A positive selection at binding site 501 in the B.1 lineage might have triggered the highly infectious sub-lineages of SARS-CoV-2.

The descendants of the B lineage are the most predominant variants among the SARS-CoV-2 virus due to the incorporation of new mutations augmenting the infectivity of the virus. There is a substantial increase in the transition transversion bias, nucleotide diversity and purifying selection on the spike protein in the descendants of the B lineage of the SARS-CoV-2 virus on a temporal scale. A strong bias for C-to-U substitutions is found in the genes encoding spike protein in this lineage. The positive selection has operated on the spike gene of B lineages and its sub-lineages. The B.1 lineage has undergone positive selection on site 501 of the receptor binding domain ultimately reflected in a key substitution N501Y in its three descendant lineages namely B.1.1.7, B.1.351 and P.1. The intensity of purifying selection on the multiple sites of the spike gene has increased substantially in the sub-lineages of B.1 in a timescale. The binding site 501 on the spike protein in B lineage is found to coevolve with other amino acid sites. This study sheds light on the evolutionary trajectory of the B lineage into highly infectious descendants in the recent past under the influence of positive and purifying selection exerted by natural immunity and vaccination of the host.

piRNAs in the human retina and retinal pigment epithelium reveal a potential role in intracellular trafficking and oxidative stress.

Long considered active only in the germline, the PIWI/piRNA pathway is now known to play a significant role in somatic cells, especially neurons. In this study, piRNAs were profiled in the human retina and retinal pigment epithelium (RPE). Furthermore, RNA immunoprecipitation with HIWI2 (PIWIL4) in ARPE19 cells yielded 261 piRNAs, and the expression of selective piRNAs in donor eyes was assessed by qRT-PCR. Intriguingly, computational analysis revealed complete and partial seed sequence similarity between piR-hsa-26131 and the sensory organ specific miR-183/96/182 cluster. Furthermore, the expression of retina-enriched piR-hsa-26131 was positively correlated with miR-182 in HIWI2-silenced Y79 cells. In addition, the lnc-ZNF169 sequence matched with two miRNAs of the let-7 family, and piRNAs, piR-hsa-11361 and piR-hsa-11360, which could modulate the regulatory network of retinal differentiation. Interestingly, we annotated four enriched motifs among the piRNAs and found that the piRNAs containing CACAATG and CTCATCAKYG motifs were snoRNA-derived piRNAs, which are significantly associated with developmental functions. However, piRNAs consisting of ACCACTANACCAC and AKCACGYTCSC motifs were mainly tRNA-derived fragments linked to stress response and sensory perception. Additionally, co-expression network analysis revealed cell cycle control, intracellular transport and stress response as the important biological functions regulated by piRNAs in the retina. Moreover, loss of piRNAs in HIWI2 knockdown ARPE19 confirmed altered expression of targets implicated in intracellular transport, circadian clock, and retinal degeneration. Moreover, piRNAs were dysregulated under oxidative stress conditions, indicating their potential role in retinal pathology. Therefore, we postulate that piRNAs, miRNAs, and lncRNAs might have a functional interplay during retinal development and functions to regulate retinal homeostasis.

Genomic coevolution of papillomavirus and immune system in placental mammals indicates the role of IFN-γ in the emergence of new variants.

Papillomaviruses (PVs) are causative agents for warts and cancers in different parts of the body in the mammalian lineage. Therefore, these viruses are proposed as model organisms to study host immune responses to pathogens causing chronic infections. The virus-associated cancer progression depends on two integral processes namely angiogenesis and immune response (AIR). The angiogenesis process aids in tumour progression through vessel formation and maturation but the host immune response, in contrast, makes every attempt to eliminate pathogens and thereby maintain healthy tissues. However, the evolutionary contribution of individual viral genes and host AIR genes in carcinogenesis is yet to be explored. Here, we applied the evolutionary genomics approach to find correlated evolution between six PV genes and 23 host AIR-related genes. We estimated that IFN-γ is the only host gene evolving in a correlated manner with all six PV genes under study. Furthermore, three papillomavirus genes, L2, E6, and E7, are found to interact with two third of host AIR-related genes. Moreover, a combined differential gene expression analysis and network analysis showed that inflammatory cytokine IFN-γ is the key regulator of hub genes in the PPI network of the differentially expressed genes. Functional enrichment of these hub genes is consistent with their established role in different cancers and viral infections. Overall, we conclude that IFN-γ maintains selective pressure on mammalian PV genes and seems to be a potential biomarker for PV-related cancers. This study demonstrates the evolutionary importance of IFN-γ in deciding the fate of carcinogenic PV variants.

Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19.

'Tripartite network' (TN) and 'combined gene network' (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as 'target genes' (TG) to identify 21 'candidate genes' (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise 'semantic similarity scores' (SSS). A new integrated 'weighted harmonic mean score' was formulated assimilating values of SSS and STRING-based 'combined score' of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and 'indispensable nodes' in CGN. Finally, six pairs sharing seven 'prevalent CGs' (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of 'prevalent CGs' has been discussed to interpret neurological phenotypes of COVID-19.

CarcinoHPVPred: An ensemble of machine learning models for HPV carcinogenicity prediction using genomic data.

Human papillomavirus (HPV) infections often show no symptoms but sometimes lead to either warts or carcinoma based on the HPV genotype. The relationship between HPV infections and cervical cancer have been well studied in the past two decades. However, distinguishing carcinogenic HPV variants from non-carcinogenic ones remains a major challenge in clinical genetic testing of HPV-induced cancer samples. All of the published HPV carcinogenicity prediction methods are neither publically available nor tested with two-thirds of available HPV variants. The nucleotide composition-based studies are the simplest and most precise methods of characterizing new genomes. Hence, there is a need for machine learning models which can predict the carcinogenic nature of newly discovered HPV based on their genomic composition. We developed a standalone and web tool, CarcinoHPVPred (h t t p :// test5.bicpu.edu.in/CarcinoHPVPred.php), for predicting the phenotype of HPV with a range of a high accuracy between 94% - 100%. This tool consists of machine learning models build upon genomic features of two genes namely E2 and E6. Overall, the accurate and early prediction of carcinogenic nature of HPV can be performed with this only available tool of its kind till date.

Matrix metallopeptidase 9 as a host protein target of chloroquine and melatonin for immunoregulation in COVID-19: A network-based meta-analysis.

AIMS: The molecular pathogenesis of COVID-19 is similar to other coronavirus (CoV) infections viz. severe acute respiratory syndrome (SARS) in human. Due to scarcity of the suitable treatment strategy, the present study was undertaken to explore host protein(s) targeted by potent repurposed drug(s) in COVID-19. MATERIALS AND METHODS: The differentially expressed genes (DEGs) were identified from microarray data repository of SARS-CoV patient blood. The repurposed drugs for COVID-19 were selected from available literature. Using DEGs and drugs, the protein-protein interaction (PPI) and chemo-protein interaction (CPI) networks were constructed and combined to develop an interactome model of PPI-CPI network. The top-ranked sub-network with its hub-bottleneck nodes were evaluated with their functional annotations. KEY FINDINGS: A total of 120 DEGs and 65 drugs were identified. The PPI-CPI network (118 nodes and 293 edges) exhibited a top-ranked sub-network (35 nodes and 174 connectivities) with 12 hub-bottleneck nodes having two drugs chloroquine and melatonin in association with 10 proteins corresponding to six upregulated and four downregulated genes. Two drugs interacted directly with the hub-bottleneck node i.e. matrix metallopeptidase 9 (MMP9), a host protein corresponding to its upregulated gene. MMP9 showed functional annotations associated with neutrophil mediated immunoinflammation. Moreover, literature survey revealed that angiotensin converting enzyme 2, a membrane receptor of SARS-CoV-2 virus, might have functional cooperativity with MMP9 and a possible interaction with both drugs. SIGNIFICANCE: The present study reveals that between chloroquine and melatonin, melatonin appears to be more promising repurposed drug against MMP9 for better immunocompromisation in COVID-19.

The cognitive and speech genes are jointly shaped by both positive and relaxed selection in the human lineage.

The emergence of a coordinated network of cognitive and speech genes in the human lineage performing overlapping functions is a great evolutionary puzzle. Prior studies on the speech gene FOXP2 are inconclusive on the nature of selection operating on this gene in the human lineage. Here, I show that the evolution of FOXP2 is accelerated in the human lineage due to relaxation of purifying selection (relaxed selection). Five potential genes associated with human-specific intelligence and speech genes have evolved under the impact of positive selection and three genes including FOXP2 have undergone relaxation of purifying selection in the human lineage. Overall, three evolutionary processes namely positive selection, relaxation of purifying selection and neutral evolution have contributed for the genomic evolution of extraordinary cognitive ability and speech in the hominin lineage. The cognitive and speech genes subjected to natural selection in the human lineage have demonstrated a coevolutionary trend.

Computational medicine: quantitative modeling of complex diseases.

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Identification of molecular biomarkers for ovarian cancer using computational approaches.

