{"results":[{"id":"ss_f756ed37780c479dfa320f54bd7caa6d6a2d38e0","title":"microeco: An R package for data mining in microbial community ecology.","authors":[{"name":"Chi Liu"},{"name":"Yaoming Cui"},{"name":"Xiangzhen Li"},{"name":"Minjie Yao"}],"abstract":"A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-'microeco' as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use, and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Medicine","Biology"],"doi":"10.1093/femsec/fiaa255","url":"https://www.semanticscholar.org/paper/f756ed37780c479dfa320f54bd7caa6d6a2d38e0","is_open_access":true,"citations":1298,"published_at":"","score":94},{"id":"ss_5a60b0393402b6bf887e6b8201718bf3a663d08d","title":"Stochastic Community Assembly: Does It Matter in Microbial Ecology?","authors":[{"name":"Jizhong Zhou"},{"name":"D. Ning"}],"abstract":"","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Medicine","Biology"],"doi":"10.1128/MMBR.00002-17","url":"https://www.semanticscholar.org/paper/5a60b0393402b6bf887e6b8201718bf3a663d08d","pdf_url":"https://mmbr.asm.org/content/mmbr/81/4/e00002-17.full.pdf","is_open_access":true,"citations":2032,"published_at":"","score":91},{"id":"ss_9bf7596c4019098d29b554cbbf17f055ba292b3d","title":"Antibiotic Pollution in the Environment: From Microbial Ecology to Public Policy","authors":[{"name":"S. Kraemer"},{"name":"A. Ramachandran"},{"name":"G. Perron"}],"abstract":"The ability to fight bacterial infections with antibiotics has been a longstanding cornerstone of modern medicine. However, wide-spread overuse and misuse of antibiotics has led to unintended consequences, which in turn require large-scale changes of policy for mitigation. In this review, we address two broad classes of corollaries of antibiotics overuse and misuse. Firstly, we discuss the spread of antibiotic resistance from hotspots of resistance evolution to the environment, with special concerns given to potential vectors of resistance transmission. Secondly, we outline the effects of antibiotic pollution independent of resistance evolution on natural microbial populations, as well as invertebrates and vertebrates. We close with an overview of current regional policies tasked with curbing the effects of antibiotics pollution and outline areas in which such policies are still under development.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Medicine","Business"],"doi":"10.3390/microorganisms7060180","url":"https://www.semanticscholar.org/paper/9bf7596c4019098d29b554cbbf17f055ba292b3d","pdf_url":"https://www.mdpi.com/2076-2607/7/6/180/pdf?version=1561178560","is_open_access":true,"citations":803,"published_at":"","score":87.09},{"id":"ss_a87276e34ef7aa13dc76b4eed295175b29c20c04","title":"Going back to the roots: the microbial ecology of the rhizosphere","authors":[{"name":"L. Philippot"},{"name":"J. Raaijmakers"},{"name":"P. Lemanceau"},{"name":"W. H. Putten"}],"abstract":"","source":"Semantic Scholar","year":2013,"language":"en","subjects":["Medicine","Biology"],"doi":"10.1038/nrmicro3109","url":"https://www.semanticscholar.org/paper/a87276e34ef7aa13dc76b4eed295175b29c20c04","is_open_access":true,"citations":3114,"published_at":"","score":87},{"id":"ss_9e8ea454cf46d802ad5dd3333804bfbffbbbe1a7","title":"Microbial Ecology","authors":[{"name":"R. Haug"}],"abstract":"▶ A dedicated international forum for the presentation of high-quality scientific investigations of how microorganisms interact each other, with neighbors, with their surroundings ▶ Offers articles of original research in full paper and note formats, as well as brief reviews, commentaries and topical position papers ▶ Editor: Karen E. Nelson, President, The J. Craig Venter Institute (JCVI) ▶ 100% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again","source":"Semantic Scholar","year":2019,"language":"en","subjects":null,"doi":"10.1090/cln/022/03","url":"https://www.semanticscholar.org/paper/9e8ea454cf46d802ad5dd3333804bfbffbbbe1a7","pdf_url":"http://www.ams.org/cln/022/cln022.pdf","is_open_access":true,"citations":613,"published_at":"","score":81.39},{"id":"ss_a12af92bd8887c424f432d27989bc2b224246d66","title":"Microbial ecology: Human gut microbes associated with