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Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool ( https://sctype.app ), and as an open-source R-package. Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish between cell types.

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. In the current preclinical drug combination screening, the top combinations for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested in Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler prioritized as top hits those synergistic drug pairs that showed higher selective efficacy (difference between efficacy and toxicity), which offers an improved likelihood for clinical success.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. The identification of treatments that selectively co-inhibit cancerous cell populations remains a challenge. Here, a machine learning approach, scTherapy, leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

Acute myeloid leukemia (AML) is an aggressive blood cancer characterized by poor survival outcomes. Further, due to the extreme molecular heterogeneity of the disease, drug treatment response varies from patient to patient. The variability of drug response can cause unnecessary treatment in more than half of the patients with no or partial therapy responses leading to severe side effects, monetary as well as time loss. Understanding the genetic risk factors underlying the drug response in AML can help with improved prediction of treatment responses and identification of biomarkers in addition to mechanistic insights to monitor treatment response. Here, we report the results of the first Exome-Wide Association Study (EWAS) of ex-vivo drug response performed to date with 175 AML cases and 47 drugs. We used information from 55,423 germline exonic SNPs to perform the analysis. We identified exome-wide significant ( p  < 9.02 × 10 − 7 ) associations for rs113985677 in CCIN with tamoxifen response, rs115400838 in TRMT5 with idelalisib response, rs11878277 in HDGFL2 with entinostat, and rs2229092 in LTA associated with vorinostat response. Further, using multivariate genome-wide association analysis, we identified the association of rs11556165 in ATRAID , and rs11236938 in TSKU with the combined response of all 47 drugs and 29 nonchemotherapy drugs at the genome-wide significance level ( p  < 5 × 10 − 8 ). Additionally, a significant association of rs35704242 in NIBAN1 was associated with the combined response for nonchemotherapy medicines ( p  = 2.51 × 10 − 8 ), and BI.2536, gefitinib, and belinostat were identified as the central traits. Our study represents the first EWAS to date on ex-vivo drug response in AML and reports 7 new associated loci that help to understand the anticancer drug response in AML patients.

Lipids foster energy production and their altered levels have been coupled with metabolic ailments. Indians feature high prevalence of metabolic diseases, yet uncharacterized for genes regulating lipid homeostasis. We performed first GWAS for quantitative lipids (total cholesterol, LDL, HDL, and triglycerides) exclusively in 5271 Indians. Further to corroborate our genetic findings, we investigated DNA methylation marks in peripheral blood in Indians at the identified loci (N = 233) and retrieved gene regulatory features from public domains. Recurrent GWAS loci-CELSR2, CETP, LPL, ZNF259, and BUD13 cropped up as lead signals in Indians, reflecting their universal applicability. Besides established variants, we found certain unreported variants at sub-genome-wide level-QKI, REEP3, TMCC2, FAM129C, FAM241B, and LOC100506207. These variants though failed to attain GWAS significance in Indians, but largely turned out to be active CpG sites in human subcutaneous adipose tissue and showed robust association to two or more lipid traits. Of which, QKI variants showed significant association to all four lipid traits and their designated region was observed to be a key gene regulatory segment denoting active transcription particularly in human subcutaneous adipose tissue. Both established and novel loci were observed to be significantly associated with altered DNA methylation in Indians for specific CpGs that resided in key regulatory elements. Further, gene-based association analysis pinpointed novel GWAS loci-LINC01340 and IQCJ-SCHIP1 for TC; IFT27, IFT88, and LINC02141 for HDL; and TEX26 for TG. Present study ascertains universality of selected known genes and also identifies certain novel loci for lipids in Indians by integrating data from various levels of gene regulation.

