Biology subjects

Hou, C.

Publications and source records attributed to Hou, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Divergent signaling profiles in mTOR gain-of-function Smith-Kingsmore syndrome (SKS) and TSC2 deficiency

Smith-Kingsmore syndrome (SKS) is a rare neurodevelopmental disorder caused by gain-of-function mutations in MTOR, yet whether these mutations phenocopy TSC2 loss or establish a distinct signaling state remains unclear. Using quantitative proteomics, phosphoproteomics, and transcriptomics in isogenic cell models of SKS (MTOR{Delta}4aa), TSC2 loss (TSC2-/-), and wild-type controls under glucose depletion and refeeding, we find that MTOR{Delta}4aa and TSC2-/- cells occupy fundamentally distinct regulatory states. TSC2-/- cells exhibit broad anabolic remodeling and a transcriptional program dominated by NF-{kappa}B- and STAT-driven inflammatory responses. MTOR{Delta}4aa cells instead display enrichment of nuclear and RNA processing programs, E2F/MYC-driven transcription, and a constrained proteomic dynamic range across nutrient states. Phosphoproteomic analysis of MTOR{Delta}4aa reveals rerouting of nutrient-responsive signaling toward MAPK/ERK- and Ca2+/CaMK-dependent pathways with limited canonical mTORC1/S6K1 engagement. These findings establish SKS as a signaling rewiring disorder distinct from classical mTORC1 hyperactivation, with implications for therapeutic targeting.

molecular biology↗

bioRxiv2026

A Novel Glycoproteomics Platform for High-Throughput Identification of Disease-Associated Glycoforms

Glycosylation is a critical post-translational modification, and its aberrant forms are potent disease biomarkers; however, the comprehensive, site-specific identification of all glycosites and glycoforms across an entire proteome remains prohibitively slow and computationally demanding. To address this bottleneck and accelerate biomarker discovery, we introduce the Glycoproteomics Data Analysis Software (GDAS), a novel, high-throughput platform designed to provide confident, proteome-scale identification of disease-specific glycoforms. GDAS streamlines the analysis through a core, multi-step workflow: it initially employs an ultrafast open search (e.g., MSFragger-Glyco) on mass spectrometry data to rapidly screen and statistically reduce the vast proteome database to a manageable subset of significantly regulated glycoproteins, conserving computational resources for subsequent, in-depth, targeted N- and O-glycosylation analysis using specialized tools (e.g., GlycReSoft and O-Pair). Furthermore, a unique Final Analysis Module utilizes an advanced statistical and machine learning pipeline (incorporating Bootstrap/Bayesian methods, XGBoost, and Random Forest) to integrate quantitative results and generate a robust, comprehensive glycosylation score. We demonstrate GDASs power to recognize biologically relevant glycosylation changes in targeted proteins by validating it using published Alzheimers disease data. GDAS can be downloaded from https://github.com/yang-lab/GDAS. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/708406v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@189b270org.highwire.dtl.DTLVardef@1221045org.highwire.dtl.DTLVardef@15a3f50org.highwire.dtl.DTLVardef@1f2d43f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Nucleophagy removes cytotoxic trapped PARP1

Poly (ADP-Ribose) Polymerase inhibitors (PARPi) induce cytotoxicity in homologous recombination repair (HRR)-deficient cancers by causing PARP1 to become trapped on chromatin, resulting in irreparable replication-associated DNA damage. Although increased clearance of trapped PARP1 from chromatin reduces the sensitivity of cancer cells to PARPi, details surrounding this process remain unclear. PARPi exposure is known to cause increased autophagy flux, whilst autophagy inhibition can hypersensitise cells to PARPi. Using various biochemical, cell biological and live imaging-based assays, we found that trapped PARP1 is cleared by nucleophagy, the selective autophagy of nuclear substrates. Specifically, the nucleophagy of trapped PARP1 was orchestrated by the selective autophagy receptor TEX264 and its partner segregase p97/VCP. TEX264 mediates this process by directly interacting with trapped PARP1, thus bridging PARP1 to the autophagosomal resident protein LC3 for processing via autophagy. Impeding this process, either chemically or genetically, heightened PARP1 trapping, leading to accumulation of protein aggregates, replication-associated DNA damage and cell lethality, re-sensitising PARPi-resistant cells to various PARPi. In conclusion, we show that nucleophagy acts in a cytoprotective manner to directly target PARPi-induced trapped PARP1 for degradation.

