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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Junker R, Valence F, Mistou MY, Chaillou S, Chiapello H. Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables. Microbiol Spectr 2024; 12:e0031224. [PMID: 38747598 DOI: 10.1128/spectrum.00312-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 03/26/2024] [Indexed: 06/06/2024] Open
Abstract
The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the "stochastic block model" method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages. IMPORTANCE Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales. These findings could be applied to the design of new fermented products.
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Affiliation(s)
- Romane Junker
- MaIAGE, INRAE, Université Paris-Saclay, Jouy-en-Josas, France
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3
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Hu Y, Cai J, Song Y, Li G, Gong Y, Jiang X, Tang X, Shao K, Gao G. Sediment DNA Records the Critical Transition of Bacterial Communities in the Arid Lake. MICROBIAL ECOLOGY 2024; 87:68. [PMID: 38722447 PMCID: PMC11082002 DOI: 10.1007/s00248-024-02365-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/07/2024] [Indexed: 05/12/2024]
Abstract
It is necessary to predict the critical transition of lake ecosystems due to their abrupt, non-linear effects on social-economic systems. Given the promising application of paleolimnological archives to tracking the historical changes of lake ecosystems, it is speculated that they can also record the lake's critical transition. We studied Lake Dali-Nor in the arid region of Inner Mongolia because of the profound shrinking the lake experienced between the 1300 s and the 1600 s. We reconstructed the succession of bacterial communities from a 140-cm-long sediment core at 4-cm intervals and detected the critical transition. Our results showed that the historical trajectory of bacterial communities from the 1200 s to the 2010s was divided into two alternative states: state1 from 1200 to 1300 s and state2 from 1400 to 2010s. Furthermore, in the late 1300 s, the appearance of a tipping point and critical slowing down implied the existence of a critical transition. By using a multi-decadal time series from the sedimentary core, with general Lotka-Volterra model simulations, local stability analysis found that bacterial communities were the most unstable as they approached the critical transition, suggesting that the collapse of stability triggers the community shift from an equilibrium state to another state. Furthermore, the most unstable community harbored the strongest antagonistic and mutualistic interactions, which may imply the detrimental role of interaction strength on community stability. Collectively, our study showed that sediment DNA can be used to detect the critical transition of lake ecosystems.
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Affiliation(s)
- Yang Hu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Jian Cai
- Xiangyang Polytechnic, Xiangyang, 441000, Hubei Province, China
| | - Yifu Song
- Nanjing Forestry University, Nanjing, 210008, China
| | | | - Yi Gong
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Xingyu Jiang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Xiangming Tang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Keqiang Shao
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Guang Gao
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China.
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Wang YL, Ikuma K, Brooks SC, Varonka MS, Deonarine A. Non-mercury methylating microbial taxa are integral to understanding links between mercury methylation and elemental cycles in marine and freshwater sediments. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 346:123573. [PMID: 38365074 DOI: 10.1016/j.envpol.2024.123573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/09/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024]
Abstract
The goal of this study was to explore the role of non-mercury (Hg) methylating taxa in mercury methylation and to identify potential links between elemental cycles and Hg methylation. Statistical approaches were utilized to investigate the microbial community and biochemical functions in relation to methylmercury (MeHg) concentrations in marine and freshwater sediments. Sediments were collected from the methylation zone (top 15 cm) in four Hg-contaminated sites. Both abiotic (e.g., sulfate, sulfide, iron, salinity, total organic matter, etc.) and biotic factors (e.g., hgcA, abundances of methylating and non-methylating taxa) were quantified. Random forest and stepwise regression were performed to assess whether non-methylating taxa were significantly associated with MeHg concentration. Co-occurrence and functional network analyses were constructed to explore associations between taxa by examining microbial community structure, composition, and biochemical functions across sites. Regression analysis showed that approximately 80% of the variability in sediment MeHg concentration was predicted by total mercury concentration, the abundances of Hg methylating taxa, and the abundances of the non-Hg methylating taxa. The co-occurrence networks identified Paludibacteraceae and Syntrophorhabdaceae as keystone non Hg methylating taxa in multiple sites, indicating the potential for syntrophic interactions with Hg methylators. Strong associations were also observed between methanogens and sulfate-reducing bacteria, which were likely symbiotic associations. The functional network results suggested that non-Hg methylating taxa play important roles in sulfur respiration, nitrogen respiration, and the carbon metabolism-related functions methylotrophy, methanotrophy, and chemoheterotrophy. Interestingly, keystone functions varied by site and did not involve carbon- and sulfur-related functions only. Our findings highlight associations between methylating and non-methylating taxa and sulfur, carbon, and nitrogen cycles in sediment methylation zones, with implications for predicting and understanding the impact of climate and land/sea use changes on Hg methylation.
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Affiliation(s)
- Yong-Li Wang
- Department of Civil, Environmental & Construction Engineering, Texas Tech University, Lubbock, TX, United States
| | - Kaoru Ikuma
- Department of Civil, Construction & Environmental Engineering, Iowa State University, Ames, IA, United States
| | - Scott C Brooks
- Oak Ridge National Laboratory, Environmental Science Division, Oak Ridge, TN, United States
| | - Matthew S Varonka
- U.S. Geological Survey, Geology, Energy & Minerals Science Center, Reston, VA, United States
| | - Amrika Deonarine
- Department of Civil, Environmental & Construction Engineering, Texas Tech University, Lubbock, TX, United States.
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Matteoli FP, Silva AMM, de Araújo VLVP, Feiler HP, Cardoso EJBN. Organic farming promotes the abundance of fungi keystone taxa in bacteria-fungi interkingdom networks. World J Microbiol Biotechnol 2024; 40:119. [PMID: 38429532 DOI: 10.1007/s11274-024-03926-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/14/2024] [Indexed: 03/03/2024]
Abstract
Soil bacteria-fungi interactions are essential in the biogeochemical cycles of several nutrients, making these microbes major players in agroecosystems. While the impact of the farming system on microbial community composition has been extensively reported in the literature, whether sustainable farming approaches can promote associations between bacteria and fungi is still unclear. To study this, we employed 16S, ITS, and 18S DNA sequencing to uncover how microbial interactions were affected by conventional and organic farming systems on maize crops. The Bray-Curtis index revealed that bacterial, fungal, and arbuscular mycorrhizal fungi communities were significantly different between the two farming systems. Several taxa known to thrive in healthy soils, such as Nitrosophaerales, Orbiliales, and Glomus were more abundant in the organic farming system. Constrained ordination revealed that the organic farming system microbial community was significantly correlated with the β-glucosidase activity, whereas the conventional farming system microbial community significantly correlated with soil pH. Both conventional and organic co-occurrence interkingdom networks exhibited a parallel node count, however, the former had a higher number of edges, thus being denser than the latter. Despite the similar amount of fungal nodes in the co-occurrence networks, the organic farming system co-occurrence network exhibited more than 3-fold the proportion of fungal taxa as keystone nodes than the conventional co-occurrence network. The genera Bionectria, Cercophora, Geastrum, Penicillium, Preussia, Metarhizium, Myceliophthora, and Rhizophlyctis were among the fungal keystone nodes of the organic farming system network. Altogether, our results uncover that beyond differences in microbial community composition between the two farming systems, fungal keystone nodes are far more relevant in the organic farming system, thus suggesting that bacteria-fungi interactions are more frequent in organic farming systems, promoting a more functional microbial community.
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Affiliation(s)
- Filipe Pereira Matteoli
- Laboratory of Microbial Bioinformatics, Department of Biological Sciences, Faculty of Sciences, São Paulo State University, Bauru, Brazil.
| | - Antonio M M Silva
- Department of Soil Sciences, University of São Paulo, "Luiz de Queiroz" College of Agriculture, Piracicaba, Brazil
| | - Victor L V P de Araújo
- Department of Soil Sciences, University of São Paulo, "Luiz de Queiroz" College of Agriculture, Piracicaba, Brazil
| | - Henrique P Feiler
- Department of Soil Sciences, University of São Paulo, "Luiz de Queiroz" College of Agriculture, Piracicaba, Brazil
| | - Elke J B N Cardoso
- Department of Soil Sciences, University of São Paulo, "Luiz de Queiroz" College of Agriculture, Piracicaba, Brazil
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6
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Berruto CA, Demirer GS. Engineering agricultural soil microbiomes and predicting plant phenotypes. Trends Microbiol 2024:S0966-842X(24)00043-X. [PMID: 38429182 DOI: 10.1016/j.tim.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.
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Affiliation(s)
- Chiara A Berruto
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Gozde S Demirer
- Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
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Zhang S, Liu S, Liu H, Li H, Luo J, Zhang A, Ding Y, Ren T, Chen W. Stochastic Assembly Increases the Complexity and Stability of Shrimp Gut Microbiota During Aquaculture Progression. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2024; 26:92-102. [PMID: 38165637 DOI: 10.1007/s10126-023-10279-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/20/2023] [Indexed: 01/04/2024]
Abstract
The gut microbiota of aquaculture species contributes to their food metabolism and regulates their health, which has been shown to vary during aquaculture progression of their hosts. However, limited research has examined the outcomes and mechanisms of these changes in the gut microbiota of hosts. Here, Kuruma shrimps from the beginning, middle, and late stages of aquaculture progression (about a time duration of 2 months between each stage) were collected and variations in the gut microbiota of Kuruma shrimp during the whole aquaculture process were examined. High-throughput sequencing demonstrated increases in the diversity and richness of the shrimp gut microbiota with aquaculture progression. In addition, the gut microbiota composition differed among cultural stages, with enrichment of Firmicutes, RF39, and Megamonas and a reduction in Proteobacteria in the mid-stage. Notably, only very few taxa were persistent in the shrimp gut microbiota during the whole aquaculture progression, while the number of taxa that specific to the end of aquaculture was high. Network analysis revealed increasing complexity of the shrimp gut microbiota during aquaculture progression. Moreover, the shrimp gut microbiota became significantly more stable towards the end of aquaculture. According to the results of neutral community model, contribution of stochastic processes for shaping the shrimp gut microbiota was elevated along the aquaculture progression. This study showed substantial variations in shrimp gut microbiota during aquaculture progression and explored the underlying mechanisms regulating these changes.
