Biology subjects

Cohen, T.

Publications and source records attributed to Cohen, T..

At least 19 recordsLinked to original sources

bioRxiv2026

RNA-sequencing of butyrate-treated human lymphoblastoid cell lines shows dysregulated genes of multiple disease-related pathways

Abstract Butyrate, a 4-carbon fatty acid produced from insoluble starch by the gut microbiome, plays key roles in mitochondrial respiration, activation of cell-surface G-protein-coupled receptors, and protein acetylation by inhibiting histone deacetylases (HDACs). Butyrate (as sodium salt) added to drinking water was shown to improve pathological symptoms in numerous model mouse disorders, including models for cancer metastasis, neuroinflammation, autism spectrum disorders, dementia, insulin resistance, and colorectal cancer. Over 5000 PubMed-listed studies feature butyrate in their title, yet its genome-wide transcriptomic effects have not been reported in human cell lines from multiple healthy donors. Here, we report the genome-wide transcriptomic effects of 1 mM sodium butyrate on human lymphoblastoid cell lines (LCLs) generated from blood samples of eight unrelated healthy donors. Paired bioinformatics analysis comparing the butyrate-treated LCLs to the same untreated cell line identified thousands of differentially expressed gene (DEG) transcripts with padj<0.05, of which over half were upregulated by butyrate. Pathway analysis showed that butyrate exposure of LCLs upregulated extracellular matrix, cell adhesion, junction organization, Wnt signaling, axonogenesis, and neurodevelopment pathways. Downregulated pathways included cell cycle, G2/M checkpoint, E2F/MYC targets, DNA replication and repair, mitotic spindle integrity, mTOR signaling, oxidative phosphorylation, unfolded-protein response, and translation/ribosome-related pathways. Our RNA-seq study, utilizing LCLs from healthy donors and paired bioinformatic analysis, opens venues for assessing butyrate effects on cellular pathways and potential therapeutics for cancer, metabolic, immune, and CNS-related disorders.

genomics↗

bioRxiv2026

A structural antibody benchmark for leakage-aware deep-learning evaluation

Deep-learning methods for antibody structure prediction, antibody-antigen interaction modelling and design are advancing rapidly. However, comparisons across studies remain difficult because training and test sets are often constructed independently, and a temporal cutoff alone does not prevent train-test leakage. We present SABLE (Structural Antibody Benchmark for deep-Learning Evaluation), a versioned structural antibody resource that couples a fixed training collection with a leakage-controlled held-out test set for reproducible machine-learning development and evaluation. SABLE combines 16,511 experimental training entries with 327 manually reviewed test entries and 3,274 high-confidence, patent-derived AlphaFold3 models spanning 476 antigens. Candidate test structures were selected after the AlphaFold3 temporal cutoff and filtered against the training set using antibody and antigen sequence similarity filters. Each test entry records its nearest training set neighbour, allowing users to quantify remaining relatedness and stratify performance by similarity. Versioned releases provide metadata, processed structures, CDR annotations, redundancy labels and model-confidence fields. A Python/PyTorch API and standardised benchmark metrics provide reproducible database access and evaluation code without requiring additional antibody-structure processing.

bioinformatics↗

bioRxiv2026

NinjaSeq: programmable restriction enzyme-based sequencing library preparation with random access for DNA data storage

DNA data storage allows sequences to be defined without biological constraints, yet readout workflows still depend on generic end-repair/dA-tailing chemistry. We developed NinjaSeq, a type IIS restriction endonuclease library-preparation strategy that incorporates recognition sites into primer flanks, enabling digestion to generate adapter-compatible overhangs and eliminating the need for conventional end preparation. By combining this chemistry with constrained coding that excludes internal recognition motifs, NinjaSeq produced sequencing quality and decoding performance consistent with standard protocols while reducing reagent burden and simplifying processing, including compatibility with one-pot restriction-ligation. The same sequence-directed design also enables physical random access during library preparation: targeting file-specific flanking sites enriched a desired file from a mixed pool by about sixteen-fold in a proof-of-concept experiment. These results position NinjaSeq as a practical ONT readout approach for DNA data storage. HIGHLIGHTSO_LINinjaSeq replaces end-repair/dA-tailing with REases for nanopore sequencing C_LIO_LIConstrained encoding excludes recognition motifs to protect payloads from cleavage C_LIO_LINinjaSeq achieves decoding accuracy comparable to standard library preparation C_LIO_LIDesigning file-specific RRS enables random access during library preparation C_LI

molecular biology↗

bioRxiv2026

A comparison of scalable approaches for the pairwise analysis of large pathogen genomic and spatial datasets: an application to studying Mycobacterium tuberculosis transmission

