Sex differences in kidney metabolism may reflect sex-dependent outcomes in human diabetic kidney disease.

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease (CKD) and progresses faster in males than in females. We identify sex-based differences in kidney metabolism and in the blood metabolome of male and female individuals with diabetes. Primary human proximal tubular epithelial cells (PTECs) from healthy males displayed increased mitochondrial respiration, oxidative stress, apoptosis, and greater injury when exposed to high glucose compared with PTECs from healthy females. Male human PTECs showed increased glucose and glutamine fluxes to the TCA cycle, whereas female human PTECs showed increased pyruvate content. The male human PTEC phenotype was enhanced by dihydrotestosterone and mediated by the transcription factor HNF4A and histone demethylase KDM6A. In mice where sex chromosomes either matched or did not match gonadal sex, male gonadal sex contributed to the kidney metabolism differences between males and females. A blood metabolomics analysis in a cohort of adolescents with or without diabetes showed increased TCA cycle metabolites in males. In a second cohort of adults with diabetes, females without DKD had higher serum pyruvate concentrations than did males with or without DKD. Serum pyruvate concentrations positively correlated with the estimated glomerular filtration rate, a measure of kidney function, and negatively correlated with all-cause mortality in this cohort. In a third cohort of adults with CKD, male sex and diabetes were associated with increased plasma TCA cycle metabolites, which correlated with all-cause mortality. These findings suggest that differences in male and female kidney metabolism may contribute to sex-dependent outcomes in DKD.

Evolution and advancements in genomics and epigenomics in OA research: How far we have come.

OBJECTIVE: Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. DESIGN: In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. RESULTS: Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. CONCLUSIONS: Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.

Immunological signatures unveiled by integrative systems vaccinology characterization of dengue vaccination trials and natural infection.

Introduction: Dengue virus infection is a global health problem lacking specific therapy, requiring an improved understanding of DENV immunity and vaccine responses. Considering the recent emerging of new dengue vaccines, here we performed an integrative systems vaccinology characterization of molecular signatures triggered by the natural DENV infection (NDI) and attenuated dengue virus infection models (DVTs). Methods and results: We analyzed 955 samples of transcriptomic datasets of patients with NDI and attenuated dengue virus infection trials (DVT1, DVT2, and DVT3) using a systems vaccinology approach. Differential expression analysis identified 237 common differentially expressed genes (DEGs) between DVTs and NDI. Among them, 28 and 60 DEGs were up or downregulated by dengue vaccination during DVT2 and DVT3, respectively, with 20 DEGs intersecting across all three DVTs. Enriched biological processes of these genes included type I/II interferon signaling, cytokine regulation, apoptosis, and T-cell differentiation. Principal component analysis based on 20 common DEGs (overlapping between DVTs and our NDI validation dataset) distinguished dengue patients by disease severity, particularly in the late acute phase. Machine learning analysis ranked the ten most critical predictors of disease severity in NDI, crucial for the anti-viral immune response. Conclusion: This work provides insights into the NDI and vaccine-induced overlapping immune response and suggests molecular markers (e.g., IFIT5, ISG15, and HERC5) for anti-dengue-specific therapies and effective vaccination development.

Intrauterine growth and the tangential expansion of the human cerebral cortex in times of food scarcity and abundance.

Tangential growth of the human cerebral cortex is driven by cell proliferation during the first and second trimester of pregnancy. Fetal growth peaks in mid-gestation. Here, we explore how genes associated with fetal growth relate to cortical growth. We find that both maternal and fetal genetic variants associated with higher birthweight predict larger cortical surface area. The relative dominance of the maternal vs. fetal variants in these associations show striking variations across birth years (1943 to 1966). The birth-year patterns vary as a function of the epigenetic status near genes differentially methylated in individuals exposed (or not) to famine during the Dutch Winter of 1944/1945. Thus, it appears that the two sets of molecular processes contribute to early cortical development to a different degree in times of food scarcity or its abundance.

Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning.

INTRODUCTION: Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult. There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management. OBJECTIVES: To identify metabolites capable of classifying patients with PsA according to their disease activity. METHODS: An in-house solid-phase microextraction (SPME)-liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance. RESULTS: The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids. CONCLUSION: Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin. Quantitative MS assays based on selected reaction monitoring, are required to quantify the candidate biomarkers identified.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies.

