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Huang, Y.

Publications and source records attributed to Huang, Y..

At least 469 records · Page 26Linked to original sources

Acetyl-CoA Synthetase Mutations S868G and V949I Do Not Confer Resistance to Antimalarial Drugs in vitro in Plasmodium falciparum.

Plasmodium falciparum acetyl-CoA synthetase (PfACAS) protein is an important source of acetyl-CoA. We detected the mutations S868G and V949I in PfACAS by whole-genome sequencing analysis in some recrudescent parasites after antimalarial treatment with artesunate and dihydroartemisinin-piperaquine, suggesting that they may confer drug resistance. Using CRISPR/Cas9 technology, we engineered parasite lines carrying the PfACAS S868G and V949I mutations in two genetic backgrounds and evaluated their susceptibility to antimalarial drugs in vitro. The results demonstrated that PfACAS S868G and V949I mutations alone or in combination were not enough to provide resistance to antimalarial drugs.

microbiology↗

ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation

Structure-based molecule generation represents a significant advancement in AI-aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of structural data on protein-ligand complexes, a challenge we term the Paradox of Sparse Chemical Space Generation. To address this limitation, we propose a novel latent variable approach that bridges the data gap between ligand-only and protein-ligand complexes, enabling the target-aware generative models to explore a broader chemical space and enhancing the quality of molecular generation. Drawing inspiration from quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables--an innovative integration of physical principles into deep learning frameworks. ECloudGen incorporates modern techniques, including latent diffusion models, Llama architectures, and a newly proposed contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a significantly broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in a case study designing V2R inhibitors. Furthermore, ECloudGens structurally ordered modeling of chemical space enables the development of a model-agnostic optimizer, extending its utility to molecular optimization tasks. This capability has been validated through a single-objective oracle benchmark and a complex multi-objective optimization scenario involving the redesign of endogenous BRD4 ligands. In conclusion, ECloudGen effectively addresses the Paradox of Sparse Chemical Space Generation through its integration of theoretical insights, advanced generative techniques, and real-world validation. The newly proposed technique of leveraging physical entities (such as electron clouds) as latent variables within a deep learning framework may prove useful for computational biology fields beyond AIDD.

bioinformatics↗

Inhibition of Endothelial Lipase by MEDI5884 Normalizes Phosphatidylinositol Levels in Coronary Artery Disease Patients

BackgroundEndothelial lipase (EL) promotes high-density lipoproteins (HDL) phospholipid degradation, increases catabolism of HDL and is an attractive target for the potential treatment for cardiovascular disease. Inhibition of EL using a monoclonal neutralizing antibody, MEDI5884, demonstrated increased quantity and function of HDL. Determinants of anti-atherosclerotic function of HDL comprise the interplay of various components of HDL structure-activity relationship: size, shape and composition (lipid and protein). Previous studies have shown that single doses of MEDI5884 administered to healthy nonhuman primates (NHPs) and healthy subjects resulted in a dose- dependent increase in plasma phospholipids (PL) and that plasma PI levels in placebo treated healthy subjects are significantly increased relative to CAD subjects participating in clinical trials NCT03001297 and NCT03351738, respectively. MethodsHerein, we characterized using LC-MS/MS the plasma lipidome of NHPs, heathy subjects and subjects with coronary artery disease (CAD) following MEDI5884 administration. ResultsMEDI5884 treated NHPs resulted in a prominent increase in phosphatidylinositols (PI) and cholesteryl esters (CE). Treatment with MEDI5884 restores near-normal levels of PI in CAD patients. PI increases in both healthy subjects and CAD patients were dose-dependent, correlated with exposure and saturated at approximately 200 mg MEDI5884 subcutaneous (SC) dose in CAD patients. Comparison of pharmacodynamic (PD) effects of repeat SC 200 mg doses of MEDI5884 in CAD patients revealed greater and more rapid increases in PI levels compared to HDL-C and HDL phospholipid (HDL-PL). The increase in PI species was inversely correlated with decreases in free EL mass levels. ConclusionsPI has previously been shown to possess anti-atherosclerotic properties and led to increases in HDL cholesterol (HDL-C) and reverse cholesterol transport (RCT). The mechanism by which CE levels increase as the result of MEDI5884 administration can be attributed to the observed increase in both substrates of the lecithin-cholesterol acyltransferase (LCAT) reaction: phosphatidylcholine/phosphatidylethanolamine (PC/PE) and cholesterol as the consequence of EL inhibition. Further characterization of the underlying biological mechanisms responsible for the decrease of the PI biomarker in CAD patient population relative to healthy subjects as well as in conjunction with pharmacological intervention by MEDI5884 may reveal more information on this clinically-relevant biomarker and potential role in CAD.

