Biology subjects

Tang, Z.

Publications and source records attributed to Tang, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Endothelial Kallikrein-Related Peptidase 8 Promotes Diabetic Nephropathy through a LIFR dependent mechanism

BackgroundDiabetic nephropathy (DN) is the primary microvascular complication of diabetes mellitus; however, the exact pathways in endothelial cells (ECs) linked to DN progress remain unclear. Tissue kallikrein-related peptidases (KLKs) participate in pathophysiological processes in ECs. We aimed to explore the roles of endothelial KLKs in DN and define the underlying mechanisms. Methods and resultsKLK8 was the most highly upregulated member of KLKs in renal tissues in streptozotocin (STZ)-induced diabetic mice and cultured glomerular ECs (GECs) upon high glucose (HG) treatment. Both global (Klk8-/-) and endothelial Klk8 knockout (Klk8{Delta}EC) mice displayed improved albuminuria, mesangial matrix expansion and glomerulosclerosis caused by STZ compared to Klk8f/f mice. Single-cell RNA seq (scRNA-seq) showed that many pathways associated with DN in ECs, mesangial cells (MCs) and tubule cells were reversed in Klk8{Delta}EC mice. Endothelial-to-mesenchymal transition (EndMT) was extensively improved in Klk8{Delta}EC mice, and by KLK8 siRNA in cultured GECs upon HG. Using proteome and other biochemical approaches, we revealed that KLK8 cleaved syndecan-4(SDC4), which contributed to loss of glycocalyx integrity in GECs in cultured cells and animal diabetic models. Furthermore, scRNA-seq showed that Lifr was one of the key genes linked to the disease progressed in ECs and MCs and regulated by endothelial Klk8. LIFR signaling contributed to HG-induced GEC dysfunction and MC activation, which was associated with endothelial Klk8. Knockdown of Lifr by lentivirus-Lifr shRNA ameliorated hallmark features of DN and improved EndMT and Sdc4 expression glomeruli of diabetic mice. LIFR was upregulated in GECs and MCs in DN patients. Circulatory levels of LIF, KLK8 and soluble SDC4 were increased in patients with DN, and KLK8 level was positively correlated with LIF, soluble SDC4 and creatinine levels. ConclusionEndothelial KLK8 promotes GECs dysfunction and abnormal crosstalk with MCs through a LIFR-dependent signaling in DN, which immediately highlights therapeutic targets for DN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=171 SRC="FIGDIR/small/662673v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@dae3b1org.highwire.dtl.DTLVardef@9aab24org.highwire.dtl.DTLVardef@59eaa5org.highwire.dtl.DTLVardef@13c138a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2025

Potent Efficacy of Computer-Aided Designed Peptide Degrader Drug on PCSK9-Mediated Hypercholesterolemia

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key regulator of low-density lipoprotein receptor (LDLR) degradation, which leads to increased levels of low-density lipoprotein cholesterol (LDL-C) and a heightened risk of hypercholesterolemia. Existing therapeutic strategies--such as monoclonal antibodies and gene-editing approaches--are limited by high production costs, potential safety concerns, and limited effectiveness in targeting intracellular PCSK9. Targeted protein degradation (TPD) represents a promising alternative capable of overcoming these limitations. In this study, we utilized computer-aided drug design (CADD) to develop a peptide-based degrader, Cadd4, aimed at selectively reducing PCSK9 levels. Through molecular docking and structural refinement, a peptide sequence with strong binding affinity to PCSK9 was identified. Functional evaluation was conducted both in vitro using human LX-2 cells and in vivo in high-fat diet (HFD)-induced hypercholesterolemic mouse models. Cellular uptake, PCSK9 degradation, and LDLR restoration were assessed via confocal microscopy, western blotting, and biochemical analysis. Safety was evaluated by monitoring liver enzyme levels and general physiological parameters. Cadd4 exhibited efficient intracellular delivery and selective PCSK9 degradation in LX-2 cell. In HFD-fed mice, hepatic PCSK9 expression was reduced by 38%, accompanied by a marked increase in LDLR levels. These changes translated into a 25% reduction in total plasma cholesterol and a 29% decrease in LDL-C concentrations. Biodistribution studies indicated liver-specific accumulation of Cadd4, consistent with its intended site of action. Experiments using human liver tissue further confirmed its ability to decrease PCSK9 and enhance LDLR levels. No signs of liver toxicity or adverse systemic effects were observed throughout the study. This work introduces Cadd4 as a CADD-designed peptide-based degrader capable of lowering plasma cholesterol by promoting LDLR recovery via PCSK9 degradation. Its intracellular mechanism of action addresses key gaps in current PCSK9-targeted therapies and supports the broader utility of TPD platforms in managing hypercholesterolemia. These findings offer a foundation for developing cost-effective, scalable therapies within the context of precision cardiovascular medicine.

