Engineering CRISPR/Cas9 therapeutics for cancer precision medicine.

The discovery of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) technology has revolutionized field of cancer treatment. This review explores usage of CRISPR/Cas9 for editing and investigating genes involved in human carcinogenesis. It provides insights into the development of CRISPR as a genetic tool. Also, it explores recent developments and tools available in designing CRISPR/Cas9 systems for targeting oncogenic genes for cancer treatment. Further, we delve into an overview of cancer biology, highlighting key genetic alterations and signaling pathways whose deletion prevents malignancies. This fundamental knowledge enables a deeper understanding of how CRISPR/Cas9 can be tailored to address specific genetic aberrations and offer personalized therapeutic approaches. In this review, we showcase studies and preclinical trials that show the utility of CRISPR/Cas9 in disrupting oncogenic targets, modulating tumor microenvironment and increasing the efficiency of available anti treatments. It also provides insight into the use of CRISPR high throughput screens for cancer biomarker identifications and CRISPR based screening for drug discovery. In conclusion, this review offers an overview of exciting developments in engineering CRISPR/Cas9 therapeutics for cancer treatment and highlights the transformative potential of CRISPR for innovation and effective cancer treatments.

Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights.

Aqueous fluoride ( F - ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from indigenous Limonia acidissima L. (fruit pericarp) for effective aqueous defluoridation. The adsorbents were characterized using instrumental analysis, e.g., TGA-DTA, ATR-FTIR, SEM-EDS, and XRD. The batch-mode study was performed to investigate the influence of experimental variables. The artificial neural network (ANN) model was employed to validate the adsorption. The dataset was fed to a backpropagation learning algorithm of the ANN (BPNN) architecture. The four-ten-one neural network model was considered to be functioning correctly with an absolute-relative-percentage error of 0.633 throughout the learning period. The results easily fit the linearly transformed Langmuir isotherm model with a correlation coefficient ( R 2 ) > 0.997. The maximum F - removal efficiency was found to be 80.8 mg/g at the optimum experimental condition of pH 7 and a dosage of 6 g/L at 30 min. The ANN model and experimental data provided a high degree of correlation ( R 2 = 0.9964), signifying the accuracy of the model in validating the adsorption experiments. The effects of interfering ions were studied with real F - water. The pseudo-second-order kinetic model showed a good fit to the equilibrium dataset. The performance of the adsorbent was also found satisfactory with field samples and can be considered a potential adsorbent for aqueous defluoridation.

Application of phytoaccumulation perspective of Monochoria hastate L. on fluoride contaminated water in hydroponic treatment: its statistical design and characterization studies.

The present research work approaches the accumulation of fluoride ions from contaminated water using an aquatic plant Monochoria hastate L. in hydroponic culture. A design of experiment (DOE) has been adopted and an analysis of variance has been conducted to establish the statistical significance of various process parameters. The different experimental factors are root and shoot (Factor A), fluoride concentration (Factor B), and experimental days (Factor C) largely influence the output response. Plants treated with 5 mg/L of fluoride solutions accumulated the highest concentration in root biomass 1.23 mg/gm, and shoot biomass 0.820 mg/gm, dry weight after 21 days' experimentation. The accumulation mechanism and potentiality of treated plants depend on root cells of the plasma membrane and energy-capturing molecules of adenosine triphosphate. Monochoria hastate L. root biomass was characterized to confirm the accumulation of fluoride ions in the experimented plants using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR) analysis.

The novelty of this study is the high fluoride accumulation efficiency in hydroponic treatment by Monochoria hastate L an excellent choice for phytoremediation technique. The Design of Experiment (DOE) has a good approach for the optimization of fluoride in the accumulation process. The maximum absorption of fluoride ions in root biomass is 1.23 mg/gm, and shoot biomass is 0.820 mg/gm, dry weight after 21 days of treatment. To know the fluoride ions in shoot and root biomass are characterized using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR).

