Kin Selection Explains the Evolution of Cooperation in the Gut Microbiota
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Ecology and Evolution of Metabolic Cross-Feeding Interactions in Bacteria† Cite This: Nat
Natural Product Reports View Article Online REVIEW View Journal | View Issue Ecology and evolution of metabolic cross-feeding interactions in bacteria† Cite this: Nat. Prod. Rep.,2018,35,455 Glen D'Souza, ab Shraddha Shitut, ce Daniel Preussger,c Ghada Yousif,cde Silvio Waschina f and Christian Kost *ce Literature covered: early 2000s to late 2017 Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has fi fi Creative Commons Attribution 3.0 Unported Licence. shown that bacterial genotypes can signi cantly bene t from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution Received 25th January 2018 and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight DOI: 10.1039/c8np00009c general principles that underlie the adaptive evolution of interconnected microbial metabolic networks rsc.li/npr as well as the evolutionary consequences that result for cells living in such communities. 1 Introduction 2.5 Mechanisms of metabolite transfer This article is licensed under a 2 Metabolic cross-feeding interactions 2.5.1 Contact-independent mechanisms 2.1 Historical account 2.5.1.1 Passive diffusion 2.2 Classication of cross-feeding interactions 2.5.1.2 Active transport 2.2.1 Unidirectional by-product cross-feeding 2.5.1.3 Vesicle-mediated transport Open Access Article. -
Inter Scientific Inquiry • Nature • Technology • Evolution • Research
Inter Scientific InquIry • nature • technology • evolutIon • research vol. 5 - august 2018 INTER SCIENTIFIC CONTENIDO CONTENTS MENSAJE DEL RECTOR/ MESSAGE FROM THE CHANCELLOR………………………………………………………..1 MENSAJE DE LA DECANA DE ASUNTOS ACADÉMICOS/ MESSAGE FROM THE DEAN OF ACADEMIC AFFAIRS………………………………………………………………………………………………1 DESDE EL ESCRITORIO DE LA EDITORA/ FROM THE EDITOR’S DESK…………………………………………....2 INVESTIGACIÓN/ RESEARCH ARTICLES………………………………………………………………………….....3-19 Host plant preference of Danaus plexippus larvae in the subtropical dry forest in Guánica, Puerto Rico………………………………………………………………………………………………......…3 Preferencia de plantas hospederas por parte de las larvas de Danaus plexippus en el bosque seco de Guánica, Puerto Rico López, Laysa, Santiago, Jonathan, and Puente-Rolón, Alberto Predatory behavior and prey preference of Leucauge sp. in Mayaguez, Puerto Rico……….…...7 Comportamiento de depredación y preferencia de presa de arañas del género Leucauge en Mayaguez, Puerto Rico Geli-Cruz, Orlando, Ramos-Maldonado, Frank, and Puente-Rolón, Alberto Construction and screening of soil metagenomic libraries: identification of hydrolytic metabolic activity………………………………………………………………………..……..…………....13 Construcción y prueba de bibliotecas metagenómicas de suelo: identificación de actividad metabólica hidrolítica Jiménez-Serrano, Edgar, Justiniano-Santiago, Jeydien, Reyes, Yazmin, Rosa-Matos, Raquel, Vélez- Cardona, Wilmarie, Huertas-Meléndez, Stephanie and Pagán-Jiménez, María LA INVESTIGACIÓN EN EL CAMPUS/ RESEARCH ON CAMPUS ……………………………………………………20 -
Pulse Generation in the Quorum Machinery of Pseudomonas Aeruginosa
This is a repository copy of Pulse generation in the quorum machinery of Pseudomonas aeruginosa. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/116745/ Version: Published Version Article: Alfiniyah, Cicik Alfiniyah, Bees, Martin Alan and Wood, Andrew James orcid.org/0000- 0002-6119-852X (2017) Pulse generation in the quorum machinery of Pseudomonas aeruginosa. Bulletin of Mathematical Biology. pp. 1360-1389. ISSN 1522-9602 https://doi.org/10.1007/s11538-017-0288-z Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Bull Math Biol (2017) 79:1360–1389 DOI 10.1007/s11538-017-0288-z ORIGINAL ARTICLE Pulse Generation in the Quorum Machinery of Pseudomonas aeruginosa Cicik Alfiniyah1,2 · Martin A. Bees1 · A. Jamie Wood1,3 Received: 28 September 2016 / Accepted: 3 May 2017 / Published online: 19 May 2017 © The Author(s) 2017. This article is an open access publication Abstract Pseudomonas aeruginosa is a Gram-negative bacterium that is responsible for a wide range of infections in humans. -
Major Evolutionary Transitions in Individuality COLLOQUIUM
PAPER Major evolutionary transitions in individuality COLLOQUIUM Stuart A. Westa,b,1, Roberta M. Fishera, Andy Gardnerc, and E. Toby Kiersd aDepartment of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom; bMagdalen College, Oxford OX1 4AU, United Kingdom; cSchool of Biology, University of St. Andrews, Dyers Brae, St. Andrews KY16 9TH, United Kingdom; and dInstitute of Ecological Sciences, Faculty of Earth and Life Sciences, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands Edited by John P. McCutcheon, University of Montana, Missoula, MT, and accepted by the Editorial Board March 13, 2015 (received for review December 7, 2014) The evolution of life on earth has been driven by a small number broken down into six questions. We explore what is already known of major evolutionary transitions. These transitions have been about