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Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

Vaccination is the best way to prevent the spread of emerging or reemerging infectious disease. Current research for vaccine development is mainly focused on recombinant-, subunit-, and peptide-based vaccine. At this point, immunoinformatics has been proven as a powerful method for identification of potential vaccine candidates, by analyzing immunodominat B- and T-cell epitopes. This method can reduce the time and cost of experiment to a great extent, by reducing the number of vaccine candidates for experimental testing for their efficacy. This chapter describes the use of immunoinformatics and molecular docking methods to screen potential vaccine candidates by taking Leptospira as a model.

Leptospirosis is the most widespread zoonotic disease, estimated to cause severe infection in more than one million people each year, particularly in developing countries of tropical areas. Several factors such as variable and nonspecific clinical manifestation, existence of large number of serovars and asymptomatic hosts spreading infection, poor sanitation and lack of an effective vaccine make prophylaxis difficult. Consequently, there is an urgent need to develop an effective vaccine to halt its spread all over the world. In this study, an immunoinformatics approach was employed to identify the most vital and effective immunogenic protein from the proteome of Leptospira interrogans serovar Copenhageni strain L1-130 that may be suitable to stimulate a significant immune response aiding in the development of peptide vaccine against leptospirosis. Both B-cell and T-cell (Helper T-lymphocyte (HTL) and cytotoxic T lymphocyte (CTL)) epitopes were predicted for the conserved and most immunogenic outer membrane lipoprotein. Further, the binding interaction of CTL epitopes with Major Histocompatibility Complex class I (MHC-I) was evaluated using docking techniques. A Molecular Dynamics Simulation study was also performed to evaluate the stability of the resulting epitope-MHC-I complexes. Overall, this study provides novel vaccine candidates and may prompt further development of vaccines against leptospirosis.

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis., Hub genes; Multiplex network; Multi-omics data; Oral cancer

Mycobacterium lepromatosis was identified as a causative agent for leprosy in the year 2008 in the United States and later more cases were identified in Canada, Singapore, Brazil, and Myanmar. It is known to cause diffuse lepromatosis leprosy among humans. Since it is invasive, the mortality rates are higher in comparison to the M. leprae. At genomic level, there exists 90.9% similarity between M. lepromatosis and M. leprae. Codon usage analysis based on analyses of 228 coding sequences (CDSs) of M. lepromatosis, revealed that the genome is GC rich. Among the total 16 dinucleotides, CpG dinucleotide possesses the highest dinucleotide frequency in M. lepromatosis, that is strikingly an unobvious observation since higher CpG is associated with higher proinflammatory cytokine production and NF-κB activation that eventually leads to high pathogenicity. To evade immune response, CpG content is generally less in pathogens. The unusually high CpG content can be explained by the fact that the nucleotide composition of M. lepromatosis is CG rich. Various forces interplay to shape codon usage pattern of any organism including selection; mutation, nucleotide composition as well as GC biased gene conversion. To understand the interplay between various forces; neutrality, parity, Nc-GC3 (Effective number of codons-GC content at 3rd position of the codon), aromaticity (AROMO) and the general average hydropathicity score (GRAVY) analyses have been carried out. The analyses revealed that selection force is the major contributory force. Along with the selection; mutation, nucleotide composition as well as GC biased gene conversion also play role in shaping codon usage bias in M. lepromatosis. This is the first report on the codon usage in M. lepromatosis.

Gymnema sylvestre is a highly valuable medicinal plant in traditional Indian system of medicine and used in many polyherbal formulations especially in treating diabetes. However, the lack of genomic resources has impeded its research at molecular level. The present study investigated functional gene profile of G. sylvestre via RNA sequencing technology. The de novo assembly of 88.9 million high quality reads yielded 23,126 unigenes, of which 18116 were annotated against databases such as NCBI nr database, gene ontology (GO), KEGG, Pfam, CDD, PlantTFcat, UniProt & GreeNC. Total 808 unigenes mapped to 78 different Transcription Factor families, whereas 39 unigenes assigned to CYP450 and 111 unigenes coding for enzymes involved in the biosynthesis of terpenoids including transcripts for synthesis of important compounds like Vitamin E, beta-amyrin and squalene. Among them, presence of six important enzyme coding transcripts were validated using qRT-PCR, which showed high expression of enzymes involved in methyl-erythritol phosphate (MEP) pathway. This study also revealed 1428 simple sequence repeats (SSRs), which may aid in molecular breeding studies. Besides this, 8 putative long non-coding RNAs (lncRNAs) were predicted from un-annotated sequences, which may hold key role in regulation of essential biological processes in G. sylvestre. The study provides an opportunity for future functional genomic studies and to uncover functions of the lncRNAs in G. sylvestre.

