Biology subjects

Luo, P.

Publications and source records attributed to Luo, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Central platelet-rich plasma administration promotes weight loss in diet-induced obese mice

Aims: Platelet-rich plasma (PRP) contains diverse bioactive factors, but its potential role in central regulation of energy balance remains unexplored. This study investigated whether central PRP administration alters metabolic homeostasis in diet-induced obese (DIO) mice and examined associated hypothalamic responses under obese and inflammatory conditions. Materials and Methods: PRP was administered intracerebroventricularly in mouse models of DIO and lipopolysaccharide (LPS)-induced systemic inflammation. Body weight, food intake, energy metabolism, and adiposity were assessed alongside hypothalamic microglial morphology and transcriptomic profiling. Results: A single intracerebroventricular PRP injection reduced body weight in DIO mice, with the effect persisting throughout the 7-day observation period. PRP also decreased cumulative food intake, relative gonadal white adipose tissue weight, and fasting glucose, accompanied by increased energy expenditure, oxygen consumption, fat utilization, and physical activity. In both DIO and LPS-treated mice, PRP reduced IBA1-positive area per microglial cell in the arcuate nucleus without altering microglial number. Hypothalamic RNA sequencing revealed changes in immune and metabolic signaling, extracellular matrix organization, and growth-related pathways, suggesting a complex transcriptional response rather than uniform suppression of inflammation. Conclusions: A single central PRP administration induces sustained weight loss and favorable metabolic changes in DIO mice, accompanied by region-specific hypothalamic microglial and transcriptional responses. These findings identify central PRP as a potential modulator of energy balance and provide a basis for investigating its active components and underlying mechanisms.

neuroscience↗

bioRxiv2026

TRACE: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification

Accurate prioritization of T-cell receptor (TCR)-epitope interactions and identification of tumor-reactive T cells are important but difficult steps in immunotherapy-oriented bioinformatics workflows. Existing methods typically address these tasks separately and either model TCR-epitope pairs as independent observations or rely primarily on transcriptomic signatures. In this study, we present DUET (Dual Unified Evaluation of TCR-Epitopes and Tumor-reactive T cells), a graph-based computational workflow that unifies both applications within a single heterogeneous graph framework. The protocol represents TCRs, epitopes, and T cells as typed nodes connected by similarity and association edges, and combines pretrained sequence embeddings with edge-aware graph attention, Laplacian positional encoding, and bidirectional cross-domain attention. Applied to the IEDB and VDJdb benchmarks, DUET achieved AUROC/AUPR values of 0.937/0.922 and 0.992/0.990, respectively, outperforming five state-of-the-art algorithms. In addition, on a single-cell RNA-seq dataset, the workflow achieved an AUROC of 0.984 and an AUPR of 0.984, substantially exceeding transcriptomic signature-based baselines for tumor-reactive T-cell identification. Ablation analysis showed that Laplacian positional encoding provided the largest performance gain, particularly in sparse graph settings. These results suggest that heterogeneous graph modeling can serve as a practical protocol for integrating receptor sequence, antigen context, and cellular phenotype in computational immunology.

bioinformatics↗

bioRxiv2026

Identification of a neural circuit that enables safe, long-term torpor in mice

Torpor, a state of regulated hypothermia and hypometabolism, is a critical survival strategy in certain mammals. Previous studies established the primacy of neuron control in initiating acute torpor. However, whether neuromodulation can safely sustain torpor over the long term, and the specific governing pathways, remain unanswered challenges limiting its translational potential. Here we identify a distinct preoptic neuronal population expressing General control nonderepressible 2 ( G neurons) in mice that is essential and sufficient for torpor induction. Strikingly, sustained and selective activation of G neurons induces a stable, weeks-long torpid state (G neuron-driven long-term torpor, GLT), from which animals arouse without obvious behavioral deficits or tissue pathology. In contrast, long-term torpor driven by pan-neuronal activation of the same brain region (PLT) causes multiple post-recovery damages, underscoring the unique safety profile of selective pathway of G neurons. In a mouse cancer model, GLT directly suppressed tumor proliferation and markedly sensitized tumors to chemotherapy, achieving profound therapeutic outcomes. Together, we delineate a distinct neuronal population safely enabling prolonged torpor. This establishes a specific paradigm for inducing a stable and sustained hypometabolic state, providing a transformative platform for fundamental physiological research and pioneering clinical strategies against chronic pathologies such as cancer.