pharmacology and toxicology↗

Soybean PHR1-regulated low phosphorus-responsive GmRALF22 increases uptake of phosphate via stimulating GmPTs expression

Phosphorus (P) is one of essential macronutrients for plant growth and development. Rapid Alkalization Factors (RALFs) play crucial roles in plant responses to nutrient stresses, however, the functions of Glycine max RALFs (GmRALFs) under low P (LP) stress remain elusive. In this study, we first identified 27 GmRALFs in soybean, then we revealed that GmRALF10, GmRALF11, and GmRALF22 are induced in both roots and leaves, while only GmRALF5, GmRALF6, and GmRALF25 are up-regulated in leaves in LP conditions. Furthermore, GmRALF22 was found to be the target gene of transcription factor GmPHR1, which binds the P1BS cis-element in the promoter of GmRALF22 via electrophoretic mobility shift assay (EMSA) and DUAL-LUC experiment. Colonization of Bacillus subtilis that deliver GmRALF22 increases the expression of high affinity phosphate (Pi) transporter gene GmPT2, GmPT11, GmPT13 and GmPT14, thus increases the total amount of dry matter and soluble Pi in soybean. RNA-sequencing uncovered that GmRALF22 alleviates LP stress by regulating the expression of JA-, SA- and immune-related genes. Finally, we verified that GmRALF22 is dependent on FERONIA to promote Arabidopsis primary root growth under LP conditions. In summary, GmPHR1-GmRALF22 module positively regulates soybeans tolerance to LP. HighlightsO_LISoybean genome has 27 GmRALFs. GmRALF5, GmRALF6, GmRALF10, GmRALF11, GmRALF22 and GmRALF25 are induced in low phosphorus (LP). C_LIO_LIGmPHR1 directly regulate the transcription of GmRALF22 via binding the promoter P1BS cis-element. C_LIO_LISecretion of GmRALF22 protein by Bacillus subtilis promoted soybean growth under LP conditions by improving soybean P nutrition through increased expression of high affinity phosphate (Pi) transporter gene. C_LIO_LIGmRALF22 regulates soybean P nutrition by JA-, SA- and immune-related genes expression at transcriptome level. That application of GmRALF22 promoted primary root growth in Arabidopsis in LP is dependent on FER receptor. C_LI

molecular biology↗

A Rigorous Multi-Laboratory Study of Known PDAC Biomarkers Identifies Increased Sensitivity and Specificity Over CA19-9 Alone

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.

cancer biology↗

Mass mortality associated with Carpione Rhabdovirus in golden pompano (Trachinotus ovatus) in China: First report