OBJECTIVE: Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. DESIGN: We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. RESULTS: Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. CONCLUSIONS: Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful.

Pathway Data Integration Portal (PathDIP) is an integrated pathway database that was developed to increase functional gene annotation coverage and reduce bias in pathway enrichment analysis. PathDIP 5 provides multiple improvements to enable more interpretable analysis: users can perform enrichment analysis using all sources, separate sources or by combining specific pathway subsets; they can select the types of sources to use or the types of pathways for the analysis, reducing the number of resulting generic pathways or pathways not related to users' research question; users can use API. All pathways have been mapped to seven representative types. The results of pathway enrichment can be summarized through knowledge-based pathway consolidation. All curated pathways were mapped to 53 pathway ontology-based categories. In addition to genes, pathDIP 5 now includes metabolites. We updated existing databases, included two new sources, PathBank and MetabolicAtlas, and removed outdated databases. We enable users to analyse their results using Drugst.One, where a drug-gene network is created using only the user's genes in a specific pathway. Interpreting the results of any analysis is now improved by multiple charts on all the results pages. PathDIP 5 is freely available at https://ophid.utoronto.ca/pathDIP.

From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports.

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

Identifying Serum Metabolomic Markers Associated with Skin Disease Activity in Patients with Psoriatic Arthritis.

Psoriatic arthritis (PsA) is a chronic, systemic, immune-mediated inflammatory disease causing cutaneous and musculoskeletal inflammation that affects 25% of patients with psoriasis. Current methods for evaluating PsA disease activity are not accurate enough for precision medicine. A metabolomics-based approach can elucidate psoriatic disease pathogenesis, providing potential objective biomarkers. With the hypothesis that serum metabolites are associated with skin disease activity, we aimed to identify serum metabolites associated with skin activity in PsA patients. We obtained serum samples from patients with PsA (n = 150) who were classified into mild, moderate and high disease activity groups based on the Psoriasis Area Severity Index. We used solid-phase microextraction (SPME) for sample preparation, followed by data acquisition via an untargeted liquid chromatography-mass spectrometry (LC-MS) approach. Disease activity levels were predicted using identified metabolites and machine learning algorithms. Some metabolites tentatively identified include eicosanoids with anti- or pro-inflammatory properties, like 12-Hydroxyeicosatetraenoic acid, which was previously implicated in joint disease activity in PsA. Other metabolites of interest were associated with dysregulation of fatty acid metabolism and belonged to classes such as bile acids, oxidized phospholipids, and long-chain fatty acids. We have identified potential metabolites associated with skin disease activity in PsA patients.

USNAP: fast unique dense region detection and its application to lung cancer.

MOTIVATION: Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. RESULTS: There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. AVAILABILITY AND IMPLEMENTATION: The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

Proteomic and genomic profiling of plasma exosomes from patients with ankylosing spondylitis.

INTRODUCTION: Recent advances in understanding the biology of ankylosing spondylitis (AS) using innovative genomic and proteomic approaches offer the opportunity to address current challenges in AS diagnosis and management. Altered expression of genes, microRNAs (miRNAs) or proteins may contribute to immune dysregulation and may play a significant role in the onset and persistence of inflammation in AS. The ability of exosomes to transport miRNAs across cells and alter the phenotype of recipient cells has implicated exosomes in perpetuating inflammation in AS. This study reports the first proteomic and miRNA profiling of plasma-derived exosomes in AS using comprehensive computational biology analysis. METHODS: Plasma samples from patients with AS and healthy controls (HC) were isolated via ultracentrifugation and subjected to extracellular vesicle flow cytometry analysis to characterise exosome surface markers by a multiplex immunocapture assay. Cytokine profiling of plasma-derived exosomes and cell culture supernatants was performed. Next-generation sequencing was used to identify miRNA populations in exosomes enriched from plasma fractions. CD4+ T cells were sorted, and the frequency and proliferation of CD4+ T-cell subsets were analysed after treatment with AS-exosomes using flow cytometry. RESULTS: The expression of exosome marker proteins CD63 and CD81 was elevated in the patients with AS compared with HC (q<0.05). Cytokine profiling in plasma-derived AS-exosomes demonstrated downregulation of interleukin (IL)-8 and IL-10 (q<0.05). AS-exosomes cocultured with HC CD4+ T cells induced significant upregulation of IFNα2 and IL-33 (q<0.05). Exosomes from patients with AS inhibited the proliferation of regulatory T cells (Treg), suggesting a mechanism for chronically activated T cells in this disease. Culture of CD4+ T cells from healthy individuals in the presence of AS-exosomes reduced the proliferation of FOXP3+ Treg cells and decreased the frequency of FOXP3+IRF4+ Treg cells. miRNA sequencing identified 24 differentially expressed miRNAs found in circulating exosomes of patients with AS compared with HC; 22 of which were upregulated and 2 were downregulated. CONCLUSIONS: Individuals with AS have different immunological and genetic profiles, as determined by evaluating the exosomes of these patients. The inhibitory effect of exosomes on Treg in AS suggests a mechanism contributing to chronically activated T cells in this disease.

