Proteomic Datasets Research Articles

Proteogenomic Identification and Analysis of KIF5B as a Prognostic Signature for Hepatocellular Carcinoma.

Metabolic disorders are significant risk factors for liver cancer, particularly Hepatocellular Carcinoma (HCC). However, the molecular genetic basis of metabolic reprogramming in the liver remains largely uncertain. This study aimed to investigate some novel prognostic biomarkers in HCC by using proteogenomic and transcriptomic analysis and explore the potential role of specific prognostic genes in HCC. Here, we have presented a proteogenomic analysis of 10 pairs of HCC. Protein co-expression and pathway analysis were performed to investigate the biological characteristics of HCC. Protein and mRNA expression profiles of multi-cohorts were integrated to detect novel prognostic protein markers of HCC. The carcinogenic roles of candidate prognostic markers were further evaluated by MTS assay, colony formation, monolayer wound healing assay, and xenograft models. A total of 2086 proteins with significantly different expressions were detected in HCC. Pathways related to oncogenic signaling and insulin-related metabolism have been found to be dysregulated and differentially regulated in HCC. We have identified the novel prognostic biomarkers, KIF5B, involved in liver metabolic reprogramming. The biomarkers were identified using multivariable COX regression analysis from two independent proteomic datasets (Fudan Cohort and our recruited cohort) and the TCGA mRNA database. Both the protein and mRNA up-regulation of KIF5B have been found to be associated with a poor clinical outcome in HCC. Insulin activated the protein expression of KIF5B in HCC. Knocking out KIF5B expression by sgRNA decreased the protein expression of FASN and SCD1 and the intracellular triglyceride concentration. Silencing KIF5B suppressed HCC cell proliferation and colony formation in vitro, as well as HCC growth in xenograft models. Our findings have suggested KIF5B protein to function as a novel prognostic biomarker in HCC. KIF5B expression has been found to activate the AKT/mTOR pathway and reprogram triglyceride metabolism, leading to HCC development. Targeting KIF5B may be an effective strategy in the clinical treatment of HCC.

Deciphering genetic and nongenetic factors underlying tumour dormancy: insights from multiomics analysis of two syngeneic MRD models of melanoma and leukemia

BackgroundTumour dormancy, a resistance mechanism employed by cancer cells, is a significant challenge in cancer treatment, contributing to minimal residual disease (MRD) and potential relapse. Despite its clinical importance, the mechanisms underlying tumour dormancy and MRD remain unclear. In this study, we employed two syngeneic murine models of myeloid leukemia and melanoma to investigate the genetic, epigenetic, transcriptomic and protein signatures associated with tumour dormancy. We used a multiomics approach to elucidate the molecular mechanisms driving MRD and identify potential therapeutic targets.ResultsWe conducted an in-depth omics analysis encompassing whole-exome sequencing (WES), copy number variation (CNV) analysis, chromatin immunoprecipitation followed by sequencing (ChIP-seq), transcriptome and proteome investigations. WES analysis revealed a modest overlap of gene mutations between melanoma and leukemia dormancy models, with a significant number of mutated genes found exclusively in dormant cells. These exclusive genetic signatures suggest selective pressure during MRD, potentially conferring resistance to the microenvironment or therapies. CNV, histone marks and transcriptomic gene expression signatures combined with Gene Ontology (GO) enrichment analysis highlighted the potential functional roles of the mutated genes, providing insights into the pathways associated with MRD. In addition, we compared “murine MRD genes” profiles to the corresponding human disease through public datasets and highlighted common features according to disease progression. Proteomic analysis combined with multi-omics genetic investigations, revealed a dysregulated proteins signature in dormant cells with minimal genetic mechanism involvement. Pathway enrichment analysis revealed the metabolic, differentiation and cytoskeletal remodeling processes involved in MRD. Finally, we identified 11 common proteins differentially expressed in dormant cells from both pathologies.ConclusionsOur study underscores the complexity of tumour dormancy, implicating both genetic and nongenetic factors. By comparing genomic, transcriptomic, proteomic, and epigenomic datasets, our study provides a comprehensive understanding of the molecular landscape of minimal residual disease. These results provide a robust foundation for forthcoming investigations and offer potential avenues for the advancement of targeted MRD therapies in leukemia and melanoma patients, emphasizing the importance of considering both genetic and nongenetic factors in treatment strategies.Graphical

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation.

