Biology subjects

Lin, Z.

Publications and source records attributed to Lin, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Real-time visualization of ligand-specific conformational dynamics of GPCR C-terminal domain in living cells

The cytoplasmic tails (C-tails) of membrane receptors are fundamental hubs for signal transductions. Yet directly investigating their dynamics in living cells is challenging. We developed a single-molecule technique to sense C-tail's conformational transitions. It was applied to investigate the ligand-induced activation of {beta}2AR, a prototypical G protein-coupled receptor (GPCR). Our data elucidate how the receptor transduces ligand binding to cell-level outcomes, with the ligand efficacy being interpreted in terms of the association and disassociation rates of the G protein. Moreover, upon G protein binding, not only the C-tail is displaced from the intracellular surface of the receptor but also the eighth helix H8 is dissociated from the plasma membrane. This work establishes a robust framework to directly decipher the activation of membrane receptors without interferences from overexpressed downstream transducers.

biophysics↗

bioRxiv2025

Thioredoxin-1 inhibits granulosa cell ferroptosis to rescue ovarian aging through mitophagy-dependent activation of BNIP3L

Ovarian aging is closely associated with a decline in fertility and an increase in reproductive dysfunction. Ovarian granulosa cells (GCs) support oocyte homeostasis and development, yet insight into GC dysfunction during aging is limited. Here, we show that aged GCs of humans and mice have indications of elevated ferroptosis, including increased ferroptosis-related metabolites, lipid peroxidation, and iron accumulation. The ferroptosis inhibitor Ferrostatin-1 reversed ovarian impairment and fertility of aged mice in vivo. We show that the age-related reduction in the expression of TXN (thioredoxin) leads to ferroptosis in human and mouse GCs by blocking BNIP3L-dependent mitophagy. Exogenous activation of TXN could promote mitophagy, thereby clearing excessive ROS and inhibiting ferroptosis. These results suggest that anti-ferroptosis-related treatments may assist in treating aging-related reproductive disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/664497v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1b97e8forg.highwire.dtl.DTLVardef@123f8d0org.highwire.dtl.DTLVardef@a44c61org.highwire.dtl.DTLVardef@92b32e_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIFerroptosis signatures are upregulated in aged GCs from human and mouse ovaries C_LIO_LITXN is deregulated in aged GCs, leading to mitochondrial and ROS metabolic dysfunction C_LIO_LITXN binds to DNA to regulate autophagy and mitophagy genes, including BNIP3L C_LIO_LIInhibition of ferroptosis can ameliorate GC dysfunction C_LI

cell biology↗

bioRxiv2025

HER2; p53 Co-mutated Cancers Show Increased Histone Acetylation and are Sensitive to Neratinib plus Trastuzumab Deruxtecan.

In metastatic breast cancer, HER2-activating mutations often co-occur with TP53 mutations, a combination linked to poor response to neratinib and worse prognosis. To model this clinical challenge, we bred HER2 V777L transgenic mice with two TP53 mutant alleles: TP53 R172H (the murine homolog of human TP53 R175H) and TP53fl/fl, which mimics p53 truncations common in human tumors. TP53 mutations accelerated tumor development and reduced survival in HER2-mutant mice. These co-mutant tumors were resistant to neratinib but remained sensitive to exatecan, the topoisomerase I (TOP1) inhibitor payload in trastuzumab deruxtecan (T-DXd). Mechanistically, TP53 mutant tumors exhibited upregulation of histone acetylation, hypertranscription of DNA repair factors, increased chromatin accessibility, and rendered cells more susceptible to TOP1 inhibitors via G2/M arrest and apoptosis. This vulnerability is dependent on transcriptional activity of TP53 mutations, highlighting a novel strategy to treat HER2;TP53 co-mutant breast cancers using TOP1-targeted therapies. Statement of SignificanceTP53 mutations sensitize HER2-mutant cancers to TOP1 inhibitors via chromatin accessibility and hyper-transcription, supporting combination therapy with neratinib and T-DXd in TP53/HER2 co-mutant breast cancers.

cancer biology↗

bioRxiv2025

Adaptive cytoskeletal responses to extracellular environment viscosity modulate cell migration

