Investigation on the utilization of beneficial microbes in modulating the gut microbiota and metabolic profiles in ageing
by Ravichandra Vemuri B. Pharmacy (Osmania University, India) Registered Pharmacist (India) Postgraduate Certificate in Clinical Research (AICTE, India)
Submitted in fulfillment of the requirements for the Doctor of Philosophy
School of Health Sciences, College of Health and Medicine University of Tasmania February 2019
Declarations
Declaration of Originality
I hereby declare that this thesis entitled “Investigation on the utilization of beneficial
microbes in modulating the gut microbiota and metabolic profiles in ageing” contains no
material which has been accepted for a degree or diploma by the University of Tasmania or any other institution, except by way of background information and duly acknowledged in the thesis, and to the best of my knowledge and belief no material has previously been published or was written by another person except where due reference is made in the text of the thesis, nor does the thesis contain any material that infringes Copyright Act 1968.
Ravichandra Vemuri
Date: 7th February 2019
Authority of Access
This thesis may be made available for loan and limited copying in accordance with the
Copyright Act 1968.
Ravichandra Vemuri
Date: 7th February 2019
i Statement Regarding Published Work Contained in Thesis
The publishers of the papers comprising Chapters 2, 3, 4, 5 and 6 hold the copyright for that content and access to the material should be sought from the respective journals. The remaining non-published content of the thesis may be made available for loan and limited copying and communication in accordance with Copyright Act 1968.
Statement of Ethical Conduct
The research associated with this thesis abides by the international and Australian codes on human and animal experimentation, the guidelines by the Australian Government's Office of the Gene Technology Regulator and the rulings of the Safety, Ethics and Institutional
Biosafety Committees of the University. All animal experiments conducted in this thesis were carried out under the approval of the University of Tasmania’s Animal Ethics
Committee; animal ethics approval number A0015840.
Publications and Statement of Co-Authorship
The following people and institutions contributed to the publication or work undertaken as part of this thesis:
Ravichandra Vemuri (candidate), University of Tasmania, Australia
1. Rohit Gundamaraju, University of Tasmania, Australia
2. Tanvi Shinde, University of Tasmania, Australia
3. Agampodi Promoda, University of Tasmania, Australia
4. Waheedha Basheer, University of Tasmania, Australia
5. Madhur Shastri, University of Tasmania, Australia
6. Shakti Dhar Shukla, University of Newcastle, Australia
7. Krishna Kumar Kalpurath, Australia
ii 8. Shankar Esaki Muthu, Central University of Tamil Nadu, India
9. Benjamin Southam, University of Tasmania
10. Shakuntala Gondalia, Swinburne University of Technology, Australia
11. Avinash Karpe, CSIRO, Australia
12. David Beale, CSIRO, Australia
13. Christopher Martoni, UAS labs, USA
14. Kiran Ahuja (Co-supervisor), University of Tasmania, Australia
15. Madeleine Ball (Co-supervisor), University of Tasmania, Australia
16. Stephen Tristram (Co-supervisor), University of Tasmania, Australia
17. Rajaraman Eri (Primary supervisory), University of Tasmania, Australia
Paper 1: Gut microbial changes, interactions, and their implications on human lifecycle: an
ageing perspective
• Located in Chapter 2
• The candidate was the primary author, contributed 80% to the concept, analysis and
interpretation of this manuscript.
• Authors 14, 15, 16, and 17 contributed significantly to the manuscript revisions.
• Candidate developed the initial draft with inputs of authors 1, 5, 6, 7, and 8.
Paper 2: Therapeutic interventions for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or fecal microbiota transplant?
• Located in Chapter 3
• The candidate was the primary author, contributed 90% to the concept, analysis and
interpretation of this manuscript.
iii • Authors 1, 2 and 17 contributed search strategies, all authors contributing to the drafts of
the manuscript.
Paper 3: A human origin probiotic Lactobacillus acidophilus DDS-1 exhibits superior in vitro probiotic efficacy in comparison to plant or dairy origin probiotics
• Located in Chapter 4
• The candidate was the primary author, contributed 75% to the concept, analysis, and
interpretation of this manuscript.
• Candidate performed all the experiments with contributions of Authors 1,2,3, and 5.
• The candidate conducted all data analysis.
• Authors 14, 15, 16, and 17 contributed significantly to the manuscript revisions, all
authors contributing to the drafts of the manuscript.
Paper 4: Lactobacillus acidophilus DDS-1 modulates the gut microbiota and improves
metabolic profiles in aging mice
• Located in Chapter 5
• The candidate was the primary author, contributed 85% to the concept, analysis, and
interpretation of this manuscript.
• The candidate conducted all data analysis.
• Authors 1, 2, 13, and 17 contributed significantly to the manuscript revisions, all authors
contributing to the drafts of the manuscript.
• Authors 10, 11, and 12 contributed to the metabolomic experimental analysis.
iv Paper 5: Distinct patterns of microbiota, improvements in immunological and short-chain
fatty acid profiles with Lactobacillus acidophilus DDS-1 supplementation in aging mice.
• Located in Chapter 6
• The candidate was the primary author, contributed 80% to the concept, analysis, and
interpretation of this manuscript.
• The candidate conducted all data analysis.
• Authors 14, 15, 16, and 17 contributed significantly to the manuscript revisions, all
authors contributing to the drafts of the manuscript.
• Authors 10, 11, and 12 contributed to the metabolomic experimental analysis.
• Candidate along with Author 9 contributed in histological analysis.
We the undersigned agree with the above stated “proportion of work undertaken” for each of
the above published or submitted peer-reviewed manuscripts contributing to this thesis.
Signed: ______
Rajarama Eri Nuala Bryne Supervisor Head of School School of Health Sciences School of Health Sciences University of Tasmania University of Tasmania
Date: 12-Feb-19 13-02-19
v List of Publications
1. Ravichandra Vemuri, Gundamaraju R, Shastri MD, Shukla SD, Kalpurath K, Ball
M, Tristram S, Shankar EM, Ahuja K, Eri R. Gut Microbial Changes, Interactions,
and Their Implications on Human Lifecycle: An Ageing Perspective. BioMed
Research International. 2018;2018.
2. Ravichandra Vemuri, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions
for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or faecal
microbiota transplantation? Beneficial Microbes. 2017 26;8(2):179-92.
3. Ravichandra Vemuri, Shinde T, Shastri MD, Perera AP, Tristram S, Martoni CJ,
Gundamaraju R, Ahuja KD, Ball M, Eri R. A human origin strain Lactobacillus
acidophilus DDS-1 exhibits superior in vitro probiotic efficacy in comparison to plant
or dairy origin probiotics. International Journal of Medical Sciences. 2018
1;15(9):840-8.
4. Ravichandra Vemuri, Shinde T, Gundamaraju R, Gondalia S, Karpe A, Beale D,
Martoni C, Eri R. Lactobacillus acidophilus dds-1 modulates the gut microbiota and
improves metabolic profiles in aging mice. Nutrients. 2018 Sep 6;10(9):1255.
5. Ravichandra Vemuri, Gundamaraju R, Eri R. Role of lactic acid probiotic bacteria
in IBD. Current Pharmaceutical Design. 2017 May 1;23(16):2352-5.
6. Ravichandra Vemuri., Gundamaraju, R., Shinde, T, Perera AP, Waheeda Basheer,
Gondalia, S., Karpe, A., Beale, D, Benjamin Southam, Stephen T Ahuja KDK
Madeleine B, Martoni CJ, Eri, R. Distinct patterns in microbiota, improvements in
immunological and short-chain fatty acid profiles with Lactobacillus acidophilus
DDS-1 supplementation in aging mice. Scientific Reports. 2019. (under review)
7. Ravichandra Vemuri, Sylvia KE, Klein SL, Forster SC, Plebanski M, Eri R,
Flanagan KL. The microgenderome revealed: sex differences in bidirectional
vi interactions between the microbiota, hormones, immunity and disease susceptibility.
In Seminars in immunopathology 2018 Oct 8 (pp. 1-11). Springer Berlin Heidelberg.
Co-author Publications
1. Gundamaraju R, Ravichandra Vemuri, Chong WC, Geraghty DP, Eri R. Cell stress
signaling cascades regulating cell fate. Current Pharmaceutical Design. 2018,11.
PMID: 29992877.
2. Gundamaraju, R., Ravichandra Vemuri., Chong, W., Myers, S., Norouzi, S., Shastri,
M. and Eri, R., 2018. The interplay between Endoplasmic Reticular Stress and
Survivin in Colonic Epithelial Cells. Cells, 7(10), p.171.
3. Shinde, T., Ravichandra Vemuri., Shastri, M.D., Perera, A.P., Tristram, S., Stanley,
R. and Eri, R., 2019. Probiotic Bacillus coagulans MTCC 5856 spores exhibit
excellent in-vitro functional efficacy in simulated gastric survival, mucosal adhesion,
and immunomodulation. Journal of Functional Foods 52, pp.100-108.
4. Gundamaraju, R., Ravichandra Vemuri., Eri, R., Scotti, L. and Scotti, M., 2018.
Biochemical Mechanisms and Therapeutic Strategies in Gastrointestinal and
Metabolic Disorders. Current Pharmaceutical Design, 24(27), pp.3153-3154.
5. Eri R, Ravichandra Vemuri, Gundamaraju R. Novel Interventional Targets for
Gastrointestinal and Metabolic Disorders. Current Pharmaceutical Design. 2017 May
1;23(16):2287-8.
6. Aditya, R., Kiran, A. R., Varma, D. S., Ravichandra Vemuri., & Gundamaraju, R.
(2017). A Review on SIRtuins in Diabetes. Current Pharmaceutical Design, 2018,11.
7. Chong, W. C., Gundamaraju, R., Ravichandra Vemuri., Scotti, M. T., & Scotti, L.
(2017). Momordicacharantia: A New Strategic Vision to Improve the therapy of
vii Endoplasmic Reticulum Stress. Current pharmaceutical design. 2017 1;23(16):2333-
43.
8. Sonia Shastri, Ravichandra Vemuri, Nuri Gueven, Madhur D. Shastri, Rajaraman
Eri (2017). Molecular mechanisms of intestinal inflammation leading to colorectal
cancer. AIMS Biophysics 4, 1, 152-177.
9. Gundamaraju, R., Ravichandra Vemuri., Eri, R., Ishiki, H. M., Coy-Barrera, E.,
Yarla, N.S., & Scotti, L. (2016). Metabolomics as a functional tool in screening
gastrointestinal diseases: Where are we in high throughput screening? Combinatorial
chemistry & high throughput screening. 2017,1;20(3):247-54.
10. Gundamaraju, R., Ravichandra Vemuri., & Eri, R. (2016). Overriding elements in
colon cancer progression: Some less known facts. Gastro Open J, 2(1), 4-8.
Conference Presentations
• September 2016: University of Tasmania Graduate Conference (Hobart, Australia),
Poster: Efficacy of probiotics in the gut health of the elderly population.
• June 2017: American Society for Microbiology Conference, Microbe (New Orleans
USA), Poster & Oral: Comparative efficacy of Human versus Plant versus Dairy origin
probiotics for the elderly.
• November 2017: 10th Asia Pacific Conference on Clinical Nutrition Conference
(Adelaide, Australia), Oral: Potential efficacy of probiotics from different origins.
• September 2018: Gastroenterological Society of Australia Conference (Adelaide,
Australia). Oral: Modulation of gut microbiota and metabolic profile after Lactobacillus
acidophilus DDS-1 through aging.
viii Funding Support, Awards, and Grants
• Tasmanian Graduate Research Scholarship, 2015- 2019
• American Society of Microbiology Student and Postdoctoral Award, USA, 2017
• University of Tasmania International Travel Grant to attend the American Society of
Microbiology, Microbe, USA, 2017
• UTAS School of Health Sciences Travel funding to attend the American Society of
Microbiology, Microbe, USA, 2017
• People’s Choice Award, Northern Research Hub, for research poster, UTAS, Launceston,
2016
ix Acknowledgments
I would like to thank my supervisors A/Prof. Raj Eri, A/Prof. Stephen Tristram, Dr. Kiran
Ahuja and Prof. Madeleine Ball for their invaluable guidance, knowledge, and support.
A/Prof. Raj was a pillar of support and gave me the freedom to develop the research project.
Dr. Steve helped hone my microbiology skills and manuscript review. Dr. Kiran and Prof.
Madeleine for timely support during project meetings, data analysis and guiding through manuscript revisions.
My special thanks to Dr. Christopher Martoni from United Agricultural Science Labs for providing the probiotic strains and giving me the opportunity to pursue my dream. I appreciate the encouragement and unwavering support to me always.
I would like to thank Dr Shankar Esakimutthu and Dr Ambati Ranga Rao for mentoring and providing timely support during my time in Malaysia and the process of transition to
Australia. Similarly, receiving a scholarship during PhD was a big dream come true for me.
It wouldn’t have been possible without Zach Duhig from Graduate Research Office, thank you very much for mentoring me throughout the scholarship application.
I sincerely thank all the administrative, research and technical staff at the School of Health
Sciences, UAS labs, and CSIRO teams. Thanks to Paul Scowen and staff at the University of
Tasmania’s Cambridge Farm facility for all their assistance in breeding animals and for their support throughout this study.
Many Thanks to Professor Dominic Geraghty and Dr Ha Hoang and the Graduate Research team for all their assistance and support. Tanvi Shinde, Rohit Gundamaraju, Waheedha
Basheer, and Promoda Perera, were kind enough to lend their support during in vivo study, data analysis, and thesis completion.
x A very special thank you to my parents, V.R.R Sarma and Indira Devi who supported me not
only throughout my PhD journey but also in every aspect of life. I wouldn’t have dared to
quit a thriving career in a clinical industry and take up research. Thank you very much for
having faith in my decisions in life. While every other family member was asking “when will
you finish your PhD?”, my parents were like “please take your time and finish it properly”. I
cannot thank you more for all the motivation.
Last, but certainly not least, my brother, Satya Kumar Vemuri: the mentor, supporter of all time. The pursuit of PhD would not even have been considered without his drive for excellence, giant brain and relentless belief in me. Satya has been my rock for almost 30 years, and his unwavering support has pushed me to get the most out of every day. This PhD journey has been great, but more than anything I am looking forward to turning my attention back to my family with the weight of this endeavor lifted. From the bottom of my heart, thank you to every special individual who has given part of themselves to mentor me along this journey.
xi General Abstract
Ageing is characterized by an inevitable, time-dependent decline in physiological functions and adaptive capacity. Specifically, altered gastrointestinal (GI) physiology can affect the nutrient absorption as well as intestinal microbiota. The intestinal microbiota is now considered as a key player in human health and well-being. Apart from host genetics, diet, antibiotic use, sex hormones, and circadian rhythm, age appears to be a vital factor in altering the GI microbial composition. Moreover, the distribution of the microbiota differs according to the location in the GI tract. Thus, imbalances in microbiota or dysbiosis during ageing may not be limited to fecal microbiota and extend to the other parts of the GI tract, especially, the cecum and colon.
The age-related imbalances in microbiota may lead to changes in metabolic phenotype and may influence the development of the mucosal immune system. Given the potential for microbiota to change with age and its influence on host metabolism and immune system, modulation of microbial composition offers an opportunity to promote digestive health.
Probiotics are natural, safe and beneficial modulators of the gut microbial composition and metabolic phenotype. Dietary supplementation of probiotics in all age groups has yielded promising outcomes. However, to be classified as a probiotic, a dietary compound must survive GI transit, adhere to the intestinal epithelium and demonstrate immune-modulatory effects. Therefore, the overall aim of the study was to understand microbial changes in ageing mice to provide dietary mechanisms utilizing probiotics to promote gut health. However, to confer any health benefits, probiotics need to survive through the hostile environment of the digestion process in sufficient numbers (at least 107 colony forming units (CFU)), tolerating acids, bile, and pancreatic digestive enzymes, and finally, adhere to the intestinal epithelium in the colon. The objectives of this study were (i) to evaluate the in in vitro efficacy of
xii different probiotics strains, (ii) to examine whether the role of Lactobacillus acidophilus
DDS-1 can modulate the host metabolic phenotype under the condition of age-affected gut microbial shifts in young and ageing C57BL/6 mice, (iii) to investigate the effect of L.
acidophilus DDS-1 supplementation on cecal and mucosal-associated microbiota, short-chain
fatty acids (SCFA) and immunological profiles in young and ageing C57BL/6J mice.
This thesis consists of seven chapters. Chapter 1 is an overall introduction to the rationale of the research, hypothesis, and aims of the research project. Chapter 2 is a review and critique of the literature and summarises the role of microbiota on the human lifecycle. Chapter 3 is a critical and systematic review of potential therapeutic interventions such as antibiotics, probiotics and fecal microbiota transplantation for dysbiosis in the ageing population.
Chapter 4 provides information on in vitro probiotic efficacy parameters including digestion simulation, adhesion to colonic cells and immune-modulation property, which enabled to select the right probiotic strain, L. acidophilus DDS-1. Chapter 5 and chapter 6 details research experiments and data generated that characterize clinical parameters, histopathology, immunological profiling, integrated metabolomics and microbiota analysis with and without
DDS-1 supplementation in young and ageing C57BL/6 mice. Chapter 5 present the effect of
L. acidophilus DDS-1 on fecal microbiota and its associated metabolic phenotype. Chapter 6 describes the research experiments and data generated that detail the effect of L. acidophilus
DDS-1 on cecal and colonic-mucosa associated microbiota, SCFA production, immune response in young and ageing C57BL/6 mice. The last Chapter 7 is a comprehensive summary of all the results obtained from the research objectives and includes potential directions for future work arising from this research project.
Probiotic strains utilized in in vitro study include L. acidophilus DDS-1, Bifidobacterium animalis ssp. lactis UABla-12, L. plantarum UALp-05 and Streptococcus thermophilus
xiii UASt-09. Screening assays such as in in vitro digestion simulation, adhesion, cell viability,
and cytokine release were used to evaluate the probiotic potential. All strains showed good
resistance in the digestion simulation process, especially DDS-1 and UALp-05, which survived up to a range of 107 to 108 CFU/mL from an initial concentration of 109 CFU/mL.
Two human colonic mucus-secreting cells, HT-29 and LS174T, were used to assess the
adhesion capacity, cytotoxicity/viability, and cytokine quantification. All strains exhibited
remarkable adhesion capacity. No significant cellular cytotoxicity or loss in cell viability was observed. DDS-1 and UALp-05 significantly upregulated anti-inflammatory IL-10 and downregulated pro-inflammatory TNF-α cytokine production. All the strains were able to downregulate IL-8 cytokine levels. Taken together, DDS-1 demonstrated superior survival rates, excellent adhesion capacity and strong immunomodulatory effect under different experimental conditions.
The in vivo study was carried out to investigate whether four weeks of DDS-1 supplementation can modulate the host metabolic phenotype under the condition of age- affected gut microbial shifts in young and ageing C57BL/6J mice. Collected fecal samples were analyzed using 16S rRNA gene sequencing for identifying gut microbiota and untargeted gas chromatography-mass spectrometry metabolomics analysis. Gut microbial shifts were observed in the control groups (young and ageing) leading to an alteration in metabolism. Principal coordinate analysis of microbiota indicated distinct separation in both the DDS-1-treated groups. L. acidophilus DDS-1 increased the relative abundances of beneficial bacteria, such as Akkermansia muciniphila and Lactobacillus spp. and reduced the relative levels of opportunistic bacteria such as Proteobacteria spp. Metabolic pathway analysis (MetPA) identified ten key pathways involving amino acid metabolism, protein synthesis and metabolism, carbohydrate metabolism and butanoate metabolism. The key metabolic pathways identified by MetPA play a vital role in probiotic-induced gut
xiv
microbiota-associated metabolic changes. To the best of our knowledge, this is the first
detailed study that demonstrates an integrated pattern recognition approach to understand the
changes in the ageing gut by exploring the metabolic network with and without probiotic supplementation.
Furthermore, we observed microbial shifts in the cecum and colonic mucosal samples in the control groups (young and ageing), with higher inter-individual variation in the mucosal- associated microbiota, leading to an alteration in SCFA levels and immune responses. DDS-1 treatment increased the abundances of beneficial bacteria such as Akkermansia spp. and
Lactobacillus spp. more effectively in cecal samples than in mucosal samples. DDS-1 also enhanced the levels of butyrate while downregulating the production of inflammatory cytokines (IL-6, IL-1β, IL-1α, MCP-1, MIP-1α, MIP-1β, IL-12, and IFN-γ) in serum and colonic explants.
To sum up, I have generated new data and knowledge on the role of microbiota in health and disease in the ageing murine model. Our results demonstrated a major shift in microbial composition from young to ageing (which may lead to dysbiosis), observed not only in fecal microbiota but also in the cecum and colonic-mucosa-associated microbiota. Our findings suggest that modulation of microbiota by L. acidophilus DDS-1 results in improvements in immunological and metabolic profiles in ageing mice. Our investigations highlight the crucial role of beneficial microbes/probiotics in determining and modulating the metabolic profile in the healthy-ageing gut.
Similar approaches could be used to identify and target specific health-promoting metabolic pathways in young and ageing populations. This study highlights the importance of evaluating the efficacy of probiotics in in vitro as well as in clinically relevant experimental models before progressing to human clinical trials. Finally, L. acidophilus DDS-1 showed
xv promising probiotic efficacy in in vitro as well as in in vivo and could be a right candidate for clinical studies in ageing individuals.
xvi Table of Contents
Declaration of Originality ______i Authority of Access ______i Statement Regarding Published Work Contained in Thesis ______ii Statement of Ethical Conduct ______ii Publications and Statement of Co-Authorship ______ii List of Publications ______vi Co-author Publications ______vii Conference Presentations ______viii Funding Support, Awards and Grants ______ix Acknowledgments ______x General Abstract ______xii List of Figures ______xx List of Tables ______xxiii List of Supplementary Materials ______xxiv List of Common Abbreviations ______xxvi Chapter 1: Introduction ______1 1.1 Microbial shifts in ageing ______2 1.2 Shifts in gut microbiota throughout GI tract in ageing ______2 1.3 Probiotics ______4 1.4 Summary ______5 1.5 Research Aims______5 1.6 Thesis organization ______6 Chapter 2: Gut Microbial Changes, Interactions, and Their Implications on Human Lifecycle: An Ageing Perspective ______9 2.1 Abstract ______10 2. 2 Introduction ______11 2.3 Microbiota and Ageing ______12 2.4 Specific gut microbial changes in the older adults ______16 2.5 Metagenomic and metabolic changes associated with gut microbiota in ageing _____ 22 2.6 Circadian rhythm, microbiome and metabolism in ageing ______23 2.7. Role of gut microbiota in immune function in the older adults ______24 2.8 Conclusion ______35 Chapter 3: Therapeutic interventions for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or fecal microbiota transplantation? ______36
xvii 3.1 Abstract ______37 3.2 Introduction ______38 3.3 Antibiotics in the elderly: broad or narrow spectrum? ______40 3.4 Probiotics, Prebiotics & Synbiotics ______45 3.5 Intestinal barrier with age ______48 3.6 Fecal microbiota transplantation ______53 3.7 Conclusion ______57 Chapter 4: A human origin strain Lactobacillus acidophilus DDS-1 exhibits superior in vitro probiotic efficacy in comparison to plant or dairy origin probiotics______58 4.1 Abstract ______59 4.2 Introduction ______60 4.3 Materials and Methods ______61 4.4 Results ______67 4.5 Discussion ______76 4.6 Conclusion ______80 Chapter 5: Lactobacillus acidophilus DDS-1 modulates the gut microbiota and improves metabolic profiles in ageing mice______81 5.1 Abstract ______82 5.2 Introduction ______83 5.3 Methods ______84 5.4 Results ______89 5.5 Discussion ______117 5.6 Conclusion ______124 Supplementary Material ______125 Supplementary Figures ______125 Supplementary Tables ______140 Chapter 6: Distinct patterns in microbiota, improvements in immunological and short- chain fatty acid profiles with Lactobacillus acidophilus DDS-1 supplementation in ageing mice ______148 6.1 Abstract ______149 6.2 Introduction ______150 6.3 Methods and Material ______152 6.4 Results ______159 6.5 Discussion ______177 Supplementary Tables ______182 Chapter 7: Thesis discussion, future directions, and conclusion ______184
xviii
7.1 Thesis discussion ______184 7.2 Limitations of the study______189 7.3 Future directions ______191 7.4 Conclusion and clinical relevance ______192 References ______193 Appendices ______236
xix
List of Figures
Figure 2.1: Overview of the development of microbiota ______19 Figure 2.2: Interplay between the immune system and gut microbiota in homeostasis, tolerance, and inflammation.______26
Figure 3.1: Mechanism of Antibiotic resistance ______44 Figure 3.2: Probiotic mechanism of action ______52
Figure 4.1: Graphical representation of chemical compositions, concentrations, digestive enzymes and pH used in in vitro simulated digestion process ______63 Figure 4.2: Survival of each bacterial strain in the mouth, stomach and intestinal simulated in vitro digestion process ______68 Figure 4.3: Adhesion capacity of each bacterial strain to HT-29 (A) & LS 174T cells (B) after 4h incubation period ______69 Figure 4.4: Effect of probiotics on cellular cytotoxicity/viability of intestinal epithelial cells ______72 Figure 4.5: Detection of cytokines released in the supernatants of HT-29 cells (control) after three conditions using Bio-Plex® 200 system ______75
Figure 5.1: Effect of DDS-1 supplementation on body weights among the four groups (YC) young control group, (YP) young probiotic group, (AC) ageing control group, (AP) ageing probiotic group ______90 Figure 5.2: The gut microbiota changes observed in the young control group (YC), young probiotic group (YP), ageing control group (AC), ageing probiotic group (AP) differentiated by PCoA (A) and UPMGA dendrogram (B). The gut microbiota composition profiles at phylum (C) and genus levels (D) in control and probiotic-treated group revealed by 16S rRNA gene sequencing (each color represents bacterial phylum and/or genus). ______91 Figure 5.3: Comparative analysis with Linear Discriminant Analysis (LDA) Effect Size (LEfSe) scoring plot using the Kruskal-Wallis rank sum test (p = 0.01 and log LDA threshold cut-off value = 1.0). ______95
xx
Figure 5.4: A. PCA plot of feces collected from young control (YC, violet color) and young treatment groups (YP, blue color) showing divergence ______100 Figure 5.5: Significance analysis of microarray (SAM) plot (A) in positive mode (Delta score = 0.5, metabolites highlighted in green are significant). Significant changes in metabolites are expressed as a heat map (B) in the young group. ______104 Figure 5.6: A. PCA plot of feces collected from ageing control (AC, red color) and ageing probiotic groups (AP, green color) showing divergence (B) Bi-plot showing compounds responsible for divergence (C) 2D-PLS-DA plot showing spatial division among groups (D) Key compounds separating AC and AP based on VIP ______106 Figure 5.7: SAM plot (A) (Delta score = 0.5, metabolites highlighted in green are significant). Significant changes in metabolites are expressed as a heat map (B) in ageing groups ____ 110 Figure 5.8: PCA plot (A) and PLS-DA plot (B) showing spatial division among all four groups. ______112 Figure 5.9: Potential pathways for the YP and AP, after DDS-1 supplementation, in fecal extracts identified using MetPA pathway analysis ______115
Figure 6.1: L. acidophilus DDS-1 viability in the diet during 24-hr of storage (A). The effect of DDS-1 supplementation on body weights (B), colon length (C), colon weight (D) and spleen weights (E) ______160 Figure 6.2: Representation of distal colon sections stained with hematoxylin and eosin (A) and Alcian blue (AB)-PAS staining (B) for sulfated mucins among the four groups. (C) The AB-PAS staining mean optical density of goblet cells ______161 Figure 6.3: Effect of L. acidophilus DDS-1 supplementation on serum cytokine levels __ 164 Figure 6.4: Effect of L. acidophilus DDS-1 supplementation on colon explants of the proximal (A) and distal colon (B) ______165 Figure 6.5: Relative abundances (%) of cecal and mucosal-associated microbiota at phylum (A) genus (B) and species level (C) ______168 Figure 6.6: Cecal and mucosal-associated microbiota changes (A) Non-metric multidimensional scaling (NMDS). Principal coordinate analysis (PCoA) plot showing the differentiation between ageing (B) and young (C) mouse groups for both Cecal (C) and Mucosal (M) samples. ______170
xxi
Figure 6.7: Biomarker analysis with Linear Discriminant Analysis (LDA) Effect Size (LEfSe) scoring plot using Kruskal-Wallis rank sum test ______173 Figure 6.8: Short chain fatty acid (SCFA) distribution observed in YC, YP, AC and AP. The concentrations of butyrate, propionate, acetate, and valeric acid in the cecal (A) and mucosal (B) samples were measured by GC-MS analysis ______176
xxii
List of Tables
Table 2.1: Changes in the gut microbiota in the healthy ageing (natural, non-antibiotic treated) ______20 Table 2. 2: Clinical studies related to variations in immunity and gut microbiota in the elderly ______33
Table 3.1: Overall gut microbiota changes with antibiotic/drug therapy throughout ageing (elderly) ______42 Table 3.2: Clinical studies on the effect of pro/pre/synbiotics in the elderly ______49 Table 3.3: Studies on fecal microbiota transplantation in GI & non-GI disease treatment __ 56
xxiii
List of Supplementary Materials
Supplementary Figures Figure S5.1: Neighbour-net showcasing the three different clusters of all the samples using Brady-Curtis Ecological Index and Ward clustering ______125 Figure S5.2: Heat map showing the Correlation analysis of fecal microbiota in ageing and young groups at phylum level using Pearson r distance method ______126 Figure S5.3: 2D O-PLS-DA plot with showing clear separation between YC and YP groups ______127 Figure S5.4: PCA plot of feces collected from YC and AC (A) and YP and AP (C) groups showing divergence. Key compounds separating YC and AC groups (B) based on variable importance in projection (VIP) score plot (D) in PLS-DA analysis ______129 Figure S5.5: A. Bi-plot showing compounds responsible for divergence in young and ageing groups B. Key compounds separating young and ageing groups based on variable importance in projection (VIP) score plot in PLS-DA analysis ______130 Figure S5.6: Significant changes in metabolites are expressed as a heat map in all four groups ______132 Figure S5.7: Overall metabolite cluster in YC, YP, AC and AP groups using Pearson’s R correlation with ward distancing method ______133 Figure S5.8: Age-wise and Group-wise correlation analysis showing the cluster of YP and AP and differences in YC and AC groups using Pearson’s correlation using ward distancing method______134 Figure S5.9: Potential pathways YP from Figure 8A (A) Valine, leucine and isoleucine biosynthesis, (B) Glycine, serine and threonine metabolism, (C) Aminoacyl-tRNA biosynthesis. Compounds in red are significantly involved in the pathways ______135 Figure S5.10: Potential pathways for ageing probiotic (AP) from Figure 5.8B ______136 Figure S5.11: KEGGS pathways of Alanine, aspartate and glutamate metabolism (A) and Galactose Metabolism (B). Compounds highlighted in red and green are directly involved in respective pathways. ______137 Figure S5.12: KEGGS pathways of Butanoate metabolism (A) and Nitrogen Metabolism (B). Compounds highlighted in red box and green are directly involved in respective pathways. ______138 Figure S5.13: KEGGS pathways of Aminoacyl-tRNA biosynthesis. Compounds highlighted in red are directly involved in respective pathways ______139
xxiv
Supplementary Tables
Table S5.1: Most significant compounds identified in YP/YC groups ______140 Table S5.2: Most significant compounds identified by OPLS-DA and SAM analysis in AP group ______141 Table S5.3: Results from ingenuity pathway analysis with MetPA in YC/YP ______143 Table S5.4: Results from metabolic pathway analysis using MetPA in AC/AP groups ___ 144 TableS5.5: Metabolites identified in fecal samples of young and ageing mice______
145
Table S6. 1: List of volatile fatty acids (VFA) identified in YC, YP, AC and AP ______182 Table S6.2: List of VFA identified in YC, YP, AC and AP ______183
xxv
List of Common Abbreviations
Alcian Blue Periodic Acid-Schiff Stain AB-PAS
Clostridium Difficile Associated Diarrhea CDAD
Colony Forming Units CFU
Gas Chromatography-Mass Spectrometry GC-MS
Gastrointestinal GI
Immunoglobulin A IgA
Interleukins IL
Lactic Acid Bacteria LAB
Linear Discriminant Analysis LDA
Linear Discriminant Analysis Effect Size LEfSe
Metabolic Pathway Analysis MetPA
Metabolite Set Enrichment Analysis MSEA
Monocyte Chemoattractant Protein MCP
Monocyte Chemoattractant Protein MIP
Non-Metric Multidimensional Scaling NMDS
Orthogonal Partial Least Squares-Discriminant Analysis (PLS-DA) O-PLS-DA
Partial Least Squares-Discriminant Analysis PLS-DA
Principal Component Analysis PCA
Principal Coordinate Analysis PCoA
Short-Chain Fatty Acids SCFA
Tumor Necrosis Factor TNF
xxvi
Chapter 1: Introduction
The overall life expectancy is increasing worldwide, leading to a rise in the ageing population
[306]. The global proportion of ageing individuals above 60 years old is estimated to increase
from 11-12% currently, to more than 25% by 2050 [306, 307, 308]. Although life expectancy
has generally increased with advances in public health and medicine, there is little evidence
of any concurrent increase in the overall health of ageing individuals [250, [306].
Ageing is associated with the loss of physiological functions that leads to the loss of physical
and mental faculties and deterioration of health [139, 163]. In particularly an altered gastrointestinal (GI) physiology can affect the amount and types of nutrients absorbed and absorbed, as well as intestinal microbiota [5]. Therefore, it is necessary to understand the dietary and nutrition-related changes in ageing individuals to provide dietary mechanisms to promote healthy ageing. [250, 252].
The intestinal microbiota, which is the collection of microbes found in the intestine, may be a promising target as it links to a myriad of host functions, is affected by environmental factors, and perturbations have been reported in the ageing population [3, 5, 6]. The microbiota is mostly affected by antibiotic use, host genetics, circadian rhythm, sex hormones, diet, and environmental factors [4, 5]. Changes in the microbiota have been implicated in diseases such as inflammatory bowel disease, obesity, type 2 diabetes, and cancer [305]. The mechanism of microbiota change with age is not totally understood because the importance of senescence of the gut and the altered physical environment therein has not yet been systematically investigated [252].
1
1.1 Microbial shifts in ageing leading to dysbiosis
Recently, human and animal studies have reported microbial shifts (from commensal to pathogenic.) in the ageing gut under healthy conditions [6,1,235]. From these studies, it is known that Bacteroidetes and Firmicutes which dominate in younger individuals are being reduced with age [18, 251]. For example, the distal gut of healthy adults is dominated by phylum Bacteroidetes [6]. Moreover, among ageing individuals, these proportions largely differ when compared to younger individuals. Proportions of bacteria belonging to
Proteobacteria phyla were found to be increased in older adults [251, 278]. Besides, the
abundance of beneficial bacteria, such as Akkermansia spp. and Lactobacillus spp. were
reduced in the ageing population [278]. Such drastic microbial shifts in ageing could lead to
increased intestinal inflammation and changes in host metabolism including lactate
metabolism and short-chain fatty acids (SCFA) production [281, 284]. These changes may
alter the levels of metabolites involved in the metabolism of amino acids, carbohydrates,
nucleotides, lipid, leading from normal to putrefactive metabolism [304]. While few human
and animal studies have pointed out the likely associations between microbial shifts (leading
to dysbiosis) and metabolism and interactions with the host, the consequences of such
alterations are not yet understood [305]. Subsequently, it is known that each member of
microbial community carries different molecular function and undertake different ecological
roles in the intestine; hence, we can expect proportionate changes in the metabolic potential
of the microbes and the way they interact with the host.
1.2 Shifts in gut microbiota throughout GI tract in ageing
The human GI tract is a complex system that starts from the oral cavity, continues through
the stomach and intestines, and finally ends at the anus [6]. The density and composition of
the microbiome change along the GI tract, with major populations being selected by the
functions performed at the various locations. The bacterial load was estimated to increase
2
from stomach to colon [1] with bacterial numbers in stomach and duodenum estimated to be around 101 to 103 cells/g, followed by jejunum and ileum ranging from 104 to 107 cells/g, and
1011 to 1012 cells/g in the colon [320]. Lower bacterial numbers are found in the upper end of the GI tract, stomach, and small intestine, in which pH is low and the transit time is short [16].
The highest biodiversity (richness and evenness) of bacteria is in the colon, in which the cell turnover rate is low, redox potential is low, and the transit time is prolonged [320, 322]. In the colon, the glycoproteins on mucosal layers foster the seed population of bacteria in the lower gut, thus allowing bacteria to colonize in more significant numbers [1]. Moreover,
these differences in the microbial composition could be due to continuous downward
peristalsis and valves present in the GI tract which prevents the retrograde interactions.
Accumulating evidence suggests that the imbalances in microbiota or dysbiosis during ageing
may not be limited to faecal microbiota and extend to the other parts of the GI tract,
especially, the cecum and colon (due to higher concentrations of microbes) [252]. A better
understanding of the composition and dynamics of this microbial population will be crucial in
developing methods such as dietary changes with defined populations of bacteria to maintain
or restore a healthy state [251].
Also, there has been increasing evidence that the gut microbiome has an impact on the host
immune system [10]. The structure of the gut microbiome appears to be altered by the ageing
process with an associated increased inflammatory status known as inflamm-ageing that may
be predictive of disease [65]. This inflammatory state could make the host more sensitive to
intestinal microbes, or vice versa, as changes in the gut microbiota composition are related to
the progression of diseases and frailty in the elderly population [6]. As the microbiome and
its role change as an individual age, understanding the linkages between ageing, metabolism,
immune system and the microbiome will be beneficial. Given the potential for gut microbiota
3
to change with age and its influence on host metabolism, modulation of gut microbial
composition offers an opportunity to promote digestive health among the ageing populations.
1.3 Probiotics
Probiotics such as the lactic acid bacteria (LAB) strains, especially Lactobacillus spp. are
considered natural, safe and beneficial modulators of the gut microbial composition and
metabolic phenotype [189]. Dietary supplementation of probiotics in all age groups has
yielded promising outcomes. Probiotics are defined as ‘live microbes which, when
administered in adequate amounts, confers a health benefit on the host’ [188]. The use of
probiotics began to show clinical evidence of their impact on human health. The advent of probiotic treatments appears to be a promising approach to reverse diseases linked to
microbiota dysbiosis [19]. Indeed, clinical and experimental studies have shown that
supplementation of Lactobacillus strains improves the overall health in all age groups [190].
Specifically, in experimental studies, probiotics are able to promote longevity [20, 21].
Although most of the studies reported the beneficial role of probiotics, the efficacy of
probiotics is strain and dose-dependent and that not all probiotics function against all
disorders and diseases. To be classified as a genuine probiotic, it must satisfy the following
three criteria: (a) resistance to gastric acidity, hydrolysis by mammalian enzymes and GI
absorption (b) adhesion to the colonic epithelium, and (c) immunomodulatory effects
associated with health and well-being [252]. Tolerance to the high concentration of bile salts
is an essential parameter to microbial survival in the intestinal tract. So far, research has
mainly focused on strains sensitivity toward low pH, proteolytic enzymes and bile salts. The
ability of probiotics to adhere to mucosal surfaces of the intestine and the subsequent long or
short-term colonization has been the most commonly encountered criteria for the selection of
probiotic strains [190]. Thus, it is beneficial to investigate the in vitro efficacy of probiotic
strains.
4
1.4 Summary
The gut microbiota plays a vital role in maintaining the overall health of individuals via interacting with the immune system and host metabolism. Distribution of the microbiota differs according to the location in the GI tract. Thus, dysbiosis during ageing may not be
limited to faecal microbiota and may extend to the entire GI tract, leading to alterations in metabolic profile and immune responses. Therefore, the overall aim of the study was to understand microbial and metabolic changes in ageing mice to provide dietary mechanisms
utilizing probiotics to promote gut health. A combination of omics-based approaches,
involving 16S rRNA gene sequencing and untargeted gas chromatography-mass spectrometry
(GC-MS) based metabolomics, were utilized to provide a comprehensive understanding of host-microbial and its associated metabolic changes with and without probiotic supplementation. Dietary supplementation with LAB probiotics such as Lactobacillus spp.
may enhance the beneficial microbial composition of cecal and mucosal-associated
microbiota in ageing.