pharmacology and toxicology↗

bioRxiv2025

AF9/KLF2 gene regulatory circuit links histone lactylation to breast cancer metastasis

Histone lysine L-lactylation (hereafter referred to as histone Kla) is a novel epigenetic mark induced by glycolytic metabolism, serving as a link between metabolic reprogramming and epigenetic regulation. In this study, we uncover an epigenetic-genetic transcriptional regulatory circuit involving AF9 and KLF2 that drives luminal breast cancer progression. AF9, identified as a reader of histone H3 lysine 9 lactylation (H3K9la), promotes KLF2 expression, while KLF2, functioning as a transcription factor for AF9, forms a positive feedback loop that amplifies lactylation-dependent effects. This circuit activates tumor-associated pathways, including TGF{beta}1, glucose and lactate transporters, and metabolic enzymes essential for glycolysis and serine biosynthesis, driving tumorigenesis and metastasis. Spatial and single-cell transcriptomics show AF9-positive tumor cells enriched in regions of active lactylation, correlated with immune evasion through interactions with M2 macrophages. Together, AF9, H3K9la, and KLF2 integrate metabolism, epigenetics, and signaling to promote tumor growth and metastasis, highlighting AF9s central role as a histone lactylation reader and a potential therapeutic target in breast cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/660498v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@56b216org.highwire.dtl.DTLVardef@1c5cbc1org.highwire.dtl.DTLVardef@c42a5corg.highwire.dtl.DTLVardef@b912ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

LSH-mediated resolution of R-loops mitigates transcription-replication conflicts to preserve genomic stability in prostate cancer cells

Prostate cancer cells exhibit heightened transcriptional activity to sustain their aggressive phenotype, yet this creates DNA topological stress, leading to RNA: DNA hybrids (R-loops) and DNA damage that compromise genomic stability. LSH, an SNF2-family chromatin remodeler, is frequently dysregulated in cancers and linked to tumor progression, but its mechanistic role in genome maintenance remains unclear. Here, we demonstrate that LSH resolves R-loops in an ATP-dependent manner, mitigating transcription- replication (TR) conflicts in prostate cancer cells. LSH depletion caused R- loop accumulation due to reduced FANCD2 recruitment, exacerbating DNA damage and impairing Rad51 filament formation. Strikingly, LSH deficiency also increased R-loops at promoters of MYC and E2F target genes, stalling RNA polymerase II elongation and disrupting transcriptional programs critical for proliferation. Our findings reveal LSH is a key guardian of genome integrity, resolving R-loops to prevent TR conflicts and maintaining DNA repair fidelity. By linking LSH to R-loop resolution and transcriptional regulation, this study reveals its dual role in prostate cancer pathogenesis-- supporting malignant proliferation while safeguarding genomic stability. These insights nominate LSH as a potential therapeutic target to disrupt oncogenic transcription and induce synthetic lethality in prostate cancer.