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

Muscle growth differences in Lijiang pigs revealed by ATAC-seq multi-omics.

As one of the largest tissues in the animal body, skeletal muscle plays a pivotal role in the production and quality of pork. Consequently, it is of paramount importance to investigate the growth and developmental processes of skeletal muscle. Lijiang pigs, which naturally have two subtypes, fast-growing and slow-growing, provide an ideal model for such studies by eliminating breed-related influences. In this study, we selected three fast-growing and three slow-growing 6-month-old Lijiang pigs as subjects. We utilized assay for transposase-accessible chromatin with sequencing (ATAC-seq) combined with genomics, RNA sequencing, and proteomics to screen for differentially expressed genes and transcription factors linked to increased longissimus dorsi muscle volume in Lijiang pigs. We identified 126 genes through ATAC-seq, including PPARA, TNRC6B, NEDD1, and FKBP5, that exhibited differential expression patterns during muscle growth. Additionally, we identified 59 transcription factors, including Foxh1, JunB, Mef2 family members (Mef2a/b/c/d), NeuroD1, and TEAD4. By examining open chromatin regions (OCRs) with significant genetic differentiation, genes such as SAV1, CACNA1H, PRKCG, and FGFR4 were found. Integrating ATAC-seq with transcriptomics and transcriptomics with proteomics, we identified differences in open chromatin regions, transcription, and protein levels of FKBP5 and SCARB2 genes in fast-growing and slow-growing Lijiang pigs. Utilizing multi-omics analysis with R packages, we jointed ATAC-seq, transcriptome, and proteome datasets, identifying enriched pathways related to glycogen metabolism and skeletal muscle cell differentiation. We pinpointed genes such as MYF6 and HABP2 that exhibit strong correlations across these diverse data types. This study provides a multi-faceted understanding of the molecular mechanisms that lead to differences in pig muscle fiber growth.

Interferon gene expression declines over time post-COVID infection and in long COVID patients

Background Interferons (IFNs) represent a first-line defense against viruses and other pathogens. It has been shown that an impaired and uncontrolled release of these glycoproteins can result in tissue damage and explain severe progression of coronavirus disease 2019 (COVID-19). However, their potential role in Long-COVID syndrome (LC) remains debateable. Objectives The objective of the present study is to shed further light on the possible role of IFNs (and related genes) gene expression patterns in the progression of COVID-19 and LC patients. Methods We carried out a multi-cohort study by analyzing the IFN gene expression patterns (using different IFN gene signatures) in five cohorts of acute COVID-19 (n = 541 samples) and LC patients (n = 188), and compared them to patterns observed in three autoimmune diseases (systemic lupus erythematous [n = 242], systemic sclerosis [n = 91], and Sjögren’s syndrome [n = 282]). Results The data show that, while the interferon signatures are strongly upregulated in severe COVID-19 patients and autoimmune diseases, it decays with the time from symptoms onset and in LC patients. Differential pathway analysis of IFN-related terms indicates an over activation in autoimmune diseases (IFN-I/II) and severe COVID-19 (IFN-I/II/III), while these pathways are mostly inactivated or downregulated in LC (IFN-I/III). By analyzing six proteomic LC datasets, we did not find evidence of a role of IFNs in this condition. Conclusion Our findings suggest a potential role of cytokine exhaustion mediated by IFN gene expression inactivation as a possible driver of LC.

Quantitative Aspect of Bacillus subtilis σB Regulatory Network on a Proteome Level-A Computational Simulation.

Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell's response to environmental stress. Experiments have proven that the amounts of free σB (SigB) are controlled by a system of anti- (RsbW) and anti-anti-sigma (RsbV) factors expressed from the same operon as SigB. Moreover, the phosphorylation state of RsbV is controlled by phosphatases RsbP and RsbU, which directly dephosphorylate RsbV. A set of chemical equations describing the network controlling the levels of free SigB was converted to a set of differential equations quantifying the dynamics of the network. The solution of these equations allowed the simulation of the kinetic behavior of the network and its components under real conditions reflected in the time series of protein expression. In this study, the time series of protein expression measured by mass spectrometry were utilized to investigate the role of phosphatases RsbU/RsbP in transmitting the environmental signal. Additionally, the influence of kinetic constants and the amounts of other network components on the functioning of the network was investigated. A comparison with the same simulation performed using a transcriptomic dataset showed that while the time series between the proteomic and transcriptomic datasets are not correlated, the results are the same. This indicates that when modeling is performed within one dataset, it does not matter whether the data come from the mRNA or protein level. In summary, the computational results based on experimental data provide a quantitative insight into the functioning of the SigB-dependent circuit and offer a template for the quantitative study of similar systems.

The Mitogenome of the Haecon-5 Strain of Haemonchus contortus and a Comparative Analysis of Its Nucleotide Variation with Other Laboratory Strains.

Haemonchus contortus (the barber's pole worm)-a highly pathogenic gastric nematode of ruminants-causes significant economic losses in the livestock industry worldwide. H. contortus has become a valuable model organism for both fundamental and applied research (e.g., drug and vaccine discovery) because of the availability of well-defined laboratory strains (e.g., MHco3(ISE).N1 in the UK and Haecon-5 in Australia) and genomic, transcriptomic and proteomic data sets. Many recent investigations have relied heavily on the use of the chromosome-contiguous genome of MHco3(ISE).N1 in the absence of a genome for Haecon-5. However, there has been no genetic comparison of these and other strains to date. Here, we assembled and characterised the mitochondrial genome (14.1 kb) of Haecon-5 and compared it with that of MHco3(ISE).N1 and two other strains (i.e., McMaster and NZ_Hco_NP) from Australasia. We detected 276 synonymous and 25 non-synonymous single nucleotide polymorphisms (SNPs) within Haecon-5. Between the Haecon-5 and MHco3(ISE).N1 strains, we recorded 345 SNPs, 31 of which were non-synonymous and linked to fixed amino acid differences in seven protein-coding genes (nad5, nad6, nad1, atp6, nad2, cytb and nad4) between these strains. Pronounced variation (344 and 435 SNPs) was seen between Haecon-5 and each of the other two strains from Australasia. The question remains as to what impact these mitogenomic mutations might have on the biology and physiology of H. contortus, which warrants exploration. The high degree of mitogenomic variability recorded here among these strains suggests that further work should be undertaken to assess the nature and extent of the nuclear genomic variation within H. contortus.

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning

BackgroundIncreasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain. MethodsWe performed a comprehensive proteomics analysis utilizing the data-independent acquisition (DIA) technique on jejunal tissues from patients with obesity and chronic diarrhea (OD, n = 33), obese patients (OB, n = 10), and healthy controls (n = 8). Differentially expressed proteins (DEPs) in OD vs. control and OD vs. OB comparisons were subjected to pathway enrichment and protein-protein interaction (PPI) network analysis. Machine learning algorithms were adopted on overlapping DEPs in both comparisons. The candidate protein was further validated using Western blot, immunohistochemistry (IHC), and in vitro experiments. ResultsWe identified 189 and 228 DEPs in OD vs. control and OD vs. OB comparisons, respectively. DEPs in both comparisons were co-enriched in extracellular matrix (ECM) organization. Downregulated DEPs were associated with tight junction and ECM-receptor interaction in OD vs. control and OD vs. OB comparisons, respectively. Machine learning algorithms selected 3 proteins from 14 overlapping DEPs in both comparisons, among which collagen alpha-1(III) chain (COL3A1) was identified as a core protein in PPI networks. Western blot and IHC verified the expression of COL3A1. Moreover, the tight junction-related proteins decreased after the knockdown of COL3A1 in Caco2 intestinal cells upon PA challenge, consistent with the proteomics results. ConclusionsWe generated in-depth profiling of a proteomic dataset from samples of OD patients and provided unique insights into disease pathogenesis. COL3A1 was involved in the crosstalk between obesity and intestinal homeostasis via the ECM-receptor interaction pathway.

