Biology subjects

Chen, P.-L.

Publications and source records attributed to Chen, P.-L..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatially resolved multimodal hallmarks of response to neoadjuvant immunotherapies in the melanoma ecosystem in 2D and 3D

Neoadjuvant immunotherapy has transformed cancer treatment, yet the spatial molecular architecture governing response and resistance across distinct immune checkpoint blockade (ICB) regimens remains poorly defined. We assembled the largest neoadjuvant ICB (NICB) spatial multi-omics cohort to date, profiling over 112 million cells at single-cell resolution across three melanoma ICB regimens using MERFISH spatial transcriptomics, multiplexed immunofluorescence, and scRNA-sequencing. These analyses revealed the full multicellular spatial architecture of the NICB tumor microenvironment, including mature TLS with germinal centers, TCF7+ stem-like T cells, myeloid cells organized into spatially distinct cellular neighborhoods with unique intercellular signaling circuits, and CCL19/CCL21-expressing fibroblasts as a previously unrecognized stromal scaffold sustaining these immune hubs. We developed three purpose-built computational tools that together enabled comprehensive quantification of this microenvironment for the first time: SCIRA for whole-slide single-cell receptor-ligand quantification, GCSCAN for molecularly grounded TLS and germinal center structural delineation, and PathNet-TLS for automated TLS detection on H&E images. Applying these tools across the cohort, we defined the immune and stromal composition and cellular neighborhood organization distinguishing responders from non-responders. We also quantified cell-cell interactions and regimen-specific immune architectures, including a markedly stronger mature TLS/germinal center response with IPI-NIVO than NIVO-RELA. Importantly, GCSCAN-quantified TLS and germinal center density each stratified disease-free survival, with responders that lack germinal centers having an elevated risk of relapse. Open-top light-sheet imaging and CODA-based 3D reconstruction further uncovered interconnected germinal center-TLS tunnels invisible to standard 2D histopathology. These findings establish a discovery-to-tool paradigm linking single-cell tumor microenvironment interrogation to clinically deployable computational pathology for biomarker-driven NICB assessment across cancer types.

cancer biology↗

bioRxiv2025

deCYPher: Star Allele-Resolution Computational Framework of Pharmacogenes for Haplotype-Resolved Long-Read Assemblies

Although existing next-generation sequencing (NGS) tools, such as Aldy and Cyrius, have been applied for allele typing, they cannot achieve complete accuracy due to various genomic challenges including pseudogenes, structural variations, hybrid genes, copy number variations, and gene deletions. These complexities make accurate pharmacogene interpretation more challenging, despite the crucial role pharmacogenomics plays in precision medicine. We developed deCYPher, a tool that generates personalized pharmacogenomic reports from haplotype-resolved assemblies. The tool enables analysis of all PharmVar 1A level genes, such as CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, DPYD, NUDT15, and SLCO1B1. Applied to all HPRC haplotypes (including both release 1 and release 2 data), deCYPher demonstrated high accuracy in resolving complex gene structures. In the case of CYP2D6, release 1 identified 6% gene multiplications, 6% full gene deletions, and 4% CYP2D6/CYP2D7 hybrids. By contrast, release 2 demonstrated an increased prevalence of multiplications (14%) and hybrids (11%), while the frequency of full gene deletions remained comparable at 5%. Comparison with pb-StarPhase revealed discrepancies in 12 of 94 assemblies in the release 1 dataset. For instance, in sample HG02257, Aldy, Cyrius, and deCYPher consistently identified the genotype as *2/*35, whereas pb-StarPhase reported *2/*2. Notably, the *35-defining variants were present in the BAM and VCF files in the pb-StarPhase pipeline, but the local read depth over the *35-specific region was only 5x in HG02257-p, suggesting that the misclassification likely resulted from insufficient coverage - a known limitation of pb-StarPhase under low-depth conditions.

bioinformatics↗

bioRxiv2025

gAIRR-wgs: An Algorithm to Genotype T Cell Receptor Alleles Using Whole Genome Sequencing Data