neuroscience↗

bioRxiv2026

RNABag: A Generalizable Transcriptome Foundation Model for Precision Oncology across Biopsy Modalities

Transcriptomic data is highly sensitive to cancer state and progression, making transcriptome-based foundation models a great promise for diverse clinical ontological inference. However, analyses of transcriptome are conventionally hindered by technical batch effects and limited generalization across platforms. Here, we introduce RNABag, a foundation model designed to generalize well to external datasets. In particular, the model only focuses on highly variable genes to reduce noise; and extensive data augmentation was utilized to pretrain RNABag to learn robust representations, invariant to batch variations. We demonstrate that RNABag achieves superior performance in pan-cancer tissue-of-origin classification and cancer detection in internal validation sets, as well as in zero-shot generalization to external cohorts and in-house clinical samples. Furthermore, RNABag, after specialized finetuning, exhibits strong capabilities in a wide range of clinical applications. The model effectively stratifies patient survival and predicts relapse risks, highlighting key molecular pathways driving tumor progression. Crucially, we extend RNABags utility to liquid biopsies, achieving high diagnostic accuracy in plasma cfRNA and tumor-educated platelets (TEPs), thereby supporting its application in non-invasive cancer monitoring. Interpretability analysis revealed pivotal role of tumor immune escape in the cancer induced plasma cfRNA signals. In summary, our study indicates that cancer states and progression may be monitored in details and precision via comprehensive modeling of transcriptome across biopsy modalities.

bioinformatics↗

bioRxiv2026

The tongue-brain axis mediates a hidden amino acid appetite

Selecting a diet containing all essential amino acids (EAAs) is critical for health. Following EAA deprivation, animals can select a nutritiously complete food source; however, the underlying mechanisms in vertebrates remain unclear. In mice, we show that leucine deficiency activates hypothalamic agouti-related protein (AgRP) neurons, which project to the paraventricular thalamus (PVT) via {gamma}-aminobutyric acid and are required for EAA deficiency-induced leucine appetite in mice. Furthermore, the peripheral tongue amino acid sensor general control nonderepressive-2 (GCN2) mediates acute EAA appetite via AgRP neurons. Together, these findings identify a tongue-AgRP-PVT circuit underlying EAA appetite, which is important for the rapid and accurate selection of essential nutrients.

neuroscience↗

bioRxiv2025

Medial preoptic area FoxO1 controls metabolic adaptation in a sexually dimorphic manner

The medial preoptic area (MPOA) of the hypothalamus is essential for metabolic adaptation to environmental challenges, though the molecular mechanisms underlying this process remain poorly understood. Here, we investigate the role of Forkhead transcription factor O1 (FoxO1), a key mediator of stress adaptation, in MPOA-dependent metabolic responses to temperature and nutritional changes. Our findings reveal sex-specific responses to both nutritional and temperature challenges. In female mice, but not males, a high-fat diet (HFD) challenge decreased FoxO1 expression in the MPOA. Specific deletion of FoxO1 in MPOA neurons (FoxO1-KOMPOA) had no effect on body weight under normal chow-fed conditions but protected females from HFD-induced obesity (DIO). These protected females exhibited increased lean mass, decreased fat mass, enhanced thermogenesis, increased energy expenditure, and reduced food intake under HFD conditions. They also showed enhanced cold-induced heat production at 6{degrees}C, though this effect vanished at thermoneutrality (30{degrees}C). The protection against DIO was abolished by ovariectomy (OVX) and was not restored by 17{beta}-estradiol supplementation, suggesting an estrogen-independent mechanism. Conversely, constitutive activation of FoxO1 in MPOA neurons (FoxO1-CAMPOA) increased DIO susceptibility in both sexes. Together, these findings demonstrate that FoxO1MPOA plays a crucial role in coordinating metabolic adaptation to nutritional and temperature challenges specifically in female mice.

physiology↗

bioRxiv2025

Bivalent ligands as a new universal chemotype of BCL6 degrader by inducing aggregation