Intestinal microRNAs and bacterial taxa in juvenile mice are associated, modifiable by allochthonous lactobacilli, and affect postnatal maturation.

The interplay between the intestinal microbiota and host is critical to intestinal ontogeny and homeostasis. MicroRNAs (miRNAs) may be an underlying link. Intestinal miRNAs are microbiota-dependent and, when shed in the lumen, affect resident microorganisms. Yet, longitudinal relationships between intestinal tissue miRNAs, luminal miRNAs, and luminal microorganisms have not been elucidated, especially in early life. Here, we investigated the postnatal cecal miRNA and microbiota populations, their relationship, and their impact on intestinal maturation in specific pathogen-free mice; we also assessed if they can be modified by intervention with allochthonous probiotic lactobacilli. We report that cecal and cecal content miRNA and microbiota signatures are temporally regulated, correlated, and modifiable by probiotics with implications for intestinal maturation. These findings help understand causal relationships within the gut ecosystem and provide a basis for preventing and managing their alterations in diseases throughout life. IMPORTANCE The gut microbiota affects intestinal microRNA (miRNA) signatures and is modified by host-derived luminal miRNA. This suggests the existence of close miRNA-microbiota relationships that are critical to intestinal homeostasis. However, an integrative analysis of these relationships and their evolution during intestinal postnatal maturation is lacking. We provide a system-level longitudinal analysis of miRNA-microbiota networks in the intestine of mice at the weaning transition, including tissue and luminal miRNA and luminal microbiota. To address causality and move toward translational applications, we used allochthonous probiotic lactobacilli to modify these longitudinal relationships and showed that they are critical for intestinal maturation in early life. These findings contribute to understand mechanisms that underlie the maturation of the intestinal ecosystem and suggest that interventions aiming at maintaining, or restoring, homeostasis cannot prescind from considering relationships among its components.

Tumor Cell-Intrinsic c-Myb Upregulation Stimulates Antitumor Immunity in a Murine Colorectal Cancer Model.

The transcription factor c-Myb is overexpressed in many different types of solid tumors, including colorectal cancer. However, its exact role in tumorigenesis is unclear. In this study, we show that tumor-intrinsic c-Myb expression in mouse models of colon cancer and melanoma suppresses tumor growth. Although no differences in proliferation, apoptosis, and angiogenesis of tumors were evident in tumors with distinct levels of c-Myb expression, we observed changes in intratumoral immune cell infiltrates. MC38 tumors with upregulated c-Myb expression showed increased numbers of CD103+ dendritic cells and eosinophils, but decreased tumor-associated macrophages (TAM). Concomitantly, an increase in the number of activated cytotoxic CD8+ T cells upon c-Myb upregulation was observed, which correlated with a pro-inflammatory tumor microenvironment and increased numbers of M1 polarized TAMs. Mechanistically, c-Myb upregulation in immunogenic MC38 colon cancer cells resulted in enhanced expression of immunomodulatory genes, including those encoding ß2-microglobulin and IFNß, and decreased expression of the gene encoding the chemokine receptor CCR2. The increased numbers of activated cytotoxic CD8+ T cells contributed to tumor growth attenuation. In poorly immunogenic CT26, LLC, and B16-BL6 tumor cells, c-Myb upregulation did not affect the immunomodulatory gene expression. Despite this, c-Myb upregulation led to reduced B16-BL6 tumor growth but it did not affect tumor growth of CT26 and LLC tumors. Altogether, we postulate that c-Myb functions as a tumor suppressor in a tumor cell-type specific manner and modulates antitumor immunity.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

The let-7b-5p, miR-326, and miR-125a-3p are associated with left ventricular systolic dysfunction in post-myocardial infarction.

