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.

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges.

Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.

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.

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.

Challenges and possible solutions for decoding extranasal olfactory receptors.

Olfactory receptors are primarily known to be expressed in the olfactory epithelium of the nasal cavity and therefore assist in odor perception. With the advent of high-throughput omics technologies such as tissue microarray or RNA sequencing, a large number of olfactory receptors have been reported to be expressed in the nonolfactory tissues. Although these technologies uncovered the expression of these olfactory receptors in the nonchemosensory tissues, unfortunately, they failed to reveal the information about their cell type of origin. Accurate characterization of the cell types should be the first step towards devising cell type-specific assays for their functional evaluation. Single-cell RNA-sequencing technology resolved some of these apparent limitations and opened new means to interrogate the expression of these extranasal olfactory receptors at the single-cell resolution. Moreover, the availability of large-scale, multi-organ/species single-cell expression atlases offer ample resources for the systematic reannotation of these receptors in a cell type-specific manner. In this Viewpoint article, we discuss some of the technical limitations that impede the in-depth understanding of these extranasal olfactory receptors, with a special focus on odorant receptors. Moreover, we also propose a list of single cell-based omics technologies that could further promulgate the opportunity to decipher the regulatory network that drives the odorant receptors expression at atypical locations.

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.

Analysis of single-cell transcriptomes links enrichment of olfactory receptors with cancer cell differentiation status and prognosis.

Ectopically expressed olfactory receptors (ORs) have been linked with multiple clinically-relevant physiological processes. Previously used tissue-level expression estimation largely shadowed the potential role of ORs due to their overall low expression levels. Even after the introduction of the single-cell transcriptomics, a comprehensive delineation of expression dynamics of ORs in tumors remained unexplored. Our targeted investigation into single malignant cells revealed a complex landscape of combinatorial OR expression events. We observed differentiation-dependent decline in expressed OR counts per cell as well as their expression intensities in malignant cells. Further, we constructed expression signatures based on a large spectrum of ORs and tracked their enrichment in bulk expression profiles of tumor samples from The Cancer Genome Atlas (TCGA). TCGA tumor samples stratified based on OR-centric signatures exhibited divergent survival probabilities. In summary, our comprehensive analysis positions ORs at the cross-road of tumor cell differentiation status and cancer prognosis.