Biology subjects

Lee, H.

Publications and source records attributed to Lee, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

The first reference genome assembly of the Chilean sea fig (Carpobrotus chilensis)

Carpobrotus chilensis (Chilean sea fig) is a coastal succulent of uncertain origin that has naturalized along the California coast, where it co-occurs and hybridizes with the invasive species, Carpobrotus edulis. Despite their ecological importance and widely supported hybridization, genomic resources for this genus remain scarce. Here, we present a draft genome assembly of C. chilensis generated from PacBio HiFi long reads. The assembled nuclear genome spans 981.7 Mb across 178 contigs. The contig N50 was 73.0 Mb, and BUSCO completeness was 96.3%. K-mer and SNP-based analyses indicate extremely low heterozygosity (3.4 x 10-), reduced genetic diversity in this population. The genome is highly repetitive, with 81.67% of the sequences composed of transposable elements, predominantly long terminal repeat (LTR) retrotransposons. Gene prediction identified 21,744 protein-coding genes, with BUSCO completeness of 95.8%. Comparative analysis with C. edulis identified 8,783 single-copy orthologous gene pairs, with a median synonymous substitution rate (dS) of 0.019, indicating low sequence divergence between the two species. This genome assembly provides a foundational resource for investigating the genomic basis of hybridization and invasion in Carpobrotus.

genomics↗

bioRxiv2026

Pore-Resolved High-Throughput Quantification of Amyloid Aggregation and Amplification

Amyloid fibrils are implicated in a wide spectrum of neurodegenerative and systemic disorders, yet their biological consequences are governed not only by total fibril content but also by how fibrillar species are organized, clustered, and amplified within heterogeneous populations. Conventional thioflavin T (ThT)-based assays provide sensitive sample-level readouts of {beta}-sheet-rich material but offer limited access to the local population structure underlying amyloid aggregation and amplification. Here, we introduce the Amyloid Pore Quantification (APQ) chip, a pore-resolved geometric partitioning platform that converts heterogeneous amyloid assembly states into fluorescence intensity distributions across thousands of defined pore-level units. Using hen egg-white lysozyme as a model amyloid-forming protein, we combine length-controlled truncated amyloid nanofibrils with vacuum-assisted ThT infiltration to establish a reproducible pore fluorescence intensity reference. APQ provided quantitative concentration-dependent calibration across a 50-fold concentration range, enabling bulk-comparable quantification while preserving pore-level distribution information. Deviations from this reference resolved pH-dependent fibril clustering near the isoelectric point as pore-signal suppression and accessibility loss, captured pepsin-associated fibrillar amplification as a population-wide increase in pore intensity, and distinguished monomer- and oligomer-driven fibril processing through coupled amplification-aggregation fingerprints. When benchmarked against ensemble fluorescence measurements and AFM morphological analysis, APQ revealed assembly-state changes that were not fully represented by sample-level ThT intensity alone. These results establish APQ as a high-throughput, distribution-aware analytical framework for translating amyloid aggregation and amplification into quantitative pore-resolved fingerprints.

bioengineering↗

bioRxiv2026

Super Learner Ensemble Modeling of CPTAC Proteomic Data for Survival Prediction in Head and Neck Squamous Cell Carcinoma

Survival analysis in head and neck squamous cell carcinoma (HNSCC) is traditionally performed using Cox proportional hazards models, alongside some exploration into black-box machine learning methods. The Super Learner (SL) algorithm addresses this model selection dilemma by combining diverse candidate algorithms into a weighted ensemble to perform comparably to the best candidate method. This study evaluates the performance of SL in HNSCC. Proteomic features as well as clinical covariates from 96 CPTAC HNSCC samples were modeled with three candidate algorithms (Cox LASSO, Cox Ridge, and Random Survival Forest) as well as the ensemble SL method. Models were optimized via Unos time-dependent Concordance Index (C-index) and tested at 1- and 3-year time horizons using 2000 bootstrap resamples. The Cox Ridge regression model achieved the highest predictive accuracy among the four total methods. However, the SL demonstrated stable performance over both time horizons (1-year C-index: 0.985; 3-year C-index: 0.960). Variable importance analysis of the Cox Ridge model successfully identified malignant proteins (ATR, MAML1, MIEN1) alongside novel potential prognostic indicators (ZNF800, KERA). This analysis emphasizes the statistical necessity for larger cohorts for ensemble learning, while providing a benchmark of proteomic indicators in HNSCC.