Cell migration is a pivotal process in metastasis, allowing cancer cells to invade surrounding tissues and disseminate to distant organs. While extracellular environment (ECE) viscosity serves as a critical modulator of cell motility, its regulatory mechanisms remain unclear. This study presents a mechanobiological model to investigate how ECE viscosity modulates cancer cell migration by regulating some key processes, including actin polymerization, retrograde flow, and adhesion adaptations. Our results reveal a biphasic response: a moderate increase in ECE viscosity enhances actin filament network density and adhesion strength, thereby accelerating migration, whereas excessively high viscosity hinders movement due to too large mechanical resistance. Furthermore, we identify a short-term migration memory phenomenon, where cancer cells exposed to high viscosity environments retain elevated migration speeds after transitioning to low viscosity conditions. This memory effect is sustained by the continued assembly of cytoskeletal proteins such as actin monomers and Arp2/3. These analyses reveal an adaptive mechano-chemo-biological mechanism by which cancer cells integrate and respond to mechanical cues from their viscous environment to optimize migration, and advance the understanding of cancer cell migration in various tissue environments.

biophysics↗

bioRxiv2025

Papaver S-determinants trigger an integrated network of mitochondrially derived ROS and disruption of energy metabolism in incompatible pollen tubes

Many plants use self-incompatibility (SI) mechanisms to prevent inbreeding by inhibiting pollen tube growth in "self" pistils. In Papaver rhoeas, SI is triggered by allele-specific interaction between the pollen and pistil S-determinants, activating a Ca2+-dependent signalling network that leads to rapid reactive oxygen species (ROS) production and eventual programmed cell death (PCD) in incompatible pollen tubes. Expression of the Papaver pollen S-determinant (PrpS) in Arabidopsis thaliana recapitulates Papaver SI when challenged with the cognate pistil ligand (PrsS). Using roGFP2-Orp1, a genetically encoded hydrogen peroxide (H2O2) sensor, combined with measurements of mitochondrial metabolism we have revealed a complex SI response. Within minutes, elevated cytosolic Ca2+ ([Ca2+]cyt) and cytosolic acidification converge to trigger mitochondrial H2O2 production (with consequent cytosolic and plastid H2O2 increase), membrane depolarisation, decreased respiration rate, and ATP depletion. In parallel, oxidative inactivation of cytosolic GAPDH inhibits glycolytic flux, resulting in decreased TCA cycle intermediates and providing a feedback loop to enhance mitochondrial disruption. Preceding production of mitochondrially-sourced ROS, we show that SI rapidly arrests pollen tube growth via inactivation of plasma membrane localised NADPH oxidase (RBOH) mediated superoxide production. Our findings provide new insights into how different ROS signatures from NADPH oxidase and mitochondria drive distinct processes. We demonstrate that mitochondrial disruption is an early and central event in the SI-response, likely driven by positive feedback loops involving altered Ca2+ signalling, cytosolic pH and ROS. Our findings provide novel insights into how SI rapidly disrupts energy metabolism in pollen tubes through interconnected signalling networks centred on mitochondria prior to PCD.

plant biology↗

bioRxiv2025

Ablation of Prdm16 and beige fat causes vascular remodeling and elevated blood pressure

While excess adiposity is a major risk factor for hypertension and cardiovascular disease, brown fat is associated with protection from these pathologies. Whether brown fat has a causal role in this process and the underlying molecular mechanisms remain unknown. Here we investigate the role of murine beige fat, as a model of inducible brown fat in humans, in adipocyte-vascular crosstalk. Using mice with an adipocyte-specific deletion of PRDM16, resulting in a loss of beige adipocyte identity, we discover a dramatic remodeling of perivascular adipose tissue, increased vascular reactivity and elevated blood pressure. We further show that the circulating enzyme Qsox1 is de-repressed in Prdm16-deficient adipocytes, and deletion of Qsox1 in PRDM16cKO mice rescues vascular fibrosis and reactivity. These results demonstrate a key new role for beige adipocytes in blood pressure regulation and identify Qsox1 as an important mediator of adipocyte-vascular crosstalk.

