The ConsensusPathDB interaction database: 2013 update

Integrative proteomic analysis reveals the potential diagnostic marker and drug target for the Type-2 diabetes mellitus

Objective

The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.

Methods

Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls. Differential expression analysis, functional enrichment analysis, receiver operating characteristic (ROC) analysis, and classification error matrix analysis were employed.

Results

Comparative proteomic analysis identified the differential expressed proteins (DEPs) and changes in biological pathways associated with T2DM. Further combined analysis refined a group of protein panel (including CA1, S100A6, and DDT), which were significantly increased in T2DM in both two cohorts. ROC analysis revealed the area under curve (AUC) values of 0.94 for CA1, 0.87 for S100A6, and 0.97 for DDT; the combined model achieved an AUC reaching 1. Classification error matrix analysis demonstrated the combined model could reach an accuracy of 1 and 0.875 in the 60% training set and 40% testing set.

Conclusions

This study incorporates different cohorts of T2DM, and refines the potential markers for T2DM with high accuracy, offering more reliable markers for clinical translation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01562-3.

Temporal phosphoproteomics reveals circuitry of phased propagation in insulin signaling

Insulin is a pleiotropic hormone that elicits its metabolic and mitogenic actions through numerous rapid and reversible protein phosphorylations. The temporal regulation of insulin's intracellular signaling cascade is highly complex and insufficiently understood. We conduct a time-resolved analysis of the global insulin-regulated phosphoproteome of differentiated human primary myotubes derived from satellite cells of healthy donors using high-resolution mass spectrometry. Identification and tracking of ~13,000 phosphopeptides over time reveal a highly complex and coordinated network of transient phosphorylation and dephosphorylation events that can be allocated to time-phased regulation of distinct and non-overlapping subcellular pathways. Advanced network analysis combining protein-protein-interaction (PPI) resources and investigation of donor variability in relative phosphosite occupancy over time identifies novel putative candidates in non-canonical insulin signaling and key regulatory nodes that are likely essential for signal propagation. Lastly, we find that insulin-regulated phosphorylation of the pre-catalytic spliceosome complex is associated with acute alternative splicing events in the transcriptome of human skeletal muscle. Our findings highlight the temporal relevance of protein phosphorylations and suggest that synchronized contributions of multiple signaling pathways form part of the circuitry for propagating information to insulin effector sites.

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

BACKGROUND

Collagen type X (ColXα1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColXα1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression.

METHODS

Immunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools.

RESULTS

Expression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as contributors to the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases.

CONCLUSIONS

COL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer.

1H Nuclear Magnetic Resonance Spectroscopy Reveals Changes in Metabolic Phenotype Associated with Disease Stage in Patients with Chronic Venous Disease

OBJECTIVE

Chronic venous disease (CVD) is a condition presenting a great burden to patients and society, with poorly characterised pathophysiology. Metabolic phenotyping can elucidate mechanisms of disease and identify candidate biomarkers. The aim of this study was to determine differences in the metabolic signature between symptomatic patients with CVD and asymptomatic volunteers using proton nuclear magnetic resonance spectroscopy (1H-NMR).

METHODS

This was a prospective case control study of consecutive patients with symptomatic CVD and asymptomatic volunteers recruited from a single centre. Participants underwent clinical assessment, venous duplex ultrasound, and blood and urine sampling. Disease stage was defined according to the Clinical-Etiology-Anatomy-Pathophysiology (CEAP) classification. 1H-NMR experiments were performed, with data analysed via multivariable statistical techniques.

RESULTS

A total of 622 participants were recruited, including 517 symptomatic patients with CVD (telangiectasia [C1] 0.6%, varicose veins [C2] 48.5%, swelling [C3] 12.0%, skin changes [C4] 27.7%, healed or active ulceration [C5/6] 11.2%) and 105 asymptomatic participants (no disease [C0] 69.5%, telangiectasia [C1] 29.6%). Multivariable analysis revealed differences between the metabolic profile of the symptomatic CVD and asymptomatic groups, and between CEAP clinical classes in the CVD group. Serum aromatic amino acids positively correlated with increasing CEAP clinical class (p < .001). Urinary formate, creatinine, glycine, citrate, succinate, pyruvate, and 2-hydroxyisobutyrate negatively correlated with increasing CEAP clinical class (p < .001). These metabolites are involved in the tricarboxylic acid cycle, hypoxia inducible factor pathway, and one carbon metabolism.

CONCLUSION

Untargeted biofluid analysis via 1H-NMR has detected metabolites associated with the presence and severity of CVD, highlighting biological pathways of relevance and providing candidate biomarkers to explore in future research.

Development of a 23-Gene Signature for Tumor Growth Mechanism in Vestibular Schwannoma

Background/Objectives: Transcriptome profiling can reveal prognostic biomarkers and therapeutic vulnerabilities for directing clinical care. Currently, there are no biomarkers that can accurately predict patient prognosis regarding tumor growth and the tumor immune microenvironment in vestibular schwannomas. This study aimed to investigate the mechanisms of tumor growth using bulk RNA-seq and single-cell data from patients with vestibular schwannomas. Methods: Gene set variation analysis was used to assess groups with high and low tumor growth using four cohorts of bulk RNA-seq data (210 patients with vestibular schwannoma), 33,081 single cells, and 558 tumor growth-related genes. Results: SIG558, a tumor growth signature gene, was enriched in Schwann cells and microglial cells with high stemness, according to stemness analysis and cell-cell communication analysis of 33,081 single cells. We identified 391 genes that were strongly expressed in Schwann cells with high stemness. In addition, we identified 23 genes related to signal transduction that are important for tumor growth through cell-cell interactions in seven cell types at the single-cell level. Conclusions: Our research demonstrates that the 23 signature genes are potential predictors and prognostic biomarkers for direct medical therapy in patients with vestibular schwannoma, and that they should be prospectively verified using large patient cohorts. These results could potentially be used in precision medicine to develop treatment strategies for vestibular schwannomas by targeting these 23 genes.

