scholarly journals A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome

2019 ◽  
Author(s):  
John L. Darcy ◽  
Alex D. Washburne ◽  
Michael S. Robeson ◽  
Tiffany Prest ◽  
Steven K. Schmidt ◽  
...  
Keyword(s):  
New Species ◽  
Microbial Communities ◽  
Phylogenetic Relationships ◽  
Temporal Dynamics ◽  
Human Microbiome ◽  
Close Relative ◽  
Data Sets ◽  
Close Relatives ◽  
Phylogenetic Overdispersion ◽  
Microbiome Data

AbstractUnderstanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely-related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e. nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the data sets we analyzed showed statistically significant phylogenetic overdispersion.

scholarly journals Scalable learning of interpretable rules for the dynamic microbiome domain

2020 ◽  
Author(s):  
Venkata Suhas Maringanti ◽  
Vanni Bucci ◽  
Georg K. Gerber
Keyword(s):  
Time Series Data ◽  
Human Microbiome ◽  
Predictive Performance ◽  
Series Data ◽  
Data Sets ◽  
Human Host ◽  
Scalable Learning ◽  
The Status ◽  
Biological Insight ◽  
Microbiome Data

AbstractThe microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understood by clinicians. Interpretable structure must take into account domainspecific information key to biologists and clinicians including evolutionary relationships (phylogeny) and dynamic behavior of the microbiome. A Bayesian model was previously developed in the field, which uses Markov Chain Monte Carlo inference to learn human interpretable rules for classifying the status of the human host based on microbiome time-series data, but that approach is not scalable to increasingly large microbiome datasets being produced. We present a new fully-differentiable model that also learns human-interpretable rules for the same classification task, but in an end-to-end gradient-descent based framework. We validate the performance of our model on human microbiome data sets and demonstrate our approach has similar predictive performance to the fully Bayesian method, while running orders-of-magnitude faster and moreover learning a larger set of rules, thus providing additional biological insight into the effects of diet and environment on the microbiome.

scholarly journals Temporal variability is a personalized feature of the human microbiome

2014 ◽  
Author(s):  
Gilberto E Flores ◽  
J Gregory Caporaso ◽  
Jessica B Henley ◽  
Jai Ram Rideout ◽  
Daniel Domogala ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Communities ◽  
Human Body ◽  
Temporal Variability ◽  
Temporal Dynamics ◽  
Human Microbiome ◽  
Wide Range ◽  
Diverse Communities ◽  
Study Population ◽  
Over Time

Background: It is now apparent that the complex microbial communities found on and in the human body (the human microbiome) vary across individuals. What has largely been missing from previous studies is an understanding of how these communities vary over time within individuals. To the extent to which it has been considered, it is often assumed that temporal variability is negligible for healthy adults. Here we address this gap in understanding by profiling the forehead, gut (fecal), palm, and tongue microbial communities in 85 adults, weekly over three months. Results: We found that skin (forehead and palm) varied most in the number of taxa present, whereas gut and tongue communities varied more in the relative abundances of taxa. Within each body habitat, there was a wide range of temporal variability across the study population, with some individuals consistently harboring more variable communities than others. The best predictor of these differences in variability across individuals was microbial diversity; individuals with more diverse gut or tongue communities were less variable than individuals with less diverse communities. Conclusions: This expanded sampling allowed us to observe consistently high levels of temporal variability in both diversity and community structure in all body habitats studied. These findings suggest that temporal dynamics may need to be considered when attempting to link changes in microbiome structure to changes in health status.Furthermore, our findings show that, not only is the composition of an individual’s microbiome highly personalized, but their degree of temporal variability is also a personalized feature.

scholarly journals Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2705
Author(s):  
Boram Kim ◽  
Eun Ju Cho ◽  
Jung-Hwan Yoon ◽  
Soon Sun Kim ◽  
Jae Youn Cheong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cirrhosis ◽  
Human Microbiome ◽  
Metabolic Reprogramming ◽  
Data Sets ◽  
Biological Interactions ◽  
Functional Potential ◽  
Metabolomic Data ◽  
Microbiome Data ◽  
Insight Into

Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host’s phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

scholarly journals GraphKKE: graph Kernel Koopman embedding for human microbiome analysis

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Kateryna Melnyk ◽  
Stefan Klus ◽  
Grégoire Montavon ◽  
Tim O. F. Conrad
Keyword(s):  
Microbial Communities ◽  
Human Microbiome ◽  
Synthetic Data ◽  
Strongly Correlated ◽  
Community Members ◽  
Real World Data ◽  
Graph Kernel ◽  
Complex Interactions ◽  
Cancer Types ◽  
Microbiome Data

AbstractMore and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.

scholarly journals The GMD-biplot and its application to microbiome data

2019 ◽  
Author(s):  
Yue Wang ◽  
Timothy W Randolph ◽  
Ali Shojaie ◽  
Jing Ma
Keyword(s):  
Human Microbiome ◽  
Simulated Data ◽  
Matrix Decomposition ◽  
Data Sets ◽  
Original Matrix ◽  
Arbitrary Matrix ◽  
Generalized Matrix ◽  
Eigen Decomposition ◽  
Value Decomposition ◽  
Microbiome Data

AbstractExploratory analysis of human microbiome data is often based on dimension-reduced graphical displays derived from similarities based on non-Euclidean distances, such as UniFrac or Bray-Curtis. However, a display of this type, often referred to as the principal coordinate analysis (PCoA) plot, does not reveal which taxa are related to the observed clustering because the configuration of samples is not based on a coordinate system in which both the samples and variables can be represented. The reason is that the PCoA plot is based on the eigen-decomposition of a similarity matrix and not the singular value decomposition (SVD) of the sample-by-abundance matrix. We propose a novel biplot that is based on an extension of the SVD, called the generalized matrix decomposition (GMD), which involves an arbitrary matrix of similarities and the original matrix of variable measures, such as taxon abundances. As in a traditional biplot, points represent the samples and arrows represent the variables. The proposed GMD-biplot is illustrated by analyzing multiple real and simulated data sets which demonstrate that the GMD-biplot provides improved clustering capability and a more meaningful relationship between the arrows and the points.

scholarly journals Temporal variability is a personalized feature of the human microbiome

2014 ◽  
Author(s):  
Gilberto E Flores ◽  
J Gregory Caporaso ◽  
Jessica B Henley ◽  
Jai Ram Rideout ◽  
Daniel Domogala ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Communities ◽  
Human Body ◽  
Temporal Variability ◽  
Temporal Dynamics ◽  
Human Microbiome ◽  
Wide Range ◽  
Diverse Communities ◽  
Study Population ◽  
Over Time

Background: It is now apparent that the complex microbial communities found on and in the human body (the human microbiome) vary across individuals. What has largely been missing from previous studies is an understanding of how these communities vary over time within individuals. To the extent to which it has been considered, it is often assumed that temporal variability is negligible for healthy adults. Here we address this gap in understanding by profiling the forehead, gut (fecal), palm, and tongue microbial communities in 85 adults, weekly over three months. Results: We found that skin (forehead and palm) varied most in the number of taxa present, whereas gut and tongue communities varied more in the relative abundances of taxa. Within each body habitat, there was a wide range of temporal variability across the study population, with some individuals consistently harboring more variable communities than others. The best predictor of these differences in variability across individuals was microbial diversity; individuals with more diverse gut or tongue communities were less variable than individuals with less diverse communities. Conclusions: This expanded sampling allowed us to observe consistently high levels of temporal variability in both diversity and community structure in all body habitats studied. These findings suggest that temporal dynamics may need to be considered when attempting to link changes in microbiome structure to changes in health status.Furthermore, our findings show that, not only is the composition of an individual’s microbiome highly personalized, but their degree of temporal variability is also a personalized feature.

scholarly journals Elachista saarelai sp. n. (Lepidoptera, Elachistidae: Elachistinae), a new species from southern Finland

