Biology subjects

Huang, K.

Publications and source records attributed to Huang, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

BiomniBench: Process-level Evaluation of LLM Agents for Real-world Biomedical Research

LLM agents now perform real biomedical research, but evaluating them rigorously is hard. Outcome-only benchmarks fail in two ways. First, a correct final answer can come from memorization, reward hacking, or wrong reasoning that produces the right number by chance. Second, valid alternative analyses are marked wrong simply because they differ from the reference. We introduce BiomniBench, a process-level evaluation framework that scores the full agent trajectory against expert-designed, task-specific rubrics. Our first release, BiomniBench-DA, contains 100 data-analysis tasks across 17 task types, 5 disease areas, and a general-biology category, each based on a paper from journals such as Nature, Cell, and Science and co-developed with an original author or a domain expert. Benchmarking frontier and open-weight models across four agent harnesses reveals three findings. Frontier and open-weight bases cluster within a few points of each other, with substantial headroom for all models. The agent harness shifts scores by more than the gap between successive model generations. Agents reliably ground claims in real sources yet consistently fall short on method selection, biological interpretation, and scientific reasoning. BiomniBench is the first process-level benchmark for LLM agents in biomedical research, providing the dimension-level diagnostics that outcome scoring cannot. Datasethuggingface.co/datasets/phylobio/BiomniBench-DA

bioinformatics↗

bioRxiv2026

TxConformal: Controlling False Discoveries in AI-Driven Therapeutic Discovery

Artificial Intelligence (AI) is transforming therapeutic discovery by scoring a large set of promising candidates and prioritizing a shortlist for further investigation. Quantifying the reliability of AI scores and preventing false positives among selected candidates is key to the efficiency of the discovery process. Conformal prediction (CP) has emerged as a popular tool for guiding such prioritization, especially via the conformal selection framework to control false discovery rates (FDR) in selecting top-ranked candidates under distributional shift1, 2. However, deploying these advances in real-world therapeutic discovery remains challenging: distribution shifts are difficult to quantify and correct in high-dimensional biomedical data, and practical workflows often require flexible error metrics. Here, we present TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW, a general framework for trustworthy decision making when building shortlists using AI scores. TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW adjusts for distribution shift by balancing the hidden representations in AI models and then provides confidence measures for true discoveries of target biological properties. These confidence measures, interpretable as p-values, can be used in conjunction with statistical multiple testing procedures to derive selection decisions with limited false positives or to estimate the errors in given selection decisions. TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW controls the false positive rate in six real-world tasks spanning various therapeutic discovery stages, modalities, and AI models with realistic data splits. When selecting promising combinatorial genetic perturbations, TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW nearly halves false-positive selections compared to baseline methods, substantially reducing unnecessary experimental costs by tens of thousands of dollars. When selecting stable protein structures under mutant shifts, TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW identifies about 10 times more proteins than baseline methods at stringent thresholds when running at a target FDR level of 10%, recovering over 90% of valuable candidates that baseline methods miss due to unaccounted distribution shifts. Furthermore, we demonstrate that TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW robustly supports various alternative error metrics suitable for resource-constrained settings. Finally, in a prospective fixed-budget virtual screening campaign for novel antibiotic discovery, TO_SCPLOWXC_SCPLOWCO_SCPLOWONFORMALC_SCPLOW predicted false positives in close agreement with experimental outcomes, with substantial improvements over simple baselines.

bioinformatics↗

bioRxiv2026

A recurrent neural network model of chronic pain development and recovery

Chronic pain presents a leading challenge in the world today for both clinicians and researchers. Because chronic pain is difficult to explain and treat, it is often managed with opioids despite providing limited relief and contributing to dependence and misuse. Persistent pain can be maintained by altered central nervous system processing even in the absence of distinct tissue damage or disease, which may limit the efficacy of conventional pharmacological therapies that target nociceptive signal transmission rather than maladaptive central nervous system dynamics often present in those with chronic pain. Although neuroimaging studies have identified this shift from nociceptive to emotional circuits during pain chronification, a quantitative framework linking these neural changes to longitudinal pain trajectories or recovery is lacking. We present a parsimonious firing-rate model that can account for the development of and recovery from chronic pain, which is based on the theoretical framework established by Wilson and Cowan. The model provides a quantitative explanation of how sensitization, anxiety, and fear maintain pain even after an injury has healed, and how calming stimulus downregulates these processes to facilitate recovery. A study applying the same principles as the model produced an average pain decrease of 3.5 on the Visual Analog Scale (VAS), with all subjects experiencing a reduction in pain. These results, coupled with our model and findings in prior studies, suggest that increasing calming stimulus can reduce pain without necessitating pharmacological or invasive, resource-intensive interventions.

