Biology subjects

Luo, J.

Publications and source records attributed to Luo, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Metabolic engineering of Acinetobacter baylyi ADP1 for efficient utilization of pentose sugars and production of glutamic acid

Efficient utilization of pentose sugars is critical for advancing sustainable biomanufacturing using lignocellulosic biomass. However, many potential host strains capable of consuming glucose and lignin-derived monomers are unable to utilize pentose sugars. Here, we engineered Acinetobacter baylyi ADP1 for the utilization of D-xylose and L-arabinose. We first modelled different pentose utilization pathways using flux balance analysis to choose the most optimal pathway. A novel marker-free approach facilitated the seamless integration of the pentose catabolic gene clusters of the selected Weimberg pathway into the A. baylyi genome, generating strains capable of efficiently utilizing both D-xylose and L-arabinose as sole carbon sources without any additional engineering or adaptation. For D-xylose, the cells achieved the highest growth rate ({micro}=0.73 h-1) reported to date for non-native hosts engineered for pentose utilization. For L-arabinose, a growth rate of {micro}=0.35 h-1 was achieved, which also surpassed the growth rate on a native substrate of A. baylyi, glucose ({micro}=0.31 h-1). Importantly, pentose utilization occurred simultaneously with glucose utilization. We then applied metabolic flux analysis using 13C labeled xylose to reveal D-xylose metabolism in the engineered strain. To demonstrate the potential for value-added bioproduct synthesis, L-glutamate was selected as a target compound. Deletion of sucAB and gabT, and the overexpression of gdhA enabled the production of L-glutamate. With the engineered strain, a carbon yield of 34% during co-utilization with succinate and 70% via whole-cell catalysis using resting cells was achieved. This study establishes a robust platform for pentose utilization and value-added bioproduct synthesis in A. baylyi ADP1 and highlights the potential for further metabolic optimization.

bioengineering↗

bioRxiv2025

Effects of alcohol on gut microbiome in adolescent and adult MMTV-Wnt1 mice

Breast cancer is the most commonly diagnosed cancer in women worldwide, with alcohol consumption recognized as a significant risk factor. While epidemiological studies consistently show a positive correlation between alcohol consumption and increased breast cancer risk, the underlying mechanisms remain unclear. Recent evidence suggests that the gut microbiome--the diverse collection of microorganisms, including bacteria, viruses, and fungi, residing in the gastrointestinal tract--plays a pivotal role in systemic health and disease. This is achieved through its regulation of key physiological processes such as metabolism, immune function, and inflammatory responses. Disruption of the gut microbiome (dysbiosis) has recently been implicated in the development of breast cancer. We hypothesized that alcohol exposure induces gut dysbiosis, which in turn drives systemic inflammation and carcinogenic processes. Previously, we demonstrated that alcohol exposure promotes mammary tumor growth and aggressiveness in MMTV-Wnt1 (Wnt1) transgenic mice, an established model for investigating mechanisms of alcohol-induced tumor promotion. In this study, we sought to determine whether alcohol exposure induces gut dysbiosis in adolescent and adult Wnt1 transgenic mice and their wild-type FVB counterparts. Our findings revealed that alcohol exposure significantly reduced microbiome richness in adult Wnt1 and FVB mice. Alcohol exposure also markedly altered microbiome composition in adolescents and adults in both strains. Additionally, we identified specific microbial taxa that were significantly affected by alcohol exposure. These results demonstrate that alcohol disrupts the gut microbiome in a preclinical breast cancer model, providing insights into the potential role of gut dysbiosis in alcohol-induced mammary tumor promotion and offering avenues for future research.

cancer biology↗

bioRxiv2025

Integrating Literature Evidence to Enhance CRISPR-based Target Screening for Hand-foot Syndrome

