Type 2 diabetes and chronic kidney disease (CKD) may share common risk factors. Here we used a 3-stage procedure to discover novel predictors of CKD by repeatedly applying a stepwise selection based on the Akaike information criterion to subsamples of a prospective complete-case cohort of 2755 patients. This cohort encompassed 25 clinical variables and 36 genetic variants associated with type 2 diabetes, obesity, or fasting plasma glucose. We compared the performance of the clinical, genetic, and clinico-genomic models and used net reclassification improvement to evaluate the impact of top selected genetic variants to the clinico-genomic model. Associations of selected genetic variants with CKD were validated in 2 independent cohorts followed by meta-analyses. Among the top 6 single-nucleotide polymorphisms selected from clinico-genomic data, three (rs478333 of G6PC2, rs7754840 and rs7756992 of CDKAL1) contributed toward the improvement of prediction performance. The variant rs478333 was associated with rapid decline (over 4% per year) in estimated glomerular filtration rate. In a meta-analysis of 2 replication cohorts, the variants rs478333 and rs7754840 showed significant associations with CKD after adjustment for conventional risk factors. Thus, this novel 3-stage approach to a clinico-genomic data set identified 3 novel genetic predictors of CKD in type 2 diabetes. This method can be applied to similar data sets containing clinical and genetic variables to select predictors for clinical outcomes.

The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear.

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a).

Increased uric acid (UA) levels have been reported to be associated with poor clinical outcomes in several conditions. However, the prognostic value of UA in patients with infective endocarditis (IE) is yet unknown.

Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.

Several studies support associations between relative leukocyte telomere length (rLTL), a biomarker of biological aging and type 2 diabetes. This study investigates the relationship between rLTL and the risk of glycemic progression in patients with type 2 diabetes.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Polycystic ovary syndrome (PCOS) remains one of the most common endocrine disorder in premenopausal women with an unfavorable metabolic risk profile. Here, we investigate whether biochemical hyperandrogenism, represented by elevated serum free testosterone, resulted in an aberrant circulating microRNA (miRNAs) expression profile and whether miRNAs can identify those PCOS women with metabolic syndrome (MetS). Accordingly, we measured serum levels of miRNAs as well as biochemical markers related to MetS in a case-control study of 42 PCOS patients and 20 Controls. Patients were diagnosed based on the Rotterdam consensus criteria and stratified based on serum free testosterone levels (≥0.034 nmol/l) into either a normoandrogenic (n = 23) or hyperandrogenic (n = 19) PCOS group. Overall, hyperandrogenic PCOS women were more insulin resistant compared to normoandrogenic PCOS women and had a higher prevalence of MetS. A total of 750 different miRNAs were analyzed using TaqMan Low-Density Arrays. Altered levels of seven miRNAs (miR-485-3p, -1290, -21-3p, -139-3p, -361-5p, -572, and -143-3p) were observed in PCOS patients when compared with healthy Controls. Stratification of PCOS women revealed that 20 miRNAs were differentially expressed between the three groups. Elevated serum free testosterone levels, adjusted for age and BMI, were significantly associated with five miRNAs (miR-1290, -20a-5p, -139-3p, -433-3p, and -361-5p). Using binary logistic regression and receiver operating characteristic curves (ROC), a combination panel of three miRNAs (miR-361-5p, -1225-3p, and -34-3p) could correctly identify all of the MetS cases within the PCOS group. This study is the first to report comprehensive miRNA profiling in different subgroups of PCOS women with respect to MetS and suggests that circulating miRNAs might be useful as diagnostic biomarkers of MetS for a different subset of PCOS.

