Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of "gene-specific" machine learning compared to "disease-specific" machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extreme gradient boosting, random forests, support vector machines, and deep neural networks. As features, we used MAFs from multiple populations, functional prediction and conservation scores, and positions of variants. The disease-specific training dataset included the gene-specific training dataset and was > 7 × larger. However, we observed that gene-specific training variants were sufficient to produce the optimal pathogenicity predictor if a suitable machine learning classifier was employed. Therefore, we recommend gene-specific over disease-specific machine learning as an efficient and effective method for predicting the pathogenicity of rare BRCA1 and BRCA2 missense variants.

Erratum to: Common and differential effects of docosahexaenoic acid and eicosapentaenoic acid on helper T-cell responses and associated pathways.

[Erratum to: BMB Reports 2021; 54(5): 278-283, PMID: 33972011] In the originally published version of this article, there was an error in the Supplementary information. Fig. 1 as following image was missing in the Supplementary Information. The Supplementary file in the original version has now been updated to include the corrected. We apologize for any inconvenience that this may have caused.

Common and differential effects of docosahexaenoic acid and eicosapentaenoic acid on helper T-cell responses and associated pathways.

Our understanding of the differential effects between specific omega-3 fatty acids is incomplete. Here, we aimed to evaluate the effects of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) on T-helper type 1 (Th1) cell responses and identify the pathways associated with these responses. Naïve CD4+ T cells were co-cultured with bone marrow-derived dendritic cells (DCs) in the presence or absence of palmitate (PA), DHA, or EPA. DHA or EPA treatment lowered the number of differentiated IFN-γ-positive cells and inhibited the secretion of IFN-γ, whereas only DHA increased IL-2 and reduced TNF-α secretion. There was reduced expression of MHC II on DCs after DHA or EPA treatment. In the DC-independent model, DHA and EPA reduced Th1 cell differentiation and lowered the cell number. DHA and EPA markedly inhibited IFN-γ secretion, while only EPA reduced TNF-α secretion. Microarray analysis identified pathways involved in inflammation, immunity, metabolism, and cell proliferation. Moreover, DHA and EPA inhibited Th1 cells through the regulation of diverse pathways and genes, including Igf1 and Cpt1a. Our results showed that DHA and EPA had largely comparable inhibitory effects on Th1 cell differentiation. However, each of the fatty acids also had distinct effects on specific cytokine secretion, particularly according to the presence of DCs. [BMB Reports 2021; 54(5): 278-283].

Escalation of liPid-lOwering therapy in patientS wiTh vascular disease receiving HIGH-intensity statins: the retrospective POST-HIGH study.

In this retrospective study, we investigated whether lipid-lowering therapy (LLT) escalation has clinical benefits in patients with atherosclerotic cardiovascular disease (ASCVD) and low-density lipoprotein cholesterol (LDL-C) levels of 55-99 mg/dL (1.4-2.6 mmol/L), post high-intensity. Out of 6317 Korean patients screened in 2005-2018, 1159 individuals with ASCVD and LDL-C levels of 55-99 mg/dL after statin use equivalent to 40 mg atorvastatin were included. After 1:2 propensity score matching, 492 patients (164 with LLT escalation, 328 controls without LLT escalation) were finally analysed. Primary outcome variables were major adverse cardiovascular and cerebrovascular events (MACCE) and all-cause death. At median follow-up (1.93 years), the escalation group had a lower MACCE rate (1.72 vs. 3.38 events/100 person-years; hazard ratio [HR] 0.34, 95% confidence interval [CI] 0.14-0.83; p = 0.018) than the control group. The incidence of all-cause death (0.86 vs. 1.02 events/100 person-years; HR 0.58, 95% CI 0.15-2.19; p = 0.42) and each MACCE component did not differ between groups. Kaplan-Meier curves exhibited lower risk of MACCE in the escalation group (HR 0.36, 95% CI 0.12-0.97; p = 0.040) but a difference not statistically significant in all-cause death (HR 0.30, 95% CI 0.04-2.48; p = 0.26). LLT escalation was associated with reduced cardiovascular risk, supporting more aggressive LLT in this population.

Cholesterol Efflux and Collateral Circulation in Chronic Total Coronary Occlusion: Effect-Circ Study.

Background The mechanism through which high-density lipoprotein (HDL) induces cardioprotection is not completely understood. We evaluated the correlation between cholesterol efflux capacity (CEC), a functional parameter of HDL, and coronary collateral circulation (CCC). We additionally investigated whether A1BP (apoA1-binding protein) concentration correlates with CEC and CCC. Methods and Results In this case-control study, clinical and angiographic data were collected from 226 patients (mean age, 58 years; male, 72%) with chronic total coronary occlusion. CEC was assessed using a radioisotope and J774 cells, and human A1BP concentration was measured using enzyme-linked immunosorbent assay. Differences between the good and poor CCC groups were compared, and associations between CEC, A1BP, and other variables were evaluated. Predictors of CCC were identified by multivariable logistic regression analysis. The CEC was higher in the good than in the poor CCC group (22.0±4.6% versus 20.2±4.7%; P=0.009). In multivariable analyses including age, sex, HDL-cholesterol levels, age (odds ratio [OR], 0.96; P=0.003), and CEC (OR, 1.10; P=0.004) were identified as the independent predictors of good CCC. These relationships remained significant after additional adjustment for diabetes mellitus, acute coronary syndrome, and Gensini score. The A1BP levels were not significantly correlated with CCC (300 pg/mL and 283 pg/mL in the good CCC and poor CCC groups, respectively, P=0.25) or CEC. Conclusions The relationship between higher CEC and good CCC indicates that well-functioning HDL may contribute to CCC and may be cardioprotective; this suggests that a specific function of HDL can have biological and clinical consequences.

PRavastatin Versus FlUVastatin After Statin Intolerance: The PRUV-Intolerance Study With Propensity Score Matching.

BACKGROUND: Limited data are available on the relapse of statin intolerance after resumption of statins. We aimed to evaluate the relapse rates of statin intolerance in patients who subsequently received pravastatin or fluvastatin and to identify associated factors. METHODS: This retrospective, propensity score-matched cohort study screened data obtained from a tertiary university hospital between 2006 and 2015. Of 8073 patients screened, 488 with statin intolerance who received pravastatin or fluvastatin with regular follow-up were enrolled. After propensity score matching of patients, 384 were finally analyzed. The primary outcome variables were relapse of statin intolerance and stopping (ie, discontinuation or switching to other statins) rate for the 2 statins. RESULTS: During the median follow-up period of 37 months, the rate of relapse of intolerance was 10.4% and 18.2% among users of pravastatin and fluvastatin, respectively (P = 0.04). However, the log-rank test showed no difference in the relapse-free rates between the 2 groups (P = 0.34). The stopping rates of the 2 statins were 36.5% and 42.2% (P = 0.30), respectively, for various reasons, including low efficacy of the drugs. After adjustment, chronic kidney disease (hazard ratio [HR] 1.83, P = 0.03) and previous creatine kinase elevation (HR 3.13, P = 0.001) were identified as independent determinants of relapse. Older age (HR 1.03, P = 0.057) and female sex (HR 1.70, P = 0.059) were associated, but not significantly, with relapse. CONCLUSION: Although a small proportion of patients taking pravastatin or fluvastatin experienced a relapse of intolerance, many patients eventually discontinued or changed these agents. Chronic kidney disease and history of creatine kinase elevation were independent determinants of relapse.