A severe epizootic outbreak occurred in a deep-water cultured golden pompano (Trachinotus ovatus) in Guangdong Province, China (August-November 2023); the cumulative mortality was 65-82%. The diseased fish showed clinical signs of lethargy, anorexia, whirling movements, and hemorrhage at the base of the fins and in the upper and lower jaws before mass mortality. A Rhabdovirus strain, CARPV2023, was successfully isolated using FHM cells. Cytopathic effects of the tissue filtrate of the diseased golden pompano on FHM and EPC cell monolayers were characterized by rounded cells, grape-like cluster formation, detachment, and lysis. Histopathology revealed congestion, hemorrhage, and necrosis in the spleen, liver, kidney, and intestinal tissues of naturally and experimentally infected golden pompanos. Electron microscopy showed that bullet-shaped viral particles 183.5-201.6-nm long and 57.3-82.7-nm wide budded from cell membrane or aggregated around the infected FHM cells. The virus remained stable for 7 days at 4-33{degrees}C and grew optimally at 28{degrees}C. Whole-genome sequencing and phylogenetic analysis based on the full genome further indicated that the CARPV2023 strain is a member of Carpione Rhabdovirus, which is related to Novirhabdovirus with unclassification. A specific nested reverse transcriptase-polymerase chain reaction confirmed the presence of CARPV in the affected golden pompano. Much higher mortality was observed in challenged golden pompanos than in the controls through intraperitoneal injection and immersion infection. In situ hybridization showed positive reactions in the fish tissues. This is the first study to report natural CARPV infections in marine fish in the world.

microbiology↗

Recombinant inbred line panels inform the genetic architecture and interactions of adaptive traits in Drosophila melanogaster

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al. 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

evolutionary biology↗

Stabilizing mammalian RNA thermometer confers neuroprotection in subarachnoid hemorrhage

Mammals tightly regulate their core body temperature, yet how cells sense and respond to small temperature changes at the molecular level remains incompletely understood. Here, we discover a significant enrichment of RNA G-quadruplex (rG4) motifs around splice sites of cold-repressed exons. These thermosensing RNA structures, when stabilized, mask splice sites, reducing exon inclusion. Focusing on cold-induced neuroprotective RBM3, we demonstrate that rG4s near splice sites of a cold-repressed poison exon are stabilized at low temperatures, leading to exon exclusion. This enables evasion of nonsense-mediated decay, increasing RBM3 expression at cold. Additionally, increasing intracellular potassium concentration stabilizes rG4s and enhances RBM3 expression, leading to RBM3-dependent neuroprotection in a mouse model of subarachnoid hemorrhage. Our findings unveil a mechanism how mammalian RNAs directly sense temperature and potassium perturbations, integrating them into gene expression programs. This opens new avenues for treating diseases arising from splicing defects and disorders benefiting from therapeutic hypothermia. HighlightsO_LIrG4s are enriched near splice sites of cassette exons repressed upon cold shock C_LIO_LIrG4s act as RNA thermometers in mammals by controlling accessibility of splice sites C_LIO_LIrG4 stability mediates temperature-dependent RBM3 expression ex vivo and in vivo C_LIO_LIStabilizing rG4s in RBM3 exon 3a reduces brain damage in subarachnoid hemorrhage C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/594628v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@e1d625org.highwire.dtl.DTLVardef@b22d21org.highwire.dtl.DTLVardef@1b0faeaorg.highwire.dtl.DTLVardef@294ac1_HPS_FORMAT_FIGEXP M_FIG C_FIG Here is a refined model of RNA G-quadruplexes acting as evolutionarily conserved thermo- and potassium sensors, modulating alternative splicing in mammals. RNA G-quadruplexes (rG4s) function as reversible temperature sensors, impacting alternative splicing dynamics. In low temperatures or high potassium conditions, rG4s can mask surrounding splice sites, rendering these sites inaccessible, thereby promoting exon skipping. Conversely, at high temperatures or under low potassium conditions, rG4s become destabilized, allowing splice sites to be exposed, and facilitating efficient exon inclusion. RBM3, a well-known cold-induced protein with neuroprotective functions, harbors a poison exon with rG4s around splice sites, that, upon inclusion, triggers NMD (non-sense mediated decay) of the RBM3 mRNA. Under low temperatures or high potassium conditions, rG4s shield the splice sites, leading to poison exon skipping and increased RBM3 expression. Stabilization of these rG4s through increased K+ promotes poison exon skipping, enabling escape from NMD, and ultimately elevating RBM3 expression. Notably, 4-AP, a clinically used pan voltage-gated potassium channel blocker, protects against neuronal damage in a subarachnoid hemorrhage mouse model in an RBM-dependent manner. (ISS: intronic splicing silencer, ESE: exon splicing enhancer, ACA: anterior cerebral artery, MCA: middle cerebral artery, PPA: pterygopalatine artery, ICA: internal carotid artery, ECA: external carotid artery, CCA: common carotid artery)