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Affiliation(s)
- Saisai Zhang
- Dalian Ocean Development Affairs Service, Dalian, Liaoning, 116023, China
| | - Shuang Liu
- Dalian Ocean Development Affairs Service, Dalian, Liaoning, 116023, China
| | - Hongwei Liu
- Dalian Ocean University, Dalian Liaoning, 116023, China
| | - Hui Li
- Dalian Ocean Development Affairs Service, Dalian, Liaoning, 116023, China
| | - Jun Luo
- Dalian Sun Asia Tourism Holding Co. Ltd., Dalian, Liaoning, 116023, China
| | - Aili Zhang
- Dalian Ocean School, Dalian, Liaoning, 116023, China
| | - Yinpeng Ding
- Dalian Ocean Development Affairs Service, Dalian, Liaoning, 116023, China
| | - Tongjun Ren
- Dalian Ocean University, Dalian Liaoning, 116023, China
| | - Wenbo Chen
- Dalian Ocean Development Affairs Service, Dalian, Liaoning, 116023, China.
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Zheludev IN, Edgar RC, Lopez-Galiano MJ, de la Peña M, Babaian A, Bhatt AS, Fire AZ. Viroid-like colonists of human microbiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576352. [PMID: 38293115 PMCID: PMC10827157 DOI: 10.1101/2024.01.20.576352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Here, we describe the "Obelisks," a previously unrecognised class of viroid-like elements that we first identified in human gut metatranscriptomic data. "Obelisks" share several properties: (i) apparently circular RNA ~1kb genome assemblies, (ii) predicted rod-like secondary structures encompassing the entire genome, and (iii) open reading frames coding for a novel protein superfamily, which we call the "Oblins". We find that Obelisks form their own distinct phylogenetic group with no detectable sequence or structural similarity to known biological agents. Further, Obelisks are prevalent in tested human microbiome metatranscriptomes with representatives detected in ~7% of analysed stool metatranscriptomes (29/440) and in ~50% of analysed oral metatranscriptomes (17/32). Obelisk compositions appear to differ between the anatomic sites and are capable of persisting in individuals, with continued presence over >300 days observed in one case. Large scale searches identified 29,959 Obelisks (clustered at 90% nucleotide identity), with examples from all seven continents and in diverse ecological niches. From this search, a subset of Obelisks are identified to code for Obelisk-specific variants of the hammerhead type-III self-cleaving ribozyme. Lastly, we identified one case of a bacterial species (Streptococcus sanguinis) in which a subset of defined laboratory strains harboured a specific Obelisk RNA population. As such, Obelisks comprise a class of diverse RNAs that have colonised, and gone unnoticed in, human, and global microbiomes.
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Affiliation(s)
- Ivan N Zheludev
- Stanford University, Department of Biochemistry, Stanford, CA, USA
| | | | - Maria Jose Lopez-Galiano
- Instituto de Biología Molecular y Celular de Plantas, Universidad Politécnica de Valencia-CSIC, Valencia, Spain
| | - Marcos de la Peña
- Instituto de Biología Molecular y Celular de Plantas, Universidad Politécnica de Valencia-CSIC, Valencia, Spain
| | - Artem Babaian
- University of Toronto, Department of Molecular Genetics, Ontario, Canada
- University of Toronto, Donnelly Centre for Cellular and Biomolecular Research, Ontario, Canada
| | - Ami S Bhatt
- Stanford University, Department of Genetics, Stanford, CA, USA
- Stanford University, Department of Medicine, Division of Hematology, Stanford, CA, USA
| | - Andrew Z Fire
- Stanford University, Department of Genetics, Stanford, CA, USA
- Stanford University, Department of Pathology, Stanford, CA, USA
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Maurice K, Bourceret A, Youssef S, Boivin S, Laurent-Webb L, Damasio C, Boukcim H, Selosse MA, Ducousso M. Anthropic disturbances impact the soil microbial network structure and stability to a greater extent than natural disturbances in an arid ecosystem. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167969. [PMID: 37914121 DOI: 10.1016/j.scitotenv.2023.167969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 11/03/2023]
Abstract
Growing pressure from climate change and agricultural land use is destabilizing soil microbial community interactions. Yet little is known about microbial community resistance and adaptation to disturbances over time. This hampers our ability to determine the recovery latency of microbial interactions after disturbances, with fundamental implications for ecosystem functioning and conservation measures. Here we examined the response of bacterial and fungal community networks in the rhizosphere of Haloxylon salicornicum (Moq.) Bunge ex Boiss. over the course of soil disturbances resulting from a history of different hydric constraints involving flooding-drought successions. An anthropic disturbance related to past agricultural use, with frequent successions of flooding and drought, was compared to a natural disturbance, i.e., an evaporation basin, with yearly flooding-drought successions. The anthropic disturbance resulted in a specific microbial network topology characterized by lower modularity and stability, reflecting the legacy of past agricultural use on soil microbiome. In contrast, the natural disturbance resulted in a network topology and stability close to those of natural environments despite the lower alpha diversity, and a different community composition compared to that of the other sites. These results highlighted the temporality in the response of the microbial community structure to disturbance, where long-term adaptation to flooding-drought successions lead to a higher stability than disturbances occurring over a shorter timescale.
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Affiliation(s)
- Kenji Maurice
- LSTM, Univ Montpellier, CIRAD, INRAE, IRD, SupAgro, UMR082 LSTM, 34398 Montpellier Cedex 5, France.
| | - Amélia Bourceret
- ISYEB, Muséum national d'Histoire naturelle, CNRS, EPHE-PSL, Sorbonne Université, 57 rue Cuvier, CP39, 75005 Paris, France
| | - Sami Youssef
- Department of Research and Development, VALORHIZ, 1900, Boulevard de la Lironde, PSIII, Parc Scientifique Agropolis, F34980 Montferrier sur Lez, France
| | - Stéphane Boivin
- LSTM, Univ Montpellier, CIRAD, INRAE, IRD, SupAgro, UMR082 LSTM, 34398 Montpellier Cedex 5, France
| | - Liam Laurent-Webb
- ISYEB, Muséum national d'Histoire naturelle, CNRS, EPHE-PSL, Sorbonne Université, 57 rue Cuvier, CP39, 75005 Paris, France
| | - Coraline Damasio
- LSTM, Univ Montpellier, CIRAD, INRAE, IRD, SupAgro, UMR082 LSTM, 34398 Montpellier Cedex 5, France
| | - Hassan Boukcim
- Department of Research and Development, VALORHIZ, 1900, Boulevard de la Lironde, PSIII, Parc Scientifique Agropolis, F34980 Montferrier sur Lez, France; ASARI, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid Ben Guerir, 43150, Morocco
| | - Marc-André Selosse
- ISYEB, Muséum national d'Histoire naturelle, CNRS, EPHE-PSL, Sorbonne Université, 57 rue Cuvier, CP39, 75005 Paris, France; Department of Plant Taxonomy and Nature Conservation, University of Gdańsk, ul. Wita Stwosza 59, 80-308 Gdańsk, Poland; Institut Universitaire de France, Paris, France
| | - Marc Ducousso
- LSTM, Univ Montpellier, CIRAD, INRAE, IRD, SupAgro, UMR082 LSTM, 34398 Montpellier Cedex 5, France
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Guan X, Zhao Z, Jiang J, Fu L, Liu J, Pan Y, Gao S, Wang B, Chen Z, Wang X, Sun H, Jiang B, Dong Y, Zhou Z. Succession and assembly mechanisms of seawater prokaryotic communities along an extremely wide salinity gradient. ENVIRONMENTAL MICROBIOLOGY REPORTS 2023; 15:545-556. [PMID: 37537784 PMCID: PMC10667648 DOI: 10.1111/1758-2229.13188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/06/2023] [Indexed: 08/05/2023]
Abstract
Salinity is an important environmental factor in microbial ecology for affecting the microbial communities in diverse environments. Understanding the salinity adaptation mechanisms of a microbial community is a significant issue, while most previous studies only covered a narrow salinity range. Here, variations in seawater prokaryotic communities during the whole salt drying progression (salinity from 3% to 25%) were investigated. According to high-throughput sequencing results, the diversity, composition, and function of seawater prokaryotic communities varied significantly along the salinity gradient, expressing as decreased diversity, enrichment of some halophilic archaea, and powerful nitrate reduction in samples with high salt concentrations. More importantly, a sudden and dramatic alteration of prokaryotic communities was observed when salinity reached 16%, which was recognized as the change point. Combined with the results of network analysis, we found the increasing of complexity but decreasing of stability in prokaryotic communities when salinity exceeded the change point. Moreover, prokaryotic communities became more deterministic when salinity exceeded the change point due to the niche adaptation of halophilic species. Our study showed that substantial variations in seawater prokaryotic communities along an extremely wide salinity gradient, and also explored the underlying mechanisms regulating these changes.
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Affiliation(s)
- Xiaoyan Guan
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Zelong Zhao
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Jingwei Jiang
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Lei Fu
- Dalian Salt Chemical Group Co., LtdDalianLiaoningPeople's Republic of China
| | - Jiaojiao Liu
- Dalian Salt Chemical Group Co., LtdDalianLiaoningPeople's Republic of China
| | - Yongjia Pan
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Shan Gao
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Bai Wang
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Zhong Chen
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Xuda Wang
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Hongjuan Sun
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Bing Jiang
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Ying Dong
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
| | - Zunchun Zhou
- Liaoning Key Laboratory of Marine Fishery Molecular Biology, Liaoning Key Lab of Germplasm Improvement and Fine Seed Breeding of Marine Aquatic AnimalsLiaoning Ocean and Fisheries Science Research InstituteDalianLiaoningPeople's Republic of China
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11
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Xu CCY, Lemoine J, Albert A, Whirter ÉM, Barrett RDH. Community assembly of the human piercing microbiome. Proc Biol Sci 2023; 290:20231174. [PMID: 38018103 PMCID: PMC10685111 DOI: 10.1098/rspb.2023.1174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/03/2023] [Indexed: 11/30/2023] Open
Abstract
Predicting how biological communities respond to disturbance requires understanding the forces that govern their assembly. We propose using human skin piercings as a model system for studying community assembly after rapid environmental change. Local skin sterilization provides a 'clean slate' within the novel ecological niche created by the piercing. Stochastic assembly processes can dominate skin microbiomes due to the influence of environmental exposure on local dispersal, but deterministic processes might play a greater role within occluded skin piercings if piercing habitats impose strong selection pressures on colonizing species. Here we explore the human ear-piercing microbiome and demonstrate that community assembly is predominantly stochastic but becomes significantly more deterministic with time, producing increasingly diverse and ecologically complex communities. We also observed changes in two dominant and medically relevant antagonists (Cutibacterium acnes and Staphylococcus epidermidis), consistent with competitive exclusion induced by a transition from sebaceous to moist environments. By exploiting this common yet uniquely human practice, we show that skin piercings are not just culturally significant but also represent ecosystem engineering on the human body. The novel habitats and communities that skin piercings produce may provide general insights into biological responses to environmental disturbances with implications for both ecosystem and human health.