A cross-sectional assessment of burnout presence among pharmacy professionals at Kathmandu Valley, Nepal.

Introduction: Burnout is a significant concern among healthcare professionals, including pharmacists, as it can lead to adverse effects on their well-being, job satisfaction, and patient care delivery. However, no previous study was conducted among pharmacy professionals in Nepal to assess their burnout cases. This study aimed to evaluate burnout presence and explore its associated factors among pharmacy professionals in Nepal. Methods: A cross-sectional study was conducted among pharmacy professionals of Kathmandu Valley, Nepal. The validated Burnout Assessment Tool measured burnout across multiple domains. Data on demographic and work-related characteristics were also collected. Descriptive statistics and Chi-square tests were used to analyze the data and identify significant associations among the variables. Results: Most participants were in the age group of 21-30 (64.7%), had a graduate degree (47.3%), and worked in hospital pharmacy settings (49.1%). Exhaustion was the most common (39.7%) burnout experienced, while mental distance and emotional and cognitive impairment were reported in one-fourth of the participants. Alternatively, only one in five participants showed secondary symptoms of burnout. Gender, working hours, exercise frequency, and substance abuse were significantly associated with burnout domains. Conclusion: This study provides valuable insights into the prevalence and factors associated with burnout among pharmacy professionals in Nepal. The findings highlight the significance of addressing burnout in this crucial healthcare sector, with gender, exercise frequency, and substance use emerging as notable contributors. These results underscore the need for targeted interventions and support systems to promote the well-being of pharmacy professionals and ensure the continued delivery of high-quality healthcare services in Nepal.

Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5.

BACKGROUND & AIMS: Small intestinal neuroendocrine tumor (SI-NET) is a rare disease, but its incidence has increased over the past 4 decades. Understanding the genetic risk factors underlying SI-NETs can help in disease prevention and may provide clinically beneficial markers for diagnosis. Here the results of the largest genome-wide association study of SI-NETs performed to date with 405 cases and 614,666 controls are reported. METHODS: Samples from 307 patients with SI-NETs and 287,137 controls in the FinnGen study were used for the identification of SI-NET risk-associated genetic variants. The results were also meta-analyzed with summary statistics from the UK Biobank (n = 98 patients with SI-NET and n = 327,529 controls). RESULTS: We identified 6 genome-wide significant (P < 5 × 10-8) loci associated with SI-NET risk, of which 4 (near SEMA6A, LGR5, CDKAL1, and FERMT2) are novel and 2 (near LTA4H-ELK and in KIF16B) have been reported previously. Interestingly, the top hit (rs200138614; P = 1.80 × 10-19) was a missense variant (p.Cys712Phe) in the LGR5 gene, a bona-fide marker of adult intestinal stem cells and a potentiator of canonical WNT signaling. The association was validated in an independent Finnish collection of 70 patients with SI-NETs, as well as in the UK Biobank exome sequence data (n = 92 cases and n = 392,814 controls). Overexpression of LGR5 p.Cys712Phe in intestinal organoids abolished the ability of R-Spondin1 to support organoid growth, indicating that the mutation perturbed R-Spondin-LGR5 signaling. CONCLUSIONS: Our study is the largest genome-wide association study to date on SI-NETs and reported 4 new associated genome-wide association study loci, including a novel missense mutation (rs200138614, p.Cys712Phe) in LGR5, a canonical marker of adult intestinal stem cells.

Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications.

Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.

Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease.

Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional "stress score" that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column.

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g-1 of F- in the bed height of 15 cm, starting concentration 1 ppm, susceptible time 120 min, adsorbate solution flow rate 0.5 mL min-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R2 = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples.

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.

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.

Implementation of medicine take-back concept at community level in Nepal: a pilot study.