cell biology↗

bioRxiv2025

BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection

1A standard task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs). This is most commonly done within one tissue section at a time because the spatial relationships between the tissue sections are typically unknown. However, large-scale spatial atlases are being generated, for example across hundreds of donors, where the goal is to identify a common set of SVGs to use for downstream analyses. One challenge is how to identify and remove SVGs that are associated with a known bias or technical artifact, such as the slide or capture area, which can lead to poor performance in downstream analyses, such as spatial domain detection. Here, we introduce BatchSVG, a tool to identify batch-biased genes in the application of SVG detection. Our approach compares the rank of per-gene deviance under a binomial model (i) with and (ii) without including a covariate in the model that is associated with the known bias or technical artifact. If the rank of a gene changes significantly between these, then we infer that this gene is likely associated with the bias or technical artifact and should be removed from the downstream analysis. We consider two SRT datasets and show how our model can improve the results of downstream analysis.

genomics↗

bioRxiv2025

MotifAE Reveals Functional Motifs from Protein Language Model: Unsupervised Discovery and Interpretability Analysis

Protein language models (pLMs) learn sequence patterns at evolutionary scale, but these patterns remain inaccessible within these "black box" models. To discover them, we developed MotifAE, an unsupervised framework based on the sparse autoencoder (SAE) architecture that projects pLM embeddings into an interpretable, sparse latent space. MotifAE introduces an additional smoothness loss to encourage coherent feature activation, which markedly improves the identification of known functional motifs compared to the standard SAE. The sequence patterns captured by MotifAE exhibit rich diversity, align with known functional motifs, and are reflected in the models weight space. Beyond short motifs, MotifAE also captures structural domains, with latent feature activation scores correlating with residue importance for different domain functions. By aligning MotifAE features with experimental data, we further identified features associated with domain folding stability. These features enable the prediction of a stability-specific fitness landscape that improves stability prediction and supports the engineering of domains with enhanced stability. Overall, MotifAE provides a general framework for systematic sequence pattern discovery and interpretation, with the potential to advance protein function analysis, mutation effect interpretation, and rational protein engineering.

bioinformatics↗

bioRxiv2025

Ethylene receptors are functionally conserved in calcium permeability across the green lineage

The gaseous hormone ethylene plays a key role in regulating plant growth and stress responses. Although Ca{superscript 2} has long been implicated in ethylene signaling, the identity of molecules controlling Ca{superscript 2} permeability involved has remained elusive. Here we show that Arabidopsis ethylene receptors ETR1 and ERS1 are Ca2+ permeable, and that ETR1 mediates ethylene-induced cytosolic Ca2+ increase and regulates hypocotyl elongation. Homologs of ETR1 from eight land plant and algal species also exhibit Ca{superscript 2} permeability, suggesting an evolutionarily conserved function. We further demonstrate ethylene enhances the Ca2+ permeability of ETR1 and its homologue from the charophyte Klebsormidium flaccidum. These findings uncover a previously unrecognized but conserved role of ethylene receptors in Ca{superscript 2} permeability regulation in the green lineage, with broad implications for Ca{superscript 2} signaling in plant development and environmental adaptation.

plant biology↗

bioRxiv2025

DEEPReditor-CMG: A deep-learning-based predictive RNA editor for crop mitochondria genomes