1.5 Research Aims
1. To evaluate the in vitro efficacy of probiotic by subjecting them to the digestion
simulation model, adhesion to colonic cells and immunomodulatory properties in
different experimental conditions.
2. To examine whether L. acidophilus DDS-1 can modulate the host metabolic phenotype
under the condition of age-affected gut microbial shifts in young and ageing C57BL/6
mice.
3. To investigate the effect of L. acidophilus DDS-1 supplementation on cecal and mucosal-
associated microbiota, SCFA and immunological profiles in young and ageing C57BL/6J
mice.
5
1.6 Thesis organization
This doctoral thesis contains a series of three studies which are aimed at understanding microbial changes (fecal, cecal and mucosal), as well as metabolism and inflammation, followed by examining and investigating whether probiotic supplementation can modulate microbial, metabolic and immunological profiles in young and ageing mice.
Chapter 1: Contains a general introduction of the themes comprising the thesis and expresses the rationale, aims, study designs, and the general layout of the thesis.
Chapter 2: Contains the first part of the literature review which deals with gut microbial changes, interactions, and their implications on the human lifecycle.
An edited version of this manuscript was published as:
Vemuri R, Gundamaraju R, Shastri MD, Shukla SD, Kalpurath K, Ball M, Tristram S,
Shankar EM, Ahuja K, Eri R. Gut Microbial Changes, Interactions, and Their Implications on
Human Lifecycle: An Ageing Perspective. BioMed research international. 2018;2018.
Weblink: https://www.hindawi.com/journals/bmri/2018/4178607/
Chapter 3: Contains the second and therapeutics part of literature review which deals with comparisons between probiotics, antibiotics and fecal microbiota transplantations in gut dysbiosis of the elderly.
An edited version of this manuscript was published as:
Vemuri RC, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or faecal microbiota transplantation?
Beneficial microbes. 2017 Apr 26;8(2):179-92.
Weblink: https://www.wageningenacademic.com/doi/abs/10.3920/BM2016.0115
6
Chapter 4: The first study utilized four different probiotics and investigated the in vitro
efficacy of each probiotic by subjecting them to the digestion simulation model, adhesion to
colonic cells and immunomodulatory properties in different experimental conditions
(research aim 1). All the probiotic strains were subjected to in vitro digestion simulation
which includes step-wise human digestive process, which is followed by adherence to
intestinal colonic cells to assess adherence and cytotoxicity. Furthermore, each probiotic
strain was tested for its possible immune-modulatory effects in three different experimental
conditions.
An edited version of this manuscript was published as:
Vemuri R, Shinde T, Shastri MD, Perera AP, Tristram S, Martoni CJ, Gundamaraju R, Ahuja
KD, Ball M, Eri R. A human origin strain Lactobacillus acidophilus DDS-1 exhibits superior
in vitro probiotic efficacy in comparison to plant or dairy origin probiotics. International
Journal of Medical Sciences. 2018 Jan 1;15(9):840-8.
Weblink: http://www.medsci.org/v15p0840.htm
Chapter 5: The second study was to examine whether L. acidophilus DDS-1 can modulate the host metabolic phenotype under the condition of age-affected gut microbial shifts in young and ageing C57BL/6 mice (research aim 2). A combination of omics-based approaches, involving 16S rRNA gene sequencing and untargeted GC-MS based metabolomics, were utilized to provide a comprehensive understanding of host-microbial and metabolic changes with and without probiotic DDS-1 supplementation.
An edited version of this manuscript was published as:
Vemuri R, Shinde T, Gundamaraju R, Gondalia S, Karpe A, Beale D, Martoni C, Eri R.
Lactobacillus acidophilus dds-1 modulates the gut microbiota and improves metabolic profiles in aging mice. Nutrients. 2018 Sep 6;10(9):1255.
7
Weblink: https://www.mdpi.com/2072-6643/10/9/1255
Chapter 6: Final study was to investigate the effect of L. acidophilus DDS-1
supplementation on cecal and mucosal-associated microbiota, SCFA and immunological
profiles in young and ageing C57BL/6J mice (research aim 3). A combination of 16S rRNA
gene sequencing, untargeted metabolomics of volatile fatty acids, and cytokine measurement
with bioplex-based immunological analysis were used to provide a comprehensive
understanding of the potential beneficial effects of DDS-1 on the intestinal microbial changes
in ageing mice.
An edited version of this manuscript is under review as:
Ravichandra Vemuri., Gundamaraju, R., Shinde, T, Perera AP, Waheeda Basheer, Gondalia,
S., Karpe, A., Beale, D, Benjamin Southam, Stephen T Ahuja KDK Madeleine B, Martoni CJ,
Eri, R. Distinct patterns in microbiota, improvements in immunological and Short-chain fatty
acid profiles with Lactobacillus acidophilus DDS-1 supplementation in aging mice. Scientific
Reports. 2019.
Chapter 7: This section comprises of the overall discussion of all the studies included in the thesis. In addition, this section includes future recommendations for researchers based on this thesis work as well as potential limitations, proposed future studies and concluding comments.
Some repetition between chapters is the result of these chapters being written as scientific papers for publication in various peer-reviewed journals.
8
Chapter 2: Gut Microbial Changes, Interactions, and Their Implications on Human Lifecycle: An Ageing Perspective
An original version of this chapter has been published in Biomed Research International
Journal as an integrative review of the literature and appears in literature as:
Vemuri Ravichandra, Gundamaraju R, Shastri MD, Shukla SD, Kalpurath K, Ball M,
Tristram S, Shankar EM, Ahuja K, Eri R. Gut Microbial Changes, Interactions, and Their
Implications on Human Lifecycle: An Ageing Perspective. BioMed research international.
2018;2018.
Clarivate Analytics/Thompson Reuters journal impact factor: 2.58
SJR journal ranking: Q1, 0.94
Citations: 7
9
2.1 Abstract
Gut microbiota is established during birth and evolves with age, mostly maintaining the commensal relationship with the host. A growing body of clinical evidence suggests an intricate relationship between the gut microbiota and the immune system. With ageing, the
gut microbiota develops significant imbalances in the major phyla such as the anaerobic
Firmicutes and Bacteroidetes as well as a diverse range of facultative organisms, resulting in
impaired immune responses. Antimicrobial therapy is commonly used for the treatment of
infections; however, this may also result in the loss of normal gut flora. Advanced age,
antibiotic use, underlying diseases, infections, hormonal differences, circadian rhythm and
malnutrition, either alone or in combination, contribute to the problem. This non-beneficial
gastrointestinal modulation may be reversed by judicious and controlled use of antibiotics,
and the appropriate use of prebiotics and probiotics. In certain persistent, recurrent settings,
the option of fecal microbiota transplantation can be explored. The aim of the current review
is to focus on the establishment and alteration of gut microbiota, with ageing. The review also
discusses the potential role of gut microbiota in regulating the immune system, together with its function in the healthy and diseased state.
10
2.2 Introduction
The human body contains a diverse range of bacterial species, with lesser representation from
viral and eukaryotic microbes, that have been referred to as a “microbial bank”. As the human
gut harbors most of the microbes, it can be considered as a “microbial organ”. All the
microbes in the gut are collectively known as “gut microbiota” and genomes associated with them represent the “gut microbiome”. Previously, the bacterial count in a healthy adult was estimated to be 1014 to 1015 cells. However, recently revised [1], the bacterial load in a healthy adult (using volume and mass as variables) was estimated to be approximately 3x1013
bacterial cells in a 70 kg adult. The microbiota is not a homogenous population of
microorganisms; rather, it is comprised of an intricate range of microbial communities that
interact with each other as well as with the host, in a way that impacts the health of the host
[2]. The Bacteroidetes and Firmicutes are the major bacterial phyla, with sub-groups such as
Fusobacteria, Cyanobacteria, Proteobacteria, Verrucomicrobia, Actinobacteria, and a few
others.
A healthy intestinal tract is relatively stable throughout adulthood, but with the ageing process, perturbations occur with exogenous factors such as antibiotics use and diet and endogenous factors like cellular stress. Ageing and related complications are leading public
health concerns worldwide [3]. Ageing is accompanied by significant physiological changes
such as alteration in the gut microbial composition (dysbiosis), immune responses and
metabolism which may lead to various gastrointestinal (GI) tract related inflammatory
conditions, and auto-immune disorders [4, 5]. The usual threshold age for defining older
adults or elderly is above 63-76 years, and around this age, the gut microbiota loses its
relative stability [6, 7]. Any compositional differences with ageing have a direct effect on the
intestinal motility and digestion [8]. Fermentation processes in the colonic gut are altered
adversely with variations in the microbiota. This affects the homeostasis in the gut, leading to
11
immunosenescence (decline in immune responses) and inflamm-ageing, i.e., low-grade inflammatory response [9, 10, 11]. In the present review, we focus on the establishment and alteration of gut microbiota with ageing. Moreover, the review also discusses the potential role of microbiota in regulating the immune system and its function in a healthy and diseased state.
2.3 Microbiota and Ageing
2.3.1 Infancy
The sterile period (in the human life cycle) is during the gestation, where the fetus grows in the uterus and remains tolerant to maternal antigens. However, a few studies have confirmed the presence of some bacteria in the amniotic fluid in the uterus, but the number and diversity that were found too low to show any effect on infant gut colonization [2, 100].
The initial colonization on the skin from environmental organisms takes place immediately after delivery, followed by gut colonization influenced by maternal and dietary factors. The nature and extent of neonatal colonization are also influenced by the method of delivery, whether vaginal or via the use of instrumental procedures (Figure 2.1). During a vaginal delivery, studies demonstrated the presence of Lactobacillus, followed by Prevotella and
Sneathia species [2, 12, 13]. After a caesarean section (C-section), Staphylococcus,
Propionibacterium, Corynebacterium species and high numbers of Clostridium difficile and
Escherichia coli were found in abundance in the gut compared to infants delivered vaginally
[14, 15].
The possible basis of the bacterial community’s establishment in the gut has been described in a study by Adlerberth et al. [13], they followed two healthy infants from birth for 10 months. Screening using fecal samples and polymerase chain reaction and denaturing gradient gel electrophoresis (PCR-DGGE), identified Streptococcus thermophiles,
12
Ruminococcus gnavas and Enterococcus raffinosus predominantly throughout the study. In the 3rd and 4th day fecal samples, there was rapid colonization with bifidobacterial species which remained strong for 3 months in both the infants. Beyond 3 months, Clostridium species colonized dominantly, which contradicted the culture-based theory. According to the culture-based evidence, Enterobacterium (gram-positive) was the first colonizer, which created and maintained a stable environment for Bacteroidetes, Bifidobacterium, and
Clostridium to grow in infants. However, the research, either culture-dependent or independent, explains that the gut colonization in infants exhibits low diversity, instability and high dynamics [16, 17]. These experiments were conducted using DNA from fecal samples of 13 infants 1-month, 3-months and 7-months old, which indicated the gut microbiota to be stable over this time frame, though there was inter-individual variability
[18]. Antibiotics administration influence infant gut colonization. Infants who were administered with antibiotics have lower proportions of lactic acid bacteria and enterococci
[1].
Besides antibiotics use, diet is also known to play a major role in the gut microbial colonization. The gut of infants who were breast-fed predominantly comprised of
Streptococci, Bifidobacteria, and E. coli. Enterobacteria, Bacteroides were common in the gut of infants who were formula-fed [19]. Changes occur to gut colonization after the introduction of solid foods. A study compared the influence of diet on the gut microbiota in children (1 to 6 years) who consumed the Western diet and compared to the African diet
(fibre-rich intake). After the Western diet, children had reduced microbial diversity with lower levels of Actinobacteria and Bacteroidetes compared to the African diet.
2.3.2 Immunity and initial colonization
13
The mucosal immune system and gut microbiota co-evolve with age. A few studies indicate
that the development of innate and adaptive immune systems require microbial interactions
during infancy [20, 21]. A study also suggested the role of the delivery mode in the
development of immunological functions and gut microbiota [22]. This study revealed that
infants born by C-section have relatively higher levels of immunoglobulins produced by
peripheral blood components compared to infants born by vaginal delivery. Immunoglobulin
A (IgA) is mainly secreted by the gut mucosal layer which contributes to gut barrier function.
IgA elicits low-grade immune responses allowing bacteria to colonize in the gut [23]. The co- relation between immunoglobulins and gut microbiota development in gnotobiotic mice was investigated by Planer et al. [24]. Briefly, to determine age-related differences in IgA
response, donor microbiota (infants) was introduced into mice, and mice fecal samples were
collected. Interestingly, the IgA responses were similar to those of the infant donor
population. This study can be used as an indicator for understanding the gut mucosal
immunity and microbiota in health and disease. IgA plays a major role in mucosal immunity,
as it is induced in response to colonization by specific commensal bacteria to protect the
mucosal surfaces and contribute to the host-microbiota mutualism.
2.3.3 Childhood, Preadolescence, and Adulthood
In early childhood (between 1 and 5 years), the expansion of bacterial diversity slows down,
and the gut microbial diversity remains lower compared to adults. The gut microbiota in
childhood is more stable and dominated by multiple members of Bacteroidetes. In the healthy
pre-adolescence (7 to 12 years), the gut microbiome is species-rich, containing many
bacterial taxa and functional genes similar to adult microbiota enriched with
Lachnospiraceae, Anaerovorax, Bifidobacterium, and Fecalibacterium. The bacterial
composition in adults is predominantly comprised of Bacteroidetes and Firmicutes (Figure
2.1). It has been shown that the relative abundance of both Bacteroidetes and Firmicutes can
14
vary between 0 to 99% [2, 25]. Other groups of researchers reported the presence of bacteria
from phyla Proteobacteria, Actinobacteria, Fusobacteria Cyanobacteria, and
Verrucomicrobia, as well as methanogenic archaea, multiple phages and Eucarya in healthy individuals [26, 27, 28, 29]. At the phyla level, the gut microbiota in adults is stable as compared to infants; however, there is a significant variation in specific microbial species and their proportions. There is no detailed study demonstrating the microbial composition of healthy adults due to vast inter-individual variation in composition. This variation can be linked to environmental factors shaping microbiota immediately after birth, low temporal variation and due to genetic variation. A holistic approach to identify conserved and similarities among healthy adults by proposing the presence of enterotypes such as
Bacteroides (enterotype I), Prevotella (enterotype II) and Ruminococcus (enterotype III) [15].
They have been shown to remain relatively stable for around six months, and evidence of fluctuations between enterotypes was revealed in a 10-year follow-up study [30]. Despite taxonomic variability, the functional properties of the adult gut microbiota are relatively consistent with pathways involving metabolism, and fermentation. In older adults, the gut microbiota becomes less diverse and unstable due to co-existing conditions and age-related factors.
2.3.4 Older adults
Imahori (1992) described ageing as “regression in physiological functions followed by advancement in age” [31]. Conventionally, people of age 65 years and above are termed as older adults by chronological measurement [32, 33, 34]. Understanding gut microbiota and its modulations are essential factors in improving the health and the well-being of older individuals. Perturbations in the gut microbial composition are associated with chronic conditions such as obesity and inflammatory diseases. The inter-individual variability is much higher in older adults than in adults or infants. There is a significant dysbiosis in gut
15
microbiota with age, together with the use of antibiotics and lack of nutrition. A reduction in
mastication ability with less salivary function and loss of dentition, can limit the nutrient
intake and thus, impact the microbial growth. Oropharyngeal and esophageal motility is
reduced with ageing resulting in swallowing propulsions and lower esophageal sphincter pressure, leading to an increased prevalence of gastroesophageal reflux. With age, there is also an increased intestinal transit time due to reduced motility, which can reduce digestion and absorption, and with reduced appetite, this may lead to malnutrition [34, 35]. In particular, hypochlorhydria is associated with ageing and is prevalent in those who have or previously had Helicobacter pylori infection. Hypochlorhydria predisposes to malabsorption, alteration in bacterial growth and vitamin B12 deficiency leading to atrophic gastritis (auto- immune), and loss of parietal cells. Malnutrition is one of the key factors affecting the growth of gut microbiota and contributes to an impaired immune system in older adults.
2.4 Specific gut microbial changes in older adults
A significant decrease in the relative proportions of Bacteroidetes and Firmicutes in older adults, when compared to adults was observed in a study by Mariat et al. [36] (Figure 2.1).
Apart from these major two phyla, a decline in Clostridium cluster IV was also observed in institutionalized older individuals [37]. The relationship between frailty scores and diversity of the microbiota in the older people was shown in a study that involved 23 older volunteers and analysis of their fecal samples [38]. A substantial decrease in Bacteroidetes, Prevotella,
Lactobacillus, Candida albicans, Streptococcus, Staphylococcus, and Fecalibacterium prausnitzii and an increase in levels of Ruminococcus, Enterobacterium, and Atopobium was reported [36, 38]. The relative variability of gut microbiome amongst the older population was measured and analyzed by Claesson et al. [37]. Fecal samples from 165 older people and nine young (controls) volunteers were collected, and microbiota analysis was performed using 16S ribosomal RNA (rRNA) gene sequencing technology. The proportions of
16
Bacteroidetes and Firmicutes in individuals varied from 3% to 92% and 7% to 94%,
respectively. A study on the fecal bacteria in healthy old volunteers (age range 63 to 90 years;
n = 35) living in the local community, old, hospitalized patients (age range 66 to 103 years; n
= 38), and old, hospitalized patients receiving antibiotic treatment (age range 65 to 100 years;
n = 21) [39], exhibited a decrease in Bacteroides-Prevotella group in the old, hospitalized
patients. Baigi et al. studied the age-related changes in the gut microbiota and host immune
system among young adults (average 30 years), older adults (65 to 75 years) and centenarians
(99 to103 years) using the Human Intestinal Tract Chip and 16S rRNA gene sequencing
methods [6]. Young adults and the older adult group showed a very comparable gut
microbiota with Bacteroidetes and Firmicutes, highly dominant and small proportions of
Proteobacteria and Actinobacteria. In contrast, there were significantly higher levels of
Proteobacteria in the centenarian population, relative changes in Firmicutes sub-groups with a
decrease in Clostridium cluster IV, and an increase in Bacillus species. The microbiota
differences between adults and older adults in four different European countries (Italy,
France, Sweden, and Germany) were studied by [40] and found to be country-specific. Other
studies also found differences in the gut microbiota with respect to age, gender and
geographical locations [40, 41].
Recent findings suggest that the gut microbial composition differs in men and women [25,
42]. Another study compared the gut microbiota of obese and lean men and women (mean
age of 60 years) with the same nutritional intake [43]. This study revealed higher levels of
Firmicutes in women regardless of age and body mass index (BMI), and men had lower
levels of Bacteroidetes than women with BMI over 33 kg/m2. The differences in the gut
microbiota between men and women may be influenced by the grade of obesity, and this could help in understanding the different prevalence of metabolic and GI diseases between males and females. The ELDERMET study by Claesson et al. [37] compared the microbial
17
composition of older and young adults and found characteristic differences in Bacteroides
and Clostridium levels. There was greater variability in microbial composition in the older people potentially contributing to greater morbidities (Table 2.1).
18
Figure 2.1: Overview of the development of microbiota
The gastrointestinal tract (GI) is most sterile during the in-utero stage. The first colonization
happens based on the mode of delivery either C-section or natural (vaginal delivery).
Corynebacterium sp. is thought to be early colonizers in C-section and Lactobacillus sp. in the vaginal delivery. As time progress, the commensal bacterial community grows and is influenced by the solid food intake. During the initial stages of microbiota establishment, the
TLR receptor actions are minimal allowing the growth of commensals. Eventually, the
immune system also grows by demarking the commensals and pathogens. Bacteroidetes
domination begins after two years of birth. The relative stability is attained at adulthood with
Bacteroidetes and Firmicutes dominating. The alteration happens with the use of antibiotics,
obesity, GI orders, and diet. During elderly the relative stability declines, commensal
community reduces, and pathogenic species like Clostridium increases. Malnutrition, alcohol
abuse, the decline in metabolism, frequent hospitalization, nosocomial infections
(Clostridium difficile), and other pathogenic infections are leading to Polypharmacy and
ultimately to various inflammatory diseases.
19
Table 2.1: Changes in the gut microbiota in the healthy ageing (natural, non-antibiotic treated)
Study Sample details Methods Changes Ref details
Comparativ n=30 Culture • Rural: High in [92] e study based Bifidobacteria, Bacilli • Rural (High fibre analysis and mainly Clostridium diet) n=15 median perfringens age 84 • Urban: Low • Urban (Low fibre Bifdobacterium diet) n=15 median adokscentis and age 68 Fusobacterium mortiferum strain
• Bacteroides, Gamma- Analysis of n =6 16S proteobacteria and [26] gut rRNA Clostridium IV, IX, XIVa microbiota gene dominated. sequenci ng, Culture- based Characteriz n=3 16S • Jejunal and Ileal [93] ation of • colon autopsy rRNA microbiota: Streptococci, jejunal, samples gene lactobacilli, ileal, caecal sequenci Gammaproteobacteria, the and recto- ng Enterococcus group, and sigmoidal the Bacteroides. colonic • Caecum:Bacteroides, microbiota Clostridium • Sigmoid-colon:C. coccoides group, the C. leptum subgroup, the Bacteroidetes group, Gammaproteobacteria, the Bifidobacterium, streptococci, and lactobacilli groups
20
Cross- n= 230 FISH • SA: Eubacterium rectale- [40] sectional and Clostridium coccoides low study • Sweden, adults (SA) Flowcyto in number. High n= 20, elderly (SE) metry Lactobacillus- n= 40 enterococcus group in both SA and SE. • Germany (GA), adults n= 23, elderly • GE: Eu., rectale-C. (GA) n= 38 coccoides cluster higher
• France, adults (FA) • FA: Low levels of n= 22, elderly (FE) Bacteroides-Prevotella n= 27 group. Low levels of bifidobacteria trend in FE • Italy, adults (IA) n=
Observation n = 249 16S • Akkermansia muciniphila [94] al study rRNA is present and colonizes • Infants n= 150, age gene the intestinal tract in early 1 to 12 months; sequenci life and develops within a ng and year to a level close to that • Adults n= 54, age: FISH observed in healthy adults. 25 to 35 years; analysis • Elderly n= 45, age:
80 to 82 years.
Comparativ n= 62 qPCR Firmicutes to Bacteroidetes [36] e and 16S ratios (in log10): assessment • Infants, n= 21, age 3 rRNA of fecal weeks to 10 months gene • Infants: 0.4 microbiota sequenci • • Adults, n= 21, age ng Adults: 10.9 25 to 45 years • Elderly: 0.6 • Elderly, n= 20, age 70 to 90 years
Comparativ n = 84 16S • Adults and Elderly: [6] e analysis rRNA Bacteroidetes and • Adults n= 20, age gene Firmicutes dominant. Low 25 to 40 years; sequenci levels of Actinobacteria, ng
21
and Proteobacteria • Elderly n= 22, age 63 to 76 years; • Centenarians: High levels of Proteobacteria. The • Centenarians n= 21, decrease in Clostridium age 99 to 104 years. cluster XIVa, an increase in Bacillus species. *Table sorted by year of publication; FISH= Fluorescence in situ-hybridization
2.5 Metagenomic and metabolic changes associated with gut microbiota in ageing
Mounting evidence indicates that gut microbiota influences metabolism [44, 45, 46], and may play a significant role in triggering metabolic diseases [47]. The relationship between the microbiota, metabolic and functional pathways in the humans was investigated by The
Human Microbiome Project (HMP). From the HMP, it is known that the human microbiome is highly variable both within a single subject and between different individuals. During the ageing process, the host is challenged by various changes, including diet, concomitant exposure to multiple medications, including antibiotics, reduced physical activity and any underlying disease, which forces microbiome reshuffling to adapt to the change [48].
A study investigated the functional differences between the gut microbiome across age groups, including young adults, older adults, and centenarians [6]. The study characterized the metabolic trajectory of the gut microbiota metagenome using Illumina shotgun sequencing on nine fecal samples. It was found that proteolytic activity was increased and there was a clear loss in the genes associated with the metabolism of carbohydrates upon ageing. The capacity of short-chain fatty acids (SCFAs) production also declined due to the age-related reduction of genetic pathways caused by overall rearrangement of the microbiome. The data on the differential abundance in microbiome indicated structural and functional changes in the microbiota in the aged population, moving from saccharolytic to a putrefactive metabolism. The study also found that many shifts in the microbiome were
22
forcing rearrangements in the core metabolic potential of the centenarian’s intestinal
microbial ecosystem. This specific microbiome analysis allows us to assess the role of
microbiota in pathophysiological conditions in the older people cohort.
Manipulating the intestinal microbiota and microbiome may be beneficial for maintaining health and treating certain disorders, particularly common among older individuals.
Nevertheless, more comprehensive clinical studies in different age groups are required to fully understand the role of the microbiome in metabolism.
2.6 Circadian rhythm, microbiome and metabolism in ageing
Circadian rhythm, metabolism, and gut microbiota are intricately linked. A study compared the human sleep-wake cycle and insulin secretions and found robust variations in glucose regulation during normal and no sleep conditions [49]. Most of the glucose tolerance occurs during sleep which influences the nocturnal brain and tissue glucose utilization. Therefore, chronic sleep disturbances and sleep apnea in older individuals may be linked to alterations in
metabolism. With reduced physiological function and sleep patterns, the older adults may
exhibit altered appetite leading to increased susceptibility to GI and other metabolic disorders.
Bass and Turek [50] demonstrated the association between sleep-wake cycle and metabolic
regulation and suggested that interventions in sleep disorders may ameliorate some of the
metabolic deficits associated with overweight and obesity [50, 51]. Another study critically
reviewed circadian rhythm, sleep, and metabolism [52]. They suggested an association
between clock gene variations, obesity, and metabolic functions in understanding the impact
of the circadian rhythm. Similarly, a study demonstrated a bi-directional relationship between
circadian clock and metabolism, tested in a high-fat diet animal model [53]. A research
scholar hypothesized that the gut microbiota and microbiome are one of the key elements in
maintaining the circadian rhythm impacting the dietary and physiological functions of the
23
body [54]. To test their hypothesis, they used germ-free mice and specific-pathogen-free mice.
The study highlights the relation between diet, gut microbial function, metabolome and
metabolic function impacting the host health. The authors speculated that any change in diet
impacts the circadian rhythm and manipulation in the gut microbial structure might restore
the metabolic balance. Age-related perturbations in gut microbial structure and microbiome
caused by diet and other factors appear to affect the circadian clock, promoting metabolic
disorders and obesity. The relationship between the gut microbiota and metabolism was first
shown in germ-free mice compared to conventional mice (both groups on a high-fat diet) [55].
Conventional mice gained weight (as expected), whereas the germ-free mice maintained their
body weight, suggesting an impaired feeding efficiency in the germ-free mice. Studies by
Turnbaugh et al. [55] confirmed this hypothesis when germ-free mice gained weight when
colonized with the microbiota from obese rather than from lean mice. The possible
mechanism could be the activation of the non-insulin dependent AMP-activated kinase pathway, which controls energy expenditure via the increase in glucose oxidation under metabolic stress conditions. Interestingly, intestinal dysbiosis was observed in the obese leptin-deprived ob/ob mice, and it was subsequently shown that there was microbial dysbiosis in obese humans when compared to lean controls; suggesting a common mechanism in mice and humans [44]. Therefore, gut microbiota can be considered dependent on the host genome as observed in the ob/ob mice. Importantly, the role of the gut microbiota in maintaining homeostasis could also play a part in the microbial genome [56].
2.7. Role of gut microbiota in immune function in the older adults
2.7.1 Pro and anti-inflammatory responses
The gut-associated immune system is the largest component (approximately 70%) of the human immune system [57]. Along with numerous commensal bacteria, there are abundant
24
innate and adaptive immune cells in the gut (Figure 2.2). Gut microbiota and the immune
system actively interact to maintain the homeostatic equilibrium [58]. This equilibrium
depends on the intestinal epithelial cells (IEC) in the colon to segregate microbes and
mucosal immune cells. Apart from IECs, enterocytes, and gut-associated lymphoid tissue
(GALT) form a specialized barrier against invading pathogens. The goblet cells of the IECs
in the colon are capable of secreting gel-like layers of mucus such as mucin and mucin 2
(MUC 2) which forms the first line of defence against the microbial breach [59, 60]. Trefoil
factor 3 (TFF3) and Resistin-like molecule-β (RELMβ) are other goblet cell-derived products that contribute to forming a physical barrier in the intestine. During inflammation, RELMβ helps to secrete MUC 2 and regulate macrophages and adaptive T cells [61]. Antimicrobial peptides (AMPs) secreted by enterocytes present in the IEC, further bolster the protective barrier. For example, in the presence of B. thetaiotaomicron and L. innocua or stimulation with lipopolysaccharide (LPS), the AMP levels were high in the IECs [62]. Enterocytes also possess specialized pathogen pattern recognition receptors (PPR) which contain Toll-like- receptors (TLRs) and nucleotide oligomerization domain-like-receptors (NOD), which recognize bacterial surface molecular structures called microbe-associated molecular patterns
(MAMPs). MAMP recognition leads to activation of an inflammatory response causing the release of nuclear factor kappa B (NF-kB), and tissue damage and NOD recognizes the damage-associated molecular patterns (DAMPs) in the host [63]. All the TLRs are present at the mRNA level of IEC with different concentration levels. TLRs 2, 4, 5, and 9 detect the presence of bacterial and fungal pathogens and TLRs 3, 7, 8 detects viral pathogens in the small and large intestine. Micro-folding (M) cells present in the sub-epithelial region with lymphocytes and dendritic cells (DCs) at the base open into the lamina propria (LP). M cells engulf pathogens by phagocytosis or endocytosis and present the antigen (APCs) to DCs.
DCs, in the presence of a high number of interleukins (IL-1β, IL-6) and transforming growth
25
factor (TGF-β), which unify the immune responses towards T helper (Th) 17 cells, then
trigger widespread inflammation by the release of IL-17A, IL-17 F, IL-21, IL-22 and IL-23
[64]. Thus, DCs triggers both pro and anti-inflammatory responses in the host.
Figure 2.2: Interplay between the immune system and gut microbiota in homeostasis, tolerance, and inflammation.
(a) Both commensals and opportunists compete for the metabolites (SCFA) and various nutrients. The intestinal epithelial cells (IEC) play a role in the steady-state environment by releasing interleukins IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) factors in the presence of SCFA, PSA (Bact. fragilis), lipopolysaccharide (LPS), and AMPs (defensins, cathelicidins, and C-type lectins). IL-25, IL-33, and various growth-related factors help in the transformation of progenitor basophils to basophils, activation of monocytes, macrophages,
26
and mast cells functioning. The bacteriocins released by segmented filamentous bacteria
(SFB) also influence the release of TSLP. The microfolding (M) cells upon sensing the
presence of the microbes act as antigen presenting cell (APC) phagocytic activity by
engulfing and presenting it to mucosal dendritic cells (DCs). In turn, DCs are endowed with
the ability to produce cytokines and other products such as IL-6 and IL-1β, tumor growth
factor (TGF-β), retinoic acid (RA), and vitamin A. DCs form a major histocompatible
complex (MHC) with the T cell receptors (TCR). In the presence of TGF-β and RA, the naïve
T cells (CD4+ cells) transform into regulatory T cells (Treg). Simultaneously during
interaction and competition of commensals and pathogens for nutrients, macrophages after
recognition microbes released proinflammatory cytokines such as IL-10, which in turn helps
with the expansion of Treg cells which are already released during homeostasis and
inflammation. Also, with the help of DCs, macrophages release certain B cell activating
factors which increase the production of secretory immunoglobulin A (SIgA) to maintain
tolerance and steady state. (b) ((1) & (2)): in the elderly, there are declined physiological
functioning and dysbiosis (reduction in commensal bacteria), resulting in an increase in
pathogens. (3) The production of IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) by
IEC reduces. (4) There is a decline in M cell/APC activity to present PSA or microbe to DCs
(activation of the inflammatory pathway). Moreover, the lack of PSA stimulation reduces the
IL-12 levels and releases T helper 2 cells. The decline in DCs not forming MHC with TCR
reduces the population of active T cells such as Treg cells. Activation of B cells to plasma
secretory cells and release of SIgA decreases. (5) The activation and function of macrophages
(low levels of IL-10) are reduced. (6) The steady state or tolerance is reduced. (7)
Macrophages (inflammatory) are activated in the presence of pathogens and release
proinflammatory cytokines (IL-1β, IL-6, and TNF-α) which leads to the production of reactive oxygen species (ROS) and causes oxidative stress. Altogether with reduced levels of
27
Treg and T helper cells and SIgA increase the pathogen invasion leading to the release of
proinflammatory cytokines and reduced anti-inflammatory cytokines increase inflammation, causing various GI disorders.
2.7.2 Role of enterocytes
Microorganisms are generally located on the outer mucosal layer of the epithelium in a compartmentalized fashion in the lumen. In order to reduce the interaction of microbiota and enterocytes, there is a dense gel-like inner layer on top of the epithelium secreted by goblet cells (MUC 2) which is devoid of microbes where in contrast the outer layer is thin and colonized with bacteria [65]. Enterocytes sense the presence of microbes within the mucus layers and monitor their proximity and density. Thus, enterocytes act as the frontline in the microbiota-immune cells crosstalk that occurs in the host. In healthy ageing, enterocytes
monitor GALT immune response, send tolerance signals towards commensals and keep DCs
in a stable mode [66]. M cells (APCs) present the antigen to naïve CD4+ cells, causing their
differentiation into CD4+ T cells (T-regulatory), which elicit an anti-inflammatory response
(IL-10, TNF-α). Concurrently, the antigens are also presented to B cells in LP, stimulating
the differentiation of immunoglobulin A (IgA). IgA helps in neutralizing the toxins produced
by microbes and prevents the adherence of the microbiota and the intestinal lumen. Secretory
IgA (SIgA) induction appear more efficient in the presence of Bacteroidetes [67].
In older adults, the pre-clinical and clinical studies indicate a decline in immune function [68,
69, 70]. In aged IEC (GALT and enterocytes), impairment and changes in immune functions
have been observed in animal models [65]. With GALT impairment, there is a reduction in
mucin and defensins (α-defensins and β-defensins) leading to an increased chance of
infection by uncontrolled growth of commensals (mutualistic to opportunistic) and pathogens.
28
When there is an increase in pathogens, enterocytes could activate certain cytokines and
chemokines forcing DCs to initiate a pro-inflammatory response by differentiation of Th-1,
Th-2, and Th-17 (effector) cells [71]. A study by [72] demonstrated Bacteroides fragilis releases polysaccharide A (PSA) recognized by TLR and induces production of IL-10 by
Treg cells (in the presence of TGF-β). Induction of Treg cells is not restricted to Bacteroides
fragilis, as the presence of an indigenous Clostridium species also promotes Treg cell
activation. A study [73] showed the role of clostridia in inducing Treg cells in germ-free mice.
A total of 17 strains from Clostridiales clusters VI, XIVa, and XVIII isolated from human
feces exhibited Treg-inducing activity, suggesting that Clostridium-dependent induction of
Tregs may contribute to the maintenance of intestinal immune homeostasis [74]. A change to
the Bacteroides-Firmicutes group triggers the release of IL-6 by DCs in the LP and activates
naïve T cells. Naïve T (T0) cells differentiate into Th-17 and Th-1 cells, which are
responsible for inflammation in colitis [75].
2.7.3 Short-chain fatty acids (SCFAs)
Gut microbiota generates a range of metabolites, particularly SCFAs such as butyrate,
propionate, and acetate. These SCFAs are linked to the elicitation of anti-inflammatory and
anti-neoplastic responses via inhibition of NF-kB favouring the growth of the intestinal
epithelium [76]. Butyrate is considered the primary source of energy to the intestinal
epithelium and helps in the production of mucin (gel-like inner layer) in the lumen which
establishes the physical barrier. Deficiency of SCFA leads to epithelial atrophy and
inflammation during colitis [77]. In the older adults, the decline in butyrate levels is
correlated to a decrease in F. prausnitzii, Eubacterium hallii and Eubacterium rectal [6]. Also,
due to the decline in butyrate and weakened physical barrier, immunologic tolerance
decreases, allowing the increase in Enterobacteriaceae in older adults. This was corroborated
in a study conducted by [6], where pro-inflammatory cytokine levels increased and were
29
related to inflamm-ageing. Additionally, a reduction in pro-inflammatory responses and
decline in SCFA levels has been linked to the risk of colorectal cancers [78, 79].
2.7.4 Sex-related differences
Both the immune and the endocrine system exhibit significant sex-specific differences. A transcriptome study on peripheral blood mononuclear cells revealed lower T-cell responses and more inflammation in older females [80]. Another study from Japan on an ageing population indicated the progression of immunosenescence in older men with a decline in T- cell proliferation when compared to older women [81]. Age-related changes in sex steroid levels enhance immunosenescence-related alterations. It is also shown that gut microbial and sex hormone differences with ageing may lead to autoimmune diseases in animal models [98].
The association of sex differences, ageing and immune response with gut microbiota was critically reviewed by [81]. The authors emphasized that early exposure to environmental influences microbiome and sex-dependent immune responses and suggested that sex should be considered as a biological variable in immunological studies.
2.7.5 Dietary influences
Due to reduced immune activity, the older adult population are generally categorized as immuno-compromised as their resistance to infections is lowered. The link between microbiota and the immune system can be demonstrated by examining the effects of the administration of various dietary supplements such as prebiotics, probiotics, and synbiotics
(Table 2.2). Dietary supplementation of a probiotic drink with Bacillus subtilis at a rate of 2 x109 colony forming units (CFU) for 18 days (n = 44) increased the levels of SIgA by 65% in
the stools and saliva during common cold infections in the older adults [82]. Consumption of
a probiotic drink containing L. casei Shirota (LcS) at 1.3 x 1010 CFU for four weeks increased
natural killer (NK) cell activity, and significantly decreased the intensity of CD25 in T resting
30
cells in healthy non-immuno-compromised older people [83, 84]. Prebiotics such as a bifidogenic growth stimulator and galacto-oligosaccharide when consumed with fermented milk for four weeks, improved and maintained antibody titers for longer periods through improving intestinal microbiota in the older adults with influenza vaccination [85]. Similarly, in a randomized, double-blind study conducted on 43 older people, supplementation of a synbiotic (containing probiotic Bifidobacterium longum and an inulin-based prebiotic synergy 1) for 4-weeks increased the bifidobacterial count and increased the counts of
Actinobacteria and Firmicutes. Proteobacteria were reduced by 1.0 log units. There was a reduction of pro-inflammatory cytokine TNF-α and an increase in butyrate production [68].
Therefore, dietary supplementation with pre/pro/synbiotics in older adults not only increases the intestinal commensal diversity but also reinforces the immune system against various pathological conditions.
2.7.6 Modulation of Gut Microbiota for Health
As discussed in the earlier sections, diet plays an important role during the establishment and development of stable gut microbiota and can assist in its modulation. Due to the high prevalence of malnutrition in older adults, dietary manipulation may play an important role.
There is a decline in SCFA production in the older adult’s gut due to a major shift in bacterial composition and malnutrition. This, in turn, contributes to a decline in anti-inflammatory response leading to an increase in infections. A high-fibre diet is widely recommended for adults and older adults as it increases the SCFA production and decreases the intestinal pH, reducing the colonization of pathogenic bacteria. However, high-fat diets with certain cooking oils containing polyunsaturated fatty acids (omega-3-PUFA) increased the levels of
Firmicutes, Actinobacteria and decrease Bacteroidetes [85].
31
Many specific therapies have been proposed for treating dysbiosis, such as the use of prebiotics, probiotics, and synbiotics. Several clinical studies demonstrated the ability of probiotics (most used are Lactobacillus, Bifidobacterium, Lactococcus, Streptococcus) in modulating the gut bacteria in infants, adults and the older adults [74, 86, 88]. These studies indicated an increase of SCFA production and improvement of the immune system [19].
Prebiotics alone was shown to improve gut microbiota and help in the production of SCFA
[99].
Broad spectrum antibiotic therapy successfully treats many infections; however, at the expense of a commensal bacterial loss. In all ages, careful use of antibiotics is preferred, and when used, the potential use of narrow spectrum or targeted therapy is preferred, as this reduces microbiota alteration [154, 167].
The concept of fecal microbiota transplantation (FMT) was introduced to control recurrent infections. FMT can be defined as a nature-tailored probiotic to control recurrent infections.