Identifying new molecular signatures and potential therapeutics for idiopathic pulmonary fibrosis: a network medicine approach

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease characterized by excessive collagen deposition and fibrosis of the lung parenchyma, leading to respiratory failure. The molecular mechanisms underlying IPF pathogenesis remain incompletely understood, hindering the development of effective therapeutic strategies. We have used a network medicine approach to comprehensively analyze molecular interactions and identify novel molecular signatures and potential therapeutics associated with IPF progression. Our integrative analysis revealed dysregulated molecular networks that are central to IPF pathophysiology. We have highlighted key molecular players and signaling pathways that are implicated in aberrant fibrotic processes. This systems-level understanding enables the identification of new biomarkers and therapeutic targets for IPF, providing potential avenues for precision medicine. Drug repurposing analysis revealed several drug candidates with anti-fibrotic, anti-inflammatory, and anti-cancer activities that could potentially slow fibrotic progression and improve patient outcomes. This study offers new insights into the molecular underpinnings of IPF and highlights network medicine approaches in uncovering complex disease mechanisms. The molecular signatures and therapeutic targets identified hold promise for developing precision therapies tailored to individual patients, ultimately advancing the management of this debilitating lung disease.

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

Background

Collagen type X (ColXα1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColXα1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression.

Methods

Immunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools.

Results

Expression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as drivers of the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases.

Conclusions

COL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer.

Multiomic analysis of familial adenomatous polyposis reveals molecular pathways associated with early tumorigenesis

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected persons and is an ideal model to study transitions of early precancer states to colorectal cancer (CRC). We performed deep multiomic profiling of 93 samples, including normal mucosa, benign polyps and dysplastic polyps, from six persons with FAP. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during precancerous transitions toward cancer formation. These involve processes such as cell proliferation, immune response, metabolic alterations (including amino acids and lipids), hormones and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin and other nonsteroidal anti-inflammatory drugs, a preventative treatment under investigation in persons with FAP. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in CRC formation and potential mechanisms of pharmaceutical prophylaxis.

Cyclin-dependent kinase 4 drives cystic kidney disease in the absence of mTORC1 signaling activity

Progression of cystic kidney disease has been linked to activation of the mTORC1 signaling pathway. Yet the utility of mTORC1 inhibitors to treat patients with polycystic kidney disease remains controversial despite promising preclinical data. To define the cell intrinsic role of mTORC1 for cyst development, the mTORC1 subunit gene Raptor was selectively inactivated in kidney tubular cells lacking cilia due to simultaneous deletion of the kinesin family member gene Kif3A. In contrast to a rapid onset of cyst formation and kidney failure in mice with defective ciliogenesis, both kidney function, cyst formation discerned by magnetic resonance imaging and overall survival were strikingly improved in mice additionally lacking Raptor. However, these mice eventually succumbed to cystic kidney disease despite mTORC1 inactivation. In-depth transcriptome analysis revealed the rapid activation of other growth-promoting signaling pathways, overriding the effects of mTORC1 deletion and identified cyclin-dependent kinase (CDK) 4 as an alternate driver of cyst growth. Additional inhibition of CDK4-dependent signaling by the CDK4/6 inhibitor Palbociclib markedly slowed disease progression in mice and human organoid models of polycystic kidney disease and potentiated the effects of mTORC1 deletion/inhibition. Our findings indicate that cystic kidneys rapidly adopt bypass mechanisms typically observed in drug resistant cancers. Thus, future clinical trials need to consider combinatorial or sequential therapies to improve therapeutic efficacy in patients with cystic kidney disease.

Microviridae bacteriophages influence behavioural hallmarks of food addiction via tryptophan and tyrosine signalling pathways

Food addiction contributes to the obesity pandemic, but the connection between how the gut microbiome is linked to food addiction remains largely unclear. Here we show that Microviridae bacteriophages, particularly Gokushovirus WZ-2015a, are associated with food addiction and obesity across multiple human cohorts. Further analyses reveal that food addiction and Gokushovirus are linked to serotonin and dopamine metabolism. Mice receiving faecal microbiota and viral transplantation from human donors with the highest Gokushovirus load exhibit increased food addiction along with changes in tryptophan, serotonin and dopamine metabolism in different regions of the brain, together with alterations in dopamine receptors. Mechanistically, targeted tryptophan analysis shows lower anthranilic acid (AA) concentrations associated with Gokushovirus. AA supplementation in mice decreases food addiction and alters pathways related to the cycle of neurotransmitter synthesis release. In Drosophila, AA regulates feeding behaviour and addiction-like ethanol preference. In summary, this study proposes that bacteriophages in the gut microbiome contribute to regulating food addiction by modulating tryptophan and tyrosine metabolism.

Vitamin C supplementation improves placental function and alters placental gene expression in smokers

Maternal smoking during pregnancy (MSDP), driven by nicotine crossing the placenta, causes lifelong decreases in offspring pulmonary function and vitamin C supplementation during pregnancy prevents some of those changes. We have also shown in animal models of prenatal nicotine exposure that vitamin C supplementation during pregnancy improves placental function. In this study we examined whether vitamin C supplementation mitigates the effects of MSDP on placental structure, function, and gene expression in pregnant human smokers. Doppler ultrasound was performed in a subset of 55 pregnant smokers participating in the "Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function" (VCSIP) randomized clinical trial (NCT01723696) and in 33 pregnant nonsmokers. Doppler ultrasound measurements showed decreased umbilical vein Doppler velocity (Vmax) in placebo-treated smokers that was significantly improved in smokers randomized to vitamin C, restoring to levels comparable to nonsmokers. RNA-sequencing demonstrated that vitamin C supplementation to pregnant smokers was associated with changes in mRNA expression in genes highly relevant to vascular and cardiac development, suggesting a potential mechanism for vitamin C supplementation in pregnant smokers to improve some aspects of offspring health.

