Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

The role of delay in vaccination rate on Covid-19.

The role of vaccination in tackling Covid-19 and the potential consequences of a time delay in vaccination rate are discussed. This study presents a mathematical model that incorporates the rate of vaccination and parameters related to the presence and absence of time delay in the context of Covid-19. We conducted a study on the global dynamics of a Covid-19 outbreak model, which incorporates a vaccinated population and a time delay parameter. Our findings demonstrate the global stability of these models. Our observation indicates that lower vaccination rates are associated with an increase in the overall number of infected individuals. The stability of the corresponding time delay model is determined by the value of the time delay parameter. If the time delay parameter is less than the critical value at which the Hopf bifurcation occurs, the model is stable. The results are supported by numerical illustrations that have epidemiological relevance.

Transcriptional advantage influence odorant receptor gene choice.

Odorant receptors (ORs) obey mutual exclusivity and monoallelic mode of expression. Efforts are ongoing to decipher the molecular mechanism that drives the 'one-neuron-one-receptor' rule of olfaction. Recently, single-cell profiling of olfactory sensory neurons (OSNs) revealed the expression of multiple ORs in the immature neurons, suggesting that the OR gene choice mechanism is much more complex than previously described by the silence-all-and-activate-one model. These results also led to the genesis of two possible mechanistic models i.e. winner-takes-all and stochastic selection. We developed Reverse Cell Tracking (RCT), a novel computational framework that facilitates OR-guided cellular backtracking by leveraging Uniform Manifold Approximation and Projection embeddings from RNA Velocity Workflow. RCT-based trajectory backtracking, coupled with statistical analysis, revealed the OR gene choice bias for the transcriptionally advanced (highest expressed) OR during neuronal differentiation. Interestingly, the observed selection bias was uniform for all ORs across different spatial zones or their relative expression within the olfactory organ. We validated these findings on independent datasets and further confirmed that the OR gene selection may be regulated by Upf3b. Lastly, our RNA dynamics-based tracking of the differentiation cascade revealed a transition cell state that harbors mixed molecular identities of immature and mature OSNs, and their relative abundance is regulated by Upf3b.

Artificial intelligence uncovers carcinogenic human metabolites.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood-brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.

Association of work and hypertension among middle-aged adults and elderly Indians.

PURPOSE: Over two-fifth of middle-aged adults and elderly (45 +) in India are hypertensive. Though studies examined prevalence, awareness and control of hypertension, little is known on the association of hypertension with work status in India. This study examines the variations of hypertension by types of work among middle-aged adults and the elderly in India. METHODS: Data were drawn from the Longitudinal Aging Survey of India (LASI), Wave 1, 2017-18, and analysis was restricted to participants aged 45 and above with complete information on employment and blood pressure (N = 59,196). RESULTS: We estimated the adjusted prevalence of hypertension at 49.2% (95% CI, 47.8-50.6) among the ever worked but not currently working and 44.5% (95% CI, 43.1-45.8) among currently working. Among eight broad categories of the currently working population, the adjusted estimates of hypertension were highest among legislators, senior officials and managers (71.5%; 95% CI, 41.9-101.0), followed by service and sales worker workers (44.7%; 95% CI, 41.2-48.2) and least among the professionals (37.1%; 95% CI, 27.1-47.2). Relative to never worked, legislators, senior officials and managers were twice more likely [adjusted OR (AOR) 2.00; 95% CI, 0.74-5.39] to be hypertensive, followed by plant and machine operators (AOR 1.33; 95% CI, 1.04-1.71). The odds of being hypertensive was least among those engaged in professional (engineering, health, education) activities. The other significant predictors are age, sex, residence, education level, household economic condition, family history of hypertension, chronic disease and depression. CONCLUSION: The risk of hypertension varies with the types of work in which older Indians are engaged. Awareness and treatment of hypertension in high-risk occupation are recommended.

OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding.

The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network-based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant-OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.

EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Dramatic genomic alterations, either inducible or in a pathological state, dismantle the core regulatory networks, leading to the activation of normally silent genes. Despite possessing immense therapeutic potential, accurate detection of these transcripts is an ever-challenging task, as it requires prior knowledge of the physiological gene expression levels. Here, we introduce EcTracker, an R-/Shiny-based single-cell data analysis web server that bestows a plethora of functionalities that collectively enable the quantitative and qualitative assessments of bona fide cell types or tissue-specific transcripts and, conversely, the ectopically expressed genes in the single-cell ribonucleic acid sequencing datasets. Moreover, it also allows regulon analysis to identify the key transcriptional factors regulating the user-selected gene signatures. To demonstrate the EcTracker functionality, we reanalyzed the CRISPR interference (CRISPRi) dataset of the human embryonic stem cells differentiated into endoderm lineage and identified the prominent enrichment of a specific gene signature in the SMAD2 knockout cells whose identity was ambiguous in the original study. The key distinguishing features of EcTracker lie within its processing speed, availability of multiple add-on modules, interactive graphical user interface and comprehensiveness. In summary, EcTracker provides an easy-to-perform, integrative and end-to-end single-cell data analysis platform that allows decoding of cellular identities, identification of ectopically expressed genes and their regulatory networks, and therefore, collectively imparts a novel dimension for analyzing single-cell datasets.

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research.

SUMMARY: Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds. AVAILABILITY AND IMPLEMENTATION: MOA is available for Windows, Mac and Linux operating systems. It's accessible at (https://ahuja-lab.in/). Source code, user manual, step-by-step guide and support is available at GitHub (https://github.com/the-ahuja-lab/Machine-Olf-Action). For results, reproducibility and hyperparameters, refer to Supplementary Notes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Cellular basis of loss of smell in 2019-nCoV-infected individuals.

A prominent clinical symptom of 2019-novel coronavirus (nCoV) infection is hyposmia/anosmia (decrease or loss of sense of smell), along with general symptoms such as fatigue, shortness of breath, fever and cough. The identity of the cell lineages that underpin the infection-associated loss of olfaction could be critical for the clinical management of 2019-nCoV-infected individuals. Recent research has confirmed the role of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) as key host-specific cellular moieties responsible for the cellular entry of the virus. Accordingly, the ongoing medical examinations and the autopsy reports of the deceased individuals indicate that organs/tissues with high expression levels of ACE2, TMPRSS2 and other putative viral entry-associated genes are most vulnerable to the infection. We studied if anosmia in 2019-nCoV-infected individuals can be explained by the expression patterns associated with these host-specific moieties across the known olfactory epithelial cell types, identified from a recently published single-cell expression study. Our findings underscore selective expression of these viral entry-associated genes in a subset of sustentacular cells (SUSs), Bowman's gland cells (BGCs) and stem cells of the olfactory epithelium. Co-expression analysis of ACE2 and TMPRSS2 and protein-protein interaction among the host and viral proteins elected regulatory cytoskeleton protein-enriched SUSs as the most vulnerable cell type of the olfactory epithelium. Furthermore, expression, structural and docking analyses of ACE2 revealed the potential risk of olfactory dysfunction in four additional mammalian species, revealing an evolutionarily conserved infection susceptibility. In summary, our findings provide a plausible cellular basis for the loss of smell in 2019-nCoV-infected patients.

Triple burden of disease and out of pocket healthcare expenditure of women in India.

