Biology subjects

Yousef, M.

Publications and source records attributed to Yousef, M..

7 recordsLinked to original sources

bioRxiv2025

Collective Microbial Effects Drive Toxin Bioremediation and Enable Rational Design

The metabolic activity of microbial communities is essential for host and environmental health, influencing processes from immune regulation to bioremediation. Given this importance, the rational design of microbiomes with targeted functional properties is an important objective. Designing microbial consortia with targeted functions is challenging due to complex community interactions and environmental heterogeneity. Community-function landscapes address this challenge by statistically inferring impacts of species presence or absence on function. Similar to fitness landscapes, community-function landscapes are shaped by both additive effects and interactions (epistasis) among species that influence function. Here, we apply the community-function landscape approach to design synthetic microbial consortia to degrade the toxic environmental contaminant bisphenol-A (BPA). Using synthetic communities of BPA-degrading isolates, we map community-function landscapes across increasing BPA concentrations, where higher BPA means greater toxicity. As toxicity increases, so does epistasis, indicating that collective effects become more important in degradation. Further, we leverage landscapes to rationally design communities with predictable BPA degradation dynamics in vitro. Remarkably, designed synthetic communities are able to remediate BPA in contaminated soils. Our results demonstrate that toxicity can drive epistatic interactions in community-function landscapes and that these landscapes can guide microbial consortia design for bioremediation.

microbiology↗

bioRxiv2024

G-S-M: A Comprehensive Framework for Integrative Feature Selection in Omics Data Analysis and Beyond

The treatment of human diseases is a major research question in many fields related to medicine. It has become clear that patient stratification is of utmost importance so that patients receive the best possible treatment. Bio/disease markers are critical to achieve stratification. Markers can come from many different sources such as genomics, transcriptomics, and proteomics. Establishing markers from such measurements often involves data analysis, machine learning, and feature selection. Traditional feature selection techniques often rely on the estimation of individual feature importance or significance by assigning a score to each feature, disregarding the inter-feature relationships. In contrast, the G-S-M (grouping scoring modeling) approach considers a group of features as a set that is organized based on prior knowledge. This approach takes into account the interdependence among features, providing a more meaningful evaluation of feature relevance and utility. Prior knowledge can encompass much compiled information such as microRNA-target interactions and protein-protein interactions. Here we present a new tool called G-S-M that presents the generalization of our previous works such as maTE, CogNet, and PriPath. The G-S-M tool combines machine learning and prior knowledge to group and score features based on their association with a binary-labeled target such as control and disease. This approach is unique in that computational and domain knowledge is utilized concurrently. Embedded feature selection, repeatedly employing machine learning during the selection process results in the identification of the most discriminative groups. Furthermore, the G-S-M tool allows for a more holistic understanding of the underlying mechanisms of a given system to be achieved through the combination of machine learning and prior domain knowledge, which can lead to new insights and discoveries. The implementation of the G-S-M workflow is freely available for download from our GitHub repository: https://github.com/malikyousef/The-G-S-M-Grouping-Scoring-Modeling-Approach. With this generalized approach we aim to make the feature selection approach available to a broader audience and hope it will be employed in medical practice. An example of such an approach is the TextNetTopics that is based on the G-S-M approach. TextNetTopics uses Latent Dirichlet Allocation (LDA) to detect topics of words, where those topics serve as groups. In the future, we aim to extend the approach to enable the incorporation of multiple lines of evidence for biomarker detection and patient stratification via combining multi-omics data.

bioinformatics↗

bioRxiv2024

RCE-IFE: Recursive Cluster Elimination with Intra-cluster Feature Elimination

The computational and interpretational difficulties caused by the ever-increasing dimensionality of biological data generated by new technologies pose a major challenge. Feature selection (FS) methods aim to reduce the dimension, and feature grouping has emerged as a foundation for FS techniques that seek to detect strong correlations among features and the existence of irrelevant features. In this work, we develop Recursive Cluster Elimination with Intra-Cluster Feature Elimination (RCE-IFE), a method that iterates clustering and elimination steps in a supervised context. Recursively, feature clusters are formed, then scored, and less contributing clusters are eliminated. Next, low-scoring features in retained clusters are eliminated. Intra-cluster feature elimination aims to reduce noisy features while keeping a minimum number of predictive features. The performance of RCE-IFE is evaluated and compared to other FS techniques in several datasets. The results show that the proposed strategy effectively reduces the size of the feature set and also improves the model performance.

