Protein-protein Network Research Articles

A statistical mechanics investigation of unfolded protein response across organisms.

Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated protein-protein interaction networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.

Causal association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Abstract Aim Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. We aimed to investigate the causality between mineral metabolism and CAVS. Methods A systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis was applied to investigate the causal effect. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n=374,277 and 653,867). Results In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery=3.081 (1.649-5.760), Pdiscovery=4.21×10-4; ORreplication=2.280 (1.461-3.558), Preplication=2.82×10-4] without evidence of reverse causation (Psteiger=7.21×10-98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS causative genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P>0.05/5=0.01). Conclusions Elevated serum FGF23 level is a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies.

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue.

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

A novel investigation of NANOG and POU5F1 associations in the pluripotent characterization of ES-like and epiblast cells

The transcription factors NANOG and POU5F1 (OCT4) play crucial roles in maintaining pluripotency in embryonic stem (ES) cells. While their functions have been well-studied, the specific interactions between NANOG and POU5F1 and their combined effects on pluripotency in ES-like and Epiblast cells remain less understood. Understanding these associations is vital for refining pluripotent stem cell characterization and advancing regenerative medicine. In this matter, we investigated the associations between NANOG and POU5F1 in maintaining pluripotency in ES-like and Epiblast cells and how these interactions contribute to the distinct pluripotent states of these cells. In the present paper, we examined the pattern of NANOG expression by the immunocytochemical method in embryonic stem-like (ES-like) cells and compared it with its expression pattern in embryonic stem cells (ESCs). Similarly, we examined the expression pattern of POU5F1 in ES-like cells, ESCs, and epiblast cells and compared the expression pattern of these two genes with each other. On the other hand, using Fluidigm Biomark system analysis, we compared the amount of NANOG mRNA in these three cell lines and differentiated and undifferentiated Spermatogonial stem cells in several passages. Microscopic observations indicated the cytoplasmic expression of NANOG in the considered cells; moreover, they showed a similar expression pattern of NANOG with POU5F1 in the experimented cells. It has also been suggested that the more limited the cell’s pluripotency, the lower the expression of these two genes. However, the decrease in NANOG expression is less than that of POU5F1. Fluidigm real-time RT-PCR analysis also confirmed these results. During the experimental process, protein-protein (PPI) network analysis shows a significant association of NANOG with other stem cell proteins, such as POU5F1. Our findings reveal distinct yet overlapping roles of NANOG and POU5F1 in maintaining pluripotency in ES-like and Epiblast cells. The differential binding patterns and functional interactions between these factors underscore the complexity of pluripotency regulation in different stem cell states. This study provides new insights into the molecular mechanisms governing pluripotency and highlights potential targets for enhancing stem cell-based therapies.

Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis.

Molecular mechanisms of the anticancer action of fustin isolated from Cotinus coggygria Scop. in MDA-MB-231 triple-negative breast cancer cell line.

The aim of the present work was to investigate some of the molecular mechanisms and targets of the anticancer action of the bioflavonoid fustin isolated from theheartwood of Cotinus coggygria Scop. in the triple-negative breast cancer cell line MDA-MB-231. For this purpose, we applied fluorescence microscopy analysis to evaluate apoptosis, necrosis, and mitochondrial integrity, wound healing assay to study fustin antimigratory potential and quantitative reverse transcription-polymerase chain reaction to analyze the expression of genes associated with cell cycle control, programmed cell death, metastasis, and epigenetic alterations. A complex network-based bioinformatic analysis was also employed for protein-protein network construction, hub genes identification, and functional enrichment. The results revealed a significant induction of early and late apoptotic and necrotic events, a slight alteration of the mitochondria-related fluorescence, and marked antimotility effect after fustin treatment. Of 34 analyzed genes, seven fustin targets were identified, of which CDKN1A, ATM, and MYC were significantly enriched in pathways such as cell cycle, intrinsic apoptotic signaling pathway in response to DNA damage and generic transcription pathway. Our findings outline some molecular mechanisms of the anticancer action of fustin pointing it out as a potential oncotherapeutic agent and provide directions for future invivo research.

A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma.