bioinformatics↗

bioRxiv2026

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes

Type 2 diabetes (T2D) arises from genetic susceptibility and chronic metabolic stress, but whether these converge on shared molecular programs in human populations remains unclear. Here, we develop a dish-to-biobank framework linking controlled {beta}-cell perturbation to population-scale disease genetics through the circulating plasma proteome, and apply it to T2D. scRNA-seq of human stem cell-derived islets under factorial glucose and palmitate exposure identifies their combination (glucolipotoxicity) as the condition eliciting the strongest SC-{beta} cell transcriptional response, with glucolipotoxicity-upregulated genes uniquely enriched for T2D heritability, monogenic diabetes genes, and rare-variant burden signals. CRISPR knockout of {beta}-cell identity regulators PAX6 and PDX1 aligns with this program, establishing convergence of environmental and genetic perturbations on a shared disease-relevant state. We then used the plasma proteome as an accessible population-scale readout of these experimentally defined {beta}-cell stress programs, scoring 45,956 UK Biobank White British participants. We define heritable stress signatures that associate with refined carbohydrate and saturated fat intake, and undergo trans-tissue genetic regulation, with a subset of variants showing diet-dependent effects. Together, these findings establish glucolipotoxicity as a genetically anchored model of {beta}-cell dysfunction and provide a generalizable framework for linking controlled cellular perturbations to human disease genetics at population scale.

genetics↗

bioRxiv2026

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs

Schizophrenia (SCZ) is a common psychiatric disorder characterized by psychosis, emotional withdrawal, and cognitive deficits. Most SCZ risk variants reside in non-coding regions of the genome and are thought to influence disease risk by modulating gene regulation. However, the target genes, biological pathways, and cell types through which these variants exert their effects remain poorly understood. To address this gap, we employed in vivo CRISPR droplet sequencing (CROP-seq) in the postnatal mouse neocortex. We perturbed 12 SCZ risk genes previously linked to functionally validated risk variants, followed by single-cell RNA sequencing. We identified 3,031 differentially expressed genes (DEGs) that recapitulate transcriptional alterations observed in postmortem SCZ brains. Integrative analysis using DEG clustering, factor analysis, and gene regulatory network inference uncovered convergent gene programs with distinct biological functions and cell type specificity. Notably, ciliary transcriptional programs consistently emerged across analytical frameworks. The primary cilium is a neurocircuit modulating signaling organelle in neurons and glia that remains understudied in SCZ. Perturbation of key contributors to the ciliary transcriptional programs led to significant alterations in ciliary structure, suggesting that SCZ genetic risk factors may influence how brain cells sense and transduce extracellular signals through synapse-independent mechanisms. Together, this study provides the first in vivo characterization of the functional consequence of common variant architecture in SCZ and implicates ciliary dysfunction as a convergent downstream mechanism.

neuroscience↗

bioRxiv2026

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated statistical limitation of commonly used ML/AI regression models for continuous outcomes: systematic prediction bias and its propagation to downstream association estimates. This issue becomes more challenging when the true outcome, biological age, is latent and therefore unobserved during ML/AI model training. We demonstrate that systematic prediction bias can distort and, in some cases, even reverse downstream association analyses that use aging clocks as ML/AI-predicted outcomes to assess their associations with exposures or clinical factors. For example, it can produce spurious associations suggesting that older predicted brain age is linked to better cognitive performance, or that older epigenetic age is associated with better kidney function. To address this problem, we introduce a principled and broadly applicable ML/AI regression framework based on constrained optimization, yielding better calibrated aging-clock estimates and valid downstream inference.

bioinformatics↗

bioRxiv2026

Reproducible and shareable bioinformatics pipelines from natural-language prompts