BCL6 naturally exists as a homodimer to exert its transcriptional repressive function. Previous studies shown that molecular glue BI3802 can induce BCL6 dimer polymerization and thus degradation by ubiquitin-proteasome system. In this study we proposed a new strategy to degrade BCL6 by its homo-bivalent ligands, which can be rationally designed by assemble two BCL6 BTB ligands via linker. These homo-bivalent degraders (HBiDs) can induce polymerization of BCL6 dimer through chain assembly and ultimately lead to its ubiquitination and degradation. Since HBiD does not require a fragment to interact with components of protein degradation system, it could exhibit better target selectivity than traditional protein degraders while avoid drug resistance caused by mutations in protein degradation components. Here we will provide the proof-of-concept evidences for HBiD as BCL6 degrader. Most HBiDs synthesized in this study could degrade BCL6 regardless of the chemotype of BCL6 ligands, with extraordinary target selectivity. The degradation of BCL6 depends on the aggregation of BCL6 dimer induced by HBiD and the recruitment of SIAH1, the natural E3 ligase of BCL6. As protein dimerization is a common phenomenon in cells, HBiD may provide us with a powerful strategy for designing protein degraders as drug candidates or molecular probes.

biochemistry↗

bioRxiv2025

ggalign: Bridging the Grammar of Graphics and Biological Multilayered Complexity

Composable visualization is essential in biomedical research, particularly when working with complex, multi-dimensional biological data. However, many existing tools focus on specific fields and lack the comprehensive features needed to capture the intricacies and interrelations of datasets. To address this, we introduce ggalign, an R package built on the grammar of graphics framework, providing an easy-to-use platform for creating composable visualizations of interconnected data. It offers a variety of layout options, including StackLayout, QuadLayout, and CircleLayout, tailored to different data types, and supports flexible multi-dimensional relationships, such as one-to-many, many-to-one, many-to-many, and crosswise connections. Additionally, ggalign enhances the clarity of visualizations through its annotation features. Overall, ggalign offers a powerful and flexible solution for visualizing complex data in biomedical research. O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/636847v1_ufig1.gif" ALT="Figure 1"> View larger version (92K): org.highwire.dtl.DTLVardef@d4bb97org.highwire.dtl.DTLVardef@67c236org.highwire.dtl.DTLVardef@1ff9cc6org.highwire.dtl.DTLVardef@8dc6a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

SPP1+ tumor-associated macrophages induce radioresistance of nasopharyngeal carcinoma through crosstalk with the tumor microenvironment

The function and phenotype of tumor-associated macrophages (TAMs) influence the efficacy of radiotherapy. However, the effect of different subsets of TAMs on the radioresistance in nasopharyngeal carcinoma (NPC) still needs to be further explored. This study utilized single-cell RNA sequencing (scRNA-seq) to analyze 120,579 cells obtained from 15 NPC samples. Cell annotation, cell-cell communication analysis, and functional annotation were used to investigate the impact of TAMs on the effectiveness of radiotherapy in NPC patients. Additionally, we employed RNA-seq data and clinical information from many patients, including 24 NPC samples from Zhujiang Hospital, 3,934 samples from patients with a history of radiotherapy, for further validation. We identified a hub TAM subpopulation, SPP1+ TAMs, that induced radioresistance in NPC based on single-cell RNA sequencing (scRNA-seq) data. We combined many public datasets to confirm their prognostic value in radiotherapy patients. We found that SPP1+ TAMs had stronger protumorigenic and immunosuppressive effects in the postradiotherapy recurrent tumor (PRRT) group. SPI1 transcription factor efficiently regulates the expression level of SPP1 in macrophages. In addition, we validated the underlying mechanisms by molecular docking, multiplex immunofluorescence and cellular experiments. In conclusions, by forming an immunosuppressive microenvironment and promoting tumor cell proliferation, SPP1+ TAMs leading to radioresistance and poor clinical prognosis in NPC patients. Targeting SPP1+ TAMs could provide new insights into improving the efficacy of radiotherapy in NPC patients.

