Biology subjects

Kang, L.

Publications and source records attributed to Kang, L..

At least 19 recordsLinked to original sources

bioRxiv2026

m6A-Driven Intratumoral Cholesterol Biosynthesis Fuels Castration-Resistant Prostate Cancer Progression

Both nuclear pore complexes (NPCs) and RNA N6-methyladenosine (m6A) machinery are indispensable for proper cellular function. Although their collaborative roles in the nuclear export of messenger RNAs (mRNAs) have been reported, it remains ambiguous whether and how this collaboration may contribute to cancer progression. Here we identify a functional cooperation between NPCs and m6A signaling that promotes the development of castration-resistant prostate cancer (CRPC). We showed that nuclear export of m6A-modified mRNAs, mediated by the interaction between RNA methyltransferase METTL3 and the nucleoporin NUP93, is functionally coupled to cholesterol biosynthesis. Given that cholesterol-fueled intratumoral androgen production is one of the mechanisms driving CRPC, we demonstrated that overexpression of the wild-type METTL3 or NUP93, but neither the enzymatically dead METTL3 nor the mutant NUP93 that loses METTL3-interacting capability, elevates intracellular levels of androgens, activates AR signaling under castrate condition, and promotes androgen-independent growth of prostate cancer cells both in vitro and in vivo. Importantly, pharmacological inhibition of METTL3 or targeted demethylation on mRNAs encoding key cholesterol biosynthesis enzymes effectively suppressed CRPC malignancy. Together, these findings uncover a therapeutically targetable m6A-METTL3-NUP93 axis that links nuclear mRNA export and metabolic reprogramming to fuel CRPC progression, providing a conceptually new strategy for the treatment of this lethal disease.

cancer biology↗

bioRxiv2026

Aperiodic EEG activity is a domain-specific marker of attentional aging in neurotypical adults

Aperiodic neural activity has been reliably shown to be reduced among older adults, and these reductions are associated with age-related cognitive decline. These results suggest that aperiodic activity, as measured by the spectral exponent, might be a viable electrophysiological biomarker for cognitive aging. However, this has not been tested longitudinally, within participants, during normal aging. Here, we used a longitudinal cohort to test whether changes in aperiodic activity between two sessions, approximately 5 years apart, track behavioral change across two domains: sustained attention (Psychomotor Vigilance Test) and executive function (modified Simon task). We found that aperiodic activity declined within individuals after the interval, independent of age at baseline. This within-person change tracked concurrent changes in reaction times during visual attention but was not associated with changes in any Simon task measure. Within a resampling framework, the aperiodic-PVT association was stable across cohort resamples and independently fitted partitions, though it was not confirmed in a small, frozen held-out set. These findings suggest the longitudinal decline in aperiodic activity specifically tracks sustained-attention processing speed rather than general cognitive aging, supporting its potential as a domain-specific biomarker of attentional aging in neurotypical populations.

neuroscience↗

bioRxiv2026

FARM: Forecasting Antibiotic Resistance in Mycobacterium tuberculosis using biophysics and machine learning

Antibiotic-resistant tuberculosis remains a major public health challenge, and rapid diagnosis of resistant infections based on genomic markers holds promise for improving time to effective treatment. However, the vast majority of clinically observed variants in resistance-associated genes remain of uncertain significance, limiting the utility of predictors. Here we develop a multimodal forecasting framework, FARM (Forecasting Antibiotic Resistance in Mycobacterium tuberculosis) to determine whether a newly observed mutation in a resistance gene may indeed cause resistance. Our framework combines structural context, biophysical energy features, protein language model features, and mutational AAIndex physicochemical descriptors. Using 345 labeled mutations from the World Health Organization 2021 catalogue, we train interpretable models that distinguish resistance-associated from non-resistance-associated variants with holdout AUCs of 0.843-0.943. In a novel temporal evaluation of 62 mutations reclassified after the training data was released, the selected Combined model achieved 80.7% recall of resistant reclassifications (resistant-class F1=86.8; AUC=0.735). Applied to 4,525 current uncertain-significance mutations, the framework prioritizes 696 candidate resistance mutations, including genes associated with the new antibiotics bedaquiline, delamanid, and pretomanid. These forecasts are intended to support future catalogue updates and experimental follow-up.