molecular biology↗

ILC2s navigate tissue redistribution during infection using stage-specific S1P receptors

Lymphocytes can circulate as well as take residence within tissues. While the mechanisms by which circulating populations are recruited to infection sites have been extensively characterized, the molecular basis for the recirculation of tissue-resident cells is less understood. Here, we show that helminth infection- or IL-25-induced redistribution of intestinal group 2 innate lymphoid cells (ILC2s) requires access to the lymphatic vessel network. Although the secondary lymphoid structure is an essential signal hub for adaptive lymphocyte differentiation and dispatch, it is redundant for ILC2 migration and effector function. Upon IL-25 stimulation, a dramatic change in epigenetic landscape occurs in intestinal ILC2s, leading to the expression of sphingosine-1-phosphate receptors (S1PRs). Among the various S1PRs, we found that S1PR5 is critical for ILC2 exit from intestinal tissue to lymph. By contrast, S1PR1 plays a dominant role in ILC2 egress from mesenteric lymph nodes to blood circulation and then to distal tissues including the lung where the redistributed ILC2s contribute to tissue repair. The requirement of two S1PRs for ILC2 migration is largely due to the dynamic expression of the tissue-retention marker CD69, which mediates S1PR1 internalization. Thus, our study demonstrates a stage-specific requirement of different S1P receptors for ILC2 redistribution during infection. We therefore propose a fundamental paradigm that innate and adaptive lymphocytes utilize a shared vascular network frame and specialized navigation cues for migration.

immunology↗

Oxidized low-density lipoprotein potentiates angiotensin II-induced Gq activation through the AT1-LOX1 receptor complex: Implications for renal dysfunction

Chronic kidney disease (CKD) and atherosclerotic heart disease, frequently associated with dyslipidemia and hypertension, represent significant health concerns. We investigated the interplay among these conditions, focusing on the role of oxidized low-density lipoprotein (oxLDL) and angiotensin II (Ang II) in renal injury via G protein q subunit (Gq) signaling. We hypothesized that oxLDL enhances Ang II-induced Gq signaling via the AT1 (Ang II type 1 receptor)-LOX1 (lectin-like oxLDL receptor) complex. Based on CHO and renal cell model experiments, oxLDL alone did not activate Gq signaling. However, when combined with Ang II, it significantly potentiated Gq-mediated inositol phosphate 1 production and calcium influx in cells expressing both LOX-1 and AT1 but not in AT1-expressing cells. This suggests a critical synergistic interaction between oxLDL and Ang II in the AT1-LOX1 complex. Conformational studies using AT1 biosensors have indicated a unique receptor conformational change due to the oxLDL-Ang II combination. In vivo, wild-type mice fed a high-fat diet with Ang II infusion presented exacerbated renal dysfunction, whereas LOX-1 knockout mice did not, underscoring the pathophysiological relevance of the AT1-LOX1 interaction in renal damage. These findings highlight a novel mechanism of renal dysfunction in CKD driven by dyslipidemia and hypertension and suggest the therapeutic potential of AT1-LOX1 receptor complex in patients with these comorbidities.

molecular biology↗

An integrated microfluidic and fluorescence platform for probing in vivo neuropharmacology