Pairwise analysis of genomic and spatial data offers opportunities to identify and estimate the associations between covariates and the transmission of pathogens between individuals. However, such pairwise analyses are computationally intensive, and may not be feasible to conduct given the high dyad count in even moderately sized datasets. Here we compare two approaches to increase the efficiency of pairwise analysis for large datasets. We quantify and compare the performance of divide-and-conquer Bayesian model fitting and pairwise case-control approaches for estimating associations between individual- and pair-level covariates and shared membership in a transmission cluster. We utilize a large dataset (n=4,154) of spatially-referenced, genomically-sequenced Mycobacterium tuberculosis isolates collected from a single city for this analysis. We find that the case-control approach produces unbiased estimates of effect sizes with expected credible interval coverage and is more robust than the divide-and-conquer method when effect sizes are large. Thus, we recommend using the case-control approach with at least three controls per case to downscale datasets for pairwise analysis when analysis of the entire dataset is not possible. This approach mitigates the computational challenges of pairwise Bayesian modeling on datasets that require significant computational resources while maintaining desired inferential properties. Author SummaryPairwise analyses of large datasets to study pathogen transmission are computationally demanding because they typically require simultaneous analysis of each possible pair of individuals in a dataset; as datasets become larger these analyses often are not feasible to conduct even with access to high-performance computing resources. In this work, we compare a case-control approach and divide-and-conquer approaches for more efficient pairwise analysis of large datasets. Using a large dataset of Mycobacterium tuberculosis isolates including genetic and spatial data, we investigate the performance of each method for estimating the associations between host covariates and genetic clustering of isolates. We find that the case-control approach is generally preferred over methods which first divide the data into subsets and then combine results. While additional extensions of these analyses are needed to test the generality of these findings to other data settings, this work provides a practical way forward for the pairwise analysis of large datasets to study pathogen transmission.

microbiology↗

bioRxiv2026

MassID provides near complete annotation of metabolomics data with identification probabilities

Liquid chromatography coupled to mass spectrometry (LC/MS) is a powerful tool in metabolomics research, generating tens-of-thousands of signals from a single biological sample. However, current software solutions for unbiased assessment of metabolomics data analysis are limited by complex sources of noise and non-quantitative metabolite identifications that make results difficult to interpret. Here, we present MassID, a cloud-based untargeted metabolomics pipeline that aims to overcome the innate challenges of unbiased metabolite analysis and perform end-to-end data processing, transforming raw spectra to normalized and identified metabolite profiles. MassID incorporates a suite of software functionalities, including deep learning-based peak detection and comprehensive noise filtering. In addition, with MassID we introduce a novel software module: DecoID2 that enables probabilistic metabolite identification for false discovery rate (FDR)-controlled metabolomics. When applied to a human plasma dataset, MassID results in near-complete signal annotation, identification of >4,000 metabolites (including >1,200 compounds at an FDR <5%) across four complementary LC/MS runs, and enables integrated downstream analyses to understand biochemical dysregulation at both the molecular and pathway level. When compared to the Metabolomics Standards Initiative (MSI) confidence levels, identification probability generally correlated with MSI levels. However, only 356/418 of MSI Level 1 compounds were identified with <5% FDR and the remaining 884 FDR < 5% compounds were identified from MSI L2-L3 compounds, highlighting the enhanced specificity and discovery potential achieved by MassID.