obesity","authors":[{"name":"R. Ley"},{"name":"P. Turnbaugh"},{"name":"S. Klein"},{"name":"J. Gordon"}],"abstract":"","source":"Semantic Scholar","year":2006,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1038/4441022a","url":"https://www.semanticscholar.org/paper/a12af92bd8887c424f432d27989bc2b224246d66","is_open_access":true,"citations":8464,"published_at":"","score":80},{"id":"ss_3b437b1b2b608a13d7011e9a13c2f6b94e5eded8","title":"Obesity alters gut microbial ecology.","authors":[{"name":"R. Ley"},{"name":"F. Bäckhed"},{"name":"P. Turnbaugh"},{"name":"C. Lozupone"},{"name":"Robin D. Knight"},{"name":"J. Gordon"}],"abstract":"","source":"Semantic Scholar","year":2005,"language":"en","subjects":["Medicine","Biology"],"doi":"10.1073/PNAS.0504978102","url":"https://www.semanticscholar.org/paper/3b437b1b2b608a13d7011e9a13c2f6b94e5eded8","pdf_url":"https://europepmc.org/articles/pmc1176910?pdf=render","is_open_access":true,"citations":5966,"published_at":"","score":80},{"id":"ss_ad487dd454a110dbf91bc6838f1a6fc13ad3dd5a","title":"Diversity and ecology of microbial sulfur metabolism","authors":[{"name":"Zhichao Zhou"},{"name":"Patricia Q. Tran"},{"name":"Elise S. Cowley"},{"name":"E. Trembath‐Reichert"},{"name":"Karthik Anantharaman"}],"abstract":"","source":"Semantic Scholar","year":2024,"language":"en","subjects":["Medicine"],"doi":"10.1038/s41579-024-01104-3","url":"https://www.semanticscholar.org/paper/ad487dd454a110dbf91bc6838f1a6fc13ad3dd5a","is_open_access":true,"citations":142,"published_at":"","score":72.25999999999999},{"id":"ss_194b2dd9cb08d1d2696171e05e3b2ee1e3e0ebce","title":"Dietary simple sugars alter microbial ecology in the gut and promote colitis in mice","authors":[{"name":"Shahanshah Khan"},{"name":"S. Waliullah"},{"name":"V. Godfrey"},{"name":"M. A. W. Khan"},{"name":"Rajalaksmy A. Ramachandran"},{"name":"B. Cantarel"},{"name":"Cassie L. Behrendt"},{"name":"Lan Peng"},{"name":"L. Hooper"},{"name":"Hasan Zaki"}],"abstract":"High-sugar diet promotes the growth of mucolytic bacteria, leading to reduction of the mucus layer and predisposition to colitis in mice. Harmful sweetness Inflammatory bowel diseases (IBDs) include multiple disorders characterized by chronic gastrointestinal inflammation. Although the etiology of these diseases is mostly unknown, Western diet and lifestyle seem to be associated with higher IBD incidence. Here, Khan et al. studied the effect of high-sugar diet on colitis in rodent models and showed that a diet high in simple sugars aggravated colitis in mouse models when administered before or after colitis induction. The effect was mediated by alteration of gut microbiota, with an increase of mucolytic bacteria that facilitated gut mucus barrier degradation. The results suggest that high-sugar diet might promote gut microbiota dysfunction and IBD development. The higher prevalence of inflammatory bowel disease (IBD) in Western countries points to Western diet as a possible IBD risk factor. High sugar, which is linked to many noncommunicable diseases, is a hallmark of the Western diet, but its role in IBD remains unknown. Here, we studied the effects of simple sugars such as glucose and fructose on colitis pathogenesis in wild-type and Il10−/− mice. Wild-type mice fed 10% glucose in drinking water or high-glucose diet developed severe colitis induced by dextran sulfate sodium. High-glucose–fed Il10−/− mice also developed a worsened colitis compared to glucose-untreated Il10−/− mice. Short-term intake of high glucose or fructose did not trigger inflammatory responses in healthy gut but markedly altered gut microbiota composition. In particular, the abundance of the mucus-degrading bacteria Akkermansia muciniphila and Bacteroides fragilis was increased. Consistently, bacteria-derived mucolytic enzymes were enriched leading to erosion of the colonic mucus layer of sugar-fed wild-type and Il10−/− mice. Sugar-induced exacerbation of colitis was not observed when mice were treated with antibiotics or maintained in a germ-free environment, suggesting that altered microbiota played a critical role in sugar-induced colitis pathogenesis. Furthermore, germ-free mice colonized with microbiota from sugar-treated mice showed increased colitis susceptibility. Together, these data suggest that intake of simple sugars predisposes to colitis and enhances its pathogenesis via modulation of gut microbiota in mice.