Biology subjects

Cha, M.

Publications and source records attributed to Cha, M..

12 recordsLinked to original sources

bioRxiv2026

Distinct control of T cell proliferation and effector function by partitioning of intracellular sulfur from cysteine

Delineating how acquired nutrients are partitioned into different intracellular pathways, and how these various fates support distinct functions in T cells is limited. We show that CD8+ T cells acquire cysteine to serve both as a substrate for glutathione (GSH) production, which modulates effector functions, and to cede its sulfur for NFS1-dependent FeS-cluster synthesis, which supports proliferation. NFS1 deletion in activated CD8+ T cells promotes exhaustion and dampens anti-cancer immunity, while blocking cysteine flux into GSH, or enforcing FeS metabolism, enhance tumor control. This role for disrupted FeS metabolism in T cell exhaustion is echoed in data from human HCC. Elucidating how different intracellular pathways use cysteine enables targeted control of cysteine flux to retain beneficial effects of cysteine while abolishing those that restrain function. We illustrate this concept for one metabolite, cysteine, but it is likely to apply to other metabolites relevant for immune cell function.

immunology↗

bioRxiv2025

From minutes to bounds: A probabilistic UV-C control and a shape-only morphological fingerprint for postharvest Colletotrichum

Postharvest losses in high-value horticultural crops such as cacao and coffee are often driven by Colletotrichum spp. and other latent fruit pathogens. Ultraviolet-C (UV-C) is increasingly deployed as a chemical-free postharvest technology, yet prescriptions remain framed in minutes rather than in probabilistic guarantees of disease control and safety. We present a dual framework that (1) establishes conservative confidence bounds on survival and (2) validates a "shape-only" morphological fingerprint. This approach addresses the biological complexity of host-pathogen interactions by quantifying isolate-specific heterogeneity, rather than averaging it away. We utilized a large Colletotrichum dataset (n = 5,363) from cacao and coffee, spanning diverse treatments including UV-C, UV-B, and sonication. First, focusing on the Coffee UV-C cohort ([~]10 min), we quantified this heterogeneity; the most conservative Clopper-Pearson upper 95% bound on survival reached 1.000, highlighting partial survival events (e.g., isolate P24-88) under otherwise high-efficacy conditions. This probabilistic framework captures the "tail-risk" of biological resilience instead of assuming complete kill. Second, we trained machine learning models on the full dataset using only geometric features (e.g., aspect ratio, asymmetry), explicitly excluding all primary size metrics. Serving as a rapid physiological indicator of UV-induced stress, these "shape-only" models successfully predicted pathogen host-origin (Accuracy {approx} 0.93) and post-treatment survival (R{superscript 2} {approx} 0.74). The signals ability to generalize across UV-B and sonication confirms that geometry, not just growth reduction, carries a robust and transferable physiological stress signature. This work provides a device-agnostic, probabilistic control platform, replacing time-based heuristics with quantitative guarantees and a generalizable, shape-based diagnostic. HighlightsO_LIA conservative upper 95 % confidence bound (on survival = 1.000) was observed for the Coffee UV-C ([~]10 min) cohort, revealing strong isolate-specific heterogeneity rather than a universal kill guarantee C_LIO_LIIsolate-specific heterogeneity (e.g., localized P24-88 survival) was quantified rather than averaged away. C_LIO_LIA size-free morphological fingerprint predicted host-origin (Accuracy {approx} 0.93) and survival (R{superscript 2} {approx} 0.74). C_LIO_LIThe shape-only signal generalized across diverse stressors, including UV-C, UV-B, and sonication. C_LIO_LIA probabilistic, device-agnostic control framework replaces traditional time-based heuristics. C_LI

microbiology↗

bioRxiv2025

Decoding pathogen ecological memory from chemical-stress phenotypes

Can standardized chemical stress reveal a reproducible, taxonomy-agnostic stress-response fingerprint that is predictive of sample-source labels (crop-of-isolation) in fungal isolates? Six coffee{square}associated Colletotrichum isolates were profiled using four phenolic{square}branched compounds and compared them with a previously characterized cacao panel. Quantitative morphology, hyperspectral imaging (HSI), and supervised machine learning (ML) yielded panel-specific fingerprints under uniform, isotropic in vitro conditions. Circularity, a measure of edge symmetry, was the most informative morphological feature, and ML classified the crop-of-isolation label (coffee vs cacao, in this panel) with 86.7% accuracy in within-panel cross-validation. HSI detected dose-dependent spectral shifts in a targeted subset of isolates and compounds, including changes near 1930 nm in the short-wave infrared, a moisture-sensitive region that warrants robustness checks (e.g., band masking or preprocessing sensitivity) prior to biochemical attribution. Multi{square}locus phylogeny showed the coffee isolates are polyphyletic, so the predictive signal should be interpreted conservatively as a taxonomy-agnostic phenotype fingerprint associated with crop background in this mixed-lineage panel, acknowledging that crop labels are partially confounded with phylogenetic structure. We propose a "chemical priors" framework as a working hypothesis, in which long-term environmental exposure may imprint stress-response pathways that become legible under simple, standardized probes. This integrative workflow supports scalable screening of eco-friendly antifungals and sensor-driven decision support for high-throughput phenotype-based screening workflows. HighlightsO_LITaxonomy-agnostic workflow integrates hyperspectral imaging and morphology. C_LIO_LIMachine learning predicts crop-source labels with 86.7% accuracy in mixed lineages. C_LIO_LISystem exhibits robustness, maintaining >86% accuracy even after feature ablation. C_LIO_LINon-linear ML captures structure missed by linear stats (1.7% variance). C_LIO_LIEnables rapid, sensor-driven antifungal screening without prior DNA sequencing. C_LI