Mucinous ovarian carcinoma (MOC) is a subtype of ovarian cancer that is distinct from all other ovarian cancer subtypes and currently has no targeted therapies. To identify novel therapeutic targets, we developed and applied a new method of differential network analysis comparing MOC to benign mucinous tumours (in the absence of a known normal tissue of origin). This method mapped the protein-protein network in MOC and then utilised structural bioinformatics to prioritise the proteins identified as upregulated in the MOC network for their likelihood of being successfully drugged. Using this protein-protein interaction modelling, we identified the strongest 5 candidates, CDK1, CDC20, PRC1, CCNA2 and TRIP13, as structurally tractable to therapeutic targeting by small molecules. siRNA knockdown of these candidates performed in MOC and control normal fibroblast cell lines identified CDK1, CCNA2, PRC1 and CDC20, as potential drug targets in MOC. Three targets (TRIP13, CDC20, CDK1) were validated using known small molecule inhibitors. Our findings demonstrate the utility of our pipeline for identifying new targets and highlight potential new therapeutic options for MOC patients.

Single-cell RNA-sequencing Analysis Reveals the Promoting Role of the Hedgehog Pathway in Epithelial Cells during Cervical Cancer Progression.

This study explored the role of the Hedgehog pathway in epithelial cells during cervical cancer [CC] progression, providing new insights for improving current CC treatment. Abnormal activation of the Hedgehog signaling pathway is associated with the malignant transformation of CC epithelial cells. Single-cell atlas of CC and the role of Hedgehog pathway in epithelial cells during CC progression remain to be explored. To comprehensively analyze the mechanism of Hedgehog pathway activation in CC epithelial cells and its impact on tumor progression by applying single-cell RNA sequencing [scRNA-seq] analysis. The scRNA-seq data were acquired from the Gene Expression Omnibus [GEO] database and then processed with the Seurat package. FindNeighbors and Find- Clusters functions were applied to cluster the cells. The CellMarker database was used for subgroup annotation. Differentially expressed genes [DEGs] in each cell subgroup were filtered by FindAllMarkers function. Biological function analysis for the gene set of interest was performed using Clusterprofiler package. AUCell function was employed to calculate the score of the Hedgehog pathway. The differentiation trajectory in epithelial cell subtypes was generated by performing Pseudotime analysis. Finally, protein-protein network [PPI] was used to investigate the interactions between the Hedgehog pathway and other pathways enriched in the gene set of interest. A total of 9 major cell subpopulations were classified and the proportion of epithelial cells was the highest in CC samples. Further analysis revealed that the Hedgehog pathway was abnormally activated in STYK1+ and TP73+ epithelial cell subtypes. Pseudo-time trajectory analysis showed that the differentiation trajectory of STYK1+ epithelial cells gradually transformed into defense-to-virus cells or into proliferation cells, while TP73+ epithelial cells eventually differentiated into two branches of response to estrogen and virus-induced proliferation. PPI analysis showed that the Hedgehog pathway was involved in the proliferation and viral process of epithelial cells in CC. The current study comprehensively analyzed the features of CC samples and differentiation trajectories of epithelial cell subtypes, as well as the role of the Hedgehog pathway in epithelial cells during CC progression. More importantly, effective target genes were discovered for the molecular diagnosis and precise treatment of CC.

Genetic association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. In this study, we employed a systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis to investigate the causal effect of mineral metabolism on CAVS. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n = 374,277 and 653,867). In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery = 3.081 (1.649–5.760), Pdiscovery = 4.21 × 10−4; ORreplication = 2.280 (1.461 – 3.558), Preplication = 2.82 × 10−4] without evidence of reverse causation (Psteiger= 7.21 × 10−98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS-associated genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P > 0.05/5 = 0.01). In conclusion, elevated serum FGF23 level may act as a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Thunbergia's Flowers Secondary Metabolites a Natural Armor Against Kidney Damage by Diclofenac.

Nephrotoxicity is a common side effect arising from exposure to drugs or toxins. The study investigates the therapeutic effects of Thunbergia alata and Thunbergia erecta flowers on diclofenac-induced renal injury. Secondary metabolite characterization by positive mode high-resolution-ESI (LC-HR-ESI-MS) was followed by assessing their renal protection against diclofenac-induced damage and molecular docking studies. Using positive LC-HR-ESI-MS, 18 compounds from T. erecta and T. alata were identified. Diclofenac administration induced significant deterioration of all parameters in the kidney in addition to renal tissue contents of several inflammatory markers. The flower extracts of T. alata and T. erecta showed a clear improvement in the treated groups compared to the diclofenac-control group. The results were confirmed by histopathological examinations followed by immunohistochemical determination of vascular endothelial growth factor (VEGF), nuclear factor erythroid 2-related factor 2 (Nrf2), and transforming growth factor beta 1 (TGF-β1) expression. Furthermore, a protein-protein network to understand the complex interplay between the target proteins and their counterparts was done in addition to a molecular docking study of the de-replicated compounds in the active sites of NF-κB, TGF-β1, and VEGFR.