C-to-U RNA editing is a pervasive phenomenon in crop mitochondria, playing a pivotal role in the processing of functional proteins encoded by mitochondrial genes and serving as a crucial regulatory mechanism for nuclear control over mitochondrial gene expression. However, identifying editing sites across the mitochondrial genome with high precision remains a formidable challenge. To address this challenge and uncover the potential regulatory patterns of C-to-U RNA editing, this study compiled a comprehensive dataset of mitochondrial genome sequences from 13 crops, including Arabidopsis thaliana, Lactuca sativa var. Capitata, Oryza sativa Japonica, and Zea mays, among others, annotated with editing sites from NCBI. Utilizing TensorFlow 2.0, we constructed a rigorous hyperparameter optimization framework, which facilitated the development of the DEEPReditor-CMG model, demonstrating superior predictive capabilities. Based on the DEEPReditor-CMG model and three interspecies relationship indices, we discovered that species with closer phylogenetic relationships exhibit more similar C-to-U RNA editing mechanisms, providing valuable insights into the evolutionary conservation of RNA editing mechanisms. We further developed executable programs for predicting C-to-U RNA editing sites in crop mitochondrial genomes, leveraging the conserved nature of the crop RNA editing machinery to maximize prediction reliability and uncover new editing sites with great potential. This research not only deepens our understanding of C-to-U RNA editing and post-transcriptional regulatory mechanisms in crop mitochondria but also paves a novel avenue for investigating RNA editing mechanisms. Experimental data, computational code, and applications are publicly accessible at https://github.com/Qinsidong/DEEPReditor-CMG. HighlightsHigh-precision prediction model for C-to-U RNA editing in crops--DEEPReditor-CMG Three interspecies relationship indices describing C to U RNA editing Prediction tools based on the conservation of crop RNA editing mechanisms Open-sourced experimental data, computational code, and applications

bioinformatics↗

bioRxiv2025

Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant effect predictors

Predictive models for missense variant pathogenicity offer little functional interpretation for intrinsically disordered regions, since they rely on conservation and coevolution across homologous sequences. To understand the extent to which biophysics modulates model performance compared to genomic conservation, we model biophysics of IDRs explicitly for improved interpretation of variant effects. We develop MDmis, a method that uses biophysical features extracted from molecular dynamics (MD) simulations of IDRs to predict pathogenicity. We find that pathogenic variants in Long IDRs manifest differently, with transient order and depleted solvent access, compared to those in Short IDRs. Using MD simulations of sequences with single missense variants, we identify stronger evidence for pathogenic effects in Long IDRs compared to Short IDRs. MDmis, when combined with conservation information, achieves strong predictive accuracy of pathogenicity of variants in Long IDRs. Overall, extracting information from MD simulations can help understand the drivers of predictive performance and elucidate biophysical behaviors affected by pathogenic variants.

bioinformatics↗

bioRxiv2025

Understanding Protein Language Model Scaling on Mutation Effect Prediction

Protein language models, and models that incorporate structure or homologous sequences, estimate sequence likelihood p(sequence) that reflects the protein fitness landscape and is commonly used in mutation effect prediction and protein design. It is widely believed in deep learning field that larger model performs better across tasks. However, for fitness prediction, language model performance declines beyond a certain size, raising concerns about their scalability. Here, we showed that model size, training dataset, and stochastic elements can bias the predicted p(sequence) away from real fitness. Model performance on fitness prediction depends on how well p(sequence) matches evolutionary patterns in homologs, which is best achieved at a moderate p(sequence) level for most proteins. At extreme predicted wild-type sequence likelihoods, models predict uniformly low or high likelihoods for nearly all mutations, failing to reflect the real fitness landscape. Notably, larger models tend to predict proteins with higher p(sequence), which may exceed the moderate range and thus reduce performance. Our findings clarify the scaling behavior of protein models on fitness prediction and provide practical guidelines for their application and future development.

bioinformatics↗

bioRxiv2024

Cryogenic electron tomography and elemental analysis of mitochondrial granules in human retinal ganglion cells