AIM: Women, unlike men, are subjected to triple burden of disease, namely, non-communicable and communicable diseases and reproductive health related diseases. To assess prevalence of triple burden of disease of currently married women and to contrast out of pocket maternal care expenditure of these diseases in India. SUBJECT AND METHODS: This study uses nationally representative unit level data from the 71st round (2014) of the National Sample Survey Organisation. Descriptive statistics and bivariate analysis are employed to assess triple burden of diseases by background of currently married women. Mean out of pocket (OOP) expenditure for healthcare care by demographic and household characteristics of women are also compared by type of diseases. Two parts model is adopted for assessment of determents of out of pocket healthcare expenditure of women. RESULTS: Overall medical and non-medical expenses of non-communicable disease are much higher than those of other disease and disability, reproductive health related and communicable diseases. OOP expenditure for treatment of non-communicable diseases, reproductive health and related diseases and other disease and disability are significantly higher than the inpatient treatment of communicable diseases and the differences are statistically significant. CONCLUSION: Out of pocket expenditure for treatment of non-communicable diseases is the highest, followed by that of other diseases & disability, then reproductive health related diseases and the least is for communicable diseases. OOP expenditures for maternal healthcare in private health facilities are much higher than in public health facilities regardless of types of disease. Women from households having insurance of any member spent less than that of women from households not having health insurance. There is an urgent need to expand the outreach of the public health system in India to rural areas.

Out-of-pocket expenditure on prenatal and natal care post Janani Suraksha Yojana: a case from Rajasthan, India.

BACKGROUND: Though Janani Suraksha Yojana (JSY) under National Rural Health Mission (NRHM) is successful in increasing antenatal and natal care services, little is known on the cost coverage of out-of-pocket expenditure (OOPE) on maternal care services post-NRHM period. METHODS: Using data from a community-based study of 424 recently delivered women in Rajasthan, this paper examined the variation in OOPE in accessing maternal health services and the extent to which JSY incentives covered the burden of cost incurred. Descriptive statistics and logistic regression analyses are used to understand the differential and determinants of OOPE. RESULTS: The mean OOPE for antenatal care was US$26 at public health centres and US$64 at private health centres. The OOPE (antenatal and natal) per delivery was US$32 if delivery was conducted at home, US$78 at public facility and US$154 at private facility. The OOPE varied by the type of delivery, delivery with complications and place of ANC. The OOPE in public health centre was US$44 and US$145 for normal and complicated delivery, respectively. The share of JSY was 44 % of the total cost per delivery, 77 % in case of normal delivery and 23 % for complicated delivery. Results from the log linear model suggest that economic status, educational level and pregnancy complications are significant predictors of OOPE. CONCLUSIONS: Our results suggest that JSY has increased the coverage of institutional delivery and reduced financial stress to household and families but not sufficient for complicated delivery. Provisioning of providing sonography/other test and treating complicated cases in public health centres need to be strengthened.

Factors associated with risky sexual practices among female sex workers in Karnataka, India.

INTRODUCTION: The objectives of this study are to develop a summary measure of risky sexual practice and examine the factors associated with this among female sex workers (FSWs) in Karnataka, India. MATERIALS AND METHODS: Data were drawn from special behavioral surveys (SBS) conducted in 2007 among 577 FSWs in two districts of Karnataka, India: Belgaum and Bangalore. FSWs were recruited using the two-stage probability sampling design. FSWs' sexual practice was considered risky if they reported inconsistent condom use with any sexual partner and reported experience of one of the following vulnerabilities to HIV risk: anal sex, alcohol consumption prior to sex and concurrent sexual relationships. RESULTS: About 51% of FSWs had engaged in risky sexual practice. The odds of engaging in risky sex were higher among FSWs who were older (35+ years) than younger (18-25 years) (58% vs. 45%, Adjusted Odds Ratio (AOR): 2.0, 95% confidence interval (CI): 1.2-3.4), who were currently married than never married (61% vs. 51%, AOR: 4.8, 95% CI: 2.5-9.3), who were in sex work for 10+ years than those who were in sex work for less than five years (66% vs. 39%, AOR: 2.6, 95% CI: 1.6-4.2), and who had sex with 3+ clients/day than those who had sex with fewer clients (67% vs. 38%, AOR: 3.7, 95% CI:2.5-5.5). CONCLUSION: FSWs who are older, currently married, practicing sex work for longer duration and with higher clientele were more likely to engage in risky sexual practices. HIV prevention programs should develop strategies to reach these most-at risk group of FSWs to optimize the effectiveness of such programs.