Global and local genomic features together modulate the spontaneous single nucleotide mutation rate.

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems. We find that the spontaneous SNM rates in our data are correlated with many genomic features in prokaryotes and unicellular eukaryotes irrespective of their sample sizes. On the other hand, only the number of coding genes was correlated with the spontaneous SNM rates in multicellular eukaryotes primarily contributed by vertebrates data. Considering local features, we notice that local GC content and CpG content significantly were correlated with the spontaneous SNM rates in the unicellular eukaryotes, while local repeat fraction is an important feature in prokaryotes and certain specific uni- and multi-cellular eukaryotes. Such predictive features of the spontaneous SNM rates often support non-linear models as the best fit compared to the linear model. We also observe that the strand asymmetry in prokaryotes plays an important role in determining the spontaneous SNM rates but the SNM spectrum does not.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates.

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Predictive modeling of moonlighting DNA-binding proteins.

Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful and comprehensive analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principles features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other functions. The source codes and data sets used for this work are freely available at https://zenodo.org/record/7299265#.Y2pO3ctBxPY.

Host-pathogen protein-nucleic acid interactions: A comprehensive review.

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention. Here, we discuss the newest advances in studies of molecular interactions involving pathogen nucleic acids and host factors, including their drug design, molecular structure and specific patterns. We observed that two groups of nucleic acid recognizing molecules, Toll-like receptors (TLRs) and the cytoplasmic retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) form the backbone of host responses to pathogen nucleic acids, with additional support provided by absent in melanoma 2 (AIM2) and DNA-dependent activator of Interferons (IFNs)-regulatory factors (DAI) like cytosolic activity. We review the structural, immunological, and other biological aspects of these representative groups of molecules, especially in terms of their target specificity and affinity and challenges in leveraging host-pathogen protein-nucleic acid interactions (HP-PNI) in drug discovery.

Unraveling molecular mechanisms of head and neck cancer.

Malignancies that develop from mucosal epithelium of the upper aerodigestive tract are known as head and neck squamous cell carcinomas (HNSCC). Heterogeneity, late stage diagnosis and high recurrence rate are big hurdles in head and neck treatment regimen. Presently, the biomarkers available for diagnosis and prognosis of HNSCC are based on smoking as the major risk habit. This review shed light on the differential environment of HNSCC in smokeless tobacco consuming Indian patients. Frequent mutation in genes involved in DNA repair pathway (p53), cell proliferation (PIK3CA, HRAS) and cell death (CASP8, FADD) are common in western population. On the contrary, the genes involved in metastasis (MMPs, YAP1), lymphocyte proliferation (TNFRSF4, CD80), cell-cell adhesion (DCC, EDNRB), miRNA processing (DROSHA) and inflammatory responses (TLR9, IL-9) are mutated in Indian HNSCC patients. Gene ontology enrichment analysis highlighted that responses to chemical stimulus, immune pathways and stress pathways are highly enriched in Indian patients.

Inadequacy of Evolutionary Profiles Vis-a-vis Single Sequences in Predicting Transient DNA-Binding Sites in Proteins.

Sequence-based prediction of DNA-binding residues in a protein is a widely studied problem for which machine learning methods with continuously improving predictive power have been developed. Concatenated rows within a sliding window of a Position Specific Substitution Matrix (PSSM) of the protein are currently used as the primary feature set in almost all the methods of predicting DNA-binding residues. Here we report that these evolutionary profiles are powerful, only for identifying conserved binding sites and fall short for the residue positions which undergo binding to non-binding transitions in closely related proteins. We created a database of highly similar protein pairs with known protein-DNA complexes and investigated differential predictability of conserved and transient binding residues within each pair. Retraining machine learning models uniformly, we compared the predictive powers of the models trained on PSSMs against similarly trained models on sparse-encoded single sequences. We found that the transient binding site predictions from evolutionary profiles are outperformed by single-sequence based models under controlled experiments by as much as 8 percentage points. Thus, we conclude that the PSSM-based models are inadequate to predict high-specificity DNA-binding residues. These findings are of critical significance for the design of mutant- and species-specific DNA ligands and for homology based modeling of protein-DNA complexes.

Understanding disorder-to-order transitions in protein-RNA complexes using molecular dynamics simulations.

