Biology subjects

Furman, O.

Publications and source records attributed to Furman, O..

5 recordsLinked to original sources

bioRxiv2026

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds

The cattle rumen microbiota represents a highly complex and dynamic ecosystem, whose organization and connection to host phenotypes are of the highest importance to food security and the environment. In this study, we analyzed the rumen microbiota, from 2,492 cattle belonging to five different breeds and production systems across five countries, categorizing them into microbial co-abundance groups referred to as Ruminosignatures. We identified twelve distinct Ruminosignatures, including two that were consistently observed across all populations and were dominated by the genus Prevotella and UBA2810. Additional Ruminosignatures showed breed-and diet-specific patterns and collectively explained 96-99% of the variance in rumen microbial composition. The abundances of several Ruminosignatures were associated with methane emissions and feed efficiency, and were influenced by host genetics, with heritability estimates ranging from 0.09 to 0.51. The Ruminosignature dominated by UAB2810 was negatively associated with methane emissions across all datasets and positively linked to feed efficiency in Holstein from Italy and crossbred from Ireland. Additionally, the type of production system affects both the occurrence of Ruminosignatures and their impact on host phenotypes, emphasizing the need for context-specific approaches to modulate the rumen microbiome. Overall, our results offer new perspectives on the assembly of ruminal microbes and underscore the potential of the Ruminosignatures framework for microbiome-informed precision agriculture and breeding initiatives aimed at enhancing feed efficiency and minimizing the environmental impact of cattle farming.

microbiology↗

bioRxiv2020

Rumen protozoa shape microbiome composition and metabolic output of fermentation

Unicellular eukaryotes are an integral part of many microbial ecosystems communities where they are known to interact with their surrounding prokaryotic community - either as predators or as a mutualistic habitat. Within the rumen, one of the most complex host-associated microbial habitats, ciliate protozoa represent the main micro-eukaryotes, accounting for up to 50% of the microbial biomass. Nonetheless, the extent of the ecological effect of protozoa on the microbial community and on the rumen metabolic output remains largely understudied. To assess the role of protozoa on the rumen ecosystem, we established an ex-vivo system in which distinct protozoa sub-communities were introduced to native rumen prokaryotic community. We show that the different protozoa communities exert a strong and differential impact on the composition of the prokaryotic community, as well as its function including methane production. Furthermore, the presence of protozoa increases prokaryotic diversity with a differential effect on specific bacterial populations such as Gammaproteobacteria, Prevotella and Spirochetes. Our results suggest that protozoa mitigate the effect of competitive exclusion between bacterial species, thereby contributing to the maintenance of prokaryotic diversity in the rumen. Our findings put forward the rumen protozoa populations as potentially important ecosystem engineers for future microbiome modulation strategies.

microbiology↗

bioRxiv2020

SCAPP: An algorithm for improved plasmid assembly in metagenomes

BackgroundMetagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples. ResultsWe developed SCAPP (Sequence Contents-Aware Plasmid Peeler) - an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets. ConclusionsSCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases, and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP.

bioinformatics↗

bioRxiv2019

Sex differences in chronic social stress models in mice

Chronic stress creates an allostatic overload that may lead to mood disorders such as anxiety and depression. Modern causes of chronic stress in humans are mostly social in nature, relating to work and relationship stress. Research into neural and molecular mechanisms of vulnerability and resilience following chronic social stress (CSS) is ongoing and uses animal models to discover efficient prevention strategies and treatments. To date, most CSS studies have neglected the female sex and used male-focused aggression-based animal models such as chronic social defeat stress (CSDS). Accumulating evidence on sex differences suggests differences in the stress response, the prevalence of stress-related illness and the treatment response, indicating that researchers should expand CSS investigation to include female-focused protocols alongside the popular CSDS protocols. Here, we describe a novel female mouse model of CSS and a parallel modified male mouse model of CSDS in C57BL/6 mice. These new models enable the investigation of vulnerability, coping and downstream effectors mediating long-term consequences of CSS in both sexes. Our data demonstrate sex differences during CSS and for many weeks following CSS. Female mice are more prone to body weight loss during CSS and hyperactive anxious behavior following CSS. Both sexes show disturbances in social interaction, but only stressed male mice show long-term changes in neuroendocrine function and memory performance after fear conditioning. We discuss future avenues of research using these models to investigate mechanisms pertaining to sensitivity to CSS as well as treatment response profiles, in a sex-suitable manner.

neuroscience↗

bioRxiv2017

Modeling the temporal dynamics of the gut microbial community in adults and infants

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 function 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 the operational taxonomic units (OTUs) 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 the microbial community temporal dynamics based on the community composition at previous time stamps. MTV-LMM can identify time-dependent microbes in time series datasets, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on three human microbiome time series datasets, and found that MTV-LMM significantly outperforms all existing methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance levels of an OTU at a later time point is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable proportion 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.

microbiology↗