Citations

AbstractBACKGROUNDClinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables.METHODSAn ultra-performance liquid chromatography–tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 − P30); goal <10%].RESULTSThe assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r2 > 0.98), and analyte recovery (98.5%–113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 − P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups.CONCLUSIONSThe new eGFRmet equation could serve as a confirmatory test for GFR estimation.

Abstract Motivation The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. Results Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. Availability and implementation LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. Supplementary information Supplementary data are available at Bioinformatics online.

Interleukin (IL)-17 is a T helper 17 cytokine implicated in the pathogenesis of many autoimmune diseases, including rheumatoid arthritis (RA). Although IL-17A has a well-established role in murine pulmonary fibrosis models, its role in the tissue remodeling and fibrosis occurring in idiopathic pulmonary fibrosis (IPF) and RA-associated interstitial lung disease (RA-ILD) is not very well defined. To address this question, we utilized complimentary studies to determine responsiveness of human normal and pathogenic lung fibroblasts to IL-17A and used lung biopsies acquired from patients with IPF and RA-ILD to determine IL-17A receptor (IL-17RA) expression. Both normal and pathogenic IPF lung fibroblasts express functional IL-17RA and respond to IL-17A stimulation with cell proliferation, generation of extracellular matrix (ECM) proteins, and induction of myofibroblast transdifferentiation. Small interfering RNA (siRNA) silencing of IL-17RA attenuated this fibroblast response to IL-17A on ECM production. These fibroblast responses to IL-17A are dependent on NF-κB-mediated signaling. In addition, inhibiting Janus activated kinase (JAK) 2 by either siRNA or a selective pharmacological inhibitor, AZD1480—but not a JAK1/JAK3 selective inhibitor, tofacitinib—also significantly reduced this IL-17A-induced fibrogenic response. Lung biopsies of RA-ILD patients demonstrate significantly higher IL-17RA expression in areas of fibroblast accumulation and fibrosis, compared with either IPF or normal lung tissue. These observations support a direct role for IL-17A in lung fibrosis that may be particularly relevant in the context of RA-ILD.