Biology subjects

Erez, A.

Publications and source records attributed to Erez, A..

13 recordsLinked to original sources

bioRxiv2026

Nodule microbiome functions shape the performance of a wild perennial legume

Nitrogen fixing legume nodules are typically viewed as the product of a bilateral mutualism between host plants and nitrogen-fixing rhizobia, yet nodules also harbor diverse non-rhizobial endophytes whose functional significance remains poorly understood, especially in wild legumes and uncultivated soil. Here, using the wild Mediterranean shrub Calicotome villosa, we performed a soil inoculation experiment to test whether plant performance is linked to the functional composition of the nodule microbiome. Soil inocula from different natural sites produced strong differences in nodulation success, plant biomass, leaf nitrogen concentration, nitrogen fixation rate, and nodule allocation under otherwise uniform conditions. Although Bradyrhizobium dominated all nodules, species composition varied among inoculation sources, and non-rhizobial endophytes reached substantial abundance in some treatments. Functional profiles of the nodule microbiome were significantly associated with plant phenotype, with the strongest coupling observed for traits related to nodule investment. Targeted and genome-wide analyses identified trait-associated genes in both symbionts and endophytes, including genes involved in nitrogen cycling, ammonium transport, denitrification, pyrimidine degradation, sulfur assimilation, and type VI secretion systems. Several of these functions were not part of the canonical symbiosis machinery, yet were strongly associated with plant nitrogen status, biomass accumulation, or nodule mass fraction. Together, our results show that legume performance is better predicted by the collective functional composition of the nodule microbiome than by the primary symbiont alone. These findings support a broader view of nodules as multipartite microbial communities.

ecology↗

bioRxiv2025

Fast and accurate taxonomic domain assignment of short metagenomic reads using BBERT

Shotgun metagenomes from complex environments such as soil uncover vast biodiversity. Yet most short reads produced by shotgun sequencing cannot be taxonomically or functionally annotated, as they lack a sufficiently comprehensive reference, obscuring the true structure and function of microbial communities. We introduce BBERT, a nucleotide large language model optimized for short reads. Testing on a large cohort of soil metagenomes, we found that BBERT identifies bacterial sequence syntax without relying on reference databases, enabling accurate assignment of taxonomic domain, coding potential, and reading frame directly from reads as short as 100 bp. BBERT is small and fast enough to analyze metagenomes using a modest GPU and can be used to convert short metagenomic reads directly to bacterial amino acid sequences for downstream applications. BBERT also improves de-novo metagenomic assembly, reducing mismatches and gaps while accelerating runtime. Using metagenomes from wild legume nodules, we demonstrate that BBERT filtering improves bin quality while significantly accelerating de-novo assembly. By providing fast, reference-free classification of short reads, BBERT unlocks large metagenomic archives for more accurate ecological and evolutionary analyses.

bioinformatics↗

bioRxiv2024

Antibiotics damage the colonic mucus barrier in a microbiota-independent manner

Antibiotic use is a risk factor for development of inflammatory bowel diseases (IBDs). IBDs are characterized by a damaged mucus layer, which does not properly separate the host intestinal epithelium from the microbiota. Here, we hypothesized that antibiotics might affect the integrity of the mucus barrier. By systematically determining the effects of different antibiotics on mucus layer penetrability we found that oral antibiotic treatment led to breakdown of the mucus barrier and penetration of bacteria into the mucus layer. Using fecal microbiota transplant, RNA sequencing followed by machine learning and ex vivo mucus secretion measurements, we determined that antibiotic treatment induces ER stress in the colonic tissue which inhibits colonic mucus secretion in a microbiota-independent manner. This mucus secretion flaw led to penetration of bacteria into the colonic mucus layer, translocation of microbial antigens into circulation and exacerbation of ulcerations in a mouse model of IBD. Thus, antibiotic use might predispose to development of intestinal inflammation by impeding mucus production.

immunology↗

bioRxiv2023

Optimal antimicrobial response to a changing microbial background at a mucus interface

Complex lifeforms host microbiota, microbes that live synergistically with their host. Accordingly, hosts have mechanisms to defend against and tolerate the microbiota. The intestinal mucus, where these systems collide, plays a pivotal role in managing this relationship, yet lacks an integrative theoretical framework. We propose a minimal model to elucidate dynamics at this interface, focusing on the ileums mucus defense. The model considers the effect of delay in host antimicrobial peptide secretion and how the host can use two different signals, from the bulk microbiota and from segmented filamentous bacteria (SFB), assuming that the SFB anticipate the bulk microbiota. We propose a theory whereby the host can optimize defense by minimizing antimicrobial peptide production and controlling bacterial exposure. Integrating two recent experiments, we show host dynamics are consistent with sensing both bulk and SFB, supporting our optimal defense hypothesis. Therefore, we propose that similar mechanisms could prove advantageous to other species and applicable beyond the ileums mucus barrier.

biophysics↗

bioRxiv2023

Grow now, pay later: when should a bacterium go into debt?