microbiology↗

bioRxiv2025

An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules

Accurate fungal ITS diagnostics rely on public sequence archives, but heterogeneous record lengths, especially frequent truncation before the LSU/28S segment, cause naive in silico benchmarking to conflate primer performance with database incompleteness. To resolve this persistent "denominator error," we present an open and fully reproducible "eligibility-aware" framework. Our pipeline first establishes eligibility by confirming both primer sites are present before applying bench-realistic performance rules, including a strict penalty for 3-terminal mismatches. It further provides mechanistic insights by analyzing binding-site conservation and uses a rarefaction-based approach to guide efficient quality control as databases grow. We demonstrate the frameworks utility using the cacao pathosystem, a context where rapid differentiation of the fungal pathogen Moniliophthora from symptomatically similar oomycetes is critical. The result is a robust, field-ready diagnostic decision tree operable under a single touchdown (TD) PCR program. By providing a transparent and barcode-agnostic template, our eligibility-aware approach offers a significant methodological advance for designing and validating molecular assays in mycology and beyond.

microbiology↗

bioRxiv2025

Unveiling differential responses to UVB (305 nm) and UVC (275 nm) in cacao-infecting Colletotrichum gloeosporioides and Pestalotiopsis sp.

Sustainable control of microbial pathogens requires alternatives to chemicals, but physical methods like Ultraviolet-C (UVC) show variable efficacy linked to poorly understood pathogen-specific responses. Here, we investigate differential UVB/UVC responses in plant pathogenic fungi (Colletotrichum gloeosporioides, Pestalotiopsis sp.). Using hyperspectral imaging and machine learning, we dissect the physiological underpinnings of UV sensitivity. UVC proves more potent than UVB, with Pestalotiopsis sp. showing significantly higher resistance than C. gloeosporioides isolates. Crucially, hyperspectral signatures correlated with resistance, revealing UVC-induced photopigment changes, biochemical disruption, and oxidative stress markers in sensitive isolates, contrasting with minimal perturbation in the resistant isolate. Machine learning accurately decoded these complex phenotypes for classification. This understanding enabled enhanced inactivation via optimized pulsed UVC and synergistic sonication. We link distinct physiological states, non-invasively detected via hyperspectral imaging, to fungal UV resistance, demonstrating how integrating advanced phenotyping and machine learning provides a mechanistic basis for optimizing physical pathogen controls.

microbiology↗

bioRxiv2025

Enhancing the protein fitness of interferon-lambda through computational design and glyco-engineering for prophylactic nasal drugs against respiratory viruses

Interferon-{lambda} (IFN-{lambda}), a type III interferon that selectively targets epithelial cells, holds strong potential as an intranasal antiviral due to its ability to suppress respiratory virus replication without inducing systemic inflammation. However, clinical translation of human IFN-{lambda}3 (hIFN-{lambda}3) is hindered by limited thermostability, protease susceptibility, and rapid mucosal clearance. In this study, instability-prone elements in hIFN-{lambda}3 are eliminated through artificial intelligence (AI)-based backbone remodeling and targeted surface hydrophobic patch engineering. A protease-sensitive loop is replaced with a de novo -helix, which shields neighboring hydrophobic patches and forms a new hydrophobic core, yielding an engineered variant (hIFN-{lambda}3-DE1) with enhanced thermostability (Tm >90{degrees}C), protease resistance, and preserved antiviral activity and structural integrity even after extended heat stress (two weeks at 50{degrees}C). Further glyco-engineering introduces an N-linked glycan at a site distant from receptor-binding interfaces, improving solubility, production yield, and diffusion through synthetic nasal mucus. Intranasal administration of the resulting variant (G-hIFN-{lambda}3-DE1) enables effective mucosal penetration and provides robust prophylactic protection against influenza A virus infection in an in vivo mouse model. These findings highlight a robust and versatile strategy that combines AI-driven structural design with glyco-engineering to develop scalable, bioavailable, and functionally enhanced nasal biologics for respiratory virus prophylaxis.

biochemistry↗

bioRxiv2024

Predicting purification process fit of monoclonal antibodies using machine learning