T cell receptor (TR) genes, including variable (TR_V), diversity (TR_D), and joining (TR_J) segments, exhibit allelic diversity that is critical to adaptive immunity. Growing evidence has identified associations between TR genes and immune-related diseases. Germline variants may influence TR gene function and subsequent usage, highlighting the importance of accurate TR allele profiling. However, accurately identifying germline TR from standard WGS data remains challenging due to short read lengths, limited depth, and high sequence similarity. To address these challenges, we developed gAIRR-wgs, for WGS-based TR allele typing. By incorporating novel alleles from HPRC individuals, gAIRR-wgs exhibited excellent performance in allele calling, with F1 scores of 100.0% for TR_D, 99.8% for TR_J, and 98.3% for TR_V. Applying this pipeline to 1,492 individuals from the Taiwan Biobank (TWB), we identified 449 novel TR alleles, 277 of which overlapped with HPRC release 1 data of mixed ethnicity and are absent in the IMGT database. Further population comparison analysis revealed significant TR allele distribution differences across global populations, showing population-specific patterns and diversity variations between ethnic groups. We also discovered TWB-specific deletion polymorphisms affecting contiguous TRGV and TRBV genes, which are not recorded in the gnomAD database and undetected by standard structural variant callers, highlighting the need for tailored approaches to resolve complex immune gene regions. In conclusion, gAIRR-wgs enables accurate TR allele calling from standard WGS data using feasible computational resources and reveals substantial immunogenetic diversity in population cohorts.

bioinformatics↗

bioRxiv2025

TypeAssembly: Copy number estimation and allele typing for haplotype assemblies

Accurately annotating complex genes in the human genome, particularly from haplotype assemblies, remains a significant challenge. To overcome this, we developed TypeAssembly, a local alignment-based framework for copy number estimation and allele typing. Operating in two modes, mode-FASTA and mode-VCF, TypeAssembly can define alleles by either sequence or variant information. We successfully applied it to annotate 41 genes in the MHC locus, 17 KIR genes, and, for the first time, 15 pharmacogenes across 466 haplotype assemblies. This study establishes TypeAssembly as a robust method for accurately annotating complex genomic regions and provides an evaluation of existing gene annotations and callers.

bioinformatics↗

bioRxiv2025

MICRORNAS REGULATED BY PREGNANCY TARGET THE HIV INTERACTOME

Innate immunity predictors of HIV risk are influenced by reproductive hormone levels, pregnancy, and lactation status. However, the molecular mechanisms underlying these associations remain unclear. MicroRNAs (miRNAs), as post-transcriptional regulators, play key roles in immune regulation and host-virus interactions. We hypothesized that physiological adaptations to pregnancy include the modulation of systemic miRNA expression, which in turn regulates host genes involved in HIV interaction, potentially influencing susceptibility during pregnancy. To test this, we leveraged a large longitudinal cohort from Uganda and Zimbabwe and analyzed 174 serum samples from 88 participants in pre-pregnancy (PP), pregnancy (P), and breastfeeding (BF) states using the HTG EdgeSeq platform. Differentially expressed (DE) miRNAs in pregnancy were identified with false discovery rate (FDR) < 0.1 (29 upregulated and 131 downregulated) by intersecting pairwise comparisons (P vs. PP and P vs. BF). Validated gene targets (2,733) were identified via miRWalk and modular enrichment analysis performed using Cytoscape/ClueGO. Enriched pathways (FDR<0.05) included Adaptive Immune Response, Hippo Signaling, Cellular Senescence, HSV-1 Infection, and two cancer-related pathways. Genes from pregnancy-enriched pathways overlapped with the known HIV-host interactome at the range of 37 to 88%. From this overlap, the Maximal Clique Centrally (MCC) score (cytoHubba) identified 47 unique hub genes acting as essential regulatory nodes of protein-protein interaction (PPI) network. These hub genes were further examined by their expression patterns predicted by DE miRNAs, and explored for their interactions with the HIV interactome, revealing connectivity with 18 HIV proteins - highest with Tat and gp120 - and impact on the HIV replication process. HLA-A was the most connected host gene, followed by BCL2L1, EIF2AK2, PTEN, and CYCS. These findings support the hypothesis that pregnancy-driven systemic miRNAs may shape HIV susceptibility by regulating hub genes with central role in viral evasion of host immunity. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=138 HEIGHT=200 SRC="FIGDIR/small/665982v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1cd2007org.highwire.dtl.DTLVardef@1f1b9d3org.highwire.dtl.DTLVardef@dee856org.highwire.dtl.DTLVardef@6796c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