Microbes grow in a wide variety of environments and must balance growth and stress resistance. Despite the prevalence of such trade-offs, understanding of their role in non-steady environments is limited. In this study, we introduce a mathematical model of "growth debt", where microbes grow rapidly initially, paying later with slower growth or heightened mortality. We first compare our model to a classical chemostat experiment, validating our proposed dynamics and quantifying Escherichia colis stress resistance dynamics. Extending the chemostat theory to include serial-dilution cultures, we derive phase diagrams for the persistence of "debtor" microbes. We find that debtors cannot coexist with non-debtors if "payment" is increased mortality but can coexist if it lowers enzyme affinity. Surprisingly, weak noise considerably extends the persistence of resistance elements, pertinent for antibiotic resistance management. Our microbial debt theory, broadly applicable across many environments, bridges the gap between chemostat and serial dilution systems. TeaserMicrobes can sacrifice future growth for immediate gains, how does this trade-off shape the structure of microbial communities?

ecology↗

bioRxiv2023

Diversity loss in microbial ecosystems undergoing gradual environmental changes

Microbial ecosystems in soils, oceans, and other environments are essential for global ecological stability. Environmental shifts are anticipated to trigger destabilizing events across the planet. In this study, we model how gradual environmental changes impact ecosystems, specifically when leading to a loss of diversity. We investigate how an ecosystem, within a serial-dilution setup, relaxes to a stable steady state. Our results reveal that as an ecosystem approaches its loss of diversity transition, its dynamics slow down. Consequently, diverse ecosystems, such as tropical rainforest soils, gradually driven past their transition point may exhibit a significant response lag. This suggests that some ecosystems may be closer to a collapse in diversity than current observations indicate. Although our model does not capture the full complexity of real-world ecosystems, it highlights critical aspects underlying the loss of biodiversity in changing environments. This has potential implications for empirical studies and when planning interventions.

ecology↗

bioRxiv2021

Nitrogen partitioning between branched-chain amino acids and urea cycle enzymes sustains renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage. HighlightsO_LIBranched-chain amino acids catabolism is reprogrammed in ccRCC tumors C_LIO_LIBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCC C_LIO_LIAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginine C_LIO_LIASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levels C_LIO_LISilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells C_LI

cancer biology↗

bioRxiv2021

Sex biases in cancer and autoimmune disease incidence are correlated across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome.

cancer biology↗

bioRxiv2021

Scalable Clustering with Supervised Linkage Methods

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner. We make HAL-x publicly available at: https://pypi.org/project/hal-x/

bioinformatics↗

bioRxiv2021

Using a recently approved tumor mutational burden biomarker to stratify patients for immunotherapy may introduce a sex bias

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB [≥]10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

cancer biology↗

bioRxiv2020

BCKDK regulates the TCA cycle through PDC to ensure embryonic development in the absence of PDK family

Pyruvate dehydrogenase kinases (PDK1-4) inhibit the TCA cycle by phosphorylating pyruvate dehydrogenase complex (PDC). Here, we show that the PDK family is dispensable for the survival of murine embryonic development and that BCKDK serves as a compensatory mechanism by inactivating PDC. First, we knocked out all four Pdk genes one by one. Surprisingly, Pdk total KO embryos developed and were born in expected ratios, but died by postnatal day 4 due to hypoglycemia or ketoacidosis. Finding that PDC was phosphorylated in these embryos suggested that another kinase compensates for the PDK family. Bioinformatic analysis implicated brunch chain ketoacid dehydrogenase kinase (Bckdk), a key regulator of branched chain amino acids (BCAA) catabolism. Indeed, knockout of Bckdk and the Pdk family led to loss of PDC phosphorylation, increment in PDC activity, elevation of Pyruvate flux into the TCA and early embryonic lethality. These findings reveal a new regulatory crosstalk hardwiring BCAA and glucose catabolic pathways, which feed the TCA cycle.

molecular biology↗

bioRxiv2019

Quantifying the dynamics of hematopoiesis by in vivo IdU pulse-chase, mass cytometry and mathematical modeling

We present a new method to directly quantify the dynamics of differentiation of multiple cellular subsets in unperturbed mice. We combine a pulse-chase protocol of IdU injections with subsequent analysis by mass cytometry (CyTOF), and mathematical modeling of the IdU dynamics. Measurements by CyTOF allow for a wide range of cells to be analyzed at once, due to the availability of a large staining panel without the complication of fluorescence spill-over. These are also compatible with direct detection of integrated iodine signal, with minimal impact on immunophenotyping based on surface markers. Mathematical modeling beyond a binary classification of surface marker abundance allows for a continuum of cellular states as the cells transition from one state to another. Thus, we present a complete and robust method for directly quantifying differentiation at the systemic level, allowing for system-wide comparisons between different mouse strains and/or experimental conditions.

immunology↗

bioRxiv2017

Modeling of cytometry data in logarithmic space: when is a bimodal distribution not bimodal?

Recent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as a readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements.

immunology↗