bioinformatics↗

bioRxiv2025

Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression

Amyotrophic lateral sclerosis (ALS) progression rates vary dramatically between patients, yet the basis of this heterogeneity remains elusive, with no prognostic biomarkers existing to guide clinical decisions or stratify patients for therapeutic trials. Here, we identify a network of coordinated immune cell types, which exhibit differential disruption across progression groups. Using mass cytometry (CyTOF) to profile 2.2 million immune cells from 35 ALS patients stratified by progression rate and 9 healthy controls, we find that the extent of immune dysfunction cannot be reflected by examining differences in individual cell type frequencies. In contrast, analyses of correlation patterns between cell types revealed distinct immune organization patterns, where coordination complexity varied with disease progression. Across all progression groups, we observed striking immune reorganization in natural killer (NK) cells and a major shift from B cell/basophil coordination hubs in healthy controls to neutrophil/T cell-dominated patterns in ALS. Having established coordinated immune patterns, we developed machine learning models to further improve our ability to stratify between disease and non-disease cohorts, achieving superior performance compared to models using cell frequencies alone. Central and effector memory (CM/EM) CD4+ T cell interactions emerged as top discriminative features for disease status, while plasmacytoid dendritic cell (pDC) relationships, especially their ratio with regulatory T cells (T-regs), distinguished progression rates, supporting T-reg-based therapeutic approaches. These findings reframe ALS as a disease of immune coordination breakdown, pointing towards cell-type specific therapeutics and biomarkers that may extend beyond ALS to other neurodegenerative diseases characterized by immune dysfunction.

immunology↗

bioRxiv2025

The global phylogeography of rapidly expanding multidrug resistant Ural lineage 4.2 Mycobacterium tuberculosis

Multidrug resistant tuberculosis (MDR-TB) is a leading global health challenge. Recent studies identified a reproductively fit Ural lineage 4.2 MDR M. tuberculosis (Mtb) variant circulating within the Republic of Moldova. We searched a large publicly available dataset of [~]200,000 Mtb whole genome sequences and identified a clade of 1604 drug-resistant Mtb sequences harboring conserved resistance-conferring mutations that are closely related to the MDR Mtb variant circulating in Moldova. We identified the Russian Federation as the most likely country of origin for this clade, and we inferred several independent migration events from Russia and Moldova to other former Soviet Republics and several European countries. Using local branching index, we found that this clade is expanding more rapidly than comparable clades of lineage 4.2. This evolutionarily successful strains presence beyond Moldova poses a threat to MDR-TB control, and enhanced surveillance is necessary to reduce the risk of MDR-TB epidemics in the region. FundingThe authors report funding from the National Institutes of Health (R01AI180209, P01AI159402)

microbiology↗

bioRxiv2024

Tiled Amplicon Sequencing Enables Culture-free Whole-Genome Sequencing of Pathogenic Bacteria From Clinical Specimens

Pathogen sequencing is an important tool for disease surveillance and demonstrated its high value during the COVID-19 pandemic. Viral sequencing during the pandemic allowed us to track disease spread, quickly identify new variants, and guide the development of vaccines. Tiled amplicon sequencing, in which a panel of primers is used for multiplex amplification of fragments across an entire genome, was the cornerstone of SARS-CoV-2 sequencing. The speed, reliability, and cost-effectiveness of this method led to its implementation in academic and public health laboratories across the world and adaptation to a broad range of viral pathogens. However, similar methods are not available for larger bacterial genomes, for which whole-genome sequencing typically requires in vitro culture. This increases costs, error rates and turnaround times. The need to culture poses particular problems for medically important bacteria such as Mycobacterium tuberculosis, which are slow to grow and challenging to culture. As a proof of concept, we developed two novel whole-genome amplicon panels for M. tuberculosis and Streptococcus pneumoniae. Applying our amplicon panels to clinical samples, we show the ability to classify pathogen subgroups and to reliably identify markers of drug resistance without culturing. Development of this work in clinical settings has the potential to dramatically reduce the time of diagnosis of drug resistance for multiple drugs in parallel, enabling earlier intervention for high priority pathogens.

genomics↗

bioRxiv2024

Evolving properties of biological materials captured via needle-based cavity expansion method