A fecal sample from a healthy donor is selected and infused into the diseased patients via colonoscopy, endoscopy, sigmoidoscopy, or enema [87]. FMT produced 90% benefit as a treatment option for diarrhoea due to recurrent C. difficile infection. In 2013 the United States
Food and Drug Administration categorized fecal samples as a biological, investigational tool for a therapeutic agent [88]. Many researchers have shown the effectiveness of FMT in IBD,
Crohn’s diseases and ulcerative colitis, but important aspects of this treatment such as the screening and selection of healthy donors are still to be defined. FMT could also possibly be used against H. pylori infection (which may cause ulcers and gastric cancers), to minimize the effects of antibiotic use, particularly in the older adult population [89].
32
Table 2.2: Clinical studies related to variations in immunity and gut microbiota in the elderly*
Study Sample details Methods CFU Outcome Ref details Placebo- n= 24 ELISA, C- 109 Daily [95] controlle • Probiotic (L. reactive protein CFU/d consumption of L. d RCT johnsonii La1 test, ay/12 johnsonii La1 (NCC533) n= PHAGOTEST, weeks (NCC533) may 12 fecal microbiota contribute to • Placebo (non- enumeration suppressing probiotic) n= infections by 12 improving the nutritional and immunological status RCT n= 209 qPCR, ELISA 109 Bifidobacterium [96] • Probiotic group: and fecal CFU/d levels in the in B. longum 2C microbiota ay/6 microbiota may be (DSM 14579) enumeration months associated with n= 56, change of cytokine • DSM 14583 n= levels. 46 • Placebo group n= 67 • Control group n= 86
33
Compara n= 84 Tract Chip The proportion of [6] tive • Adults n= 20, (HITChip), centenarians analysis age 25 to 40 qPCR, 16S showing a high years; rRNA gene inflammation • Elderly n= 22, sequencing, score was age 63 to 76 ELISA and significantly years; Flowcytometry higher than in the • Centenarians n= other age groups, 21, age 99 to confirming the 104 years. inflamm-ageing hypothesis RCT n= 45 RT-PCR, 5×1010/ The potential of [74] • B. longum ELISA, T- day/12 long-term BB536 n= RFLPs weeks ingestion of 23 BB536 in • Placebo increasing the cell (Dextrin) number of n= 22 bifidobacteria in intestinal
Investiga n=33(Bacillus FISH, Gas 1x Thei bi t dietaryd [97] tive RCT coagulans GBI- chromatography 109/day inclusion of 30, 6086 (BC30)) and / 28 probiotics such as Flowcytometry days BC30 may provide a beneficial option for enhancing markers of GI health comparison with placebo. *Table sorted by year of publication; RCT= Randomized double-blinded clinical trial.
34
2.8 Conclusion
Gut microbial diversity declines with age and its function in metabolism and regulation of the immune system is reduced. This provides a chance for opportunistic pathogens to invade and inflame the gut giving rise to various diseases ranging from low-grade chronic ill health to those causing hospitalization and even death. Despite significant research on gut microbiota, the optimal therapy to reduce/prevent the dysbiosis in older adults is yet to be identified. Diet plays a role as a manipulator of the gut microbiota throughout life, and this may be particularly important in older adults. The use of broad-spectrum antibiotics almost certainly has an adverse effect on gut bacteria. We believe probiotic supplementation has significant potential to restore the diversity of the gut microbiota and improve immune function.
However, the specifics of supplementation, dosage, and other parameters are still unclear.
More well-conducted randomized studies on probiotics/prebiotics/synbiotics in older adults are needed.
35
Chapter 3: Therapeutic interventions for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or fecal microbiota transplantation?
An original version of this chapter has been published in Beneficial Microbes Journal as an integrative review of the literature and appears in literature as:
Vemuri Ravichandra, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions for gut dysbiosis and related disorders in the elderly: antibiotics, probiotics or faecal microbiota transplantation? Beneficial microbes. 2017, 26;8(2):179-92.
Clarivate Analytics/Thompson Reuters journal impact factor: 2.3
SJR journal ranking: Q2, 0.96
Citations: 11
The word faecal has been changed to fecal to maintain consistency throughout chapters.
Chapter 3 has been removed for copyright or proprietary reasons.
36
Chapter 4: A human origin strain Lactobacillus acidophilus DDS-1 exhibits superior in vitro probiotic efficacy in comparison to plant or dairy origin probiotics
An original version of this chapter has been published in the International Journal of Medical
Sciences an original research investigation and appears in the literature as:
Vemuri Ravichandra, Shinde T, Shastri MD, Perera AP, Tristram S, Martoni CJ,
Gundamaraju R, Ahuja KD, Ball M, Eri R. A human origin strain Lactobacillus acidophilus
DDS-1 exhibits superior in vitro probiotic efficacy in comparison to plant or dairy origin probiotics. International Journal of Medical Sciences. 2018 Jan 1;15(9):840-8.
Clarivate Analytics/Thompson Reuters journal impact factor: 2.28
SJR journal ranking: Q1, 0.
Citations: 1
The all the figure layouts are modified from landscape to portrait format to maintain consistency.
58
4.1 Abstract
The health benefits of probiotics are well established and known to be strain-specific.
However, the role of probiotics obtained from different origins and their efficacy largely
remains unexplored. The aim of this study is to investigate the in vitro efficacy of probiotics
from different origins. Probiotic strains utilized in this study include Lactobacillus
acidophilus DDS-1 (human origin), Bifidobacterium animalis ssp. lactis UABla-12 (human
origin), L. plantarum UALp-05 (plant origin) and Streptococcus thermophilus UASt-09
(dairy origin). Screening assays such as in vitro digestion simulation, adhesion, cell viability, and cytokine release were used to evaluate the probiotic potential. All strains showed good
resistance in the digestion simulation process, especially DDS-1 and UALp-05, which survived up to a range of 107 to 108 CFU/mL from an initial concentration of 109 CFU/mL.
Two human colonic mucus-secreting cells, HT-29 and LS174T, were used to assess the
adhesion capacity, cytotoxicity/viability, and cytokine quantification. All strains exhibited
good adhesion capacity. No significant cellular cytotoxicity or loss in cell viability was
observed. DDS-1 and UALp-05 significantly upregulated anti-inflammatory IL-10 and downregulated pro-inflammatory TNF-α cytokine production. All the strains were able to downregulate IL-8 cytokine levels. Of the four strains tested, DDS-1 demonstrated superior survival rates, good adhesion capacity and strong immunomodulatory effect under different experimental conditions.
59
4.2 Introduction
Probiotic microorganisms are living, natural and safe modulators of gut microbiota. The term
probiotic has been defined as “Live microorganisms that, when administered in adequate
amounts, confer a health benefit on the host” [137]. Probiotics restore the beneficial
composition of the gut microbiota by adhering to the intestinal epithelium, establishing
microbial-host crosstalk and modulating immune responses [189]. Moreover, many strains
produce regulatory metabolites, such as short-chain fatty acids, which strengthen the
intestinal epithelium to maintain a healthy state. Lactic acid bacteria (LAB) such as
Lactobacillus, Bifidobacterium, Streptococcus, and Enterococcus species are widely used as
probiotics, as they were noted to have a clinical effect in reducing the symptoms of diarrhoea,
inflammatory bowel disease and irritable bowel syndrome [190-192]. To confer any health
benefits, probiotics need to survive through the hostile environment of the digestion process
in sufficient numbers (at least 106 colony forming units (CFU), tolerating acids, bile, and pancreatic digestive enzymes, and finally, adhere to the intestinal epithelium in the colon.
Interestingly, most of the LAB probiotics have demonstrated high survival rates under simulated conditions of gastric juice and high bile salts concentrations [193, 194].
Adherence of probiotics to the intestinal mucosal surfaces is also considered an essential
probiotic trait as adhesion to colonic mucosa is the first step for gut colonization [193, 194].
The possible mechanism of adherence is via close interaction between surface adhesion
proteins of bacteria and host cells [189, 193, 195]. Probiotics as generally considered safe as
functional foods. Several studies show that LAB strains are not toxic to intestinal epithelial
cells [196, 197]. The safety of probiotics for human use has been the subject of several
reviews by experts in food safety [198-200]. These reviews support the safety and suitability
of LAB for use as oral probiotics, a conclusion that is largely based on their long history of
safe use in food and as dietary supplements.
60
Experimental and human studies suggest that increased levels of pro-inflammatory cytokines
TNF-α and IL-8 play a significant role in inducing an intestinal inflammatory response [201].
LAB strains have been reported to reduce inflammation by downregulating IL-8 and TNF-α and by upregulating IL-10 levels [194, 201, 202]. This immuno-modulatory effect of probiotic bacteria offers additional potential benefits for the prevention and management of gastrointestinal inflammation.
Probiotic strain selection is often debated and linked to their origin. To date, many in vitro studies have reported the efficacies/functional properties of various probiotics obtained from different origins such as human, plant, dairy, and animal. However, most of these studies evaluated the efficacy of probiotic strain from a single origin strain, such as human [205,
206], dairy [202, 205], plant [206] or animal [193] to study their probiotic potential. To our knowledge, the current study is the first to compare probiotics of different origins. In our study, we investigated the efficacies of probiotics isolated from human, plant and dairy sources. To do this, the resistance and survival of these strains during the digestion process was assessed throughout buccal, stomach and intestinal conditions in a simulated in vitro setting. Subsequently, adhesion capacity and cytotoxicity studies were conducted on HT-29 and LS174T cells, and finally, the immunomodulation effect of these strains, particularly on
IL-8, TNF-α and IL-10, was investigated on HT-29 cell supernatants. These findings would be of great importance to identify the right probiotic strain for gut health.
4.3 Materials and Methods
4.3.1 Bacterial strains and origins
Bacterial strains utilized in the study include Lactobacillus acidophilus DDS-1(human origin),
Bifidobacterium animalis ssp. lactis UABla-12 (human origin), L. plantarum UALp-05 (plant origin) and Streptococcus thermophilus UASt-09 (dairy origin), and were obtained in freeze-
61
dried, free-flowing lyophilized form from UAS labs, Madison, WI, USA. All the strains were
routinely grown on De Man Rogosa agar supplemented with 0.05% (w/v) of L-cysteine
(MRS-C) except UASt-09, which was grown on M17 agar supplemented with 10% (w/v)
lactose, under anaerobic conditions at 37 0C for 48 h.
4.3.2 Survival of strains in static in vitro digestion model
A continuous, static in vitro digestion model was employed to assess the survival of selected
strains as described by Versantvoort et al. [207]. The model includes a step-wise human
digestive process, simulating mouth, stomach, and intestinal compartments. However, it does
not represent the continuous process of digestion. To mimic the continuous in vitro process of
the human gastrointestinal tract, the present model was modified and adapted from Chavarri
et al. [208] and Belguesmia et al. [204]. The chemical compositions of digestive fluids,
enzymes, pH, temperature and residence time-period in each compartment are reproduced in
the modified model (Figure 4.1). Firstly, to reproduce the buccal conditions, 1 mL of each
bacterial strain comprising approximately 109 CFU were individually added to 9 mL of
simulated saliva juice (pH adjusted to 6.8 ± 0.2) and incubated anaerobically at 37 0C for 5
min. After incubation, the suspension was centrifuged at 3000 rpm for 5 min at 4 0C to
recover the pellet containing each strain and to maintain the continuous process. The pellet
was then re-suspended in 9 mL of simulated gastric juice comprised of 9 g/L of NaCl containing 0.3% (w/v) of porcine pepsin (Sigma Aldrich, St. Louis, MO, USA), and the pH adjusted to 3.0 ± 0.2 and incubated anaerobically at 37 0C for 120 min to mimic gastric
conditions. After incubation, the gastric fluid was neutralized to pH 7.0 with phosphate
buffered saline to stop the pepsin digestion. The supernatants were discarded after
centrifugation, and intestinal digestion process was initiated. The intestinal compartment
included 0.3% (w/v) bovine bile (Sigma Aldrich, St. Louis, MO, USA) and 0.1% (w/v)
pancreatic enzymes (MP Biomedical, CA, USA), pH was adjusted to 7.0 ± 0.2 before
62
incubating anaerobically at 37 0C for 120 min. Overall, the bacterial strains were exposed to the various digestive conditions for a total of 240 min. After each step of the simulated digestion process, bacterial samples were collected and diluted in the saline buffer. Lastly,
100 µL of each sample was plated on MRS-C/M17 agar plates and incubated at 37 0C for 24-
72 h anaerobically to allow sufficient growth. The number of CFU/mL of each probiotic strain was calculated.
Figure 4.1: Graphical representation of chemical compositions, concentrations, digestive enzymes and pH used in in vitro simulated digestion process. (qs = quantity sufficient)
63
4.3.3 Cell culture
Human colonic epithelial cells (HT-29 and LS174T) were purchased from American Type
Cell Culture (ATCC, Virginia, USA). For initial growth, HT-29 and LS174T were cultured in
75 cm2 tissue culture flasks and grown to confluence in the complete medium [McCoy and
RPMI 1640 medium respectively supplemented with 10% fetal bovine serum, 2 mM L-
glutamine and 100 U/mL of antibiotics (penicillin and streptomycin)]. At confluence, the
adhered cells were washed with PBS, dissociated using 0.1 w/v TrypLE® Express (Gibco,
Victoria, Australia) and re-suspended in 24-well cell culture plates at a density of
approximately 5 x 104 cells/mL. Cells were incubated for 24 h and grown to confluency
before exposing them to different treatments for quantitative measurement of bacterial cell
adhesion, cytotoxicity, and cytokine quantification.
4.3.4 Adhesion assay
Each wells containing cells was washed thrice with serum-free culture medium before adding
the 1 mL of the bacterial suspension. Approximately 109 CFU/mL of each bacterial strain was
first suspended in the respective cell culture medium without serum and antibiotics. The cells containing bacterial suspensions were incubated for 4 h with 5% CO2. After 4 h incubation,
cell monolayers were washed thrice with Hank’s balanced salt solution to remove the non-
adherent bacteria in the wells. The cells were aspirated, lysed using 0.1% w/v TrypLE®
Express for 10-15 min and collected in saline solution. All cell lysates were serially diluted,
plated on MRS-C/M17 agar and incubated anaerobically for 48-72 h. The percentage of
adhesion was calculated by the log of the number of adherent bacteria (log CFU) divided by
the log of the total number of bacteria inoculated, multiplied by 100. Each determination was
performed in triplicate.
64
4.3.5 Cell cytotoxicity and viability
Each bacterial suspension at approximately 109 CFU/mL in respective serum-free cell culture medium was added to the cell monolayer in a 24-well plate and incubated for 8 h. After incubation, the supernatants were collected for determination of cytotoxicity using the lactate
dehydrogenase (LDH) assay [209] and cell viability was assessed by standard trypan blue
exclusion assay.
The cellular cytotoxicity was assessed by the LDH in-vitro cytotoxicity assay (TOX7, Sigma-
Aldrich, St. Louis, MO, USA). Briefly, the culture supernatants were centrifuged at 250×g for 4 min. An aliquot containing 50 µL of either blank (complete medium) or control (cells only) and cells treated with 100 µL of each bacterial supernatant obtained after 8 h incubation, was mixed with 100 µL of a solution containing LDH assay mixture (LDH substrate, LDH dye, and LDH cofactor). The mixture was then incubated at room temperature for 20–30 min and the reaction were quenched by the addition of 1N hydrochloric acid (15 μL). The absorbance was measured spectrophotometrically using a plate reader (Spectra Max M2 microplate reader, Sunnyvale, CA, USA) at a wavelength of 490 nm. The cellular viability was examined by Trypan Blue exclusion staining assay using a Countess Automated Cell
Counter (Thermo-Fisher, Waltham, MA, USA).
4.3.6 Quantification of cytokines
HT-29 cells were used for quantification of IL-8, TNF-α, and IL-10 cytokine levels. The cells
were subjected to probiotic/lipopolysaccharide (LPS) treatment under three different
conditions, i.e., no-, co- and post- treatments, and LPS alone (control) in individual 24-well plates [210]. For no-treatment (probiotic only + HT-29 cells) conditions, 1 mL of each
bacterial suspension (109 CFU/mL) was first added to each well containing cells and
65
0 incubated at 37 C, 5 % CO2 for 8 h. For co-treatment, each bacterial suspension was added to the cells and simultaneously challenged with LPS (100 ng/mL) (Sigma-Aldrich St. Louis,
MO, USA) followed by incubation for 8 h. Lastly, in post-treatment, the cells were first
challenged with LPS (100 ng/mL) and incubated for 4 h, after which, each bacterial
suspension (1 mL of 109 CFU/mL) was added and re-incubated for 4 h. The supernatants
from each well were collected and used for quantification of cytokines using Bio-Plex® Pro
human cytokine assay kit (Bio-Rad®), according to the manufacturer’s protocol. Briefly, 50
µL of cytokine beads were added to the 96-well plate and incubated for 30 min before washing twice with wash buffer. Then 50 µL of each standard, blank and samples were added to the respective wells and incubated at room temperature on a shaker at 850 rpm for 30 min.
After incubation, the wells were washed thrice, and 25 µL of detection antibody was added to each well and incubated at room temperature on a shaker at 850 rpm for 30 min. 50 µL of streptavidin-PE was then added to each well and incubated at room temperature in a shaker at
850 rpm for 10 min. After three washes, 125 μL assay buffer was added to each well and incubated at room temperature for 30 sec. After incubation, the plates were read on the Bio-
Plex® 200 system and data were analyzed in Bio-Plex Data ProTM Software. All the experiments were performed in triplicates.
4.3.7 Statistical analysis
All the data are expressed as mean ± SEM calculated over three independent experiments with triplicates within each experiment. All the statistical analyses were performed using
Graph Pad Prism software (version 6.0). The statistical differences between groups were measured using One-way ANOVA and Tukey’s multiple comparison tests for adhesion and cytotoxicity assays. Two-way ANOVA was used for digestion simulation and cytokine
quantification. P-values have been corrected to account for false discovery rates using Holm-
Bonferroni method and Benjamin Hochberg correction.
66
4.4 Results
4.4.1 In vitro simulated digestion process
All tested bacterial strains survived passage through the mouth compartment followed by the
stomach and intestinal compartments during the simulated digestion process. The strains
retained over 75% of their initial concentration of 109 CFU/mL (Figure 4.2). DDS-1
exhibited the highest survival rates. No significant change in bacterial numbers of the strains
was observed in the mouth compartment. During the transit from mouth to stomach, only
UABla-12 (p < 0.001) and UASt-09 (p = 0.03) strains displayed a decrease of one logarithmic unit, which was significant when compared to DDS-1. In the intestinal compartment, DDS-1 and UALp-05 showed a decrease of one logarithmic unit, while
UABla-12 and UASt-09 further reduced by two logarithmic units. Overall, DDS-1 (p =
0.034), showed the highest resistance, followed by UALp-05 (p = 0.008), UASt-09 (p =
0.002) and UABla-12 (p < 0.001) compared to initial concentration. The differences were statistically significant for UABla-12 (p < 0.001), UALp-05 (p = 0.01) and UASt-09 (p <
0.001) when compared to DDS-1.
67
9 * *** **** 8 ** **** **** a * *** **** **** b 7 DDS-1 **** **** cb UABla-12 **** c 6 UALp-05 lo g C F U /m L U A S t-09
5
0 0 5 60 120 180 240
Tim e (m in) Mouth Stomach Intestine
Figure 4.2: Survival of each bacterial strain in the mouth, stomach and intestinal simulated in
in vitro digestion process
The samples were taken at each step, and the number of CFU/ml was evaluated on agar
medium. The data represents mean ± SEM of three replicates. Statistical analysis was by
Two-way ANOVA. The values of groups designated with different letters are significantly
different in the mouth and intestinal compartments (p < 0.05). For overall survival of
individual strain *p < 0.01, **p < 0.005, ***p < 0.001 compared to DDS-1.
4.4.2 Adhesion capacity
The adhesion assay demonstrated the adherence capability of each probiotic strain to HT-29
and LS174T human colonic cells. All the strains were able to adhere efficiently to both
colonic cell types. Although, DDS-1 and UALp-05 showed the highest adhesion capacity
(87-93% and 89-92%, respectively) followed by UABla-12 and UASt-09 (79-86% and 80-
68
83%, respectively) for HT-29 cells (Figure 4.3A) the difference was not statistically significant. Comparatively, DDS-1 and UALp-05 showed relatively more adherence (81-88% and 80-87% respectively, Figure 4.3B) to LS174T cells. The adhesion capacities for the strain UABla-12 (p = 0.02) was significantly lower than DDS-1 and UALp-05 (p = 0.
(A) HT-29
100
80
60
40
% Adhesion 20
0
D D S-1 U A St-09 UABla-12 U A Lp-05 S train s
(B) LS174T
* 100 *
80
60
40 % Adhesion 20
0
D D S-1 U A St-09 UABla-12 U A Lp-05 S train s
Figure 4.3: Adhesion capacity of each bacterial strain to HT-29 (A) & LS 174T cells (B) after the 4h incubation period. Data represent mean ± SEM from three independent
69
measurements. Statistical analysis was performed by One-way ANOVA with Tukey’s multiple comparison (pairwise) tests. *p < 0.01.
4.4.3 Cell cytotoxicity and viability
To assess the safety of the tested strains and any cytotoxicity, LDH and trypan blue assays were performed on epithelial cell culture supernatants (Figure 4.4). After 8 h incubation, all the other strains except DDS-1 showed a slightly increased LDH level, but, no significant cytotoxicity was observed. Similarly, after 8 h incubation, DDS-1 showed no significant loss of cell viability, but a slight loss of cell viability was observed with UABla-12, UASt-09, and
UALp-05 when compared to untreated cells
HT 29
70
(C) LS174T
120
100
80
60
40 % LDH Release 20
0 C ells LS174T LS174T LS174T LS174T LS174T S train s - DDS-1 UABla-12 UALp-05 UASt-09
71
(D) * ** ** 100
80
60
40 % V iability
20
0 C ells LS174T LS174T LS174T LS174T LS174T S train s - DDS-1 UABla-12 UALp-05 UASt-09
Figure 4.4: Effect of probiotics on cellular cytotoxicity/viability of intestinal epithelial cells
Cell cytotoxicity and viability on HT 29 (A-B) and LS174T cells (C-D) was determined after
8 h incubation with each probiotic strain. Cell cytotoxicity was measured using LDH assay
and data was represented as percentage LDH release. Viability was measured by trypan blue
exclusion assay and data was represented as percentage viability. All the data is presented as
mean ±SEM. Statistical analysis was performed by Two-way ANOVA test. * p < 0.05
4.4.4 Immuno-modulatory effects by probiotic strains
All strains modulated specific pro and anti-inflammatory cytokines (Figure 4.5). Each strain
had a significant effect on the release of pro-inflammatory IL-8, and TNF-α as well as anti-
inflammatory IL-10. When HT-29 cells were challenged with LPS for 8 h, this resulted in
upregulation of IL-8 and TNF-α levels (1182.19 ± 165.78 and 2.56 ± 0.24 pg/mL,
respectively). IL-8 secretion was significantly downregulated during no-treatment (Figure
4.5A) when compared to HT-29 cells alone as a control (574.24 ± 24.25 pg/mL). During co-
(Figure 4.5B) and post-treatment (Figure 4.5C), all the strains significantly downregulated
72
IL-8 levels (p < 0.0001) when compared to LPS control with the greatest effect shown by
DDS-1 followed by UALp-05, UABla-12, and UASt-09. Overall, there was a statistical by
significant difference observed between DDS-1 and UASt-09 (p = 0.002) under no- and post- treatment, while there was no change in the IL-8 secretion between strains under co-treatment.
Similar to the observed inhibition of release of IL-8, DDS-1 marked reduced TNF-α levels under the three tested conditions (Figures 4.5D-F). For no-treatment condition, TNF-α levels were downregulated by 1.22 ± 0.51 pg/mL by DDS-1 and 0.98 ± 0.49 pg/mL by UALp-05 compared to the cells alone control (2.0 ± 0.32 pg/mL). Interestingly, UASt-09 significantly upregulated the TNF-α levels (3.50 ± 0.50 pg/mL, p = 0.002). Upon co-treatment, TNF-α levels were found to be downregulated to 1.0 ± 0.51 pg/mL by DDS-1, and (1.0 ± 0.26 pg/mL) by UALp-05, while no major change in TNF-α levels was observed with UABla-12 compared to LPS treated control (2.32 ± 0.51 pg/mL). Relative to LPS treated control during the post-treatment, DDS-1 downregulated the TNF-α levels from 2.32 ± 0.51 pg/mL to 0.68 ±
0.10 pg/mL followed by UALp-05 which reduced levels to 1.19 ± 0.51 pg/mL. UABla-12 and UASt-09 did not affect the TNF-α levels. The differences were statistically significant between DDS-1 and UASt-09 (p = 0.002) under no-treatments and between DDS-1, UABl-12
(p = 0.01) and UASt-09 (p = 0.01) under post-treatment.
All the strains, to some extent, upregulated the levels of anti-inflammatory cytokine IL-10 under no-treatment condition (as shown in Figure 4.5G). The basal levels of IL-10 on cells alone (control) were 2.72 ± 0.51 pg/mL and the strains upregulated by 25.25 ± 1.99 pg/mL
(DDS-1, p = 0.0003), 38.65 ± 6.40 pg/mL (UABla-12, p < 0.0001), 40.65 ± 3.75 pg/mL
(UALp-05, p < 0.001) and 24.85 ± 8.12 pg/mL (UASt-09, p < 0.001). Upon co-treatment,
DDS-1 significantly upregulated the levels of IL-10 by 45.53 ± 6.71, followed by UABla-12
(30.02 ± 4.26 pg/mL), UALp-05 (30.32 ± 6.21 pg/mL) and UASt-09 (21.82 ± 5.6 pg/mL)
(Figure 4.5H) compared to LPS treated control (3.45 ± 0.5 pg/mL). During post-treatment
73
condition, only DDS-1 and UABla-12 were able to upregulate IL-10 levels by 76 pg/mL and
62.84 pg/mL (p < 0.0001) respectively (Figure 4.5I). The statistically significant differences in IL-10 secretions were found between DDS-1 and UALp-05 (p < 0.001) under no-treatment and between DDS-1, UABla-12 (p < 0.001), UALp-05 (p < 0.001) and UASt-09 (p < 0.001) under co-treatment. There was no difference between strains under post-treatments.
74
Figure 4.5: Detection of cytokines released in the supernatants of HT-29 cells (control) after
three conditions using the Bio-Plex® 200 system.
Detection of IL-8 levels (A, B, C), TNF levels (D, E, F) and IL-10 levels (G, H, I) after no
(probiotic only), co (LPS and probiotics together) and post-treatment (challenged with LPS first for 4 h and later probiotics for another 4 h). The values of groups designated with different letters are significantly different (p < 0.05). Statistical analysis was done by Two-
way ANOVA. * indicates the significant differences. *p <0.05, **p <0.005, ***p < 0.001,
****p < 0.0001. nd = not detected.
75
4.5 Discussion
This study provides insightful information on the differences in the probiotic potential of individual LAB strains obtained from different origins.
The role of the origin of probiotic strain selection is often debated. Theoretically, human gut colonization is facilitated by selecting probiotics of human origin. However, numerous studies have shown that maximum probiotic effect after human consumption is strain-specific but not restricted to human origin strains. For example, B. animalis ssp. lactis of animal origin proved to be safe and modulated the gut microbiota in human subjects [211]. Similarly,
B. lactis CNCM I‐2494 of dairy origin have alleviated minor digestive symptoms in healthy women [212]. Although there are numerous in vitro studies showcasing the functional properties of probiotics, there are no studies comparing probiotic efficacies with origins. To our knowledge, this is the first study to elucidate the link between origin and efficacy of the probiotics in vitro with an assessment of the survival of each strain under simulated digestion process, their adhesion capacities to human colonic cells and their immunomodulatory effects.
Resistance to gastric and intestinal environments after oral administration is considered as one of the most important traits of probiotics. Many LAB strains have been reported to exhibit excellent resistance to pH around 2.5 ± 3.0 and 6.5 ± 0.5 in gastric and intestinal conditions respectively [194, 204, 213, 214]. Generally, the L. acidophilus species is considered well adapted to survive harsh conditions of digestion. In fact, due to this ability, L. acidophilus has been considered an ideal vehicle for mucosal-targeted delivery of vaccines and biotherapeutics [215, 216]. In the present study, probiotic strains obtained from different origins were tested for their survivability in an in vitro simulated digestion static model, and all of them displayed good survivability (106-108 CFU/mL) compared to their initial concentrations of 109 CFU/mL. However, the concentrations of UABla-12 and UASt-09
76
decreased around one logarithmic unit compared to their initial concentrations after exposing
the strains to low pH and digestive enzyme in the gastric compartment. The viability loss of
UABla-12 may be linked to its obligate anaerobic nature, and for UASt-09 its acid tolerance capacity is strain-specific [193, 194, 195, 202, 204, 223]. The acid/enzyme tolerance by lactobacilli (and most of the gram-positive strains) can be attributed, in part, to the presence of a constant gradient between extracellular and cytoplasmic pH [217, 218]. However, the strains in our study showed significantly better resistance in the gastric compartment compared to other studies [202, 204].
In our study, the strains were not only exposed to different pH ranges, salivary, gastric and intestinal juices, but also to bile and digestive enzymes such as pepsin, and pancreatic enzymes in a continuous process. Overall, all the strains were decreased by only one logarithmic unit in the intestinal compartment. Specifically, DDS-1 maintained higher
concentrations followed by UALp-05, UABla-12, and UASt-09. This phenomenon may be linked to the ability of these strains to produce bile salt hydrolase (BSH), and previous studies have demonstrated BSH genes in lactobacilli strains which are believed to be responsible for their resistance [219, 220, 221, 222].
Adhesion capacity to colonic cells also plays a role in the selection of probiotic bacteria [204].
The human colonic cells such as HT-29, LS174T, and Caco2 are commonly used to test probiotic bacterial adhesion capacity [194, 202, 223]. High adhesion of LAB strains to colonic cells, as seen in our study, is reported in most studies [194, 204, 202, 223]. Toscano and colleagues [194] tested three different strains of Bifidobacterium on HT-29 cells and found moderate to low adhesion capacity. The relatively low adhesion capacity of
Bifidobacteria [194] was quite similar to that observed with UABla-12 strain in our study.
The probiotic adhesion ability involves various biophysical and biochemical properties of
77
probiotics and epithelial cells, includes electrostatic forces, hydrophobicity, steric & passive
forces and specific cellular structures [202].
Bu and colleagues [224] compared the differences in biochemical and cellular properties of
LS174T and HT-29 cells, is a study extensively based on mucin expressions among the cells
involving their role in adhesion. Mucus is mostly composed of mucins protein blocks which
are encoded with MUC genes. Out of 20 identified human mucins, MUC 2 and MUC 5 are
implicated in various ligand-receptor interactions. Especially, MUC 2, major gastrointestinal
mucin secreted by goblet cells, plays a particularly important role in conferring the mucin barrier function of the gut. Morphologically, LS174T cells have many surface microvilli and more intercellular spaces than HT-29 cells. Surface microvilli play a role in the adhesion
capacity of microbes to epithelial surfaces, and Bennett and colleagues [225] demonstrated
that more surface microvilli establish an electrostatic barrier which may repel the microbes
from adhering to epithelial cells. High adhesion capacity of the strains used in the present
study might be linked to the presence of MUC 2 gene and comparatively fewer surface microvilli.
Lactobacillus and Bifidobacterium species strains are generally considered to be safe [197,
204]. In the present study, none of the tested strains showed cytotoxic effects on HT-29 and
LS174T cells. This is in agreement with the safety profile observed clinically with these strains [226, 227]. Furthermore, many studies have revealed the ability of various lactobacilli and bifidobacteria strains to limit the cytotoxicity caused by many entero-pathogens [228,
229]. In particular, the human origin strains, DDS-1 and UABla-12, have previously been shown to be safe and efficacious in multiple randomized controlled clinical studies. In randomized controlled trial enrolling 96 preschool children with moderate-to-severe atopic dermatitis, subjects receiving DDS-1 and UABla-12 experienced a more rapid improvement in atopic dermatitis questionnaire scores, as well as improved immune markers, when
78
compared to placebo over an 8-week intervention period [226]. Similarly, in another
randomized controlled trial involving 225 children, short-term use of DDS-1 and UABla-12 was found to significantly shorten the duration of acute respiratory tract infection, when compared to placebo [227].
Immunomodulatory capacity is considered as an additional factor for the selection of probiotics. A number of in vitro probiotic studies have investigated the anti-inflammatory properties of probiotics to modulate the release of pro- and anti-inflammatory cytokines including IL-6, IL-8, TNF-α and IL-10 [202, 204, 230, 231]. In our study, most of the tested strains downregulated the pro-inflammatory IL-8, and TNF-α and upregulated IL-10 levels when compared to controls, but the rate and extent of the immunomodulatory effect varied between the strains and under different conditions. The upregulation of pro-inflammatory cytokines upon LPS treatment could be attributed to T cell proliferation and activation, and the upregulation of anti-inflammatory cytokines in the presence of these strains could be associated with counteracting molecular mechanisms leading to T cell activation [210]. As shown in previous studies, probiotics could influence the activation of dendritic cells (DCs) and macrophages of the innate immune system [190, 232]. Upon activation, DCs release anti- inflammatory TGF-β and IL-10, which allows the proliferation of adaptive T cells.
All the tested strains significantly suppressed IL-8 levels under no-, co- and post-treatment conditions. NF-ĸB and IκB pathways are crucial targets in the initiation of inflammatory responses evoked by cytokines including IL-6, IL-8, and pathogens. Ohkusa and colleagues
[231] showed that VSL#3 probiotic suppressed IL-8 levels by inhibiting nuclear translocation of NF-ĸB. Reduction of IL-8 levels in our study could be attributed to inhibition of NF-ĸB.
Only DDS-1 and UALp-05 were able to downregulate TNF-α levels in all conditions, which is in agreement with previous probiotic studies [202, 210]. Both UABla-12 and UASt-09 having no stimulatory effect on TNF levels under co- and post-treatment was consistent with
79
the previous studies which showed an increase in TNF-α levels with probiotic treatment [202,
230]. The mechanism of downregulation of TNF-α levels by LAB strains has been linked to activation of certain pathways like extracellular-signal-regulated kinase (ERK), p38 and phosphoinositide 3-kinase (PI3K) pathways [233]. The beneficial effects of probiotics can be immuno-stimulatory rather than immuno-suppressive, suggesting that probiotics can restore the breach of the innate immune system and prevent the onset of inflammation by stimulation of TNF-α [189, 190]. All the tested strains induced the release of anti-inflammatory cytokine
IL-10 during no- and co-treatment itself. However, only DDS-1 and UABla-12 were able to induce the secretion of IL-10 levels during post-treatment, which is consistent with previous studies conducted in colonic cells [210, 233]. In this particular case, a few studies reported that the release of IL-10 by probiotics is a strain, origin, and dose-dependent [202, 230, 234].
Overall, all the strains demonstrated strong immunomodulatory effects; but specifically, the strain DDS-1, which strengthens its probiotic potential.
4.6 Conclusion
Taken together, all the strains showed good survival in the simulated digestion process, strongly adhered to the intestinal epithelium and demonstrated an adequate immunomodulatory effect. However, the comparative analysis of the data suggests that origin may affect their probiotic potential. This was particularly in the case of human origin DDS-1
strain, which showed superior characteristics compared to other strains and could be an
interesting candidate for more intensive research to elucidate and harness the bioactive effects.
80
Chapter 5: Lactobacillus acidophilus DDS-1 modulates the gut microbiota and improves metabolic profiles in ageing mice
An original version of this chapter has been published in Nutrients (Journal of Human
Nutrition MDPI) as a research investigation and appears in the literature as:
Vemuri Ravichandra, Shinde T, Gundamaraju R, Gondalia S, Karpe A, Beale D, Martoni C,
Eri R. Lactobacillus acidophilus DDS-1 modulates the gut microbiota and improves
metabolic profiles in aging mice. Nutrients. 2018,6;10(9):1255.
Clarivate Analytics/Thompson Reuter journal impact factor: 4.19
SJR journal ranking: Q1, 1.56
Citations: 2
The word aging has been changed to ageing to maintain consistency throughout chapters.
The all the figure layouts are modified from landscape to portrait format to maintain
consistency.
81
5.1 Abstract
Recent evidence suggests that gut microbiota shifts can alter host metabolism even during healthy ageing. Lactobacillus acidophilus DDS-1, a probiotic strain, has shown promising probiotic character in vitro as well as in clinical studies. The present study was carried out to investigate whether DDS-1 can modulate the host metabolic phenotype under the condition of age-affected gut microbial shifts in young and ageing C57BL/6J mice. Collected fecal samples were analyzed using 16S rRNA gene sequencing for identifying gut microbiota and untargeted gas chromatography-mass spectrometry (GC-MS) metabolomics analysis. Gut microbial shifts were observed in the control groups (young and ageing) leading to an alteration in metabolism. Principal coordinate analysis (PCoA) of microbiota indicated distinct separation in both the DDS-1-treated groups. L. acidophilus DDS-1 increased the relative abundances of beneficial bacteria, such as Akkermansia muciniphila and
Lactobacillus spp., and reduced the relative levels of opportunistic bacteria such as
Proteobacteria spp. Metabolic pathway analysis identified 10 key pathways involving amino acid metabolism, protein synthesis and metabolism, carbohydrate metabolism and butanoate metabolism. These findings suggest that modulation of gut microbiota by DDS-1 results in
improvement of metabolic phenotype in the ageing mice.
82
5.2 Introduction
Microbes residing in a mammalian gut co-evolve with age. These microbes, known as the
‘gut microbiota’, interact with each other as well as the host and, impact on the health of the
host [235]. Alterations in gut microbial communities are known to not only influence but also
act as a possible causative factor for various gastrointestinal (GI) and metabolic disorders
[235, 236, 237]. Apart from antibiotics, diet, and environmental factors, the age of the host is
also linked with the gut microbial changes [236, 238, 239]. Recently, human and animal
studies have reported microbial shifts in the ageing gut under healthy conditions [235, 236,
237, 239]. From these studies, it is known that Bacteroidetes and Firmicutes which dominate
in younger individuals are being reduced with age [236, 238, 240]. Also, the abundance of
beneficial bacteria, such as Akkermansia spp. and Lactobacillus spp. were reduced in the
ageing population [241, 242, 243]. Such drastic microbial shifts in healthy-ageing could lead
to increased intestinal inflammation and changes in host metabolism.
Along these lines, a few animal and clinical studies demonstrated metabolic trajectory
associated with gut microbiome upon ageing [244, 245, 246]. Primarily, these studies
indicated alterations in levels of metabolites involved in the metabolism of amino acids,
carbohydrates, nucleotides, lipid and short-chain fatty acids (SCFA) in the ageing group
compared to the young group, leading from normal to putrefactive metabolism [244, 248,
249]. Given the potential for gut microbiota to change with age and its influence on host metabolism, modulation of gut microbial composition offers an opportunity to promote digestive health among the ageing populations. A number of studies have demonstrated that the lactic acid bacteria (LAB) probiotic strains, especially Lactobacillus spp. are natural, safe and beneficial modulators of the gut microbial composition and metabolic phenotype [250,
251, 252]. Indeed, clinical studies have shown that supplementation of Lactobacillus strains improved the overall health in elderly individuals [251,253, 254, 255, 256, 257].
83
Previously, we showed that the DDS-1 strain has a promising ability to survive under stressed
conditions (pH, salts and digestive enzymes), colonize in the gut and modulate the immune
system [252]. Clinically, the DDS-1 strain has alleviated the symptoms associated with
lactose intolerance in healthy volunteers between 18 to 75 years of age [258]. In addition, this
particular probiotic strain has reduced symptoms of atopic dermatitis by improving immune
markers and, significantly shorten the acute respiratory tract infection [259, 260,261].
However, there are no studies on DDS-1 evaluating its capacity to alter the microbiota and
metabolic phenotypes. The above-mentioned studies [252, 258, 261] encouraged us to
hypothesize that the probiotic strain, L. acidophilus DDS-1 could further improve the
beneficial microbial composition of a healthy gut and host metabolism. Precise identification of metabolites associated with DDS-1 treatment throughout ageing will be beneficial to identify disease-modifying, clinical accessible biomarkers for host health [262].