The experimental high-throughput screening (HTS) methods, exemplified by CRISPR- based screening techniques, have revolutionized target identification in drug discovery. However, such screens frequently yield extensive, often unrelated target lists necessitating costly and time-intensive experimental evaluation and validation. To address this challenge, we propose a dual-filter strategy that integrates literature-mined targets with CRISPR/Cas9 screening outputs, systematically prioritizing the most credible candidates and thereby reducing the experimental validation burden and increasing success rate. To validate this strategy, we applied it with hand-foot syndrome (HFS), a clinically challenging side effect induced by fluoropyrimidine treatment. We identified ATF4 as a key regulator of 5-fluorouracil (5-FU) toxicity in the skin and revealed forskolin as a potential therapeutic agent of HFS through the strategy. Mechanistically, forskolin triggers MEK/ERK-dependent ATF4 induction, subsequently driving 5-FU detoxification via the ATF4-mediated eIF2/I{kappa}B signaling pathway. Our findings demonstrate that this dual-filter strategy could notably accelerate drug discovery by reducing experimental validation burden after target screening.

pharmacology and toxicology↗

bioRxiv2025

Automated Discovery of Therapeutic Biomaterial for Renally Impaired Hyperuricemia Patients by Natural Language Processing and Machine Learning

The exponential growth of scientific publications presents opportunities for researchers to identify valuable knowledge, especially in the highly interdisciplinary field --- biomaterials, where exploiting possible connections between unmet clinical needs and materials properties from literatures is crucial. However, with traditional literature reading, it is extremely challenging to marry unmet clinical needs with existing materials reported for different applications or other purposes. Here, to provide a not-renally cleared therapeutics for renally impaired hyperuricemia patients, we designed a multi-tiered framework MatWISE that fuses state-of-the-art natural language processing, semantic relationship mapping, and machine learning to automate the complex process of material discovery from a sea of scientific literatures published until December of 2022, and successfully identified and optimized {delta}-MnO2 into an orally administered, nonabsorbable uric acid (UA) lowering biomaterial. {delta}-MnO2 had superior serum and urine UA-lowering effect in three hyperuricemia mouse models, by comparing with a standard of care drug. {delta}-MnO2 is highly promising to serve as a safe and effective UA-lowering drug for renally impaired hyperuricemia patients. We demonstrated a new research paradigm for biomaterials that combining state-of-the-art machine learning techniques and a handful of experiments to discover a translationally relevant material from the massive existing research, for an unmet clinical need.

biochemistry↗

bioRxiv2025

Sky islands of Southwest China. II: Unraveling hidden species diversity of talpid moles using phylogenomics and skull-based deep learning

The sky islands of Southwest China, characterized by dramatic topographical and climatic variations, are prominent hotspots of biodiversity and endemism. Organisms inhabiting middle-to-high elevation habitats in this region are geographically isolated within distinct mountain chains, which over geological time have been subjected to isolation-by-distance and isolation-by-environment. These processes have led to profound allopatric diversification and strong phylogeographic structuring, resulting in a plethora of genetically distinct cryptic species, as is becoming increasingly evident for many small mammal families. However, morphological conservatism can pose significant challenges in delineating these clades within species complexes. In this study, we leverage artificial intelligence technologies to unravel the hidden species diversity of moles (family Talpidae) in Southwest Chinas sky islands. We first employed ultraconserved elements (UCEs) to investigate the evolutionary history of talpid moles, conducted molecular species delimitation using mitochondrial and multi-locus genes, and utilized both traditional and geometric morphometrics to examine their morphological disparity. To address the challenges of morphology based cryptic species identification, we developed a deep learning Hierarchical Identification of Species NETwork (HIS-NET) to create an image-based model that analyzes four different views of the skull/mandible to distinguish genera and species hierarchically. HIS-NET not only achieved expert-level accuracy in species identification but also effectively distinguished between cryptic and known species, aiding in the identification of key morphological variation intervals. Our results support the recognition of allopatrically distributed taxa in Euroscaptor and Parascaptor as full species, thereby confirming that species diversity in this region remains underestimated. Beyond advancing our understanding of speciation in this unique and fragile ecosystem, our study serves as a proof-of-concept, demonstrating the power of deep learning in unraveling hidden biodiversity within this and other species complexes.

zoology↗

bioRxiv2025

No evidence that human GIGYF2 interacts with growth factor receptor-bound protein 10 (GRB10): implication for human disease