bioinformatics↗

bioRxiv2026

Computational demands shape seizure susceptibility in recurrent neural networks

Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has focused on anatomical and physiological factors, here we observed a fundamental contribution from the computations performed by the underlying neural network. Handcrafted and trained recurrent neural networks supporting continuous representations respond to seizure perturbations with higher activity and earlier performance decline relative to matched networks stabilizing discrete, well-separated states. Consistent with this prediction, in vivo recordings revealed that medial entorhinal cortex, whose grid cells exhibit continuous attractor dynamics, drives acute epileptiform discharges with stronger involvement and smoother state trajectories compared to CA3, a hippocampal subfield associated with discrete memory storage. Moreover, selective synaptic silencing demonstrated that this difference in seizure responses depends on intact entorhinal connectivity. Thus, the computations that enable neural networks to process information also influence their vulnerability to pathological transitions.

neuroscience↗

bioRxiv2026

Interspecies variation of 45S ribosomal DNA in vertebrates

Ribosomal DNA (rDNA) remains one of the least resolved components of complex genomes despite its central role in cellular function and evolutionary inference. Using chromosome-scale assemblies from the Vertebrate Genomes Project, we present a comparative analysis of the 45S rDNA locus across vertebrates. rDNA organization varies widely among clades, with lineage-specific differences in chromosomal distribution, copy number, and positional bias. Mammals and birds typically restrict rDNA to few, often terminal loci, whereas amphibians and several fish lineages exhibit highly dispersed architectures, including extreme cases spanning dozens of chromosomes. Copy number varies by more than an order of magnitude and does not scale simply with repeat-unit size or predicted functional demand, consistent with rDNA functioning as a flexible genomic reservoir rather than a direct proxy for ribosome production. At the sequence level, the 45S unit resolves into distinct evolutionary regimes. Genes encoding rRNAs (18S, 5.8S, 28S) remain highly conserved and retain strong phylogenetic signal, whereas internal transcribed spacers show rapid divergence, lineage-specific expansion, and pronounced asymmetry between intergenic spacers ITS1 and ITS2. Sequence-structure analyses reveal a gradient of constraint across the locus, from tightly coupled evolution in rRNA-encoding regions to increasingly permissive regimes in spacers. Structure-informed phylogenetic inference yields modest but consistent improvements under high divergence, highlighting its value when sequence signal degrades. Together, these results establish the 45S rDNA locus as a multi-layered evolutionary system integrating deep conservation with structural plasticity across vertebrates.

genomics↗

bioRxiv2026

BARTsc identifies key transcriptional regulators from single-cell omics data

Inference of transcriptional regulatory mechanisms from single-cell (sc) omics data, such as scRNA-seq, scATAC-seq, and scMultiome, remains an important problem in single-cell biology and functional genomics. Most existing methods for predicting functional transcriptional regulators (TRs) from single-cell data rely on co-expression between regulator and target genes and/or sequence motif enrichment, holding inherent limitations. Here, we present BARTsc, a computational method that accurately predicts functional TRs from clustered single-cell omics data by leveraging a large collection of public ChIP-seq profiles. BARTsc implements a novel framework to infer a cis-regulatory profile from differential genomic features from either unimodal (RNA or ATAC) or bimodal (scMultiome) single-cell profiling data and identify TRs whose binding profiles most associate with the cis-regulatory profile. BARTsc can quantify TR activity across cell clusters and predict key regulators for each cell cluster. We demonstrate that BARTsc can successfully identify active TRs in each cell type and cell-type-defining key regulators across diverse biological systems, including mouse cortex, human peripheral blood mononuclear cells (PBMCs), and human pancreatic ductal adenocarcinoma (PDAC). Using a generative-AI-assisted, literature-supported collection of cell-type key regulators as benchmarks, we show that BARTsc consistently outperforms existing state-of-the-art methods. We apply BARTsc to identify critical regulators in PDAC, including NEFLA, a novel PDAC key regulator, and validate its function in pancreatic tumor proliferation by experiments. As a robust and versatile computational method, BARTsc provides deeper insights into cell-type-specific regulatory programs, facilitating the discovery of key regulators across diverse biological systems.

bioinformatics↗

bioRxiv2026

Linking biochemical and cellular efficacy of MERS coronavirus main protease inhibitors