Community Resource: Large-Scale Proteogenomics to Refine Wheat Genome Annotations.

Triticum aestivum is an important crop whose reference genome (International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.1) offers a valuable resource for understanding wheat genetic structure, improving agronomic traits, and developing new cultivars. A key aspect of gene model annotation is protein-level evidence of gene expression obtained from proteomics studies, followed up by proteogenomics to physically map proteins to the genome. In this research, we have retrieved the largest recent wheat proteomics datasets publicly available and applied the Basic Local Alignment Search Tool (tBLASTn) algorithm to map the 861,759 identified unique peptides against IWGSC RefSeq v2.1. Of the 92,719 hits, 83,015 unique peptides aligned along 33,612 High Confidence (HC) genes, thus validating 31.4% of all wheat HC gene models. Furthermore, 6685 unique peptides were mapped against 3702 Low Confidence (LC) gene models, and we argue that these gene models should be considered for HC status. The remaining 2934 orphan peptides can be used for novel gene discovery, as exemplified here on chromosome 4D. We demonstrated that tBLASTn could not map peptides exhibiting mid-sequence frame shift. We supply all our proteogenomics results, Galaxy workflow and Python code, as well as Browser Extensible Data (BED) files as a resource for the wheat community via the Apollo Jbrowse, and GitHub repositories. Our workflow could be applied to other proteomics datasets to expand this resource with proteins and peptides from biotically and abiotically stressed samples. This would help tease out wheat gene expression under various environmental conditions, both spatially and temporally.

Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia.

Chronic Lymphocytic Leukemia (CLL) is the most common B-cell malignancy in the Western world, characterized by frequent relapses despite temporary remissions. Our study integrated publicly available proteomic, transcriptomic, and patient survival datasets to identify key differences between healthy and CLL samples. We exposed approximately 1000 proteins that differentiate healthy from cancerous cells, with 608 upregulated and 415 downregulated in CLL cases. Notable upregulated proteins include YEATS2 (an epigenetic regulator), PIGR (Polymeric immunoglobulin receptor), and SNRPA (a splicing factor), which may serve as prognostic biomarkers for this disease. Key pathways implicated in CLL progression involve RNA processing, stress resistance, and immune response deficits. Furthermore, we identified three existing drugs-Bosutinib, Vorinostat, and Panobinostat-for potential further investigation in drug repurposing in CLL. We also found limited correlation between transcriptomic and proteomic data, emphasizing the importance of proteomics in understanding gene expression regulation mechanisms. This generally known disparity highlights once again that mRNA levels do not accurately predict protein abundance due to many regulatory factors, such as protein degradation, post-transcriptional modifications, and differing rates of translation. These results demonstrate the value of integrating omics data to uncover deregulated proteins and pathways in cancer and suggest new therapeutic avenues for CLL.

Proteomic Characterisation of Heart Failure Reveals a Unique Molecular Phenotype for Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a disease, which is difficult to diagnose at an early stage and for which there is a pressing need for more effective treatment options. The purpose of this study was to compare the molecular profile of HCM to that of ischaemic cardiomyopathy (ISCM) and dilated cardiomyopathy (DCM) for identification of protein and pathway targets that could support the development of better diagnostic and treatment options for HCM. A high-throughput mass spectrometry workflow was applied to achieve deep quantitative coverage of left ventricular tissue from HCM, DCM, ISCM and non-heart-failure control patients. HCM had a diverse proteomic profile compared to that of DCM and ISCM. Differentially expressed proteins unique to HCM were identified based on an observed fold change of ≥1.5 or ≤0.67 and q-value ≤ 0.05. Candidate proteins of interest were found to be significantly associated with clinical features of HCM. The significant association between these proteins and HCM was validated in an independent dataset. This represents one of the largest and deepest proteomic datasets for myocardial tissue reported to date. The dataset highlights the diverse proteomic profile of HCM, relative to other cardiomyopathies, and reveals disease-relevant pathways and promising biomarker candidates that are uniquely associated with HCM.

