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.

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.

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.

Gonadotropin releasing hormone (GnRH) neurones of triploid catfish, Heteropneustes fossilis (Bloch): an immunocytochemical study.

Gonadotropin-releasing hormone (GnRH), a regulator of gonadal maturation in vertebrates, is primarily secreted by neurosecretory cells of the pre-optic area (POA) in the forebrain of teleosts. GnRH-immunoreactive (GnRH-ir) cells of this area demonstrate positive correlation in number and size of soma with gonadal maturity and directly innervate the pituitary in most teleosts. Gonadal development in triploid fish remains impaired due to genetic sterility. The gonadal immaturity in triploid fish may be due to low levels of gonadotropin and sex steroids during the vitellogenic phase of reproductive cycle. However, the nature of GnRH-ir cells in triploid fish is not yet known. Triploid catfish (H. fossilis) showed significant decrease (P<0.001) in size and number of immunoreactive-GnRH cells of POA and low immunoreactivity in pituitary in comparison to their diploid full-sibs during the late pre-spawning phase of ovarian cycle. This study suggests that low activity of GnRH-cells in triploid may be due to lack of positive feedback stimulation by sex steroids and/or reduced responsiveness of sensory cells to environmental cues required for gonadal maturation in teleosts.

Testosterone triggers the brain-pituitary-gonad axis of juvenile female catfish (Heteropneustes fossilis Bloch) for precocious ovarian maturation.

The brain-pituitary-gonad axis of precociously matured females (PMFs) of Indian catfish (Heteropneustes fossilis), produced by testosterone treatment during juvenile stages, was analyzed by studies on immunoreactive gonadotropin-releasing hormone (ir-GnRH) secreting cells of the preoptic area of brain, plasma levels of gonadotropin (GtH-II), testosterone (T), and estradiol-17 beta (E(2)). GnRH cells of PMFs were large and strongly immunoreactive in comparison to control females. PMFs showed higher plasma levels of GtH-II, T, and E(2) than did control females. The ovaries of PMFs contained ripe ova, whereas control females had ova at maturing stages. This study suggests testosterone-mediated activation of the brain-pituitary-ovarian axis for precocious maturation in juvenile catfish.