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1126/scitranslmed.aay6218","url":"https://www.semanticscholar.org/paper/194b2dd9cb08d1d2696171e05e3b2ee1e3e0ebce","is_open_access":true,"citations":246,"published_at":"","score":71.38},{"id":"ss_a4d9f641da78017975efb32cb89dbf782b55c853","title":"Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring","authors":[{"name":"Ryan B. Ghannam"},{"name":"S. Techtmann"}],"abstract":"Graphical abstract","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.csbj.2021.01.028","url":"https://www.semanticscholar.org/paper/a4d9f641da78017975efb32cb89dbf782b55c853","pdf_url":"https://doi.org/10.1016/j.csbj.2021.01.028","is_open_access":true,"citations":207,"published_at":"","score":71.21000000000001},{"id":"ss_1f25bc00105d6387fc64780654014c5b0df31a26","title":"Carbon source shaped microbial ecology, metabolism and performance in denitrification systems.","authors":[{"name":"Yuan Pan"},{"name":"R. Sun"},{"name":"Yan Wang"},{"name":"Guanzhong Chen"},{"name":"Yingcai Fu"},{"name":"Hangqin Yu"}],"abstract":"The limited information on microbial interactions and metabolic patterns in denitrification systems, especially those fed with different carbon sources, has hindered the establishment of ecological linkages between microscale connections and macroscopic reactor performance. In this work, denitrification performance, metabolic patterns, and ecological structure were investigated in parallel well-controlled bioreactors with four representative carbon sources, i.e., methanol, glycerol, acetate, and glucose. After long-term acclimation, significant differences were observed among the four bioreactors in terms of denitrification rates, organic utilization, and heterotrophic bacterial yields. Different carbon sources induced the succession of denitrifying microbiota toward different ecological structures and exhibited distinct metabolic patterns. Methanol-fed reactors showed distinctive microbial carbon utilization pathways and a more intricate microbial interaction network, leading to significant variations in organic utilization and metabolite production compared to other carbon sources. Three keystone taxa belonging to the Verrucomicrobiota phylum, SJA-15 order and the Kineosphaera genus appeared as network hubs in the methanol, glycerol, and acetate-fed systems, playing essential roles in their ecological functions. Several highly connected species were also identified within the glucose-fed system. The close relationship between microbial metabolites, ecological structures, and system performances suggests that this complex network relationship may greatly contribute to the efficient operation of bioreactors.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.watres.2023.120330","url":"https://www.semanticscholar.org/paper/1f25bc00105d6387fc64780654014c5b0df31a26","is_open_access":true,"citations":133,"published_at":"","score":70.99000000000001},{"id":"ss_0a2391d5fb3c90400545992caf909f3a16e548d7","title":"Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses","authors":[{"name":"A. E. Pérez-Cobas"},{"name":"Laura Gomez-Valero"},{"name":"C. Buchrieser"}],"abstract":"Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1099/mgen.0.000409","url":"https://www.semanticscholar.org/paper/0a2391d5fb3c90400545992caf909f3a16e548d7","pdf_url":"https://doi.org/10.1099/mgen.0.000409","is_open_access":true,"citations":233,"published_at":"","score":70.99000000000001},{"id":"ss_d9ddc753e2b80a0283205e5fb10e5d9f9147befe","title":"Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology","authors":[{"name":"A. Molinaro"},{"name":"Pierre Bel Lassen"},{"name":"Marcus Henricsson"},{"name":"Hao Wu"},{"name":"S. Adriouch"},{"name":"Eugeni Belda"},{"name":"R. Chakaroun"},{"name":"T. Nielsen"},{"name":"Per-Olof Bergh"},{"name":"C. Rouault"},{"name":"S. André"},{"name":"Florian Marquet"},{"name":"F. Andreelli"},{"name":"J. Salem"},{"name":"K. Assmann"},{"name":"J. Bastard"},{"name":"Sofia K. Forslund"},{"name":"E. Le