Combining three-dimensional (3D) visualization with elemental analysis of vitrified cells can provide crucial insights into subcellular structures and elemental compositions in their native environments. We present a coordinated approach using cryogenic electron energy loss spectroscopy (cryoEELS) and cryogenic electron tomography (cryoET) to characterize the elemental distribution and ultrastructure of vitrified cells. We applied this method to examine calcium disposition in the mitochondria of cultured human retinal ganglion cells (RGCs) exposed to pro-calcifying conditions relevant to optic disc drusen pathology. Our cryoEELS analysis revealed mitochondrial granules with elevated calcium signals, offering direct evidence of mitochondrial calcification. Additionally, cryoET coupled with artificial intelligence-based analysis enabled quantification of the volume and spatial distribution of these calcium granules. This integrated workflow can be broadly applied to various cell types, facilitating the study of ultrastructure and elemental distribution in subcellular structures under diverse physiological and pathological conditions, as well as in response to therapeutic interventions.

biophysics↗

bioRxiv2024

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties

Structural dynamics are fundamental to protein functions and mutation effects. Current protein deep learning models are predominantly trained on sequence and/or static structure data, which often fail to capture the dynamic nature of proteins. To address this, we introduce SeqDance and ESMDance, two protein language models trained on dynamic biophysical properties derived from molecular dynamics simulations and normal mode analyses of over 64,000 proteins. Both models can be directly applied to predict dynamic properties of unseen ordered and disordered proteins. SeqDance, trained from scratch, has attentions that capture dynamic interaction and co-movement between residues, and its embeddings encode rich representations of protein dynamics that can be further utilized to predict conformational properties beyond the training tasks via transfer learning. SeqDance predicted dynamic property changes reflect mutation effect on protein folding stability. ESMDance, built upon ESM2 (Evolutionary Scale Model II) outputs, substantially outperforms ESM2 in zero-shot prediction of mutation effects for designed and viral proteins which lack evolutionary information. Together, SeqDance and ESMDance offer a new framework for integrating protein dynamics into language models, enabling more generalizable predictions of protein behavior and mutation effects. Significance StatementThe sequence--structure (ensemble)--function relationship is central to biology. Protein dynamics in the structure ensemble play a decisive role in determining function and mutation effects, and are widely used to study thermodynamics, folding pathways, and dynamic interactions of ordered proteins, as well as the conformational variability of intrinsically disordered proteins. However, current state-of-the-art protein deep learning models, such as AlphaFold2,3 and ESM, focus on static structures and sequences, which failed to directly capture protein dynamics. Here, we address this gap by developing protein language models to learn dynamic properties of over 64,000 proteins. We show that the models Transformer attentions capture protein dynamic interactions, and our model can be applied to predict conformational properties and mutation effects.

bioinformatics↗

bioRxiv2024

A Small-molecule Antagonist Radiotracer for Positron Emission Tomography Imaging of the Mu Opioid Receptor.

The opioid crisis is a catastrophic health emergency catalyzed by the misuse of opioids that target and activate the mu opioid receptor. Traditional radioligands used to study the mu opioid receptor are often tightly regulated owing to their abuse and respiratory depression potential. In the present study, we sought to design and characterize a library of 24 non-agonist ligands for the mu opioid receptor. Ligands were evaluated for the binding affinity, intrinsic activity, and predicted blood-brain barrier permeability. Several ligands demonstrated single-digit nM binding affinity for the mu opioid receptor while also demonstrating selectivity over the delta and kappa opioid receptors. The antagonist behavior of 1A and 3A at the mu opioid receptor indicate that these ligands would likely not induce opioid-dependent respiratory depression. Therefore, these ligands can enable a safer means to interrogate the endogenous opioid system. Based on binding affinity, selectivity, and potential off-target binding, [11C]1A was prepared via metallophotoredox of the aryl-bromide functional group to [11C]methyl iodide. The nascent radiotracer demonstrated brain uptake in a rhesus macaque model and accumulation in the caudate and putamen. Naloxone was able to reduce [11C]1A binding, though the interactions were not as pronounced as naloxones ability to displace [11C]carfentanil. These results suggest that GSK1521498 and related congeners are amenable to radioligand design and can offer a safer way to query opioid neurobiology.