BACKGROUND: Most households may have leftover, unwanted, unused and expired (UUE) medicines. The present research aimed to analyze feasibility of implementation of medicine take-back in select communities in Nepal. METHODS: Exploratory (i.e. feasibility) study was conducted among 400 adults from July 2017 to January 2018. Study sites and participants were selected by simple random sampling and respondents were interviewed about their awareness about medicine disposal, hazards and willingness to support take-back program using semi-structured questionnaire. Multinomial logistic regression analysis was applied to explore relationship of take-back related outcomes with the predictors. The P-value < 0.05 was statistically significant at 95% confidence level. RESULTS: Land pollution and effect on health of children was significantly related with inappropriate disposal of medicines such as site of disposal (P value < 0.01), river (P value, 0.02), garbage (P value, 0.04) and dumping site (P value, 0.01). Analysis of willingness to follow take-back program with the techniques of support showed significant relationship with the establishment of collection center and participation on seminar (P value < 0.01). CONCLUSION: Most participants were interested to support take-back, if implemented in their community but main constraint was the budget. Take-back concept could be initiated and implemented on government funding or other sources.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers.

INTRODUCTION: The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). AREA COVERED: In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. EXPERT OPINION: The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

Analysis of the payments from the coronavirus food assistance program and the market facilitation program to minority producers.

This paper examines the payments made to minority producers, focused on African American producers, from the COVID-19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of the other more recent ad hoc program payments, the Market Facilitation Program (MFP). There were two rounds of the CFAP, and combinedly (as of March 2022), the program made direct payments of $31.0 billion ($11.8 billion from CFAP 1 and $19.2 billion from CFAP 2) starting in 2020. The MFP made a total payment of $23.5 billion (in two rounds, MFP 2018 and MFP 2019) to producers affected by the retaliatory tariffs placed on US producers by trade partners across multiple years. CFAP made almost $600 million in direct payments to minority producers, including Black or African American producers. Black or African American only producers received more than $52 million in CFAP payments. CFAP payments were proportional to the value of agricultural commodity sold for most minority producers. The 2017 Census of Agriculture showed that the majority of minority producers, including African American producers but excluding Asian producers, raised livestock. CFAP made the highest payments to livestock minority producers. The CFAP payment distribution pattern shows that payments reached minority producers who often did not receive Government payments. CFAP made more payments and as a share of total program outlays to minority producers compared to MFP. However, for Black or African American only producers, even though the magnitude increased (because CFAP disbursed more funds compared to MFP), the share of payment received did not increase.

Removal of Cr (VI) from aqueous solution by activated charcoal derived from Sapindus trifoliate L fruit biomass using continuous fixed bed column studies.

In this study, the removal of hexavalent chromium from aqueous solution were examined using activated charcoal derived from Sapindus trifoliate L fruit biomass in continuous fixed-bed column studies. The activated S. trifoliate L fruit charcoal was prepared by treating the fruit powder using concentrated nitric acid solution. Experiments were performed to investigate the effect of bed height and initial concentration on the breakthrough and saturation times. The breakthrough and saturation time increases with increase in bed height and initial concentration of chromium solutions. The maximum adsorption capacity of S. trifoliate L charcoal for hexavalent chromium was found to be 1.719 mg/g in the bed height 15 cm and initial concentration 10 mg/L, respectively. Column data required at various conditions were explained using Bohart-Adams and Thomas model. Two models were found to be suitable to describe the definite part of the dynamic behaviour of the column with regard to bed-height and initial concentration of hexavalent chromium. On comparison of Adjusted R2 and estimated standard error, the Thomas model was found to best-fitted model and can be used to predict the adsorption of the hexavalent chromium in fixed-bed column studies. Activated S. trifoliate L fruit charcoal was characterised by SEM-EDX and FTIR analysis.

A Narrative Review of Personal Protective Equipment Uses in Coronavirus Disease 2019 and Its Disposable Practices.

Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for causing coronavirus disease 2019 (COVID-19), is transmitted through close contact and droplets, people, especially those at risk of infection, must follow preventive measures in the community and healthcare settings. Healthcare personnel (HCP) must appropriately select and use personal protective equipment (PPE) with sensible donning and doffing and disposal practices. A narrative review of the existing literature was conducted, in which articles from Scopus, PubMed, Google Scholar, ScienceDirect, and Web of Science were collected. The primary findings of the retained articles were reviewed according to official recommendations on PPE use. The World Health Organization (WHO), US Centers for Disease Control and Prevention (CDC), and European Center for Disease Control and Prevention (ECDC) recommend standard precautions for contact transmission, respiratory transmission, and droplet precautions among HCPs caring for patients with COVID-19. Indeed, healthcare workers working in high-risk areas, as well as the public, when social distancing cannot be assured, must wear PPE such as face mask and protective eyewear to prevent the transmission of SARS-CoV-2 infection. Due to the increased use of PPE, the potentially infectious waste stream has been rapidly increasing, requiring safe and adequate solid waste management. The proper use of PPE and management of waste generated from COVID-19 care centers can reduce the risk of COVID-19 infection. These measures should be implemented to counter the rapid spread and any long-term impacts of the current pandemic.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

Factors Associated With Health Complaints Among Leather Tannery Workers in Bangladesh.

Background: Few studies have reported associations between occupational exposure to tannery chemicals with breathing difficulty and skin diseases and none have been conducted in Bangladesh. The aim of this study was to investigate the associations of health complaints with types of work and length of employment among tannery workers in Bangladesh, where occupational health and safety regulations are less restricted compared with the developed world. Methods: One hundred sixty-seven (n = 167) workers from 10 tanneries were interviewed using a questionnaire adapted from the European Community Respiratory Health Survey (ECRHS) and the Tasmanian Longitudinal Health Study (TAHS) to collect information on occupational exposures and health outcomes. Workers' length of employment was examined, as well as their areas of work including beamhouse, wet finishing, dry finishing, and miscellaneous. Univariate and multivariate logistic regressions were performed to investigate potential associations while controlling for confounders. Results: Length of employment was positively associated with breathing difficulty (odds ratio [OR]: 1.32, 95% confidence interval [CI]:1.07-1.64). Workers involved in the wet finishing (OR: 11.75, 95% CI: 2.12-65.10) and dry finishing (OR: 13.38, 95% CI: 1.00-181.70) had higher odds of breathing difficulty; while, working in the beamhouse was associated with an increased risk of developing skin diseases (OR: 4.36, 95% CI: 1.10-17.32). Conclusion/Application to Practice: Length of employment and types of work were associated with increased risk of health complaints, including breathing difficulty and skin disease among tannery workers. Stronger regulations with regular enforcement, regular health surveillance, and worker and employer education are necessary for reducing these exposures and improving the health outcomes of the tannery workers.

Poor and Unsatisfactory Disposal of Expired and Unused Pharmaceuticals: A Global Issue.

Pharmaceuticals are beneficial to humankind and emerged as crucial arms to treat/manage multiple disease pathogenesis in the present era. In analogous, these medicines/ medical devices should be used cautiously as they possess a potential threat to induce multiple undesired effects that may be related to human health or the environment. Daunting effects may arise due to the improper disposal of unused/expired medicines. Hence, to minimize such harm, there should be adequate knowledge and practice among the population regarding the safe disposal of unused/expired medicines or related pharmaceutical devices. The lack of approved information regarding safe disposal of such substances may invite serious concerns like environmental pollution, which may induce immediate health hazards to the present population and upcoming future generations. There are numerous ways to dispose of, or manage the unused and expired pharmaceutical substances. Sharing the medicines among siblings, friends, and family members are never free from serious health risks. Storing the unused and expired medicines in the home increases the risk of intentional or accidental ingestion of such substances and may create a health emergency. Disposing medicines like household and municipal waste may lead to environmental pollution and harm to humans and animals. The present review finds the multiple unsafe ways of disposal of unutilized medications/tools. Furthermore, it also summarizes the disposal pattern of unutilized medications among the few developed and undeveloped nations.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.