0In early stage development of therapeutic monoclonal antibodies, assessment of ease of purification process development typically requires extensive experimentation. However, upstream protein expression and downstream purification experiments are often in conflict with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models which predict monoclonal antibody chromatographic binding behavior from the amino acid sequence. This work describes a QSPR strategy for in silico monoclonal antibody purification process fit assessment. Principal Component Analysis is applied to extract a one-dimensional basis for comparison of molecular chromatographic binding behavior from multi-dimensional high-throughput batch-binding screen data. Kernel Ridge Regression is employed to predict the first principal component for new molecular sequences. This workflow is demonstrated with a set of 97 monoclonal antibodies for five chromatography resins in two salt types across a range of pH and salt concentrations. Model development benchmarks four descriptor sets from biophysical structural models and protein language models. The investigation illustrates the value QSPR models can provide to purification process fit assessment, and selection of resins and operating conditions from sequence alone.

bioinformatics↗

bioRxiv2023

SUBTLE: An unsupervised platform with temporal link embedding that maps animal behavior

While huge strides have recently been made in language-based machine learning, the ability of artificial systems to comprehend the sequences that comprise animal behavior has been lagging behind. In contrast, humans instinctively recognize behaviors by finding similarities in behavioral sequences. Here, we develop an unsupervised behavior-mapping framework, SUBTLE (spectrogram-UMAP-based temporal-link embedding), to capture comparable behavioral repertoires from 3D action skeletons. To find the best embedding method, we devise a temporal proximity index as a metric to gauge temporal representation in the behavioral embedding space. The method achieves the best performance compared to current embedding strategies. Its spectrogram-based UMAP clustering not only identifies subtle inter-group differences but also matches human-annotated labels. SUBTLE framework automates the tasks of both identifying behavioral repertoires like walking, grooming, standing, and rearing, and profiling individual behavior signatures like subtle inter-group differences by age. SUBTLE highlights the importance of temporal representation in the behavioral embedding space for human-like behavioral categorization. One Sentence SummaryUnsupervised behavior-mapping from 3D action skeletons achieves superior performance, captures behavioral repertoires, and identifies inter-group differences, emphasizing how temporal representation is critical in the behavioral embedding space.

neuroscience↗

bioRxiv2023

Predicting the efficacy of BH3 mimetics through the profiling of multiple protein complexes

BH3 mimetics are protein-protein interaction (PPI) inhibitors that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells, a prominent example of protein complex-targeting therapies. Despite the remarkable success of the BH3 mimetic ABT-199 in treating hematological malignancies, only a fraction of patients respond to ABT-199 and eventually develop resistance, necessitating the predictive biomarkers for both initial responses and resistance development. We here used the single-molecule pull-down and co-immunoprecipitation platform to quantify more than 20 different types of PPI complexes using [~]1.2x106 cells in total, revealing the rewired status of the BCL2 family PPI network. By comparing the obtained multi-dimensional data with BH3 mimetic efficacies determined ex vivo, we constructed an analysis model for ABT-199 efficacy that designates the BCL2-BAX and BCLxL-BAK complexes as the primary mediators of drug effectiveness and resistance. We then applied this model to assist in therapeutic decision-making for acute myeloid leukemia patients in a prospective manner. Our work demonstrates a capability for the extensive characterization of PPI complexes in clinical specimens, potentially opening a new avenue of precision medicine for protein complex-targeting therapies.

molecular biology↗

bioRxiv2023

Systematic elucidation of genetic mechanisms underlying cholesterol uptake

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we have substantially improved the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

genetics↗

bioRxiv2021

Peptide fusion improves prime editing efficiency

Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, PepSEq, to measure how fusion of 12,000 85-amino acid peptides derived from human DNA repair-related proteins influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites.

bioengineering↗

bioRxiv2020

Elucidation of remdesivir cytotoxicity pathways through genome-wide CRISPR-Cas9 screening and transcriptomics

The adenosine analogue remdesivir has emerged as a frontline antiviral treatment for SARS-CoV-2, with preliminary evidence that it reduces the duration and severity of illness1. Prior clinical studies have identified adverse events1,2, and remdesivir has been shown to inhibit mitochondrial RNA polymerase in biochemical experiments7, yet little is known about the specific genetic pathways involved in cellular remdesivir metabolism and cytotoxicity. Through genome-wide CRISPR-Cas9 screening and RNA sequencing, we show that remdesivir treatment leads to a repression of mitochondrial respiratory activity, and we identify five genes whose loss significantly reduces remdesivir cytotoxicity. In particular, we show that loss of the mitochondrial nucleoside transporter SLC29A3 mitigates remdesivir toxicity without a commensurate decrease in SARS-CoV-2 antiviral potency and that the mitochondrial adenylate kinase AK2 is a remdesivir kinase required for remdesivir efficacy and toxicity. This work elucidates the cellular mechanisms of remdesivir metabolism and provides a candidate gene target to reduce remdesivir cytotoxicity.

genetics↗