neuroscience↗

bioRxiv2026

Glutamine addiction is a therapeutic target to block emergency myelopoiesis

Inflammation-driven emergency myelopoiesis (EM) contributes to the progression of many solid cancers and inflammatory diseases, yet therapeutic strategies to selectively suppress EM without compromising hematopoiesis remain lacking. Here, we use functional and single-cell transcriptomic analyses to determine metabolic programs organizing the hematopoietic hierarchy, myeloid lineage commitment, and myeloid differentiation. We identify de novo glutamine biosynthesis as a stem cell-specific survival mechanism allowing independence from exogenous glutamine. We show that myeloid differentiation is characterized by Myc-driven upregulation of mitochondrial respiration, which is hyperactivated during EM and renders myeloid progenitors dependent on glutaminolysis to fuel the TCA cycle. Both genetic and pharmacologic targeting of glutaminase suppresses EM and impairs breast tumor progression by reducing intratumoral neutrophil infiltration. Our study defines a central role for Myc-glutaminolysis in driving EM, identifies glutaminolysis as a therapeutic target to normalize maladaptive EM, and highlights myeloid overproduction as a systemic problem requiring HSPC targeting. HIGHLIGHTSO_LIHSC survival depends on de novo glutamine biosynthesis via glutamine synthetase C_LIO_LIMyc hyperactivation drives mitochondrial biogenesis during emergency myelopoiesis C_LIO_LIMyeloid progenitors become glutamine-addicted to fuel Myc-driven TCA cycle activity C_LIO_LIGlutaminase deficiency in HSPCs blunts tumor-promoting neutrophil production C_LI ETOC BLURBOlson et al. show that emergency myelopoiesis, the inflammatory overproduction of myeloid cells that drives regeneration, depends on Myc-driven mitochondrial respiration and glutamine addiction in hematopoietic progenitors. Targeting glutaminase in hematopoietic stem and progenitor cells suppresses pathological myelopoiesis, reduces tumor-promoting neutrophil production, and slows breast tumor growth.

immunology↗

bioRxiv2026

EthoClaw: An Integrated AI Workflow Platform for Automated Analysis in Neuroethology

Computational methods have advanced the analysis of animal behavior, yet significant challenges remain in data standardization, analytical reproducibility, and workflow integration. Existing computational solutions often demand extensive programming proficiency or compel users to navigate a highly fragmented ecosystem of disconnected tools for tracking, statistical analysis, and visualization. Here, we present EthoClaw, an open-source, artificial intelligence-driven workflow platform built upon the OpenClaw agentic framework, functioning as a locally deployable AI assistant for behavioral research. EthoClaw provides an integrated computational infrastructure that seamlessly bridges the gap between raw behavioral video acquisitions and publishable scientific results. In this study, we demonstrate the platforms capacity to natively ingest video data via a dual-mode tracking architecture: utilizing ultra-fast image processing for rapid object detection, and leveraging the SuperAnimal methods for precise, markerless postural tracking. To ensure maximal interoperability, EthoClaw automatically converts various tracking data formats into DeepLabCut-compatible formats, enabling high-throughput phenotyping by generating publication-quality visualizations alongside rigorous multidimensional statistical profiling. Furthermore, the platform incorporates a large language model (LLM)-driven reporting module that dynamically synthesizes analytical documents, ensuring methodological transparency. Through an open field test, we validate the practical usability of EthoClaw while accelerating computational throughput by localizing heavy video processing to circumvent cloud bandwidth bottlenecks. Operating via an omnichannel natural language interface that integrates seamlessly with ubiquitous instant messaging software, EthoClaw democratizes advanced computational behavioral analysis, offering a holistic, highly efficient ecosystem that enforces experimental reproducibility and open science principles.

