Reactome Pathway Research Articles

P-1980. Decoding the Complexity of Post COVID Conditions: Transcriptomic Analyses of Machine-Learning-Based Chronic Symptom Phenotypes

Abstract Background Post-COVID conditions (PCC, ‘Long COVID’) remain a military health concern, and symptom heterogeneity limits understanding of PCC pathogenesis. To address this, we used machine learning to define PCC phenotypes in Military Health System (MHS) beneficiaries. Here, we extend these findings to identify early transcriptomic profiles which may predict the development of these distinct symptom-based PCC phenotypes. (A) Machine learning techniques identified three groups of participants based on chronic symptom-clustering. Radial charts display the distribution of post-COVID condition (PCC) symptoms. Each plot’s axes represent different symptoms, with the length of each spoke indicating symptom prevalence. Shorter spokes indicate lower prevalence, while longer spokes indicate higher prevalence. (B) Clinical and demographic characteristics of 154 military health system beneficiaries by identified symptom-clusters. (C) Schematic representation of pathway enrichment analysis using the differentially expressed signature and pathway databases. (D-E) Jaccard similarity analysis identifies the similarity of leading-edge pathways within each symptom-cluster phenotype, with a result close to 1 indicating high similarity and close to 0 indicating significant differences. (F) Network analysis depicts pathway interactions between each symptom-cluster phenotype, with larger nodes representing clusters and smaller nodes indicating individual pathways. Grey lines indicate associations between clusters and pathways, revealing both overlapping and distinct pathway sets. Upregulated and downregulated pathways are highlighted in red and blue, respectively. (G) Non-overlapping leading-edge pathways are identified, with the size of each pathway node corresponding to the normalized enrichment score (NES), with an upward arrow and red color indicates upregulation, while a downward arrow and blue color denotes downregulation. Methods The EPICC cohort study evaluates SARS-CoV-2 infection in MHS beneficiaries. PCC phenotypes were identified using symptom data; transcriptome profiling was performed on early post-infection blood samples from participants who did and did not develop 3-month symptom PCC phenotypes. Participants with pre-COVID comorbidities were excluded to delineate gene responses for PCC from pre-existing illnesses. Comparison of transcriptomic signatures associated with symptom clusters involved utilizing Reactome, KEGG, BioCarta, and Hallmark pathway enrichment analysis. Hypergeometric p-values and the Jaccard coefficient (JC) assess pathway distinctiveness between symptom clusters and those with no PCC. Results We analyzed 154 participants with no reported comorbidities (Fig. 1), who completed a symptom survey 3-months after a SARS-CoV-2 positive test, and who had an early blood sample (median: 36 days post-infection). Clustering analysis stratified 3 distinct PCC symptom clusters: Sensory, Fatigue/difficulty thinking, and Difficulty breathing/exercise intolerance. Transcriptomic analysis noted post-infection signatures which predicted the development of each cluster (JC: 0.18–0.28). Leading-edge pathways included cytokine signaling in the Sensory cluster (Normalized Enrichment Score, NES=16.16, P < 0.001), nervous system development in the Fatigue/difficulty thinking cluster (NES=10.23, P < 0.001), and MYC Targets V1 in the Difficulty thinking/exercise intolerance cluster (NES=8.89, P < 0.001). Conclusion Distinct symptom-based PCC phenotypes are associated with different early post-infection gene expression pathways. These findings inform mechanistic hypotheses and the development of PCC prediction tools. These methods may be applied for prediction of post-acute sequelae in other pathogens. Disclosures Timothy Burgess, MD, MPH, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response Simon Pollett, MBBS, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID Response

Distinct single cell transcriptional profile in CD4+ T-lymphocytes among obese children with asthma.