cancer biology↗

bioRxiv2025

A Reductive Uric Acid Degradation Pathway in Anaerobic Bacteria

Uric acid is a key intermediate in purine degradation across diverse organisms, while its accumulation in humans leads to inflammation and gout disease. Aerobic organisms degrade uric acid via a well-known "oxidative pathway" involving dearomatization of the purine core catalyzed by uric acid oxidases or dehydrogenases. The ability to degrade uric acid is also widespread in anaerobic bacteria, including gut bacteria, although the mechanisms are incompletely understood. Here, we report the biochemical characterization of a recently identified uric acid degradation gene cluster from Escherichia coli, and show that it encodes a "reductive pathway" for uric acid degradation. In this pathway, uric acid is first reduced to isoxanthine (2,8-dioxopurine) by a xanthine dehydrogenase homolog (XdhD), followed by dearomatization of the purine core catalyzed by a flavin-dependent reductase (YgfK). Stepwise cleavage of the pyrimidine and imidazole rings forms 2,3-diureidopropionate, and stepwise cleavage of the 2- and 3-ureido groups then forms 2,3-diaminopropionate, which is cleaved by a pyridoxal 5-phosphate-dependent lyase (YgeX) to pyruvate and ammonia. Detection of isoxanthine in clinical serum samples suggests it is a physiologically relevant circulating metabolite. A probiotic E coli Nissle strain was engineered for constitutive overexpression of the gene cluster, and oral administration in a uricase-knockout hyperuricemic mouse model significantly reduced the serum uric acid level and alleviated associated kidney injury, suggesting a potential route towards uricolytic probiotics.

biochemistry↗

bioRxiv2025

GenVS TBDB: An Open AI-Generated andVirtual Screened Small-Molecule Database for Tuberculosis Drug Discovery

Tuberculosis (TB) remains a leading global health threat, with over 10 million new cases and 1.25 million deaths reported in 2023. Current TB therapies rely on a limited drug repertoire and prolonged treatment courses that have changed little in four decades. Here, we present GenVS-TBDB, a target-aware AI-generated and virtual-screened small-molecule database, which expands the chemical space against Mycobacterium tuberculosis (M.tb) essential proteins. We first identified 460 probable small-molecule binding pockets across 377 essential M.tb proteins by integrating multiple sources. Then, by leveraging the target-aware molecule generative model, over 1.2 million novel small molecules tailored to these pockets were produced. The key physicochemical properties were computed for all compounds to ensure medicinal chemistry tractability. These compounds were also evaluated using molecular docking and anti-TB specific graph neural network model, yielding binding propensity ranking and whole-cell activity prioritization for each target. To substantiate the integrated AI-driven workflow for anti-TB discovery, 30 compounds were obtained, including 22 AI-designed molecules synthesized de novo and 8 commercially available analogs. In validation, 2 synthesized compounds demonstrated significant thermal stabilization of FtsZ, confirming target engagement. 6 compounds exhibited cellular inhibition below 50 {micro}M, the most potent at 12 {micro}M. Furthermore, GDI-11785 showed binding to the cell wall biosynthesis pathway with 35 {micro}M cellular activity, establishing a promising starting point for tuberculosis drug discovery. Our findings contribute a significant library of bioactive molecules which may hasten the preliminary phase of tuberculosis drug discovery. The GenVS-TBDB small library is publicly accessible and can be downloaded freely at https://datascience.ghddi.org/database/view.

pharmacology and toxicology↗

bioRxiv2025

scTransMIL Bridges Patient-Level Phenotypes and Single-Cell Transcriptomics for Cancer Screening and Heterogeneity Inference