A best-match approach for gene set analyses in embedding spaces

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Spermidine is essential for fasting-mediated autophagy and longevity

Caloric restriction and intermittent fasting prolong the lifespan and healthspan of model organisms and improve human health. The natural polyamine spermidine has been similarly linked to autophagy enhancement, geroprotection and reduced incidence of cardiovascular and neurodegenerative diseases across species borders. Here, we asked whether the cellular and physiological consequences of caloric restriction and fasting depend on polyamine metabolism. We report that spermidine levels increased upon distinct regimens of fasting or caloric restriction in yeast, flies, mice and human volunteers. Genetic or pharmacological blockade of endogenous spermidine synthesis reduced fasting-induced autophagy in yeast, nematodes and human cells. Furthermore, perturbing the polyamine pathway in vivo abrogated the lifespan- and healthspan-extending effects, as well as the cardioprotective and anti-arthritic consequences of fasting. Mechanistically, spermidine mediated these effects via autophagy induction and hypusination of the translation regulator eIF5A. In summary, the polyamine-hypusination axis emerges as a phylogenetically conserved metabolic control hub for fasting-mediated autophagy enhancement and longevity.

Immune profiling of premalignant lesions in patients with Peutz-Jeghers syndrome

BACKGROUND

Peutz-Jeghers syndrome (PJS), is a rare autosomal dominant hereditary disease characterized by an elevated risk of various cancers. Serine/Threonine Kinase 11 (STK11) gene is a major tumor suppressor crucial for immune evasion with and beyond tumorigenic cells. It has garnered increasing attention in the realm of oncology treatment, particularly in the context of immunotherapy development.

OBJECTIVE

This study aimed to assess the suitability of polyps obtained from individuals with PJS, resulting from germline STK11 deficiency, for immunotherapy. Additionally, we seek to identify potential shared mechanisms related to immune evasion between PJS polyps and cancers. To achieve this, we examined PJS polyps alongside familial adenomatous polyposis (FAP) and sporadic polyps.

METHODS

Polyps were compared among themselves and with either the paracancerous tissues or colon cancers. Pathological and gene expression profiling approaches were employed to characterize infiltrating immune cells and assess the expression of immune checkpoint genes.

RESULTS

Our findings revealed that PJS polyps exhibited a closer resemblance to cancer tissues than other polyps in terms of their immune microenvironment. Notably, PJS polyps displayed heightened expression of the immune checkpoint gene CD80 and an accumulation of myeloid cells, particularly myeloid-derived suppressor cells (MDSCs).

CONCLUSION

The findings suggest an immunobiological foundation for the increased cancer susceptibility in PJS patients, paving the way for potential immune therapy applications in this population. Furthermore, utilizing PJS as a model may facilitate the exploration of immune evasion mechanisms, benefiting both PJS and cancer patients.

Rosiglitazone and trametinib exhibit potent anti-tumor activity in a mouse model of muscle invasive bladder cancer

Muscle invasive bladder cancers (BCs) can be divided into 2 major subgroups-basal/squamous (BASQ) tumors and luminal tumors. Since Pparg has low or undetectable expression in BASQ tumors, we tested the effects of rosiglitazone, Pparg agonist, in a mouse model of BASQ BC. We find that rosiglitazone reduces proliferation while treatment with rosiglitazone plus trametinib, a MEK inhibitor, induces apoptosis and reduces tumor volume by 91% after 1 month. Rosiglitazone and trametinib also induce a shift from BASQ to luminal differentiation in tumors, which our analysis suggests is mediated by retinoid signaling, a pathway known to drive the luminal differentiation program. Our data suggest that rosiglitazone, trametinib, and retinoids, which are all FDA approved, may be clinically active in BASQ tumors in patients.

Diagnostic Value of Menstrual Blood Lipidomics in Endometriosis: A Pilot Study

Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patients in a control group) was acquired using reverse-phase high-performance liquid chromatography-mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann-Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. Plasmenylphosphatidylethanolamine PE P-16:0/18:1 and cardiolipin CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening.

Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis

Our objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

Single-B cell analysis correlates high-lactate secretion with stress and increased apoptosis

While cellular metabolism was proposed to be a driving factor of the activation and differentiation of B cells and the function of the resulting antibody-secreting cells (ASCs), the study of correlations between cellular metabolism and functionalities has been difficult due to the absence of technologies enabling the parallel measurement. Herein, we performed single-cell transcriptomics and introduced a direct concurrent functional and metabolic flux quantitation of individual murine B cells. Our transcriptomic data identified lactate metabolism as dynamic in ASCs, but antibody secretion did not correlate with lactate secretion rates (LSRs). Instead, our study of all splenic B cells during an immune response linked increased lactate metabolism with acidic intracellular pH and the upregulation of apoptosis. T cell-dependent responses increased LSRs, and added TLR4 agonists affected the magnitude and boosted LSRhigh B cells in vivo, while resulting in only a few immunoglobulin-G secreting cells (IgG-SCs). Therefore, our observations indicated that LSRhigh cells were not differentiating into IgG-SCs, and were rather removed due to apoptosis.

Inhibition of p53-MDM2 binding reduces senescent cell abundance and improves the adaptive responses of skeletal muscle from aged mice

Skeletal muscle adaptation to external stimuli, such as regeneration following injury and hypertrophy in response to resistance exercise, are blunted with advanced age. The accumulation of senescent cells, along with defects in myogenic progenitor cell (MPC) proliferation, have been strongly linked as contributing factors to age-associated impairment in muscle adaptation. p53 plays an integral role in all these processes, as upregulation of p53 causes apoptosis in senescent cells and prevents mitotic catastrophe in MPCs from old mice. The goal of this study was to determine if a novel pharmaceutical agent (BI01), which functions by upregulating p53 through inhibition of binding to MDM2, the primary p53 regulatory protein, improves muscle regeneration and hypertrophy in old mice. BI01 effectively reduced the number of senescent cells in vitro but had no effect on MPC survival or proliferation at a comparable dose. Following repeated oral gavage with 2 mg/kg of BI01 (OS) or vehicle (OV), old mice (24 months) underwent unilateral BaCl2 injury in the tibialis anterior (TA) muscle, with PBS injections serving as controls. After 7 days, satellite cell number was higher in the TA of OS compared to OV mice, as was the expression of genes involved in ATP production. By 35 days, old mice treated with BI01 displayed reduced senescent cell burden, enhanced regeneration (higher muscle mass and fiber cross-sectional area) and restoration of muscle function relative to OV mice. To examine the impact of 2 mg/kg BI01 on muscle hypertrophy, the plantaris muscle was subjected to 28 days of mechanical overload (MOV) in OS and OV mice. In response to MOV, OS mice had larger plantaris muscles and muscle fibers than OV mice, particularly type 2b + x fibers, associated with reduced senescent cells. Together our data show that BI01 is an effective senolytic agent that may also augment muscle metabolism to enhance muscle regeneration and hypertrophy in old mice.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Methyl-CpG binding domain protein 2 (Mbd2) drives breast cancer progression through the modulation of epithelial-to-mesenchymal transition

