INVESTIGATIONS TO IRON LIMITATION IN STREPTOCOCCUS PNEUMONIAE
I n a u g u r a l d i s s e r t a t i o n
zur
Erlangung des akademischen Grades eines
Doktors der Naturwissenschaften (Dr. rer. nat.)
der
Mathematisch-Naturwissenschaftlichen Fakultät
der
Universität Greifswald
vorgelegt von Juliane Hoyer geboren am 18.08.1988 in Potsdam
Greifswald, den 18.12.2018
Dekan: Prof. Dr. Werner Weitschies
1. Gutachter: Prof. Dr. Dörte Becher
2. Gutachter: Prof. Dr. Jan Maarten van Dijl
Tag der Promotion: 25.04.2019
Table of contents
TABLE OF CONTENTS
Table of contents ...... I
Abbreviations ...... V
1. Summary ...... 1
2. Zusammenfassung ...... 3
3. Introduction ...... 7
3.1. Streptococcus pneumoniae ...... 7
3.1.1. Historical and general aspects ...... 7
3.1.2. Carriage and diseases ...... 7
3.1.3. Treatment and prevention ...... 8
3.2. Proteomics ...... 10
3.2.1. Mass spectrometry-based proteomic workflow ...... 10
3.2.2. Mass spectrometry-based protein quantification ...... 12
3.3. Iron ...... 14
3.3.1. Role of iron and its acquisition by bacteria ...... 14
3.3.2. Iron acquisition by pneumococci ...... 22
3.3.3. Establishment of in vitro iron starvation ...... 28
3.4. Objective of the thesis ...... 30
4. Materials and methods ...... 31
4.1. Bacterial strain ...... 31
4.2. Chemicals ...... 31
4.3. Water and media ...... 33
4.3.1. Water ...... 33
4.3.2. Chemically defined medium ...... 33
4.3.3. Stable Isotope Labeling by Amino Acids in Cell Culture - CDM ...... 34
4.3.4. Todd-Hewitt broth with yeast extract medium ...... 34
4.4. Consumables...... 34
I
Table of contents
4.5. Instruments ...... 35
4.6. Computer software ...... 36
4.7. Experimental design and cultivation ...... 37
4.7.1. Strain maintenance ...... 37
4.7.2. Cultivation and sampling ...... 37
4.7.3. Cell harvest and disruption ...... 42
4.7.4. Gram-staining ...... 43
4.7.5. Electron microscopy ...... 43
4.8. Protein analysis ...... 46
4.8.1. Determination of protein concentration ...... 46
4.8.2. Preparation of heavy labeled internal standard ...... 46
4.8.3. In-solution protein digestion ...... 47
4.8.4. Peptide purification ...... 47
4.9. Mass spectrometric analyses ...... 48
4.9.1. Liquid chromatography coupled to tandem-mass spectrometry (LC-MS/MS) .... 48
4.9.2. Inductively coupled plasma-mass spectrometry (ICP-MS) ...... 49
4.10. Data analyses ...... 49
4.10.1. Determination of incorporation rate ...... 49
4.10.2. Proteome analysis ...... 49
5. Results ...... 51
5.1. Prerequisites for proteome analyses ...... 51
5.1.1. Modified cultivation workflow ...... 51
5.1.2. Generation of Voronoi treemap layout for S. pneumoniae ...... 53
5.2. Pneumococcal adaptation to iron limitation in CDM ...... 56
5.2.1. Determination of suitable BIP concentration and BIP toxicity test ...... 56
5.2.2. Determination of incorporation rate for SILAC quantification ...... 58
5.2.3. Proteome analysis ...... 60
5.2.4. Cell morphology ...... 63
II
Table of contents
5.2.5. Iron concentrations of CDM and THY ...... 65
5.3. Pneumococcal adaptation to iron limitation in THY ...... 65
5.3.1. Determination of suitable BIP concentration ...... 65
5.3.2. Proteome analysis ...... 66
5.4. Comparison of CDM vs. THY ...... 71
5.4.1. Proteomic adaptation to media ...... 71
5.4.2. Comparative analysis of cell morphology ...... 73
6. Discussion ...... 77
6.1. Functional categorization of pneumococcal proteins ...... 77
6.2. Comparison of protein expression in response to media ...... 78
6.3. The importance of iron and the choice of the iron chelator ...... 80
6.4. Adaptation to iron limitation ...... 83
6.5. Conclusion ...... 93
6.6. Publication of main results ...... 93
References ...... 95
7. Publications ...... 121
7.1. Scientific articles ...... 121
7.2. Poster presentations ...... 121
7.3. Oral presentations ...... 121
8. Appendix ...... 123
III
Abbrevations
ABBREVIATIONS
1D One-dimensional 2,5-DHBA 2,5-Dihydroxybenzoic acid 2D Two-dimensional ABC ATP-binding cassette ATP Adenosine triphosphate B. anthracis Bacillus anthracis B. licheniformis Bacillus licheniformis B. subtilis Bacillus subtilis BALF Bronchoalveolar lavage fluid BIP 2,2‘-Bipyridine, 2,2‘-Bipyridyl, 2,2‘-Dipyridine, 2,2‘-Dipyridyl BR Biological replicate BSA Bovine serum albumin C. diphtheriae Corynebacterium diphtheria CDC Centers for Disease Control and Prevention CDM Chemically defined medium CID Collision-induced dissociation CSP Competence-stimulating peptide DFO Desferoxamine, deferoxamine, Desferal DNA Desoxyribonucleic acid E. coli Escherichia coli e.g. For example EDDHA Ethylenediamine-di-(o-hydroxyphenylacetic acid) EDTA Ethylenediaminetetraacetic acid EM Electron microscopy ESI Electron spray ionization FASP Filter-aided sample preparation FC Fold change FDR False discovery rate FESEM Field scanning electron microscopy G+C Guanine + cytosine H. influenzae Haemophilus influenza HZI Helmholtz-Zentrum für Infektionsforschung iBAQ Intensity-based absolute quantification
V
Abbrevations
ICP Inductively coupled plasma
Kd Dissociation constant L Length L. monocytogenes Listeria monocytogenes LC Liquid chromatography LFQ Label-free quantification M. catarrhalis Moraxella catarrhalis m/z Mass-over-charge ratio MALDI Matrix-assisted laser desorption/ionization MS Mass spectrometry MS/MS Tandem mass spectrometry MS1 Precursor ion level/survey scan MS2 Fragment ion level N. gonorrhoeae Neisseria gonorrhoeae N. meningitidis Neisseria meningitides NE Norepinephrine NEAT NEAr Transporter NGAL Neutrophil gelantinase-associated lipocalin (lipocalin 2, siderocalin) NIS NRPS-independent siderophore NRPS Nonribosomal peptide synthetase
OD 600 nm Optical density at 600 nm PAGE Polyacrylamide gel electrophoresis PBV PnuBioVax (multi-antigen, serotype-independent prophylactic vaccine) PCA Principal component analysis PCV Pneumococcal conjugate vaccine PCV10 10-valent PCV (Synflorix) PCV13 13-valent PCV (Prevnar 13) PCV7 7-valent PCV (Prevnar) PPSV Pneumococcal polysaccharide vaccine PPSV23 23-valent PPSV (Pneumovax23) PSM Peptide spectral match PTM Post-translational modification RNA Ribonucleic acid ROS Reactive oxygen species
VI
Abbrevations
RPMI Roswell Park Memorial Institute (specific CDM) RT Room temperature RTEC Respiratory tract epithelial cell RTG 1870 Research training group 1870 RTLF Respiratory tract lining fluid S. aureus, Staphylococcus aureus S. lugdunensis Staphylococcus lugdunensis S. pneumoniae Streptococcus pneumoniae S. pyogenes Streptococcus pyogenes SAM Significance analysis of microarrays SILAC Stable isotope labeling by amino acids in cell culture TEM Transmission electron microscopy THB Todd-Hewitt broth THY Todd-Hewitt broth with yeast extract TOF Time-of-flight tRNA Transfer RNA vs. Versus
VII
Summary
1. SUMMARY
Streptococcus pneumoniae is one of the leading human pathogen causing morbidity and mortality worldwide. The pneumococcus can cause a variety of different diseases ranging from mild illnesses like otitis media and sinusitis to life-threatening diseases such as pneumonia, meningitis and sepsis. Mostly affected are infants, elderly and immune- suppressed patients. Although, there are vaccines against pneumococci available, still hundreds of thousands of people got infected each year. These vaccines are targeting the pneumococcal polysaccharide capsule. Because of the high number of different serotypes, it is not possible to generate a vaccine against all present serotypes. In the last years a shift to non-vaccine serotypes was noticed. This strengthens the need for the development of vaccines which do not target polysaccharides. Thus, proteins came into focus as potential new vaccine candidates or targets for drug treatment, because several proteins are highly conserved among different strains or even genera. Proteome analyses can give insights into the protein composition in a certain state of a bacterium. So, targets can be identified, which are especially expressed under infection-relevant conditions. Iron limitation is one of these conditions and the knowledge on iron acquisition in pneumococci is still limited. Iron is an essential trace element and as redox-active catalyst or as cofactor involved in various key metabolic pathway in nearly all living organisms and thus also in bacteria. For instance, iron is necessary during biosynthesis of amino acids and in electron transport as well as in DNA replication. Within the human host iron is extremely limited due to its high insolubility under physiological conditions, which is part of the nutritional immunity of its human host. Hence, bacteria had to evolve mechanism to overcome iron starvation. In this thesis the adaptation process triggered by iron limitation in the S. pneumoniae serotype 2 strain D39 was investigated in a global mass spectrometry-based proteome analysis. In preceding growth experiments the pneumococcal growth was adapted to the needs of proteomic workflows. In order to investigate the pneumococcal response to iron limitation, the organic iron-chelating agent 2,2’-bipyridine (BIP) was applied. For the quantification of changes in protein abundances comparing stress to control conditions the very reliable and robust metabolic labeling technique Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) was used. This method requires the bacterial cultivation in a chemically defined medium, for which reason modified RPMI 1640 medium was chosen. A pooled protein extract with heavy labeled amino acids was applied as an internal standard, which included proteins expressed under control and stress condition, to control, BIP and BIP-iron-complex (BIP control experiment) samples. Samples were analyzed by liquid chromatography coupled directly to a tandem mass spectrometer. It is described that under iron-restricted
1
Summary
conditions proteins associated to pathogenesis are higher abundant in pathogenic bacteria like Staphylococcus aureus. Hence, similar observations were expected also for the proteomic adaptation of S. pneumoniae, but the first results showed a reduction in protein abundance of virulence factors. In order to explain these results inductively-coupled-plasma mass spectrometry was executed to determine the iron concentration of chemically defined medium (CDM) used in this experiment. The analysis revealed a relatively low iron concentration of approximately 190 µg l-1. Therefore, the iron concentration of the complex medium THY, in which pneumococci are usually grown, was investigated. THY contains four- fold (740 µg l-1) more iron than the CDM. Subsequently, an additional iron limitation approach was carried out in THY. As SILAC is not applicable in complex media like THY, MaxLFQ was applied as quantification method in this case. Because two different media were used, an additional comparative proteome analysis with regard to the two investigated media was executed. Comparing the protein composition in both cultivation media it became clear that pneumococci exhibit a totally different proteome depending on the medium. Major differences were found in metabolisms of amino acids, vitamins and cofactors as well as in pathogenesis-associated proteins. These differences have to be taken into account during the analyses of both iron limitation approaches. Overall, more proteins were identified and quantified in CDM samples. The pneumococcal adaptation to iron limitation in both media was different; especially, the alterations in protein abundances of virulence factors. In contrast to the iron limitation in CDM, proteins involved in pathogenesis were higher abundant under iron limitation in THY, which was the expected result. Because of proteomic changes of cell division and lipid metabolism involved proteins in iron-limited pneumococci in CDM, electron microscopic pictures were taken in order to proof cell morphology. The pictures showed an impaired cell division in iron-limited CDM, but not in THY medium. However, both datasets have similarities as well. Thus, the iron uptake protein PiuA is strongly increased in iron-restricted conditions and the abundance of the iron storage protein Dpr is significantly decreased in both datasets. Notably, PiuA and Dpr seem to have important roles during the pneumococcal adaptation to iron-restricted environments. One the basis of these results, it could be shown that the proteomic response of pneumococci to iron limitation is strongly dependent to the initial iron concentration of the environment. Hence, pneumococci will adapt differently to varying niches and thus potential vaccine candidates should be expressed independently of the localization within the human host.
2
Zusammenfassung
2. ZUSAMMENFASSUNG
Streptococcus pneumoniae ist eines der wichtigsten Humanpathogene, das weltweit zu hohen Krankheits- und Sterblichkeitsraten führt. Der Pneumokokkus kann viele verschiedene Krankheiten verursachen, so zum Beispiel leichtere Infektionen wie Mittelohrentzündungen (Otitis media) und Nasennebenhöhlenentzündungen (Sinusitis) als auch schwerwiegende Krankheiten wie beispielsweise Lungenentzündungen (Pneumonie), Hirnhautentzündungen (Meningitis) und Blutvergiftung (Sepsis). Von diesen Infektionen sind meistens Kleinkinder, Senioren und immunsupprimierte Patienten betroffen. Jedes Jahr werden mehrere tausend Menschen durch Pneumokokken infiziert, obwohl gegen diesen Krankheitserreger Impfstoffe erhältlich sind. Diese Vakzine wirken gegen die Polysaccharide innerhalb der Pneumokokkenkapsel. Dadurch, dass sehr viele verschiedene Serotypen vorhanden sind, ist es nur schwer möglich einen Impfstoff gegen alle vorkommenden Pneumokokkenserotypen zu entwickeln. In den letzten Jahren wurde festgestellt, dass Serotypen, die von keinem Impfstoff abgedeckt sind, nunmehr häufiger vorkommen. Das verstärkt die Notwendigkeit einen Impfstoff zu produzieren, der nicht gegen Polysaccharide gerichtet ist. Proteine stellen einen alternativen Angriffspunkt für potentielle Impfstoff oder auch Wirkstoffziele dar. Mithilfe von Proteomanalysen können Einblicke in die Proteinzusammensetzungen von Bakterien, die unter bestimmten Bedingungen gewachsen sind, gewonnen werden. So können Zielproteine identifiziert werden, die besonders unter infektionsrelevanten Bedingungen exprimiert werden. Eisenmangel ist eine dieser infektionsrelevanten Bedingungen. Das Wissen über Eisenaufnahmemechanismen in Pneumokokken ist bisher noch eingeschränkt. Eisen ist ein Spurenelement, das als redoxaktiver Katalysator als auch als Kofaktor in unterschiedlichen, metabolischen Wegen von fast allen Lebewesen und insbesondere von Bakterien involviert ist. So zum Beispiel ist Eisen während der Aminosäurebiosynthese, als auch während des Elektronentransports und bei der DNA-Replikation beteiligt. Innerhalb des humanen Wirts ist freies Eisen, aufgrund dessen geringer Löslichkeit unter physiologischen Bedingungen und durch eisenbindende Proteine, die einen Teil der humanen Immunabwehr darstellen, extrem limitiert. Deshalb haben Bakterien Mechanismen entwickelt, um den Eisenmangel zu überwinden. In dieser Arbeit wurde der Anpassungsprozess von dem S. pneumoniae Serotyp-2-Stamm D39 an Eisen-limitierte Umgebungen in einer umfassenden Massenspektrometrie basierten Proteomanalyse untersucht. Zunächst wurde das Pneumokokkenwachstum in Wachstumsversuchen an die Bedürfnisse der proteomanalytischen Arbeitsprozesse angepasst. Um die proteomische Antwort auf Eisenlimitation in Pneumokokken zu untersuchen, wurde der organische Eisenchelator 2,2‘-
3
Zusammenfassung
Bipyridin (BIP) eingesetzt. Für die relative Quantifizierung von Änderungen der Proteinmengen im Vergleich von Stress- zu Kontrollbedingungen wurde das sehr zuverlässige und robuste metabolische Markierungsverfahren SILAC (Stable Isotope Labeling by Amino Acids in Cell Culture, Stabile Isotopenmarkierung mittels Aminosäuren in Zellkulturen) verwendet. Eine Voraussetzung dieser Methode ist die Anwendung eines chemisch definierten Mediums (CDM), weshalb das modifiziert RPMI 1640-Medium ausgewählt wurde. Ein gemischter Proteinextrakt, der schwer markierte Aminosäuren enthält, ist als interner Standard zu den Kontroll-, BIP- und BIP-Komplexproben hinzugegeben worden. Der interne SILAC-Standard ist eine Vereinigung möglichst aller Proteine, die sowohl unter Kontroll- als auch unter Stressbedingungen in schwer markierten CDM von Pneumokokken exprimiert wurden. Die Peptidproben wurden mittels Flüssigkeitschromatografie mit Tandem-Massenspektrometrie analysiert. Aus früheren Studien, beispielsweise in Staphylococcus aureus ist bekannt, dass unter Eisen-limitierenden Bedingungen Pathogenese-assoziierte Proteine im Vergleich zu Kontrollbedingungen deutlich erhöht sind. Aus diesem Grund wurden auch für die Anpassung der Pneumokokken ähnliche Ergebnisse erwartet. Allerdings wiesen die ersten Resultate eine Reduktion der Virulenzfaktoren auf. Um diese Ergebnisse zu verstehen, wurde die Eisenkonzentration des CDMs mittels Massenspektrometrie mit induktiv gekoppeltem Plasma (ICP-MS) bestimmt. Es zeigte sich, dass die Eisenkonzentration mit 190 µg l-1 im Medium relativ gering ist. Deshalb wurde auch die Eisenkonzentration des komplexen Mediums THY bestimmt, in welchem Pneumokokken für gewöhnlich kultiviert werden. THY hat in etwa eine vierfach höhere Eisenkonzentration (740 µg l-1) als CDM. Anschließend wurde der Eisenlimitationsversuch auch in THY durchgeführt. Da in diesem Medium die SILAC- Methode nicht angewendet werden kann, wurde auf die markierungsfreie Methode MaxLFQ zurückgegriffen. Dadurch, dass zwei grundverschiedene Medien untersucht wurden, wurde eine weitere Proteomanalyse, die die Proteinzusammensetzung der Pneumokokken in Abhängigkeit des Wachstumsmediums miteinander vergleicht, durchgeführt. Wie zu erwarten war, wiesen die Pneumokokken im Vergleich der Proteinzusammensetzung in beiden Medien eine unterschiedliche Proteinexpression auf. Vor allem wurden im Aminosäuremetabolismus als auch im Metabolismus von Vitaminen und Kofaktoren und auch bei Pathogenese-assoziierten Proteinen große Unterschiede festgestellt. Diese Unterschiede müssen zur Interpretation der beiden Eisenmangelexperimente berücksichtigt werden, um Fehlinterpretationen zu vermeiden. Insgesamt konnten wesentlich mehr Proteine im CDM identifiziert als auch quantifiziert werden. Des Weiteren war die Anpassung der Pneumokokken an die Eisenlimitation in den beiden Medien unterschiedlich. Dabei sind besonders die gegensätzlichen Proteinmengen
4
Zusammenfassung der Virulenzfaktoren aufgefallen. Im Gegensatz zu den Ergebnissen der Eisenlimitation in CDM, wurden Pathogenese-assoziierte Proteine im Eisen-limitierten THY deutlich stärker exprimiert. Dieses Ergebnis entsprach den Erwartungen, wie zuvor beschrieben. Zusätzlich wurden elektronenmikroskopische Aufnahmen angefertigt, nachdem im CDM-Datensatz auffallende Veränderungen bezüglich der Zellteilung deutlich geworden sind. Tatsächlich konnte man in den Bildern der Eisen-limitierten CDM-Proben eine gestörte Zellteilung der Pneumokokken feststellen. Diese Beobachtungen wurden bei Eisen-limitierten Pneumokokken, die in THY gewachsen sind, nicht gemacht. Auch die dazugehörigen Proteomdaten wiesen keine deutlichen Veränderungen der Proteinmengen auf. Neben den genannten Unterschieden zeigten sich in beiden Datensätzen auch Gemeinsamkeiten. So war zum einen die Menge des Eisentransportproteins PiuA nach Eisenlimitation in beiden Medien stark erhöht und zum anderen die Menge des Eisenspeicherproteins Dpr signifikant verringert. Insbesondere scheinen diese beiden Proteine, PiuA und Dpr, wichtige Rollen während der Anpassung an Eisenmangel innezuhaben. Basierend auf diesen Ergebnissen konnte gezeigt werden, dass die proteomische Antwort der Pneumokokken auf Eisenlimitation von der Ausgangseisenkonzentration abhängig ist. Daraus lässt sich schließen, dass die Anpassung von S. pneumoniae an das Nährstoffangebot in den verschiedenen Wirtsnischen unterschiedlich sein könnte. Aus diesem Grund wäre es von Vorteil, wenn potentielle Vakzinkandidaten, unabhängig vom Nährstoffangebot der Umgebung im humanen Wirt, exprimiert werden.
5
Introduction
3. INTRODUCTION
3.1. STREPTOCOCCUS PNEUMONIAE
3.1.1. HISTORICAL AND GENERAL ASPECTS
In 1875 the German-Swiss pathologist Edwin Klebs discovered the bacterium Streptococcus pneumoniae, the pneumococcus, in pulmonary tissue. Independently from each other George Sternberg and Louis Pasteur isolated the bacterium from saliva for the first time in 1880 in the United States and in France, respectively (Austrian, 1999; Watson et al., 1993). The Gram-positive bacterium is lancet-shaped, occurs in diplococci and can form short chains. It is described as catalase-negative, facultative anaerobic and -hemolytic (Bridy-
Pappas et al., 2005; Brown, 1919). S. pneumoniae belongs to the phylum Firmicutes,α the class Bacilli, the order Lactobacillales, the family Streptococcaceae within the genus Streptococcus and the species S. pneumoniae (Facklam, 2002). Another well investigated characteristic of the pneumococcus is its polysaccharide capsule, which plays an important role in virulence and immune evasion. To date 97 different pneumococcal serotypes, based in their polysaccharide capsule, are identified and described (Geno et al., 2015). In 1928 Griffith discovered that genetic information was transferred by a process named transformation investigating rough (unencapsulated) and smooth pneumococcal (encapsulated) strains (Griffith, 1928). Several years later, Avery, MacLeod and McCarty could prove that deoxyribonucleic acid (DNA) act as the carrier of the genetic material (Avery et al., 1944). In the focus of their studies was also the pneumococcal serotype 2 strain D39 (NCTC 7466), which is a clinical isolate from 1916 and was fully sequenced in 2007 (Lanie et al., 2007). Since 1916, S. pneumoniae D39 did not become less important and was often used in pneumonia, meningitis and sepsis as a model organism (Holmes et al., 2001; Oggioni et al., 2004; Winter et al., 1997). In this work the unencapsulated mutant of D39 was used as experimental strain.
3.1.2. CARRIAGE AND DISEASES
The pneumococcus is part of the commensal flora of its natural habitat, the human host. Besides Moraxella catarrhalis, Neisseria meningitidis, Haemophilus influenzae and Staphylococcus aureus, S. pneumoniae colonizes asymptomatically the mucosal surfaces of the upper respiratory tract. The colonization is usually not followed by infection. Typically,
7
Introduction
every human being is colonized at least once in her/his life with pneumococci (Bogaert et al., 2004). Approximately 27 to 65 percent of children and less than ten percent of adults are carriers of this opportunistic pathogen (Weiser et al., 2018). The transmission is occurring by person-to-person contact with carriers by respiratory droplets, especially during cold and dry months, when airway secretions are increased (Mehr and Wood, 2012; Weiser et al., 2018). Furthermore, contact of contaminated surfaces contribute to contagion with pneumococci (Weiser et al., 2018). The colonization is influenced by the age, as infants exhibit the highest carriage rates. In addition, viral coinfections and missing vaccination can also be responsible for an eased pneumococcal establishment (colonization) (Siemens et al., 2017; Weiser et al., 2018). Also environmental factors as winter, passive or active smoking and large crowds facilitate contagion and colonization (Mehr and Wood, 2012). The listed factors here are just a minor selection of influencing factors. Pneumococci are able to migrate from colonized mucosal surfaces of the upper respiratory tract to deeper tissues and sterile fluids or even to the bloodstream causing life-threatening infections (Mehr and Wood, 2012; Weiser et al., 2018). Thus, colonization can lead to invasive pneumococcal disease (IPD) such as meningitis, septicemia and community- acquired pneumonia or to respiratory tract infections like otitis media or sinusitis (Bogaert et al., 2004; Kadioglu et al., 2008). In addition to young children, elderly persons and immunosuppressed patients are also affected by colonization and infection with S. pneumoniae (Bogaert et al., 2004; Musher, 1992).
3.1.3. TREATMENT AND PREVENTION
Since the first half of the 20th century, penicillin and other antibiotics were the treatment of choice for pneumococcal and other bacterial infections. But with increasing resistance to antibiotics, due to misuse, it is even harder and more expensive to treat the diseases. Additionally, via horizontal gene transfer antibiotic resistances are widely distributed among bacteria (Coffey et al., 1995; Dowson et al., 1997). The key to circumvent bacterial respiratory diseases is to prevent the colonization of the host by pathogens. In 1983 the first polysaccharide-based pneumococcal vaccine was introduced and thus, prevented infection and disease. Pneumovax23 (PPSV23) contains 23 polysaccharide antigens and is recommended for adults older than 65 years and children older than two years, who have an increased risk for disease (Centers for Disease Control and Prevention (CDC), 2017). Because of poor antibody response to these polysaccharide antibodies, the application to children under age of 18 months is not recommended (Pomat et al., 1994). With the introduction of Prevnar (PCV7) in 2000 the prevalence of invasive IPDs
8
Introduction caused by vaccine-serotypes decreased, but unfortunately IPDs due to non-vaccine serotypes increased (Gamez and Hammerschmidt, 2012; Liñares et al., 2010). In addition, PCV7 is a pneumococcal conjugate vaccine, which is also immunogenic in children under the age of two years (Oosterhuis-Kafeja et al., 2007). In 2003 the second PCV, Synflorix (PCV10) was introduced, which includes three additional serotypes. Nowadays, Prevnar 13 (PCV13), which protects against serotypes included in PCV7 plus 6 additional serotypes, is recommended in infants and young children, but also in elderly person older than 65 years (Centers for Disease Control and Prevention (CDC), 2017). As all available vaccines are serotype-dependent, not all serotypes are covered by the PCVs or PPSV. Moreover, in the last decades it was shown that non-vaccine serotypes become more and more prevalent. This process is termed serotype replacement (Balsells et al., 2017). Hence, novel vaccine strategies had to be developed and proteins of the pneumococcal surface came into focus. During infection many virulent surface proteins are expressed on the cell surface such as choline-binding proteins or lipoproteins (Gamez and Hammerschmidt, 2012; Kadioglu et al., 2008; Kohler et al., 2016). A protein-based vaccine would be beneficial in many ways as it would be, for instance, serotype-independent, cheap in production and would ensure protection in all age cohorts (Bogaert et al., 2004). For the elucidation of bacterial virulence, it is of great interest to gain knowledge of the pneumococcal lifestyle during changing environmental conditions. For instance, the nutrient availability changes from niche to niche within the different human host compartments encountered by pneumococci. Thus, comprehensive proteome analyses of specific stress signatures will give additional information on pneumococcal adaptation to their environment (Hempel et al., 2011; van Oudenhove and Devreese, 2013).
9
Introduction
3.2. PROTEOMICS
The term "proteomics" comprises a whole field in natural sciences, in which proteins are in focus of investigation. In 1996 Wilkins and coauthors defined the proteome as an "entire protein complement expressed by a genome, or cell or tissue type" (Wilkins et al., 1996). A few years later, Anderson and Anderson characterized proteomics as "the use of quantitative protein-level measurements of gene expression to characterize biological processes (e.g., disease processes and drug effects) and decipher the mechanisms of gene expression control" (Anderson and Anderson, 1998). Briefly, proteomics describes the comprehensive analyses of proteomes (van Oudenhove and Devreese, 2013). The proteome contains much more information than the genome alone as the proteome is varying in its size, properties and post-translational modifications (PTMs). Proteome analyses aim for the identification of proteins, the quantitative measurements in protein abundances, identification of PTMs and its sites as well as the response to certain environmental conditions. With all those information on protein networks, their dynamics and structures can be functionally analyzed (van Oudenhove and Devreese, 2013). The first tool to analyze proteomes was the two dimensional polyacrylamide gel electrophoresis (2D-PAGE) technique. 2D-PAGE was introduced in 1975 independently by Klose, O'Farrell and Scheele (Klose, 1975; O'Farrell, 1975; Scheele, 1975). In this method proteins of a sample are first separated according to their isoelectric point and subsequently to their molecular weight. Afterwards, the protein expression patterns of two different conditions are compared to each other. Since the introduction of MALDI-MS (matrix-assisted laser desorption/ionization mass spectrometry) in 1985 by Karas and colleagues (Karas et al., 1985), the traditional quantitative proteomics was realized in a combination of 2D-PAGE and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI- TOF-MS). However, this method possesses a limited dynamic range and only most abundant proteins could be observed (Gygi et al., 2000). In 1992, Hunt and coworkers published the first article on LC-MS/MS (liquid chromatography coupled to tandem mass spectrometer) as a further tool for proteome analysis (Hunt et al., 1992). This method paved its way, as meanwhile most prominent technique, within proteomic analyses. The first milestones of mass spectrometry in proteomics are published in a very comprehensive review by Aebersold and Mann (Aebersold and Mann, 2003).
3.2.1. MASS SPECTROMETRY-BASED PROTEOMIC WORKFLOW
A classical MS-based proteomic workflow in microbiology, which was also applied in this particular work, is depicted in Figure 3-1. After the bacteria are cultivated under conditions
10
Introduction of interest, the supernatant is separated from cells by centrifugation. Subsequently, the cells are lysed and the proteins from cell lysate are digested tryptically. In the next step, the complexity of the sample will be reduced during chromatographic separation on an LC system. Finally, the peptides are ionized and directly injected into the mass spectrometer. After monitoring the mass spectra, the raw files are searched against theoretical spectra during a classical database search using MaxQuant via the Andromeda algorithm (Cox et al., 2009; Cox et al., 2011). In the end, the data are evaluated by the software Perseus (Tyanova et al., 2016).
Figure 3-1 A classical proteomic workflow in microbiology. Parts of the figure were taken from Servier Medical Art1. Scissors by Creative Stall and Orbitrap by Fredrik Edfors from the Noun Project.
Additional modifications of the workflow can be applied according to the scientific question. Hence, proteins of the supernatant can be precipitated or enriched (Otto et al., 2017). Proteins of the cell lysate can be additionally separated in an 1D-gel or subcellular fractionated (Otto et al., 2016). Moreover, in a so called "top-down" approach intact proteins can be directly injected to the LC-MS/MS system. The protein digestion can also be executed by usage of other proteinases (Tsiatsiani and Heck, 2015) and in-gel (Otto et al., 2010) or on a filter as in FASP ; several other digestion methods are available (Ludwig et al., 2018). Besides(Wiśniewski, the classical 2016) database search raw spectra can be matched against spectra from a spectral library in a data independent approach (Griss, 2015; Lam and Aebersold, 2011). Also diverse computational proteomic software for database search and data evaluation is commercially available as well as available free of charge.
1 “Cell culture and microbiology” and "Chemistry" Servier Medical Art by Servier, used under CC BY 3.0 / Recolored from original, and the original version can be found here: https://smart.servier.com/wp-content/uploads/2016/10/Microbiology_cellculture.ppt https://smart.servier.com/wp-content/uploads/2016/10/Chemistry.ppt.
11
Introduction
3.2.2. MASS SPECTROMETRY-BASED PROTEIN QUANTIFICATION
The proteome analysis of pathogenic microorganisms aims for the characterization of infection-relevant processes and thus, the discovery of novel targets for antibiotics or even potential vaccine candidates. Hence, to gather all those information bacteria grown under control and also under condition of interest can be compared on proteome level. The protein abundances can be quantified by various proteomic methods. In the last two decades numerous MS-based protein quantification methods have been evolved (Bantscheff et al., 2007; Bantscheff et al., 2012; Ong and Mann, 2005). Within the present thesis the proteomic adaptation of Streptococcus pneumoniae to iron- limiting condition is in focus. Thereby, the protein abundances of pneumococci grown under iron-limited conditions should be relatively quantified to the protein abundances of pneumococci grown under control conditions. In order to gain very robust and reliable results, it was decided to apply the metabolic labeling technique Stable Isotope Labeling by Amino Acids in Cell Culture, short SILAC (Ong et al., 2002). Within metabolic labeling techniques stable isotope labels are introduced at the earliest time point of the experiment, during cell growth. This leads to decreased variance between compared experimental states and to an exact quantification accuracy (Lindemann et al., 2017), because biochemical and mass spectrometric procedures affect the comparing samples likewise. Although SILAC was developed for cell culture experiments, this method can also be applied to microorganisms, which are auxotroph for certain amino acids (Mann, 2006). In principle, stable isotope labeled amino acids in a culture medium are incorporated into an entire proteome of an organism or cell culture. A minimum of at least five generation times are necessary for a nearly complete incorporation of heavy labeled amino acids as the incorporation rate depends on protein synthesis, degradation and turnover (Chen et al., 2015). To ensure complete incorporation, amino acids are chosen, which are essential for the survival of eukaryotic or prokaryotic cells, such as leucine (Ong et al., 2002), lysine (Fröhlich et al., 2013) or methionine (Ong and Mann, 2006). Additionally, the nonessential amino acids arginine and tyrosine have been applied to SILAC approaches (Molina et al., 2009; Tzouros et al., 2013). Since deuterated amino acids tend to introduce retention time shifts and the co-elution of 13C- and 15N-labeled amino acids with its light counterparts has proven to be very reproducible, the latter labeled amino acids modification became widely accepted. Moreover, the combination of heavy labeled lysine and arginine together with the application of trypsin, which is the most commonly used proteolytic enzyme in proteomic workflows, is preferable as all tryptic peptides but the C-terminal peptide will be labeled theoretically (Ong and Mann, 2006).
12
Introduction
In the present thesis the chemically defined medium modified RPMI 1640 was used (see 4.3.2 and 4.3.3), in which it is possible to exchange arginine and lysine by their heavy labeled counterparts. Based on unexpected results of the iron limitation experiment in CDM, it was decided to repeat the experiment in the complex medium THY. In contrast to a chemically defined medium, it is not possible to exchange specific amino acids within complex media, because they usually contain any kind of broth, which is rich in proteins, peptides as well as amino acids. For this reason another quantification method for the iron limitation experiment in THY medium had to be chosen. Thus, it was decided to apply an intensity-based label-free quantification (LFQ) method on the THY dataset. Within the intensity-based quantification methods the MS signal intensities of peptide precursor ion are used for quantitation. Here, ion chromatograms are extracted from liquid chromatography. Therefore, high accuracy mass spectrometers, an optimized chromatographic profile and the right balance between acquisition of MS1 and MS2 scans are required (Bantscheff et al., 2007). For instance, iBAQ (intensity-based absolute quantification) protein intensities are calculated as the sum of the intensities of all identified peptides, which is divided by the number of theoretical observable peptides (Schwanhäusser et al., 2011). Whereas in MaxLFQ complex algorithms are applied to generated robust and reliable results for label-free quantification especially for larger datasets as published by Cox and coworkers (Cox et al., 2014). The latter method was chosen for the data evaluation of the THY dataset, because label-free approaches are low-costs methodologies as no label is introduced, which goes hand in hand with less time consumption for sample preparation and MS analysis. In addition, the number of comparable experiments is not limited to specific number of available labels. On the other hand, LFQ is at least accurate, so the overall experimental steps should be kept to a minimum in favor of reproducibility (Bantscheff et al., 2007).
13
Introduction
3.3. IRON
3.3.1. ROLE OF IRON AND ITS ACQUISITION BY BACTERIA
Transition metals are involved in many key metabolic pathways of all living organisms. These metal cofactors have different functional roles. On the one hand, some cofactors, which are redox-inert, such as magnesium or zinc, have a structural function, for example in the stabilization of negative charges or activation of substrates in metalloenzymes. On the other hand, redox-active metal ions can function in the processes described above and in a catalytic way. The most prevalent metal ion in enzymatic redox reactions, which is directly involved, is iron, followed by manganese, cobalt, molybdenum, copper and nickel (Andreini et al., 2008). Metal cofactors function in DNA replication, transcription, cellular respiration, oxidative stress response and other biological processes (Palmer and Skaar, 2016). Pathogenic bacteria rely on the acquisition of essential nutrients. Iron, alongside with other transition metals such as manganese or zinc, are vital for the bacterial survival within its human host. Hence, the human host developed a mechanism, which is called the "nutritional immunity", to prevent colonization and subsequently infection with pathogenic microorganisms. Therefore, nutritional immunity can be described as a host-mediated restriction of essential transition metals or/and using their toxicity against the invading microbes (Hood and Skaar, 2012).
Role of iron in bacteria The most abundant metal on earth is iron. As a transition metal it is involved in many different biological processes as cofactor or redox-active catalyst. Due to its redox-active properties, iron plays a major role in the transition of electrons, e.g., during the cellular respiration and photosynthesis. Moreover, as a cofactor for enzymes and other proteins iron affects their catalytic reactions and/or their structures. Iron-dependent proteins can be found for instance in the amino acid biosynthesis, the tricarboxylic acid cycle, nitrogen fixation and in DNA replication (Sheldon et al., 2016; Sheldon and Heinrichs, 2015). The redox-active role of iron in a living organism is ambivalent, whereat its oxidation state changes between +2 and +3 during electron transfers. One the one hand, it is a vital nutrient and required in many biological pathways, but on the other hand, under iron ion excess, the so called Fenton chemistry can take place. Briefly, during aerobic growth organisms reduce
oxygen to water. This reduction is partially incomplete and superoxide radical anions (O2•-) are generated. The enzyme superoxide dismutase catalyzes the reaction from superoxide
•- 2+ radical anion (O2 ) to hydrogen peroxide (H2O2). Freely available Fe reacts with hydrogen
peroxide (H2O2) forming a hydroxyl radical (OH•) and a hydroxide ion (OH-) while being
14
Introduction oxidized to Fe3+. In addition, Fe3+ is reduced by superoxide radical anion (O2•-) forming Fe2+ and oxygen (O2). Overall, the sum of both reaction forms the “iron-catalyzed Haber-Weiss reaction”, in which hydroxyl radicals (OH•) and hydroxide ions (OH-) are products of the reaction from hydrogen peroxide (H2O2) with superoxide radical anion (O2•-) (Wardman and
Candeias, 1996). In this case, superoxide radical anion (O2•-) and hydroxyl radical (OH•) are referred to as reactive oxygen species (ROS). This process can lead to a high abundance of ROS, which can cause damage on DNA, lipids and also proteins (Cassat and Skaar, 2013). This balance between benefits and harm of iron is well regulated. Before the iron acquisition by pathogens is described in detail, it is important to know, where the iron sources within the natural habitat, the human host, are located. Iron is found predominantly in heme-binding proteins such as hemoglobin and myoglobin. In erythrocytes hemoglobin is circulating through the human body. If erythrocytes lyse, free hemoglobin is sequestered by haptoglobin and free heme-molecules are captured by hemopexin to prevent harmful levels of free heme. The divalent form of iron, ferrous iron, plays an important role in many biological processes. Hence, before taken up into a cell, iron is first released from heme, if necessary, and ferric iron is reduced to its ferrous form. Intracellularly, ferrous iron can then be used in biological processes as described above, stored in the iron storage protein ferritin or be exported by ferroportin. Outside of the cell ferrous iron is oxidized to ferric iron. Depending on the localization free ferric iron is bound to the glycoproteins transferrin in the serum or lactoferrin at the mucosal surfaces. With these mechanisms the concentration of free available iron under physiological conditions is between 10-8 to 10-9 M and can be drastically decreased to 10-18 M by the host through activation of iron sequestration processes (Raymond and Carrano, 1979; Sheldon et al., 2016; Theil and Goss, 2009). Hence, pathogenic bacteria had to evolve a diverse repertoire of iron acquisition mechanisms in this extremely iron-restricted environment.
Regulation of iron homeostasis The regulation of iron homeostasis in Gram-positive bacteria is organized by two highly conserved transcriptional regulators. The sensory signal for the regulation processes is the iron concentration in the bacterial environment. During iron-limited conditions the expression of genes associated to iron acquisition mechanisms and virulence is activated. In low G + C content Firmicutes such as Staphylococcus spp., Bacillus spp. and Listeria monocytogenes the ferric uptake regulator Fur is responsible for the regulation of iron homeostasis (Baichoo et al., 2002; Escolar et al., 1999; Harvie et al., 2005; Heidrich et al., 1996; Johnson et al., 2005; Ollinger et al., 2006; Pi et al., 2016; Sineva et al., 2012; Torres et al., 2010; Xiong et al., 2000). In addition, in high G+C content Actinomycetes
15
Introduction
Corynebacterium diphtheriae the regulation of iron acquisition proteins and virulence factors is under the control of the diphtheria toxin repressor DtxR (Boyd et al., 1990; Kunkle and Schmitt, 2003, 2005; Schmitt and Holmes, 1991, 1994). Interestingly, although Streptococcus spp. also belongs to the phylogenetic phyla of Firmicutes they express a DtxR homolog. In this way it was shown that the metal transporter of streptococci regulator MtsR in Streptococcus pyogenes is a divergent DtxR homolog, which is involved in the control of iron uptake and virulence (Bates et al., 2005; Hanks et al., 2006; Toukoki et al., 2010). Also the manganese-dependent DtxR homolog SloR from Streptococcus mutans is involved in the expression of virulence factors (Rolerson et al., 2006; Spatafora et al., 2015) and it was recently shown that it is, next to its function in Mn2+-uptake, also involved in Fe2+-uptake (Monette et al., 2018). Fur, DtxR and homologous of the latter show a low sequence homology, but they are structurally and functionally analogous. The monomers of the dimeric regulators have an N-terminal helix-turn-helix motif for DNA binding, a dimerization domain and at the C-terminus a metal binding site for ferrous iron (Fe2+) (Baichoo and Helmann, 2002; Bates et al., 2005; Escolar et al., 1999; Guedon and Helmann, 2003; White et al., 1998). Under iron-replete conditions the dimers form a complex with one Fe2+ ion. The Fur-Fe2+, DtxR-Fe2+ and MtsR-Fe2+ complexes block the RNA-polymerase binding site in the promotor region and thus, inhibit transcription of target genes. During iron-limited conditions the ferrous iron is removed from the complex, which results in the dissociation of the repressor from the RNA polymerase binding site. Subsequently, the gene expression of iron acquisition proteins and of virulence factors is no longer repressed (Bates et al., 2005; Boyd et al., 1990; Johnson et al., 2011; Torres et al., 2010; Toukoki et al., 2010).
Iron acquisition mechanisms The iron acquisition system of Gram-positive bacteria can be roughly divided by the iron acquisition mechanisms. The majority of iron is bound to heme or heme-binding proteins such as hemoglobin or hemopexin, which is the first mechanism to be named here. Secondly, iron can be sequestered by small molecules, which are characterized by a very high affinity to iron, the so called siderophores. Furthermore, iron can also be directly acquired from iron storage proteins of the host such as transferrin and lactoferrin (Palmer and Skaar, 2016; Weinberg, 1978). Besides the aforementioned mechanisms not much attention was paid on the uptake of free inorganic iron, because of its low abundance within the host (Sheldon and Heinrichs, 2015).
Heme-iron acquisition The iron acquisition from heme or heme-binding proteins is also referred as "iron thievery". As mentioned above heme is with approximately 75% the predominant source of iron in
16
Introduction mammalians (Sheldon et al., 2016), and thus, heme is the preferred iron source for pathogenic bacteria like Staphylococcus aureus (Skaar et al., 2004). Heme or heme-binding proteins are usually found within erythrocytes. To make heme-iron accessible, bacteria secret under iron-restricted conditions cytolytic enzymes like hemolysin to lyse erythrocytes. Locally, the heme concentration is than increased. Afterwards the pathogens express proteins, which can capture heme or hemoglobin (Skaar, 2010). These heme- and hemoglobin-binding proteins are either secreted or are associated to the bacterial cell surface. In addition, some bacteria are also capable of obtaining iron from heme-hemopexin, hemoglobin-haptoglobin or serum-albumin complexes (Sheldon et al., 2016). Furthermore, bacteria are described to synthesize and secrete small soluble heme-binding proteins called hemophores, which can bind heme or capture heme from heme proteins in the extracellular space. The binding of heme to hemophores is facilitated by NEAT domains (near iron transporter). The hemophores transport heme to heme-binding receptor on the cell wall of Gram-positive bacteria (Honsa et al., 2011). One of the best described NEAT-containing hemophore systems is the iron-regulated surface determinants (Isd) pathway. NEAT domains were already described for pathogenic and also for non-pathogenic bacteria in the phylum of Firmicutes. Overall, the NEAT domains show a low sequence homology, but their structure is highly conserved. The domains consist of - -helix, which structure results in a hydrophobic pocket and suspectedeight to beβ barrels the heme and-binding one α site (Honsa et al., 2014). A highly conserved motif YXXXY coordinates the heme molecule within the NEAT domain (Grigg et al., 2006). As previously mentioned, the structure of those hemophores is not well conserved and also the function differs from organism to organism (Sheldon et al., 2016). As Gram-positive bacteria have a thick peptidoglycan layer of approximately 20 to 40 nm, the heme transport is not trivial. Hence, Gram-positive bacteria, like S. aureus, Staphylococcus lugdunensis, Bacillus anthracis, L. monocytogenes, S. pyogenes and C. diphtheriae, evolved the Isd pathway as a heme shuttle system through this barrier (Sheldon and Heinrichs, 2015). The Isd pathway is well described in the major human pathogen S. aureus. The genome of S. aureus encodes nine proteins of the heme acquisition system Isd, namely IsdA-I. In addition, the sortase B (srtB) gene is genetically-linked to that region (Mazmanian et al., 2002; Mazmanian et al., 2003). Briefly, free heme of heme-binding proteins are sequestered by hemophores (Dryla et al., 2007; Pishchany et al., 2014; Torres et al., 2006), which transfer the heme to surface-associated receptors (Grigg et al., 2007; Muryoi et al., 2008; Pluym et al., 2007; Tiedemann et al., 2012). Afterwards, heme is transported via the ABC (ATP [adenosine triphosphate]-binding cassette) transporter system IsdEF into the cytoplasm (Grigg et al., 2007; Grigg et al., 2010), where it is extracted by heme-degrading
17
Introduction
enzymes (Matsui et al., 2013; Wu et al., 2005). In several publications it was shown that the Isd pathway in S. aureus is very important for its virulence (Cheng et al., 2009; Hempel et al., 2011; Kim et al., 2010; Miajlovic et al., 2010; Pishchany et al., 2009; Pishchany et al., 2010; Pishchany et al., 2014; Reniere and Skaar, 2008; Torres et al., 2006). The Isd pathway is just one heme-iron acquisition system. An alternative analogous heme- uptake machinery consists of the streptococcal heme protein receptor Shr, the streptococcal heme-associated cell surface protein Shp and the heme ABC transporter system HtsABC in S. pyogenes (Aranda et al., 2007; Bates et al., 2003; Lei et al., 2003). Unlike in IsdB/H, the NEAT domains within Shr only bind heme, whereas an unique N-terminal domain is able to bind hemoglobin (Ouattara et al., 2010). Furthermore, it was shown that Shr can bind also other heme proteins such as myoglobin, heme-albumin and the hemoglobin-haptoglobin complex (Bates et al., 2003). The cell surface protein Shp has structural similarities to IsdA/C and HtsA is a functional homolog to IsdE (Aranda et al., 2007). Similar to S. aureus heme-iron acquisition system, this streptococcal iron acquisition system has a crucial role in the virulence (Dahesh et al., 2012).