MICRORNAS AS REGULATORS OF DRUG METABOLISM AND TRANSPORT IN PREGNANT AND LACTATING WOMEN

BackgroundPhysiological changes during pregnancy result in altered maternal drug metabolism that impacts efficacy and safety of therapeutics in pregnant women. Pregnancy induced hormonal, immunologic, or metabolic changes may also influence and alter drug disposition. Despite research efforts focused on pharmacokinetics of medications used in pregnant women in the past decade, knowledge gaps exist in understanding how pregnancy influences drug disposition and placental drug transporters. Moreover, there is a scarcity of research in understanding the safety and effectiveness of therapeutics in lactating women. This study aimed to determine the effect of pregnancy on levels of miRNAs regulating drug metabolizing enzymes and transporters (DMET). MethodsWe utilized longitudinal serum specimens collected in 3-month intervals from 88 women who became pregnant during follow-up in a large prospective study of hormonal contraception and HIV acquisition in Uganda and Zimbabwe. We used the HTG EdgeSeq platform coupled with Illumina sequencing to obtain the global miRNA transcriptome in paired specimens collected before, during and after pregnancy. To identify differentially expressed (DE) miRNAs that distinguish pregnancy from pre-conception or breastfeeding we used mixed effect model accounting for multiple samples from the pregnancy event and controlling for fixed effects of batch, country, Nugent score category and sexually transmitted infections. To identify hormonally regulated miRNAs independently associated with Box-Cox-transformed levels of progesterone (P4), {beta}-estradiol (E2), and sex-hormone binding protein (SHBG) we controlled in addition for age, pregnancy and breastfeeding P-values were corrected using the Benjamini-Hochberg false discovery rate (FDR). DMET-targeting miRNAs were identified using miRTarBase focusing on interactions verified by 3-UTR luciferase reporter assay and overlapped with DE miRNAs with FDR < 0.05. ResultsOf 140 DMET-targeting miRNAs among the 2079 miRNAs in the global peripheral blood transcriptome, 41 unique DMET-targeting miRNAs were found to be DE during pregnancy - 38 differentiating pregnancy from preconception and 9 differentiating pregnancy from breastfeeding. The 56 DMETs confirmed as targets of the DE miRNAs included 8 members of the ABC (ATP-binding cassette) transporter family, all abundantly expressed in the placenta, and 4 members of the cytochrome P450 Phase 1 enzyme family with major role in xenobiotics detoxification. The study also revealed a strong (FDR<0.05), predominantly positive association between specific DMET-targeting miRNAs and sex hormone-binding globulin (SHBG) levels, suggesting a miRNA-mediated downregulation of DMETs as SHBG levels rise during pregnancy. ConclusionThis research provides crucial insights into the molecular mechanisms underlying altered drug disposition in pregnant and lactating women, paving the way for improved therapeutic management and personalized medicine in these populations.

molecular biology↗

bioRxiv2025

Abnormal Vaginal Microbiota Associated with miRNA Targeting the HIV-Host Interactome

Understanding the molecular mechanisms underlying the ability of vaginal dysbiosis to alter the mucosal barrier to HIV acquisition is an essential step toward prevention. We hypothesized that micro(mi)-RNAs dysregulated by vaginal pathobiont bacteria epigenetically control host pathways exploited by the virus. The impact of these endogenous non-coding short RNAs on the anti-viral mucosal barrier function in the female reproductive tract is largely unknown. This study utilized cervicovaginal specimens collected during the luteal and follicular phase of the menstrual cycle along with data on age, race, ethnicity, education, and body mass index from 141 healthy reproductive-age women confirmed negative for sexually transmitted infections. Vaginal microbiota was classified by Nugent scoring. Shot-gun vaginal microbiome sequencing and metagenome taxonomic classification was performed on a subset of 21 women. Levels of miRNAs in exosomes isolated from cervicovaginal secretions were quantified using the EdgeSeq-NextGen global transcriptome platform. Differential expression (DE) was determined using R. Epigenetic target prediction was performed using MirTarBase. MiRNA profiles varied by both Nugent score categories (0-3 scores = normal, 4-6 = intermediate, and 7-10 = bacterial vaginosis, BV) and by metagenome classification. Higher microbiome diversity was associated with higher number of significantly dysregulated miRNAs (588 in BV compared to Nugent 0-3 versus 42 in Nugent 4-6 compared to Nugent 0-3, false discovery rate FDR<0.01) affecting over 400 experimentally validated genes targeted for post-transcriptional regulation. The miRNAs dysregulated by G. vaginalis-dominated compared to L. crispatus-dominated metagenomes included 24 DE miRNAs (92% overlap with BV by Nugent score) and 112 validated target genes. BV-dysregulated miRNA mediated the immunosuppressive effects of BV on cytokine levels previously associated with HIV acquisition risk. The gene ontology predictions based on BV-dysregulated miRNAs identified enrichment for 445 downregulated and 50 upregulated genes previously validated as part of the HIV-host interactome. miRNAs mediation revealed a mechanism of suppressed immunity by BV predictive of HIV risk. In conclusion, miRNAs dysregulated by vaginal dysbiosis may facilitate immune imbalance and cellular pathways associated with HIV risk.