Large language models (LLMs) are increasingly used to generate bioinformatics pipelines and to carry out analyses from natural-language prompts. However, the resulting analyses are often difficult to reproduce across sessions, owing to the non-deterministic nature of LLM-driven conversations and heterogeneity of local execution environments, and cannot run on remote high-performance computing (HPC) servers or be shared and reused. We present Autopipe, a platform that guides any Model Context Protocol (MCP) - compatible LLM to produce, execute, and publish source-preserved, re-executable containerized pipelines. Autopipe enables users to execute bioinformatics pipelines on any on-premises remote servers - supported by comprehensive setup documentation aimed at researchers without prior server-administration experience - and to visualize results through an extensible web-based viewer. The Autopipe platform comprises four components: a desktop application with an embedded MCP server for pipeline management and remote execution, an online registry for pipeline and plugin discovery, a web-based result viewer, and a CLI tool for customizing viewer plugins. Autopipe turns conversational analysis into re-executable and shareable workflows. Autopipe is freely available at https://autopipe.org/.

bioinformatics↗

bioRxiv2026

Mechanochemically Programmed, Oligomer-Selective Amyloid Assembly via Axial Rotation

Amyloid oligomers have been widely implicated as primary cytotoxic intermediates; however, their selective and scalable production remains challenging due to rapid fibril amplification. Here, we demonstrated that sustained axial rotation enables programmable mechanochemical control over amyloid pathway selection without the use of chemical additives. Using a thermal axial rotator, native monomeric hen egg-white lysozyme was incubated at 60 {degrees}C under quantitatively tunable rotational speeds, imposing defined centrifugal forces and wall-associated shear that restructured the hydrodynamic boundary conditions. A discrete transition emerged near 600 RPM, separating the two distinct assembly regimes. Below this threshold, aggregation followed a fibril-amplifying pathway characterized by elevated {beta}-sheet content and elongated fibrillar morphologies. Above this threshold, fibrillar growth was strongly attenuated and oligomer-dominant assemblies predominated. Spectroscopic analyses and atomic force microscopy revealed that axial rotation redistributes the amyloid assembly states rather than simply suppressing aggregation. Functionally, the RPM-defined assemblies exhibited kinetically distinct seeding behaviors and induced divergent cytotoxic phenotypes in SH-SY5Y neuroblastoma cells. These findings establish axial rotation-mediated hydrodynamic boundary control as a scalable, chemical-free strategy for reprogramming amyloid assembly pathways and producing oligomer-rich assemblies with well-defined structural and functional properties.

bioengineering↗

bioRxiv2026

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

Large-scale perturbation screens have begun to map cell intrinsic gene-function relationships, yet how genes shape tissue architecture remains largely unexplored. To address this gap, we developed PerturbSpace, a novel approach that integrates CRISPR perturbations with spatially hashed single-cell multiomics. This approach enables the first high-throughput, spatially resolved Perturb-seq analysis across complex tissue architecture in vivo. Notably, PerturbSpace enables spatial transcriptome-wide perturbation readouts at organ scale and can be seamlessly integrated with orthogonal modalities. We combine PerturbSpace with surface proteomics and expressed lineage tracing barcodes to demonstrate multimodal compatibility. We use PerturbSpace to study the genetic determinants of tissue architecture. First, we map how 40 transcriptional regulators determine the size and lineage composition of colonies in the spleen during regenerative hematopoiesis. Second, we characterize immune-niche interactions in the liver by dissecting the extrinsic effects mediated by cytokine-secreting immune cells on their neighboring cells. Collectively, our work establishes PerturbSpace as a scalable and cost-effective approach for transcriptome-wide spatial profiling of whole cells while remaining compatible with the single-cell multiomics workflows that the field has already adopted at scale.

genomics↗

bioRxiv2026

An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures

Spontaneous thought is pervasive in everyday human cognition, yet datasets capturing its neural dynamics under minimally interrupted conditions remain limited. The current dataset was acquired from a think-aloud functional MRI experiment in which 118 participants continuously verbalized their spontaneous thoughts during 10-minute scanning sessions. The raw MRI data and verbal transcripts with sentence-level timestamps were previously released and analyzed in our prior study examining neural activity associated with thought transitions. Building on that release, we additionally provide preprocessed MRI data, speech transcriptions with word-level timestamps aligned to image acquisition, large language model-generated ratings of transcribed thoughts across emotional and sensory dimensions, and self-report survey measures assessing personality, mental health, and cognitive abilities. Validation analyses demonstrated activation in expected cortical regions associated with speech production and sensory content identified from transcript annotations, agreement between language model and human ratings, and adequate internal consistency of survey measures, supporting the datasets overall quality. This dataset enables reuse for investigations of spontaneous thought, speech generation, and individual differences using naturalistic functional MRI data.