In the current study, we employed a healthy ageing-based model to evaluate the direct effects
of DDS-1 on gut microbiota changes associated with fecal metabolome composition. A
combination of omics-based approaches involving 16S rRNA gene sequencing and
untargeted GC-MS based metabolomics were utilized to provide a comprehensive
understanding of changes in host metabolic phenotype of altering gut microbiota with and
without DDS-1 supplementation.
5.3 Methods
5.3.1 Ethics statement
All animal procedures were performed in accordance with the Australian Code of Practice for
the Care and Use of Animals for Scientific Purposes of the National Health and Medical
Research Council. The study was approved by the Animal Ethics Committee of the
University of Tasmania (A0015840).
84
5.3.2 Bacterial culture and probiotic feed preparation
The bacterial strain utilized in the study, L. acidophilus DDS-1, was obtained in freeze-dried,
free-flowing lyophilized form from UAS Labs, Madison, WI, USA. The bacterial culture was suspended in autoclaved water before making the probiotic-chow mix at a concentration of approximately 3 × 109 CFU/g in a manner similar to Kuo et al. [263].
5.3.3 Animals and probiotic treatments
A total of 32 mice were used in the present study. Young (age = 3 – 4 weeks, n = 16) and
ageing (age = 35 - 36 weeks, n = 16) C57BL/6J mice of both sexes were obtained from the
University of Tasmania animal breeding facility. Animals were housed within individually
ventilated cages containing a corncob bedding (Andersons, Maumee, OH, United States), in a
room with a temperature maintained at 21-22°C, with a 12-h light/dark cycle. Mice were
allowed access to radiation-sterilized rodent feed (Barastoc Rat and Mouse (102108), Ridley
Agriproducts, Melbourne, Australia) and water available ad libitum. The young and ageing
mice were randomly divided into 4 groups (n = 8) and housed in individual cages containing,
(1) Young control group (YC) fed with normal chow, (2) Young probiotic group (YP) fed
with probiotic chow, (3) Ageing control group (AC) fed with normal chow and, (4) Ageing probiotic group (AP) fed with probiotic chow. All the mice were fed for 4 weeks with group- specific chow
5.3.4 Fecal Sample collection and preparation
Throughout the experiment, the weight of each mouse was recorded each day, and fecal samples were collected on the 28th day in a manner similar to Langille et al. [236]. To minimize contamination, on the day of sampling mice had no access to food and water, and sterile forceps were used for fecal sample collection. At least 2 pellets (100 - 150 mg of the sample) were collected from each mouse and immediately transferred into a sterile
85
microcentrifuge tube, stored at -80 0C for 16S rRNA gene sequencing and metabolomic
analysis.
5.3.5 Microbiota analysis using 16S rRNA High-Throughput sequencing
The total DNA was extracted from fecal samples using the QIAamp DNA Stool Mini Kit
(Qiagen, Melbourne, VIC, Australia). The samples underwent high-throughput sequencing on
the Illumina MiSeq platform at the Australian Genome Research Facility (University of
Queensland, Brisbane, QLD, Australia). PCR amplicons spanning the 16S rRNA V3-V4
hypervariable region with forward Primer (27F) AGAGTTTGATCMTGGCTCAG reverse
Primer (519R) GWATTACCGCGGCKGCTG were sequenced. Paired-end reads were assembled by aligning the forward and reverse reads using PEAR1 (version 0.9.5) [264].
Primers were identified and trimmed. Trimmed sequences were processed using Quantitative
Insights into Microbial Ecology (QIIME 1.8) 4 USEARCH 2.3 (version 8.0.1623) and
UPARSE software [265]. Using USEARCH tools, sequences were quality filtered; full-length
duplicate sequences were removed and sorted by abundance. Singletons or unique reads in
the data set were discarded. Sequences were clustered followed by chimera filtered using
“rdp_gold” database as a reference [266, 267]. To obtain a number of reads in each
Operational taxonomic units (OTUs), reads were mapped back to OTUs with a minimum identity of 97%. Using QIIME taxonomy was assigned using Greengenes database5 (Version
13_8, Aug 2013). Image analysis was performed in real time by the MiSeq Control Software
(MCS) v2.6.2.1 and Real-Time Analysis (RTA) v1.18.54, running on the instrument
computer. RTA performs real-time base calling on the MiSeq instrument computer. Then the
Illumina bcl2fastq 2.20.0.422 pipeline [266] was used to generate the sequence data. 16S
rRNA gene sequences were analyzed using MEGAN6 (Community edition version) [268],
Microbiome analyst [269] and QIIME. Statistical analysis of Brady-Curtis dissimilarities
86
calculated using the relative abundances of bacterial genera was conducted using Adonis
function in R (version 3.2).
5.3.6 Fecal Metabolomics
The samples were subjected to derivatisation to increase volatility before subjecting to GC-
MS analysis. Briefly, fecal samples (n = 5, weight = 40 mg) were freeze-dried and suspended
in 1 mL methanol (LC-MS grade, Merck, Castle Hill, NSW, Australia), supplemented with
10 µg/mL adonitol (Analytical grade, Sigma Aldrich, Castle Hill, NSW, Australia) as an
internal standard in sterile 2 mL bead-beating tube. The samples were homogenized by bead beating for 30 s and then centrifuged at 570 g/ 4°C for 15 minutes. The supernatant (50 µL) was transferred to a fresh centrifuge tube (1.5 mL) and dried in a vacuum evaporator centrifuge (LabGear, Brisbane, QLD, Australia) at 35°C. Methoxyamine-HCl (20 mg/mL in
Pyridine) (both, Analytical grade, Sigma Aldrich, Castle Hill, NSW, Australia) was added
(40 µL), and samples were incubated at 37°C/ 1400 rpm (ThermoMixer C, Eppendorf,
Hamburg, Germany) for 45 minutes. This was followed by silylation with 70 µL BSTFA at
70°C/ 1400 rpm for 60 minutes. Pre-derivatized 13C-Stearic acid (10 µg/mL) was added (1
µL) as the QA/QC internal standard. The mixture was briefly vortexed and centrifuged at
15,700 g for 5 min. The aliquot was transferred to vials for GC-MS analysis.
The GC-MS analysis was performed on an Agilent 6890B gas chromatograph (GC) oven coupled to a 5977B mass spectrometer (MS) detector (Agilent Technologies, Mulgrave, VIC,
Australia) fitted with a MPS autosampler (Gerstel GmbH & Co.KG, Deutschland, Germany).
The GC-MS conditions were as stated previously [270, 271, 272]. Data acquisition and spectral analysis were performed using the Qualitative Analysis software (Version B.08.00) of MassHunter workstation. Qualitative identification of the compounds was performed according to the Metabolomics Standard Initiative (MSI) chemical analysis workgroup [273,
87
274] using standard GC-MS reference metabolite libraries (NIST 17, Fiehn Metabolomics
RTL Library [G166766A, Agilent Technologies] and the Golm database) and with the use of
Kovats retention indices based on a reference n-alkane standard (C8-C40 Alkanes Calibration
Standard, Sigma-Aldrich, Castle Hill, NSW, Australia). For peak integration, a 5- point
detection filtering (default settings) was set with a start threshold of 0.2 and a stop threshold
of 0.0 for 10 scans per sample. Procedural blanks (n = 7) were analyzed randomly throughout
the sequence batch. The obtained data was processed on Quantitative analysis platform of
MassHunter workstation and exported as a Microsoft Excel output file for statistical analysis.
5.3.7 Data analysis and Multivariate analysis
GC-MS data imported to Microsoft Excel platform was normalized with respect to the
internal standard adonitol (relative standard deviation = 11.257%). The normalized data was
further log-transformed and auto-scaled (mean-centered) before statistical analysis [274]. To determine overall microbial variation in four groups, we used a principal coordinate analysis
(PCoA), Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram,
Neighbour-Net (a distance-based method for constructing phylogenetic networks) hierarchical clustering with Brady-Curtis ecological indexing and Euclidean distances as the similarity measure and Ward’s linkage as clustering algorithm [237, 274]. For analysis of metabolome variations, Principal component analysis (PCA), partial least squares- discriminant analysis (PLS-DA) and orthogonal (O) PLS-DA were used. Because PLS-DA can overfit data, we used 1000 permutations to validate these models. The OPLS-DA was used to identify discrimination between metabolites contributing to classification. For heatmaps, the Euclidean distances were used as the similarity measure and Ward’s linkage as a clustering algorithm. Metabolite set enrichment analysis (MSEA) and Metabolic pathway analysis (MetPA) methods were used to perform correlation analysis and to identify treatment
88
associated biochemical pathways [275]. P-values have been corrected to account for false discovery rates using Holm-Bonferroni method and Benjamin Hochberg correction.
5.3.8 Statistical analysis Graph Pad Prism version 7.0 for Windows was used for the statistical analysis. The data were analyzed using the Wilcoxon Mann–Whitney Test (multiple comparisons), with p < 0.05 set as the level of statistical significance. For microbial comparative analysis, a linear discriminant effect size (LEfSe) analysis was performed (α = 0.05), logarithmic Linear
Discriminant Analysis (LDA) score threshold = 1.0. A MetaboAnalyst (Version 4.0) data annotation approach and Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway
Database were used for the hierarchical clustering analysis, significance analysis for microarrays (SAM) along with the variable importance of projection (VIP) [276]. The SAM and VIP methods are a well-established statistical method for metabolites, was used to select the most discriminant and interesting biomarkers [277].
5.4 Results
5.4.1 Body weight during days of treatments
There was no significant change in body weights of mice treated with DDS-1 (Figure 5.1).
89
AC 40 AP YC 30 YP
20
10 Body weight (g) weight Body
0 0 1 5 10 15 20 25 30 Day of Dietary treament
Figure 5.1: Effect of DDS-1 supplementation on body weights among the four groups (YC)
Young control group, (YP) young probiotic group, (AC) ageing control group, (AP) ageing
probiotic group
5.5.2 Gut Microbial changes at the phylum level
Fecal microbiota profiling was performed using 16S rRNA gene sequencing-based method.
To compare the changes among the groups, PCoA was used. The PCoA plot of phylogeny
with Brady-Curtis ecological indexing using ward clustering showed a clear separation of
each group with three distinct clusters (Figure 5.2A) at the OTU level among 4 groups. After
DDS-1 treatment, both AP and YP groups segregated when compared to control groups.
Particularly, the AP group more significantly separated from AC as depicted in UPGMA
hierarchical clustering dendrogram (p < 0.01) (Figure 5.2B) and Neighbour-Net (Figure
S5.1). Moreover, around 99% of the total microbial abundance was classified into nine major
phyla in the ageing group and seven in the young group; rest were allocated as unclassified or others (Figure 5.2C). The dominant phyla were Bacteroidetes and Firmicutes with their
90
numbers higher in the young group (86.46 ± 2.10% and 8.20 ± 1.20%) compared with the
ageing group (63.35 ± 3.10% and 26.33 ± 4.45%) (p < 0.001).
Figure 5.2: The gut microbiota changes observed in the young control group (YC), young probiotic group (YP), ageing control group (AC), ageing probiotic group (AP) differentiated
by PCoA (A) and UPMGA dendrogram (B). The gut microbiota composition profiles at
phylum (C) and genus levels (D) in control and probiotic-treated group revealed by 16S rRNA gene sequencing (each color represents bacterial phylum and/or genus).
A.
PC2: 24.9%
PC3: 17.5% PC1: 43.2%
91
B.
C.
92
D.
The treatment with DDS-1 helped increased Firmicutes levels from 8.40% to 9.32% (YP) (p
< 0.001) and 26.33% to 30.72% (AP) (p < 0.001). However, it decreased the levels of
Bacteroidetes in both the groups reflecting the effect of DDS-1 supplementation on overall abundance of Firmicutes. Verrucomicrobia abundance was significantly increased in both treatment groups (YP: 0.72% to 2.10%; AP: 0.62 to 4.20%) (p < 0.001). Proteobacteria levels were decreased from 1.70% to 0.58% in the ageing group after DDS-1 treatment. These changes in the ageing groups were further confirmed with LEfSe analysis (α = 0.05), LDA approach keeping the cut-off value for p = 0.01 and log LDA threshold score = 1.0 (Fig.
5.3A). Also, under phylum Bacteroidetes, the relatively novel family S24-7 were overrepresented in both the control groups (YC: 70.30% and AC: 52.8%), and their relative abundances were further decreased with DDS-1 treatment (YC: 67.50% and AC: 50.8%).
Overall correlation analysis was performed using Spearman correlation analysis (Figure S5.2) with p < 0.01 as significant.
93
5.4.3 Gut microbial changes at the genus and species level among four groups
At the genus level, the distribution of microbial populations of the ageing groups was markedly different from the young groups. The AC group had significantly higher abundances of Staphylococcus, Ruminococcus, and Sutterella relative to the AP group. The
DDS-1 treatment has increased the abundances of Akkermansia, Adlercreutzia, Allobaculum,
Rikenella, and Anaeroplasma while reduced the abundances of Dorea, Oscillospira, and
Ruminococcus (Figure 5.2D). It is noteworthy that the Lactobacillus (p < 0.001) abundances significantly increased in the AP group and was further confirmed with the LDA approach
(Figure 5.3B). At the species level, DDS-1 treated groups have significantly increased A. muciniphila (p < 0.001) and decreased the levels of R. gnavus, Bacteroides acidifaciences and B. uniformis compared to the AC group (Figure 5.3D). Adding to this change, an abundance of Mucispirillum schaedleri (0.04 ± 0.02% to 0.10 ± 0.06%) was also increased in the AP group.
The YC group was enriched with Prevotella, Ruminococcus, and Sutterella while very low levels of Adlercreutzia, Allobaculum, Alphaproteobacteria, were identified compared to the
AC group. Anaeroplasma and Staphylococcus were absent compared to the AC group. The treatment with DDS-1 increased the levels of Akkermansia (p < 0.05), Parabacteroides and
Odoribacter (Figure 5.2D). It also further increased Prevotella and Sutterella levels (p <
0.05) and was confirmed using the LDA approach (Figure 5.3C) in YP group. Similar to AP group, at the species level in YP group, A. muciniphila (p < 0.001) levels were considerably increased, and R. gnavus, Bacteroides acidifaciences, and B. uniformis level were decreased compared to the YC group (Figure 5.3D). Certain bacterial species, such as M. schaedleri, were undetected in young groups. The beneficial effects of DDS-1 on modulating the gut
94
microbiota were found to have a differential effect between the two age groups. The
treatment with DDS-1 significantly altered AP group which was evident with the higher
levels of Lactobacillus in fecal samples.
Figure 5.3: Comparative analysis with Linear Discriminant Analysis (LDA) Effect Size
(LEfSe) scoring plot using the Kruskal-Wallis rank sum test (p = 0.01 and log LDA threshold
cut-off value = 1.0). Ageing groups at Phylum (A) and genus (B) level. Young groups at
genus (C) level. Significant changes at the species (D) level. (YC) Young control group, (YP) young probiotic group, (AC) ageing control group, (AP) ageing probiotic group.
A.
AP
AC
95
B.
AP
AC
96
C.
YP
YC
97
D.
AP
AC
YP
YC
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Bacteroides acidifaciens Bacteroides uniformis Parabacteroides distasonis Dehalobacterium Ruminococcus gnavus Akkermansia munciphilia Lactobacillus_unknown Mucispirillum Schaedleri
5.4.4 Metabolic phenotyping of YC and YP groups
To gain an untargeted overview of probiotic-induced changes in dominant gut metabolites, we analyzed fecal samples using a GC-MS platform. A total of 68 metabolites of different functional groups such as sugars, amino acids, SCFAs, biogenic amines were detected. An unsupervised PCA was used to obtain an overview of the samples, and all samples were discriminated as shown in Figure 5.4A. PCA analysis showed distinct clustering of the control group and DDS-1 treated group fecal samples. The biplot for PC1 and PC2 showed the compounds with the greatest impact on the division among the samples as shown in
Figure 5.4B. In order to provide better visualization and to carry out the class separating information of each variable, a supervised PLS-DA approach was used to evaluate the metabolic patterns of YC group and YP group (Figure 5.4C). This analysis indicated the
98
clear differences in the metabolic profiles of mice in YP and YC groups, which suggests that
DDS-1 treatment induced significant biochemical changes. The accuracy, R2X, R2Y and Q2 of PLS-DA score analysis were 0.9, 0.86 and 0.82, respectively (Figure 5.4C), indicating the classifications was well suited for the models, and the control and treated groups were classified clearly.
5.4.5 Identification of potential fecal metabolites associated with the YP/YC groups
The supervised orthogonal PLS-DA (OPLS-DA) was employed to further enhance the observed group separation in PCA and PLS-DA. Combination of PCA, PLS-DA and VIP scores and O-PLS-DA (R2Y= 0.993 (p = 0.01), Q2 = 0.811(p = 0.01)) (Figure S5.3A) and
SAM enabled us to identify potential biomarkers (Figure 5.5A). The metabolites with VIP score > 1 and SAM (Figure 5.5A) could be considered as potential biomarkers responsible for altering the metabolic profile post-DDS-1 treatment. The results showed 36 statistically significant metabolites contributing to the clustering, with their SAM scores and KEGG,
InChI Key IDs listed in Table S5.1. Of the confirmed identifications, the most significant compounds were 4-Guanidinobutanoic acid (Fold changes (FC) = 149.21, p = 0.0055),
Iminodiacetic acid (FC = 75.57, p = 0.022), L-Aspartic acid (FC = 47.02, p = 0.002) and L-
Proline (FC = 31.96, p = 0.001). The varied tendencies of the identified fecal metabolites post
DDS-1 treatment are depicted in the heat map (Figure 5.5B). Finally, the number of markers making a significant contribution was 21 in positive mode and 15 in negative mode using
SAM and VIP analysis as shown in Figure 5.4D.
99
Figure 5.4: A. PCA plot of feces collected from young control (YC, violet color) and young treatment groups (YP, blue color) showing divergence. B. Bi-plot is showing compounds responsible for divergence. C. 2D-PLS-DA plot showing spatial division among groups. D.
Key compounds separating YC and YP based on variable importance in projection (VIP) score plot in PLS-DA analysis.
A.
100
B.
101
C.
102
D.
103
Figure 5.5: Significance analysis of microarray (SAM) plot (A) in positive mode (Delta score = 0.5, metabolites highlighted in green are significant). Significant changes in metabolites are expressed as a heat map (B) in the young group. (YC) Young Control group,
(YP) young probiotic group
A.
104
B.
5.4.6 Metabolic phenotyping of AC and AP groups
Similar to young groups, the ageing groups also yielded 68 metabolites of different functional groups such as sugars, amino acids, SCFAs, and biogenic amines. The PCA and PLS-DA models were performed to evaluate the metabolic phenotyping of the AC group and AP group
(Figure 5.6A, C). The biplot for PC1 and PC2 showed the compounds with the greatest impact on the division among the samples as shown in Figure 5.6B. The metabolic profile of mice in the AP group differed from the AC group indicating significant biochemical changes
105
induced by DDS-1. The accuracy, R2Y, and Q2 of PLS-DA score analysis were 0.763, 0.959
and 0.824, respectively (Figure 5.6C), indicating the classifications was well suited for the
models, and the control and treated groups were classified clearly.
Figure 5.6: A. PCA plot of feces collected from ageing control (AC, red color) and ageing probiotic groups (AP, green color) showing divergence. (B) Bi-plot showing compounds responsible for divergence (C) 2D-PLS-DA plot showing spatial division among groups (D)
Key compounds separating AC and AP based on variable importance in projection (VIP)
score plot in PLS-DA analysis
A.
106
B.
107
C.
108
D.
5.4.7 Identification of potential fecal metabolites associated with the AP/AC groups
The supervised OPLS-DA model was utilized to enhance biomarker identification. The
OPLS-DA method separated the control group and probiotic group into two different blocks
indicating better discrimination (R2Y= 0.994 (p = 0.01), Q2 = 0.863(p = 0.1)) (Figure
S5.3B). From VIP scores (Figure 5.6D), PCA, PLS-DA, and O-PLS-DA approach, we could identify a total of 19 statistically significant metabolites that contribute to the overall cluster
(Table S5.2). Out of identified 19 significantly changed metabolites in feces, 7 compounds were selected as potential biomarkers involved in the classification of AP gut using SAM analysis (Delta value = 0.5) (Figure 5.7A). Of the confirmed identifications, the most significant compounds were Lactose (Fold changes (FC) = 3.16, p = 0.003), Melibiose (FC =
109
3.13, p = 0.04), Cellobiose (FC = 3.06, p = 0.02). The varied tendencies of the identified fecal metabolites post DDS-1are depicted in the heat map for each group (Figure 5.7B).
Figure 5.7: Significance analysis of microarray (SAM) plot (A) (Delta score = 0.5, metabolites highlighted in green are significant). Significant changes in metabolites are expressed as a heat map (B) in ageing groups. (AC) Ageing control group, (AP) Ageing probiotic group.
A.
110
B.
5.4.8 Comparative metabolite phenotyping in young and ageing groups
PCA and PLS-DA analyses of the fecal metabolites showed a divergence between all four
groups (Figure 5.8A and B). The samples from YC and AC were different from YP and AP
groups respectively (Figure S5.4A-D). Fecal samples from the groups YC and YP formed
two clusters. Both the clusters from the young groups were different and showed great
divergence compared to the ageing groups. Similarly, the AP group formed a cluster which
was different from the AC group and more closely related to the YP group (Figure 5.8B).
The bi-plot for PC1 and PC2 shows the variation in the dataset and indicates that specific metabolites associated with the four groups are responsible for divergence among the samples
(Figure S5.5A). VIP-value plot (Figure S5.5B) of the most significant metabolites and
111
Figure S5.5-7 presents heat map correlation analysis showing the relative concentrations of significant metabolites in all four groups.
Figure 5.8: PCA plot (A) and PLS-DA plot (B) showing spatial division among all four groups. (YC) young control group, (YP) young probiotic group, (AC) ageing control group,
(AP) ageing probiotic group
A.
112
B.
5.4.9 Identification of key metabolic pathways using MetPA
Detailed analysis of functional correlations and networks influenced throughout ageing and with DDS-1 supplementation were performed by MetPA and MSEA methods. In order to identify the pathways that had an impact on altering gut microbiota-associated metabolic signature, the ions contributing to the separation of controls and probiotic-supplemented mice were analyzed using MetPA, MetaboAnalyst 4.0 (http://www.metaboanalyst.ca) [274, 276].
A comprehensive metabolic network was mapped using joint pathway analysis by integration of all potential biomarkers identified in the current research.
113
In the young group, metabolic pathway analysis revealed that the metabolites which were
identified as significant are important for host response to DDS-1 treatment (Figure 5.9A and Table S5.3). An impact value was set to 0.10 as a threshold, and any pathway receiving >
0.10 value was filtered out as potential target pathways [236, 262, 274, 275, 276]. These pathways include Valine, leucine and isoleucine biosynthesis, Alanine, aspartate and glutamate metabolism, Pentose and glucuronate interconversions, Aminoacyl-tRNA biosynthesis, Glycine, serine and threonine metabolism, Galactose metabolism and Cysteine and methionine metabolism. In the ageing groups, a total of 5 metabolic pathways were identified (impact > 0.10) (Figure 5.9B and Table S5.4). D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, galactose metabolism, nitrogen metabolism, and butanoate metabolism, were recognized as key pathways in DDS-1-induced changes in metabolic phenotype of the ageing gut. Omics Net was used to select and isolate individual KEGGS-genome-metabolite mapping (http://www.omicsnet.ca) [274, 276]
(Figure S5.9-13). Of all the pathways identified, two pathways were found to be common
among both age groups, specifically galactose metabolism and alanine, aspartate and
glutamate metabolism. These two pathways may play a role in the ability of LAB probiotics
to modulate host metabolism via with the gut microbiota.
114
Figure 5.9: Potential pathways for the young probiotic group (YP) and ageing probiotic group (AP), after DDS-1 supplementation, in fecal extracts identified using MetPA pathway analysis. YP group (A) with pathways (a) Valine, leucine, and isoleucine biosynthesis (b)
Aminoacyl-tRNA biosynthesis (c) Glycine, serine and threonine metabolism (d) Galactose metabolism (e) Cysteine and methionine metabolism. AP group (B) with pathways (f) D-
Glutamine and D-glutamate metabolism (g) Alanine, aspartate, and glutamate metabolism (h)
Galactose metabolism (i) Nitrogen metabolism (j) Butanoate metabolism.
A.
a
c
b
d e
115
B.
f
g
i h
j
116
5.5 Discussion
In our study, we used a combination of 16S rRNA sequencing analysis and untargeted
metabolomics profiling of fecal samples to identify specific gut microbiota changes and
numerous gut metabolites that were associated with response to DDS-1 supplementation in
young and ageing mice. Importantly, DDS-1 increased Firmicutes abundance and enriched
the populations of Akkermansia and Lactobacillus. Metabolic profiling of fecal samples
indicated substantial changes to metabolites generated by gut microbes. Specifically, DDS-1
improved metabolism of pathways associated with amino acids, proteins, and carbohydrates.
Taxonomic differences were evaluated in this study among young and ageing groups with
and without DDS-1 treatment. In line with previous findings, our study confirms a microbial
shift in the ageing mouse gut [236, 239, 278]. At the phylum level, Bacteroidetes and
Firmicutes were most dominant, and their relative abundances were increased in the YC
group compared to the AC group. Acidobacteria and Tenericutes were not detected in the
young groups. DDS-1 treatment increased the Firmicutes population in both YP and AP
groups, reflecting the role of Lactobacillus in overall abundance [236]. It is noteworthy that
Verrucomicrobia levels were also significantly increased with DDS-1 treatment.
Furthermore, Proteobacteria levels, which have been associated with inflammatory bowel
disease (IBD) and colon cancer [238, 250, 279], were doubled in AC relative to the YC group.
DDS-1 treatment was able to reduce their levels indicating a potentially beneficial role as it
relates to controlling intestinal inflammation [252, 258, 263]. Importantly, more than 50% of
the mouse gut was enriched with relatively new family S-24-7, that belongs to Bacteroidetes phylum. Only a few studies could hypothesize the role of S-24-7 in butyrate production.
However, the results were inconclusive, and further research should be performed to understand its role [240, 280].
117
We observed noticeable changes at the genus and species level within the ageing mice groups.
Interestingly, our data found an increase in the abundance of A. muciniphila and
Lactobacillus with DDS-1 treatment specific to the ageing gut, which is consistent with previous studies [242, 251, 281]. Increase in the relative abundance of the phylum Firmicutes could be associated with an increase of beneficial bacterial species, such as Lactobacillus species [250]. The mucin-feeding species, A. muciniphila are thought to be biomarkers of intestinal health and their enrichment has been inversely correlated with IBD and metabolic disorders [236, 250, 278]. Recently, a study on ageing obese mice demonstrated the extracellular vesicles of A. muciniphila improved body weight and lipid profiles of the mice concomitantly with metformin treatment [281]. In several prior studies, animals treated with
A. muciniphila reported improvements in body weight, metabolic profiles, inflammation, and overall gut homeostasis by influencing immunity, mucin-layer thickness, and intestinal epithelial barrier proteins [235, 278, 281]. This was consistent with changes observed in body weights of the mice treated with DDS-1 in our study. Moreover, in our study, the levels of
Allobaculum were increased in mice fed with DDS-1. Interestingly, the major end products of
Allobaculum fermentation is butyrate (SCFA), which serves as a primary energy source in the gut [243]. In addition, bacterial species such as R. gnavus were present in both YC and AC groups. This particular member of Clostridia was directly correlated with the severity of IBD, suggesting its causal role in a dysbiotic shift in the ageing gut [282]. DDS-1 supplementation was able to decrease R. gnavus abundance and thus could possibly reduce inflammation associated with IBD or other conditions 252, 258, 260, 261, 282].
The small molecules present in feces are products of co-metabolism of microbes and host cells. Therefore, metabolite profiling in feces provides insight on novel metabolic biomarkers for health [237, 262]. In an attempt to gain functional insight into changes in the gut microbiome, we have used a functional pathway analysis on probiotic treated gut microbiota-
118
derived metabolites. Untargeted metabolite profiling of control groups and DDS-1 supplemented groups not only allowed us to identify specific metabolites in the young and ageing gut but also to understand the mechanistic role of DDS-1. Our analysis revealed varying levels of metabolites in young and ageing mice. The metabolite profile in the YC group was dissimilar from the AC group, which could be correlated with microbial shift [236,
238, 239]. Interestingly, we found that the metabolic phenotype of AP mice was clustered more towards YP mice (Figure 7B). The DDS-1 treatment allowed us to identify specific metabolites and their pathway-specific expressions profiles which demonstrated changes in metabolism are discussed below [275, 283].
5.5.1 Amino acid and Protein metabolism
In many metabolic pathways, amino acids serve as crucial regulatory substrates [284]. Fecal branched-chain amino acids (BCAAs) such as valine, leucine, and isoleucine were significantly down-regulated in the YP group compared to the YC group. In particular, increases in a small cluster of essential amino acids including the BCAAs (i.e., leucine, valine, and isoleucine) are associated with a ~5-fold increased the risk of prospectively developing diabetes mellitus [237, 240, 275]. On the other hand, Clostridium, Propionibacterium,
Fusobacterium, Streptococcus, and Lactobacillus bacteria are involved in the proteolytic activity to generate amino acids in the gut and linked to metabolic changes [283]. Specifically, altered levels of Streptococcus have been associated with metabolic disorders [240]. In our study, the relative abundance of Streptococcus is decreased in probiotic-treated groups.
Moreover, glycine, serine, and threonine metabolism was increased in the YP group. This could be associated with decreased levels of L-serine in YP and AP; these changes could be positively correlated with Proteobacteria, Prevotella and negatively correlated with
Bacteroides [237, 285].
119
Glutamine, another nonessential amino acid and a precursor of glutamate, is able to promote
beta-cells to secrete insulin under the stimulation of glucose. Glutamate, which can be
converted into gamma-aminobutyric acid (GABA), is of vital importance in the
decomposition of metabolites [286]. In addition, glutamate can also be converted into
ornithine. On the other hand, aspartate can be transformed into asparagine and alanine. A
study by Wang et al. [275] reported that elevated levels of alanine and aspartate have a
positive association with obesity. In our study, the levels of alanine and aspartate were
significantly decreased in the feces of AP group, indicating a potentially beneficial role of
DDS-1 in ageing, which can be positively correlated with an increase in levels of A.
muciniphila. In addition, the ageing process can have a profound effect on protein
metabolism, as well as overall nutritional status influenced by food intake [287, 288].
Therefore, our results speculate that alanine, aspartate, and glutamate metabolism post-
enrichment of A. muciniphila may prevent metabolic syndrome.
Cysteine and methionine are the only two sulfur-containing amino acids that integrate into proteins [289]. Accumulating evidence indicates that the intestinal metabolism of dietary sulfur-containing amino acids methionine and cysteine are nutritionally important for normal intestinal mucosal growth [290]. The cysteine and methionine metabolism pathway was upregulated in the YP group which could be associated with beneficial modulation of gut microbiota and basic metabolism of growth in youth [289]. Our MetPA analysis indicated that L-serine, with importance score of 0.07407, was equally involved in the pathway.
Studies have reported the role of the LAB, such as Lactobacillus species, in the biosynthesis of cysteine from serine, suggesting the role of DDS-1. In particular, this could be positively correlated with decreased levels of Desulfovibrionales in DDS-1 treated groups, which are associated with bacterial infections and Crohn’s disease [250].
120
Aminoacyl-tRNA (A-tRNA) plays a vital role in RNA translation (tRNA) and gene
expression associated with protein biosynthesis [291]. Amino-acylation of tRNA involves A-
tRNA synthetases to catalyze and form an association between cognate amino acid and tRNA,
which is the initial step of protein synthesis. This involves in adenylation of amino acid to A-
tRNA in adenosine triphosphate (ATP) dependent manner to form Aminoacyl-adenosine
monophosphate (AMP). This is followed by the formation of A-tRNA and AMP in the
presence of A-AMP. In turn, an accurate A-tRNA biosynthesis is required for the survival of
all cells [292]. In addition, specific aminoacyl tRNA synthetases are implicated in various
metabolic and neurological disorders [293]. Specifically, tetracyclines, a class of antibiotics,
interfere with the initiation step of protein synthesis by inhibiting the binding of aminoacyl
tRNA to the A-site of the ribosome leading to bacteriostatic effect [294]. The activation of A-
tRNA biosynthesis pathway could be an important factor in amino acid metabolism and
activation of other amino acid pathways mentioned above [295]. It is noteworthy that A-
tRNA biosynthesis pathway was only detected in the YP group. In particular, MetPA
indicated that L-aspartic acid, L-serine, L-valine, L-lysine, L-threonine, Isoleucine with importance scores ≥ 0.02, were significantly involved in this pathway. The altered levels of the above-mentioned amino acids in YP were well correlated with the beneficial role of DDS-
1 in protein synthesis and metabolism.
Nitrogen balance is an essential part of protein metabolism. The biological process of the nitrogen cycle is a complex interplay among many gut microbes catalysing different reactions, where nitrogen is found in various oxidation states ranging from +5 in nitrate to -3 in ammonia [296]. Nitrogen metabolism pathway was only activated in the AP group when compared with the AC group. This could be linked to activation of amino acid metabolism, majorly, D-Glutamine and D-glutamate metabolism, where Glutamine (importance score =
0.11) can serve as a nitrogen donor in the production of amino sugars, nucleotides, and other
121
amino acids 288, 298]. Thus, glutamate/glutamine and aspartate are also associated with
nitrogen metabolism (Figure S5.12 B). On the other hand, removing amino acid nitrogen
from the body is known as transaminations [298]. Transaminations are a class of reactions
which helps in the dissociation of nitrogen from amino acids either by deamination or
oxidation process into a small group of compounds [299]. This result in ammonia production
and their amine groups are converted to urea by the urea cycle [300]. Here, we used the total
amount of urea present in the feces as a factor to measure nitrogen metabolism. The increased
concentration of urea in the AP group indicates that DDS-1 has a regulatory effect on
nitrogen metabolism perturbation [301].
5.5.2 Carbohydrate metabolism
Digestion of lactose produces glucose and galactose, both of which are transported through
the hepatic portal vein directly to the liver [288]. Galactose is metabolized by conversion
initially to glucose 1-phosphate, which can then be converted either to glucose 6-phosphate or
to glycogen. Galactose is crucial for human metabolism, with an established role in energy
delivery and galactosylation of complex molecules [302]. Its main metabolic pathway is
highly conserved in nature, being present in all living organisms. In humans, galactose is
particularly important in early development. Genetic disorders that impair its metabolism
inevitably cause disease, drawing attention to its key role [303]. Excess of galactose
accumulation in tissue leads to various metabolic disorders. According to our MetPA analysis,
metabolites such as D-galactose, melibiose, and lactose are shown to be directly involved in
the D-galactose pathway. In our study, levels of these three metabolites were decreased in both YP and AP which is directly correlated to active galactose metabolism demonstrating the beneficial role of DDS-1. Specifically, this could be correlated with increased in beneficial bacteria, such as Akkermansia species following probiotic treatment [237, 251].
122
5.5.3 Butanoate metabolism
The detailed functional analysis using MetPA analysis in our study indicated the importance
of L-glutamic acid and oxoglutaric acid in Butanoate metabolism. Oxoglutaric levels were
increased in AP when compared to AC. Butanoate or butyrate is a short chain fatty acid and
secondary metabolite produced by gut microbes upon carbohydrate fermentation [241].
Although it was not detected in the untargeted metabolomics, butanoate (SCFA) could be
linked to increased butyrate-producing bacteria in the gut. Bacteria classified under
Firmicutes are directly involved in the Butanoate production [275]. In turn, SCFA act as a
critical energy source for intestinal epithelial cells. Butanoate metabolism pathway activation
in AP could be linked to increased relative abundances of Firmicutes, especially increased
levels of Lactobacillus. A. muciniphila and Allobaculum enrichment in treatment groups is
also positively correlated with SCFA production [250]. The DDS-1 treatment significantly ameliorated and modulated the gut microbiota associated metabolic pathways.
To the best of our knowledge, this is the first detailed study that demonstrates an integrated pattern recognition approach to understand the changes in the ageing gut by exploring the
metabolic network with and without probiotic supplementation. The key metabolic pathways
identified by MetPA and MSEA play an important role in probiotic-induced gut microbiota-
associated metabolic changes. Understanding the complex nature of specific gut microbiome
and metabolome of the mouse, our study corroborates previous findings of gut microbiota
shifts in ageing could result in significant alterations in metabolic phenotype of the host with relevance to human health [236, 239, 281, 288]. Any specific change to metabolic phenotype
may lead to various GI and metabolic disorders [251, 287, 288]. The current model of
probiotic-induced gut microbial and metabolic profiling is largely explained by a multi-omics
approach using fecal 16S rRNA sequencing analysis and metabolomics [304]. Our results
suggest that the ageing-associated shifts in gut microbiota can be modulated by the dietary
123
intervention of L. acidophilus DDS-1. In particular, this intervention enriched several beneficial bacteria, such as Akkermansia, Allobaculum, and Lactobacillus spp. in the gut and
directly contributed to the improvement of the host metabolic phenotype [242]. Translation of
these outcomes to clinical accessibility warrants furthers larger mouse cohorts with extreme
older mice before clinical studies. Indeed, DDS-1 intervention intended to selectively
promote bacteria could become a vital dietary strategy to counteract ageing-associated dysbiosis.
5.6 Conclusion
Our results highlight the key role of beneficial microbes/probiotics in determining and modulating the metabolic profile in the healthy-ageing gut. Similar approaches could be used as a reference to identify and target specific health-promoting metabolic pathways in young and ageing populations.
124
Supplementary Material
Supplementary Figures
Figure S5.1: Neighbour-net showcasing the three different clusters of all the samples using
Brady-Curtis Ecological Index and Ward clustering (p < 0.01). (YC) Young control group,
(YP) young probiotic group, (AC) ageing control group, (AP) ageing probiotic group.
125
Figure S5.2: Heat map showing the Correlation analysis of fecal microbiota in ageing and young groups at phylum level using Pearson r distance method. (p < 0.01)
126
Figure S5.3: 2D O-PLS-DA plot with showing a clear separation between YC and YP groups. (YC) Young control group, (YP) young probiotic group, (AC) ageing control group,
(AP) ageing probiotic group
A.
127
B.
128
Figure S5.4: PCA plot of feces collected from YC and AC (A) and YP and AP (C) groups showing divergence. Key compounds separating YC and AC groups (B) based on variable importance in projection (VIP) score plot (D) in PLS-DA analysis. (YC) Young control group,
(YP) Young probiotic group, (AC) Ageing control group, (AP) Ageing probiotic group.
A B
C D
129
Figure S5.5: A. Bi-plot showing compounds responsible for divergence in young and ageing groups B. Key compounds separating young and ageing groups based on variable importance in projection (VIP) score plot in PLS-DA analysis
A.
130
B.
131
Figure S5.6: Significant changes in metabolites are expressed as a heat map in all four groups. (YC) Young control group, (YP) Young probiotic group, (AC) Ageing control group,
(AP) Ageing probiotic group.
132
Figure S5.7: Overall metabolite cluster in YC, YP, AC and AP groups using Pearson’s R correlation with ward distancing method (p < 0.01). (YC) Young control group, (YP) young probiotic group, (AC) Ageing control group, (AP) Ageing probiotic group
133
Figure S5.8: Age-wise and Group-wise correlation analysis showing the cluster of YP and
AP and differences in YC and AC groups using Pearson’s correlation using ward distancing
method (p < 0.01). (YC) Young control group, (YP) Young probiotic group, (AC) Ageing control group, (AP) Ageing probiotic group
YC AC AP YP
134
Figure S5.9: Potential pathways for Young probiotic (YP) from Figure 8A (A) Valine, leucine and isoleucine biosynthesis, (B) Glycine, serine and threonine metabolism, (C)
Aminoacyl-tRNA biosynthesis. Compounds in red are significantly involved in the pathways
A B
C
135
Figure S5.10: Potential pathways for ageing probiotic (AP) from Figure 5.8B, (A) D-
Glutamine and D-glutamate metabolism (B) Alanine, aspartate and glutamate metabolism (C)
Nitrogen metabolism, (D) Butanoate metabolism. Compounds in red are significantly involved in the pathways.