Adolescence is the most critical phase of human growth that radically alters physiology of the body and wherein any inconsistency can lead to serious health consequences in adulthood. The timing and pace at which various developmental events occur during adolescence is highly diverse and thus results in a drastic change in blood biochemistry. Monitoring the physiological levels of various biochemical measures in ample number of individuals during adolescence can call up for an early intervention in managing metabolic diseases in adulthood. Today, only a couple of studies in different populations have investigated blood biochemistry in a small number of adolescents however, there is no comprehensive biochemical data available worldwide. In view, we performed a cross-sectional study in a sizeable group of 7,618 Indian adolescents (3,333 boys and 4,285 girls) aged between 11-17 years to inspect the distribution of values of common biochemical parameters that generally prevails during adolescence and we observed that various parameters considerably follow the reported values from different populations being 3.56-6.49mmol/L (fasting glucose), 10.60-199.48pmol/L (insulin), 0.21-3.22nmol/L (C-peptide), 3.85-6.25% (HbA1c), 2.49-5.54mmol/L (total cholesterol), 1.16-3.69mmol/L (LDL), 0.78-1.85mmol/L (HDL), 0.33-2.24mmol/L (triglycerides), 3.56-11.45mmol/L (urea), 130.01-440.15μmol/L (uric acid) and 22.99-74.28μmol/L (creatinine). Barring LDL and triglycerides, all parameters differed significantly between boys and girls (p< 0.001). Highest difference was seen for uric acid (p = 1.3 x10-187) followed by C-peptide (p = 6.6 x10-89). Across all ages during adolescence, glycemic and nitrogen metabolites parameters varied markedly with gender. Amongst lipid parameters, only HDL levels were found to be significantly associated with gender following puberty (p< 0.001). All parameters except urea, differed considerably in obese and lean adolescents (p< 0.0001). The present study asserts that age, sex and BMI are the essential contributors to variability in blood biochemistry during adolescence. Our composite data on common blood biochemical measures will benefit future endeavors to define reference intervals in adolescents especially when the global availability is scarce.

Indians, a rapidly growing population, constitute vast genetic heterogeneity to that of Western population; however they have become a sedentary population in past decades due to rapid urbanization ensuing in the amplified prevalence of metabolic syndrome (MetS). We performed a genome-wide association study (GWAS) of MetS in 10,093 Indian individuals (6617 MetS and 3476 controls) of Indo-European origin, that belong to our previous biorepository of The Indian Diabetes Consortium (INDICO). The study was conducted in two stages—discovery phase (N = 2158) and replication phase (N = 7935). We discovered two variants within/near the CETP gene—rs1800775 and rs3816117—associated with MetS at genome-wide significance level during replication phase in Indians. Additional CETP loci rs7205804, rs1532624, rs3764261, rs247617, and rs173539 also cropped up as modest signals in Indians. Haplotype association analysis revealed GCCCAGC as the strongest haplotype within the CETP locus constituting all seven CETP signals. In combined analysis, we perceived a novel and functionally relevant sub-GWAS significant locus—rs16890462 in the vicinity of SFRP1 gene. Overlaying gene regulatory data from ENCODE database revealed that single nucleotide polymorphism (SNP) rs16890462 resides in repressive chromatin in human subcutaneous adipose tissue as characterized by the enrichment of H3K27me3 and CTCF marks (repressive gene marks) and diminished H3K36me3 marks (activation gene marks). The variant displayed active DNA methylation marks in adipose tissue, suggesting its likely regulatory activity. Further, the variant also disrupts a potential binding site of a key transcription factor, NRF2, which is known for involvement in obesity and metabolic syndrome.

Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here, we implement DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for accurate prediction of drug combination synergy in a given sample. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully measured dose–response matrices. Measuring only the matrix diagonal provides an accurate and practical option for combinatorial screening. The minimal-input web implementation enables applications of DECREASE to both pre-clinical and translational studies. Drug combinations are often an effective means of managing complex diseases, but understanding the synergies of drug combinations requires extensive resources. The authors developed an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for the accurate prediction of synergistic and antagonistic drug combinations.