Single-cell sequencing technology has revolutionized cancer research by revealing unprecedented insights into tumor heterogeneity. However, reliably connecting patient-level cancer phenotypes with single-cell transcriptomic profiles remains challenging, due to technical constraints and labeling ambiguity. This further hinders the precise cancer screening and intensive study of tumor mechanism based on single-cell sequencing. To bridge this gap, we introduce scTransMIL, a scRNA-seq Transformer-based Multi-Instance Learning framework that learns whole-genome context to deliver comprehensive cancer insights at the sample, cell, and gene levels across three biological scales: (1) accurate patient-level cancer phenotype prediction, (2) precise single-cell disease scoring (validated on 4 million single cells), and (3) genome-wide biomarker discovery. Benchmark experiments demonstrate scTransMILs exceptional performance, including robust out-of-distribution generalization and clinically relevant prediction of metastatic tissue-of-origin, a crucial capability for identifying cancers of unknow primary. At single-cell resolution, scTransMIL identified both known and novel biomarkers for tumor B cells that conventional differential expression analysis failed to detect while maintaining consistent concordance with malignant cell annotations. scTransMILs adaptability is exemplified in acute myelocytic leukemia, where minimal fine-tuning with only a few patient sample labels enabled: (i) discovery of novel disease subtypes with distinct clinical outcomes, (ii) reconstruction of differentiation trajectories at the single-cell resolution, and (iii) identification of subtype-specific gene signatures. In summary, by systematically linking cellular and molecular profiles with clinical disease phenotypes, scTransMIL emerges as a transformative tool poised to advance both basic cancer research and precision oncology applications.

bioinformatics↗

bioRxiv2025

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis

Analyzing animal behavior is crucial in advancing neuroscience, yet quantifying and deciphering its intricate dynamics remains a significant challenge. Traditional machine vision approaches, despite their ability to detect spontaneous behaviors, fall short due to limited interpretability and reliance on manual labeling, which restricts the exploration of the full behavioral spectrum. Here, we introduce MouseGPT, a Vision-Language Model (VLM) that integrates visual cues with natural language to revolutionize mouse behavior analysis. Built upon our first-of-its-kind dataset--incorporating pose dynamics and open-vocabulary behavioral annotations across over 42 million frames of diverse psychiatric conditions--MouseGPT provides a novel, context-rich method for comprehensive behavior interpretation. Our holistic analysis framework enables detailed behavior profiling, clustering, and novel behavior discovery, offering deep insights without the need for labor-intensive manual annotation. Evaluations reveal that MouseGPT surpasses existing models in precision, adaptability, and descriptive richness, positioning it as a transformative tool for ethology and for unraveling complex behavioral dynamics in animal models.

animal behavior and cognition↗

bioRxiv2025

In Situ Graphene-Seq: Spatial Transcriptomics and Chronic Electrophysiological Characterization of Tissue Microenvironments

Biological systems are composed of diverse, interconnected cell types, yet capturing both their functional dynamics and molecular identities at high spatiotemporal resolution remains challenging. While electrophysiological measurements provide real-time insights into cellular activities, they cannot fully describe the molecular architecture and states of the measured cells. Conversely, transcriptomics reveals cell gene expression patterns but does not capture functional states. Bridging these modalities is essential for a holistic understanding of the molecular mechanisms driving functional changes. In this study, we introduce in situ graphene-sequencing (graphene-seq), a unique platform that seamlessly integrates chronic electrophysiology with imaging-based, spatially resolved 3D transcriptomics, overcoming longstanding limitations of current multimodal approaches. This system leverages stretchable mesh nanoelectronics for long-term, single-cell-level interfacing and incorporates transparent graphene/PEDOT:PSS electrodes, enabling seamless integration of electrical recordings and optical imaging. By combining electrophysiology with high-throughput, imaging-based in situ sequencing, this platform allows comprehensive multimodal, spatially resolved analysis of cell microenvironment within spatially heterogeneous tissues. We validate in situ graphene-seq by charting multimodal profiles of human-induced pluripotent stem cell-derived cardiomyocyte and endothelial cell co-cultures, examining how spatial heterogeneity in cell composition influences both electrophysiological activity and gene expression. This scalable, integrated approach offers a powerful tool for studying the complex interplay between cellular function and molecular identity. It also provides insights into how tissue microenvironments shape cell behavior and molecular states, advancing applications in regenerative medicine, stem cell therapy, and disease modeling.

bioengineering↗

bioRxiv2025

scKAN: Interpretable Single-cell Analysis for Cell-type-specific Gene Discovery and Drug Repurposing via Kolmogorov-Arnold Networks