A B A
C D
136
Figure S5.11: KEGGS pathways of Alanine, aspartate and glutamate metabolism (A) and
Galactose Metabolism (B). Compounds highlighted in red and green are directly involved in respective pathways.
A
B
137
Figure S5.12: KEGGS pathways of Butanoate metabolism (A) and Nitrogen Metabolism (B).
Compounds highlighted in red box and green are directly involved in respective pathways.
A
B
138
Figure S5.13: KEGGS pathways of Aminoacyl-tRNA biosynthesis. Compounds highlighted in red are directly involved in respective pathways
139
Supplementary Tables
Table S5.1: Most significant compounds identified in YP/YC groups
Compound InChI Key KEGG FC Log2(FC) SAM (p Name value) Cellobiose GUBGYTABKSRVRQQ C06422 0.030 -5.010 0.00033333 RZGKKJRA-N Cellobiose2 GUBGYTABKSRVRQQ - 0.030 -5.019 0.00066667 RZGKKJRA-N L-Proline ONIBWKKTOPOVIA- C00148 31.966 4.998 0.001 BYPYZUCNSA-N L-Aspartic CKLJMWTZIZZHCS- C00049 47.016 5.555 0.002 acid REOHCLBHSA-N Glycerol PEDCQBHIVMGVHV- C00116 0.079 -3.650 0.0023333 UHFFFAOYSA-N Malonic OFOBLEOULBTSOW- C00383 22.051 4.462 0.0028333 acid UHFFFAOYSA-N L-Serine MTCFGRXMJLQNBG- C00065 34.464 5.107 0.0031667 REOHCLBHSA-N L-Valine KZSNJWFQEVHDMF- C00183 28.623 4.839 0.0035 BYPYZUCNSA-N Sucrose CZMRCDWAGMRECN- C00089 0.024 -5.363 0.0046667 UGDNZRGBSA-N D-Glucose WQZGKKKJIJFFOK- C00031 0.164 -2.599 0.005 GASJEMHNSA-N 4- TUHVEAJXIMEOSA- C01035 149.21 7.221 0.0055 Guanidinob UHFFFAOYSA-N utanoic acid GDP-6- LQEBEXMHBLQMDB- C02977 0.180 -2.473 0.0058333 deoxy-D- UUZHTGJLSA-N talose Thymine RWQNBRDOKXIBIV- C00178 11.418 3.513 0.0088333 UHFFFAOYSA-N Uracil ISAKRJDGNUQOIC- C00106 9.485 3.245 0.0091667 UHFFFAOYSA-N L- FFEARJCKVFRZRR- C00073 22.344 4.481 0.0105 Methionine BYPYZUCNSA-N Stearic acid QIQXTHQIDYTFRH- C01530 0.222 -2.169 0.011167 UHFFFAOYSA-N L-Lysine KDXKERNSBIXSRK- C00047 26.333 4.718 0.012167 YFKPBYRVSA-N L- AYFVYJQAPQTCCC- C00188 25.073 4.648 0.0125 Threonine GBXIJSLDSA-N Hypoxanthi FDGQSTZJBFJUBT- C00262 12.996 3.701 0.016333 ne UHFFFAOYSA-N Alpha- GUBGYTABKSRVRQ- C00243 0.029 3.245 0.0185 Lactose QKKXKWKRSA-N Melibiose DLRVVLDZNNYCBX- C05402 0.029 -5.079 0.019333 ABXHMFFYSA-N
140
Iminodiacet NBZBKCUXIYYUSX- C19911 75.569 6.239 0.022 ic acid UHFFFAOYSA-N L- AGPKZVBTJJNPAG- C00407 20.298 4.343 0.023167 Isoleucine WHFBIAKZSA-N Cholesterol HVYWMOMLDIMFJA- C00187 0.344 -1.538 0.026167 DPAQBDIFSA-N D-Xylitol HEBKCHPVOIAQTA- C00379 0.340 -1.555 0.026667 NGQZWQHPSA-N Urea XSQUKJJJFZCRTK- C00086 0.005 -7.569 0.031667 UHFFFAOYSA-N Allose WQZGKKKJIJFFOK- C01487 0.25 0.043667 IVMDWMLBSA-N 7 - D- WQZGKKKJIJFFOK- C00984 4.16 0.049 Galactose PHYPRBDBSA-N 7 2.059 Myristic TUNFSRHWOTWDNC- C06424 0.445 -1.165 0.053667 acid UHFFFAOYSA-N L- LRQKBLKVPFOOQJ- C01933 13.767 3.783 0.059667 Norleucine YFKPBYRVSA-N D-Arabitol HEBKCHPVOIAQTA- C01904 0.431 -1.221 0.064333 QWWZWVQMSA-N D-Threitol UNXHWFMMPAWVPI C16884 0.4 0.064333 QWWZWVQMA-N 31 -1.211
L-Sorbose LKDRXBCSQODPBY- C00247 0.28365 -1.817 0.111 AMVSKUEXSA-N Oxalic acid MUBZPKHOEPUJKR- C00209 0.44955 -1.153 0.124 UHFFFAOYSA-N 5- JJMDCOVWQOJGCB- C00431 0.44995 - 0.143 Aminopent UHFFFAOYSA-N anoic acid (International Chemical Identifiers (InChI) and standard InChI hashes (InChIKey); KEGG= Kyoto Encyclopaedia of Genes and Genomes; FC= fold change)
Table S5.2: Most significant compounds identified by OPLS-DA and SAM analysis in the
AP group. (*First 7 compounds are identified by SAM)
Compound InChI Key KEGG FC Log2(F SAM (p Name C) values) Glycerol* PEDCQBHIVMGVHV- C00116 0.252 -1.985 0.01 UHFFFAOYSA-N 5- JJMDCOVWQOJGCB- C00431 0.219 -2.190 0.0115 Aminopentanoi UHFFFAOYSA-N c acid* Cellobiose* GUBGYTABKSRVRQ- C06422 3.068 1.617 0.0255 QRZGKKJRSA-N Cellobiose2* GUBGYTABKSRVRQ- C06422 3.066 1.618 0.0255 QRZGKKJRSA-N Phenyl BHHGXPLMPWCGHP- C05332 0.052 -4.257 0.032833 ethylamine* UHFFFAOYSA-N
141
Lactose* GUBGYTABKSRVRQQKKXK C00243 3.163 1.661 0.0365 WKRSA-N Melibiose* DLRVVLDZNNYCBX- C05402 3.131 1.646 0.043 ABXHMFFYSA-N Cholic acid BHQCQFFYRZLCQQ- C00695 0.167 -2.581 OELDTZBJSA-N Oxoglutaric KPGXRSRHYNQIFN- C00026 0.558 -0.840 acid UHFFFAOYSA-N Tyramine DZGWFCGJZKJUFP- C00483 1.277 0.353 UHFFFAOYSA-N Phenylethylami BHHGXPLMPWCGHP- C05332 0.052 -4.257 ne UHFFFAOYSA-N Glucosamine MSWZFWKMSRAUBDIVMDW C00329 1.609 MLBSA-N D-Arabitol HEBKCHPVOIAQTAQWWZWV C01904 0.255 -1.971 QMSA-N Glycolic acid AEMRFAOFKBGASWUHFFFA C00160 0.352 -1.502 OYSA-N
D-Threitol UNXHWFMMPAWVPI- C16884 0.255 -1.971 QWWZWVQMSA-N Cholesterol HVYWMOMLDIMFJA- C00187 1.207 0.153 DPAQBDIFSA-N
Myristic acid TUNFSRHWOTWDNC- C06424 0.730 0.177 UHFFFAOYSA-N
L-Lactic acid JVTAAEKCZFNVCJ- C00186 0.844 -0.243 REOHCLBHSA-N L-Glutamic WHUUTDBJXJRKMK- C00025 0.988 0.155 acid VKHMYHEASA-N
(International Chemical Identifiers (InChI) and standard InChI hashes (InChIKey); KEGG= Kyoto Encyclopaedia of Genes and Genomes; FC = Fold change)
142
Table S5.3: Results from ingenuity pathway analysis with MetPA in YC/YP
Pathway Name p value -log(p) Holm p FDR Impact Importance scores for compounds Valine, leucine 0.019846 3.9198 1 0.40684 0.28572 Isoleucine: 0.142, and isoleucine Valine: 0.14, biosynthesis
Aminoacyl- 0.0075182 4.8904 0.60146 0.2055 0.18 Aspartic acid: tRNA 0.02, Serine: 0.04, biosynthesis Methionine: 0.02, Valine: 0.02, Isoleucine: 0.02
Galactose 0.0015514 6.4686 0.12567 0.063609 0.12904 Melibiose: 0.06, metabolism Lactose: 0.064, Galactose: 0.06
Glycine, serine 0.1306 2.0356 1.0 0.97358 0.17143 Threonine: 0.02, and threonine Serine: 0.1 metabolism
Cysteine and 0.1036 2.2672 1.0 0.94388 0.11111 Methionine: methionine 0.037, Serine: metabolism. 0.07, Cysteine: 0.11 (The raw p is the original p-value calculated from the MSEA analysis; the Impact is the pathway impact value calculated from pathway analysis; FDR= False discovery rate Holm p = Holm-Bonferroni method)
143
Table S5.4: Results from metabolic pathway analysis using MetPA in AC/AP groups
Pathway Match P -log(p) Holm p FDR Impact Importance Name Status scores for compounds D- 2/5 0.0014908 6.5084 0.12225 0.12225 0.33333 D-Glutamine: Glutamine 0.33333 and D- glutamate metabolism
Alanine, 2/24 0.035665 3.3336 1.0 0.73112 0.16 Alanine: 0.04 aspartate aspartate: 0.04 and Glutamate: 0.16 glutamate metabolism
Galactose 3/26 0.003731 5.5911 0.30221 0.15297 0.12904 Melibiose: metabolism 0.06452, Alpha-Lactose: 0.06452, D-Galactose: 0.06452 Nitrogen 1/9 0.10898 2.2166 1.0 1.0 0.11111 L-Glutamic metabolism acid: 0.11111, Ammonia: 0.33333 Butanoate 2/22 0.030301 3.4966 1.0 0.73112 0.10526 L-Glutamic metabolism acid: 0.05263, Oxoglutaric acid: 0.05263 (The raw p is the original p-value calculated from the MSEA analysis; the Impact is the pathway impact value calculated from pathway analysis; FDR= False discovery rate Holm p = Holm- Bonferroni method)
144
Table S5.5: Metabolites identified in fecal samples of young and ageing mice
Identified Putatively Putatively named Putatively spectr amount of compounds annotated characterized al trimethyl compounds compound match silyl classes (%) (TMS) groups Lactic acid L - (+) lactic α-hydroxypropionic carboxylic 86 - acid acid acids Glycolic acid Glycolic acid 2-Hydroxyethanoic acid Alpha hydroxy 90 1TMS acids L-Alanine L-alanine 1 (S)-2-Aminopropanoic Amino acid 78 - acid Oxalic acid oxalic acid oxalic acid dicarboxylic 89 1TMS ethanedioic acid acid L-Valine L-valine 2 N-(tert- Amino acid 81 - Butoxycarbonyl)-L- valine Urea urea Urea carbamide nitrogenous 78 1TMS Benzoic acid benzoic acid Dracylic acid aromatic 94 1TMS carboxylic acid L-Norleucine L-norleucine 2-aminohexanoic acid Amino acid 91 - 2 Glycerol glycerol 1,2,3-Propanetriol trihydroxy 90 3TMS sugar alcohol Phosphoric phosphoric trihydroxidooxidophosp tribasic acid 87 3TMS acid acid horus phosphoric acid Nicotinic acid nicotinic acid Pyridine-3-carboxylic pyridinecarbox 79 1TMS acid ylic acid L-Isoleucine DL-isoleucine (2S,3S)-2-amino-3- Amino acid 89 - 2 methylpentanoic acid L-Proline L-proline 2 Pyrrolidine-2- Amino acid 85 - carboxylic acid Glycine glycine 2-Aminoethanoic acid Amino acid 79 2TMS Succinic acid succinic acid 1,4-Butanedioic acid dicarboxylic 90 acid Glyceric acid glyceric acid 2,3- trihydroxy 3TMS Dihydroxypropanoic sugar alcohol acid Uracil uracil Pyrimidine- nucleic acid 88 - 2,4(1H,3H)-dione L-Serine L-serine 2 2-Amino-3- Amino acid 87 - hydroxypropanoic acid L-Threonine L-threonine 2 2-Amino-3- Amino acid 86 - hydroxybutanoic acid Thymine thymine 5-Methylpyrimidine- nucleic acid 96 - 2,4(1H,3H)-dione Malonic acid malonic acid Propanedioic acid dicarboxylic 84 1TMS 1 acid Iminodiacetic iminodiacetic 2,2'-Azanediyldiacetic dicarboxylic 74 1TMS acid acid 2 acid acid amine
145
L-Methionine L-methionine 2-amino-4- Amino acid 87 2TMS 2 (methylthio)butanoic acid L-Aspartic aspartic acid 2 Aminobutanedioic acid Amino acid 88 1TMS acid 4- 4- 4-Guanidinobutyric acids 89 2TMS Guanidinobut guanidinobuty acid yric acid ric acid 2 Phenylethyla phenylethyla 1-phenylethylamine. amines 81 2TMS mine mine Oxoglutaric alpha 2-Oxopentanedioic acid Ketone 83 11TMS acid ketoglutaric derivative acid L-Glutamic L-glutamic 2-Aminopentanedioic Amino acid 94 - acid acid 2 acid 5- 5- 5-Aminobutanoic acid acids 78 5TMS Aminovaleric aminovaleric acid acid 1 D- D- D-Lyxosylamine amines 67 11TMS Lyxosylamine lyxosylamine 2 D-Lyxose D-lyxose 1 2,3,4,5- monosaccharid 90 2TMS Tetrahydroxypentanal e D-Threitol D-threitol (2R,3R)-Butane- sugar alcohol 66 1,2,3,4-tetrol D-Arabitol arabitol (2R,4R)-Pentane- sugar alcohol 89 5TMS 1,2,3,4,5-pentol 6-Deoxy-D- 6-deoxy-D- 6-deoxy-D-glucose sugar 90 11TMS Glucose glucose 2 D-Xylitol xylitol (2R,3R,4S)-Pentane- sugar 76 11TMS 1,2,3,4,5-pentol Hypoxanthine hypoxanthine 1H-purin-6(9H)-one Purine 79 1TMS derivative Myristic acid myristic acid 1-tetradecanoic acid Fatty 85 1TMS acid/esters D-Tagatose tagatose 1 1,3,4,5,6- monosaccharid 88 - Pentahydroxy-hexan-2- e one L-Sorbose L- sorbose 2 L-xylo-Hexulose monosaccharid 87 - e Allantoin allantoin 1 (2,5-Dioxo-4- purines and 76 - imidazolidinyl) urea their derivatives D-Galactose D (+) - sugars 73 5TMS galactose 2 Allose D-allose 2 (2R,3R,4R,5R)- aldohexose 77 5TMS 2,3,4,5,6- sugar Pentahydroxyhexanal Tyramine tyramine 4- Trace amine 78 - hydroxyphenylacetalde hyde, D-Talose talose 1 (3S,4S,5R,6R)-6- monosaccharid 84 5TMS (Hydroxymethyl) e oxane-2,3,4,5-tetrol
146
D-Glucose D-glucose 1 D-glucopyranose sugar 96 5TMS L-Lysine L-lysine 2 (2S)-2,6- Amino acid 95 5TMS Diaminohexanoic acid D-Altrose D (+) altrose (2S,3R,4R)-2,3,4,5,6- aldohexose 93 4TMS Pentahydroxyhexanal sugar L-Tyrosine tyrosine 2 L-2-Amino-3-(4- Amino acid 65 1TMS hydroxyphenyl) propanoic acid Palmitic acid palmitic acid Hexadecenoic acid saturated fatty 83 3TMS acid Allo-inositol allo-inositol cyclohexane-1,2,3,4,5- - 75 - pentaol Linoleic acid linoleic acid (9Z,12Z)-octadeca- polyunsaturate 68 1TMS 9,12-dienoic acid d omega-6 fatty acid Oleic acid oleic acid (9Z)-Octadec-9-enoic fatty acid 76 1TMS acid Stearic acid stearic acid Octadecanoic acid saturated fatty 81 2TMS acid Sucrose Sucrose β-D-Fructofuranans α- Disaccharide/s 78 - D-glucopyranoside ugar Cellobiose2 cellobiose 2 β-cellobiose Disaccharide/s 81 5TMS ugar Cellobiose cellobiose 1 α-cellobiose Disaccharide/s 83 5TMS ugar Melibiose melibiose 2 Oxymethyl- oxane- Disaccharide/s 93 5TMS 2,3,4,5-tetrol ugar Lactose lactose 1 β-D-glucopyranosyl- Disaccharide/s 91 8TMS (1→4)-D-glucose ugar Cholesterol cholesterol (3β)-cholest-5-en-3-ol lipids 89 1TMS Cholic acid cholic acid 1 3α,7α,12α-trihydroxy- Bile acid 75 1TMS 5β-cholan-24-oic acid
147
Chapter 6: Distinct patterns in microbiota, improvements in immunological and short-chain fatty acid profiles with Lactobacillus acidophilus DDS-1 supplementation in ageing mice
An original version of this chapter has been submitted and under review in Nutrients as a
research investigation as:
Ravichandra Vemuri., Gundamaraju, R., Shinde, T, Perera AP, Waheeda Basheer, Gondalia, S., Karpe, A., Beale, D, Benjamin Southam, Stephen T Ahuja KDK Madeleine B, Martoni CJ, Eri, R. Distinct patterns in microbiota, improvements in immunological and Short-chain fatty acid profiles with Lactobacillus acidophilus DDS-1 supplementation in aging mice. Scientific Reports. 2019.
Clarivate Analytics/Thompson Reuter journal impact factor: 4.15
SJR journal ranking: Q1, 1.53
The word aging has been changed to ageing to maintain consistency throughout chapters.
The all the figure layouts are modified from landscape to portrait format to maintain consistency.
148
6.1 Abstract
Distribution of the microbiota differs according to the location in the gastrointestinal (GI) tract. Thus, dysbiosis during ageing may not be limited to fecal microbiota and extend to the other parts of the GI tract, especially the cecum and colon. Lactobacillus acidophilus DDS-1,
a probiotic strain, has been shown to modulate fecal microbiota and its associated metabolic
phenotype in ageing mice. In the present study, we investigated the effect of L. acidophilus
DDS-1 supplementation on cecal and mucosal-associated microbiota, short-chain fatty acids
(SCFA) and immunological profiles in young and ageing C57BL/6J mice. Besides differences in the young and ageing control groups, we observed microbial shifts in cecal and mucosal samples, leading to an alteration in SCFA levels and immune response. DDS-1 treatment increased the abundances of beneficial bacteria such as Akkermansia spp. and
Lactobacillus spp. more effectively in cecal samples than in mucosal samples. DDS-1 also enhanced the levels of butyrate, while downregulating the production of inflammatory cytokines (IL-6, IL-1β, IL-1α, MCP-1, MIP-1α, MIP-1β, IL-12, and IFN-γ) in serum and colonic explants. Our findings suggest distinct patterns of microbiota, and improvements in immunological and SCFA profiles with DDS-1 supplementation in ageing mice.
149
6.2 Introduction
The global proportion of ageing individuals above 60 years old is estimated to increase from
11-12% currently, to more than 25% by 2050 [305, 306]. Although life expectancy has generally increased with advances in public health and medicine, there is little evidence of any concurrent increase in the overall health of ageing individuals [307, 308]. Ageing is characterized by a decline in physiological functions of the human body [305, 309]. Altered gastrointestinal (GI) physiology affects the nutritional status of ageing individuals by impacting the host’s metabolism as well as their gut microbiota [309, 310]. Therefore, it is necessary to understand the dietary and nutrition-related changes in ageing individuals to provide dietary mechanisms to promote healthy ageing.
The gut microbiota plays a vital role in maintaining the overall health of individuals via interacting with the immune system and host metabolism. Gut microbiota is established during birth and evolves with age, mostly remaining stable through adulthood [309, 310, 311].
However, a few human and animal studies suggest a major shift in the gut microbial composition in ageing populations when compared to younger groups [310, 311, 312].
Recent studies have reported that Bacteroidetes, Firmicutes, and Verrucomicrobia are overrepresented in younger individuals, but their abundance tends to decrease with age [309,
315, 316, 317]. Moreover, during such microbial shifts, bacteria belonging to the
Enterobacteriaceae family (Proteobacteria) increased, which has been implicated in the etiology of various GI and metabolic diseases [313, 318, 319].
Most of the previous studies have investigated the microbial profile in fecal samples.
However, very recently, differences of microbiota along the GI tract have been reported in human and animal studies, suggesting a likely distinction between fecal and mucosal- associated microbiota [320, 321, 322]. This could be due to an increase of bacterial load from
150
the stomach to the colon. Along these lines, a few studies have indicated differences in the cecal and fecal microbiota, which lead to alteration in metabolic profiles in mice [323, 324,
325]. In a study on ageing rats, the abundance of Lactobacillaceae in the ileum and
Ruminococcaceae and Lachnospiraceae in the cecum and feces were higher compared to the control young group [320]. From the studies mentioned above, it can be speculated that dysbiotic shifts may extend to the other parts of the GI tract, especially the cecum and colonic-mucosa, where the microbial concentration is at a higher level [311].
Concomitantly with microbiota shifts, immunity becomes impaired in ageing individuals
[326]. The decline in immune responses or low-grade chronic inflammation, known as
‘inflamm-ageing’ is a hallmark of ageing [327]. During this condition, homeostasis between pro-inflammatory cytokines and the regulatory response (microbes-immune system axis) is disoriented, which is likely to contribute to the pathophysiology of various GI diseases [326,
327, 328]. Gut microbiota also produces short-chain fatty acids (SCFA) which regulate intestinal barrier integrity and immune homeostasis [329]. In human and animal studies, the
SCFA levels were found to be decreased with an imbalance in gut microbiota, affecting immune homeostasis [307, 330, 331]. Hence, for healthy-ageing the maintenance of proper microbial populations of cecal and mucosal sites is essential.
Probiotics are living microbes that, when administered in adequate amounts, confer health benefits on the host [309, 318]. In a variety of animal models, treatment with probiotics containing lactic acid bacteria (LAB) have been reported to modulate the gut microbiota,
SCFA production and inflammatory responses [310, 325, 331]. Previously, we have reported that L. acidophilus DDS-1, a clinically documented probiotic strain [332, 333, 334, 335], was able to improve the metabolic phenotype via modulating fecal microbiota composition in ageing mice [310]. Therefore, we hypothesized that this probiotic L. acidophilus DDS-1
151
could also enhance the beneficial microbial composition of cecal and mucosal-associated microbiota in ageing.
In the present study, we employed a healthy-ageing-based mice model to investigate the dynamic effect of cecal and mucosal-associated microbiota in ageing. A combination of 16S rRNA gene sequencing, untargeted metabolomics of volatile fatty acids, and cytokine measurement with bioplex-based immunological analysis were used to provide a
comprehensive understanding of the potential beneficial effects of DDS-1 on the intestinal
microbial changes in ageing mice.
6.3 Methods and Material
6.3.1 Ethics statement
All animal procedures were performed in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes of the National Health and Medical
Research Council. The study was approved by the Animal Ethics Committee of the
University of Tasmania (A0015840).
6.3.2 Bacterial culture and Probiotic DDS-1 viability in the feed
The bacterial strain utilized in the study, L. acidophilus DDS-1, was obtained in freeze-dried, free-flowing lyophilized form from UAS labs, Madison, WI, USA. The bacterial culture was suspended in saline before making the probiotic chow at a concentration of 3 × 109 colony
forming units (CFU)/g. The viability of the probiotic DDS-1 in the chow was assessed at the
following intervals over a 24 h period (at 0 min, 1 h, 2 h, 5 h, and 24 h) and formulated to
deliver 3 × 109 CFU/g as shown by Kuo et al. [310, 336].
6.3.3 Animals and probiotic treatments
152
A total of 32 mice were used. Young (age = 3 – 4 weeks, n = 16) and ageing (age = 35 - 36 weeks, n = 16) C57BL/6J mice were obtained from the University of Tasmania animal breeding facility. Animals were housed within individually ventilated cages containing a corncob bedding (Andersons, Maumee, OH, USA), in a room with a temperature maintained at 21 - 22°C, with a 12-h light/dark cycle. Mice were allowed access to radiation-sterilized rodent feed (Barastoc Rat and Mouse (102108), Ridley Agriproducts, Melbourne, Australia) and water was available ad libitum. The young and ageing mice were divided into 4 groups (n
= 8) and housed in individual cages containing, (1) Young control group (YC) fed with normal chow, (2) Young probiotic group (YP) fed with probiotic chow, (3) Ageing control group (AC) fed with normal chow and, (4) Ageing probiotic group (AP) fed with probiotic chow. All mice were fed for four weeks with group-specific chow.
6.3.4 Clinical parameters and Sample collection
Throughout the experiment, the weight of each mouse was recorded each day. Cecal and mucosal samples were collected at different time periods in a manner similar to the work of
Langille et al. [317]. To minimize contamination, mice had no access to food and water on the day of sampling. Sterile forceps were used for cecal and mucosal sample collection. The luminal contents of cecum and colonic-mucosa were carefully collected in at least two sets
(100 - 150 mg of the sample) from each mouse and immediately transferred into a sterile microcentrifuge tube. These samples were immediately stored at -80°C for subsequent 16S rRNA gene sequencing and metabolomic analysis. At the end of the experiment, the mice were euthanized by CO2 asphyxiation. Colon tissues were excised and snap frozen immediately before further analysis.
6.3.5 Histological analysis
153
The length of the colon (i.e., from the ileocecal junction to the rectum) was recorded. The
colon was subsequently opened along its longitudinal axis, and the luminal contents were
removed before weighing the organ. The colon was bisected longitudinally, and one half was
prepared using the Swiss roll technique [337]. Swiss roll 24 h fixation was performed in 10%
(v/v) neutral-buffered formalin. Swiss rolls were subsequently transferred to 70% (v/v)
ethanol prior to progressive dehydration, clearing, and infiltration with HistoPrep paraffin
wax (Fisher Scientific, Philadelphia, PA, United States). Swiss rolls were then embedded in
wax, and 5 μm sections (proximal & distal colon) cut at least three levels 50 μm apart using a
rotary microtome. Sections were routinely stained with haematoxylin and eosin Y (H&E; HD
Scientific, Sydney, Australia) histology and Periodic Acid–Schiff (PAS)/Alcian blue as
described previously [338]. Alcian blue staining was used to identify acidic carbohydrates
and PAS for neutral carbohydrates, both of which occur on the Muc2 glycoprotein. The
staining intensity (IOD) was analyzed using the Image-Pro Plus software (Version 5.1, Media
Cybernetics, Inc.) and it was used for comparison among groups.
6.3.6 Serum collection
Blood from individual mice was collected by cardiac puncture at the end of the treatment into vacutainer tube(s) containing no anticoagulant. The vacutainer was incubated in an upright position at room temperature for 30 - 45 min to allow clotting, then centrifuged at 3000 g for
15 min, and the supernatant (serum) was collected in cryovials and stored at -800C.
6.3.7 Explant culture
Each collected colonic tissue was cut, weighed and washed with cold (40C) PBS before
transferring to a 12 well plate containing 0.5 mL / well of RPMI1640 culture medium (In
vitro Technologies Pty Ltd, Melbourne, Australia) supplemented with 10% v/v fetal calf
serum (Gibco, Life Technologies, Melbourne, Australia), penicillin (100 mU/L) and
154
streptomycin (100 mg/L) (Sigma-Aldrich Pty Ltd, Sydney, Australia). After 24 h of
incubation, the supernatant was collected from each well and stored at -800C until further
analysis.
6.3.8 Cytokine measurement
Cytokine concentrations in culture supernatants and serum were determined by immunoassay
using a Bio-Plex Pro Mouse cytokine 23-plex kit (Bio-Rad Laboratories, Inc., Hercules, CA,
USA) following the manufacturer’s instructions. Briefly, standards were prepared by reconstituting the cytokine standard with culture media. A solution containing coupled magnetic beads was added into each well of a 96 well plate. Standards and samples were added in duplicate and incubated for 30 mins. Detection antibodies were then added and incubated before final incubation with streptavidin-phycoerythrin. Cytokine levels were analyzed using a Bio-Plex 200 instrument and Bio-plex Manager software, version 6 (both
Bio-Rad Laboratories). The cytokine levels were normalized by dividing the cytokine results
(pg/mL) by the measured colon weight (g) to obtain pg/mL of cytokines/ g of tissue.
6.3.9 Microbiota analysis using 16S rRNA High-Throughput sequencing
The total DNA was extracted from cecal and mucosal samples using the QIAamp DNA Stool
Mini Kit (Qiagen, Melbourne, VIC, Australia). The samples underwent high-throughput sequencing on the Illumina MiSeq platform at the Australian Genome Research Facility
(University of Queensland, Brisbane, QLD, Australia). Polymerase chain reaction (PCR) amplicons spanning the 16S rRNA V3-V4 hypervariable region with a 27F forward primer
(5’-AGAGTTTGATCMTGGCTCAG-3’) and 519R reverse primer (5’-
GWATTACCGCGGCKGCTG-3’) were sequenced. Paired-end reads were assembled by aligning the forward and reverse reads using PEAR1 (version 0.9.5). Primers were identified and trimmed. Trimmed sequences were processed using Quantitative Insights into Microbial
155
Ecology (QIIME 1.8) 4 USEARCH 2.3 (version 8.0.1623) and UPARSE software [339].
Using USEARCH tools, sequences were quality filtered; full-length duplicate sequences were
removed and sorted by abundance. Singletons or unique reads in the data set were discarded.
Sequences were clustered followed by chimera filtering using "rdp_gold" database as a
reference [340]. To obtain several reads in each Operational taxonomic units (OTUs), reads
were mapped back to OTUs with a minimum identity of 97%. Using QIIME, taxonomy was
assigned using Greengenes database5 (Version 13_8, Aug 2013) [341]. Image analysis was
performed in real time by the MiSeq Control Software (MCS) v2.6.2.1 and Real-Time
Analysis (RTA) v1.18.54, running on the instrument computer. RTA performs real-time base calling on the MiSeq instrument computer. Then the Illumina bcl2fastq 2.20.0.422 pipeline was used to generate the sequence data [340, 341]. 16S rRNA gene sequences were analyzed using MEGAN6 (Community edition version) [342], Microbiome analyst [343] and QIIME.
Statistical analysis of Brady-Curtis dissimilarities was calculated using the relative abundances of bacterial genera using Adonis function in R (version 3.2).
6.3.10 Metabolomics analysis
All samples (cecal and mucosal) were prepared and derivatized following the protocol developed by Furuhashi et al. [344] with some modifications. Briefly, cecal and mucosal samples (stored at -80°C) were weighed to ±0.1 mg accuracy. These samples (100 – 150 mg
fresh weight) were added to a sterile 1.5 mL bead-beating tube (NAVY Rino Lysis tubes,
Next Advance, Troy, NY, USA). Isobutanol (10% in water, volume = 1.0 mL, LC-MS grade,
Merck, Castle Hill, NSW, Australia) was added to each sample, followed by two 30 second,
4,000 rpm homogenization pulses sandwiched between a 20-second pause interval (Precellys
Evolution Homogenizer, Bertin Instruments, Montigny-le-Bretonneux, France). The samples
were subsequently centrifuged at 15,700 g for 6 minutes.
156
The supernatant (675 µL) was transferred to a clean round bottomed 2 mL centrifuge tube
(Eppendorf South Pacific Pty. Ltd., Macquarie Park, NSW, Australia) and NaOH (20 mM,
125 µL, Merck, Australia) and chloroform (400 µL, LC-MS grade, Merck, Castle Hill, NSW,
Australia) were added. The samples were briefly vortexed and centrifuged at 15,700 rpm for
3 minutes. The aqueous phase (upper layer, 400 µL) was transferred to a new clean round bottomed 2 mL centrifuge tube (Eppendorf South Pacific Pty. Ltd., Australia) containing a boiling chip (Sigma Aldrich, Castle Hill, NSW, Australia). Pyridine (100µL), isobutanol (80
µL) (both LC-MS grade, Sigma Aldrich, Castle Hill, NSW, Australia) and milliQ water (70
µL) were added and the samples were subjected to gentle hand vortexing (swirling action) followed by the addition of 50 µL isobutyl chloroformate (Assay = 98%, Sigma Aldrich,
Castle Hill, NSW, Australia). The tube was kept opened to release any generated gases and was allowed to stand for about 1 minute. Hexane (150 µL, LC-MS grade, Sigma Aldrich,
Castle Hill, NSW, Australia) was then added to each tube, which was capped and vortexed prior to centrifugation at 15,700 g for 4 minutes. The upper phase (100 µL) was subsequently transferred to clean GC autosampler vials fitted with salinized glass inserts; Malathion (1 µL, equivalent to 2.5 µg/mL dry weight) was added as an internal standard.
The GC-MS analysis was performed on an Agilent 6890B gas chromatograph (GC) oven coupled to a 5977B mass spectrometer (MS) detector (Agilent Technologies, Mulgrave, VIC,
Australia) fitted with a MPS autosampler (Gerstel GmbH & Co.KG, Mülheim an der Ruhr,
Germany). The GC oven was fitted with two 15 m HP‐5MS columns (0.25 mm ID and
0.25 µm film thickness; 19091S-431 UI, Agilent Technologies, Mulgrave, VIC, Australia) coupled to each other through a purged ultimate union (PUU) for the use of post-run backflushing. The sample (1.0 µL) was introduced via a multimode inlet (MMI) operated in split mode (1:20). The column was maintained at 40°C for 5 min, followed by an increase to
250°C at a rate of 10°C/min. This was followed by a second increment to 310°C at a rate of
157
60°C/min. The column was held at 310°C for 1 min. The mass spectrometer was kept in
extractor ion mode (EI mode) at 70 eV. The GC-MS ion source temperature and transfer line
were kept at 250°C and 280°C, respectively. Detector voltage was kept at 1054 V. The MS
detector was turned off for the first 3 min and, at 4.0 – 4.8 min and 12.5 – 13.2-min time windows until the excess derivatization reagent and chloroformate/hexane solvents were eluted from the column. This ensured that the source filament was not saturated and damaged.
The scan range was kept in the range of m/z 35 – 350 (35 – 350 Daltons). Data acquisition and spectral analysis were performed as described in our previous study [311] and qualitative identification of metabolites was performed according to the Metabolomics Standard
Initiative (MSI) chemical analysis workgroup [345] using standard GC-MS reference metabolite libraries (NIST 17, Agilent Fiehn RTL Library [G166766A, Agilent Technologies] with the use of Kovats retention indices based on a reference n-alkane standard (C8-C40
Alkanes Calibration Standard, Sigma-Aldrich, Castle Hill, NSW, Australia).
6.3.11 Multivariate and Statistical analysis
To determine overall microbial variation in the four groups, a principal coordinate analysis
(PCoA) and non-metric multidimensional scaling (NMDS) was used with Brady-Curtis ecological indexing and Euclidean distances as the similarity measure and Ward’s linkage as a clustering algorithm [311]. Data are presented as mean values ± standard error from multiple individual experiments each carried out in triplicate measurements in a representative experiment. Graph Pad Prism version 7.0 for Windows was used for the statistical analysis. Statistical analyses were done using an unpaired two-tailed t-test, for two groups study. The data were evaluated with one-way analysis of variance (ANOVA) and using Tukey’s test for multiple comparisons with a statistical significance of p < 0.05. For comparative microbial analysis, a linear discriminant effect size (LEfSe) analysis was performed (α = 0.05), logarithmic Linear Discriminant Analysis (LDA) score threshold = 1.0.
158
Beta diversity of microbial data was performed using NMDS with permutational multivariate
analysis of variance statistical method. P-values have been corrected to account for false
discovery rates using Holm-Bonferroni method and Benjamin Hochberg correction.
6.4 Results
6.4.1 DDS-1 viable in the diet after 24 h and exerts no effect on body weights, colon & spleen
weights and lengths
The viability of DDS-1 in the chow was maintained, and no significant loss was observed
even at the 24 h time point (Figure 6.1A). There was no significant difference in body weights and spleen weights (Figure 6.1B and E). Colon weights of the YP group (p < 0.001) were significantly different when compared to the YC group (Figure 6.1D). The colon length was different only in the YP group (p = 0.02) when compared to the YC group (Figure 6.1C).
159
Figure 6.1: L. acidophilus DDS-1 viability in the diet during 24-hr of storage (A). The effect
of DDS-1 supplementation on body weights (B), colon length (C), colon weight (D) and
spleen weights (E) observed in Young control (YC), Young probiotic group (YP), Ageing
control group (AC) and Ageing probiotic group (AP) * P < 0.001 (one-way ANOVA with
Tukey’s posthoc test and data are expressed as the mean ± SEM).
6.4.2 DDS-1 supplementation improves goblet cell structure
The colonic H & E staining showed normal anatomical architecture. However, there was a
difference in the size and number of goblet cells (Figure 6.2A) in the DDS-1 treated groups.
Noticeable changes in PAS–Alcian blue-positive goblet cells were observed in the colon of mice in both YP and AP groups (Figure 6.2B). In addition, the mean optical density (OD)
reflecting goblet cell density of both YP and AP groups (OD = 0.32 ± 0.04) were
160
significantly higher than that of the control groups (OD = 0.20 ± 0.03, p < 0.05). The mucus layer was significantly thicker in YP and AP groups compared with the control groups.
Figure 6.2: Representation of distal colon sections stained with hematoxylin and eosin (A) and Alcian blue (AB)-PAS staining (B) for sulfated mucins among the four groups. (C) The
AB-PAS staining mean optical density of goblet cells observed in Young control (YC),
Young probiotic group (YP), Ageing control group (AC) and Ageing probiotic group (AP).
Magnification is reported, and the arrow indicates location goblet cells. *P < 0.005 (one-way
ANOVA with Tukey’s posthoc test and data are expressed as the mean ± SEM).
AC AP YC YP
A
10X
40X
B
40X
161
C.
0.4 * *
0.3
0.2
0.1 AB-PAS mean OD 0.0 AC AP YC YP
6.4.3 DDS-1 supplementation reduces local and systemic pro-inflammatory cytokine levels
The serum cytokine analysis detected certain pro-inflammatory cytokines such as TNF-α, IL-
1β, IL-1α, IL-2, IL-5, MCP-1, MIP-1α, MIP-1β, and an anti-inflammatory cytokine, IL-10 in the 4 groups (Figure 6.3). Of these, IL-6, IL-1β, IL-1α, MCP-1, MIP-1α, MIP-1β showed differences with DDS-1 treatment. Relative to the YC group, the levels of IL-1α (from 21.2 ±
2.2 pg/mL to 13.8 ± 4.30 pg/mL, p = 0.053) and IL-1β (74.38 ± 9.38 pg/mL to 38.73 ± 19.37 pg/mL, p = 0.01) were significantly lower in YP group. Similarly, IL-2 (from 52.49 ± 8.51 to
24.74 ± 9.14 pg/mL, p = 0.01), MCP-1 (from 1271 ± 100 to 844.34 ± 212.66 pg/mL, p =
0.004), MIP-1α (from 13.46 ± 2.12 to 3.33 ± 1.39 pg/mL, p = 0.0001) and MIP-1β (59.74 ±
22.56 to 27.01 ± 9.17 pg/mL, p = 0.002) levels were significantly downregulated in AP group compared to the AC group. Furthermore, IL-5 levels were downregulated in both AP (70.03
± 4.41 pg/mL, p = 0.003) and YP (39.28 ± 23.75 pg/mL, p = 0.003) groups compared to AC and YC groups, respectively. Although not significant, TNF-α levels (from YC to YP: 229.07
± 6.51 to 191.89 ± 23.4 pg/mL; AC to AP: 197.94 ± 31.6 to 164.19 ± 8.04 pg/mL) were notably reduced and IL-10 levels (from YC to YP 127.59 ± 23.27 to 171.59 ± 18.05 pg/mL;
AC to AP: 131.26 ± 7.83 to 157.19 ± 8.41 pg/mL) were altered following treatment with
DDS-1 in YP and AP groups, compared to YC and AC groups, respectively.
162
At the tissue level, certain pro-inflammatory cytokines such as IL-1α, IL-1β, IL-12, and IFN-
γ were detected in proximal and distal ends of the colon in the 4 groups (Figure 6.4A & B).
