Biology subjects

Bashan, A.

Publications and source records attributed to Bashan, A..

8 recordsLinked to original sources

bioRxiv2026

Individual-specific recovery of the healthy human gut microbiota after antibiotics

Following disruption by broad-spectrum antibiotics, the human gut microbiome undergoes a prolonged recovery where coarse ecological features, such as species richness and diversity, are largely restored. However, as these features are shared by most healthy individuals, it remains unclear whether, even after strong perturbation, individuals preferentially return toward their own pre-treatment composition ('individual-specific recovery') or instead switch to alternative equally healthy states. Resolving this question is fundamental for evaluating the long-term impacts of antibiotics, because an individual's health is affected by their unique microbiome composition, affecting immune regulation and metabolic processing, so a state switch, even to a broadly healthy configuration, could carry unintended consequences. Here we compare each subject's post-antibiotic endpoint with their own baseline and with the baselines of every other subject, across four cohorts (n = 121) whose regimens ranged from a five-day fluoroquinolone course to a four-day three-drug combination given to deplete the gut community. The recovered community was significantly closer to the subject's own baseline than to the alternative healthy baselines in more than 90% of individuals under the mildest regimen and in two thirds of those under regimens that broadly target the gut anaerobes, remaining above chance in every cohort and the majority outcome even where most baseline taxa fell below detection during treatment. This specificity is not merely the persistence and fast regrowth of survivors: it persists among baseline taxa that remain undetected for weeks to months, and the same taxon re-establishes more readily in a host where it was previously detected than in one where it was not. Newly established taxa are consistently more functionally similar to the pre-treatment community than transient arrivals, and their identity correlates with the surviving community's composition in a substantial fraction of subjects. State switching, in contrast, was uncommon and occurred almost exclusively among subjects with the largest reduction in detected taxa during the treatment. Together, these observations reveal the individual-specific nature of the microbiome resilience to broad-spectrum antibiotics, whereas the compositional changes are not random but rather functionally and ecologically associated with the subject's baseline.

ecology↗

bioRxiv2026

An Epstein-Barr virus-encoded snoRNA directs 2'-O-methylation of human rRNAs to control translation and the viral lytic switch

Epstein-Barr virus (EBV) establishes life-long latency in human B-cells yet the molecular strategies that balance its persistence with lytic replication remain incompletely understood. Here, we identify the EBV-encoded small nucleolar RNA, v-snoRNA1, as a bona fide 2'-O-methylation guide that directs methylation of host ribosomal RNAs at 18S-C621 and 28S-U1760, two conserved residues in the ribosomal A-site. V-snoRNA1-mediated hypermethylation impairs 18S rRNA maturation and compromises translational fidelity and output, resulting in slower cellular proliferation. Infection with a v-snoRNA1 deleted virus ({Delta}v-snoRNA1) leads to enhanced protein synthesis and increased proliferation, together with extensive rewiring of host and viral gene expression. This rewiring includes suppression of immune and interferon pathways and alterations in transcription factor activities important for B-cell differentiation. Importantly, we find that v-snoRNA1 is required to facilitate viral production. Our findings reveal a molecular strategy by which EBV directly controls translation to promote infection.

molecular biology↗

bioRxiv2025

Assessment of methods for network analysis of single-time-point microbial samples

Harnessing information from a single-time-point microbial sample holds transformative potential for advancing personalized medicine. Traditional techniques, including alpha-diversity (richness-based), beta-diversity (dissimilarity-based) measures and neural network models, effectively pinpoint compositional differences and species abundance variations between the test sample and a reference population. Recently, two novel approaches have been proposed, that, instead of assessing abundance of the individual species, focus on the inter-species relationships in the test sample, the network-impact (NI) approach and the individualized dissimilarity-overlap analysis (IDOA). The NI is a measure of the contribution of the test sample to the calculated inter-species correlations in a bottom-up approach, while the IDOA analyzes the relationships between species assemblages and their abundances in a top-down approach. In this research, we comprehensively evaluate and compare the traditional and the new approaches. Employing the Generalized Lotka-Volterra (GLV) model, we create synthetic samples to rigorously test the classification capabilities of each measure in both supervised and semi-supervised setups. Our findings reveal that when the species of the test sample share similar self-dynamics as the reference cohort but distinct inter-species interactions, they typically can not be classified by conventional dissimilarity-based measures. In contrast, IDOA and NI parameters emerge as successful tools in assessing microbial relationships within a single-time-point sample, while a neural network is highly dependent on the training set size. This study underscores the potential of extracting information from the intricate inter-species relationships based on individual microbial snapshots, paving the way for improving microbiome-based personalized medicine.

systems biology↗

bioRxiv2023

Individualized network analysis reveals a link between the gut microbiome, diet intervention and Gestational Diabetes Mellitus