Introduction: Obesity is a risk factor for asthma morbidity, associated with less responsiveness to inhaled corticosteroids. CD4+ T-cells are central to the immunology of asthma and may contribute to the unique obese asthma phenotype. We sought to characterize the single cell CD4+ Transcriptional profile differences in obese children with asthma compared to normal weight children with asthma. Methods: Eight normal weight and obese participants with asthma were clinically phenotyped and matched based on asthma control. Peripheral blood (PB) CD4+ T-cells were sorted, and single cell RNA sequencing was conducted. Cell clusters were identified by canonical gene expression, differential gene expression and reactome pathway analysis was applied. The obese PB bulk transcriptomic signature from the U-BIOPRED pediatric cohort was assessed in our cohort as well. Results: Obese children with asthma have a distinct CD4+ transcriptional profile with differential gene expression. There were more activated protein tyrosine phosphate receptor type C (PTPRC)high cells and less PTPRClow in the obese children. Obese children had higher enrichment of the neutrophil degranulation, interleukin-7 (IL-7) receptor and IL-7-related janus kinase-signal transducer and activator of transcription signaling pathways. Genes previously associated with more severe asthma, IL-32, FKBP5 gene expression, IL-6 and Rho transcriptional signaling, were also enriched in obese children with asthma. Discussion: Our findings shed insight into the molecular mechanisms underpinning more severe and steroid-resistant asthma among obese children. Further investigation is needed to identify potential new therapeutic targets for this group.

Genetic Polymorphism of Zinc Transporter-8 Gene (SLC30A8), Serum Zinc Concentrations, and Proteome Profiles Related to Type 2 Diabetes in Elderly

Background and Aims: Older adults are particularly susceptible to type 2 diabetes mellitus (T2DM) due to factors such as age-related insulin resistance, decreased physical activity, and deficiency of micronutrients, especially zinc. Studies have suggested that the risk allele of the zinc transporter 8 gene (SLC30A8) single-nucleotide poly-morphism (SNP) rs13266634 may contribute to T2DM susceptibility in addition to the complex protein interactions and alterations in the protein expressions and modifications associated with T2DM. This study was implemented to study the associations between SLC30A8 polymorphism, serum zinc levels, and the profiles of proteins differentially expressed in nondiabetic (n = 116) and prediabetic/diabetic (n = 149) subjects. Methods: SNP genotyping using TaqMan® assay and proteomic analysis by LC-MS/MS were performed in each group. Results: The results showed a higher risk of diabetes in individuals with the risk genotype CC accompanied by a low serum zinc level than in those with other genotypes. Profiles of proteins differentially expressed between the groups were identified and shown to be particularly associated with zinc-related functions, zinc transporter 8, and glucose metabolism. Proteins exclusively expressed in prediabetes/diabetes were assigned to a Reactome pathway related to zinc transporter and insulin processing. Conclusions: Our findings suggest that individuals carrying at least one copy of SLC30A8 rs13266634 accompanied by a low serum zinc level might be susceptible to T2DM, which could be due to alterations in insulin signaling and zinc metabolism. Understanding this relationship deepens our understanding of the genetic and molecular mechanisms underlying T2DM risk, offering potential targets for therapeutic intervention and prevention strategies.

KEAP1 mutations as key crucial prognostic biomarkers for resistance to KRAS-G12C inhibitors

BackgroundKRAS-G12C inhibitors mark a notable advancement in targeted cancer therapies, yet identifying predictive biomarkers for treatment efficacy and resistance remains essential for optimizing clinical outcomes.MethodsThis systematic meta-analysis synthesized studies available through September 2024 across PubMed, Cochrane Library, SpringerLink, and Embase. Using CRISPR/Cas9 technology, this study generated cells with KEAP1 and STK11 knockouts, and utilized lentiviral vectors to overexpress PD-L1. Cellular sensitivity to KRAS-G12C inhibitors—AMG510, MRTX849, and JAB-21822—was evaluated through CCK-8 assays. Comprehensive bioinformatics analyses on stably transfected cell lines were conducted to elucidate pathways mediating resistance.ResultsAnalysis of 13 studies involving 1132 patients highlighted the significant efficacy of KRAS-G12C inhibitor monotherapy, particularly among elderly and female patients. Treatment response was notably affected by liver and brain metastases, with KEAP1 mutations identified as a primary negative prognostic factor, closely associated with early resistance to treatment. Validation studies in NCI-H358 cells showed a marked increase in IC50 values for AMG510, MRTX849, and JAB-21822 after KEAP1 knockout (P < 0.0001), with IC50 values rising from 27.78 nm, 116.9 nm, and 118.7 nm in controls, respectively. Comparative analysis of differentially expressed genes in KEAP1 knockout cells versus controls, utilizing GO, KEGG, and Reactome pathway analyses, revealed substantial enrichment in pathways linked to extracellular matrix organization and cell adhesion processes. Although STK11 mutations and heightened PD-L1 expression indicated a trend toward poorer outcomes, these correlations lacked statistical significance.ConclusionThis research affirms KRAS-G12C inhibitors as promising treatments, especially for certain patient subgroups, and underscores KEAP1 mutations as key biomarkers for resistance. The findings highlight the urgent need for alternative therapeutic approaches in KEAP1-mutant patients and emphasize the role of molecular profiling in tailored treatment strategies.