the factors facilitating transitions, examining the extent to characterized by individuals that could previously replicate inde- which we can generalize across the different transitions. Ultimately, pendently, cooperating to form a new, more complex life form. we are interested in the underlying evolutionary and ecological For example, archaea and eubacteria formed eukaryotic cells, and factors that drive major transitions. cells formed multicellular organisms. However, not all cooperative Defining Major Transitions groups are en route to major transitions. How can we explain why major evolutionary transitions have or haven’t taken place on dif- A major evolutionary transition has been most broadly defined as a change in the way that heritable information is stored and ferent branches of the tree of life? We break down major transi- transmitted (2). We focus on the major transitions that lead to a tions into two steps: the formation of a cooperative group and the new form of individual (Table 1), where the same problems arise, transformation of that group into an integrated entity. -
Human Microbiome: Your Body Is an Ecosystem
Human Microbiome: Your Body Is an Ecosystem This StepRead is based on an article provided by the American Museum of Natural History. What Is an Ecosystem? An ecosystem is a community of living things. The living things in an ecosystem interact with each other and with the non-living things around them. One example of an ecosystem is a forest. Every forest has a mix of living things, like plants and animals, and non-living things, like air, sunlight, rocks, and water. The mix of living and non-living things in each forest is unique. It is different from the mix of living and non-living things in any other ecosystem. You Are an Ecosystem The human body is also an ecosystem. There are trillions tiny organisms living in and on it. These organisms are known as microbes and include bacteria, viruses, and fungi. There are more of them living on just your skin right now than there are people on Earth. And there are a thousand times more than that in your gut! All the microbes in and on the human body form communities. The human body is an ecosystem. It is home to trillions of microbes. These communities are part of the ecosystem of the human Photo Credit: Gaby D’Alessandro/AMNH body. Together, all of these communities are known as the human microbiome. No two human microbiomes are the same. Because of this, you are a unique ecosystem. There is no other ecosystem like your body. Humans & Microbes Microbes have been around for more than 3.5 billion years. -
New Insights Into the Co-Occurrences of Glycoside Hydrolase Genes Among Prokaryotic Genomes Through Network Analysis
microorganisms Article New Insights into the Co-Occurrences of Glycoside Hydrolase Genes among Prokaryotic Genomes through Network Analysis Alei Geng * , Meng Jin, Nana Li, Daochen Zhu , Rongrong Xie, Qianqian Wang, Huaxing Lin and Jianzhong Sun * Biofuels Institute, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China; [email protected] (M.J.); [email protected] (N.L.); [email protected] (D.Z.); [email protected] (R.X.); [email protected] (Q.W.); [email protected] (H.L.) * Correspondence: [email protected] (A.G.); [email protected] (J.S.) Abstract: Glycoside hydrolase (GH) represents a crucial category of enzymes for carbohydrate uti- lization in most organisms. A series of glycoside hydrolase families (GHFs) have been classified, with relevant information deposited in the CAZy database. Statistical analysis indicated that most GHFs (134 out of 154) were prone to exist in bacteria rather than archaea, in terms of both occurrence frequencies and average gene numbers. Co-occurrence analysis suggested the existence of strong or moderate-strong correlations among 63 GHFs. A combination of network analysis by Gephi and functional classification among these GHFs demonstrated the presence of 12 functional categories (from group A to L), with which the corresponding microbial collections were subsequently labeled, respectively. Interestingly, a progressive enrichment of particular GHFs was found among several types of microbes, and type-L as well as type-E microbes were deemed as functional intensified Citation: Geng, A.; Jin, M.; Li, N.; species which formed during the microbial evolution process toward efficient decomposition of Zhu, D.; Xie, R.; Wang, Q.; Lin, H.; lignocellulose as well as pectin, respectively. -