2010 ◽  
Vol 21 (3) ◽  
pp. 129-138 ◽  
Author(s):  
Lauri Kaila ◽  
Leo Sippola
Keyword(s):  
Life History ◽  
New Species ◽  
Host Plants ◽  
Close Relative ◽  
Diagnostic Characters ◽  
Close Relatives ◽  
A New Species

Elachista (Elachista) saarelai sp. n. is described on the basis of specimens reared from larvae in southern Finland. The new species belongs to the Elachista tetragonella group, and is a close relative of E. trapeziella Stainton, E. ornithopodella Frey, E. occidentalis Frey and E. kebneella Traugott-Olsen & Schmidt Nielsen. It differs from all these species by details in morphology and life history. The new species inhabits sheltered habitats. Carex digitata and probably also C. pediformis are recorded as its host plants. Diagnostic characters and illustrations are provided for the close relatives of E. saarelai sp. n.

Two new species of Neocosmospora from China

Phytotaxa ◽  
2017 ◽  
Vol 319 (2) ◽  
pp. 175
Author(s):  
ZHAO-QING ZENG ◽  
WEN-YING ZHUANG
Keyword(s):  
New Species ◽  
Phylogenetic Relationships ◽  
Smooth Surface ◽  
Molecular Data ◽  
Hunan Province ◽  
Autonomous Region ◽  
Tibet Autonomous Region ◽  
Two New Species ◽  
Close Relatives

Recent collections of nectriaceous fungi from Hunan Province and Tibet Autonomous Region were examined. Using combined analyses of morphology and molecular data, Neocosmospora bomiensis and N. hengyangensis are described and illustrated as new species. Neocosmospora bomiensis can be easily recognized by commonly non-stromatic perithecia that are superficial, solitary, subglobose to globose or pyriform and becoming lateral collapsed when dry; clavate asci with eight ascospores that are ellipsoidal, uniseptate, hyaline, and smooth; sickle-shaped, slightly curved macroconidia with (3–)5–6-septate. Neocosmospora hengyangensis is characterized by non-stromatic perithecia that are solitary to gregarious, subglobose to globose, becoming laterally collapsed upon drying; clavate asci with eight ascospores that are ellipsoidal to subfusiform, uniseptate, and have a smooth surface; sickle-shaped, 4–6-septate macroconidia that are slightly curved; and allantoid to rod-shaped, aseptate microconidia. Their morphological distinctions from and phylogenetic relationships with the close relatives are discussed.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

The revised Permian genus Dagmarita Reitlinger, 1965 (Dagmaritinae, Foraminifera) and its phylogenetic relationships

2019 ◽  
Vol 94 (2) ◽  
pp. 202-216
Author(s):  
Valerio Gennari ◽  
Roberto Rettori
Keyword(s):  
New Species ◽  
Thin Section ◽  
Phylogenetic Relationships ◽  
Morphological Character ◽  
Taxonomic Composition ◽  
3D Reconstructions ◽  
Nw Iran ◽  
Smaller Foraminifers ◽  
Worldwide Distribution ◽  
First Time

AbstractAmong Permian smaller foraminifers, the genus Dagmarita is one of the most studied due to its worldwide distribution. The detailed study of the Zal (NW Iran) and Abadeh (Central Iran) stratigraphic sections led to redescription of the genus Dagmarita and its taxonomic composition. In Dagmarita, a peculiar generic morphological character, represented by a secondary valvular projection, has been detected for the first time among globivalvulinid foraminifers. The phylogeny of Dagmarita, and in particular its ancestor Sengoerina, is discussed and the new species, D. ghorbanii n. sp. and D. zalensis n. sp., are introduced. Analogies and differences among all the species belonging to Dagmarita are highlighted and morphological features of the new taxa are shown in 3D reconstructions, useful for understanding differently oriented sections of the specimens in thin section.UUID: http://zoobank.org/3d8eb14c-7757-4cbd-877c-4bacd2d156da

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