neuroscience↗

bioRxiv2026

Dependencies in heterogeneous, lineage plastic patient-derived prostate cancer organoids revealed through integrated single-cell multiomics and CRISPR screening

Lineage plasticity and tumor heterogeneity limit the effectiveness of targeted therapies, yet the functional dependencies used to nominate therapeutic targets are often derived from homogeneous systems that fail to capture this complexity. Here, we establish a framework to resolve state-specific genetic vulnerabilities by integrating single-cell multiomics (RNA and ATAC) with pooled CRISPR-Cas9 screening across a large panel of patient-derived organoids (PDOs) from castrate-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). We generate a single-cell multiome atlas spanning >190,000 cells across 22 PDOs, defining seven lineage states--including intermediate and plastic populations not resolved by bulk profiling--and demonstrate that these lineage programs robustly classify independent transcriptomic datasets from prostate cancer patient tumors. By systematically coupling this atlas to subtype-resolved CRISPR screens, we construct a functional dependency map linking cell state in heterogeneous 3D human tumor models. We show that intratumoral heterogeneity fundamentally reshapes the interpretation of gene essentiality, whereby gene-level depletion reflects the composite behavior of co-existing subpopulations, and identify a general principle in which resistant "limiting" populations disproportionately determine aggregate fitness effects. This framework reveals both canonical and previously unrecognized lineage-restricted dependencies within highly plastic tumor and NEPC states, including a therapeutically targetable dependency on the aryl hydrocarbon receptor (AHR) in a novel hybrid stem-like/ASCL1 population. Together, these data establish an extensive multi-dimensional prostate cancer resource, identify novel lineage-resolved biology, and provide a generalizable strategy for interpreting functional genomics in heterogeneous human tumors.

cancer biology↗

bioRxiv2026

Cognitive load modulates cortical excitability in the human prefrontal cortex

The prefrontal cortex dynamically adjusts its excitability to meet changing cognitive demands, yet our understanding of how these rapid state changes unfold in real time remains limited. Existing evidence relies predominantly on correlational neuroimaging and invasive electrophysiology in clinical populations, neither of which affords causal measurement in healthy humans. Here, using concurrent single-pulse TMS-EEG during a parametric cognitive multi-source interference task (MSIT) in 27 healthy participants, we causally probed left prefrontal excitability across conditions spanning a range of cognitive interference levels. Prefrontal excitability scaled systematically with cognitive load, peaking at intermediate incongruency levels where trial-type uncertainty was greatest, and differentiating high-from low-conflict trials at the single-subject level. Critically, prefrontal excitability and midfrontal theta power showed parallel state dependency patterns, linking causal perturbation of prefrontal circuit responsiveness to an established oscillatory marker of cognitive control. The observed excitability modulation was selective to the left prefrontal cortex: stimulation of right dlPFC, pre-SMA, and vertex in a separate control cohort (N=5) produced no load-dependent effects. These results position prefrontal excitability as a real-time, regionally selective physiological readout of how the human prefrontal cortex adapts to cognitive demand. HighlightsO_LIWe evaluated the links between cognitive interference, prefrontal excitability, and intrinsic theta activity during a multi-source interference task C_LIO_LICognitive load, estimated based on behavioral performance, was greatest at at intermediate interference levels (33% congruent), where trial-type uncertainty was maximal C_LIO_LIPrefrontal excitability, measured via single-pulse TMS-evoked potentials, scaled with cognitive load C_LIO_LIEffects were observed at the single subject level and were specific to prefrontal stimulation C_LIO_LIPrefrontal excitability changes and midfrontal theta power showed parallel scaling with cognitive load, linking causal probing to established oscillatory markers C_LIO_LIThis noninvasive technique provides causal measurement of prefrontal engagement during cognitive processing C_LIO_LIThe methodology demonstrates potential for tracking brain state dynamics in both healthy cognition and neuropsychiatric disorders C_LI

neuroscience↗

bioRxiv2026

Metacontam: A Negative Control-Free Decontamination Method for Metagenomic Analysis