Methyl-CpG-binding domain protein 2 (Mbd2), a reader of DNA methylation, has been implicated in different types of malignancies, including breast cancer. However, the exact role of Mbd2 in various stages of breast cancer growth and progression in vivo has not been determined. To test whether Mbd2 plays a causal role in mammary tumor growth and metastasis, we performed genetic knockout (KO) of Mbd2 in MMTV-PyMT transgenic mice and compared mammary tumor progression kinetics between the wild-type (PyMT-Mbd2+/+) and KO (PyMT-Mbd2-/-) groups. Our results demonstrated that deletion of Mbd2 in PyMT mice impedes primary tumor growth and lung metastasis at the experimental endpoint (postnatal week 20). Transcriptomic and proteomic analyses of primary tumors revealed that Mbd2 deletion abrogates the expression of several key determinants involved in epithelial-to-mesenchymal transition, such as neural cadherin (N-cadherin) and osteopontin. Importantly, loss of the Mbd2 gene impairs the activation of the PI3K/AKT pathway, which is required for PyMT-mediated oncogenic transformation, growth, and survival of breast tumor cells. Taken together, the results of this study provide a rationale for further development of epigenetic therapies targeting Mbd2 to inhibit the progression of breast cancer.

Circadian disruption dysregulates lung gene expression associated with inflammatory lung injury

Rationale

Circadian systems drive the expression of multiple genes in nearly all cells and coordinate cellular-, tissue-, and system-level processes that are critical to innate immunity regulation.

Objective

We examined the effects of circadian rhythm disorganization, produced by light shift exposure, on innate immunity-mediated inflammatory lung responses including vascular permeability and gene expression in a C57BL/6J murine model of inflammatory lung injury.

Methods

A total of 32 C57BL/6J mice were assigned to circadian phase shifting (CPS) with intratracheal phosphate-buffered saline (PBS), CPS with intratracheal lipopolysaccharide (LPS), control (normal lighting) condition with intratracheal PBS, and control condition with intratracheal LPS. Bronchoalveolar lavage (BAL) protein, cell counts, tissue immunostaining, and differentially expressed genes (DEGs) were measured in lung tissues at 2 and 10 weeks.

Measurements and results

In mice exposed to both CPS and intratracheal LPS, both BAL protein and cell counts were increased at both 2 and 10 weeks compared to mice exposed to LPS alone. Multiple DEGs were identified in CPS-LPS-exposed lung tissues compared to LPS alone and were involved in transcriptional pathways associated with circadian rhythm disruption, regulation of lung permeability, inflammation with Rap1 signaling, and regulation of actin cytoskeleton. The most dysregulated pathways included myosin light chain kinase, MAP kinase, profilin 2, fibroblast growth factor receptor, integrin b4, and p21-activated kinase.

Conclusion

Circadian rhythm disruption results in exacerbated immune response and dysregulated expression of cytoskeletal genes involved in the regulation of epithelial and vascular barrier integrity-the mechanistic underpinnings of acute lung injury. Further studies need to explore circadian disorganization as a druggable target.

Comparative analysis of diisononyl phthalate and di(isononyl)cyclohexane-1,2 dicarboxylate plasticizers in regulation of lipid metabolism in 3T3-L1 cells

Diisononyl phthalate (DINP) and di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) are plasticizers introduced to replace previously used phthalate plasticizers in polymeric products. Exposure to DINP and DINCH has been shown to impact lipid metabolism. However, there are limited studies that address the mechanisms of toxicity of these two plasticizers. Here, a comparative toxicity analysis has been performed to evaluate the impacts of DINP and DINCH on 3T3-L1 cells. The preadipocyte 3T3-L1 cells were exposed to 1, 10, and 100 μM of DINP or DINCH for 10 days and assessed for lipid accumulation, gene expression, and protein analysis. Lipid staining showed that higher concentrations of DINP and DINCH can induce adipogenesis. The gene expression analysis demonstrated that both DINP and DINCH could alter the expression of lipid-related genes involved in adipogenesis. DINP and DINCH upregulated Pparγ, Pparα, C/EBPα Fabp4, and Fabp5, while both compounds significantly downregulated Fasn and Gata2. Protein analysis showed that both DINP and DINCH repressed the expression of FASN. Additionally, we analyzed an independent transcriptome dataset encompassing temporal data on lipid differentiation within 3T3-L1 cells. Subsequently, we derived a gene set that accurately portrays significant pathways involved in lipid differentiation, which we subsequently subjected to experimental validation through quantitative polymerase chain reaction. In addition, we extended our analysis to encompass a thorough assessment of the expression profiles of this identical gene set across 40 discrete transcriptome datasets that have linked to diverse pathological conditions to foreseen any potential association with DINP and DINCH exposure. Comparative analysis indicated that DINP could be more effective in regulating lipid metabolism.

