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.

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.

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.

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.

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.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation.

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

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.

Osteoarthritis Data Integration Portal (OsteoDIP): A web-based gene and non-coding RNA expression database.

Objective: OsteoDIP aims to collect and provide, in a simple searchable format, curated high throughput RNA expression data related to osteoarthritis. Design: Datasets are collected annually by searching "osteoarthritis gene expression profile" in PubMed. Only publications containing patient data and a list of differentially expressed genes are considered. From 2020, the search has expanded to include non-coding RNAs. Moreover, a search in GEO for "osteoarthritis" datasets has been performed using 'Homo sapiens' and 'Expression profiling by array' filters. Annotations for genes linked to osteoarthritis have been downloaded from external databases. Results: Out of 1204 curated papers, 63 have been included in OsteoDIP, while GEO curation led to the collection of 28 datasets. Literature data provides a snapshot of osteoarthritis research derived from 1924 human samples, while GEO datasets provide expression for additional 1012 patients. Similar to osteoarthritis literature, OsteoDIP data has been created mostly from studies focused on knee, and the tissue most frequently investigated is cartilage. GEO data sets were fully integrated with associated clinical data. We showcase examples and use cases applicable for translational research in osteoarthritis. Conclusions: OsteoDIP is publicly available at http://ophid.utoronto.ca/OsteoDIP. The website is easy to navigate and all the data is available for download. Data consolidation allows researchers to perform comparisons across studies and to combine data from different datasets. Our examples show how OsteoDIP can integrate with and improve osteoarthritis researchers' pipelines.

Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture.

Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.

Circulating microRNAs differentiate fast-progressing from slow-progressing and non-progressing knee osteoarthritis in the Osteoarthritis Initiative cohort.

Introduction: The objective of this study is to identify circulating microRNAs that distinguish fast-progressing radiographic knee osteoarthritis (OA) in the Osteoarthritis Initiative cohort by applying microRNA-sequencing. Methods: Participants with Kellgren-Lawrence (KL) grade 0/1 at baseline were included (N = 106). Fast-progressors were defined by an increase to KL 3/4 by 4-year follow-up (N = 20), whereas slow-progressors showed an increase to KL 2/3/4 only at 8-year follow-up (N = 35). Non-progressors remained at KL 0/1 by 8-year follow-up (N = 51). MicroRNA-sequencing was performed on plasma collected at baseline and 4-year follow-up from the same participants. Negative binomial models were fitted to identify differentially expressed (DE) microRNAs. Penalized logistic regression (PLR) analyses were performed to select combinations of DE microRNAs that distinguished fast-progressors. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate predictive ability. Results: DE analyses revealed 48 microRNAs at baseline and 2 microRNAs at 4-year follow-up [false discovery rate (FDR) < 0.05] comparing fast-progressors with both slow-progressors and non-progressors. Among these were hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-320e, which were predicted to target gene families, including members of the 14-3-3 gene family, involved in signal transduction. PLR models included miR-320 members as top predictors of fast-progressors and yielded AUC ranging from 82.6 to 91.9, representing good accuracy. Conclusion: The miR-320 family is associated with fast-progressing radiographic knee OA and merits further investigation as potential biomarkers and mechanistic drivers of knee OA.

Vesicular traffic-mediated cell-to-cell signaling at the immune synapse in Ankylosing Spondylitis.

The chronic inflammatory disease ankylosing spondylitis (AS) is marked by back discomfort, spinal ankylosis, and extra-articular symptoms. In AS, inflammation is responsible for both pain and spinal ankylosis. However, the processes that sustain chronic inflammation remain unknown. Despite the years of research conducted to decipher the intricacy of AS, little progress has been made in identifying the signaling events that lead to the development of this disease. T cells, an immune cell type that initiates and regulates the body's response to infection, have been established to substantially impact the development of AS. T lymphocytes are regarded as a crucial part of adaptive immunity for the control of the immune system. A highly coordinated interaction involving antigen-presenting cells (APCs) and T cells that regulate T cell activation constitutes an immunological synapse (IS). This first phase leads to the controlled trafficking of receptors and signaling mediators involved in folding endosomes to the cellular interface, which allows the transfer of information from T cells to APCs through IS formation. Discrimination of self and nonself antigen is somatically learned in adaptive immunity. In an autoimmune condition such as AS, there is a disturbance of self/nonself antigen discrimination; available findings imply that the IS plays a preeminent role in the adaptive immune response. In this paper, we provide insights into the genesis of AS by evaluating recent developments in the function of vesicular trafficking in IS formation and the targeted release of exosomes enriched microRNAs (miRNA) at the synaptic region in T cells.