animal behavior and cognition↗

bioRxiv2026

The Epigenetic Factor PHF13 Governs Trophoblast Stemness and Differentiation

Differentiation of trophoblast stem (TS) cells or progenitor cytotrophoblasts (CTBs) into multinucleated syncytiotrophoblasts (STBs) is essential for placental development. Disruption of this process contributes to major obstetrical syndromes, including fetal growth restriction and preeclampsia, and Trisomy 21. However, the chromatin mechanisms governing trophoblast stemness and differentiation remain inadequately defined. Here we identify the chromatin-associated factor PHF13, uncovered through a high-throughput microRNA target screen, as a key regulator of trophoblast cell fate. PHF13 knockout TS cells exhibited defects that ultimately resulted in loss of cell viability, whereas PHF13 knockdown promoted expression of fusion-associated genes, including ERVFRD-1 and human chorionic gonadotropin (hCG). Consistently, PHF13 depletion in BeWo trophoblast cells increased hCG expression and secretion while reducing expression of canonical stemness-associated transcription factors ELF5 and TEAD4. Integrated genomic analyses further revealed that PHF13 target genes comprise a gene regulatory network that maintains trophoblast stemness and restrains differentiation. Notably, the pluripotency-associated transcription factor THAP11 partially co-occupies genomic sites with PHF13. Together, these findings establish PHF13 as a previously unrecognized chromatin regulator of trophoblast stemness and differentiation, providing mechanistic insight into pathways critical for placental development and function. HighlightsO_LIPHF13 preserves trophoblast stem cell identity C_LIO_LIPHF13 restrains syncytiotrophoblast differentiation C_LIO_LIPHF13 controls trophoblast chromatin programs with THAP11 C_LI

molecular biology↗

bioRxiv2026

Chd8 haploinsufficiency leads to molecular layer heterotopias and age-dependent cortical expansion

Mutations in the chromatin remodeler CHD8 are associated with autism and macrocephaly. While mouse models of Chd8 haploinsufficiency recapitulate brain overgrowth, the specific cellular mechanisms and developmental timing that lead to these anatomical abnormalities remain poorly understood. Here, we conducted 3D imaging of Chd8V986*/+ mouse brains using magnetic resonance imaging followed by tissue clearing and cellular resolution light-sheet microscopy across embryonic and postnatal developmental stages. We found that brain overgrowth occurs postnatally, driven by an increase in non-neuronal cells prior to volumetric expansion. Unexpectedly, we identified prevalent molecular layer heterotopias (MLH) within the frontal cortex of Chd8V986*/+ mice composed of neurons breaking through the pial surface during embryonic development and persisting throughout life. Increased incidence of MLH, previously identified in individuals with idiopathic autism, was replicated across three independent Chd8+/- mouse models and present across multiple genomic backgrounds, establishing aberrant neuronal migration as a core feature of Chd8 haploinsufficiency.

neuroscience↗

bioRxiv2026

Unleashed condensation by recurrent mutations of an epigenetic regulator promotes cancer