cancer biology↗

bioRxiv2024

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning

BackgroundRadiotherapy resistance is a major obstacle to the long-term survival of nasopharyngeal cancer patients, as it is a primary cause of recurrence and metastasis. Identifying radiotherapy-associated biomarkers can help improve the survival prognosis of nasopharyngeal cancer patients. Consequently, discovering biomarkers associated with radiosensitization is crucial. MethodsWe evaluated 113 combinations of machine learning algorithms and ultimately selected 48 to construct a radiotherapy sensitivity score (NPC-RSS) that can predict radiosensitivity in nasopharyngeal cancer patients. Furthermore, we analyzed the relationship between NPC-RSS and the expression of genes associated with immune and radiotherapy sensitivity profiles. We employed GSEA and ssGSEA to investigate the connection between NPC-RSS and signaling pathways. ResultsWe selected the combined model glmBoost+NaiveBayes, which had the best AUC among 48 models, for our subsequent study. The NPC-RSS, built based on the 18 genes included in this model, can predict the results of the public dataset and the in-house dataset of Zhujiang Hospital, Southern Medical University, with considerable efficiency. The key genes of NPC-RSS are closely associated with immune characteristics, including chemokine and chemokine receptor families, and histocompatibility complex (MHC), and show more active immune processes. Meanwhile, these key genes were significantly associated with the expression of radiosensitization-related genes. Furthermore, GSVA and GSEA analyses demonstrated that different expression levels of key NPC-RSS genes influenced signaling pathways, such as the Wnt/{beta}-catenin signaling pathway, JAK-STAT signaling pathway,NF-kappa B signaling pathway and T cell receptor signaling pathway, which are associated with immunity and disease progression. The consistency of the expression of key genes SMARCA2 and CD9 with NPC-RSS was validated in in-house cell lines. The radiosensitive group, classified according to NPC-RSS, exhibited a more enriched and activated state of immune infiltration compared to the radioresistant group. Moreover, in single-cell samples, NPC-RSS was higher in the radiotherapy-sensitive group, with immune cells playing a predominant role. ConclusionsIn this study, we used machine learning to construct a predictive score, called NPC-RSS, associated with radiosensitivity in nasopharyngeal carcinoma patients; moreover, NPC-RSS is strongly associated with immune characteristics, expression of radiosensitivity-related genes, and signaling pathways related to disease progression. We hope that the NPC-RCC will enable more precise selection of the NPC population of potential beneficiaries of radiation therapy.

bioinformatics↗

bioRxiv2024

Grifola frondosa polysaccharide Ameliorates Inflammation and Insulin Resistance by Regulating Macrophage Polarization of liver in Type 2 Diabetes Mellitus Rats

Type 2 diabetes mellitus (T2DM) is a common metabolic disease characterized by a lack of insulin secretion, insulin resistance (IR), and hyperglycemia. Given its high prevalence and multifarious complications, diabetes is globally ranked as the third leading cause of mortality. It is critical to discover efficient medication substitutes in order to lessen the drawbacks and adverse effects of traditional diabetes medication therapy. Previous studies have shown that Grifola frondosa polysaccharide (GFP) have the positive effects in regulating blood glucose and insulin resistance, but the understanding of its regulatory mechanism is still limited. Therefore, this study aimed to investigate the effects of GFP on liver inflammation induced by a high-fat diet (HFD) combined with streptozotocin (STZ) in type 2 diabetic rats and to explore its possible mechanisms. The results showed that GFP intervention reduced weight loss and hyperglycemia symptoms, as well as lowered FINS, HOMA-IR, IPGTT-AUC, and IPITT-AUC to different degrees in T2DM rats. At the same time, after GFP intervention, the secretion level of inflammatory factors (TNF-, IL-1{beta}, MCP-1) was down-regulated and the secretion level of anti-inflammatory factor (IL-10) was up-regulated in the liver tissue of T2DM rats. Furthermore, GFP reduced macrophage infiltration in liver tissue, inhibited macrophage M1-type polarization, and promoted M2-type polarization. These results suggest that GFP intervention could attenuate the hepatic inflammatory response in T2DM rats; possible mechanisms for this effect include hepatic macrophage infiltration and M1/M2 polarization. Summary statementThis study revealed the improvement effect of GFP on hepatic inflammation and insulin resistance in T2DM rats and to explore the possible key roles of hepatic macrophages and their potential mechanisms.

immunology↗

bioRxiv2024

IOBR2: Multidimensional Decoding Tumor Microenvironment for Immuno-Oncology Research