molecular biology↗

bioRxiv2025

Unmasking Human T Cell Receptor Germline Diversity: 335 Novel Alleles Identified in 47 Pangenome Reference Individuals Using the gAIRR Suite

The adaptive immune receptor repertoire (AIRR), composed of V(D)J-recombined T cell receptors (TR) and immunoglobulins (IG), is central to adaptive immunity. Accurate AIRR profiling depends on a comprehensive and diverse germline gene set encoding AIRR (gAIRR). Furthermore, gAIRR alleles themselves are associated with immune-related phenotypes and diseases. However, for TR genes, the IMGT database - the primary gAIRR reference - has not been updated since 2020 and lacks sufficient population diversity and complete flanking sequences. To address these limitations, we investigated 47 high-quality genomes from the Human Pangenome Reference Consortium (HPRC) using the gAIRR Suite and identified 335 novel TR alleles - 305 TRV and 30 TRJ - representing 91.6% and 30.9% increases over IMGT records. All novel alleles were crosschecked using two orthogonal pipelines. We also established a comprehensive flanking sequence database, including recombination signal sequences (RSS). We have made all resources publicly available to support immunogenomics research and clinical applications.

genomics↗

bioRxiv2024

Toward Automatic Variant Interpretation: Discordant Genetic Interpretation Across Variant Annotations for ClinVar Pathogenic Variants

PurposeHigh-throughput sequencing has revolutionized genetic disorder diagnosis, but variant pathogenicity interpretation is still challenging. Even though the Human Genome Variation Society (HGVS) provides recommendations for variant nomenclature, discrepancies in annotation remain a significant hurdle. MethodsThis study evaluated the annotation concordance between three tools-- ANNOVAR, SnpEff, and Variant Effect Predictor (VEP)--using 164,549 two-star variants from ClinVar. The analysis used HGVS nomenclature string-match comparisons to assess annotation consistency from each tool, corresponding coding impacts, and associated ACMG criteria inferred from the annotations. ResultsThe analysis revealed variable concordance rates, with 58.52% agreement for HGVSc, 84.04% for HGVSp, and 85.58% for the coding impact. SnpEff showed the highest match for HGVSc (0.988), while VEP bettered for HGVSp (0.977). The substantial discrepancies were noted in the Loss-of-Function (LoF) category. Incorrect PVS1 interpretations affected the final pathogenicity and downgraded PLP variants (ANNOVAR 55.9%, SnpEff 66.5%, VEP 67.3%), risking false negatives of clinically relevant variants in reports. ConclusionsThese findings highlight the critical challenges in accurately interpreting variant pathogenicity due to discrepancies in annotations. To enhance the reliability of genetic variant interpretation in clinical practice, standardizing transcript sets and systematically cross-validating results across multiple annotation tools is essential. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/617756v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@9f6378org.highwire.dtl.DTLVardef@3b9a2corg.highwire.dtl.DTLVardef@106fb58org.highwire.dtl.DTLVardef@15f70c8_HPS_FORMAT_FIGEXP M_FIG This study examined the consistency of variant annotations produced by three widely used open-source toolsANNOVAR, SnpEff, and VEPagainst 164,549 ClinVar two starts variants. The investigation covers HGVS-based transcript, protein nomenclature and coding impact annotation. The results showed that none of the tools were fully consistent with ClinVar across all coding impact categories, particularly in the LoF category, which exhibited the poorest consistency. This inconsistency may lead to discrepancies in PVS1 interpretation, affecting the final pathogenicity assessment. PVS1 loss resulted in a significant downgrading of PLP variants, potentially leading to the omission of clinically relevant variants in reports. C_FIG

genomics↗

bioRxiv2024

Identification of high sugar diet-induced dysregulated metabolic pathways in muscle using tissue-specific metabolic models in Drosophila