BBSdb, an open resource for bacterial biofilm-associated proteins.

Bacterial biofilms are organized heterogeneous assemblages of microbial cells encased within a self-produced matrix of exopolysaccharides, extracellular DNA and proteins. Over the last decade, more and more biofilm-associated proteins have been discovered and investigated. Furthermore, omics techniques such as transcriptomes, proteomes also play important roles in identifying new biofilm-associated genes or proteins. However, those important data have been uploaded separately to various databases, which creates obstacles for biofilm researchers to have a comprehensive access to these data. In this work, we constructed BBSdb, a state-of-the-art open resource of bacterial biofilm-associated protein. It includes 48 different bacteria species, 105 transcriptome datasets, 21 proteome datasets, 1205 experimental samples, 57,823 differentially expressed genes (DEGs), 13,605 differentially expressed proteins (DEPs), 1,930 'Top 5% differentially expressed genes', 444 'Threshold-based DEGs' and a predictor for prediction of biofilm-associated protein. In addition, 1,781 biofilm-associated proteins, including annotation and sequences, were extracted from 942 articles and public databases via text-mining analysis. We used E. coli as an example to represent how to explore potential biofilm-associated proteins in bacteria. We believe that this study will be of broad interest to researchers in field of bacteria, especially biofilms, which are involved in bacterial growth, pathogenicity, and drug resistance. Availability and implementation: The BBSdb is freely available at http://124.222.145.44/#!/.

Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson’s disease

Parkinson’s disease is highly heterogeneous across disease symptoms, clinical manifestations and progression trajectories, hampering the identification of therapeutic targets. Despite knowledge gleaned from genetics analysis, dysregulated proteome mechanisms stemming from genetic aberrations remain underexplored. In this study, we develop a three-phase system-level proteogenomic analytical framework to characterize disease-associated proteins and dysregulated mechanisms. Proteogenomic analysis identified 577 proteins that enrich for Parkinson’s disease-related pathways, such as cytokine receptor interactions and lysosomal function. Converging lines of evidence identified nine proteins, including LGALS3, CSNK2A1, SMPD3, STX4, APOA2, PAFAH1B3, LDLR, HSPB1, BRK1, with potential roles in disease pathogenesis. This study leverages the largest population-scale proteomics dataset, the UK Biobank Pharma Proteomics Project, to characterize genetically-driven protein disturbances associated with Parkinson’s disease. Taken together, our work contributes to better understanding of genome-proteome dynamics in Parkinson’s disease and sets a paradigm to identify potential indirect mediators connected to GWAS signals for complex neurodegenerative disorders.

Distinct functional and molecular profiles between physiological and pathological atrial enlargement offer potential new therapeutic opportunities for atrial fibrillation.

Atrial fibrillation (AF) remains challenging to prevent and treat. A key feature of AF is atrial enlargement. However, not all atrial enlargement progresses to AF. Atrial enlargement in response to physiological stimuli such as exercise is typically benign and reversible. Understanding the differences in atrial function and molecular profile underpinning pathological and physiological atrial remodelling will be critical for identifying new strategies for AF. The discovery of molecular mechanisms responsible for pathological and physiological ventricular hypertrophy has uncovered new drug targets for heart failure. Studies in the atria have been limited in comparison. Here, we characterised mouse atria from (1) a pathological model (cardiomyocyte-specific transgenic (Tg) that develops dilated cardiomyopathy [DCM] and AF due to reduced protective signalling [PI3K]; DCM-dnPI3K), and (2) a physiological model (cardiomyocyte-specific Tg with an enlarged heart due to increased insulin-like growth factor 1 receptor; IGF1R). Both models presented with an increase in atrial mass, but displayed distinct functional, cellular, histological and molecular phenotypes. Atrial enlargement in the DCM-dnPI3K Tg, but not IGF1R Tg, was associated with atrial dysfunction, fibrosis and a heart failure gene expression pattern. Atrial proteomics identified protein networks related to cardiac contractility, sarcomere assembly, metabolism, mitochondria, and extracellular matrix which were differentially regulated in the models; many co-identified in atrial proteomics data sets from human AF. In summary, physiological and pathological atrial enlargement are associated with distinct features, and the proteomic dataset provides a resource to study potential new regulators of atrial biology and function, drug targets and biomarkers for AF.

