Biology subjects

Zeevi, D.

Publications and source records attributed to Zeevi, D..

12 recordsLinked to original sources

bioRxiv2025

Learning the DNA syntax of human microbiomes to infer health and disease

The human microbiome is a key factor in human health and alterations in community structure are associated with diverse pathological conditions. However, defining universal criteria to distinguish healthy from altered microbiome configurations remains challenging due to inter- and intra-individual variability, database-dependent approaches, and the complexity of analyzing numerous microbial features simultaneously. Here, we developed an approach that learns the syntax of the entire DNA of human microbial communities, using Sequence-Informed GC-normalized 4-mers (SIG-mers) that feed into statistical and machine learning frameworks. We identified distinct SIG-mer signatures that differentiate microbiomes of body sites across diverse healthy human populations. These signatures reveal both global microbiome shifts and individual-specific dynamics in response to antibiotic treatments and in chronic inflammatory disease. Leveraging machine learning models, we inferred health- and disease-associated microbiome states from SIG-mer profiles, capturing the degree of perturbation and disease severity. Our findings highlight SIG-mer profiling as a robust, unbiased and broadly applicable approach for personalized microbiome diagnostics and guiding therapeutic interventions.

microbiology↗

bioRxiv2025

Synergy in viral-bacterial coinfection expedites algal bloom demise

Algal blooms are hotspots for diverse microbial interactions affecting bloom dynamics. Viral infections often regulate the demise of these blooms, recycling >25% of oceanic fixed carbon. Although these blooms collapse synchronously, only 10-30% of algal cells show active viral infection, suggesting additional mortality agents. Using microbial communities from natural coccolithophore blooms, we discovered that bacteria significantly enhanced virus-mediated algal mortality. We quantified this interaction using two novel metrics: the Synergy Index (SI) measuring viral-bacterial interaction strength, and the Benefit Index (BI) assessing changes in bacterial fitness. We identified Alteromonas bacteria as drivers of viral-bacterial coinfection with consistently high SI and BI. In natural communities, Alteromonas exhibited chemoattraction towards infected algal cells, leading to their enrichment in bloom demise particles. This viral-bacterial synergy represents a previously unrecognized mortality agent controlling bloom termination, modulating the balance between the carbon-recycling viral shunt and the carbon-exporting viral shuttle pathways, likely influencing global carbon cycling.

microbiology↗

bioRxiv2025

TinkerHap - A Novel Read-Based Phasing Algorithm with Integrated Multi-Method Support for Enhanced Accuracy

Phasing, the assignment of alleles to their respective parental chromosomes, is fundamental to studying genetic variation and identifying disease-causing variants. Traditional approaches, including statistical, pedigree-based, and read-based phasing, face challenges such as limited accuracy for rare variants, reliance on external reference panels, and constraints in regions with sparse genetic variation. To address these limitations, we developed TinkerHap, a novel and unique phasing algorithm that integrates a read-based phaser, based on a pairwise distance-based unsupervised classification, with external phased data, such as statistical or pedigree phasing. We evaluated TinkerHaps performance against other phasing algorithms using 1,040 parent-offspring trios from the UK Biobank (Illumina short-reads) and GIAB Ashkenazi trio (PacBio long-reads). TinkerHaps read-based phaser alone achieved higher phasing accuracies than all other algorithms with 95.1% for short-reads (second best: 94.8%) and 97.5% for long-reads (second best: 95.5%). Its hybrid approach further enhanced short-read performance to 96.3% accuracy and was able to phase 99.5% of all heterozygous sites. TinkerHap also extended haplotype block sizes to a median of 79,449 base-pairs for long-reads (second best: 68,303 bp) and demonstrated higher accuracy for both SNPs and indels. This combination of a robust read-based algorithm and hybrid strategy makes TinkerHap a uniquely powerful tool for genomic analyses.