The pathological mechanisms and significance for the prevalent hotspot mutations in disordered protein regions are poorly understood. ASXL1 is an obligate co-factor for BAP1 in H2AK119 deubiquitination. ASXL1 mutations are very frequent in myeloid malignancies, and are mostly C-terminal truncating mutations concentrated in a specific disordered region of ASXL1. ASXL1 truncations are gain-of-function mutations that promote myeloid malignancies, but the underlying mechanisms remain poorly understood. Here we show that the frequently truncated mutants of ASXL1 possess an intrinsic property of forming phase-separated biomolecular condensates, and this property is normally suppressed by the frequently deleted regions. A disease-mutant of the endogenous ASXL1 in leukemia cells forms dynamic nuclear co-condensates with other endogenous factors important for gene activation. The ASXL1 disease-mutants can greatly enhance H2A deubiquitination activity of BAP1 in cells and in vitro reconstituted system, enhance myeloid leukemia cell growth, and promote leukemogenesis in a mouse transplant model by turning on myeloid leukemogenic transcriptional programs. However, substitution of residues important for condensation disrupted or impaired all these abilities, suggesting that the condensation property is crucially important for the ASXL1 mutants in promoting cancer. Moreover, we discover that the conserved negative charges in the highly disordered and frequently deleted region on ASXL1 suppress the condensation of the wild type ASXL1. Charge-neutralizing mutations in this region restores condensation of the full-length ASXL1, and are sufficient to turn ASXL1 into a leukemogenic protein. Biochemical, biophysical, and simulation analyses suggest the intramolecular interactions normally mask the N-terminal region in engaging intermolecular interactions required for phase separation, and disease truncations escape from the regulatory interactions and unleash the phase separation property to form nuclear hubs to promote expression of tumorigenic gene programs. Finally, by showing a striking correlation of the mutation frequencies with the condensation properties and leukemogenesis activity for a series of human patient mutations, we suggest that dysregulation of condensation is a central mechanism for ASXL1 mutations in promoting myeloid malignancies. This suggests that dysregulation of condensation may be a key mechanism for some of the prevalent hotspot disease mutations in the disordered proteomes.

biochemistry↗

bioRxiv2026

The progression of sex differences in brain networks across the lifespan

Sex differences in brain connectivity are well documented, yet how these differences evolve across the human lifespan remains poorly understood. Rigorously assessing sex-dependent trajectories of brain network organization is challenging due to difficulty in acquiring, processing, and modeling high-dimensional connectomes. Here, we analyzed 15 types of functional and structural connectivity networks from 1286 healthy individuals aged 8-100+ years, using our new AI-based Krakencoder to derive a low-dimensional multimodal "fusion" connectome representation. Sex differences were minimal in early childhood, pronounced in young to mid-adulthood, and diverged across modalities in later life: functional connectivity grew less distinct and structural connectivity grew more distinct from midlife onward. Functional differences were driven predominantly by higher-order association networks (default mode, control), while structural differences concentrated in lower-order cerebellar and subcortical pathways. These findings provide a lifespan-wide, multimodal map of sex differences in brain networks which may help inform sex-specific vulnerability and resilience to brain disorders.

neuroscience↗

bioRxiv2026

Unraveling AMPK and BET regulation of immune checkpoint biology: implications for personalized medicine

Triple negative breast cancer (TNBC) patients with comorbid Type 2 diabetes (T2D) show worse survival compared to nondiabetic TNBC patients. Immune checkpoint blockade (ICB) has unclear benefit in TNBC. Immune suppression in T2D, and use of metformin, an activator of 5 Adenosine Monophosphate-activated Protein Kinase (AMPK), in such patients, prompted us to examine AMPK regulation of immune checkpoint expression. Improved ICB efficacy may optimize outcomes for certain TNBC patients. We have also been exploring the role of Bromodomain and ExtraTerminal domain (BET) proteins (BRD2, BRD3, BRD4) in regulation of checkpoint molecules in immune cell subsets, including CD4+, CD8+ T cells, and NK cells. BET proteins are important transcriptional co-regulators, critical for proliferation and metastasis in many cancer types, including TNBC. We observed differential BET regulation of immune checkpoint proteins, specifically TIM-3, TIGIT, PD-1 and CTLA-4, on CD3/CD28-stimulated peripheral blood mononuclear cells by flow cytometry. Chemical inhibition of AMPK with Compound C, and with the pan-BET inhibitor JQ1 or the BRD4-selective PROTAC inhibitor MZ-1, revealed that BET proteins regulate PD-1 and CTLA-4 through an AMPK-dependent pathway and TIM-3 and TIGIT through an AMPK-independent pathway. Personalized approaches to ICB treatment of TNBC patients with comorbid T2D should improve outcomes.