Shotgun metagenomic sequencing enables high-resolution profiling of host-associated microbial communities. However, contaminant DNA can substantially distort biological interpretations, especially in low-biomass samples. Here, we introduce Metacontam, a control-free method for species-level decontamination of shotgun metagenomic data. Metacontam integrates blacklist-guided community detection within a species correlation network with average nucleotide identity (ANI) to identify contaminants arising from shared sources. Across diverse low-biomass and mixed-biomass datasets, Metacontam outperformed existing approaches, improving the detection of low-abundance and low-prevalence contaminants while retaining biologically plausible taxa. It also reduces kit-specific biases in skin metagenomes and improves downstream analyses of tissue microbiome data. Together, these results demonstrate that Metacontam enables accurate identification of contaminant taxa across diverse metagenomic datasets, even in the absence of negative controls.

bioinformatics↗

bioRxiv2026

Comparative Analysis of Relative Ligand Binding Free Energy Simulation Methods: Amber-TI, GROMACS-NETI, OpenMM-FEP, and BLaDE-MSLD

Structure-based drug design has become increasingly important in the pharmaceutical industry for accelerating the discovery of effective drug candidates. In particular, ligand binding free energy serves as a critical metric for predicting drug efficacy during the key stages of hit discovery and lead optimization. Continuous progresses have been made in the prediction of ligand binding free energies, but direct comparisons of different methods using the same force field remain challenging due to their unique implementations into different simulation engines. In this study, we present a direct comparison of four popular methodologies (Amber-TI, GROMACS-NETI, OpenMM-FEP, and BLaDE-MSLD) for calculating relative binding free energies ({Delta}{Delta}Gbind) with the same Amber protein and ligand force fields using MolCube Alchemical Free Energy Simulator (MolCube-AFES), which provides an input generation workflow to support {Delta}{Delta}Gbind calculations of all four methods. We used 80 alchemical transformations (among the JACS benchmark set by Wang et al.) and two additional applications to compare the predicted {Delta}{Delta}Gbind from the four methods against experimental measurements. All four methods reproduced experimentally observed trends with most transformations within {+/-}2 kcal/mol from experiments and show broadly comparable accuracy with no statistically significant performance differences across the benchmark dataset. These results demonstrate that MolCube-AFES enables controlled, cross platform benchmarking and show that all four different alchemical free energy methods deliver statistically equivalent accuracy, with method selection guided by workflow requirements such as throughput, portability, and perturbation network design rather than expected differences in performances.

biophysics↗

bioRxiv2026

Landscape-scale navigation unlocks antibody CDR structural logic for AI-guided rescue and therapeutic optimization

While AI offers transformative potential for therapeutic antibody design, the lack of ground-truth data fundamentally constrains our ability to model the epistatic topology of fitness landscapes. Here, we establish a high-throughput workflow to characterize tens of thousands of antibody variants per week with gold-standard biophysical precision. By combinatorially assembling functional variants from deep mutational scanning, we charted antibody fitness landscapes comprising over 17,000 data points, which revealed an extremely rugged, non-navigable epistatic topology. Yet, navigating at this unprecedented scale enabled the discovery of rare peak clusters exhibiting simultaneous enhancements in affinity and productivity. Strikingly, ProteinMPNN predicted the CDR-dependent productivity landscape with remarkable accuracy, suggesting that sequence-structure compatibility within CDRs gates cellular productivity. This insight enabled a structure-guided rescue strategy combining AlphaFold3 and ProteinMPNN, which successfully restored the cellular productivity of high-affinity, low-productivity clones via single amino acid substitutions. Two elite variants drawn directly from peak clusters further demonstrated 20- to 100-fold in vivo efficacy gains in a murine psoriasis model. Our findings establish CDR structural fitness as a fundamental determinant of antibody cellular productivity and validate landscape-scale navigation as a powerful framework for therapeutic antibody optimization.