Compounds that bind to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (MPro) often produce biphasic concentration-response curves (CRCs) in biochemical assays; low concentrations activate the enzyme and high concentrations inhibit it. This biphasic behavior complicates data analysis. Here, we compare three approaches to data analysis: fitting the Hill equation to the activation phase, fitting it to the inhibition phase, and fitting an enzyme kinetics model that incorporates dimerization and ligand binding to the complete CRC. In the latter case, cellular efficacy is predicted by extrapolating the model to high enzyme concentrations. For compounds in our drug lead series, all three procedures yield inhibitory concentrations that are correlated with live-virus antiviral assays. The latter procedure provides the most accurate forecast of cellular efficacy rank. These data analysis procedures may be valuable for antiviral drug discovery against MERS-CoV MPro and other enzymes with similar kinetics.

biochemistry↗

bioRxiv2025

Suppression of Glucosylceramide Synthase Reverses Drug Resistance in Cancer Cells Harbor Homozygous p53 Mutants

Glucosylceramide synthase (GCS) catalyzes ceramide glycosylation in response to cell stress that produces glucosylceramide and other glycosphingolipids. GCS overexpression is a cause of drug resistance and enriches cancer stem cells (CSCs) during cancer chemotherapy. Previous studies showed that GCS modulates expression of p53 mutants and oncogenic gain-of-function (GOF) in heterozygous knock-in cell models (TP53 R273H-/+). However, it is unclear whether GCS can modulate the effects of homozygous p53 mutations, which are common in many cancer cases. We report herewith that inhibition of GCS, via UGCG-knockout and using new inhibitor (Genz-161), effectively re-sensitizes drug resistance and diminishes CSCs in colon cancer cells carrying the homozygous p53 R273H mutation. In aggressive WiDr cells carrying TP53 R273H mutation, knockout of UGCG gene using CRISPR/Cas9 editing or inhibition of GCS with Genz-161 sensitized cancer cells to oxaliplatin, irinotecan and paclitaxel. With decreased ceramide glycosylation in lipidomic profiling, both UGCG-knockout and Genz-161 treatments substantially decreased wound healing, and diminished CSCs and tumor growth under chemotherapy. Interestingly, inhibition of RNA m6A methylation by neplanocin A reactivated p53 function and reversed drug resistance. Mechanistic investigation revealed that GCS inhibition downregulated METTL3 expression and repressed RNA-m6A modification on mutant p53 R273H effects. Altogether, our findings demonstrate that ceramide glycosylation promotes METTL3 expression and RNA m6A methylation in response to drug-induced stress, thereby promoting mutant p53 expression and associated GOF. Conversely, inhibition of GCS can diminish CSCs and drug resistance via reduction of m6A modification and reactivation of p53 function. GCS inhibition is an achievable approach for mutant cancer treatment.

cancer biology↗

bioRxiv2025

Force loading on molecular clutches governs the stability of cell lamellipodia

Cells can utilize the lamellipodia, a thin actin-rich membrane protrusion, to probe the mechanical properties of microenvironments. During the mechanosensing process, the lamellipodium usually exhibits instability in dynamics, i.e., protrusion-retraction cycles. However, how mechanical instability arises in lamellipodia, along with the functional role of dynamic instability in mechanosensing, is poorly understood. Here, we develop a minimal mechanochemical model for lamellipodia dynamics that integrates membrane deformation, myosin contractility, and binding kinetics of adhesion molecules (molecular clutches). Through stochastic simulations and analytical mean-field analysis, we demonstrate that both force-loading rate and magnitude applied by myosin-driven retrograde flow mediate the clutch binding kinetics, governing lamellipodial stability and hence the mechanosensing. Specifically, a slow force loading rate allows the clutches to bind and traction to accumulate, while a high loading magnitude collapses the bound clutches, causing protrusion-retraction cycles (instability) in lamellipodia. Our model predicts that a stiffer substrate stabilizes the lamellipodia by increasing the force loading rate, consistence with previous experiments. Furthermore, our model predictions on the biphasic regulation effect of myosin perturbations have been quantitatively validated by experimental results. Overall, the theoretical framework highlights the force loading as the key mechanical input driving lamellipodial instability and cell mechanosensing, advancing our understanding of mechanotransductions role in cell behaviors. SignificanceCell behavior can be highly dynamic, showing periodic protrusion and retraction at the leading edge, and this dynamic instability is key to migration, immune response, and cancer invasion. How such instability arises from mechanotransduction between intercellular components and extracellular matrices remains unclear. Here, through theoretical modeling, we identify the rate and magnitude of force loading on adhesion molecules (molecular clutches) as the key factors for controlling lamellipodial stability. We find that a slow loading rate enables more clutches to bind and traction to accumulate, while a high loading magnitude collapses the bound clutches, causing protrusion-retraction cycles in lamellipodia. This work reveals a physical mechanism of how cells sense mechanical cues and adapt their dynamics, offering insights into diverse biological processes.