FLNB overexpression promotes tumor progression and associates with immune suppression, evasion and stemness in pancreatic cancer

Pancreatic cancer (PC) is an immunosuppressive cancer. Immune-based therapies that enhance or recruit antitumor immune cells into the tumor microenvironment (TME) remain promising strategies for PC treatment. Consequently, a deeper understanding of the molecular mechanisms involved in PC immune suppression is critical for developing immune-based therapies to improve survival rates. In this study, weighted gene co-expression network analysis (WGCNA) was used to identify Filamin B (FLNB) correlated with the infiltration of CD8+ T cells and tumor-associated macrophages (TAMs). The clinical significance and potential biological function of FLNB were evaluated using bioinformatic analysis. The oncogenic role of FLNB in PC was determined using in vitro and in vivo studies. We further analyzed possible associations between FLNB expression and tumor immunity using CIBERSORT, single sample gene set enrichment analysis, and ESTIMATE algorithms. We found FLNB was overexpressed in PC tissues and was correlated with poorer overall survival, tumor recurrence, larger tumor size, and higher histologic grade. Moreover, FLNB overexpression was associated with the mutation status and expression of driver genes, especially for KRAS and SMAD4. Functional enrichment analysis identified the role of FLNB in the regulation of cell cycle, focal adhesion, vascular formation, and immune regulation. Knockdown of FLNB expression inhibited cancer cell proliferation and migration in-vitro and suppressed tumor growth in-vivo. Furthermore, FLNB overexpression caused high infiltration of Treg cells, Th2 cells, and TAMs, but reduced infiltration of CD8+ T cells and Th1/Th2. Collectively, our findings suggest FLNB promotes PC progression and may be a novel biomarker for PC.

A novel risk score system based on immune subtypes for identifying optimal mRNA vaccination population in hepatocellular carcinoma

PURPOSE

Although mRNA vaccines have shown certain clinical benefits in multiple malignancies, their therapeutic efficacies against hepatocellular carcinoma (HCC) remains uncertain. This study focused on establishing a novel risk score system based on immune subtypes so as to identify optimal HCC mRNA vaccination population.

METHODS

GEPIA, cBioPortal and TIMER databases were utilized to identify candidate genes for mRNA vaccination in HCC. Subsequently, immune subtypes were constructed based on the candidate genes. According to the differential expressed genes among various immune subtypes, a risk score system was established using machine learning algorithm. Besides, multi-color immunofluorescence of tumor tissues from 72 HCC patients were applied to validate the feasibility and efficiency of the risk score system.

RESULTS

Twelve overexpressed and mutated genes associated with poor survival and APCs infiltration were identified as potential candidate targets for mRNA vaccination. Three immune subtypes (e.g. IS1, IS2 and IS3) with distinct clinicopathological and molecular profiles were constructed according to the 12 candidate genes. Based on the immune subtype, a risk score system was developed, and according to the risk score from low to high, HCC patients were classified into four subgroups on average (e.g. RS1, RS2, RS3 and RS4). RS4 mainly overlapped with IS3, RS1 with IS2, and RS2+RS3 with IS1. ROC analysis also suggested the significant capacity of the risk score to distinguish between the three immune subtypes. Higher risk score exhibited robustly predictive ability for worse survival, which was further independently proved by multi-color immunofluorescence of HCC samples. Notably, RS4 tumors exhibited an increased immunosuppressive phenotype, higher expression of the twelve potential candidate targets and increased genome altered fraction, and therefore might benefit more from vaccination.

CONCLUSIONS

This novel risk score system based on immune subtypes enabled the identification of RS4 tumor that, due to its highly immunosuppressive microenvironment, may benefit from HCC mRNA vaccination.

Increased neutrophil-derived IL-17A identified in generalized pustular psoriasis

Generalized pustular psoriasis (GPP) is considered to be a distinct clinical entity from psoriasis vulgaris (PV), with different clinical and histological manifestations. The pathogenesis of GPP has not been thoroughly elucidated, especially in those patients lacking interleukin (IL)36RN. In present study, we performed RNA sequence analysis on skin lesions from 10 GPP patients (4 with and 6 without IL36RN mutation) and 10 PV patients without IL36RN mutation. Compared with PV, significantly overexpressed genes in GPP patients were enriched in IL-17 signalling pathway (MMP1, MMP3, DEFB4A and DEFB4B, etc.) and associated with neutrophil infiltration (MMP1, MMP3, ANXA and SERPINB, etc.). GPP with IL36RN mutations evidenced WNT11 upregulation and IL36RN downregulation in comparison to those GPP without IL36RN mutations. The expression of IL-17A/IL-36 in skin or serum and the origin of IL-17A in skin were also investigated. IL-17A expression in skin was significantly higher in GPP than PV patients, whereas, there were no differences in skin IL-36α/IL-36γ/IL-36RA or serum IL-17A/IL-36α/IL-36γ between GPP than PV. Besides, double immunofluorescence staining of MPO/IL-17A or CD3/IL-17A further confirmed that the majority of IL-17A in GPP skin was derived from neutrophils, but not T cells. These data emphasized the role of neutrophil-derived IL-17A in the pathogenesis of GPP with or without IL36RN mutations. Targeting neutrophil-derived IL-17A might be a promising treatment for GPP.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Modeling transcriptional regulation of the cell cycle using a novel cybernetic-inspired approach

Quantitative understanding of cellular processes, such as cell cycle and differentiation, is impeded by various forms of complexity ranging from myriad molecular players and their multilevel regulatory interactions, cellular evolution with multiple intermediate stages, lack of elucidation of cause-effect relationships among the many system players, and the computational complexity associated with the profusion of variables and parameters. In this paper, we present a modeling framework based on the cybernetic concept that biological regulation is inspired by objectives embedding rational strategies for dimension reduction, process stage specification through the system dynamics, and innovative causal association of regulatory events with the ability to predict the evolution of the dynamical system. The elementary step of the modeling strategy involves stage-specific objective functions that are computationally determined from experiments, augmented with dynamical network computations involving endpoint objective functions, mutual information, change-point detection, and maximal clique centrality. We demonstrate the power of the method through application to the mammalian cell cycle, which involves thousands of biomolecules engaged in signaling, transcription, and regulation. Starting with a fine-grained transcriptional description obtained from RNA sequencing measurements, we develop an initial model, which is then dynamically modeled using the cybernetic-inspired method, based on the strategies described above. The cybernetic-inspired method is able to distill the most significant interactions from a multitude of possibilities. In addition to capturing the complexity of regulatory processes in a mechanistically causal and stage-specific manner, we identify the functional network modules, including novel cell cycle stages. Our model is able to predict future cell cycles consistent with experimental measurements. We posit that this innovative framework has the promise to extend to the dynamics of other biological processes, with a potential to provide novel mechanistic insights.