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Affiliation(s)
- Charles C. Y. Xu
- Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0C4
- Department of Biology, McGill University, Montreal, Quebec, Canada H3A 1B1
| | - Juliette Lemoine
- Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0C4
- Department of Biology, McGill University, Montreal, Quebec, Canada H3A 1B1
- Department of Ecology and Evolution, University of Lausanne, Lausanne 1015, Switzerland
| | - Avery Albert
- Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0C4
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada H9X 3V9
- Trottier Space Institute, McGill University, Montreal, Quebec, Canada H3A 2A7
| | | | - Rowan D. H. Barrett
- Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0C4
- Department of Biology, McGill University, Montreal, Quebec, Canada H3A 1B1
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12
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Colgate ER, Klopfer C, Dickson DM, Lee B, Wargo MJ, Alam A, Kirkpatrick BD, Hébert-Dufresne L. Network analysis of patterns and relevance of enteric pathogen co-infections among infants in a diarrhea-endemic setting. PLoS Comput Biol 2023; 19:e1011624. [PMID: 37992129 PMCID: PMC10664872 DOI: 10.1371/journal.pcbi.1011624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open
Abstract
Despite significant progress in recent decades toward ameliorating the excess burden of diarrheal disease globally, childhood diarrhea remains a leading cause of morbidity and mortality in low-and-middle-income countries (LMICs). Recent large-scale studies of diarrhea etiology in these populations have revealed widespread co-infection with multiple enteric pathogens, in both acute and asymptomatic stool specimens. We applied methods from network science and ecology to better understand the underlying structure of enteric co-infection among infants in two large longitudinal birth cohorts in Bangladesh. We used a configuration model to establish distributions of expected random co-occurrence, based on individual pathogen prevalence alone, for every pathogen pair among 30 enteropathogens detected by qRT-PCR in both diarrheal and asymptomatic stool specimens. We found two pairs, Enterotoxigenic E. coli (ETEC) with Enteropathogenic E. coli (EPEC), and ETEC with Campylobacter spp., co-infected significantly more than expected at random (both pairs co-occurring almost 4 standard deviations above what one could expect due to chance alone). Furthermore, we found a general pattern that bacteria-bacteria pairs appear together more frequently than expected at random, while virus-bacteria pairs tend to appear less frequently than expected based on model predictions. Finally, infants co-infected with leading bacteria-bacteria pairs had more days of diarrhea in the first year of life compared to infants without co-infection (p-value <0.0001). Our methods and results help us understand the structure of enteric co-infection which can guide further work to identify and eliminate common sources of infection or determine biologic mechanisms that promote co-infection.
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Affiliation(s)
- E. Ross Colgate
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America
| | - Connor Klopfer
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Dorothy M. Dickson
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America
| | - Benjamin Lee
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Pediatrics, University of Vermont Larner College of Medicine, Burlington, Vermont, United States of America
| | - Matthew J. Wargo
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America
| | - Ashraful Alam
- International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Beth D. Kirkpatrick
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, Vermont, United States of America
| | - Laurent Hébert-Dufresne
- Translational Global Infectious Disease Research Center, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America
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13
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Dundore-Arias JP, Michalska-Smith M, Millican M, Kinkel LL. More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes. ANNUAL REVIEW OF PHYTOPATHOLOGY 2023; 61:403-423. [PMID: 37217203 DOI: 10.1146/annurev-phyto-021021-041457] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.
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Affiliation(s)
- J P Dundore-Arias
- Department of Biology and Chemistry, California State University, Monterey Bay, Seaside, California, USA
| | - M Michalska-Smith
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, USA;
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, USA
| | | | - L L Kinkel
- Department of Plant Pathology, University of Minnesota, St. Paul, Minnesota, USA;
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14
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Kishore D, Birzu G, Hu Z, DeLisi C, Korolev KS, Segrè D. Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation. mSystems 2023; 8:e0096122. [PMID: 37338270 PMCID: PMC10469762 DOI: 10.1128/msystems.00961-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.
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Affiliation(s)
- Dileep Kishore
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Gabriel Birzu
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Applied Physics, Stanford University, Stanford, California, USA
| | - Zhenjun Hu
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Charles DeLisi
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Kirill S. Korolev
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Physics, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
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15
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Hanusch M, He X, Janssen S, Selke J, Trutschnig W, Junker RR. Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents. Ecosystems 2023; 26:1819-1840. [PMID: 38106357 PMCID: PMC10721710 DOI: 10.1007/s10021-023-00867-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 07/06/2023] [Indexed: 12/19/2023]
Abstract
Complex links between biotic and abiotic constituents are fundamental for the functioning of ecosystems. Although non-monotonic interactions and associations are known to increase the stability, diversity, and productivity of ecosystems, they are frequently ignored by community-level standard statistical approaches. Using the copula-based dependence measure qad, capable of quantifying the directed and asymmetric dependence between variables for all forms of (functional) relationships, we determined the proportion of non-monotonic associations between different constituents of an ecosystem (plants, bacteria, fungi, and environmental parameters). Here, we show that up to 59% of all statistically significant associations are non-monotonic. Further, we show that pairwise associations between plants, bacteria, fungi, and environmental parameters are specifically characterized by their strength and degree of monotonicity, for example, microbe-microbe associations are on average stronger than and differ in degree of non-monotonicity from plant-microbe associations. Considering directed and non-monotonic associations, we extended the concept of ecosystem coupling providing more complete insights into the internal order of ecosystems. Our results emphasize the importance of ecological non-monotonicity in characterizing and understanding ecosystem patterns and processes. Supplementary Information The online version contains supplementary material available at 10.1007/s10021-023-00867-9.
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Affiliation(s)
- Maximilian Hanusch
- Department of Environment and Biodiversity, Paris-Lodron-University Salzburg, 5020 Salzburg, Austria
| | - Xie He
- Department of Environment and Biodiversity, Paris-Lodron-University Salzburg, 5020 Salzburg, Austria
| | - Stefan Janssen
- Algorithmic Bioinformatics, Justus-Liebig-University Giessen, 35390 Giessen, Germany
| | - Julian Selke
- Algorithmic Bioinformatics, Justus-Liebig-University Giessen, 35390 Giessen, Germany
| | - Wolfgang Trutschnig
- Department for Artificial Intelligence & Human Interfaces, Paris-Lodron-University Salzburg, 5020 Salzburg, Austria
| | - Robert R. Junker
- Department of Environment and Biodiversity, Paris-Lodron-University Salzburg, 5020 Salzburg, Austria
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, 35043 Marburg, Germany
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16
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Hoosein S, Neuenkamp L, Trivedi P, Paschke MW. AM fungal-bacterial relationships: what can they tell us about ecosystem sustainability and soil functioning? FRONTIERS IN FUNGAL BIOLOGY 2023; 4:1141963. [PMID: 37746131 PMCID: PMC10512368 DOI: 10.3389/ffunb.2023.1141963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/05/2023] [Indexed: 09/26/2023]
Abstract
Considering our growing population and our continuous degradation of soil environments, understanding the fundamental ecology of soil biota and plant microbiomes will be imperative to sustaining soil systems. Arbuscular mycorrhizal (AM) fungi extend their hyphae beyond plant root zones, creating microhabitats with bacterial symbionts for nutrient acquisition through a tripartite symbiotic relationship along with plants. Nonetheless, it is unclear what drives these AM fungal-bacterial relationships and how AM fungal functional traits contribute to these relationships. By delving into the literature, we look at the drivers and complexity behind AM fungal-bacterial relationships, describe the shift needed in AM fungal research towards the inclusion of interdisciplinary tools, and discuss the utilization of bacterial datasets to provide contextual evidence behind these complex relationships, bringing insights and new hypotheses to AM fungal functional traits. From this synthesis, we gather that interdependent microbial relationships are at the foundation of understanding microbiome functionality and deciphering microbial functional traits. We suggest using pattern-based inference tools along with machine learning to elucidate AM fungal-bacterial relationship trends, along with the utilization of synthetic communities, functional gene analyses, and metabolomics to understand how AM fungal and bacterial communities facilitate communication for the survival of host plant communities. These suggestions could result in improving microbial inocula and products, as well as a better understanding of complex relationships in terrestrial ecosystems that contribute to plant-soil feedbacks.
