NetVA: an R package for network vulnerability and influence analysis.

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.Communicated by Ramaswamy H. Sarma.

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues.

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

Subtype-specific network organization of molecular complexes in breast cancer.

Breast cancer, a leading cause of death in women, is a complex heterogeneous disease comprising multiple molecular subtypes with different treatment responses and hence clinical outcomes. The present study aims to gain a deeper insight into the disease complexities at the level of molecular subtypes. For this, first, three subtype networks of breast cancer, viz., ER-/HER2-, ER+/HER2-, and HER2+, were constructed utilizing mRNA expression profiles of tumor tissues. Subsequently, these networks were used to construct three exclusively subtype-specific networks. Further, the mRNA expression profiles of all three subtypes were analyzed using differential correlations based on z-statistics of the F-test. Finally, functional enrichment analysis was carried out to elucidate functions and processes of important genes involved in subtype networks. From this analysis, it was observed that these subtype networks share a commonality among them in terms of preserved patterns. However, these networks possess specific patterns that result in exclusively subtypespecific networks having unique sets of wiring among the genes. Additionally, the significantly differentially correlated gene pairs between two subtypes demonstrate subtype-specific expressional patterns which make them different at the molecular level. Furthermore, the network analysis also revealed ER-/HER2--specific genes, viz., LUM, RARB, and ERCC6. Thus, the present analysis provides new insights for further research on breast cancer subtypes and hence the development of the most effective diagnosis and treatment.

BCLncRDB: a comprehensive database of LncRNAs associated with breast cancer.

Breast cancer, the most common cancer in women, is characterized by high morbidity and mortality worldwide. Recent evidence has shown that long non-coding RNAs (lncRNAs) play a crucial role in the development and progression of breast cancer. However, despite increasing data and evidence indicating the implication of lncRNAs in breast cancer, no web resource or database exists primarily for lncRNAs associated with only breast cancer. Therefore, we developed a manually curated, comprehensive database, "BCLncRDB," for lncRNAs associated with breast cancer. For this, we collected, processed, and analyzed available data on breast cancer-associated lncRNAs from different sources, including previously published research articles, the Gene Expression Omnibus (GEO) Database of the National Centre for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), and the Ensembl database; subsequently, these data were hosted at BCLncRDB for public access. Currently, the database contains 5324 unique breast cancer-lncRNA associations and has the following features: (i) a user-friendly, easy-to-use web interface for searching and browsing about lncRNAs of the user's interest, (ii) differentially expressed and methylated lncRNAs, (iii) stage- and subtype-specific lncRNAs, and (iv) drugs, subcellular localization, sequence, and chromosome information of these lncRNAs. Thus, the BCLncRDB provides a one-stop dedicated platform for exploring breast cancer-related lncRNAs to advance and support the ongoing research on this disease. The BCLncRDB is publicly available for use at http://sls.uohyd.ac.in/new/bclncrdb_v1 .

A core and pan gene map of Leptospira genus and its interactions with human host.

Leptospira species are the etiological agent of an emerging zoonotic disease known as "Leptospirosis" that substantially affects both human health and economy across the globe. Despite the global importance of the disease, pathogenetic features, host-adaptation and proper diagnosis of this bacteria remains lacking. To accomplish these gaps, pan-genome of Leptospira genus was explored in the present study. The pan-genome of Leptospira genus was comprised of core (692) and accessory parts (softcore:1804, shell:6432, cloud:16,600). The functional analysis revealed the abundancy of "Translation, ribosomal structure and biogenesis" COG class in core-genes; whereas in accessory parts, genes involved in signal transduction was the most abundant. Furthermore, pathogen-host interaction (PHI) analysis of core and accessory proteins with human proteins showed the presence of a total of 599 and 510 interactions, respectively. There were eight hubs in core PHI network and five hubs in PHI network of accessory proteins. The human's proteins involved in these interactions were found functionally enriched in metabolic processes, responses to stimulus and immune system processes. Further, pan-genome based phylogeny separated the Leptospira genus in three major clades (belonging to P1, P2 and S) which relates with their pathogenicity level. Additionally, pathogenic and saprophytic clade specific genes of Leptospira have also been identified and functionally annotated for COG, KEGG and virulence factors. The results revealed the presence of 102 pathogenic and 215 saprophytic group specific gene clusters. The COG functional annotation of pathogen specific genes showed that defence mechanism followed by signal transduction mechanisms category were most significantly enriched COG categories; whereas in saprophytic group, signal transduction mechanisms was the most abundant COG, suggesting their role in adaptation and hence important for microbe's evolution and survival. In conclusion, this study provides a new insight of genomic features of Leptospira genus which may further be implemented for development of better control actions of the disease.

Whole genome sequencing and de novo assembly of three virulent Indian isolates of Leptospira.

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.

Author Correction: Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory.

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

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory.

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.

Exploring Leptospiral proteomes to identify potential candidates for vaccine design against Leptospirosis using an immunoinformatics approach.

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.

Cross-Kingdom Regulation of Putative miRNAs Derived from Happy Tree in Cancer Pathway: A Systems Biology Approach.

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.