Siderophore-mediated iron acquisition In most cases iron is stored in its ferric form in three main proteins. Intracellular it is stored in ferritin, whereas extracellular ferric iron is bound to transferrin in serum (Cassat and Skaar, 2013) or to lactoferrin in milk, saliva, tears, mucus, neutrophil secondary granules and other exocrine secretions (Baker and Baker, 2005; Birgens, 1985; Iigo et al., 2009; Kanwar et al., 2015). The main mechanism, in which free and also protein-bound ferric iron is sequestered, is mediated by low molecular weight molecules termed siderophores. These molecules have a remarkably high affinity for Fe3+ and their mass is generally less than 1 kDa. Siderophores are secreted and imported by many different bacteria (Cassat and Skaar, 2013). For instance the siderophore enterobactin from Enterobacteriaceae is able to bind Fe3+ with a Kd (dissociation constant) of 10-51 to 1049, whereas the binding of iron to transferrin is weaker with a Kd of 10-24 to 10-20 (Carrano and Raymond, 1979; Loomis and Raymond, 1991). Also in other organisms siderophores were found. So for example in fungi as well as in graminaceous plants (Gründlinger et al., 2013; Haas, 2014; Hider and Kong, 2010) and even in mammalians siderophores like 2,5-DHBA (2,5-dihydroxybenzoic acid) were discovered (Correnti and Strong, 2012; Devireddy et al., 2010; Liu et al., 2014). To date 500 siderophores were identified and from approximately 270 also the structure was elucidated (Hider and Kong, 2010). Based on their chemical nature and functional groups, they can be classified into four groups as follows: Catecholate, hydroxama -hydroxycarboxylate and a mixed type of the
te, α 18
Introduction aforementioned (Miethke and Marahiel, 2007). The siderophore biosynthesis is occurring in two different ways. The biosynthesis can be differentiated in nonribosomal peptide synthetases (NRPS)-based siderophores and NRPS-independent siderophores (NIS). Within the first group non-proteinogenic amino acids are incorporated (Crosa and Walsh, 2002). In contrast, in NIS' an alternative synthetase family is active and via condensation amino alcohols, alcohols, dicarboxylic acids and diamines are fused together in an alternating fashion (Challis, 2005; Oves-Costales et al., 2009). After secretion and iron sequestration siderophores are transported through the outer membrane by the TonB-system and channeled through the inner membrane by an ABC transporter system in case of Gram- negative bacteria. In Gram-positive bacteria the import is realized by a substrate-binding lipoprotein and an ABC transporter (Miethke and Marahiel, 2007). There are two common mechanisms for the release of iron from siderophores: Within the first system Fe3+ is reduced to Fe2+ by an unspecific ferrisiderophore reductase, afterwards the ferrous iron is either spontaneously released or competitively sequestered by other molecules. The other mechanism is based on the hydrolysis of the iron-siderophore complex, in which the stability of the complex is drastically lowered. In contrast to the reductive iron release, the siderophores cannot be recycled in the hydrolysis pathway (Miethke and Marahiel, 2007). Furthermore, it was shown that some bacteria, so for instance S. lugdunensis, L. monocytogenes and S. pyogenes, are able to absorb siderophores produced by other organism in a microbial community. Those siderophores are then called xenosiderophores. The incorporation of xenosiderophores is beneficial for the cheaters as no energy has to be spent on siderophore production in the competition with other microorganisms (Griffin et al., 2004; Sheldon et al., 2016). In some populations this led to loss of siderophore production of cheated organisms and resulted in a reduced fitness within the iron-limited environment of the host (Andersen et al., 2015). The human host expresses siderophore-binding proteins such as lipocalin 2, also termed siderocalin or neutrophil gelantinase-associated lipocalin (NGAL), which sequesters siderophores produced by invading pathogens, as a defense mechanisms of the innate immune response (Flo et al., 2004). During this ongoing battle of host and pathogen, bacteria have evolved stealth-siderophores in response to proteins like lipocalin 2. Stealth- siderophores, for example salmochelin from pathogenic enterobacteria or petrobactin from B. anthracis, are not recognized by lipocalin 2 (Abergel et al., 2006; Barber and Elde, 2015; Hantke et al., 2003).
19
Introduction
Iron acquisition through transferrin and lactoferrin-binding proteins Besides the iron sequestration from transferrin and lactoferrin mediated by siderophores, several bacteria express transferrin- and lactoferrin-binding proteins on their cell surface. The process, in which iron is obtained from proteins associated to nutritional immunity, is called “iron piracy” (Sheldon et al., 2016). The best described systems are mainly from Gram- negative bacteria such as Neisseria gonorrhoeae, Neisseria meningitidis, Moraxella catarrhalis and Haemophilus influenzae. These bacteria colonize either the mucosal surface of the urogenital tract (N. gonorrhoeae) or the upper respiratory tract (N. meningitidis, M. catarrhalis and H. influenzae). As the pathogens reside extracellularly, they have evolved specialized cell surface-associated transferrin- and lactoferrin-binding proteins to obtain iron (Campagnari et al., 1994; Schryvers and Gray-Owen, 1992; West and Sparling, 1985). Briefly, the best investigated mechanism is the Fur-regulated system of the two proteins transferrin-binding protein A and B, TbpA and TbpB, which are a TonB-dependent outer membrane receptor and a surface-associated lipoprotein, respectively. Within this system TbpA binds both apo- and holo-transferrin, whereas TbpB binds only holo-transferrin. In N. meningitidis a specific protein domain of TbpB binds to a variable domain of human transferrin. After binding transferrin, TbpB does not change its structural confirmation, in which the iron remains still bound. Not till TbpA is also binding to transferrin, subsequently, the iron will be freed from this complex and transported through the membrane (Alcantara et al., 1993; Calmettes et al., 2012; Noinaj et al., 2012). A similar mechanism exists for lactoferrin. Accordingly, again the system is composed of a TonB-dependent outer membrane lactoferrin-binding protein LbpA (lactoferrin-binding protein A) and a lactoferrin-binding lipoprotein LbpB (lactoferrin-binding protein B). This system is functioning analogous to TbpA/TbpB-mechanism (Bonnah et al., 1995; Schryvers and Morris, 1988). Several years ago, a transferrin-binding protein could be identified in S. aureus. The staphylococcal transferrin-binding protein A, StbA, contains in its C-terminal region an LPXTG motif and is covalently-linked to the cell wall. The regulation of its gene should be under the control of Fur. Moreover, the authors found out that the stab gene lies within a region of iron-regulated genes with unknown function (Taylor and Heinrichs, 2002). Unfortunately, no more studies about the investigation of this transferrin-binding protein were published. Although the knowledge on transferrin-binding proteins in Gram-positive bacteria remains limited, several lactoferrin-binding proteins could be identified and characterized in Streptococcus species, several strains of S. aureus, Gardnerella vaginalis and in Bifidobacteria
20
Introduction
(Fang and Oliver, 2006; Jarosik and Land, 2000; Kim et al., 2002; Moshynskyy et al., 2003; Naidu et al., 1991; Naidu et al., 1992; Oda et al., 2014; Park et al., 2006; Rahman et al., 2008; Rainard, 1992). But the overall molecular mechanisms, which underlie the iron acquisition process mediated by lactoferrin receptors on the bacterial cell surface of Gram-positive bacteria is so far not elucidated.
Inorganic iron uptake Although inorganic iron within bacterial host environment is extremely limited, two inorganic iron transporter systems for the uptake of inorganic iron have been described. One is the EfeUOB/FepABC, which is homologous to iron acquisition system Fet3p-Ftrp1 in Saccharomyces cerevisiae (Cao et al., 2007; Große et al., 2006; Kosman, 2003). This Fur- regulated system is widely spread among many different microbial species. In Escherichia coli the acid-induced elemental ferrous iron uptake system EfeUOB is specific for ferrous iron, but it can also channel ferric iron and heme (Létoffé et al., 2009; Turlin et al., 2013). Beyond, the substrate specificity of EfeUOB/FepABC is varying between the different bacteria (Sheldon et al., 2016). The other one is the ferrous iron transport system FeoAB(C), which is broadly distributed in Enterobacteriaceae such as E. coli and Helicobacter pylori (Cartron et al., 2006; Hantke, 2003; Lau et al., 2016) and plays a role in the virulence of bacteria residing in the gastrointestinal tract of their host (Sheldon et al., 2016). This system depends on iron and oxygen, whereby it is regulated by Fur and the anaerobic transcriptional regulators Fnr and/or AcrA (Carpenter and Payne, 2014). The overall function of the three proteins is not fully understood yet, but FeoB is a G Protein-regulated ferrous iron permease (Marlovits et al., 2002) and both FeoA and FeoC are small, hydrophilic proteins of the cytoplasm (Cartron et al., 2006; Kammler et al., 1993b). It was shown in E. coli, Salmonella enterica and Vibrio cholera that FeoA is required for the Fe2+ uptake by FeoB (Kim et al., 2012; Weaver et al., 2013). So far -proteobacteria, where it might be involved in the stabilization ,of FeoC FeoB was under identified iron- and only oxygen in γ -limited conditions (Cartron et al., 2006; Kim et al., 2013). Even less is known on the inorganic iron acquisition in Gram-positive bacteria, but three Feo homologous have been described, namely: SitABC, MntABC and MtsABC (Sheldon and Heinrichs, 2015). The first system is the staphylococcal iron transporter system SitABC identified in Staphylococcus epidermidis (Cockayne et al., 1998). This Feo homolog is under the control of the staphylococcal iron regulator repressor SirR, which responds interestingly to ferrous iron and manganese (Hill et al., 1998). In S. aureus, on the other hand, is the analog to SitABC designated as manganese transporter MntABC system, which is under the control of the DtxR-like regulator MntR. Although this system is described
21
Introduction
as manganese transporter, it is not clear, which metal the actual substrate is (Horsburgh et al., 2002; Kehl-Fie et al., 2013). The third Feo homolog known so far is the metal transporter in streptococcus MtsABC discovered in S. pyogenes and responsible for the transport of iron and manganese (Janulczyk et al., 1999; Janulczyk et al., 2003). The expression of MtsABC is dependent on extracellular concentrations of ferric iron and manganese and regulated by MtsR (Hanks et al., 2006). The substrate specificity for this transporter system is more diverse, than only iron and manganese. The preference among the metals was described as follows: Fe2+ > Fe3+ > Cu2+ > Mn2+ > Zn2+ (Janulczyk et al., 2003; Sun et al., 2008).
3.3.2. IRON ACQUISITION BY PNEUMOCOCCI
The iron acquisition mechanisms of S. pneumoniae remains largely unknown compared to other Gram-positive bacteria, for instance in S. aureus. To date, three iron uptake ABC transporter systems, PiaABCD, PiuBCDA and PitABCD (Brown et al., 2001a; Brown et al., 2002; Brown and Holden, 2002), have been identified and partially characterized, although the actual iron acquisition mechanisms are not entirely elucidated yet. Moreover, it was shown that pneumococci can utilize ferrous as well as ferric iron and iron, which is bound to heme, hemin and hemoglobin. Pneumococci are also are able to obtain iron from xenosiderophores, e.g. they use siderophores produced by other microorganism (Pramanik and Braun, 2006; Tai et al., 1993; Tai et al., 2003). Another important protein for pneumococcal iron homeostasis is the non-heme iron-containing ferritin Dpr. Mainly, this iron storage protein has a pivotal role in the oxidative stress response of pneumococci (Pericone et al., 2003). Additionally, the regulation of these iron transporter system and the iron storage protein are connected to three regulators, namely RitR, CodY and IdtR (PsaR; DtxR homolog) (Gupta et al., 2013; Hendriksen et al., 2008; Johnston et al., 2005; Ulijasz et al., 2004). An overview of the iron acquisition systems of pneumococci is shown in Figure 3-2.
22
Introduction
Figure 3-2 Iron acquisition in S. pneumoniae.
Iron transporter systems Roughly 25 years ago, Tai and coworkers could describe for the first hemin uptake of a Gram- positive bacterium. Remarkably, this bacterium was S. pneumoniae and used hemin as iron source. Additionally, they showed that pneumococci can utilize hemoglobin as well (Tai et al., 1993; Tai et al., 1997). These studies were the starting point for investigations on iron acquisition systems in pneumococci. As mentioned above, three iron transporter systems have been described. PiaABCD (originally named Pit2ABCD), PiuBCDA (originally named Pit1ABCD; also referred as Fat/FecDCEB, (Ferrándiz and La Campa, 2014)) and PitABCD were identified by Brown and colleagues in the early 2000's. In a phenotypic characterization study they could show that a double deletion mutant in piaA and piuB was not able to utilize hemoglobin as iron source and had an attenuated virulence in a murine infection model. Furthermore, both iron transporters work independently from each other and in a single knock out mutant of one transporter, the other transporter can partially compensate the iron acquisition activity of the other (Brown et al., 2001a). A deletion of pitA had no growth effect, therefore the authors claimed that PitABCD is not as important for pneumococcal survival as PiaABCD and PiuBCDA (Brown et al., 2002). Additionally to the three iron transporter system described by Brown and colleagues, another novel iron transporter has been discovered only two years ago (Yang et al., 2016). In a very recent study, it was shown that all four iron transporters play a crucial role in the pneumococcal survival in manganese-deficient environment. In their proteomic study Cao and coworkers could demonstrate that during manganese
23
Introduction
deprivation the manganese transporter system PsaABC was higher abundant, plus all of the previously named iron transporters were present in much higher amounts. Furthermore, the addition of iron to the manganese-limited medium increased the pneumococcal growth. Thus, the authors stated that enhanced iron uptake can compensate the lack of manganese (Cao et al., 2018a). The same group published recently a study, in which another iron transporter was identified. SPD_1590, which was previously annotated as general stress protein, was shown to bind hemin and they assume that it has similar function as PiuA, but only in a minor role (Miao et al., 2018). The in silico analysis of the pneumococcal genome, which confirmed also the three aforementioned iron transporters (Pia, Piu and Pit), it suggested that pneumococci do not contain a gene encoding a for a transferrin-binding receptor protein and there is also no evidence of genes encoding siderophore biosynthetic proteins (Brown and Holden, 2002; Tettelin et al., 2001). The latter is proven by siderophore bioassay results demonstrating no production of these small iron scavenging molecules (Tai et al., 1993). However, pneumococci can acquire iron from xenosiderophores by binding to the PiaABCD transporter system. Because of the uptake of the ferric hydroxamates ferrichrome and ferroxamine B, PiaABCD is also referred as FhuCDBG (ferric hydroxamate uptake) analogous to the hydroxamte-mediated iron uptake of E. coli (Kadner et al., 1980; Pramanik and Braun, 2006). A few years ago, the human hormone norepinephrine (NE), which is also a known siderophore, was shown to affect pneumococcal iron homeostasis. NE shall benefit bacterial growth. In the presence of NE, the expression of the piu operon was downregulated, which led to a decreased PiuA-mediated adherence (Gonzales et al., 2013). Contrary observations have been made by Sandrini and coworkers, in which the attachment of pneumococci was increased by NE and piuA was highly upregulated. Interestingly, within this study the authors observed transferrin-binding to pneumococci and acquisition of transferrin-bound iron (Sandrini et al., 2014). This observation hints to a potential transferrin-binding protein, although no genetic evidence for a transferrin-binding protein was found (Brown and Holden, 2002; Tettelin et al., 2001). In an earlier study it was demonstrated that pneumococci are not able to obtain iron from the glycoproteins transferrin and lactoferrin (Tai et al., 1993). Nevertheless, Hammerschmidt and coworkers identified the pneumococcal surface protein A, PspA as a lactoferrin-binding protein (Hammerschmidt et al., 1999). Human apo-lactoferrin is bactericidal against several bacteria and also against S .pneumoniae (Arnold et al., 1980). Mirza and coworkers observed that a short peptide, lactoferricin, of apo-lactoferrin is responsible for the bactericidal effect on pneumococci. Furthermore, the authors suggested that PspA is blocking the active site(s) of apo-lactoferrin and thus prevent killing by the human glycoprotein (Mirza et al., 2004). Hence, PspA facilitate rather a
24
Introduction protection mechanism from pneumococcal killing during colonization on mucosal surfaces by human apo-lactoferrin (Mirza et al., 2004; Mirza et al., 2011) than an iron acquisition system. Additionally, it was shown that pneumococci cannot obtain iron from ferritin (Brown et al., 2001a). As iron transport protein systems are associated to the pneumococcal surface, they came into focus of several vaccination studies (Brown et al., 2001b; Jomaa et al., 2006; Whalan et al., 2005, 2006). The comparison of gene sequences among a variety of different pneumococcal serotypes resulted in > 99% conservation of the piuA and piaA genes, but the gene sequence of PitA varies significantly among the investigated serotype. Hence, PiaA and PiuA are good vaccine candidates, whereas PitA is none (Jomaa et al., 2006). Whalan and coworkers could demonstrate that sera from patients with septicemia have a serotype- independent antibody response to PiaA and PiuA (Whalan et al., 2005). Moreover, they investigated the distribution and genetic diversity of PiaA and PiuA among the family of Streptococcaceae. The gene piuA was shown to be present in Streptococcus mitis and Streptococcus oralis, but piaA was only present in S. pneumoniae. Thus, a potential vaccine based on PiuA could cover not only pneumococci, but also closely related streptococci (Whalan et al. 2006). Recently a vaccination study was published, in which a potential serotype-independent vaccine was introduced. The PnuBioVax (PBV) is a multi-antigen prophylactic vaccine against pneumococcal infections. Amongst other antigens also a PiuA-antigen is included. Meanwhile PVB is in the first phase of clinical trial and seems to be a promising novel vaccine candidate (Hill et al., 2018). The knowledge about the PitABCD transporter system is very rare. However, recently it was shown in a comparative sequence analysis that PitA is widely distributed among bacteria, occurring in four different variants (Cao et al., 2018b), but still the function of PitABC transporter system remains unclear. In addition, several hemoglobin and heme-binding proteins were detected and partially characterized (Romero-Espejel et al., 2013; Romero-Espejel et al., 2016; Vázquez-Zamorano et al., 2014).
Iron storage in pneumococci S. pneumoniae lacks the enzyme catalase and produces via pyruvate oxidase, SpxB, large amounts of endogenous hydrogen peroxide. Pericone and colleagues investigated the question, how pneumococci can survive high endogenous hydrogen peroxide concentrations without expressing the catalase enzyme. Hydrogen peroxide forms together with ferrous iron via the Fenton reaction reactive oxygen species (ROS') such as hydroxyl radicals, which
25
Introduction
can damage DNA, lipids and proteins. The authors assumed, as one possible explanation, that the formation of toxic ROS' is prevented by the iron-storage protein Dpr (Dps[DNA-binding protein from starved cells]-like peroxide resistance), which binds with high affinity iron. Hence, they wanted to investigate a dpr deletion mutant, but unfortunately this mutant could not be generated. The authors therefore concluded that Dpr has to be essential for pneumococcal survival (Pericone et al., 2003). With the production of high concentrations of hydrogen peroxide pneumococci have a competitive advantage towards co-colonizing bacteria (Pericone et al., 2000). Ferrous iron is taken up by Dpr and is subsequently bound to its ferroxidase center. Afterwards, the iron is oxidized to the ferric form and stored within Dpr (Havukainen et al., 2008). Hua and colleagues were finally able to generate a dpr deletion mutant in S. pneumoniae. With this study they could show that the deletion mutant is sensitive to pH, heat, higher iron concentrations and oxidative stress triggered by hydrogen peroxide. In addition, they executed a murine colonization model and observed a reduced colonization and a better clearance of the nasopharynx. Accordingly, the non-heme iron- containing ferritin Dpr is important for pneumococcal stress resistance and for colonization of the upper respiratory tract of pneumococci's host (Hua et al., 2014).
Regulation of iron homeostasis-associated proteins The expression of the proteins mentioned in this chapter is regulated by three different iron- dependent regulators known so far. RitR (repressor of iron transport regulator) is an orphan two-component signal transduction response regulator, which is involved in the regulation of protein expression belonging to oxidative stress response, DNA repair, sugar uptake and heme biosynthesis. One of its main functions is the maintenance of the iron homeostasis. Thus, RitR represses the translation of the piu operon and activates Dpr expression. This was shown by Ulijasz and coworkers in a transcriptome analysis investigating a ritR deletion mutant as the expression of piu genes were upregulated and dpr was downregulated. Still, they could not demonstrate that Dpr expression is directly regulated by RitR or if the regulation is indirect by a high intracellular iron concentration (Ulijasz et al., 2004). RitR itself is regulated by a phosphorylation at the DNA-binding domain. The same group assumes that StkP/PhpP signaling couple (serine- threonine kinase and phosphatase) is responsible for phosphorylation status of RitR. Thus, the StkP/PhpP couple seems to be necessary for the translation of the piu operon, wherein PhpP shall positively regulate the expression of PiuBCDA transporter proteins (Ulijasz et al., 2009). Recently, it was published that RitR is an archetype as a redox sensor in streptococci. All so far known redox-sensing transcription factors are missing in pneumococci. RitR is required for the nasopharyngeal colonization, which is enabled by this redox switch. The
26
Introduction redox switch of RitR is described to be a conserved cysteine residue, which is situated on the linker between the receiver and the DNA-binding domain. During higher concentrations of hydrogen peroxide two RitR proteins form an inter-protomer connected by their two cysteines via a disulfide bridge. In this form RitR is able to bind to the DNA and thus, repress the translation of the piu operon. In addition, they could show that the translation of the pia operon is not controlled by RitR (Glanville et al., 2018). Another regulator, which is also associated to the iron homeostasis in S. pneumoniae, is the pleiotropic global repressor CodY. Proteins from amino acid metabolism and cellular processes such as carbon metabolism and iron uptake are under the control of CodY. Hendriksen and coworkers investigated in a codY deletion mutant the gene and protein expression. Alongside with the altered regulation in the expression of proteins involved in amino acid metabolism, biosynthesis and uptake, they could show an upregulation of the piu operon in the deletion mutant. Hence, CodY represses the piu gene expression. Additionally, CodY is an activator for dpr translation (Hendriksen et al., 2008). Interestingly, in a codY deletion mutant two additional mutations were found in fatC (piuC; truncated FatC gene product) and in amiC (oligopeptide ABC transporter permease encoding gene). They were called suppressor mutations, as they allow pneumococci the tolerance of codY inactivation. CodY is essential for pneumococcal survival, as the repression of piu operon is required to prevent an uncontrolled import of iron. Moreover, parallels between CodY and RitR were identified, for instance, in the activation of Dpr expression and the repression of the piu operon (Caymaris et al., 2010). The described suppressor mutations were summarized under the term suppressor of codY (SocY). Johnston and colleagues compared suppressing mutations of three studies, in which codY deletion mutants were generated, and the three mutants had three additional sets of mutations. One mutant contained truncated fatC (piuC) and amiC (Caymaris et al., 2010), the second mutant a truncated fecE (piuD) and amiC (Kloosterman et al., 2006), and the third mutant had also a truncated fecE (piuD) (Härtel et al., 2012). With these findings the authors suggested that the co-inactivation of the fat/fec (piu) operon is crucial for the survival of S. pneumoniae missing codY gene (Johnston et al., 2015). Besides the already mentioned and well characterized regulators, a third regulator is described. Gupta and colleagues termed this regulator iron-dependent transcriptional regulator, IdtR and suggested that IdtR has a role in the modulation of virulence and in the repression of certain virulence factors. This was assumed after analyzing a deletion mutant of idtR in a murine sepsis models. It was postulated that IdtR is essential during the transition from nasopharyngeal mucosa to submucosal tissues and blood (Gupta et al., 2013). However, this protein has been described a few years earlier as a manganese-dependent regulator
27
Introduction
called PsaR. Johnston and coworkers could show that in the presence of manganese the transcription of several virulence factor encoding genes as well as the psaBCA operon (encoding manganese ABC transporter system) are repressed. PsaR is a homolog to DtxR, which was described above (see 3.3.1). Furthermore, they found that PsaR is required for full virulence in a murine pneumonia model (Johnston et al., 2005). In addition, the presence of zinc, cobalt and nickel was shown to lead to derepression of PsaR (Kloosterman et al., 2008; Manzoor et al., 2015a; Manzoor et al., 2015b). Although, the iron acquisition mechanisms and iron homeostasis in pneumococci have been studied within the last three decades, still more effort is required to shed more light on this topic. So far, four iron transport systems have been described and potential substrates have been identified. However, if there are more iron transporter systems in pneumococci is not clear. Additionally, the mechanisms underlying the known iron acquisition mechanisms remain largely unexplained. Moreover, the regulation of iron homeostasis is not yet fully elucidated. With the help of iron limitation experiments, it is possible to identify iron- dependent proteins and regulatory mechanisms. This could provide a basis for further studies to elucidate iron homeostasis in pneumococci in order to identify new potential vaccine candidates or drug targets.
3.3.3. ESTABLISHMENT OF IN VITRO IRON STARVATION
An artificial iron starvation can be generated in different ways. What all methods have in common is that iron is deprived, more or less specifically, from the cultivation medium. For iron limitation approaches in microorganisms the most prominent methods are examined using chemical iron chelators, biological compounds or two media with or without iron. In the latter method usually chemically defined media are used, in which it is possible to omit the iron source. This was done in a shotgun proteomics study on Bordetella pertussis (Alvarez Hayes et al., 2015). One disadvantage of this method is that also last traces of iron can still be present, because other compounds of chemically defined media are usually contaminated with iron. In an iron starvation experiment the natural iron-restricted conditions in the mammalian host shall be investigated. Hence, it is obvious to use biological compounds, which have high iron-binding affinities, for the introduction of iron-depleted conditions. So for instance human transferrin, which in a major human iron-binding protein, is widely applied as apo- transferrin in various bacteria, e.g. in Acinetobacter baumannii (Actis et al., 1993; Dorsey et al., 2004), Vibrio anguillarum (Dorsey et al., 2004), Staphylococcus epidermidis (Bonsdorff et al., 2006) and in combination with other chelators in S. aureus (Lin et al., 2014). In a further
28
Introduction study, Rooijakkers and colleagues investigated the effect of human serum, and especially apo-transferrin, on the growth of Bacillus anthracis. Additionally, as comparative Gram- positive bacteria, they also investigated S. aureus and S. pneumoniae. They could show that apo-transferrin inhibits the growth of B. anthracis by its iron-sequestering properties. In contrast, pneumococci and S. aureus were unaffected by purified apo-transferrin (Rooijakkers et al., 2010). Thus, an iron limitation approach in pneumococci by applying apo- transferrin might not be promising. Some microorganisms are producing small molecules with iron-chelating properties, which are termed siderophores. One of the best known siderophores is desferoxamine (Desferal, DFO). This siderophore and others are applied in numerous iron limitation experiments (Chart and Rowe, 1993; Lin et al., 2014; Ma et al., 2015; Vries et al., 2012). Although DFO is used in so many studies as iron-chelating agent, it happens that DFO can be utilized by microorganisms as iron source in form of a xenosiderophore. This is, in fact, the case in fungi of the order of Mucorales. It was shown that in the development of the disease mucormycosis is paradoxically increased, because the fungi were not iron-restricted by DFO application they rather used it as xenosiderophore for iron acquisition (Boelaert et al., 1993; Ibrahim et al., 2007). Therefore, the application of a siderophore for iron limitation experiment can be difficult as pneumococci are able to utilize siderophores produced by other microorganisms (Pramanik and Braun, 2006; Tai et al., 1993; Tai et al., 2003). Preliminary experiments in terms of practicability of selected siderophore would have been necessary. Besides iron chelators deriving from a natural source, also chemical compounds are known to have a high affinity for iron. Three preeminent chemicals in connection to iron starvation studies in bacteria are ethylenediamine-di-(o-hydroxyphenylacetic acid) (EDDHA), Chelex 100, and 2,2’-bipyridine (BIP). EDDHA is able to form ferric complexes (Gómez-Gallego et al., 2002) and is broadly used in iron restriction analyses in microorganisms (Actis et al., 1993; Aguila et al., 2001; Chung et al., 2012; Dorsey et al., 2004). In contrast to EDDHA, Chelex 100 binds nearly all transition metals and is usually used for ultrapurification of samples and solutions. Historically, Chelex 100 was applied in columns as a resin material; nowadays it can also be directly added to culture medium before bacterial growth. As Chelex 100 binds all essential metal ions for microorganisms, the supplementation of these metal ions, especially magnesium, is a vital requirement for such an iron limitation approach (Kadurugamuwa et al., 1987). Also Chelex 100 is often used in the large field of microbiology, so for instance in studies on Listeria monocytogenes (Ledala et al., 2010), S. aureus (Aguila et al., 2001)and S. pneumoniae (Nanduri et al., 2008).
29
Introduction
One of the most frequently used iron chelators in analyses of pathogenic microorganisms is the organic compound BIP. Bipyridines (also referred as bipyridyls, dipyridines or dipyridyls) are a family of six isomers (2,2’; 2,3’; 2,4’; 3,3’; 3,4’ and 4,4’), of which all are biologically active and five isomers are also found in the environment. There are two major sources; on the one hand bipyridines are degradation products from paraquat and related herbicides, and one the other hand these compounds occur as pyrolytic degradation products in tobacco. Additionally, the 2,2’-bipyridine (BIP) isomer is also detected in diquat (Li et al., 2004). Besides iron, BIP can also form diverse complexes together with ions of other transition metals (e.g.: manganese, cobalt, nickel, copper, zinc, ruthenium, osmium cadmium, iridium and mercury) and ligands (Holyer et al., 1965; Saji and Aoyagui, 1975; Watts et al., 1977; Yadav et al., 2013; Zhang et al., 2013).
3.4. OBJECTIVE OF THE THESIS
This thesis is the first global and comprehensive study on S. pneumoniae under iron limitation applying LC-MS/MS-based quantitative proteomics. The aim was the elucidation of the proteomic response of pneumococci to iron depletion based on the iron-chelating agent 2,2’-bipyridine. The alterations of protein abundances after iron limitation will extend the knowledge of the pneumococcal physiology during its adaptation to iron-restricted host environment.
30
Materials and methods
4. MATERIALS AND METHODS
4.1. BACTERIAL STRAIN
In this work the serotype 2 strain S. pneumoniae cps (deletion SPD_0312 to SPD_0333) was investigated. This strain was prepared by ClaudiaD39Δ Rennemeier as described in her PhD thesis (Rennemeier, 2007).
4.2. CHEMICALS
Table 4-1 Overview of applied chemicals
Chemical Manufacturer Fluka Analytical, Sigma-Aldrich Chemie 2,2’-Bipyridine GmbH, Darmstadt, Germany Carl Roth GmbH + Co. KG, Karlsruhe, Acetic acid Germany Acetonitrile VWR International, Radnor, PA, USA Carl Roth GmbH + Co. KG, Karlsruhe, Acetonitrile with 0.1% acetic acid Germany Carl Roth GmbH + Co. KG, Karlsruhe, Adenine Germany Thermo Fisher Scientific, Waltham, MA, BSA Fraction V USA Sigma-Aldrich Chemie GmbH, Darmstadt, Choline chloride Germany OXOID Limited, Hampshire, United Columbia agar with sheep blood plus Kingdom cOmplete Tablets, Mini EDTA-free, F. Hoffmann-La Roche AG, Basel, EASYpack Switzerland Carl Roth GmbH + Co. KG, Karlsruhe, di-Sodium hydrogen phosphate dihydrate Germany EDTA disodium salt dihydrate AppliChem GmbH, Darmstadt, Germany Carl Roth GmbH + Co. KG, Karlsruhe, Ethanol Germany Gibco® RPMI 1640 without L-glutamine, L- Thermo Fisher Scientific, Waltham, MA, lysine and L-arginine USA
31
Materials and methods
Chemical Manufacturer
Carl Roth GmbH + Co. KG, Karlsruhe, Glutaraldehyde 25% Germany Carl Roth GmbH + Co. KG, Karlsruhe, Glycine Germany HyClone RPMI-1640 Medium (1x) GE Healthcare GmbH, Solingen, Germany Sigma-Aldrich Chemie GmbH, Darmstadt, Iodoacetamide Germany Carl Roth GmbH + Co. KG, Karlsruhe, Iron(II) sulphate heptahydrate Germany Sigma-Aldrich Chemie GmbH, Darmstadt, Iron(III) chloride hexahydrate Germany High-Purity Standards, Charleston, SC, Iron standard USA Sigma-Aldrich Chemie GmbH, Darmstadt, Kanamycin Germany L-Arginine HCl, Arg-10, 13C,15N Silantes GmbH, München, Germany Fluka analytical, Sigma-Aldrich Chemie L-Glutamine GmbH, Darmstadt, Germany L-Lysine HCl, Lys-8, 13C,15N Silantes GmbH, München, Germany Methanol Merck KGaA, Darmstadt, Germany Carl Roth GmbH + Co. KG, Karlsruhe, Osmium tetroxide 5% Germany Carl Roth GmbH + Co. KG, Karlsruhe, Paraformaldehyde Germany Carl Roth GmbH + Co. KG, Karlsruhe, Potassium chloride Germany Carl Roth GmbH + Co. KG, Karlsruhe, Potassium dihydrogen phosphate Germany Carl Roth GmbH + Co. KG, Karlsruhe, Roti-Nanoquant Germany Sigma-Aldrich Chemie GmbH, Darmstadt, Sodium bicarbonate Germany
32
Materials and methods
Chemical Manufacturer Carl Roth GmbH + Co. KG, Karlsruhe, Sodium chloride Germany Sodium dihydrogen phosphate Carl Roth GmbH + Co. KG, Karlsruhe, monohydrate Germany
Carl Roth GmbH + Co. KG, Karlsruhe, Todd-Hewitt-Bouillon Germany Fluka analytical, Sigma-Aldrich Chemie Triethylammonium bicarbonate buffer GmbH, Darmstadt, Germany Carl Roth GmbH + Co. KG, Karlsruhe, Trifluoroacetic acids Germany Carl Roth GmbH + Co. KG, Karlsruhe, Tris Germany Tris-(2-carboxyethyl)-phosphine Carl Roth GmbH + Co. KG, Karlsruhe, hydrochloride Germany Trypsin Promega GmbH, Mannheim, Germany Trypsin Resuspension buffer Promega GmbH, Mannheim, Germany Uracil BioChemica AppliChem GmbH, Darmstadt, Germany Carl Roth GmbH + Co. KG, Karlsruhe, Water with 0.1% acetic acid Germany Carl Roth GmbH + Co. KG, Karlsruhe, Yeast extract Germany Carl Roth GmbH + Co. KG, Karlsruhe, -D(+)-Glucose monohydrate Germany α 4.3. WATER AND MEDIA
4.3.1. WATER
In this work the term “MS water” is used, which stands for ultrapure water ASTM type1.
4.3.2. CHEMICALLY DEFINED MEDIUM
The chemically defined medium (CDM) is modified RPMI 1640 medium supplemented with buffer solution and adenine/uracil solution as described by (Schulz et al., 2014).
33
Materials and methods
CDM: 500 ml RPMI 1640 40.54 ml Buffer solution 5 ml Adenine/uracil solution Buffer solution: 30.52 mM Glucose
21.85 mM NaHCO3 1.10 mM Glycine 0.24 mM Choline chloride
1.72 mM NaH2PO4 2O
3.85 mM Na2HPO4 ∙ H 2O In a. dest.; sterile filtration;∙ 2H storage at 4 °C Adenine/uracil solution: 4.0 g l-1 Adenine (0.27 mM) 8.0 g l-1 Uracil (0.65 mM) Solve in 40 ml 1 N HCl at 90 °C; fill up to 100 ml with a. dest.; store at RT; before use reheat to 90 °C and sterile filtration
4.3.3. STABLE ISOTOPE LABELING BY AMINO ACIDS IN CELL CULTURE - CDM
For the preparation of SILAC CDM RPMI 1640 without glutamine, lysine and arginine was used, but the supplements as described above (4.3.2) were not changed. The absent amino acids were solved in buffer solution and added to the medium. The amounts of missing amino acids accord to amino acids concentration in unlabeled RPMI medium (glutamine 300 mg l-1, arginine 200 mg l-1, lysine 40 mg l-1).
4.3.4. TODD-HEWITT BROTH WITH YEAST EXTRACT MEDIUM
For the Todd-Hewitt broth with yeast extract medium (THY) 36.4 l-1 Todd-Hewitt-Bouillon and 0.5% (w/v) yeast extract were solved in a. dest. and filled up to 1 l.
4.4. CONSUMABLES
Table 4-2 Overview of applied consumables
Consumable Company 0.22 µm Rotilabo-syringe filters, CME, Carl Roth GmbH + Co. KG, Karlsruhe, sterile Germany 8 mm Screw cap VWR International, Radnor, PA, USA C18, Aeris PEPTIDE 3.6 µ xB phenomenex, Aschaffenburg, Germany Cotton swabs A. Hartenstein GmbH, Würzburg, Germany
34
Materials and methods
Consumable Company Fused Silica Capillaries 30 µm Postnova Analytics GmbH, Landsberg, Germany Glass beads 0.10-0.11 mm Sartorius AG, Göttingen, Germany iRT Kit Biognosys AG, Schlieren, Switzerland Micro-Insert 0.1 ml VWR International, Radnor, PA, USA StageTips Thermo Fisher Scientific, Waltham, MA, USA Vial screw 1.5 ml VWR International, Radnor, PA, USA ZipTip Merck KGaA, Darmstadt, Germany
4.5. INSTRUMENTS
Table 4-3 Overview of used instruments
Instrument Company Allegra X-15R Centrifuge Beckman Coulter, Brea, CA, USA Balance 510 KERN & SOHN GmbH, Balingen, Germany Balance ABJ-NM/ABS-N KERN & SOHN GmbH, Balingen, Germany Balance PRECISION Plus OHAUS Europe GmbH, Greifensee, Switzerland Balance Sartorius Basic Sartorius AG, Göttingen, Germany BioPhotometer plus Eppendorf AG, Hamburg, Germany Bunsen burner WLD-TEC GmbH (Wartewig), Göttingen, Germany Centrifuge 5417R Eppendorf AG, Hamburg, Germany Clean Bench Hera Safe Thermo Fisher Scientific, Waltham, MA, USA EASY-nLC 1000 Thermo Fisher Scientific, Waltham, MA, USA EASY-nLC 1200 Thermo Fisher Scientific, Waltham, MA, USA EASY-nLC II Thermo Fisher Scientific, Waltham, MA, USA Heating plate, MR 3001 Heidolph Instruments GmbH & Co.KG
35
Materials and methods
Instrument Company Heraeus Biofuge Primo R Centrifuge Thermo Fisher Scientific, Waltham, MA, USA Homogenizer FastPrep-24 MP Biomedicals, Santa Ana, CA, USA Homogenizer Precellys 24 Bertin, Montigny-le-Bretonneux, France Incubator Heracell 150 Thermo Fisher Scientific, Waltham, MA, USA Orbitrap Elite Thermo Fisher Scientific, Waltham, MA, USA Orbitrap Velos Pro Thermo Fisher Scientific, Waltham, MA, USA Photometer Genesys 10S Vis Thermo Fisher Scientific, Waltham, MA, USA Ultrasonic water bath RK 102 H BANDELIN electronic GmbH & Co. KG, Berlin, Germany Vacuum centrifuge, Concentrator plus Eppendorf AG, Hamburg, Germany Water bath GFL Gesellschaft für Labortechnik mbH, Burgwedel, Germany ICP-MS 7500c Agilent Technologies, Waldbrunn, Germany
4.6. COMPUTER SOFTWARE
Table 4-4 Overview of applied software
Software Company/Reference MaxQuant 1.5.3.30 Cox and Mann, 2008 Paver 2.0 DECODON GmbH, Greifswald, Germany Perseus 1.5.6.0 Tyanova et al., 2016 RStudio 1.0.136 RStudio, Boston, MA, USA Scaffold 4.4.7 Proteome Software, Inc., Portland, OR, USA Sorcerer 3.5 release Sage-N Research, Inc., Milpitas, CA, USA Xcalibur 2.2 SP1.48 Qual Browser Thermo Fisher Scientific, Waltham, MA, USA
36
Materials and methods
4.7. EXPERIMENTAL DESIGN AND CULTIVATION
4.7.1. STRAIN MAINTENANCE
Pneumococci were spread on a blood agar plate with 50 µg ml-1 cps mutant selection and incubated for 8 hours at 37 °C and 5% CO2 atmosphere.kanamycin All colonies for Δ from the blood agar plate were collected with a cotton swab and transferred into cryo storage tubes containing THY with 20% (v/v) glycerol. The bacteria were stored at -80 °C for long-term storage.
4.7.2. CULTIVATION AND SAMPLING
Overnight cultures on blood agar plates For overnight cultures pneumococci from long term storage (see above) were spread with an inoculation loop on blood agar plates with 50 µg ml-1 cps mutant selection and incubated overnight for maximal 10 hours at 37 °C kanamycinand 5% CO 2for atmosphere. Δ Modification of pneumococcal cultivation protocol The cultivation workflow for S. pneumoniae D39 cps is depicted in Figure 4-1. The first step was the transfer bacteria from long term storageΔ at -80 °C to a blood agar plate without antibiotics, allowing the pneumococci to adapt to changed environmental conditions without selection pressure. The plate was incubated at 37 °C and 5% CO2 atmosphere for maximal 8 hours. In the next step pneumococci colonies were transferred onto a blood agar plate with 50 µg ml-1 kanamycin cps mutant selection. This plate was incubated at 37 °C and 5%
CO2 atmosphere for maximalfor Δ 10 hours. The last step was the inoculation of the liquid main culture directly from blood agar plate.
Figure 4-1 Initial cultivation workflow for S. pneumoniae cps proteome analyses. Parts of the figure were
2 taken from Servier Medical Art . D39Δ
2 “Cell culture and microbiology” Servier Medical Art by Servier, used under CC BY 3.0 / Desaturated from original, and the original version can be found here: https://smart.servier.com/wp- content/uploads/2016/10/Microbiology_cellculture.ppt.
37
Materials and methods
Due to unreproducible growth (see 5.1.1), an additional liquid preculture step was introduced to the cultivation workflow as depicted in Figure 4-2. Additionally, it was observed that S. pneumoniae D39 cps, which were transferred directly from -80 °C storage to an antibiotic blood agar plate showedΔ no difference in growth compared to the bacteria cultivated following the workflow with two blood agar plate steps. This modified cultivation workflow was used for all growth experiments in this work.
Figure 4-2 Modified cultivation workflow for S. pneumoniae cps proteome analyses. Parts of the figure were
(see footnote Figure 4-1) taken from Servier Medical Art2 . D39Δ Determination of suitable BIP concentration for iron limitation experiments In order to determine a suitable BIP concentration for iron limitation experiments different BIP concentrations were tested. Therefore, liquid precultures were inoculated from
overnight cultures (see above) to an OD600 nm between 0.05 and 0.07 in CDM and incubated
at 37 °C until cultures reached OD600 nm 0.2. The main cultures were inoculated to OD600 nm
0.08 in CDM. After reaching OD600 nm 0.2 different concentrations (0.1 mM, 0.25 mM, 0.5 mM, 1mM) of BIP were added to the medium. The growth curves were recorded and the BIP concentration was defined with 0.5 mM (see 5.2.1), because at this concentration pneumococci showed an inhibited growth. For the determination of a suitable BIP concentration to induce iron limitation in THY, the results of iron concentration determination with ICP-MS were used (see 5.2.5). This analysis showed that THY contains fourfold more iron than CDM, for which reason the BIP concentration in the iron limitation experiment in THY was defined to be 2 mM. Thus, liquid precultures were inoculated from overnight cultures (see before) to an OD 600 nm between
0.06 to 0.07 in THY and incubated at 37 °C until cultures reached OD 600 nm 0.35. The main
cultures were inoculated to OD 600 nm 0.6 in THY. Thirty minutes after inoculation the cultures were stressed with different BIP concentrations (0.2 mM, 0.5 mM, 1 mM, 2 mM, 5 mM). The growth was monitored for six hours.
38
Materials and methods
BIP solution: 30 mM BIP in a. dest. Solve at 30 °C in ultrasonic bath in the dark Filtration with 0.2 µm filter
BIP toxicity test In addition to the determination of the BIP concentration, it was also examined, if BIP itself has effects on pneumococcal growth. Therefore, pneumococci were cultured in CDM. The liquid precultures were inoculated from blood agar plates to an OD 600 nm between 0.06 and
0.08 and grown until OD 600 nm 0.2. The main cultures were inoculated to OD 600 nm 0.06 to 0.07. Thirty minutes after inoculation bacterial cultures were treated with 0.167 mM BIP-iron(II)- complex (Complex) and compared to control and stress experiment (0.5 mM BIP). The BIP- iron(II)-complex had the same amount of BIP molecules as in BIP stress experiment. One iron(II)-ion complexes three BIP molecules, that means 0.5 mM BIP with 0.167 mM iron(II)- salt yields in 0.167 mM BIP-iron(II)-complex. The pneumococcal growth was observed for six hours.
Iron(II) salt solution: 100 mM Fe2SO4 in a. dest. BIP-iron(II)-complex solution: 30 mM BIP solution (see above)
10 mM Fe2SO4 Filtration with 0.2 µm filter
Incorporation rates of heavy labeled amino acids In order to reach almost complete incorporation of heavy labeled amino acids into the bacterial proteome, two liquid precultures were prepared using CDM with SILAC RPMI 1640 from Gibco deficient in glutamine, lysine and arginine and supplemented with 2 mM L- glutamine, 0.21 mM 13C15N-labeled lysine, and 0.91 mM 13C15N-labeled arginine. The precultures were inoculated to OD600 nm 0.04 and transferred to the next culture after reaching an OD600 nm between 0.15 and 0.2. The main culture was inoculated to OD600 nm 0.06. The main culture was harvested by centrifugation after entry into early stationary phase.
Heavy labeled SILAC standard The cultivation for the heavy labeled SILAC standard was done as described above for the “Incorporation Rates of Heavy Labeled Amino Acids”. For control samples pneumococci were harvested after 4.5 hours. The stress cultures were treated with 0.5 mM BIP 30 minutes after inoculation. The BIP treated pneumococci were harvested after 2.5 hours in early stationary growth phase. The growth is illustrated in Figure 4-3.