GIGYF2 (Growth factor receptor bound protein 10 (GRB10)-interacting GYF (glycine-tyrosine-phenylalanine) protein 2) reduces mRNA stability and translation via microRNAs, ribosome quality control, and several RNA-binding proteins. GIGYF2 was first identified in mouse cell lines as an interacting partner with Growth factor receptor-bound protein 10 (GRB10), which binds to the insulin receptor and insulin-like growth-factor receptor 1. Mutations in the human GIGYF2 gene were reported in autism spectrum disorder and Parkinsons disease. In mouse models, mutations in the gene encoding GIGYF2 exhibited disease phenotypes. It was, therefore, thought that the GIGYF2-associated disease in humans is caused by defective GRB10 signaling. We show here that GIGYF2 does not interact with GRB10 in human cell lines, as determined by proximity ligation and co-immunoprecipitation assays. Size exclusion chromatography assay further confirmed these findings. The lack of interaction is explained by the absence of the critical GYF-domain binding PPG{Phi} sequence in the human GRB10 protein. These results are in contrast with the current understanding that a GIGYF2/GRB10 complex is associated with human disease via IR and IGF-1R signaling and underscore alternative mechanisms responsible for the observed phenotypes related to mutations in the human GIGYF2 gene.

molecular biology↗

bioRxiv2025

Excavating Podocyte-Protective Targets of phloroglucinol-terpene hybrids Utilizing AI Models and Omics Analysis

Lupus nephritis (LN) is a multifactorial autoimmune inflammatory disorder, with podocyte dysfunction or structural abnormalities playing a pivotal role in its pathogenesis. Eucalyptus leaves, traditionally employed in folk medicine, have demonstrated notable efficacy in managing non-infectious inflammations. Eucarbwenstols D (R14) is a novel phloroglucinol-terpene that has been isolated from Eucalyptus robusta. To further refine potential targets, we utilized an AI scoring system and successfully identified the epidermal growth factor receptor (EGFR) as the novel target of R14. This discovery was built upon the foundation of transcriptome and connectivity mapping (CMap) analysis. Molecular docking and molecular dynamics simulations were employed to analyze the interaction between R14 and EGFR, surface plasmon resonance (SPR) and homogeneous time-resolved fluorescence (HTRF) assays were utilized to accurately quantify the binding affinity between R14 and EGFR. Western blot (WB) and immunofluorescence assays were then conducted to explore the downstream signaling pathways initiated by EGFR, specifically the PI3K/AKT/mTOR pathway. These assays demonstrated that R14 modulates the expression of apoptotic proteins Bax and Bcl-2 through this pathway, thereby exerting a protective effect on podocytes from undergoing apoptosis. The modulation of key podocyte proteins, nephrin and podocin, further substantiates this protective effect. Overall, we have applied a novel AI algorithm model that transcends bioinformatics analysis to precise identification of the novel podocyte target of R14. In this study, R14 was identified for the first time as a novel scaffold compound for EGFR inhibition. Moreover, R14 has demonstrated its capacity to inhibit podocyte apoptosis, thereby protecting these critical cells. This discovery establishes a robust theoretical foundation for the use of phloroglucinol-terpene hybrids (PTHs) in the treatment of LN.

cell biology↗

bioRxiv2025

Learning maximally spanning representations improves protein function annotation

Automated protein function annotation is a fundamental problem in computational biology, crucial for understanding the functional roles of proteins in biological processes, with broad implications in medicine and biotechnology. A persistent challenge in this problem is the imbalanced, long-tail distribution of available function annotations: a small set of well-studied function classes account for most annotated proteins, while many other classes have few annotated proteins, often due to investigative bias, experimental limitations, or intrinsic biases in protein evolution. As a result, existing machine learning models for protein function prediction tend to only optimize the prediction accuracy for well-studied function classes overrepresented in the training data, leading to poor accuracy for understudied functions. In this work, we develop MSRep, a novel deep learning-based protein function annotation framework designed to address this imbalance issue and improve annotation accuracy. MSRep is inspired by an intriguing phenomenon, called neural collapse (NC), commonly observed in high-accuracy deep neural networks used for classification tasks, where hidden representations in the final layer collapse to class-specific mean embeddings, while maintaining maximal inter-class separation. Given that NC consistently emerges across diverse architectures and tasks for high-accuracy models, we hypothesize that inducing NC structure in models trained on imbalanced data can enhance both prediction accuracy and generalizability. To achieve this, MSRep refines a pre-trained protein language model to produce NC-like representations by optimizing an NC-inspired loss function, which ensures that minority functions are equally represented in the embedding space as majority functions, in contrast to conventional classification methods whose embedding spaces are dominated by overrepresented classes. In evaluations across four protein function annotation tasks on the prediction of Enzyme Commission numbers, Gene3D codes, Pfam families, and Gene Ontology terms, MSRep demonstrates superior predictive performance for both well- and underrepresented classes, outperforming several state-of-the-art annotation tools. We anticipate that MSRep will enhance the annotation of understudied functions and novel, uncharacterized proteins, advancing future protein function studies and accelerating the discovery of new functional proteins. The source code of MSRep is available at https://github.com/luo-group/MSRep.