Integrating network pharmacology and experimental verification to reveal the ferroptosis-associated mechanism of Changpu-Yizhi-Wan in the treatment of Alzheimer's disease.

To explore the pharmacological mechanism of Changpu-Yizhi-Wan (CYW) in the treatment of Alzheimer's disease (AD) from the perspective of ferroptosis based on network pharmacology and experimental verification. The Encyclopedia of Traditional Chinese Medicine 2.0 (ETCM2.0) database was used to collect the active components of CYW, and the putative targets were predicted in ETCM2.0 and SwissTargetPrediction database. The AD related targets were collected from GeneCards, comparative toxicogenomics database (CTD), Online Mendelian Inheritance in Man (OMIM), DisGeNET and Therapeutic Target Database (TTD), the ferroptosis related targets were collected from FerrDb V2 database, and the common targets of CYW, AD and ferroptosis were calculated by Venny2.1 platform. Protein-protein interaction (PPI) analysis was performed by STRING database, and the active compounds-target network and the PPI network were constructed using Cytoscape software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway enrichment analysis were performed through DAVID database. RSL3 was used to induce HT22 cells to establish a neuronal ferroptosis cell model, and the inhibitory effect of CYW on neuronal ferroptosis was evaluated by cell viability assay, intracellular iron assay and lipid peroxidation staining. The ferroptosis-associated key protein expressions of Nrf2, SLC7A11, GPX4 and FTH1 were detected by Western blot. A total of 100 candidate compounds were identified from CYW, and 1129 putative targets were obtained. 3924 AD-related targets and 564 ferroptosis-related targets were collected, respectively. There were 78 common targets between them and CYW targets, which were potential targets for CYW to regulate ferroptosis in the treatment of AD. PPI network analysis identified 10 key targets, including TP53, IL6, STAT3, HIF1A, NFE2L2, and others. GO, KEGG and Reactome enrichment analysis showed that 78 potential targets were involved in the regulation of ferroptosis and Nrf2-mediated gene transcription. Molecular docking showed that some active components of CYW had good affinity with Nrf2. In RSL3-induced HT22 cells, CYW significantly improved cell viability, reduced intracellular iron levels and inhibited lipid peroxidation, and improved the protein expression of Nrf2, SLC7A11, GPX4 and FTH1. The pharmacological mechanism of CYW in the treatment of AD may be related to the regulation of Nrf2/SLC7A11/GPX4/FTH1 axis to inhibit neuronal ferroptosis.

Long-term effects of linear versus macrocyclic GBCAs on gene expression in the central nervous system of mice

