Untargeted Metabolomics Identifies a Novel Panel of Markers for Autologous Blood Transfusion.

Untargeted metabolomics was used to analyze serum and urine samples for biomarkers of autologous blood transfusion (ABT). Red blood cell concentrates from donated blood were stored for 35−36 days prior to reinfusion into the donors. Participants were sampled at different time points post-donation and up to 7 days post-transfusion. Metabolomic profiling was performed using ACQUITY ultra performance liquid chromatography (UPLC), Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The markers of ABT were determined by principal component analysis and metabolites that had p < 0.05 and met ≥ 2-fold change from baseline were selected. A total of 11 serum and eight urinary metabolites, including two urinary plasticizer metabolites, were altered during the study. By the seventh day post-transfusion, the plasticizers had returned to baseline, while changes in nine other metabolites (seven serum and two urinary) remained. Five of these metabolites (serum inosine, guanosine and sphinganine and urinary isocitrate and erythronate) were upregulated, while serum glycourdeoxycholate, S-allylcysteine, 17-alphahydroxypregnenalone 3 and Glutamine conjugate of C6H10O2 (2)* were downregulated. This is the first study to identify a panel of metabolites, from serum and urine, as markers of ABT. Once independently validated, it could be universally adopted to detect ABT.

Cardiac myosin binding protein-C variants in paediatric-onset hypertrophic cardiomyopathy: natural history and clinical outcomes.

BACKGROUND: Variants in the cardiac myosin-binding protein C gene (MYBPC3) are a common cause of hypertrophic cardiomyopathy (HCM) in adults and have been associated with late-onset disease, but there are limited data on their role in paediatric-onset HCM. The objective of this study was to describe natural history and clinical outcomes in a large cohort of children with HCM and pathogenic/likely pathogenic (P/LP) MYBPC3 variants. METHODS AND RESULTS: Longitudinal data from 62 consecutive patients diagnosed with HCM under 18 years of age and carrying at least one P/LP MYBPC3 variant were collected from a single specialist referral centre. The primary patient outcome was a major adverse cardiac event (MACE). Median age at diagnosis was 10 (IQR: 2-14) years, with 12 patients (19.4%) diagnosed in infancy. Forty-seven (75%) were boy and 31 (50%) were probands. Median length of follow-up was 3.1 (IQR: 1.6-6.9) years. Nine patients (14.5%) experienced an MACE during follow-up and five (8%) died. Twenty patients (32.3%) had evidence of ventricular arrhythmia, including 6 patients (9.7%) presenting with out-of-hospital cardiac arrest. Five-year freedom from MACE for those with a single or two MYBPC3 variants was 95.2% (95% CI: 78.6% to 98.5%) and 68.4% (95% CI: 40.6% to 88.9%), respectively (HR 4.65, 95% CI: 1.16 to 18.66, p=0.03). CONCLUSIONS: MYBPC3 variants can cause childhood-onset disease, which is frequently associated with life-threatening ventricular arrhythmia. Clinical outcomes in this cohort vary substantially from aetiologically and genetically mixed paediatric HCM cohorts described previously, highlighting the importance of identifying specific genetic subtypes for clinical management of childhood HCM.

Analysis of Anti-RNA Polymerase III Antibody-positive Systemic Sclerosis and Altered GPATCH2L and CTNND2 Expression in Scleroderma Renal Crisis.

OBJECTIVE: Scleroderma renal crisis (SRC) is a life-threatening complication of systemic sclerosis (SSc) strongly associated with anti-RNA polymerase III antibody (ARA) autoantibodies. We investigated genetic susceptibility and altered protein expression in renal biopsy specimens in ARA-positive patients with SRC. METHODS: ARA-positive patients (n = 99) with at least 5 years' follow-up (49% with a history of SRC) were selected from a well characterized SSc cohort (n = 2254). Cases were genotyped using the Illumina Human Omni-express chip. Based on initial regression analysis, 9 single-nucleotide polymorphisms (SNP) were chosen for validation in a separate cohort of 256 ARA-positive patients (40 with SRC). Immunostaining of tissue sections from SRC or control kidney was used to quantify expression of candidate proteins based upon genetic analysis of the discovery cohort. RESULTS: Analysis of 641,489 SNP suggested association of POU2F1 (rs2093658; P = 1.98 × 10-5), CTNND2 (rs1859082; P = 5.58 × 10-5), HECW2 (rs16849716; P = 1.2 × 10-4), and GPATCH2L (rs935332; P = 4.92 × 10-5) with SRC. Further, the validation cohort showed an association between rs935332 within the GPATCH2L region, with SRC (P = 0.025). Immunostaining of renal biopsy sections showed increased tubular expression of GPATCH2L (P = 0.026) and glomerular expression of CTNND2 (P = 0.026) in SRC samples (n = 8) compared with normal human kidney controls (n = 8), despite absence of any genetic replication for the associated SNP. CONCLUSION: Increased expression of 2 candidate proteins, GPATCH2L and CTNND2, in SRC compared with control kidney suggests a potential role in pathogenesis of SRC. For GPATCH2L, this may reflect genetic susceptibility in ARA-positive patients with SSc based upon 2 independent cohorts.

Expanding the horizons of microRNA bioinformatics.

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.