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1
600 nm 0.1 Control OD 0.5 mM BIP
0.01 0 50 100 150 200 250 300 Time [min]
Figure 4-3 Growth of S. pneumoniae cps under control and stress condition in heavy labeled CDM. Addition
of BIP is indicated by a lightning. TimeD39Δ points of cell harvest are marked by arrows. Iron limitation experiment S. pneumoniae cps was spread on blood agar plates with 50 µg ml-1 kanamycin and was inoculated for maximalD39Δ 10 hours at 37 °C in 5% CO2 atmosphere. Pneumococci were grown in 50 ml centrifuge tubes filled with 40 ml CDM without agitation at 37 °C. For each biological replicate two cell cultures were combined to gain sufficient protein amounts. Liquid precultures were inoculated to an OD600 nm between 0.06 and 0.08 and cultured to OD600 nm 0.2. The main cultures were inoculated from the precultures to an
OD600 nm between 0.06 and 0.07. After 30 minutes 0.5 mM BIP was added to stress samples to induce the iron limitation. In order to deduce the effects of BIP, which are independent of its iron-chelating properties, 0.167 mM BIP-iron(II)-complex (BIP molecules equal to 0.5 mM) were added to another pneumococcal culture. Bacteria treated with BIP or BIP- iron(II)-complex, respectively, were harvested 150 minutes after inoculation in early stationary phase. Control culture samples were harvested 200 minutes after inoculation at the same growth rate as BIP-treated bacteria. The pneumococcal growth is illustrated in Figure 4-4.
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1
CDM Control
600 nm 0.1 CDM 0.5 mM BIP OD
CDM 0.167 mM Complex
0.01 0 50 100 150 200 250 Time [min]
Figure 4-4 Growth of S. pneumoniae cps under iron limitation in CDM. Addition of BIP or BIP-iron(II)- complex is indicated by a lightning. TimeD39Δ points of cell harvest are marked by arrows. In order to compare the response of S. pneumoniae to iron limitation in CDM to an adaptation in a medium with a different initial iron concentration, pneumococcal cells were cultivated in 40 ml THY, without agitation at 37 °C. Liquid precultures were inoculated to an OD600 nm between 0.06 and 0.07 and grown maximally to OD600 nm 0.4. The main cultures were inoculated from the precultures to an OD600 nm between 0.05 and 0.07. After 30 minutes 0.5 mM and 2.0 mM BIP or 0.167 mM and 0.67 mM BIP-iron(II)-complex were added to the medium. On the one hand the concentrations of BIP and BIP-iron(II)-complex were the same as for the CDM experiments (0.5 mM and 0.167 mM, respectively). As ICP-MS analyses revealed that CDM contains about 190 µg l-1 and THY contains 740 µg l-1 iron (see 5.2.5), which is approximately fourfold more iron than in CDM, higher concentrations of BIP and BIP-iron(II)-complex (2.0 mM and 0.67 mM, respectively) were adapted to main cultures in THY to take into account the higher native iron concentration in THY compared to CDM. Control samples and bacteria treated with BIP or BIP-iron(II)-complex, respectively, were harvested 180 minutes after inoculation (see Figure 4-5, early stationary phase). All experiments in CDM and THY medium were carried out in three independent replicates.
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10
1
THY Control 600 nm THY 2.0 mM BIP OD
0.1 THY 0.67 mM Complex
0.01 0 50 100 150 200 Time [min]
Figure 4-5 Growth of S. pneumoniae cps under iron limitation in THY. Addition of BIP or BIP-iron(II)-
complex is indicated by a lightning. TimeD39Δ points of cell harvest are marked by arrows.
4.7.3. CELL HARVEST AND DISRUPTION
All pneumococcal cultures were harvested by centrifugation at 4,500 x g at 4 °C for 10 minutes. The cell pellets were washed twice with ice cold 1x PBS supplemented with 1% (w/v) choline chloride and cOmplete protease inhibitor cocktail (application according to manufacturer’s protocol). The pneumococcal cell pellets were resuspended in 750 µl TE buffer and transferred in cryo storage tube filled with 0.5 ml glass beads. The cell disruption was performed with the homogenizer Precellys 24 or with FastPrep-24 at 6,400 x g for 6 x 30 seconds and 4 °C. The glass beads and cell debris were removed by two centrifugation steps (first: 5,000 x g, 15 minutes, 4 °C; second: 10,000 x g, 45 minutes, 4 °C).
1x PBS (phosphate buffer saline): 137 mM NaCl 2.7 mM KCl
10 mM Na2HPO4
1.8 M KH2PO4 pH 7.4 TE buffer: 50 mM Tris 10 mM EDTA pH 7.4
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4.7.4. GRAM-STAINING
For Gram-straining pneumococci under control and iron-limited conditions were cultivated in CDM as described above (see 4.7.2). A culture droplet was set onto a microscope slide. Subsequently, the droplet was dried and fixed with heat from the flame of a Bunsen burner. The fixed bacteria were treated for 1 minute with crystal violet solution and washed with distilled water. The remaining water was washed away with Lugol’s solution. The bacteria were incubated with Lugol’s solution for 1 minute followed by a washing step with distilled water. The remaining water was washed away with Safranin T solution. Afterwards pneumococci were incubated with Safranin T solution for 1 minute. The prepared microscope slide was dried. The stained samples were examined with a light microscope.
Crystal violet solution: 1 g Crystal violet Solve in 100 ml a. dest., filtration with fluted filter Lugol’s solution: 1 g Iodine 2 g Potassium iodide Solve in 5 ml a. dest., fill up to 300 ml Safranin T solution: 1 g Safranin T Solve in 100 ml a. dest.
4.7.5. ELECTRON MICROSCOPY
First sample preparation method for electron microscopy For transmission electron microscopy (TEM) pneumococci under control and stress condition were cultivated in CDM as described above (see 4.7.2), but no BIP-iron(II)-complex were investigated in this approach. The results of this first cell morphology analysis are described in chapter 5.2.4. The bacterial cells were harvested by centrifugation at 4,500 x g at 4 °C for 10 minutes. The supernatant was discarded and the bacteria were resuspended in fixation solution I and incubated for 20 minutes on ice, followed by centrifugation. Bacterial pellets were washed twice with washing solution. In the next step the pellets were resuspended in fixation solution II and incubated for 2 hours on ice. The samples were washed three times with washing solution. Afterwards they were treated with a solution of 800 µl washing solution and 200 µl 5% (w/v) osmium tetroxide, which was incubated for 1 hour at room temperature, followed by a washing step. Subsequently, the bacteria were washed with cacodylate buffer and centrifuged. Finally, the pneumococci were covered with cacodylate buffer and sent to Prof. Dr. Manfred Rohde (HZI Braunschweig) for further processing and analysis by TEM.
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2x Caco buffer: 0.2 M Sodium cacodylate trihydrate 0.02 M Calcium chloride 0.02 M Magnesium chloride Solve in a. dest., fill up to 1 l pH 6.9 Cacodylate buffer: 2x Caco buffer 0.18 M Saccharose 25% (w/v) Formaldehyde solution: 25 g Paraformaldehyde 90 ml a. dest., heat to 60 °C Titrate 10 M NaOH until paraformaldehyde is solved Fill up to 100 ml with a. dest. Filtration through fluted filter, storage at RT Before use centrifuge aliquots at 13,000 rpm Fixation solution I: 0.5 ml Cacodylate buffer with 1.5% ruthenium red 2% (w/v) Paraformaldehyde solution 2.5% (v/v) Glutardialdehyde 7.5 mM Lysine Fill up to 1 ml with a. dest. Fixation solution II: Fixation solution I without lysine Washing solution: 0.5 ml Cacodylate buffer with 1.5% (w/v) ruthenium red Fill up to 100 ml with a. dest.
Second sample preparation method for electron microscopy For the second electron microscopy (EM) analysis pneumococci were cultivated as described above (see 4.7.2) under control and stress condition in THY and CDM. During this work the protocol for sample preparation for EM analysis changed in agreement with Prof. Dr. Manfred Rohde and was used for the comparative cell morphology analysis in the two examined media. The results are presented in chapter 5.4.2. Samples were fixed with 5% (w/v) formaldehyde and 2% (v/v) glutaraldehyde in growth medium on ice and kept at 7 °C. For further processing and analysis by FESEM (field emission scanning electron microscopy) and TEM the fixed samples were sent to Prof. Dr. Manfred Rohde (HZI Braunschweig) for electron microscopy.
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25% (w/v) Formaldehyde solution: 25 g Paraformaldehyde 90 ml a. dest., heat to 60 °C Titrate 10 M NaOH until paraformaldehyde is solved Fill up to 100 ml with a. dest. Filtration through fluted filter, storage at RT Before use centrifuge aliquots at 13,000 rpm
Methods for EM sample preparation by Prof. Dr. Manfred Rohde in Braunschweig were provided by himself. For reasons of completeness the original text is added to this work:
Field emission scanning electron microscopy (FESEM) Samples were fixed with 5% formaldehyde and 2% glutaraldehyde in growth medium on ice and kept at 7 °C, then washed twice with TE buffer (20 mM tris, 2 mM EDTA, pH 6.9). Bacteria were attached onto poly-L-lysine coated cover slips (12 mm in diameter) for 15 minutes, fixed with 1% glutaraldehyde in TE buffer, twice washed with TE buffer, and dehydrated with an increasing series of acetone (10, 30, 50, 70, 90, 100%) on ice for 15 minutes for each step. Samples in the 100% acetone step were allowed to reach room temperature before another change in 100% acetone. Samples were then subjected to critical-point drying with liquid CO2 (CPD 030, Bal-Tec). Dried samples were mounted with carbon adhesive tape onto aluminium stubs and coated with an approximately 8 nm thick gold-palladium film by sputter coating (SCD 500 Bal-Tec) before examination in a field emission scanning electron microscope Zeiss Merlin using the Everhart-Thornley SE-detector alone or together with the Inlens SE-detector in a 75:25 ratio at an acceleration voltage of 5 kV.
Embedding in LRWhite resin and transmission electron microscopy (TEM) Fixed bacteria were centrifuged and the resulting pellet mixed with an equal volume of 1.75% water agar. After solidification the agar was cut into small cubes and then samples were dehydrated with an increasing series of ethanol, each step 30 minutes (10, 30, 50%). Samples were treated with 2% uranyl acetate for overnight in the 70%, further dehydrated with 90% ethanol for 30 minutes. The 100% ethanol step was repeated twice before the samples were infiltrated with one part 100% ethanol and one part LRWhite resin (London resin Company) for overnight. The next day samples were infiltrated with one part 100% ethanol and two parts LRWhite resin for 24 hours, and subsequently infiltrated with pure LRWhite resin with two changes over two days. The next day 1 µl starter was added to 10 ml LRWhite resin, stirred and resin was put into 0.5 ml gelatin capsules. The samples were placed into the tip of 0.5 ml gelatin capsules, followed by polymerization for four days at
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50 °C. Ultrathin sections were cut with a diamond knife. Sections were counter-stained with 4% aqueous uranyl acetate for one minute. Samples were examined in a Zeiss TEM 910 transmission electron microscope at an acceleration voltage of 80 kV and at calibrated magnifications. Images were recorded digitally at calibrated magnifications with a Slow-Scan CCD-Camera (ProScan, 1024x1024) with ITEM-Software (Olympus Soft Imaging Solutions).
4.8. PROTEIN ANALYSIS
4.8.1. DETERMINATION OF PROTEIN CONCENTRATION
The determination of protein concentrations was carried out with the modified Bradford assay (Bradford, 1976) using Roti-Nanoquant. All samples for mass spectrometric analysis were subjected to determination of protein concentration. Therefore, a suitable sample volume was filled up to 200 µl with a. dest. and mixed in a cuvette with 800 µl 1x Roti- Nanoquant solution. The absorbance of the samples was measured after 5 minutes at 590 nm and 450 nm against a. dest. as blank. In parallel, a BSA calibration curve with known protein concentration (1 - 40 µg µl-1) was measured. By means of the calibration curves the protein
concentration of the samples⋅ could be determined using the quotient of the sample values measured at 590 nm and 450 nm.
4.8.2. PREPARATION OF HEAVY LABELED INTERNAL STANDARD
For the SILAC experiment in CDM the heavy labeled samples were used to prepare an internal standard (Figure 4-6). Therefore, the protein extracts from control and stress conditions were pooled in a 1:1 ratio based on their protein concentrations determined before. In the thus generated spike-in standard (MacCoss et al., 2003) all proteins expressed in CDM under control and iron limitation conditions are combined. Hence, almost all proteins present in the obtained data set should be quantifiable. Additionally, the number of comparable samples is not limited by the number of available labels, since the actual experiment is independent of the labeling procedure (Geiger et al., 2011).
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Figure 4-6 Scheme of SILAC experiment. The upper part shows the generation of the heavy labeled SILAC standard, in which control and stress samples are combined. The lower part depicts the actual iron limitation experiment in CDM. In a 1:1 ratio the control and the stress samples are mixed with the heavy labeled standard. As the standard is the same in both samples they can be compared via the heavy to light ratio of the proteins.
4.8.3. IN-SOLUTION PROTEIN DIGESTION
For LC-MS/MS analyses protein samples were reduced with 500 mM TCEP for 45 minutes at 65 °C and subsequently alkylated with 500 mM iodoacetamide for 15 minutes at RT in the dark. Afterwards, protein samples were incubated with 1:100 trypsin (relating to used protein amount) over night at 37 °C and 900 rpm. The digest was stopped with 1% (v/v) TFA. In the experiment for determination of the incorporation of heavy labeled amino acids 20 µg protein was digested. For LC-MS/MS analyses 100 µg of protein from CDM control and from all THY samples were applied. Additionally, for the SILAC approach 50 µg total protein extract of each biological replicate from CDM samples were mixed with 50 µg of the heavy labeled SILAC standard before further sample preparation.
TCEP solution: 500 mM TCEP in 1 M TEAB IAA solution: 500 mM IAA in 50 mM TEAB 10x trypsin: 1 vial trypsin (20 µg) in 100 µl trypsin resuspension buffer
4.8.4. PEPTIDE PURIFICATION
Samples from CDM cultures were desalted using StageTips and samples from THY cultures were desalted using ZipTips according to manufacturer’s protocols. The samples were eluted with either 70% or 60% solution B in solution A. The change in purification method was for practical reasons.
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Solution A: 0.1% acetic acid in water Solution B: 0.1% acetic acid in acetonitrile The eluted samples were dried in a vacuum centrifuge and resuspended in 1x iRT peptide solution.
4.9. MASS SPECTROMETRIC ANALYSES
4.9.1. LIQUID CHROMATOGRAPHY COUPLED TO TANDEM-MASS SPECTROMETRY (LC- MS/MS)
The LC-MS/MS analyses were performed with EASY-nLC II, EASY-nLC 1000 or EASY-nLC 1200 coupled to an Orbitrap Velos Pro or to an Orbitrap Elite. Tryptic peptides were loaded on a self-made analytical column (Aeris PEPTIDE 3.6 µm XB – C18, length 20 cm, OD360 µm, ID 10 µm) and eluted by a binary nonlinear gradient of solution A and B (see Figure 4-7) over a period of 180 minutes with a flow rate of 300 nl min-1. For MS analysis a full scan in the Orbitrap (m/z 300-1,700) with a resolution of 30,000 was followed by CID MS/MS experiments of the twenty most abundant precursor ions acquired in the linear ion trap.
Solution A: 0.1% acetic acid in water Solution B: 0.1% acetic acid in acetonitrile
120
100
80
60 Elite 40 Velos Pro Mixture [%B] Mixture
20
0 0 50 100 150 200 Time [min]
Figure 4-7 Non-linear binary gradient of solution A and B for elution of tryptic peptides.
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4.9.2. INDUCTIVELY COUPLED PLASMA-MASS SPECTROMETRY (ICP-MS)
Determination of iron concentration in CDM and THY To determine the iron concentration in CDM and THY 250 µl aliquots of sterile media were dried in a vacuum centrifuge and chemically digested with 200 µl concentrated nitric acid for four hours. As internal standard 800 µl of a 10 ng ml-1 yttrium-solution were added to the digested samples. The isotopes 56Fe and 57Fe and the internal standard were analyzed by ICP- MS 7500c. The ICP-MS was equipped with a PFA concentric microFlow nebulizer and a Scott type spray chamber, and operated at 1350 watt in He collision mode. Other parameters, including the position of the plasma torch and lens voltages, were optimized as necessary. Different dilutions of an iron standard were measured among the samples for external calibration and iron isotope intensities were corrected for the internal standard.
4.10. DATA ANALYSES
4.10.1. DETERMINATION OF INCORPORATION RATE
For the application of SILAC as quantification method a nearly complete incorporation of the chosen heavy labeled amino acids is crucial. Therefore, the incorporation rate of the heavy labeled amino acids into the pneumococcal proteome has to be verified. The peptide identification was carried out by classical database search using the Sorcerer-Sequest platform. Therefore, a S. pneumoniae D39 database was downloaded on 11th December 2014 from UniProt (The UniProt Consortium, 2017) and contaminants and iRT peptide sequences were added. Using the program Scaffold 4.4.7 reverse sequences were created and added to the database. The next step was the database search. Following settings were stored in the search profile: full digest using trypsin (KR), two missed cleavage sites allowed, mass tolerance was set to 10 ppm and differential modifications for oxidation on methionine, carbamidomethylation on cysteine and 13C15N-labeled arginine and lysine. The database search results were analyzed with Scaffold. Randomly selected peptides were checked for light and heavy variants. The spectra of all selected peptides were investigated using Xcalibur Qual Browser. The spectrum intensities from light and heavy peptide form were compared to each other and an incorporation rate was calculated.
4.10.2. PROTEOME ANALYSIS
In iron limitation experiments the database search was carried out using MaxQuant with the implemented Andromeda algorithm. A more recent pneumococci database (STRP2, 1914 proteins) was downloaded on 9th March 2016 from UniProt (The UniProt Consortium, 2017).
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The database was supplemented with the addition of iRT peptide sequences. Additionally, MaxQuant’s generic contamination list was included during database search. The CDM data set was quantified using SILAC ratios. Hence the number of multiplicity set to two in the search profile, as the samples contained peptides with light labeled amino acids from the experiment samples and peptides with heavy labeled amino acids of the internal SILAC standard. In contrast, the THY samples were quantified using LFQ intensities, because the exchange of light labeled amino acids with their heavy labeled counterparts is not possible in complex media like THY. Hence, the option LFQ was checked (min. ratio count set to two). For the comparative media analysis, the control samples from both media were relatively quantified using iBAQ (intensity based absolute quantification) values. Thus, the option iBAQ was checked in the search profile. The remaining search profile settings for all three approaches were the same: Trypsin/P (C-terminal cleavage after arginine and lysine independent of proline) was chosen for the generation of in silico peptides. The false discovery rates (FDRs) of protein and peptide spectrum match (PSM) levels were set to 0.01 and two missed cleavage sites were allowed for identification. Oxidation on methionine was set as variable modification and carbamidomethylation on cysteines was set as fixed modification. After database search the analyses of the shot-gun proteomic data were examined using Perseus. The data from MaxQuant output files were filtered for contaminants and log2- transformed for the next steps. A protein was identified in a biological replicate if two or st two technical replicates.
Formore quantification unique peptides only with proteins a FDR≤0.01 were considered were identified, which inwere at lea identified in two out of three biological replicates. In the next step, the mean protein ratio was calculated for all quantified proteins in the biological replicates for each condition (control, BIP, BIP-iron(II)-complex). Afterwards, the fold change (FC) of stress versus control condition was calculated, which gives value for the protein alteration. The results were visualized in Voronoi treemaps using the software Paver. For the statistical analysis of the quantified protein data the program RStudio and in detail the R-script SAM (significance analysis of microarrays (Tusher et al., 2001)) by Mike Seo (https://github.com/MikeJSeo/SAM) was used. The settings in the script were as follows: “response type” was set to “two class unpaired” and “are data in log scale” was filled with “yes”. The other settings remained in default options. After the script was executed the “minimum fold change” was set to 1.5 and the “data value” was manually adapted until the q-value in the “significant genes” tab was below 0.01.
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5. RESULTS
5.1. PREREQUISITES FOR PROTEOME ANALYSES
5.1.1. MODIFIED CULTIVATION WORKFLOW
For proteomic analysis reproducibility is a key element in terms of sample preparation and also protein quantification (Röst et al., 2015). The growth of bacteria represents the first step within the analysis of proteomic investigations of bacterial physiology (see Figure 3-1). Hence, scientists have to ensure that for all biological replicates for the same analysis the bacterial cultures are harvested in the same state or at the same time point. Otherwise, differences in protein composition of biological replicates can occur and so increase the variance between the samples, which would make the analysis less exact and influence the statistical significance. The proteome of each living organism is highly dynamic and responds to the smallest environmental changes, for which reason the cultivation condition have to be exactly determined and precisely executed. Even the handling of different investigators can lead to a different outcome (Lithgow et al., 2017). For all those reasons the first task in this work was to establish a reproducible pneumococcal growth for proteomic analysis. As described in 4.7.2 the cultivation workflow had to be modified for proteomic approaches, because the growth of pneumococci was not sufficiently reproducible. In the initial workflow the bacteria were spread on a blood agar plate over six hours, followed by maximally ten hours on blood agar plate with antibiotic. Afterwards, the bacteria were transferred directly in the liquid main culture. This resulted in varying growth behaviors in CDM, which are exemplarily presented in Figure 5-1.
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1
600 nm BR1
OD BR2 0.1 BR3
0.01 0 100 200 300 400 500 600 700 Time [min]
Figure 5-1 Growth behaviors of S. pneumoniae cps in CDM without liquid preculture. Pneumococci grown in
CDM without additional preculture showed threeD39Δ different growth behaviors. The samples were prepared always in the same manner, but independent of each other.
Growth behavior of BR1 (biological replicate 1) has a characteristic drop within the first hour, which could be explained with the transfer of already dead, but not yet lysed, pneumococcal cells from blood agar plate to liquid culture. Contrary, growth curves of BR2 and BR3 do not show this drastic drop at the cultivation start. The lag phase is in both cases shorter than in BR1. Curves of BR2 and BR3 differ in the stationary phase, where BR2 stayed steady in OD 600 nm, the OD 600 nm that was observed in growth behavior of BR3, decreased. The reasons for these observations can only be assumed. Perhaps it is due to different start-
ODs 600 nm of growth cultures, which resulted in varying fitness in stationary phase, and/or different fitness of bacteria in the overnight culture, which influenced the main cultures. However, different and unreproducible growth behavior may have an influence on the pneumococcal proteome. In particular, unreproducible bacterial growth may lead to varying compositions of expressed proteins and their abundances. To ensure that growth of pneumococci is comparable in all growth experiments, an additional liquid culture as preculture was used in the modified cultivation workflow. Although the growth behaviors of liquid precultures were varying in the length of the lag phases (data not shown), the main cultures showed a better reproducibility than in the first non-adapted workflow and also a consistent growth behavior. The growth of the different biological replicates was very similar as depicted in Figure 5-2. Pneumococci started growing in exponential phase directly after inoculation, omitting another lag-phase. After approximately 200 minutes, bacteria
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1
600 nm BR1
OD BR2 0.1 BR3
0.01 0 50 100 150 200 250 300 350 400 Time [min]
Figure 5-2 Growth behavior of S. pneumoniae cps with liquid preculture in modified workflow. Pneumococci were grown in CDM and inoculated from liquidD39Δ preculture.
5.1.2. GENERATION OF VORONOI TREEMAP LAYOUT FOR S. PNEUMONIAE
For visualization of large proteomic data sets Voronoi treemaps were applied. Voronoi treemaps are used to illustrate hierarchically organized data structures, which can be categorized with available classifications systems (Bernhardt et al., 2013). The interpretation and visualization of the result data in Voronoi treemaps requires a comprehensive protein annotation, which allows to draw conclusions in accordance with the biological question under investigation (Mehlan et al., 2013). Several functional annotation databases are online available, so for instance KEGG BRITE (Kanehisa et al., 2006; Kanehisa et al., 2008) and TIGRFAMs (Haft et al., 2001; Haft et al., 2013), which were used for the protein annotation of the pneumococcal strain D39 in the present study. The protein annotation was a tedious process, and therefore, performed in close collaboration with Claudia Hirschfeld and Christian Hentschker from the group of Prof. Dr. Becher. Afterwards, the protein annotation table was rechecked by Alejandro Gómez-Mejia from Prof. Dr. Hammerschmidt's group. The basis of the protein annotation was a protein table downloaded from UniProt (The UniProt Consortium, 2017) with 1,914 predicted proteins of the S. pneumoniae strain D39. Within the KEGG BRITE database to 655 proteins functions were assigned, which is roughly one third of the predicted proteome. As this
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number is relatively low, it was decided to add TIGRFAMs to the annotation table. In this way additional 247 proteins could be allocated to a specific function. Afterwards, still more than half of the proteins were not assigned to a protein function. Hence, all remaining proteins with unknown function were searched in literature and additional databases, namely UniProt (The UniProt Consortium, 2017) and Ensembl (Zerbino et al., 2018). Thus, 362 protein functions could be determined. Still 650 proteins remained without a specific function and were tagged with "Unknown function". In spite of every effort, approximately 30% of the pneumococcal D39 predicted proteome remains without specific function (see in the appendix section 8, file B1). All numbers of the protein annotation table are summarized in Table 5-1.
Table 5-1 Assembly of protein annotation table
Number of Proportion of all Sum of single Origin of function proteins predicted proteins rows KEGG BRITE 655 34.22% - TIGRFAMs 247 12.90% 902 Manually added 362 18.91% 1,264 Unknown function 650 33.96% 1,914
Sum 1,914 100.00% -
Each protein of S. pneumoniae D39 was assigned to a main role, a subrole, an operon number and a protein acronym. In Table 5-2 an excerpt of 14 of the 1914 annotated proteins of S. pneumoniae D39 is listed exemplarily. The comprehensive annotation table can be found in the appendix file B1.
Table 5-2 Excerpt of the protein annotation table used as template for the Voronoi treemap layout.
4th level 1st level 2nd level 3rd level Identifier (protein (main role) (subrole) (operon) acronym) DNA replication, DNA SPD_0001 recombination and 1 DnaA metabolism repair DNA replication, DNA SPD_0002 recombination and 1 DnaN metabolism repair Unknown SPD_0003 Unknown function 2 SPD_0003 function
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4th level 1st level 2nd level 3rd level Identifier (protein (main role) (subrole) (operon) acronym) Unknown SPD_0004 General 3 YchF function Protein SPD_0005 Other 3 Pth synthesis DNA replication, DNA SPD_0006 recombination and 3 Mfd metabolism repair SPD_0007 Transcription Other 3 SPD_0007
Cellular SPD_0008 Cell division 3 FtsB processes Unknown SPD_0009 Unknown function 3 SPD_0009 function Cellular Toxin production and SPD_0010 3 AmpC processes resistance Protein tRNA and rRNA base SPD_0011 3 TilS synthesis modification Nucleotide SPD_0012 Purine metabolism 3 Hpt metabolism Degradation of SPD_0013 Protein fate proteins, peptides and 3 FtsH glycopeptides Signal Two-component SPD_0014 4 ComX1 transduction system
… … … … …
The annotation table provides the template for the Voronoi treemap layout shown in Figure 5-3. This layout was used for all treemaps in this work. The Voronoi treemap shows all 1914 annotated proteins of S. pneumoniae D39 clustered accordingly to their function. The “first level” depicts the main role, the “second level” the subrole, the “third level” the operon number (assigned as running number) and the “fourth level” the protein acronym or if not present the locus ID (Identifier). Each protein is displayed as one framed spot. The operon level provides an additional clustering within proteins that are already on gene level in close contact to each other. This level is not visible in the final treemaps. Detailed treemap layouts can be found in the appendix (chapter 8) files A2 – A5.
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Figure 5-3 Voronoi treemap template for visualization of proteomic data. The fundament for this Voronoi treemaps is the protein annotation of S. pneumoniae D39 based on the genome sequence. The first level classifies the main role, the second level the subrole, the third level the operon number and the fourth level the protein acronym of a specific protein. Thereby, proteins of the same functional group are clustered together.
5.2. PNEUMOCOCCAL ADAPTATION TO IRON LIMITATION IN CDM
5.2.1. DETERMINATION OF SUITABLE BIP CONCENTRATION AND BIP TOXICITY TEST
For the iron limitation experiment BIP was applied to induce iron-restricted conditions. Therefore, different BIP concentrations were tested for growth inhibition in comparison to a control sample in CDM. In Figure 5-4 the different pneumococcal growth curves for four tested BIP concentration in CDM are depicted. BIP was applied to cultures after reaching an
OD 600 nm of approximately 0.2 (black lightning). Samples with 0.25 mM BIP or higher
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Results concentrations showed inhibited growth. As 0.5 mM BIP sample did not differ so much from 0.25 mM BIP sample and the growth rate was roughly halved, 0.5 mM BIP was defined as the concentration to be used for subsequent iron limitation experiments in CDM.
1
Control 0.1 mM BIP
600 nm 0.1 0.25 mM BIP OD 0.5 mM BIP 1 mM BIP
0.01 0 100 200 300 400 Time [min]
Figure 5-4 Determination of suitable BIP concentration for pneumococcal stress experiments in CDM. Pneumococci were cultivated in CDM with addition of different concentrations of BIP (0.1 mM, 0.25 mM, 0.5 mM, 1 mM), which is indicated by a lightning. The bacterial growth was recorded for six hours.
Besides the determination of a suitable BIP concentration, it was also tested, if BIP itself has toxic effects on pneumococci. Therefore, BIP, in the determined concentration, was mixed in a 1:3 ratio with iron(II) salt to form the stable BIP-iron(II)-complex, as one iron ion complexes three BIP molecules (see Figure 5-5). In this way, BIP molecules were saturated with iron, so that no iron ions deriving from the bacterial proteins were sequestered by BIP. Hence, the observed effect after addition of the BIP-iron(II)-complex should represent the effect of BIP itself and not the effect of iron limitation.
Figure 5-5 Reaction equation of BIP-iron(II)-complex formation. One iron ion coordinates three BIP molecules and the BIP- iron(II)-complex is formed.
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In Figure 5-6 the growth results of the BIP toxicity test are depicted. In comparison to the control sample, the BIP and also the BIP-iron(II)-complex sample showed a decreased growth. Comparing BIP and BIP-iron(II)-complex sample to each other, there is a difference in the end-OD 600 nm, which is less in BIP than in BIP-iron(II)-complex sample. Moreover, after reaching the stationary phase, the complex sample is more stable than the BIP sample as the
OD 600 nm decreased faster. The higher variability in BIP sample is due to the start of cell lysis in one biological replicate after 240 minutes. To conclude, BIP itself affects the pneumococcal growth, but the additional effect of iron limitation led to even more inhibited growth. The BIP-iron(II)-complex approach was used in latter proteome analyses as the complemented condition to distinguish between adaptation of the pneumococcal proteome to iron limitation or to BIP itself.
1
600 nm 0.1 Control
OD 0.5 mM BIP 0.167 mM Complex
0.01 0 100 200 300 400 Time [min]
Figure 5-6 Elucidation of BIP toxicity on pneumococcal growth in CDM. Addition of BIP or BIP-iron(II)-complex is indicated by a lightning. The bacterial growth was recorded for six hours.
5.2.2. DETERMINATION OF INCORPORATION RATE FOR SILAC QUANTIFICATION
SILAC was applied as quantification method of the iron limitation experiment in CDM. Therefore, the incorporation rate of heavy labeled amino acids in the pneumococcal proteome had to be determined. Wherein, heavy and light peptide spectra of the same peptide were compared to each other. The investigated peptides were selected randomly. In Figure 5-7 an example is depicted, explaining how incorporation rate of a heavy/light- peptide couple was determined. With a peptide list of the proteomic software Scaffold (upper left corner) a peptide was selected. Using the “observed mass” of this peptide, the peptide
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Figure 5-7 Determination of incorporation rate on the example of the peptide (R)AMIALDGTPNK(G). After database search via Sorcerer the output file was analyzed using the Scaffold software. Peptides were selected randomly and the peptide spectrum was displayed in the raw-data file. Using the mass difference from heavy arginine or lysine to its light form and the charge of the peptide ion, the mass-to-charge-ratio of light peptide form was determined. After identifying the heavy-light peptide spectrum couple, its intensities were used to calculate the incorporation rate.
In Table 5-3 the incorporations rates for the selected peptides are listed. For all peptides the incorporation rate was above 95%. Hence, it can be assumed that the incorporation of heavy labeled amino acids into the pneumococcal proteome was successful.
Table 5-3 Incorporation rates of randomly selected heavy/light-peptide couples
Protein Peptide Incorporation rate SPD_1012, Eno (R)IEDQLGEVAEYR(G) 100% SPD_1012, Eno (R)AMIALDGTPNK(G) % SPD_1012, Eno (R)AAADYLEIPLYSYLGGFNTK(V) ≈ % SPD_0558, PrtA (K)VSASAITTDSLTDR(L) ≈ 99.04% SPD_0558, PrtA (K)IANIYPLDSNGNPQDAQLER(G) ≈ 99.29% SPD_1087, Fhs (K)LILVTAINPTPAGEGK(S) ≈ 96.25% SPD_1087, Fhs (K)DALTEENVEAVR(A) ≈ 100% SPD_0212, RplO (K)SAEEAITAK(G) ≈ 100 % SPD_1004, GapN (R)EGNLLWPVLFDQVTK(D) ≈ 100 % ≈ 98.75 ≈ 98.73 59
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5.2.3. PROTEOME ANALYSIS
The pneumococcal proteome adaptation to iron limitation in CDM was investigated using the metabolic labeling technique SILAC for quantification as it has proven to be very robust and thereby providing reliable results (Mann, 2006). Besides the control and iron limitation condition also the addition of BIP-iron(II)-complex was considered for proteome analysis in order to analyze the effect of BIP itself on pneumococci. All raw-files were searched together resulting in one dataset. From 1,914 predicted pneumococcal proteins 803 proteins were identified, of which 546 proteins could be quantified. The distribution of the quantified proteins in the three conditions is illustrated in Figure 5-8.
Figure 5-8 Distribution of quantified proteins in CDM dataset.
Roughly 70% of the identified proteins in CDM control samples could be quantified. In BIP and BIP-iron(II)-complex treated pneumococci 5% and 10%, respectively, less proteins could be quantified (Figure 5-9).
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800
700
600
500 70.6% 65.2% 400 59.8% Identified Quantified 300
Number proteins of 200
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0 Control BIP Complex
Figure 5-9 Distribution of identified and quantified proteins within the three analyzed conditions in CDM dataset.
In order to evaluate the quality of the biological replicates in all three conditions in CDM a principle component analysis (PCA) based on the quantified data was examined. The PCA is depicted in Figure 5-10. The conditions, in which the pneumococci were cultivated, can be clearly distinguished on proteome level, which is indicated by the clusters formed by all three replicates of one condition. In addition, the LFQ values of biological replicates in one conditions correlate, which shows that the replicates are reproducible (R2 is between 0.76 and 0.92; see in the appendix Figure 8-1).
Figure 5-10 PCA of biological replicates of the CDM dataset.
The SILAC ratios of proteins were used for the calculation of changes between BIP stress sample to control condition and BIP-iron(II)-complex samples to control condition.
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Statistically analyses (FC > 1.5 , q-value < 0.01) using the SAM script (see 4.10) revealed 163
proteins to be significantly∣ altered∣ comparing BIP stress to control sample. The calculated values are illustrated in the Voronoi treemap applying a color gradient (Figure 5-11, Voronoi treemap levels with better resolution can be found in appendix files A1 in Figure 1 – Figure 4).
Figure 5-11 Adaptation of S. pneumoniae cps proteome to iron limitation in CDM. Quantified protein
amounts of pneumococci grown in CDM wereD39Δ visualized with Voronoi treemaps. The orange fields indicate proteins that are higher abundant and turquoise fields illustrate lower abundant proteins under iron limitation. In the first level proteins are clustered to their general function and the second level displays more specific protein functions. The third level indicates the assigned operon numbers of proteins and the fourth level shows the protein acronyms. Proteins, which were quantified in another iron limitation experiment, but not in both displayed conditions, are indicated in dark gray.
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In detail, most of the quantified transport and binding proteins were lower abundant under iron-limited conditions (Figure 5-11, 1st level, left middle, appendix file A1 in Figure 1), whereas the abundance of iron uptake protein SPD_1652, PiuA, was strikingly increased under iron restriction. In addition, the non-heme iron-containing ferritin Dpr (SPD_1402) was significantly less abundant. Furthermore, the abundances of proteins which are associated to pneumococcal pathogenesis and cell division (Figure 5-11, 2nd level, right middle, appendix file A1 in Figure 2) were decreased. On the other hand, proteins involved in lipid metabolism were slightly higher abundant (Figure 5-11, 1st level, upper left corner, appendix file A1 in Figure 1). Statistical analyses (FC > 1.5 , q-value < 0.01) using the SAM script (see 4.10) revealed 162 proteins to be significantly∣ altered∣ after addition of BIP-iron(II)-complex. The calculated values are illustrated in the Voronoi treemap applying a color gradient (see appendix file A1 in Figure 5 – Figure 9). In comparison to the actual iron limitation experiment, the majority of transport proteins are not altered after BIP-iron(II)-complex treatment. As described above, the iron transporter PiuA was higher abundant after iron limitation. This is not the case after BIP-iron(II)-complex addition, because the transporter showed no alteration in abundance. The iron storage protein Dpr is here strikingly higher abundant in contrast to iron limitation. This hints to an increased intracellular iron concentration. Moreover, proteins involved in electron transport are highly increased in abundance. This is supported by increased abundance of proteins involved in protein folding and stabilization. All quantitative protein data are listed in Table 8-1.
5.2.4. CELL MORPHOLOGY
For the cell morphology analyses pneumococci were cultivated under control and iron- limited conditions and the harvested cells were prepared for Gram-Staining (see 4.7.4). The pictures showed no clear differences between control and stress condition. Thus, additional transmission electron microscopy (TEM) was performed (see 4.7.5, first EM sample preparation method). For the first TEM analysis pneumococci were cultivated under control (A, C; longitudinal [A] and transverse [C] sections of the same sample) and stress (B, D; longitudinal [B] and transverse [D] sections of the same sample)) conditions. The pictures shown in Figure 5-12 illustrate several differences in pneumococcal cell morphology in both examined approaches. Bacteria grown under control condition had a 16.36 nm (average) thick cell wall (C) and compacted DNA localized in the center of each coccus (A, C). In contrast, pneumococci grown under iron-limited condition possessed a thicker cell wall of about 22.45 nm (D),
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which is significantly thicker compared to control samples (Student’s t-test, p- value = 1.09 10-6). The results of the measurements of cell wall thickness are listed in
Table 5-4. Moreover⋅ , the DNA is delocalized in stress samples (B, D). Another striking difference was the presence of multiple septum formation sites within diplococci (B).
Figure 5-12 TEM pictures of S. pneumoniae cps cultivated in CDM. The TEM pictures illustrate S. pneumoniae
cps grown in CDM under control (A, CD39Δ; longitudinal [A] and transverse [C] sections of the same sample) and D39Δunder iron-limited (B, D; longitudinal [B] and transverse [D] sections of the same sample) conditions.
Table 5-4 Cell wall thickness measurements of pneumococci grown in CDM under control and iron-limited conditions
# Measurements Control BIP 1 15.25 nm 21.11 nm 2 16.68 nm 23.29 nm 3 17.22 nm 22.76 nm 4 15.15 nm 23.59 nm 5 17.75 nm 22.36 nm 6 16.10 nm 21.56 nm Mean 16.36 nm 22.45 nm
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5.2.5. IRON CONCENTRATIONS OF CDM AND THY
The iron concentrations of the applied media were elucidated by ICP-MS. The analysis resulted in 190 µg l-1 and 740 µg l-1 iron in CDM and THY, respectively. This analysis gives no indication about the bioavailability of iron in the media; but gives a total iron concentration only. The THY medium includes yeast extract with intact proteins, which can contain metal ions. With the protocol described in chapter 4.9.2, no separation of protein bound and free iron was performed.
5.3. PNEUMOCOCCAL ADAPTATION TO IRON LIMITATION IN THY
5.3.1. DETERMINATION OF SUITABLE BIP CONCENTRATION
The determination of a suitable BIP concentration in THY depends on the iron concentration of the investigated media. As THY contains fourfold more iron than CDM, the BIP concentration was defined to be 2 mM. Nevertheless, also a growth experiment with different concentrations of BIP was carried out in THY. The results are depicted in Figure 5-13. BIP was added in different concentration to THY main cultures 30 minutes after inoculation. Growths in different approaches were very similar, despite the 5 mM BIP sample, where the growth was slower in comparison to other samples. This outcome could be caused by dilution effects as in this case a higher volume of 30 mM BIP stock solution was used to reach the desired end concentration in the sample. Although there were just minor differences in growth between control and 2 mM BIP stress sample, the maximal OD was lowered. Because of growth effects and the fact that THY medium contains fourfold more iron than CDM the BIP concentration for the iron limitation experiment in THY was defined to be 2 mM. Moreover, although no differences in growth at varying BIP concentrations were observed, it is still possible that an adaptation on proteome level can be detected.
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10
1
Control 600 nm 0.5 mM BIP OD 2 mM BIP 0.1 5 mM BIP*
0.01 0 100 200 300 400 Time [min]
Figure 5-13 Determination of suitable BIP concentration for pneumococcal stress experiments in THY. Pneumococci were cultivated in THY with addition of different concentrations of BIP (0.5 mM, 2 mM, 5 mM), which is indicated by the lightning. The bacterial growth was recorded for six hours.
5.3.2. PROTEOME ANALYSIS
The proteome analysis of pneumococci for the iron limitation experiment in THY is based on label-free quantification using the integration of peptide peak areas, because SILAC is not applicable on complex media like THY. This dataset comprises proteins identified and quantified under control condition, BIP stress and BIP-iron(II)-complex treatment. In total, from 1914 predicted pneumococcal proteins 529 could be identified and from those 346 proteins were quantified. The distribution of the quantified proteins under the three conditions is illustrated in Figure 5-14.
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Figure 5-14 Distribution of quantified protein in THY dataset.
The proportion of identified proteins, which could also be quantified, is around 70% in all three conditions in THY (Figure 5-15).
600
500
400
Identified 300 67.1% 70.1% 69.4% Quantified 200 Number proteins of
100
0 Control BIP Complex
Figure 5-15 Distribution of identified and quantified proteins within the three analyzed conditions in THY dataset.
In order to evaluate the quality of the biological replicates in all three conditions in THY a PCA based on the quantified data was examined, which is depicted in Figure 5-16. Most of biological replicates of all three conditions are arranged on the right site of the PCA plot. In comparison to the PCA of the CDM dataset (see Figure 5-10), the conditions analyzed in THY cannot be differentiated on proteome level as clearly as in CDM. Although it seems that the biological replicate K1 is an outlier, it could be shown in an additional analysis that the
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biological replicates of one condition correlate well (R2 is between 0.81 and 0.99; see in the appendix Figure 8-2) and thus are very reproducible.
Figure 5-16 PCA of biological replicates of the THY dataset.
For the calculation of protein FCs, LFQ intensities were used; first, for the comparison between control and BIP stress sample, and second, for the comparison of control against BIP-iron(II)-complex samples. The statistical analyses were executed with the R script SAM as described in chapter 4.10. Twelve proteins were significantly altered (FC > 1.5 , q- value < 0.01) in the iron limitation approach. While 16 proteins were statistically signifi∣ ∣cant regulated comparing control to BIP-iron(II)-complex samples. The calculated values for the iron limitation experiment in THY are illustrated in Figure 5-17 (detailed treemaps of single levels can be found in the appendix file A1 Figure 10 – Figure 13). The results of the comparison from control to BIP-iron(II)-complex treatment are shown in the appendix file A1 in Figure 14 (more details can be found in Figure 15 – Figure 18).
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Figure 5-17 Adaptation of S. pneumoniae cps proteome to iron limitation in THY. Quantified protein amounts of pneumococci grown in THY were visualizedD39Δ with Voronoi treemaps. The orange fields indicate proteins that are higher abundant and turquoise fields illustrate lower abundant proteins under iron limitation. In the first level proteins are clustered to their general function and the second level displays more specific protein function. The third level indicates the assigned operon numbers of proteins and the fourth level shows the protein acronyms. Proteins, which were quantified in another iron limitation experiment, but not in both displayed conditions, are indicated in dark gray.
With the results shown here, the assumption of the increase in abundance of pathogenesis associated proteins under iron limitation was confirmed for S. pneumoniae. Proteins involved in virulence were either significantly higher abundant (PhpA [SPD_1038]), have a FC higher than 1.6 (PspA [SPD_0126], PhtD [SPD_0889], Ply [SPD_1726]) or could be
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quantified (PhtE [SPD_0890], SPD_0335, SPD_1037) exclusively under iron-restricted condition. Additionally, the enzyme sortase, SrtA (SPD_1076) was switched on (only quantified in stress sample). Proteins connected to iron sulfur cluster assembly were higher abundant or switched on (SufC [SPD_0762], SufS [SPD_0764], SPD_0765). Contrarily, proteins, which use iron sulfur clusters as prosthetic groups, could not be quantified under iron limitation (Fer [SPD_1430, putatively containing Fe-S-cluster], IlvD [SPD_1956], PyrK [SPD_0851]). In this data set two iron transporters were identified, the iron uptake transporter PiuA (SPD_1652) and the iron acquisition transporter PiaA (SPD_0915). The first one was only identified under iron-limited conditions and the latter one showed no significant alterations. Additionally, the non-heme iron-containing ferritin Dpr (SPD_1402) was significantly less abundant after treatment with BIP. Furthermore, several proteins using an iron or another metal ion as a cofactor (exclusively annotated metal bindings) were lower abundant after BIP addition to THY cultures (ManA [SPD_0641], AdhA [SPD_0265], DeoB [SPD_0724], Rpe [SPD_1780], Adh2 [SPD_1865], CarB [SPD_1131], PyrE [SPD_0609]). Besides the control and iron limitation condition, the addition of BIP-iron(II)-complex was also considered for proteome analysis for the identification of proteomic changes deriving from BIP itself and not from its metal chelating properties. The protein regulations observed in the iron limitation experiment were not detected in the comparison of control condition to BIP-iron(II)-complex treatment. There was no increase in abundance of pathogenesis- associated proteins and all the other considered regulations were not significantly altered. Proteins, which were either not quantified in control, but in BIP or BIP-iron(II)-complex samples (switched off) or quantified in control, but not in BIP or BIP-iron(II)-complex samples (switched on), may give an indication for the pneumococcal adaptation to BIP itself, and not to its metal chelating properties. The proteins, which were switched on (SPD_2053, SPD_1376, Exp5 [SPD_0661], RpsO_2 [SPD_1429]) or off (AtpG [SPD_1336], PyrR [SPD_1134]) after addition of BIP or BIP-iron(II)-complex, give no indication which metabolic pathways might be influenced, as there is no evidence about a connection to each other. Detailed information is provided in Table 8-2.