genomics↗

bioRxiv2025

Global DNA signatures of temperature and nutrient limitation in prokaryotes

Microorganisms adapt to their environment through changes in both their genes and overall genome composition. However, identifying universal principles of genomic adaptation remains challenging because different environments present multiple, overlapping selective pressures. To overcome this challenge, we analyzed DNA composition patterns across diverse environments using machine learning. By examining tetranucleotide frequencies from 1,112 marine and soil metagenomic samples, we discovered that environmental temperature can be accurately predicted from DNA composition alone (R2=0.82). This temperature signal remained robust even when analyzing individual bacterial phyla and classes, suggesting a fundamental adaptive response. To examine this adaptation mechanism, we analyzed GC content relationships with temperature and found opposing relationships in different environments: positive correlations in soil but negative correlations in marine samples. We show that this inverse relationship in marine environments is driven by nutrient availability, as GC content increases with nutrient levels while nutrients decrease with temperature in marine ecosystems. Despite these contrasting GC patterns, we identified specific tetranucleotides composed of equal numbers of GC and AT bases (50% GC content) that showed consistent temperature correlations across all environments. These findings reveal a complex interplay between temperature adaptation and nutrient limitation in shaping microbial genomes.

microbiology↗

bioRxiv2024

Metagenomic co-assembly reveals mobile antibiotic resistance genes in airborne microbiomes in the Eastern Mediterranean

Antibiotic resistance is a significant threat to global ecological health. Mobile resistance genes are of particular concern due to their ability to transfer between organisms. In environmental settings and within the microbiota of humans and animals, these genes can sometime move into pathogens, promoting the spread of resistance. While various dispersal mechanisms have been studied, the role of air masses and dust storms in spreading antibiotic resistance genes globally remains largely unexplored. We analyzed air samples from non-dusty conditions and Middle Eastern dust storms using a metagenomic co-assembly approach to identify mobile antibiotic resistance genes dispersed by these air masses. Our analysis shows that co-assembling low-biomass air samples of similar origin produces longer genome fragments, allowing for a more detailed bioinformatic analysis that links functional features to specific taxa. Through this method, we identified genes associated with resistance to antibiotics such as aminoglycosides, beta-lactams, bleomycin, fosfomycin, chloramphenicol, daunorubicin, fluoroquinolones, glycopeptides, quinolones, streptomycin, and tetracyclines. Approximately 10% of these genes were linked to mobile genetic elements. These findings demonstrate the potential for global spread of resistance genes across ecosystems via air masses, particularly dust storms, and underscore the need for continuous atmospheric monitoring and further research to develop strategies that mitigate their impact on public health, the environment, and agriculture.

microbiology↗

bioRxiv2024

Dust storm-driven dispersal of potential pathogens and antimicrobial resistance genes in the Eastern Mediterranean

The atmosphere hosts a microbiome that connects distant ecosystems yet remains relatively unexplored. In this study, we tested the hypothesis that dust storms enhance the spread of pathogenic microorganisms and whether these microorganisms carry antibiotic resistance and virulence-related genes in the Eastern Mediterranean. We collected air samples during a seasonal transition period, capturing data from 13 dusty days originating from Middle Eastern sources and 32 clear days, with temperatures ranging from 16.5 to 27.1 {degrees}C. Using metagenomic analysis, we identified several facultative pathogens like Klebsiella pneumoniae, Stenotrophomonas maltophilia, and Aspergillus fumigatus, which are linked to human respiratory diseases, and others like Zymoseptoria tritici, Fusarium poae, and Puccinia striiformis, which are harmful to wheat. The abundance of these pathogens increased during dust storms and with rising temperatures. Although we did not find strong evidence that these species harbored antibiotic resistance or virulence-related genes, which could be linked to their pathogenic potential, dust storms transported up to 125 times more total antibiotic resistance genes, as measured by RPKM abundance, compared to clear conditions. These levels during dust storms far exceeded those found in other ecosystems. While further research is needed to determine whether dust storms and temperature variations pose an immediate threat to public health and the environment, our findings underscore the importance of continuous monitoring of atmospheric microbiomes. This surveillance is crucial for assessing potential risks to human health and ecosystem stability, particularly in the face of accelerating global climate change.

ecology↗

bioRxiv2024

Spec2Class: Accurate Prediction of Plant Secondary Metabolite Class using Deep Learning