Intrinsically disordered regions (IDRs) in proteins are characterized by their flexibilities and low complexity regions, which lack unique 3 D structures in solution. IDRs play a significant role in signaling, regulation, and binding multiple partners, including DNA, RNA, and proteins. Although various experiments have shown the role of disordered regions in binding with RNA, a detailed computational analysis is required to understand their binding and recognition mechanism. In this work, we performed molecular dynamics simulations of 10 protein-RNA complexes to understand the binding governed by intrinsically disordered regions. The simulation results show that most of the disordered regions are important for RNA-binding and have a transition from disordered-to-ordered conformation upon binding, which often contribute significantly towards the binding affinity. Interestingly, most of the disordered residues are present at the interface or located as a linker between two regions having similar movements. The DOT regions are overlaped or flanked with experimentally reported functionally important residues in the recognition of protein-RNA complexes. This study provides additional insights for understanding the role and recognition mechanism of disordered regions in protein-RNA complexes.Communicated by Ramaswamy H. Sarma.

Molecular Characterization of HOXA2 and HOXA3 Binding Properties.

The highly conserved HOX homeodomain (HD) transcription factors (TFs) establish the identity of different body parts along the antero-posterior axis of bilaterian animals. Segment diversification and the morphogenesis of different structures is achieved by generating precise patterns of HOX expression along the antero-posterior axis and by the ability of different HOX TFs to instruct unique and specific transcriptional programs. However, HOX binding properties in vitro, characterised by the recognition of similar AT-rich binding sequences, do not account for the ability of different HOX to instruct segment-specific transcriptional programs. To address this problem, we previously compared HOXA2 and HOXA3 binding in vivo. Here, we explore if sequence motif enrichments observed in vivo are explained by binding affinities in vitro. Unexpectedly, we found that the highest enriched motif in HOXA2 peaks was not recognised by HOXA2 in vitro, highlighting the importance of investigating HOX binding in its physiological context. We also report the ability of HOXA2 and HOXA3 to heterodimerise, which may have functional consequences for the HOX patterning function in vivo.

Current Insights and Advancements in Head and Neck Cancer: Emerging Biomarkers and Therapeutics with Cues from Single Cell and 3D Model Omics Profiling.

Head and neck cancer (HNC) is among the ten leading malignancies worldwide, with India solely contributing one-third of global oral cancer cases. The current focus of all cutting-edge strategies against this global malignancy are directed towards the heterogeneous tumor microenvironment that obstructs most treatment blueprints. Subsequent to the portrayal of established information, the review details the application of single cell technology, organoids and spheroid technology in relevance to head and neck cancer and the tumor microenvironment acknowledging the resistance pattern of the heterogeneous cell population in HNC. Bioinformatic tools are used for study of differentially expressed genes and further omics data analysis. However, these tools have several challenges and limitations when analyzing single-cell gene expression data that are discussed briefly. The review further examines the omics of HNC, through comprehensive analyses of genomics, transcriptomics, proteomics, metabolomics, and epigenomics profiles. Patterns of alterations vary between patients, thus heterogeneity and molecular alterations between patients have driven the clinical significance of molecular targeted therapies. The analyses of potential molecular targets in HNC are discussed with connotation to the alteration of key pathways in HNC followed by a comprehensive study of protein kinases as novel drug targets including its ATPase and additional binding pockets, non-catalytic domains and single residues. We herein review, the therapeutic agents targeting the potential biomarkers in light of new molecular targeted therapies. In the final analysis, this review suggests that the development of improved target-specific personalized therapies can combat HNC's global plight.

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review.

Objective: This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate. Introduction: Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care. Inclusion Criteria: Studies that used artificial intelligence to aid the diagnosis, treatment, or its planning in individuals with cleft lip and/or palate were included. Methodology: A search of the Pubmed, Embase, and IEEE Xplore databases was conducted using search terms artificial intelligence and cleft lip and/or palate. Gray literature was searched using Google Scholar. The study was conducted according to the PRISMA- ScR guidelines. Results: The initial search identified 458 results, which were screened based on title and abstracts. After the screening, removal of duplicates, and a full-text reading of selected articles, 26 publications were included. They explored the use of AI in cleft lip and/or palate to aid in decisions regarding diagnosis, treatment, especially speech therapy, and prediction. Conclusion: There is active interest and immense potential for the use of artificial intelligence in cleft lip and/or palate. Most studies currently focus on speech in cleft palate. Multi-center studies that include different populations, with collaboration amongst academicians and researchers, can further develop the technology.