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5.4. COMPARISON OF CDM VS. THY
5.4.1. PROTEOMIC ADAPTATION TO MEDIA
The environment has a huge impact on bacterial protein expression as the nutrient availability of the habitat determines the activated metabolic pathways, which are substantial for survival. Hence, various biosynthetic pathways will be differentially regulated, just as it is needed. In this work two media with different iron concentration were under investigation. So, the different niches in the pneumococcal host shall be mimicked. The composition of both media is so different from each other that the adaptation of the pneumococcal proteome to the two investigated media had to be analyzed additionally. Therefore, the control samples of the CDM and THY iron limitation experiment were compared to each other. The comparison of protein abundances was based on iBAQ intensities. In this dataset 968 proteins were identified and 919 proteins were quantified. In total, 495 proteins were found in both media, but the number of exclusively detected proteins in CDM was 409. In contrast to this, only 14 proteins were exclusively found in THY. From the shared proteins approximately half (257) showed statistically significant altered protein abundances (FC > 1.5 , q-value < 0.01). The differences in protein amount between CDM and
THY cultures are illustrated∣ ∣ in Voronoi treemaps (Figure 5-18), detailed treemaps can be found in the appendix file A1 Figure 19 – Figure 22.
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Figure 5-18 Comparison of pneumococcal protein expression in CDM and THY displayed in a Voronoi treemap. For each protein the protein amount based on iBAQ intensities were calculated and compared between CDM and THY. Each protein is depicted as one blot. In the first level proteins are clustered to their general function and the second level displays more specific protein function. The third level indicates the assigned operon numbers of proteins and the fourth level shows the protein acronyms. Turquoise fields indicate proteins that are more abundant in CDM and orange fields illustrate proteins, which are more abundant in THY medium.
In almost every metabolic pathway significantly altered protein amounts were observed. In the chemically defined medium additional pathways were switched on, so for example in amino acid metabolism and transport and binding proteins. Contrarily, pathogenesis associated proteins are already higher abundant before the induction of iron-restricted condition in CDM than in THY medium. Additionally, differences in the proteome pattern of
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S. pneumoniae were also observed in lipid metabolism, glycan biosynthesis and cell division. Detailed quantitative data are provided in Table 8-3.
5.4.2. COMPARATIVE ANALYSIS OF CELL MORPHOLOGY
The results from the comparative media analysis of observed alteration in lipid metabolism, glycan biosynthesis and cell division led to the assumption of modified cell morphology. Hence, additional scanning and transmission electron microscopy analyses of control, iron- limited and complemented conditions were executed in both media as described in chapter 4.7.5 (second EM sample preparation method). The FESEM pictures are presented in Figure 5-19 and the TEM pictures in Figure 5-20, in which pneumococci grown in CDM and in THY under control, iron-restricted and complemented condition are pictured.
Figure 5-19 FESEM pictures of S. pneumoniae cps morphology. The FESEM pictures of pneumococcal cells under control (A, D), iron limitation (B, E) and D39Δcomplemented conditions (C, F) in CDM (A-C) or THY (D-F) show differences in cell shape, cell surface and cell division (B, arrows).
Comparing the two control samples the shape of diplococci appears to be round in THY and more pointed in CDM. Additionally, vesicles were detected on cell surface in THY samples. The picture with pneumococcal cells grown under BIP treatment in CDM shows most striking
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differences. The bacterial cells are stretched and show incomplete septum formation. The complex treated pneumococci revealed no visible differences compared to control samples in both media. Furthermore, in the three samples of S. pneumoniae grown in THY less morphological variations were observed compared to cells derived from cultivation in CDM.
Figure 5-20 TEM pictures of S. pneumoniae D cps morphology. The TEM pictures of S. pneumoniae cps
under control (A, D), iron limitation (addition39Δ of BIP) (B, E) and complemented conditions (addition of D39ΔBIP iron complex) (C, F) in CDM (A-C) or THY (D-F) illustrate clear difference in cell morphology. The darker structures around the cells represent the cell wall and the central light inner structure is DNA. Pneumococci grown in CDM under iron-limited conditions (B) show disturbed cell division as they are longer in comparison to bacteria grown under control conditions (A).
Pictures from TEM analysis (see Figure 5-20) support the observations described above. Moreover, the insights into S. pneumoniae cells show clear differences in cell composition. Both control samples in comparison to each other reveal that the DNA is denser in bacteria
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6. DISCUSSION
In the present work, the major human pathogen S. pneumoniae and its proteomic response to iron limitation was in focus to gain deeper insight into pneumococcal adaptation processes during infections. For the implementation of iron-restricted conditions the often used chelating agent BIP was applied. In an MS-based quantitative proteome analysis the metabolic labeling technique SILAC was employed, as SILAC is a very robust and reliable quantitation method, resulting in highly accurate data. Anyway, the achieved results were not according to the expectations, as a higher expression of virulence factors and a clear differential expression of iron-associated proteins was anticipated (Hempel et al., 2011). Hence, the iron concentration was determined, in order to understand the results. Because of the relatively low iron concentration of 190 µg l-1 in CDM, another medium with higher iron concentration, THY (740 µg l-1), was additionally investigated. And indeed, the expected protein expression could be observed; thus, it was reasoned that the iron concentration of the applied medium seems to be decisive for the pneumococcal adaptation on proteome level. Transferring the latter observation to a physiological state, the iron concentration of a niche in the human body, in which pneumococci are residing, is pivotal for the proteomic adaptation of this pathogen.
6.1. FUNCTIONAL CATEGORIZATION OF PNEUMOCOCCAL PROTEINS
For the generation of Voronoi treemaps usually well annotated protein annotation tables are the basis. Here in this work such an annotation table for S. pneumoniae was created (see 5.1.2). In order to estimate the quality of the constructed pneumococcal annotation table a comparison to a well annotated protein annotation from a model organism was done. Probably the best studied Gram-positive bacterium is the model microorganism Bacillus subtilis. Stülke and coworkers published in 2009 the comprehensive B. subtilis wiki, the SubtiWiki (Flórez et al., 2009; Zhu and Stülke, 2018). Using the "genes export wizard" an annotation table including gene/protein functions can be downloaded. After filtering RNA genes out, only protein-encoding genes remain. In total, 4437 proteins are predicted and 1863 are not assigned with any function. This equates to 41.99%, which is slightly higher than the proportion of proteins with unknown function in S. pneumoniae (33.96%, see Table 5-1). Hence, the annotation table constructed in cooperation with Claudia Hirschfeld, Christian Hentschker and Alejandro Gómez-Mejia should be sufficient for a meaningful biological data interpretation. Using this annotation table, it is possible to describe bacterial adaptation to environmental changes under investigation, because main metabolic pathways
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are well annotated. Moreover, proteins, which function is not yet known, play no role in the biological interpretation of the scientific question. Nevertheless, the relatively high numbers of proteins with unknown function strengthen the need for a better annotation not only for pneumococci, but also for other organisms. Protein and genome annotations are of great importance for data evaluation and interpretation of scientific questions. Consequently, the development of bioinformatic annotation tools is important for genome and proteome data analyses, and thus can help to unravel more biological functions of unassigned genes and protein. However, bioinformatics tools are only tools and cannot replace laboratory experiments. Bioinformatic predictions must always be validated by experimental approaches.
6.2. COMPARISON OF PROTEIN EXPRESSION IN RESPONSE TO MEDIA
In the present work, two fundamentally different media were used for the iron limitation experiments in S. pneumoniae. The actual idea was the investigation of iron limitation in a medium, in which the data interpretation is based on a very reliable and robust protein quantification method. Hence, it was decided to work with a chemically defined medium, namely a modified RPMI1640, where it is possible to exchange lysine and arginine with their heavy isotope labeled counterparts, so that the changes in protein abundances can be quantified with the SILAC method. From previous work on S. aureus (Hempel et al., 2011) and other organisms, we know that proteins associated to pathogenesis are higher abundant under iron-restricted condition than under control condition, suggesting that iron-limiting conditions induced by nutritional immunity trigger the expression of virulence factors in pathobionts. Therefore, it was expected to observe a similar proteomic response to iron restriction in pneumococci, but this was not the case. For this reason, it was decided to determine the iron concentration of the CDM in an element analysis by ICP-MS. The total iron concentration in this CDM amounts to 190 µg l-1, which is in comparison to the freely available iron concentration the human extracellular environment (water/plasma; 10-18 M
-14 -1 5.6 10 µg l iron) (Theil and Goss, 2009) relatively high. Still, it was suggested to apply≙ another medium with a higher iron concentration, because iron was only present in trace in medium ingredients and no extra iron was added, for instance, as iron salt. Ideally, the best option would have been the addition of iron to the CDM medium. Unfortunately, this was not practicable, because the addition of iron sulfate led to precipitation of poorly soluble iron phosphate, as the CDM is a phosphate buffered medium. Thereupon, it was decided to use the complex medium THY, which is commonly used for pneumococcal growth experiments. THY medium contains 740 µg l-1 iron in total, which is approximately four-fold higher than in
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CDM. However, this analysis gives no indication of the biological availability of iron, because THY contains yeast extract and therefore, it is not clear how much iron is actually bound to proteins deriving from THY medium. The different composition of the two media and thus differing proteome of pneumococci has to be considered during the analysis of the iron limitation experiments. Overall, it became clear that the proteome composition expressed in both media is very different from each other. Noticeably more proteins were expressed in the chemically defined medium than in the nutrient rich medium. Due to medium compositions, pneumococci have to activate suitable metabolic pathways for the production of substrates needed. This was also observed in other studies, where the protein expression in defined or rather minimal and complex medium was under investigation. This was the case, for example, in proteome analyses in Bacillus licheniformis (Voigt et al., 2004), in E. coli (Li et al., 2014) and in three strains of the genus Xanthomonas (Park et al., 2017). Additionally, bacteria and also other living organisms actively adapt their protein expression to their ever changing environment, e.g. the expression pattern of major soluble proteins from Arabidopsis thaliana, a plant model, is significantly influenced by the culture medium (Sarry et al., 2006). The protein expression of specific metabolic pathways is activated or inactivated as required. Particularly, the proteins assigned to amino acids metabolism are much higher abundant in minimal or defined medium in the present thesis, which is in accordance with previous studies in B. licheniformis, E. coli and Xanthomonas species (Li et al., 2014; Park et al., 2017; Voigt et al., 2004). The chemically defined medium, applied in this study, is mainly composed of a phosphate buffered system, all proteinogenic amino acids and vitamins. But chemically defined media usually do not contain any other protein or peptide sources as for instance yeast cell extract as in THY medium. Hence, the bacteria have to produce metabolic precursors by their own. Additionally, to the increased amino acids metabolism, also the transport and binding proteins responsible for amino acid, peptide and amine trafficking are higher abundant in CDM than in THY. Moreover, in CDM additional transport and binding proteins were present, which were not identified in THY. Similar observations have been made in E. coli (Li et al., 2014). Consequently, the protein synthesis and tRNA aminoacylation pathways are increased in pneumococci grown in CDM. This was observed in a similar manner in B. licheniformis (Voigt et al., 2004). Also in the metabolism of vitamins and cofactors more expressed proteins and higher protein abundances were observed in CDM. This is in accordance with the proteome study on B. licheniformis as higher abundances of proteins ascribed to metabolism of coenzymes and prosthetic groups were found in minimal medium (Voigt et al., 2004). That could be a consequences of low levels of vitamins in the
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growth environment, for which reason the bacteria have to produce their own cofactors. In addition, Otto and colleagues investigated Clostridium difficile in minimal and complex medium. Besides the higher protein abundances in the metabolism of cofactors and vitamins, proteins involved in purine metabolism were highly increased in minimal medium (Otto et al., 2016). This was also the case in the present comparative analysis of both investigated media. Moreover, a study on a pathogenic E. coli strain comparing the protein expression in a chemically defined (simulated colonic environment medium) and in a complex medium (tryptic soy broth medium) could show that besides the differential expression of proteins belonging to protein biosynthesis, purine metabolism and other general metabolic pathways, also the protein amount of virulence factors and toxin was higher in CDM than in complex medium (Polzin et al., 2013). In the present thesis similar observations have been made as proteins involved in pathogenesis, in addition to the aforementioned metabolic pathways, were higher abundant in pneumococci grown in CDM than in THY. Besides the significantly higher amount of virulence factors, also more toxins were expressed and present in increased abundance in CDM. Since many proteins from these groups are independent from iron, it is likely that the observed differences display a general adaptation to the different media.
6.3. THE IMPORTANCE OF IRON AND THE CHOICE OF THE IRON CHELATOR
Iron is involved in many diverse processes in nearly all organisms. Borrelia burgdorferi and Treponema pallidum, obligate intracellular parasites, are two exceptions within microorganisms that do not require iron and are only indirectly dependent on this metal, as they are relying on iron-dependent metabolic processes of their hosts (Andrews et al., 2003). As iron has besides its benefits also a detrimental site, in terms of ROS generation and their toxic effect on proteins, lipids and especially DNA, the concentration of freely available iron is extremely restricted in the habitat human (Brown and Holden, 2002; Cassat and Skaar, 2013). Hence, colonization and infection of the human is in direct connection with the ability of pathogens to acquire iron. Therefore, bacteria evolved a handful of strategies as described in the introduction chapter (3.3.1). Within the human host different niches can be colonized by pneumococci. In doing so, the bacteria will probably face varying iron concentrations. The human contains approximately 4,000 mg iron in total. The biggest part with 2,500 mg is located within erythrocytes and 1,000 mg are stored in splenic and hepatic macrophages. Only three milligrams of iron are bound to transferrin and thus circulate in blood serum (Waldvogel-Abramowski et al., 2014). As aforementioned, pneumococci are able to colonize on respiratory tract epithelial cells
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(RTECs) at different sites. Hence, they can colonize both upper and lower respiratory tract of their human host (Kadioglu et al., 2002). The interface between RTECs and external environment is build up of the so called respiratory tract lining fluids (RTLFs; nasal lavage fluid, sputum, bronchoalveolar lavage fluid [BALF]); these fluids are covering the RTECs from nasal mucosa to alveoli. Within the RTLFs low molecular mass antioxidants (e.g. glutathione), metal ion-binding proteins (e.g. transferrin, lactoferrin, ferritin), specific antioxidant enzymes (e.g. superoxide dismutase, catalase), sacrificial proteins and unsaturated lipids are included (Cross et al., 1994). The composition of the RTLFs is heterogeneous and depends on the individual regional characteristic (Reynolds and Chrétien, 1984). So for instance, the concentration of lactoferrin is higher in the upper RTLFs, in which it is assumed that lactoferrin might have a role in the prevention of ROS generation by binding iron (Cross et al., 1994). There are a few studies on iron concentration in BALF. Stites and coworkers could not detect free iron in BALF (limit of detection = 1 µg dl-1). But they could determine the total ferritin concentration, which was roughly 9 ng ml-1. Additionally, they found that the concentration of transferrin was approximately 3 µg ml-1 (Stites et al., 1999). In another study the iron concentration in BALF was determined to be approximately 7.2 µg ml-1 and the transferrin concentration was about 3 µg ml-1 (Mateos et al., 1998). Supposing that the iron concentration within RTLFs correlates with the concentration of RTLFs’ antioxidants, it can be assumed that pneumococci are facing different iron concentration dependent on the colonized site within the human respiratory tract and therefore might have a different proteome adaptation to varying niches. Subsequently, the question arises, if the initial iron concentration plays a role in the formation of virulence within host niches. In order to mimic the low physiological iron availability artificial iron starvation experiments are a suitable tool. An in vitro iron limitation can be established in several different ways (see chapter 3.3.3). In the present thesis the iron chelator BIP was used. BIP is able to form complexes together with other transition metal ions, too. Hence, while analyzing the pneumococcal adaptation to iron limitation, not only proteins connected to iron limitation should be discussed, but the fate of other metal-dependent proteins should be taken into account. Nevertheless, BIP is commonly used as iron chelator within iron limitation experiments in diverse microorganisms. BIP was applied in various studies on Gram- negative bacteria, so for instance on Acinetobacter baumannii (Actis et al., 1993; Dorsey et al., 2004; Léséleuc et al., 2012), Vibrio anguillarum (Dorsey et al., 2004), E. coli (Kammler et al., 1993a; Ma et al., 2015; Pakarian and Pawelek, 2016), Edwardsiella ictaluri (Dumpala et al., 2015) and Fusobacterium necrophorum (Antiabong et al., 2015). In addition, also Gram- positive bacteria like S. aureus (Hempel et al., 2011; Stentzel et al., 2014),
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Acinetobacillus pleuropneumoniae (Klitgaard et al., 2010) and Enterococcus faecalis (López et al., 2012) were treated with BIP to introduce iron-limiting conditions. For the investigation of the proteomic response to iron limitation in S. pneumoniae in the present thesis, BIP was applied for the introduction of this infection-relevant condition. There are several reasons for the choice of BIP in this study. As mentioned before, the use of iron chelators from natural source like apo-transferrin or siderophores could have no or contrary effects to the iron limitation experiments in pneumococci (see chapter 3.3.3). A further alternative are chemicals substances with iron-coordinating properties. Beyond the chemical iron chelators, BIP is certainly one of the most utilized iron-chelating agents. Moreover, our lab had already experience with BIP as iron chelator (Hempel et al., 2011). An alternative iron chelator would have been EDDHA, but for all these aforementioned reasons it was decided to apply BIP as suitable iron chelator. As mentioned above, BIP is also able to generate complexes with other divalent metal ions. Thus, the change in abundance of proteins, which are annotated to contain a divalent metal ion, were also considered for data evaluation while analyzing the proteomic adaptation to iron limitation in CDM and THY. To what extent BIP has an influence on other divalent metal cations in CDM and THY cannot be precisely stated, since, for instance, zinc-dependent proteins (ManA, AdhA, Fba, Adh2, FtsH, PepT, DnaJ, SPD_0855, AlaS, ThrS, Rnz) were altered variously in BIP treated samples in comparison to control condition. Additional zinc- (Adh, AccD, Eep, SecA, IleS, Tgt, SPD_1523, ZmpB) and also manganese-dependent (DeoB, AckA, PpaC, Ddl, RdgB, CarB, PhpP) proteins were exclusively found in the CDM dataset showing ambivalent proteins abundances. Interestingly, the zinc and manganese transporter proteins AdcA as well as MtsA (also referred as PsaA) and PsaB, respectively, were less abundant (FC < -5) in BIP- treated CDM samples than in control CDM samples, what could lead to the conclusion that these metal ions did not underlie a limitation (proteome data can be found in Table 8-1 and in Table 8-2). In theory, approximately 30 to 40% of all proteins within an organism contain a metal cofactor (Andreini et al., 2009; Putignano et al., 2018). In the present study, only a minor proportion of proteins had an annotated metal c 5.33%) or other cofactor (total
7.00%), respectively (cofactorofactor (≈ annotation from UniProt for annotatedS. pneumoniae cofactors strain D39; ≈ (The UniProt Consortium, 2017)). Hence, there is a large gap between known and unknown protein cofactor interactions. There are several metal ion- binding prediction tools like MetalPDB (Putignano et al., 2018) or COFACTOR (Zhang et al., 2017) available. Theoretical data together with measured data from metal-dependent
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6.4. ADAPTATION TO IRON LIMITATION
In the first iron limitation study in pneumococci, the adaptation of the strain TIGR4 to iron depletion was under investigation and the analysis was performed in a 2D-LC-MS/MS approach (Nanduri et al., 2008). The authors of this study used Chelex 100 for the depletion of metals and supplemented the treated medium with calcium, magnesium and manganese ions. Although, this study was an MS-based proteome analysis, only 172 proteins could be identified (Nanduri et al., 2008), which estimates roughly 5.4% of the total pneumococcal proteome. It has to be taken into account that this study was already performed ten years ago, when the mass spectrometry was only applied a few years earlier for global microbial proteome analysis and MS-based methods were still in their infancy. Nonetheless, Nanduri and colleagues were able to show that iron is involved in several biological processes, so for instance in stress response, phase variation and biofilm formation as well as in pneumococcal virulence (Nanduri et al., 2008). Since then, the development of novel MS- based technologies and quantification methods were tremendous. Hence, an up-to-date global quantitative proteome analysis, focusing on iron limitation, will broaden the knowledge of iron homeostasis in pneumococci and might give new starting points for other scientist for the identification of novel vaccine or drug targets. Additionally, for the first time two media with regard to different iron concentrations were under investigation. Within this chapter the main results of both iron limitation experiments shall be discussed. Regarding the data interpretation and discussion of the iron limitation approaches, the data evaluation of the comparative media analysis with respect to iron homeostasis is of fundamental relevance. Since there are major proteomic differences in the pneumococcal protein levels in response to both media, those differences have to be considered during the interpretation of the results of the iron limitation experiment.
Regulation Within the datasets of the comparative media analysis and of both iron limitation experiments in CDM and THY (see in Table 8-1, Table 8-2 and Table 8-3) two of the three known iron-associated regulators could be identified, namely CodY and IdtR (also known as PsaR). The global nutritional repressor CodY has an important regulatory function in the iron homeostasis of pneumococci. It was shown that CodY is required to prevent the uncontrolled import of iron (Caymaris et al., 2010) by repressing directly the expression of the piuBCDA
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operon (Hendriksen et al., 2008). Additionally, it is also known to play a key role in the amino acid metabolism and uptake via the repression of the ami operon (Hendriksen et al., 2008). In the iron limitation dataset of CDM samples the protein abundance of CodY is significantly decreased (FC = -4.35). Hence, the expression of the piuBCDA operon is not as repressed as under iron-replete condition; consequently, the protein abundance of the gene products should increase. Indeed, PiuA is much higher abundant in the iron-restricted environment in comparison to control condition in CDM. Although CodY is also described to repress the expression of the ami operon, no increase in the abundance of ami gene products was detected. Quite the contrary, the abundances of AmiA, AmiC, AmiD, AmiE and AmiF were eased. This observation might be in connection with the overall decrease of transport and binding proteins under iron-limited condition in CDM. The ami operon is also known to antagonize the competence of pneumococci (Johnston et al., 2015). To what extent the alterations in competence proteins (see below) and AmiFEDCA are connected in this thesis, cannot be elucidated without further in-depth analysis. Moreover, in an early study on codY in pneumococci it was shown that the deletion of this gene leads to the downregulation of dpr (Hendriksen et al., 2008). If this is a direct or indirect effect is not clear yet (Caymaris et al., 2010). In the present study it was shown that the amount of Dpr is significantly decreased (FC = -16.67). The changes in protein abundances of PiuA and Dpr fit very well to the known regulations of CodY. CodY was not identified in the iron limitation experiment in THY medium; probably due to very low abundance of this regulator. Moreover, CodY and IdtR (PsaR) could be quantified solely in CDM samples of the media comparison dataset (only CDM and THY control samples). In addition to its other functions (discussed before), CodY was shown to be required for a successful colonization (Hendriksen et al., 2008) and its gene is described to be essential, because its deletion leads to further suppressor mutations (Caymaris et al., 2010). Although this regulator seems to be very important for survival of pneumococci, only in one technical replicate of a THY sample the gene product CodY was identified in a relatively low amount. In contrast, CodY could be identified and even quantified in CDM in reasonable amounts. The presence of CodY might explain the high abundance of pathogenesis-associated proteins in CDM even under control conditions as it is possibly directly or indirectly involved in the expression of virulence factors (Hendriksen et al., 2008). It seems that pneumococci grown in CDM are already more virulent than in THY, although the bacteria were not iron-limited yet. IdtR (PsaR) was only quantified in very low amounts in CDM samples of the media comparison dataset. Not very much is known about the function of the latter regulator in the iron homeostasis (Gupta et al., 2013), but this DtxR homolog was shown to be directly involved in the manganese
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Iron and iron/sulfur cluster as cofactors In the dataset comparing CDM and THY control samples, proteins, which are involved in the assembly of iron/sulfur clusters (SufB, SufC, SufD, SufS, SPD_0765), were clearly higher abundant in CDM than in THY, at which the differences of SufC and SufS amounts are significant. This is in accordance with the higher abundance of proteins involved in metabolism of vitamins and cofactors as already described above and is not a direct consequence of lower iron concentration in the medium rather a response to the low iron/sulfur cluster reservoir in the CDM. Moreover, proteins (Nth, PflA, IlvD, PyrK, LuxS, NrdF, SPD_0136, Def) using iron ions or iron/sulfur clusters as coenzymes or prosthetic groups do not show a differential expression in favor of any of the two media under control condition. Analyzing the iron-dependent proteins (LuxS, IlvD, NrdF, Def, SufC, SufD, SufB, SufS) in the iron limitation experiment in CDM, the FC values gave no conclusive indications on the iron limitation effect on the pneumococcal proteome. In contrast, pneumococci grown in THY under iron-limited conditions showed an increase of the iron/sulfur cluster assembly proteins SufC, SufS and SPD_0765. Additionally, proteins (IlvD, PyrK, Fer) containing iron/sulfur clusters as prosthetic group could not be identified in BIP-treated samples in THY. Although, the results of the iron limitation experiments, regarding iron/sulfur cluster, differ from each other, it can be assumed that this observation is a direct response to iron limitation of pneumococci grown in THY medium.
Energy metabolism Within the CDM iron limitation dataset, it has been noticed that the abundance of ATPase subunits AtpD, AtpG and AtpA (FC < -1.5) was decreased. Additionally, AtpH as well as AtpF were not identified at all. This indicates that less ATP is produced during oxidative phosphorylation, and thus the energy metabolism might be disturbed in iron-depleted CDM. Such observations were made neither in the media comparison dataset nor during iron limitation in THY. To investigate whether pneumococci have an altered energy metabolism in CDM under iron-limited conditions, this would require a detailed determination of the concentrations of ADP and/or ATP (Kanamori et al., 1990).
StkP and cell division In the iron limitation experiment in CDM, alterations in abundances of proteins involved in lipid metabolism, cell division and glycan biosynthesis were detected. Also electron
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microscopic pictures confirmed an impaired cell division and an abnormal cell shape (5.2.4 and 5.4.2). Proteins belonging to the divisome were either very low abundant (FtsA, FtsZ, FtsE, FtsX, Pbp1A, StkP, EzrA), not identified in BIP-treated samples (Pbp2A, MreC, MapZ) or not altered in their protein abundances (DivIVA, GpsB, YlmF [also referred as SepF], PhpP); the FC values of proteins mentioned can be found in the appendix section 8, file B3. The proteins of the latter group are partially regulated by the kinase StkP via phosphorylation. In the first phase of cell division MapZ and FtsZ are forming the framework for the divisome and the polymerization of FtsZ at midcell forms the so called Z-ring (Garcia et al., 2016). Together with FtsA, StkP and DivIVA the basis of the divisome is established and many more proteins (above mentioned and others) take part in the highly complex cell division network (Beilharz et al., 2012). This conglomerate defines the cell morphology of a bacterium, which is determined by the presence or absence of specific shape-determining factors (Massidda et al., 2013). It seems that the cell division after BIP-treatment is differentially regulated. One kind of regulation in prokaryotes is facilitated by phosphorylations. The serine/threonine- protein kinase StkP is involved in cell division, virulence, competence and stress resistance. Known targets of this kinase are DivIVA, MapZ, PpaC, FtsZ, FtsA, MurC, GlmM and StkP itself amongst others (Garcia et al., 2016; Massidda et al., 2013; Nováková et al., 2010). Beilharz and colleagues investigated a StkP deletion mutant, which showed a similar phenotype as witnessed in electron microscopic pictures of pneumococci grown in iron-depleted CDM. They described the cell morphology as elongated with multiple, often unconstructed FtsA and DivIVA rings (Beilharz et al., 2012). The same observation has been made in the present study. Here, the StkP was only found in significantly low amounts after induction or iron restriction. In addition, also other proteins involved in cell division were found in much lower amounts by comparison of BIP-treated samples with control samples. Interestingly, although also cell-division-associated proteins showed differential alteration in their abundances in BIP-complex samples, pneumococci did not seem to have visible perturbation in cell division (see Figure 5-19 and Figure 5-20). One major difference of proteomic data is the significantly higher levels of StkP in BIP-complex-treated samples in comparison to BIP- treated samples. These results strengthen the assumption that the iron availability plays a role in the pneumococcal cell division in the investigated CDM. The extent to which iron has an influence on cell division is difficult to say, because the impaired cell division was only observed under iron-limited condition in CDM and not in THY medium. One can speculate whether the initial iron concentration of the medium is decisive for this outcome in CDM. Another explanation for the impaired cell division might be a consequence of the disturbed energy metabolism as described above. StkP is an ATP-dependent phosphorylase (Nováková et al., 2005) and if not enough ATP is present, the function of StkP might be defective. In
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Competence and virulence As aforementioned, StkP is also involved in pneumococcal competence. The competence- inducing system ComCDE has an important role in the quorum sensing regulation of competence in pneumococci. ComC is processed and secreted by the ABC transporter system ComAB as the pheromone competence-stimulating peptide (CPS). CPS is sensed by the receptor kinase ComD, which activates the response regulator ComE. Subsequently, ComE via ComX induces the expression of the comCDE operon (Hakenbeck, 2000; Morrison and Lee, 2000). The kinase StkP is additionally required for the activation of the comCDE operon. The positive regulation of comCDE expression by StkP is balanced by the repression through CiaRH (Echenique et al., 2004). In the present study the ABC transporter system ComAB and the two-component system ComDE could not be identified or only present in significantly lower abundance, respectively, under iron-limited conditions in CDM. In contrast, the protein level of CiaR did not alter and ComX was not identified at all in the whole dataset. From the obtained data it can be assumed that less CPS is released to the extracellular milieu, because no ComA and B could be identified in CDM after BIP-treatment. Hence, the two-component system ComDE is less stimulated, resulting in a decreased expression of the comCDE operon. Consequently, iron-restricted pneumococci, grown in CDM, are less competent and probably less virulent, which is in accordance with the observed decrease of pathogenesis-associated protein abundances. Also in case of pneumococcal competence no direct effect of iron- limiting condition could be shown. Probably it is a multiple effect of iron limitation and low nutrient availability. As mentioned before, pneumococci grown in CDM showed lower levels of pathogenesis- associated proteins in BIP- (depicted in Figure 6-1, turquoise color) and BIP-complex samples; this proteomic response in the iron-limited CDM was contrary to the expectations. Hence, this response might be an effect based on BIP itself and not of its iron-restricting ability. This conclusion is supported by the results of the analysis of BIP-complex-treated bacterial cells, which showed a comparable response. In this case it is necessary to include the results from the media comparison. The comparative proteome analysis in the media comparison dataset showed that proteins associated to virulence were already much higher abundant in pneumococci grown in CDM without iron limitation, due to a lower iron concentration in CDM. Pneumococci in a lower iron content environment seem to have different adaptation strategies to an induced iron limitation.
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However, pneumococci grown in iron-limited THY medium showed an increase of pathogenesis associated proteins (see in Figure 6-1, orange color); this effect was not observed after BIP complex addition (control experiment), what means that the increased protein abundance of virulence factors is a direct result of the iron-limiting properties of BIP. Strikingly, all four known polyhistidine triad (Pht) proteins PhpA (PhtA), SPD_1037 (PhtB), PhtD and PhtE increased in their proteins abundance, in which PhtB as well as PhtE were only identified in BIP-treated samples, PhpA was significantly higher abundant (FC = 24.42) and PhtD was slightly higher abundant under iron limitation in comparison to control samples. Pht proteins possess five histidine triad motifs (HXXHXH) and these proteins are described to contribute to the pneumococcal virulence (Plumptre et al., 2012). Although their physiological functions are not fully understood yet, it was suggested that, for instance, PhtD protects pneumococci of toxic zinc ion concentrations by scavenging and storage of excessive zinc ions (Bersch et al., 2013). Moreover, it was shown that Pht proteins function in the adhesion to respiratory epithelial cells during nasopharyngeal colonization (Kallio et al., 2014). As colonization is the first step in the development of an infection, Pht proteins might be one of the first protein groups, which allow pneumococci to adhere to human host cells. Hence, the drop of iron concentration might trigger Pht protein expression. To highlight the differences in proteins abundance of virulence factors, Figure 6-1 shows the FC values of the pathogenesis-associated proteins found in both iron limitation experiments. It is worth to mention that the vast majority of virulence factors quantified in this study exhibit a contrary response to iron limitation between CDM and THY. While all seven proteins display negative FC values after iron depletion in CDM, six proteins are higher abundant in THY medium in response to BIP treatment.
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6 On
4
2 ) B-K 0 log2 (FC -2
-4
Off -6 PspA PhtD PhtE SPD_1037 PhpA Ply CbpA CDM THY
Figure 6-1 Protein FCs of virulence factors during iron limitation in CDM (orange) and THY (turquoise) in comparison to control samples of the respective medium.
Iron transport As already described in the introduction, the three iron ABC transporter systems Pia, Piu and Pit (Brown et al., 2001a; Brown et al., 2002) as well as a recently discovered putative iron transporter SPD_1590 (Miao et al., 2018) are encoded within the genome of the pneumococcal strain D39. In the media comparison dataset, in which THY and CDM samples under control condition were compared to each other, two proteins of the PiaABCD transporter system could be quantified, namely PiaA and PiaD. PiaA was significantly higher abundant CDM than in THY and PiaD was only identified in CDM. This fit very well to the assumption that iron transport proteins are more expressed in a medium with lower iron concentration. Anyhow, no proteins from PiuBCDA or PitBCDA systems could be quantified within the media comparison dataset; PiuA was identified only in one biological replicate in CDM. PiaABCD is known to be the dominant iron transporter system in pneumococci (Brown et al., 2002). In addition, SPD_1590 was quantified at similar levels in both media. In both iron limitation experiments the iron-compound-binding protein PiuA was highly abundant in iron-restricted environment. It is known that PiuA and PiaA work independently from each other and they can even compensate the iron uptake function of the other; this was shown in knockout mutants (Brown et al., 2002). After iron starvation was induced, PiaA
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levels were decreased in CDM. Overall, all proteins involved in transport mechanisms are less abundant in BIP-treated CDM samples. The only quantified transport and binding protein, which is more abundant under iron limitation (see in the appendix section 8, file B3), was PiuA. In iron-limited THY medium, the abundance of PiaA did not alter much, unlike PiuA, which abundance increased dramatically (FC = 19.28) (see in the appendix section 8, file B4). It seems that the increase of PiuA is an iron-limitation-dependent accumulation of the iron transporter protein. Hence, iron uptake by PiuA might be predominant during infection. The abundance of SPD_1590 did not change significantly after BIP treatment. Hence, the change in iron concentration or any other difference of both media (see above) seems to have no effect on the expression of this protein. All identified iron transport proteins of the iron limitation experiments are visualized in Figure 6-2, which shows clearly the impressive increase of PiuA in both iron-limited media. From the third ABC transporter system Pit, no associated protein could be identified in any of the three datasets.
6 On
4
2 ) B-K 0 log2 (FC -2
-4
Off -6 PiaA PiuA SPD_1590 Dpr CDM THY
Figure 6-2 Protein FCs of iron transport and storage proteins under iron limitation in CDM (orange) and THY (turquoise) in comparison to control samples of the respective medium.
Iron storage Another important protein in the pneumococcal iron homeostasis is the iron storage protein non-heme iron-containing ferritin Dpr. Its main function is the storage of excessive iron within the cell, and therefore, Dpr is crucial in the prevention of ROS generated by Fenton Chemistry. In a deletion mutant study it was shown that Dpr is substantial for pneumococcal
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Discussion stress resistance and also for the colonization of the upper respiratory tract (Hua et al., 2014). Within the media comparison analysis under control condition, Dpr is significantly higher abundant in THY samples than in CDM samples, which is in accordance with the higher iron concentration in THY medium. In both iron limitation experiments Dpr was significantly less abundant after BIP-treatment in comparison to control condition (see Figure 6-2). This suggests that the intracellular iron concentration was actually decreased in both media, because of iron deprivation by BIP. Two iron-related adaptations in both media were very similar, namely the changes in protein abundances of the iron transporter PiuA and the iron storage protein Dpr, which are highly more or less abundant, respectively, under iron limited conditions in both examined media. This strongly suggests that BIP-treatment indeed resulted in a limitation of intracellular iron in both media and that additionally the aforementioned differential responses of virulence factors are most likely a result of the initial iron content of the environment.
Classification of iron limitation results in current scientific context Recently, Jiménez-Munguía and coworkers published a multi-omics study on iron starvation in S. pneumoniae TIGR4. Pneumococci were grown in Todd-Hewitt broth (THB) medium, the same was used in this thesis but additionally yeast extract was added (THY). The iron starvation was induced by addition the iron chelator, desferoxamine mesylate. This group investigated the pneumococcal adaptation to iron deprivation on proteome, transcriptome and metabolome level (Jiménez-Munguía et al., 2018). The proteome analysis was executed in a 2D-gel-based MS-experiment, in which cell extracts and secreted proteins were analyzed. They could demonstrate that proteins, especially enzymes from primary and amino sugar metabolism, which use divalent metal ions as cofactors, were differentially expressed. In addition, the protein abundance of other ion- binding proteins was differentially altered (Jiménez-Munguía et al., 2018). By comparing the proteomic results from the iron limitation experiment in THY medium with the study of Jiménez-Munguía and colleagues, several proteins showed similar alterations in their abundances (CbpA, Def, PpaC, Gap, AtpD, Adh2, Pgk, Tuf, Pyk, Eno). This could have been expected, because in both approaches nearly the same growth medium was applied. However, comparing the pneumococcal growth curves in THY and THB shows that the bacteria had a higher end OD in THY (approximately 1.2; Figure 5-13) than in THB (approximately 0.6; (Jiménez-Munguía et al., 2018)). To what extent the yeast extract has an effect on the proteomic response cannot be said. This would require an additional study. Still, it is difficult to compare 2D-gel-based proteomic method with a global gel-free MS-based
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approach, because much more proteins are detected by more sensitive LC-MS/MS than in 2DE/MALDI-TOF approaches. This fact makes it difficult to make a statement about differences and similarities in both studies on proteome level. Furthermore, the authors measured the iron concentration of THB medium, which was 734 µg l-1. The result is very similar to the value obtained in this thesis (740 µg l-1). In addition to the proteome analysis, also the transcriptomic response to iron starvation was investigated. The transcriptomic results showed changes in the expression of several biological processes like oxidative phosphorylation, competence and fatty acid biosynthesis (Jiménez-Munguía et al., 2018). In contrast to their proteomic results, the transcriptomic outcome is in accordance to some extent with the results of the present thesis. So for instance, it was shown that genes of the suf operon as well as of the piu operon were upregulated and genes encoding enzymes of the pyrimidine metabolism were downregulated (Jiménez-Munguía et al., 2018), which is in line with the protein abundances of gene products in the iron-limited THY dataset. Contrarily, the iron-limited CDM dataset showed lower abundances of proteins belonging to iron-sulfur-cluster assembly and differential alterations of proteins involved in pyrimidine metabolism, which is not surprising, considering that THY as well as THB are very different from CDM regarding the media composition. But still, PiuA was in both iron limitation approaches of the present thesis much higher abundant, which is in accordance with the upregulation of the piu operon as described in (Jiménez-Munguía et al., 2018). Interestingly, the transcriptomic results showed that genes encoding proteins involved in the synthesis of branched-chain amino acids and competence were downregulated under iron-restricted condition (Jiménez- Munguía et al., 2018). This is in accordance with changed protein abundances of those metabolic pathways in the iron-limited CDM dataset, as described before. How this is related cannot be said at this point without further investigation. Furthermore, in metabolome experiments the only altered metabolites were elevated levels of amino sugars, which are involved in the cell wall biosynthesis (Jiménez-Munguía et al., 2018). Both the proteomic response to iron limitation in THY and CDM concerning the peptidoglycan biosynthesis are not consistent with the metabolome data. In the THY dataset the FC values are not significantly altered in any direction. In addition, in CDM the protein abundances of nearly all peptidoglycan-biosynthesis-associated proteins are decreased. Additionally to the differences of the applied media, also the iron chelator was different in the study of Jiménez-Munguía and colleagues. Hence, it can be assumed that the response to the different iron chelators might also result in a different proteomic response of pneumococci. This further strengthens the necessity to apply appropriate controls such as the BIP-complex samples in the present study. In addition, in both studies different
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Discussion pneumococcal strains, namely the unencapsulated serotype 2 strain D39 and the encapsulated serotype 4 strain TIGR4 were investigated. TIGR4 may have different strategies for the adaptation to iron limitation. However, what causes the difference in the peptidoglycan biosynthesis remains unanswered. Whether the iron chelator has an influence on any metabolic pathway or the different strains adapt differentially would be speculative. Again, further investigations would be necessary.
6.5. CONCLUSION
In summary, from both studies (this thesis and Jiménez-Munguía et al., 2018) we can assume that the pneumococcal response to an iron-limited environment impacts many different physiological pathways. One the one hand, the ABC iron transporter system PiuBCDA seems to play a very important role in the uptake of iron during its limitation and hence is crucial during infection. Although Dpr was only described in this thesis, it is obviously important in handling the intracellular iron concentration and thus the prevention of ROS generation. On the other hand, iron seems to be involved in virulence and competence as well as in branched-chain amino acid and pyrimidine metabolism. Additionally, in this thesis, it was shown that the initial iron concentration of the medium and also the nutritional composition has a huge impact on the pneumococcal proteome adaptation. These observations can be transferred to pneumococci residing in diverse niches and probably respond to the different habitats in a varying protein composition, adapted to the present nutrient and also to the available iron. Maybe this makes the difference between colonization and infection. Hence, it would be interesting to figure out which specific factor or which factor group is necessary to evoke the transition from colonization to infection.
6.6. PUBLICATION OF MAIN RESULTS
The main results of this PhD thesis regarding the iron limitation experiments in CDM and THY with respect to the iron concentration of the environment were summarized and published in a comprehensive research article with the title "Proteomic response of Streptococcus pneumoniae to iron limitation" (article: Hoyer, J.; Bartel, J.; Gómez-Mejia, A.; Rohde, M.; Hirschfeld, C.; Heß, N.; Sura, T.; Maaß, S.; Hammerschmidt, S.; Becher, D. 2018. Int J Med Microbiol. doi: 10.1016/j.ijmm.2018.02.001.). In particular, the publication is based on the results from chapters 5.2.3, 5.2.4, 5.2.5, 5.3.2, 5.4.1 and 5.4.2. Additionally, partial results were presented in several contributions on conferences (see chapter 7).
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References
REFERENCES
Abergel, R.J., Wilson, M.K., Arceneaux, J.E.L., Hoette, T.M., Strong, R.K., Byers, B.R., Raymond, K.N., 2006. Anthrax pathogen evades the mammalian immune system through stealth siderophore production. Proceedings of the National Academy of Sciences 103 (49), 18499. Actis, L.A., Tolmasky, M.E., Crosa, L.M., Crosa, J.H., 1993. Effect of iron-limiting conditions on growth of clinical isolates of Acinetobacter baumannii. Journal of Clinical Microbiology 31 (10), 2812. Aebersold, R., Mann, M., 2003. Mass spectrometry-based proteomics. Nature 422 (6928), 198-207. Aguila, A., Herrera, A.G., Morrison, D., Cosgrove, B., Perojo, A., Montesinos, I., Pérez, J., Sierra, G., Gemmell, C.G., Brock, J.H., 2001. Bacteriostatic activity of human lactoferrin against Staphylococcus aureus is a function of its iron-binding properties and is not influenced by antibiotic resistance. FEMS Immunology & Medical Microbiology 31 (2), 145–152. Alcantara, J., Yu, R.H., Schryvers, A.B., 1993. The region of human transferrin involved in binding to bacterial transferrin receptors is iocalized in the C-lobe. Molecular Microbiology 8 (6), 1135–1143. Alvarez Hayes, J., Lamberti, Y., Surmann, K., Schmidt, F., Völker, U., Rodriguez, M.E., 2015. Shotgun proteome analysis of Bordetella pertussis reveals a distinct influence of iron availability on the bacterial metabolism, virulence, and defense response. Proteomics 15 (13), 2258–2266. Andersen, S.B., Marvig, R.L., Molin, S., Krogh Johansen, H., Griffin, A.S., 2015. Long-term social dynamics drive loss of function in pathogenic bacteria. Proceedings of the National Academy of Sciences 112 (34), 10756. Anderson, N.L., Anderson, N.G., 1998. Proteome and proteomics: New technologies, new concepts, and new words. Electrophoresis 19 (11), 1853–1861. Andreini, C., Bertini, I., Cavallaro, G., Holliday, G.L., Thornton, J.M., 2008. Metal ions in biological catalysis: From enzyme databases to general principles. Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry 13 (8), 1205–1218. Andreini, C., Bertini, I., Rosato, A., 2009. Metalloproteomes: A Bioinformatic Approach. Accounts of Chemical Research 42 (10), 1471–1479. Andrews, S.C., Robinson, A.K., Rodríguez-Quiñones, F., 2003. Bacterial iron homeostasis. FEMS Microbiology Reviews 27 (2-3), 215–237.
95
References
Antiabong, J.F., Ball, A.S., Brown, M.H., 2015. The effects of iron limitation and cell density on prokaryotic metabolism and gene expression: Excerpts from Fusobacterium necrophorum strain 774 (sheep isolate). Gene 563 (1), 94–102. Aranda, R., Worley, C.E., Liu, M., Bitto, E., Cates, M.S., Olson, J.S., Lei, B., Phillips, G.N., 2007. Bis- methionyl Coordination in the Crystal Structure of the Heme-binding Domain of the Streptococcal Cell Surface Protein Shp. Journal of Molecular Biology 374 (2), 374–383. Arnold, R.R., Brewer, M., Gauthier, J.J., 1980. Bactericidal activity of human lactoferrin: Sensitivity of a variety of microorganisms. Infection and Immunity 28 (3), 893–898. Austrian, R., 1999. The Pneumococcus at the Millennium: Not Down, Not Out. Journal of Infectious Diseases 179 (Supplement_2), S338-S341. Avery, O.T., MacLeod, C.M., McCarty, M., 1944. Studies on the Chemical Nature of the Substance Inducing Transformation of Pneumococcal Types: Induction of Transformation by a Desoxyribonucleic Acid Fraction Isolated from Pneumococcus Type III. Journal of Experimental Medicine 79 (2), 137–158. Baichoo, N., Helmann, J.D., 2002. Recognition of DNA by Fur: A Reinterpretation of the Fur Box Consensus Sequence. Journal of Bacteriology 184 (21), 5826–5832. Baichoo, N., Wang, T., Ye, R., Helmann, J.D., 2002. Global analysis of the Bacillus subtilis Fur regulon and the iron starvation stimulon. Molecular Microbiology 45 (6), 1613–1629. Baker, E.N., Baker, H.M., 2005. Lactoferrin: Molecular structure, binding properties and dynamics of lactoferrin. Cellular and Molecular Life Sciences 62 (22), 2531. Balsells, E., Guillot, L., Nair, H., Kyaw, M.H., 2017. Serotype distribution of Streptococcus pneumoniae causing invasive disease in children in the post-PCV era: A systematic review and meta-analysis. PLoS One 12 (5), e0177113. Bantscheff, M., Lemeer, S., Savitski, M.M., Kuster, B., 2012. Quantitative mass spectrometry in proteomics: Critical review update from 2007 to the present. Analytical and Bioanalytical Chemistry 404 (4), 939–965. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J., Kuster, B., 2007. Quantitative mass spectrometry in proteomics: A critical review. Analytical and Bioanalytical Chemistry 389 (4), 1017–1031. Barber, M.F., Elde, N.C., 2015. Buried Treasure: Evolutionary Perspectives on Microbial Iron Piracy. Trends in Genetics 31 (11), 627–636. Bates, C.S., Montañez, G.E., Woods, C.R., Vincent, R.M., Eichenbaum, Z., 2003. Identification and Characterization of a Streptococcus pyogenes Operon Involved in Binding of Hemoproteins and Acquisition of Iron. Infection and Immunity 71 (3), 1042–1055.