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Affiliation(s)
- Shabana Hoosein
- Department of Forest and Rangeland Stewardship/Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Lena Neuenkamp
- Institute of Landscape Ecology, Münster University, Münster, Germany
- Department of Ecology and Multidisciplinary Institute for Environment Studies “Ramon Margalef,” University of Alicante, Alicante, Spain
| | - Pankaj Trivedi
- Microbiome Network, Department of Agricultural Biology, Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Mark W. Paschke
- Department of Forest and Rangeland Stewardship/Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
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17
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Shu X, Liu W, Hu Y, Xia L, Fan K, Zhang Y, Zhang Y, Zhou W. Ecosystem multifunctionality and soil microbial communities in response to ecological restoration in an alpine degraded grassland. FRONTIERS IN PLANT SCIENCE 2023; 14:1173962. [PMID: 37593047 PMCID: PMC10431941 DOI: 10.3389/fpls.2023.1173962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 07/07/2023] [Indexed: 08/19/2023]
Abstract
Linkages between microbial communities and multiple ecosystem functions are context-dependent. However, the impacts of different restoration measures on microbial communities and ecosystem functioning remain unclear. Here, a 14-year long-term experiment was conducted using three restoration modes: planting mixed grasses (MG), planting shrub with Salix cupularis alone (SA), and planting shrub with Salix cupularis plus planting mixed grasses (SG), with an extremely degraded grassland serving as the control (CK). Our objective was to investigate how ecosystem multifunctionality and microbial communities (diversity, composition, and co-occurrence networks) respond to different restoration modes. Our results indicated that most of individual functions (i.e., soil nutrient contents, enzyme activities, and microbial biomass) in the SG treatment were significantly higher than in the CK treatment, and even higher than MG and SA treatments. Compared with the CK treatment, treatments MG, SA, and SG significantly increased the multifunctionality index on average by 0.57, 0.23 and 0.76, respectively. Random forest modeling showed that the alpha-diversity and composition of bacterial communities, rather than fungal communities, drove the ecosystem multifunctionality. Moreover, we found that both the MG and SG treatments significantly improved bacterial network stability, which exhabited stronger correlations with ecosystem multifunctionality compared to fungal network stability. In summary, this study demonstrates that planting shrub and grasses altogether is a promising restoration mode that can enhance ecosystem multifunctionality and improve microbial diversity and stability in the alpine degraded grassland.
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Affiliation(s)
- Xiangyang Shu
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Weijia Liu
- Institute of Agricultural Bioenvironment and Energy, Chengdu Academy of Agriculture and Forestry Sciences, Chengdu, China
| | - Yufu Hu
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Longlong Xia
- Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
| | - Kunkun Fan
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China
| | - Yanyan Zhang
- College of Resources, Sichuan Agricultural University, Chengdu, China
| | - Yulin Zhang
- Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Wei Zhou
- College of Resources, Sichuan Agricultural University, Chengdu, China
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18
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Wang Q, Nute M, Treangen TJ. Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities. Bioinformatics 2023; 39:i47-i56. [PMID: 37387148 DOI: 10.1093/bioinformatics/btad236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities. RESULTS We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions. AVAILABILITY AND IMPLEMENTATION Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.
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Affiliation(s)
- Qi Wang
- Systems, Synthetic, and Physical Biology (SSPB) Graduate Program, Rice University, Houston, TX 77005, United States
| | - Michael Nute
- Department of Computer Science, Rice University, Houston, TX 77005, United States
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX 77005, United States
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19
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Bonal M, Goetghebuer L, Joseph C, Gonze D, Faust K, George IF. Deciphering Interactions Within a 4-Strain Riverine Bacterial Community. Curr Microbiol 2023; 80:238. [PMID: 37294449 DOI: 10.1007/s00284-023-03342-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023]
Abstract
The dynamics of a community of four planktonic bacterial strains isolated from river water was followed in R2 broth for 72 h in batch experiments. These strains were identified as Janthinobacterium sp., Brevundimonas sp., Flavobacterium sp. and Variovorax sp. 16S rRNA gene sequencing and flow cytometry analyses were combined to monitor the change in abundance of each individual strain in bi-cultures and quadri-culture. Two interaction networks were constructed that summarize the impact of the strains on each other's growth rate in exponential phase and carrying capacity in stationary phase. The networks agree on the absence of positive interactions but also show differences, implying that ecological interactions can be specific to particular growth phases. Janthinobacterium sp. was the fastest growing strain and dominated the co-cultures. However, its growth rate was negatively affected by the presence of other strains 10 to 100 times less abundant than Janthinobacterium sp. In general, we saw a positive correlation between growth rate and carrying capacity in this system. In addition, growth rate in monoculture was predictive of carrying capacity in co-culture. Taken together, our results highlight the necessity to take growth phases into account when measuring interactions within a microbial community. In addition, evidence that a minor strain can greatly influence the dynamics of a dominant one underlines the necessity to choose population models that do not assume a linear dependency of interaction strength to abundance of other species for accurate parameterization from such empirical data.
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Affiliation(s)
- Mathias Bonal
- Laboratory of Ecology of Aquatic Systems, Brussels Bioengineering School, Université Libre de Bruxelles, 1050, Brussels, Belgium
- Laboratory of Molecular Bacteriology (Rega Institute), Department of Microbiology, Immunology and Transplantation, KU Leuven, 3000, Louvain, Belgium
| | - Lise Goetghebuer
- Laboratory of Ecology of Aquatic Systems, Brussels Bioengineering School, Université Libre de Bruxelles, 1050, Brussels, Belgium
| | - Clémence Joseph
- Laboratory of Molecular Bacteriology (Rega Institute), Department of Microbiology, Immunology and Transplantation, KU Leuven, 3000, Louvain, Belgium
| | - Didier Gonze
- Unit of Theoretical Chronobiology, Faculty of Sciences, Université Libre de Bruxelles, 1050, Brussels, Belgium
| | - Karoline Faust
- Laboratory of Molecular Bacteriology (Rega Institute), Department of Microbiology, Immunology and Transplantation, KU Leuven, 3000, Louvain, Belgium
| | - Isabelle F George
- Laboratory of Ecology of Aquatic Systems, Brussels Bioengineering School, Université Libre de Bruxelles, 1050, Brussels, Belgium.
- Laboratory of Marine Biology, Department of Biology, Université Libre de Bruxelles, 1050, Brussels, Belgium.
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20
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Benincà E, Pinto S, Cazelles B, Fuentes S, Shetty S, Bogaards JA. Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome. Sci Rep 2023; 13:8042. [PMID: 37198426 DOI: 10.1038/s41598-023-34713-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/05/2023] [Indexed: 05/19/2023] Open
Abstract
Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.
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Affiliation(s)
- Elisa Benincà
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
| | - Susanne Pinto
- Biomedical Data Sciences, Leiden UMC, Leiden, The Netherlands
| | - Bernard Cazelles
- CNRS UMR-8197, IBENS, Ecole Normale Supérieure, Paris, France
- Sorbonne Université, UMMISCO, Paris, France
| | - Susana Fuentes
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Sudarshan Shetty
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Medical Microbiology and Infection Prevention, UMC Groningen, Groningen, The Netherlands
| | - Johannes A Bogaards
- Department of Epidemiology & Data Science, Amsterdam UMC location VUMC, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam UMC, Amsterdam, The Netherlands
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21
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Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
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22
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Deutschmann IM, Krabberød AK, Latorre F, Delage E, Marrasé C, Balagué V, Gasol JM, Massana R, Eveillard D, Chaffron S, Logares R. Disentangling temporal associations in marine microbial networks. MICROBIOME 2023; 11:83. [PMID: 37081491 PMCID: PMC10120119 DOI: 10.1186/s40168-023-01523-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 03/19/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Microbial interactions are fundamental for Earth's ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network. RESULTS A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. CONCLUSIONS Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems. Video Abstract.
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Affiliation(s)
- Ina Maria Deutschmann
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
| | - Anders K Krabberød
- Department of Biosciences/Section for Genetics and Evolutionary Biology (EVOGENE), University of Oslo, p.b. 1066 Blindern, N-0316, Oslo, Norway
| | - Francisco Latorre
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
| | - Erwan Delage
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France
- Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, F-75016, Paris, France
| | - Cèlia Marrasé
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
| | - Vanessa Balagué
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
| | - Josep M Gasol
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
| | - Ramon Massana
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
| | - Damien Eveillard
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France
- Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, F-75016, Paris, France
| | - Samuel Chaffron
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France
- Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, F-75016, Paris, France
| | - Ramiro Logares
- Institute of Marine Sciences (ICM), CSIC, Passeig Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain.
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23
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Choy CT, Chan UK, Siu PLK, Zhou J, Wong CH, Lee YW, Chan HW, Tsui JCC, Loo SKF, Tsui SKW. A Novel E3 Probiotics Formula Restored Gut Dysbiosis and Remodelled Gut Microbial Network and Microbiome Dysbiosis Index (MDI) in Southern Chinese Adult Psoriasis Patients. Int J Mol Sci 2023; 24:ijms24076571. [PMID: 37047542 PMCID: PMC10094986 DOI: 10.3390/ijms24076571] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Psoriasis is a common chronic immune-mediated inflammatory skin disease with the association of various comorbidities. Despite the introduction of highly effective biologic therapies over the past few decades, the exact trigger for an immune reaction in psoriasis is unclear. With the majority of immune cells residing in the gut, the effect of gut microbiome dysbiosis goes beyond the gastrointestinal site and may exacerbate inflammation and regulate the immune system elsewhere, including but not limited to the skin via the gut-skin axis. In order to delineate the role of the gut microbiome in Southern Chinese psoriasis patients, we performed targeted 16S rRNA sequencing and comprehensive bioinformatic analysis to compare the gut microbiome profile of 58 psoriasis patients against 49 healthy local subjects presumably with similar lifestyles. Blautia wexlerae and Parabacteroides distasonis were found to be enriched in psoriasis patients and in some of the healthy subjects, respectively. Metabolic functional pathways were predicted to be differentially abundant, with a clear shift toward SCFA synthesis in healthy subjects. The alteration of the co-occurrence network was also evident in the psoriasis group. In addition, we also profiled the gut microbiome in 52 of the 58 recruited psoriasis patients after taking 8 weeks of an orally administrated novel E3 probiotics formula (with prebiotics, probiotics and postbiotics). The Dermatological Life Quality Index (p = 0.009) and Psoriasis Area and Severity Index (p < 0.001) were significantly improved after taking 8 weeks of probiotics with no adverse effect observed. We showed that probiotics could at least partly restore gut dysbiosis via the modulation of the gut microbiome. Here, we also report the potential application of a machine learning-derived gut dysbiosis index based on a quantitative PCR panel (AUC = 0.88) to monitor gut dysbiosis in psoriasis patients. To sum up, our study suggests the gut microbial landscape differed in psoriasis patients at the genera, species, functional and network levels. Additionally, the dysbiosis index could be a cost-effective and rapid tool to monitor probiotics use in psoriasis patients.