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Philips et al. BMC Genomics (2020) 21:402 https://doi.org/10.1186/s12864-020-06810-9 RESEARCH ARTICLE Open Access Analysis of oral microbiome from fossil human remains revealed the significant differences in virulence factors of modern and ancient Tannerella forsythia Anna Philips1, Ireneusz Stolarek1, Luiza Handschuh1, Katarzyna Nowis1, Anna Juras2, Dawid Trzciński2, Wioletta Nowaczewska3, Anna Wrzesińska4, Jan Potempa5,6 and Marek Figlerowicz1,7* Abstract Background: Recent advances in the next-generation sequencing (NGS) allowed the metagenomic analyses of DNA from many different environments and sources, including thousands of years old skeletal remains. It has been shown that most of the DNA extracted from ancient samples is microbial. There are several reports demonstrating that the considerable fraction of extracted DNA belonged to the bacteria accompanying the studied individuals before their death. Results: In this study we scanned 344 microbiomes from 1000- and 2000- year-old human teeth. The datasets originated from our previous studies on human ancient DNA (aDNA) and on microbial DNA accompanying human remains. We previously noticed that in many samples infection-related species have been identified, among them Tannerella forsythia, one of the most prevalent oral human pathogens. Samples containing sufficient amount of T. forsythia aDNA for a complete genome assembly were selected for thorough analyses. We confirmed that the T. forsythia-containing samples have higher amounts of the periodontitis-associated species than the control samples. Despites, other pathogens-derived aDNA was found in the tested samples it was too fragmented and damaged to allow any reasonable reconstruction of these bacteria genomes. The anthropological examination of ancient skulls from which the T. -
Understanding Microbial Cooperation
Journal of Theoretical Biology 299 (2012) 31–41 Contents lists available at ScienceDirect Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi Understanding microbial cooperation James A. Damore, Jeff Gore à Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, United States article info abstract Available online 17 March 2011 The field of microbial cooperation has grown enormously over the last decade, leading to improved Keywords: experimental techniques and a growing awareness of collective behavior in microbes. Unfortunately, Evolutionary game theory many of our theoretical tools and concepts for understanding cooperation fail to take into account the Kin selection peculiarities of the microbial world, namely strong selection strengths, unique population structure, Hamilton’s rule and non-linear dynamics. Worse yet, common verbal arguments are often far removed from the math Multilevel selection involved, leading to confusion and mistakes. Here, we review the general mathematical forms of Price’s Price’s equation equation, Hamilton’s rule, and multilevel selection as they are applied to microbes and provide some intuition on these otherwise abstract formulas. However, these sometimes overly general equations can lack specificity and predictive power, ultimately forcing us to advocate for more direct modeling techniques. & 2011 Elsevier Ltd. All rights reserved. 1. Introduction do not hold, both methods resort to abstract generalities, making application difficult and prone to error. Cooperation presents a fundamental challenge to customary Microbes present a unique opportunity for scientists interested in evolutionary thinking. If only the fittest organisms survive, why the evolution of cooperation because of their well-characterized and would an individual ever pay a fitness cost for another organism simple genetics, fast generation times, and easily manipulated and to benefit? Traditionally, kin selection and group selection have measured interactions. -
A Model of Symmetry Breaking in Collective Decision-Making
A Model of Symmetry Breaking in Collective Decision-Making Heiko Hamann1, Bernd Meyer2, Thomas Schmickl1, and Karl Crailsheim1 1 Artificial Life Lab of the Dep. of Zoology, Karl-Franzens University Graz, Austria {heiko.hamann, thomas.schmickl, karl.crailsheim}@uni-graz.at 2 FIT Centre for Research in Intelligent Systems, Monash University, Melbourne [email protected] Abstract. Symmetry breaking is commonly found in self-organized col- lective decision making. It serves an important functional role, specifi- cally in biological and bio-inspired systems. The analysis of symmetry breaking is thus an important key to understanding self-organized deci- sion making. However, in many systems of practical importance avail- able analytic methods cannot be applied due to the complexity of the scenario and consequentially the model. This applies specifically to self- organization in bio-inspired engineering. We propose a new modeling approach which allows us to formally analyze important properties of such processes. The core idea of our approach is to infer a compact model based on stochastic processes for a one-dimensional symmetry parameter. This enables us to analyze the fundamental properties of even complex collective decision making processes via Fokker–Planck theory. We are able to quantitatively address the effectiveness of symmetry breaking, the stability, the time taken to reach a consensus, and other parameters. This is demonstrated with two examples from swarm robotics. 1 Introduction Self-organization is one of the fundamental mechanism used in nature to achieve flexible and adaptive behavior in unpredictable environments [1]. Particularly collective decision making in social groups is often driven by self-organizing pro- cesses. -
Quorum Sensing