Ovarian cancer is one of the major causes of mortality among women. This is partly because of highly asymptomatic nature, lack of reliable screening techniques and non-availability of effective biomarkers of ovarian cancer. The recent availability of high-throughput data and consequently the development of network medicine approach may play a key role in deciphering the underlying global mechanism involved in a complex disease. This novel approach in medicine will pave the way in translating the new molecular insights into an effective drug therapy applying better diagnostic, prognostic and predictive tests for a complex disease. In this study, we performed reconstruction of gene co-expression networks with a query-based method in healthy and different stages of ovarian cancer to identify new potential biomarkers from the reported biomarker genes. We proposed 17 genes as new potential biomarkers for ovarian cancer that can effectively classify a disease sample from a healthy sample. Most of the predicted genes are found to be differentially expressed between healthy and diseased states. Moreover, the survival analysis showed that these genes have a significantly higher effect on the overall survival rate of the patient than the established biomarkers. The comparative analyses of the co-expression networks across healthy and different stages of ovarian cancer have provided valuable insights into the dynamic nature of ovarian cancer.

Increased Plasma Nitrite and von Willebrand Factor Indicates Early Diagnosis of Vascular Diseases in Chemotherapy Treated Cancer Patients.

Chemotherapy induced cardiotoxicity leads to development of hypertension, conduction abnormalities, and congestive heart failure. However, there is no simple test to detect and assess cardiovascular risk in a chemotherapy treated cancer patient. The aim of the present study on cancer patients treated with (n = 66) and without (n = 66) chemotherapy is to identify indicators from plasma for vascular injury. The levels of plasma nitrite, asymmetric dimethyl arginine (ADMA), von Willebrand factor (vWF), cardiac troponins, lipid peroxidation (MDA), and lactate dehydrogenase (LDH) were estimated. An R package, namely, Optimal Cutpoints, and a machine learning method-support vector machine (SVM) were applied for identifying the indicators for cardiovascular damage. We observed a significant increase in nitrite (p < 0.001) and vWF (p < 0.001) level in chemotherapy treated patients compared to untreated cancer patients and healthy controls. An increased MDA and LDH activity from plasma in chemotherapy treated cancer patients was found. The R package analysis and SVM model developed using three indicators, namely, nitrite, vWF, and MDA, can distinguish cancer patients before and after chemotherapy with an accuracy of 87.8% and AUC value of 0.915. Serum collected from chemotherapy treated patients attenuates angiogenesis in chick embryo angiogenesis (CEA) assay and inhibits migration of human endothelial cells. Our work suggests that measurement of nitrite along with traditional endothelial marker vWF could be used as a diagnostic strategy for identifying susceptible patients to develop cardiovascular dysfunctions. The results of the present study offer clues for early diagnosis of subclinical vascular toxicity with minimally invasive procedure. Schematic representation of chemotherapy induced elevated plasma nitrite level in cancer patients.

Inhibition of the MurA enzyme in Fusobacterium nucleatum by potential inhibitors identified through computational and in vitro approaches.

Fusobacterium nucleatum plays a key role in several diseases such as periodontitis, gingivitis, appendicitis, and inflammatory bowel disease (IBD). The development of antibiotic resistance by this bacterium demands novel therapeutic intervention. Our recent study has reported UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA) as one of the potential target proteins in F. nucleatum. In this study, we proposed two novel MurA inhibitors through in silico screening and evaluated their mode of inhibition by in vitro experiments. It was found that MurA structural arrangement (inside-out α/ß barrel) was stabilized by L/FXXXG(A) motif-based interactions. The protein was maintained in an open or substrate-free conformation due to repulsive forces between two parallelly arranged positively charged residues of domain I and II. In this conformation, we identified six best compounds that held key interactions with the substrate-binding pocket via a structure-based virtual screening of natural and chemical compound libraries. However, among these, only orientin and quercetin-3-O-d-glucuronide (Q3G) showed better interaction capability through consistent H-bond occupancy and lowest binding free energy during molecular dynamic simulations. In vitro inhibition studies evidenced the mixed and uncompetitive mode of inhibition by orientin and Q3G, respectively, with purified MurA protein. This explains the binding of orientin in both open and closed (substrate-bound) conformations of MurA, and Q3G binding in only closed conformation. Therefore, the Q3G binding mode was predicted on a MurA-substrate complex, which highlighted its constant H-bond with Cys118, a phosphoenolpyruvate (PEP) interacting residue. This suggests that Q3G may interrupt the PEP binding, thereby inhibiting the MurA activity. Thus, the current study discusses the structure of MurA and demonstrates the inhibitory action of two novel compounds.