Molecular Recognition of Moringa oleifera Active Compounds for Stunted Growth Prevention Using Network Pharmacology and Molecular Modeling Approach

In this study, network pharmacology was used to analyze the active compounds of Moringa oleifera as food supplements for stunted growth prevention. Thirty-eight important proteins were discovered that may be strongly related to stunting. Those proteins were uploaded to several online tool platforms in order to determine the shared genes' pathways. Six pathways were identified that may be correlated with human growth. Furthermore, ligands for molecular docking analysis were retrieved from the top 5 active substances discovered through experimental investigation. In the meantime, the first-degree rank based on the protein-protein interaction (PPI) topological analysis was utilized to choose albumin protein (ALB) as a receptor. Our docking results showed that every ligand binds to the receptors, indicating that they can bind to the binding site of the ALB protein to form a complex formation. Further, MD simulation was used to verify the stability of the ligand in complex with the protein in the TIP3P water model. Based on the validation parameters, our results suggested that all models achieved a stable phase along the simulation. Additionally, the MM-GBSA method was used to calculate the binding energies of all models. Ligands 2 and 4 have strong binding to the binding pocket of ALB, followed by ligands 3, 5, and 2, suggesting that those ligands could be promising food supplements that can be utilized for the prevention of stunted growth in children.

Synthetic lethality in human bladder cancer cells by curcumin via concurrent Aurora A inhibition and autophagy induction

Combination therapies to induce mixed-type cell death and synthetic lethality have the potential to overcome drug resistance in cancer. In this study, we demonstrated that the curcumin-enhanced cytotoxicity of cisplatin/carboplatin in combination with gemcitabine was associated with Aurora A suppression-mediated G2/M arrest, and thus apoptosis, as well as MEK/ERK-mediated autophagy in human bladder cancer cells. Animal study data confirmed that curcumin combined with cisplatin/gemcitabine reduced tumorigenesis of xenograft in mice and this phenomenon was associated with elevated expressions of p-ERK and reduced p-Aurora A in tumors. Gene analyses using data repositories further revealed that reduced Aurora A expression alone did not significantly elevate the sensitivity of human bladder carcinoma cells to these anticancer drugs. Unlike other major cancer types, human bladder urothelial carcinoma tissue coexpressed higher AURKA and lower MAP1LC3B than normal tissue, and reduced Aurora A and induction of autophagy have been clinically associated with a better prognosis in patients with early but not advanced stage bladder cancer. Therefore, our results suggest that treatment strategies can utilize the synthetic lethal pair to concurrently suppress oncogenic Aurora A and induce autophagy by coadministrating curcumin with anticancer drugs for early-stage bladder cancer with high expression of Aurora A.

The overlapping genetic architecture of psychiatric disorders and cortical brain structure

Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remains elusive. In this study, we employed pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area, and seven psychiatric disorders in approximately 700,000 individuals of European ancestry. Aggregating regional measures, we identified 50 genetic loci shared between psychiatric disorders and surface area, as well as 26 genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder increased regional brain size while other risk alleles decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multi-scale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways, and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.

Inflammatory response to bacterial lipopolysaccharide drives iron accumulation in human adipocytes

The association among increased inflammation, disrupted iron homeostasis, and adipose tissue dysfunction in obesity has been widely recognized. However, the specific impact of inflammation on iron homeostasis during human adipogenesis and in adipocytes remains poorly understood. In this study, we investigated the effects of bacterial lipopolysaccharide (LPS) on iron homeostasis during human adipocyte differentiation, in fully differentiated adipocytes, and in human adipose tissue. We found that LPS-induced inflammation hindered adipogenesis and led to a gene expression profile indicative of intracellular iron accumulation. This was accompanied by increased expression of iron importers (TFRC and SLC11A2), markers of intracellular iron accumulation (FTH, CYBA, FTL, and LCN2), and decreased expression of iron exporter-related genes (SLC40A1), concomitant with elevated intracellular iron levels. Mechanistically, RNA-seq analysis and gene knockdown experiments revealed the significant involvement of iron importers SLC39A14, SLC39A8, and STEAP4 in LPS-induced intracellular iron accumulation in human adipocytes. Notably, markers of LPS signaling pathway-related inflammation were also associated with a gene expression pattern indicative of intracellular iron accumulation in human adipose tissue, corroborating the link between LPS-induced inflammation and iron accumulation at the tissue level. In conclusion, our findings demonstrate that induction of adipocyte inflammation disrupts iron homeostasis, resulting in adipocyte iron overload.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Multiomics of Tissue Extracellular Vesicles Identifies Unique Modulators of Atherosclerosis and Calcific Aortic Valve Stenosis

BACKGROUND

Fewer than 50% of patients who develop aortic valve calcification have concomitant atherosclerosis, implying differential pathogenesis. Although circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs are associated with early mineralization, but their cargoes, functions, and contributions to disease remain unknown.

METHODS

Disease stage-specific proteomics was performed on human carotid endarterectomy specimens (n=16) and stenotic aortic valves (n=18). Tissue EVs were isolated from human carotid arteries (normal, n=6; diseased, n=4) and aortic valves (normal, n=6; diseased, n=4) by enzymatic digestion, (ultra)centrifugation, and a 15-fraction density gradient validated by proteomics, CD63-immunogold electron microscopy, and nanoparticle tracking analysis. Vesiculomics, comprising vesicular proteomics and small RNA-sequencing, was conducted on tissue EVs. TargetScan identified microRNA targets. Pathway network analyses prioritized genes for validation in primary human carotid artery smooth muscle cells and aortic valvular interstitial cells.