Core microbiome-associated proteins associated with ulcerative colitis interact with cytokines for synergistic or antagonistic effects on gut bacteria.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is associated with a loss or an imbalance of host-microorganism interactions. However, such interactions at protein levels remain largely unknown. Here, we applied a depletion-assisted metaproteomics approach to obtain in-depth host-microbiome association networks of IBD, where the core host proteins shifted from those maintaining mucosal homeostasis in controls to those involved in inflammation, proteolysis, and intestinal barrier in IBD. Microbial nodes such as short-chain fatty-acid producer-related host-microbial crosstalk were lost or suppressed by inflammatory proteins in IBD. Guided by protein-protein association networks, we employed proteomics and lipidomics to investigate the effects of UC-related core proteins S100A8, S100A9, and cytokines (IL-1β, IL-6, and TNF-α) on gut bacteria. These proteins suppressed purine nucleotide biosynthesis in stool-derived in vitro communities, which was also reduced in IBD stool samples. Single species study revealed that S100A8, S100A9, and cytokines can synergistically or antagonistically alter gut bacteria intracellular and secreted proteome, with combined S100A8 and S100A9 potently inhibiting beneficial Bifidobacterium adolescentis. Furthermore, these inflammatory proteins only altered the extracellular but not intracellular proteins of Ruminococcus gnavus. Generally, S100A8 induced more significant bacterial proteome changes than S100A9, IL-1β, IL-6, and TNF-α but gut bacteria degrade significantly more S100A8 than S100A9 in the presence of both proteins. Among the investigated species, distinct lipid alterations were only observed in Bacteroides vulgatus treated with combined S100A8, S100A9, and cytokines. These results provided a valuable resource of inflammatory protein-centric host-microbial molecular interactions.

An integrated approach to identifying sex-specific genes, transcription factors, and pathways relevant to Alzheimer's disease

BackgroundAge represents a significant risk factor for the development of Alzheimer's disease (AD); however, recent research has documented an influencing role of sex in several features of AD. Understanding the impact of sex on specific molecular mechanisms associated with AD remains a critical challenge to creating tailored therapeutic interventions. MethodsThe exploration of the sex-based differential impact on disease (SDID) in AD used a systematic review to first select transcriptomic studies of AD with data regarding sex in the period covering 2002 to 2021 with a focus on the primary brain regions affected by AD - the cortex (CT) and the hippocampus (HP). A differential expression analysis for each study and two tissue-specific meta-analyses were then performed. Focusing on the CT due to the presence of significant SDID-related alterations, a comprehensive functional characterization was conducted: protein-protein network interaction and over-representation analyses to explore biological processes and pathways and a VIPER analysis to estimate transcription factor activity. ResultsWe selected 8 CT and 5 HP studies from the Gene Expression Omnibus (GEO) repository for tissue-specific meta-analyses. We detected 389 significantly altered genes in the SDID comparison in the CT. Generally, female AD patients displayed more affected genes than males; we grouped said genes into six subsets according to their expression profile in female and male AD patients. Only subset I (repressed genes in female AD patients) displayed significant results during functional profiling. Female AD patients demonstrated more significant impairments in biological processes related to the regulation and organization of synapsis and pathways linked to neurotransmitters (glutamate and GABA) and protein folding, Aβ aggregation, and accumulation compared to male AD patients. These findings could partly explain why we observe more pronounced cognitive decline in female AD patients. Finally, we detected 23 transcription factors with different activation patterns according to sex, with some associated with AD for the first time. All results generated during this study are readily available through an open web resource Metafun-AD (https://bioinfo.cipf.es/metafun-ad/). ConclusionOur meta-analyses indicate the existence of differences in AD-related mechanisms in female and male patients. These sex-based differences will represent the basis for new hypotheses and could significantly impact precision medicine and improve diagnosis and clinical outcomes in AD patients.

