Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.
Ontology highlight
ABSTRACT:
SUBMITTER: Martino C
PROVIDER: S-EPMC7878194 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.
Martino Cameron C Shenhav Liat L Marotz Clarisse A CA Armstrong George G McDonald Daniel D Vázquez-Baeza Yoshiki Y Morton James T JT Jiang Lingjing L Dominguez-Bello Maria Gloria MG Swafford Austin D AD Halperin Eran E Knight Rob R
Nature biotechnology 20200831 2
The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are r ...[more]
Similar Datasets
Project description:Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.
| S-EPMC9677468 | biostudies-literature
Project description:Gut microbiota stimulates the immune system and inhibits pathogens, and thus, it is critical for disease prevention. Probiotics represent an effective alternative to antibiotics used for the therapy and prevention of bacterial diseases. Probiotic bacteria are commonly used in vertebrates, although their use in invertebrates is still rare. We manipulated the gut microbiome of the African Armyworm (Spodoptera exempta Walker) using antibiotics and field-collected frass, in an attempt to understand the interactions of the gut microbiome with the nucleopolyhedrovirus, SpexNPV. We found that S. exempta individuals with supplemented gut microbiome were significantly more resistant to SpexNPV, relative to those with a typical laboratory gut microbiome. Illumina MiSeq sequencing revealed the bacterial phyla in the S. exempta gut belonged to 28 different classes. Individuals with an increased abundance of Lactobacillales had a higher probability of surviving viral infection. In contrast, there was an increased abundance of Enterobacteriales and Pseudomonadales in individuals dying from viral infection, corresponding with decreased abundance of these two Orders in surviving caterpillars, suggesting a potential role for them in modulating the interaction between the host and its pathogen. These results have important implications for laboratory studies testing biopesticides.
| S-EPMC9764207 | biostudies-literature
Project description:Meta-analysis of Klebsiella pneumoniae genomes from multiple studies
| PRJEB45529 | ENA
Project description:Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.
| S-EPMC3002994 | biostudies-literature
Project description:Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.
| S-EPMC6597035 | biostudies-literature
Project description:BackgroundSynthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level, standard methods for analysis of shotgun metagenomic sequencing do not account for ambiguous mapping between closely related strains, and other methods such as quantitative PCR (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing paired with a k-mer-based normalization strategy to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes.ResultsBoth in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy than other tools utilizing shotgun metagenomic sequencing reads and can resolve complex mixtures of highly related strains. Through experimental validation and benchmarking, we demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. Further, it is capable of scaling to communities of hundreds of strains and efficiently utilizes memory being capable of running both on personal computers and high-performance computing nodes.ConclusionsUsing shotgun metagenomic sequencing reads is a viable method for determining accurate strain-level abundances in synthetic communities using StrainR2.
| S-EPMC11326212 | biostudies-literature
Project description:Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems. Such approaches provide a multi-dimensional, time-dependent view of the system wavefunction represented on a coordinate grid; in the case of non-adiabatic simulations, additional information about the state populations adds a further layer of complexity. As such, wavepacket motion on potential energy surfaces which couple many nuclear and electronic degrees-of-freedom can be extremely challenging to analyse in order to extract physical insight beyond the usual expectation-value picture. Here, we show that non-linear dimensionality reduction (NLDR) methods, notably diffusion maps, can be adapted to extract information from grid-based wavefunction dynamics simulations, providing insight into key nuclear motions which explain the observed dynamics. This approach is demonstrated for 2-D and 9-D models of proton transfer in salicylaldimine, as well as 8-D and full 12-D simulations of cis-trans isomerization in ethene; these simulations demonstrate how NLDR can provide alternative views of wavefunction dynamics, and also highlight future developments.
| S-EPMC8708769 | biostudies-literature
Project description:Dimensionality reduction offers unique insights into high-dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa levels are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic nonlinear tensor factorization approach. Like normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. Using multiple real and synthetic datasets, we show that a very small number of ECNs can accurately approximate microbiome dynamics. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.
| S-EPMC10282069 | biostudies-literature
Project description:BackgroundLignocellulose is the most abundant renewable carbon resource that can be used for biofuels and commodity chemicals production. The ability of complex microbial communities present in natural environments that are specialized in biomass deconstruction can be exploited to develop lignocellulose bioconversion processes. Termites are among the most abundant insects on earth and play an important role in lignocellulose decomposition. Although their digestive microbiome is recognized as a potential reservoir of microorganisms producing lignocellulolytic enzymes, the potential to enrich and maintain the lignocellulolytic activity of microbial consortia derived from termite gut useful for lignocellulose biorefinery has not been assessed. Here, we assessed the possibility of enriching a microbial consortium from termite gut and maintaining its lignocellulose degradation ability in controlled anaerobic bioreactors.ResultsWe enriched a termite gut-derived consortium able to transform lignocellulose into carboxylates under anaerobic conditions. To assess the impact of substrate natural microbiome on the enrichment and the maintenance of termite gut microbiome, the enrichment process was performed using both sterilized and non-sterilized straw. The enrichment process was carried out in bioreactors operating under industrially relevant aseptic conditions. Two termite gut-derived microbial consortia were obtained from Nasutitermes ephratae by sequential batch culture on raw wheat straw as the sole carbon source. Analysis of substrate loss, carboxylate production and microbial diversity showed that regardless of the substrate sterility, the diversity of communities selected by the enrichment process strongly changed compared to that observed in the termite gut. Nevertheless, the community obtained on sterile straw displayed higher lignocellulose degradation capacity; it showed a high xylanase activity and an initial preference for hemicellulose.ConclusionsThis study demonstrates that it is possible to enrich and maintain a microbial consortium derived from termite gut microbiome in controlled anaerobic bioreactors, producing useful carboxylates from raw biomass. Our results suggest that the microbial community is shaped both by the substrate and the conditions that prevail during enrichment. However, when aseptic conditions are applied, it is also affected by the biotic pressure exerted by microorganisms naturally present in the substrate and in the surrounding environment. Besides the efficient lignocellulolytic consortium enriched in this study, our results revealed high levels of xylanase activity that can now be further explored for enzyme identification and overexpression for biorefinery purposes.
| S-EPMC6191919 | biostudies-literature
Project description:Our intestine is host to a large microbial community (microbiota) that educates the immune system and confers niche protection. Profiling of the gut-associated microbial community reveals a dominance of obligate anaerobic bacteria in healthy individuals. However, intestinal inflammation is associated with a disturbance of the microbiota-known as dysbiosis-that often includes an increased prevalence of facultative anaerobic bacteria. This group contains potentially harmful bacterial species, the bloom of which can further exacerbate inflammation. Here, we review the mechanisms that generate changes in the microbial community structure during inflammation. One emerging concept is that electron acceptors generated as by-products of the host inflammatory response feed facultative anaerobic bacteria selectively, thereby increasing their prevalence within the community. This new paradigm has broad implications for understanding dysbiosis during gut inflammation and identifies potential targets for intervention strategies.
| S-EPMC3615657 | biostudies-other