neuroscience↗

bioRxiv2024

HuBMAPR: an R Client for the HuBMAP Data Portal

SummaryThe Human BioMolecular Atlas Program (HuBMAP) constructs the worldwide available platform to research the human body at the cellular level. The HuBMAP Data Portal encompasses a wide range of data resources measured on emerging experimental technologies at a spatial resolution. To broaden access to the HuBMAP Data Portal, we introduce an R client called HuBMAPR available on Bioconductor. This gives an efficient and programmatic interface, enabling researchers to discover and retrieve HuBMAP data easier and faster. AvailabilityThis package is available on GitHub (https://github.com/christinehou11/HuBMAPR) and has been submitted to Bioconductor.

bioinformatics↗

bioRxiv2024

CryoViT: Efficient Segmentation of Cryogenic Electron Tomograms with Vision Foundation Models

Cryogenic electron tomography (cryoET) directly visualizes subcellular structures in 3D at the nanometer scale. Quantitative analyses of cryoET data can reveal structural biomarkers of diseases, provide novel mechanistic insights, and inform the effects of treatments on phenotype. However, existing automated annotation approaches primarily focus on localizing molecular features with few methods accurately quantifying complex structures such as organelles. We address this challenge with CryoViT, a paradigm shift from traditional convolutional neural networks that leverages vision transformers to enhance the segmentation of large pleomorphic structures that can occupy almost the entire field of view in high-magnification images, such as mitochondria. CryoViT is powered by a large-scale vision foundation model and overcomes limitations of popular U-Net based methods, particularly when training data are scarce. We demonstrate the efficacy of CryoViT on a large cryoET dataset of neurons differentiated from iPSCs derived from Huntington disease (HD) patients and cultured HD mouse model neurons.

bioinformatics↗

bioRxiv2024

PhaSeDis: A Manually Curated Database of Phase Separation-Disease Associations and Corresponding Small Molecules

Biomacromolecules form membraneless organelles through liquid-liquid phase separation in order to regulate the efficiency of particular biochemical reactions. Dysregulation of phase separation might result in pathological condensation or sequestration of biomolecules, leading to diseases. Thus, phase separation and phase separating factors may serve as drug targets for disease treatment. Nevertheless, such associations have not yet been integrated into phase separation related databases. Therefore, based on MloDisDB, a database for membraneless organelle factor-disease association previously developed by our lab, we constructed PhaSeDis, the phase separation-disease association database. We increased the number of phase separation entries from 52 to 185, and supplemented the evidence provided by the original article verifying the phase separation nature of the factors. Moreover, we included the information of interacting small molecules with low or high-throughput evidence that might serve as potential drugs for phase separation entries. PhaSeDis strives to offer comprehensive descriptions of each entry, elucidating how phase separating factors induce pathological conditions via phase separation and the mechanisms by which small molecules intervene. We believe that PhaSeDis would be very important in the application of phase separation regulation in treating related diseases. PhaSeDis is available at http://mlodis.phasep.pro.

bioinformatics↗

bioRxiv2024

NSD2 is a requisite subunit of the AR/FOXA1 neo-enhanceosome in promoting prostate tumorigenesis