BackgroundWe examined chronic gadolinium retention impact on gene expression in the mouse central nervous system (CNS) after injection of linear or macrocyclic gadolinium-based contrast agents (GBCAs).MethodsFrom 05/2022 to 07/2023, 36 female mice underwent weekly intraperitoneal injections of gadodiamide (2.5 mmol/kg, linear), gadobutrol (2.5 mmol/kg, macrocyclic), or saline. Mice were sacrificed on day 29 or 391 after a 1-year washout. Assessments included magnetic resonance imaging (MRI), mechanical hyperalgesia tests, and inductively coupled plasma mass spectrometry to measure gadolinium levels. Ribonucleic acid (RNA) sequencing and bioinformatic analyses identified differentially expressed genes (DEGs), with validation by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot (WB).ResultsPost-gadodiamide, MRI showed increased signal intensity in the deep cerebellar nuclei (pre, 0.997 ± 0.006 versus post, 1.086 ± 0.013, p < 0.001). Mechanical hyperalgesia tests indicated transient sensory changes. After 1-year, gadolinium retention was noted in the brain (5.92 ± 0.32 nmol/kg) and spinal cord (1.23 ± 0.66 nmol/kg) with gadodiamide, compared to saline controls (0.06 ± 0.02 nmol/kg in brains and 0.28 ± 0.06 nmol/kg in spinal cords). RNA sequencing identified 17 shared DEGs between brain and spinal cord in the gadodiamide group on day 391, with altered Hmgb2 and Sgk1 expression confirmed by qRT-PCR and WB. Reactome pathway analysis showed enrichment in neuroinflammation pathways. No DEGs were detected in brains on day 29.ConclusionChronic gadolinium deposition from repeated linear GBCA but not macrocyclic administration causes significant gene expression alterations in the mouse CNS, particularly affecting neuroinflammation pathways.Relevance statementThis study examined the long-term impact of chronic gadolinium retention on gene expression in the mouse CNS, uncovering significant changes associated with neuroinflammation pathways after repeated administration of linear GBCA, but not with macrocyclic GBCA. These findings highlight the importance of further research on the long-term safety of linear GBCA in medical imaging.Key PointsChronic gadolinium retention alters gene expression in the mouse central nervous system.Significant neuroinflammatory pathway changes were observed after linear gadodiamide exposure.MRI showed increased signal intensity in deep cerebellar nuclei after gadodiamide injection.Graphical

A collaborative network analysis for the interpretation of transcriptomics data in Huntington’s disease

Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives.We analyzed a Huntington’s disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington’s disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation.In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

Untangling the complex mechanisms associated with Alzheimer's disease in elderly patients using high-throughput RNA sequencing data and next-generation knowledge discovery methods: Focus on potential gene signatures and drugs for dementia.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that primarily affects elderly individuals. This study aimed to elucidate the intricate mechanisms underlying AD in elderly patients compared with healthy aged individuals using high-throughput RNA sequencing (RNA-seq) data and next-generation knowledge discovery methods (NGKD), with a focus on identifying potential therapeutic agents. High-throughput RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE104704). These data were derived from healthy and diseased human brains (eight young healthy brains [young], 10 aged healthy brains [Old], and 12 aged diseased brains [AD]). We used NGKD tools such as GEO RNA-seq Experiments Interactive Navigator (GREIN) to obtain differentially expressed genes (DEGs) by comparing the AD versus Old RNA-seq data and further filtered and normalized to obtain differentially regulated Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and Panther pathways using ExpressAnalyst tool. Besides, WebGestalt was used to identify differentially regulated Gene Ontologies (GO) and the pre-ranked Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. The X2K web tool was used to infer upstream regulator networks and X2K Appyter tool for obtaining transcription factors (TFs) and kinase network information. LFW1000 and L1000CDS2 tools were used to identify specific drugs that reverse AD-associated gene signatures in elderly patients. Our study revealed significant downregulation of pathways related to neuroactive receptor-ligand interaction, synaptic vesicle cycle, and neuronal system in elderly individuals with AD. GO analysis showed negative enrichment of functions related to cognition, potassium ion transport, receptor-ligand activity, SNARE binding, and primary lysosomes. The transcription factors SUZ12 and REST, along with increased MAPK signaling, were identified as key regulators of downregulated genes. Several drugs and natural products, including dihydroergocristine, mepacrine, gedunin, amlodipine, and disulfiram have been identified as potential therapeutic agents for reversing AD-associated gene signatures. This comprehensive analysis of AD in elderly individuals using RNA-seq data and NGKD tools revealed multiple differentially regulated pathways, gene signatures, and potential drugs for dementia treatment. These findings highlight the complex molecular mechanisms underlying AD and provide insights into potential therapeutic strategies. Further research is needed to validate these findings and to develop personalized treatment approaches for AD in elderly patients.