Single-cell analysis has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency and interpretability. In this study, we present scKAN, a framework that leverages Kolmogorov-Arnold Networks for interpretable single-cell analysis through three key innovations: efficient knowledge transfer from large language models through a lightweight distillation strategy; systematic identification of cell-type-specific functional gene sets through KANs learned activation curves; and precise marker gene discovery enabled by KANs importance scores with potential for drug repurposing applications. The model achieves superior performance on cell-type annotation with a 6.63% improvement in macro F1 score compared to state-of-the-art methods. Furthermore, scKANs learned activation curves and importance scores provide interpretable insights into cell-type-specific gene patterns, facilitating both gene set identification and marker gene discovery. We demonstrate the practical utility of scKAN through a case study on pancreatic ductal adenocarcinoma, where it successfully identified novel therapeutic targets and potential drug candidates, including Doconexent as a promising repurposing candidate. Molecular dynamics simulations further validated the stability of the predicted drug-target complexes. Our approach offers a comprehensive framework for bridging single-cell analysis with drug discovery, accelerating the translation of single-cell insights into therapeutic applications.

bioinformatics↗

bioRxiv2025

SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer

Spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular ecosystems. However, these technologies face challenges such as sparse gene signals and limited gene detection capacities, which hinder their ability to fully capture comprehensive spatial gene expression profiles. To address these limitations, we propose leveraging single-cell RNA sequencing (scRNA-seq), which provides comprehensive gene expression data but lacks spatial context, to enrich ST profiles. Herein, we introduce SpaIM, an innovative style transfer learning model that utilizes scRNA-seq information to predict unmeasured gene expressions in ST data, thereby improving gene coverage and expressions. SpaIM segregates scRNA-seq and ST data into data-agnostic contents and data-specific styles, with the contents capture the commonalities between the two data types, while the styles highlight their unique differences. By integrating the strengths of scRNA-seq and ST, SpaIM overcomes data sparsity and limited gene coverage issues, making significant advancements over 12 existing methods. This improvement is demonstrated across 53 diverse ST datasets, spanning sequencing- and imaging-based spatial technologies in various tissue types. Additionally, SpaIM enhances downstream analyses, including the detection of ligand-receptor interactions, spatial domain characterization, and identification of differentially expressed genes. Released as open-source software, SpaIM increases accessibility for spatial transcriptomics analysis. In summary, SpaIM represents a pioneering approach to enrich spatial transcriptomics using scRNA-seq data, enabling precise gene expression imputation and advancing the field of spatial transcriptomics research.

bioinformatics↗

bioRxiv2025

Upstream open reading frame translation enhances immunogenic peptide presentation in mitotically arrested cancer cells

Mitosis is a critical phase of the cell cycle and a vulnerable point where cancer cells can be effectively disrupted, leading to cell death and inhibition of tumor growth. However, challenges such as drug resistance remain significant in clinical applications. During mitosis, mRNA translation is generally downregulated, while non-canonical translation of specific transcripts proceeds. Here, we demonstrate that mitotic cancer cells redistribute ribosomes toward the 5 untranslated region (5 UTR) and the start of the coding sequence (CDS), enhancing the translation of thousands of upstream open reading frames (uORFs) and upstream overlapping open reading frames (uoORFs). This mitotic induction of uORF/uoORF enriches the presentation of immunopeptides at the surface of cancer cells following treatment with mitotic inhibitors. Functional assays indicate the potential of such neoepitopes to provoke cancer-cell killing by T cells. Altogether, our findings highlight the therapeutic potential of targeting uORF/uoORF-derived neoepitopes in combination with mitotic inhibitors to enhance immune recognition and tumor cell elimination.

cancer biology↗

bioRxiv2024

Isoliquiritigenin attenuated cognitive impairment, cerebral tau phosphorylation and oxidative stress in a streptozotocin-induced mouse model of Alzheimers disease