bioinformatics↗

bioRxiv2024

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

Engineering functional microbiomes is challenging due to complex interactions between bacteria and their environments1-6. Using a set of 848 gut commensal strains and clearance of multi-drug resistant Klebsiella pneumoniae (Kp-MH258) as a target function, we engineered a functional 15-member synthetic microbiome--SynCom15--through a statistical approach agnostic to strain phenotype, mechanism of action, bacterial interactions, or composition of natural microbiomes. Our approach involved designing, building, and testing 96 metagenomically diverse consortia, learning a generative model using community strain presence/absence as input, and distilling model constraints through statistical inference. SynCom15 cleared Kp-MH258 across in vitro, ex vivo, and in vivo environments, matching the efficacy of a fecal microbiome transplant in a clinically relevant murine model of infection. The mechanism of suppression by SynCom15 was related to fatty acid production coupled with environmental acidification. SynCom15 also suppressed other pathogens--Clostridioides difficile, Escherichia coli, and other K. pneumoniae strains--but through different mechanisms. Sensitivity analysis revealed models trained on strain presence/absence captured the statistical structure of pathogen suppression, illustrating that community representation was key to our approach succeeding. Our framework, Constraint Distillation, could be a general and efficient strategy for building emergent complex systems, offering a path towards synthetic ecology more broadly.

synthetic biology↗

bioRxiv2023

Learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines emergent function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes - analogs to fitness landscapes - that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically-inferred landscapes quantitatively predict community functions from knowledge of strain presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species, and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes are typically not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show this observation is generic across many ecological models, suggesting community-function landscapes can be applied broadly across many contexts. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

ecology↗

bioRxiv2020

Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome

Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and their intriguing patterns of site specificity amidst the lack of dispersal limitation. Yet, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among the taxa that belong to recently discovered branches of microbial life. Here we assembled metagenomes from daily tongue and dental plaque samples from multiple individuals and reconstructed 790 non-redundant genomes, 43 of which resolved to TM7 that formed six monophyletic clades distinctly associated either with plaque or with tongue. Both pangenomic and phylogenomic analyses grouped tongue-specific TM7 clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 grouped together with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and the mobilome of the cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of human associated microbes. Additionally, we report that prophages are widespread amongst oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment.

microbiology↗

bioRxiv2020

Early-life microbial intervention reduces colitis risk promoted by antibiotic-induced gut dysbiosis

Perturbations in the early life gut microbiome are associated with increased risk to complex immune disorder like inflammatory bowel diseases. We previously showed maternal antibiotic-induced gut dysbiosis vertically passed to offspring increases experimental colitis risk in IL-10 gene deficient (IL-10-/-) mice. While this could arise from emergence of pathobionts or loss/lack of essential microbes needed for appropriate immunological education, our findings suggest the latter. A dominant Bacteroides strain absent following antibiotic-induced perturbation was cultivated from murine fecal samples. Addition of this strain into mice with antibiotic-induced dysbiosis significantly promoted immune tolerance and reduced incidence of colitis in IL-10-/- mice, but only if engrafted early in life, and not during adulthood. Thus, key members of the gut microbiome are essential for development of immune tolerance to commensal microbes in early life and their addition in presence of gut dysbiosis during this period can reduce colitis risk in genetically prone hosts. HighlightsO_LISpecific gut microbes promote early life immune tolerance to key commensal microbes C_LIO_LILoss of early life keystone microbes increases colitis risk in genetically prone hosts C_LIO_LIEmergence of absent commensal microbes late in life worsened colitis outcome C_LIO_LIEarly life exposure to a missing keystone Bacteroides strain reduced colitis risk C_LI

microbiology↗