Individual tissues perform highly specialized metabolic functions to maintain whole-body metabolic homeostasis. Although Drosophila serves as a powerful model for studying human metabolic diseases, modeling tissue-specific metabolism has been limited in this organism. To address this gap, we reconstruct 32 tissue-specific genome-scale metabolic models (GEMs) by integrating a curated Drosophila metabolic network with pseudo-bulk single-nuclei transcriptomics data, revealing distinct metabolic network structures and subsystem coverage across tissues. We validate enriched pathways identified through tissue-specific GEMs, particularly in muscle and fat body, using metabolomics and pathway analysis. Moreover, to demonstrate the utility in disease modeling, we apply muscle-GEM to investigate high sugar diet (HSD)-induced metabolic dysregulation. Constraint-based semi-quantitative flux and sensitivity analyses identify altered NAD(H)-dependent reactions and distributed control of glycolytic flux, including GAPDH. This prediction is further validated through in vivo 13C-glucose isotope tracing study. Notably, decreased glycolytic flux, including GAPDH, is linked to increased redox modifications. Finally, our pathway-level flux analyses identify dysregulation in fructose metabolism. Together, this work establishes a quantitative framework for tissue-specific metabolic modeling in Drosophila, demonstrating its utility for identifying dysregulated reactions and pathways in muscle in response to HSD.

systems biology↗

bioRxiv2023

Graph-KIR: Graph-based KIR Copy Number Estimation and Allele Calling Using Short-read Sequencing Data

MotivationThe Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce a tool Graph-KIR that aims to estimate the copy number of genes and to call full-resolution (7-digit) KIR alleles from a whole genome sequencing (WGS) sample. ResultsGraph-KIR, unlike most KIR tools, is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 100% accuracy in copy number estimation and high accuracy of allele typing: 91.2% at 7-digit resolution, 97.0% at 5-digit resolution, 97.2% at 3-digit resolution, and 99.6% at gene-level resolution. Graph-KIR outperforms existing tools such as PINGs WGS version (91.9% accuracy) and T1K (84.6% accuracy) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves an accuracy of 85.0%, better than PINGs WGS version (75.5% accuracy) at 5-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples, ensuring reliable performance. AvailabilityThe Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

bioinformatics↗

bioRxiv2023

Genetic Diversity and Structural Complexity of the Killer-Cell Immunoglobulin-Like Receptor Gene Complex: A Comprehensive Analysis using Human Pangenome Assemblies

The killer-cell immunoglobulin-like receptor (KIR) gene complex, a highly polymorphic region of the human genome that encodes proteins involved in immune responses, poses strong challenges in genotyping due to its remarkable genetic diversity and structural intricacy. Accurate analysis of KIR alleles, including their structural variations, is crucial for understanding their roles in various immune responses. Leveraging the high-quality genome assemblies from the Human Pangenome Reference Consortium (HPRC), we present a novel bioinformatic tool, the Structural KIR annoTator (SKIRT), to investigate gene diversity and facilitate precise KIR allele analysis. We applied SKIRT on 47 HPRC-phased assemblies and identified a recurrent novel KIR2DS4/3DL1 fusion gene in the paternal haplotype of HG02630 and maternal haplotype of NA19240. Additionally, SKIRT accurately identifies eight structural variants and 17 novel nonsynonymous alleles, all of which were independently validated using short-read data or quantitative polymerase chain reaction. Our study has discovered a total of 570 novel alleles, among which eight haplotypes harbor at least one KIR gene duplication, six haplotypes have lost at least one framework gene, and 75 out of 94 haplotypes (79.8%) carry at least five novel alleles, thus confirming KIR genetic diversity. These findings are pivotal in providing insights into KIR gene diversity and serve as a solid foundation for understanding the functional consequences of KIR structural variations. High-resolution genome assemblies offer unprecedented opportunities to explore polymorphic regions that are challenging to investigate using short-read sequencing methods. The SKIRT pipeline emerges as a highly efficient tool, enabling the comprehensive detection of the complete spectrum of KIR alleles within human genome assemblies.

genomics↗

bioRxiv2023

Somatic mutation detection workflow validity distinctly influences clinical decision.