96
References
Bates, C.S., Toukoki, C., Neely, M.N., Eichenbaum, Z., 2005. Characterization of MtsR, a New Metal Regulator in Group A Streptococcus, Involved in Iron Acquisition and Virulence. Infection and Immunity 73 (9), 5743–5753. Beilharz, K., Nováková, L., Fadda, D., Branny, P., Massidda, O., Veening, J.-W., 2012. Control of cell division in Streptococcus pneumoniae by the conserved Ser/Thr protein kinase StkP. Proceedings of the National Academy of Sciences 109 (15), E905. Bernhardt, J., Michalik, S., Wollscheid, B., Völker, U., Schmidt, F., 2013. Proteomics approaches for the analysis of enriched microbial subpopulations and visualization of complex functional information. Current Opinion in Biotechnology 24 (1), 112–119. Bersch, B., Bougault, C., Roux, L., Favier, A., Vernet, T., Durmort, C., Dmitriev, O.Y., 2013. New Insights into Histidine Triad Proteins: Solution Structure of a Streptococcus pneumoniae PhtD Domain and Zinc Transfer to AdcAII. PLoS One 8 (11), e81168. Birgens, H.S., 1985. Lactoferrin in plasma measured by an ELISA technique: Evidence that plasma lactoferrin is an indicator of neutrophil turnover and bone marrow activity in acute leukaemia. Scandinavian Journal of Haematology 34 (4), 326–331. Boelaert, J.R., Locht, M. de, van Cutsem, J., Kerrels, V., Cantinieaux, B., Verdonck, A., van Landuyt, H.W., Schneider, Y.J., 1993. Mucormycosis during deferoxamine therapy is a siderophore-mediated infection. In vitro and in vivo animal studies. Journal of Clinical Investigation 91 (5), 1979–1986. Bogaert, D., Groot, R. de, Hermans, P.W.M., 2004. Streptococcus pneumoniae colonisation: The key to pneumococcal disease. The Lancet Infectious Diseases 4 (3), 144–154. Bonnah, R.A., Yu, R.-H., Schryvers, A.B., 1995. Biochemical analysis of lactoferrin receptors in the Neisseriaceae: Identification of a second bacterial lactoferrin receptor protein. Microbial Pathogenesis 19 (5), 285–297. Bonsdorff, L., Sahlstedt, L., Ebeling, F., Ruutu, T., Parkkinen, J., 2006. Apotransferrin administration prevents growth of Staphylococcus epidermidis in serum of stem cell transplant patients by binding of free iron. FEMS Immunology & Medical Microbiology 37 (1), 45–51. Boyd, J., Oza, M.N., Murphy, J.R., 1990. Molecular cloning and DNA sequence analysis of a diphtheria tox iron-dependent regulatory element (dtxR) from Corynebacterium diphtheriae. Proceedings of the National Academy of Sciences 87 (15), 5968. Bradford, M.M., 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Analytical Biochemistry 72 (1), 248–254.
97
References
Bridy-Pappas, A.E., Margolis, M.B., Center, K.J., Isaacman, D.J., 2005. Streptococcus pneumoniae: Description of the pathogen, disease epidemiology, treatment, and prevention. Pharmacotherapy 25 (9), 1193–1212. Brown, J.H., 1919. The use of blood agar for the study of streptococci. The Rockefeller Institute for Medical Research, New York. Brown, J.S., Gilliland, S.M., Holden, D.W., 2001a. A Streptococcus pneumoniae pathogenicity island encoding an ABC transporter involved in iron uptake and virulence. Molecular Microbiology 40 (3), 572–585. Brown, J.S., Gilliland, S.M., Ruiz-Albert, J., Holden, D.W., 2002. Characterization of pit, a Streptococcus pneumoniae iron uptake ABC transporter. Infection and Immunity 70 (8), 4389–4398. Brown, J.S., Holden, D.W., 2002. Iron acquisition by Gram-positive bacterial pathogens. Microbes and Infection 4 (11), 1149–1156. Brown, J.S., Ogunniyi, A.D., Woodrow, M.C., Holden, D.W., Paton, J.C., 2001b. Immunization with components of two iron uptake ABC transporters protects mice against systemic Streptococcus pneumoniae infection. Infection and Immunity 69 (11), 6702–6706. Calmettes, C., Alcantara, J., Yu, R.-H., Schryvers, A.B., Moraes, T.F., 2012. The structural basis of transferrin sequestration by transferrin-binding protein B. Nature Structural & Molecular Biology 19, 358-360. Campagnari, A.A., Shanks, K.L., Dyer, D.W., 1994. Growth of Moraxella catarrhalis with human transferrin and lactoferrin: Expression of iron-repressible proteins without siderophore production. Infection and Immunity 62 (11), 4909–4914. Cao, J., Woodhall, M.R., Alvarez, J., Cartron, M.L., Andrews, S.C., 2007. EfeUOB (YcdNOB) is a tripartite, acid-induced and CpxAR-regulated, low-pH Fe2+ transporter that is cryptic in Escherichia coli K-12 but functional in E. coli O157:H7. Molecular Microbiology 65 (4), 857–875. Cao, K., Lai, F., Zhao, X.-L., Wei, Q.-X., Miao, X.-Y., Ge, R., He, Q.-Y., Sun, X., 2018a. The mechanism of iron-compensation for manganese deficiency of Streptococcus pneumoniae. Journal of Proteomics 184, 62–70. Cao, K., Zhang, J., Miao, X.-Y., Wei, Q.-X., Zhao, X.-L., He, Q.-Y., Sun, X., 2018b. Evolution and molecular mechanism of PitAs in iron transport of Streptococcus species. Journal of Inorganic Biochemistry 182, 113–123. Carpenter, C., Payne, S.M., 2014. Regulation of iron transport systems in Enterobacteriaceae in response to oxygen and iron availability. Journal of Inorganic Biochemistry 133, 110– 117.
98
References
Carrano, C.J., Raymond, K.N., 1979. Ferric ion sequestering agents. 2. Kinetics and mechanism of iron removal from transferrin by enterobactin and synthetic tricatechols. Journal of the American Chemical Society 101 (18), 5401–5404. Cartron, M.L., Maddocks, S., Gillingham, P., Craven, C.J., Andrews, S.C., 2006. Feo – Transport of Ferrous Iron into Bacteria. BioMetals 19 (2), 143–157. Cassat, J.E., Skaar, E.P., 2013. Iron in Infection and Immunity. Cell Host & Microbe 13 (5), 509– 519. Caymaris, S., Bootsma, H.J., Martin, B., Hermans, P.W.M., Prudhomme, M., Claverys, J.-P., 2010. The global nutritional regulator CodY is an essential protein in the human pathogen Streptococcus pneumoniae. Molecular Microbiology 78 (2), 344–360. Centers for Disease Control and Prevention (CDC), 2017. Pneumococcal Vaccination: What Everyone Should Know. National Center for Immunization and Respiratory Diseases. https://www.cdc.gov/vaccines/vpd/pneumo/public/index.html. Challis, G.L., 2005. A Widely Distributed Bacterial Pathway for Siderophore Biosynthesis Independent of Nonribosomal Peptide Synthetases. ChemBioChem 6 (4), 601–611. Chart, H., Rowe, B., 1993. Iron restriction and the growth of Salmonella enteritidis. Epidemiology and Infection 110 (1), 41–47. Chen, X., Wei, S., Ji, Y., Guo, X., Yang, F., 2015. Quantitative proteomics using SILAC: Principles, applications, and developments. Proteomics 15 (18), 3175–3192. Cheng, A.G., Kim, H.K., Burts, M.L., Krausz, T., Schneewind, O., Missiakas, D.M., 2009. Genetic requirements for Staphylococcus aureus abscess formation and persistence in host tissues. The FASEB Journal 23 (10), 3393–3404. Chung, J.W., Küster-Schöck, E., Gibbs, B.F., Jacques, M., Coulton, J.W., 2012. Immunoproteomic analyses of outer membrane antigens of Actinobacillus pleuropneumoniae grown under iron-restricted conditions. Veterinary Microbiology 159 (1), 187–194. Cockayne, A., Hill, P.J., Powell, N.B.L., Bishop, K., Sims, C., Williams, P., 1998. Molecular Cloning of a 32-Kilodalton Lipoprotein Component of a Novel Iron-Regulated Staphylococcus epidermidis ABC Transporter. Infection and Immunity 66 (8), 3767–3774. Coffey, T.J., -
Lactam-Dowson,Resistant C.G., Pneumococci. Daniels, M., Microbial Spratt, B.G., Drug 1995. Resistance Genetics 1 and(1), Molecular29–34. Biology of β Correnti, C., Strong, R.K., 2012. Mammalian Siderophores, Siderophore-binding Lipocalins, and the Labile Iron Pool. Journal of Biological Chemistry 287 (17), 13524–13531. Cox, J., Hein, M.Y., Luber, C.A., Paron, I., Nagaraj, N., Mann, M., 2014. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Molecular & Cellular Proteomics 13 (9), 2513–2526.
99
References
Cox, J., Mann, M., 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology 26 (12), 1367–1372. Cox, J., Matic, I., Hilger, M., Nagaraj, N., Selbach, M., Olsen, J.V., Mann, M., 2009. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nature Protocols 4 (5), 698-705. Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V., Mann, M., 2011. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment. Journal of Proteome Research 10 (4), 1794–1805. Crosa, J.H., Walsh, C.T., 2002. Genetics and Assembly Line Enzymology of Siderophore Biosynthesis in Bacteria. Microbiology and Molecular Biology Reviews 66 (2), 223–249. Cross, C.E., van der Vliet, A., O'Neill, C.A., Louie, S., Halliwell, B., 1994. Oxidants, antioxidants, and respiratory tract lining fluids. Environmental Health Perspectives 102 (Suppl 10), 185–191. Dahesh, S., Nizet, V., Cole, J.N., 2012. Study of streptococcal hemoprotein receptor (Shr) in iron acquisition and virulence of M1T1 group A streptococcus. Virulence 3 (7), 566–575. Devireddy, L.R., Hart, D.O., Goetz, D.H., Green, M.R., 2010. A Mammalian Siderophore Synthesized by an Enzyme with a Bacterial Homolog Involved in Enterobactin Production. Cell 141 (6), 1006–1017. Dorsey, C.W., Tomaras, A.P., Connerly, P.L., Tolmasky, M.E., Crosa, J.H., Actis, L.A., 2004. The siderophore-mediated iron acquisition systems of Acinetobacter baumannii ATCC 19606 and Vibrio anguillarum 775 are structurally and functionally related. Microbiology 150 (11), 3657–3667. Dowson, C.G., Barcus, V., King, S., Pickerill, P., Whatmore, A., Yeo, M., 1997. Horizontal gene transfer and the evolution of resistance and virulence determinants in Streptococcus. Journal of Applied Microbiology 83 (S1), 42S-51S. Dryla, A., Hoffmann, B., Gelbmann, D., Giefing, C., Hanner, M., Meinke, A., Anderson, A.S., Koppensteiner, W., Konrat, R., Gabain, A. von, Nagy, E., 2007. High-Affinity Binding of the Staphylococcal HarA Protein to Haptoglobin and Hemoglobin Involves a Domain with an Antiparallel Eight- -Barrel Fold. Journal of Bacteriology 189 (1), 254–264.
Dumpala, P.R., Peterson,Stranded B.C., Lawrence, β M.L., Karsi, A., Wang, W., 2015. Identification of Differentially Abundant Proteins of Edwardsiella ictaluri during Iron Restriction. PLoS One 10 (7), e0132504. Echenique, J., Kadioglu, A., Romao, S., Andrew, P.W., Trombe, M.-C., 2004. Protein Serine/Threonine Kinase StkP Positively Controls Virulence and Competence in Streptococcus pneumoniae. Infection and Immunity 72 (4), 2434.
100
References
Escolar, L., Pérez-Martín, J., Lorenzo, V. de, 1999. Opening the Iron Box: Transcriptional Metalloregulation by the Fur Protein. Journal of Bacteriology 181 (20), 6223–6229. Facklam, R., 2002. What Happened to the Streptococci: Overview of Taxonomic and Nomenclature Changes. Clinical Microbiology Reviews 15 (4), 613–630. Fang, W., Oliver, S.P., 2006. Identification of lactoferrin-binding proteins in bovine mastitis- causing Streptococcus uberis. FEMS Microbiology Letters 176 (1), 91–96. Ferrándiz, M.-J., La Campa, A.G. de, 2014. The fluoroquinolone levofloxacin triggers the transcriptional activation of iron transport genes that contribute to cell death in Streptococcus pneumoniae. Antimicrobial agents and chemotherapy 58 (1), 247–257. Flo, T.H., Smith, K.D., Sato, S., Rodriguez, D.J., Holmes, M.A., Strong, R.K., Akira, S., Aderem, A., 2004. Lipocalin 2 mediates an innate immune response to bacterial infection by sequestrating iron. Nature 432, 917 EP -. Flórez, L.A., Roppel, S.F., Schmeisky, A.G., Lammers, C.R., Stülke, J., 2009. A community- curated consensual annotation that is continuously updated: The Bacillus subtilis centred wiki SubtiWiki. Database 2009, bap012-bap012. Fröhlich, F., Christiano, R., Walther, T.C., 2013. Native SILAC: Metabolic labeling of proteins in prototroph microorganisms based on lysine synthesis regulation. Molecular & cellular proteomics : MCP 12 (7), 1995–2005. Gamez, G., Hammerschmidt, S., 2012. Combat pneumococcal infections: Adhesins as candidates for protein-based vaccine development. Current drug targets 13 (3), 323– 337. Garcia, P.S., Simorre, J.-P., Brochier-Armanet, C., Grangeasse, C., 2016. Cell division of Streptococcus pneumoniae: Think positive! Current Opinion in Microbiology 34, 18–23. Geiger, T., Wisniewski, J.R., Cox, J., Zanivan, S., Kruger, M., Ishihama, Y., Mann, M., 2011. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nature Protocols 6 (2), 147–157. Geno, K.A., Gilbert, G.L., Song, J.Y., Skovsted, I.C., Klugman, K.P., Jones, C., Konradsen, H.B., Nahm, M.H., 2015. Pneumococcal Capsules and Their Types: Past, Present, and Future. Clinical Microbiology Reviews 28 (3), 871–899. Glanville, D.G., Han, L., Maule, A.F., Woodacre, A., Thanki, D., Abdullah, I.T., Morrissey, J.A., Clarke, T.B., Yesilkaya, H., Silvaggi, N.R., Ulijasz, A.T., 2018. RitR is an archetype for a novel family of redox sensors in the streptococci that has evolved from two-component response regulators and is required for pneumococcal colonization. PLoS Pathogens 14 (5), e1007052. Gómez-Gallego, M., Sierra, M.A., Alcázar, R., Ramírez, P., Piñar, C., Mancheño, M.J., García- Marco, S., Yunta, F., Lucena, J.J., 2002. Synthesis of o,p-EDDHA and Its Detection as the
101
References
Main Impurity in o,o-EDDHA Commercial Iron Chelates. Journal of Agricultural and Food Chemistry 50 (22), 6395–6399. Gonzales, X.F., Castillo-Rojas, G., Castillo-Rodal, A.I., Tuomanen, E., López-Vidal, Y., 2013. Catecholamine norepinephrine diminishes lung epithelial cell adhesion of Streptococcus pneumoniae by binding iron. Microbiology 159 (Pt 11), 2333–2341. Griffin, A.S., West, S.A., Buckling, A., 2004. Cooperation and competition in pathogenic bacteria. Nature 430 (7003), 1024-7. Griffith, F., 1928. The Significance of Pneumococcal Types. The Journal of Hygiene 27 (2), 113–159. Grigg, J.C., Ukpabi, G., Gaudin, C.F.M., Murphy, M.E.P., 2010. Structural biology of heme binding in the Staphylococcus aureus Isd system. Journal of Inorganic Biochemistry 104 (3), 341–348. Grigg, J.C., Vermeiren, C.L., Heinrichs, D.E., Murphy, M.E.P., 2006. Haem recognition by a Staphylococcus aureus NEAT domain. Molecular Microbiology 63 (1), 139–149. Grigg, J.C., Vermeiren, C.L., Heinrichs, D.E., Murphy, M.E.P., 2007. Heme Coordination by Staphylococcus aureus IsdE. Journal of Biological Chemistry 282 (39), 28815–28822. Griss, J., 2015. Spectral library searching in proteomics. Proteomics 16 (5), 729–740. Große, C., Scherer, J., Koch, D., Otto, M., Taudte, N., Grass, G., 2006. A new ferrous iron-uptake transporter, EfeU (YcdN), from Escherichia coli. Molecular Microbiology 62 (1), 120–131. Gründlinger, M., Yasmin, S., Lechner, B.E., Geley, S., Schrettl, M., Hynes, M., Haas, H., 2013. Fungal siderophore biosynthesis is partially localized in peroxisomes. Molecular Microbiology 88 (5), 862–875. Guedon, E., Helmann, J.D., 2003. Origins of metal ion selectivity in the DtxR/MntR family of metalloregulators. Molecular Microbiology 48 (2), 495–506. Gupta, R., Bhatty, M., Swiatlo, E., Nanduri, B., 2013. Role of an iron-dependent transcriptional regulator in the pathogenesis and host response to infection with Streptococcus pneumoniae. PLoS One 8 (2), e55157. Gygi, S.P., Corthals, G.L., Zhang, Y., Rochon, Y., Aebersold, R., 2000. Evaluation of two- dimensional gel electrophoresis-based proteome analysis technology. Proceedings of the National Academy of Sciences 97 (17), 9390. Haas, H., 2014. Fungal siderophore metabolism with a focus on Aspergillus fumigatus. Natural Product Reports 31 (10), 1266–1276. Haft, D.H., Loftus, B.J., Richardson, D.L., Yang, F., Eisen, J.A., Paulsen, I.T., White, O., 2001. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Research 29 (1), 41–43.
102
References
Haft, D.H., Selengut, J.D., Richter, R.A., Harkins, D., Basu, M.K., Beck, E., 2013. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Research 41 (D1), D387-D395. Hakenbeck, R., 2000. Transformation in Streptococcus pneumoniae: Mosaic genes and the regulation of competence. Research in Microbiology 151 (6), 453–456. Hammerschmidt, S., Bethe, G., Remane, P.H., Chhatwal, G.S., 1999. Identification of pneumococcal surface protein A as a lactoferrin-binding protein of Streptococcus pneumoniae. Infection and Immunity 67 (4), 1683–1687. Hanks, T.S., Liu, M., McClure, M.J., Fukumura, M., Duffy, A., Lei, B., 2006. Differential Regulation of Iron- and Manganese-Specific MtsABC and Heme-Specific HtsABC Transporters by the Metalloregulator MtsR of Group A Streptococcus. Infection and Immunity 74 (9), 5132–5139. Hantke, K., 2003. Is the bacterial ferrous iron transporter FeoB a living fossil? Trends in Microbiology 11 (5), 192–195. Hantke, K., Nicholson, G., Rabsch, W., Winkelmann, G., 2003. Salmochelins, siderophores of Salmonella enterica and uropathogenic Escherichia coli strains, are recognized by the outer membrane receptor IroN. Proceedings of the National Academy of Sciences 100 (7), 3677. Härtel, T., Eylert, E., Schulz, C., Petruschka, L., Gierok, P., Grubmüller, S., Lalk, M., Eisenreich, W., Hammerschmidt, S., 2012. Characterization of central carbon metabolism of Streptococcus pneumoniae by isotopologue profiling. Journal of Biological Chemistry 287 (6), 4260–4274. Harvie, D.R., Vílchez, S., Steggles, J.R., Ellar, D.J., 2005. Bacillus cereus Fur regulates iron metabolism and is required for full virulence. Microbiology 151 (2), 569–577. Havukainen, H., Haataja, S., Kauko, A., Pulliainen, A.T., Salminen, A., Haikarainen, T., Finne, J., Papageorgiou, A.C., 2008. Structural basis of the zinc- and terbium-mediated inhibition of ferroxidase activity in Dps ferritin-like proteins. Protein Science 17 (9), 1513–1521. Heidrich, C., Hantke, K., Bierbaum, G., Sahl, H.-G., 1996. Identification and analysis of a gene encoding a Fur-like protein of Staphylococcus epidermidis. FEMS Microbiology Letters 140 (2), 253–259. Hempel, K., Herbst, F.-A., Moche, M., Hecker, M., Becher, D., 2011. Quantitative Proteomic View on Secreted, Cell Surface-Associated, and Cytoplasmic Proteins of the Methicillin- Resistant Human Pathogen Staphylococcus aureus under Iron-Limited Conditions. Journal of Proteome Research 10 (4), 1657–1666. Hendriksen, W.T., Bootsma, H.J., Estevão, S., Hoogenboezem, T., Jong, A. de, Groot, R. de, Kuipers, O.P., Hermans, P.W.M., 2008. CodY of Streptococcus pneumoniae: Link between nutritional gene regulation and colonization. Journal of Bacteriology 190 (2), 590–601.
103
References
Hider, R.C., Kong, X., 2010. Chemistry and biology of siderophores. Natural Product Reports 27 (5), 637–657. Hill, P.J., Cockayne, A., Landers, P., Morrissey, J.A., Sims, C.M., Williams, P., 1998. SirR, a Novel Iron-Dependent Repressor in Staphylococcus epidermidis. Infection and Immunity 66 (9), 4123–4129. Hill, S., Entwisle, C., Pang, Y., Joachim, M., McIlgorm, A., Dalton, K., Burbidge, P., Colaco, C., Brown, J., Goldblatt, D., Bailey, C., 2018. Immunogenicity and mechanisms of action of PnuBioVax, a multi-antigen serotype-independent prophylactic vaccine against infection with Streptococcus pneumoniae. Vaccine 36 (29), 4255–4264. Holmes, A.R., McNab, R., Millsap, K.W., Rohde, M., Hammerschmidt, S., Mawdsley, J.L., Jenkinson, H.F., 2001. The pavA gene of Streptococcus pneumoniae encodes a fibronectin- binding protein that is essential for virulence. Molecular Microbiology 41 (6), 1395– 1408. Holyer, R.H., Hubbard, C.D., Kettle, S.F.A., Wilkins, R.G., 1965. The Kinetics of Replacement Reactions of Complexes of the Transition Metals with 1,10-Phenanthroline and 2,2'- Bipyridine. Inorganic Chemistry 4 (7), 929–935. Honsa, E.S., Fabian, M., Cardenas, A.M., Olson, J.S., Maresso, A.W., 2011. The Five Near-iron Transporter (NEAT) Domain Anthrax Hemophore, IsdX2, Scavenges Heme from Hemoglobin and Transfers Heme to the Surface Protein IsdC. Journal of Biological Chemistry 286 (38), 33652–33660. Honsa, E.S., Maresso, A.W., Highlander, S.K., 2014. Molecular and Evolutionary Analysis of NEAr-Iron Transporter (NEAT) Domains. PLoS One 9 (8), e104794. Hood, M.I., Skaar, E.P., 2012. Nutritional immunity: Transition metals at the pathogen-host interface. Nature Reviews Microbiology 10 (8), 525–537. Horsburgh, M.J., Wharton, S.J., Cox, A.G., Ingham, E., Peacock, S., Foster, S.J., 2002. MntR modulates expression of the PerR regulon and superoxide resistance in Staphylococcus aureus through control of manganese uptake. Molecular Microbiology 44 (5), 1269–1286. Hoyer, J., Bartel, J., Gómez-Mejia, A., Rohde, M., Hirschfeld, C., Heß, N., Sura, T., Maaß, S., Hammerschmidt, S., Becher, D., 2018. Proteomic response of Streptococcus pneumoniae to iron limitation. International Journal of Medical Microbiology 308 (6), 713–721. Hua, C.-Z., Howard, A., Malley, R., Lu, Y.-J., 2014. Effect of nonheme iron-containing ferritin Dpr in the stress response and virulence of pneumococci. Infection and Immunity 82 (9), 3939–3947. Hunt, D.F., Henderson, R.A., Shabanowitz, J., Sakaguchi, K., Michel, H., Sevilir, N., Cox, A.L., Appella, E., Engelhard, V.H., 1992. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255 (5049), 1261.
104
References
Ibrahim, A.S., Gebermariam, T., Fu, Y., Lin, L., Husseiny, M.I., French, S.W., Schwartz, J., Skory, C.D., Edwards, J.E., JR., Spellberg, B.J., 2007. The iron chelator deferasirox protects mice from mucormycosis through iron starvation. Journal of Clinical Investigation 117 (9), 2649–2657. Iigo, M., Alexander, D.B., Long, N., Xu, J., Fukamachi, K., Futakuchi, M., Takase, M., Tsuda, H., 2009. Anticarcinogenesis pathways activated by bovine lactoferrin in the murine small intestine. Biochimie 91 (1), 86–101. Janulczyk, R., Pallon, J., Björck, L., 1999. Identification and characterization of a Streptococcus pyogenes ABC transporter with multiple specificity for metal cations. Molecular Microbiology 34 (3), 596–606. Janulczyk, R., Ricci, S., Björck, L., 2003. MtsABC Is Important for Manganese and Iron Transport, Oxidative Stress Resistance, and Virulence of Streptococcus pyogenes. Infection and Immunity 71 (5), 2656–2664. Jarosik, G.P., Land, C.B., 2000. Identification of a Human Lactoferrin-Binding Protein in Gardnerella vaginalis. Infection and Immunity 68 (6), 3443–3447. Jiménez-Munguía, I., Calderón-Santiago, M., Rodríguez-Franco, A., Priego-Capote, F., Rodríguez-Ortega, M.J., Leite, L., 2018. Multi-omic profiling to assess the effect of iron starvation in Streptococcus pneumoniae TIGR4. PeerJ 6, e4966. Johnson, M., Cockayne, A., Williams, P.H., Morrissey, J.A., 2005. Iron-Responsive Regulation of Biofilm Formation in Staphylococcus aureus Involves Fur-Dependent and Fur- Independent Mechanisms. Journal of Bacteriology 187 (23), 8211–8215. Johnson, M., Sengupta, M., Purves, J., Tarrant, E., Williams, P.H., Cockayne, A., Muthaiyan, A., Stephenson, R., Ledala, N., Wilkinson, B.J., Jayaswal, R.K., Morrissey, J.A., 2011. Fur is required for the activation of virulence gene expression through the induction of the sae regulatory system in Staphylococcus aureus. International Journal of Medical Microbiology 301 (1), 44–52. Johnston, C., Bootsma, H.J., Aldridge, C., Manuse, S., Gisch, N., Schwudke, D., Hermans, P.W.M., Grangeasse, C., Polard, P., Vollmer, W., Claverys, J.-P., 2015. Co-Inactivation of GlnR and CodY Regulators Impacts Pneumococcal Cell Wall Physiology. PLoS One 10 (4), e0123702. Johnston, J.W., Briles, D.E., Myers, L.E., Hollingshead, S.K., 2005. Mn(2+)-Dependent Regulation of Multiple Genes in Streptococcus pneumoniae through PsaR and the Resultant Impact on Virulence. Infection and Immunity 74 (2), 1171–1180. Jomaa, M., Terry, S., Hale, C., Jones, C., Dougan, G., Brown, J., 2006. Immunization with the iron uptake ABC transporter proteins PiaA and PiuA prevents respiratory infection with Streptococcus pneumoniae. Vaccine 24 (24), 5133–5139.
105
References
Kadioglu, A., Taylor, S., Iannelli, F., Pozzi, G., Mitchell, T.J., Andrew, P.W., 2002. Upper and Lower Respiratory Tract Infection by Streptococcus pneumoniae Is Affected by Pneumolysin Deficiency and Differences in Capsule Type. Infection and Immunity 70 (6), 2886. Kadioglu, A., Weiser, J.N., Paton, J.C., Andrew, P.W., 2008. The role of Streptococcus pneumoniae virulence factors in host respiratory colonization and disease. Nature Reviews Microbiology 6 (4), 288–301. Kadner, R.J., Heller, K., Coulton, J.W., Braun, V., 1980. Genetic control of hydroxamate- mediated iron uptake in Escherichia coli. Journal of Bacteriology 143 (1), 256–264. Kadurugamuwa, J.L., Anwar, H., Brown, M.R., Shand, G.H., Ward, K.H., 1987. Media for study of growth kinetics and envelope properties of iron-deprived bacteria. Journal of Clinical Microbiology 25 (5), 849–855. Kallio, A., Sepponen, K., Hermand, P., Denoël, P., Godfroid, F., Melin, M., Camilli, A., 2014. Role of Pht Proteins in Attachment of Streptococcus pneumoniae to Respiratory Epithelial Cells. Infection and Immunity 82 (4), 1683–1691. Kammler, M., Schön, C., Hantke, K., 1993a. Characterization of the ferrous iron uptake system of Escherichia coli. Journal of Bacteriology 175 (19), 6212–6219. Kammler, M., Schön, C., Hantke, K., 1993b. Characterization of the ferrous iron uptake system of Escherichia coli. Journal of Bacteriology 175 (19), 6212–6219. Kanamori, K., Weiss, R.L., Roberts, J.D., 1990. Efficiency factors and ATP/ADP ratios in nitrogen-fixing Bacillus polymyxa and Bacillus azotofixans. Journal of Bacteriology 172 (4), 1962–1968. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y., 2008. KEGG for linking genomes to life and the environment. Nucleic Acids Research 36 (suppl_1), D480-D484. Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., Hirakawa, M., 2006. From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Research 34 (suppl_1), D354-D357. Kanwar, J.R., Roy, K., Patel, Y., Zhou, S.-F., Singh, M.R., Singh, D., Nasir, M., Sehgal, R., Sehgal, A., Singh, R.S., Garg, S., Kanwar, R.K., 2015. Multifunctional iron bound lactoferrin and nanomedicinal approaches to enhance its bioactive functions. Molecules 20 (6), 9703– 9731. Karas, M., Bachmann, D., Hillenkamp, F., 1985. Influence of the wavelength in high-irradiance ultraviolet laser desorption mass spectrometry of organic molecules. Analytical Chemistry 57 (14), 2935–2939.
106
References
Kehl-Fie, T.E., Zhang, Y., Moore, J.L., Farrand, A.J., Hood, M.I., Rathi, S., Chazin, W.J., Caprioli, R.M., Skaar, E.P., 2013. MntABC and MntH Contribute to Systemic Staphylococcus aureus Infection by Competing with Calprotectin for Nutrient Manganese. Infection and Immunity 81 (9), 3395–3405. Kim, H., Lee, H., Shin, D., 2012. The FeoA protein is necessary for the FeoB transporter to import ferrous iron. Biochemical and Biophysical Research Communications 423 (4), 733–738. Kim, H., Lee, H., Shin, D., 2013. The FeoC Protein Leads to High Cellular Levels of the Fe(II) Transporter FeoB by Preventing FtsH Protease Regulation of FeoB in Salmonella enterica. Journal of Bacteriology 195 (15), 3364–3370. Kim, H.K., DeDent, A., Cheng, A.G., McAdow, M., Bagnoli, F., Missiakas, D.M., Schneewind, O., 2010. IsdA and IsdB antibodies protect mice against Staphylococcus aureus abscess formation and lethal challenge. Vaccine 28 (38), 6382–6392. Kim, W.-S., Tanaka, T., Kumura, H., Shimazaki, K.-i., 2002. Lactoferrin-binding proteins in Bifidobacterium bifidum. Biochemistry and cell biology 80 (1), 91–94. Klitgaard, K., Friis, C., Angen, Ø., Boye, M., 2010. Comparative profiling of the transcriptional response to iron restriction in six serotypes of Actinobacillus pleuropneumoniae with different virulence potential. BMC Genomics 11 (1), 698. Kloosterman, T.G., Hendriksen, W.T., Bijlsma, J.J.E., Bootsma, H.J., van Hijum, S.A.F.T., Kok, J., Hermans, P.W.M., Kuipers, O.P., 2006. Regulation of glutamine and glutamate metabolism by GlnR and GlnA in Streptococcus pneumoniae. Journal of Biological Chemistry 281 (35), 25097–25109. Kloosterman, T.G., Witwicki, R.M., van der Kooi-Pol, M.M., Bijlsma, J.J.E., Kuipers, O.P., 2008. Opposite Effects of Mn(2+) and Zn(2+) on PsaR-Mediated Expression of the Virulence Genes pcpA, prtA, and psaBCA of Streptococcus pneumoniae. Journal of Bacteriology 190 (15), 5382–5393. Klose, J., 1975. Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. Humangenetik 26 (3), 231–243. Kohler, S., Voß, F., Gómez Mejia, A., Brown, J.S., Hammerschmidt, S., 2016. Pneumococcal lipoproteins involved in bacterial fitness, virulence, and immune evasion. FEBS Letters 590 (21), 3820–3839. Kosman, D.J., 2003. Molecular mechanisms of iron uptake in fungi. Molecular Microbiology 47 (5), 1185–1197. Kunkle, C.A., Schmitt, M.P., 2003. Analysis of the Corynebacterium diphtheriae DtxR Regulon: Identification of a Putative Siderophore Synthesis and Transport System That Is Similar
107
References
to the Yersinia High-Pathogenicity Island-Encoded Yersiniabactin Synthesis and Uptake System. Journal of Bacteriology 185 (23), 6826–6840. Kunkle, C.A., Schmitt, M.P., 2005. Analysis of a DtxR-Regulated Iron Transport and Siderophore Biosynthesis Gene Cluster in Corynebacterium diphtheriae. Journal of Bacteriology 187 (2), 422–433. Lam, H., Aebersold, R., 2011. Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics. Methods 54 (4), 424–431. Lanie, J.A., Ng, W.-L., Kazmierczak, K.M., Andrzejewski, T.M., Davidsen, T.M., Wayne, K.J., Tettelin, H., Glass, J.I., Winkler, M.E., 2007. Genome sequence of Avery's virulent serotype 2 strain D39 of Streptococcus pneumoniae and comparison with that of unencapsulated laboratory strain R6. Journal of Bacteriology 189 (1), 38–51. Lau, C.K.Y., Krewulak, K.D., Vogel, H.J., 2016. Bacterial ferrous iron transport: The Feo system. FEMS Microbiology Reviews 40 (2), 273–298. Ledala, N., Sengupta, M., Muthaiyan, A., Wilkinson, B.J., Jayaswal, R.K., 2010. Transcriptomic Response of Listeria monocytogenes to Iron Limitation and Mutation. Applied and Environmental Microbiology 76 (2), 406. Lei, B., Liu, M., Voyich, J.M., Prater, C.I., Kala, S.V., DeLeo, F.R., Musser, J.M., 2003. Identification and Characterization of HtsA, a Second Heme-Binding Protein Made by Streptococcus pyogenes. Infection and Immunity 71 (10), 5962–5969. Léséleuc, L. de, Harris, G., KuoLee, R., Chen, W., 2012. In Vitro and In Vivo Biological Activities of Iron Chelators and Gallium Nitrate against Acinetobacter baumannii. Antimicrobial agents and chemotherapy 56 (10), 5397. Létoffé, S., Heuck, G., Delepelaire, P., Lange, N., Wandersman, C., 2009. Bacteria capture iron from heme by keeping tetrapyrrol skeleton intact. Proceedings of the National Academy of Sciences 106 (28), 11719. Li, S., Crooks, P.A., Wei, X., Leon, J., 2004. Toxicity of Dipyridyl Compounds and Related Compounds. Critical Reviews in Toxicology 34 (5), 447–460. Li, Z., Nimtz, M., Rinas, U., 2014. The metabolic potential of Escherichia coli BL21 in defined and rich medium. Microbial Cell Factories 13 (1), 45. Lin, L., Pantapalangkoor, P., Tan, B., Bruhn, K.W., Ho, T., Nielsen, T., Skaar, E.P., Zhang, Y., Bai, R., Wang, A., Doherty, T.M., Spellberg, B., 2014. Transferrin Iron Starvation Therapy for Lethal Bacterial and Fungal Infections. The Journal of Infectious Diseases 210 (2), 254– 264. Liñares, J., Ardanuy, C., Pallares, R., Fenoll, A., 2010. Changes in antimicrobial resistance, serotypes and genotypes in Streptococcus pneumoniae over a 30-year period. Clinical Microbiology and Infection 16 (5), 402–410.
108
References
Lindemann, C., Thomanek, N., Hundt, F., Lerari, T., Meyer, H.E., Wolters, D., Marcus, K., 2017. Strategies in relative and absolute quantitative mass spectrometry based proteomics. Biological Chemistry 398 (5-6), 687–699. Lithgow, G.J., Driscoll, M., Phillips, P., 2017. A long journey to reproducible results. Nature 548 (7668), 387–388. Liu, Z., Reba, S., Chen, W.-D., Porwal, S.K., Boom, W.H., Petersen, R.B., Rojas, R., Viswanathan, R., Devireddy, L., 2014. Regulation of mammalian siderophore 2,5-DHBA in the innate immune response to infection. The Journal of Experimental Medicine 211 (6), 1197. Loomis, L.D., Raymond, K.N., 1991. Solution equilibria of enterobactin and metal- enterobactin complexes. Inorganic Chemistry 30 (5), 906–911. López, G., Latorre, M., Reyes-Jara, A., Cambiazo, V., González, M., 2012. Transcriptomic response of Enterococcus faecalis to iron excess. BioMetals 25 (4), 737–747. Ludwig, K.R., Schroll, M.M., Hummon, A.B., 2018. Comparison of In-Solution, FASP, and S-Trap Based Digestion Methods for Bottom-Up Proteomic Studies. Journal of Proteome Research 17 (7), 2480–2490. Ma, L., Gao, Y., Maresso, A.W., Schneewind, O., 2015. Escherichia coli Free Radical-Based Killing Mechanism Driven by a Unique Combination of Iron Restriction and Certain Antibiotics. Journal of Bacteriology 197 (23), 3708. MacCoss, M.J., Wu, C.C., Liu, H., Sadygov, R., Yates, J.R., 2003. A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data. Analytical Chemistry 75 (24), 6912–6921. Mann, M., 2006. Functional and quantitative proteomics using SILAC. Nature Reviews Molecular Cell Biology 7 (12), 952–958. Manzoor, I., Shafeeq, S., Kloosterman, T.G., Kuipers, O.P., 2015a. Co(2+)-dependent gene expression in Streptococcus pneumoniae: Opposite effect of Mn(2+) and Co(2+) on the expression of the virulence genes psaBCA, pcpA, and prtA. Frontiers in Microbiology 6, 748. Manzoor, I., Shafeeq, S., Kuipers, O.P., van Schaik, W., 2015b. Ni(2+)-Dependent and PsaR- Mediated Regulation of the Virulence Genes pcpA, psaBCA, and prtA in Streptococcus pneumoniae. PLoS One 10 (11), e0142839. Marlovits, T.C., Haase, W., Herrmann, C., Aller, S.G., Unger, V.M., 2002. The membrane protein FeoB contains an intramolecular G protein essential for Fe(II) uptake in bacteria. Proceedings of the National Academy of Sciences 99 (25), 16243. Massidda, O., Nováková, L., Vollmer, W., 2013. From models to pathogens: How much have we learned about Streptococcus pneumoniae cell division? Environmental Microbiology 15 (12), 3133–3157.
109
References
Mateos, F., Brock, J.H., Pérez-Arellano, J.L., 1998. Iron metabolism in the lower respiratory tract. Thorax 53 (7), 594. Matsui, T., Nambu, S., Ono, Y., Goulding, C.W., Tsumoto, K., Ikeda-Saito, M., 2013. Heme Degradation by Staphylococcus aureus IsdG and IsdI Liberates Formaldehyde Rather Than Carbon Monoxide. Biochemistry 52 (18), 3025–3027. Mazmanian, S.K., Skaar, E.P., Gaspar, A.H., Humayun, M., Gornicki, P., Jelenska, J., Joachmiak, A., Missiakas, D.M., Schneewind, O., 2003. Passage of Heme-Iron Across the Envelope of Staphylococcus aureus. Science 299 (5608), 906. Mazmanian, S.K., Ton-That, H., Su, K., Schneewind, O., 2002. An iron-regulated sortase anchors a class of surface protein during Staphylococcus aureus pathogenesis. Proceedings of the National Academy of Sciences 99 (4), 2293. Mehlan, H., Schmidt, F., Weiss, S., Schüler, J., Fuchs, S., Riedel, K., Bernhardt, J., 2013. Data visualization in environmental proteomics. Proteomics 13 (18-19), 2805–2821. Mehr, S., Wood, N., 2012. Streptococcus pneumoniae – a review of carriage, infection, serotype replacement and vaccination. Paediatric respiratory reviews 13 (4), 258–264. Miajlovic, H., Zapotoczna, M., Geoghegan, J.A., Kerrigan, S.W., Speziale, P., Foster, T.J., 2010. Direct interaction of iron-regulated surface determinant IsdB of Staphylococcus aureus with the GPIIb/IIIa receptor on platelets. Microbiology 156 (3), 920–928. Miao, X., He, J., Zhang, L., Zhao, X., Ge, R., He, Q.-Y., Sun, X., 2018. A Novel Iron Transporter SPD_1590 in Streptococcus pneumoniae Contributing to Bacterial Virulence Properties. Frontiers in Microbiology 9, 1624. Miethke, M., Marahiel, M.A., 2007. Siderophore-Based Iron Acquisition and Pathogen Control. Microbiology and Molecular Biology Reviews 71 (3), 413–451. Mirza, S., Hollingshead, S.K., Benjamin, W.H., Briles, D.E., 2004. PspA protects Streptococcus pneumoniae from killing by apolactoferrin, and antibody to PspA enhances killing of pneumococci by apolactoferrin corrected. Infection and Immunity 72 (9), 5031–5040. Mirza, S., Wilson, L., Benjamin, W.H., Novak, J., Barnes, S., Hollingshead, S.K., Briles, D.E., 2011. Serine protease PrtA from Streptococcus pneumoniae plays a role in the killing of S. pneumoniae by apolactoferrin. Infection and Immunity 79 (6), 2440–2450. Molina, H., Yang, Y., Ruch, T., Kim, J.-W., Mortensen, P., Otto, T., Nalli, A., Tang, Q.-Q., Lane, M.D., Chaerkady, R., Pandey, A., 2009. Temporal Profiling of the Adipocyte Proteome during Differentiation Using a Five-Plex SILAC Based Strategy. Journal of Proteome Research 8 (1), 48–58. Monette, P., Brach, R., Cowan, A., Winters, R., Weisman, J., Seybert, F., Goguen, K., Chen, J., Glasfeld, A., Spatafora, G., 2018. Autoregulation of the S. mutans SloR metalloregulator is
110
References
constitutive and driven by an independent promoter. Journal of Bacteriology 200 (14), e00214-18. Morrison, D.A., Lee, M.S., 2000. Regulation of competence for genetic transformation in Streptococcus pneumoniae: A link between quorum sensing and DNA processing genes. Research in Microbiology 151 (6), 445–451. Moshynskyy, I., Jiang, M., Fontaine, M.C., Perez-Casal, J., Babiuk, L.A., Potter, A.A., 2003. Characterization of a bovine lactoferrin binding protein of Streptococcus uberis. Microbial Pathogenesis 35 (5), 203–215. Muryoi, N., Tiedemann, M.T., Pluym, M., Cheung, J., Heinrichs, D.E., Stillman, M.J., 2008. Demonstration of the Iron-regulated Surface Determinant (Isd) Heme Transfer Pathway in Staphylococcus aureus. Journal of Biological Chemistry 283 (42), 28125–28136. Musher, D.M., 1992. Infections Caused by Streptococcus pneumoniae: Clinical Spectrum, Pathogenesis, Immunity, and Treatment. Clinical Infectious Diseases 14 (4), 801–807. Naidu, A.S., Andersson, M., Forsgren, A., 1992. Identification of a human lactoferrin-binding protein in Staphylococcus aureus. Journal of Medical Microbiology 36 (3), 177–183. Naidu, A.S., Miedzobrodzki, J., Musser, J.M., Rosdahl, V.T., HedstrÖm, S.-Å., Forsgren, A., 1991. Human lactoferrin binding in clinical isolates of Staphylococcus aureus. Journal of Medical Microbiology 34 (6), 323–328. Nanduri, B., Shah, P., Ramkumar, M., Allen, E.B., Swiatlo, E., Burgess, S.C., Lawrence, M.L., 2008. Quantitative analysis of Streptococcus pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS. Proteomics 8 (10), 2104–2114. Noinaj, N., Easley, N.C., Oke, M., Mizuno, N., Gumbart, J., Boura, E., Steere, A.N., Zak, O., Aisen, P., Tajkhorshid, E., Evans, R.W., Gorringe, A.R., Mason, A.B., Steven, A.C., Buchanan, S.K., 2012. Structural basis for iron piracy by pathogenic Neisseria. Nature 483 (7387), 53–58. Nováková, L., Bezoušková, S., Pompach, P., Špidlová, P., Sasková, L., Weiser, J., Branny, P., 2010. Identification of Multiple Substrates of the StkP Ser/Thr Protein Kinase in Streptococcus pneumoniae. Journal of Bacteriology 192 (14), 3629.
Nováková,M.-C., Branny, L., Sasková, P., 2005. L., Pallová, Characterization P., Janeček, ofJ., Novotná,a eukaryotic J., Ulrych, type serine/threonA., Echenique,ine J., Trombe, protein kinase and protein phosphatase of Streptococcus pneumoniae and identification of kinase substrates. The FEBS Journal 272 (5), 1243–1254. Oda, H., Wakabayashi, H., Yamauchi, K., Abe, F., 2014. Lactoferrin and bifidobacteria. BioMetals 27 (5), 915–922. O'Farrell, P.H., 1975. High resolution two-dimensional electrophoresis of proteins. Journal of Biological Chemistry 250 (10), 4007–4021.
