Exploring the midgut physiology of the non-haematophagous mosquito Toxorhynchites theobaldi.

Toxorhynchites mosquitoes have an exclusively phytophagous feeding habit as adults, which leads to significant differences in their morphophysiology compared with haematophagous mosquitoes. However, the molecular mechanisms of digestion in this mosquito are not well understood. In this study, RNA sequencing of the posterior midgut (PMG) of the mosquito Toxorhynchites theobaldi was undertaken, highlighting its significance in mosquito digestion. Subsequently, a comparison was made between the differential gene expression of the PMG and that of the anterior midgut. It was found that the most abundant proteases in the PMG were trypsin and chymotrypsin, and the level of gene expression for enzymes essential for digestion (such as serine protease, α-amylase and pancreatic triacylglycerol lipase) and innate immune response (including catalase, cecropin-A2 and superoxide dismutase) was like that of haematophagous mosquitoes. Peritrophin-1 was detected in the entire midgut, with an elevated expression level in the PMG. Based on our findings, it is hypothesized that a non-haematophagic habit might have been exhibited by the ancestor of Tx. theobaldi, and this trait may have been retained. This study represents a pioneering investigation at the molecular level of midgut contents in a non-haematophagous mosquito. The findings offer valuable insights into the evolutionary aspects of feeding habits in culicids.

Distinct molecular profile of the chick organizer as a stem zone during axial elongation.

The vertebrate organizer plays a crucial role in building the main (antero-posterior) axis of the embryo: it neuralizes the surrounding ectoderm, and is the site of emigration for cells making axial and paraxial mesendoderm during elongation. The chick organizer becomes a stem zone at the onset of elongation; it stops recruiting cells from the neighbouring ectoderm and generates all its derivatives from the small number of resident cells it contains at the end of gastrulation stages. Nothing is known about the molecular identity of this stem zone. Here, we specifically labelled long-term resident cells of the organizer and compared their RNA-seq profile to that of the neighbouring cell populations. Screening by reverse transcription-polymerase chain reaction and in situ hybridization identified four genes (WIF1, PTGDS, ThPO and UCKL1) that are upregulated only in the organizer region when it becomes a stem zone and remain expressed there during axial elongation. In experiments specifically labelling the resident cells of the mature organizer, we show that only these cells express these genes. These findings molecularly define the organizer as a stem zone and offer a key to understanding how this zone is set up, the molecular control of its cells' behaviour and the evolution of axial growth zones.

Phylogenomics recovers multiple origins of portable case making in caddisflies (Insecta: Trichoptera), nature's underwater architects.

Caddisflies (Trichoptera) are among the most diverse groups of freshwater animals with more than 16 000 described species. They play a fundamental role in freshwater ecology and environmental engineering in streams, rivers and lakes. Because of this, they are frequently used as indicator organisms in biomonitoring programmes. Despite their importance, key questions concerning the evolutionary history of caddisflies, such as the timing and origin of larval case making, remain unanswered owing to the lack of a well-resolved phylogeny. Here, we estimated a phylogenetic tree using a combination of transcriptomes and targeted enrichment data for 207 species, representing 48 of 52 extant families and 174 genera. We calibrated and dated the tree with 33 carefully selected fossils. The first caddisflies originated approximately 295 million years ago in the Permian, and major suborders began to diversify in the Triassic. Furthermore, we show that portable case making evolved in three separate lineages, and shifts in diversification occurred in concert with key evolutionary innovations beyond case making.

Construction of m6A-Related Gene Prediction Model and Subtype Analysis in Non-Obstructive Azoospermia Based on Bioinformatics.

PURPOSE: Non-obstructive azoospermia (NOA) is a severe and common cause of male infertility. Currently, the most reliable predictor of sperm retrieval success in NOA is histopathology, but preoperative testicular biopsy often increases the difficulty of sperm retrieval surgery. This study aims to explore the characteristics of N6-methyladenosine (m6A) modification in NOA patients and investigate the potential biomarkers and molecular mechanisms for pathological diagnosis and treatment of NOA using m6A-related genes. METHODS: NOA-related datasets were downloaded from the GEO database. Based on the results of LASSO regression analysis, a prediction model was established from differentially expressed m6A-related genes, and the predictive performance of the model was evaluated using ROC curves. Cluster analysis was performed based on differentially expressed m6A-related genes to evaluate the differences in different m6A modification patterns in terms of differentially expressed genes (DEGs), biological features, and immune features. RESULTS: There were significant differences in eight m6A-related genes between NOA samples and healthy controls. The ROC curves showed excellent predictive performance for the diagnostic models constructed with ALKBH5 and FTO. DEGs of two m6A modification subtypes indicated the influence of m6A-related genes in the biological processes of mitosis and meiosis in NOA patients, and there were significant immune differences between the two subtypes. CONCLUSION: The NOA pathological diagnostic models constructed with FTO and ALKBH5 have good predictive ability. We have identified two different m6A modification subtypes, which may help predict sperm retrieval success rate and treatment selection in NOA patients.