At the proximal end, DDS-1 treatment in the YP group modulated the levels of IL-1α from
64.01 ± 14.87 to 22.09 ± 3.27 (p = 0.0003), IL-1β from 94.31 ± 13.78 to 40.03 ± 10.71 (p =
0.0005), IL-12 from 831.44 ±22.66 to 664.47 ± 32.88 (p = 0.006), and IFN- γ from 61.15 ±
7.85 to 42.72 ± 6.18 (p = 0.01), when compared to the YC group. Similar changes were observed in the distal end of the YP group relative to the YC group. The basal levels of IL-1α,
IL-1β, IL-12, and IFN- γ were (pg/mL per g) 143.86 ± 23.34, 143.86 ± 23.34, 1368.1 ± 27.4 and 89.92 ± 6.93 and DDS-1 downregulated these levels in the YP group to 78.47 ± 24.03 (p
= 0.01 ), 84.89 ± 33.53 (p = 0.001), 880.3 ± 34.7 (p = 0.04) and 59.33 ± 4.67 (p = 0.01),
respectively. Among ageing mice, at the proximal end, DDS-1 was shown to downregulate
the levels of IL-1α from 41.07 ± 4.59 to 16.26 ± 1.90 (p = 0.008), IL-1β from 85.48 ± 10.14
to 57.56 ± 6.93 (p = 0.025), IL-12 levels from 602.04 ± 105.46 to 347.43 ± 11.73 (p =
0.0001), and IFN- γ levels from 62.47 ± 1.03 to 30.92 ± 7.68 (p = 0.0003) (from AC to AP
groups). However, at the distal end in the AP group, DDS-1 only modulated IL-1β (101.29 ±
7.67 to 40.93 ± 3.10, p = 0.01) and IFN- γ (112.33 ± 13.67 to 75.66 ± 1.34, p = 0.001) compared to the AC group.
163
way ANOVA with -
group probiotic ing (AP). *P < 0.05, **P < 0.01, ***P < 0.001 (one e
probiotic Young (YC), control in Young observed levels cytokine on serum 1 supplementation - DDS
L. acidophilus ing control group (AC) (AC) Agingand control group hoc test and data are expressed as the mean ± SEM).
- Effect of
: 3
Tukey’s post Tukey’s 6. Figure (YP),e Ag group
164
colon (B) observed in observed (B) colon distal
(A) and
proximal ing probiotic group (AP). *P < 0.05, **P < 0.01,
test comparisons and data expressed as the mean ± SEM). ing control ing control (AC) Aggroup and e
hoc -
1 supplementation on colon explants of the the explants on colon of 1 supplementation - DDS Tukey’s post
L. acidophilus way ANOVA with -
Effect of
: 4 Figure 6. Figure (YC),control probioticYoung (YP), group e Ag ***P < 0.001 (one
165
6.4.4 Distinct modulation of intestinal microbiota with DDS-1 supplementation
6.4.4.1 Changes at the phylum level
Taxonomic and functional profiles of 32 samples (n = 6 per group) including the cecal and
mucosal content of all controls and DDS-1 supplemented groups were generated using the
16S rRNA gene sequencing-based method. To compare the changes among the groups,
NMDS and PCoA were used. Both NMDS (F-value = 2.438, R2 = 0.415, p < 0.001, NMDS
stress = 0.149) and PCoA plots of phylogeny with Brady-Curtis ecological indexing using ward clustering showed a clear separation of each group with three distinct clusters at the
OTU level among 4 groups (Figure 6.5A-C) suggesting that DDS-1 modulated cecal and mucosal microbiota. Notably, the inter-individual variation was higher among mucosal samples compared to cecal samples in all four groups. That is, while microbial communities of mucosal samples were scattered, those of cecal samples were more closely gathered.
Figure 6.6A indicates the phylum-level changes in the cecal and mucosal microbiota, which is significantly dominated by Bacteroidetes and Firmicutes and moderately by
Verrucomicrobia.
6.4.4.2 Cecal microbiota
Around 99% of the total microbial abundance was classified into six major phyla, while the rest were allocated as unclassified or others, as shown in the LEfSe scoring plot (Figure
6.7A). DDS-1 increased the abundance of Firmicutes and decreased the abundance of
Bacteroidetes. In cecal samples in young mice, Firmicutes levels were increased from 19.58% to 28.30% and Bacteroidetes levels were reduced from 75.40% to 60.80% (from YC to YP group) (Figure 6.5C). Similarly, in ageing mice, when comparing AC and AP groups, the
166
Firmicutes abundance (25.50% to 32.8%) was increased, while Bacteroidetes (69.30% to
63.60%) was reduced (Figure 6.5B).
6.4.4.3 Mucosal microbiota
Regarding mucosal samples, the DDS-1 treatment in the YP group marginally increased the abundance of Bacteroidetes from 81.10% to 83% and decreased Firmicutes levels from 13.50% to 3.80% when compared to the YC group (Figure 6A). The abundance of Bacteroidetes in
AP was increased from 68.20% to 80.70%, while Firmicutes levels were decreased from
22.68% to 17.53% when compared to the AC group. Additionally, Verrucomicrobia abundance in cecal and mucosal microbiota was significantly increased in both treatment groups when compared to respective control groups (p < 0.05).
6..4.4.4 Specific beneficial alterations at the genus and species level
At the genus level, the distribution of microbial populations of ageing mice was markedly different when compared to young mice, in both cecal and mucosal microbiota (Figure 6.6B).
In cecal and mucosal microbiota, the DDS-1 treatment enriched the populations of
Akkermansia, Lactobacillus, Odoribacter, Oscillospira, and Rikenella while reducing the abundances of Prevotella, Oscillospira and Ruminococcus in YP and AP compared to the YC and AC groups as shown by LEfSe analysis (Figure 6.6B). Parabacteroides and
Anaeroplasma were undetected in the cecal microbiota, and Desulfovibrio and Dorea were undetected in mucosal microbiota in all four groups. Interestingly, the relatively new family
S24-7 of the phylum Bacteroidetes were only present in cecal microbiota. At the species level,
DDS-1 treatment significantly increased A. muciniphilia (p < 0.05) levels, and decreased R.
gnavus, Parabacteroides distasonis, Bacteroides acidifaciences, and B. uniformis levels but
the differences were not statistically significant (Figure 6.6C).
167
Figure 6.5: Relative abundances (%) of cecal and mucosal-associated microbiota at phylum
(A) genus (B) and species level (C) observed in Young control (YC), Young probiotic group
(YP), Ageing control group (AC) and Ageing probiotic group (AP). Cecal (C), Mucosal (M)
A.
100% 90%
80%
70%
60% 50%
40% 30% 20% Relative abundance (%) abundance Relative 10% 0% C-AC C-AP C-YC C-YP M-AC M-AP M-YC M-YP
Bacteroidetes Cyanobacteria Firmicutes Proteobacteria
TM7 Verrucomicrobia Others
168
B.
100%
90%
80% 70% 60% 50%
40% 30% 20% Relative abundance (%) abundance Relative 10% 0% C-AC C-AP C-YC C-YP M-AC M-AP M-YC M-YP
Akkermansia Anaeroplasma Bacteroides Odoribacter Oscillospira Parabacteroides Lactobacillus Desulfovibrio
Dorea S24-7_others Prevotella Rikenella
Ruminococcus Sutterella Others
C.
100% Sphingomonas suberifaciens 90% Parabacteroides distasonis 80% Akkermansia muciniphila 70% Desulfovibrio C21_c20 60% Ruminococcus gnavus 50% Lactobacillus_others 40% 30% Mucispirillum schaedleri
Relative abundance (%) abundance Relative 20% Bacteroides uniformis 10% Bacteroides acidifaciens 0% C-AC C-AP C-YC C-YP M-ACM-APM-YCM-YP
169
Figure 6.6: Cecal and mucosal-associated microbiota changes observed in Young control
(YC), Young probiotic group (YP), Ageing control group (AC) and Ageing probiotic group
(AP) differentiated by (A) Non-metric multidimensional scaling (NMDS). Principal coordinate analysis (PCoA) plot showing the differentiation between ageing (B) and young
(C) mouse groups for both Cecal (C) and Mucosal (M) samples.
A.
170
B.
PC2: 24.9%
PC3: 17.5%
PC1: 43.2%
171
C.
PC2: 35.9%
PC3: 4.5% PC1: 56.2%
172
Figure 6.7: Biomarker analysis with Linear Discriminant Analysis (LDA) Effect Size (LEfSe) scoring plot using the Kruskal-Wallis rank sum test. Ageing and young mouse groups at
phylum (A) and genus (B) level
A.
173
B.
174
6.4.5 A significant increase in the production of SCFAs
A total of 30 volatile compounds were identified in our untargeted metabolomics from 46 cecum and mucosal samples in all four groups (Table 6S.1 and 6S.2). These 30 metabolites included the three major SCFA of butyrate, propionate, and acetate. DDS-1 differentially elevated butyrate, propionate, and acetate in the cecum and mucosal samples across groups
(Figure 6.8A and B). In both cecal and mucosal samples, the butyrate and propionate levels were significantly increased in both YP and AP groups compared to the YC group and AC group, respectively. There was no change observed in acetate levels in cecal and mucosal samples with DDS-1 treatment. Notably, valeric acid levels were upregulated in mucosal samples with DDS-1 treatment in both YP and AP groups.
175
- ing e
*P < 0.05 (One
Young controlYoung (YC), group probiotic Young (YP),group Ag
study. the of end the at contents mucosal cecal from & MS analysis -
ing probiotic group (AP). The concentrations of butyrate, propionate, acetate, and valeric acid acid valeric in cecal and the propionate, acetate, butyrate, of (AP). concentrations probiotic The ing group
Short chain fatty (SCFA)Short distribution chainacid fatty in observed :
8
Figure 6. Figure and e (AC) Ag group control (A) and mucosal (B) samples were measured by GC
176
6.5 Discussion
Distribution of the microbiota differs according to the location in the GI tract [305, 307, 315,
320, 321]. Thus, dysbiosis during ageing may not be limited to fecal microbiota and may
extend to the entire GI tract, leading to alterations in metabolic profile and immune responses
[310, 328, 346]. Therefore, the present study aimed to evaluate the comparison of cecal and
mucosal-associated microbiota, SCFA production, and immune responses with L. acidophilus
DDS-1 supplementation in relation to age (young and ageing) utilizing 16S rRNA gene
sequencing analysis and untargeted metabolomics of volatile fatty acids. Besides differences
in the young and ageing control groups, our study confirms microbial shifts in cecal and
mucosal samples. The taxa showing altered abundance might mediate dysbiosis in ageing
mice. Importantly, treatment with DDS-1 increased the abundance of specific bacterial
species such as A. muciniphila, helped modulate the overall intestinal microbiota, increased
butyrate production and reduced pro-inflammatory responses.
In our study, 16S rRNA gene sequencing analysis of the intestinal microbiota revealed
noticeable changes in microbial communities. Bacteroidetes, Firmicutes, and
Verrucomicrobia were the most dominant phyla across age groups, which was consistent with
our previous study [311]. We observed greater inter-individual variation in mucosal samples when compared to cecal samples. Such differences can be explained by the transient nature of mucosal-associated microbiota, as described by Lee et al. [320] and are consistent with
previous similar studies on young/old mice and rats [317, 321, 325, 346, 347]. Interestingly,
DDS-1 had differential effects on cecal and mucosal microbiota at the phylum level. Similar
to fecal microbiota profiles [311], DDS-1 increased Firmicutes in both age groups in cecal
samples, reflecting the role of Lactobacillus spp. in overall abundance.
177
In contrast, Bacteroidetes showed modest increases post-DDS-1 among mucosal samples. We speculate this change could be due to the transient nature of mucosal-associated intestinal microbiota and needs further examination. Although Verrucomicrobia was present in all four groups of cecal and mucosal samples, their relative abundance was higher in DDS-1 treated groups. It is noteworthy that more than 50% of mouse cecum was enriched by the relatively new family S24-7, that belongs to Bacteroidetes phylum and is consistent with our previous study [311]. However, S24-7 was completely undetected in mucosal samples. The consequences of these fluctuations in abundance of S24-7 is unknown, and a few researchers have hypothesized that they could play a role in butyrate production [348, 349].
At the genus and species level, Lactobacillus levels were mostly enriched in ageing mice administered DDS-1. Notable changes in this study were an increase of R. gnavus and B. acidifaciens in ageing mice cecal and mucosal samples when compared to younger mice.
Such differences were in line with previously reported fecal microbiota analysis [311, 350].
Several lines of evidence indicate that mucin-degrading bacteria such as R. gnavus and B. acidifaciens may play an essential role in dysbiosis by increasing total mucosa-associated bacteria in inflammatory bowel diseases [319, 350]. DDS-1 was shown to reduce the abundance of both R. gnavus, and B. acidifaciens and thus could play a role in reducing inflammation. A. muciniphilia levels were increased by DDS-1, irrespective of age, and could be related to improvement in metabolic profiles. These changes are consistent with previous studies on young/ageing mice [311, 316, 325, 350].
The gut microbiota produces SCFA, which ameliorate host energy metabolism and inflammation [346]. A series of animal studies have previously demonstrated that dietary supplementation with probiotics of Lactobacillus spp. have induced SCFA production by modulation of gut microbiota [307, 330, 331, 350]. Specifically, the phylum Bacteroidetes members mainly produce acetate and propionate, whereas the phylum Firmicutes have
178
butyrate as its primary metabolic end product [350]. Consistent with previous studies,
butyrate production was increased in cecal samples across age groups in treated mice and was
shown to be directly correlated with an increase in Firmicutes levels [307, 331]. Butyrate is a
primary energy substrate for colonocytes, and it has been suggested to play a role in the
prevention and treatment of distal ulcerative colitis, Crohn’s disease, and cancer [350].
Previously, a study by Lee et al. [315] demonstrated that butyrate increased mucin
production, which was also regulated by A. muciniphila, and the mucus layer thickness was
highly related with metabolic improvement, consistent with mucin production in the present
study. Propionate levels were also found to be increased in both DDS-1 treated groups of
cecal and mucosal samples, and it could also be positively correlated with increased
Akkermansia [358]. Unlike the other SCFA, however, very little is known about the effects of valeric acid on intestinal health and on health in general. A limited number of studies have reported that valeric acid may stimulate intestinal growth and ameliorate the pathogenesis of
diseases ranging from colitis and cancer to cardio-metabolic diseases [353, 354, 355].
Moreover, a variety of animal and human studies have reported very low to undetectable concentrations of valeric acid in feces, colon, cecum, and ileum [356, 357, 358, 359].
Interestingly, in the present study, valeric acid levels were detected and upregulated among
mucosal samples in both DDS-1 treated groups and were positively correlated with an
increase in Bacteroidetes level [357]. Further research is needed to understand the role of
valeric acid in gut health parameters.
Age-related alteration in tissue and circulating cytokine production contributes to low-grade
chronic inflammation and release of pro-inflammatory factors such as nuclear factor-kappa B
(NF-κB) [305, 307, 326, 327, 328]. Pro-inflammatory cytokines such as IL-1α, IL-1β, IL-2,
IL-5, IL-6, IFN-γ, MCP-1, MIP-1α, MIP-1β, and TNF-α are shown to play an essential role
in inducing age-related chronic inflammation [326, 327, 328, 360]. Notably, studies on
179
ageing mice have suggested an imbalance in cytokine type 1 T helper (h) cell/type 2 T helper
cell production to be associated with chronic inflammation [360, 361, 362]. This could be due
to imbalances in the intestinal microbiota that lead to compromised gut barrier integrity,
enhanced inflammatory responses and subsequent development of chronic diseases [326,
363]. For example, the segmented filamentous bacteria have been involved in the induction
of Th cell subsets in the gut [305, 364]. In contrast, polysaccharide A produced by B. fragilis
and certain Clostridium spp. have shown to induce anti-inflammatory IL-10 releasing T
regulatory cells in the gut [311]. Consistent with these studies, we observed more pro-
inflammation in ageing mice both in serum and colonic explants in our study.
Previously, we demonstrated the immuno-modulatory capacity of DDS-1 on lipopolysaccharide-induced human colonic epithelial cells [365]. Similarly, in this study,
DDS-1 helped normalize, to some extent, the age-specific pro-inflammatory response and is consistent with previous probiotic studies [366, 367]. This could be positively correlated to an increase in abundance of certain beneficial microbes, such as Akkermansia and
Lactobacillus spp. and a decrease in opportunistic microbes such as R. gnavus and B. acidifaciens. Increases in the abundance of Akkermansia and Lactobacillus spp. are reported
to be directly correlated with enhanced SCFA production [307, 329, 331]. Moreover, SCFAs
have a multitude of benefits for the host, notably butyrate, which has demonstrated anti-
inflammatory effects not only locally but also systemically [351]. Further, SCFAs are known
to inhibit NF-κB activation via G protein-coupled receptor 109A receptors and could be
responsible for the immuno-modulation observed in this study [357].
To our knowledge, this is one of the first detailed studies comparing intestinal site-specific
microbial changes in ageing mice with or without probiotic supplementation. Increases in
SCFA production, concurrent with immune homeostasis identified in this study, appear to be
connected to probiotic-induced microbial and metabolic changes. Consistent with the
180
complex nature of the microbial composition of the mouse, our study confirms differential
effects in the cecal and mucosal-associated microbiota. This further suggests the importance
of site-specific assessment in the GI microbiota. Our results demonstrate that
supplementation with L. acidophilus DDS-1 can help modulate shifts in the microbiota in
ageing mice. DDS-1 not only enriched beneficial bacteria such as Akkermansia spp. and
Lactobacillus spp. in the intestine but also reduced opportunistic bacteria such as R. gnavus
and B. acidifaciens. Altogether, DDS-1 reduced low-grade inflammation by the release of
intestinal microbiota-induced-SCFAs such as butyrate. Further studies, including clinical investigation, are needed to confirm these outcomes. The clinical relevance of this study is that by selectively promoting beneficial bacteria, L. acidophilus DDS-1 supplementation could be an important dietary strategy to counteract ageing-associated dysbiosis.
181
Supplementary Tables
Table S6. 1: List of volatile fatty acids (VFA) identified in Young control group (YC), Young probiotic group (YP), Ageing control group (AC) and Ageing probiotic group (AP). The concentrations of VFA in the cecal content (samples were measured by GC-MS analysis from cecal contents at the end of the study). *P < 0.05. (One-way ANOVA with Tukey’s posthoc test and data are expressed as the mean ± SEM
Volatile fatty acids AC AP YC YP Isobutyl acetate 11.47 ± 3.50 6.9 ± 2.33* 7.7 ± 2.32 10.92 ± 0.82*
Isobutyl ether 2.46 ± 0.36 2.43 ± 0.36 2.14 ± 0.41 2.83 ± 0.56 d-Proline, N- 1.36 ± 0.26 1.35 ± 0.28 1.17 ± 0.03 1.58 ± 0.85 methoxycarbonyl-, heptyl ester 2-Propen-1-ol, 2- 2.05 ± 0.20 1.93 ± 0.25 1.59 ± 0.22 2.28 ± 0.48 methyl Carbonic acid 8.69 ± 1.69 8.59 ± 0.56 7.02 ± 1.31 8.40 ± 1.43
Ethanedioic acid 0.99 ± 0.22 0.68 ± 0.28 0.77 ± 0.13 0.57 ± 0.11
Butane, 1,1-dibutoxy 0.51 ± 0.08 0.50 ± 0.06 0.64 ± 0.22 0.67 ± 0.16
D-Alanine, N- 1.06 ± 0.17 0.98 ± 0.12 0.78 ± 0.10 1.08 ± 0.18 allyloxycarbonyl-, nonyl ester L-Isoleucine, N- 0.63 ± 0.11 0.59 ± 0.07 0.46 ± 0.07 0.65 ± 0.10 isopropyl-, methyl ester Propanedioic acid, 0.08 ± 0.05 0.10 ± 0.04 0.06 ± 0.03 0.08 ± 0.02 methyl-, dibutyl ester Succinic acid, 3- 1.62 ± 0.67 1.07 ± 0.18 0.63 ± 0.29 0.92 ± 0.32 chlorophenyl 4- methoxybenzyl ester d-Proline, N- 0.93 ± 0.57 0.33 ± 0.03 0.36 ± 0.25 0.21 ± 0.02 isobutoxycarbonyl-, isobutyl ester Carbonic acid, 0.88 ± 0.40 0.14 ± 0.18 ± 0.07 0.16 ± 0.05 monoamide, N- 0.03* propyl-N-(hept-2- yl)-, butyl ester l-Norvaline, N- 1.28 ± 0.60 0.30 ± 0.09 0.38 ± 0.07 0.36 ± 0.02 isobutoxycarbonyl-, isobutyl ester Benzeneacetic acid, 0.03 ± 0.02 0.05 ± 0.04 0.04 ± 0.05 0.03 ± 0.01 2-methylpropyl ester
182
Table S6.2: List of volatile fatty acids (VFA) identified in Young control group (YC), Young probiotic group (YP), Ageing control group (AC) and Ageing probiotic group (AP). The concentrations of VFA in the mucosal content (samples were measured by GC-MS analysis from mucosal contents at the end of the study). *P < 0.05 (One-way ANOVA with Tukey’s posthoc test and data are expressed as the mean ± SEM)
Volatile fatty acids AC AP YC YP Isobutyl acetate 12.58 ± 9.39 16.26 ± 3.42 8.69 ± 11.67 12.67 ± 5.23
Isobutyl ether 5.15 ± 2.86 7.12 ± 2.72* 3.01 ± 0.15 5.1 ± 1.63 d-Proline 2.88 ± 1.56 4.09 ± 1.76* 1.62 ± 1.54 2.90 ± 0.85 2-Propen-1-ol, 2- 4.14 ± 2.31 5.75 ± 2.4 2.17 ± 2.46 4.20 ± 1.36* methyl Carbonic acid 17.15 ± 12.72 21.43 ± 8.01* 9.01 ± 9.44 17.17 ± 6.88* Ethanedioic acid 1.22 ± 0.77 1.22 ± 0.36 0.47 ± 0.56 1.42 ± 0.96*
Butane, 1,1-dibutoxy 1.06 ± 0.50 1.59 ± 0.43 1.08 ± 1.22 1.31 ± 0.13 D-Alanine, N- 2.27 ± 1.36 3.31 ± 1.56 1.22 ± 1.49 2.25 ± 0.92 allyloxycarbonyl-, nonyl ester L-Isoleucine, N- 1.36 ± 0.77 2.01 ± 0.93 0.73 ± 0.90 1.36 ± 0.54 isopropyl-, methyl ester Propanedioic acid, 0.50 ± 0.19 0.45 ± 0.30 0.13 ± 0.15 0.62 ± 0.05 methyl-, dibutyl ester Succinic acid, 3- 1.72 ± 0.84 1.07 ± 0.18 0.74 ± 0.88 2.22 ± 1.22 chlorophenyl 4- methoxybenzyl ester d-Proline, N- 0.90 ± 0.20 0.83 ± 0.50 0.13 ± 0.10 0.67 ± 0.14 isobutoxycarbonyl-, isobutyl ester Carbonic acid, 0.17 ± 0.05 0.22 ± 0.05 0.06 ± 0.05 0.25 ± 0.23 monoamide, N- propyl-N-(hept-2- yl)-, butyl ester l-Norvaline, N- 0.46 ± 0.40 0.48 ± 0.20 0.17 ± 0.07 0.54 ± 0.40 isobutoxycarbonyl-, isobutyl ester Benzeneacetic acid, 0.02 ± 0.01 0.17 ± 0.05 0.06 ± 0.05 0.25 ± 0.23 2-methylpropyl ester
183
Chapter 7: Thesis discussion, future directions, and conclusion
7.1 Thesis discussion
Ageing is a highly complex process affecting a wide array of physiological, genomic,
metabolic, and immunological functions [6]. Indeed, any such disturbances may be one of the
primary risk factors for age-related increased predisposition to various chronic maladies
including cardiovascular disorders, infections, bowel diseases, autoimmune diseases, cancers,
diabetes, obesity, and neurodegenerative diseases [235]. Given that the gut microbiota is
closely associated with most of the features mentioned above, the ageing-related issue may
contribute to the increased predisposition to various infectious and gut-associated diseases by
causing alterations in the microbiota.
The most important findings arising from the studies undertaken in this thesis are in line with a wide range of pre-clinical as well as clinical studies [245, 30]. Clinical studies have indicated that the gut microbiota plays a vital role in maintaining the overall health of individuals via interacting with the immune system and host metabolism [241, 310]. Both pre-clinical and clinical studies on ageing have reported microbial shifts over the host's entire lifespan, suggesting there may be some parallel shifts that occur in ageing human and mouse populations [235, 241, 310]. These microbial shifts could be non-beneficial to the host and may lead to dysbiosis condition. Dysbiosis in the gut microbiome and microbial metabolites is known to be associated with aberrations of gut barrier integrity and enhanced pro- inflammatory cytokines, and all these elements can also potentially or partly underlie the pathogenesis and progression of various metabolic diseases [6, 9].
Distribution of the microbiota differs according to the location in the GI tract. Thus, dysbiosis during ageing may not be limited to fecal microbiota and may extend to the entire GI tract, leading to alterations in metabolic profile and immune responses [30, 310]. A wide diversity
184
of microbes are needed to utilize the many nutrients in adult diets. In addition, a low gut microbiota diversity has been associated with an increasing number of GI and metabolic conditions [236]. Also, there has been increasing evidence that the gut microbiome has an impact on the host immune system [9]. The structure of the gut microbiome appears to be altered by the ageing process with an associated increased inflammatory status such as inflamm-ageing that may be predictive of disease [10]. Owing to the recent revelation of the association between ageing and the intestinal microbiota, it is not surprising that the gut microbial ecosystem is receiving considerable attention as a potential target for developing novel strategies for healthier ageing and well-being of ageing individuals [246].
Probiotics are “live microbes that, when administered in adequate amounts, confer a health benefit on the host” [136]. Probiotic strains can help maintain and restore normal microbial balance (homeostasis) in the gastrointestinal tract by increasing the populations of beneficial bacteria while checking the growth of indigenous pathobionts and opportunistic pathogens
[30]. Considering that major age-related changes in microbiota composition can be attenuated by probiotics, it is not surprising that probiotics can be successfully applied in elderly individuals for treating a variety of GI and metabolic diseases [251]. An ideal probiotic strain intended for any human use must be (a) devoid of potential virulence genes, (b) tolerant to gastric and intestinal physicochemical conditions; (c) able to adhere to the intestinal epithelial membrane and (g) reduce inflammation in the gut [252]. So, it was imperative to investigate the in vitro efficacy of probiotics. Probiotic strains utilized in this study include L. acidophilus DDS-1 (human origin), B. lactis UABla-12 (human origin), L. plantarum UALp-
05 (plant origin) and S. thermophilus UASt-09 (dairy origin).
To our knowledge, this is the first study to elucidate the link between origin and efficacy of the probiotics in in vitro with an assessment of the survival of each strain under simulated digestion process, their adhesion capacities to human colonic cells and their
185
immunomodulatory effects (chapter 4) [252]. Taken together, all the strains showed good
survival in the simulated digestion process, strongly adhered to the intestinal epithelium and
demonstrated an adequate immunomodulatory effect. However, the comparative analysis of
the data suggests that origin may affect their probiotic potential. This was particularly in the
case of human origin DDS-1 strain, which showed superior characteristics compared to other
strains and could be an interesting candidate for more intensive research to elucidate and
harness the bioactive effects.
Further, as the probiotic strain L. acidophilus DDS-1 showed promising probiotic character in
vitro, it was investigated in in vivo. A healthy ageing-based model (young and ageing
C57BL/6J mice) was employed to evaluate the direct effects of DDS-1 on gut microbiota
changes associated with fecal metabolome composition (chapter 5) [310]. Subsequently, we
investigated the effect of L. acidophilus DDS-1 supplementation on cecal and mucosal-
associated microbiota, SCFA and immunological profiles in young and ageing C57BL/6J
mice (chapter 6). A combination of omics-based approaches involving 16S rRNA gene
sequencing, untargeted GC-MS based metabolomics, untargeted metabolomics of volatile
fatty acids, and cytokine measurement with bioplex-based immunological analysis were
utilized to provide a comprehensive understanding of changes in host metabolic phenotype of
altering gut microbiota with and without DDS-1 supplementation.
Our results corroborate previous findings of microbiome shifts in ageing adults and identify
changes in taxonomy and function with relevance to ageing [310]. Besides differences in the
young and ageing control groups, our study confirms microbial shifts in cecal and mucosal samples. This further suggests the importance of site-specific assessment in the GI microbiota.
The taxa showing altered abundance might mediate dysbiosis in ageing mice [317].
Additionally, the distribution of microbiota and associated inflammation in the different colonic segments is a key finding of the present study. Importantly, treatment with DDS-1
186
increased the relative abundances of beneficial bacteria, such as A. muciniphila and
Lactobacillus spp., and reduced the relative levels of opportunistic bacteria such as
Proteobacteria spp, reduced opportunistic bacteria such as R. gnavus and B. acidifaciens.
Consistent with the complex nature of the microbial composition of the mouse, our study confirms the differential effects of DDS-1 on the cecal and mucosal-associated microbiota
[311].
To gain functional insight into changes in the gut microbiome, we used a functional pathway analysis on probiotic treated gut microbiota-derived metabolites. Untargeted metabolite profiling of control groups and DDS-1 supplemented groups not only allowed us to identify specific metabolites in the young and ageing gut but also to understand the mechanistic role of DDS-1. Our analysis revealed varying levels of metabolites in young and ageing mice. The metabolite profile in the young control (YC) group was dissimilar from the ageing control
(AC) group, which could be correlated with the microbial shift. Metabolic pathway analysis identified ten key pathways involving amino acid metabolism, protein synthesis and metabolism, carbohydrate metabolism and butanoate metabolism [310].
The gut microbiota produces SCFA, which ameliorate host energy metabolism and inflammation [346]. A total of 30 volatile compounds were identified in our untargeted metabolomics from 46 cecum and mucosal samples in all four groups. These 30 metabolites included the three major SCFA of butyrate, propionate, and acetate. DDS-1 differentially elevated butyrate, propionate, and acetate in the cecum and mucosal samples across groups.
In both cecal and mucosal samples, the butyrate and propionate levels were significantly increased in both young probiotics (YP) and ageing probiotic (AP) groups compared to the
YC group and AC group, respectively. Notably, valeric acid levels were upregulated in mucosal samples with DDS-1 treatment in both groups. Consistent with previous studies, butyrate production was increased in cecal samples across age groups in treated mice and was
187
directly correlated with an increase in Firmicutes levels [307, 331]. Butyrate is a primary
energy substrate for colonocytes, and it has been suggested to play a role in the prevention
and treatment of distal ulcerative colitis, Crohn’s disease, and cancer [350]. From previous
studies, it is known that butyrate increases mucin production, which was also regulated by A.
muciniphila, and the mucus layer thickness was highly related with metabolic improvement,
consistent with mucin production in the present study [315]. Propionate levels were also
found to be increased in both DDS-1 treated groups of cecal and mucosal samples, and it
could also be positively correlated with increased Akkermansia [358].
Age-related alteration in tissue and circulating cytokine production contributes to low-grade
chronic inflammation and release of pro-inflammatory factors such as nuclear factor-kappa B
(NF-κB) [305, 306, 307, 326]. Pro-inflammatory cytokines such as IL-1α, IL-1β, IL-2, IL-5,
IL-6, IFN-γ, MCP-1, MIP-1α, MIP-1β, and TNF-α are shown to play an essential role in inducing age-related chronic inflammation [326, 360, 328]. Notably, studies on ageing mice have suggested an imbalance in cytokine type 1 T helper (h) cell/type 2 T helper cell production to be associated with chronic inflammation [360]. This could be due to imbalances in the intestinal microbiota that lead to compromised gut barrier integrity, enhanced inflammatory responses and subsequent development of chronic diseases [305].
Previously, we demonstrated the immuno-modulatory capacity of DDS-1 on lipopolysaccharide-induced human colonic epithelial cells (chapter 4). Similarly, in this study,
DDS-1 helped normalize, to some extent, the age-specific pro-inflammatory response and is consistent with previous probiotic studies [307, 366, 367]. This could be positively correlated to an increase in abundance of certain beneficial microbes, such as Akkermansia and
Lactobacillus spp. and a decrease in opportunistic microbes such as R. gnavus and B. acidifaciens. Increases in the abundance of Akkermansia and Lactobacillus spp. are reported to be directly correlated with enhanced SCFA production [351]. Altogether, DDS-1 reduced
188
low-grade inflammation by the release of intestinal microbiota-induced-SCFAs such as
butyrate.
To the best of our knowledge, this is the first detailed study comparing fecal, cecal and
mucosal microbial changes and utilizing an integrated pattern recognition approach to
understand the changes in the ageing gut by exploring the metabolic network with and without probiotic supplementation. The key metabolic pathways identified by MetPA and
MSEA play an important role in probiotic-induced gut microbiota-associated metabolic changes. The pathways that we identified by our integrative approach also opens a new avenue of research, aimed to identify the individual contributions of these molecular and metabolomic systems to the ageing process. These findings suggest that modulation of gut microbiota by DDS-1 results in improvement of metabolic phenotype in the ageing mice.
Although some of these pathways have already been investigated in relation to ageing, further
research is needed to establish their relevance, also in humans fully.
7.2 Limitations of the study
7.2.1 Probiotic strains and combinations
Although with most of the probiotic studies, it is understood that probiotic activity is strain dependent and that not all probiotics function against all disorders and diseases studied. In the present research, a single probiotic strain demonstrated superior probiotic character in vitro as
well as in vivo studies (chapters 5 and 6). Several commercial products consist of multiple
strains of probiotics. In fact, it seems to be a recent development to create mixtures of
potential probiotic isolates, rather than to rely on single strains [250]. However, it is usually
unclear how these strains interact with one another, and whether they act synergistically or
antagonistically. Some combinations are based on results of in vitro experiments, although,
interactions between strains are usually not studied [75]. Some mixtures consist of 6–8 strains,
189
and it is imperative that the individual activities of the components are established (preferably
even clinically), if the mode of action of probiotics, and certainly of mixtures, is to be deciphered. As discussed earlier in the section on synbiotics, combinations of pro- and
prebiotics are also arbitrarily chosen. Sometimes the selected probiotic cannot even ferment
the selected prebiotic. It is unusual that the components of synbiotics are tested individually
in clinical trials, although this does sometimes occur in preclinical studies.
7.2.2 Dietary intervention and mice
Another limitation is that we did not test the influence of these probiotics in any disease phenotype. However, very few studies have examined the effects of probiotics on a healthy
(disease-free) gut microbiome, which was our focus here. This also explains why we did not use any reference probiotic strains since we sought to test how inoculation with probiotic-like strains could influence the diversity and composition of the gut microbiota under normal conditions. Whole diet approaches have been specifically designed to slow down the onset of
age and its related diseases. We supplemented the probiotics for 4-weeks, and we have not assessed the pre-treatment and post-treatment microbial changes in the mice. One of the vital focus here was to understand the changes associated with DDS-1 intervention, as this is the first study to show the effects of DDS-1 in in vitro as well as in in vivo. Therefore, the length
of dietary intervention periods depends on the primary research question and the logistical methodologies for prolonged dietary control, which become exponentially more difficult during long-term interventions. In many ways, a shorter intervention, as modelled in the studies within this thesis (Chapter 5, 6), are ideal for investigating GI microbiota due to the rapid turnover of GI microbiota and metabolites. However, it can be argued that longer interventions would allow time more time for physiological changes to manifest, such as changes in the microbiome, and may better capture the effects of dietary interventions on GI
190
measures. The age of mice utilized in the present study might not be the exact representation in the clinical scenario. However, the results can be correlated with the clinical studies.
Further work with larger mouse cohorts, particularly older mice will be needed to separate the effects of ageing on the microbiome. More extensive longitudinal studies would also clarify the relationship between specific clinical attributes and changes to the microbiome during ageing.
7.3 Future directions a. Longitudinal studies envisaging metagenomics sequencing and in-depth phylogenetic analysis, as well as an extensive phenotypic characterization using up-to-date omics
(metabolomics, and metagenomics), are urgently needed.
In the last few years, prodigious progress has been made in understanding the role of gut
microbiota in human health-span, ageing, and longevity. Numerous limitations and
challenges, however, still exist within this newly emerging field, which requires further
investigation and improvement. Since microbiota composition can be profoundly affected by
several confounding factors, the interpretation of these data should be conducted with caution.
b. Specific probiotic and their combinations need to be further investigated to effectively use them to their full potential.
Although it is known that probiotics can confer health benefits, many commercially available products have not been standardized and may not provide any clinical benefits. Specific probiotic and their combinations need to be further investigated to effectively use them to their full potential. This may prove to be difficult considering the number of different microbes are harboured within the gut, as well as the fact that what constitutes a ‘normal’ microbiota varies according to differences in environment and diet.
191
c. Future studies aimed at investigating the influence of probiotics on the human microbiome,
metabolism, and associated diseases are required.
From the current and recent research, it is understood that (a) the gut microbiota and
intestinal environment significantly varies between individuals, (b) the effects of probiotics
are highly strain-specific and host-specific, (c) the effects of prebiotics on microbiota
composition are compound-specific and host-specific, it is exceedingly challenging to elucidate and validate the precise health effects and underlying mechanisms, and hence a lot more research is still awaited to substantiate the beneficial effects of probiotics and prebiotics in healthy as well as in diseased subjects.
7.4 Conclusion and clinical relevance
New evidence from the research conducted throughout this thesis shows that the mouse
microbiome changes throughout various life stages and suggests that similar changes in
humans may have a significant effect on health. Probiotic supplementation with L.
acidophilus DDS-1 can counter ageing associated GI disorder. Further studies, including clinical investigation, are needed to confirm these outcomes. The clinical relevance of this study is that by selectively promoting beneficial bacteria, L. acidophilus DDS-1
supplementation could be an important dietary strategy to counteract ageing-associated
dysbiosis.
192
References
1. Sender R, Fuchs S, Milo R. Are We Really Vastly Outnumbered? Revisiting the Ratio of
Bacterial to Host Cells in Humans. Cell. 2016;164(3):337-40.
2. Clemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on
human health: an integrative view. Cell. 2012;148(6):1258-70.
3. Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The
pandemic of physical inactivity: global action for public health. Lancet.
2012;380(9838):294-305.
4. Cherubini A, Corsonello A, Lattanzio F. Underprescription of beneficial medicines in
older people: causes, consequences, and prevention. Drugs Ageing. 2012;29(6):463-75.
5. Li H, Qi Y, Jasper H. Preventing Age-Related Decline of Gut Compartmentalization
Limits Microbiota Dysbiosis and Extends Lifespan. Cell Host Microbe. 2016;19(2):240-
53.
6. Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, et al. Through ageing, and
beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One.
2010;5(5):e10667.
7. Quinlan N, O'Neill D. "Older" or "elderly"--are medical journals sensitive to the wishes
of older people? J Am Geriatr Soc. 2008;56(10):1983-4.
8. Kleessen B, Sykura B, Zunft HJ, Blaut M. Effects of inulin and lactose on fecal
microflora, microbial activity, and bowel habit in elderly constipated persons. Am J Clin
Nutr. 1997;65(5):1397-402.
9. Franceschi C. Inflammageing as a major characteristic of old people: can it be prevented
or cured? Nutr Rev. 2007;65(12 Pt 2):S173-6.
10. Ostan R, Bucci L, Capri M, Salvioli S, Scurti M, Pini E, et al. Immunosenescence and
immunogenetics of human longevity. Neuroimmunomodulation. 2008;15(4-6):224-40.
193
11. Magrone T, Jirillo E. The interplay between the gut immune system and microbiota in
health and disease: nutraceutical intervention for restoring intestinal homeostasis. Curr
Pharm Des. 2013;19(7):1329-42.
12. Nagpal R, Tsuji H, Takahashi T, Kawashima K, Nagata S, Nomoto K, et al. Sensitive
Quantitative Analysis of the Meconium Bacterial Microbiota in Healthy Term Infants
Born Vaginally or by Cesarean Section. Front Microbiol. 2016; 7:1997.
13. Benno Y, Endo K, Mizutani T, Namba Y, Komori T, Mitsuoka T. Comparison of fecal
microflora of elderly persons in rural and urban areas of Japan. Appl Environ Microbiol.