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

MicroRNAs are small sized endogenous non-coding RNA, of ~18‐25 (nt) that regulates the expression of their target genes in plants at post transcriptional level and play very significant roles in multiple biological processes. These ribosw itches may play the role of master regulator in important biological processes and plant metabolic pathways. In the present study, we performed small RNA profiling to identify miRNAs present in the holy tree Aegle marmelos and their involvement in different metabolic pathways. A total of 9,192,725 (88.52%) clean reads out of 10,384,409 (100%) reads were identified with size ranging from 15‐ to 24 nt. Using computational tools and bioinformatics approaches we revealed around 253 conserved miRNAs accounting into 20 miRNA families and a total of 395 annotated targets were found for 6 novel miRNAs within the plant. Functional annotation revealed Homeobox-leucine zipper protein and ERF like transcription factors as targets of conserved miRNAs. The present study provides a first report on elucidation of genome-wide miRNA profile of A. marmelos which can be further explored to manipulate their expression to scale-up biosynthesis of active metabolites using metabolic engineering approaches.

Leptospirosis is a re-emerging bacterial zoonosis caused by pathogenic Leptospira, with a worldwide distribution and becoming a major public health concern. Prophylaxis of this disease is difficult due to several factors such as non-specific variable clinical manifestation, presence of a large number of serovar, species and asymptomatic reservoir hosts, lack of proper diagnostics and vaccines. Despite its global importance and severity of the disease, knowledge about the molecular mechanism of pathogenesis and evolution of pathogenic species of Leptospira remains limited. In this study, we sequenced and analyzed three highly pathogenic species of Indian isolates of Leptospira (interrogans, santarosai, and kirschneri). Additionally, we identified some virulence-related and CRISPR-Cas genes. The virulent analysis showed 232 potential virulence factors encoding proteins in L. interrogans strain Salinem and L. santarosai strain M-4 genome. While the genome of L. kirschneri strain Wumalasena was predicted to encode 198 virulence factor proteins. The variant calling analysis revealed 1151, 19,786, and 22,996 single nucleotide polymorphisms (SNPs) for L. interrogans strain Salinem, L. kirschneri strain Wumalasena and L. santarosai strain M-4, respectively, with a maximum of 5315 missense and 12,221 synonymous mutations for L. santarosai strain M-4. The structural analyses of genomes indicated potential evidence of inversions and structural rearrangment in all three genomes. The availability of these genome sequences and in silico analysis of Leptospira will provide a basis for a deeper understanding of their molecular diversity and pathogenesis mechanism, and further pave a way towards proper management of the disease.

Natural products and secondary metabolites are produced through plant biosynthetic pathways and hold enormous opportunities for treating human ailments. Andrographis paniculata (Kalmegh), commonly known as “king of bitters” is widely exploited for its usage in array of polyherbal formulations. Andrographolide, a diterpene lactone is the major bioactive metabolite with widespread pharmaceutical properties, extracted primarily from the leaves of this plant. Using high throughput NGS (next generation sequencing), we have generated whole transcriptome profile of leaves of A. paniculata using semiconductor-based Ion Torrent technology. Functional annotation of transcripts revealed 20,498 unigenes annotating to plant nr database with 8695 belonging to biological processes, 7605 to molecular function and 14,448 to cellular component. KEGG pathway analysis annotated 2592 transcripts to different biological pathways with 631 mapping to metabolic pathways, 297 to secondary metabolic pathways and 157 to biosynthesis of antibiotics etc. Additionally, to enrich the information, a co-expression analysis was performed with publically available datasets generating an expression matrix of 4270 genes. Co-expression analysis using WGCNA package revealed 10 transcripts as major hub/bottleneck genes known to be majorly involved in developmental and metabolic processes of plants e.g. flower development (POE52202.1, protein heading date 3a), oxidoreductase activity and lipid metabolic process (PIN06124.1, Delta (12)-fatty acid dehydrogenase) etc., implying the impact of genetic as well as environmental factors on their expression.