bioinformatics↗

bioRxiv2025

CDP-DAG synthases regulate plant growth and broad-spectrum disease resistance

Phosphatidic acid (PA) functions as a cell membrane component and signaling molecule in plants. PA metabolism has multiple routes, in one of which PA is converted into cytidine diphosphate diacylglycerol (CDP-DAG) by CDP-DAG synthases (CDSs). CDS genes are highly conserved in plants. Here, we found that knock-down of the CDS gene enhanced the resistance of Arabidopsis thaliana to multiple pathogens, with a growth penalty. When Arabidopsis leaves were treated with chitin or flg22, reactive oxygen species (ROS) production in cds mutants was significantly higher than that in the wild-type (WT). Similarly, phosphorylation of mitogen-activated protein kinases (MAPKs) in the cds1cds2 double mutant was significantly increased compared to the WT. By integrating lipidomics, transcriptomics, and metabolomics data, PA accumulation was observed in mutants cds1cds2, activating the jasmonic acid (JA) and salicylic acid (SA) signaling pathway, and increasing transcript levels of plant defense-related genes. Significant accumulation of the downstream metabolites including serotonin and 5-methoxyindole was also found, which plays important roles in plant immunity. In conclusion, our study indicated the role of CDSs in broad-spectrum disease resistance in Arabidopsis and that CDSs are involved in plant metabolic regulation.

plant biology↗

bioRxiv2025

B7-H3 as a Dual Clinically Relevant Checkpoint and Antibody Drug Conjugate Target Expressed Across Adenocarcinoma and Neuroendocrine Prostate Cancers

Background and ObjectivesCD276 (B7-H3) has recently emerged as a promising presumptive immune checkpoint inhibitor (ICI) and a potential antibody-drug conjugate (ADC) target for prostate cancer (PCa). We evaluated B7-H3 and 15 other clinically relevant ADC and ICI targets for expression at the RNA and protein level across the PCa continuum--hormone-sensitive, castration-resistant, and neuroendocrine. MethodsCCLE data analysis and western blot experiments were performed for quantifying RNA and protein expression variability across PCa cell lines. Inter- and intratumoral heterogeneity was evaluated by integrating single-cell RNA sequencing data across 595k cells and 102 patients, spanning the disease continuum. AR and B7-H3 knockouts of PCa cell lines were developed and investigated using R1881 and Enzalutamide to elucidate the AR-CD276 signaling pathway through qRT-PCR and flow cytometry. Key FindingsB7-H3 showed high expression in tumor and myeloid cells (tumor microenvironment - TME), lowest heterogeneity across all ADC and ICI targets, and was negatively regulated by androgen signaling. LimitationsFurther validation of ADC and ICI target protein expression in human samples and more exhaustive exploration of the AR-CD276 signaling cascade is required. ConclusionB7-H3 demonstrates the least susceptibility to selective pressure due to its stable expression across PCa disease states. Clinical ImplicationsB7-H3 represents an important ADC target for PCa due to its potential to minimize drug resistance and likely ability to be a valid target across the prostate cancer continuum. Additionally, its expression in myeloid cells supports a dual role as an ICI, further enhancing its therapeutic relevance. Graphical AbstractDiscovery of optimal checkpoints and antibody drug conjugates (ADCs) in Prostate Cancer (PCa). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/632253v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@1af1115org.highwire.dtl.DTLVardef@8a9d70org.highwire.dtl.DTLVardef@6659aeorg.highwire.dtl.DTLVardef@189e200_HPS_FORMAT_FIGEXP M_FIG C_FIG Patient SummaryIn this study, we demonstrate that prostate cancer expresses high amounts of a cell surface protein called B7-H3 both when first diagnosed and throughout various stages of metastatic disease. Furthermore, we demonstrate a clear and yet to be fully resolved cross-talk between B7-H3 and the androgen signaling pathway central to prostate cancer development. We conclude that B7-H3 is therefore a highly promising therapeutic cell surface protein expressed by prostate cancer due to its stable expression potentially leading to low selective pressure and reduced drug resistance--thereby having significant implications for clinical trial design and patient inclusion considerations. Advancing PracticePCa is a highly heterogeneous disease with an increasingly wide treatment landscape, governed by various factors including clinical covariates, histopathological features, and molecular factors. B7-H3 has recently emerged as a promising clinical target for immunotherapy in the localized setting and antibody drug targeting in the metastatic setting. Despite the clinical significance, B7-H3 expression has not yet been thoroughly evaluated against other clinically relevant immune checkpoint and antibody drug targets, especially along the continuum of PCa treatment stages--hormone sensitive, castration resistant, and neuroendocrine. To our knowledge, this is the first study to characterize B7-H3 in this continuum using representative cell lines and the largest collection of PCa patient single cell sequencing data yet assembled in the literature, thus accounting for the substantial heterogeneity across PCa disease states as well as between datasets. Take Home MessageThis article explores the therapeutic relevance of clinical stage ADC targets and immune checkpoints for Prostate Cancer (PCa). We show that B7-H3 is a highly promising therapeutic candidate for PCa due to its stable expression across various disease states.