Understanding the Molecular Mechanisms of Incomptine A in Treating Non-Hodgkin Lymphoma Associated with U-937 Cells: Bioinformatics Approaches, Part I.

Background: Incomptine A (IA) has been reported to have cytotoxic activity in non-Hodgkin lymphoma cancer cell lines and have effects on U-937 cells, including the induction of apoptosis, the production of reactive oxygen species, and the inhibition of glycolytic enzymes. Also, IA has cytotoxic activity in the triple-negative subtypes, HER2+, and luminal A of breast cancer cells, with its properties being associated with an effect on the antiapoptotic function of Hexokinase II (HKII). Objectives: In this research, we reviewed the altered levels of proteins present in the lymph nodes of male Balb/c mice inoculated with U-937 cells and treated with IA or methotrexate, as well as mice only inoculated with cancer cells. Methods: Five approaches, including Tandem Mass Tag (TMT), Gene ontology (GO), Reactome, KEGG pathway analysis, and molecular docking, were used. Results: TMT showed that 74 proteins were differentially expressed, out of which 12 presented overexpression (FC ≥ 1.5) and 62 were under expressed (FC ≤ 0.67). In general, the TMT approach showed that IA had a better effect on proteins than methotrexate. Gene ontology, Reactome, and KEGG pathway analysis showed that proteins with altered levels may be implicated in several processes, including gene silencing by RNA, oxidative phosphorylation, glycolysis/gluconeogenesis, cytoskeleton organization, and ATP metabolic and energetic processes. The molecular docking analysis, which used 23 altered proteins as targets, revealed that IA interacted with all the proteins used. Conclusions: The results obtained using the five bioinformatic approaches provide information and show that IA could be used to treat non-Hodgkin lymphoma induced with the U-937 cell line. Also, it could provide a basis for future research and the development of clinical trials.

Transcriptomic Changes in Human Tonsil-Derived Mesenchymal Stem Cells Across Culture Passages.

Tonsil-derived mesenchymal stem cells (TMSCs) are in the limelight in regenerative medicine due to their high proliferation and differentiation potential. It is important to conduct studies to determine the optimal conditions for achieving the maximum yield while maintaining the optimal differentiation capacity of TMSCs. This study explores the impact of serial subculture on TMSCs by analyzing gene expression at passages 2, 4, 6, and 8. For each culture passage, genes with significant differences in RNA expression from previous passages were selected and their characteristics were observed performing enrichment analysis including KEGG (Kyoto Encyclopedia of Genes and Genomes) and Reactome pathway. At each passage, a "cell cycle" term was ranked high with statistical significance in the KEGG and Reactome pathway. Cell cycle gene expression, including Cyclin-dependent kinases (CDKs) and cyclins, increased until passage 6, then decreased by passage 8. The cell cycle is known to be important not only for proliferation but also for determining whether stem cells maintain pluripotency or differentiate into various lineages. The results suggest that cell cycle gene expression can guide the timing for differentiation induction, with passage 6 potentially being a critical point for initiating differentiation.

Screening of the key genes and signaling pathways for schizophrenia using bioinformatics and next generation sequencing data analysis

Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Researchers have identified numerous proteins associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia through exhaustive bioinformatics and next generation sequencing (NGS) data analyses using GSE106589 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software, and the differentially expressed genes (DEGs) were identified. Gene ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction network, module, micro-RNA (miRNA)-hub gene regulatory network, transcription factor (TF)-hub gene regulatory network, and drug-hub gene interaction network analysis were performed to identify the hub genes, miRNA, TFs, and drug molecules. Potential hub genes were analyzed using receiver operating characteristic curves in the R package. In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably upregulated and 477 genes were distinctly downregulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, G protein-coupled receptor ligand binding, regulation of cellular processes, and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A, and BTK were considered the potential hub genes. The MiRNA-hub gene regulatory network, TF-hub gene regulatory network, and drug-hub gene interaction network were constructed successfully and predicted key miRNAs, TFs, and drug molecules for schizophrenia diagnosis and treatment. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

Faecal proteomics links neutrophil degranulation with mortality in patients with alcohol-associated hepatitis

ObjectivePatients with alcohol-associated hepatitis (AH) have a high mortality. Alcohol exacerbates liver damage by inducing gut dysbiosis, bacterial translocation and inflammation, which is characterised by increased numbers of circulating and...