physiology↗

bioRxiv2025

A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the datasets utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

neuroscience↗

bioRxiv2025

An autonomous AI agent for universal behavior analysis

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit research scalability and accessibility. We introduce BehaveAgent, an autonomous multimodal AI agent designed to automate behavior analysis from video input without retraining or manual intervention. Unlike conventional methods that require manual behavior annotation, video segmentation, task-specific model training, BehaveAgent leverages the reasoning capabilities of multimodal large language models (LLM) to generalize across novel behavioral domains without need for additional training. It integrates LLMs, vision-language models (VLMs), and large-scale visual grounding modules, orchestrated through a multimodal context memory and goal-directed attention mechanism, to enable robust zero-shot visual reasoning across species and experimental paradigms, including plants, insects, rodents, primates, and humans. Upon receiving a video input, BehaveAgent autonomously identifies the correct analysis strategy and performs end-to-end behavior analysis and interpretation without human supervision. Leveraging vision-language representations, it performs general-purpose tracking, pose estimation and segmentation. We demonstrate BehaveAgents universal applicability to autonomously (1) identify the behavioral paradigm and develop an action plan specialized for the identified paradigm, (2) identify relevant subjects and objects, (3) track those features, (4) identify behavioral sequences with explicit reasoning, (5) generate and execute code for targeted analysis and (6) generate comprehensive research reports that integrate behavioral findings with relevant scientific literature. Through interpretable agentic reasoning, BehaveAgent makes its internal decision-making process transparent, clarifying why particular features are tracked or behaviors inferred. By reducing the time and expertise required for behavior analysis, BehaveAgent introduces a scalable, generalizable, and explainable paradigm for advancing biological and behavioral research.

neuroscience↗

bioRxiv2025

MALER: a web server to build, evaluate, and apply machine learning models for numerical biomedical data analysis

The explosive growth of numerical biomedical data poses a challenge in uncovering meaningful insights within from vast omics and clinical data. In recent years, machine learning has emerged as a powerful tool for processing and dissecting numerical biomedical data, making it a popular choice for addressing analytical challenges in biomedical research. Nevertheless, the intricate algorithms and complex optimization processes of machine learning model frameworks have deterred numerous non-machine learning experts. To address the need for comprehensive signatures exploration in biomedical field, we have developed Maler (http://www.inbirg.com/maler/home), a platform designed to facilitate the effortless and efficient application of machine learning models to high-dimensional biomedical data for bioscientists. Maler primarily offers four machine learning modes and a variety of machine learning algorithms to accommodate diverse analysis requirements. Simultaneously, it employs multiple feature selection methods to acquire relevant features and selects the optimal combinations of features based on machine learning models to achieve the best predictive outcomes. Maler is expected to provide a user-friendly and efficient platform for marker discovery, catering to the needs of biologists and clinical experts.

bioinformatics↗

bioRxiv2025

SIRT4 Controls Macrophage Function and Wound Healing through Control of Protein Itaconylation in Mice

Proper regulation of inflammatory responses is essential for organismal health. Dysregulation can lead to accelerated development of the diseases of aging and the aging process itself. Here, we identify a novel enzymatic activity of the mitochondrial sirtuin SIRT4 as a lysine deitaconylase that regulates macrophage inflammatory responses. Itaconate is a metabolite abundantly produced in activated macrophages. We find it forms a protein modification called lysine itaconylation. Using biochemical and proteomics approaches, we demonstrate that SIRT4 efficiently removes this modification from target proteins both in vitro and in vivo. In macrophages, elevated protein itaconylation increases upon LPS stimulation, coinciding with elevated SIRT4 expression. SIRT4-deficient macrophages exhibit significantly increased IL-1{beta} production in response to LPS stimulation. This phenotype is intrinsic to macrophages, as demonstrated by both lentiviral over-expression and acute SIRT4 knockdown models. Mechanistically, we identify key enzymes in branched-chain amino acid (BCAA) metabolism as targets of hyperitaconylation in SIRT4-deficient macrophages. The BCKDH complex component dihydrolipoamide branched chain transacylase E2 (DBT) is hyperitaconylated and has reduced BCKDH activity in SIRT4KO macrophages. Physiologically, SIRT4-deficient mice exhibit significantly delayed wound healing, demonstrating a consequence of dysregulated macrophage function. Our data reveal a novel protein modification pathway in immune cells and establish SIRT4 as a critical regulator at the intersection of metabolism and inflammation. These findings have implications for understanding immune dysregulation in aging and metabolic disease.

molecular biology↗

bioRxiv2025

SAKPE: A Site Attention Kinetic Parameters Prediction Method for Enzyme Engineering