cancer biology↗

bioRxiv2025

Anticancer synthetic arylsulfonamides with Wnt1-modulating activity

Dysregulation of the Wnt1/{beta}-catenin signaling pathway has been demonstrated to be a driving factor in the propagation of several human cancers. Previous studies have discovered methyl 3-{[(4-methylphenyl)sulfonyl]amino}benzoate (MSAB) as a selective inhibitor of the Wnt1/{beta}-catenin signaling pathway, which putatively functions through direct engagement of {beta}-catenin. To understand how changes to the identity and position of the methyl ester affect the in vitro potency of this compound in Wnt1-driven mammalian cell lines, we prepared and evaluated three analogs of MSAB with 3- and 4-substituted methyl and ethyl esters. In MTT assays, analogs with methyl esters showed significantly more activity than their ethyl ester counterparts and both 4-substituted esters exhibited significantly attenuated antiproliferative activity, with MSAB exhibiting dose-dependent activity across cancerous cell lines. Further analysis by flow cytometry reveals low-Annexin V signal, suggesting that these compounds do not function via a pro-apoptotic pathway. Additionally, through a TCF/LEF-activated luciferase reporter cell assay, we observe that the 4-substituted methyl ester analogous to MSAB exhibits slightly diminished Wnt1-inhibitory activity, while 3- and 4-substituted ethyl esters exhibit minimal Wnt1-inhibitory activity. This difference in potency with a simple ester substitution might be attributed to several factors that ultimately drive antiproliferative activity, prompting the investigation of other potential substituents to further investigate the structure-activity relationship of these compounds as Wnt1-based antiproliferative agents.

cancer biology↗

bioRxiv2025

Niacin protects against vascular calcification via enhanced SIRT1 and SIRT6 signaling and promoted VSMC autophagy

BackgroundVascular calcification (VC) is a common pathologic state that often accompanies calcium-phosphorus metabolism disorder and chronic kidney diseases (CKDs) in aging individuals. Vascular smooth muscle cell (VSMC) has been widely acknowledged as one of the main cell types that involved in this process. Niacin, a lipid-lowering reagent, has been demonstrated to be beneficial in atherosclerotic disease. The correlation and mechanism between niacin and vascular calcification have not been reported so far. Methods and resultsRCS analysis based on large clinical databases indicated that diet that riches in niacin can protect against abdominal artery calcification (AAC). Our data showed that niacin treatment remarkably reduced VSMC osteogenic differentiation and senescence-associated markers under osteogenic stimulation. Moreover, niacin treatment alleviated CKD and vitamin D3-induced vascular calcification in C57BL/6J mice. Mechanistically, we for the first time demonstrated that niacin supplementation inhibited vascular calcification via maintaining both Sirtuin 1 (SIRT1) and Sirtuin 6 (SIRT6) levels. Moreover, we verified that niacin activated SIRT1 and SIRT6 promoted VSMC autophagy flux. ConclusionsThese findings may help to develop novel therapeutic strategies in the treatment and prevention of vascular calcification.