Evolution of the SRGAP2 Gene Is Linked to Intelligence in Mammals.

BACKGROUND/AIMS: A recent duplication of the gene encoding SLIT-ROBO Rho GTPase-activating protein 2 (SRGAP2) in the primate lineage has been proposed to be associated with the human-specific extraordinary development of intelligence. There is no report regarding the role of the SRGAP2 gene in the expression of neural traits indicating intelligence in mammals. METHODS: A phylogenetic tree of the SRGAP2 gene from 11 mammals was reconstructed using MrBayes. The evolution of neural traits along the branches of the phylogenetic tree was modeled in the BayesTraits, and the dN/dS ratio (i.e. the ratio between the number of nonsynonymous substitutions per nonsynonymous site and the number of synonymous substitutions per synonymous site) was estimated using the codon-based maximum likelihood method (CODEML) in PAML (phylogenetic analysis by maximum likelihood). RESULTS: Two neural traits, namely brain mass and the number of cortical neurons, showed statistical dependency on the underlying evolutionary history of the SRGAP2 gene in mammals. A significant positive correlation between the increase in cortical neurons and the rate of nucleotide substitutions in the SRGAP2 gene was observed concomitantly with a significant negative correlation between the increase in cortical neurons and the rate of nonsynonymous substitutions in the gene. The SRGAP2 gene appears to be under intense pressure of purifying selection in all mammalian lineages under stringent functional constraint. CONCLUSION: This work indicates a key role of the SRGAP2 gene in the rapid expansion of neurons in the brain cortex, thereby facilitating the evolution of remarkable intelligence in mammals.

MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features.

INTRODUCTION: The paucity of morphometric markers for hemispheric asymmetries and gender variations in hippocampi and amygdalae in temporal lobe epilepsy (TLE) calls for better characterization of TLE by finding more useful prognostic MRI parameter(s). METHODS: T1-weighted MRI (3 T) morphometry using multiple parameters of hippocampus-parahippocampus (angular and linear measures, volumetry) and amygdalae (volumetry) including their hemispheric asymmetry indices (AI) were evaluated in both genders. The cutoff values of parameters were statistically estimated from measurements of healthy subjects to characterize TLE (57 patients, 55% male) alterations. RESULTS: TLE had differential categories with hippocampal atrophy, parahippocampal angle (PHA) acuteness, and several other parametric changes. Bilateral TLE categories were much more prevalent compared to unilateral TLE categories. Female patients were considerably more disposed to bilateral TLE categories than male patients. Male patients displayed diverse categories of unilateral abnormalities. Few patients (both genders) had combined bilateral appearances of hippocampal atrophy, amygdala atrophy, PHA acuteness, and increase in hippocampal angle (HA) where medial distance ratio (MDR) varied among genders. TLE had gender-specific and hemispheric dominant alterations in AI of parameters. Maximum magnitude of parametric changes in TLE includes (a) AI increase in HA of both genders, (b) HA increase (bilateral) in female patients, and (c) increase in ratio of amygdale/hippocampal volume (unilateral, right hemispheric), and AI decrease in MDR, in male patients. CONCLUSION: Multiparametric MRI studies of hippocampus and amygdalae, including their hemispheric asymmetry, underscore better characterization of TLE. Rapidly measurable single-slice parameters (HA, PHA, MDR) can readily delineate TLE in a time-constrained clinical setting, which contrasts with customary three-dimensional hippocampal volumetry that requires many slice computation.

Insights on the functional impact of microRNAs present in autism-associated copy number variants.

Autism spectrum disorder is a complex neurodevelopmental disorder that appears during the first three years of infancy and lasts throughout a person's life. Recently a large category of genomic structural variants, denoted as copy number variants (CNVs), were established to be a major contributor of the pathophysiology of autism. To date almost all studies have focussed only on the genes present in the CNV loci, but the impact of non-coding regulatory microRNAs (miRNAs) present in these regions remain largely unexplored. Hence we attempted to elucidate the biological and functional significance of miRNAs present in autism-associated CNV loci and their target genes by using a series of computational tools. We demonstrate that nearly 11% of the CNV loci harbor miRNAs and a few of these miRNAs were previously reported to be associated with autism. A systematic analysis of the CNV-miRNAs based on their interactions with the target genes enabled the identification of top 10 miRNAs namely hsa-miR-590-3p, hsa-miR-944, hsa-miR-570, hsa-miR-34a, hsa-miR-124, hsa-miR-548f, hsa-miR-429, hsa-miR-200b, hsa-miR-195 and hsa-miR-497 as hub molecules. Further, the CNV-miRNAs formed a regulatory loop with transcription factors and their downstream target genes, and annotation of these target genes indicated their functional involvement in neurodevelopment and synapse. Moreover, miRNAs present in deleted and duplicated CNV loci may explain the difference in dosage of the crucial genes controlled by them. These CNV-miRNAs can also impair the global processing and biogenesis of all miRNAs by targeting key molecules in the miRNA pathway. To our knowledge, this is the first report to highlight the significance of CNV-microRNAs and their target genes to contribute towards the genetic heterogeneity and phenotypic variability of autism.