Chatelier"},{"name":"G. Falony"},{"name":"N. Pons"},{"name":"Edi Prifti"},{"name":"B. Quinquis"},{"name":"H. Roume"},{"name":"S. Vieira-Silva"},{"name":"T. Hansen"},{"name":"H. Pedersen"},{"name":"C. Lewinter"},{"name":"Nadja B Sønderskov"},{"name":"Renato Chloe Ehm Astrid Andersson Olivier Jean-Paul Magal Alves Amouyal Galijatovic Barthelemy Batisse Berla"},{"name":"Renato J Alves"},{"name":"C. Amouyal"},{"name":"Ehm Astrid Andersson Galijatovic"},{"name":"O. Barthelemy"},{"name":"J. Batisse"},{"name":"M. Berland"},{"name":"R. Bittar"},{"name":"H. Blottière"},{"name":"Frédéric Bosquet"},{"name":"Rachid Boubrit"},{"name":"O. Bourron"},{"name":"M. Camus"},{"name":"D. Cassuto"},{"name":"J. Chilloux"},{"name":"C. Ciangura"},{"name":"Luis Pedro Coelho"},{"name":"J. Collet"},{"name":"M. Dao"},{"name":"M. Djebbar"},{"name":"A. Doré"},{"name":"L. Engelbrechtsen"},{"name":"S. Fellahi"},{"name":"L. Fezeu"},{"name":"S. Fromentin"},{"name":"P. Giral"},{"name":"J. Gøtze"},{"name":"A. Hartemann"},{"name":"J. Holst"},{"name":"S. Hercberg"},{"name":"G. Helft"},{"name":"M. Hornbak"},{"name":"J. Hulot"},{"name":"R. Isnard"},{"name":"Sophie Jaqueminet"},{"name":"N. Jørgensen"},{"name":"Hanna Julienne"},{"name":"J. Justesen"},{"name":"J. Kammer"},{"name":"N. Krarup"},{"name":"M. Kerneis"},{"name":"J. Khemis"},{"name":"N. B. Kristensen"},{"name":"Michael Kuhn"},{"name":"V. Léjard"},{"name":"F. Levenez"},{"name":"Léa Lucas-Martini"},{"name":"Robin Massey"},{"name":"N. Maziers"},{"name":"Jonathan Medina-Stamminger"},{"name":"G. Montalescot"},{"name":"S. Moutel"},{"name":"Laetitia Pasero Le Pavin"},{"name":"C. Poitou"},{"name":"F. Pousset"},{"name":"Laurence Pouzoulet"},{"name":"Sebastien Schmidt"},{"name":"L. Moitinho-Silva"},{"name":"J. Silvain"},{"name":"Nataliya Sokolovska"},{"name":"Sothea Touch"},{"name":"Mathilde Svendstrup"},{"name":"Timothy Swartz"},{"name":"Thierry Vanduyvenboden"},{"name":"C. Vatier"},{"name":"Stefan Walther"},{"name":"L. Køber"},{"name":"H. Vestergaard"},{"name":"T. Hansen"},{"name":"Jean-Daniel Zucker"},{"name":"P. Galan"},{"name":"M. Dumas"},{"name":"J. Raes"},{"name":"J. Oppert"},{"name":"Ivica Letunic"},{"name":"J. Nielsen"},{"name":"P. Bork"},{"name":"S. Ehrlich"},{"name":"M. Stumvoll"},{"name":"O. Pedersen"},{"name":"J. Aron‐Wisnewsky"},{"name":"K. Clément"},{"name":"F. Bäckhed"}],"abstract":"Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism. Gut microbial metabolism of nutrients contributes to metabolic diseases, and the histidine metabolite imidazole propionate (ImP) is produced by type 2 diabetes (T2D) associated microbiome. Here the authors report that circulating ImP levels are increased in subjects with prediabetes or T2D in three European populations, and this increase associates with altered gut microbiota rather than dietary histidine.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Medicine","Chemistry"],"doi":"10.1038/s41467-020-19589-w","url":"https://www.semanticscholar.org/paper/d9ddc753e2b80a0283205e5fb10e5d9f9147befe","pdf_url":"https://www.nature.com/articles/s41467-020-19589-w.pdf","is_open_access":true,"citations":207,"published_at":"","score":70.21000000000001},{"id":"arxiv_2603.16896","title":"Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution","authors":[{"name":"Gerda Claeskens"},{"name":"Céline Cunen"},{"name":"Nils Lid Hjort"}],"abstract":"Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale.","source":"arXiv","year":2026,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/2603.16896","pdf_url":"https://arxiv.org/pdf/2603.16896","is_open_access":true,"published_at":"2026-03-03T10:07:06Z","score":70},{"id":"doaj_10.1002/mbo3.70227","title":"Revisiting Hyaluronan Catabolism in Bacteroides: Pathway Conservation, Overlooked Proteins, and Predictive Accuracy","authors":[{"name":"Martin Sindelar"},{"name":"Anna Kocurkova"},{"name":"Matej Simek"},{"name":"Pavel Roudnicky"},{"name":"Gabriela Ambrozova"},{"name":"Lukas Kubala"},{"name":"Kristyna Turkova"}],"abstract":"ABSTRACT The ability of gut microbes to degrade host‐ and diet‐derived glycans is central to microbiome ecology and host interactions, yet predicting these functions in silico remains challenging. Hyaluronan (HA), a glycosaminoglycan (GAG) abundant in host tissues and dietary supplements, is depolymerized by specialized polysaccharide utilization loci (PULs) in Bacteroides. Here, we combined comparative protein analysis, functional assays, and quantitative proteomics to evaluate the reliability of sequence‐based predictions of HA utilization. Clustering of more than 3900 PL8 and GH88 protein sequences from 54 Bacteroides species did not distinguish known HA degraders from nondegraders, underscoring the limited predictive power of these enzymes alone. Experimental validation in Bacteroides acidifaciens DSM 111135 and Bacteroides thetaiotaomicron DSM 2079 confirmed HA degradation, as HA‐derived fragments were identified by liquid chromatography–mass spectrometry. Proteomic profiling revealed coordinated induction of both canonical GAG‐specific PULs‐encoded proteins and noncanonical accessory proteins (BT4410/BT4411) in response to HA in both species. Incorporating such noncanonical components into comparative frameworks may improve prediction of glycan utilization potential and help link microbial genomic content to ecological function in the gut.","source":"DOAJ","year":2026,"language":"","subjects":["Microbiology"],"doi":"10.1002/mbo3.70227","url":"https://doi.org/10.1002/mbo3.70227","is_open_access":true,"published_at":"","score":70},{"id":"ss_281340dbb98efe8babade25b23df05a3ca86a027","title":"Sourdough production: fermentation strategies, microbial ecology, and use of non-flour ingredients","authors":[{"name":"L. De Vuyst"},{"name":"Andrea Comasio"},{"name":"S. V. Kerrebroeck"}],"abstract":"Abstract Sourdough production is an ancient method to ferment flour from cereals for the manufacturing of baked goods. This review deals with the state-of-the-art of current fermentation strategies for sourdough production and the microbial ecology of mature sourdoughs, with a particular focus on the use of non-flour ingredients. Flour fermentation processes for sourdough production are typically carried out by heterogeneous communities of lactic acid bacteria and yeasts. Acetic acid bacteria may also occur, although their presence and role in sourdough production can be criticized. Based on the inoculum used, sourdough productions can be distinguished in fermentation processes using backslopping procedures, originating from a spontaneously fermented flour-water mixture (Type 1), starter culture-initiated fermentation processes (Type 2), and starter culture-initiated fermentation processes that are followed by backslopping (Type 3). In traditional recipes for the initiation and/or propagation of Type 1 sourdough productions, non-flour ingredients are often added to the flour-water mixture. These ingredients may be the source of an additional microbial inoculum and/or serve as (co-)substrates for fermentation. An example of the former is the addition of yoghurt; an example of the latter is the use of fruit juices. The survival of microorganisms transferred from the ingredients to the fermenting flour-water mixture depends on the competitiveness toward particular strains of the microbial species present under the harsh conditions of the sourdough ecosystem. Their survival and growth is also determined by the presence of the appropriate substrates, whether or not carried over by the ingredients added.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1080/10408398.2021.1976100","url":"https://www.semanticscholar.org/paper/281340dbb98efe8babade25b23df05a3ca86a027","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10408398.2021.1976100?needAccess=true","is_open_access":true,"citations":152,"published_at":"","score":69.56},{"id":"ss_b4a01b2dd4e5db534ce44f3200dc8b248f201e0f","title":"Metagenomic tools in microbial ecology research.","authors":[{"name":"N. Taş"},{"name":"Anniek E E de Jong"},{"name":"Yao-ming Li"},{"name":"G. Trubl"},{"name":"Yaxin Xue"},{"name":"Nicholas C. Dove"}],"abstract":"Ability to directly sequence DNA from the environment permanently changed microbial ecology. Here, we review the new insights to microbial life gleaned from the applications of metagenomics, as well as the extensive set of analytical tools that facilitate exploration of diversity and function of complex microbial communities. While metagenomics is shaping our understanding of microbial functions in ecosystems via gene-centric and genome-centric methods, annotating functions, metagenome assembly and binning in heterogeneous samples remains challenging. Development of new analysis and sequencing platforms generating high-throughput long-read sequences and functional screening opportunities will aid in harnessing metagenomes to increase our understanding of microbial taxonomy, function, ecology, and evolution in the environment.