Background: Acute ST-elevation myocardial infarction (STEMI) can lead to adverse cardiac remodeling, resulting in left ventricular systolic dysfunction (LVSd) and heart failure. Epigenetic regulators, such as microRNAs, may be involved in the physiopathology of LVSd. Objective: This study explored microRNAs in peripheral blood mononuclear cells (PBMC) of post-myocardial infarction patients with LVSd. Methods: Post-STEMI patients were grouped as having (LVSd, n = 9) or not LVSd (non-LVSd, n = 16). The expression of 61 microRNAs was analyzed in PBMC by RT-qPCR and the differentially expressed microRNAs were identified. Principal Component Analysis stratified the microRNAs based on the development of dysfunction. Predictive variables of LVSd were investigated through logistic regression analysis. A system biology approach was used to explore the regulatory molecular network of the disease and an enrichment analysis was performed. Results: The let-7b-5p (AUC: 0.807; 95% CI: 0.63-0.98; p = 0.013), miR-125a-3p (AUC: 0.800; 95% CI: 0.61-0.99; p = 0.036) and miR-326 (AUC: 0.783; 95% CI: 0.54-1.00; p = 0.028) were upregulated in LVSd (p < 0.05) and discriminated LVSd from non-LVSd. Multivariate logistic regression analysis showed let-7b-5p (OR: 16.00; 95% CI: 1.54-166.05; p = 0.020) and miR-326 (OR: 28.00; 95% CI: 2.42-323.70; p = 0.008) as predictors of LVSd. The enrichment analysis revealed association of the targets of these three microRNAs with immunological response, cell-cell adhesion, and cardiac changes. Conclusion: LVSd alters the expression of let-7b-5p, miR-326, and miR-125a-3p in PBMC from post-STEMI, indicating their potential involvement in the cardiac dysfunction physiopathology and highlighting these miRNAs as possible LVSd biomarkers.

Association of Nonconcussive Repetitive Head Impacts and Intense Physical Activity With Levels of Phosphorylated Tau181 and Total Tau in Plasma of Young Elite Soccer Players.

Importance: Head impacts resulting in traumatic brain injury (TBI) lead to the elevation of phosphorylated tau protein (p-tau181) in plasma. To our knowledge, this study is the first to investigate dynamics of p-tau181 levels and the ratio of p-tau181 to total tau in individuals after nonconcussive head impacts. Objective: To determine the association of repetitive low-intensity head impacts on p-tau181 and total tau protein levels in the plasma of young adult elite soccer players and assess the possible association of head impacts with focused attention and cognitive flexibility. Design, Setting, and Participants: In this cohort study, young elite soccer players performed intense physical activity with and without heading the ball. The study was conducted at a university facility in Slovakia from October 1, 2021, to May 31, 2022. Eligible participants were selected based on similarities in demographic variables, excluding those with a history of TBI. Main Outcomes and Measures: The primary study outcomes were the levels of total tau protein and p-tau181 in plasma samples and the cognitive status of the study participants. Results: A total of 37 male athletes participated in the study (mean [SD] age: exercise group, 21.6 [1.6] years; heading group, 21.2 [1.5] years). We found significantly elevated levels of total tau and p-tau181 in the plasma of soccer players 1 hour after physical exercise (tau, 1.4-fold; 95% CI, 1.2-1.5; P < .001; p-tau181, 1.4-fold; 95% CI, 1.3-1.5, P < .001) and repetitive head impacts (tau, 1.3-fold; 95% CI, 1.2-1.4; P < .001; p-tau181, 1.5-fold; 95% CI, 1.4-1.7 P < .001). The ratio of p-tau181 to tau was significantly higher 1 hour after exercise and heading training, and remained elevated specifically in the heading group even after 24 hours (1.2-fold; 95% CI, 1.1-1.3; P = .002). Performance in cognitive tests revealed a significant decline in focused attention and cognitive flexibility after physical exercise and heading training; physical exercise of higher intensity without heading training was associated with a greater negative cognitive performance than heading only. Conclusions and Relevance: In this cohort study of young elite soccer players, the elevation of p-tau181 and tau was observed after acute intense physical activity and nonconcussive repetitive head impacts. The increase of p-tau181 levels relative to tau after 24 hours indicated an acute enrichment of phosphorylated tau fraction in the periphery when compared with preimpact levels; an imbalance of tau proteins may have long-lasting consequences in the brain of head-impacted individuals.