The chondrocyte "mechanome": Activation of the mechanosensitive ion channels TRPV4 and PIEZO1 drives unique transcriptional signatures.

The mechanosensitive ion channels Transient Receptor Potential Vanilloid 4 (TRPV4) and PIEZO1 transduce physiologic and supraphysiologic magnitudes of mechanical signals in the chondrocyte, respectively. TRPV4 activation promotes chondrogenesis, while PIEZO1 activation by supraphysiologic deformations drives cell death. The mechanisms by which activation of these channels discretely drives changes in gene expression to alter cell behavior remain to be determined. To date, no studies have contrasted the transcriptomic response to activation of these channels nor has any published data attempted to correlate these transcriptomes to alterations in cellular function. This study used RNA sequencing to comprehensively investigate the transcriptomes associated with activation of TRPV4 or PIEZO1, revealing that TRPV4 and PIEZO drive distinct transcriptomes and also exhibit unique co-regulated clusters of genes. Notably, activation of PIEZO1 through supraphysiologic deformation induced a transient inflammatory profile that overlapped with the interleukin (IL)-1-responsive transcriptome and contained genes associated with cartilage degradation and osteoarthritis progression. However, both TRPV4 and PIEZO1 were also shown to elicit anabolic effects. PIEZO1 expression promoted a pro-chondrogenic transcriptome under unloaded conditions, and daily treatment with PIEZO1 agonist Yoda1 significantly increased sulfated glycosaminoglycan deposition in vitro. These findings emphasize the presence of a broad "mechanome" with distinct effects of TRPV4 and PIEZO1 activation in chondrocytes, suggesting complex roles for PIEZO1 in both the physiologic and pathologic responses of chondrocytes. The identification of transcriptomic profiles unique to or shared by PIEZO1 and TRPV4 (distinct from IL-1-induced inflammation) could inform future therapeutic designs targeting these channels for the management and treatment of osteoarthritis.

Exploring the role of cellular senescence in cancer prognosis across multiple tumor types.

Background: Cellular senescence is a common biological process with a well-established link to cancer. However, the impact of cellular senescence on tumor progression remains unclear. To investigate this relationship, we utilized transcriptomic data from a senescence gene set to explore the connection between senescence and cancer prognosis. Methods: We developed the senescence score by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. We obtained transcriptomic information of the senescence gene set from The Cancer Genome Atlas (TCGA) program. Additionally, we created a nomogram that integrates these senescence scores with clinical characteristics, providing a more comprehensive tool for prognosis evaluation. Results: We calculated the senescence score based on the expression level of 42 senescence-related genes. We established the nomogram based on the senescence score and clinical characteristics. The senescence score showed a positive correlation with epithelial-to-mesenchymal transition, cell cycle, and glycolysis, and a negative correlation with autophagy. Furthermore, we carried out Gene Ontology (GO) analysis to explore the signaling pathways and biological process in different senescence score groups. Conclusions: The senescence score, a novel tool constructed in this study, shows promise in predicting survival outcomes across various cancer types. These findings not only highlight the complex interplay between senescence and cancer but also indicate that cellular senescence might serve as a biomarker for tumor prognosis.

Unique regulatory network of dragon fruit simultaneously mitigates the effect of vanadium pollutant and environmental factors.

Under changing climatic conditions, plants are simultaneously facing conflicting stresses in nature. Plants can sense different stresses, induce systematic ROS signals, and regulate transcriptomic, hormonal, and stomatal responses. We performed transcriptome analysis to reveal the integrative stress response regulatory mechanism underlying heavy metal stress alone or in combination with heat and drought conditions in pitaya (dragon fruit). A total of 70 genes were identified from 31,130 transcripts with conserved differential expression. Furthermore, weighted gene co-expression network analysis (WGCNA) identified trait-associated modules. By integrating information from three modules and protein-protein interaction (PPI) networks, we identified 10 interconnected genes associated with the multifaceted defense mechanism employed by pitaya against co-occurring stresses. To further confirm the reliability of the results, we performed a comparative analysis of 350 genes identified by three trait modules and 70 conserved genes exhibiting their dynamic expression under all treatments. Differential expression pattern of genes and comparative analysis, have proven instrumental in identifying ten putative structural genes. These ten genes were annotated as PLAT/LH2, CAT, MLP, HSP, PB1, PLA, NAC, HMA, and CER1 transcription factors involved in antioxidant activity, defense response, MAPK signaling, detoxification of metals and regulating the crosstalk between the complex pathways. Predictive analysis of putative candidate genes, potentially governing single, double, and multifactorial stress response, by several signaling systems and molecular patterns. These findings represent a valuable resource for pitaya breeding programs, offering the potential to develop resilient "super pitaya" plants.