Gestational Diabetes Mellitus (GDM), a serious complication during pregnancy which is defined by abnormal glucose regulation, is commonly treated by diabetic diet and lifestyle changes. While recent findings place the microbiome as a natural mediator between diet interventions and diverse disease states, its role in GDM is still unknown. Here, based on observation data from healthy pregnant control group and GDM patients, we developed a new network approach using patterns of co-abundance of microorganism to construct microbial networks that represent human-specific information about gut microbiota in different groups. By calculating network similarity in different groups, we analyze the gut microbiome from 27 GDM subjects collected before and after two weeks of diet therapy compared with 30 control subjects to identify the health condition of microbial community balance in GDM subjects. Although the microbial communities remain similar after the diet phase, we find that the structure of their inter-species co-abundance network is significantly altered, which is reflected in that the ecological balance of GDM patients was not "healthier" after the diet intervention. In addition, we devised a method for individualized network analysis of the microbiome, thereby a pattern is found that individuals with large deviations in microbial networks are usually accompanied by their abnormal glucose regulation. This approach may help the development of individualized diagnosis strategies and microbiome-based therapies in the future. Author SummaryIn this study, we aimed to investigate the role of the gut microbiome in gestational diabetes mellitus (GDM), a condition that affects pregnant women and is characterized by abnormal glucose regulation. Specifically, we asked whether and how the gut microbiome is affected by diabetic diet which is commonly used to treat GDM patients. We developed a new network approach to analyze patterns of co-abundance of microorganisms in the gut microbiota of GDM patients and healthy pregnant women. Our findings show that although the microbial communities remained similar after the diet phase, the structure of their inter-species co-abundance network was significantly altered, indicating that the ecological balance of GDM patients was not "healthier" after the diet intervention. Furthermore, we suggest that abnormal glucose regulation is associated with large network deviations, which could lead to the development of individualized microbiome-based therapies in the future. Our work highlights the importance of studying the microbiome from a network perspective to better understand the dynamic interactions among microorganisms in the community balance of the microbiome.

systems biology↗

bioRxiv2022

Model-free prediction of microbiome compositions

The recent recognition of the importance of the microbiome to the hosts health and well-being, has yielded efforts to develop therapies that aim to shift the microbiome from a disease-associated steady-state to a healthier one. Direct manipulation techniques of the species assemblage are currently available, e.g., using probiotics or narrow-spectrum antibiotics to introduce or eliminate specific taxa. However, predicting the species abundances at the new steady-state remains a challenge, mainly due to the difficulties of deciphering the delicate underlying network of ecological interactions or constructing a predictive model for such complex ecosystems. Here, we propose a model-free method to predict the species abundances at the new steady state based on their presence/absence configuration by utilizing a multi-dimensional k-nearest-neighbors (kNN) regression algorithm. By analyzing data from numeric simulations of ecological dynamics, we show that our predictions, which consider the presence/absence of all species holistically, outperform both the null model that uses the statistics of each species independently and a predictive neural network model. We analyze real metagenomic data of human-associated microbial communities and found that by relying on a small number of neighboring samples, i.e., samples with similar species assemblage, the kNN predicts the species abundance better than the whole-cohort average. By studying both real metagenomic and simulated data, we show that the predictability of our method is tightly related to the dissimilarity-overlap relationship of the training data. Our results demonstrate how model-free methods can prove useful in predicting microbial communities and may facilitate the development of microbial-based therapies.

microbiology↗

bioRxiv2021

Top-down identification of keystone species in the microbiome

Keystone species in ecological communities are native species that play an especially important role in the stability of their ecosystem and can also be potentially used as its main drivers. However, we still lack an effective framework for identifying these species from the available metagenomic data without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the species. Our method does not assume pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real metagenomic data of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module - multiple candidate keystones species with correlated occurrence. The keystones analysis of single-time-point cross-sectional data is also later verified by evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities.

ecology↗

bioRxiv2021

Complexity-stability relationship in empirical microbial ecosystems

Mays stability theory [1, 2], which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology [3-7]. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge, with inconsistent results [8]. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks [9-11]. Here, we introduce a novel computational framework for estimating an ecosystems complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites [12] and sponge-associated microbial communities from different geographical locations [13], we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity.

ecology↗

bioRxiv2017

On the Stability Landscape of the Human Gut Microbiome: Implications for Microbiome-based Therapies

Understanding how gut microbial species determine their abundances is crucial in developing any microbiome-based therapy. Towards that end, we show that the compositions of our gut microbiota have characteristic and attractive steady states, and hence respond to perturbations in predictable ways. This is achieved by developing a new method to analyze the stability landscape of the human gut microbiome. In order to illustrate the efficacy of our method and its ecological interpretation in terms of asymptotic stability, this novel method is applied to various human cohorts, including large cross-sectional studies, long longitudinal studies with frequent sampling, and perturbation studies via fecal microbiota transplantation, antibiotic and probiotic treatments. These findings will facilitate future ecological modeling efforts in human microbiome research. Moreover, the method allows for the prediction of the compositional shift of the gut microbiome during the fecal microbiota transplantation process. This result holds promise for translational applications, such as, personalized donor selection when performing fecal microbiota transplantations.\n\nOne Sentence SummaryA new method for analyzing the stability landscape of the human gut microbiome and predicting its steady-state composition is developed.

ecology↗