BackgroundThe mechanical properties of biological tissues change over time and with disease progression. Quantifying these mechanical properties can thus be instrumental for medical diagnosis and for evaluation of tissue viability for transplant. However, soft and biological materials are exceptionally challenging to mechanically characterize using conventional testing methods, which are hindered by limitations of sample size, fixturing capabilities, and sample preparation. ObjectiveWe hypothesize that Volume Controlled Cavity Expansion (VCCE) is well-suited to capture subtle mechanical differences in biological tissue. The objective of this work is therefore twofold: first, we seek to quantify how stiffness of liver and gelatin evolve with age. In achieving this understanding, we aim to demonstrate the precision of VCCE in measuring subtle changes in the mechanical properties of biological tissues. MethodsPerforming VCCE tests over 15 days in samples of gelatin and liver (porcine and bovine), we track the evolving pressure-volume response and deformation limits of the materials. ResultsIn both materials, we observed time-dependent variation of the stiffness and fracture thresholds. In gelatin VCCE repeatably captured stiffening over time, which was correlated with a higher fracture stress. This was in contrast to observations in bovine liver, where stiffening corresponded to a lower fracture stress. Porcine liver initially stiffened, then reversed this trend and relaxed. ConclusionThrough this work we show that liver and gelatin stiffen with age, and that this trend is measurable via VCCE. These results highlight the utility of VCCE and call attention to the need for a new class of mechanism based constitutive models that are capable of capturing variations in material over time with a minimal number of parameters.

bioengineering↗

bioRxiv2024

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogens genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIMs capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

bioinformatics↗

bioRxiv2024

Large-scale genomic analysis of SARS-CoV-2 Omicron BA.5 emergence in the United States

The COVID-19 pandemic is marked by the continuing emergence of novel SARS-CoV-2 variants. Questions remain about the mechanisms with which these lineages establish themselves in new geographical areas. In this study, we performed a discrete phylogeographic analysis on [~]19,000 SARS-CoV-2 sequences of Omicron BA.5 lineages between February and June 2022 to better understand how it emerged in different regions of the United States (U.S.). We found that the earliest introductions came from Africa, the putative origin of the variant, but the majority were from Europe, correlating with the high volume of air travelers. Additionally, the analysis revealed extensive domestic transmission between different regions of the U.S. driven by population size and cross-country transmission. Results suggest that most of the within-U.S. spread was between three regions that include California, New York, and Florida. Our results form a framework for understanding novel SARS-CoV-2 variant emergence in the U.S.

genomics↗

bioRxiv2024

A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission

BackgroundMixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections. MethodsWe employed a Gaussian mixture model to cluster allele frequencies at mixed sites (hSNPs) in each sample to identify signals of multiple strains. Building upon our previous tool, MixInfect, we increased the accuracy of classifying in vitro mixed samples through multiple improvements to the bioinformatic pipeline. Major and minor proportion constituent strains were reconstructed using three approaches and assessed by comparing the estimated sequence to the known constituent strain sequence. Lastly, mixed infections in a real-world Mycobacterium tuberculosis population from Moldova were detected with MixInfect2 and clusters of recent transmission that included major and minor constituent strains were built. ResultsAll 36/36 in vitro mixed and 12/12 non-mixed samples were correctly classified with MixInfect2, and major strain proportions estimated with high accuracy, outperforming previous tools. Reconstructed major strain sequences closely matched the true constituent sequence by taking the allele at the highest frequency at hSNPs, while the best performing approach to reconstruct the minor proportion strain sequence was identifying the closest non-mixed isolate in the same population, though no approach was effective when the minor strain proportion was at 5%. Finally, fewer mixed infections were identified in Moldova than previous estimates (6.6% vs 17.4%) and we found multiple instances where the constituent strains of mixed samples were present in transmission clusters. ConclusionsMixInfect2 accurately detects samples with evidence of mixed infection from WGS data and provides an excellent estimate of the mixture proportions. While there are limitations in reconstructing the constituent strain sequences of mixed samples, we present recommendations for the best approach to include these isolates in further analyses.

genomics↗

bioRxiv2024

CAR-engineered lymphocyte persistence is governed by a FAS ligand/FAS auto-regulatory circuit