Combined analysis of single-cell sequencing and bulk transcriptome sequencing reveals new mechanisms for non-healing diabetic foot ulcers.

Diabetic foot ulcers (DFUs) pose a significant challenge in diabetes care. Yet, a comprehensive understanding of the underlying biological disparities between healing and non-healing DFUs remains elusive. We conducted bioinformatics analysis of publicly available transcriptome sequencing data in an attempt to elucidate these differences. Our analysis encompassed differential analysis to unveil shifts in cell composition and gene expression profiles between non-healing and healing DFUs. Cell communication alterations were explored employing the Cellchat R package. Pseudotime analysis and cytoTRACE allowed us to dissect the heterogeneity within fibroblast subpopulations. Our findings unveiled disruptions in various cell types, localized low-grade inflammation, compromised systemic antigen processing and presentation, and extensive extracellular matrix signaling disarray in non-healing DFU patients. Some of these anomalies partially reverted in healing DFUs, particularly within the abnormal ECM-receptor signaling pathway. Furthermore, we distinguished distinct fibroblast subpopulations in non-healing and healing DFUs, each with unique biological functions. Healing-associated fibroblasts exhibited heightened extracellular matrix (ECM) remodeling and a robust wound healing response, while non-healing-associated fibroblasts showed signs of cellular senescence and complement activation, among other characteristics. This analysis offers profound insights into the wound healing microenvironment, identifies pivotal cell types for DFU healing promotion, and reveals potential therapeutic targets for DFU management.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Integrative analysis of blood transcriptome profiles in small-cell lung cancer patients for identification of novel chemotherapy resistance-related biomarkers.

Introduction: Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched. Methods: In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance. Results: Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed PRICKLE3, TNFSFI0, ACSLl and EP300 as potential contributors to primary resistance, with SNWl, SENP2 and SMNDCl emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC. Discussion: These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.

Pyroptosis-Related Genes as Diagnostic Markers in Chronic Obstructive Pulmonary Disease and Its Correlation with Immune Infiltration.

Background: Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between pyroptosis-related genes (PRGs) and COPD diagnosis in the context of immune infiltration, ultimately proposing a PRG-based diagnostic model for predicting COPD outcomes. Methods: Clinical data and PRGs of COPD patients were sourced from the GEO database. The "ConsensusClusterPlus" package was employed to generate molecular subtypes derived from PRGs that were identified through differential expression analysis and LASSO Cox analysis. A diagnostic signature including eight genes (CASP4, CASP5, ELANE, GPX4, NLRP1, GSDME, NOD1and IL18) was also constructed. Immune cell infiltration calculated by the ESTIMATE score, Stroma scores and Immune scores were also compared on the basis of pyroptosis-related molecular subtypes and the risk signature. We finally used qRT - PCR to detect the expression levels of eight genes in COPD patient and normal. Results: The diagnostic model, anchored on eight PRGs, underwent validation with an independent experimental cohort. The area under the receiver operating characteristic (ROC) curves (AUC) for the diagnostic model showcased values of 0.809, 0.765, and 0.956 for the GSE76925, GSE8545, and GSE5058 datasets, respectively. Distinct expression patterns and clinical attributes of PRGs were observed between the comparative groups, with functional analysis underscoring a disparity in immune-related functions between them. Conclusion: In this study, we developed a potential as diagnostic biomarkers for COPD and have a significant role in modulating the immune response. Such insights pave the way for novel diagnostic and therapeutic strategies for COPD.

Single-cell analysis of Crohn's disease: Unveiling heterogeneity and evaluating ustekinumab outcomes.

BACKGROUND: The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue. METHODS: We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways. RESULTS: Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level. CONCLUSIONS: Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.

Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.

Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.

Identification of unique cell type responses in pancreatic islets to stress.

Diabetes involves the death or dysfunction of pancreatic ß-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that ß-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

Identification of a novel lactylation-related gene signature predicts the prognosis of multiple myeloma and experiment verification.