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Affiliation(s)
- Chi Tung Choy
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Un Kei Chan
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Pui Ling Kella Siu
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Junwei Zhou
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Chi Ho Wong
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Yuk Wai Lee
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | - Ho Wang Chan
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
| | | | - Steven King Fan Loo
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
- Hong Kong Institute of Integrative Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Dermatology Centre, CUHK Medical Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Stephen Kwok Wing Tsui
- Microbiome Research Centre, BioMed Laboratory Company Limited, Hong Kong, China
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Centre for Microbial Genomics and Proteomics, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Hong Kong, China
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24
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Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth. mSystems 2023; 8:e0083622. [PMID: 36815773 PMCID: PMC10134828 DOI: 10.1128/msystems.00836-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R2 of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.
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25
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Liu C, Li C, Jiang Y, Zeng RJ, Yao M, Li X. A guide for comparing microbial co-occurrence networks. IMETA 2023; 2:e71. [PMID: 38868345 PMCID: PMC10989802 DOI: 10.1002/imt2.71] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 06/14/2024]
Abstract
The article provides a pipeline for comparing microbial co-occurrence networks based on the R microeco package and meconetcomp package. It has high flexibility and expansibility and can help users efficiently compare networks built from different groups of samples or different construction approaches.
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Affiliation(s)
- Chi Liu
- Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhouChina
| | - Chaonan Li
- Key Laboratory of Environmental and Applied Microbiology, CAS, Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of BiologyChinese Academy of SciencesChengduChina
| | - Yanqiong Jiang
- Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhouChina
| | - Raymond J. Zeng
- Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhouChina
| | - Minjie Yao
- Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhouChina
| | - Xiangzhen Li
- Key Laboratory of Environmental and Applied Microbiology, CAS, Environmental Microbiology Key Laboratory of Sichuan Province, Chengdu Institute of BiologyChinese Academy of SciencesChengduChina
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26
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Endo H, Umezawa Y, Takeda S, Suzuki K. Haptophyte communities along the Kuroshio current reveal their geographical sources and ecological traits. Mol Ecol 2023; 32:110-123. [PMID: 36221794 DOI: 10.1111/mec.16734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 12/29/2022]
Abstract
Haptophytes are one of the most ecologically successful phytoplankton groups in the modern ocean and tend to maintain balanced and stable communities across various environments. However, little is known about the mechanisms that enable community stability and ecological success. To reveal the community characteristics and interactions among haptophytes, we conducted comprehensive observations from the upstream to downstream regions of the Kuroshio Current. Haptophyte abundance and taxonomy were assessed using quantitative polymerase chain reaction and metabarcoding of 18S rRNA sequences, respectively. The haptophyte community structure changed abruptly at sites on the shelf-slope of the East China Sea, indicating the strong influence of shelf waters with high phytoplankton biomass on downstream communities. Correlation network analysis combined with the phylogeny suggested that haptophytes can coexist with their close relatives, possibly owing to their nutritional flexibility, thereby escaping from resource competition. Consistently, some noncalcifying haptophyte genera with high mixotrophic capacities such as Chrysochromulina constituted a major component of the co-occurrence network, whereas coccolithophores such as Emiliania/Gephyrocapsa were rarely observed. Our study findings suggest that noncalcifying haptophytes play crucial roles in community diversity and stability, and in sustaining the food web structure in the Kuroshio ecosystems.
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Affiliation(s)
- Hisashi Endo
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Yu Umezawa
- Department of Environmental Science on Biosphere, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Shigenobu Takeda
- Faculty of Environmental Earth Science, Hokkaido University, Hokkaido, Sapporo, Japan
| | - Koji Suzuki
- Graduate School of Fisheries and Environmental Sciences, Nagasaki University, Nagasaki, Japan
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27
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Scarsella E, Jha A, Sandri M, Stefanon B. Network-based gut microbiome analysis in dogs. ITALIAN JOURNAL OF ANIMAL SCIENCE 2022. [DOI: 10.1080/1828051x.2022.2124932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Elisa Scarsella
- Dipartimento di Scienze Agroalimentari, Ambientali e Animali, University of Udine, Udine, Italy
| | - Aashish Jha
- Genetic Heritage Group, Program in Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Misa Sandri
- Dipartimento di Scienze Agroalimentari, Ambientali e Animali, University of Udine, Udine, Italy
| | - Bruno Stefanon
- Dipartimento di Scienze Agroalimentari, Ambientali e Animali, University of Udine, Udine, Italy
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28
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Puntin G, Sweet M, Fraune S, Medina M, Sharp K, Weis VM, Ziegler M. Harnessing the Power of Model Organisms To Unravel Microbial Functions in the Coral Holobiont. Microbiol Mol Biol Rev 2022; 86:e0005322. [PMID: 36287022 PMCID: PMC9769930 DOI: 10.1128/mmbr.00053-22] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Stony corals build the framework of coral reefs, ecosystems of immense ecological and economic importance. The existence of these ecosystems is threatened by climate change and other anthropogenic stressors that manifest in microbial dysbiosis such as coral bleaching and disease, often leading to coral mortality. Despite a significant amount of research, the mechanisms ultimately underlying these destructive phenomena, and what could prevent or mitigate them, remain to be resolved. This is mostly due to practical challenges in experimentation on corals and the highly complex nature of the coral holobiont that also includes bacteria, archaea, protists, and viruses. While the overall importance of these partners is well recognized, their specific contributions to holobiont functioning and their interspecific dynamics remain largely unexplored. Here, we review the potential of adopting model organisms as more tractable systems to address these knowledge gaps. We draw on parallels from the broader biological and biomedical fields to guide the establishment, implementation, and integration of new and emerging model organisms with the aim of addressing the specific needs of coral research. We evaluate the cnidarian models Hydra, Aiptasia, Cassiopea, and Astrangia poculata; review the fast-evolving field of coral tissue and cell cultures; and propose a framework for the establishment of "true" tropical reef-building coral models. Based on this assessment, we also suggest future research to address key aspects limiting our ability to understand and hence improve the response of reef-building corals to future ocean conditions.
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Affiliation(s)
- Giulia Puntin
- Department of Animal Ecology and Systematics, Marine Holobiomics Lab, Justus Liebig University Giessen, Giessen, Germany
| | - Michael Sweet
- Aquatic Research Facility, Environmental Sustainability Research Centre, University of Derby, Derby, United Kingdom
| | - Sebastian Fraune
- Institute for Zoology and Organismic Interactions, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mónica Medina
- Department of Biology, Pennsylvania State University, State College, Pennsylvania, USA
| | - Koty Sharp
- Department of Biology, Marine Biology, and Environmental Science, Roger Williams University, Bristol, Rhode Island, USA
| | - Virginia M. Weis
- Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
| | - Maren Ziegler
- Department of Animal Ecology and Systematics, Marine Holobiomics Lab, Justus Liebig University Giessen, Giessen, Germany
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29
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Bates KA, Friesen J, Loyau A, Butler H, Vredenburg VT, Laufer J, Chatzinotas A, Schmeller DS. Environmental and Anthropogenic Factors Shape the Skin Bacterial Communities of a Semi-Arid Amphibian Species. MICROBIAL ECOLOGY 2022:10.1007/s00248-022-02130-5. [PMID: 36445401 DOI: 10.1007/s00248-022-02130-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
The amphibian skin microbiome is important in maintaining host health, but is vulnerable to perturbation from changes in biotic and abiotic conditions. Anthropogenic habitat disturbance and emerging infectious diseases are both potential disrupters of the skin microbiome, in addition to being major drivers of amphibian decline globally. We investigated how host environment (hydrology, habitat disturbance), pathogen presence, and host biology (life stage) impact the skin microbiome of wild Dhofar toads (Duttaphrynus dhufarensis) in Oman. We detected ranavirus (but not Batrachochytrium dendrobatidis) across all sampling sites, constituting the first report of this pathogen in Oman, with reduced prevalence in disturbed sites. We show that skin microbiome beta diversity is driven by host life stage, water source, and habitat disturbance, but not ranavirus infection. Finally, although trends in bacterial diversity and differential abundance were evident in disturbed versus undisturbed sites, bacterial co-occurrence patterns determined through network analyses revealed high site specificity. Our results therefore provide support for amphibian skin microbiome diversity and taxa abundance being associated with habitat disturbance, with bacterial co-occurrence (and likely broader aspects of microbial community ecology) being largely site specific.
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Affiliation(s)
- K A Bates
- Department of Zoology, University of Oxford, Oxford, UK.
| | - J Friesen
- Centre for Environmental Biotechnology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - A Loyau
- Department of Experimental Limnology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany
- Laboratoire Écologie Fonctionnelle et Environnement, Université de Toulouse, INPT, UPS, Toulouse, France
| | - H Butler
- Department of Biology, San Francisco State University, San Francisco, CA, USA
| | - V T Vredenburg
- Department of Biology, San Francisco State University, San Francisco, CA, USA
| | - J Laufer
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - A Chatzinotas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
- Institute of Biology, Leipzig University, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany
| | - D S Schmeller
- Laboratoire Écologie Fonctionnelle et Environnement, Université de Toulouse, INPT, UPS, Toulouse, France
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30
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Tran HNH, Thu TNH, Nguyen PH, Vo CN, Doan KV, Nguyen Ngoc Minh C, Nguyen NT, Ta VND, Vu KA, Hua TD, Nguyen TNT, Van TT, Pham Duc T, Duong BL, Nguyen PM, Hoang VC, Pham DT, Thwaites GE, Hall LJ, Slade DJ, Baker S, Tran VH, Chung The H. Tumour microbiomes and Fusobacterium genomics in Vietnamese colorectal cancer patients. NPJ Biofilms Microbiomes 2022; 8:87. [PMID: 36307484 PMCID: PMC9616903 DOI: 10.1038/s41522-022-00351-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/14/2022] [Indexed: 12/24/2022] Open
Abstract
Perturbations in the gut microbiome have been associated with colorectal cancer (CRC), with the colonic overabundance of Fusobacterium nucleatum shown as the most consistent marker. Despite its significance in the promotion of CRC, genomic studies of Fusobacterium is limited. We enrolled 43 Vietnamese CRC patients and 25 participants with non-cancerous colorectal polyps to study the colonic microbiomes and genomic diversity of Fusobacterium in this population, using a combination of 16S rRNA gene profiling, anaerobic microbiology, and whole genome analysis. Oral bacteria, including F. nucleatum and Leptotrichia, were significantly more abundant in the tumour microbiomes. We obtained 53 Fusobacterium genomes, representing 26 strains, from the saliva, tumour and non-tumour tissues of six CRC patients. Isolates from the gut belonged to diverse F. nucleatum subspecies (nucleatum, animalis, vincentii, polymorphum) and a potential new subspecies of Fusobacterium periodonticum. The Fusobacterium population within each individual was distinct and in some cases diverse, with minimal intra-clonal variation. Phylogenetic analyses showed that within four individuals, tumour-associated Fusobacterium were clonal to those isolated from non-tumour tissues. Genes encoding major virulence factors (Fap2 and RadD) showed evidence of horizontal gene transfer. Our work provides a framework to understand the genomic diversity of Fusobacterium within the CRC patients, which can be exploited for the development of CRC diagnostic and therapeutic options targeting this oncobacterium.
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Affiliation(s)
- Hoang N H Tran
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | | | | | - Chi Nguyen Vo
- Binh Dan Hospital, Ho Chi Minh City, Vietnam
- Tan Tao University, Long An, Vietnam
| | - Khanh Van Doan
- Department of Oral Biology, Yonsei University College of Dentistry, Seoul, Korea
| | | | | | | | | | | | | | - Tan Trinh Van
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Trung Pham Duc
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | | | | | | | - Duy Thanh Pham
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Guy E Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Lindsay J Hall
- Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
- Intestinal Microbiome, School of Life Sciences, ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Daniel J Slade
- Department of Biochemistry, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Stephen Baker
- Department of Medicine, Cambridge Institute of Therapeutic Immunology and Infectious Diseases (CITIID), University of Cambridge, Cambridge, United Kingdom
| | | | - Hao Chung The
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.
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31
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Abstract
Despite an ever-growing number of data sets that catalog and characterize interactions between microbes in different environments and conditions, many of these data are neither easily accessible nor intercompatible. These limitations present a major challenge to microbiome research by hindering the streamlined drawing of inferences across studies. Here, we propose guiding principles to make microbial interaction data more findable, accessible, interoperable, and reusable (FAIR). We outline specific use cases for interaction data that span the diverse space of microbiome research, and discuss the untapped potential for new insights that can be fulfilled through broader integration of microbial interaction data. These include, among others, the design of intercompatible synthetic communities for environmental, industrial, or medical applications, and the inference of novel interactions from disparate studies. Lastly, we envision potential trajectories for the deployment of FAIR microbial interaction data based on existing resources, reporting standards, and current momentum within the community.
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Affiliation(s)
| | - Charlie Pauvert
- Functional Microbiome Research Group, Institute of Medical Microbiology, University Hospital of RWTH, Aachen, Germany
| | - Dileep Kishore
- Bioinformatics Program and Biological Design Center, Boston University, Boston, Massachusetts, USA
| | - Daniel Segrè
- Bioinformatics Program and Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biology, Department of Biomedical Engineering, Department of Physics, Boston University, Boston Massachusetts, USA
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Yang D, Kato H, Kawatsu K, Osada Y, Azuma T, Nagata Y, Kondoh M. Reconstruction of a Soil Microbial Network Induced by Stress Temperature. Microbiol Spectr 2022; 10:e0274822. [PMID: 35972265 PMCID: PMC9602341 DOI: 10.1128/spectrum.02748-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 08/01/2022] [Indexed: 01/04/2023] Open
Abstract
The microbial community is viewed as a network of diverse microorganisms connected by various interspecific interactions. While the stress gradient hypothesis (SGH) predicts that positive interactions are favored in more stressful environments, the prediction has been less explored in complex microbial communities due to the challenges of identifying interactions. Here, by applying a nonlinear time series analysis to the amplicon-based diversity time series data of the soil microbiota cultured under less stressful (30°C) or more stressful (37°C) temperature conditions, we show how the microbial network responds to temperature stress. While the genera that persisted only under the less stressful condition showed fewer positive effects, the genera that appeared only under the more stressful condition received more positive effects, in agreement with SGH. However, temperature difference also induced reconstruction of the community network, leading to an increased proportion of negative interactions at the whole-community level. The anti-SGH pattern can be explained by the stronger competition caused by increased metabolic rate and population densities. IMPORTANCE By combining amplicon-based diversity survey with recently developed nonlinear analytical tools, we successfully determined the interaction networks of more than 150 natural soil microbial genera under less or more temperature stress and explored the applicability of the stress gradient hypothesis to soil microbiota, shedding new light on the well-known hypothesis.
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Affiliation(s)
- Dailin Yang
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Hiromi Kato
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Kazutaka Kawatsu
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Yutaka Osada
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | | | - Yuji Nagata
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Michio Kondoh
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
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Lam TJ, Ye Y. Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes. Sci Rep 2022; 12:17482. [PMID: 36261472 PMCID: PMC9581956 DOI: 10.1038/s41598-022-22541-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023] Open
Abstract
The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.
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Affiliation(s)
- Tony J Lam
- Luddy School of Informatics, Computing and Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA
| | - Yuzhen Ye
- Luddy School of Informatics, Computing and Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA.
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Wu X, Wang C, Wang D, Huang YX, Yuan S, Meng F. Simultaneous methanogenesis and denitrification coupled with nitrifying biofilm for high-strength wastewater treatment: Performance and microbial mechanisms. WATER RESEARCH 2022; 225:119163. [PMID: 36206686 DOI: 10.1016/j.watres.2022.119163] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
A combined system consisting of an upflow blanket filter (UBF) and a moving-bed biofilm reactor (MBBR) was developed for the simultaneous removal of organic matters and ammonia from high-strength wastewater. With a constant COD of approximately 2000 mg/L and ammonium nitrogen in a series of concentrations (e.g., 50, 200 and 400 mg/L in stages I to III) of the influent wastewater, the removal efficiencies of COD, ammonium nitrogen and total nitrogen reached 96.10%-98.19%, 100%, and 79.12%-82.15%, respectively. With the increase of influent ammonia nitrogen concentration, the specific methanogenic activity of the UBF granules decreased significantly, while the specific denitrification rates of the UBF granules and specific nitrification rates of the MBBR biofilms increased significantly. Microbial community analysis showed that Methanobacterium and Methanosaeta were the dominant methanogens in the UBF granules, while Candidatus Competibacter, Thauera and Acinetobacter were identified as dominant denitrifiers. In addition, nitrifiers were enriched in MBBR biofilms at 11.33% and 13.87% of the average abundance of Nitrosomonas and Nitrospira, respectively, at stage III (influent ammonium at 400 mg/L, COD/NH4+-N = 5). The ecological network analysis, including full-networks and sub-networks, indicated that the interactions between methanogens and denitrifiers in the UBF granules were strong when the influent ammonium concentration reached 400 mg/L. No intensive interactions were observed among the functional bacteria in the MBBR biofilms over the entire operation. Overall, this study provides a new strategy for the application and construction of efficient biological processes to achieve simultaneous removal of organic matter and nitrogen for high-strength wastewater treatment.
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Affiliation(s)
- Xueshen Wu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China
| | - Chao Wang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China
| | - Depeng Wang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China
| | - Yu-Xi Huang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China
| | - Shasha Yuan
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China
| | - Fangang Meng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, PR China; National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Changsha, Hunan 410125, PR China.
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Hou L, Li J, Wang H, Chen Q, Su JQ, Gad M, Ahmed W, Yu CP, Hu A. Storm promotes the dissemination of antibiotic resistome in an urban lagoon through enhancing bio-interactions. ENVIRONMENT INTERNATIONAL 2022; 168:107457. [PMID: 35963060 DOI: 10.1016/j.envint.2022.107457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/07/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Antibiotic-resistance genes (ARGs) and resistant bacteria (ARB) are abundant in stormwater that could cause serious infections, posing a potential threat to public health. However, there is no inference about how stormwater contributes to ARG profiles as well as the dynamic interplay between ARGs and bacteria via vertical gene transfer (VGT) or horizontal gene transfer (HGT) in urban water ecosystems. In this study, the distribution of ARGs, their host communities, and the source and community assembly process of ARGs were investigated in Yundang Lagoon (China) via high-throughput quantitative PCR, 16S rRNA gene amplicon sequencing, and application of SourceTracker before, after and recovering from an extreme precipitation event (132.1 mm). The abundance of ARGs and mobile genetic elements (MGEs) was the highest one day after precipitation and then decreased 2 days after precipitation and so on. Based on SourceTracker and NMDS analysis, the ARG and bacterial communities in lagoon surface water from one day after precipitation were mainly contributed by the wastewater treatment plant (WWTP) influent and effluent. However, the contribution of WWTP to ARG communities was minor 11 days after the precipitation, suggesting that the storm promoted the ARG levels by introducing the input of ARGs, MGEs, and ARB from point and non-point sources, such as sewer overflow and land-applied manure. Based on a novel microbial network analysis framework, the contribution of positive biological interactions between ARGs and MGEs or bacteria was the highest one day after precipitation, indicating a promoted VGT and HGT for ARG dissemination. The microbial networks deconstructed 11 days after precipitation, suggesting the stormwater practices (e.g., tide gate opening, diversion channels, and pumping) alleviated the spread of ARGs. These results advanced our understanding of the distribution and transport of ARGs associated with their source in urban stormwater runoff.
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Affiliation(s)
- Liyuan Hou
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jiangwei Li
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China; Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Hongjie Wang
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China; Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Qingfu Chen
- Yundang Lake Management Center, Xiamen, Fujian 361004, China
| | - Jian-Qiang Su
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, China
| | - Mahmoud Gad
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Water Pollution Research Department, National Research Centre, Giza 12622, Egypt
| | - Warish Ahmed
- CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Qld 4102, Australia
| | - Chang-Ping Yu
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Anyi Hu
- CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Fujian Key Laboratory of Watershed Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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Species abundance correlations carry limited information about microbial network interactions. PLoS Comput Biol 2022; 18:e1010491. [PMID: 36084152 PMCID: PMC9518925 DOI: 10.1371/journal.pcbi.1010491] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 09/28/2022] [Accepted: 08/15/2022] [Indexed: 11/25/2022] Open
Abstract
Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation. The bacteria in and on our body (the human microbiome) largely determine how our body functions, and whether we stay healthy or get sick. These bacteria do not live on their own, but interact among each other and with their human host. Finding out which bacteria interact with each other is cumbersome, but patterns of joint occurrence between species might provide a clue to their ecological dependencies. We investigated whether correlations in species abundance can be used for the purpose of ecological network reconstruction. We simulated different bacterial communities with known interactions according to a theoretical population model. After having collected virtual samples from our simulated data, we performed a correlation analysis and then compared the correlation network with our known interaction network. We found that correlations can be informative for underlying interactions, but ecological conclusions should be drawn carefully. An obvious limitation of correlation analysis is that direction of interaction cannot be recovered from co-occurrence data, making correlations insensitive for detection of asymmetric interactions. In addition, we found that competitive and exploitative interactions can induce positive as well as negative correlations. We recommend careful interpretation and validation when inferring networks from cross-sectional abundance data.
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Ko YJ, Kim S, Pan CH, Park K. Identification of Functional Microbial Modules Through Network-Based Analysis of Meta-Microbial Features Using Matrix Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2851-2862. [PMID: 34329170 DOI: 10.1109/tcbb.2021.3100893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As the microbiome is composed of a variety of microbial interactions, it is imperative in microbiome research to identify a microbial sub-community that collectively conducts a specific function. However, current methodologies have been highly limited to analyzing conditional abundance changes of individual microorganisms without considering group-wise collective microbial features. To overcome this limitation, we developed a network-based method using nonnegative matrix factorization (NMF) to identify functional meta-microbial features (MMFs) that, as a group, better discriminate specific environmental conditions of samples using microbiome data. As proof of concept, large-scale human microbiome data collected from different body sites were used to identify body site-specific MMFs by applying NMF. The statistical test for MMFs led us to identify highly discriminative MMFs on sample classes, called synergistic MMFs (SYMMFs). Finally, we constructed a SYMMF-based microbial interaction network (SYMMF-net) by integrating all of the SYMMF information. Network analysis revealed core microbial modules closely related to critical sample properties. Similar results were also found when the method was applied to various disease-associated microbiome data. The developed method interprets high-dimensional microbiome data by identifying functional microbial modules on sample properties and intuitively representing their systematic relationships via a microbial network.
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Kundu P, Mondal S, Ghosh A. Bacterial species metabolic interaction network for deciphering the lignocellulolytic system in fungal cultivating termite gut microbiota. Biosystems 2022; 221:104763. [PMID: 36029916 DOI: 10.1016/j.biosystems.2022.104763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 11/25/2022]
Abstract
Fungus-cultivating termite Odontotermes badius developed a mutualistic association with Termitomyces fungi for the plant material decomposition and providing a food source for the host survival. The mutualistic relationship sifted the microbiome composition of the termite gut and Termitomyces fungal comb. Symbiotic bacterial communities in the O. badius gut and fungal comb have been studied extensively to identify abundant bacteria and their lignocellulose degradation capabilities. Despite several metagenomic studies, the species-wide metabolic interaction pattern of bacterial communities in termite gut and fungal comb remains unclear. The bacterial species metabolic interaction network (BSMIN) has been constructed with 230 bacteria identified from the O. badius gut and fungal comb microbiota. The network portrayed the metabolic map of the entire microbiota and highlighted several inter-species biochemical interactions like cross-feeding, metabolic interdependency, and competition. Further, the reconstruction and analysis of the bacterial influence network (BIN) quantified the positive and negative pairwise influences in the termite gut and fungal comb microbial communities. Several key macromolecule degraders and fermentative microbial entities have been identified by analyzing the BIN. The mechanistic interplay between these influential microbial groups and the crucial glycoside hydrolases (GH) enzymes produced by the macromolecule degraders execute the community-wide functionality of lignocellulose degradation and subsequent fermentation. The metabolic interaction pattern between the nine influential microbial species has been determined by considering them growing in a synthetic microbial community. Competition (30%), parasitism (47%), and mutualism (17%) were predicted to be the major mode of metabolic interaction in this synthetic microbial community. Further, the antagonistic metabolic effect was found to be very high in the metabolic-deprived condition, which may disrupt the community functionality. Thus, metabolic interactions of the crucial bacterial species and their GH enzyme cocktail identified from the O. badius gut and fungal comb microbiota may provide essential knowledge for developing a synthetic microcosm with efficient lignocellulolytic machinery.
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Affiliation(s)
- Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, 721302, India.
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Griessenberger F, Trutschnig W, Junker RR. qad
: An R‐package to detect asymmetric and directed dependence in bivariate samples. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
| | | | - Robert R. Junker
- Department of Environment and Biodiversity University of Salzburg Salzburg Austria
- Evolutionary Ecology of Plants, Department of Biology Philipps‐University Marburg Marburg Germany
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40
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Wang C, Lin Q, Yao Y, Xu R, Wu X, Meng F. Achieving simultaneous nitrification, denitrification, and phosphorus removal in pilot-scale flow-through biofilm reactor with low dissolved oxygen concentrations: Performance and mechanisms. BIORESOURCE TECHNOLOGY 2022; 358:127373. [PMID: 35623607 DOI: 10.1016/j.biortech.2022.127373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/18/2022] [Accepted: 05/21/2022] [Indexed: 06/15/2023]
Abstract
In this pilot-scale study, a flow-through biofilm reactor (FTBR) was investigated for municipal wastewater treatment. The removal efficiencies for ammonium, total nitrogen, total phosphorus, and chemical oxygen demand were 87.2 ± 17.9%, 61.1 ± 13.9%, 83.5 ± 11.9%, and 92.6 ± 1.7%, respectively, at low dissolved oxygen concentrations (averaged at 0.59 mg/L), indicating the feasibility and robustness of the FTBR for a simultaneous nitrification, denitrification, and phosphorous removal (SNDPR) process. The co-occurrence network of bacteria in the dynamic biofilm was complex, with equivalent bacterial cooperation and competition. Nevertheless, the bacterial interactions in the suspended sludge were mainly cooperative. The presence of dynamic biofilms increased bacterial diversity by creating niche differentiation, which enriched keystone species closely related to nutrient removal. Overall, this study provides a novel FTBR-based SNDPR process and reveals the ecological mechanisms responsible for nutrient removal.
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Affiliation(s)
- Chao Wang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China
| | - Qining Lin
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China
| | - Yuanyuan Yao
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China
| | - Ronghua Xu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China
| | - Xueshen Wu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China
| | - Fangang Meng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, PR China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Guangzhou 510006, PR China.
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Mercado JV, Koyama M, Nakasaki K. Co-occurrence network analysis reveals loss of microbial interactions in anaerobic digester subjected to repeated organic load shocks. WATER RESEARCH 2022; 221:118754. [PMID: 35759844 DOI: 10.1016/j.watres.2022.118754] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
Fluctuations in the anaerobic digestion (AD) organic loading rate (OLR) cause shocks to the AD microbiome, which lead to unstable methane productivity. Managing these fluctuations requires a larger digester, which is impractical for community-scale applications, limiting the potential of AD in advancing a circular economy. To allow operation of small-scale AD while managing OLR fluctuations, we need to tackle the issue through elucidation of the microbial community dynamics via 16S rRNA gene sequencing. This study elucidated the interrelation of the AD performance and the dynamics of the microbial interactions within its microbiome in response to repeated high OLR shocks at different frequencies. The OLR shocks were equivalent to 4 times the baseline OLR of 2 g VS/L/d. We found that less frequent organic load shocks result to deterioration of methane productivity. Co-occurrence network analysis shows that this coincides with the breakdown of the microbiome network structure. This suggests loss of microbial interactions necessary in maintaining stable AD. Identification of species influencing the network structure revealed that a species under the genus Anaerovorax has the greatest influence, while orders Spirochaetales and Synergistales represent the greatest number of the influential species. We inferred that the impact imposed by the OLR shocks shifted the microbiome activity towards biochemical pathways that are not contributing to methane production. Establishing a small-scale AD system that permits OLR fluctuations would require developing an AD microbiome resilient to infrequent organic loading shocks.
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Affiliation(s)
- Jericho Victor Mercado
- School of Environment and Society, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Mitsuhiko Koyama
- School of Environment and Society, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Kiyohiko Nakasaki
- School of Environment and Society, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
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Lee KK, Kim H, Lee YH. Cross-kingdom co-occurrence networks in the plant microbiome: Importance and ecological interpretations. Front Microbiol 2022; 13:953300. [PMID: 35958158 PMCID: PMC9358436 DOI: 10.3389/fmicb.2022.953300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/05/2022] [Indexed: 12/04/2022] Open
Abstract
Microbial co-occurrence network analysis is being widely used for data exploration in plant microbiome research. Still, challenges lie in how well these microbial networks represent natural microbial communities and how well we can interpret and extract eco-evolutionary insights from the networks. Although many technical solutions have been proposed, in this perspective, we touch on the grave problem of kingdom-level bias in network representation and interpretation. We underscore the eco-evolutionary significance of using cross-kingdom (bacterial-fungal) co-occurrence networks to increase the network’s representability of natural communities. To do so, we demonstrate how ecosystem-level interpretation of plant microbiome evolution changes with and without multi-kingdom analysis. Then, to overcome oversimplified interpretation of the networks stemming from the stereotypical dichotomy between bacteria and fungi, we recommend three avenues for ecological interpretation: (1) understanding dynamics and mechanisms of co-occurrence networks through generalized Lotka-Volterra and consumer-resource models, (2) finding alternative ecological explanations for individual negative and positive fungal-bacterial edges, and (3) connecting cross-kingdom networks to abiotic and biotic (host) environments.