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The increasing popularity of multi-omics sequencing, single-cell transcriptome sequencing (scRNA), and spatial transcriptome sequencing has led to numerous new discoveries. However, these findings require clinical phenotypic validation with a large sample size. To enhance the integration of multi-omics in advancing research on the tumor microenvironment, we have developed a systematic and comprehensive analytical tool (Immuno-Oncology Biological Research 2, IOBR2) based on our prior work. IOBR2 offers six modules for TME analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. IOBR2 integrates multiple vital microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell data, IOBR2 also provides a way to construct a reference matrix for TME deconvolution from single-cell RNAseq. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumor-immune interactions. A comprehensive gitbook (https://iobr.github.io/book/) is available with a user-friendly manual and complete analysis workflow for each module.

cancer biology↗

bioRxiv2023

THER: Integrative Web Tool for Tumor Hypoxia Exploration and Research

Hypoxia is an important factor in the adaptation of tumor cells to their environment, contributes to their malignant progression, and affects tumor prognosis and drug sensitivity. Although there is a wealth of transcriptomic data stored in public databases, there is a lack of web-based tools for analyzing these data to explore the link between hypoxia and the mechanisms of tumorigenesis and progression. To this end, we have developed an interactive web-based tool called THER, which is designed to help users easily identify potential targets, mechanisms of action and effective drugs for treating hypoxic tumors. THER integrates 63 transcriptomic tumor hypoxia datasets from the Gene Expression Omnibus (GEO) database, covering 3 species, 18 tumor types and 42 cell line types. This web tool provides five modules that allow users to perform differential expression analysis, expression profiling analysis, correlation analysis, enrichment analysis and drug sensitivity analysis on different datasets based on different oxygen statuses. We expect that users will be able to use the tool to identify valuable biomarkers, further reveal the molecular mechanisms of tumor hypoxia, and identify effective drugs, thus providing a scientific basis for tumor diagnosis and treatment. THER is open to all users and can be accessed without login at https://smuonco.shinyapps.io/THER/.

cancer biology↗

bioRxiv2023

TCCIA: A Comprehensive Resource for Exploring CircRNA in Cancer Immunotherapy

BackgroundImmunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers. MethodsTo address these gaps, we constructed the Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multi-cancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune-checkpoint blockades (ICBs). ResultsTCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy. ConclusionsTCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology. What is already known on this topicPrior knowledge indicated that circRNAs are involved in tumor immunity and have potential as predictive biomarkers for immunotherapy efficacy. However, there lacked a comprehensive database that integrated circRNA profiles and immunotherapy response data, necessitating this study. What this study addsThis study introduces TCCIA, a database that combines circRNA profiles, immunotherapy response data, and clinical outcomes. It provides a diverse collection of clinical samples and an interactive platform, enabling in-depth exploration of circRNAs in the context of checkpoint-blockade immunotherapy. How this study might affect research, practice or policyThe findings of this study offer valuable insights into the roles of circRNAs in tumor-immune interactions and provide a resource for researchers and clinicians in the field of immune-oncology. TCCIA has the potential to guide personalized immunotherapeutic strategies and contribute to future research, clinical practice, and policy decisions in checkpoint-blockade immunotherapy and biomarker development.

bioinformatics↗

bioRxiv2023

Illumination of the complement receptors C3aR and C5aR signaling by anaphylatoxins

The complement receptors C3aR and C5aR, whose signaling are selectively activated by anaphylatoxins C3a and C5a, are important regulators of both innate and adaptive immune responses. Dysregulations of C3aR and C5aR signaling lead to multiple inflammatory disorders, including sepsis, asthma, and acute respiratory distress syndrome (ARDS). The mechanism underlying endogenous anaphylatoxin recognition and activation of C3aR and C5aR remains elusive. Here we reported the structures of C3a-bound C3aR and C5a-bound C5aR1 as well as an apo C3aR structure. These structures, combined with mutagenesis analysis, reveal a conserved recognition pattern of anaphylatoxins to the complement receptors that is different from chemokine receptors, unique pocket topologies of C3aR and C5aR1 that mediate ligand selectivity, and a common mechanism of receptor activation. These results provide crucial insights into the molecular understandings of C3aR and C5aR1 signaling and structural templates for rational drug design for treating inflammation disorders.