Magnesium Supplementation Modifies Arthritis Synovial and Splenic Transcriptomic Signatures Including Ferroptosis and Cell Senescence Biological Pathways.

Rheumatoid arthritis (RA) is a common systemic autoimmune inflammatory disease that can cause joint damage. We have recently reported that oral magnesium supplementation significantly reduces disease severity and joint damage in models of RA. In the present study, we analyzed the transcriptome of spleens and synovial tissues obtained from mice with KRN serum-induced arthritis (KSIA) consuming either a high Mg supplemented diet (Mg2800; n = 7) or a normal diet (Mg500; n = 7). Tissues were collected at the end of a 15-day KSIA experiment. RNA was extracted and used for sequencing and analyses. There was an enrichment of differentially expressed genes (DEGs) belonging to Reactome and Gene Ontology (GO) pathways implicated in RA pathogenesis such as RHO GTPases, the RUNX1 pathway, oxidative stress-induced senescence, and the senescence-associated secretory phenotype. Actc1 and Nr4a3 were among the genes with the highest expression, while Krt79 and Ffar2 were among the genes with the lowest expression in synovial tissues of the Mg2800 group compared with the Mg500 group. Spleens had an enrichment for the metabolism of folate and pterines and the HSP90 chaperone cycle for the steroid hormone receptor. We describe the tissue transcriptomic consequences of arthritis-protecting Mg supplementation in KSIA mice. These results show that oral Mg supplementation may interfere with the response to oxidative stress and senescence and other processes known to participate in RA pathogenesis. We provide new evidence supporting the disease-suppressing effect of increased Mg intake in arthritis and its potential to become a new addition to the therapeutic options for RA and other autoimmune and inflammatory diseases.

Differential proteomic expression in non-functional pituitary neuroendocrine tumours and pituitary glands.

Pituitary neuroendocrine tumours (PitNETs) are common accounting for 10 to 25 % of all intracranial tumours. This project describes the feasibility of developing a novel membrane-based biomarker that could be used for fluorescent guided surgery. The aim was to catalogue the differential expression of membrane proteins between non-functional PitNETs and pituitary glands. Ten pituitary gland tissue specimens were obtained from the National Institute of Health (NIH) NeuroBio-Bank and twenty non-functional PitNETs were obtained from the Northwestern University Nervous System Tumour Bank. Mass spectrometry analysis using an Orbitrap Fusion Lumos Tribrid Mass Spectrometer linked to a Dionex Ultimate 3000 UPLC system was undertaken. Data Dependent Acquisition Mass Spectrometry and Data Independent Acquisition Mass Spectrometry was then completed. Pathway enrichment analysis was performed using clusterProfiler v4.6.0. Functional enrichment analysis was conducted using Gene Ontology terms and Reactome pathways. Differential expression analysis between the two groups revealed a total of 2110 significant differently expressed proteins (DEPs), with 1387 of these also having a Log2 fold change either greater than 1, or less than -1. Of the 2110 DEPs, 925 were upregulated in tumours compared to control, while 1185 were downregulated. We have demonstrated a proteomic comparison between non-functional PitNETs and normal pituitary glands. These results demonstrate differences consistent with contemporary literature but shows that NOTCH3 and PTPRJ are up-regulated in non-functional PitNETs compared to pituitary glands.

Shared Genetic Links Between Sleep, Neurodevelopmental and Neuropsychiatric Conditions: A Genome-Wide and Pathway-Based Polygenic Score Analysis.

