Sequencing identifies a distinct signature of circulating microRNAs in early radiographic knee osteoarthritis

OBJECTIVE: MicroRNAs act locally and systemically to impact osteoarthritis (OA) pathophysiology, but comprehensive profiling of the circulating miRNome in early vs late stages of OA has yet to be conducted. Sequencing has emerged as the preferred method for microRNA profiling since it offers high sensitivity and specificity. Our objective is to sequence the miRNome in plasma from 91 patients with early [Kellgren-Lawrence (KL) grade 0 or 1 (n = 41)] or late [KL grade 3 or 4 (n = 50)] symptomatic radiographic knee OA to identify unique microRNA signatures in each disease state. DESIGN: MicroRNA libraries were prepared using the QIAseq miRNA Library Kit and sequenced on the Illumina NextSeq 550.Counts were produced for microRNAs captured in miRBase and for novel microRNAs. Statistical, bioinformatics, and computational biology approaches were used to refine and interpret the final list of microRNAs. RESULTS: From 215 differentially expressed microRNAs (FDR \u3c 0.01), 97 microRNAs showed an increase or decrease in expression in ≥85% of samples in the early OA group as compared to the median expression in the late OA group. Increasing this threshold to ≥95%, seven microRNAs were identified: hsa-miR-335-3p, hsa-miR-199a-5p, hsa-miR-671-3p, hsa-miR-1260b, hsa-miR-191-3p, hsa-miR-335-5p, and hsa-miR-543. Four novel microRNAs were present in ≥50% of early OA samples and had 27 predicted gene targets in common with the prioritized set of predicted gene targets from the 97 microRNAs, suggesting common underlying mechanisms. CONCLUSION: Applying sequencing to well-characterized patient cohorts produced unbiased profiling of the circulating miRNome and identified a unique panel of 11 microRNAs in early radiographic knee OA

Integration of DNA Copy Number Alterations and Transcriptional Expression Analysis in Human Gastric Cancer

Background: Genomic instability with frequent DNA copy number alterations is one of the key hallmarks of carcinogenesis. The chromosomal regions with frequent DNA copy number gain and loss in human gastric cancer are still poorly defined. It remains unknown how the DNA copy number variations contributes to the changes of gene expression profiles, especially on the global level. Principal Findings: We analyzed DNA copy number alterations in 64 human gastric cancer samples and 8 gastric cancer cell lines using bacterial artificial chromosome (BAC) arrays based comparative genomic hybridization (aCGH). Statistical analysis was applied to correlate previously published gene expression data obtained from cDNA microarrays with corresponding DNA copy number variation data to identify candidate oncogenes and tumor suppressor genes. We found that gastric cancer samples showed recurrent DNA copy number variations, including gains at 5p, 8q, 20p, 20q, and losses at 4q, 9p, 18q, 21q. The most frequent regions of amplification were 20q12 (7/72), 20q12-20q13.1 (12/72), 20q13.1-20q13.2 (11/72) and 20q13.2-20q13.3 (6/72). The most frequent deleted region was 9p21 (8/72). Correlating gene expression array data with aCGH identified 321 candidate oncogenes, which were overexpressed and showed frequent DNA copy number gains; and 12 candidate tumor suppressor genes which were down-regulated and showed frequent DNA copy number losses in human gastric cancers. Three networks of significantly expressed genes in gastric cancer samples were identified by ingenuity pathway analysis. Conclusions: This study provides insight into DNA copy number variations and their contribution to altered gene expression profiles during human gastric cancer development. It provides novel candidate driver oncogenes or tumor suppressor genes for human gastric cancer, useful pathway maps for the future understanding of the molecular pathogenesis of this malignancy, and the construction of new therapeutic targets. © 2012 Fan et al.published_or_final_versio

TYK2 Kinase Activity Is Required for Functional Type I Interferon Responses In Vivo

Tyrosine kinase 2 (TYK2) is a member of the Janus kinase (JAK) family and is involved in cytokine signalling. In vitro analyses suggest that TYK2 also has kinase-independent, i.e., non-canonical, functions. We have generated gene-targeted mice harbouring a mutation in the ATP-binding pocket of the kinase domain. The Tyk2 kinase-inactive (Tyk2K923E) mice are viable and show no gross abnormalities. We show that kinase-active TYK2 is required for full-fledged type I interferon- (IFN) induced activation of the transcription factors STAT1-4 and for the in vivo antiviral defence against viruses primarily controlled through type I IFN actions. In addition, TYK2 kinase activity was found to be required for the protein’s stability. An inhibitory function was only observed upon over-expression of TYK2K923E in vitro. Tyk2K923E mice represent the first model for studying the kinase-independent function of a JAK in vivo and for assessing the consequences of side effects of JAK inhibitors