Evaluating robustness of somatic mutation detections is essential when utilizing whole exome sequencing (WES) for treatment decision-making. A comprehensive evaluation was conducted using tumor WES from the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, in which multiple library kits sequenced identical DNA materials across three labs to benchmark analytical validity. These workflows included various read aligner (BWA, Bowtie2, DRAGEN-Aligner, DRAGMAP, and HISAT2) and mutation caller (Mutect2, TNscope, DRAGEN-Caller, and DeepVariant) combinations. The results revealed that DRAGEN exhibited superior performance, achieving mean F1-scores of 0.966 and 0.791 for SNV and INDEL detection, respectively. Among open-source software, BWA Mutect2 and HISAT2 Mutect2 combinations showed the highest mean F1-scores for SNV (0.949) and IN-DEL (0.722), respectively. The analyses indicated that high-quality data can be analyzed as having worse results, and vice versa. Evaluations of COSMIC reported mutations unveiled discrepancies across enrichment kits. IDT enrichment kits showed a higher false negative rate, while Agilent WES kits tended to miss mutations in CBL and IDH1, and Roche library kits tended to miss the mutations in PIK3CB. For drug-related biomarkers, Sentieon TNscope tended to underestimate tumor mutation burden and overlook crucial drug-resistance mutations such as FLT3 (c.G1879A: p.A627T) for cytarabine resistance in leukemia and MAP2K1 (c.G199A:p.D67N) for BRAF inhibitors in melanoma. The findings highlight the importance of robust bioinformatic analysis in identifying tumor mutations and guiding clinical decision-making. HighlightsO_LIMutation callers had a significantly higher effect on overall sensitivity than aligners. C_LIO_LIBenchmarking analyses demonstrated that high-quality sequencing reads can be analyzed as having worse results, and vice versa. C_LIO_LIDRAGEN exhibited the best performance among other aligner-caller combinations. C_LIO_LIThe combination of BWA with Mutect2 and HISAT2 with Mutect2 yielded the highest mean F1 scores for detecting SNVs and INDELs by open-source software, respectively. C_LIO_LISentieon TNscope tended to underestimate the tumor mutation burden and missed several drug-resistant mutations. C_LI

bioinformatics↗

bioRxiv2023

Mitochondrial CISD1/Cisd accumulation blocks mitophagy and genetic or pharmacological inhibition rescues neurodegenerative phenotypes in Pink1/parkin models

BackgroundMitochondrial dysfunction and toxic protein aggregates have been shown to be key features in the pathogenesis of neurodegenerative diseases, such as Parkinsons disease (PD). Functional analysis of genes linked to PD have revealed that the E3 ligase Parkin and the mitochondrial kinase PINK1 are important factors for mitochondrial quality control. PINK1 phosphorylates and activates Parkin, which in turn ubiquitinates mitochondrial proteins priming them and the mitochondrion itself for degradation. However, it is unclear whether dysregulated mitochondrial degradation or the toxic build-up of certain Parkin ubiquitin substrates is the driving pathophysiological mechanism leading to PD. The iron-sulphur cluster containing proteins CISD1 and CISD2 have been identified as major targets of Parkin in various proteomic studies. MethodsWe employed in vivo Drosophila and human cell culture models to study the role of CISD proteins in cell and tissue viability as well as aged-related neurodegeneration, specifically analysing aspects of mitophagy and autophagy using orthogonal assays. ResultsWe discovered that the Drosophila homolog Cisd accumulates during aging, as well as in Pink1 and parkin mutant flies. We observed that build-up of Cisd is particularly toxic in neurons, resulting in mitochondrial defects and Ser65-phospho-Ubiquitin accumulation. Age-related increase of Cisd blocks mitophagy and impairs autophagy flux. Importantly, reduction of Cisd levels upregulates mitophagy in vitro and in vivo, and ameliorates pathological phenotypes in locomotion, lifespan and neurodegeneration in Pink1/parkin mutant flies. In addition, we show that pharmacological inhibition of CISD1/2 by rosiglitazone and NL-1 induces mitophagy in human cells and rescues the defective phenotypes of Pink1/parkin mutants. ConclusionAltogether, our studies indicate that Cisd accumulation during aging and in Pink1/parkin mutants is a key driver of pathology by blocking mitophagy, and genetically and pharmacologically inhibiting CISD proteins may offer a potential target for therapeutic intervention.