Genetic correlations have been reported between chronotype and both autism (AUT) and schizophrenia (SCZ), as well as between insomnia and attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BP), schizophrenia (SCZ) and major depression (MDD). Our study aimed to investigate these shared genetic variations using genome-wide and pathway-based polygenic score analyses. We computed polygenic scores using summary statistics from genome-wide association studies (GWAS) of ADHD (N = 225,534), AUT (N = 46,350), BP (N = 353,899), MDD (N = 500,199) and SCZ (N = 160,779). We tested their performance in predicting chronotype (N = 409,630) and insomnia (N = 239,918) status of UK Biobank participants. For pathway-based polygenic scores, we restricted genetic variation to SNPs that mapped to genes within 1377 Reactome pathways. Genome-wide polygenic scores for AUT, BP, MDD and SCZ were associated with an evening chronotype (p < 2.2 × 10-16, p = 4.8 × 10-3, p = 8.07 × 10-4 and p < 2.2 × 10-16, respectively). Polygenic scores for ADHD, AUT, BP, MDD SCZ were associated with insomnia (p < 2.2 × 10-16, p = 2.93 × 10-3, p = 2.9 × 10-7, p < 2.2 × 10-16 and p = 8.86 × 10-3, respectively). While pathway-based polygenic score analysis identified the KEAP1-NRF2 (p = 1.29 × 10-8) and mRNA Splicing-Minor Pathways (p = 1.52 × 10-8) as enriched for genetic variation overlapping between chronotype and BP, the majority of tested pathways yielded null findings, suggesting that specific shared genetic mechanisms between sleep-related phenotypes and neurodevelopmental/psychiatric conditions (NDPC) may be limited to a subset of pathways. Colocalisation analysis identified BP-associated SNPs in CUL3 and SF3B1 as being linked to changes in their expression. Our results strengthen evidence for shared genetic variation between NDPC and sleep-related phenotypes. We identify the KEAP1-NRF2 and mRNA Splicing-Minor Pathways as potentially mediating the disrupted circadian rhythm phenotype of BP.

Sex Differences in Ependymoma Methylation by Methylation-Defined Subgroup.

Ependymoma is the second most common malignant paediatric brain tumour composed of nine methylation-defined, clinically relevant subgroups. It is unclear if there are sex differences in methylation profiles within these subgroups which could guide future treatment options. We obtained available methylation data from the National Center for Biotechnology Information Gene Expression Omnibus (GEO). Differentially methylated probes (DMPs) between sexes were identified in each ependymoma sample and mapped to genes. Reactome pathways resulting from genes were identified. Survival was estimated for each sex within molecular subgroups. There were 492 cases included in the main analysis: PF-EPN-A (n = 238) PF-EPN-B (n = 52), PF-SE (n = 34), SP-MPE (n = 26), SP-EPN (n = 21), ST-EPN-RELA (n = 87), ST-EPN-YAP1 (n = 13) and ST-SE (n = 21). Females were observed to have better, but statistically nonsignificant, 5-year overall survival (OS) and better, marginally significant 5-year progression-free survival (PFS) than males. One subgroup, ST-EPN-RELA, showed significantly better OS in females. There was a difference in immune cell composition within tumour subgroups. One gene, RFTN1, was consistently differentially methylated by sex among all subgroups. There were biologic pathways identified from genes with differential methylation by sex in the following subgroups: PF-EPN-B, PF-SE, ST-EPN-RELA and ST-EPN-YAP1. Many of the identified pathways may be options for potential therapeutic targets.

The identification of key genes and pathways in polycystic ovary syndrome by bioinformatics analysis of next-generation sequencing data

BackgroundPolycystic ovary syndrome (PCOS) is a reproductive endocrine disorder. The specific molecular mechanism of PCOS remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS.MethodsThe next-generation sequencing (NGS) dataset GSE199225 was downloaded from the gene expression omnibus (GEO) database and NGS dataset analyzed is obtained from in vitro culture of PCOS patients’ muscle cells and muscle cells of healthy lean control women. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the gene ontology (GO) and REACTOME pathways of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed. The hub genes were validated by using receiver operating characteristic (ROC) curve analysis.ResultsWe have identified 957 DEG in total, including 478 upregulated genes and 479 downregulated gene. GO terms and REACTOME pathways illustrated that DEG were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling, and aggregation. The top 5 upregulated hub genes including HSPA5, PLK1, RIN3, DBN1, and CCDC85B and top 5 downregulated hub genes including DISC1, AR, MTUS2, LYN, and TCF4 might be associated with PCOS. The hub gens of HSPA5 and KMT2A, together with corresponding predicted miRNAs (e.g., hsa-mir-34b-5p and hsa-mir-378a-5p), and HSPA5 and TCF4 together with corresponding predicted TF (e.g., RCOR3 and TEAD4) were found to be significantly correlated with PCOS.ConclusionsThese study uses of bioinformatics analysis of NGS data to obtain hub genes and key signaling pathways related to PCOS and its associated complications. Also provides novel ideas for finding biomarkers and treatment methods for PCOS and its associated complications.