0.The quantitative determination of enzyme kinetic parameters traditionally relies on experimental methods that are both time-intensive and costly. Machine learning models have demonstrated significant potential for predicting enzyme kinetic parameters in recent years. Despite this promise, these methods face challenges, including limited training data, inadequate sensitivity to subtle mutations, and poor alignment with practical enzyme engineering contexts. Here, we introduce SAKPE (Site Attention Kinetic Parameters Prediction Method for Enzyme Engineering), a novel machine-learning framework designed to predict enzyme kinetic parameters with enhanced accuracy in practical application scenarios. By incorporating protein representation, substrate representation, and protein representation with weights for important sites, SAKPE significantly outperforms existing methods in predicting enzymatic kinetic parameters, including turnover number (kcat), Michaelis constant (Km), and inhibition constant (Ki). Incorporating protein representation with weights for important sites enables SAKPE to effectively capture the impact of mutations, especially mutations of important sites and their surrounding amino acids of interest in enzyme engineering, on enzyme kinetics parameters. SAKPE offers a robust and practical tool for predicting enzyme kinetic parameters, providing a superior tool for enzyme engineering scenarios such as enzyme design, directed evolution, and industrial applications.

bioinformatics↗

bioRxiv2025

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Transcription initiation is a critical regulatory step in plant gene expression, yet its sequence determinants remain largely elusive. Here we introduce GenoRetriever, an interpretable deep learning model that deciphers the sequence basis of transcriptional initiation regulation across plant genomes. Trained on STRIPE-seq data from 16 soybean tissues and six other crop species, GenoRetriever identifies 27 core sequence motifs that govern transcription start site (TSS) selection and usage. The model predicts TSS locations and usage levels with high accuracy, as validated by in silico motif insertions, saturation mutagenesis, and CRISPR-Cas9 promoter editing. It further reveals that 31.85% of natural variation between wild and domesticated soybean drives shifts in promoter motif usage during domestication, and uncovers lineage-specific motif effects between monocots and dicots. This interpretable model and its user-friendly web server for promoter analysis and design make GenoRetriever both a methodological innovation and practical tool for plant functional genomics and crop improvement.

plant biology↗

bioRxiv2025

A new chromosome-level genome assembly for western painted turtle Chrysemys picta belli, a model for extreme physiological adaptations

The western painted turtle, Chrysemys picta bellii, has the greatest tolerance to anoxia of any tetrapod studied to date. These turtles reside in the northern United States and southern Canada, and survive months of anoxia while submerged in ice-locked ponds and bogs. Reference genomes provide an important resource for elucidating the molecular bases for such unique physiological traits. An initial reference genome for this species was published in 2013, but the assembly is highly fragmented which poses several limitations for downstream analyses and biological interpretation. In this study, we created a new and improved assembly by combining PacBio HiFi, 10x Genomics Chromium, Hi-C sequence data and BioNano optical mapping derived from a single individual to generate a new haplotype-resolved chromosome-level assembly for C. picta bellii, called "SLU_Cpb5.0". The genome size of the primary assembly is 2.372 Gb with a scaffold N50 of 133.6 Mb, which is a 6.5-fold improvement over the existing assembly. Genome annotation of SLU_Cpb5.0 revealed 12,242 novel genes compared to previous assemblies. Our PacBio Iso-Seq RNA sequencing data for twelve tissues unraveled over 100,000 novel transcript isoforms and 4,325 novel genes that were not annotated by standard NCBI pipeline. We also observed distinct patterns of tissue-specific isoform expression, creating a robust foundation for future characterization of the functions of these genes. The improved genome assembly and annotation will facilitate comparative genomics studies to better understand the genetic basis of C.picta belliis extreme physiological adaptations and other aspects of its biology.

genomics↗

bioRxiv2025

Spatial transcriptomics AI agent charts hPSC-pancreas maturation in vivo

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs - such as flexible reasoning, contextual understanding, and cross-modal integration - which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral -cells surrounding {beta}-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

genomics↗

bioRxiv2025

Machine learning-assisted enzyme engineering through ultra-high throughput sorting and large-scale sequence-function data generation