IntroductionTau hyperphosphorylation, mitochondrial dysfunction and oxidative stress play important roles in Alzheimers disease (AD). Isoliquiritigenin, a natural flavonoid isolated from the root of liquorice, has been shown to exert inhibitory effects on oxidative stress. Here, we assessed the neuroprotective effects of isoliquiritigenin on a streptozotocin-injected mouse model. MethodMolecular docking analysis performed for isoliquiritigenin with mTOR and ERK2. The mice (n = 27, male) were intracerebroventricularly injected with streptozotocin, treated with isoliquiritigenin (intraperitoneal, 2 days) and assessed using the Morris water maze. Oxidative stress, tau phosphorylation, mitochondrial dysfunction and synaptic impairment were evaluated in the cortex and hippocampal tissues of the mice by using biochemical assays and immunofluorescence staining. ResultsIsoliquiritigenin treatment mitigated the spatial memory capacity of streptozotocin-injected mice and alleviated tau phosphorylation at Ser396; the production of reactive oxygen species; the mitochondrial fission proteins Mfn1 and Mfn2; neuronal loss; and synaptic impairment (PSD95, SNAP25). Isoliquiritigenin treatment reduced the levels of mTOR Ser2448 and ERK1/2 T202/Y204 and upregulated the level of GSK-3{beta}Ser9 in the cortex and hippocampus of streptozotocin-injected mice. ConclusionIn conclusion, our findings suggest that isoliquiritigenin ameliorates streptozotocin-induced cognitive impairment, hyperphosphorylated tau, oxidative stress, mitochondrial dysfunction and synaptic impairment by decreasing mTOR and ERK activity and increasing GSK-3{beta} activity.

neuroscience↗

bioRxiv2024

mTOR inhibition alleviated tau phosphorylation-induced mitochondrial impairment, oxidative stress, and cognitive impairment

AimHyperphosphorylated tau is a hallmark of Alzheimers disease (AD). However, whether mammalian target of rapamycin (mTOR) directly regulates tau phosphorylation at Ser214, Ser356, and Thr231 and how such regulation affects mitochondrial function remain unclear. In this study, we aimed to determine whether mTOR-mediated tau phosphorylation disrupts mitochondrial and metabolic profiles and cognitive function and whether these effects can be reversed by the mTOR inhibitor rapamycin. MethodsTau3E-overexpressing mice were generated by bilateral injection of adeno-associated virus vectors encoding the phosphomimetic TauS214E/T231E/S356E (Tau3E) variant into the hippocampal CA3 region of 2-month-old C57BL/6 mice. The mice received intraperitoneal rapamycin for one week. Cognitive performance was assessed using the Morris water maze. MALDI-mass spectrometry imaging was conducted to evaluate metabolic alterations in the brain tissue slices. mTOR, p70S6K, and tau phosphorylation protein expression; mitochondrial dynamics markers; and reactive oxygen species (ROS) levels were analyzed by Western blotting, immunofluorescence, and flow cytometry in HT22 cells, Tau3E mice, and postmortem AD brain tissues. ResultsPhosphorylated mTORS2448 colocalized with p-TauSer214, p-TauSer356, and p-TauThr231 in the hippocampal CA3 region of AD brains. HT22 cells and Tau3E mice exhibited increased levels of p-mTOR, p-p70S6K, and ROS production; mitochondrial fragmentation; and tau phosphorylation at Ser214, Ser356, and Thr231. Rapamycin treatment partially ameliorated cognitive deficits, reduced oxidative stress and mitochondrial dysfunction, and restored metabolic profile particularly the inosine-pentose phosphate pathway-glycolysis metabolic axis, structural lipidome axis and the neurotransmitter homeostasis. ConclusionmTOR activation contributes to site-specific tau hyperphosphorylation, mitochondrial dysfunction, and cognitive impairment. Pharmacological inhibition of mTOR by rapamycin attenuated tau pathology, regulated metabolic alterations, preserved mitochondrial homeostasis, and improved cognitive function, suggesting a potential therapeutic strategy for AD.