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.copbio.2021.01.019","url":"https://www.semanticscholar.org/paper/b4a01b2dd4e5db534ce44f3200dc8b248f201e0f","pdf_url":"https://doi.org/10.1016/j.copbio.2021.01.019","is_open_access":true,"citations":149,"published_at":"","score":69.47},{"id":"ss_9b17621b186b33ff6d000d446435de66a5edd322","title":"Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology","authors":[{"name":"L. Tedersoo"},{"name":"M. Albertsen"},{"name":"Sten Anslan"},{"name":"B. Callahan"}],"abstract":"Short-read, high-throughput sequencing (HTS) methods have yielded numerous important insights into microbial ecology and function. Yet, in many instances short-read HTS techniques are suboptimal, for example, by providing insufficient phylogenetic resolution or low integrity of assembled genomes. Single-molecule and synthetic long-read (SLR) HTS methods have successfully ameliorated these limitations. ABSTRACT Short-read, high-throughput sequencing (HTS) methods have yielded numerous important insights into microbial ecology and function. Yet, in many instances short-read HTS techniques are suboptimal, for example, by providing insufficient phylogenetic resolution or low integrity of assembled genomes. Single-molecule and synthetic long-read (SLR) HTS methods have successfully ameliorated these limitations. In addition, nanopore sequencing has generated a number of unique analysis opportunities, such as rapid molecular diagnostics and direct RNA sequencing, and both Pacific Biosciences (PacBio) and nanopore sequencing support detection of epigenetic modifications. Although initially suffering from relatively low sequence quality, recent advances have greatly improved the accuracy of long-read sequencing technologies. In spite of great technological progress in recent years, the long-read HTS methods (PacBio and nanopore sequencing) are still relatively costly, require large amounts of high-quality starting material, and commonly need specific solutions in various analysis steps. Despite these challenges, long-read sequencing technologies offer high-quality, cutting-edge alternatives for testing hypotheses about microbiome structure and functioning as well as assembly of eukaryote genomes from complex environmental DNA samples.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Medicine"],"doi":"10.1128/AEM.00626-21","url":"https://www.semanticscholar.org/paper/9b17621b186b33ff6d000d446435de66a5edd322","pdf_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357291","is_open_access":true,"citations":137,"published_at":"","score":69.11},{"id":"doaj_10.1186/s40168-025-02041-w","title":"Microbial network inference for longitudinal microbiome studies with LUPINE","authors":[{"name":"Saritha Kodikara"},{"name":"Kim-Anh Lê Cao"}],"abstract":"Abstract Background The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal microbiome studies are becoming increasingly popular. These studies enable researchers to infer taxa associations towards the understanding of coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, are limited due to the data characteristics of microbiome data (sparse, compositional, multivariate). Several network inference methods have been proposed, but have been largely unexplored in a longitudinal setting. Results We introduce LUPINE (LongitUdinal modelling with Partial least squares regression for NEtwork inference), a novel approach that leverages on conditional independence and low-dimensional data representation. This method is specifically designed to handle scenarios with small sample sizes and small number of time points. LUPINE