Neurotechnologies and genetic tools for dissecting neural circuit functions have advanced rapidly over the past decade, although the development of complementary pharmacological method-ologies has comparatively lagged. Understanding the precise pharmacological mechanisms of neuroactive compounds is critical for advancing basic neurobiology and neuropharmacology, as well as for developing more effective treatments for neurological and neuropsychiatric disorders. However, integrating modern tools for assessing neural activity in large-scale neural networks with spatially localized drug delivery remains a major challenge. Here, we present a dual microfluidic-photometry platform that enables simultaneous intracranial drug delivery with neural dynamics monitoring in the rodent brain. The integrated platform combines a wireless, battery-free, miniaturized fluidic microsystem with optical probes, allowing for spatially and temporally specific drug delivery while recording activity-dependent fluorescence using genetically encoded calcium indicators (GECIs), neurotransmitter sensors GRABNE and GRABDA, and neuropeptide sensors. We demonstrate the performance this platform for investigating neuropharmacological mechanisms in vivo and characterize its efficacy in probing precise mechanistic actions of neuroactive compounds across several rapidly evolving neuroscience domains.

neuroscience↗

Naturalistic acute pain states decoded from neural and facial dynamics

Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.

neuroscience↗

Estimating effective population size trajectories from time-series Identity-by-Descent (IBD) segments

Long, identical haplotypes shared between pairs of individuals, known as identity-by-descent (IBD) segments, result from recently shared co-ancestry. Various methods have been developed to utilize IBD sharing for demographic inference in contemporary DNA data. Recent methodological advances have enabled the screening for IBD in ancient DNA (aDNA) data, making demographic inference based on IBD also possible for aDNA. However, aDNA data typically have varying sampling times, but most demographic inference methods designed for modern data assume that sampling is contemporaneous. Here, we present TTNE (Time-Transect Ne), which models time-transect sampling to improve inference of recent effective population size trajectories. Using simulations, we show that utilizing IBD sharing in time series has increased resolution to infer recent fluctuations in effective population sizes compared to methods that only use contemporaneous samples. Finally, we developed an approach for estimating and modeling IBD detection errors in empirical IBD analysis. To showcase the practical utility of TTNE, we applied it to two time transects of ancient genomes, individuals associated with the Corded Ware Culture (CWC) and Medieval England. In both cases, we found evidence of a growing population, a signal consistent with archaeological records.

evolutionary biology↗

Multi-omics Reveals Immune Response and Metabolic Profiles during High-Altitude Mountaineering

The physiological perturbations induced by high-altitude exposure in mountain climbers, manifesting as metabolic and immunologic deviations, have been previously reported but are not fully understood. In this study, we obtained longitudinal multi-omic profiles of blood samples for healthy mountain climbers during two mountaineering stages (acclimatization and extreme altitude mountaineering). Our integrative assay included metabolomics and lipidomics profiling of plasma coupled with single-cell transcriptomic analysis of 375,722 immune cells. Longitudinal analysis revealed dynamic immune response profiles, during the acclimatization period, characterized by the downregulation of inflammatory responses in monocytes and classical dendritic cells (cDCs) and an increase in the proportion of cytotoxic CD8+ T cells with enhanced immune effector processes. In contrast, during extreme altitude mountaineering, the activation of inflammatory responses and impairment of T cell effector function were observed, concomitant with an increased cellular response to hypoxia and oxidative stress pathways. Furthermore, we found upregulated glycolysis and antioxidant gene expression during extreme altitude mountaineering, which was primarily orchestrated by HIF1A and NFE2L2, while decreased expression of these genes was observed in dysregulated plasmacytoid dendritic cells (pDCs). Finally, high-resolution plasma metabolic analysis revealed significant alterations in the metabolism of climbers, involving enhanced glutamine and fatty acid metabolism.