Mass spectrometry (MS)-based data is commonly used in studying metabolism and natural products, but typically requires domain-specific skill and experience to analyze. Existing computational tools for non-targeted metabolite analysis (i.e., metabolomics) mostly rely on comparison to reference MS spectral libraries for metabolite identification, limiting the annotation of metabolites for which reference spectra do not exist. This is the case in plant secondary metabolites, where most spectral features remain unidentified. Here, we developed Spec2Class, a deep-learning algorithm for the identification and classification of plant secondary metabolites from liquid chromatography (LC)-MS/MS spectra. We used the in-house spectral library of 7973 plant metabolite chemical standards, alongside publicly available data, to train Spec2Class to classify LC-MS/MS spectra to 43 common plant secondary metabolite classes. Tested on held out sets, our algorithm achieved an overall accuracy of 73%, outperforming state-of-the-art classification. We further established a prediction certainty parameter to set a threshold for low-confidence results. Applying this threshold, we reached an accuracy of 93% on an unseen dataset. We show a high robustness of our prediction to noise and to the data acquisition method. Spec2Class is publicly available and is anticipated to facilitate metabolite identification and accelerate natural product discovery. Significance StatementUntargeted mass spectrometry (MS) is essential for natural product discovery but is limited by product identification, which is often manual and requires domain-specific skills. Spec2Class addresses this limitation by accurately classifying plant secondary metabolites from LC-MS/MS spectra without reliance on reference spectral libraries. Trained on a substantial dataset and using a prediction certainty threshold, it outperforms state-of-the-art algorithms with 93% accuracy. This tool demonstrates high robustness against noise and different data acquisition methods, promising to streamline metabolite identification and expedite natural product research. Spec2Class is open-source, publicly available, and easy to integrate into natural product discovery pipelines.

biochemistry↗

bioRxiv2024

Competitive fungal commensalism mitigates candidiasis pathology

The mycobiota are a critical part of the gut microbiome, but host-fungal interactions and specific functional contributions of commensal fungi to host fitness remain incompletely understood. Here we report the identification of a new fungal commensal, Kazachstania heterogenica var. weizmannii, isolated from murine intestines. K. weizmannii exposure prevented Candida albicans colonization and significantly reduced the commensal C. albicans burden in colonized animals. Following immunosuppression of C. albicans colonized mice, competitive fungal commensalism thereby mitigated fatal candidiasis. Metagenome analysis revealed K. weizmannii presence among human commensals. Our results reveal competitive fungal commensalism within the intestinal microbiota, independent of bacteria and immune responses, that could bear potential therapeutic value for the management of C. albicans-mediated diseases.

immunology↗

bioRxiv2019

Patterns of purifying selection in marine microbes reveal optimization in the genetic code

Nutrient limitation is a strong selective force, driving competition for resources. However, much is unknown about how selective pressures resulting from nutrient limitation shape microbial coding sequences. Here, we study this resource-driven selection using metagenomic and single-cell data of marine microbes, alongside environmental measurements. We show that a significant portion of the selection exerted on microbes is explained by the environment and is strongly associated with nitrogen availability. We further demonstrate that this resource conservation optimization is encoded in the structure of the standard genetic code, providing robustness against mutations that increase carbon and nitrogen incorporation into protein sequences. Overall, we demonstrate that nutrient conservation exerts a significant selective pressure on coding sequences and may have even contributed to the evolution of the genetic code.

microbiology↗

bioRxiv2019

Screening human embryos for polygenic traits has limited utility

Genome-wide association studies have led to the development of polygenic score (PS) predictors that explain increasing proportions of the variance in human complex traits. In parallel, progress in preimplantation genetic testing now allows genome-wide genotyping of embryos generated via in vitro fertilization (IVF). Jointly, these developments suggest the possibility of screening embryos for polygenic traits such as height or cognitive function. There are clear ethical, legal, and societal concerns regarding such a procedure, but these cannot be properly discussed in the absence of data on the expected outcomes of screening. Here, we use theory, simulations, and real data to evaluate the potential gain of PS-based embryo selection, defined as the expected difference in trait value between the top-scoring embryo and an average, unselected embryo. We observe that the gain increases very slowly with the number of embryos, but more rapidly with increased variance explained by the PS. Given currently available polygenic predictors and typical IVF yields, the average gain due to selection would be {approx}2.5cm if selecting for height, and {approx}2.5 IQ (intelligence quotient) points if selecting for cognitive function. These mean values are accompanied by wide confidence intervals; in real data drawn from nuclear families with up to 20 offspring each, we observe that the offspring with the highest PS for height was the tallest only in 25% of the families. We discuss prospects and limitations of PS-based embryo selection for the foreseeable future.

genetics↗

bioRxiv2018

Analysis of the genetic basis of height in large Jewish nuclear families

Despite intensive study, most genetic factors that contribute to variation in human height remain undiscovered. We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage, compared to previous family-based studies. We identified loci that together explain an estimated 6% of the variance in height. We showed that these loci are not tagging known common variants associated with height. Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.

genetics↗

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↗