O-265 Mapping decidualization resistance at multi-omic level

Abstract Study question What are the cellular and molecular features of decidualization resistance (DR) at single-cell resolution? Summary answer There is an absence of the stromal-epithelial transition with aberrant crosstalk among cell types in essential pathways (e.g., endoglin, WNT) confirmed at the protein level. What is known already DR refers to the inability of the endometrium to undergo decidualization changes preparing the endometrium for a conceptus, noted in disorders like endometriosis, recurrent pregnancy loss, miscarriage and preeclampsia. The molecular mechanisms causing this disorder remains unknown. It has been demonstrated that there is a transcriptomic signature of DR in the endometrium of former preeclampsia patients, during and years after suffering from the disease. Our objective is to dilucidated what is DR at single-cell resolution. Study design, size, duration This cross-sectional study compares late secretory phase endometrial biopsies from patients that previously have suffered severe Preeclampsia (sPE). The sample size consisted of 11 sPE patients and 12 control participants for single-cell RNA sequencing (scRNA-seq), 8 sPE patients and 8 controls for spatial transcriptomic mapping covering 95 tissue regions, and 10 sPE patients and 10 controls for proteomic analysis. The study spanned 2 years, from sample collection to omics analysis. Participants/materials, setting, methods Late secretory phase endometrial biopsies from sPE patients and controls underwent molecular analyses. Methods included 10x Genomics protocol for single-cell RNA sequencing, MiloR to calculate cell abundance, CellChat to infer cell-to-cell communication and ClusterProfiler for gene ontology. We solved spatial mapping at transcriptomic level using Nanostring. Proteomics data was collected by laser-capture microdissection coupled mass spectrometry. Protein-protein networks, involving PR and ESR1 receptors as master regulators of decidualization, were built using String. Main results and the role of chance Initial morphological analyses of endometrial biopsies collected during the late secretory phase revealed gross anatomical defects in glandular morphology that were histologically confirmed. scRNA-seq revealed sPE-associated alterations in cell composition involving stromal and epithelial subtypes, denoting a higher proportion of cells expressing proliferative markers in the stroma (MMP11, SFRP1) and epithelium (IHH, EMID), and a near absent epithelial-stromal transition that is normally linked to decidualization. Further, abnormal abundance and gene dysregulation were detected in the three decidualized stages identified. Mapping cell-cell communication networks confirmed aberrant crosstalk among cell types involved in essential pathways (e.g., endoglin, WNT, SPP1). Spatial transcriptomic in a replication cohort of samples revealed a landscape of dysregulated genes in stromal, luminal and glandular enriched regions, corroborating sPE-associated DR. Laser capture microdissection/mass spectrometry of samples revealed differential protein expression at a global level and confirmed many of the scRNA-seq results, such as sPE-exclusive proteins related to proliferation and the absence of a stromal to epithelial transition at a pathway level. These data lend insights into potential molecular mechanisms underlying the pathogenesis of DR. Limitations, reasons for caution DR analysed in this work is associated with sPE but we do not know if this decidualization defect would be the same in other DR associated conditions such as endometriosis, recurrent pregnancy loss, or miscarriage. Wider implications of the findings Here we present the most comprehensive snapshot of DR to date, providing a full range of markers potentially responsible for this decidualization disorder linked to sPE. We concluded that stromal and epithelial cell populations are altered revealing a near absence of the stromal-epithelial transition, cellular crosstalk and glandular constitution. Trial registration number not aplicable

Designing an mRNA Vaccine against P. jirovecii Involved in Fatal Pneumonia Infections via Comparative Proteomics and Reverse Vaccinology Approaches.