Multiple myeloma (MM) is an incurable hematological malignancy with poor survival. Accumulating evidence reveals that lactylation modification plays a vital role in tumorigenesis. However, research on lactylation-related genes (LRGs) in predicting the prognosis of MM remains limited. Differentially expressed LRGs (DELRGs) between MM and normal samples were investigated from the Gene Expression Omnibus database. Univariate Cox regression and LASSO Cox regression analysis were applied to construct gene signature associated with overall survival. The signature was validated in two external datasets. A nomogram was further constructed and evaluated. Additionally, Enrichment analysis, immune analysis, and drug chemosensitivity analysis between the two groups were investigated. qPCR and immunofluorescence staining were performed to validate the expression and localization of PFN1. CCK-8 and flow cytometry were performed to validate biological function. A total of 9 LRGs (TRIM28, PPIA, SOD1, RRP1B, IARS2, RB1, PFN1, PRCC, and FABP5) were selected to establish the prognostic signature. Kaplan-Meier survival curves showed that high-risk group patients had a remarkably worse prognosis in the training and validation cohorts. A nomogram was constructed based on LRGs signature and clinical characteristics, and showed excellent predictive power by calibration curve and C-index. Moreover, biological pathways, immunologic status, as well as sensitivity to chemotherapy drugs were different between high- and low-risk groups. Additionally, the hub gene PFN1 is highly expressed in MM, knocking down PFN1 induces cell cycle arrest, suppresses cell proliferation and promotes cell apoptosis. In conclusion, our study revealed that LRGs signature is a promising biomarker for MM that can effectively early distinguish high-risk patients and predict prognosis.

Integrated transcriptome and microRNA analysis reveals molecular responses to high-temperature stress in the liver of American shad (Alosa sapidissima).

BACKGROUND: Fish reproduction, development and growth are directly affected by temperature, investigating the regulatory mechanisms behind high temperature stress is helpful to construct a finer molecular network. In this study, we systematically analyzed the transcriptome and miRNA information of American shad (Alosa sapidissima) liver tissues at different cultivation temperatures of 24 â„ƒ (Low), 27 â„ƒ (Mid) and 30 â„ƒ (High) based on a high-throughput sequencing platform. RESULTS: The results showed that there were 1594 differentially expressed genes (DEGs) and 660 differentially expressed miRNAs (DEMs) in the LowLi vs. MidLi comparison group, 473 DEGs and 84 DEMs in the MidLi vs. HighLi group, 914 DEGs and 442 DEMs in the LowLi vs. HighLi group. These included some important genes and miRNAs such as calr, hsp90b1, hsp70, ssa-miR-125a-3p, ssa-miR-92b-5p, dre-miR-15a-3p and novel-m1018-5p. The DEGs were mainly enriched in the protein folding, processing and export pathways of the endoplasmic reticulum; the target genes of the DEMs were mainly enriched in the focal adhesion pathway. Furthermore, the association analysis revealed that the key genes were mainly enriched in the metabolic pathway. Interestingly, we found a significant increase in the number of genes and miRNAs involved in the regulation of heat stress during the temperature change from 24 °C to 27 °C. In addition, we examined the tissue expression characteristics of some key genes and miRNAs by qPCR, and found that calr, hsp90b1 and dre-miR-125b-2-3p were significantly highly expressed in the liver at 27 â„ƒ, while novel-m0481-5p, ssa-miR-125a-3p, ssa-miR-92b-5p, dre-miR-15a-3p and novel-m1018-5p had the highest expression in the heart at 30℃. Finally, the quantitative expression trends of 10 randomly selected DEGs and 10 DEMs were consistent with the sequencing data, indicating the reliability of the results. CONCLUSIONS: In summary, this study provides some fundamental data for subsequent in-depth research into the molecular regulatory mechanisms of A. sapidissima response to heat stress, and for the selective breeding of high temperature tolerant varieties.

Regulatory gene network for coffee-like color morph of TYRP1 mutant of oujiang color common carp.

BACKGROUND: Neither a TYRP1-mediated highly conserved genetic network underlying skin color towards optimum defense nor the pathological tendency of its mutation is well understood. The Oujiang Color Common Carp (Cyprinus carpio var. color) as a model organism, offering valuable insights into genetics, coloration, aquaculture practices, and environmental health. Here, we performed a comparative skin transcriptome analysis on TYRP1 mutant and wild fishes by applying a conservative categorical approach considering different color phenotypes. RESULTS: Our results reveal that an unusual color phenotype may be sensitized with TYRP1 mutation as a result of upregulating several genes related to an anti-inflammatory autoimmune system in response to the COMT-mediated catecholamine neurotransmitters in the skin. Particularly, catecholamines-derived red/brown, red with blue colored membrane attack complex, and brown/grey colored reduced eumelanin are expected to be aggregated in the regenerated cells. CONCLUSIONS: It is, thus, concluded that the regenerated cells with catecholamines, membrane attack complex, and eumelanin altogether may contribute to the formation of the unusual (coffee-like) color phenotype in TYRP1 mutant.

Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks.

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.