bioinformatics↗

Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods

As scRNA-seq becomes increasingly accessible, providing a cost-efficient method to augment surface protein levels from gene expression measurements are desirable. We proposed a machine learning approach that includes a novel geneset neural network (GS-NN) that aims to learn robust and biologically meaningful features and a highly efficient transfer learning strategy to address cross-dataset differences. We conducted comprehensive experiments to show the improvements of the proposed methods. Specifically, we demonstrate that GS-NN learns more robust features to achieve better cross-subject performance than other machine learning approaches. Transfer learning further improves that of GS-NN by reducing dataset differences through highly efficient fine-tuning. The unique genesets design of GS-NN also allows identification of functions contributing to the prediction and improvement of the proposed strategy. Overall, this study reports a novel approach to robustly augment. Key PointsO_LIThe article presents a machine learning approach, Geneset Neural Network(GS-NN) to augment surface protein levels from single-cell RNA sequencing(scRNA-seq) gene expression data. C_LIO_LIThe GS-NN aims to learn robust and biologically meaningful features, and the approach includes a highly efficient transfer learning strategy to address cross-dataset differences in scRNA-seq data. C_LIO_LIComprehensive experiments demonstrate that GS-NN learns more robust features using trasfer learning techniques achieving better cross-subject performance compared to other machine learning approaches. C_LIO_LIThe unique geneset-based architecture of GS-NN allows the identification and interpretion of biological functions contributing to the prediction of cell surface protein level. C_LIO_LIGS-NNs architecture is conveniently transferrable across datasets, making it valuable tool for researchers working with diverse scRNA-seq datasets. C_LI

bioinformatics↗

Riluzole reverses blood-testis barrier loss to rescue chemotherapy induced male infertility by binding to TRPC

Cancer treatments, including cytotoxic therapy, often result in male infertility, necessitating the development of safe and effective strategies to preserve male reproductive potential during chemotherapy. Notably, our study uncovers the potential of repurposing riluzole, an FDA-approved drug for amyotrophic lateral sclerosis (ALS), in enhancing spermatogenesis. Hence, this research aims to explore the feasibility of utilizing riluzole to alleviate male infertility induced by busulfan (BSF), a commonly used chemotherapy drug. We established a BSF-induced oligospermia model in 4-week-old male mice and found that riluzole could effectively countered the detrimental effects of BSF on sperm production in mice with oligospermia. By restoring blood-testis barrier (BTB) functionality, riluzole improves sperm quality and reduces testicular atrophy. Through transcriptomic and molecular docking analyses, we identify transient receptor potential canonical subfamily member 5 (TRPC5) as a potential target for riluzole-mediated regulation of blood-testis barrier function. These findings propose riluzole as a promising therapeutic option for chemotherapy-induced male infertility, thereby addressing the fertility challenges associated with cancer treatments. Moreover, repurposing riluzole could streamline the drug development process, providing a cost-effective approach with reduced risk compared to developing entirely new drugs.

pharmacology and toxicology↗

Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

bioinformatics↗

Generation of a zebrafish neurofibromatosis model via inducible knockout of nf2

Neurofibromatosis Type 2 (NF-2) is a dominantly inherited genetic disorder that results from mutations in the tumor suppressor gene, neurofibromin 2 (NF2) gene. Here, we report the generation of a conditional zebrafish model of neurofibromatosis established by an inducible genetic knockout of nf2a/b, the zebrafish homolog of human NF2. Analysis of nf2a and nf2b expression reveals ubiquitous expression of nf2b in the early embryo, with overlapping expression in the neural crest and its derivatives and in the cranial mesenchyme. In contrast, nf2a displays lower expression levels. Induction of nf2a/b knockout at early stages increases the proliferation of larval Schwann cells and meningeal fibroblasts. Subsequently, in adult zebrafish, nf2a/b knockout triggers the development of a spectrum of tumors, including vestibular schwannomas, spinal schwannomas, meningiomas, and retinal hamartomas, mirroring the tumor manifestations observed in patients with NF-2. Collectively, these findings highlight the generation of a novel zebrafish model that mimics the complexities of the human NF-2 disorder. Consequently, this model holds significant potential for facilitating therapeutic screening and elucidating key driver genes implicated in NF-2 onset.

developmental biology↗