Pneumocystis jirovecii is the most emerging life-threating health problem that causes acute and fatal pneumonia infection. It is rare and more contagious for patients with leukemia and immune-deficiency disorders. Until now there is no treatment available for this infection therefore, it is needed to develop any treatment against this pathogen. In this work, we used comparative proteomics, robust immune-informatics, and reverse vaccinology to create an mRNA vaccine against Pneumocystis jirovecii by targeting outer and transmembrane proteins. Using a comparative subtractive proteomic analysis of two Pneumocystis jirovecii proteomes, a distinct non-redundant Pneumocystis jirovecii (strain SE8) proteome was chosen. Seven Pneumocystis jirovecii transmembrane proteins were chosen from this proteome based on hydrophilicity, essentiality, virulence, antigenicity, pathway interaction, protein-protein network analysis, and allergenicity. The reverse vaccinology approach was used to predict the immunogenic and antigenic epitopes of major histocompatibility complex (MHC) I, II and B-cells from the selected proteins on the basis of their antigenicity, toxicity and allergenicity. These immunogenic epitopes were linked together to construct the mRNA-based vaccine. To enhance the immunogenicity, suitable adjuvant, linkers (GPGPG, KK, and CYY), and PRDRE sequences were used. Through predictive modeling and confirmation via the Ramachandran plot, we assessed secondary and 3D structures. The adjuvant RpfE was incorporated to enhance the vaccine construct's immunogenicity (GRAVY index: -0.271, instability index: 39.53, antigenicity: 1.0428). The physiochemical profiling of vaccine construct was predicted it an antigenic, efficient, and potential vaccine. Notably, strong interactions were observed between the vaccine construct and TLR-3/TLR-4 (-1301.7 kcal/mol-1 and -1374.7 kcal/mol-1). The results predicted that mRNA-based vaccines trigger a cellular and humoral immune response, making the vaccine potential candidate against Pneumocystis jirovecii and it is more suitable for in-vitro analysis and validation to prove its effectiveness.

Quantitative analysis of the lysine acetylome reveals the role of SIRT3-mediated HSP60 deacetylation in suppressing intracellular Mycobacterium tuberculosis survival.

Protein acetylation is crucial for the onset, development, and outcome of tuberculosis (TB). Our study comprehensively investigated the dynamics of lysine acetylation during M. tb infection, shedding light on the intricate host-pathogen interactions that underlie the pathogenesis of tuberculosis. Using an advanced quantitative lysine proteomics approach, different profiles of acetylation sites and proteins in macrophages infected with M. tb were identified. Functional enrichment and protein-protein network analyses revealed significant associations between acetylated proteins and key cellular pathways, highlighting their critical role in the host response to M. tb infection. Furthermore, the deacetylation of HSP60 and its influence on macrophage-mediated clearance of M. tb underscore the functional significance of acetylation in tuberculosis pathogenesis. In conclusion, this study provides valuable insights into the regulatory mechanisms governing host immune responses to M. tb infection and offers promising avenues for developing novel therapeutic interventions against TB.

Precision Drug Repurposing: A Deep Learning Toolkit for Identifying 34 Hyperpigmentation-Associated Genes and Optimizing Treatment Selection.

Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offered. However, these interventions are not devoid of risks and are associated with postinflammatory hyperpigmentation. In the quest for novel therapeutic potentials, this study aims to investigate computational methods in the identification of new targeted therapies in the treatment of hyperpigmentation. We used a comprehensive approach, which integrated text mining, interpreting gene lists through enrichment analysis and integration of diverse biological information (GeneCodis), protein-protein association networks and functional enrichment analyses (STRING), and plug-in network centrality parameters (Cytoscape) to pinpoint genes closely associated with hyperpigmentation. Subsequently, analysis of drug-gene interactions to identify potential drugs (Cortellis) was utilized to select drugs targeting these identified genes. Lastly, we used Deep Learning Based Drug Repurposing Toolkit (DeepPurpose) to conduct drug-target interaction predictions to ultimately identify candidate drugs with the most promising binding affinities. Thirty-four hyperpigmentation-related genes were identified by text mining. Eight key genes were highlighted by utilizing GeneCodis, STRING, Cytoscape, gene enrichment, and protein-protein interaction analysis. Thirty-five drugs targeting hyperpigmentation-associated genes were identified by Cortellis, and 29 drugs, including 16 M2PK1 inhibitors, 11 KRAS inhibitors, and 2 BRAF inhibitors were recommended by DeepPurpose. The study highlights the promise of advanced computational methodology for identifying potential treatments for hyperpigmentation.

METABOLIC DISRUPTION IMPAIRS RIBOSOMAL PROTEIN LEVELS, RESULTING IN ENHANCED AMINOGLYCOSIDE TOLERANCE.