1989;55(5):1100-5.
14. Hayashi H, Sakamoto M, Benno Y. Phylogenetic analysis of the human gut microbiota
using 16S rDNA clone libraries and strictly anaerobic culture-based methods. Microbiol
Immunol. 2002;46(8):535-48.
15. Hayashi H, Takahashi R, Nishi T, Sakamoto M, Benno Y. Molecular analysis of jejunal,
ileal, caecal and recto-sigmoidal human colonic microbiota using 16S rRNA gene
libraries and terminal restriction fragment length polymorphism. J Med Microbiol.
2005;54(Pt 11):1093-101.
16. Collado MC, Derrien M, Isolauri E, de Vos WM, Salminen S. Intestinal integrity and
Akkermansia muciniphila, a mucin-degrading member of the intestinal microbiota
present in infants, adults, and the elderly. Appl Environ Microbiol. 2007;73(23):7767-70.
17. Mueller S, Saunier K, Hanisch C, Norin E, Alm L, Midtvedt T, et al. Differences in fecal
microbiota in different European study populations in relation to age, gender, and
country: a cross-sectional study. Appl Environ Microbiol. 2006;72(2):1027-33.
18. Mariat D, Firmesse O, Levenez F, Guimaraes V, Sokol H, Dore J, et al. The
Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC
Microbiol. 2009; 9:123.
194
19. Fukushima Y, Miyaguchi S, Yamano T, Kaburagi T, Iino H, Ushida K, et al.
Improvement of nutritional status and incidence of infection in hospitalised, enterally fed
elderly by feeding of fermented milk containing probiotic Lactobacillus johnsonii La1
(NCC533). Br J Nutr. 2007;98(5):969-77.
20. Ouwehand AC, Bergsma N, Parhiala R, Lahtinen S, Gueimonde M, Finne-Soveri H, et al.
Bifidobacterium microbiota and parameters of immune function in elderly subjects.
FEMS Immunol Med Microbiol. 2008;53(1):18-25.
21. Akatsu H, Iwabuchi N, Xiao JZ, Matsuyama Z, Kurihara R, Okuda K, et al. Clinical
effects of probiotic Bifidobacterium longum BB536 on immune function and intestinal
microbiota in elderly patients receiving enteral tube feeding. JPEN J Parenter Enteral
Nutr. 2013;37(5):631-40.
22. Nyangale EP, Farmer S, Cash HA, Keller D, Chernoff D, Gibson GR. Bacillus coagulans
GBI-30, 6086 Modulates Fecalibacterium prausnitzii in Older Men and Women. J Nutr.
2015;145(7):1446-52.
23. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human
infant intestinal microbiota. PLoS Biol. 2007;5(7):e177.
24. Adlerberth I, Wold AE. Establishment of the gut microbiota in Western infants. Acta
Paediatr. 2009;98(2):229-38.
25. Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al.
Delivery mode shapes the acquisition and structure of the initial microbiota across
multiple body habitats in newborns. Proc Natl Acad Sci U S A. 2010;107(26):11971-5.
26. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al.
Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174-80.
195
27. Favier CF, Vaughan EE, De Vos WM, Akkermans AD. Molecular monitoring of
succession of bacterial communities in human neonates. Appl Environ Microbiol.
2002;68(1):219-26.
28. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, et al. Metagenomic
analysis of the human distal gut microbiome. Science. 2006;312(5778):1355-9.
29. Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H, Toyoda A, et al. Comparative
metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA
Res. 2007;14(4):169-81.
30. Lakshminarayanan B, Stanton C, O'Toole PW, Ross RP. Compositional dynamics of the
human intestinal microbiota with ageing: implications for health. J Nutr Health Ageing.
2014;18(9):773-86.
31. O'Hara AM, Shanahan F. The gut flora as a forgotten organ. EMBO reports.
2006;7(7):688-93.
32. Chow J, Lee SM, Shen Y, Khosravi A, Mazmanian SK. Host-bacterial symbiosis in
health and disease. Adv Immunol. 2010; 107:243-74.
33. Huurre A, Kalliomaki M, Rautava S, Rinne M, Salminen S, Isolauri E. Mode of delivery
- effects on gut microbiota and humoral immunity. Neonatology. 2008;93(4):236-40.
34. Peterson DA, McNulty NP, Guruge JL, Gordon JI. IgA response to symbiotic bacteria as
a mediator of gut homeostasis. Cell Host Microbe. 2007;2(5):328-39.
35. Planer JD, Peng Y, Kau AL, Blanton LV, Ndao IM, Tarr PI, et al. Development of the
gut microbiota and mucosal IgA responses in twins and gnotobiotic mice. Nature.
2016;534(7606):263-6.
36. Saraswati S, Sitaraman R. Ageing and the human gut microbiota-from correlation to
causality. Front Microbiol. 2014; 5:764.
196
37. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid
assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol.
2007;73(16):5261-7.
38. Breitbart M, Haynes M, Kelley S, Angly F, Edwards RA, Felts B, et al. Viral diversity
and dynamics in an infant gut. Res Microbiol. 2008;159(5):367-73.
39. Virgin HW, Wherry EJ, Ahmed R. Redefining chronic viral infection. Cell.
2009;138(1):30-50.
40. Lynch SV, Pedersen O. The Human Intestinal Microbiome in Health and Disease. N
Engl J Med. 2016;375(24):2369-79.
41. Imahori K. How I understand ageing. Nutr Rev. 1992;50(12):351-2.
42. Singh S, Bajorek B. Defining 'elderly' in clinical practice guidelines for pharmacotherapy.
Pharm Pract (Granada). 2014;12(4):489.
43. WHO, “Definition of an older orelderly person,” World Health Organisation, Geneva,
Switzerland, 2010, http://www.who.int/healthinfo/survey/ageingdefnolder/en/index.html.
44. Soenen S, Rayner CK, Horowitz M, Jones KL. Gastric Emptying in the Elderly. Clin
Geriatr Med. 2015;31(3):339-53.
45. Claesson MJ, Cusack S, O'Sullivan O, Greene-Diniz R, de Weerd H, Flannery E, et al.
Composition, variability, and temporal stability of the intestinal microbiota of the elderly.
Proc Natl Acad Sci U S A. 2011;108 Suppl 1:4586-91.
46. van Tongeren SP, Slaets JP, Harmsen HJ, Welling GW. Fecal microbiota composition
and frailty. Appl Environ Microbiol. 2005;71(10):6438-42.
47. Bartosch S, Fite A, Macfarlane GT, McMurdo ME. Characterization of bacterial
communities in feces from healthy elderly volunteers and hospitalized elderly patients by
using real-time PCR and effects of antibiotic treatment on the fecal microbiota. Appl
Environ Microbiol. 2004;70(6):3575-81.
197
48. Zwielehner J, Liszt K, Handschur M, Lassl C, Lapin A, Haslberger AG. Combined PCR-
DGGE fingerprinting and quantitative-PCR indicates shifts in fecal population sizes and
diversity of Bacteroides, bifidobacteria and Clostridium cluster IV in institutionalized
elderly. Exp Gerontol. 2009;44(6-7):440-6.
49. Dominianni C, Sinha R, Goedert JJ, Pei Z, Yang L, Hayes RB, et al. Sex, body mass
index, and dietary fiber intake influence the human gut microbiome. PLoS One.
2015;10(4):e0124599.
50. Haro C, Rangel-Zuniga OA, Alcala-Diaz JF, Gomez-Delgado F, Perez-Martinez P,
Delgado-Lista J, et al. Intestinal Microbiota Is Influenced by Gender and Body Mass
Index. PLoS One. 2016;11(5):e0154090.
51. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters
gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102(31):11070-5.
52. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, et al. Changes in
gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-
induced obesity and diabetes in mice. Diabetes. 2008;57(6):1470-81.
53. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of
human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541-
6.
54. Ussar S, Griffin NW, Bezy O, Fujisaka S, Vienberg S, Softic S, et al. Interactions
between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity
and Metabolic Syndrome. Cell Metab. 2015;22(3):516-30.
55. Burcelin R. Gut microbiota and immune crosstalk in metabolic disease. Molecular
metabolism. 2016;5(9):771-81.
56. Van Cauter E, Polonsky KS, Scheen AJ. Roles of circadian rhythmicity and sleep in
human glucose regulation. Endocr Rev. 1997;18(5):716-38.
198
57. Bass J, Turek FW. Sleepless in America: a pathway to obesity and the metabolic
syndrome? Arch Intern Med. 2005;165(1):15-6.
58. Dibner C, Schibler U, Albrecht U. The mammalian circadian timing system: organization
and coordination of central and peripheral clocks. Annu Rev Physiol. 2010;72:517-49.
59. Huang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism.
J Clin Invest. 2011;121(6):2133-41.
60. Kohsaka A, Laposky AD, Ramsey KM, Estrada C, Joshu C, Kobayashi Y, et al. High-fat
diet disrupts behavioral and molecular circadian rhythms in mice. Cell Metab.
2007;6(5):414-21.
61. Leone V, Gibbons SM, Martinez K, Hutchison AL, Huang EY, Cham CM, et al. Effects
of diurnal variation of gut microbes and high-fat feeding on host circadian clock function
and metabolism. Cell Host Microbe. 2015;17(5):681-9.
62. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-
associated gut microbiome with increased capacity for energy harvest. Nature.
2006;444(7122):1027-31.
63. Burcelin R, Serino M, Chabo C, Garidou L, Pomie C, Courtney M, et al. Metagenome
and metabolism: the tissue microbiota hypothesis. Diabetes Obes Metab. 2013;15 Suppl
3:61-70.
64. Weiner HL, da Cunha AP, Quintana F, Wu H. Oral tolerance. Immunol Rev.
2011;241(1):241-59.
65. Magrone T, Jirillo E. The interaction between gut microbiota and age-related changes in
immune function and inflammation. Immun Ageing. 2013;10(1):31.
66. Johansson ME, Phillipson M, Petersson J, Velcich A, Holm L, Hansson GC. The inner of
the two Muc2 mucin-dependent mucus layers in colon is devoid of bacteria. Proc Natl
Acad Sci U S A. 2008;105(39):15064-9.
199
67. Randall-Demllo S, Chieppa M, Eri R. Intestinal epithelium and autophagy: partners in
gut homeostasis. Front Immunol. 2013;4:301.
68. Nair MG, Guild KJ, Du Y, Zaph C, Yancopoulos GD, Valenzuela DM, et al. Goblet cell-
derived resistin-like molecule beta augments CD4+ T cell production of IFN-gamma and
infection-induced intestinal inflammation. J Immunol. 2008;181(7):4709-15.
69. Bevins CL, Salzman NH. Paneth cells, antimicrobial peptides and maintenance of
intestinal homeostasis. Nat Rev Microbiol. 2011;9(5):356-68.
70. Williams A, Flavell RA, Eisenbarth SC. The role of NOD-like Receptors in shaping
adaptive immunity. Curr Opin Immunol. 2010;22(1):34-40.
71. Kanai T, Mikami Y, Sujino T, Hisamatsu T, Hibi T. RORgammat-dependent IL-17A-
producing cells in the pathogenesis of intestinal inflammation. Mucosal Immunol.
2012;5(3):240-7.
72. Dicarlo AL, Fuldner R, Kaminski J, Hodes R. Aging in the context of immunological
architecture, function and disease outcomes. Trends Immunol. 2009;30(7):293-4.
73. Hooper LV, Macpherson AJ. Immune adaptations that maintain homeostasis with the
intestinal microbiota. Nat Rev Immunol. 2010;10(3):159-69.
74. Mantis NJ, Rol N, Corthesy B. Secretory IgA's complex roles in immunity and mucosal
homeostasis in the gut. Mucosal Immunol. 2011;4(6):603-11.
75. Macfarlane S, Cleary S, Bahrami B, Reynolds N, Macfarlane GT. Synbiotic consumption
changes the metabolism and composition of the gut microbiota in older people and
modifies inflammatory processes: a randomised, double-blind, placebo-controlled
crossover study. Aliment Pharmacol Ther. 2013;38(7):804-16.
76. Wong CP, Magnusson KR, Ho E. Increased inflammatory response in aged mice is
associated with age-related zinc deficiency and zinc transporter dysregulation. J Nutr
Biochem. 2013;24(1):353-9.
200
77. Maynard CL, Weaver CT. Intestinal effector T cells in health and disease. Immunity.
2009;31(3):389-400.
78. Stefka AT, Feehley T, Tripathi P, Qiu J, McCoy K, Mazmanian SK, et al. Commensal
bacteria protect against food allergen sensitization. Proc Natl Acad Sci U S A.
2014;111(36):13145-50.
79. Feng T, Wang L, Schoeb TR, Elson CO, Cong Y. Microbiota innate stimulation is a
prerequisite for T cell spontaneous proliferation and induction of experimental colitis. J
Exp Med. 2010;207(6):1321-32.
80. De Vuyst L, Leroy F. Cross-feeding between bifidobacteria and butyrate-producing
colon bacteria explains bifdobacterial competitiveness, butyrate production, and gas
production. Int J Food Microbiol. 2011;149(1):73-80.
81. Schiffrin EJ, Morley JE, Donnet-Hughes A, Guigoz Y. The inflammatory status of the
elderly: the intestinal contribution. Mutat Res. 2010;690(1-2):50-6.
82. Manichanh C, Borruel N, Casellas F, Guarner F. The gut microbiota in IBD. Nat Rev
Gastroenterol Hepatol. 2012;9(10):599-608.
83. Guigoz Y, Dore J, Schiffrin EJ. The inflammatory status of old age can be nurtured from
the intestinal environment. Curr Opin Clin Nutr Metab Care. 2008;11(1):13-20.
84. Marttila S, Jylhava J, Nevalainen T, Nykter M, Jylha M, Hervonen A, et al.
Transcriptional analysis reveals gender-specific changes in the aging of the human
immune system. PLoS One. 2013;8(6):e66229.
85. Hirokawa K, Utsuyama M, Hayashi Y, Kitagawa M, Makinodan T, Fulop T. Slower
immune system aging in women versus men in the Japanese population. Immun Ageing.
2013;10(1):19.
86. Klein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol.
2016;16(10):626-38.
201
87. Fransen F, van Beek AA, Borghuis T, Meijer B, Hugenholtz F, van der Gaast-de Jongh C,
et al. The Impact of Gut Microbiota on Gender-Specific Differences in Immunity. Front
Immunol. 2017;8:754.
88. Lefevre M, Racedo SM, Ripert G, Housez B, Cazaubiel M, Maudet C, et al. Probiotic
strain Bacillus subtilis CU1 stimulates immune system of elderly during common
infectious disease period: a randomized, double-blind placebo-controlled study. Immun
Ageing. 2015;12:24.
89. Dong H, Rowland I, Thomas LV, Yaqoob P. Immunomodulatory effects of a probiotic
drink containing Lactobacillus casei Shirota in healthy older volunteers. Eur J Nutr.
2013;52(8):1853-63.
90. Carasi P, Racedo SM, Jacquot C, Romanin DE, Serradell MA, Urdaci MC. Impact of
kefir derived Lactobacillus kefiri on the mucosal immune response and gut microbiota.
Journal of immunology research. 2015;2015:361604.
91. Nagafuchi S, Yamaji T, Kawashima A, Saito Y, Takahashi T, Yamamoto T, et al. Effects
of a Formula Containing Two Types of Prebiotics, Bifidogenic Growth Stimulator and
Galacto-oligosaccharide, and Fermented Milk Products on Intestinal Microbiota and
Antibody Response to Influenza Vaccine in Elderly Patients: A Randomized Controlled
Trial. Pharmaceuticals (Basel). 2015;8(2):351-65.
92. D'Aimmo MR, Mattarelli P, Biavati B, Carlsson NG, Andlid T. The potential of
bifidobacteria as a source of natural folate. J Appl Microbiol. 2012;112(5):975-84.
93. Chung WS, Walker AW, Louis P, Parkhill J, Vermeiren J, Bosscher D, et al. Modulation
of the human gut microbiota by dietary fibres occurs at the species level. BMC Biol.
2016;14:3.
94. Brown K, DeCoffe D, Molcan E, Gibson DL. Diet-induced dysbiosis of the intestinal
microbiota and the effects on immunity and disease. Nutrients. 2012;4(8):1095-119.
202
95. Round JL, Mazmanian SK. Inducible Foxp3+ regulatory T-cell development by a
commensal bacterium of the intestinal microbiota. Proc Natl Acad Sci U S A.
2010;107(27):12204-9.
96. Khoruts A, Weingarden AR. Emergence of fecal microbiota transplantation as an
approach to repair disrupted microbial gut ecology. Immunol Lett. 2014;162(2 Pt A):77-
81.
97. Agrawal M, Aroniadis OC, Brandt LJ, Kelly C, Freeman S, Surawicz C, et al. The long-
term efficacy and safety of fecal microbiota transplant for recurrent, severe, and
complicated Clostridium difficile infection in 146 elderly individuals. J Clin
Gastroenterol. 2016;50(5):403-7.
98. Ahmed M, Prasad J, Gill H, Stevenson L, Gopal P. Impact of consumption of different
levels of Bifidobacterium lactis HN019 on the intestinal microflora of elderly human
subjects. Journal of Nutrition Health and Aging. 2007;11(1):26.
99. Akatsu H, Nagafuchi S, Kurihara R, Okuda K, Kanesaka T, Ogawa N, et al. Enhanced
vaccination effect against influenza by prebiotics in elderly patients receiving enteral
nutrition. Geriatrics & gerontology international. 2016;16(2):205-13.
100. Allen S, Wareham K, Wang D, Bradley C, Sewell B, Hutchings H, et al. PWE-008
Placide: Probiotics in the Prevention of Antibiotic Associated Diarrhoea (AAD) and
Clostridium Difficile Associated Diarrhoea (CDD) in Elderly Patients Admitted to
Hospital–Results of a Large Multi-Centre RCT in the UK. Gut. 2013;62(Suppl 1):A133-
A.
101. Amati L, Marzulli G, Martulli M, Pugliese V, Caruso C, Candore G, et al.
Administration of a synbiotic to free-living elderly and evaluation of serum cytokines. A
pilot study. Current pharmaceutical design. 2010;16(7):854-8.
203
102. Andrews P, Borody T, Shortis N, Thompson S, editors. BACTERIOTHERAPY FOR
CHRONIC CONSTIPATION-A LONG-TERM FOLLOW-UP. Gastroenterology; 1995:
WB SAUNDERS CO INDEPENDENCE SQUARE WEST CURTIS CENTER, STE
300, PHILADELPHIA.
103. Angelberger S, Reinisch W, Makristathis A, Lichtenberger C, Dejaco C, Papay P, et al.
Temporal bacterial community dynamics vary among ulcerative colitis patients after
fecal microbiota transplantation. The American journal of gastroenterology.
2013;108(10):1620.
104. Aroniadis OC, Brandt LJ. Fecal microbiota transplantation: past, present and future.
Curr Opin Gastroenterol. 2013;29(1):79-84.
105. Aroniadis OC, Brandt LJ, Greenberg A, Borody T, Kelly CR, Mellow M, et al. Long-
term Follow-up Study of Fecal Microbiota Transplantation for Severe and/or
Complicated Clostridium difficile Infection. J Clin Gastroenterol. 2016;50(5):398-402.
106. Bakken JS. Fecal bacteriotherapy for recurrent Clostridium difficile infection. Anaerobe.
2009;15(6):285-9.
107. Bartosch S, Fite A, Macfarlane GT, McMurdo ME. Characterization of bacterial
communities in feces from healthy elderly volunteers and hospitalized elderly patients by
using real-time PCR and effects of antibiotic treatment on the fecal microbiota. Appl
Environ Microbiol. 2004;70(6):3575-81.
108. Bergonzelli GE, Blum S, Brüssow H, Corthésy-Theulaz I. Probiotics as a treatment
strategy for gastrointestinal diseases? Digestion. 2005;72(1):57-68.
109. Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, et al. Through ageing, and
beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One.
2010;5(5):e10667.
204
110. Biliński J, Grzesiowski P, Muszyński J, Wróblewska M, Mądry K, Robak K, et al. Fecal
microbiota transplantation inhibits multidrug-resistant gut pathogens: preliminary report
performed in an immunocompromised host. Arch Immunol Ther Exp (Warsz).
2016;64(3):255-8.
111. Boll EVJ, Ekström LM, Courtin CM, Delcour JA, Nilsson AC, Björck IM, et al. Effects
of wheat bran extract rich in arabinoxylan oligosaccharides and resistant starch on
overnight glucose tolerance and markers of gut fermentation in healthy young adults. Eur
J Nutr. 2016;55(4):1661-70.
112. Borody TJ, George L, Andrews P, Brandl S, Noonan S, Cole P, et al. Bowel-flora
alteration: a potential cure for inflammatory bowel disease and irritable bowel syndrome?
The Medical journal of Australia. 1989;150(10):604.
113. Borody TJ, Warren EF, Leis SM, Surace R, Ashman O, Siarakas S. Bacteriotherapy
using fecal flora: toying with human motions. J Clin Gastroenterol. 2004;38(6):475-83.
114. Bouhnik Y, Achour L, Paineau D, Riottot M, Attar A, Bornet F. Four-week short chain
fructo-oligosaccharides ingestion leads to increasing fecal bifidobacteria and cholesterol
excretion in healthy elderly volunteers. Nutr J. 2007;6(1):42.
115. Brandt LJ, Aroniadis OC, Mellow M, Kanatzar A, Kelly C, Park T, et al. Long-term
follow-up of colonoscopic fecal microbiota transplant for recurrent Clostridium difficile
infection. The American journal of gastroenterology. 2012;107(7):1079.
116. Chang JY, Antonopoulos DA, Kalra A, Tonelli A, Khalife WT, Schmidt TM, et al.
Decreased diversity of the fecal microbiome in recurrent Clostridium difficile—
associated diarrhea. The Journal of infectious diseases. 2008;197(3):435-8.
117. Charpentier C, Salleron J, Savoye G, Fumery M, Merle V, Laberenne J-E, et al. Natural
history of elderly-onset inflammatory bowel disease: a population-based cohort study.
Gut. 2014;63(3):423-32.
205
118. Chassard C, Dapoigny M, Scott KP, Crouzet L, Del'homme C, Marquet P, et al.
Functional dysbiosis within the gut microbiota of patients with constipated‐irritable
bowel syndrome. Aliment Pharmacol Ther. 2012;35(7):828-38.
119. Chung Y-C, Hsu C-K, Ko C-Y, Chan Y-C. Dietary intake of xylooligosaccharides
improves the intestinal microbiota, fecal moisture, and pH value in the elderly. Nutr Res.
2007;27(12):756-61.
120. Claesson MJ, Jeffery IB, Conde S, Power SE, O’connor EM, Cusack S, et al. Gut
microbiota composition correlates with diet and health in the elderly. Nature.
2012;488(7410):178.
121. Cui B, Feng Q, Wang H, Wang M, Peng Z, Li P, et al. Fecal microbiota transplantation
through mid‐gut for refractory C rohn's disease: Safety, feasibility, and efficacy trial
results. J Gastroenterol Hepatol. 2015;30(1):51-8.
122. Cusack S, O'toole PW. Challenges and implications for biomedical research and
intervention studies in older populations: Insights from the ELDERMET study.
Gerontology. 2013;59(2):114-21.
123. Damman C, Brittnacher M, Hayden H, Radey M, Hager K, Miller S, et al. Su1403
single colonoscopically administered fecal microbiota transplant for ulcerative colitis-a
pilot study to determine therapeutic benefit and graft stability. Gastroenterology.
2014;146(5):S-460.
124. Deng F, Wang Y, Zhang Y, Shen Z. Characterization of the genetic environment of the
ribosomal RNA methylase gene erm (B) in Campylobacter jejuni. J Antimicrob
Chemother. 2014;70(2):613-5.
125. Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of an antibiotic on
the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol.
2008;6(11):e280.
206
126. Dong D, Zhang L, Chen X, Jiang C, Yu B, Wang X, et al. Antimicrobial susceptibility
and resistance mechanisms of clinical Clostridium difficile from a Chinese tertiary
hospital. Int J Antimicrob Agents. 2013;41(1):80-4.
127. Dunne C, Murphy L, Flynn S, O’Mahony L, O’Halloran S, Feeney M, et al. Probiotics:
from myth to reality. Demonstration of functionality in animal models of disease and in
human clinical trials. Lactic Acid Bacteria: Genetics, Metabolism and Applications:
Springer; 1999. p. 279-92.
128. Eiseman á, Silen W, Bascom G, Kauvar A. Fecal enema as an adjunct in the treatment
of pseudomembranous enterocolitis. Surgery. 1958;44(5):854-9.
129. Fang H, Elina T, Heikki A, Seppo S. Modulation of humoral immune response through
probiotic intake. FEMS Immunol Med Microbiol. 2000;29(1):47-52.
130. Frank DN, Amand ALS, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecular-
phylogenetic characterization of microbial community imbalances in human
inflammatory bowel diseases. Proceedings of the National Academy of Sciences.
2007;104(34):13780-5.
131. Gibson GR, Probert HM, Van Loo J, Rastall RA, Roberfroid MB. Dietary modulation of
the human colonic microbiota: updating the concept of prebiotics. Nutr Res Rev.
2004;17(2):259-75.
132. Gopal PK, Prasad J, Gill HS. Effects of the consumption of Bifidobacterium lactis
HN019 (DR10TM) and galacto-oligosaccharides on the microflora of the gastrointestinal
tract in human subjects. Nutr Res. 2003;23(10):1313-28.
133. Granata M, Brandi G, Borsari A, Gasbarri R, Gioia DD. Synbiotic yogurt consumption
by healthy adults and the elderly: the fate of bifidobacteria and LGG probiotic strain. Int
J Food Sci Nutr. 2013;64(2):162-8.
207
134. Hamilton MJ, Weingarden AR, Sadowsky MJ, Khoruts A. Standardized frozen
preparation for transplantation of fecal microbiota for recurrent Clostridium difficile
infection. The American journal of gastroenterology. 2012;107(5):761.
135. Higgins JA, Brown IL. Resistant starch: a promising dietary agent for the
prevention/treatment of inflammatory bowel disease and bowel cancer. Curr Opin
Gastroenterol. 2013;29(2):190-4.
136. Hill C, Guarner F, Reid G, Gibson GR, Merenstein DJ, Pot B, et al. Expert consensus
document: The International Scientific Association for Probiotics and Prebiotics
consensus statement on the scope and appropriate use of the term probiotic. Nature
Reviews Gastroenterology and Hepatology. 2014;11(8):506.
137. Holvoet T, Boelens J, Joossens M, Raes J, De Vos M, De Looze D. Tu2025 Fecal
Microbiota Transplantation in Irritable Bowel Syndrome With Bloating: Results From a
Prospective Pilot Study. Gastroenterology. 2015;148(4):S-963-S-4.
138. Hopkins M, Macfarlane G. Changes in predominant bacterial populations in human
faeces with age and with Clostridium difficile infection. J Med Microbiol.
2002;51(5):448-54.
139. Imahori K. How I understand aging. Nutr Rev. 1992;50(12):351-2.
140. Jernberg C, Löfmark S, Edlund C, Jansson JK. Long-term ecological impacts of
antibiotic administration on the human intestinal microbiota. The ISME journal.
2007;1(1):56.
141. Jernberg C, Löfmark S, Edlund C, Jansson JK. Long-term impacts of antibiotic exposure
on the human intestinal microbiota. Microbiology. 2010;156(11):3216-23.
142. Katz JA. Probiotics for the prevention of antibiotic-associated diarrhea and Clostridium
difficile diarrhea. J Clin Gastroenterol. 2006;40(3):249-55.
208
143. Khoruts A, Dicksved J, Jansson JK, Sadowsky MJ. Changes in the composition of the
human fecal microbiome after bacteriotherapy for recurrent Clostridium difficile-
associated diarrhea. J Clin Gastroenterol. 2010;44(5):354-60.
144. Khoruts A, Weingarden AR. Emergence of fecal microbiota transplantation as an
approach to repair disrupted microbial gut ecology. Immunol Lett. 2014;162(2 Pt A):77-
81.
145. Kleessen B, Sykura B, Zunft HJ, Blaut M. Effects of inulin and lactose on fecal
microflora, microbial activity, and bowel habit in elderly constipated persons. Am J Clin
Nutr. 1997;65(5):1397-402.
146. Lahtinen SJ, Tammela L, Korpela J, Parhiala R, Ahokoski H, Mykkanen H, et al.
Probiotics modulate the Bifidobacterium microbiota of elderly nursing home residents.
Age (Dordrecht, Netherlands). 2009;31(1):59-66.
147. Lakshminarayanan B, Stanton C, O'Toole PW, Ross RP. Compositional dynamics of the
human intestinal microbiota with aging: implications for health. J Nutr Health Aging.
2014;18(9):773-86.
148. Likotrafiti E, Tuohy KM, Gibson GR, Rastall RA. An in vitro study of the effect of
probiotics, prebiotics and synbiotics on the elderly fecal microbiota. Anaerobe.
2014;27:50-5.
149. Likotrafiti E, Tuohy KM, Gibson GR, Rastall RA. Antimicrobial activity of selected
synbiotics targeted for the elderly against pathogenic Escherichia coli strains. Int J Food
Sci Nutr. 2016;67(2):83-91.
150. Lobley GE, Holtrop G, Bremner DM, Calder AG, Milne E, Johnstone AM. Impact of
short term consumption of diets high in either non-starch polysaccharides or resistant
starch in comparison with moderate weight loss on indices of insulin sensitivity in
subjects with metabolic syndrome. Nutrients. 2013;5(6):2144-72.
209
151. Louie TJ, Cannon K, Byrne B, Emery J, Ward L, Eyben M, et al. Fidaxomicin preserves
the intestinal microbiome during and after treatment of Clostridium difficile infection
(CDI) and reduces both toxin reexpression and recurrence of CDI. Clin Infect Dis.
2012;55 Suppl 2:S132-42.
152. Louie TJ, Miller MA, Crook DW, Lentnek A, Bernard L, High KP, et al. Effect of age
on treatment outcomes in Clostridium difficile infection. J Am Geriatr Soc.
2013;61(2):222-30.
153. Makivuokko H, Tiihonen K, Tynkkynen S, Paulin L, Rautonen N. The effect of age and
non-steroidal anti-inflammatory drugs on human intestinal microbiota composition. Br J
Nutr. 2010;103(2):227-34.
154. Mathur H, O'Connor PM, Hill C, Cotter PD, Ross RP. Analysis of anti-Clostridium
difficile activity of thuricin CD, vancomycin, metronidazole, ramoplanin, and
actagardine, both singly and in paired combinations. Antimicrob Agents Chemother.
2013;57(6):2882-6.
155. McGuckin MA, Eri R, Simms LA, Florin TH, Radford-Smith G. Intestinal barrier
dysfunction in inflammatory bowel diseases. Inflamm Bowel Dis. 2009;15(1):100-13.
156. Moore T, Rodriguez A, Bakken JS. Fecal microbiota transplantation: a practical update
for the infectious disease specialist. Clin Infect Dis. 2014;58(4):541-5.
157. Moroti C, Souza Magri LF, de Rezende Costa M, Cavallini DC, Sivieri K. Effect of the
consumption of a new symbiotic shake on glycemia and cholesterol levels in elderly
people with type 2 diabetes mellitus. Lipids Health Dis. 2012;11:29.
158. Nagata S, Asahara T, Wang C, Suyama Y, Chonan O, Takano K, et al. The
Effectiveness of Lactobacillus Beverages in Controlling Infections among the Residents
of an Aged Care Facility: A Randomized Placebo-Controlled Double-Blind Trial. Ann
Nutr Metab. 2016;68(1):51-9.
210
159. Neto JV, de Melo CM, Ribeiro SM. Effects of three-month intake of synbiotic on
inflammation and body composition in the elderly: a pilot study. Nutrients.
2013;5(4):1276-86.
160. Nord CE, Edlund C. Ecological effects of antimicrobial agents on the human intestinal
microflora. Microbial ecology in health and disease. 1991 ;4(4):193-207.
161. O'sullivan Ó, Coakley M, Lakshminarayanan B, Conde S, Claesson MJ, Cusack S,
Fitzgerald AP, O'toole PW, Stanton C, Ross RP. Alterations in intestinal microbiota of
elderly Irish subjects post-antibiotic therapy. Journal of Antimicrobial Chemotherapy.
2012;68(1):214-21.
162. O'Donoghue C, Solomon K, Fenelon L, Fitzpatrick F, Kyne L. Effect of proton pump
inhibitors and antibiotics on the gut microbiome of hospitalised older persons. Journal of
Infection. 2016;72(4):498-500.
163. Orimo H, Ito H, Suzuki T, Araki A, Hosoi T, Sawabe M. Reviewing the definition of
“elderly”. Geriatrics & gerontology international. 2006;6(3):149-58.
164. Ouwehand AC, Salminen S, Isolauri E. Probiotics: an overview of beneficial effects.
InLactic Acid Bacteria: Genetics, Metabolism and Applications 2002 (pp. 279-289).
Springer, Dordrecht.
165. Pamer EG. Fecal microbiota transplantation: effectiveness, complexities, and lingering
concerns. Mucosal Immunol. 2014;7(2):210-4.
166. Pellino G, Sciaudone G, Candilio G, Camerlingo A, Marcellinaro R, De Fatico S, et al.
Early postoperative administration of probiotics versus placebo in elderly patients
undergoing elective colorectal surgery: a double-blind, randomized controlled trial. BMC
Surg. 2013;13(2):S57.
211
167. Pérez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K, Otto W,
Rojo D, Bargiela R, von Bergen M, Neulinger SC. Gut microbiota disturbance during
antibiotic therapy: a multi-omic approach. Gut. 2013;62(11):1591-601.
168. Pitkala KH, Strandberg TE, Finne Soveri UH, Ouwehand AC, Poussa T, Salminen S.
Fermented cereal with specific bifidobacteria normalizes bowel movements in elderly
nursing home residents. A randomized, controlled trial. J Nutr Health Aging.
2007;11(4):305-11.
169. Rampelli S, Candela M, Severgnini M, Biagi E, Turroni S, Roselli M, et al. A
probiotics-containing biscuit modulates the intestinal microbiota in the elderly. J Nutr
Health Aging. 2013;17(2):166-72.
170. Rea MC, Alemayehu D, Casey PG, O’Connor PM, Lawlor PG, Walsh M, Shanahan F,
Kiely B, Ross RP, Hill C. Bioavailability of the anti-clostridial bacteriocin thuricin CD in
gastrointestinal tract. Microbiology. 2014;160(2):439-45.
171. Rea MC, Alemayehu D, Casey PG, O'Connor PM, Lawlor PG, Walsh M, et al.
Bioavailability of the anti-clostridial bacteriocin thuricin CD in gastrointestinal tract.
Microbiology. 2014;160(Pt 2):439-45.
172. Rodríguez-Julbe, MC, Ramírez-Ronda, CH, Arroyo, E, Maldonado, G, Saavedra, S,
Meléndez, B, González, G. and Figueroa, J. Antibiotics in older adults. Puerto Rico
Health Sciences Journal. 2010; 23: 25-33.
173. Salazar N, Dewulf EM, Neyrinck AM, Bindels LB, Cani PD, Mahillon J, et al. Inulin-
type fructans modulate intestinal Bifidobacterium species populations and decrease fecal
short-chain fatty acids in obese women. Clin Nutr. 2015;34(3):501-7.
174. Sears P, Ichikawa Y, Ruiz N, Gorbach S. Advances in the treatment of Clostridium
difficile with fidaxomicin: a narrow spectrum antibiotic. Ann N Y Acad Sci. 2013;
1291:33-41.
212
175. Sekirov I, Russell SL, Antunes LC, Finlay BB. Gut microbiota in health and disease.
Physiological reviews. 2010;90(3):859-904.
176. Singh S, Bajorek B. Defining 'elderly' in clinical practice guidelines for
pharmacotherapy. Pharm Pract (Granada). 2014;12(4):489.
177. Sjolund M, Wreiber K, Andersson DI, Blaser MJ, Engstrand L. Long-term persistence
of resistant Enterococcus species after antibiotics to eradicate Helicobacter pylori. Ann
Intern Med. 2003;139(6):483-7.
178. Stewardson AJ, Gaia N, Francois P, Malhotra-Kumar S, Delemont C, Martinez de
Tejada B, et al. Collateral damage from oral ciprofloxacin versus nitrofurantoin in
outpatients with urinary tract infections: a culture-free analysis of gut microbiota. Clin
Microbiol Infect. 2015;21(4):344.e1-11.
179. Swidsinski A, Ladhoff A, Pernthaler A, Swidsinski S, Loening–Baucke V, Ortner M,
Weber J, Hoffmann U, Schreiber S, Dietel M, Lochs H. Mucosal flora in inflammatory
bowel disease. Gastroenterology. 2002;122(1):44-54.
180. Van den Nieuwboer M, Klomp-Hogeterp A, Verdoorn S, Metsemakers-Brameijer L,
Vriend T, Claassen E, et al. Improving the bowel habits of elderly residents in a nursing
home using probiotic fermented milk. Benef Microbes. 2015;6(4):397-403.
181. Vrieze A, Van Nood E, Holleman F, Salojarvi J, Kootte RS, Bartelsman JF, et al.
Transfer of intestinal microbiota from lean donors increases insulin sensitivity in
individuals with metabolic syndrome. Gastroenterology. 2012;143(4):913-6.e7.
182. Vulevic J, Juric A, Walton GE, Claus SP, Tzortzis G, Toward RE, et al. Influence of
galacto-oligosaccharide mixture (B-GOS) on gut microbiota, immune parameters and
metabonomics in elderly persons. Br J Nutr. 2015;114(4):586-95.
183. Wallace RJ, Jr., Dukart G, Brown-Elliott BA, Griffith DE, Scerpella EG, Marshall B.
Clinical experience in 52 patients with tigecycline-containing regimens for salvage
213
treatment of Mycobacterium abscessus and Mycobacterium chelonae infections. J
Antimicrob Chemother. 2014;69(7):1945-53.
184. Woodmansey EJ, McMurdo ME, Macfarlane GT, Macfarlane S. Comparison of
compositions and metabolic activities of fecal microbiotas in young adults and in
antibiotic-treated and non-antibiotic-treated elderly subjects. Appl Environ Microbiol.
2004;70(10):6113-22.
185. Youngster I, Sauk J, Pindar C, Wilson RG, Kaplan JL, Smith MB, et al. Fecal microbiota
transplant for relapsing Clostridium difficile infection using a frozen inoculum from
unrelated donors: a randomized, open-label, controlled pilot study. Clin Infect Dis.
2014;58(11):1515-22.
186. Zhang F, Luo W, Shi Y, Fan Z, Ji G. Should we standardize the 1,700-year-old fecal
microbiota transplantation? Am J Gastroenterol. 2012;107(11):1755.
187. World Health Organization (WHO), 2016. Health statistics and information system.
WHO, Geneva, Switzerland. Available at: http://tinyurl.com/q8l8ovm.
188. Hill C, Guarner F, Reid G, Gibson GR, Merenstein DJ, Pot B, et al. Expert consensus
document. The International Scientific Association for Probiotics and Prebiotics
consensus statement on the scope and appropriate use of the term probiotic. Nat Rev
Gastroenterol Hepatol. 2014;11(8):506-14.
189. Vemuri R, Gundamaraju R, Eri R. Role of Lactic Acid Probiotic Bacteria in IBD. Curr
Pharm Des. 2017;23(16):2352-5.
190. Vemuri RC, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions for gut
dysbiosis and related disorders in the elderly: antibiotics, probiotics or fecal microbiota
transplantation? Benef Microbes. 2017;8(2):179-92.
214
191. Akoglu B, Loytvedb A, Nuidingb H, Zeuzemc S, Faustb D. Probiotic Lactobacillus casei
Shirota improves kidney function, inflammation and bowel movements in hospitalized
patients with acute gastroenteritis - A prospective study. J Funct Foods. 2015;17:305–13.
192. Orel R, Kamhi Trop T. Intestinal microbiota, probiotics and prebiotics in inflammatory
bowel disease. World J Gastroenterol. 2014;20(33):11505-24.
193. Valeriano VD, Parungao-Balolong MM, Kang DK. In vitro evaluation of the mucin-
adhesion ability and probiotic potential of Lactobacillus mucosae LM1. J Appl Microbiol.