RESULTS

Disease progression drove significant convergence (P<0.0001) of carotid artery plaque and calcified aortic valve proteomes (2318 proteins). Each tissue also retained a unique subset of differentially enriched proteins (381 in plaques; 226 in valves; q<0.05). Vesicular gene ontology terms increased 2.9-fold (P<0.0001) among proteins modulated by disease in both tissues. Proteomics identified 22 EV markers in tissue digest fractions. Networks of proteins and microRNA targets changed by disease progression in both artery and valve EVs revealed shared involvement in intracellular signaling and cell cycle regulation. Vesiculomics identified 773 proteins and 80 microRNAs differentially enriched by disease exclusively in artery or valve EVs (q<0.05); multiomics integration found tissue-specific EV cargoes associated with procalcific Notch and Wnt signaling in carotid arteries and aortic valves, respectively. Knockdown of tissue-specific EV-derived molecules FGFR2, PPP2CA, and ADAM17 in human carotid artery smooth muscle cells and WNT5A, APP, and APC in human aortic valvular interstitial cells significantly modulated calcification.

CONCLUSIONS

The first comparative proteomics study of human carotid artery plaques and calcified aortic valves identifies unique drivers of atherosclerosis versus aortic valve stenosis and implicates EVs in advanced cardiovascular calcification. We delineate a vesiculomics strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Integration of vesicular proteomics and transcriptomics by network approaches revealed novel roles for tissue EVs in modulating cardiovascular disease.

Pancancer network analysis reveals key master regulators for cancer invasiveness

BACKGROUND

Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness.

MATERIALS AND METHODS

We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository.

RESULTS

Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis.

CONCLUSION

Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

Adipose tissue coregulates cognitive function

Obesity is associated with cognitive decline. Recent observations in mice propose an adipose tissue (AT)-brain axis. We identified 188 genes from RNA sequencing of AT in three cohorts that were associated with performance in different cognitive domains. These genes were mostly involved in synaptic function, phosphatidylinositol metabolism, the complement cascade, anti-inflammatory signaling, and vitamin metabolism. These findings were translated into the plasma metabolome. The circulating blood expression levels of most of these genes were also associated with several cognitive domains in a cohort of 816 participants. Targeted misexpression of candidate gene ortholog in the Drosophila fat body significantly altered flies memory and learning. Among them, down-regulation of the neurotransmitter release cycle-associated gene SLC18A2 improved cognitive abilities in Drosophila and in mice. Up-regulation of RIMS1 in Drosophila fat body enhanced cognitive abilities. Current results show previously unidentified connections between AT transcriptome and brain function in humans, providing unprecedented diagnostic/therapeutic targets in AT.

XGDAG: explainable gene-disease associations via graph neural networks

MOTIVATION

Disease gene prioritization consists in identifying genes that are likely to be involved in the mechanisms of a given disease, providing a ranking of such genes. Recently, the research community has used computational methods to uncover unknown gene-disease associations; these methods range from combinatorial to machine learning-based approaches. In particular, during the last years, approaches based on deep learning have provided superior results compared to more traditional ones. Yet, the problem with these is their inherent black-box structure, which prevents interpretability.

RESULTS

We propose a new methodology for disease gene discovery, which leverages graph-structured data using graph neural networks (GNNs) along with an explainability phase for determining the ranking of candidate genes and understanding the model's output. Our approach is based on a positive-unlabeled learning strategy, which outperforms existing gene discovery methods by exploiting GNNs in a non-black-box fashion. Our methodology is effective even in scenarios where a large number of associated genes need to be retrieved, in which gene prioritization methods often tend to lose their reliability.

AVAILABILITY AND IMPLEMENTATION

The source code of XGDAG is available on GitHub at: https://github.com/GiDeCarlo/XGDAG. The data underlying this article are available at: https://www.disgenet.org/, https://thebiogrid.org/, https://doi.org/10.1371/journal.pcbi.1004120.s003, and https://doi.org/10.1371/journal.pcbi.1004120.s004.

Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β  = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q < 0.05), including elafin leukocyte elastase inhibitor and mucin-associated TFF2 (trefoil factor 2; β = -4.3 ml/yr, Q = 0.049; β = -6.1 ml/yr, Q = 0.032, respectively). Pathways and processes highlighted by our study include aberrant extracellular matrix remodeling, enhanced innate immune response, dysregulation of angiogenesis, and coagulation. Conclusions: In this study, we identify and validate novel biomarkers and pathways associated with lung function traits in a racially diverse population. In addition, we identify novel protein markers associated with FEV1 decline. Several protein findings are supported by previously reported genetic signals, highlighting the plausibility of certain biologic pathways. These novel proteins might represent markers for risk stratification, as well as novel molecular targets for treatment of COPD.

Capturing time-dependent activation of genes and stress-response pathways using transcriptomics in iPSC-derived renal proximal tubule cells

Transcriptomic analysis is a powerful method in the utilization of New Approach Methods (NAMs) for identifying mechanisms of toxicity and application to hazard characterization. With this regard, mapping toxicological events to time of exposure would be helpful to characterize early events. Here, we investigated time-dependent changes in gene expression levels in iPSC-derived renal proximal tubular-like cells (PTL) treated with five diverse compounds using TempO-Seq transcriptomics with the aims to evaluate the application of PTL for toxicity prediction and to report on temporal effects for the activation of cellular stress response pathways. PTL were treated with either 50 μM amiodarone, 10 μM sodium arsenate, 5 nM rotenone, or 300 nM tunicamycin over a temporal time course between 1 and 24 h. The TGFβ-type I receptor kinase inhibitor GW788388 (1 μM) was used as a negative control. Pathway analysis revealed the induction of key stress-response pathways, including Nrf2 oxidative stress response, unfolding protein response, and metal stress response. Early response genes per pathway were identified much earlier than 24 h and included HMOX1, ATF3, DDIT3, and several MT1 isotypes. GW788388 did not induce any genes within the stress response pathways above, but showed deregulation of genes involved in TGFβ inhibition, including downregulation of CYP24A1 and SERPINE1 and upregulation of WT1. This study highlights the application of iPSC-derived renal cells for prediction of cellular toxicity and sheds new light on the temporal and early effects of key genes that are involved in cellular stress response pathways.