is the first method of its kind to infer microbial networks across time, while considering information from all past time points and is thus able to capture dynamic microbial interactions that evolve over time. We validate LUPINE and its variant, LUPINE_single (for single time point analysis) in simulated data and four case studies, where we highlight LUPINE’s ability to identify relevant taxa in each study context, across different experimental designs (mouse and human studies, with or without interventions, and short or long time courses). To detect changes in the networks across time and groups or in response to external disturbances, we used different metrics to compare the inferred networks. Conclusions LUPINE is a simple yet innovative network inference methodology that is suitable for, but not limited to, analysing longitudinal microbiome data. The R code and data are publicly available for readers interested in applying these new methods to their studies. Video Abstract","source":"DOAJ","year":2025,"language":"","subjects":["Microbial ecology"],"doi":"10.1186/s40168-025-02041-w","url":"https://doi.org/10.1186/s40168-025-02041-w","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.1128/msphere.00245-25","title":"Global phylogeography and microdiversity of the marine diazotrophic photoautotrophs Trichodesmium and UCYN-A","authors":[{"name":"Angie Nguyen"},{"name":"Lucas J. Ustick"},{"name":"Alyse A. Larkin"},{"name":"Adam C. Martiny"}],"abstract":"ABSTRACT Photoautotrophic diazotrophs, specifically the genera Trichodesmium and UCYN-A, play a pivotal role in marine nitrogen cycling through their capacity for nitrogen fixation. Despite their global distribution, the microdiversity and environmental drivers of these diazotrophs remain underexplored. This study provides a comprehensive analysis of the global diversity and distribution of Trichodesmium and UCYN-A using the nitrogenase gene (nifH) as a genetic marker. We sequenced 954 samples from the Pacific, Atlantic, and Indian Oceans as part of the Bio-GO-SHIP project. Our results reveal significant phylogenetic and biogeographic differences between and within the two genera. Trichodesmium exhibited greater microdiversity compared to UCYN-A, with clades showing region-specific distribution. Trichodesmium clades were primarily influenced by temperature and nutrient availability. They were particularly frequent in regions of phosphorus stress. In contrast, UCYN-A was most frequently observed in regions experiencing iron stress. UCYN-A clades demonstrated more homogeneous distributions, with a single sequence variant within the UCYN-A1 clade dominating across varied environments. The biogeographic patterns and environmental correlations of Trichodesmium and UCYN-A highlight the role of microdiversity in their ecological adaptation and reflect their different ecological strategies. These findings underscore the importance of characterizing the global patterns of fine-scale genetic diversity to better understand the functional roles and distribution of marine nitrogen-fixing photoautotrophs.IMPORTANCEThis study provides insights into the global diversity and distribution of nitrogen-fixing photoautotrophs, specifically Trichodesmium and UCYN-A. We sequenced 954 oceanic samples of the nifH nitrogenase gene and uncovered significant differences in microdiversity and environmental associations between these genera. Trichodesmium showed high levels of sequence diversity and region-specific clades influenced by temperature and nutrient availability. In contrast, UCYN-A exhibited a more uniform distribution, thriving in iron-stressed regions. Quantifying these fine-scale genetic variations enhances our knowledge of their ecological roles and adaptations, emphasizing the need to characterize the genetic diversity of marine nitrogen-fixing prokaryotes.","source":"DOAJ","year":2025,"language":"","subjects":["Microbiology"],"doi":"10.1128/msphere.00245-25","url":"https://journals.asm.org/doi/10.1128/msphere.00245-25","is_open_access":true,"published_at":"","score":69}],"total":1190051,"page":1,"page_size":20,"sources":["arXiv","DOAJ","Semantic Scholar"],"query":"Microbial ecology"}