Deciphering the Role of Residues Involved in Disorder-To-Order Transition Regions in Archaeal tRNA Methyltransferase 5.

tRNA methyltransferase 5 (Trm5) enzyme is an S-adenosyl methionine (AdoMet)-dependent methyltransferase which methylates the G37 nucleotide at the N1 atom of the tRNA. The free form of Trm5 enzyme has three intrinsically disordered regions, which are highly flexible and lack stable three-dimensional structures. These regions gain ordered structures upon the complex formation with tRNA, also called disorder-to-order transition (DOT) regions. In this study, we performed molecular dynamics (MD) simulations of archaeal Trm5 in free and complex forms and observed that the DOT residues are highly flexible in free proteins and become stable in complex structures. The energetic contributions show that DOT residues are important for stabilising the complex. The DOT1 and DOT2 are mainly observed to be important for stabilising the complex, while DOT3 is present near the active site to coordinate the interactions between methyl-donating ligands and G37 nucleotides. In addition, mutational studies on the Trm5 complex showed that the wild type is more stable than the G37A tRNA mutant complex. The loss of productive interactions upon G37A mutation drives the AdoMet ligand away from the 37th nucleotide, and Arg145 in DOT3 plays a crucial role in stabilising the ligand, as well as the G37 nucleotide, in the wild-type complex. Further, the overall energetic contribution calculated using MMPBSA corroborates that the wild-type complex has a better affinity between Trm5 and tRNA. Overall, our study reveals that targeting DOT regions for binding could improve the inhibition of Trm5.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis.

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype-phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance-associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm.

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium.

Intestinal Epithelium-Derived Luminally Released Extracellular Vesicles in Sepsis Exhibit the Ability to Suppress TNF-a and IL-17A Expression in Mucosal Inflammation.

Sepsis is a systemic inflammatory disorder induced by a dysregulated immune response to infection resulting in dysfunction of multiple critical organs, including the intestines. Previous studies have reported contrasting results regarding the abilities of exosomes circulating in the blood of sepsis mice and patients to either promote or suppress inflammation. Little is known about how the gut epithelial cell-derived exosomes released in the intestinal luminal space during sepsis affect mucosal inflammation. To study this question, we isolated extracellular vesicles (EVs) from intestinal lavage of septic mice. The EVs expressed typical exosomal (CD63 and CD9) and epithelial (EpCAM) markers, which were further increased by sepsis. Moreover, septic-EV injection into inflamed gut induced a significant reduction in the messaging of pro-inflammatory cytokines TNF-a and IL-17A. MicroRNA (miRNA) profiling and reverse transcription and quantitative polymerase chain reaction (RT-qPCR) revealed a sepsis-induced exosomal increase in multiple miRNAs, which putatively target TNF-a and IL-17A. These results imply that intestinal epithelial cell (IEC)-derived luminal EVs carry miRNAs that mitigate pro-inflammatory responses. Taken together, our study proposes a novel mechanism by which IEC EVs released during sepsis transfer regulatory miRNAs to cells, possibly contributing to the amelioration of gut inflammation.

Potential of age distribution profiles for the prediction of COVID-19 infection origin in a patient group.

The COVID-19 pandemic is a serious and global public health concern. It is now well known that COVID-19 cases may result in mild symptoms leading to patient recovery. However, severity of infection, fatality rates, and treatment responses across different countries, age groups, and demographic groups suggest that the nature of infection is diverse, and a timely investigation of the same is needed for evolving sound treatment and preventive strategies. This paper reports an the analysis of age distribution patterns in six groups of Indian COVID-19 patient populations based on their likely geographical origin of infection viz. the United Kingdom, North America, the European Union, the Middle East, and Asian countries. It was observed that patient groups stratified in this way had a distinct age profile and that some of these groups e.g. patient groups from Asia, the European Union, and the United Kingdom formed a different cluster than those from North America, the Middle East, and other regions. Patient age profiles of a population were found to be highly predictive of the group they belong to, and there are indications of their distinct recovery and fatality rates across gender. Altogether this study provides a scalable framework to estimate the source of infection in a new population of COVID-19 patients with unknown origin. It is also concluded that greater public availability of age and other demographic profile details of patients may be helpful in gaining robust insights into COVID-19 infection origins. Datasets and scripts used in this work are shared at http://covid.sciwhylab.org.