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level

Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically heterogenous. The application of omics technologies—mainly studying nucleic acids—has significantly improved MB classification and stratification, but treatment options are still unsatisfactory. The proteome and their N-glycans hold the potential to discover clinically relevant phenotypes and targetable pathways. We compile a harmonized proteome dataset of 167 MBs and integrate findings with DNA methylome, transcriptome and N-glycome data. We show six proteome MB subtypes, that can be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pG3myc), and synapses/immunological processes (pSHHs, pG3 and pG4). Multiomic analysis reveals different conservation levels of proteome features across MB subtypes at the DNA methylome level. Aggressive pGroup3myc MBs and favorable pWNT MBs are most similar in cluster hierarchies concerning overall proteome patterns but show different protein abundances of the vincristine resistance-associated multiprotein complex TriC/CCT and of N-glycan turnover-associated factors. The N-glycome reflects proteome subtypes and complex-bisecting N-glycans characterize pGroup3myc tumors. Our results shed light on targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.

Integration analysis of pituitary proteome and transcriptome reveals fertility-related biomarkers in FecB mutant Small Tail Han sheep.

The Booroola fecundity mutation (FecB) in Small Tail Han sheep has been shown to enhance ovulation rates and litter sizes by affecting the hypothalamic-pituitary-gonadal (HPG) axis. Despite the pituitary's role in reproductive regulation, its involvement in FecB-induced ovulation remains understudied. Our study aimed to fill this gap by analyzing pituitary tissues from FecB homozygous (BB) and wild-type (WW) ewes during luteal and follicular phases using tandem mass tag-based protein quantification and the DIABLO framework for proteomic and transcriptomic data integration. Significant differences in 277 proteins were observed across estrus periods, with network analysis highlighting the voltage-dependent calcium channel L-type alpha-1C as a key convergence point in oxytocin signaling and GnRH secretion pathways. The DIABLO method revealed a strong correlation (0.98) between proteomic and transcriptomic datasets, indicating a coordinated response in FecB ewes. Notably, higher expression levels of Follicle Stimulating Hormone Subunit Beta (FSHB) and Luteinizing Hormone Subunit Beta (LHB) were found in BB ewes during the follicular phase, potentially due to elevated E2 concentrations. Furthermore, our analysis identified genes related to the Gamma-aminobutyric acid type A receptor family (GABRA2, GABRG1, GABRB1) in the pituitary, with GABRB1 showing higher expression in BB ewes. This suggests a role for GABA in modulating GnRH and gonadotropin feedback loops, potentially contributing to the FecB mutation's effect on ovulation. This study provides novel insights into the pituitary's role in fertility among FecB sheep, identifying GABA as a potential regulatory factor within the HPG axis. The findings also open avenues for discovering new biomarkers in pituitary endocrinology for sheep breeding purposes.

Secretome analysis of oligodendrocytes and precursors reveals their roles as contributors to the extracellular matrix and potential regulators of inflammation.

Oligodendrocytes form myelin that ensheaths axons and accelerates the speed of action potential propagation. Oligodendrocyte progenitor cells (OPCs) proliferate and replenish oligodendrocytes. While the myelin-forming role of oligodendrocytes and OPCs is well-established, potential additional roles of these cells are yet to be fully explored. Here, we analyzed the secreted proteome of oligodendrocytes and OPCs in vitro to determine whether these cell types are major sources of secreted proteins in the central nervous system (CNS). Interestingly, we found that both oligodendrocytes and OPCs secret various extracellular matrix proteins. Considering the critical role of neuroinflammation in neurological disorders, we evaluated the responses and potential contributions of oligodendrocytes and OPCs to this process. By characterizing the secreted proteomes of these cells after pro-inflammatory cytokine treatment, we discovered the secretion of immunoregulators such as C2 and B2m. This finding sheds new light on the hitherto underappreciated role of oligodendrocytes and OPCs in actively modulating neuroinflammation. Our study provides a comprehensive and unbiased proteomic dataset of proteins secreted by oligodendrocyte and OPC under both physiological and inflammatory conditions. It revealed the potential of these cells to secrete matrix and signaling molecules, highlighting their multifaceted function beyond their conventional myelin-forming roles.