Aminoglycoside antibiotics display broad-spectrum activity against Gram-negative and Grampositive bacteria by targeting their ribosomes. Herein, we have demonstrated that energy metabolism plays a crucial role in aminoglycoside tolerance, as knockout strains associated with the tricarboxylic acid cycle (TCA) and the electron transport chain (ETC) exhibited increased tolerance to aminoglycosides in the mid-exponential growth phase of Escherichia coli cells. Given that aminoglycoside uptake relies on the energy-driven electrochemical potential across the cytoplasmic membrane, our initial expectation was that these genetic perturbations would decrease the proton motive force (PMF), subsequently affecting the uptake of aminoglycosides. However, our results did not corroborate this assumption. We found no consistent metabolic changes, ATP levels, cytoplasmic pH variations, or membrane potential differences in the mutant strains compared to the wild type. Additionally, intracellular concentrations of fluorophore-labeled gentamicin remained similar across all strains. To uncover the mechanism responsible for the observed tolerance in mutant strains, we employed untargeted mass spectrometry to quantify the proteins within these mutants and subsequently compared them to their wild-type counterparts. Our comprehensive analysis, which encompassed protein-protein association networks and functional enrichment, unveiled a noteworthy upregulation of proteins linked to the TCA cycle in the mutant strains during the mid-exponential growth phase, suggesting that these strains compensate for the perturbation in their energy metabolism by increasing TCA cycle activity to maintain their membrane potential and ATP levels. Furthermore, our pathway enrichment analysis shed light on local network clusters displaying downregulation across all mutant strains, which were associated with both large and small ribosomal binding proteins, ribosome biogenesis, translation factor activity, and the biosynthesis of ribonucleoside monophosphates. These findings offer a plausible explanation for the observed tolerance of aminoglycosides in the mutant strains. Altogether, this research has the potential to uncover mechanisms behind aminoglycoside tolerance, paving the way for novel strategies to combat such cells.

Identification of superior haplotypes for flowering time in pigeonpea through candidate gene-based association study of a diverse minicore collection.

In current study candidate gene (261 genes) based association mapping on 144 pigeonpea accessions for flowering time and related traits and 29 MTAs producing eight superior haplotypes were identified. In the current study, we have conducted an association analysis for flowering-associated traits in a diverse pigeonpea mini-core collection comprising 144 accessions using the SNP data of 261 flowering-related genes. In total, 13,449 SNPs were detected in the current study, which ranged from 743 (ICP10228) to 1469 (ICP6668) among the individuals. The nucleotide diversity (0.28) and Watterson estimates (0.34) reflected substantial diversity, while Tajima's D (-0.70) indicated the abundance of rare alleles in the collection. A total of 29 marker trait associations (MTAs) were identified, among which 19 were unique to days to first flowering (DOF) and/or days to fifty percent flowering (DFF), 9 to plant height (PH), and 1 to determinate (Det) growth habit using 3years of phenotypic data. Among these MTAs, six were common to DOF and/or DFF, and four were common to DOF/DFF along with the PH, reflecting their pleiotropic action. These 29 MTAs spanned 25 genes, among which 10 genes clustered in the protein-protein network analysis, indicating their concerted involvement in floral induction. Furthermore, we identified eight haplotypes, four of which regulate late flowering, while the remaining four regulate early flowering using the MTAs. Interestingly, haplotypes conferring late flowering (H001, H002, and H008) were found to be taller, while those involved in early flowering (H003) were shorter in height. The expression pattern of these genes, as inferred from the transcriptome data, also underpinned their involvement in floral induction. The haplotypes identified will be highly useful to the pigeonpea breeding community for haplotype-based breeding.

Dynamic molecular choreography induced by acute heat exposure in human males: a longitudinal multi-omics profiling study.

Extreme heat events caused by occupational exposure and heat waves are becoming more common. However, the molecular changes underlying the response to heat exposure in humans remain to be elucidated. This study used longitudinal multi-omics profiling to assess the impact of acute heat exposure (50°C for 30 min) in 24 subjects from a mine rescue team. Intravenous blood samples were collected before acute heat exposure (baseline) and at 5 min, 30 min, 1 h, and 24 h after acute heat exposure (recovery). In-depth multi-omics profiling was performed on each sample, including plasma proteomics (untargeted) and metabolomics (untargeted). After data curation and annotation, the final dataset contained 2,473 analytes, including 478 proteins and 1995 metabolites. Time-series analysis unveiled an orchestrated molecular choreography of changes involving the immune response, coagulation, acid-base balance, oxidative stress, cytoskeleton, and energy metabolism. Further analysis through protein-protein interactions and network analysis revealed potential regulators of acute heat exposure. Moreover, novel blood-based analytes that predicted change in cardiopulmonary function after acute heat exposure were identified. This study provided a comprehensive investigation of the dynamic molecular changes that underlie the complex physiological processes that occur in human males who undergo heat exposure. Our findings will help health impact assessment of extreme high temperature and inspire future mechanistic and clinical studies.