Publisher Correction: Successional trajectory of bacterial communities in soil are shaped by plant-driven changes during secondary succession.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Successional trajectory of bacterial communities in soil are shaped by plant-driven changes during secondary succession.

This study investigated the potential role of a nitrogen-fixing early-coloniser Alnus Nepalensis D. Don (alder) in driving the changes in soil bacterial communities during secondary succession. We found that bacterial diversity was positively associated with alder growth during course of ecosystem development. Alder development elicited multiple changes in bacterial community composition and ecological networks. For example, the initial dominance of actinobacteria within bacterial community transitioned to the dominance of proteobacteria with stand development. Ecological networks approximating species associations tend to stabilize with alder growth. Janthinobacterium lividum, Candidatus Xiphinematobacter and Rhodoplanes were indicator species of different growth stages of alder. While the growth stages of alder has a major independent contribution to the bacterial diversity, its influence on the community composition was explained conjointly by the changes in soil properties with alder. Alder growth increased trace mineral element concentrations in the soil and explained 63% of variance in the Shannon-diversity. We also found positive association of alder with late-successional Quercus leucotrichophora (Oak). Together, the changes in soil bacterial community shaped by early-coloniser alder and its positive association with late-successional oak suggests a crucial role played by alder in ecosystem recovery of degraded habitats.

Role of disordered regions in transferring tyrosine to its cognate tRNA.

Aminoacyl tRNA synthetase (AARS) plays an important role in transferring each amino acid to its cognate tRNA. Specifically, tyrosyl tRNA synthetase (TyrRS) is involved in various functions including protection from DNA damage due to oxidative stress, protein synthesis and cell signaling and can be an attractive target for controlling the pathogens by early inhibition of translation. TyrRS has two disordered regions, which lack a stable 3D structure in solution, and are involved in tRNA synthetase catalysis and stability. One of the disordered regions undergoes disorder-to-order transition (DOT) upon complex formation with tRNA whereas the other remains disordered (DR). In this work, we have explored the importance of these disordered regions using molecular dynamics simulations of both free and RNA-complexed states. We observed that the DOT and DR regions of the first subunit acts as a flap and interact with the acceptor arm of the tRNA. The DOT-DR flap closes when tyrosine (TyrRSTyr) is present at the active site of the complex and opens in the presence of tyrosine monophosphate (TyrRSYMP). The DOT and DR regions of the second subunit interact with the anticodon stem as well as D-loop of the tRNA, which might be involved in stabilizing the complex. The anticodon loop of the tRNA binds to the structured region present in the C-terminal of the protein, which is observed to be flexible during simulations. Detailed energy calculations also show that TyrRSTyr complex has stronger binding energy between tRNA and protein compared to TyrRSYMP; on the contrary, the anticodon is strongly bound in TyrRSYMP. The results obtained in the present study provide additional insights for understanding catalysis and the involvement of disordered regions in Tyr transfer to cognate tRNA.

Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied to unequally sized protein sequences. PSSM summaries representing column averages and their amino-acid wise versions have been effectively used for the task, but it remains unclear if these features carry all the PSSM's predictive power, traditionally harnessed for binding site predictions. Here we evaluate if PSSMs scaled up to a fixed size by zero-vector padding (pPSSM) could perform better than the summary based features on similar models. Using multilayer perceptron (MLP) and deep convolutional neural network (CNN), we found that (a) Summary features work well for single-genome (human-only) data but are outperformed by pPSSM for diverse PDB-derived data sets, suggesting greater summary-level redundancy in the former, (b) even when summary features work comparably well with pPSSM, a consensus on the two outperforms both of them (c) CNN models comprehensively outperform their corresponding MLP models and (d) actual predicted scores from different models depend on the choice of input feature sets used whereas overall performance levels are model-dependent in which CNN leads the accuracy.