Biology subjects

Weinberger, A.

Publications and source records attributed to Weinberger, A..

7 recordsLinked to original sources

bioRxiv2025

Bacterial strains in the human gut associate with host physiology

The human microbiota influences host physiology, yet much of its complexity lies beneath the species level. Here, we analyzed the intra-species genetic diversity of 936 gut bacteria across 24,997 individuals from three countries. Our findings show that highly abundant species exhibit greater strain stability, whereas low-abundance species display increased in-clonal mutations. Clonal strains are often mutually exclusive, while genetically variable strains tend to coexist. Strain turnover is associated with the presence of annotated chemotaxis and sporulation genes in reference genomes, whereas strain coexistence is associated with quorum sensing and secretion systems. Leveraging deep phenotypic data, we constructed an atlas detailing strain-level associations with diverse host physiological domains. For example, Phocaeicola vulgatus sub-types relate to host obesity, Lachnospira eligens to sleep, and Parabacteroides distasonis to iron hemostasis. This resource may guide personalized microbiome-based interventions to improve human health.

microbiology↗

bioRxiv2025

Self-supervised learning enables robust microbiome predictions in data-limited and cross-cohort settings

The gut microbiome plays a crucial role in human health, but machine learning applications in this field face significant challenges, including limited labeled data availability, high dimensionality, and batch effects across different cohorts. To address these limitations, we developed representation learning models for gut microbiome metagenomic data, drawing inspiration from foundation models approaches based on self-supervised and transfer learning principles. By leveraging a large collection of 85,364 metagenomic samples, we implemented multiple self-supervised learning methods, including masked autoencoders with varying masking rates and adapted single-cell RNA sequencing models (scVI and scGPT), to generate embeddings from bacterial abundance profiles. These learned representations demonstrated significant advantages over raw bacterial abundances in two key scenarios: first, when training predictive models with very limited labeled data, improving prediction performance for age (r = 0.14 vs. 0.06), BMI (r = 0.16 vs. 0.11), visceral fat mass (r = 0.25 vs. 0.18), and drug usage classification (PR-AUC = 0.81 vs. 0.73); and second, when generalizing predictions across different cohorts, consistently outperforming models based on raw abundances in cross-dataset evaluation. Our approach provides a valuable framework for leveraging self-supervised representation learning to overcome the data limitations inherent in microbiome research, potentially enabling more robust and generalizable machine learning applications in this field.

systems biology↗

bioRxiv2024

Genetic underpinnings of predicted changes in cardiovascular function using self supervised learning

BackgroundThe genetic underpinnings of cardiovascular disease remain elusive. Contrastive learning algorithms have recently shown cutting-edge performance in extracting representations from electrocardiogram (ECG) signals that characterize cross-temporal cardiovascular state. However, there is currently no connection between these representations and genetics. MethodsWe designed a new metric, denoted as Delta ECG, which measures temporal shifts in patients cardiovascular state, and inherently adjusts for inter-patient differences at baseline. We extracted this measure for 4,782 patients in the Human Phenotype Project using a novel self-supervised learning model, and quantified the associated genetic signals with Genome-Wide-Association Studies (GWAS). We predicted the expression of thousands of genes extracted from Peripheral Blood Mononuclear Cells (PBMCs). Downstream, we ran enrichment and overrepresentation analysis of genes we identified as significantly predicted from ECG. FindingsIn a Genome-Wide Association Study (GWAS) of Delta ECG, we identified five associations that achieved genome-wide significance. From baseline embeddings, our models significantly predict the expression of 57 genes in men and 9 in women. Enrichment analysis showed that these genes were predominantly associated with the electron transport chain and the same immune pathways as identified in our GWAS. ConclusionsWe validate a novel method integrating self-supervised learning in the medical domain and simple linear models in genetics. Our results indicate that the processes underlying temporal changes in cardiovascular health share a genetic basis with CVD, its major risk factors, and its known correlates. Moreover, our functional analysis confirms the importance of leukocytes, specifically eosinophils and mast cells with respect to cardiac structure and function.

systems biology↗

bioRxiv2021

In-depth characterization of the serum antibody epitope repertoire in inflammatory bowel disease using phage-displayed immunoprecipitation sequencing

Inflammatory bowel diseases (IBD), e.g. Crohns disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases. A comprehensive overview of an IBD-specific antibody epitope repertoire is, however, lacking. We leveraged a high-throughput phage-displayed immunoprecipitation sequencing (PhIP-seq) workflow to identify antibodies against 344,000 antimicrobial, immune and food antigens in 497 IBD patients as compared to 1,326 controls. IBD was characterized by 373 differentially abundant antibodies (202 overrepresented and 171 underrepresented), with 17% shared by both IBDs, 55% unique to CD and 28% unique to UC. Antibodies against bacterial flagellins dominated in CD and were associated with ileal involvement, fibrostenotic disease and anti-Saccharomyces cerevisiae antibody positivity, but not with fecal microbiome composition. Antibody epitope repertoires accurately discriminated CD from controls (AUC=0.89), and similar discrimination was achieved when using only ten antibodies (AUC=0.87). IBD patients thus show a distinct antibody repertoire against selected peptides, allowing patient stratification and discovery of immunological targets.

immunology↗

bioRxiv2021

Genetic, environmental and intrinsic determinants of the human antibody epitope repertoire

Phage-displayed immunoprecipitation sequencing (PhIP-Seq) has successfully enabled high-throughput profiling of human antibody profiles. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is currently lacking. In this study, we aimed to investigate the effects of genetic, environmental and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-Seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We demonstrate individual-specificity, temporal consistency and co-housing similarities in antibody repertoire. Genetic analyses showed involvement of the HLA, IGHV and FUT2 regions. Furthermore, we uncovered associations between 48 phenotypic factors and 544 antibody-bound peptides, including age, cell counts, sex, smoking behavior and allergies, among others. Overall, our results indicate that human antibody epitope repertoires are shaped by both host genetics and environmental exposures and highlight unique signatures of distinct phenotypes and genotypes.

immunology↗

bioRxiv2017

Deciphering Transcriptional Regulation of Human Core Promoters

Despite its pivotal role in regulating transcription, our understanding of core promoter function, architecture, and cis-regulatory elements is lacking. Here, we devised a highthroughput assay to quantify the activity of [~]15,000 fully designed core promoters that we integrated and expressed from a fixed location within the human genome. We find that core promoters drive transcription unidirectionally, and that sequences originating from promoters exhibit stronger activity than sequences originating from enhancers. Testing multiple combinations and distances of core promoter elements, we observe a positive effect of TATA and Initiator, a negative effect of BREu and BREd, and a 10bp periodicity in the optimal distance between the TATA and the Initiator. By comprehensively screening TF binding-sites, we show that site orientation has little effect, that the effect of binding site number on expression is factor-specific, and that there is a striking agreement between the effect of binding site multiplicity in our assay and the tendency of the TF to appear in homotypic clusters throughout the genome. Overall, our results systematically assay the elements that drive expression in core- and proximal-promoter regions and shed light on organization principles of regulatory regions in the human genome.

genomics↗

bioRxiv2017

Environmental factors dominate over host genetics in shaping human gut microbiota composition

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

genetics↗