BackgroundIdentification of genetic variants that are associated with disease is an important goal in elucidating the genetic causes of diseases. The genetic patterns that are associated with common diseases are complex and may involve multiple interacting genetic variants. The Relief family of algorithms is a powerful tool for efficiently identifying genetic variants that are associated with disease, even if the variants have nonlinear interactions without significant main effects. Many variations of Relief have been developed over the past two decades and several of them have been applied to single nucleotide polymorphism (SNP) data.ResultsWe developed a new spatially weighted variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*), and applied it to synthetic SNP data. When compared to ReliefF and SURF*, which are two algorithms that have been applied to SNP data for identifying interactions, SWRF* had significantly greater power. Furthermore, we developed a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of the Relief algorithm, and we used MoRF to develop the SWRF* algorithm.ConclusionsMoRF allows easy development of new Relief algorithms by specifying different interchangeable functions for the component terms. Using MORF, we developed a new Relief algorithm called SWRF* that had greater ability to identify interacting genetic variants in synthetic data compared to existing Relief algorithms.

Ozanimod (RPC1063) is a specific and potent small molecule modulator of the sphingosine 1-phosphate receptor 1 (S1PR1) and receptor 5 (S1PR5), which has shown therapeutic benefit in clinical trials of relapsing multiple sclerosis and ulcerative colitis. Ozanimod and its active metabolite, RP-101075, exhibit a similar specificity profile at the S1P receptor family in vitro and pharmacodynamic profile in vivo. The NZBWF1 mouse model was used in therapeutic dosing mode to assess the potential benefit of ozanimod and RP-101075 in an established animal model of systemic lupus erythematosus. Compared with vehicle-treated animals, ozanimod and RP-101075 reduced proteinuria over the duration of the study and serum blood urea nitrogen at termination. Additionally, ozanimod and RP-101075 reduced kidney disease in a dose-dependent manner, as measured by histological assessment of mesangial expansion, endo- and exo-capillary proliferation, interstitial infiltrates and fibrosis, glomerular deposits, and tubular atrophy. Further exploration into gene expression changes in the kidney demonstrate that RP-101075 also significantly reduced expression of fibrotic and immune-related genes in the kidneys. Of note, RP-101075 lowered the number of plasmacytoid dendritic cells, a major source of interferon alpha in lupus patients, and reduced all B and T cell subsets in the spleen. Given the efficacy demonstrated by ozanimod and its metabolite RP-101075 in the NZBWF1 preclinical animal model, ozanimod may warrant clinical evaluation as a potential treatment for systemic lupus erythematosus.

BackgroundRanking and identifying biomarkers that are associated with disease from genome-wide measurements holds significant promise for understanding the genetic basis of common diseases. The large number of single nucleotide polymorphisms (SNPs) in genome-wide studies (GWAS), however, makes this task computationally challenging when the ranking is to be done in a multivariate fashion. This paper evaluates the performance of a multivariate graph-based method called label propagation (LP) that efficiently ranks SNPs in genome-wide data.ResultsThe performance of LP was evaluated on a synthetic dataset and two late onset Alzheimer’s disease (LOAD) genome-wide datasets, and the performance was compared to that of three control methods. The control methods included chi squared, which is a commonly used univariate method, as well as a Relief method called SWRF and a sparse logistic regression (SLR) method, which are both multivariate ranking methods. Performance was measured by evaluating the top-ranked SNPs in terms of classification performance, reproducibility between the two datasets, and prior evidence of being associated with LOAD.On the synthetic data LP performed comparably to the control methods. On GWAS data, LP performed significantly better than chi squared and SWRF in classification performance in the range from 10 to 1000 top-ranked SNPs for both datasets, and not significantly different from SLR. LP also had greater ranking reproducibility than chi squared, SWRF, and SLR. Among the 25 top-ranked SNPs that were identified by LP, there were 14 SNPs in one dataset that had evidence in the literature of being associated with LOAD, and 10 SNPs in the other, which was higher than for the other methods.ConclusionLP performed considerably better in ranking SNPs in two high-dimensional genome-wide datasets when compared to three control methods. It had better performance in the evaluation measures we used, and is computationally efficient to be applied practically to data from genome-wide studies. These results provide support for including LP in the methods that are used to rank SNPs in genome-wide datasets.

effect. The link with disease but not apremilast effect may offer a plausible molecular connection consistent with an overall lack of apremilast clinical response in this trial (Supplementary Fig. 2).

Multiple myeloma (MM) is a plasma cell malignancy characterised by aberrant production of immunoglobulins requiring survival mechanisms to adapt to proteotoxic stress. We here show that glutamyl-prolyl-tRNA synthetase (GluProRS) inhibition constitutes a novel therapeutic target. Genomic data suggest that GluProRS promotes disease progression and is associated with poor prognosis, while downregulation in MM cells triggers apoptosis. We developed NCP26, a novel ATP-competitive ProRS inhibitor that demonstrates significant anti-tumour activity in multiple in vitro and in vivo systems and overcomes metabolic adaptation observed with other inhibitor chemotypes. We demonstrate a complex phenotypic response involving protein quality control mechanisms that centers around the ribosome as an integrating hub. Using systems approaches, we identified multiple downregulated proline-rich motif-containing proteins as downstream effectors. These include CD138, transcription factors such as MYC, and transcription factor 3 (TCF3), which we establish as a novel determinant in MM pathobiology through functional and genomic validation. Our preclinical data therefore provide evidence that blockade of prolyl-aminoacylation evokes a complex pro-apoptotic response beyond the canonical integrated stress response and establish a framework for its evaluation in a clinical setting.