biophysics↗

bioRxiv2025

The functional divergence of plant ESCRT components TOL3, SNF7.1, and VPS4 during salt stress response

Salinity, a major abiotic stressor, impacts protein trafficking and alters transport routes. The endosomal sorting complex required for transport (ESCRT)is a key player for membrane protein sorting and is increasingly recognized as pivotal element in abiotic stress responses. However, the functional role of ESCRT proteins in cereal crops under salt stress remains unexplored. Using the as near as possible to nature DRD-BIBLOX (Dark-Root device Brick Black Box), barley (Hordeum vulgare, L.), although relatively salt-tolerant, exhibits impaired germination, seedling growth, and an altered root system architecture (RSA) under high salinity. Shotgun proteomic analysis revealed spatio-temporal regulation of ESCRT proteins in shoots and roots during seedling development. We identified HvSNF7.1 and HvVPS4 as critical, but differently acting modulators of salt stress responses in barley tissues. In this study we further reveal novel functions of ESCRT-0-like TOM-1-like proteins (TOLs): Previously described as gatekeepers of degradative protein sorting and as regulators of abscisic acid signaling, growth, and heat stress responses in Arabidopsis (Arabidopsis thaliana), HvTOLs had not been characterized for their role in salinity adaptation during plant development. Functional analyses demonstrated lethality in Hvtoldouble and Hvtolquadruple mutants under standard growth conditions. In Arabidopsis, loss of AtTOL3 alone impaired germination and root growth under salt stress, underscoring the essential role of individual TOL proteins in the spatio-temporal regulation of abiotic stress adaptation. These findings highlight ESCRT-mediated trafficking as a key determinant of cereal resilience under saline conditions and provide a molecular framework for improving crop stress tolerance.

plant biology↗

bioRxiv2025

A high-quality bread wheat genome unravels adaptive evolution of wheat end-use quality

Understanding the genetics of superior dough performance is essential for improving wheat end-use quality. Here, we presented a de novo assembly of the genome of JM44, a Chinese wheat cultivar known for its exceptional end-use quality. The JM44 genome achieved reference-level quality (QV = 66.74), depicting a complete picture of complex regions containing gluten genes. Our microsynteny analysis across the Triticum-Aegilops complex showed that high-molecular-weight glutenin subunits (HMW-GSs) loci are highly conserved, while low-molecular-weight glutenin subunits (LMW-GSs) and -/{beta}-gliadins exhibited greater structural variation. These variable loci appear to have been preferentially selected by humans and contributed substantially to the evolution of wheat quality traits. Moreover, we observed that epistatic interactions between gluten genes are strong in modern cultivars, but markedly weaker in landraces, indicating the importance of epistatic selection during modern breeding. Our findings shed light on the genomics and evolution of wheat quality traits, providing valuable guidance for future breeding efforts.

genomics↗

bioRxiv2025

Genome-Wide Investigation of Transcription Factor Occupancy and Dynamics Using cFOOT-seq

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences. Notably, cFOOT-seq, combined with FootTrack analysis, predicts TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.

molecular biology↗

bioRxiv2025

DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases led to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieved much higher sequence coverages. DiNovo also showed great potential as a powerful complement or alternative to database search for peptide identification with quality control.

bioinformatics↗

bioRxiv2025

Inhibition of methylthioadenosine phosphorylase provides protection from experimental acute kidney injury