SBT1103 / MICROBIOLOGY / UNIT V / BIOTECH/BIOMED/BIOINFO II SEMESTER / I YEAR UNIT – V APPLICATIONS OF MICROBIOLOGY 1 SBT1103 / MICROBIOLOGY / UNIT V / BIOTECH/BIOMED/BIOINFO II SEMESTER / I YEAR Microbial ecology The great plate count anomaly. Counts of cells obtained via cultivation are orders of magnitude lower than those directly observed under the microscope. This is because microbiologists are able to cultivate only a minority of naturally occurring microbes using current laboratory techniques, depending on the environment.[1] Microbial ecology (or environmental microbiology) is the ecology of microorganisms: their relationship with one another and with their environment. It concerns the three major domains of life—Eukaryota, Archaea, and Bacteria—as well as viruses. Microorganisms, by their omnipresence, impact the entire biosphere. Microbial life plays a primary role in regulating biogeochemical systems in virtually all of our planet's environments, including some of the most extreme, from frozen environments and acidic lakes, to hydrothermal vents at the bottom of deepest oceans, and some of the most familiar, such as the human small intestine.[3][4] As a consequence of the quantitative magnitude of microbial life (Whitman and coworkers calculated 5.0×1030 cells, eight orders of magnitude greater than the number of stars in the observable universe[5][6]) microbes, by virtue of their biomass alone, constitute a significant carbon sink.[7] Aside from carbon fixation, microorganisms’ key collective metabolic processes (including -
The Human Gut Microbiota: a Dynamic Interplay with the Host from Birth to Senescence Settled During Childhood
Review nature publishing group The human gut microbiota: a dynamic interplay with the host from birth to senescence settled during childhood Lorenza Putignani1, Federica Del Chierico2, Andrea Petrucca2,3, Pamela Vernocchi2,4 and Bruno Dallapiccola5 The microbiota “organ” is the central bioreactor of the gastroin- producing immunological memory (2). Indeed, the intestinal testinal tract, populated by a total of 1014 bacteria and charac- epithelium at the interface between microbiota and lymphoid terized by a genomic content (microbiome), which represents tissue plays a crucial role in the mucosa immune response more than 100 times the human genome. The microbiota (2). The IS ability to coevolve with the microbiota during the plays an important role in child health by acting as a barrier perinatal life allows the host and the microbiota to coexist in a against pathogens and their invasion with a highly dynamic relationship of mutual benefit, which consists in dispensing, in modality, exerting metabolic multistep functions and stimu- a highly coordinated way, specific immune responses toward lating the development of the host immune system, through the biomass of foreign antigens, and in discriminating false well-organized programming, which influences all of the alarms triggered by benign antigens (2). The failure to obtain growth and aging processes. The advent of “omics” technolo- or maintain this complex homeostasis has a negative impact gies (genomics, proteomics, metabolomics), characterized by on the intestinal and systemic health (2). Once the balance complex technological platforms and advanced analytical and fails, the “disturbance” causes the disease, triggering an abnor- computational procedures, has opened new avenues to the mal inflammatory response as it happens, for example, for the knowledge of the gut microbiota ecosystem, clarifying some inflammatory bowel diseases in newborns (2). -
Metabolic Modeling of Microbial Community Interactions for Health, En- Vironmental and Biotechnological Applications
Send Orders for Reprints to [email protected] 712 Current Genomics, 2018, 19, 712-722 REVIEW ARTICLE Metabolic Modeling of Microbial Community Interactions for Health, En- vironmental and Biotechnological Applications Kok Siong Ang1, Meiyappan Lakshmanan1, Na-Rae Lee2 and Dong-Yup Lee1,2,3,* 1Bioprocessing Technology Institute (BTI), A*STAR, Singapore 138668, Singapore; 2Department of Chemical and Bio- molecular Engineering, and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National Uni- versity of Singapore, Singapore 117585, Singapore; 3School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea Abstract: In nature, microbes do not exist in isolation but co-exist in a variety of ecological and bio- logical environments and on various host organisms. Due to their close proximity, these microbes in- teract among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrin- sic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial A R T I C L E H I S T O R Y communities. If detailed species-specific information is available, segregated models of individual or- Received: June 25, 2017 ganisms can be constructed and connected via metabolite exchanges; otherwise, the community may Revised: November 08, 2017 be represented as a lumped network of metabolic reactions. The constructed models can then be simu- Accepted: November 11, 2017 lated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments.