The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis
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ABSTRACT:
SUBMITTER: Yu S
PROVIDER: S-EPMC10570731 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Microbial communities that degrade lignocellulosic biomass are typified by high levels of species- and strain-level complexity, as well as synergistic interactions between both cellulolytic and non-cellulolytic microorganisms. Here we deconvoluted a highly efficient cellulose-degrading and methanogenic consortium (SEM1b) that is co-dominated by Clostridium (Ruminiclostridium) thermocellum and multiple heterogenic strains affiliated to C. proteolyticus. A time-series analysis was performed over the entire lifetime span of the microbial community and comprised of metagenomic, metatranscriptomic, metabolomics, metaproteomic and 16S rRNA gene analysis for 8 time points, in triplicate. Metagenomic analysis of SEM1b recovered metagenome-assembled genomes (MAGs) for each constituent population, whereas in parallel two novel strains of C. proteolyticus were isolated and sequenced. Both the recovered MAGs and the isolated strains were used as a database for further functional meta-omics. Absolute quantitative metatranscriptomics was performed thanks the spike-in of an in vitro transcribed RNA as an internal standard and label-free quantification was used for the metaproteomic analysis. The present dataset has been used for several publications. The first aim of the project was to characterize the interactions between uncultured populations in a lignocellulose-degrading community. Furthermore, because of the in-depth multi-omics characterization of the community, the dataset was used to develop new approaches for meta-omics integration as well as to assess the protein-to-RNA ratio of multiple microbial populations simultaneously. Modifications of multi-omics toolkits allowed us to assess the linearity between transcriptome and proteome for each population over time and reveal deeper functional-related trends and integrative co-dependent metabolisms that drive the overall phenotype of microbial communities.
2020-08-21 | PXD016242 | Pride
Project description:There is a growing interest in controlling-promoting or avoiding-the invasion of microbial communities by new community members. Resource availability and community structure have been reported as determinants of invasion success. However, most invasion studies do not adhere to a coherent and consistent terminology nor always include rigorous interpretations of the processes behind invasion. Therefore, we suggest that a consistent set of definitions and a rigorous conceptual framework are needed. We define invasion in a microbial community as the establishment of an alien microbial type in a resident community and argue how simple criteria to define aliens, residents, and alien establishment can be applied for a wide variety of communities. In addition, we suggest an adoption of the community ecology framework advanced by Vellend (2010) to clarify potential determinants of invasion. This framework identifies four fundamental processes that control community dynamics: dispersal, selection, drift and diversification. While selection has received ample attention in microbial community invasion research, the three other processes are often overlooked. Here, we elaborate on the relevance of all four processes and conclude that invasion experiments should be designed to elucidate the role of dispersal, drift and diversification, in order to obtain a complete picture of invasion as a community process.
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Project description:Recent work with microbial communities has demonstrated an adaptive response to artificial selection at the level of the ecosystem. The reasons for this response and the level at which adaptation occurs are unclear: does selection act implicitly on traits of individual species, or are higher-level traits genuinely being selected? If the ecosystem response is just the additive combination of the responses of the constituent species, then the ecosystem response could be predicted a priori, and the ecosystem-level selection process is superfluous. However, if the ecosystem response results from ecological interactions among species, then selection at a higher level is necessary. Here we perform artificial ecosystem selection experiments on an individual-based evolutionary simulation model of microbial ecology and observe a similar response to that seen with real ecosystems. We demonstrate that a significant fraction of artificially selected ecosystem responses cannot be accounted for by implicit lower-level selection of a single type of organism within the community, and that interactions among different types of organism contribute significantly to the response in the majority of cases. However, when the ecological problem posed by the artificial ecosystem selection process can be easily solved by a single dominant species, it often is.
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Project description:Microbial communities play essential and preponderant roles in all ecosystems. Understanding the rules that govern microbial community assembly will have a major impact on our ability to manage microbial ecosystems, positively impacting, for instance, human health and agriculture. Here, I present a phylogenetically constrained community assembly principle grounded on the well-supported facts that deterministic processes have a significant impact on microbial community assembly, that microbial communities show significant phylogenetic signal, and that microbial traits and ecological coherence are, to some extent, phylogenetically conserved. From these facts, I derive a few predictions which form the basis of the framework. Chief among them is the existence, within most microbial ecosystems, of phylogenetic core groups (PCGs), defined as discrete portions of the phylogeny of varying depth present in all instances of the given ecosystem, and related to specific niches whose occupancy requires a specific phylogenetically conserved set of traits. The predictions are supported by the recent literature, as well as by dedicated analyses. Integrating the effect of ecosystem patchiness, microbial social interactions, and scale sampling pitfalls takes us to a comprehensive community assembly model that recapitulates the characteristics most commonly observed in microbial communities. PCGs' identification is relatively straightforward using high-throughput 16S amplicon sequencing, and subsequent bioinformatic analysis of their phylogeny, estimated core pan-genome, and intra-group co-occurrence should provide valuable information on their ecophysiology and niche characteristics. Such a priori information for a significant portion of the community could be used to prime complementing analyses, boosting their usefulness. Thus, the use of the proposed framework could represent a leap forward in our understanding of microbial community assembly and function.
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Project description:Microbial communities, acting as key drivers of ecosystem processes, harbour immense potential for sustainable agriculture practices. Phosphate-solubilising microorganisms, for example, can partially replace conventional phosphate fertilisers, which rely on finite resources. However, understanding the mechanisms and engineering efficient communities poses a significant challenge. In this study, we employ two artificial selection methods, environmental perturbation, and propagation, to construct phosphate-solubilising microbial communities. To assess trait transferability, we investigate the community performance in different media and a hydroponic system with Chrysanthemum indicum. Our findings reveal a distinct subset of phosphate-solubilising bacteria primarily dominated by Klebsiella and Enterobacterales. The propagated communities consistently demonstrate elevated levels of phosphate solubilisation, surpassing the starting soil community by 24.2% in activity. The increased activity of propagated communities remains consistent upon introduction into the hydroponic system. This study shows the efficacy of community-level artificial selection, particularly through propagation, as a tool for successfully modifying microbial communities to enhance phosphate solubilisation.
| S-EPMC10884399 | biostudies-literature
Project description:IntroductionShock-induced endotheliopathy (SHINE), defined as a profound sympathoadrenal hyperactivation in shock states leading to endothelial activation, glycocalyx damage, and eventual compromise of end-organ perfusion, was first described in 2017. The aggressive resuscitation therapies utilised in treating shock states could potentially lead to further worsening endothelial activation and end-organ dysfunction.ObjectiveThis study aimed to systematically review the literature on resuscitation-associated and resuscitation-induced endotheliopathy.MethodsA predetermined structured search of literature published over an 11-year and 6-month period (1 January 2011 to 31 July 2023) was performed in two indexed databases (PubMed/MEDLINE and Embase) per PRISMA guidelines. Inclusion was restricted to original studies published in English (or with English translation) reporting on endothelial dysfunction in critically ill human subjects undergoing resuscitation interventions. Reviews or studies conducted in animals were excluded. Qualitative synthesis of studies meeting the inclusion criteria was performed. Studies reporting comparable biomarkers of endothelial dysfunction post-resuscitation were included in the quantitative meta-analysis.ResultsThirty-two studies met the inclusion criteria and were included in the final qualitative synthesis. Most of these studies (47%) reported on a combination of mediators released from endothelial cells and biomarkers of glycocalyx breakdown, while only 22% reported on microvascular flow changes. Only ten individual studies were included in the quantitative meta-analysis based on the comparability of the parameters assessed. Eight studies measured syndecan-1, with a heterogeneity index, I2 = 75.85% (pooled effect size, mean = 0.27; 95% CI - 0.07 to 0.60; p = 0.12). Thrombomodulin was measured in four comparable studies (I2 = 78.93%; mean = 0.41; 95% CI - 0.10 to 0.92; p = 0.12). Three studies measured E-selectin (I2 = 50.29%; mean = - 0.15; 95% CI - 0.64 to 0.33; p = 0.53), and only two were comparable for the microvascular flow index, MFI (I2 = 0%; mean = - 0.80; 95% CI - 1.35 to - 0.26; p < 0.01).ConclusionResuscitation-associated endotheliopathy (RAsE) refers to worsening endothelial dysfunction resulting from acute resuscitative therapies administered in shock states. In the included studies, syndecan-1 had the highest frequency of assessment in the post-resuscitation period, and changes in concentrations showed a statistically significant effect of the resuscitation. There are inadequate data available in this area, and further research and standardisation of the ideal assessment and panel of biomarkers are urgently needed.
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Project description:Interventions: healthy people, intestinal polyp group and intestinal cancer group.:Nil
Primary outcome(s): bacteria;fungi;phages
Study Design: Factorial
| 2702334 | ecrin-mdr-crc
Project description:IntroductionThe USA has the highest rate of community gun violence of any developed democracy. There is an urgent need to develop feasible, scalable and community-led interventions that mitigate incident gun violence and its associated health impacts. Our community-academic research team received National Institutes of Health funding to design a community-led intervention that mitigates the health impacts of living in communities with high rates of gun violence.Methods and analysisWe adapted 'Building Resilience to Disasters', a conceptual framework for natural disaster preparedness, to guide actions of multiple sectors and the broader community to respond to the man-made disaster of gun violence. Using this framework, we will identify existing community assets to be building blocks of future community-led interventions. To identify existing community assets, we will conduct social network and spatial analyses of the gun violence episodes in our community and use these analyses to identify people and neighbourhood blocks that have been successful in avoiding gun violence. We will conduct qualitative interviews among a sample of individuals in the network that have avoided violence (n=45) and those living or working on blocks that have not been a location of victimisation (n=45) to identify existing assets. Lastly, we will use community-based system dynamics modelling processes to create a computer simulation of the community-level contributors and mitigators of the effects of gun violence that incorporates local population-based based data for calibration. We will engage a multistakeholder group and use themes from the qualitative interviews and the computer simulation to identify feasible community-led interventions.Ethics and disseminationThe Human Investigation Committee at Yale University School of Medicine (#2000022360) granted study approval. We will disseminate study findings through peer-reviewed publications and academic and community presentations. The qualitative interview guides, system dynamics model and group model building scripts will be shared broadly.
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Project description:Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840-0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
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