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

CIRCULATING MIR-126-3P IS ELEVATED IN LATE-STAGE RADIOGRAPHIC KNEE OSTEOARTHRITIS

Purpose: There is an outstanding need to identify minimally invasive biomarkers for reliable detection of knee osteoarthritis (OA). Current clinical diagnostic methods are limited since OA symptoms do not always correlate with structural degeneration in the joint. Soluble biochemical markers provide a better readout of disease activity, and a variety of blood, synovial fluid, and urine biomarkers have been explored in OA, including microRNAs. As small, non-coding RNAs, microRNAs are promising biomarker candidates since they are easy to detect in biofluids, are relatively stable (i.e. resistant to enzymatic degradation), and can be reliably quantified such that levels can be linked to disease. Furthermore, microRNAs are known drivers of OA pathology, and their expression may precede joint degeneration, when opportunities for intervention still exist. Based on this, circulating microRNAs have strong potential to serve as biomarkers for knee OA, but a major limitation is lack of reproducibility across studies profiling circulating microRNAs in OA. While sequencing is the gold standard method for unbiased profiling of microRNAs, there are critical experimental design and analysis parameters that can impact the results. The objectives of this study are to identify circulating microRNAs in late-stage radiographic knee OA compared to non-OA controls using existing microRNA-sequencing data, and to validate the findings using our recently established Henry Ford Health System (HFHS) Osteoarthritis cohort. Methods: We searched the literature for microRNA-sequencing studies profiling circulating microRNAs in OA versus non-OA participants and identified two studies, one conducted in Norway (Aae et al., 2020) and the other in France (Rousseau et al., 2020). We obtained raw sequencing data from the authors and re-analyzed the data by applying our recently reported method for microRNA-sequencing analysis. Among other changes (e.g. normalization method), we re-defined the cohorts to include participants with only Kellgren-Lawrence (KL) grades 3 and 4 in the OA group (compared to KL 0 to 4 and total knee arthroplasty in the original Norway study and KL 2 and 3 in the original France study) and with KL grade 0 in the non-OA group (consistent with the original Norway study and compared to KL 0 and 1 in the original France study). Following differential expression analysis using a multivariate model adjusted for age, sex, and body mass index, we prioritized microRNAs that were common to the OA groups in both cohorts. We next performed validation by real-time PCR in the HFHS Osteoarthritis cohort utilizing plasma samples from participants with unilateral and/or bilateral knee and/or hip OA and non-OA controls. Results: As reported by the two original microRNA-sequencing studies, there were no significant differences in the Norway cohort and 3 differentially expressed microRNAs in OA (miR-139-5p, miR-1299, miR-200a-3p) in the France cohort, though none achieved validation in real-time PCR experiments. Following our re-analysis, we identified 23 and 82 differentially expressed microRNAs (p\u3c0.1) in the Norway and France cohorts, respectively, with 3 microRNAs in common between the OA groups: miR-126-3p, miR-30c-2-3p, and miR-144-5p. Of these, miR-126-3p had the highest counts-per-million in both cohorts, showed an increased fold change in OA in both cohorts (p\u3c0.05; Figure 1A and 1B), and was found in 100% and 91% of OA samples and 0% and 35% of non-OA samples in the Norway and France cohorts, respectively. Furthermore, a report in 2014 by Borgonio Cuadra et al. identified circulating miR-126 to be elevated in OA (KL 2 and 3) compared to non-OA (KL 0) by both real-time PCR array and real-time PCR validation experiments (Figure 1C). This led us to explore miR-126-3p expression in plasma samples from the HFHS Osteoarthritis cohort where we found a consistent increase in knee OA (symptomatic, KL 3 or 4), irrespective of unilateral or bilateral, compared to non-OA controls (asymptomatic, KL 0), yet no significant increase in hip OA (Figure 1D). Conclusions: Through application of our microRNA-sequencing analysis method, we identified circulating miR-126-3p to be increased in late-stage radiographic knee OA compared to non-OA controls in two studies originally reporting no validated differences. This finding is supported by previous literature identifying circulating miR-126 to be elevated in knee OA compared to non-OA controls and is extended by our data showing that the increase may be unique to knee OA and not hip OA. Taken together, there are now data from four independent cohorts demonstrating an increase in circulating miR-126-3p in knee OA, suggesting that this microRNA may have utility as a biomarker for OA. [Formula presented