cell biology↗

bioRxiv2025

Ultraviolet B Induces Cutaneous Lupus Erythematosus by Triggering a Ribotoxic-Stress-Response-Dependent MIF-p38 Feedback Loop in Keratinocytes

Ultraviolet B (UVB) is one of the most common triggers of cutaneous lupus erythematosus (CLE), but the mechanism is still unclear. Here, we discovered that the expression of macrophage migration inhibitory factor (MIF), a multipotent cytokine, was significantly increased in keratinocytes of lupus patients in scRNA-seq data and clinical samples. We further discovered that UVB exposure induced MIF releasing from lupus keratinocytes and contributed to the tissue remodeling and inflammation by regulating the expression of matrix metalloproteinases (MMPs) and pro-inflammatory cytokines in keratinocytes and fibroblasts. Mechanistically, UVB exposure triggered a ribotoxic stress response (RSR) in lupus keratinocytes, which was featured by the increased level of phospho-ZAK. The RSR induced MIF releasing through the activation of p38 and GSDMD cleavage. The released MIF could further promote the activation of p38 through its receptor CD74, forming a positive feedback loop triggered by UVB exposure in lupus keratinocytes. Intradermal injection of a Mif-shRNA AAV significantly improved the skin lesions in lupus mice. To further investigate the potentials of MIF as a therapeutic target, we developed a microneedle patch capable of intradermal administration of a MIF inhibitor, which effectively protected lupus mice from skin lesions induced by UVB exposure. In conclusion, our findings suggest a novel mechanism by which UVB exposure contributes to the development of CLE by triggering a positive feedback loop of MIF-p38 in lupus keratinocytes. Our results also demonstrate the translational potentials of microneedle patch targeting MIF in the treatment of CLE.

immunology↗

bioRxiv2025

AncestryHub: A web server for local ancestry analysis

SummaryAncestryHub is a web version of local ancestry analysis software. It contains four built-in software and a set of built-in population reference panels. AncestryHub will significantly reduce the efforts and requirements for the users on their personal computational skills and computational hardware environment for ancestry analysis. Availabilityhttps://www.ancestryhub.4dgenome.com Contact: lima@4dgenome.com

bioinformatics↗

bioRxiv2024

Cellular cartography reveals mouse prostate organization and determinants of castration resistance

Inadequate response to androgen deprivation therapy (ADT) frequently arises in prostate cancer, driven by cellular mechanisms that remain poorly understood. Here, we integrated single-cell RNA sequencing, single-cell multiomics, and spatial transcriptomics to define the transcriptional, epigenetic, and spatial basis of cell identity and castration response in the mouse prostate. Leveraging these data along with a meta-analysis of human prostates and prostate cancer, we identified cellular orthologs and key determinants of ADT response and resistance. Our findings reveal that mouse prostates harbor lobe-specific luminal epithelial cell types distinguished by unique gene regulatory modules and anatomically defined androgen-responsive transcriptional programs, indicative of divergent developmental origins. Androgen-insensitive, stem-like epithelial populations - resembling human club and hillock cells - are notably enriched in the urethra and ventral prostate but are rare in other lobes. Within the ventral prostate, we also uncovered two additional androgen-responsive luminal epithelial cell types, marked by Pbsn or Spink1 expression, which align with human luminal subsets and may define the origin of distinct prostate cancer subtypes. Castration profoundly reshaped luminal epithelial transcriptomes, with castration-resistant luminal epithelial cells activating stress-responsive and stemness programs. These transcriptional signatures are enriched in tumor cells from ADT-treated and castration-resistant prostate cancer patients, underscoring their likely role in driving treatment resistance. Collectively, our comprehensive cellular atlas of the mouse prostate illuminates the importance of lobe-specific contexts for prostate cancer modeling and reveals potential therapeutic targets to counter castration resistance. Significance StatementAndrogen deprivation therapy is a mainstay in prostate cancer treatment, yet many patients eventually develop castration-resistant disease--a lethal progression driven by poorly understood cellular mechanisms. Our study provides a comprehensive cellular map of the prostate, identifying key determinants of normal organization and castration-induced remodeling. By pinpointing the cell types and molecular programs that confer ADT responsiveness or resistance, our findings offer new directions for prostate cancer modeling and pave the way toward novel therapeutic strategies aimed at enhancing ADT efficacy and preventing the emergence of castration-resistant prostate cancer.