111
References
Oggioni, M.R., Memmi, G., Maggi, T., Chiavolini, D., Iannelli, F., Pozzi, G., 2004. Pneumococcal zinc metalloproteinase ZmpC cleaves human matrix metalloproteinase 9 and is a virulence factor in experimental pneumonia. Molecular Microbiology 49 (3), 795–805. Ollinger, J., Song, K.-B., Antelmann, H., Hecker, M., Helmann, J.D., 2006. Role of the Fur Regulon in Iron Transport in Bacillus subtilis. Journal of Bacteriology 188 (10), 3664–3673. Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., Mann, M., 2002. Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics. Molecular & Cellular Proteomics 1 (5), 376–386. Ong, S.-E., Mann, M., 2005. Mass spectrometry–based proteomics turns quantitative. Nature Chemical Biology 1 (5), 252-62. Ong, S.-E., Mann, M., 2006. Identifying and Quantifying Sites of Protein Methylation by Heavy Methyl SILAC. Current Protocols in Protein Science 46 (1), 14.9.1–14.9.12. Oosterhuis-Kafeja, F., Beutels, P., van Damme, P., 2007. Immunogenicity, efficacy, safety and effectiveness of pneumococcal conjugate vaccines (1998–2006). Vaccine 25 (12), 2194– 2212. Otto, A., Bernhardt, J., Meyer, H., Schaffer, M., Herbst, F.-A., Siebourg, J., Mäder, U., Lalk, M., Hecker, M., Becher, D., 2010. Systems-wide temporal proteomic profiling in glucose- starved Bacillus subtilis. Nature Communications 1, 137. Otto, A., Maaß, S., Bonn, F., Büttner, K., Becher, D., 2017. Chapter One - An Easy and Fast Protocol for Affinity Bead-Based Protein Enrichment and Storage of Proteome Samples, in: Shukla, A.K. (Ed.), Methods in Enzymology : Proteomics in Biology, Part A, vol. 585. Academic Press, pp. 1–13. Otto, A., Maaß, S., Lassek, C., Becher, D., Hecker, M., Riedel, K., Sievers, S., 2016. The protein inventory of Clostridium difficile grown in complex and minimal medium. Proteomics Clinical Applications 10 (9-10), 1068–1072. Ouattara, M., Bentley Cunha, E., Li, X., Huang, Y.-S., Dixon, D., Eichenbaum, Z., 2010. Shr of group A streptococcus is a new type of composite NEAT protein involved in sequestering haem from methaemoglobin. Molecular Microbiology 78 (3), 739–756. Oves-Costales, D., Kadi, N., Challis, G.L., 2009. The long-overlooked enzymology of a nonribosomal peptide synthetase-independent pathway for virulence-conferring siderophore biosynthesis. Chemical Communications (43), 6530–6541. Pakarian, P., Pawelek, P.D., 2016. A novel set of vectors for Fur-controlled protein expression under iron deprivation in Escherichia coli. BMC Biotechnology 16 (1), 68. Palmer, L.D., Skaar, E.P., 2016. Transition Metals and Virulence in Bacteria. Annual Review of Genetics 50 (1), 67–91.
112
References
Park, H.-J., Bae, N., Park, H., Kim, D.-W., Han, S.-W., 2017. Comparative Proteomic Analysis of Three Xanthomonas spp. Cultured in Minimal and Rich Media. Proteomics 17 (23-24). Park, H.-M., Almeida, R.A., Oliver, S.P., 2006. Identification of lactoferrin-binding proteins in Streptococcus dysgalactiae subsp. dysgalactiae and Streptococcus agalactiae isolated from cows with mastitis. FEMS Microbiology Letters 207 (1), 87–90. Pericone, C.D., Overweg, K., Hermans, P.W.M., Weiser, J.N., 2000. Inhibitory and Bactericidal Effects of Hydrogen Peroxide Production by Streptococcus pneumoniae on Other Inhabitants of the Upper Respiratory Tract. Infection and Immunity 68 (7), 3990–3997. Pericone, C.D., Park, S., Imlay, J.A., Weiser, J.N., 2003. Factors Contributing to Hydrogen Peroxide Resistance in Streptococcus pneumoniae Include Pyruvate Oxidase (SpxB) and Avoidance of the Toxic Effects of the Fenton Reaction. Journal of Bacteriology 185 (23), 6815–6825. Pi, H., Patel, S.J., Argüello, J.M., Helmann, J.D., 2016. The Listeria monocytogenes Fur-regulated virulence protein FrvA is an Fe(II) efflux P1B4-type ATPase. Molecular Microbiology 100 (6), 1066–1079. Pishchany, G., Dickey, S.E., Skaar, E.P., 2009. Subcellular Localization of the Staphylococcus aureus Heme Iron Transport Components IsdA and IsdB. Infection and Immunity 77 (7), 2624–2634. Pishchany, G., McCoy, A.L., Torres, V.J., Krause, J.C., Crowe, J.E., Fabry, M.E., Skaar, E.P., 2010. Specificity for Human Hemoglobin Enhances Staphylococcus aureus Infection. Cell Host & Microbe 8 (6), 544–550. Pishchany, G., Sheldon, J.R., Dickson, C.F., Alam, M.T., Read, T.D., Gell, D.A., Heinrichs, D.E., Skaar, E.P., 2014. IsdB-dependent Hemoglobin Binding Is Required for Acquisition of Heme by Staphylococcus aureus. Journal of Infectious Diseases 209 (11), 1764–1772. Plumptre, C.D., Ogunniyi, A.D., Paton, J.C., 2012. Polyhistidine triad proteins of pathogenic streptococci. Trends in Microbiology 20 (10), 485–493. Pluym, M., Vermeiren, C.L., Mack, J., Heinrichs, D.E., Stillman, M.J., 2007. Heme binding properties of Staphylococcus aureus IsdE. Biochemistry 46 (44), 12777–12787. Polzin, S., Huber, C., Eylert, E., Elsenhans, I., Eisenreich, W., Schmidt, H., 2013. Growth Media Simulating Ileal and Colonic Environments Affect the Intracellular Proteome and Carbon Fluxes of Enterohemorrhagic Escherichia coli. Applied and Environmental Microbiology 79 (12), 3703. Pomat, W.S., Lehmann, D., Sanders, R.C., Lewis, D.J., Wilson, J., Rogers, S., Dyke, T., Alpers, M.P., 1994. Immunoglobulin G antibody responses to polyvalent pneumococcal vaccine in children in the highlands of Papua New Guinea. Infection and Immunity 62 (5), 1848– 1853.
113
References
Pramanik, A., Braun, V., 2006. Albomycin uptake via a ferric hydroxamate transport system of Streptococcus pneumoniae R6. Journal of Bacteriology 188 (11), 3878–3886. Putignano, V., Rosato, A., Banci, L., Andreini, C., 2018. MetalPDB in 2018: A database of metal sites in biological macromolecular structures. Nucleic Acids Research 46 (D1), D459- D464. Rahman, M.M., Kim, W.-S., Ito, T., Kumura, H., Shimazaki, K., 2008. Examination of bovine lactoferrin binding to bifidobacteria. Applied Biochemistry and Microbiology 44 (5), 478–481. Rainard, P., 1992. Binding of bovine lactoferrin to Streptococcus agalactiae. FEMS Microbiology Letters 98 (1-3), 235–239. Raymond, K.N., Carrano, C.J., 1979. Coordination chemistry and microbial iron transport. Accounts of Chemical Research 12 (5), 183–190. Reniere, M.L., Skaar, E.P., 2008. Staphylococcus aureus haem oxygenases are differentially regulated by iron and haem. Molecular Microbiology 69 (5), 1304–1315. Rennemeier, C., 2007. Charakterisierung der Thrombospondin-1 vermittelten Anheftung von Streptococcus pneumoniae an humane Wirtszellen. Dissertation, Würzburg. Reynolds, H.Y., Chrétien, J., 1984. Respiratory tract fluids: Analysis of content and contemporary use in understanding lung diseases. Disease-a-Month 30 (5), 1–103. Rolerson, E., Swick, A., Newlon, L., Palmer, C., Pan, Y., Keeshan, B., Spatafora, G., 2006. The SloR/Dlg Metalloregulator Modulates Streptococcus mutans Virulence Gene Expression. Journal of Bacteriology 188 (14), 5033–5044. Romero-Espejel, M.E., González-López, M.A., Olivares-Trejo, J.d.J., 2013. Streptococcus pneumoniae requires iron for its viability and expresses two membrane proteins that bind haemoglobin and haem. Metallomics 5 (4), 384–389. Romero-Espejel, M.E., Rodríguez, M.A., Chávez-Munguía, B., Ríos-Castro, E., Olivares-Trejo, J.d.J., 2016. Characterization of Spbhp-37, a Hemoglobin-Binding Protein of Streptococcus pneumoniae. Frontiers in Cellular and Infection Microbiology 6, 47. Rooijakkers, S.H.M., Rasmussen, S.L., McGillivray, S.M., Bartnikas, T.B., Mason, A.B., Friedlander, A.M., Nizet, V., 2010. Human Transferrin Confers Serum Resistance against Bacillus anthracis. Journal of Biological Chemistry 285 (36), 27609–27613. Röst, H.L., Malmström, L., Aebersold, R., 2015. Reproducible quantitative proteotype data matrices for systems biology. Molecular biology of the cell 26 (22), 3926–3931. Saji, T., Aoyagui, S., 1975. Polarographic studies on bipyridine complexes: I. Correlation between reduction potentials of iron(II), ruthenium(II) and osmium(II) complexes and those of free ligands. Journal of Electroanalytical Chemistry and Interfacial Electrochemistry 58 (2), 401–410.
114
References
Sandrini, S., Alghofaili, F., Freestone, P., Yesilkaya, H., 2014. Host stress hormone norepinephrine stimulates pneumococcal growth, biofilm formation and virulence gene expression. BMC Microbiology 14, 180. Sarry, J.-E., Kuhn, L., Le Lay, P., Garin, J., Bourguignon, J., 2006. Dynamics of Arabidopsis thaliana soluble proteome in response to different nutrient culture conditions. Electrophoresis 27 (2), 495–507. Scheele, G.A., 1975. Two-dimensional gel analysis of soluble proteins. Charaterization of guinea pig exocrine pancreatic proteins. Journal of Biological Chemistry 250 (14), 5375– 5385. Schmitt, M.P., Holmes, R.K., 1991. Iron-dependent regulation of diphtheria toxin and siderophore expression by the cloned Corynebacterium diphtheriae repressor gene dtxR in C. diphtheriae C7 strains. Infection and Immunity 59 (6), 1899–1904. Schmitt, M.P., Holmes, R.K., 1994. Cloning, sequence, and footprint analysis of two promoter/operators from Corynebacterium diphtheriae that are regulated by the diphtheria toxin repressor (DtxR) and iron. Journal of Bacteriology 176 (4), 1141–1149. Schryvers, A.B., Gray-Owen, S., 1992. Iron Acquisition in Haemophilus influenzae: Receptors for Human Transferrin. Journal of Infectious Diseases 165 (Supplement_1), S103-S104. Schryvers, A.B., Morris, L.J., 1988. Identification and characterization of the human lactoferrin-binding protein from Neisseria meningitidis. Infection and Immunity 56 (5), 1144–1149. Schulz, C., Gierok, P., Petruschka, L., Lalk, M., Mäder, U., Hammerschmidt, S., 2014. Regulation of the arginine deiminase system by ArgR2 interferes with arginine metabolism and fitness of Streptococcus pneumoniae. mBio 5 (6). Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., Selbach, M., 2011. Global quantification of mammalian gene expression control. Nature 473 (7347), 337–342. Sheldon, J.R., Heinrichs, D.E., 2015. Recent developments in understanding the iron acquisition strategies of gram positive pathogens. FEMS Microbiology Reviews 39 (4), 592–630. Sheldon, J.R., Laakso, H.A., Heinrichs, D.E., 2016. Iron Acquisition Strategies of Bacterial Pathogens. Microbiology Spectrum 4 (2). Siemens, N., Oehmcke-Hecht, S., Mettenleiter, T.C., Kreikemeyer, B., Valentin-Weigand, P., Hammerschmidt, S., 2017. Port d’Entrée for Respiratory Infections – Does the Influenza A Virus Pave the Way for Bacteria? Frontiers in Microbiology 8, 2602. Sineva, E., Shadrin, A., Rodikova, E.A., Andreeva-Kovalevskaya, Z.I., Protsenko, A.S., Mayorov, S.G., Galaktionova, D.Y., Magelky, E., Solonin, A.S., 2012. Iron Regulates Expression of
115
References
Bacillus cereus Hemolysin II via Global Regulator Fur. Journal of Bacteriology 194 (13), 3327–3335. Skaar, E.P., 2010. The Battle for Iron between Bacterial Pathogens and Their Vertebrate Hosts. PLoS Pathogens 6 (8), e1000949. Skaar, E.P., Humayun, M., Bae, T., DeBord, K.L., Schneewind, O., 2004. Iron-Source Preference of Staphylococcus aureus Infections. Science 305 (5690), 1626. Spatafora, G., Corbett, J., Cornacchione, L., Daly, W., Galan, D., Wysota, M., Tivnan, P., Collins, J., Nye, D., Levitz, T., Breyer, W.A., Glasfeld, A., 2015. Interactions of the Metalloregulatory Protein SloR from Streptococcus mutans with Its Metal Ion Effectors and DNA Binding Site. Journal of Bacteriology 197 (22), 3601–3615. Stentzel, S., Vu, H.C., Weyrich, A.M., Jehmlich, N., Schmidt, F., Salazar, M.G., Steil, L., Völker, U., Bröker, B.M., 2014. Altered immune proteome of Staphylococcus aureus under iron- restricted growth conditions. Proteomics 14 (16), 1857–1867. Stites, S.W., Plautz, M.W., Bailey, K., O'Brien-Ladner, A.R., Wesselius, L.J., 1999. Increased Concentrations of Iron and Isoferritins in the Lower Respiratory Tract of Patients with Stable Cystic Fibrosis. American Journal of Respiratory and Critical Care Medicine 160 (3), 796–801. Sun, X., Ge, R., Chiu, J.-F., Sun, H., He, Q.-Y., 2008. Lipoprotein MtsA of MtsABC in Streptococcus pyogenes primarily binds ferrous ion with bicarbonate as a synergistic anion. FEBS Letters 582 (9), 1351–1354. Tai, S.S., Lee, C.J., Winter, R.E., 1993. Hemin utilization is related to virulence of Streptococcus pneumoniae. Infection and Immunity 61 (12), 5401–5405. Tai, S.S., Wang, T.R., Lee, C.J., 1997. Characterization of hemin binding activity of Streptococcus pneumoniae. Infection and Immunity 65 (3), 1083–1087. Tai, S.S., Yu, C., Lee, J.K., 2003. A solute binding protein of Streptococcus pneumoniae iron transport. FEMS Microbiology Letters 220 (2), 303–308. Taylor, J., Heinrichs, D.E., 2002. Transferrin binding in Staphylococcus aureus: Involvement of a cell wall-anchored protein. Molecular Microbiology 43 (6), 1603–1614. Tettelin, H., Nelson, K.E., Paulsen, I.T., Eisen, J.A., Read, T.D., Peterson, S., Heidelberg, J., DeBoy, R.T., Haft, D.H., Dodson, R.J., Durkin, A.S., Gwinn, M., Kolonay, J.F., Nelson, W.C., Peterson, J.D., Umayam, L.A., White, O., Salzberg, S.L., Lewis, M.R., Radune, D., Holtzapple, E., Khouri, H., Wolf, A.M., Utterback, T.R., Hansen, C.L., McDonald, L.A., Feldblyum, T.V., Angiuoli, S., Dickinson, T., Hickey, E.K., Holt, I.E., Loftus, B.J., Yang, F., Smith, H.O., Venter, J.C., Dougherty, B.A., Morrison, D.A., Hollingshead, S.K., Fraser, C.M., 2001. Complete genome sequence of a virulent isolate of Streptococcus pneumoniae. Science 293 (5529), 498–506.
116
References
The UniProt Consortium, 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Research 45 (D1), D158-D169. Theil, E.C., Goss, D.J., 2009. Living with Iron (and Oxygen): Questions and Answers about Iron Homeostasis. Chemical Reviews 109 (10), 4568–4579. Tiedemann, M.T., Heinrichs, D.E., Stillman, M.J., 2012. Multiprotein heme shuttle pathway in Staphylococcus aureus: Iron-regulated surface determinant cog-wheel kinetics. Journal of the American Chemical Society 134 (40), 16578–16585. Torres, V.J., Attia, A.S., Mason, W.J., Hood, M.I., Corbin, B.D., Beasley, F.C., Anderson, K.L., Stauff, D.L., McDonald, W.H., Zimmerman, L.J., Friedman, D.B., Heinrichs, D.E., Dunman, P.M., Skaar, E.P., 2010. Staphylococcus aureus Fur Regulates the Expression of Virulence Factors That Contribute to the Pathogenesis of Pneumonia. Infection and Immunity 78 (4), 1618–1628. Torres, V.J., Pishchany, G., Humayun, M., Schneewind, O., Skaar, E.P., 2006. Staphylococcus aureus IsdB Is a Hemoglobin Receptor Required for Heme Iron Utilization. Journal of Bacteriology 188 (24), 8421–8429. Toukoki, C., Gold, K.M., McIver, K.S., Eichenbaum, Z., 2010. MtsR is a dual regulator that controls virulence genes and metabolic functions in addition to metal homeostasis in the group A streptococcus. Molecular Microbiology 76 (4), 971–989. Tsiatsiani, L., Heck, A.J.R., 2015. Proteomics beyond trypsin. FEBS Journal 282 (14), 2612– 2626. Turlin, E., Débarbouillé, M., Augustyniak, K., Gilles, A.-M., Wandersman, C., 2013. Staphylococcus aureus FepA and FepB Proteins Drive Heme Iron Utilization in Escherichia coli. PLoS One 8 (2), e56529. Tusher, V.G., Tibshirani, R., Chu, G., 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences 98 (9), 5116–5121. Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M.Y., Geiger, T., Mann, M., Cox, J., 2016. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 13, 731 EP -. Tzouros, M., Golling, S., Avila, D., Lamerz, J., Berrera, M., Ebeling, M., Langen, H., Augustin, A., 2013. Development of a 5-plex SILAC Method Tuned for the Quantitation of Tyrosine Phosphorylation Dynamics. Molecular & Cellular Proteomics 12 (11), 3339–3349. Ulijasz, A.T., Andes, D.R., Glasner, J.D., Weisblum, B., 2004. Regulation of iron transport in Streptococcus pneumoniae by RitR, an orphan response regulator. Journal of Bacteriology 186 (23), 8123–8136.
117
References
Ulijasz, A.T., Falk, S.P., Weisblum, B., 2009. Phosphorylation of the RitR DNA-binding domain by a Ser-Thr phosphokinase: Implications for global gene regulation in the streptococci. Molecular Microbiology 71 (2), 382–390. van Oudenhove, L., Devreese, B., 2013. A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics. Applied Microbiology and Biotechnology 97 (11), 4749–4762. Vázquez-Zamorano, Z.E., González-López, M.A., Romero-Espejel, M.E., Azuara-Liceaga, E.I., López-Casamichana, M., Olivares-Trejo, J.d.J., 2014. Streptococcus pneumoniae secretes a glyceraldehyde-3-phosphate dehydrogenase, which binds haemoglobin and haem. BioMetals 27 (4), 683–693. Voigt, B., Schweder, T., Becher, D., Ehrenreich, A., Gottschalk, G., Feesche, J., Maurer, K.-H., Hecker, M., 2004. A proteomic view of cell physiology of Bacillus licheniformis. Proteomics 4 (5), 1465–1490. Vries, S.P.W., Burghout, P., Langereis, J.D., Zomer, A., Hermans, P.W.M., Bootsma, H.J., 2012. Genetic requirements for Moraxella catarrhalis growth under iron-limiting conditions. Molecular Microbiology 87 (1), 14–29. Waldvogel-Abramowski, S., Waeber, G., Gassner, C., Buser, A., Frey, B.M., Favrat, B., Tissot, J.- D., 2014. Physiology of Iron Metabolism. Transfusion Medicine and Hemotherapy 41 (3), 213–221. Wardman, P., Candeias, L.P., 1996. Fenton Chemistry: An Introduction. Radiation Research 145 (5), 523–531. Watson, D.A., Musher, D.M., Jacobson, J.W., Verhoef, J., 1993. A Brief History of the Pneumococcus in Biomedical Research: A Panoply of Scientific Discovery. Clinical Infectious Diseases 17 (5), 913–924. Watts, R.J., Harrington, J.S., van Houten, J., 1977. A stable monodentate 2,2'-bipyridine complex of iridium(III): A model for reactive intermediates in ligand displacement reactions of tris-2,2'-bipyridine metal complexes. Journal of the American Chemical Society 99 (7), 2179–2187. Weaver, E.A., Wyckoff, E.E., Mey, A.R., Morrison, R., Payne, S.M., 2013. FeoA and FeoC Are Essential Components of the Vibrio cholerae Ferrous Iron Uptake System, and FeoC Interacts with FeoB. Journal of Bacteriology 195 (21), 4826–4835. Weinberg, E.D., 1978. Iron and infection. Microbiological Reviews 42 (1), 45–66. Weiser, J.N., Ferreira, D.M., Paton, J.C., 2018. Streptococcus pneumoniae: Transmission, colonization and invasion. Nature Reviews Microbiology 16 (6), 355–367.
118
References
West, S.E., Sparling, P.F., 1985. Response of Neisseria gonorrhoeae to iron limitation: Alterations in expression of membrane proteins without apparent siderophore production. Infection and Immunity 47 (2), 388–394. Whalan, R.H., Funnell, S.G.P., Bowler, L.D., Hudson, M.J., Robinson, A., Dowson, C.G., 2005. PiuA and PiaA, iron uptake lipoproteins of Streptococcus pneumoniae, elicit serotype independent antibody responses following human pneumococcal septicaemia. FEMS Immunology and Medical Microbiology 43 (1), 73–80. Whalan, R.H., Funnell, S.G.P., Bowler, L.D., Hudson, M.J., Robinson, A., Dowson, C.G., 2006. Distribution and genetic diversity of the ABC transporter lipoproteins PiuA and PiaA within Streptococcus pneumoniae and related streptococci. Journal of Bacteriology 188 (3), 1031–1038. White, A., Ding, X., vanderSpek, J.C., Murphy, J.R., Ringe, D., 1998. Structure of the metal-ion- activated diphtheria toxin repressor/ tox operator complex. Nature 394 (6692), 502-6. Wilkins, M.R., Sanchez, J.-C., Gooley, A.A., Appel, R.D., Humphery-Smith, I., Hochstrasser, D.F., Williams, K.L., 1996. Progress with Proteome Projects: Why all Proteins Expressed by a Genome Should be Identified and How To Do It. Biotechnology and Genetic Engineering Reviews 13 (1), 19–50. Winter, A.J., Comis, S.D., Osborne, M.P., Tarlow, M.J., Stephen, J., Andrew, P.W., Hill, J., Mitchell, T.J., 1997. A role for pneumolysin but not neuraminidase in the hearing loss and cochlear damage induced by experimental pneumococcal meningitis in guinea pigs. Infection and Immunity 65 (11), 4411–4418.
Wiśniewski,and Multienzyme J.R., 2016. Digestion Quantitative FASP EvaluationProtocols. Analyticalof Filter Aided Chemistry Sample 88 Preparation(10), 5438– 5443.(FASP) Wu, R., Skaar, E.P., Zhang, R., Joachimiak, G., Gornicki, P., Schneewind, O., Joachimiak, A., 2005. Staphylococcus aureus IsdG and IsdI, Heme-degrading Enzymes with Structural Similarity to Monooxygenases. Journal of Biological Chemistry 280 (4), 2840–2846. Xiong, A., Singh, V.K., Cabrera, G., Jayaswal, R.K., 2000. Molecular characterization of the ferric-uptake regulator, Fur, from Staphylococcus aureus. Microbiology 146 (3), 659–668. Yadav, Y.J., Mastropietro, T.F., Szerb, E.I., Talarico, A.M., Pirillo, S., Pucci, D., Crispini, A., -
crystallineGhedini, M., solid 2013. state 2,2′ properties.Bipyridine New Zn(ii) Journal complexes: of Chemistry Effect 37of (5),the 1484,4′6 substituents–1493. theon Yang, X.-Y., He, K., Du, G., Wu, X., Yu, G., Pan, Y., Zhang, G., Sun, X., He, Q.-Y., 2016. Integrated Translatomics with Proteomics to Identify Novel Iron-Transporting Proteins in Streptococcus pneumoniae. Frontiers in Microbiology 7, 78.
119
References
Zhang, C., Freddolino, P.L., Zhang, Y., 2017. COFACTOR: Improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Research 45 (W1), W291-W299. Zhang, F., Lin, Q.-Y., Hu, W.-L., Song, W.-J., Shen, S.-T., Gui, P., 2013. Interaction with biomacromolecules and antiproliferative activities of Mn(II), Ni(II), Zn(II) complexes of -bipyridine. Spectrochimica Acta Part A: Molecular and
demethylcantharateBiomolecular Spectroscopy and 2,2′ 110, 100–107. Zhu, B., Stülke, J., 2018. SubtiWiki in 2018: From genes and proteins to functional network annotation of the model organism Bacillus subtilis. Nucleic Acids Research 46 (D1), D743- D748.
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7. PUBLICATIONS
7.1. SCIENTIFIC ARTICLES
Hoyer, J.; Bartel, J.; Gómez-Mejia, A.; Rohde, M.; Hirschfeld, C.; Heß, N.; Sura, T.; Maaß, S.; Hammerschmidt, S.; Becher, D. 2018. Int J Med Microbiol. doi: 10.1016/j.ijmm.2018.02.001
7.2. POSTER PRESENTATIONS
Hoyer, J.; Gómez-Mejia, A.; Hammerschmidt, S.; Becher, D. Analysis of Streptococcus pneumoniae under iron-limited conditions. 2nd German Pneumococcal and Streptococcal Symposium (11th – 13th June 2015, Rostock, Germany)
Hoyer, J.; Hirschfeld, C.; Hentschker, C.; Gómez-Mejia, A.; Hammerschmidt, S.; Maaß, S.; Becher, D. Investigations to Streptococcus pneumoniae under iron limitation. 3rd German Pneumococcal and Streptococcal Symposium (9th – 10th September 2016, Braunschweig, Germany)
Hoyer, J.; Hirschfeld, C.; Hentschker, C.; Gómez-Mejia, A.; Hammerschmidt, S.; Maaß, S.; Becher, D. Investigations to Streptococcus pneumoniae under iron limitation. 1st Summer School “Infection Biology” (28th – 30th September 2016, Greifswald/Riems, Germany)
Hoyer, J.; Bartel, J.; Gómez-Mejia, A.; Rohde, M.; Hammerschmidt, S.; Maaß, S.; Becher, D. Proteomic response of Streptococcus pneumoniae to treatment with 2,2’-bipyridine – an iron limitation approach. 11th European Summer School “Advanced Proteomics” (30th July – 5th August 2017, Brixen/Bressanone, South Tirol, Italy)
Hoyer, J.; Bartel, J.; Gómez-Mejia, A.; Rohde, M.; Hammerschmidt, S.; Maaß, S.; Becher, D. Proteomic response of Streptococcus pneumoniae to iron limitation. 1st International Conference on Respiratory Pathogens (1st – 3rd November 2017, Rostock, Germany)
7.3. ORAL PRESENTATIONS
Hoyer, J.; Bartel, J.; Gómez-Mejia, A.; Rohde, M.; Hirschfeld, C.; Heß, N.; Sura, T.; Maaß, S.; Hammerschmidt, S.; Becher, D. Proteomic response of Streptococcus pneumoniae to iron limitation. Annual Conference of the Association for General and Applied Microbiology (VAAM) 2018 (15th- 18th April 2018, Wolfsburg, Germany)
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Appendix
8. APPENDIX
Figure 8-1 Scatter plot of biological replicates in CDM. For these scatter plots the quantified proteome data from pneumococci grown in CDM under control and stress condition as well as after addition of BIP-Fe(II)-complex were used.
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Appendix
Figure 8-2 Scatter plot of biological replicates in THY. For these scatter plots the quantified proteome data from pneumococci grown in THY under control and stress condition as well as after addition of BIP-Fe(II)-complex were used.
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Appendix
Table 8-1 Quantified proteome data of iron limitation experiment in CDM. More details can be found in appendix file B3.
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0248 GlmS Amino acid Metabolism -2.63* -2.17 SPD_0448 GlnA Amino acid Metabolism 1.65 -2.27* SPD_1158 GdhA Amino acid Metabolism 1.69 2.93 SPD_1373 AspC Amino acid Metabolism 1.77 1.86* SPD_1768 AsnA Amino acid Metabolism -8.33* -1.33 SPD_1791 AlaA Amino acid Metabolism -1.11 -1.89* SPD_0823 ProA Amino acid Metabolism Off Off SPD_1976 ArgF Amino acid Metabolism 2.06* 1.84 SPD_0309 LuxS Amino acid Metabolism 2.01* 12.85* SPD_0664 MetK Amino acid Metabolism -1.61 -1.01 SPD_2037 CysK Amino acid Metabolism -1.37 -1.56 SPD_0377 LysC Amino acid Metabolism -2.86* -2.33* SPD_0910 GlyA Amino acid Metabolism 1.16 -1.92* SPD_1194 ThrB Amino acid Metabolism Off -2.56 SPD_1877 ThrC Amino acid Metabolism 1.64 -2.22* SPD_0041 AraT Amino acid Metabolism -1.06 -1.96 SPD_0900 Asd Amino acid Metabolism -1.08 -2.56 SPD_0901 DapA Amino acid Metabolism 1.58 -1.67 SPD_1195 Hom Amino acid Metabolism -5.88* -2.44* SPD_1387 DapB Amino acid Metabolism 1.89* -2.86 SPD_1775 LysA Amino acid Metabolism Off Off SPD_1923 DapD Amino acid Metabolism -1.64 -1.14 SPD_0812 Lys1 Amino acid Metabolism -2.94 -1.43 SPD_1151 AroA Amino acid Metabolism 1.25 10.81 SPD_1205 AroA Amino acid Metabolism 1.95* 2.48* SPD_1208 AroC Amino acid Metabolism -3.45* -1.14 SPD_1211 AroD Amino acid Metabolism -1.19 Off SPD_1510 AroF Amino acid Metabolism -3.85* -1.72 SPD_1511 AroG Amino acid Metabolism -3.33 -2.27 SPD_1597 TrpB Amino acid Metabolism Off Off SPD_0405 IlvH Amino acid Metabolism -2.27 -1.92 SPD_0406 IlvC Amino acid Metabolism -1.45 -3.03*
125
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0409 IlvA Amino acid Metabolism -4.35 Off SPD_0749 IlvE Amino acid Metabolism 1.33 -1.49 SPD_1956 IlvD Amino acid Metabolism -1.96* 1.26 Biosynthesis of other SPD_1327 Bta 1.29 -1.20 secondary metabolites SPD_0874 GlmU Carbohydrate Metabolism -2.44* -1.14 SPD_1246 NagB Carbohydrate Metabolism -1.39 1.98* SPD_1390 GlmM Carbohydrate Metabolism -2.44* 1.10 SPD_1531 ScrK Carbohydrate Metabolism 1.46 -1.37 SPD_1866 NagA Carbohydrate Metabolism -1.10 9.51* SPD_0641 ManA Carbohydrate Metabolism 1.29 Off SPD_1050 LacD Carbohydrate Metabolism Off -1.10 SPD_1432 GalE-1 Carbohydrate Metabolism -1.61 -1.79* SPD_1919 GalU Carbohydrate Metabolism 1.23 -1.10 SPD_0222 GpmB Carbohydrate Metabolism Off Off SPD_0265 AdhA Carbohydrate Metabolism On On SPD_0445 Pgk Carbohydrate Metabolism 1.23 -1.35 SPD_0526 Fba Carbohydrate Metabolism -1.59 1.05 SPD_0580 Gki Carbohydrate Metabolism 1.00 -2.08* SPD_0789 PfkA Carbohydrate Metabolism -2.86* -1.06 SPD_0790 Pyk Carbohydrate Metabolism 1.20 -1.89* SPD_1004 GapN Carbohydrate Metabolism -1.23 -3.03* SPD_1012 Eno Carbohydrate Metabolism 1.48 -2.17* SPD_1025 LpdA Carbohydrate Metabolism 2.84 7.91 SPD_1078 Ldh Carbohydrate Metabolism -4.00* -1.79* SPD_1326 Pgm Carbohydrate Metabolism -2.94* -1.43 SPD_1404 TpiA Carbohydrate Metabolism 1.87* 2.82* SPD_1468 GpmA Carbohydrate Metabolism 1.79 1.87* SPD_1636 Adh Carbohydrate Metabolism On SPD_1823 Gap Carbohydrate Metabolism 1.32 1.37 SPD_1834 AdhE Carbohydrate Metabolism -1.52 -2.22* SPD_1897 Pgi Carbohydrate Metabolism 1.46 -1.67 SPD_0285 XylS Carbohydrate Metabolism 1.11 Off SPD_0403 PrfC Carbohydrate Metabolism -1.61 -1.12
126
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1002 PulA Carbohydrate Metabolism 1.72 Off SPD_0343 Gnd Carbohydrate Metabolism 1.44 -4.00* SPD_0723 RpiA Carbohydrate Metabolism 1.69 2.05 SPD_0724 DeoB Carbohydrate Metabolism 1.66 -1.61 SPD_0980 Prs2 Carbohydrate Metabolism -3.33 -1.72 SPD_1100 Zwf Carbohydrate Metabolism 1.39 -4.55 SPD_1333 Pgl Carbohydrate Metabolism 1.16 Off SPD_1839 Tkt Carbohydrate Metabolism 1.01 -2.13* SPD_1865 Adh2 Carbohydrate Metabolism 3.61* 63.70* SPD_0420 PflB Carbohydrate Metabolism 1.99* 1.18 SPD_0621 LctO Carbohydrate Metabolism 1.13 -2.56* SPD_0636 SpxB Carbohydrate Metabolism -1.69 -2.17* SPD_0850 GloA Carbohydrate Metabolism 1.59 1.95* SPD_0953 Ppc Carbohydrate Metabolism -3.13* 1.19 SPD_0985 EutD Carbohydrate Metabolism 1.44 -1.30 SPD_1853 AckA Carbohydrate Metabolism -1.61 -3.03* SPD_0311 DexB Carbohydrate Metabolism 2.92 Off SPD_1932 MalP Carbohydrate Metabolism 5.84* -3.57* SPD_1933 MalQ Carbohydrate Metabolism 4.37 Off SPD_0663 RheB Cellular processes Off -2.86 SPD_1193 MsrAB1 Cellular processes -1.85* -1.85* SPD_1242 SPD_1242 Cellular processes Off Off SPD_1360 SPD_1360 Cellular processes 1.27 6.16* SPD_1402 SPD_1402 Cellular processes -16.67* 5.73* SPD_0339 GpsB Cellular processes -1.02 2.85* SPD_0342 MapZ Cellular processes Off Off SPD_0659 FtsE Cellular processes -3.85* -2.27* SPD_0660 FtsX Cellular processes -5.26 -2.33 SPD_0710 EzrA Cellular processes -5.00* -1.04 SPD_1474 DivIVA Cellular processes 1.31 5.11* SPD_1477 YlmF Cellular processes -1.04 2.18* SPD_1479 FtsZ Cellular processes -2.08* 1.98* SPD_1480 FtsA Cellular processes -4.55* -3.57* SPD_1122 SPD_1122 Cellular processes -3.57* -1.39
127
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0126 PspA Cellular processes -7.69* -10.00* SPD_0345 CbpC Cellular processes Off Off SPD_0889 PhtD Cellular processes -16.67* -11.11* SPD_0890 PhtE Cellular processes Off Off SPD_1037 SPD_1037 Cellular processes Off Off (PhtB) SPD_1038 PhpA (PhtA) Cellular processes -16.67 -100.00 SPD_1726 Ply Cellular processes -1.16 -1.69 SPD_2017 CbpA (PspC) Cellular processes -2.78* 1.05 SPD_0549 VanY Cellular processes -2.33* -1.61 SPD_0672 PPIA Cellular processes -2.22* 1.45 SPD_0454 HsdM DNA Metabolism 1.29 -2.27 SPD_0997 Hup DNA Metabolism 1.74 5.25* SPD_0001 DnaA DNA Metabolism -4.35 Off SPD_0002 DnaN DNA Metabolism 1.07 -1.23 SPD_0181 SPD_0181 DNA Metabolism -1.04 2.64* SPD_0490 SPD_0490 DNA Metabolism 1.71 8.11* SPD_0709 GyrB DNA Metabolism Off Off SPD_0827 SPD_0827 DNA Metabolism On SPD_1077 GyrA DNA Metabolism Off Off SPD_1120 TopA DNA Metabolism 1.30 Off SPD_1369 Ssb DNA Metabolism 1.16 17.60* SPD_1626 Xth DNA Metabolism -2.04* -2.70* SPD_1739 RecA DNA Metabolism -4.76* -1.11 SPD_1903 MutS DNA Metabolism Off Off SPD_0667 SodA Energy Metabolism 2.16* 1.38 SPD_1041 NrdH Energy Metabolism -2.86* 5.03* SPD_1152 Fld Energy Metabolism 1.53 8.76* SPD_1287 TrxB Energy Metabolism 1.54 5.98* SPD_1298 Nox Energy Metabolism 1.52 -3.57* SPD_1415 SPD_1415 Energy Metabolism 1.38 -3.13* SPD_1464 PsaD Energy Metabolism 1.17 -3.23* SPD_1567 Trx Energy Metabolism -1.08 4.22* SPD_0030 CynT Energy Metabolism Off Off
128
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1335 AtpD Energy Metabolism -2.17* -1.43 SPD_1336 AtpG Energy Metabolism -2.33 -1.59 SPD_1337 AtpA Energy Metabolism -2.13* -1.79 SPD_1338 AtpH Energy Metabolism Off Off SPD_1339 AtpF Energy Metabolism Off 2.94 SPD_1363 PpaC Energy Metabolism 1.53 1.46 Glycan biosynthesis and SPD_0558 PrtA -12.50* -7.14* Metabolism Glycan biosynthesis and SPD_0577 ZmpB -1.18 2.58* Metabolism Glycan biosynthesis and SPD_1018 Iga -1.56 -3.23* Metabolism Glycan biosynthesis and SPD_1504 NanA On Metabolism Glycan biosynthesis and SPD_1737 LytA 1.00 -1.18 Metabolism Glycan biosynthesis and SPD_0099 CapD -5.56 -1.64 Metabolism Glycan biosynthesis and SPD_0336 Pbp1A -3.13* -2.17* Metabolism Glycan biosynthesis and SPD_0598 MurD Off -1.09 Metabolism Glycan biosynthesis and SPD_0767 DacC -3.85 Off Metabolism Glycan biosynthesis and SPD_0967 MurA-1 -1.02 Off Metabolism Glycan biosynthesis and SPD_1349 MurC 1.50 Off Metabolism Glycan biosynthesis and SPD_1484 Ddl 1.73 Off Metabolism Glycan biosynthesis and SPD_1486 PenA Off -2.44 Metabolism Glycan biosynthesis and SPD_1619 SPD_1619 1.75 -1.33 Metabolism
129
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Glycan biosynthesis and SPD_1764 MurA-2 -2.17* -1.33 Metabolism Glycan biosynthesis and SPD_1821 Pbp2A Off Off Metabolism Glycan biosynthesis and SPD_2045 MreC Off Off Metabolism SPD_0378 FabM Lipid Metabolism 2.06 Off SPD_0380 FabH Lipid Metabolism 1.32 Off SPD_0381 AcpP Lipid Metabolism 2.24* 8.67* SPD_0382 FabK Lipid Metabolism -1.75 -1.79 SPD_0383 FabD Lipid Metabolism 2.46* -1.22 SPD_0384 FabG Lipid Metabolism -2.08* -1.56 SPD_0385 FabF Lipid Metabolism 2.26* -3.45* SPD_0386 AccB Lipid Metabolism 2.49 14.46 SPD_0388 AccC Lipid Metabolism -1.56 Off SPD_0389 AccD Lipid Metabolism 1.30 -2.94* SPD_0390 AccA Lipid Metabolism 1.26 Off SPD_1918 GpsA Lipid Metabolism -5.56* -2.04* Metabolism of cofactors and SPD_0271 FolE 1.89* 3.34 vitamins Metabolism of cofactors and SPD_1346 YceG -2.86* -1.25 vitamins Metabolism of cofactors and SPD_1250 NadE 1.40 Off vitamins Metabolism of cofactors and SPD_1251 PncB -1.33 -3.13 vitamins Metabolism of cofactors and SPD_1740 CinA Off -1.45 vitamins Metabolism of cofactors and SPD_0721 FolD -3.85* -1.89 vitamins Metabolism of cofactors and SPD_1087 Fhs 1.05 -2.50* vitamins Metabolism of cofactors and SPD_0762 SufC -5.00* -2.08* vitamins
130
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Metabolism of cofactors and SPD_0763 SufD -5.26* -2.22* vitamins Metabolism of cofactors and SPD_0766 SufB -7.14* -2.70* vitamins Metabolism of cofactors and SPD_1296 SPD_1296 -1.23 -1.45 vitamins Metabolism of cofactors and SPD_1297 SPD_1297 2.18* 1.23 vitamins Metabolism of cofactors and SPD_0994 RibF -6.25* -1.18 vitamins Metabolism of cofactors and SPD_0623 ThiM On vitamins Metabolism of cofactors and SPD_1423 PdxK -1.92 -1.19 vitamins Metabolism of other amino SPD_0685 Gor 1.35 4.35* acids Metabolism of other amino SPD_0700 PepN -1.85* -2.94* acids Metabolism of other amino SPD_1427 PhnA On On acids Metabolism of other amino SPD_0764 SufS 1.17 1.65 acids Metabolism of other amino SPD_1393 TrxB Off -1.49 acids Metabolism of terpenoids SPD_1127 IspD On and polyketides Metabolism of terpenoids SPD_1537 MvaS On On and polyketides SPD_0012 Hpt Nucleotide Metabolism 1.36 -2.33* SPD_0024 PurA Nucleotide Metabolism -1.20 10.11* SPD_0033 PrsA Nucleotide Metabolism Off Off SPD_0052 PurL Nucleotide Metabolism -1.47 1.05 SPD_0057 PurH Nucleotide Metabolism -3.23* 1.04 SPD_0058 PurD Nucleotide Metabolism 1.39 Off