Correlated evolution of gonadotropin-releasing hormone and gonadotropin-inhibitory hormone and their receptors in mammals.

BACKGROUND: Evolutionary rate variation in genes (proteins) is manifested both within the species (genome) and between the species (genomes). However, the interdependent components of a biological system in form of a gene or a protein are expected to evolve in a correlated manner under a common functional constraint. METHODS: The phylogenetic analysis and correlation analysis of gonadotropin-releasing hormone (GnRH) and gonadotropin-inhibitory hormone (GnIH) and their receptors (GnRHR and GnIHR) was conducted along with other control neuropeptides. RESULTS: Both neuropeptides and their receptors regulating the reproductive neuroendocrine axis in vertebrates exhibited a correlated evolution under a common physiological constraint to regulate the release of gonadotropin. This result supports a coordinated substitution of amino acids in the GnRH and the GnIH neuropeptides along with their receptors in terms of similar evolutionary rates and distances with similar nucleotide composition of genes. CONCLUSION: This is the first demonstration of the correlated evolution of two components of an endocrine system regulating the release of gonadotropin which are acting in concert for successful reproduction.

The coordinated expression, interaction and evolution of the neuroendocrine genes.

The neuroendocrine system is a complex biological system controlled by various neuropeptides and hormones. The evolution and network properties of neuroendocrine genes are analyzed along with their expression profiles. The neuroendocrine genes show very similar expression profiles and local network properties across a wide range of tissues consistent with the physiological roles of their proteins. Moreover, the coordinated evolution of 10 neuroendocrine genes involved in mammalian reproduction and homeostasis is demonstrated using several methods, such as correlated evolution, relative-rate test, relative-ratio test and codon usage bias. The neuroendocrine genes seem to evolve predominantly under similar selective strengths and regimes of purifying selection, which is well reflected in their evolutionary fingerprints. This result demonstrates for the first time a key role of natural selection in creating and maintaining a well-designed neuroendocrine system at the genomic level. It also indicates that component properties of a complex system at a higher physiological scale may determine component properties at a lower genomic scale and/or vice versa.

The severity of mental disorders is linked to interaction among candidate genes.

There is a considerable overlap in the manifestation of symptoms in three mental disorders namely unipolar disorder, bipolar disorder and schizophrenia. A gene coexpression network was developed based on a mutual information approach including four candidate genes (NRG1, DISC1, BDNF and COMT) along with other coexpressing genes in unipolar disorder, bipolar disorder and schizophrenia. There is a significant difference in the degree distribution of nodes between normal and bipolar disorder network and bipolar disorder network and schizophrenia network. Moreover, there is a differential direct connectivity among candidate genes in various mental disorders and between normal and mental disorders. All candidate genes are directly connected to each other in schizophrenia except one pair (NRG1-BDNF) indicating a strong role of inter-gene interactions in the manifestation of severe symptoms in this disease. DISC1 and NRG1 are key hub genes in the unipolar disorder network and the bipolar disorder network but have lost the role of hub genes in schizophrenia network, despite their significant association with schizophrenia. This study indicates that the three psychiatric diseases may not have discrete classes but three phenotypic manifestations of the same continuous disease based on severity.

Maintaining optimal state probabilities in biological systems.

A biological problem is usually studied experimentally by reducing it into a number of modules. In contrast, the systems biology approach seeks to address the collective behavior of interacting molecules vis-a-vis the corresponding higher level behavior. Various attributes of a biological system are conditionally dependent on each other, and these conditionalities are usually represented through Bayesian networks for computing easily the joint probability for a state of an attribute. In this article, a genetic algorithm is investigated to a biological system, by representing it through a Bayesian network, for evaluating the optimum state probabilities of different attributes, in order to obtain a desired joint probability for a given state of an attribute. We believe that such a study would be helpful in achieving a desired health condition by maintaining various attributes of a system to their estimated optimum levels.