Biology subjects

HUANG, S.

Publications and source records attributed to HUANG, S..

4 recordsLinked to original sources

bioRxiv2026

memVar: an integrated resource for interpreting human membrane protein variation in molecular and disease contexts

Interpreting membrane protein variation requires connecting clinical evidence to the membrane environments and molecular interactions that govern protein functions. Here, we introduce memVar (https://memvar.sunnylab.org), an integrated database of 10,906,688unique single-nucleotide variants from ClinVar, COSMIC, and gnomAD across 7,732 human membrane-associated proteins. memVar reconciles genomic, transcript, and protein coordinates to link variants with membrane topology, bilayer depth, lipid contacts, functional sites, partner-specific interaction interfaces, and variant-effect predictions, alongside disease, tissue-expression and regulatory annotations. Coordinated sequence, structure and variant views retain source provenance and distinguish clinical assertions, experimental observations, and computational predictions. Our analysis reveals that pathogenic/likely pathogenic missense substitutions are enriched in transmembrane segments and predicted protein-protein interaction sites relative to benign/likely benign substitutions, with pooled odds ratios of 2.47 and 6.67, respectively. IntAct-derived interaction loss or decrease was experimentally recorded for 57.9% of pathogenic/likely pathogenic substitutions versus 7.4% of benign/likely benign substitutions. CFTR examples contextualize the established G551D gating defect and prioritize W202R, a variant of uncertain significance, for tests of protein maturation, surface abundance and channel activity. By connecting variant records to specific molecular questions, memVar potentially supports mechanism-oriented investigation and prioritization of functional experiments.

bioinformatics↗

bioRxiv2025

Modulation of signal response by small RNAs through feed-forward loops

In response to environmental changes, bacteria have evolved sophisticated regulatory networks that incorporate small RNAs (sRNAs) and transcription factors (TFs) to fine-tune cellular physiology. Both sRNAs and TFs modulate gene expression, but the former function via post-transcriptional mechanisms, while the latter act at the transcriptional level. However, it remains unclear why both regulatory layers are conserved through evolution, rather than one being sufficient. Here, we experimentally identified that CRP, a global regulator, regulates 25 small RNAs (sRNAs) in Escherichia coli. Interestingly, CRP also controls 80% of the target genes of these sRNAs. This architecture led us to identify 34 novel sRNA-mediated feed-forward loops (sFFLs)--circuits where CRP regulates both an sRNA and its target--within the CRP regulon. Quantitative PCR analysis of 16 such sFFLs revealed that each type possesses a distinct cAMP dose-response profile, suggesting that different sFFL structures embody unique regulatory logic. Specifically, coherent and incoherent type 3 and 4 sFFLs exhibit broader dynamic ranges in their dose-response compared to open-loop controls. Coherent and incoherent type 1 and 2 sFFLs appear more energy-efficient. Altogether, we propose that sRNAs cooperate with TFs through sFFLs to optimize both the energy efficiency and the diversity of signal response profiles. Therefore, sRNAs serve as critical components integrating transcriptional and post-transcriptional networks across diverse cellular pathways.

systems biology↗

bioRxiv2022

INSIDER: Interpretable Sparse Matrix Decomposition for Bulk RNA Expression Data Analysis

RNA-Seq is widely used to capture transcriptome dynamics across tissues from different biological entities even across biological conditions, with the aim of understanding the contribution of gene activities to phenotypes of biosamples. However, due to variation from tissues and biological entities (or other biological conditions), joint analysis of bulk RNA expression profiles across multiple tissues from a number of biological entities to achieve the aim is hindered. Moreover, it is crucial to consider interactions between biological variables. For example, different brain disorders may affect brain regions heterogeneously. Thus, modeling the disorder-region interaction can shed light on the heterogeneity. To address these key challenges, we propose a general and flexible statistical framework based on matrix factorization, named INSIDER (https://github.com/kai0511/insider). INSIDER decomposes variation from different biological variables into a shared low-rank latent space. In particular, it considers interactions between biological variables and introduces the elastic net penalty to induce sparsity, thus facilitating interpretation. In the framework, the biological variables and interaction terms can be defined based on the research questions and study design. Besides, it enables us to compute the adjusted expression profiles for biological variables that control variation from other biological variables. Lastly, it allows various downstream analyses, such as clustering donors with donor representations, revealing development trajectory in its application to the BrainSpan data, and uncovering mechanisms underlying variables like phenotype and interactions between biological variables (e.g., phenotypes and tissues).

bioinformatics↗

bioRxiv2020

Coordination and assembly of protein complexes encoded across mitochondrial and nuclear genomes is assisted by CLPP2 in Arabidopsis thaliana

Protein homeostasis in eukaryotic organelles and their progenitor prokaryotes is regulated by a series of proteases including the caseinolytic protease (CLPP). CLPP has essential roles in chloroplast biogenesis and maintenance, but the significance of the plant mitochondrial CLPP remains unknown and factors that aid coordination of nuclear and mitochondrial encoded subunits for complex assembly in mitochondria await discovery. We generated knock-out lines of the single gene for the mitochondrial CLP protease subunit, CLPP2, in Arabidopsis thaliana. Mutants had higher abundance of transcripts from mitochondrial genes encoding OXPHOS protein complexes, while transcripts for nuclear genes encoding other subunits of the same complexes showed no change in abundance. In contrast, the protein abundance of specific nuclear-encoded subunits in OXPHOS complexes I and V increased in CLPP2 knockouts, without accumulation of mitochondrial-encoded counterparts in the same complex. Protein complexes mainly or entirely encoded in the nucleus were unaffected. Analysis of protein import, assembly and function of Complex I revealed that while function was retained, protein homeostasis was disrupted through decreased assembly, leading to accumulation of soluble subcomplexes of nuclear-encoded subunits. Therefore, CLPP2 contributes to the mitochondrial protein degradation network through supporting coordination and assembly of protein complexes encoded across mitochondrial and nuclear genomes. One sentence summaryCLPP contributes to the mitochondrial protein degradation network through supporting coordination and assembly of protein complexes encoded across mitochondrial and nuclear genomes.

plant biology↗