neuroscience↗

bioRxiv2024

Uncertainty-aware genomic deep learning with knowledge distillation

Deep neural networks (DNNs) have advanced predictive modeling for regulatory genomics, but challenges remain in ensuring the reliability of their predictions and understanding the key factors behind their decision making. Here we introduce DEGU (Distilling Ensembles for Genomic Uncertainty-aware models), a method that integrates ensemble learning and knowledge distillation to improve the robustness and explainability of DNN predictions. DEGU distills the predictions of an ensemble of DNNs into a single model, capturing both the average of the ensembles predictions and the variability across them, with the latter representing epistemic (or model-based) uncertainty. DEGU also includes an optional auxiliary task to estimate aleatoric, or data-based, uncertainty by modeling variability across experimental replicates. By applying DEGU across various functional genomic prediction tasks, we demonstrate that DEGU-trained models inherit the performance benefits of ensembles in a single model, with improved generalization to out-of-distribution sequences and more consistent explanations of cis-regulatory mechanisms through attribution analysis. Moreover, DEGU-trained models provide calibrated uncertainty estimates, with conformal prediction offering coverage guarantees under minimal assumptions. Overall, DEGU paves the way for robust and trustworthy applications of deep learning in genomics research.

genomics↗

bioRxiv2024

Human skin rejuvenation via mRNA

Aging is characterized by a gradual decline in function, partly due to accumulated molecular damage. Human skin undergoes both chronological aging and environmental degradation, particularly UV-induced photoaging. Detrimental structural and physiological changes caused by aging include epidermal thinning due to stem cell depletion and dermal atrophy associated with decreased collagen production. Here, we present a comprehensive single-cell atlas of skin aging, analyzing samples from young, middle-aged, and elderly individuals, including both sun-exposed and sun-protected areas. This atlas reveals age-related changes in cellular composition and function across various skin cell types, including epidermal stem cells, fibroblasts, hair follicles, and endothelial cells. Using our atlas, we have uncovered basal stem cells as a highly variable population across aging, more so than other skin cell populations, such as fibroblasts. In basal stem cells, we identified ATF3 as a novel regulator of skin aging. ATF3 is a transcriptional factor for genes involved in the aging process, with its expression reduced by 20% during aging. Based on this discovery, we developed an innovative mRNA-based treatment to mitigate the effects of skin aging. After treatment with ATF3 mRNA, cell senescence decreased 25%, and we observed an over 20% increase in proliferation in treated basal stem cells. Importantly, we also found communication between keratinocytes and fibroblasts as a critical component of therapeutic interventions, with ATF3 mRNA rescue of basal cells significantly enhancing fibroblast collagen production by approximately 200%. Furthermore, we validated the efficacy of ATF3 mRNA treatment in ex vivo human skin and in vivo mouse models. In ex vivo human skin, microneedle-mediated delivery of ATF3 mRNA induced robust rejuvenation, expanding the basal stem-cell population and enhancing dermal ECM reconstruction. In a wound healing mouse model, ATF3 mRNA reduced scarring, demonstrating strong regenerative potential for reversing age-related skin decline. We conclude that ATF3 mRNA treatment effectively reverses the effects of skin aging by modulating specific cellular mechanisms, offering a novel, targeted approach to human skin rejuvenation.

synthetic biology↗

bioRxiv2024

A new synonym for Viburnum erosum (Viburnaceae) in East China, based on morphological and molecular evidence

The critical observations of living plants in the field, along with the examination of type specimens and protologues, led us to conclude that the key characteristics, including the length of the petiole, stipules and peduncles, as well as the shape of leaves of Viburnum fengyangshanense, all fall within the variation range of V. erosum. Additionally, molecular analysis of nuclear ribosomal internal transcribed spacer (nrITS) and three plastid DNA markers (rbcL, matK and ndhF) indicates that V. fengyangshanense and V. erosum are deeply nested within a clade. Therefore, based on morphological and molecular evidence, it is demonstrated that V. fengyangshanense should be regarded as a new synonym of V. erosum.

plant biology↗

bioRxiv2024

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

biochemistry↗