131
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0059 PurE Nucleotide Metabolism 1.63 1.51 SPD_0060 PurK Nucleotide Metabolism Off 1.12 SPD_0062 PurB Nucleotide Metabolism 1.67 -2.78* SPD_0187 NrdD Nucleotide Metabolism -6.25* -4.17 SPD_0214 Adk Nucleotide Metabolism 1.21 4.41* SPD_0726 PunA Nucleotide Metabolism 1.51 -3.45 SPD_0730 DeoD Nucleotide Metabolism 1.02 Off SPD_1042 NrdE Nucleotide Metabolism -7.69* -2.70* SPD_1043 NrdF Nucleotide Metabolism -5.26* -2.17* SPD_1107 GuaC Nucleotide Metabolism 2.47* 1.04 SPD_1274 GuaA Nucleotide Metabolism -3.03* -1.56 SPD_1407 Apt Nucleotide Metabolism -2.22 -2.04 SPD_1628 Xpt Nucleotide Metabolism -4.00 -1.22 SPD_1660 RdgB Nucleotide Metabolism Off -2.08 SPD_2055 GuaB Nucleotide Metabolism -1.23 1.37 SPD_0027 Dut Nucleotide Metabolism Off 12.96 SPD_0442 PyrG Nucleotide Metabolism 1.45 -1.75 SPD_0581 ThyA Nucleotide Metabolism Off Off SPD_0649 Upp Nucleotide Metabolism -1.03 -1.23 SPD_0665 PyrDa Nucleotide Metabolism 1.63 -1.72 SPD_0834 PyrH Nucleotide Metabolism -2.86* -2.50* SPD_1030 PyrC Nucleotide Metabolism 2.52* -1.43 SPD_1131 CarB Nucleotide Metabolism Off Off SPD_1133 PyrB Nucleotide Metabolism 1.03 Off SPD_1428 Cmk Nucleotide Metabolism -1.02 3.65* SPD_0013 FtsH Protein fate -4.55* -1.96* SPD_0173 SPD_0173 Protein fate On SPD_0177 PepP Protein fate 1.33 -4.17* SPD_0245 Eep Protein fate Off -1.85 SPD_0258 PepS Protein fate -1.49 -4.00* SPD_0261 PepC Protein fate 1.31 -3.57* SPD_0542 PepV Protein fate 1.85* -1.64 SPD_0650 ClpP Protein fate -2.22 1.12 SPD_0717 ClpE Protein fate -2.56 -1.49
132
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0753 Pcp Protein fate 1.06 1.22 SPD_0866 PepF Protein fate 1.05 1.20 SPD_0894 PepT Protein fate 1.83* 1.78 SPD_1399 ClpX Protein fate -3.13 -1.28 SPD_1418 PepQ Protein fate -1.16 -1.56 SPD_1460 PepO Protein fate 1.10 -3.23* SPD_1571 SPD_1571 Protein fate 1.44 -2.13* SPD_1647 PepA Protein fate 1.62 13.98* SPD_1922 SPD_1922 Protein fate -1.79 Off SPD_2022 SPD_2022 Protein fate -4.76 -2.78 SPD_0365 Tig Protein fate 1.44 10.54* SPD_0459 GrpE Protein fate 1.66 10.63* SPD_0460 DnaK Protein fate 1.30 6.89* SPD_0461 DnaJ Protein fate -1.92* -1.27 SPD_0868 PrsA Protein fate 1.28 -1.64 SPD_1709 GroEL Protein fate -1.08 3.44* SPD_1710 GroES Protein fate 1.39 7.19* SPD_2015 HslO Protein fate -1.54 Off SPD_0970 Map Protein fate -1.96* 1.80* SPD_1024 LplA Protein fate Off Off SPD_1285 Def Protein fate -1.33 -1.39 SPD_1542 StkP Protein fate -4.17* 2.60* SPD_1543 PhpP Protein fate -1.25 4.10* SPD_1837 SPD_1837 Protein fate On On SPD_1076 SrtA Protein fate On SPD_1101 FtsY Protein fate Off Off SPD_1142 Ffh Protein fate -3.70* -1.61 SPD_1389 SPD_1389 Protein fate -2.70* -1.52 SPD_1512 SecA Protein fate -5.88* -1.79 SPD_1773 YidC Protein fate On SPD_0836 SPD_0836 Protein synthesis 1.85 Off SPD_0855 SPD_0855 Protein synthesis On SPD_0857 Era Protein synthesis Off Off SPD_0871 YbaK Protein synthesis -1.72 2.76*
133
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0964 SPD_0964 Protein synthesis -3.23* -1.39 SPD_1398 SPD_1398 Protein synthesis Off Off SPD_1519 SPD_1519 Protein synthesis -3.70* -1.43 SPD_0083 RpsD Protein synthesis -4.76* -1.15 SPD_0192 RpsJ Protein synthesis -4.17* -1.09 SPD_0193 RplC Protein synthesis 1.26 1.20 SPD_0194 RplD Protein synthesis -2.78* -1.82* SPD_0195 RplW Protein synthesis -1.04 1.29 SPD_0196 RplB Protein synthesis 1.02 -3.57* SPD_0197 RpsS Protein synthesis 1.23 -1.33 SPD_0198 RplV Protein synthesis 1.12 4.47* SPD_0199 RpsC Protein synthesis -5.00* -1.33 SPD_0200 RplP Protein synthesis 1.13 Off SPD_0201 RpmC Protein synthesis 1.28 7.20 SPD_0202 RpsQ Protein synthesis 1.22 -1.45 SPD_0203 RplN Protein synthesis -3.70* -1.35 SPD_0204 RplX Protein synthesis 1.33 7.43* SPD_0205 RplE Protein synthesis -3.33* 1.00 SPD_0206 RpsN Protein synthesis On SPD_0207 RpsH Protein synthesis -1.56 2.14* SPD_0208 RplF Protein synthesis 1.04 1.33 SPD_0209 RplR Protein synthesis 1.40 13.04* SPD_0210 RpsE Protein synthesis -2.94* -1.14 SPD_0211 RpmD Protein synthesis -2.04* Off SPD_0212 RplO Protein synthesis 1.34 9.01* SPD_0216 RpsM Protein synthesis 1.19 -1.05 SPD_0217 RpsK Protein synthesis 1.07 -1.43 SPD_0219 RplQ Protein synthesis 1.22 4.44* SPD_0251 RpsL Protein synthesis -1.19 -1.11 SPD_0252 RpsG Protein synthesis -2.38* -1.47 SPD_0274 RplM Protein synthesis -3.13* -1.01 SPD_0275 RpsI Protein synthesis 1.33 1.35 SPD_0401 RpmB Protein synthesis 1.25 -1.06 SPD_0478 RimP Protein synthesis -1.52 1.35
134
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0550 RplK Protein synthesis -1.47 5.44* SPD_0551 RplA Protein synthesis -4.00* -1.75 SPD_0674 RpsP Protein synthesis 1.24 7.70* SPD_0732 RpsT Protein synthesis -1.15 10.12* SPD_0757 RpsA Protein synthesis -3.33* -1.37 SPD_0849 RplT Protein synthesis 1.30 -1.96* SPD_0989 RplU Protein synthesis 1.47 -1.01 SPD_0991 RpmA Protein synthesis 1.05 17.08* SPD_1148 RplS Protein synthesis -3.85* -1.30 SPD_1154 RpmE2 Protein synthesis -1.15 12.02* SPD_1187 RplL Protein synthesis 1.22 13.04* SPD_1188 RplJ Protein synthesis -1.47 2.02* SPD_1245 RpsU Protein synthesis 1.37 -1.54 SPD_1368 RpsR Protein synthesis 1.56 7.23* SPD_1370 RpsF Protein synthesis 1.30 4.06* SPD_1439 RpsO Protein synthesis 1.41 -1.33 SPD_1573 PrmA Protein synthesis Off Off SPD_1964 RpmG Protein synthesis -1.28 7.28 SPD_2031 RplI Protein synthesis -2.78* 2.13* SPD_2042 RpsB Protein synthesis -2.78* 1.47 SPD_0215 InfA Protein synthesis 1.07 1.83 SPD_0253 FusA Protein synthesis -3.23* -1.61 SPD_0395 Efp Protein synthesis 1.31 2.63* SPD_0399 PrfC Protein synthesis -4.55* -1.96* SPD_0482 InfB Protein synthesis -3.85* -1.30 SPD_0593 SPD_0593 Protein synthesis -3.33* -1.01 SPD_0835 Frr Protein synthesis -1.20 6.48* SPD_0906 PrfA Protein synthesis -1.96* 1.08 SPD_1318 Tuf Protein synthesis -2.78* -1.18 SPD_2033 YfiA Protein synthesis -1.25 2.41* SPD_2041 Tsf Protein synthesis -1.56 -1.03 SPD_0238 LeuS Protein synthesis -3.33* -1.69 SPD_0246 ProS Protein synthesis -2.38* -1.92* SPD_0375 SerS Protein synthesis -1.82* -1.11
135
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0396 GatB Protein synthesis 1.18 -1.67 SPD_0397 GatA Protein synthesis 1.14 -1.45 SPD_0398 GatC Protein synthesis 1.37 10.69* SPD_0494 ValS Protein synthesis -2.17* -2.44* SPD_0504 PheS Protein synthesis Off Off SPD_0506 PheT Protein synthesis -3.57* -2.33 SPD_0620 LysS Protein synthesis -2.13* -1.43 SPD_0689 MetG Protein synthesis 1.23 -2.38* SPD_1216 AlaS Protein synthesis -3.57* -1.22 SPD_1304 GlyS Protein synthesis -3.45* -2.00* SPD_1305 GlyQ Protein synthesis -3.03* -1.69 SPD_1371 AsnC Protein synthesis -5.56* -1.85* SPD_1444 ThrS Protein synthesis -6.25* -2.22 SPD_1472 IleS Protein synthesis 1.18 -3.23* SPD_1545 Fmt Protein synthesis -1.10 -1.64 SPD_1896 GltX Protein synthesis -1.85* -1.18 SPD_1905 ArgS Protein synthesis -4.17* -1.75 SPD_1926 TyrS Protein synthesis -4.55* -1.67 SPD_1941 AspS Protein synthesis -4.35 Off SPD_1950 HisS Protein synthesis -1.52 -2.78 SPD_2056 TrpS Protein synthesis -4.00 Off SPD_0127 MnmA Protein synthesis 1.20 Off SPD_0129 GidA Protein synthesis -1.59 1.53 SPD_0833 Gid Protein synthesis 1.83* -2.04 SPD_0902 TrmE Protein synthesis Off Off SPD_1247 QueA Protein synthesis Off Off SPD_1654 RluB Protein synthesis 1.63 -1.22 SPD_1713 PheT_2 Protein synthesis 1.12 1.24 SPD_1868 Tgt Protein synthesis 2.17* Off SPD_0467 BlpS Regulatory functions 1.31 Off SPD_1291 SPD_1291 Regulatory functions -1.19 1.83* SPD_1412 CodY Regulatory functions -4.35* -1.69 SPD_1523 SPD_1523 Regulatory functions Off Off SPD_1725 SPD_1725 Regulatory functions 1.65 3.70*
136
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1776 PurR Regulatory functions -1.23 Off SPD_1797 CcpA Regulatory functions -1.27 -2.70* SPD_0049 ComA Signal transduction Off -1.56 SPD_0050 ComB Signal transduction Off -1.19 SPD_0468 BlpR Signal transduction Off -2.04 SPD_0469 BlpH Signal transduction Off Off SPD_0701 CiaR Signal transduction 1.02 -1.14 SPD_1085 VicR Signal transduction -1.39 Off SPD_2063 ComE Signal transduction -14.29* -2.44* SPD_2064 ComD Signal transduction -12.50* -1.61 SPD_2068 DegP Signal transduction 1.76 -1.33 SPD_0130 Rnj Transcription 1.07 -1.82* SPD_0512 Pnp Transcription 1.11 -2.63* SPD_0533 Rnj Transcription -1.01 -1.72 SPD_0862 Rnr Transcription 1.34 Off SPD_1397 SPD_1397 Transcription 1.15 10.35* SPD_1413 DeaD Transcription -2.08* -1.22 SPD_1549 Rny Transcription Off -1.67 SPD_0218 RpoA Transcription 1.11 1.31 SPD_0441 RpoE Transcription 1.12 12.35 SPD_1547 RpoZ Transcription 1.27 10.45 SPD_1758 RpoC Transcription 1.17 -2.22* SPD_1759 RpoB Transcription 1.08 -2.94* SPD_0483 RbfA Transcription -1.69 2.63* SPD_0586 Rnz Transcription 1.21 1.42 SPD_0064 SPD_0064 Transcription -2.04* -2.04* SPD_0447 GlnR Transcription Off 3.64 SPD_0479 NusA Transcription -1.92* -1.49 SPD_0958 RpoD Transcription 1.02 2.09* SPD_0976 Rex Transcription -2.63 -1.12 SPD_1044 LacR2 Transcription -2.86 Off SPD_1345 GreA Transcription 1.40 10.96* SPD_1487 SPD_1487 Transcription Off Off SPD_1524 SPD_1524 Transcription 1.23 2.07
137
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1525 SPD_1525 Transcription -5.26* -1.32 SPD_1819 NusG Transcription -1.64 -1.06 Transport and binding SPD_0150 SPD_0150 -2.44* -1.27 proteins Transport and binding SPD_0153 MetN Off -1.25 proteins Transport and binding SPD_0411 LysY -5.00* -1.54 proteins Transport and binding SPD_0412 LysX1 -5.26* -1.43 proteins Transport and binding SPD_0540 TcyJ -1.92 1.20 proteins Transport and binding SPD_0652 LivJ -2.22* -1.28 proteins Transport and binding SPD_0720 GlnQ -2.44 Off proteins Transport and binding SPD_0954 SPD_0954 -7.14 -1.33 proteins Transport and binding SPD_1098 SPD_1098 -4.55* -1.64 proteins Transport and binding SPD_1099 SPD_1099 -2.33 -1.01 proteins Transport and binding SPD_1170 GsiB -2.33 -2.13* proteins Transport and binding SPD_1289 TcyC -5.56 -3.33 proteins Transport and binding SPD_1290 SPD_1290 Off -2.27 proteins Transport and binding SPD_1328 AatB -2.86* 1.14 proteins Transport and binding SPD_1329 SPD_1329 -2.04 Off proteins Transport and binding SPD_1667 AmiF -4.17* -1.33 proteins
138
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Transport and binding SPD_1668 AmiE -5.00* -2.27* proteins Transport and binding SPD_1669 AmiD -4.55 -1.67 proteins Transport and binding SPD_1670 AmiC -5.56* -1.92* proteins Transport and binding SPD_1671 AmiA -2.86* -1.85* proteins Transport and binding SPD_1227 SPD_1227 -3.70* -1.39 proteins Transport and binding SPD_1228 PstB -4.17 -1.61 proteins Transport and binding SPD_0262 ManZ -7.69* -1.49 proteins Transport and binding SPD_0264 ManL -2.56* 2.45* proteins SPD_0560; Transport and binding GatB 2.01 6.67 SPD_1057 proteins Transport and binding SPD_0740 SPD_0740 -3.23 -1.33 proteins Transport and binding SPD_1039 PtsI -2.17* -1.23 proteins Transport and binding SPD_1040 PtsH 1.46 8.96* proteins Transport and binding SPD_1934 MalX -1.22 1.02 proteins Transport and binding SPD_0077 TrkA -2.08* -1.37 proteins Transport and binding SPD_0915 PiaA -3.33* 1.21 proteins Transport and binding SPD_1383 PacL Off -1.08 proteins Transport and binding SPD_1461 PsaB -8.33* -1.79 proteins
139
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Transport and binding SPD_1527 NatB Off Off proteins Transport and binding SPD_1642 ProWX -3.85 -1.30 proteins Transport and binding SPD_1643 ProV -3.33 Off proteins Transport and binding SPD_1652 PiuA 19.28 1.34 proteins Transport and binding SPD_1997 AdcA -6.67 Off proteins Transport and binding SPD_0267 PbuO -7.14* 1.31 proteins Transport and binding SPD_0151 MetQ -1.92* 1.40 proteins Transport and binding SPD_0888 Lmb -25.00 -16.67 proteins Transport and binding SPD_1357 AliB -2.94* 1.44 proteins Transport and binding SPD_1409 MsmX -2.63 -1.30 proteins Transport and binding SPD_1463 MtsA (PsaA) -7.14* -1.49 proteins Transport and binding SPD_1528 SPD_1528 Off -2.13 proteins Transport and binding SPD_2025 SPD_2025 -2.78 -1.35 proteins Transport and binding SPD_2057 SPD_2057 -2.44* -1.82* proteins Transport and binding SPD_0554 SPD_0554 On proteins Transport and binding SPD_0686 SPD_0686 -4.55* -1.02 proteins Transport and binding SPD_1263 SPD_1263 Off Off proteins
140
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Transport and binding SPD_1264 SPD_1264 Off Off proteins SPD_0266 SPD_0266 Unknown function -2.22 -1.12 SPD_1102 SPD_1102 Unknown function -1.27 3.57* SPD_1146 SPD_1146 Unknown function -1.64 -1.16 SPD_1367 SPD_1367 Unknown function -2.13* 1.58 SPD_1659 SPD_1659 Unknown function -2.78 -1.28 SPD_1788 SPD_1788 Unknown function On SPD_1794 SPD_1794 Unknown function -2.13 -1.03 SPD_0004 YchF Unknown function -2.33* -1.02 SPD_0707 SPD_0707 Unknown function 1.29 1.71 SPD_0987 SPD_0987 Unknown function 1.71 5.80* SPD_1060 LepA Unknown function -4.76* -1.45 SPD_2016 SPD_2016 Unknown function Off -1.39 SPD_0091 SPD_0091 Unknown function 1.92* -3.45* SPD_0114 SPD_0114 Unknown function -4.00 -1.64 SPD_0131 SPD_0131 Unknown function 1.08 Off SPD_0160 SPD_0160 Unknown function -3.85 Off SPD_0179 SPD_0179 Unknown function -1.64 -1.16 SPD_0180 SPD_0180 Unknown function 1.55 10.14* SPD_0249 SPD_0249 Unknown function -4.00* -1.85* SPD_0310 SPD_0310 Unknown function -1.85* -3.33 SPD_0341 RlmL Unknown function 1.90* Off SPD_0373 SPD_0373 Unknown function -10.00* -3.45* SPD_0394 SPD_0394 Unknown function -1.14 -1.15 SPD_0410 SPD_0410 Unknown function 1.16 Off SPD_0462 SPD_0462 Unknown function Off Off SPD_0463 SPD_0463 Unknown function 2.23 2.07 SPD_0466 BlpT Unknown function 1.00 2.14* SPD_0480 YlxR Unknown function On SPD_0481 SPD_0481 Unknown function -4.76 Off SPD_0489 SPD_0489 Unknown function 1.48 Off SPD_0541 SPD_0541 Unknown function -1.02 Off SPD_0547 SPD_0547 Unknown function 1.68 7.48*
141
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0582 SPD_0582 Unknown function -1.79* 5.20* SPD_0590 MoeZ Unknown function Off Off SPD_0622 TenA_2 Unknown function -1.19 Off SPD_0646 SPD_0646 Unknown function 1.93 -1.75 SPD_0675 SPD_0675 Unknown function 1.34 Off SPD_0680 NrdD_2 Unknown function -5.00 -2.17* SPD_0683 SPD_0683 Unknown function 1.36 16.54* SPD_0688 SPD_0688 Unknown function Off -1.16 SPD_0693 SPD_0693 Unknown function 2.07 Off SPD_0714 SPD_0714 Unknown function -1.89* 1.68 SPD_0718 YkuJ Unknown function 1.27 3.77* SPD_0739 SPD_0739 Unknown function -7.69* 1.17 SPD_0754 SPD_0754 Unknown function -1.10 3.68 SPD_0799 SPD_0799 Unknown function On SPD_0911 SPD_0911 Unknown function -2.70 Off SPD_0913 SPD_0913 Unknown function 3.68 3.32* SPD_0974 SPD_0974 Unknown function Off Off SPD_0978 SPD_0978 Unknown function On SPD_1063 SPD_1063 Unknown function -1.06 1.48 SPD_1123 LicC Unknown function -2.70 Off SPD_1125 Pck Unknown function -1.96* -1.79 SPD_1130 LicD2 Unknown function Off Off SPD_1136 SPD_1136 Unknown function -1.03 3.68* SPD_1139 LemA Unknown function -2.94 1.95* SPD_1166 SPD_1166 Unknown function 2.30 Off SPD_1190 MtaD Unknown function -2.56* -1.61 SPD_1197 SPD_1197 Unknown function -6.25* -2.22* SPD_1202 Psr Unknown function -2.33 Off SPD_1206 SPD_1206 Unknown function 1.02 4.75* SPD_1226 SPD_1226 Unknown function -2.27* 2.73* SPD_1241 SPD_1241 Unknown function -5.88 5.97* SPD_1303 SPD_1303 Unknown function Off Off SPD_1308 SPD_1308 Unknown function -2.70* -1.59 SPD_1311 MocA Unknown function -2.50 Off
142
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1320 SPD_1320 Unknown function -5.56 1.05 SPD_1375 SPD_1375 Unknown function -4.76* -2.33* SPD_1380 SPD_1380 Unknown function -5.56* -1.35 SPD_1391 SPD_1391 Unknown function Off -1.52 SPD_1396 SPD_1396 Unknown function Off Off SPD_1429 RpsO_2 Unknown function -5.88 Off SPD_1520 SPD_1520 Unknown function 1.75 -1.59 SPD_1558 SPD_1558 Unknown function On SPD_1566 SPD_1566 Unknown function 1.15 12.40 SPD_1576 SPD_1576 Unknown function 1.22 Off SPD_1590 SPD_1590 Unknown function -2.17* 2.13* SPD_1591 SPD_1591 Unknown function -1.75 -1.37 SPD_1662 SPD_1662 Unknown function -5.00 -2.13* SPD_1727 SPD_1727 Unknown function -2.08* 1.05 SPD_1728 SPD_1728 Unknown function -4.35 Off SPD_1729 SPD_1729 Unknown function -6.67 1.16 SPD_1765 YlbL Unknown function Off -1.56 SPD_1777 Cbf1 Unknown function -2.86 -1.41 SPD_1849 SPD_1849 Unknown function -1.72 1.34 SPD_1874 SPD_1874 Unknown function On On SPD_1899 SPD_1899 Unknown function -1.69 1.19 SPD_1928 SPD_1928 Unknown function Off 5.42 SPD_1962 SPD_1962 Unknown function Off Off SPD_1984 SPD_1984 Unknown function -6.25* -1.15 SPD_2028 CbpD Unknown function -1.67 Off SPD_2050 SPD_2050 Unknown function -6.67 Off Xenobiotics biodegradation SPD_1238 Hydrolase -1.96 Off and Metabolism Xenobiotics biodegradation SPD_0903 XylH 1.61 Off and Metabolism
143
Appendix
Table 8-2 Quantified proteome data of iron limitation experiment in THY. More details can be found in appendix file B4.
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0248 GlmS Amino acid Metabolism -1.14 1.01 SPD_0448 GlnA Amino acid Metabolism -1.69 -1.35 SPD_1158 GdhA Amino acid Metabolism -1.59 -3.33 SPD_1768 AsnA Amino acid Metabolism -1.39 -1.25 SPD_1976 ArgF Amino acid Metabolism 1.62 -9.09 SPD_1977 ArcC Amino acid Metabolism -1.35 1.07 SPD_0309 LuxS Amino acid Metabolism -1.32 1.14 SPD_0664 MetK Amino acid Metabolism -1.32 1.29 SPD_2037 CysK Amino acid Metabolism 2.60* 1.49 SPD_0910 GlyA Amino acid Metabolism Off -1.03 SPD_1151 AroA Amino acid Metabolism -1.69 -1.37 SPD_1208 AroC Amino acid Metabolism On SPD_1956 IlvD Amino acid Metabolism Off -1.16 Biosynthesis of other SPD_1327 Bta 1.49 1.55 secondary metabolites SPD_0874 GlmU Carbohydrate Metabolism -1.56 -1.39 SPD_0965 SPD_0965 Carbohydrate Metabolism 1.06 1.02 SPD_1246 NagB Carbohydrate Metabolism -1.04 1.79 SPD_1390 GlmM Carbohydrate Metabolism 1.13 1.46 SPD_1497 NanE-1 Carbohydrate Metabolism -1.09 1.34 SPD_1866 NagA Carbohydrate Metabolism -1.23 -1.43 SPD_0641 ManA Carbohydrate Metabolism Off -1.45 SPD_0772 FruK Carbohydrate Metabolism -2.13 -1.01 SPD_1053 LacA Carbohydrate Metabolism 1.41 1.84* SPD_1432 GalE-1 Carbohydrate Metabolism -1.96 -1.37 SPD_1919 GalU Carbohydrate Metabolism -1.52 1.17 SPD_0265 AdhA Carbohydrate Metabolism Off 1.02 SPD_0445 Pgk Carbohydrate Metabolism -1.49 1.37 SPD_0526 Fba Carbohydrate Metabolism -1.27 1.77 SPD_0789 PfkA Carbohydrate Metabolism -1.27 1.44 SPD_0790 Pyk Carbohydrate Metabolism -2.63 -1.64 SPD_1012 Eno Carbohydrate Metabolism -3.85 -3.45 SPD_1025 LpdA Carbohydrate Metabolism 1.16 3.42* SPD_1078 Ldh Carbohydrate Metabolism -1.43 1.22 SPD_1326 Pgm Carbohydrate Metabolism -1.19 1.28 SPD_1404 TpiA Carbohydrate Metabolism -1.09 1.88* SPD_1468 GpmA Carbohydrate Metabolism -1.08 1.64 SPD_1823 Gap Carbohydrate Metabolism -3.13 -4.00 SPD_1834 AdhE Carbohydrate Metabolism -4.55 1.21 SPD_1897 Pgi Carbohydrate Metabolism -1.03 -4.76 SPD_0403 PrfC Carbohydrate Metabolism 1.28 1.76
144
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0343 Gnd Carbohydrate Metabolism -1.54 -1.15 SPD_0723 RpiA Carbohydrate Metabolism -1.11 -1.54 SPD_0724 DeoB Carbohydrate Metabolism -14.28* -1.67 SPD_1780 Rpe Carbohydrate Metabolism -6.25* 1.15 SPD_1839 Tkt Carbohydrate Metabolism -1.64 -1.32 SPD_1865 Adh2 Carbohydrate Metabolism -2.38* 1.57 SPD_0420 PflB Carbohydrate Metabolism 1.78 1.24 SPD_0621 LctO Carbohydrate Metabolism 1.23 1.36 SPD_0636 SpxB Carbohydrate Metabolism 1.86 -1.03 SPD_0850 GloA Carbohydrate Metabolism -1.82 -1.43 SPD_1932 MalP Carbohydrate Metabolism -2.00 -1.39 SPD_1360 SPD_1360 Cellular processes 4.62* -1.11 SPD_1402 SPD_1402 Cellular processes -100.00* -1.92 SPD_0339 GpsB Cellular processes 1.01 1.43 SPD_0342 MapZ Cellular processes 1.43 1.62 SPD_0369 SPD_0369 Cellular processes 1.45 1.45 SPD_0710 EzrA Cellular processes 1.40 1.19 SPD_1474 DivIVA Cellular processes -1.16 2.00* SPD_1477 YlmF Cellular processes 1.16 1.75 SPD_1479 FtsZ Cellular processes 1.02 1.88 SPD_1480 FtsA Cellular processes -1.05 1.28 SPD_0126 PspA Cellular processes 1.95 1.01 SPD_0335 SPD_0335 Cellular processes On SPD_0889 PhtD Cellular processes 1.67 -1.79 SPD_0890 PhtE Cellular processes On SPD_1037 SPD_1037 Cellular processes On (PhtB) SPD_1038 PhpA (PhtA) Cellular processes 24.42* 2.04 SPD_1726 Ply Cellular processes 1.92 1.27 SPD_2017 CbpA (PspC) Cellular processes -1.47 1.25 SPD_0549 VanY Cellular processes 1.03 1.08 SPD_0672 PPIA Cellular processes 1.14 1.63 SPD_1082 SPD_1082 Cellular processes 1.20 1.48 SPD_1066 XseB DNA Metabolism 1.70 1.25 SPD_0997 Hup DNA Metabolism 1.05 1.03 SPD_0002 DnaN DNA Metabolism -1.47 -3.85 SPD_0181 SPD_0181 DNA Metabolism 1.57 1.55 SPD_0490 SPD_0490 DNA Metabolism 1.13 1.56 SPD_1369 Ssb DNA Metabolism 1.51 1.67 SPD_2053 SPD_2053 DNA Metabolism On On SPD_0667 SodA Energy Metabolism 1.63 2.10* SPD_1041 NrdH Energy Metabolism 5.16* -1.25 SPD_1152 Fld Energy Metabolism 1.13 1.57 SPD_1287 TrxB Energy Metabolism 2.83* 1.17 SPD_1298 Nox Energy Metabolism -1.56 -1.23
145
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1415 SPD_1415 Energy Metabolism -1.79 -2.13 SPD_1430 Fer Energy Metabolism Off 1.64 SPD_1464 PsaD Energy Metabolism 4.30 -1.39 SPD_1567 Trx Energy Metabolism -1.08 1.05 SPD_1335 AtpD Energy Metabolism -2.70 -2.38 SPD_1336 AtpG Energy Metabolism Off Off SPD_1337 AtpA Energy Metabolism -1.96 -1.59 SPD_1339 AtpF Energy Metabolism 1.34 1.18 SPD_1363 PpaC Energy Metabolism -2.50* 1.39 Glycan biosynthesis and SPD_0080 SPD_0080 1.13 1.03 Metabolism Glycan biosynthesis and SPD_0558 PrtA -1.14 -1.79 Metabolism Glycan biosynthesis and SPD_0577 ZmpB 1.35 -1.22 Metabolism Glycan biosynthesis and SPD_1018 Iga 1.81 1.29 Metabolism Glycan biosynthesis and SPD_1376 SPD_1376 On On Metabolism Glycan biosynthesis and SPD_1737 LytA Off 1.30 Metabolism Glycan biosynthesis and SPD_1619 SPD_1619 -1.06 2.35 Metabolism Glycan biosynthesis and SPD_2003 DltC 1.16 1.68 Metabolism SPD_0381 AcpP Lipid Metabolism 1.42 1.23 SPD_0382 FabK Lipid Metabolism 1.23 1.16 SPD_0384 FabG Lipid Metabolism -1.37 -1.64 SPD_0385 FabF Lipid Metabolism -1.59 -1.72 SPD_0386 AccB Lipid Metabolism 1.36 1.63 SPD_1918 GpsA Lipid Metabolism -1.23 1.02 Metabolism of cofactors and SPD_0271 FolE 1.82 -1.75 vitamins Metabolism of cofactors and SPD_1346 YceG 1.42 1.26 vitamins Metabolism of cofactors and SPD_1250 NadE Off 1.92 vitamins Metabolism of cofactors and SPD_0762 SufC 3.27 1.22 vitamins Metabolism of cofactors and SPD_0765 SPD_0765 On vitamins Metabolism of cofactors and SPD_1297 SPD_1297 -12.50 -2.13 vitamins Metabolism of cofactors and SPD_0623 ThiM On vitamins Metabolism of other amino SPD_0685 Gor 1.39 -1.20 acids Metabolism of other amino SPD_0700 PepN -1.72 -1.41 acids
146
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Metabolism of other amino SPD_1427 PhnA 1.54 1.65 acids Metabolism of other amino SPD_0764 SufS 2.39 -1.56 acids Metabolism of terpenoids SPD_1537 MvaS 1.03 -2.04 and polyketides SPD_0024 PurA Nucleotide Metabolism 1.66 -1.09 SPD_0187 NrdD Nucleotide Metabolism 2.75 1.86 SPD_0214 Adk Nucleotide Metabolism 1.30 1.92 SPD_1042 NrdE Nucleotide Metabolism 1.13 1.05 SPD_1043 NrdF Nucleotide Metabolism Off 1.25 SPD_1107 GuaC Nucleotide Metabolism -6.25 -100.00 SPD_2055 GuaB Nucleotide Metabolism 1.16 -2.44 SPD_0027 Dut Nucleotide Metabolism 1.00 -4.00 SPD_0609 PyrE Nucleotide Metabolism -2.08* 1.63 SPD_0649 Upp Nucleotide Metabolism 2.56 1.19 SPD_0665 PyrDa Nucleotide Metabolism -3.70 -4.55 SPD_0851 PyrK Nucleotide Metabolism Off -2.63 SPD_0852 PyrDb Nucleotide Metabolism Off -1.96 SPD_1131 CarB Nucleotide Metabolism Off 1.29 SPD_1134 PyrR Nucleotide Metabolism Off Off SPD_1428 Cmk Nucleotide Metabolism 1.31 1.33 SPD_1757 Ndk Nucleotide Metabolism -1.06 -2.50 SPD_0074 SPD_0074 Nucleotide Metabolism 1.03 1.37 SPD_0013 FtsH Protein fate -1.43 1.02 SPD_0258 PepS Protein fate -1.14 1.60 SPD_0261 PepC Protein fate -1.54 -4.55 SPD_0308 ClpL Protein fate 1.85 -1.33 SPD_0542 PepV Protein fate -2.04 1.26 SPD_0753 Pcp Protein fate -1.54 1.17 SPD_0894 PepT Protein fate -1.35 3.60* SPD_1196 MecA Protein fate 1.10 -1.02 SPD_1418 PepQ Protein fate -1.22 1.16 SPD_1460 PepO Protein fate 1.24 1.17 SPD_1647 PepA Protein fate -50.00 -3.85 SPD_0365 Tig Protein fate 1.46 1.33 SPD_0459 GrpE Protein fate 1.83 1.19 SPD_0460 DnaK Protein fate 1.49 1.45 SPD_0461 DnaJ Protein fate 1.12 -1.01 SPD_0868 PrsA Protein fate 1.19 1.12 SPD_1709 GroEL Protein fate 1.18 1.55 SPD_1710 GroES Protein fate 1.56 1.28 SPD_0970 Map Protein fate -1.19 3.26* SPD_1285 Def Protein fate -1.49 1.28 SPD_1542 StkP Protein fate 1.69 -1.41
147
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1543 PhpP Protein fate -1.12 1.50 SPD_1837 SPD_1837 Protein fate -1.25 1.51 SPD_1076 SrtA Protein fate On SPD_0855 SPD_0855 Protein synthesis -1.79 1.18 SPD_0871 YbaK Protein synthesis -1.08 1.39 SPD_0083 RpsD Protein synthesis -1.20 -1.12 SPD_0192 RpsJ Protein synthesis 1.02 1.93 SPD_0193 RplC Protein synthesis 1.04 1.31 SPD_0194 RplD Protein synthesis -1.33 1.26 SPD_0195 RplW Protein synthesis -1.12 -1.04 SPD_0196 RplB Protein synthesis -1.01 1.21 SPD_0197 RpsS Protein synthesis 1.46 -1.03 SPD_0198 RplV Protein synthesis 1.14 -1.22 SPD_0199 RpsC Protein synthesis -1.30 -1.19 SPD_0200 RplP Protein synthesis -1.47 1.18 SPD_0201 RpmC Protein synthesis 1.61 1.46 SPD_0202 RpsQ Protein synthesis -1.30 -1.05 SPD_0203 RplN Protein synthesis -1.25 1.24 SPD_0204 RplX Protein synthesis 1.38 1.39 SPD_0205 RplE Protein synthesis -1.23 1.51 SPD_0206 RpsN Protein synthesis 1.48 1.43 SPD_0207 RpsH Protein synthesis -1.15 1.16 SPD_0208 RplF Protein synthesis -1.19 1.05 SPD_0209 RplR Protein synthesis 1.77 1.22 SPD_0210 RpsE Protein synthesis -1.04 1.22 SPD_0211 RpmD Protein synthesis 1.32 1.69 SPD_0212 RplO Protein synthesis 1.31 1.13 SPD_0216 RpsM Protein synthesis -1.05 -1.22 SPD_0217 RpsK Protein synthesis -1.19 -1.22 SPD_0219 RplQ Protein synthesis 1.48 1.38 SPD_0251 RpsL Protein synthesis -1.61 1.74 SPD_0252 RpsG Protein synthesis -1.14 1.28 SPD_0274 RplM Protein synthesis -1.08 1.00 SPD_0275 RpsI Protein synthesis -2.13* 2.13 SPD_0401 RpmB Protein synthesis 1.28 1.46 SPD_0478 RimP Protein synthesis 1.35 1.79 SPD_0550 RplK Protein synthesis 1.45 1.54 SPD_0551 RplA Protein synthesis -1.16 1.18 SPD_0674 RpsP Protein synthesis 1.41 1.34 SPD_0732 RpsT Protein synthesis 1.12 1.48 SPD_0757 RpsA Protein synthesis -1.28 1.23 SPD_0848 RpmI Protein synthesis 4.10 1.92 SPD_0849 RplT Protein synthesis 1.09 -1.96 SPD_0989 RplU Protein synthesis -1.12 1.10
148
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0991 RpmA Protein synthesis 1.78 1.59 SPD_1148 RplS Protein synthesis -1.82 -1.49 SPD_1154 RpmE2 Protein synthesis 1.48 1.33 SPD_1187 RplL Protein synthesis 1.39 1.36 SPD_1188 RplJ Protein synthesis 1.18 1.25 SPD_1245 RpsU Protein synthesis 1.34 1.10 SPD_1368 RpsR Protein synthesis 1.42 1.18 SPD_1370 RpsF Protein synthesis 1.17 -1.15 SPD_1439 RpsO Protein synthesis 1.04 -1.18 SPD_1964 RpmG Protein synthesis -1.10 1.08 SPD_2031 RplI Protein synthesis -1.10 1.21 SPD_2042 RpsB Protein synthesis -1.18 1.27 SPD_0215 InfA Protein synthesis 1.13 -1.03 SPD_0253 FusA Protein synthesis -1.32 1.00 SPD_0395 Efp Protein synthesis 1.14 1.76 SPD_0482 InfB Protein synthesis 2.04 1.39 SPD_0593 SPD_0593 Protein synthesis 1.31 1.56 SPD_0835 Frr Protein synthesis 1.54 1.47 SPD_0847 InfC Protein synthesis -1.27 -1.35 SPD_0906 PrfA Protein synthesis 1.16 1.03 SPD_1318 Tuf Protein synthesis -1.05 1.17 SPD_2033 YfiA Protein synthesis 1.21 1.34 SPD_2041 Tsf Protein synthesis -1.43 -1.10 SPD_0375 SerS Protein synthesis -1.30 1.20 SPD_0396 GatB Protein synthesis 1.12 1.06 SPD_0397 GatA Protein synthesis -1.19 -1.05 SPD_0398 GatC Protein synthesis 1.53 1.32 SPD_0620 LysS Protein synthesis -1.06 -1.43 SPD_1216 AlaS Protein synthesis -3.33 2.01* SPD_1304 GlyS Protein synthesis 1.04 1.07 SPD_1444 ThrS Protein synthesis 1.30 1.23 SPD_1896 GltX Protein synthesis -1.02 1.13 SPD_1905 ArgS Protein synthesis -1.30 1.15 SPD_1926 TyrS Protein synthesis -1.20 -1.15 SPD_1654 RluB Protein synthesis -1.41 -1.01 SPD_1713 PheT_2 Protein synthesis -1.08 1.12 SPD_0178 SPD_0178 Regulatory functions Off 1.03 SPD_1291 SPD_1291 Regulatory functions 1.32 -1.10 SPD_1725 SPD_1725 Regulatory functions 1.31 1.53 SPD_1797 CcpA Regulatory functions 1.17 -2.94 SPD_2068 DegP Signal transduction 1.41 1.21 SPD_0130 Rnj Transcription 18.31 1.39 SPD_0533 Rnj Transcription 15.03 1.81 SPD_1397 SPD_1397 Transcription 1.02 -1.39
149
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_0218 RpoA Transcription -1.25 1.55 SPD_0441 RpoE Transcription 1.70 1.94* SPD_1547 RpoZ Transcription 1.66 1.29 SPD_1758 RpoC Transcription -1.18 -1.16 SPD_1759 RpoB Transcription -1.79 -1.27 SPD_0483 RbfA Transcription 1.20 -1.16 SPD_0586 Rnz Transcription -1.02 1.66 SPD_0447 GlnR Transcription 1.00 1.40 SPD_0479 NusA Transcription Off 1.23 SPD_0668 YutD Transcription Off -1.47 SPD_0958 RpoD Transcription -1.08 1.65 SPD_1345 GreA Transcription 1.75 1.45 SPD_1819 NusG Transcription -1.05 1.16 Transport and binding SPD_0150 SPD_0150 1.28 1.01 proteins Transport and binding SPD_1667 AmiF 1.56 Off proteins Transport and binding SPD_1671 AmiA -1.75 -1.75 proteins Transport and binding SPD_1227 SPD_1227 On proteins Transport and binding SPD_1232 SPD_1232 -1.03 1.15 proteins Transport and binding SPD_0262 ManZ -1.08 -1.43 proteins Transport and binding SPD_0264 ManL 1.27 1.18 proteins SPD_0560;SP Transport and binding GatB 1.62 1.39 D_1057 proteins Transport and binding SPD_0661 Exp5 On On proteins Transport and binding SPD_0773 FruAB 1.07 1.10 proteins Transport and binding SPD_1039 PtsI -1.02 1.14 proteins Transport and binding SPD_1040 PtsH 1.14 -1.64 proteins Transport and binding SPD_1664 TreP -1.19 1.01 proteins Transport and binding SPD_1934 MalX -1.19 1.19 proteins Transport and binding SPD_1960 UlaB 1.74 1.48 proteins Transport and binding SPD_0915 PiaA -1.18 -1.02 proteins Transport and binding SPD_1652 PiuA On proteins Transport and binding SPD_0151 MetQ 1.43 1.33 proteins
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Appendix
Locus ID Protein General function I FCB-K I I FCC-K I Transport and binding SPD_1463 MtsA (PsaA) -1.85 -1.30 proteins Transport and binding SPD_0686 SPD_0686 1.34 1.42 proteins SPD_1102 SPD_1102 Unknown function -1.45 2.44* SPD_1367 SPD_1367 Unknown function 1.36 1.75 SPD_1556 SPD_1556 Unknown function Off 2.24* SPD_1788 SPD_1788 Unknown function -4.35 -1.52 SPD_0707 SPD_0707 Unknown function Off 1.95 SPD_0987 SPD_0987 Unknown function 1.53 1.11 SPD_1554 SPD_1554 Unknown function -1.23 1.16 SPD_0039 SPD_0039 Unknown function On SPD_0084 SPD_0084 Unknown function -3.85 -7.14 SPD_0091 SPD_0091 Unknown function On SPD_0093 SPD_0093 Unknown function 1.41 1.19 SPD_0131 SPD_0131 Unknown function 1.48 1.43 SPD_0180 SPD_0180 Unknown function 1.66 1.28 SPD_0184 SPD_0184 Unknown function On SPD_0188 SPD_0188 Unknown function 3.75 -1.45 SPD_0220 SPD_0220 Unknown function -1.54 1.45 SPD_0394 SPD_0394 Unknown function -1.33 1.65 SPD_0402 Asp Unknown function Off 2.82 SPD_0410 SPD_0410 Unknown function -1.23 -1.22 SPD_0462 SPD_0462 Unknown function 1.42 1.13 SPD_0463 SPD_0463 Unknown function 1.54 -1.18 SPD_0480 YlxR Unknown function 1.43 1.41 SPD_0489 SPD_0489 Unknown function 1.26 1.66 SPD_0541 SPD_0541 Unknown function -1.33 1.17 SPD_0547 SPD_0547 Unknown function -5.88 -1.72 SPD_0582 SPD_0582 Unknown function -1.45 1.07 SPD_0646 SPD_0646 Unknown function Off 1.70 SPD_0675 SPD_0675 Unknown function 1.24 1.03 SPD_0683 SPD_0683 Unknown function 1.15 1.47 SPD_0714 SPD_0714 Unknown function 1.05 1.55 SPD_0718 YkuJ Unknown function -1.32 1.53 SPD_0739 SPD_0739 Unknown function 1.22 1.67 SPD_0754 SPD_0754 Unknown function -1.04 -1.16 SPD_0799 SPD_0799 Unknown function 2.04 1.47 SPD_0837 SPD_0837 Unknown function -1.37 -1.61 SPD_0878 SPD_0878 Unknown function 1.47 2.47 SPD_0913 SPD_0913 Unknown function -1.15 1.32 SPD_0974 SPD_0974 Unknown function On SPD_1061 SPD_1061 Unknown function Off 1.57 SPD_1063 SPD_1063 Unknown function -1.12 1.10 SPD_1136 SPD_1136 Unknown function 1.83 1.68
151
Appendix
Locus ID Protein General function I FCB-K I I FCC-K I SPD_1139 LemA Unknown function -1.01 1.19 SPD_1197 SPD_1197 Unknown function -1.27 -1.04 SPD_1206 SPD_1206 Unknown function 1.39 1.26 SPD_1241 SPD_1241 Unknown function -1.05 1.36 SPD_1253 SPD_1253 Unknown function On SPD_1303 SPD_1303 Unknown function 1.55 1.37 SPD_1320 SPD_1320 Unknown function -1.30 -1.56 SPD_1350 SPD_1350 Unknown function 1.45 1.32 SPD_1411 SPD_1411 Unknown function On SPD_1429 RpsO_2 Unknown function On On SPD_1551 SPD_1551 Unknown function 1.82 1.54 SPD_1558 SPD_1558 Unknown function -1.54 -1.14 SPD_1566 SPD_1566 Unknown function 1.28 -1.23 SPD_1590 SPD_1590 Unknown function 1.32 1.69 SPD_1591 SPD_1591 Unknown function 2.18 1.32 SPD_1646 SPD_1646 Unknown function 1.00 1.31 SPD_1662 SPD_1662 Unknown function 1.21 1.15 SPD_1714 SPD_1714 Unknown function 1.25 1.15 SPD_1727 SPD_1727 Unknown function 2.12 1.09 SPD_1849 SPD_1849 Unknown function -1.49 -1.82 SPD_1867 OatA Unknown function Off 1.22 SPD_1895 SPD_1895 Unknown function 1.02 1.84 SPD_1928 SPD_1928 Unknown function 1.66 1.11 SPD_1948 SPD_1948 Unknown function 1.13 1.04 SPD_1984 SPD_1984 Unknown function -1.03 1.03 Xenobiotics biodegradation SPD_0903 XylH 1.27 1.59 and Metabolism
152
Appendix
Table 8-3 Quantified proteome data of comparative analysis of CDM versus THY. More details can be found in appendix file B2.