Acute kidney injury (AKI) increases mortality risk and predisposes individuals to chronic kidney disease. Metabolic pathways play a crucial role in AKI pathophysiology. Here, we investigate the potential of methylthioadenosine phosphorylase (MTAP) inhibition as a novel renoprotective strategy in AKI. Using AKI mouse models, we demonstrate that a small molecule MTAP inhibitor significantly reduces kidney injury markers and improves renal histology. RNA sequencing reveals that MTAP inhibition modulates pathways associated with inflammation, oxidative phosphorylation, and cell survival. Additionally, analysis of human single-cell RNA sequencing data links MTAP expression to kidney injury marker in AKI. This study provides evidence of MTAP inhibition as a potential therapeutic strategy for AKI, highlighting metabolic dysregulation as a target for future clinical interventions.

pathology↗

bioRxiv2025

Igf2 regulates early postnatal DPP4+ preadipocyte pool expansion

Adipose tissue is rapidly expanding early in life. Identifying the queues facilitating this process will advance our understanding of metabolically healthy obesity. Single-cell RNA sequencing revealed compositional differences between pre-wean and adult murine subcutaneous adipose tissue. We identify a dipeptidyl peptidase-4 (Dpp4) positive precursor population in pre-wean mice residing at the reticular interstitium of subcutaneous adipose tissue expressing insulin growth factor 2 (Igf2). We show that IGF2 drives proliferation rather than differentiation in these cells. Moreover, loss of Igf2 in Dpp4+ progenitor cells promotes adipogenesis. We show that expression of Igf2 at a limited timeframe during development promotes preadipocyte expansion.

developmental biology↗

bioRxiv2025

Enzyme kinetics model for the coronavirus main protease including dimerization and ligand binding

The coronavirus main protease (MPro) plays a pivotal role in viral replication and is the target of several antivirals against SARS-CoV-2. In some species, CRCs of MPro enzymatic activity can exhibit biphasic behavior in which low ligand concentrations activate the enzyme whereas higher ones inhibit it. While this behavior has been attributed to ligand-induced dimerization, quantitative enzyme kinetics models have not been fit to it. Here, we develop a kinetic model integrating dimerization and ligand binding. We perform a Bayesian regression to globally fit the model to multiple types of biochemical and biophysical data. The reversible covalent inhibitor GC376 strongly induces dimerization and binds to the dimer with no cooperativity. In contrast, the fluorescent peptide substrate has a minor effect on dimerization but binds to the dimer with positive cooperativity. The biphasic concentration response curve occurs because compared to substrate, the inhibitor accelerates turnover in the opposite catalytic site.

biochemistry↗

bioRxiv2024

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

bioinformatics↗

bioRxiv2024

A novel functional coordination in UNC-13 regulates neurotransmitter release

Munc13 plays a crucial role in short-term synaptic plasticity by regulating synaptic vesicle (SV) exocytosis and neurotransmitter release at the presynaptic terminals. However, the intricate mechanisms governing these processes have remained elusive due to the presence of multiple functional domains within Munc13, each playing distinct roles in neurotransmitter release. Here we report a coordinated mechanism in the C. elegans Munc13 homolog UNC-13 that controls the functional switch of UNC-13 during synaptic transmission. Mutations disrupting the interactions of C1 and C2B with diacylglycerol (DAG) and phosphatidylinositol 4,5-bisphosphate (PIP2) on the plasma membrane induced the gain-of-function state of UNC-13L, the long UNC-13 isoform, resulting in enhanced SV release. Concurrent mutations in both domains counteracted this enhancement, highlighting the functional interdependence of C1 and C2B. Intriguingly, the individual C1 and C2B domains exhibited significantly stronger facilitation of SV release compared to the presence of both domains, supporting a mutual inhibition of C1 and C2B under basal conditions. Moreover, the N-terminal C2A and X domains exhibited opposite regulation on the functional switch of UNC-13L. Furthermore, we identified the polybasic motif in the C2B domain that facilitates SV release. Finally, we found that disruption of C1 and C2B membrane interaction in UNC-13S, the short isoform, leads to functional switch between gain-of-function and loss-of-function. Collectively, our findings provide a novel mechanism for SV exocytosis wherein UNC-13 undergoes functional switches through the coordination of its major domains, thereby regulating synaptic transmission and short-term synaptic plasticity.

neuroscience↗