biochemistry↗

bioRxiv2022

PanCanSurvPlot: A Large-scale Pan-cancer Survival Analysis Web Application

The identification of reliable tumor prognostic markers can help clinicians and researchers predict tumor development and patient survival outcomes more accurately, which plays a vital role in clinical diagnosis, treatment effectiveness assessment, and prognostic evaluation. Existing web tools supporting online survival analysis are gradually failing to meet the increasing demands of researchers in terms of the dataset size, richness of survival analysis methods, and diversity of customization features. Therefore, there is an urgent need for a large-scale, one-stop pan-cancer survival analysis web server. We developed PanCanSurvPlot (https://smuonco.shinyapps.io/PanCanSurvPlot/), a Shiny web tool that has incorporated a total of 215 cancer-related datasets from the GEO and TCGA databases, covering nearly 100,000 genes (mRNAs, miRNAs, and lncRNAs), approximately 45,000 samples, 51 different cancer types, and 13 different survival outcomes. The website also provides two cutoff methods based on median and optimal cutpoints. All survival analysis results from the log-rank test and univariate Cox regression are presented in a clear and straightforward summary table. Finally, users can customize color schemes and cutpoint levels to quickly obtain high-quality Kaplan-Meier survival plots that meet publication requirements.

cancer biology↗

bioRxiv2022

Transcriptomic landscape and potential therapeutic targets for human testicular aging revealed by single-cell RNA sequencing

BackgroundTesticular aging is known to cause male age-related fertility decline and hypogonadism, but the underlying molecular mechanisms remain unclear. MethodsWe survey the single-cell transcriptomic landscape of testes from young and old men and examine age-related changes in germline and somatic niche cells. ResultsIn-depth evaluation of the gene expression dynamics of germline cells reveals that disturbance of base-excision repair pathway is a major feature of aging spermatogonial stem cells (SSCs), suggesting that defective DNA repair of SSCs may serve as a potential driver for increased de novo germline mutations with age. Further analysis of aging-associated transcriptional changes shows that stress-related changes and apoptotic signaling pathway accumulate in aged somatic cells. We identify age-related impairment of redox homeostasis in aged Leydig cells and find that pharmacological treatment with antioxidants alleviate this cellular dysfunction of Leydig cells and promote testosterone production. Lastly, our results reveal that decreased pleiotrophin (PTN) signaling is a contributing factor for testicular aging. ConclusionsThese findings provide a comprehensive understanding of the cell-type-specific mechanisms underlying human testicular aging at a single-cell resolution, and suggest potential therapeutic targets that may be leveraged to address age-related male fertility decline and hypogonadism. FundingThis work was supported by the National Key Research and Development Program of China (2018YFA0107200, 2018YFA0801404), the National Natural Science Foundation of China (32130046, 82171564, 82101669, 81871110, 81971759), the Key Research and Development Program of Guangdong Province (2019B020234001), the Natural Science Foundation of Guangdong Province, China (2022A1515010371), the Major Project of Medical Science and Technology Development Research Center of National Health Planning Commission, China (HDSL202001000), the Open Project of NHC Key Laboratory of Male Reproduction and Genetics (Family Planning Research Institute of Guangdong Province) (KF202001), the Guangdong Province Regional Joint Fund-Youth Fund Project (2021A1515110921), the China Postdoctoral Science Foundation (2021M703736).

developmental biology↗

bioRxiv2022

SurvivalML: an integrative platform for the discovery and exploration of prognostic models in multi-center cancer cohorts

Advances in multi-omics and big-data technologies have led to numerous prognostic signatures aimed at improving current clinicopathological staging systems. Due to the lack of reproducibility and independent confirmation, few signatures have been translated into clinical routine. As high-quality datasets accumulate, identifying robust signatures across multiple independent cohorts becomes possible. Nonetheless, inaccurate data retrieval, different versions of genome annotations, disparate expression distributions, difficult data cleaning, inconsistent clinical information, algorithm selection, and parameter tuning have impeded model development and validation in multi-center datasets. Hence, for the first time, we introduced SurvivalML (https://rookieutopia.com/app_direct/SurvivalML/), a web application for helping develop and validate prognostic models across multi-center datasets. SurvivalML included 37,325 samples (253 eligible datasets) with both transcriptome data and survival information from 21 cancer types, which were renewedly and uniformly re-annotated, normalized, and cleaned. This application provided 10 survival machine-learning algorithms for flexibly training models via tuning essential parameters online and delivered four aspects for model evaluation, including Kaplan-Meier survival analysis, time-dependent ROC, calibration curve, and decision curve analysis. Overall, we believe that SurvivalML can serve as an attractive platform for model discovery from multi-center datasets.

bioinformatics↗