Increased genital mucosal cytokines in Canadian women associate with higher antigen-presenting cells, inflammatory metabolites, epithelial barrier disruption, and the depletion of L. crispatus

BACKGROUND

Cervicovaginal inflammation has been linked to negative reproductive health outcomes including the acquisition of HIV, other sexually transmitted infections, and cervical carcinogenesis. While changes to the vaginal microbiome have been linked to genital inflammation, the molecular relationships between the functional components of the microbiome with cervical immunology in the reproductive tract are understudied, limiting our understanding of mucosal biology that may be important for reproductive health.

RESULTS

In this study, we used a multi'-omics approach to profile cervicovaginal samples collected from 43 Canadian women to characterize host, immune, functional microbiome, and metabolome features of cervicovaginal inflammation. We demonstrate that inflammation is associated with lower amounts of L. crispatus and higher levels of cervical antigen-presenting cells (APCs). Proteomic analysis showed an upregulation of pathways related to neutrophil degranulation, complement, and leukocyte migration, with lower levels of cornified envelope and cell-cell adherens junctions. Functional microbiome analysis showed reductions in carbohydrate metabolism and lactic acid, with increases in xanthine and other metabolites. Bayesian network analysis linked L. crispatus with glycolytic and nucleotide metabolism, succinate and xanthine, and epithelial proteins SCEL and IVL as major molecular features associated with pro-inflammatory cytokines and increased APCs.

CONCLUSIONS

This study identified key molecular and immunological relationships with cervicovaginal inflammation, including higher APCs, bacterial metabolism, and proteome alterations that underlie inflammation. As APCs are involved in HIV transmission, parturition, and cervical cancer progression, further studies are needed to explore the interactions between these cells, bacterial metabolism, mucosal immunity, and their relationship to reproductive health. Video Abstract.

VGLL3 expression is associated with macrophage infiltration and predicts poor prognosis in epithelial ovarian cancer

Background/objective

High-grade serous ovarian carcinoma (HGSOC) is the most common histologic type of epithelial ovarian cancer (EOC). Due to its poor survival outcomes, it is essential to identify novel biomarkers and therapeutic targets. The hippo pathway is crucial in various cancers, including gynaecological cancers. Herein, we examined the expression of the key genes of the hippo pathway and their relationship with clinicopathological significance, immune cells infiltration and the prognosis of HGSOC.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data were curated to analyse the mRNA expression as well as the clinicopathological association and correlation with immune cell infiltration in HGSOC. The protein levels of significant genes in the HGSOC tissue were analysed using Tissue Microarray (TMA)-based immunohistochemistry. Finally, DEGs pathway analysis was performed to find the signalling pathways associated with VGLL3.

Results

VGLL3 mRNA expression was significantly correlated with both advanced tumor stage and poor overall survival (OS) (p=0.046 and p=0.003, respectively). The result of IHC analysis also supported the association of VGLL3 protein with poor OS. Further, VGLL3 expression was significantly associated with tumor infiltrating macrophages. VGLL3 expression and macrophages infiltration were both found to be independent prognostic factors (p=0.003 and p=0.024, respectively) for HGSOC. VGLL3 was associated with four known and three novel cancer-related signalling pathways, thus implying that VGLL3 is involved in the deregulation of many genes and pathways.

Conclusion

Our study revealed that VGLL3 may play a distinct role in clinical outcomes and immune cell infiltration in patients with HGSOC and that it could potentially be a prognostic marker of EOC.

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease

Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.

MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis

MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE3, accessible at http://csbg.cnb.csic.es/mbrole3. This new version contains updated annotations from previously included databases as well as a wide variety of new functional annotations, such as additional pathway databases and Gene Ontology terms. Of special relevance is the inclusion of a new category of annotations, 'indirect annotations', extracted from the scientific literature and from curated chemical-protein associations. The latter allows to analyze enriched annotations of the proteins known to interact with the set of chemical compounds of interest. Results are provided in the form of interactive tables, formatted data to download, and graphical plots.

piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets

Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .

HPV16 status predicts potential protein biomarkers and therapeutics in head and neck squamous cell carcinoma

Human papillomavirus (HPV) infection, especially HPV16, is one of the causative factors for the development of head and neck squamous cell (HNSC) carcinoma. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features, so they should be evaluated differently depending on their HPV status. Given the tremendous variation in HNSC cancers depending on HPV, our goal in this study was to present biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs). By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we introduce eight modules as potential biomarkers specialized for either positive or negative phenotype. Finally, raloxifene was repositioned for the first time as a drug candidate for the treatment of HPV16-positive HNSC patients.

Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors

PURPOSE

Non-functioning pituitary neuroendocrine tumors are challengingly diagnosed tumors in the clinic. Transsphenoidal surgery remains the first-line treatment. Despite the development of state-of-the-art techniques, no drug therapy is currently approved for the treatment. There are also no randomized controlled trials comparing therapeutic strategies or drug therapy for the management after surgery. Therefore, novel therapeutic interventions for the therapeutically challenging NF-PitNETs are urgently needed.

METHODS

We integrated epigenome and transcriptome data (both coding and non-coding) that elucidate disease-specific signatures, in addition to biological and pharmacological data, to utilize rational pathway and drug prioritization in NF-PitNETs. We constructed an epigenome- and transcriptome-based PPI network and proposed hub genes. The signature-based drug repositioning based on the integration of multi-omics data was performed.

RESULTS

The construction of a disease-specific network based on three different biological levels revealed DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3 as hub proteins. Signature-based drug repositioning using hub proteins yielded repositioned drug candidates that were confirmed in silico via molecular docking. As a result of molecular docking simulations, palbociclib, linifanib, trametinib, eplerenone, niguldipine, and zuclopenthixol showed higher binding affinities with hub genes compared to their inhibitors and were proposed as potential repositioned therapeutics for the management of NF-PitNETs.

CONCLUSION

The proposed systems' biomedicine-oriented multi-omics data integration for drug repurposing to provide promising results for the construction of effective clinical therapeutics. To the best of our knowledge, this is the first study reporting epigenome- and transcriptome-based drug repositioning for NF-PitNETs using in silico confirmations.

Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation

BACKGROUND

Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation.

METHODS

Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures.

RESULTS

We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data.

CONCLUSION

N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.