bioengineering↗

bioRxiv2026

Early Visual Cortex Represents Sensory and Mnemonic Orientations in Separate Subspaces with Preserved Geometry

How early visual cortex (EVC) represents working memory (WM) content while continuing to process incoming sensory input remains unclear. Using fMRI with prolonged delays to isolate mnemonic activity, together with analyses of cross-decoding, low-dimensional subspace structure, and representational geometry, we examined the relationship between sensory and mnemonic orientation representations in human EVC. Cross-decoding generalized poorly between sensory and mnemonic epochs, but this did not imply unrelated codes. Rather, the two occupied separable low-dimensional subspaces while preserving representational geometry across epochs. During discrimination and estimation, sensory- and mnemonic-trained decoders yielded dissociable readouts of concurrent sensory and mnemonic information from the same EVC measurements. Mnemonic coding showed little dependence on the retinotopic radial bias that characterized sensory coding, and trial-by-trial variability in mnemonic representation predicted both discrimination choices and estimation reports. Our findings support a population-level account in which mnemonic information in EVC is re-expressed in a separable but geometrically preserved format.

neuroscience↗

bioRxiv2026

Coordinated Tuning of Ionizable Lipids and Formulation Redirects mRNA Vaccines Toward Lymphoid-Specific CD4+ T Cell Immunity

The global success of mRNA vaccines has underscored the pivotal role of lipid nanoparticles (LNPs), yet how subtle chemical variations in ionizable lipids and their formulation parameters orchestrate complex immune landscapes remains largely elusive. Here, we report a novel ionizable lipid, N4Z, and demonstrate that its distinct chemical signature selectively intensifies early innate immune programs compared to its structural analogue, N4Y. Single-cell transcriptomic profiling at the injection site reveals that N4Z-based LNPs uniquely prime inflammatory and type I interferon-related transcriptional programs, accompanied by a rapid influx of B and CD4+ T cells. Beyond lipid chemistry, we show that formulation-level tuning, that is independent of the ionizable lipid structure, can reshape the systemic biodistribution from hepatic dominance toward lymphoid tissues. This optimization substantially enhances macrophage-associated antigen expression, which in turn amplifies polyfunctional CD4+ T cell responses, T follicular helper cell differentiation, and germinal center reactions. Our findings establish that the coordinated interplay between lipid engineering and formulation design provides a programmable platform for precision mRNA vaccination, achieving superior protective efficacy and neutralizing activity over clinically validated benchmarks.

bioengineering↗

bioRxiv2026

Influence of Lipomannan and Lipoarabinomannan Concentration on Mycobacterial Inner Membranes Characterized by All-atom Simulations

Mycobacteria are responsible for causing severe illnesses like tuberculosis and leprosy in humans. Studying the mycobacteria cell envelope presents a significant challenge due to its intricate lipid compositions and structural variations and also its harmful nature in a typical experiment setting. In this study, we use all-atom molecular dynamics simulation to study mycobacterial inner membranes (MIMs). By incorporating different types of phosphatidyl-myo-inositol-mannosides (PIMs) and their glycoconjugates such as lipomannans (LM) and lipoarabinomannans (LAM) lipoglycans, we have constructed both symmetric and asymmetric membrane systems to study the MIM structure and dynamics under varying compositions of each lipid type. Our results show that the phospholipid/PIM-rich inner leaflet remains a stable, fluid bilayer, and the outer leaflet structure and dynamics are heavily governed by lipoglycan surface density. Importantly, as LM/LAM concentration increases, the polysaccharide chains shift from flexible, membrane-lying orientations to a compact brush-like state aligned with the membrane normal. This crowding significantly reduces the solvent-accessible volume and limits direct interactions between LM/LAM sugars and the outer leaflet surface. Furthermore, we observe that high lipoglycan presence in the outer leaflet slows lipid diffusion across the entire bilayer, demonstrating a dynamic coupling between the two leaflets. By resolving these LM/LAM sugar-level dynamics and their impact on membrane-wide properties, this study provides a molecular framework for future MIM modeling and simulation with various (peripheral) membrane proteins to better understand how the MIM functions as a regulated physical barrier and a platform for mycobacterial virulence.

biophysics↗