Leptospirosis is a potentially fatal zoo-anthroponosis caused by pathogenic species of Leptospira belonging to the family of Leptospiraceae, with a worldwide distribution and effect, in terms of its burden and risk to human health. The ‘LeptoDB’ is a single window dedicated architecture (5 948 311 entries), modeled using heterogeneous data as a core resource for global Leptospira species. LeptoDB facilitates well-structured knowledge of genomics, proteomics and therapeutic aspects with more than 500 assemblies including 17 complete and 496 draft genomes encoding 1.7 million proteins for 23 Leptospira species with more than 250 serovars comprising pathogenic, intermediate and saprophytic strains. Also, it seeks to be a dynamic compendium for therapeutically essential components such as epitope, primers, CRISPR/Cas9 and putative drug targets. Integration of JBrowse provides elaborated locus centric description of sequence or contig. Jmol for structural visualization of protein structures, MUSCLE for interactive multiple sequence alignment annotation and analysis. The data on genomic islands will definitely provide an understanding of virulence and pathogenicity. Phylogenetics analysis integrated suggests the evolutionary division of strains. Easily accessible on a public web server, we anticipate wide use of this metadata on Leptospira for the development of potential therapeutics. Database URL: http://leptonet.org.in

MicroRNAs (miRNAs) are well-known key regulators of gene expression primarily at the post-transcriptional level. Plant-derived miRNAs may pass through the gastrointestinal tract, entering into the body fluid and regulate the expression of endogenous mRNAs. Camptotheca acuminata, a highly important medicinal plant known for its anti-cancer potential was selected to investigate cross-kingdom regulatory mechanism and involvement of miRNAs derived from this plant in cancer-associated pathways through in silico systems biology approach. In this study, total 33 highly stable putative novel miRNAs were predicted from the publically available 53,294 ESTs of C. acuminata, out of which 14 miRNAs were found to be regulating 152 target genes in human. Functional enrichment, gene-disease associations and network analysis of these target genes were carried out and the results revealed their association with prominent types of cancers like breast cancer, leukemia and lung cancer. Pathways like focal adhesion, regulation of lipolysis in adipocytes and mTOR signaling pathways were found significantly associated with the target genes. The regulatory network analysis showed the association of some important hub proteins like GSK3B, NUMB, PEG3, ITGA2 and DLG2 with cancer-associated pathways. Based on the analysis results, it can be suggested that the ingestion of the C. acuminata miRNAs may have a functional impact on tumorigenesis in a cross-kingdom way and may affect the physiological condition at genetic level. Thus, the predicted miRNAs seem to hold potentially significant role in cancer pathway regulation and therefore, may be further validated using in vivo experiments for a better insight into their mechanism of epigenetic action of miRNA.

Head and neck cancer is the sixth most common cancer worldwide, with tobacco as the leading cause. However, it is increasing in non-tobacco users also, hence limiting our understanding of its underlying molecular mechanisms. RNA-seq analysis of cancers has proven as effective tool in understanding disease etiology. In the present study, RNA-Seq of 86 matched Tumor/Normal pairs, of tobacco smoking (TOB) and non-smokers (N-TOB) HNSCC samples analyzed, followed by validation on 375 similar datasets. Total 2194 and 2073 differentially expressed genes were identified in TOB and N-TOB tumors, respectively. GO analysis found muscle contraction as the most enriched biological process in both TOB and N-TOB tumors. Pathway analysis identified muscle contraction and salivary secretion pathways enriched in both categories, whereas calcium signaling and neuroactive ligand-receptor pathway was more enriched in TOB and N-TOB tumors respectively. Network analysis identified muscle development related genes as hub node i. e. ACTN2, MYL2 and TTN in both TOB and N-TOB tumors, whereas EGFR and MYH6, depicts specific role in TOB and N-TOB tumors. Additionally, we found enriched gene networks possibly be regulated by tumor suppressor miRNAs such as hsa-miR-29/a/b/c, hsa-miR-26b-5p etc., suggestive to be key riboswitches in regulatory cascade of HNSCC. Interestingly, three genes PKLR, CST1 and C17orf77 found to show opposite regulation in each category, hence suggested to be key genes in separating TOB from N-TOB tumors. Our investigation identified key genes involved in important pathways implicated in tobacco dependent and independent carcinogenesis hence may help in designing precise HNSCC diagnostics and therapeutics strategies.