cancer biology↗

bioRxiv2026

Benchmarking algorithms for RNA velocity inference

RNA velocity is a computational framework for single-cell RNA sequencing (scRNA-seq) that estimates the future transcriptional state of individual cells, thereby capturing the direction and rate of cell state transitions rather than providing a purely static snapshot. Since its introduction in 2018, multiple RNA velocity methods have been developed, differing in their modeling assumptions, required inputs, computational complexity, and robustness. However, there remains limited consensus on how best to evaluate these methods or on which tools are most reliable under specific biological and technical settings. Here, we perform a systematic comparison of 29 velocity inference algorithms across 114 simulated datasets with known ground-truth cell dynamics and 62 real scRNA-seq datasets, and we extend the evaluation to spatial and multi-omics levels where velocity is increasingly applied. We benchmark RNA velocity methods using a unified framework that decomposes performance into four practical dimensions: accuracy, scalability, stability, and usability. Our results show that performance rankings vary substantially across metrics and datasets, indicating no single method is uniformly optimal and that practical deployment is often constrained by feasibility and robustness as much as by accuracy. Based on these results, we provide actionable guidance for selecting RNA velocity tools according to data modality, available priors, and computational constraints. Finally, we identify key bottlenecks that currently limit RNA velocity development and deployment, including scalability to large size of datasets, sensitivity to gene selection, and the lack of genuinely multimodal and spatially explicit velocity models for spot-based technologies.

bioinformatics↗

bioRxiv2026

An ER-Inner Membrane Complex Bridge via VAP-VPS13A-DAT1 Drives Daughter Budding in Toxoplasma gondii

All alveolates including apicomplexa parasites contain an inner membrane complex (IMC) underneath the plasma membrane. The IMC is synthesized de novo during the daughter cell budding (endodyogeny) within the mother cell and serves as a crucial scaffold for supporting cytoskeletal structures and the glideosome machinery for the parasite locomotion. However, the mechanism(s) underlying the membrane biogenesis in the IMC are not well understood. Using clinically-relevant and globally-prevalent pathogenic protist model, Toxoplasma gondii, we identified the TgVAP-TgVPS13A-TgDAT1 complex bridging the IMC to the endoplasmic reticulum (ER) - the major site of phospholipid synthesis. Individual components of this complex play a crucial role in the IMC biogenesis, where the multi-modular TgVPS13A protein interacts with the lipid scramblase TgDAT1 in the IMC via its C-terminal VAB domain, and with the ER-resident TgVAP through its N-terminal region. DAT1 is recruited for the progeny formation sites during the early stages of budding. Conditional depletion of TgVPS13A, TgDAT1 or TgVAP results in collapse of the inner membrane complex, leading to parasite death, as visualized by endodyogeny-specific organelle markers. LactC2-GFP, a biosensor of phosphatidylserine and phosphatidylthreonine lipids made in the ER and enriched in the IMC, also mislocalizes upon protein depletion. In conclusion, we propose that TgVAP-TgVPS13A-TgDAT1 bridge the ER and IMC and mediate the inter-organelle transport of lipids, thus contributing to the organelle biogenesis and daughter budding in T. gondii. Author SummaryThe inner membrane complex (IMC) in apicomplexan parasites is essential for maintaining the structural stability of the parasite, as well as for its budding and motility. The IMC is composed of flattened vesicles, alveolins, and microtubules. However, the mechanism behind the biogenesis of these flattened vesicles remains unclear. In this study, we provide evidence that a protein complex formed by TgVAP, TgVPS13A, and TgDAT1 exists between the endoplasmic reticulum (ER) and the IMC. Phenotypic analysis indicates that the absence of any individual component of this protein complex disrupts the biogenesis of daughter IMCs, which in turn affects the budding of daughter parasites. We also demonstrated that TgDAT1 functions as a scramblase. Based on our findings, we propose a model in which the TgVAP-TgVPS13A-TgDAT1 complex mediates lipid transport from the ER to the nascent IMC, driving the expansion of the IMC membrane and the budding of daughter parasites in T. gondii. Furthermore, TgDAT1 may facilitate the transfer of lipids from the outer leaflet to the inner leaflet of the IMC sacs, promoting a balanced composition of the IMC membrane components.

microbiology↗

bioRxiv2025

Plant Face: Machine learning decodes genetic, environmental and developmental imprints in leaf appearance