Machine learning (ML) shows great promise in protein engineering but has yet to be integrated with ultra-high throughput sorting (ultra-HTS) and NGS for large-scale sequence-function data generation to harness its capability to explore wider search space and more complex mutation events. Here, we introduce PUSDA, a framework that rapidly sorts mutant libraries into multiple performance groups with good accuracy and generates large-scale sequence-function data to power ML-driven protein design. As a demonstration, PUSDA generated over five million sequence-function data of an enzyme, with data processing revealing over 1.3 million unique enzyme mutants being sorted in a single day. With a trained ML model that achieved 93.52% accuracy, we further analysed combinatorial mutation events and applied a ratio-based selection approach to design novel enzyme sequences. Validation experiment demonstrated a 16.67-fold improvement in efficiency of identifying high-performance enzymes using PUSDA compared to using ultra-HTS alone. The designed novel enzyme achieved 8.23-fold increase in productivity compared to wild type. PUSDA lays a foundation to integrate ultra-HTS, NGS, and ML for future predictive enzyme engineering, offering a data-driven tool for accelerating breakthroughs in biotechnology.

synthetic biology↗

bioRxiv2025

In Situ Graphene-Seq: Spatial Transcriptomics and Chronic Electrophysiological Characterization of Tissue Microenvironments

Biological systems are composed of diverse, interconnected cell types, yet capturing both their functional dynamics and molecular identities at high spatiotemporal resolution remains challenging. While electrophysiological measurements provide real-time insights into cellular activities, they cannot fully describe the molecular architecture and states of the measured cells. Conversely, transcriptomics reveals cell gene expression patterns but does not capture functional states. Bridging these modalities is essential for a holistic understanding of the molecular mechanisms driving functional changes. In this study, we introduce in situ graphene-sequencing (graphene-seq), a unique platform that seamlessly integrates chronic electrophysiology with imaging-based, spatially resolved 3D transcriptomics, overcoming longstanding limitations of current multimodal approaches. This system leverages stretchable mesh nanoelectronics for long-term, single-cell-level interfacing and incorporates transparent graphene/PEDOT:PSS electrodes, enabling seamless integration of electrical recordings and optical imaging. By combining electrophysiology with high-throughput, imaging-based in situ sequencing, this platform allows comprehensive multimodal, spatially resolved analysis of cell microenvironment within spatially heterogeneous tissues. We validate in situ graphene-seq by charting multimodal profiles of human-induced pluripotent stem cell-derived cardiomyocyte and endothelial cell co-cultures, examining how spatial heterogeneity in cell composition influences both electrophysiological activity and gene expression. This scalable, integrated approach offers a powerful tool for studying the complex interplay between cellular function and molecular identity. It also provides insights into how tissue microenvironments shape cell behavior and molecular states, advancing applications in regenerative medicine, stem cell therapy, and disease modeling.

bioengineering↗

bioRxiv2025

Spike sorting AI agent

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.

neuroscience↗

bioRxiv2025

DMEM and EMEM are suitable surrogate media to mimic host environment and expand leptospiral pathogenesis studies using in vitro tools

Pathogenic Leptospira species can survive and thrive in a wide range of environments. Distinct environments expose the bacteria to different temperatures, osmolarities, and amounts and sources of nutrition. However, leptospires are mostly cultured, in a laboratory setting under in vitro conditions that do not reflect natural environments. This constraint on laboratory cultures limits the applicability of in vitro studies to the understanding of even simple pathogenic processes. Here we report, investigate, and identify a medium and conditions that mimic the host environment during leptospirosis infection, expanding the available in vitro tools to evaluate leptospiral pathogenesis. We quantified genome-wide gene expression of pathogenic Leptospira interrogans cultured in different in vitro media compositions (EMJH, DMEM, EMEM, and HAN). Using EMJH as standard, we compared gene expression in these compositions to genome-wide gene expression gathered in a host environment: whole blood (WB) of hamsters after infection with pathogenic leptospires. Leptospires cultured in DMEM and EMEM media shared 40% and 47% of all differentially expressed genes (DEGs) of leptospires present within WB (FDR<0.01), while leptospires cultured in HAN media only shared 20% of DEGs with those from WB. Furthermore, gene and pathway expression of leptospires cultured on DMEM and EMEM media exhibited a better correlation with leptospires grown in WB, including promoting expression of a similar leptospiral lipid A profile to the one identified directly in host tissues. Taken together, these results indicate that commercial cell-culture media EMEM or DMEM are better surrogates for in vivo pathogenic studies than EMJH or HAN media in Leptospira. These alternative culture conditions, using media that are a standard supply worldwide, provide a reproducible and cost-effective approach that can accelerate research investigation and reduce the number of animal infections necessary for basic research of leptospirosis.

microbiology↗