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Affiliation(s)
- Kiseok Keith Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea
| | - Hyun Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea
| | - Yong-Hwan Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, South Korea
- Interdisciplinary Program in Agricultural Genomics, Seoul National University, Seoul, South Korea
- Center for Plant Microbiome Research, Seoul National University, Seoul, South Korea
- Plant Immunity Research Center, Seoul National University, Seoul, South Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, South Korea
- *Correspondence: Yong-Hwan Lee,
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43
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Chiyomaru K, Takemoto K. Adversarial attacks on voter model dynamics in complex networks. Phys Rev E 2022; 106:014301. [PMID: 35974603 DOI: 10.1103/physreve.106.014301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
This paper investigates adversarial attacks conducted to distort voter model dynamics in complex networks. Specifically, a simple adversarial attack method is proposed to hold the state of opinions of an individual closer to the target state in the voter model dynamics. This indicates that even when one opinion is the majority the vote outcome can be inverted (i.e., the outcome can lean toward the other opinion) by adding extremely small (hard-to-detect) perturbations strategically generated in social networks. Adversarial attacks are relatively more effective in complex (large and dense) networks. These results indicate that opinion dynamics can be unknowingly distorted.
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Affiliation(s)
- Katsumi Chiyomaru
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
| | - Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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Favila N, Madrigal-Trejo D, Legorreta D, Sánchez-Pérez J, Espinosa-Asuar L, Eguiarte LE, Souza V. MicNet toolbox: Visualizing and unraveling a microbial network. PLoS One 2022; 17:e0259756. [PMID: 35749381 PMCID: PMC9231805 DOI: 10.1371/journal.pone.0259756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 04/05/2022] [Indexed: 11/19/2022] Open
Abstract
Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code's functions with ease.
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Affiliation(s)
- Natalia Favila
- Laboratorio de Inteligencia Artificial, Ixulabs, Mexico City, Mexico
| | - David Madrigal-Trejo
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Daniel Legorreta
- Laboratorio de Inteligencia Artificial, Ixulabs, Mexico City, Mexico
| | - Jazmín Sánchez-Pérez
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Laura Espinosa-Asuar
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Luis E. Eguiarte
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Valeria Souza
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Centro de Estudios del Cuaternario de Fuego-Patagonia y Antártica (CEQUA), Punta Arenas, Chile
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45
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Guseva K, Darcy S, Simon E, Alteio LV, Montesinos-Navarro A, Kaiser C. From diversity to complexity: Microbial networks in soils. SOIL BIOLOGY & BIOCHEMISTRY 2022; 169:108604. [PMID: 35712047 PMCID: PMC9125165 DOI: 10.1016/j.soilbio.2022.108604] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 05/07/2023]
Abstract
Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.
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Affiliation(s)
- Ksenia Guseva
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
- Corresponding author.
| | - Sean Darcy
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | - Eva Simon
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, Austria
| | - Lauren V. Alteio
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | - Alicia Montesinos-Navarro
- Centro de Investigaciones sobre Desertificación (CIDE, CSIC-UV-GV), Carretera de Moncada-Náquera Km 4.5, 46113, Moncada, Valencia, Spain
| | - Christina Kaiser
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
- Corresponding author.
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46
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Hu Y, Amir A, Huang X, Li Y, Huang S, Wolfe E, Weiss S, Knight R, Xu ZZ. Diurnal and eating-associated microbial patterns revealed via high-frequency saliva sampling. Genome Res 2022; 32:1112-1123. [PMID: 35688483 DOI: 10.1101/gr.276482.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 05/18/2022] [Indexed: 11/24/2022]
Abstract
The oral microbiome is linked to oral and systemic health, but its fluctuation under frequent daily activities remains elusive. Here, we sampled saliva at 10- to 60-min intervals to track the high-resolution microbiome dynamics during the course of human activities. This dense time series data showed that eating activity markedly perturbed the salivary microbiota, with tongue-specific Campylobacter concisus and Oribacterium sinus and dental plaque-specific Lautropia mirabilis, Rothia aeria, and Neisseria oralis increased after every meal in a temporal order. The observation was reproducible in multiple subjects and across an 11-mo period. The microbiome composition showed significant diurnal oscillation patterns at different taxonomy levels with Prevotella/Alloprevotella increased at night and Bergeyella HMT 206/Haemophilus slowly increased during the daytime. We also identified microbial co-occurring patterns in saliva that are associated with the intricate biogeography of the oral microbiome. Microbial source tracking analysis showed that the contributions of distinct oral niches to the salivary microbiome were dynamically affected by daily activities, reflecting the role of saliva in exchanging microbes with other oral sites. Collectively, our study provides insights into the temporal microbiome variation in saliva and highlights the need to consider daily activities and diurnal factors in design of oral microbiome studies.
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Affiliation(s)
- Yichen Hu
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Amnon Amir
- Department of Pediatrics, University of California San Diego, La Jolla, California 92093, USA.,Sheba Medical Center, Ramat Gan 52621, Israel
| | - Xiaochang Huang
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Yan Li
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, PR China
| | - Shi Huang
- Department of Pediatrics, University of California San Diego, La Jolla, California 92093, USA
| | - Elaine Wolfe
- Department of Pediatrics, University of California San Diego, La Jolla, California 92093, USA
| | - Sophie Weiss
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, Colorado 80309, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California 92093, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California 92093, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California 92093, USA
| | - Zhenjiang Zech Xu
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, Jiangxi 330047, PR China.,Shenzhen Stomatology Hospital (Pingshan), Southern Medical University, Shenzhen 518001, China.,Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, China
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47
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Metataxonomic signature of beef burger perishability depends on the meat origin prior grinding. Food Res Int 2022; 156:111103. [DOI: 10.1016/j.foodres.2022.111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/22/2022]
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48
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Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model. Diagnostics (Basel) 2022; 12:diagnostics12040958. [PMID: 35454006 PMCID: PMC9029337 DOI: 10.3390/diagnostics12040958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/24/2022] [Accepted: 03/04/2022] [Indexed: 02/04/2023] Open
Abstract
Through a multitude of studies, the gut microbiota has been recognized as a significant influencer of both homeostasis and pathophysiology. Certain microbial taxa can even affect treatments such as cancer immunotherapies, including the immune checkpoint blockade. These taxa can impact such processes both individually as well as collectively through mechanisms from quorum sensing to metabolite production. Due to this overarching presence of the gut microbiota in many physiological processes distal to the GI tract, we hypothesized that mice bearing tumors at extraintestinal sites would display a distinct intestinal microbial signature from non-tumor-bearing mice, and that such a signature would involve taxa that collectively shift with tumor presence. Microbial OTUs were determined from 16S rRNA genes isolated from the fecal samples of C57BL/6 mice challenged with either B16-F10 melanoma cells or PBS control and analyzed using QIIME. Relative proportions of bacteria were determined for each mouse and, using machine-learning approaches, significantly altered taxa and co-occurrence patterns between tumor- and non-tumor-bearing mice were found. Mice with a tumor had elevated proportions of Ruminococcaceae, Peptococcaceae.g_rc4.4, and Christensenellaceae, as well as significant information gains and ReliefF weights for Bacteroidales.f__S24.7, Ruminococcaceae, Clostridiales, and Erysipelotrichaceae. Bacteroidales.f__S24.7, Ruminococcaceae, and Clostridiales were also implicated through shifting co-occurrences and PCA values. Using these seven taxa as a melanoma signature, a neural network reached an 80% tumor detection accuracy in a 10-fold stratified random sampling validation. These results indicated gut microbial proportions as a biosensor for tumor detection, and that shifting co-occurrences could be used to reveal relevant taxa.
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Chen L, Wan H, He Q, He S, Deng M. Statistical Methods for Microbiome Compositional Data Network Inference: A Survey. J Comput Biol 2022; 29:704-723. [PMID: 35404093 DOI: 10.1089/cmb.2021.0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Microbes can be found almost everywhere in the world. They are not isolated, but rather interact with each other and establish connections with their living environments. Studying these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A widely used approach toward this objective involves the inference of microbiome interaction networks. However, owing to the compositional, high-dimensional, sparse, and heterogeneous nature of observed microbial data, applying network inference methods to estimate their associations is challenging. In addition, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this article, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks, and differential networks. Their assumptions, high-level ideas, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial interaction study and future prospects. Finally, we point out several feasible directions of microbial network inference analysis and highlight that future research requires the joint promotion of statistical computation methods and experimental techniques.
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Affiliation(s)
- Liang Chen
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Hui Wan
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Qiuyan He
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Shun He
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China.,Center for Statistical Science, Peking University, Beijing, China.,Center for Quantitative Biology, Peking University, Beijing, China
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50
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Peimbert M, Alcaraz LD. Where environmental microbiome meets its host: subway and passenger microbiome relationships. Mol Ecol 2022; 32:2602-2618. [PMID: 35318755 DOI: 10.1111/mec.16440] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 12/17/2022]
Abstract
Subways are urban transport systems with high capacity. Every day around the world, there are more than 150 million subway passengers. Since 2013, thousands of microbiome samples from various subways worldwide have been sequenced. Skin bacteria and environmental organisms dominate the subway microbiomes. The literature has revealed common bacterial groups in subway systems; even so, it is possible to identify cities by their microbiome. Low-frequency bacteria are responsible for specific bacterial fingerprints of each subway system. Furthermore, daily subway commuters leave their microbial clouds and interact with other passengers. Microbial exchange is quite fast; the hand microbiome changes within minutes, and after cleaning the handrails, the bacteria are re-established within minutes. To investigate new taxa and metabolic pathways of subway microbial communities, several high-quality metagenomic-assembled genomes (MAG) have been described. Subways are harsh environments unfavorable for microorganism growth. However, recent studies have observed a wide diversity of viable and metabolically active bacteria. Understanding which bacteria are living, dormant, or dead allows us to propose realistic ecological interactions. Questions regarding the relationship between humans and the subway microbiome, particularly the microbiome effects on personal and public health, remain unanswered. This review summarizes our knowledge of subway microbiomes and their relationship with passenger microbiomes.
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Affiliation(s)
- Mariana Peimbert
- Departamento de Ciencias Naturales, Unidad Cuajimalpa, Universidad Autónoma Metropolitana. Ciudad de México, México
| | - Luis D Alcaraz
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, México
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