The androgen receptor (AR) is a ligand-responsive transcription factor that binds at enhancers to drive terminal differentiation of the prostatic luminal epithelia. By contrast, in tumors originating from these cells, AR chromatin occupancy is extensively reprogrammed to drive hyper-proliferative, metastatic, or therapy-resistant phenotypes, the molecular mechanisms of which remain poorly understood. Here, we show that the tumor-specific enhancer circuitry of AR is critically reliant on the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2), a histone 3 lysine 36 di-methyltransferase. NSD2 expression is abnormally gained in prostate cancer cells and its functional inhibition impairs AR trans-activation potential through partial off-loading from over 40,000 genomic sites, which is greater than 65% of the AR tumor cistrome. The NSD2-dependent AR sites distinctly harbor a chimeric AR-half motif juxtaposed to a FOXA1 element. Similar chimeric motifs of AR are absent at the NSD2-independent AR enhancers and instead contain the canonical palindromic motifs. Meta-analyses of AR cistromes from patient tumors uncovered chimeric AR motifs to exclusively participate in tumor-specific enhancer circuitries, with a minimal role in the physiological activity of AR. Accordingly, NSD2 inactivation attenuated hallmark cancer phenotypes that were fully reinstated upon exogenous NSD2 re-expression. Inactivation of NSD2 also engendered increased dependency on its paralog NSD1, which independently maintained AR and MYC hyper-transcriptional programs in cancer cells. Concordantly, a dual NSD1/2 PROTAC degrader, called LLC0150, was preferentially cytotoxic in AR-dependent prostate cancer as well as NSD2-altered hematologic malignancies. Altogether, we identify NSD2 as a novel subunit of the AR neo-enhanceosome that wires prostate cancer gene expression programs, positioning NSD1/2 as viable paralog co-targets in advanced prostate cancer.

cancer biology↗

bioRxiv2024

Nephrectomy and high-salt diet inducing pulmonary hypertension and kidney damage by increasing Ang II concentration in rats

BackgroundPulmonary hypertension (PH) is a common complication in patients with chronic kidney disease (CKD), affecting prognosis. However, the pathogenesis is not clear, and the lack of a stable animal model is a significant factor. MethodsIn this study, a rat model of chronic kidney disease with pulmonary hypertension (CKD-PH) was developed through 5/6 nephrectomy combined with a high-salt diet. The models hemodynamics and pathological changes in multiple organs were dynamically assessed. Lung tissues and serum were collected from the model rats to measure the expression of ACE2, the expression levels of vascular active components related to the renin-angiotensin-aldosterone system (RAAS), and changes in the serum metabolic profile of the model. ResultsAfter 14 weeks post-surgery, the CKD-PH rat model exhibited significant changes in hemodynamic parameters indicative of pulmonary arterial hypertension, along with alterations such as right ventricular hypertrophy. However, no evidence of pulmonary vascular remodeling was observed. An imbalance in the renin-angiotensin-aldosterone system was identified in the CKD-PH rat models. Downregulation of ACE2 expression was observed in pulmonary tissues. The serum metabolic profile of the CKD-PH rat models showed distinct differences compared to the sham surgery group. ConclusionsThe development of pulmonary arterial hypertension in CKD-PH rats may be primarily attributed to the disruption of the renin-angiotensin-aldosterone system (RAAS), coupled with a decrease in ACE2 expression in pulmonary vascular endothelial tissues and metabolic disturbances.

pathology↗

bioRxiv2023

High-resolution diploid 3D genome reconstruction using Pore-C data

In diploid organisms, spatial variations between homologous chromosomes are essential to many biological phenomena. Currently, it is still challenging to efficiently reconstruct a high-quality diploid 3D human genome. Here, we introduce Dip3D, reconstructing the diploid 3D human genome using Pore-C data of one sample. Dip3D has solved multiple problems in genome-wide SNV calling and haplo-tagging caused by the high sequencing error rates in Pore-C type data. Dip3D capitalizes on the high-order chromosomal interaction characteristics, enabling robust haplotype imputation and intricate haplotype-specific 3D structure discovery. Dip3D outperforms previous methods in data utilization rate, contact matrix resolution, and completeness by one order of magnitude. Moreover, Dip3D allows capturing haplotype high-order interactions that are unseen in Hi-C type data. We demonstrated the identified haplotype substructures such as Topologically Associating Domains (TADs) in the constructed 3D human genome, and unraveled connections between genic haplotype-specific high-order interactions and imbalanced allelic expression.

genomics↗