Differences in the cerebral amyloid angiopathy proteome in Alzheimer’s disease and mild cognitive impairment

Cerebral amyloid angiopathy (CAA) is characterized by amyloid beta (Aβ) deposition in cerebrovasculature. It is prevalent with aging and Alzheimer’s disease (AD), associated with intracerebral hemorrhage, and contributes to cognitive deficits. To better understand molecular mechanisms, CAA(+) and CAA(−) vessels were microdissected from paraffin-embedded autopsy temporal cortex of age-matched Control (n = 10), mild cognitive impairment (MCI; n = 4), and sporadic AD (n = 6) cases, followed by label-free quantitative mass spectrometry. 257 proteins were differentially abundant in CAA(+) vessels compared to neighboring CAA(−) vessels in MCI, and 289 in AD (p < 0.05, fold-change > 1.5). 84 proteins changed in the same direction in both groups, and many changed in the same direction among proteins significant in at least one group (p < 0.0001, R2 = 0.62). In CAA(+) vessels, proteins significantly increased in both AD and MCI were particularly associated with collagen-containing extracellular matrix, while proteins associated with ribonucleoprotein complex were significantly decreased in both AD and MCI. In neighboring CAA(−) vessels, 61 proteins were differentially abundant in MCI, and 112 in AD when compared to Control cases. Increased proteins in CAA(−) vessels were associated with extracellular matrix, external encapsulating structure, and collagen-containing extracellular matrix in MCI; collagen trimer in AD. Twenty two proteins were increased in CAA(−) vessels of both AD and MCI. Comparison of the CAA proteome with published amyloid-plaque proteomic datasets identified many proteins similarly enriched in CAA and plaques, as well as a protein subset hypothesized as preferentially enriched in CAA when compared to plaques. SEMA3G emerged as a CAA specific marker, validated immunohistochemically and with correlation to pathology levels (p < 0.0001; R2 = 0.90). Overall, the CAA(−) vessel proteomes indicated changes in vessel integrity in AD and MCI in the absence of Aβ, and the CAA(+) vessel proteome was similar in MCI and AD, which was associated with vascular matrix reorganization, protein translation deficits, and blood brain barrier breakdown.

Molecular overlaps of neurological manifestations of COVID-19 and schizophrenia from a proteomic perspective.

COVID-19, a complex multisystem disorder affecting the central nervous system, can also have psychiatric sequelae. In addition, clinical evidence indicates that a diagnosis of a schizophrenia spectrum disorder is a risk factor for mortality in patients with COVID-19. In this study, we aimed to explore brain-specific molecular aspects of COVID-19 by using a proteomic approach. We analyzed the brain proteome of fatal COVID-19 cases and compared it with differentially regulated proteins found in postmortem schizophrenia brains. The COVID-19 proteomic dataset revealed a strong enrichment of proteins expressed by glial and neuronal cells and processes related to diseases with a psychiatric and neurodegenerative component. Specifically, the COVID-19 brain proteome enriches processes that are hallmark features of schizophrenia. Furthermore, we identified shared and distinct molecular pathways affected in both conditions. We found that brain ageing processes are likely present in both COVID-19 and schizophrenia, albeit possibly driven by distinct processes. In addition, alterations in brain cell metabolism were observed, with schizophrenia primarily impacting amino acid metabolism and COVID-19 predominantly affecting carbohydrate metabolism. The enrichment of metabolic pathways associated with astrocytic components in both conditions suggests the involvement of this cell type in the pathogenesis. Both COVID-19 and schizophrenia influenced neurotransmitter systems, but with distinct impacts. Future studies exploring the underlying mechanisms linking brain ageing and metabolic dysregulation may provide valuable insights into the complex pathophysiology of these conditions and the increased vulnerability of schizophrenia patients to severe outcomes.