Leaf appearance is a crucial plant phenotype. However, traditional methods for extracting this information are inefficient, limiting its full utilization. Deep learning based on convolutional neural networks (CNNs) enables us to capture previously inaccessible information from images. In this study, we made the surprising discovery that the leaf appearance of each individual plant is unique. Using deep learning, leaves from one plant could be efficiently distinguished from those of another plant of the same species and cultivar. We term this phenomenon the "Plant Face" and suggest the potential to develop a "plant face recognition system," analogous to human facial recognition. We also applied similar methods to study the relationship between leaflet appearance and their position on compound leaves, leaf bilateral symmetry, and differences in leaves from twining stems with different chirality. These results collectively indicate that plant genetic characteristics, growth conditions, and developmental features can be stored within their appearance. With appropriate decoding, leaf appearance is poised to play an increasingly important role in phenomics. SignificanceThe saying "no two leaves in the world are identical" holds philosophical significance, as such variation encompasses considerable contingency and randomness. Here, we assert that no two trees have identical leaves; meaning that even for plants of the same species and cultivar, the leaf morphology of each individual plant is distinct at the population level, even though single leaves may overlap in appearance. Genetic, environmental, and developmental information is recorded in some manner within the phenotypic appearance of leaves. With advancements in computational technologies like artificial intelligence, this information can now be decoded. HighlightsO_LIThe leaves of each individual plant are statistically unique. C_LIO_LIThe relationship between leaflet appearances in compound leaves hints at their developmental patterns. C_LIO_LILeaves are not necessarily bilaterally symmetric in a statistical sense. C_LIO_LILeaves from stems with different chirality (twining direction) exhibit distinct appearances. C_LI

plant biology↗

bioRxiv2025

Saxiphilin functions as a "toxin sponge" protein that counteracts the effects of saxitoxin poisoning

Saxitoxin (STX) is among the most potent toxins known, is classified as a chemical weapon, and is the archetype of the paralytic shellfish toxin (PST) family produced by marine and freshwater harmful algal blooms (HAB)1-3. STX causes paralysis and death through inhibition of voltage-gated sodium channels (NaVs), has no antidote, and poses a public health and commercial fishing threat due to its accumulation in seafood and increasing HAB occurrences1,4. Although STX is lethal to varied vertebrates5-7, including humans4,8,9, certain frogs resist STX poisoning6,7. This phenotype is thought to depend on soluble toxin sponge high-affinity STX binding proteins (saxiphilins, Sxphs)6,7,10,11 that provide resistance through a competition mechanism different from classic target site ion channel mutation mechanisms12-14. Here, we test this idea directly and show that a single dose of American bullfrog (Rana catesbeiana) Sxph (RcSxph) is sufficient to counteract STX neurotoxicity and lethality in mice using paradigms where STX and Sxph are administered together or sequentially in either order. Importantly, this function requires the RcSxph high-affinity STX binding site. Our findings provide in vivo validation that Sxphs are toxin sponges that protect against STX poisoning and highlight the potential to harness this protein class as antidotes for PSTs and other toxins.

pharmacology and toxicology↗

bioRxiv2025

DNA Framework Nanoreactor for Programmable Membrane Fusion

Membrane fusion is a fundamental yet transient process that has long resisted direct structural and kinetic dissection. Here we introduce a DNA framework vesicle (DFV) nanoreactor that transforms this elusive biological phenomenon into a programmable, visualizable, and quantifiable process-a nanoreaction confined in space yet extended in time. By embedding lipid membranes and SNARE proteins within precisely defined DNA apertures, DFVs convert stochastic vesicle collisions into geometry-controlled fusion events with tunable kinetics. Cryo-electron microscopy resolves a complete sequence of six intermediates, revealing how nanoscale confinement reshapes the energetic landscape of bilayer merger. Quantitative fluorescence and nano-flow cytometry further establish a direct link between spatial design and fusion probability. Extending this concept to living cells, DFVs enable controllable membrane fusion and augmentation on VAMP2-expressing membranes, achieving direct cytosolic delivery of functional siRNA via fusion-driven transfer rather than endocytosis. This framework bridges structural precision and functional mimicry, offering a unified platform to reconstruct, quantify, and harness membrane fusion as a programmable process for synthetic biology and nanomedicine.