Chimeric antigen receptor (CAR)-engineered T and NK cells can cause durable remission of B-cell malignancies; however, limited persistence restrains the full potential of these therapies in many patients. The FAS ligand (FAS-L)/FAS pathway governs naturally-occurring lymphocyte homeostasis, yet knowledge of which cells express FAS-L in patients and whether these sources compromise CAR persistence remains incomplete. Here, we constructed a single-cell atlas of diverse cancer types to identify cellular subsets expressing FASLG, the gene encoding FAS-L. We discovered that FASLG is limited primarily to endogenous T cells, NK cells, and CAR-T cells while tumor and stromal cells express minimal FASLG. To establish whether CAR-T/NK cell survival is regulated through FAS-L, we performed competitive fitness assays using lymphocytes modified with or without a FAS dominant negative receptor ({Delta}FAS). Following adoptive transfer, {Delta}FAS-expressing CAR-T and CAR-NK cells became enriched across multiple tissues, a phenomenon that mechanistically was reverted through FASLG knockout. By contrast, FASLG was dispensable for CAR-mediated tumor killing. In multiple models, {Delta}FAS co-expression by CAR-T and CAR-NK enhanced antitumor efficacy compared with CAR cells alone. Together, these findings reveal that CAR-engineered lymphocyte persistence is governed by a FAS-L/FAS auto-regulatory circuit.

immunology↗

bioRxiv2023

Elasticity of Whole Blood Clots Measured via Volume Controlled Cavity Expansion

Measuring and understanding the mechanical properties of blood clots can provide insights into disease progression and the effectiveness of potential treatments. However, several limitations hinder the use of standard mechanical testing methods to measure the response of soft biological tissues, like blood clots. These tissues can be difficult to mount, and are inhomogeneous, irregular in shape, scarce, and valuable. To remedy this, we employ in this work Volume Controlled Cavity Expansion (VCCE), a technique that was recently developed, to measure local mechanical properties of soft materials in their natural environment. Through a highly controlled volume expansion of a water bubble at the tip of an injection needle, paired with simultaneous measurement of the resisting pressure, we obtain a local signature of whole blood clot mechanical response. Comparing this data with predictive theoretical models, we find that a 1-term Ogden model is sufficient to capture the nonlinear elastic response observed in our experiments and produces shear modulus values that are comparable to values reported in the literature. Moreover, we find that bovine whole blood stored at 4{degrees}C for greater than 2 days exhibits a statistically significant shift in the shear modulus from 2.53 {+/-} 0.44 kPa on day 2 (N = 13) to 1.23 {+/-} 0.18 kPa on day 3 (N = 14). In contrast to previously reported results, our samples did not exhibit viscoelastic rate sensitivity within strain rates ranging from 0.22 - 21.1 s-1. By surveying existing data on whole blood clots for comparison, we show that this technique provides highly repeatable and reliable results, hence we propose the more widespread adoption of VCCE as a path forward to building a better understanding of the mechanics of soft biological materials. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/528966v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1cf427borg.highwire.dtl.DTLVardef@15d7b7org.highwire.dtl.DTLVardef@53ab4org.highwire.dtl.DTLVardef@1513aee_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVolume controlled cavity expansion overcomes common obstacles to testing biological samples C_LIO_LIWhole blood clot elasticity is well captured by the Ogden hyperelastic material model C_LIO_LIShear modulus strain-rate sensitivity was not observed in clots for moderate rates C_LI

bioengineering↗

bioRxiv2022

Structure modeling and specificity of peptide-MHC class I interactions using geometric deep learning

Major Histocompatibility Complex (MHC) plays a major role in the adaptive immune response by recognizing foreign proteins through binding to their peptides. In humans alone there are several hundred different MHC alleles, where each allele binds a specific subset of peptides. The peptide-MHC complex on a cell surface is identified by a T-cell receptor (TCR) and this binding invokes an immune response. Therefore, predicting the binding specificity of peptide-MHC pairs is necessary for understanding the immune recognition mechanism. Here, we develop an end-to-end novel deep learning model, MHCfold, that consists of structure and specificity prediction modules for simultaneous modeling of peptide-MHC class I (pMHCI) complexes and prediction of their specificity based on their modeled structure. MHCfold produces highly accurate structures of pMHCI complexes with mean C RMSD of 0.98[A] and 1.50[A] for the MHC chain and the peptide, respectively. The binding specificity is also predicted with high accuracy (mean AUC of 0.94). Furthermore, the structure modeling component is orders of magnitudes faster than state-of-the-art methods (modeling of 100,000 pMHCI pairs in four hours on a standard computer), enabling high-throughput applications for large immunopeptidomics datasets. While peptide-MHC specificity can be accurately predicted from the sequence alone, TCR specificity prediction likely requires modeling of the 3D structures. We anticipate our model can be further used in structure-based prediction of TCR specificity. MHCfold is available @https://github.com/dina-lab3D/MHCfold

bioinformatics↗

bioRxiv2021

NanoNet: Rapid end-to-end nanobody modeling by deep learning at sub angstrom resolution

Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the C[a] atoms of the entire VH domain. For the Nb test set, NanoNet achieves 1.7[A] overall average RMSD and 3.0[A] average RMSD for the most variable CDR3 loops. The accuracy for antibody VH domains is even higher: overall average RMSD < 1[A] and 2.2[A] RMSD for CDR3. NanoNet runtimes allow generation of ~1M nanobody structures in less than an hour on a standard CPU computer enabling high-throughput structure modeling.

bioinformatics↗

bioRxiv2021

Potent neutralizing nanobodies resist convergent circulating variants of SARS-CoV-2 by targeting novel and conserved epitopes

There is an urgent need to develop effective interventions resistant to the evolving variants of SARS-CoV-2. Nanobodies (Nbs) are stable and cost-effective agents that can be delivered by novel aerosolization route to treat SARS-CoV-2 infections efficiently. However, it remains unknown if they possess broadly neutralizing activities against the prevalent circulating strains. We found that potent neutralizing Nbs are highly resistant to the convergent variants of concern that evade a large panel of neutralizing antibodies (Abs) and significantly reduce the activities of convalescent or vaccine-elicited sera. Subsequent determination of 9 high-resolution structures involving 6 potent neutralizing Nbs by cryoelectron microscopy reveals conserved and novel epitopes on virus spike inaccessible to Abs. Systematic structural comparison of neutralizing Abs and Nbs provides critical insights into how Nbs uniquely target the spike to achieve high-affinity and broadly neutralizing activity against the evolving virus. Our study will inform the rational design of novel pan-coronavirus vaccines and therapeutics.

biophysics↗

bioRxiv2021

Mechanical stress determines morphogenesis and cell ordering in confined bacterial biofilms

Biofilms are aggregates of bacterial cells surrounded by an extracellular matrix. Much progress has been made in studying biofilm growth on solid substrates; however, little is known about the biophysical mechanisms underlying biofilm development in three-dimensional confined environments, in which the biofilm-dwelling cells must push against and even damage the surrounding environment to proliferate. Here, combining single-cell imaging, mutagenesis, and rheological measurement, we reveal the key morphogenesis steps of Vibrio cholerae biofilms embedded in hydrogels as they grow by four orders of magnitude from their initial size. We show that the morphodynamics and cell ordering in embedded biofilms are fundamentally different from those of biofilms on flat surfaces. Treating embedded biofilms as inclusions growing in an elastic medium, we quantitatively show that the stiffness contrast between the biofilm and its environment determines biofilm morphology and internal architecture, selecting between spherical biofilms with no cell ordering and oblate ellipsoidal biofilms with high cell ordering. When embedded in stiff gels, cells self-organize into a bipolar structure that resembles the molecular ordering in nematic liquid crystal droplets. In vitro biomechanical analysis shows that cell ordering arises from stress transmission across the biofilm-environment interface, mediated by specific matrix components. Our imaging technique and theoretical approach are generalizable to other biofilm-forming species, and potentially to biofilms embedded in mucus or host tissues as during infection. Our results open an avenue to understand how confined cell communities grow by means of a compromise between their inherent developmental program and the mechanical constraints imposed by the environment. Significance StatementBiofilms are microbial cities in which bacterial cells reside in a polymeric matrix. They are commonly found inside soft confining environments such as food matrices and host tissues, against which bacteria must push to proliferate. Here, by combining single-cell live imaging and mechanical characterization, we show that the confining environment determines the dynamics of biofilm shape and internal structure. This self-organized evolution of biofilm architecture is caused by force transmission between the environment and the biofilm, mediated by the extracellular matrix secreted by the cells. Our findings lead to new ways to understand how bacterial communities develop under mechanical constraints, and potentially to new strategies for preventing and controlling biofilm growth in three-dimensional environments.

microbiology↗