cancer biology↗

bioRxiv2024

Nucleosome Spacing Can Fine-Tune Higher Order Chromatin Assembly

Cellular chromatin displays heterogeneous structure and dynamics, properties that control diverse nuclear processes. Models invoke phase separation of conformational ensembles of chromatin fibers as a mechanism regulating chromatin organization in vivo. Here we combine biochemistry and molecular dynamics simulations to examine, at single base-pair resolution, how nucleosome spacing controls chromatin phase separation. We show that as DNA linkers extend from 25 bp to 30 bp, as examplars of 10N+5 and 10N (integer N) bp lengths, chromatin condensates become less thermodynamically stable and nucleosome mobility increases. Simulations reveal that this is due to trade-offs between inter- and intramolecular nucleosome stacking, favored by rigid 10N+5 and 10N bp linkers, respectively. A remodeler can induce or inhibit phase separation by moving nucleosomes, changing the balance between intra- and intermolecular stacking. The intrinsic phase separation capacity of chromatin enables fine tuning of compaction and dynamics, likely contributing to heterogeneous chromatin organization in vivo.

biophysics↗

bioRxiv2024

DRUGSYNC: Prediction of Synergistic Drug Combinations Using GCN Graph Convolutional Network based and Pre-learned Drug-induced Gene Expression Profiles

Drug-drug synergy (DDS) is crucial for identifying novel and effective drug combinations. With the accumulation of experimental data (DrugCombDB) and the uprising of deep learning approaches, many efforts have been made to computationally predict effective drug pairs. However, most models had limited success, with the highest ROC AUC of 0.65 for predicting pairs of unseen drugs. Drug-induced gene expressions profiles (DGEP) from datasets like LINCS L1000 offer a robust proxy to describe drug effects. However, overlap between DrugCombDB and experimental DGEP data is sparse only [~]2%. To address these limitations, we propose the DRUGSYNC (Drug Response and Utilization for Graph-based SYNergy Classification) prediction framework that leverages pre-learned drug-target interaction (DTI) and pre-learned DGEP as features. Graph Convolutional Network (GCN) was used to represent the inherent network of genetic interactions. Our pre-learned DGEP achieved 0.877 Pearson correlation coefficients (PCC). Furthermore, our framework excels by integrating pre-learned DGEP and DTI of paired drugs for DDS prediction, achieving superior performance over benchmark technologies. Notably, it demonstrates exceptional accuracy in predicting interactions between unseen drugs, with an ROC AUC score of 0.854. The analysis further emphasized the importance of accurate DGEP predictions. This method can be extended to any cell lines.

bioinformatics↗

bioRxiv2024

Coverage landscape of the human genome in nucleus DNA and cell-free DNA

For long, genome-wide coverage has been used as a measure of sequencing quality and quantity, but the biology hidden beneath has not been fully exploited. Here we performed a comparative analysis on genome-wide coverage profiles between nucleus genome DNA (gDNA) samples from the 1000 Genomes Project (n=3,202) and cell-free DNA (cfDNA) samples from healthy controls (n=113) or cancer patients (n=362). Regardless of sample type, we observed an overall conserved landscape with segmentation of coverage, where adjacent windows of genome positions present similar coverage. Besides GC-content, we identified protein-coding gene density and nucleosome density as major factors influencing the coverage of gDNA and cfDNA, respectively. Differential coverage of cfDNA vs gDNA was found in immune-receptor loci, intergenic regions and non-coding genes, reflecting distinct genome activities in different cell types. A further rise in coverage at non-coding genes and intergenic regions plus a further drop of coverage at protein-coding genes and genic regions within cancer cfDNA samples indicated a loss of contribution by normal cells. Importantly, we observed the distinctive feature of coverage convergence in cancer-derived cfDNA, with the extent of convergence positively correlated to stages. Based on the findings, we developed and validated an outlier-detection approach for cfDNA-based cancer screening without the need of cancer samples for training, outperforming current benchmarks on condition-matched and condition-unmatched cancer detection tasks.

genomics↗