Locus ID Protein General function I FCTHY-CDM I SPD_0248 GlmS Amino acid Metabolism -35.30* SPD_0448 GlnA Amino acid Metabolism -19.93* SPD_1158 GdhA Amino acid Metabolism -2.72 SPD_1373 AspC Amino acid Metabolism -1.38 SPD_1768 AsnA Amino acid Metabolism -464.52* SPD_1791 AlaA Amino acid Metabolism -59.38 SPD_1796 AnsA Amino acid Metabolism CDM SPD_1878 SPD_1878 Amino acid Metabolism CDM SPD_0786 ArgR Amino acid Metabolism CDM SPD_0813 NspC Amino acid Metabolism CDM SPD_0814 AguA Amino acid Metabolism CDM SPD_0815 AguB Amino acid Metabolism CDM SPD_0822 ProB Amino acid Metabolism CDM SPD_0823 ProA Amino acid Metabolism CDM SPD_0824 ProC Amino acid Metabolism CDM SPD_1976 ArgF Amino acid Metabolism 5.98 SPD_1977 ArcC Amino acid Metabolism -1.29 SPD_0309 LuxS Amino acid Metabolism 8.60* SPD_0510 MetE Amino acid Metabolism 1.08 SPD_0664 MetK Amino acid Metabolism -30.48* SPD_1353 MetB Amino acid Metabolism 8.09 SPD_2037 CysK Amino acid Metabolism -135.10* SPD_0102 SdhA Amino acid Metabolism CDM SPD_0377 LysC Amino acid Metabolism -555.02 SPD_0910 GlyA Amino acid Metabolism -655.89* SPD_1194 ThrB Amino acid Metabolism CDM SPD_1877 ThrC Amino acid Metabolism -288.88 SPD_0041 AraT Amino acid Metabolism CDM SPD_0152 DapE Amino acid Metabolism CDM SPD_0900 Asd Amino acid Metabolism -216.37 SPD_0901 DapA Amino acid Metabolism -1721.58 SPD_1195 Hom Amino acid Metabolism CDM SPD_1387 DapB Amino acid Metabolism -233.46 SPD_1775 LysA Amino acid Metabolism CDM SPD_1923 DapD Amino acid Metabolism -349.67* SPD_0809 Cad Amino acid Metabolism CDM SPD_0812 Lys1 Amino acid Metabolism CDM SPD_1151 AroA Amino acid Metabolism -2.44* SPD_1203 PheA2 Amino acid Metabolism CDM SPD_1205 AroA Amino acid Metabolism -2.04 SPD_1207 TyrA Amino acid Metabolism CDM
153
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1208 AroC Amino acid Metabolism -41.60* SPD_1209 AroB Amino acid Metabolism CDM SPD_1211 AroD Amino acid Metabolism CDM SPD_1510 AroF Amino acid Metabolism CDM SPD_1511 AroG Amino acid Metabolism CDM SPD_1596 TrpA Amino acid Metabolism CDM SPD_1597 TrpB Amino acid Metabolism CDM SPD_1600 TrpD Amino acid Metabolism CDM SPD_1601 TrpG Amino acid Metabolism CDM SPD_1602 TrpE Amino acid Metabolism CDM SPD_0405 IlvH Amino acid Metabolism CDM SPD_0406 IlvC Amino acid Metabolism -3568.85 SPD_0409 IlvA Amino acid Metabolism CDM SPD_0749 IlvE Amino acid Metabolism CDM SPD_1956 IlvD Amino acid Metabolism -7.00 Biosynthesis of other secondary SPD_1327 Bta -13.45* metabolites SPD_0874 GlmU Carbohydrate Metabolism -10.24 SPD_0965 SPD_0965 Carbohydrate Metabolism THY SPD_1246 NagB Carbohydrate Metabolism -1.78 SPD_1390 GlmM Carbohydrate Metabolism -65.96* SPD_1497 NanE-1 Carbohydrate Metabolism -2.08 SPD_1531 ScrK Carbohydrate Metabolism 7.44* SPD_1866 NagA Carbohydrate Metabolism 9.61* SPD_1224 BudA Carbohydrate Metabolism CDM SPD_0324 Cps2I Carbohydrate Metabolism 2.26 SPD_0641 ManA Carbohydrate Metabolism -51.32 SPD_0772 FruK Carbohydrate Metabolism 6.47 SPD_1199 SPD_1199 Carbohydrate Metabolism CDM SPD_1050 LacD Carbohydrate Metabolism -32.62* SPD_1052 LacB Carbohydrate Metabolism CDM SPD_1053 LacA Carbohydrate Metabolism 6.64* SPD_1432 GalE-1 Carbohydrate Metabolism -129.80* SPD_1919 GalU Carbohydrate Metabolism -3.09* SPD_0222 GpmB Carbohydrate Metabolism CDM SPD_0265 AdhA Carbohydrate Metabolism 38.71* SPD_0445 Pgk Carbohydrate Metabolism -2.61* SPD_0526 Fba Carbohydrate Metabolism -1.02 SPD_0580 Gki Carbohydrate Metabolism -113.64 SPD_0789 PfkA Carbohydrate Metabolism -6.59* SPD_0790 Pyk Carbohydrate Metabolism -12.43* SPD_1004 GapN Carbohydrate Metabolism -1943.02 SPD_1012 Eno Carbohydrate Metabolism -2.67 SPD_1025 LpdA Carbohydrate Metabolism -1.91 SPD_1026 PdhC Carbohydrate Metabolism CDM
154
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1027 PdhB Carbohydrate Metabolism CDM SPD_1028 AcoA Carbohydrate Metabolism CDM SPD_1078 Ldh Carbohydrate Metabolism -33.48* SPD_1326 Pgm Carbohydrate Metabolism -145.91* SPD_1404 TpiA Carbohydrate Metabolism 5.74* SPD_1468 GpmA Carbohydrate Metabolism 1.64 SPD_1636 Adh Carbohydrate Metabolism 1.94 SPD_1823 Gap Carbohydrate Metabolism 6.58* SPD_1834 AdhE Carbohydrate Metabolism -27.88* SPD_1897 Pgi Carbohydrate Metabolism -3.87 SPD_0285 XylS Carbohydrate Metabolism CDM SPD_0403 PrfC Carbohydrate Metabolism -4.03* SPD_0657 SPD_0657 Carbohydrate Metabolism CDM SPD_1002 PulA Carbohydrate Metabolism CDM SPD_1126 TarJ Carbohydrate Metabolism CDM SPD_0343 Gnd Carbohydrate Metabolism -260.60* SPD_0723 RpiA Carbohydrate Metabolism 1.14 SPD_0724 DeoB Carbohydrate Metabolism -1.52 SPD_0737 DeoC Carbohydrate Metabolism CDM SPD_0980 Prs2 Carbohydrate Metabolism -13.36 SPD_1100 Zwf Carbohydrate Metabolism CDM SPD_1333 Pgl Carbohydrate Metabolism CDM SPD_1780 Rpe Carbohydrate Metabolism THY SPD_1839 Tkt Carbohydrate Metabolism -76.78* SPD_0237 GldA Carbohydrate Metabolism 5.13 SPD_1865 Adh2 Carbohydrate Metabolism 525.84* SPD_0235 Pfl Carbohydrate Metabolism THY SPD_0420 PflB Carbohydrate Metabolism -2.11* SPD_0621 LctO Carbohydrate Metabolism -164.51 SPD_0636 SpxB Carbohydrate Metabolism -92.20* SPD_0850 GloA Carbohydrate Metabolism -1.78 SPD_0953 Ppc Carbohydrate Metabolism CDM SPD_0985 EutD Carbohydrate Metabolism 2.84 SPD_1853 AckA Carbohydrate Metabolism -782.90* SPD_0250 AmyA Carbohydrate Metabolism CDM SPD_0311 DexB Carbohydrate Metabolism -33.58 SPD_1005 GlgB Carbohydrate Metabolism CDM SPD_1006 GlgC Carbohydrate Metabolism CDM SPD_1007 GlgD Carbohydrate Metabolism CDM SPD_1534 ScrB Carbohydrate Metabolism CDM SPD_1932 MalP Carbohydrate Metabolism -8.39* SPD_1933 MalQ Carbohydrate Metabolism -1.48 SPD_0573 MsrAB2 Cellular processes CDM SPD_0663 RheB Cellular processes -87.44
155
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1193 MsrAB1 Cellular processes -340.36* SPD_1242 SPD_1242 Cellular processes -45.20 SPD_1360 SPD_1360 Cellular processes 2.67 SPD_1402 Dpr Cellular processes 3.90* SPD_1458 RelA Cellular processes CDM SPD_0339 GpsB Cellular processes -1.65 SPD_0342 MapZ Cellular processes -3.78* SPD_0369 SPD_0369 Cellular processes 8.13* SPD_0496 SPD_0496 Cellular processes CDM SPD_0600 DivIB Cellular processes CDM SPD_0659 FtsE Cellular processes CDM SPD_0660 FtsX Cellular processes CDM SPD_0710 EzrA Cellular processes -86.75* SPD_0774 FtsK Cellular processes CDM SPD_0952 SPD_0952 Cellular processes CDM SPD_1474 DivIVA Cellular processes 3.58* SPD_1477 YlmF Cellular processes 1.44 SPD_1479 FtsZ Cellular processes -4.11* SPD_1480 FtsA Cellular processes -107.99* SPD_1122 SPD_1122 Cellular processes CDM SPD_0126 PspA Cellular processes -2.35 SPD_0335 SPD_0335 Cellular processes 6.67 SPD_0345 CbpC Cellular processes CDM SPD_0889 PhtD Cellular processes -36.16* SPD_0890 PhtE Cellular processes -45.92* SPD_1037 SPD_1037 (PhtB) Cellular processes -109.62 SPD_1038 PhpA (PhtA) Cellular processes -2004.2* SPD_1278 CppA Cellular processes CDM SPD_1726 Ply Cellular processes -276.72* SPD_2017 CbpA (PspC) Cellular processes -2.64* SPD_0306 PbpX Cellular processes CDM SPD_0549 VanY Cellular processes -11.09* SPD_0672 PPIA Cellular processes -3.85* SPD_0864 TehB Cellular processes CDM SPD_1082 SPD_1082 Cellular processes 9.35* SPD_1785 SPD_1785 Cellular processes CDM SPD_1104 Smc DNA Metabolism CDM SPD_1066 XseB DNA Metabolism 22.07* SPD_1067 XseA DNA Metabolism CDM SPD_0451 HsdS DNA Metabolism CDM SPD_0453 HsdS DNA Metabolism CDM SPD_0454 HsdM DNA Metabolism -11.91 SPD_0455 HsdR DNA Metabolism CDM SPD_0782 SPD_0782 DNA Metabolism CDM
156
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0784 HsdR DNA Metabolism CDM SPD_0997 Hup DNA Metabolism 14.94* SPD_1630 DpnD DNA Metabolism CDM SPD_0001 DnaA DNA Metabolism -16.74 SPD_0002 DnaN DNA Metabolism -2.28 SPD_0038 PolA DNA Metabolism CDM SPD_0165 MutL DNA Metabolism CDM SPD_0170 RuvA DNA Metabolism CDM SPD_0176 UvrA DNA Metabolism CDM SPD_0181 SPD_0181 DNA Metabolism -1.33 SPD_0254 PolC DNA Metabolism 1.78 SPD_0368 RnhC DNA Metabolism CDM SPD_0371 MutS2 DNA Metabolism CDM SPD_0490 SPD_0490 DNA Metabolism 99.19* SPD_0532 RecJ DNA Metabolism CDM SPD_0709 GyrB DNA Metabolism CDM SPD_0746 ParE DNA Metabolism CDM SPD_0748 ParC DNA Metabolism CDM SPD_0760 DnaX DNA Metabolism CDM SPD_0827 SPD_0827 DNA Metabolism 3.28 SPD_0858 MutM DNA Metabolism CDM SPD_0879 DnaQ DNA Metabolism CDM SPD_0957 DnaG DNA Metabolism 10.25 SPD_0973 PcrA DNA Metabolism CDM SPD_1001 LigA DNA Metabolism CDM SPD_1016 RexA DNA Metabolism -1.25 SPD_1031 MutX DNA Metabolism CDM SPD_1032 Ung DNA Metabolism CDM SPD_1062 RecN DNA Metabolism CDM SPD_1077 GyrA DNA Metabolism CDM SPD_1086 MutY DNA Metabolism 1.33 SPD_1096 UvrB DNA Metabolism CDM SPD_1120 TopA DNA Metabolism -3.39 SPD_1135 Nth DNA Metabolism CDM SPD_1369 Ssb DNA Metabolism 14.27* SPD_1405 DnaD DNA Metabolism CDM SPD_1485 RecR DNA Metabolism CDM SPD_1507 RecG DNA Metabolism CDM SPD_1546 PriA DNA Metabolism CDM SPD_1626 Xth DNA Metabolism CDM SPD_1711 Ssb DNA Metabolism CDM SPD_1739 RecA DNA Metabolism -732.89 SPD_1778 SPD_1778 DNA Metabolism CDM SPD_1903 MutS DNA Metabolism 1.22
157
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_2030 DnaB DNA Metabolism CDM SPD_2053 SPD_2053 DNA Metabolism THY SPD_0667 SodA Energy Metabolism -3.23* SPD_1041 NrdH Energy Metabolism 3.82 SPD_1152 Fld Energy Metabolism 16.04* SPD_1287 TrxB Energy Metabolism 2.41 SPD_1298 Nox Energy Metabolism -55.98* SPD_1415 SPD_1415 Energy Metabolism -411.58* SPD_1430 Fer Energy Metabolism 53.30 SPD_1464 PsaD Energy Metabolism -3001.90* SPD_1567 Trx Energy Metabolism 2.56* SPD_0030 CynT Energy Metabolism CDM SPD_1334 AtpC Energy Metabolism CDM SPD_1335 AtpD Energy Metabolism -18.63* SPD_1336 AtpG Energy Metabolism -34.99* SPD_1337 AtpA Energy Metabolism -10.89* SPD_1338 AtpH Energy Metabolism -21.24 SPD_1339 AtpF Energy Metabolism 1.17 SPD_1363 PpaC Energy Metabolism -6.72* SPD_1927 SPD_1927 Energy Metabolism CDM SPD_0080 SPD_0080 Glycan biosynthesis and Metabolism THY SPD_0444 SPD_0444 Glycan biosynthesis and Metabolism -6.64 SPD_0558 PrtA Glycan biosynthesis and Metabolism -159.81* SPD_0577 ZmpB Glycan biosynthesis and Metabolism -3.44 SPD_1018 Iga Glycan biosynthesis and Metabolism -2.07 SPD_0562 BgaA Glycan biosynthesis and Metabolism CDM SPD_0853 LytB Glycan biosynthesis and Metabolism CDM SPD_1403 LytC Glycan biosynthesis and Metabolism CDM SPD_1504 NanA Glycan biosynthesis and Metabolism CDM SPD_1737 LytA Glycan biosynthesis and Metabolism -87.36* SPD_0099 CapD Glycan biosynthesis and Metabolism -62.93 SPD_0336 Pbp1A Glycan biosynthesis and Metabolism -230.67* SPD_0536 FibB Glycan biosynthesis and Metabolism CDM SPD_0598 MurD Glycan biosynthesis and Metabolism CDM SPD_0599 MurG Glycan biosynthesis and Metabolism CDM SPD_0767 DacC Glycan biosynthesis and Metabolism CDM SPD_0967 MurA-1 Glycan biosynthesis and Metabolism CDM SPD_1222 MurB Glycan biosynthesis and Metabolism CDM SPD_1309 PgdA Glycan biosynthesis and Metabolism CDM SPD_1349 MurC Glycan biosynthesis and Metabolism CDM SPD_1359 MurE Glycan biosynthesis and Metabolism CDM SPD_1416 MurE Glycan biosynthesis and Metabolism CDM SPD_1483 MurF Glycan biosynthesis and Metabolism CDM SPD_1484 Ddl Glycan biosynthesis and Metabolism 2.83
158
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1486 PenA Glycan biosynthesis and Metabolism CDM SPD_1619 SPD_1619 Glycan biosynthesis and Metabolism -27.17* SPD_1661 MurI Glycan biosynthesis and Metabolism CDM SPD_1764 MurA-2 Glycan biosynthesis and Metabolism CDM SPD_1821 Pbp2A Glycan biosynthesis and Metabolism CDM SPD_1925 Pbp1B Glycan biosynthesis and Metabolism -1.04 SPD_2003 DltC Glycan biosynthesis and Metabolism 22.33* SPD_2005 DltA Glycan biosynthesis and Metabolism CDM SPD_2045 MreC Glycan biosynthesis and Metabolism CDM SPD_0378 FabM Lipid Metabolism -27.94 SPD_0380 FabH Lipid Metabolism -92.20 SPD_0381 AcpP Lipid Metabolism 17.90* SPD_0382 FabK Lipid Metabolism -73.11* SPD_0383 FabD Lipid Metabolism -126.34 SPD_0384 FabG Lipid Metabolism -206.44* SPD_0385 FabF Lipid Metabolism -63.62* SPD_0386 AccB Lipid Metabolism 11.51* SPD_0388 AccC Lipid Metabolism CDM SPD_0389 AccD Lipid Metabolism CDM SPD_0390 AccA Lipid Metabolism -40.49 SPD_0043 PlsX Lipid Metabolism CDM SPD_0961 Mgs Lipid Metabolism CDM SPD_1437 PlsC Lipid Metabolism CDM SPD_1918 GpsA Lipid Metabolism -312.62* SPD_2012 GlpO Lipid Metabolism CDM Metabolism of cofactors and SPD_1417 SPD_1417 CDM vitamins Metabolism of cofactors and SPD_0183 FolC CDM vitamins Metabolism of cofactors and SPD_0269 FolP CDM vitamins Metabolism of cofactors and SPD_0271 FolE 4.87* vitamins Metabolism of cofactors and SPD_0272 SulD CDM vitamins Metabolism of cofactors and SPD_0578 PabB 76.86* vitamins Metabolism of cofactors and SPD_1346 YceG -24.24* vitamins Metabolism of cofactors and SPD_0983 PpnK 5.74 vitamins Metabolism of cofactors and SPD_1250 NadE -18.10* vitamins Metabolism of cofactors and SPD_1251 PncB 1.54 vitamins Metabolism of cofactors and SPD_1557 NadD CDM vitamins
159
Appendix
Locus ID Protein General function I FCTHY-CDM I Metabolism of cofactors and SPD_1740 CinA CDM vitamins Metabolism of cofactors and SPD_0721 FolD CDM vitamins Metabolism of cofactors and SPD_1087 Fhs -11.01 vitamins Metabolism of cofactors and SPD_0762 SufC -506.74* vitamins Metabolism of cofactors and SPD_0763 SufD -2426.00 vitamins Metabolism of cofactors and SPD_0765 SPD_0765 -2.01 vitamins Metabolism of cofactors and SPD_0766 SufB -3983.83 vitamins Metabolism of cofactors and SPD_1088 CoaB CDM vitamins Metabolism of cofactors and SPD_1296 SPD_1296 CDM vitamins Metabolism of cofactors and SPD_1297 SPD_1297 -1.15 vitamins Metabolism of cofactors and SPD_0166 RibH CDM vitamins Metabolism of cofactors and SPD_0994 RibF CDM vitamins Metabolism of cofactors and SPD_0623 ThiM CDM vitamins Metabolism of cofactors and SPD_0632 ThiD CDM vitamins Metabolism of cofactors and SPD_0776 IscS CDM vitamins Metabolism of cofactors and SPD_0979 IscS CDM vitamins Metabolism of cofactors and SPD_1779 ThiN CDM vitamins Metabolism of cofactors and SPD_1423 PdxK CDM vitamins SPD_0811 SpeE Metabolism of other amino acids CDM SPD_0685 Gor Metabolism of other amino acids 5.88* SPD_0700 PepN Metabolism of other amino acids -199.94* SPD_1427 PhnA Metabolism of other amino acids 18.56* SPD_0764 SufS Metabolism of other amino acids -13.90* SPD_1393 TrxB Metabolism of other amino acids CDM Metabolism of terpenoids and SPD_0243 UppS CDM polyketides Metabolism of terpenoids and SPD_0347 MvaD CDM polyketides Metabolism of terpenoids and SPD_1127 IspD -23.27 polyketides Metabolism of terpenoids and SPD_1537 MvaS 16.01* polyketides SPD_0012 Hpt Nucleotide Metabolism -143.17*
160
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0024 PurA Nucleotide Metabolism 10.03* SPD_0033 PrsA Nucleotide Metabolism -12.86 SPD_0051 PurC Nucleotide Metabolism CDM SPD_0052 PurL Nucleotide Metabolism 3.56 SPD_0053 PurF Nucleotide Metabolism CDM SPD_0054 PurM Nucleotide Metabolism CDM SPD_0057 PurH Nucleotide Metabolism CDM SPD_0058 PurD Nucleotide Metabolism CDM SPD_0059 PurE Nucleotide Metabolism CDM SPD_0060 PurK Nucleotide Metabolism CDM SPD_0062 PurB Nucleotide Metabolism -131.11 SPD_0187 NrdD Nucleotide Metabolism -67.90 SPD_0214 Adk Nucleotide Metabolism 3.14 SPD_0726 PunA Nucleotide Metabolism 10.92 SPD_0730 DeoD Nucleotide Metabolism -211.45 SPD_0875 NudF Nucleotide Metabolism 1.09 SPD_1042 NrdE Nucleotide Metabolism -9.03 SPD_1043 NrdF Nucleotide Metabolism -74.44* SPD_1107 GuaC Nucleotide Metabolism -10.65 SPD_1274 GuaA Nucleotide Metabolism -362.85 SPD_1407 Apt Nucleotide Metabolism -52.67* SPD_1548 Gmk Nucleotide Metabolism CDM SPD_1628 Xpt Nucleotide Metabolism CDM SPD_1660 RdgB Nucleotide Metabolism CDM SPD_2055 GuaB Nucleotide Metabolism -9.44 SPD_0027 Dut Nucleotide Metabolism 25.33* SPD_0442 PyrG Nucleotide Metabolism -20.11 SPD_0581 ThyA Nucleotide Metabolism CDM SPD_0609 PyrE Nucleotide Metabolism 4.98* SPD_0648 ComEB Nucleotide Metabolism CDM SPD_0649 Upp Nucleotide Metabolism -2.94* SPD_0665 PyrDa Nucleotide Metabolism -46.43* SPD_0825 Tmk Nucleotide Metabolism CDM SPD_0834 PyrH Nucleotide Metabolism -224.53 SPD_0851 PyrK Nucleotide Metabolism THY SPD_0852 PyrDb Nucleotide Metabolism THY SPD_1030 PyrC Nucleotide Metabolism -2.19 SPD_1131 CarB Nucleotide Metabolism 1.34 SPD_1133 PyrB Nucleotide Metabolism -30.97 SPD_1134 PyrR Nucleotide Metabolism -21.14 SPD_1428 Cmk Nucleotide Metabolism 11.32* SPD_1757 Ndk Nucleotide Metabolism 28.70* SPD_0074 SPD_0074 Nucleotide Metabolism 6.49* SPD_0013 FtsH Protein fate -9.87*
161
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0173 SPD_0173 Protein fate CDM SPD_0177 PepP Protein fate 4.65 SPD_0245 Eep Protein fate -61.19 SPD_0258 PepS Protein fate -36.38* SPD_0261 PepC Protein fate -8.91 SPD_0308 ClpL Protein fate 164.35 SPD_0542 PepV Protein fate 2.32 SPD_0650 ClpP Protein fate -14.45* SPD_0717 ClpE Protein fate -20.64 SPD_0753 Pcp Protein fate -3.15 SPD_0866 PepF Protein fate CDM SPD_0894 PepT Protein fate -62.61* SPD_1138 HtpX Protein fate 2.76 SPD_1196 MecA Protein fate -2.06* SPD_1399 ClpX Protein fate -36.93* SPD_1418 PepQ Protein fate -15.35* SPD_1460 PepO Protein fate -942.94* SPD_1571 SPD_1571 Protein fate CDM SPD_1647 PepA Protein fate 14.92* SPD_1922 SPD_1922 Protein fate CDM SPD_2022 SPD_2022 Protein fate CDM SPD_2029 SPD_2029 Protein fate 1.48 SPD_0365 Tig Protein fate 17.51* SPD_0459 GrpE Protein fate 51.32* SPD_0460 DnaK Protein fate 21.28* SPD_0461 DnaJ Protein fate -3.85* SPD_0868 PrsA Protein fate -38.02* SPD_0876 SPD_0876 Protein fate CDM SPD_1709 GroEL Protein fate 3.25* SPD_1710 GroES Protein fate 35.87* SPD_2015 HslO Protein fate CDM SPD_0970 Map Protein fate -19.77* SPD_1024 LplA Protein fate -1.19 SPD_1285 Def Protein fate -9.02* SPD_1542 StkP Protein fate 4.21 SPD_1543 PhpP Protein fate -1.09 SPD_1680 BirA Protein fate -1.54 SPD_1774 PflA Protein fate CDM SPD_1837 SPD_1837 Protein fate 2.21 SPD_0115 SPD_0115 Protein fate CDM SPD_0213 SecY Protein fate CDM SPD_0367 LepB Protein fate CDM SPD_1076 SrtA Protein fate -4.25 SPD_1101 FtsY Protein fate CDM
162
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1142 Ffh Protein fate -432.68 SPD_1389 SPD_1389 Protein fate -170.37 SPD_1512 SecA Protein fate -846.41* SPD_1773 YidC Protein fate -3.31* SPD_1838 YajC Protein fate -5.74 SPD_1850 YidC Protein fate CDM SPD_1861 CglC Protein fate -34.99 SPD_1863 CglA Protein fate CDM SPD_0836 SPD_0836 Protein synthesis CDM SPD_0855 SPD_0855 Protein synthesis 1.75 SPD_0857 Era Protein synthesis CDM SPD_0863 SmpB Protein synthesis 1.99 SPD_0871 YbaK Protein synthesis 8.88* SPD_0964 SPD_0964 Protein synthesis CDM SPD_1019 SPD_1019 Protein synthesis CDM SPD_1381 Def-2 Protein synthesis CDM SPD_1398 SPD_1398 Protein synthesis CDM SPD_1519 SPD_1519 Protein synthesis -54.22 SPD_1559 SPD_1559 Protein synthesis -53.52 SPD_0083 RpsD Protein synthesis -28.73* SPD_0192 RpsJ Protein synthesis -1.29 SPD_0193 RplC Protein synthesis 3.02* SPD_0194 RplD Protein synthesis -11.47* SPD_0195 RplW Protein synthesis -1.02 SPD_0196 RplB Protein synthesis -2.74* SPD_0197 RpsS Protein synthesis 15.06* SPD_0198 RplV Protein synthesis 13.29* SPD_0199 RpsC Protein synthesis -44.88* SPD_0200 RplP Protein synthesis -2.62 SPD_0201 RpmC Protein synthesis 31.79* SPD_0202 RpsQ Protein synthesis 3.96* SPD_0203 RplN Protein synthesis -5.50* SPD_0204 RplX Protein synthesis 24.05* SPD_0205 RplE Protein synthesis -2.88* SPD_0206 RpsN Protein synthesis 48.72* SPD_0207 RpsH Protein synthesis 2.54* SPD_0208 RplF Protein synthesis 1.03 SPD_0209 RplR Protein synthesis 23.20* SPD_0210 RpsE Protein synthesis -4.30* SPD_0211 RpmD Protein synthesis 12.11* SPD_0212 RplO Protein synthesis 23.10* SPD_0216 RpsM Protein synthesis -1.19 SPD_0217 RpsK Protein synthesis -1.46 SPD_0219 RplQ Protein synthesis 8.76*
163
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0251 RpsL Protein synthesis 2.75 SPD_0252 RpsG Protein synthesis -3.41* SPD_0274 RplM Protein synthesis -1.08 SPD_0275 RpsI Protein synthesis 6.92* SPD_0401 RpmB Protein synthesis 47.94* SPD_0478 RimP Protein synthesis -16.48* SPD_0550 RplK Protein synthesis 7.54* SPD_0551 RplA Protein synthesis -9.26* SPD_0674 RpsP Protein synthesis 15.49* SPD_0732 RpsT Protein synthesis 83.51* SPD_0757 RpsA Protein synthesis -10.42* SPD_0848 RpmI Protein synthesis 17.67* SPD_0849 RplT Protein synthesis 8.26* SPD_0989 RplU Protein synthesis 14.60* SPD_0991 RpmA Protein synthesis 31.79* SPD_1148 RplS Protein synthesis -72.39* SPD_1154 RpmE2 Protein synthesis 14.64* SPD_1187 RplL Protein synthesis 22.04* SPD_1188 RplJ Protein synthesis -2.46* SPD_1245 RpsU Protein synthesis 52.20* SPD_1368 RpsR Protein synthesis 30.72* SPD_1370 RpsF Protein synthesis 2.74* SPD_1439 RpsO Protein synthesis -1.33 SPD_1573 PrmA Protein synthesis CDM SPD_1964 RpmG Protein synthesis 13.94* SPD_2031 RplI Protein synthesis 2.59* SPD_2042 RpsB Protein synthesis -2.50* SPD_0215 InfA Protein synthesis 14.79* SPD_0253 FusA Protein synthesis -46.39* SPD_0395 Efp Protein synthesis 2.25 SPD_0399 PrfC Protein synthesis -62.43 SPD_0482 InfB Protein synthesis -11.39* SPD_0593 SPD_0593 Protein synthesis -35.80* SPD_0658 PrfB Protein synthesis -21.12 SPD_0835 Frr Protein synthesis 14.69* SPD_0847 InfC Protein synthesis 19.93* SPD_0906 PrfA Protein synthesis -38.98* SPD_1318 Tuf Protein synthesis -8.52* SPD_1781 RsgA Protein synthesis CDM SPD_2033 YfiA Protein synthesis 1.37 SPD_2041 Tsf Protein synthesis -9.48* SPD_0238 LeuS Protein synthesis -113.64* SPD_0246 ProS Protein synthesis -1189.16* SPD_0375 SerS Protein synthesis -52.30*
164
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0396 GatB Protein synthesis -196.76* SPD_0397 GatA Protein synthesis -33.21* SPD_0398 GatC Protein synthesis 115.12* SPD_0494 ValS Protein synthesis -1227.83 SPD_0504 PheS Protein synthesis CDM SPD_0506 PheT Protein synthesis -176.97* SPD_0515 CysS Protein synthesis -7.66 SPD_0620 LysS Protein synthesis -42.01* SPD_0689 MetG Protein synthesis -3.74 SPD_1216 AlaS Protein synthesis -28.76* SPD_1304 GlyS Protein synthesis -138.80* SPD_1305 GlyQ Protein synthesis -88.50 SPD_1371 AsnC Protein synthesis -67.69 SPD_1444 ThrS Protein synthesis -70.04* SPD_1472 IleS Protein synthesis -128.90 SPD_1545 Fmt Protein synthesis CDM SPD_1896 GltX Protein synthesis -54.43* SPD_1905 ArgS Protein synthesis -117.80* SPD_1926 TyrS Protein synthesis -289.17* SPD_1941 AspS Protein synthesis -114.66 SPD_1950 HisS Protein synthesis -169.86 SPD_2056 TrpS Protein synthesis -58.73 SPD_0127 MnmA Protein synthesis CDM SPD_0129 GidA Protein synthesis CDM SPD_0134 YdiC Protein synthesis 173.82 SPD_0260 RsuA-1 Protein synthesis 31.85 SPD_0477 TrmB Protein synthesis CDM SPD_0679 TrmD Protein synthesis CDM SPD_0777 ThiI Protein synthesis CDM SPD_0820 RluD Protein synthesis 1.19 SPD_0833 Gid Protein synthesis CDM SPD_0902 TrmE Protein synthesis CDM SPD_0914 RumA-1 Protein synthesis CDM SPD_1140 GidB Protein synthesis CDM SPD_1247 QueA Protein synthesis CDM SPD_1286 SPD_1286 Protein synthesis CDM SPD_1386 PcnB Protein synthesis CDM SPD_1544 Sun Protein synthesis CDM SPD_1572 SPD_1572 Protein synthesis CDM SPD_1654 RluB Protein synthesis 3.00 SPD_1713 PheT_2 Protein synthesis -8.78* SPD_1782 KsgA Protein synthesis CDM SPD_1868 Tgt Protein synthesis -13.68 SPD_0178 SPD_0178 Regulatory functions 2.91
165
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0458 HrcA Regulatory functions CDM SPD_0467 BlpS Regulatory functions CDM SPD_0999 SPD_0999 Regulatory functions CDM SPD_1291 SPD_1291 Regulatory functions -2.52* SPD_1412 CodY Regulatory functions CDM SPD_1518 SPD_1518 Regulatory functions CDM SPD_1523 SPD_1523 Regulatory functions CDM SPD_1725 SPD_1725 Regulatory functions 6.38* SPD_1776 PurR Regulatory functions CDM SPD_1797 CcpA Regulatory functions -40.49* SPD_1904 ArgR Regulatory functions CDM SPD_2032 Pde1 Signal transduction CDM SPD_0049 ComA Signal transduction CDM SPD_0050 ComB Signal transduction CDM SPD_0081 SaeR Signal transduction CDM SPD_0082 SaeS Signal transduction CDM SPD_0468 BlpR Signal transduction CDM SPD_0469 BlpH Signal transduction CDM SPD_0701 CiaR Signal transduction 1.80 SPD_1083 VicX Signal transduction CDM SPD_1084 VicK Signal transduction CDM SPD_1085 VicR Signal transduction -94.16 SPD_1908 PhoB1 Signal transduction CDM SPD_2002 DltD Signal transduction CDM SPD_2063 ComE Signal transduction CDM SPD_2064 ComD Signal transduction CDM SPD_2068 DegP Signal transduction 2.36* SPD_0130 Rnj Transcription -44.39 SPD_0512 Pnp Transcription -53.30 SPD_0533 Rnj Transcription -76.32 SPD_0862 Rnr Transcription -5.99 SPD_1397 SPD_1397 Transcription 2.25 SPD_1413 DeaD Transcription CDM SPD_1549 Rny Transcription CDM SPD_0007 SPD_0007 Transcription 54.60 SPD_0218 RpoA Transcription -5.70* SPD_0441 RpoE Transcription 14.78* SPD_1547 RpoZ Transcription 29.61* SPD_1758 RpoC Transcription -437.47* SPD_1759 RpoB Transcription -289.74* SPD_0483 RbfA Transcription -2.77* SPD_0586 Rnz Transcription -2.51 SPD_0678 RimM Transcription CDM SPD_1105 Rnc Transcription CDM
166
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0064 SPD_0064 Transcription -152.02 SPD_0301 RegR Transcription CDM SPD_0344 CbpJ_2 Transcription CDM SPD_0379 MarR_1 Transcription CDM SPD_0393 NusB Transcription CDM SPD_0447 GlnR Transcription 16.96* SPD_0479 NusA Transcription -63.94* SPD_0588 CpsY Transcription CDM SPD_0668 YutD Transcription -2.13 SPD_0958 RpoD Transcription -15.60* SPD_0976 Rex Transcription CDM SPD_1044 LacR2 Transcription -34.88* SPD_1262 SoxS Transcription CDM SPD_1345 GreA Transcription 12.95* SPD_1450 SPD_1450 Transcription CDM SPD_1487 SPD_1487 Transcription CDM SPD_1524 SPD_1524 Transcription -3.20 SPD_1525 SPD_1525 Transcription CDM SPD_1586 SPD_1586 Transcription CDM SPD_1605 SPD_1605 Transcription CDM SPD_1645 SPD_1645 Transcription CDM SPD_1741 SPD_1741 Transcription CDM SPD_1819 NusG Transcription -5.96* SPD_1938 MalR Transcription CDM SPD_2000 AdcR Transcription CDM SPD_2020 SPD_2020 Transcription CDM SPD_0150 SPD_0150 Transport and binding proteins -59.38* SPD_0153 MetN Transport and binding proteins 5.69* SPD_0411 LysY Transport and binding proteins 6.90* SPD_0412 LysX1 Transport and binding proteins CDM SPD_0522 Vex2 Transport and binding proteins CDM SPD_0523 Vex3 Transport and binding proteins CDM SPD_0540 TcyJ Transport and binding proteins CDM SPD_0652 LivJ Transport and binding proteins -16.41 SPD_0655 LivG Transport and binding proteins CDM SPD_0656 LivF Transport and binding proteins CDM SPD_0719 SPD_0719 Transport and binding proteins CDM SPD_0720 GlnQ Transport and binding proteins CDM SPD_0954 SPD_0954 Transport and binding proteins CDM SPD_1098 SPD_1098 Transport and binding proteins -83.01* SPD_1099 SPD_1099 Transport and binding proteins -81.61 SPD_1167 GsiA Transport and binding proteins CDM SPD_1168 AppC Transport and binding proteins 41.47 SPD_1170 GsiB Transport and binding proteins CDM
167
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1221 PotA Transport and binding proteins CDM SPD_1289 TcyC Transport and binding proteins -38.74 SPD_1290 SPD_1290 Transport and binding proteins CDM SPD_1328 AatB Transport and binding proteins -4.29 SPD_1329 SPD_1329 Transport and binding proteins CDM SPD_1330 SPD_1330 Transport and binding proteins CDM SPD_1667 AmiF Transport and binding proteins -7.34* SPD_1668 AmiE Transport and binding proteins CDM SPD_1669 AmiD Transport and binding proteins CDM SPD_1670 AmiC Transport and binding proteins CDM SPD_1671 AmiA Transport and binding proteins -22.11* SPD_1227 SPD_1227 Transport and binding proteins -242.26* SPD_1228 PstB Transport and binding proteins -165.67 SPD_1229 PstB Transport and binding proteins -26.10 SPD_1230 PstA Transport and binding proteins CDM SPD_1232 SPD_1232 Transport and binding proteins -2.04* SPD_0262 ManZ Transport and binding proteins -40.13* SPD_0263 ManM Transport and binding proteins CDM SPD_0264 ManL Transport and binding proteins -1.66 SPD_0560;SPD GatB Transport and binding proteins 11.21* _1057 SPD_0661 Exp5 Transport and binding proteins -2.66 SPD_0740 SPD_0740 Transport and binding proteins CDM SPD_0773 FruAB Transport and binding proteins 2.90* SPD_0956 SPD_0956 Transport and binding proteins CDM SPD_1039 PtsI Transport and binding proteins -20.47* SPD_1040 PtsH Transport and binding proteins 10.18* SPD_1244 HprK Transport and binding proteins CDM SPD_1414 OxlT Transport and binding proteins CDM SPD_1495 SPD_1495 Transport and binding proteins -4.09 SPD_1664 TreP Transport and binding proteins THY SPD_1832 CelA Transport and binding proteins CDM SPD_1934 MalX Transport and binding proteins 1.37 SPD_1960 UlaB Transport and binding proteins THY SPD_0077 TrkA Transport and binding proteins CDM SPD_0175 CorA Transport and binding proteins CDM SPD_0430 TrkA Transport and binding proteins 2.35 SPD_0635 SPD_0635 Transport and binding proteins CDM SPD_0915 PiaA Transport and binding proteins -11.29* SPD_0918 PiaD Transport and binding proteins CDM SPD_1021 SPD_1021 Transport and binding proteins CDM SPD_1383 PacL Transport and binding proteins CDM SPD_1384 SPD_1384 Transport and binding proteins CDM SPD_1436 SPD_1436 Transport and binding proteins 2.28 SPD_1461 PsaB Transport and binding proteins -220.96
168
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1527 NatB Transport and binding proteins CDM SPD_1642 ProWX Transport and binding proteins CDM SPD_1643 ProV Transport and binding proteins -149.90 SPD_1997 AdcA Transport and binding proteins CDM SPD_1999 AdcC Transport and binding proteins CDM SPD_0267 PbuO Transport and binding proteins CDM SPD_0090 LplA Transport and binding proteins 84.86 SPD_0151 MetQ Transport and binding proteins -2.32 SPD_0434 EcfA1 Transport and binding proteins CDM SPD_0687 MacB_1 Transport and binding proteins CDM SPD_0888 Lmb Transport and binding proteins -1782.91* SPD_1192 ABCC-BAC Transport and binding proteins CDM SPD_1267 EcfA1 Transport and binding proteins CDM SPD_1357 AliB Transport and binding proteins -20.97* SPD_1409 MsmX Transport and binding proteins -56.49 SPD_1463 MtsA (PsaA) Transport and binding proteins -16.10* SPD_1514 SPD_1514 Transport and binding proteins CDM SPD_1528 SPD_1528 Transport and binding proteins CDM SPD_1608 AfuC Transport and binding proteins CDM SPD_1621 SPD_1621 Transport and binding proteins 2.38 SPD_1622 SPD_1622 Transport and binding proteins CDM SPD_1721 SPD_1721 Transport and binding proteins CDM SPD_1902 SPD_1902 Transport and binding proteins -4.27 SPD_2025 SPD_2025 Transport and binding proteins CDM SPD_2057 SPD_2057 Transport and binding proteins -292.66 SPD_0148 SPD_0148 Transport and binding proteins CDM SPD_0554 SPD_0554 Transport and binding proteins -60.89 SPD_0686 SPD_0686 Transport and binding proteins -136.46* SPD_0998 SPD_0998 Transport and binding proteins CDM SPD_1191 SPD_1191 Transport and binding proteins CDM SPD_1263 SPD_1263 Transport and binding proteins -8.96* SPD_1264 SPD_1264 Transport and binding proteins 2.43 SPD_1385 SPD_1385 Transport and binding proteins CDM SPD_1784 SPD_1784 Transport and binding proteins 89.30 SPD_0266 SPD_0266 Unknown function CDM SPD_0645 SPD_0645 Unknown function CDM SPD_0816 SPD_0816 Unknown function CDM SPD_0828 SPD_0828 Unknown function CDM SPD_0926 SPD_0926 Unknown function CDM SPD_1034 SPD_1034 Unknown function CDM SPD_1102 SPD_1102 Unknown function 1.38 SPD_1103 SPD_1103 Unknown function CDM SPD_1146 SPD_1146 Unknown function CDM SPD_1367 SPD_1367 Unknown function -1.94
169
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1556 SPD_1556 Unknown function 7.94* SPD_1659 SPD_1659 Unknown function CDM SPD_1743 SPD_1743 Unknown function 1.28 SPD_1788 SPD_1788 Unknown function 4.96* SPD_1794 SPD_1794 Unknown function CDM SPD_0004 YchF Unknown function -101.49 SPD_0136 SPD_0136 Unknown function CDM SPD_0584 HflX Unknown function CDM SPD_0707 SPD_0707 Unknown function -10.03* SPD_0722 SPD_0722 Unknown function CDM SPD_0987 SPD_0987 Unknown function 31.82* SPD_0996 SPD_0996 Unknown function CDM SPD_1060 LepA Unknown function CDM SPD_1388 SPD_1388 Unknown function CDM SPD_1434 SPD_1434 Unknown function CDM SPD_1554 SPD_1554 Unknown function -2.02 SPD_2016 SPD_2016 Unknown function CDM SPD_0039 SPD_0039 Unknown function -10.69* SPD_0084 SPD_0084 Unknown function -5.50 SPD_0091 SPD_0091 Unknown function CDM SPD_0093 SPD_0093 Unknown function 2.48 SPD_0114 SPD_0114 Unknown function CDM SPD_0131 SPD_0131 Unknown function 3.96* SPD_0145 SPD_0145 Unknown function CDM SPD_0159 SPD_0159 Unknown function CDM SPD_0160 SPD_0160 Unknown function -156.33 SPD_0161 SPD_0161 Unknown function CDM SPD_0174 SPD_0174 Unknown function -19.75* SPD_0179 SPD_0179 Unknown function -51.01 SPD_0180 SPD_0180 Unknown function 4.65* SPD_0184 SPD_0184 Unknown function -3.43 SPD_0189 SPD_0189 Unknown function CDM SPD_0220 SPD_0220 Unknown function -1.45 SPD_0249 SPD_0249 Unknown function CDM SPD_0310 SPD_0310 Unknown function -28.25 SPD_0341 RlmL Unknown function CDM SPD_0373 SPD_0373 Unknown function -1418.00 SPD_0394 SPD_0394 Unknown function -4.20* SPD_0402 Asp Unknown function -4.17* SPD_0410 SPD_0410 Unknown function -10.32* SPD_0414 SPD_0414 Unknown function CDM SPD_0462 SPD_0462 Unknown function 3.00 SPD_0463 SPD_0463 Unknown function 1.32 SPD_0464 SPD_0464 Unknown function CDM
170
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0466 BlpT Unknown function CDM SPD_0476 CotS Unknown function CDM SPD_0480 YlxR Unknown function 22.51* SPD_0489 SPD_0489 Unknown function 7.53* SPD_0514 SPD_0514 Unknown function CDM SPD_0534 EstA Unknown function CDM SPD_0541 SPD_0541 Unknown function -1.56 SPD_0547 SPD_0547 Unknown function 16.15* SPD_0568 SPD_0568 Unknown function CDM SPD_0579 SPD_0579 Unknown function CDM SPD_0582 SPD_0582 Unknown function 9.41* SPD_0585 SPD_0585 Unknown function CDM SPD_0587 SPD_0587 Unknown function CDM SPD_0590 MoeZ Unknown function -63.62 SPD_0622 TenA_2 Unknown function CDM SPD_0637 SPD_0637 Unknown function CDM SPD_0646 SPD_0646 Unknown function -6.09* SPD_0651 SPD_0651 Unknown function CDM SPD_0671 SPD_0671 Unknown function -1.11 SPD_0675 SPD_0675 Unknown function 7.24* SPD_0680 NrdD_2 Unknown function CDM SPD_0681 SPD_0681 Unknown function CDM SPD_0683 SPD_0683 Unknown function 2.80 SPD_0688 SPD_0688 Unknown function CDM SPD_0693 SPD_0693 Unknown function CDM SPD_0704 Spr0710 Unknown function CDM SPD_0714 SPD_0714 Unknown function -2.36 SPD_0718 YkuJ Unknown function 2.62 SPD_0739 SPD_0739 Unknown function 1.08 SPD_0741 SPD_0741 Unknown function CDM SPD_0751 SPD_0751 Unknown function CDM SPD_0754 SPD_0754 Unknown function 9.68* SPD_0759 SPD_0759 Unknown function CDM SPD_0775 SPD_0775 Unknown function THY SPD_0792 SPD_0792 Unknown function 7.86* SPD_0799 SPD_0799 Unknown function 25.13 SPD_0802 YhgF Unknown function -1.55 SPD_0818 SPD_0818 Unknown function CDM SPD_0829 SPD_0829 Unknown function CDM SPD_0837 SPD_0837 Unknown function THY SPD_0838 SPD_0838 Unknown function CDM SPD_0867 SPD_0867 Unknown function CDM SPD_0873 SPD_0873 Unknown function CDM SPD_0878 SPD_0878 Unknown function 58.03*
171
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_0886 SPD_0886 Unknown function CDM SPD_0911 SPD_0911 Unknown function CDM SPD_0913 SPD_0913 Unknown function 10.28* SPD_0959 SPD_0959 Unknown function CDM SPD_0969 SPD_0969 Unknown function CDM SPD_0974 SPD_0974 Unknown function -10.91 SPD_0978 SPD_0978 Unknown function CDM SPD_0981 SPD_0981 Unknown function CDM SPD_0990 SPD_0990 Unknown function 1.82 SPD_1061 SPD_1061 Unknown function -1.61 SPD_1063 SPD_1063 Unknown function -3.81* SPD_1072 SPD_1072 Unknown function -2.52 SPD_1079 SPD_1079 Unknown function CDM SPD_1080 SPD_1080 Unknown function CDM SPD_1092 SPD_1092 Unknown function CDM SPD_1109 SPD_1109 Unknown function CDM SPD_1121 SPD_1121 Unknown function CDM SPD_1123 LicC Unknown function -52.04 SPD_1125 Pck Unknown function -199.34 SPD_1130 LicD2 Unknown function CDM SPD_1136 SPD_1136 Unknown function -5.04* SPD_1139 LemA Unknown function -17.29 SPD_1145 SPD_1145 Unknown function CDM SPD_1153 SPD_1153 Unknown function CDM SPD_1165 SPD_1165 Unknown function CDM SPD_1166 SPD_1166 Unknown function 7.84 SPD_1190 MtaD Unknown function CDM SPD_1197 SPD_1197 Unknown function -99.09 SPD_1198 SPD_1198 Unknown function CDM SPD_1201 LicD3 Unknown function CDM SPD_1202 Psr Unknown function -8.2* SPD_1206 SPD_1206 Unknown function 5.02* SPD_1226 SPD_1226 Unknown function -91.47 SPD_1241 SPD_1241 Unknown function 6.24* SPD_1293 SPD_1293 Unknown function CDM SPD_1294 SPD_1294 Unknown function 3.50 SPD_1301 Azr_2 Unknown function CDM SPD_1302 SPD_1302 Unknown function CDM SPD_1303 SPD_1303 Unknown function 11.54* SPD_1308 SPD_1308 Unknown function CDM SPD_1311 MocA Unknown function -27.94 SPD_1312 CshA_1 Unknown function CDM SPD_1320 SPD_1320 Unknown function -26.76* SPD_1331 SPD_1331 Unknown function CDM
172
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1332 SPD_1332 Unknown function CDM SPD_1350 SPD_1350 Unknown function 337.31 SPD_1375 SPD_1375 Unknown function CDM SPD_1378 SPD_1378 Unknown function CDM SPD_1380 SPD_1380 Unknown function -607.29 SPD_1382 SPD_1382 Unknown function CDM SPD_1391 SPD_1391 Unknown function -23.64 SPD_1392 SPD_1392 Unknown function CDM SPD_1395 SPD_1395 Unknown function CDM SPD_1396 SPD_1396 Unknown function CDM SPD_1408 RebM Unknown function CDM SPD_1411 SPD_1411 Unknown function -20.95* SPD_1429 RpsO_2 Unknown function -25.46 SPD_1435 SPD_1435 Unknown function CDM SPD_1447 SPD_1447 Unknown function CDM SPD_1448 SPD_1448 Unknown function CDM SPD_1449 SPD_1449 Unknown function 1.41 SPD_1516 SPD_1516 Unknown function CDM SPD_1517 SPD_1517 Unknown function CDM SPD_1520 SPD_1520 Unknown function 2.60 SPD_1521 DnaI Unknown function CDM SPD_1522 SPD_1522 Unknown function CDM SPD_1551 SPD_1551 Unknown function 103.54 SPD_1558 SPD_1558 Unknown function -1.65 SPD_1566 SPD_1566 Unknown function 3.86* SPD_1576 SPD_1576 Unknown function CDM SPD_1580 SPD_1580 Unknown function CDM SPD_1588 SPD_1588 Unknown function CDM SPD_1590 SPD_1590 Unknown function -2.01 SPD_1591 SPD_1591 Unknown function 3.35 SPD_1595 SPD_1595 Unknown function CDM SPD_1646 SPD_1646 Unknown function THY SPD_1658 SPD_1658 Unknown function CDM SPD_1662 SPD_1662 Unknown function -3.91* SPD_1706 SPD_1706 Unknown function -1.20 SPD_1714 SPD_1714 Unknown function 13.41* SPD_1717 SPD_1717 Unknown function CDM SPD_1727 SPD_1727 Unknown function -23.76* SPD_1728 SPD_1728 Unknown function -85.11 SPD_1729 SPD_1729 Unknown function -180.73 SPD_1761 SPD_1761 Unknown function CDM SPD_1762 SPD_1762 Unknown function CDM SPD_1765 YlbL Unknown function -39.65* SPD_1771 SPD_1771 Unknown function CDM
173
Appendix
Locus ID Protein General function I FCTHY-CDM I SPD_1777 Cbf1 Unknown function CDM SPD_1793 SPD_1793 Unknown function CDM SPD_1836 SPD_1836 Unknown function CDM SPD_1849 SPD_1849 Unknown function -2.67 SPD_1867 OatA Unknown function -1.29 SPD_1874 SPD_1874 Unknown function CDM SPD_1875 SPD_1875 Unknown function CDM SPD_1895 SPD_1895 Unknown function THY SPD_1899 SPD_1899 Unknown function CDM SPD_1928 SPD_1928 Unknown function 2.79 SPD_1948 SPD_1948 Unknown function THY SPD_1962 SPD_1962 Unknown function CDM SPD_1979 SPD_1979 Unknown function 2.17 SPD_1984 SPD_1984 Unknown function -195.39* SPD_2018 SPD_2018 Unknown function CDM SPD_2028 CbpD Unknown function CDM SPD_2043 SPD_2043 Unknown function -8.52 SPD_2050 SPD_2050 Unknown function CDM Xenobiotics biodegradation and SPD_1238 Hydrolase CDM Metabolism Xenobiotics biodegradation and SPD_0903 XylH 28.59* Metabolism
Voronoi treemaps, protein annotation table and detailed proteome data tables are to be found on extra CD-ROM. In Table 8-4 the file names with the respective file description are listed.
Table 8-4 List of detailed Voronoi treemaps, protein annotation table and evaluated proteome data tables
File name Description A1 Overview of detailed Voronoi treemaps with figure captions A2 Treemap layout main role (1st level) A3 Treemap layout subrole (2nd level) A4 Treemap layout operon number (3rd level) A5 Treemap layout protein acronym (4th level) B1 Annotation table B2 Proteome dataset media comparison CDM vs. THY under control condition B3 Proteome dataset CDM iron limitation experiment B4 Proteome dataset THY iron limitation experiment
174