Diabetic nephropathy (DN) is a polygenic disorder with few risk variants showing robust replication in large-scale genome-wide association studies. To understand the role of DNA methylation, it is important to have the prevailing genomic view to distinguish key sequence elements that influence gene expression. This is particularly challenging for DN because genome-wide methylation patterns are poorly defined. While methylation is known to alter gene expression, the importance of this causal relationship is obscured by array-based technologies since coverage outside promoter regions is low. To overcome these challenges, we performed methylation sequencing using leukocytes derived from participants of the Finnish Diabetic Nephropathy (FinnDiane) type 1 diabetes (T1D) study (n = 39) that was subsequently replicated in a larger validation cohort (n = 296). Gene body-related regions made up more than 60% of the methylation differences and emphasized the importance of methylation sequencing. We observed differentially methylated genes associated with DN in 3 independent T1D registries originating from Denmark (n = 445), Hong Kong (n = 107), and Thailand (n = 130). Reduced DNA methylation at CTCF and Pol2B sites was tightly connected with DN pathways that include insulin signaling, lipid metabolism, and fibrosis. To define the pathophysiological significance of these population findings, methylation indices were assessed in human renal cells such as podocytes and proximal convoluted tubule cells. The expression of core genes was associated with reduced methylation, elevated CTCF and Pol2B binding, and the activation of insulin-signaling phosphoproteins in hyperglycemic cells. These experimental observations also closely parallel methylation-mediated regulation in human macrophages and vascular endothelial cells.

Stroke is a major cause of death and disability throughout the world. A combination of Panax Ginseng and Ginkgo biloba extracts (CGGE) is an effective treatment for nervous system diseases, but the neuroprotective mechanism underlying CGGE remains unclear. Both network analysis and experimental research were employed to explore the potential mechanism of CGGE in treating ischemic stroke (IS). Network analysis identified a total number of 133 potential targets for 34 active ingredients and 239 IS-related targets. What's more, several processes that might involve the regulation of CGGE against IS were identified, including long-term potentiation, cAMP signaling pathway, neurotrophin signaling pathway, and Nod-like receptor signaling pathway. Our studies in animal models suggested that CGGE could reduce inflammatory response by inhibiting the activity of Nod-like receptor, pyrin containing 3 (NLRP3) inflammasome, and maintain the balance of glutamate (Glu)/gamma-aminobutyric acid (GABA) via activating calmodulin-dependent protein kinase type Ⅳ (CAMK4)/cyclic AMP-responsive element-binding protein (CREB) pathway. These findings indicated the neuroprotective effects of CGGE, possibly improving neuroinflammation and excitotoxicity by regulating the NLRP3 inflammasome and CAMK4/CREB pathway.

Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits.

Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells.

Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Inadequate bowel preparation leads to lower polyp detection rates, longer procedure times and lower cecal intubation rates. However, there is no consensus about high-quality bowel preparation, so our study evaluated graphical education and appropriate time before elective colonoscopy.

Diabetic retinopathy (DR) is a major microvascular complication of diabetes. Susceptibility genes for type 2 diabetes may also impact the susceptibility of DR. This case-control study investigated the effects of 88 type 2 diabetes susceptibility loci on DR in a Chinese population with type 2 diabetes performed in two stages. In stage 1, 88 SNPs were genotyped in 1,251 patients with type 2 diabetes, and we found that ADAMTS9-AS2 rs4607103, WFS1 rs10010131, CDKAL1 rs7756992, VPS26A rs1802295 and IDE-KIF11-HHEX rs1111875 were significantly associated with DR. The association between CDKAL1 rs7756992 and DR remained significant after Bonferroni correction for multiple comparisons (corrected P = 0.0492). Then, the effect of rs7756992 on DR were analysed in two independent cohorts for replication in stage 2. Cohort (1) consisted of 380 patients with DR and 613 patients with diabetes for ≥5 years but without DR. Cohort (2) consisted of 545 patients with DR and 929 patients with diabetes for ≥5 years but without DR. A meta-analysis combining the results of stage 1 and 2 revealed a significant association between rs7756992 and DR, with the minor allele A conferring a lower risk of DR (OR 0.824, 95% CI 0.743-0.914, P = 2.46 × 10-4).

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.

Few large-scale prospective studies have investigated associations between relative leucocyte telomere length (rLTL) and kidney dysfunction in individuals with type 2 diabetes. We examined relationships between rLTL and incident end-stage kidney disease (ESKD) and the slope of eGFR decline in Chinese individuals with type 2 diabetes.