Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

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.

Competing endogenous RNAs in head and neck squamous cell carcinoma: a review.

Our understanding of RNA biology has evolved with recent advances in research from it being a non-functional product to molecules of the genome with specific regulatory functions. Competitive endogenous RNA (ceRNA), which has gained prominence over time as an essential part of post-transcriptional regulatory mechanism, is one such example. The ceRNA biology hypothesis states that coding RNA and non-coding RNA co-regulate each other using microRNA (miRNA) response elements. The ceRNA components include long non-coding RNAs, pseudogene and circular RNAs that exert their effect by interacting with miRNA and regulate the expression level of its target genes. Emerging evidence has revealed that the dysregulation of the ceRNA network is attributed to the pathogenesis of various cancers, including the head and neck squamous cell carcinoma (HNSCC). This is the most prevalent cancer developed from the mucosal epithelium in the lip, oral cavity, larynx and pharynx. Although many efforts have been made to comprehend the cause and subsequent treatment of HNSCC, the morbidity and mortality rate remains high. Hence, there is an urgent need to understand the holistic progression of HNSCC, mediated by ceRNA, that can have immense relevance in identifying novel biomarkers with a defined therapeutic intervention. In this review, we have made an effort to highlight the ceRNA biology hypothesis with a focus on its involvement in the progression of HNSCC. For the identification of such ceRNAs, we have additionally highlighted a number of databases and tools.

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 .

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis.

Cervical cancer is the primary cause of mortality among women in developing countries. Preventing cervical cancer is partially possible through early vaccination against the human papillomavirus, the most common cause of the disease. Nevertheless, it is imperative to understand the genetics of the disease progression to develop new therapeutic strategies. The present study aims to identify potential genes and associated pathways associated with cervical squamous cell carcinoma progression. We used an integrative approach by combining differential expression analysis, network biology, and functional enrichment analysis with survival analysis. In the present study, differential expression analysis of the microarray-based gene expression profiles of cervical cancer resulted in identifying a total of 544 significantly differentially expressed genes (DEGs). Further, centrality and network vulnerability analysis of the protein-protein interaction network (PPIN) and not well documented in cervical cancer, resulted in seven proteins (FN1, MCM5, TRIP13, KIF11, TTK, CDC45, and BUB1B), in which four proteins were vulnerable. These genes are mostly enriched in biological processes of cell division, mitotic nuclear division, cell cycle checkpoint, and cell proliferation in gene ontology analysis. The KEGG pathway enrichment analysis of the proteins lists them as mainly associated with the cell cycle. In the survival analysis, it was found that the genes MCM5, FN1, KIF11, and CDC45 were statistically significant prognostic factors for cervical cancer. The outcome of the current study identifies and explores the key role of the candidate genes involved in the progression of cervical cancer.

Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor.

It is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever-evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence-based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.

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.

Discovery of dual cation-π inhibitors of acetylcholinesterase: design, synthesis and biological evaluation.

BACKGROUND: Alzheimer's disease (AD) is a widespread dementia-related disease affecting mankind worldwide. A cholinergic hypothesis is considered the most effective target for treating mild to moderate AD. Present study aims to identify new scaffolds for inhibiting acetylcholinesterase activity. METHODS: To find Acetylcholinesterase (AChE) inhibitors, we computationally designed and chemically synthesized a series of cation-π inhibitors based on novel scaffolds that potentially block AChE. The cytotoxic effect of inhibitors were determined by MTT. AChE inhibition experiment was performed by Ellman and the Amplex red method in the SH-SY5Y cell line. Further, the experimental data on designed compounds corroborate with various computational studies that further elucidate the binding mode of interactions and binding affinity. RESULTS: The inhibitors were designed to promote dual binding and were incorporated with groups that may facilitate any of the cation- π, hydrophobic and hydrogen-bonding interactions with the conserved and hot-spot residues in the binding site. The inhibitors possessing pyridine-N-methylated pyridinium group and thereby involved in cation- π interactions are highly active relative to the marketed drug Donepezil as well as the designed analogs that lack the group. In vitro enzymatic Ellman assay and Amplex red assay on SH-SY5Y cell line estimated IC50 of the designed compounds in nM range with one having binding affinity higher than Donepezil. Compounds exhibit no significant toxicity up to µM range. CONCLUSIONS: Compounds possessing methylidenecyclohexanone scaffolds, with characteristic dual-binding and involving strong cation-π interactions, serves as new leads for AChE and opens a new direction for drug discovery efforts.

MedPServer: A database for identification of therapeutic targets and novel leads pertaining to natural products.

Natural products have been the source of treatment for various human diseases from time immemorial. Interests in natural product-based scaffolds for the discovery of modern drugs have grown in recent years. However, research on exploring the traditional medicinal systems for modern therapeutics is severely limited due to our incomplete understanding of the therapeutic mechanism of action. One possible solution is to develop computational approaches, based on ligand- and structure-based screening tools, for fast and plausible target identification, leading to elucidation of the therapeutic mechanism. In the present work, we present two methods based on shape-based and pharmacophore search to predict targets of natural products and elucidate their mechanism, and to identify natural product-based leads. These methods were tested on an in-house developed database of medicinal plants that include information from a largely unexplored North-East region of India, known as one of the twelve mega biodiversity regions. However, depending on the choice of the lead molecules, any existing databases can be used for screening. MedPServer is an open access resource available at http://bif.uohyd.ac.in/medserver/.

In silico screening for identification of novel ß-1,3-glucan synthase inhibitors using pharmacophore and 3D-QSAR methodologies.