An integrated multiomics analysis of rectal cancer patients identified POU2F3 as a putative druggable target and entinostat as a cytotoxic enhancer of 5-fluorouracil.

Rectal cancer (RC) accounts for one-third of colorectal cancers (CRC), and 40% of these are locally advanced rectal cancers (LARC). The use of neoadjuvant chemoradiotherapy (nCRT) significantly reduces the rate of local recurrence compared to adjuvant therapy or surgery alone. However, after nCRT, up to 40%-60% of patients show a poor pathological response, while only about 20% achieve a pathological complete response. In this scenario, the identification of novel predictors of tumor response to nCRT is urgently needed to reduce LARC mortality and to spare poorly responding patients from unnecessary treatments. Therefore, by combining gene and microRNA expression datasets with proteomic data from LARC patients, we developed an integrated network centered on seven hub-genes putatively involved in the response to nCRT. In an independent validation cohort of LARC patients, we confirmed that differential expression of NFKB1, TRAF6 and STAT3 is correlated with the response to nCRT. In addition, the functional enrichment analysis also revealed that these genes are strongly related to hallmarks of cancer and inflammation, whose dysfunction may causatively affect LARC patient's response to nCRT. Furthermore, by constructing the transcription factor-module network, we hypothesized a protective role of POU2F3 gene, which could be used as a new drug target in LARC patients. Finally, we identified and tested in vitro entinostat, a histone deacetylase inhibitor, as a chemical compound that could be combined with a classical therapeutic regimen in order to design more efficient therapeutic strategies in LARC management.

Integrative systems immunology uncovers molecular networks of the cell cycle that stratify COVID-19 severity.

Several perturbations in the number of peripheral blood leukocytes, such as neutrophilia and lymphopenia associated with Coronavirus disease 2019 (COVID-19) severity, point to systemic molecular cell cycle alterations during severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, the landscape of cell cycle alterations in COVID-19 remains primarily unexplored. Here, we performed an integrative systems immunology analysis of publicly available proteome and transcriptome data to characterize global changes in the cell cycle signature of COVID-19 patients. We found significantly enriched cell cycle-associated gene co-expression modules and an interconnected network of cell cycle-associated differentially expressed proteins (DEPs) and genes (DEGs) by integrating the molecular data of 1469 individuals (981 SARS-CoV-2 infected patients and 488 controls [either healthy controls or individuals with other respiratory illnesses]). Among these DEPs and DEGs are several cyclins, cell division cycles, cyclin-dependent kinases, and mini-chromosome maintenance proteins. COVID-19 patients partially shared the expression pattern of some cell cycle-associated genes with other respiratory illnesses but exhibited some specific differential features. Notably, the cell cycle signature predominated in the patients' blood leukocytes (B, T, and natural killer cells) and was associated with COVID-19 severity and disease trajectories. These results provide a unique global understanding of distinct alterations in cell cycle-associated molecules in COVID-19 patients, suggesting new putative pathways for therapeutic intervention.

MirDIP 5.2: tissue context annotation and novel microRNA curation.

MirDIP is a well-established database that aggregates microRNA-gene human interactions from multiple databases to increase coverage, reduce bias, and improve usability by providing an integrated score proportional to the probability of the interaction occurring. In version 5.2, we removed eight outdated resources, added a new resource (miRNATIP), and ran five prediction algorithms for miRBase and mirGeneDB. In total, mirDIP 5.2 includes 46 364 047 predictions for 27 936 genes and 2734 microRNAs, making it the first database to provide interactions using data from mirGeneDB. Moreover, we curated and integrated 32 497 novel microRNAs from 14 publications to accelerate the use of these novel data. In this release, we also extend the content and functionality of mirDIP by associating contexts with microRNAs, genes, and microRNA-gene interactions. We collected and processed microRNA and gene expression data from 20 resources and acquired information on 330 tissue and disease contexts for 2657 microRNAs, 27 576 genes and 123 651 910 gene-microRNA-tissue interactions. Finally, we improved the usability of mirDIP by enabling the user to search the database using precursor IDs, and we integrated miRAnno, a network-based tool for identifying pathways linked to specific microRNAs. We also provide a mirDIP API to facilitate access to its integrated predictions. Updated mirDIP is available at https://ophid.utoronto.ca/mirDIP.