MatrixDB 2024: an increased coverage of extracellular matrix interactions, a new Network Explorer and a new web interface.

MatrixDB, a member of the International Molecular Exchange consortium (IMEx), is a curated interaction database focused on interactions established by extracellular matrix (ECM) constituents including proteins, proteoglycans, glycosaminoglycans and ECM bioactive fragments. The architecture of MatrixDB was upgraded to ease interaction data export, allow versioning and programmatic access and ensure sustainability. The new version of the database includes more than twice the number of manually curated and experimentally-supported interactions. High-confidence predicted interactions were imported from the Integrated Interactions Database to increase the coverage of the ECM interactome. ECM and ECM-associated proteins of five species (human, murine, bovine, avian and zebrafish) were annotated with matrisome divisions and categories, which are used for computational analyses of ECM -omic datasets. Biological pathways from the Reactome Pathway Knowledgebase were also added to the biomolecule description. New transcriptomic and expanded proteomic datasets were imported in MatrixDB to generate cell- and tissue-specific ECM networks using the newly developed in-house Network Explorer integrated in the database. MatrixDB is freely available at https://matrixdb.univ-lyon1.fr.

Quantitative proteomic analysis of residual host cell protein retention across adeno-associated virus affinity chromatography

To better understand host cell protein (HCP) retention in adeno-associated virus (AAV) downstream processes, sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) was used to quantitatively profile residual HCPs for four AAV serotypes (AAV2, -5, -8, and -9) produced with HEK293 cells and purified using POROS CaptureSelect AAVX affinity chromatography. A broad range of residual HCPs were detected in affinity eluates after purification (N total = 2,746), and HCP profiles showed universally present species (N universal = 1,117) and species unique to one or more AAV serotype. SWATH-MS revealed that HCP persistence was dominated by high-abundance conserved species (HACS), which appeared across all serotype conditions studied. Due to the notable contribution of these species to overall residual HCP levels, physical and functional characteristics of HACS were examined to determine trends that coincide with persistence. Subnetwork interaction mapping and Gene Ontology function enrichment analysis revealed extensive physical interactions between these proteins and significant enrichment for biological processes, molecular functions, and reactome pathways related to protein folding, nucleic acid binding, and cellular stress. The abundant and conserved nature of these HCPs and their functions offers a new perspective for mechanistic evaluations of impurity retention for AAV downstream processes.

De novo transcriptome analysis of the Indian squid Uroteuthis duvaucelii (Orbigny, 1848) from the Indian Ocean

Cephalopods have dominated the oceans for hundreds of millions of years and are unquestionably at the peak of molluscan evolution. The development of the large brain and a well-sophisticated sensory system contributed significantly to its success. Therefore, it is considered the best example of convergent evolution and attracted the attention of scientists from various disciplines of biology. The aim of the present study is to construct a reference transcriptome in the Indian squid Uroteuthis duvaucelii to gain insights into cephalopod evolution and enrich the existing cephalopod database. Around 72 million short Illumina reads were generated from five different tissues, including the brain, eye, gill, heart and gonads, and assembled using the Trinity assembler. About 26230 protein-coding sequences were annotated from the assembled transcripts. The BUSCO completeness of the assembly was 71.71% compared to the Mollusca_Odb10 gene set. KEGG and REACTOME pathway analyzes revealed that U. duvaucelii shares many genes and pathways with higher vertebrates.