miRAnno-network-based functional microRNA annotation.

MOTIVATION: Functional annotation is a common part of microRNA (miRNA)-related research, typically carried as pathway enrichment analysis of the selected miRNA targets. Here, we propose miRAnno, a fast and easy-to-use web application for miRNA annotation. RESULTS: miRAnno uses comprehensive molecular interaction network and random walks with restart to measure the association between miRNAs and individual pathways. Independent validation shows that miRAnno achieves higher signal-to-noise ratio compared to the standard enrichment analysis. AVAILABILITY AND IMPLEMENTATION: miRAnno is freely available at https://ophid.utoronto.ca/miRAnno/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis.

Improved bioassays have significantly increased the rate of identifying new protein-protein interactions (PPIs), and the number of detected human PPIs has greatly exceeded early estimates of human interactome size. These new PPIs provide a more complete view of disease mechanisms but precise understanding of how PPIs affect phenotype remains a challenge. It requires knowledge of PPI context (e.g. tissues, subcellular localizations), and functional roles, especially within pathways and protein complexes. The previous IID release focused on PPI context, providing networks with comprehensive tissue, disease, cellular localization, and druggability annotations. The current update adds developmental stages to the available contexts, and provides a way of assigning context to PPIs that could not be previously annotated due to insufficient data or incompatibility with available context categories (e.g. interactions between membrane and cytoplasmic proteins). This update also annotates PPIs with conservation across species, directionality in pathways, membership in large complexes, interaction stability (i.e. stable or transient), and mutation effects. Enrichment analysis is now available for all annotations, and includes multiple options; for example, context annotations can be analyzed with respect to PPIs or network proteins. In addition to tabular view or download, IID provides online network visualization. This update is available at http://ophid.utoronto.ca/iid.

Improving Analysis and Annotation of Microarray Data with Protein Interactions.

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.

Pathway Enrichment Analysis of Microarray Data.

Microarray analyses usually result in a list of differential genes that need to be annotated to link them the phenotype being studied, help planning validation experiments and interpretation of the results. Pathway enrichment analyses are frequently used for such purpose, where pathways are human created models of molecular activities and processes. While different types of pathway enrichment are available, we focus this protocol on the most frequent type-overrepresentation analysis. Many databases collect different sets of pathways and curate different sets of genes for the same pathways, so it is important to carefully choose the most suitable pathway source to perform enrichment analysis. To provide a comprehensive pathway analysis, in this protocol we will use pathDIP, which supports comprehensive enrichment analysis by integrating 22 main pathway databases. We will also describe the steps needed to visualize the enriched pathways using GSOAP.

Estrogen Receptor 1 Inhibition of Wnt/ß-Catenin Signaling Contributes to Sex Differences in Hepatocarcinogenesis.