2014;117(2):485-97.
194. Toscano M, De Vecchi E, Gabrieli A, Zuccotti GV, Drago L. Probiotic characteristics
and in vitro compatibility of a combination of Bifidobacterium breve M-16 V,
Bifidobacterium longum subsp. infantis M-63 and Bifidobacterium longum subsp.
longum BB536. Ann Microbiol. 2015;65:1079–86.
195. Arai T, Obuchi S, Eguchi K, Seto Y. In vitro investigation of molecules involved in
Lactobacillus gasseri SBT2055 adhesion to host intestinal tract components. J Appl
Microbiol. 2016;120(6):1658-67.
196. Messaoudi S, Madi A, Prevost H, Feuilloley M, Manai M, Dousset X, et al. In vitro
evaluation of the probiotic potential of Lactobacillus salivarius SMXD51. Anaerobe.
2012;18(6):584-9.
197. Sevda ER, Koparal AT, Kivanç M. Cytotoxic effects of various lactic acid bacteria on
Caco-2 cells. Turkish J Biol. 2015;39:23–30.
198. Bernardeau M, Guguen M, Vernoux JP. Beneficial lactobacilli in food and feed: long-
term use, biodiversity and proposals for specific and realistic safety assessments. FEMS
Microbiol Rev. 2006;30(4):487-513.
199. Adams MR. Safety of industrial lactic acid bacteria. J Biotechnol. 1999;68(2-3):171-8.
215
200. Salminen S, von Wright A, Morelli L, Marteau P, Brassart D, de Vos WM, et al.
Demonstration of safety of probiotics -- a review. Int J Food Microbiol. 1998;44(1-
2):93-106.
201. Park JS, Joe I, Rhee PD, Jeong CS, Jeong G. A lactic acid bacterium isolated from
kimchi ameliorates intestinal inflammation in DSS-induced colitis. J Microbiol.
2017;55(4):304-10.
202. Ranadheera CS, Evans CA, Adams MC, Baines SK. Effect of dairy probiotic
combinations on in vitro gastrointestinal tolerance, intestinal epithelial cell adhesion and
cytokine secretion. J Funct Foods. 2014;8:18–25.
203. Bahrami B, Macfarlane S, Macfarlane GT. Induction of cytokine formation by human
intestinal bacteria in gut epithelial cell lines. J Appl Microbiol. 2011;110(1):353-63.
204. Belguesmia Y, Domenger D, Caron J, Dhulster P, Ravallec R, Drider D. et al. Novel
probiotic evidence of lactobacilli on immunomodulation and regulation of satiety
hormones release in intestinal cells. J Funct Foods. 2016;24:276–86.
205. Thakur K, Tomar SK. Invitro study of riboflavin producing lactobacilli as potential
probiotic. LWT- Food Sci Technol. 2016;68:570–78.
206. Nomura M, Kobayashi M, Narita T, Kimoto-Nira H, Okamoto T. Phenotypic and
molecular characterization of Lactococcus lactis from milk and plants. J Appl Microbiol.
2006;101(2):396-405.
207. Versantvoort CH, Oomen AG, Van de Kamp E, Rompelberg CJ, Sips AJ. Applicability
of an in vitro digestion model in assessing the bioaccessibility of mycotoxins from food.
Food Chem Toxicol. 2005;43(1):31-40.
208. Chavarri M, Maranon I, Ares R, Ibanez FC, Marzo F, Villaran Mdel C.
Microencapsulation of a probiotic and prebiotic in alginate-chitosan capsules improves
216
survival in simulated gastro-intestinal conditions. Int J Food Microbiol. 2010;142(1-
2):185-9.
209. Weidmann E, Brieger J, Jahn B, Hoelzer D, Bergmann L, Mitrou PS. Lactate
dehydrogenase-release assay: a reliable, nonradioactive technique for analysis of
cytotoxic lymphocyte-mediated lytic activity against blasts from acute myelocytic
leukemia. Ann Hematol. 1995;70(3):153-8.
210. Duary RK, Batish VK, Grover S. Immunomodulatory activity of two potential probiotic
strains in LPS-stimulated HT-29 cells. Genes Nutr. 2014;9(3):398.
211. Holmes E, Kinross J, Gibson GR, Burcelin R, Jia W, Pettersson S, et al. Therapeutic
modulation of microbiota-host metabolic interactions. Sci Transl Med.
2012;4(137):137rv6.
212. Marteau P, Guyonnet D, Lafaye de Micheaux P, Gelu S. A randomized, double-blind,
controlled study and pooled analysis of two identical trials of fermented milk containing
probiotic Bifidobacterium lactis CNCM I-2494 in healthy women reporting minor
digestive symptoms. Neurogastroenterol Motil. 2013;25(4):331-e252.
213. Mater DD, Bretigny L, Firmesse O, Flores MJ, Mogenet A, Bresson JL, et al.
Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus survive
gastrointestinal transit of healthy volunteers consuming yogurt. FEMS Microbiol Lett.
2005;250(2):185-7.
214. Kebouchi M, Galia W, Genay M, Soligot C, Lecomte X, Awussi AA, et al. Implication
of sortase-dependent proteins of Streptococcus thermophilus in adhesion to human
intestinal epithelial cell lines and bile salt tolerance. Appl Microbiol Biotechnol.
2016;100(8):3667-79.
217
215. Mohamadzadeh M, Duong T, Sandwick SJ, Hoover T, Klaenhammer TR. Dendritic cell
targeting of Bacillus anthracis protective antigen expressed by Lactobacillus acidophilus
protects mice from lethal challenge. Proc Natl Acad Sci U S A. 2009;106(11):4331-6.
216. Kajikawa A, Zhang L, Long J, Nordone S, Stoeker L, LaVoy A, et al. Construction and
immunological evaluation of dual cell surface display of HIV-1 gag and Salmonella
enterica serovar Typhimurium FliC in Lactobacillus acidophilus for vaccine delivery.
Clin Vaccine Immunol. 2012;19(9):1374-81.
217. Corcoran BM, Stanton C, Fitzgerald GF, Ross RP. Growth of probiotic lactobacilli in the
presence of oleic acid enhances subsequent survival in gastric juice. Microbiology.
2007;153(Pt 1):291-9.
218. Corcoran BM, Stanton C, Fitzgerald GF, Ross RP. Survival of probiotic lactobacilli in
acidic environments is enhanced in the presence of metabolizable sugars. Appl Environ
Microbiol. 2005;71(6):3060-7.
219. Chae JP, Valeriano VD, Kim GB, Kang DK. Molecular cloning, characterization and
comparison of bile salt hydrolases from Lactobacillus johnsonii PF01. J Appl Microbiol.
2013;114(1):121-33.
220. Kim GB, Brochet M, Lee BH. Cloning and characterization of a bile salt hydrolase (bsh)
from Bifidobacterium adolescentis. Biotechnol Lett. 2005;27(12):817-22.
221. Kim GB, Yi SH, Lee BH. Purification and characterization of three different types of
bile salt hydrolases from Bifidobacterium strains. J Dairy Sci. 2004;87(2):258-66.
222. Jiang J, Hang X, Zhang M, Liu X, Li D, Yang H. Diversity of bile salt hydrolase
activities in different lactobacilli toward human bile salts. Ann Microbiol. 2010;60:81–88.
223. Saxami G, Karapetsas A, Lamprianidou E, Kotsianidis I, Chlichlia A, Tassou C. et al.
Two potential probiotic lactobacillus strains isolated from olive microbiota exhibit
218
adhesion and anti-proliferative effects in cancer cell lines. J Funct Foo ds. 2016;24:461–
71.
224. Kainulainen V, Tang Y, Spillmann T, Kilpinen S, Reunanen J, Saris PE, et al. The canine
isolate Lactobacillus acidophilus LAB20 adheres to intestinal epithelium and attenuates
LPS-induced IL-8 secretion of enterocytes in vitro. BMC Microbiol. 2015;15:4.
225. Bu XD, Li N, Tian XQ, Huang PL. Caco-2 and LS174T cell lines provide different
models for studying mucin expression in colon cancer. Tissue Cell. 2011;43(3):201-6.
226. Bennett KM, Walker SL, Lo DD. Epithelial microvilli establish an electrostatic barrier to
microbial adhesion. Infect Immun. 2014;82(7):2860-71.
227. Gerasimov SV, Vasjuta VV, Myhovych OO, Bondarchuk LI. Probiotic supplement
reduces atopic dermatitis in preschool children: a randomized, double-blind, placebo-
controlled, clinical trial. Am J Clin Dermatol. 2010;11(5):351-61.
228. Gerasimov SV, Ivantsiv VA, Bobryk LM, Tsitsura OO, Dedyshin LP, Guta NV, et al.
Role of short-term use of L. acidophilus DDS-1 and B. lactis UABLA-12 in acute
respiratory infections in children: a randomized controlled trial. Eur J Clin Nutr.
2016;70(4):463-9.
229. Tejero-Sarinena S, Barlow J, Costabile A, Gibson GR, Rowland I. In vitro evaluation of
the antimicrobial activity of a range of probiotics against pathogens: evidence for the
effects of organic acids. Anaerobe. 2012;18(5):530-8.
230. Schoster A, Kokotovic B, Permin A, Pedersen PD, Dal Bello F, Guardabassi L. In vitro
inhibition of Clostridium difficile and Clostridium perfringens by commercial probiotic
strains. Anaerobe. 2013;20:36-41.
231. Ferreira Dos Santos T, Alves Melo T, Almeida ME, Passos Rezende R, Romano CC.
Immunomodulatory Effects of Lactobacillus plantarum Lp62 on Intestinal Epithelial and
Mononuclear Cells. BioMed research international. 2016;2016:8404156.
219
232. Ohkusa T, Yoshida T, Sato N, Watanabe S, Tajiri H, Okayasu I. Commensal bacteria can
enter colonic epithelial cells and induce proinflammatory cytokine secretion: a possible
pathogenic mechanism of ulcerative colitis. J Med Microbiol. 2009;58(Pt 5):535-45.
233. Resta-Lenert S, Barrett KE. Probiotics and commensals reverse TNF-alpha- and IFN-
gamma-induced dysfunction in human intestinal epithelial cells. Gastroenterology.
2006;130(3):731-46.
234. Candela M, Perna F, Carnevali P, Vitali B, Ciati R, Gionchetti P, et al. Interaction of
probiotic Lactobacillus and Bifidobacterium strains with human intestinal epithelial cells:
adhesion properties, competition against enteropathogens and modulation of IL-8
production. Int J Food Microbiol. 2008;125(3):286-92.
235. Vemuri R, Gundamaraju R, Shastri MD, Shukla SD, Kalpurath K, Ball M, et al. Gut
Microbial Changes, Interactions, and Their Implications on Human Lifecycle: An
Ageing Perspective. Biomed Res Int. 2018;2018.
236. Langille MG, Meehan CJ, Koenig JE, Dhanani AS, Rose RA, Howlett SE, et al.
Microbial shifts in the aging mouse gut. Microbiome. 2014;2(1):50.
237. Yu M, Jia H-M, Zhou C, Yang Y, Sun L-L, Zou Z-M. Urinary and fecal metabonomics
study of the protective effect of Chaihu-Shu-Gan-San on antibiotic-induced gut
microbiota dysbiosis in rats. Sci Rep. 2017;7:46551.
238. Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, et al. Through ageing, and
beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One.
2010;5(5):e10667.
239. Fahlström A, Yu Q, Ulfhake B. Behavioral changes in aging female C57BL/6 mice.
Neurobiol Aging. 2011;32(10):1868-80.
220
240. Krych Ł, Nielsen DS, Hansen AK, Hansen CHF. Gut microbial markers are associated
with diabetes onset, regulatory imbalance, and IFN-γ level in NOD mice. Gut microbes.
2015;6(2):101-9.
241. Vemuri R, Gundamaraju R, Eri R. Role of lactic acid probiotic bacteria in IBD. Curr
Pharm Des. 2017;23(16):2352-5.
242. Schneeberger M, Everard A, Gómez-Valadés AG, Matamoros S, Ramírez S, Delzenne
NM, et al. Akkermansia muciniphila inversely correlates with the onset of inflammation,
altered adipose tissue metabolism and metabolic disorders during obesity in mice. Sci
Rep. 2015;5:16643.
243. Sybille T, June Z, Michael K, Roy M, Maria L M. The intestinal microbiota in aged
mice is modulated by dietary resistant starch and correlated with improvements in host
responses. FEMS Microbiol Ecol. 2013;83(2):299-309.
244. Azzu V, Valencak TG. Energy metabolism and ageing in the mouse: a mini-review.
Gerontology. 2017;63(4):327-36.
245. Valdes AM, Glass D, Spector TD. Omics technologies and the study of human ageing.
Nat Rev Genet. 2013;14(9):601.
246. Rampelli S, Candela M, Turroni S, Biagi E, Collino S, Franceschi C, et al. Functional
metagenomic profiling of intestinal microbiome in extreme ageing. Aging (Albany N Y).
2013;5(12):902.
247. Son N, Hur HJ, Sung MJ, Kim M-S, Hwang J-T, Park JH, et al. Liquid
chromatography–mass spectrometry-based metabolomic analysis of livers from aged rats.
J Proteome Res. 2012;11(4):2551-8.
248. Deda O, Gika HG, Taitzoglou I, Raikos Ν, Theodoridis G. Impact of exercise and aging
on rat urine and blood metabolome. An LC-MS based metabolomics longitudinal study.
Metabolites. 2017;7(1):10.
221
249. Kim S, Cheon H-S, Song J-C, Yun S-M, Park SI, Jeon J-P. Aging-related changes in
mouse serum glycerophospholipid profiles. Osong Public Health Res Perspect
2014;5(6):345-50.
250. Shi Y, Zhao X, Zhao J, Zhang H, Zhai Q, Narbad A, et al. A mixture of Lactobacillus
species isolated from traditional fermented foods promote recovery from antibiotic‐
induced intestinal disruption in mice. J Appl Microbiol. 2018;124(3):842-54.
251. Vemuri R, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions for gut dysbiosis
and related disorders in the elderly: antibiotics, probiotics or fecal microbiota
transplantation? Benef Microbes. 2017;8(2):179-92.
252. Vemuri R, Shinde T, Shastri MD, Perera AP, Tristram S, Martoni CJ, et al. A human
origin strain Lactobacillus acidophilus DDS-1 exhibits superior in vitro probiotic
efficacy in comparison to plant or dairy origin probiotics. Int J Med Sci. 2018;15(9):840-
8.
253. Nagata S, Asahara T, Wang C, Suyama Y, Chonan O, Takano K, et al. The effectiveness
of Lactobacillus beverages in controlling infections among the residents of an aged care
facility: a randomized placebo-controlled double-blind trial. Ann Nutr Metab.
2016;68(1):51-9.
254. Van den Nieuwboer M, Klomp-Hogeterp A, Verdoorn S, Metsemakers-Brameijer L,
Vriend T, Claassen E, et al. Improving the bowel habits of elderly residents in a nursing
home using probiotic fermented milk. Benef Microbes. 2015;6(4):397-403.
255. Rampelli S, Candela M, Severgnini M, Biagi E, Turroni S, Roselli M, et al. A
probiotics-containing biscuit modulates the intestinal microbiota in the elderly. J Nutr
Health Aging. 2013;17(2):166-72.
256. Pellino G, Sciaudone G, Candilio G, Camerlingo A, Marcellinaro R, De Fatico S, et al.
Early postoperative administration of probiotics versus placebo in elderly patients
222
undergoing elective colorectal surgery: a double-blind randomized controlled trial. BMC
Surg. 2013;13(2):S57.
257. Allen S, Wareham K, Wang D, Bradley C, Sewell B, Hutchings H, et al. PWE-008
Placide: Probiotics in the Prevention of Antibiotic Associated Diarrhoea (AAD) and
Clostridium Difficile Associated Diarrhoea (CDD) in Elderly Patients Admitted to
Hospital–Results of a Large Multi-Centre RCT in the UK. Gut. 2013;62(Suppl 1):A133-
A.
258. Pakdaman MN, Udani JK, Molina JP, Shahani M. The effects of the DDS-1 strain of
lactobacillus on symptomatic relief for lactose intolerance-a randomized, double-blind,
placebo-controlled, crossover clinical trial. Nutr J. 2015;15(1):56.
259. Hulshof L, van’t Land B, Sprikkelman AB, Garssen J. Role of microbial modulation in
management of atopic dermatitis in children. Nutrients. 2017;9(8):854.
260. Gerasimov SV, Vasjuta VV, Myhovych OO, Bondarchuk LI. Probiotic supplement
reduces atopic dermatitis in preschool children. Am J Clin Dermatol. 2010;11(5):351-61.
261. Gerasimov S, Ivantsiv V, Bobryk L, Tsitsura O, Dedyshin L, Guta N, et al. Role of
short-term use of L. acidophilus DDS-1 and B. lactis UABLA-12 in acute respiratory
infections in children: a randomized controlled trial. Eur J Clin Nutr. 2016;70(4):463.
262. Wang X, Yang B, Zhang A, Sun H, Yan G. Potential drug targets on insomnia and
intervention effects of Jujuboside A through metabolic pathway analysis as revealed by
UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach. J Proteomics.
2012;75(4):1411-27.
263. Kuo S-M, Merhige PM, Hagey LR. The effect of dietary prebiotics and probiotics on
body weight, large intestine indices, and fecal bile acid profile in wild type and IL10−/−
mice. PLoS One. 2013;8(3):e60270.
223
264. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-
End reAd mergeR. Bioinformatics. 2013;30(5):614-20.
265. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads.
Nat Methods. 2013;10(10):996.
266. Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R. Using
QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc
Microbiol. 2012;27(1):1E. 5.1-E. 5.20.
267. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity
and speed of chimera detection. Bioinformatics. 2011;27(16):2194-200.
268. Huson DH, Mitra S. Introduction to the analysis of environmental sequences:
metagenomics with MEGAN. Evolutionary Genomics. 856: Springer; 2012. p. 415-29.
269. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: a web-
based tool for comprehensive statistical, visual and meta-analysis of microbiome data.
Nucleic Acids Res. 2017;45(W1):W180-W8.
270. Beale DJ, Marney D, Marlow DR, Morrison PD, Dunn MS, Key C, et al. Metabolomic
analysis of Cryptosporidium parvum oocysts in water: A proof of concept demonstration.
Environmental Pollution. 2013;174:201-3.
271. Karpe AV, Beale DJ, Harding IH, Palombo EA. Optimization of degradation of winery-
derived biomass waste by Ascomycetes. J Chem Tech Biotechnol. 2015;90(10):1793-801.
272. Beale D, Morrison P, Key C, Palombo E. Metabolic profiling of biofilm bacteria known
to cause microbial influenced corrosion. Water Science and Technology. 2014;69(1):1-8.
273. Sansone SA, Fan T, Goodacre R, Griffin JL, Hardy NW, Kaddurah-Daouk R, et al. The
metabolomics standards initiative. Nature biotechnology. 2007;25(8):846-8.
224
274. French KE, Harvey J, McCullagh JS. Targeted and Untargeted Metabolic Profiling of
Wild Grassland Plants identifies Antibiotic and Anthelmintic Compounds Targeting
Pathogen Physiology, Metabolism and Reproduction. Sci Rep. 2018;8(1):1695.
275. Wang M, Zhang X, Wang Y, Li Y, Chen Y, Zheng H, et al. Metabonomic strategy for
the detection of metabolic effects of probiotics combined with prebiotic supplementation
in weaned rats. RSC Advances. 2018;8(9):5042-57.
276. Sun H, Zhang A, Yan G, Piao C, Li W, Sun C, et al. Metabolomic analysis of key
regulatory metabolites in HCV-infected tree shrews. Mol Cell Proteomics. 2012:mcp.
M112. 019141.
277. Hayakawa K, Matsuda F, Shimizu H. Metabolome analysis of Saccharomyces cerevisiae
and optimization of culture medium for S-adenosyl-l-methionine production. AMB
Express. 2016;6(1):38.
278. Cani PD. Human gut microbiome: hopes, threats and promises. Gut. 2018:gutjnl-2018-
316723.
279. Robinson AM, Gondalia SV, Karpe AV, Eri R, Beale DJ, Morrison PD, et al. Fecal
microbiota and metabolome in a mouse model of spontaneous chronic colitis: Relevance
to human inflammatory bowel disease. Inflamm Bowel Dis. 2016;22(12):2767-87.
280. Welly RJ, Liu T-W, Zidon TM, Rowles III JL, Park Y-M, Smith TN, et al. Comparison
of diet vs. exercise on metabolic function & gut microbiota in obese rats. Med Sci Sports
Exerc. 2016;48(9):1688.
281. Lee H, Lee Y, Kim J, An J, Lee S, Kong H, et al. Modulation of the gut microbiota by
metformin improves metabolic profiles in aged obese mice. Gut microbes. 2018:1-11.
282. Hall AB, Yassour M, Sauk J, Garner A, Jiang X, Arthur T, et al. A novel Ruminococcus
gnavus clade enriched in inflammatory bowel disease patients. Genome Med.
2017;9(1):103.
225
283. Vital M, Howe AC, Tiedje JM. Revealing the bacterial butyrate synthesis pathways by
analyzing (meta) genomic data. MBio. 2014;5(2):e00889-14.
284. Neis EP, Dejong CH, Rensen SS. The role of microbial amino acid metabolism in host
metabolism. Nutrients. 2015;7(4):2930-46.
285. Vernocchi P, Del Chierico F, Quagliariello A, Ercolini D, Lucidi V, Putignani L. A
metagenomic and in silico functional prediction of gut microbiota profiles may concur in
discovering new cystic fibrosis patient-targeted probiotics. Nutrients. 2017;9(12):1342.
286. Hertz L. The glutamate–glutamine (GABA) cycle: importance of late postnatal
development and potential reciprocal interactions between biosynthesis and degradation.
Front Endocrinol (Lausanne). 2013;4:59.
287. Fisher DY, Fukagawa NK. Protein and Amino Acid Metabolism in the Elderly.
Methods for Investigation of Amino Acid and Protein Metabolism: Routledge; 2017. p.
167-75.
288. Munro HN, Gersovitz M, Young VR. Human aging: protein and amino acid metabolism
and implications for protein and amino acid requirements. Nutritional approaches to
aging research: CRC Press; 2018. p. 58-93.
289. Bauchart-Thevret C, Stoll B, Burrin DG. Intestinal metabolism of sulfur amino acids.
Nutr Res Rev. 2009;22(2):175-87.
290. Bogicevic B, Berthoud H, Portmann R, Bavan T, Meile L, Irmler S. Cysteine
biosynthesis in Lactobacillus casei: identification and characterization of a serine
acetyltransferase. FEMS Microbiol Lett. 2016;363(4).
291. Hausmann CD, Ibba M. Aminoacyl‐tRNA synthetase complexes: molecular
multitasking revealed. FEMS Microbiol Rev. 2008;32(4):705-21.
226
292. Bhatt TK, Kapil C, Khan S, Jairajpuri MA, Sharma V, Santoni D, et al. A genomic
glimpse of aminoacyl-tRNA synthetases in malaria parasite Plasmodium falciparum.
BMC Genomics. 2009;10(1):644.
293. Park SG, Schimmel P, Kim S. Aminoacyl tRNA synthetases and their connections to
disease. Proc Natl Acad Sci U S A. 2008.
294. Jafarnejad SM, Kim S-H, Sonenberg N. Aminoacylation of Proteins: New Targets for
the Old ARSenal. Cell Metab. 2018;27(1):1-3.
295. Kong J, Fang P, Madoux F, Spicer TP, Scampavia L, Kim S, et al. High-Throughput
Screening for Protein Synthesis Inhibitors Targeting Aminoacyl-tRNA Synthetases.
SLAS Discov. 2018;23(2):174-82.
296. Finkel T. The metabolic regulation of aging. Nat Med. 2015;21(12):1416.
297. De Las Heras J, Aldámiz-Echevarría L, Martínez-Chantar M-L, Delgado TC. An update
on the use of benzoate, phenylacetate and phenylbutyrate ammonia scavengers for
interrogating and modifying liver nitrogen metabolism and its implications in urea cycle
disorders and liver disease. Expert Opin Drug Metab Toxicol. 2017;13(4):439-48.
298. Yudkoff M. Interactions in the Metabolism of Glutamate and the Branched-Chain
Amino Acids and Ketoacids in the CNS. Neurochem Res. 2017;42(1):10-8.
299. Garibotto G, Verzola D, Vettore M, Tessari P. The contribution of muscle, kidney, and
splanchnic tissues to leucine transamination in humans. Can J Physiol Pharmacol.
2017;96(4):382-7.
300. Waisbren SE, Cuthbertson D, Burgard P, Holbert A, McCarter R, Cederbaum S, et al.
Pharmacol. Res. J Inherit Metab Dis. 2018:1-11.
301. Davila-Gay A-M, Blachier F, Gotteland M, Andriamihaja M, Benetti P-H, Sanz Y, et al.
Intestinal luminal nitrogen metabolism: Role of the gut microbiota and consequences for
the host. Pharmacol Res. 2013;68(1):95-107.
227
302. Coelho AI, Berry GT, Rubio-Gozalbo ME. Galactose metabolism and health. Curr Opin
Clin Nutr Metab Care. 2015;18(4):422-7.
303. Hobbs ME, Williams HJ, Hillerich B, Almo SC, Raushel FM. l-Galactose metabolism in
Bacteroides vulgatus from the human gut microbiota. Biochemistry. 2014;53(28):4661-
70.
304. Gundamaraju R, Vemuri R, Eri R, M Ishiki H, Coy-Barrera E, Sastry Yarla N, et al.
Metabolomics as a Functional Tool in Screening Gastro Intestinal Diseases: Where are
we in High Throughput Screening? Comb Chem High Throughput Screen.
2017;20(3):247-54.
305. An R, Wilms E, Masclee AA, Smidt H, Zoetendal EG, Jonkers D. Age-dependent
changes in GI physiology and microbiota: time to reconsider? Gut. 2018:gutjnl-2017-
315542.
306. Nations U. World population prospects: The 2015 revision. United Nations Econ Soc Aff.
2015;33(2):1-66.
307. Mu W-C, VanHoosier E, Elks C, Grant R. Long-Term Effects of Dietary Protein and
Branched-Chain Amino Acids on Metabolism and Inflammation in Mice. Nutrients.
2018;10(7):918.
308. Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel J-P, et al. The World
report on ageing and health: a policy framework for healthy ageing. The Lancet.
2016;387(10033):2145-54.
309. Vemuri R, Gundamaraju R, Shinde T, Eri R. Therapeutic interventions for gut dysbiosis
and related disorders in the elderly: antibiotics, probiotics or fecal microbiota
transplantation? Benef Microbes. 2017;8(2):179-92.
228
310. Vemuri R, Shinde T, Gundamaraju R, Gondalia S, Karpe A, Beale D, et al.
Lactobacillus acidophilus DDS-1 Modulates the Gut Microbiota and Improves Metabolic
Profiles in Aging Mice. Nutrients. 2018;10(9):1255.
311. Vemuri R, Gundamaraju R, Shastri MD, Shukla SD, Kalpurath K, Ball M, et al. Gut
Microbial Changes, Interactions, and Their Implications on Human Lifecycle: An
Ageing Perspective. Biomed Res Int. 2018;2018.
312. Vernocchi P, Del Chierico F, Quagliariello A, Ercolini D, Lucidi V, Putignani L. A
metagenomic and in silico functional prediction of gut microbiota profiles may concur in
discovering new cystic fibrosis patient-targeted probiotics. Nutrients. 2017;9(12):1342.
313. Biagi E, Nylund L, Candela M, Ostan R, Bucci L, Pini E, et al. Through ageing, and
beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS One.
2010;5(5):e10667.
314. Deda O, Gika HG, Taitzoglou I, Raikos Ν, Theodoridis G. Impact of exercise and aging
on rat urine and blood metabolome. An LC-MS based metabolomics longitudinal study.
Metabolites. 2017;7(1):10.
315. Lee H, Lee Y, Kim J, An J, Lee S, Kong H, et al. Modulation of the gut microbiota by
metformin improves metabolic profiles in aged obese mice. Gut microbes. 2018:1-11.
316. Yu M, Jia H-M, Zhou C, Yang Y, Sun L-L, Zou Z-M. Urinary and fecal metabonomics
study of the protective effect of Chaihu-Shu-Gan-San on antibiotic-induced gut
microbiota dysbiosis in rats. Sci Rep. 2017;7:46551.
317. Langille MG, Meehan CJ, Koenig JE, Dhanani AS, Rose RA, Howlett SE, et al.
Microbial shifts in the aging mouse gut. Microbiome. 2014;2(1):50.
318. Vemuri R, Gundamaraju R, Eri R. Role of lactic acid probiotic bacteria in IBD. Curr
Pharm Des. 2017;23(16):2352-5.
229
319. Hall AB, Yassour M, Sauk J, Garner A, Jiang X, Arthur T, et al. A novel Ruminococcus
gnavus clade enriched in inflammatory bowel disease patients. Genome Med.
2017;9(1):103.
320. Lee SM, Kim N, Park JH, Nam RH, Yoon K, Lee DH. Comparative Analysis of Ileal
and Cecal Microbiota in Aged Rats. J Cancer Prev. 2018;23(2):70.
321. Hirano A, Umeno J, Okamoto Y, Shibata H, Ogura Y, Moriyama T, et al. Comparison
of the microbial community structure between inflamed and non‐inflamed sites in
patients with ulcerative colitis. J Gastroenterol Hepatol. 2018.
322. Gu S, Chen D, Zhang J-N, Lv X, Wang K, Duan L-P, et al. Bacterial community
mapping of the mouse gastrointestinal tract. PLoS One. 2013;8(10):e74957.
323. Pang W, Vogensen FK, Nielsen DS, Hansen AK. Fecal and caecal microbiota profiles of
mice do not cluster in the same way. Lab Anim. 2012;46(3):231-6.
324. Tanca A, Manghina V, Fraumene C, Palomba A, Abbondio M, Deligios M, et al.
Metaproteogenomics reveals taxonomic and functional changes between cecal and fecal
microbiota in mouse. Front Microbiol. 2017;8:391.
325. Shi Y, Zhao X, Zhao J, Zhang H, Zhai Q, Narbad A, et al. A mixture of Lactobacillus
species isolated from traditional fermented foods promote recovery from antibiotic‐
induced intestinal disruption in mice. J Appl Microbiol. 2018;124(3):842-54.
326. Franceschi C, Capri M, Monti D, Giunta S, Olivieri F, Sevini F, et al. Inflammaging and
anti-inflammaging: a systemic perspective on aging and longevity emerged from studies
in humans. Mech Ageing Dev. 2007;128(1):92-105.
327. Flynn MG, Markofski MM, Carrillo AE. Elevated Inflammatory Status and Increased
Risk of Chronic Disease in Chronological Aging: Inflamm-aging or Inflamm-inactivity?
Aging Dis. 2018:0.
230
328. Monteiro-Junior RS, de Tarso Maciel-Pinheiro P, da Matta Mello Portugal E, da Silva
Figueiredo LF, Terra R, Carneiro LS, et al. Effect of exercise on inflammatory profile of
older persons: systematic review and meta-analyses. Journal of Physical Activity and
Health. 2018;15(1):64-71.
329. Bier A, Braun T, Khasbab R, Di Segni A, Grossman E, Haberman Y, et al. A high salt
diet modulates the gut microbiota and short chain fatty acids production in a salt-
sensitive hypertension rat model. Nutrients. 2018;10(9):1154.
330. Nagpal R, Wang S, Ahmadi S, Hayes J, Gagliano J, Subashchandrabose S, et al.
Human-origin probiotic cocktail increases short-chain fatty acid production via
modulation of mice and human gut microbiome. Sci Rep. 2018;8(1):12649.
331. Huang Y-C, Wu B-H, Chu Y-L, Chang W-C, Wu M-C. Effects of Tempeh Fermentation
with Lactobacillus plantarum and Rhizopus oligosporus on Streptozotocin-Induced Type
II Diabetes Mellitus in Rats. Nutrients. 2018;10(9):1143.
332. Gerasimov S, Ivantsiv V, Bobryk L, Tsitsura O, Dedyshin L, Guta N, et al. Role of
short-term use of L. acidophilus DDS-1 and B. lactis UABLA-12 in acute respiratory
infections in children: a randomized controlled trial. Eur J Clin Nutr. 2016;70(4):463.
333. Pakdaman MN, Udani JK, Molina JP, Shahani M. The effects of the DDS-1 strain of
lactobacillus on symptomatic relief for lactose intolerance-a randomized, double-blind,
placebo-controlled, crossover clinical trial. Nutr J. 2015;15(1):56.
334. Nagala R, Routray C. Clinical case study multispecies probiotic supplement minimizes
symptoms of irritable Bowel Syndrome. US Gastroenterol Hepatol Rev. 2011;7(1):36-7.
335. Gerasimov SV, Vasjuta VV, Myhovych OO, Bondarchuk LI. Probiotic supplement
reduces atopic dermatitis in preschool children. Am J Clin Dermatol. 2010;11(5):351-61.
231
336. Kuo S-M, Merhige PM, Hagey LR. The effect of dietary prebiotics and probiotics on
body weight, large intestine indices, and fecal bile acid profile in wild type and IL10−/−
mice. PLoS One. 2013;8(3):e60270.
337. Randall-Demllo S, Fernando R, Brain T, Sohal SS, Cook AL, Guven N, et al.
Characterisation of colonic dysplasia-like epithelial atypia in murine colitis. World
Journal of Gastroenterology. 2016;22(37):8334-48.
338. Zapata DJ, Rodríguez BdJ, Ramírez MC, López A, Parra J. Escherichia coli
lipopolysaccharide affects intestinal mucin secretion in weaned pigs. Revista
Colombiana de Ciencias Pecuarias. 2015;28(3):209-17.
339. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads.
Nat Methods. 2013;10(10):996.
340. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity
and speed of chimera detection. Bioinformatics. 2011;27(16):2194-200.
341. Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R. Using
QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc
Microbiol. 2012;27(1):1E. 5.1-E. 5.20.
342. Huson DH, Mitra S. Introduction to the analysis of environmental sequences:
metagenomics with MEGAN. Evolutionary Genomics. 856: Springer; 2012. p. 415-29.
343. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: a web-
based tool for comprehensive statistical, visual and meta-analysis of microbiome data.
Nucleic Acids Res. 2017;45(W1):W180-W8.
344. Furuhashi T, Sugitate K, Nakai T, Jikumaru Y, Ishihara G. Rapid profiling method for
mammalian feces short chain fatty acids by GC-MS. Anal Biochem. 2018;543:51-4.
345. Sansone SA, Fan T, Goodacre R, Griffin JL, Hardy NW, Kaddurah-Daouk R, et al. The
metabolomics standards initiative. Nature biotechnology. 2007;25(8):846-8.
232
346. Wang M, Zhang X, Wang Y, Li Y, Chen Y, Zheng H, et al. Metabonomic strategy for
the detection of metabolic effects of probiotics combined with prebiotic supplementation
in weaned rats. RSC Advances. 2018;8(9):5042-57.
347. Fahlström A, Yu Q, Ulfhake B. Behavioral changes in aging female C57BL/6 mice.
Neurobiol Aging. 2011;32(10):1868-80.
348. Krych Ł, Nielsen DS, Hansen AK, Hansen CHF. Gut microbial markers are associated
with diabetes onset, regulatory imbalance, and IFN-γ level in NOD mice. Gut microbes.
2015;6(2):101-9.
349. Welly RJ, Liu T-W, Zidon TM, Rowles III JL, Park Y-M, Smith TN, et al. Comparison
of diet vs. exercise on metabolic function & gut microbiota in obese rats. Med Sci Sports
Exerc. 2016;48(9):1688.
350. Wang Y, Guo Y, Chen H, Wei H, Wan C. Potential of Lactobacillus plantarum
ZDY2013 and Bifidobacterium bifidum WBIN03 in relieving colitis by gut microbiota,
immune, and anti-oxidative stress. Can J Microbiol. 2018;64(5):327-37.
351. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host
physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016;165(6):1332-
45.
352. Sybille T, June Z, Michael K, Roy M, Maria L M. The intestinal microbiota in aged
mice is modulated by dietary resistant starch and correlated with improvements in host
responses. FEMS Microbiol Ecol. 2013;83(2):299-309.
353. Liang Y, Liang S, Zhang Y, Deng Y, He Y, Chen Y, et al. Oral Administration of
compound probiotics ameliorates HFD-induced gut microbe dysbiosis and chronic
metabolic inflammation via the G protein-coupled receptor 43 in non-alcoholic fatty liver
disease rats. Probiotics and antimicrobial proteins. 2018:1-11.
233
354. Yuille S, Reichardt N, Panda S, Dunbar H, Mulder IE. Human gut bacteria as potent
class I histone deacetylase inhibitors in vitro through production of butyric acid and
valeric acid. PLoS One. 2018;13(7):e0201073.
355. Onrust L, Van Driessche K, Ducatelle R, Schwarzer K, Haesebrouck F, Van Immerseel
F. Valeric acid glyceride esters in feed promote broiler performance and reduce the
incidence of necrotic enteritis. Poult Sci. 2018.
356. Wang X, Brown I, Khaled D, Mahoney M, Evans A, Conway P. Manipulation of
colonic bacteria and volatile fatty acid production by dietary high amylose maize
(amylomaize) starch granules. J Appl Microbiol. 2002;93(3):390-7.
357. Huda-Faujan N, Abdulamir A, Fatimah A, Anas OM, Shuhaimi M, Yazid A, et al. The
impact of the level of the intestinal short chain fatty acids in inflammatory bowel disease
patients versus healthy subjects. The open biochemistry journal. 2010;4:53.
358. Gancarčíková S, Nemcová R, Popper M, Hrčková G, Sciranková Ľ, Maďar M, et al. The
Influence of Feed-Supplementation with Probiotic Strain Lactobacillus reuteri CCM
8617 and Alginite on Intestinal Microenvironment of SPF Mice Infected with Salmonella
Typhimurium CCM 7205. Probiotics and antimicrobial proteins. 2018:1-16.
359. Brestenský M, Nitrayová S, Bomba A, Strojný L, Patráš P, Heger J. Effect of probiotics
and prebiotics supplemented to the diet of growing pigs on the content of short chain
fatty acids in the jejunum and cecum. J Anim Sci. 2016;94(suppl_3):219-21.
360. van Beek A, Borghuis T, Aidy S, Hugenholtz F, van der Gaast-de Jongh C, Savelkoul H,
et al. Aged Gut Microbiota Contributes to Systemical Inflammaging after Transfer to
Germ-Free Mice. 2017.
361. Sharma R, Kapila R, Dass G, Kapila S. Improvement in Th1/Th2 immune homeostasis,
antioxidative status and resistance to pathogenic E. coli on consumption of probiotic
Lactobacillus rhamnosus fermented milk in aging mice. Age. 2014;36(4):9686.
234
362. Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U, et al. Induction of
intestinal Th17 cells by segmented filamentous bacteria. Cell. 2009;139(3):485-98.
363. Vemuri R, Sylvia KE, Klein SL, Forster SC, Plebanski M, Eri R, et al. The
microgenderome revealed: sex differences in bidirectional interactions between the
microbiota, hormones, immunity and disease susceptibility. Semin Immunopathol.
2018:1-11.
364. Mazmanian SK, Round JL, Kasper DL. A microbial symbiosis factor prevents intestinal
inflammatory disease. Nature. 2008;453(7195):620.
365. Vemuri R, Shinde T, Shastri MD, Perera AP, Tristram S, Martoni CJ, et al. A human
origin strain Lactobacillus acidophilus DDS-1 exhibits superior in vitro probiotic
efficacy in comparison to plant or dairy origin probiotics. Int J Med Sci. 2018;15(9):840-
8.
366. Sharma R, Kapila R, Kapasiya M, Saliganti V, Dass G, Kapila S. Dietary
supplementation of milk fermented with probiotic Lactobacillus fermentum enhances
systemic immune response and antioxidant capacity in aging mice. Nutr Res.
2014;34(11):968-81.
367. Kaburagi T, Yamano T, Fukushima Y, Yoshino H, Mito N, Sato K. Effect of
Lactobacillus johnsonii La1 on immune function and serum albumin in aged and
malnourished aged mice. Nutrition. 2007;23(4):342-50.
235
Appendices
American Society for Microbiology Student and Postdoctoral Travel Award Certification
236