bioengineering↗

bioRxiv2025

Ptf1a robustly drives the gliogenic switch in the rodent embryonic cortex in a dosage-dependent manner by activating pro-glial gene expression programs

It is widely believed that the gliogenic switch during rodent embryonic development is governed by the orchestrated crosstalk between a cohort of genes and extracellular cues. Here we report that ectopic expression of the single bHLH factor, pancreas transcription factor 1 (PTF1A), is sufficient to drive radial glial progenitors (RGPs)-regardless of progenitor heterogeneity and developmental stage-to differentiate into glial cells. Ptf1a- expressing RGPs exhibit progenitor behaviors indicative of a neurogenic-to-gliogenic fate transition, resembling endogenous progenitors at the late stages of embryonic development, and preferentially produce OLIG2+ oligodendrocytes in vivo, some of which are positive for PDGFR or CC1. This robust gliogenic competency depends on the dosage of stable Ptf1a expression in RGPs. RNAseq reveals the glial transcriptome, including upregulated expression of several notch signaling components and the endothelin receptors in Ptf1a RGPs. We further identify Ednrb as one of the downstream targets of Ptf1a that directs RGPs toward gliogenesis via the ERK1/2 signaling pathway. In summary, our study uncovers a novel and robust role for Ptf1a in glial fate specification, offering a potential strategy for generating human oligodendrocytes in vitro.

neuroscience↗

bioRxiv2025

A Structure-Guided Kinase-Transcription Factor Interactome Atlas Reveals Docking Landscapes of the Kinome

AbstractProtein kinases orchestrate cellular processes through phosphorylation, yet the structural basis for their specific binding partner interactions remains largely unmapped. Here, we present a structure-guided atlas of the human and Drosophila kinome, built by applying a new interface-aware scoring framework (iLIS) to AlphaFold-Multimer predictions. The resulting atlas recapitulates hallmark sequence preferences, confirms previously reported and functionally related protein-protein interactions, and uncovers unrecognized docking interactions. Notably, our analysis predicts a potentially widespread docking motif on homeodomain transcription factors that mediates interactions with basophilic kinases. Furthermore, we map putative allosteric interaction hotspots across the kinome and provide proof-of-concept evidence that targeting these surfaces can inhibit kinase activity. Finally, we demonstrate the physiological utility of the atlas by identifying a novel regulatory mechanism between Sgg/GSK3 and Hnf4 that controls lipid metabolism in vivo. This resource provides a blueprint for dissecting signaling networks and for the rational design of docking-site-specific kinase modulators.

bioinformatics↗

bioRxiv2025

In silico analysis of the Chikungunya virus and SARS-CoV-2 Macrodomain

The macrodomain of the Chikungunya virus NSP3 protein (ChikV Mac1) hydrolyzes mono-ADP ribose post-translational modifications on the human host proteins. Mutations in Mac1 reduce ChikV virulence. Thus, ChikV Mac1 is a viral drug target. While no potent ChikV Mac1 inhibitors are available, high-affinity inhibitors were developed for Mac1 in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we rationalize this difference in the ligand binding affinity in terms of three differences in the binding site structure and dynamics. First, the apo ChikV Mac1 binding site exhibits substantially more conformational heterogeneity than that of SARS-CoV-2, based on microsecond-scale molecular dynamics simulations; it also has a less druggable binding site, based on program SiteMap. Second, water binding sites overlapping with the ligand binding site in ChikV Mac1 are predicted by program WaterMap to have a stronger affinity for water molecules than the corresponding sites in SARS-CoV-2 Mac1, thus decreasing ligand binding affinity. Finally, a smaller number of Mac1 residues interacts persistently with ADP-ribose in holo ChikV Mac1 than in SARS-CoV-2. With these rationalizations in hand, we designed ligands of ChikV Mac1 using a fragment growth strategy; subsequent molecular dynamics simulations of a representative ligand in complex with ChikV Mac1 substantiated our design.

bioinformatics↗