neuroscience↗

bioRxiv2023

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible at http://pgsb.tw23.org.

bioinformatics↗

bioRxiv2022

Computer simulations reveal pathogenicity and inheritance modes of hearing loss-causing germinal variants

Variants in the gap junction beta-2 (GJB2) gene are the most common cause of hereditary hearing impairment. However, how GJB2 variants lead to local physicochemical and structural changes in the hexameric ion channels of connexin 26 (Cx26), resulting in hearing impairment, remains elusive. In the present study, using molecular dynamics (MD) simulations, we showed that detached inner-wall N-terminal "plugs" aggregated to reduce the channel ion flow in a highly prevalent V37I variant in humans. To examine the predictability of the computational platform, an artificial mutant, V37M, of which the effect was previously unknown in hearing loss, was created. Microsecond simulations showed that homomeric V37M Cx26 hemichannels had an abnormal affinity between the inner edge and N-termini to block the narrower side of the cone-shaped Cx26, while the most stable heteromeric channels did not. Consistent with these predictions, homozygous V37M transgenic mice exhibited apparent hearing loss, but not their heterozygous counterparts, indicating a recessive inheritance mode. Reduced channel conductivity was found in Gjb2V37M/V37M outer sulcus cells and Claudius cells but not in Gjb2WT/WT cells. We view that the current computational platform could serve as an assessment tool for the pathogenesis and inheritance of GJB2-related hearing impairments and other diseases caused by connexin dysfunction.

biophysics↗

bioRxiv2021

Chromatin state transition underlies the temporal changes in gene expression during cardiomyocyte maturation

Congenital heart disease (CHD) is often rooted in gene expression anomalies that occur during heart development. As cells commit to a specific lineage, chromatin dynamics and developmental plasticity generally become more limited. However, it remains unclear how differentiated cardiomyocytes (CMs) undergo morphological and functional adaptations to the postnatal environment during their maturation. In this work, we sought to identify the regulatory mechanisms controlling postnatal cardiac gene networks. A time-series transcriptomic analysis of postnatal hearts revealed an integrated, time-ordered transcriptional network that regulates CM maturation. Remarkably, depletion of histone H2B ubiquitin ligase RNF20 after formation of the four-chamber heart disrupted these highly coordinated gene networks. Its ablation also caused early-onset cardiomyopathy, a phenotype reminiscent of CHD. Furthermore, we found that dynamic RNF20-mediated modulation of chromatin accessibility during CM maturation was necessary for the operative binding of cardiac transcription factors known to drive transcriptional gene networks. Together, our results reveal how epigenetic-mediated chromatin state transitions modulate time-ordered gene expression during CM maturation.

developmental biology↗

bioRxiv2020

Profiling Germline Adaptive Immune Receptor Repertoire with gAIRR Suite

Genetic profiling of germline adaptive immune receptor repertoire (AIRR), including T cell receptor (TR) and immunoglobulin (IG), is imaginably relevant to numerous immune-related conditions, but currently insurmountable due to high genetic complexity. Our gAIRR Suite comprises three modules. gAIRR-seq, a probe capture-based targeted sequencing pipeline, profiles AIRR from individual DNA samples. gAIRR-call and gAIRR-annotate call alleles from gAIRR-seq reads and annotate whole-genome assemblies respectively. We gAIRR-seqed TRV and TRJ of seven Genome in a Bottle (GIAB) DNA samples with 100% accuracy, and discovered novel alleles. We also gAIRR-seqed and gAIRR-called a subject from both the peripheral blood mononuclear cells (PBMC) and oral mucosal cells. The calling results from these two cell types have a high concordance (99% for all known AIRR alleles). We gAIRR-annotated 36 genomes to cumulatively unearth 325 novel TRV alleles and 29 novel TRJ alleles. We could further profile the flanking sequences, including the recombination signal sequence (RSS). We validated two structural variants for HG002. We uncovered substantial conflicts of AIRR genes in references GRCh37 and GRCh38. The gAIRR Suite can potentially benefit future genetic study and clinical applications of various immune-related phenotypes.

genomics↗