BACKGROUND: Hepatocellular Carcinoma (HCC) is a sexually dimorphic cancer, with female sex being independently protective against HCC incidence and progression. The aim of our study was to understand the mechanism of estrogen receptor signaling in driving sex differences in hepatocarcinogenesis. METHODS: We integrated 1,268 HCC patient sample profiles from publicly available gene expression data to identify the most differentially expressed genes (DEGs). We mapped DEGs into a physical protein interaction network and performed network topology analysis to identify the most important proteins. Experimental validation was performed in vitro on HCC cell lines, in and in vivo, using HCC mouse model. RESULTS: We showed that the most central protein, ESR1, is HCC prognostic, as increased ESR1 expression was protective for overall survival, with HR=0.45 (95%CI 0.32-0.64, p=4.4E-06), and was more pronounced in women. Transfection of HCC cell lines with ESR1 and exposure to estradiol affected expression of genes involved in the Wnt/ß-catenin signaling pathway. ER-α (protein product of ESR1) agonist treatment in a mouse model of HCC resulted in significantly longer survival and decreased tumor burden (p<0.0001), with inhibition of Wnt/ß-Catenin signaling. In vitro experiments confirmed colocalization of ß-catenin with ER-α, leading to inhibition of ß-catenin-mediated transcription of target genes c-Myc and Cyclin D1. CONCLUSION: Combined, the centrality of ESR1 and its inhibition of the Wnt/ß-catenin signaling axis provide a biological rationale for protection against HCC incidence and progression in women.

A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis.

Discovery of non-coding RNAs continues to provide new insights into some of the key molecular drivers of musculoskeletal diseases. Among these, microRNAs have received widespread attention for their roles in osteoarthritis and rheumatoid arthritis. With evidence to suggest that long non-coding RNAs and circular RNAs function as competing endogenous RNAs to sponge microRNAs, the net effect on gene expression in specific disease contexts can be elusive. Studies to date have focused on elucidating individual long non-coding-microRNA-gene target axes and circular RNA-microRNA-gene target axes, with a paucity of data integrating experimentally validated effects of non-coding RNAs. To address this gap, we curated recent studies reporting non-coding RNA axes in chondrocytes from human osteoarthritis and in fibroblast-like synoviocytes from human rheumatoid arthritis. Using an integrative computational biology approach, we then combined the findings into cell- and disease-specific networks for in-depth interpretation. We highlight some challenges to data integration, including non-existent naming conventions and out-of-date databases for non-coding RNAs, and some successes exemplified by the International Molecular Exchange Consortium for protein interactions. In this perspective article, we suggest that data integration is a useful in silico approach for creating non-coding RNA networks in arthritis and prioritizing interactions for further in vitro and in vivo experimentation in translational research.

Increased Autoantibodies Against Ro/SS-A, CENP-B, and La/SS-B in Patients With Kidney Allograft Antibody-mediated Rejection.

Antibody-mediated rejection (AMR) causes more than 50% of late kidney graft losses. In addition to anti-human leukocyte antigen (HLA) donor-specific antibodies, antibodies against non-HLA antigens are also linked to AMR. Identifying key non-HLA antibodies will improve our understanding of AMR. METHODS: We analyzed non-HLA antibodies in sera from 80 kidney transplant patients with AMR, mixed rejection, acute cellular rejection (ACR), or acute tubular necrosis. IgM and IgG antibodies against 134 non-HLA antigens were measured in serum samples collected pretransplant or at the time of diagnosis. RESULTS: Fifteen non-HLA antibodies were significantly increased (P < 0.05) in AMR and mixed rejection compared with ACR or acute tubular necrosis pretransplant, and 7 at diagnosis. AMR and mixed cases showed significantly increased pretransplant levels of IgG anti-Ro/Sjögren syndrome-antigen A (SS-A) and anti-major centromere autoantigen (CENP)-B, compared with ACR. Together with IgM anti-CENP-B and anti-La/SS-B, these antibodies were significantly increased in AMR/mixed rejection at diagnosis. Increased IgG anti-Ro/SS-A, IgG anti-CENP-B, and IgM anti-La/SS-B were associated with the presence of microvascular lesions and class-II donor-specific antibodies (P < 0.05). Significant increases in IgG anti-Ro/SS-A and IgM anti-CENP-B antibodies in AMR/mixed rejection compared with ACR were reproduced in an external cohort of 60 kidney transplant patients. CONCLUSIONS: This is the first study implicating autoantibodies anti-Ro/SS-A and anti-CENP-B in AMR. These antibodies may participate in the crosstalk between autoimmunity and alloimmunity in kidney AMR.