Biology subjects

Cen, H.

Publications and source records attributed to Cen, H..

4 recordsLinked to original sources

bioRxiv2026

Structure-resolved fold annotation of the medicinal-leech salivary-EV cargo: a Ly-6/uPAR three-finger scaffold and a structurally dark fraction

Many proteins that medicinal leeches package into salivary extracellular vesicles (EVs) carry no interpretable sequence annotation, so the least-characterised part of a pharmacologically important secretome lacks even a fold assignment. A companion Astral-DIA study of Hirudinaria manillensis nominated 17 ultra-enriched candidate EV cargo proteins. Here we ask whether structure prediction and structural search can assign folds where sequence methods fail, and which candidates remain unresolved under prespecified criteria. With an offline pipeline (ESMFold, TM-align, Foldseek against AFDB-SwissProt and PDB100, UniRef90 search, protein-to-genome alignment), the candidates separate into intracellular/membrane machinery and a secreted, cysteine-dense fraction of three proteins with no Pfam domain. The predicted structure of the leech Protein-sleepless homolog g21166 supports its assignment to the Ly-6/uPAR three-finger fold (TM 0.58 vs CD59; best significant PDB match Lynx1); no function is inferred. g436 and g20791 returned no match meeting the criterion (alnTM [≥] 0.5, E [≤] 0.05) in either database, g436 even after homolog-informed modelling. Gene structure and splice junctions support a conserved locus for the two-Trypsin-segment model g8114 (catalytic unit not established). A Boltz-2 thrombin pilot did not support target engagement, and a pre-registered nicotinic test was terminated as undecidable. The results yield a structure-informed shortlist for experimental follow-up.

bioinformatics↗

bioRxiv2025

An explainable artificial intelligence framework reveals mutations associated with drug resistance in Mycobacterium tuberculosis

Understanding the mechanisms of drug resistance in Mycobacterium tuberculosis (MTB) is essential for the rapid detection of resistance and for guiding effective treatment, ultimately contributing to reducing the global burden of tuberculosis (TB). Under anti-TB drugs pressure, MTB continues to accumulate resistance loci. The current repertoire of known resistance-associated mutations requires further refinement, necessitating efficient methods for the timely identification of potential resistance sites. Here, we introduce xAI-MTBDR, an explainable artificial intelligence framework designed to identify new resistance-associated mutations and predict drug resistance in MTB. It outperforms state-of-the-art methods in predicting drug resistance for all first-line drugs, and scoring each mutations contribution to resistance. By leveraging public whole-genome sequencing data from nearly 40,000 MTB isolates, the framework identified 788 candidate resistance-related mutations and revealed 30 potential resistance markers, several of which are structurally located closer to their respective drugs compared to known resistance mutations. Furthermore, these scores enabled the framework to efficiently subgroup isolates with different resistance mechanisms and reflect varying levels of resistance. The framework serves as a valuable tool for accurate detection of drug-resistant MTB and offers new insights into its underlying mechanisms.

microbiology↗

bioRxiv2019

Ins2 gene bursting activity defines a mature beta-cell state

Transcriptional and functional cellular specialization has been described for insulin-secreting {beta}-cells of the endocrine pancreas. However, it is not clear whether {beta}-cell heterogeneity is stable or reflects dynamic cellular states. We investigated the temporal kinetics of endogenous insulin gene activity using live cell imaging, with complementary experiments employing FACS and single cell RNA sequencing, in {beta}-cells from Ins2GFP knock-in mice. In vivo staining and FACS analysis of islets from Ins2GFP mice confirmed that at a given moment, ~25% of {beta}-cells exhibited significantly higher activity at the conserved insulin gene Ins2. Live cell imaging captured Ins2 gene activity dynamics in single {beta}-cells over days. Autocorrelation analysis revealed a subset of cells with oscillating behavior, with mean oscillation periods of 17 hours. Increased glucose concentrations stimulated more cells to oscillate and resulted in higher average Ins2 gene activity per cell. Single cell RNA sequencing showed that Ins2(GFP)HIGH {beta}-cells were enriched for markers of {beta}-cell maturity. Ins2(GFP)HIGH {beta}-cells were also significantly less viable at all glucose concentrations and in the context of ER stress. Collectively, our results demonstrate that the heterogeneity of insulin production, observed in mouse and human {beta}-cells, can be accounted for by dynamic states of insulin gene activity. BlurbPreviously reported pancreatic {beta}-cell heterogeneity reflects {beta}-cell state transitions.

cell biology↗

bioRxiv2019

Modulation of Insr and insulin receptor signaling by hyperinsulinemia in vitro and in vivo

Hyperinsulinemia is commonly viewed as a compensatory response to insulin resistance, yet studies have suggested that chronically elevated insulin may also drive insulin resistance. The molecular mechanisms underpinning this potentially cyclic process remain poorly defined, especially on a transcriptome-wide level. To study the direct effects of prolonged exposure to excess insulin in muscle cells, we incubated C2C12 myotubes with elevated insulin for 16 hours, followed by 6 hours of serum starvation, and established that acute AKT and ERK signaling were attenuated in this model of in vitro hyperinsulinemia. Global RNA-sequencing of cells both before and after nutrient withdrawal highlighted genes in the insulin signaling, FOXO signaling, and glucose metabolism pathways indicative of hyperinsulinemia and starvation programs. We observed that hyperinsulinemia led to a substantial reduction in insulin receptor (Insr) gene expression, and subsequently a reduced surface INSR and total INSR protein, both in vitro and in vivo. Transcriptomic meta-analysis in >450 human samples demonstrated that fasting insulin reliably and negatively correlated with insulin receptor (INSR) mRNA in skeletal muscle. Bioinformatic modeling combined with RNAi, identified SIN3A as a negative regulator of Insr mRNA (and JUND, MAX, and MXI as positive regulators of Irs2 mRNA). Together, our analysis identifies novel mechanisms which may explain the cyclic processes underlying hyperinsulinemia-induced insulin resistance in muscle, a process directly relevant to the etiology and disease progression of type 2 diabetes.

physiology↗