The enzyme ß-1,3-glucan synthase, which catalyzes the synthesis of ß-1,3-glucan, an essential and unique structural component of the fungal cell wall, has been considered as a promising target for the development of less toxic anti-fungal agents. In this study, a robust pharmacophore model was developed and structure activity relationship analysis of 42 pyridazinone derivatives as ß-1,3-glucan synthase inhibitors were carried out. A five-point pharmacophore model, consisting of two aromatic rings (R) and three hydrogen bond acceptors (A) was generated. Pharmacophore based 3D-QSAR model was developed for the same reported data sets. The generated 3D-QSAR model yielded a significant correlation coefficient value (R (2) = 0.954) along with good predictive power confirmed by the high value of cross-validated correlation coefficient (Q (2) = 0.827). Further, the pharmacophore model was employed as a 3D search query to screen small molecules database retrieved from ZINC to select new scaffolds. Finally, ADME studies revealed the pharmacokinetic efficiency of these compounds.

Modeling of babesipain-1 and identification of natural and synthetic leads for bovine babesiosis drug development.

Babesiosis is a tick-borne, zoonotic disease caused by species of the intraerythrocytic protozoan Babesia. It is distributed all around the world and affects various domestic and wild animals, mainly cattle. Recently, the cysteine protease enzyme, babesipain-1 from Babesia bigemina has been identified as a potential target for designing new anti-babesiosis drugs. In the present study, a three-dimensional structural model of babesipain-1 was developed. An active site with three pockets (S1, S2, and S3), which is congruent with its homolog, falcipain-3, was also identified. Moreover, the conservation of active site residues was consistent with the cysteine protease family. In order to identify potential inhibitors, a virtual screening workflow was employed with a chemical library containing natural and synthetic compounds. Potential inhibitors interacting with all the three subsites were identified. Further, molecular dynamic simulations were carried out to assess the interactions and stability of the inhibitors. The informatics approach, and the findings presented in this study will assist researchers in further development of potential anti-babesiosis molecules.

ClosIndb: A resource for computationally derived information from clostridial genomes.

Over the past few years, several clostridial genomes have been sequenced, and since then new sequencing projects are also under way. Clostridia is one of the most sequenced genera, and presently, complete genome sequences of 49 clostridial species are available in public archives. Unraveling this wealth of genomic information opens up potential avenues in clostridial research. In the present study, we have carried out in silico analysis to decipher the genomic data. Subsequently, a web resource, ClosIndb, has been developed which collates the computationally derived information associated with all clostridial genes. It features various aspects of coding regions as well as non-coding regions, such as putative orthologs, proteins physicochemical properties, operons and cis-regulatory elements. It provides users with comparative details of all clostridial proteins across the firmicutes. ClosIndb is a comprehensive resource for all completely sequenced clostridial genomes and is under constant development. ClosIndb is freely accessible at http://bif.uohyd.ac.in/closindb/.

Rv0494 is a starvation-inducible, auto-regulatory FadR-like regulator from Mycobacterium tuberculosis.

Fatty acid metabolism plays an important role in the survival and pathogenesis of Mycobacterium tuberculosis. Lipids are assumed to be the major source of energy during dormancy. Here, we report the characterization of a starvation-inducible, lipid-responsive transcriptional regulator, Rv0494, divergently transcribed from the Rv0493c probable operon. The striking difference in the transcriptional regulatory apparatus between mycobacteria and other well-studied organisms, such as Escherichia coli, is the organization of mycobacterial promoters. Mycobacterial promoters have diverse architectures and most of these promoters function inefficiently in E. coli. In this study, we characterized the promoter elements of Rv0494 along with the sigma factors required for transcription initiation. Rv0494 promoter activity increased under nutrient starvation conditions and was transcribed via two promoters: the promoter proximal to the translational start site was active under standard growth conditions, whilst both promoters contributed to the increased activity seen during starvation, with the major contribution from the distal promoter. Furthermore, Rv0494 translation initiated at a codon located 9 bp downstream of the annotated start codon. Rv0494 bound to its upstream sequence to auto-regulate its own expression; this binding was responsive to long-chain fatty acyl-CoA molecules. We further report Rv0494-mediated transcriptional regulation of the Rv2326c gene - a probable transmembrane ATP-binding transporter encoding gene.

In silico analysis of mt 16S rRNA sequences of ocypodid crabs.

The family Ocypodidae comprises of four sub-families Dotillinae, Heloeciinae, Macrophthalminae and Ocypodinae. Ocypodidae mt 16S rRNA sequences were analysed for studying the conserved regions and phylogeny. Conserved regions will be helpful in molecular identification at the sub-family level and in interpretation of the species relationships in a better way. In the current phylogeny, the Ocypodidae family exhibits different lineages, indicating the family is polyphyletic along with its sub-families Dotillinae and Ocypodinae. The genera Ilyoplax of Dotillinae, with three different lineages, and the Uca genera of Ocypodinae, with four different lineages, exhibit polyphyly. Ocypode and Uca show a close relationship within the sub-family Ocypodinae.

OrFin: A web tool for detection of putative orthologs.

UNLABELLED: Identification of ortholog is one of the important tasks to understand a novel genome. It helps to assign functional annotations, from one organism to another organism. To identify the putative ortholog, Reciprocal Best BLAST hit (RBBH) method is known to be an efficient approach. OrFin makes use of the same approach to identify pair of orthologous proteins for a given set of sequences of two species. It is a user-friendly web tool which works with user defined parameters to search RBBHs. Results are produced in both html and text format. AVAILABILITY: This web tool is freely available at http://bifl.uohyd.ac.in/orfin.

MycoRRdb: a database of computationally identified regulatory regions within intergenic sequences in mycobacterial genomes.

The identification of regulatory regions for a gene is an important step towards deciphering the gene regulation. Regulatory regions tend to be conserved under evolution that facilitates the application of comparative genomics to identify such regions. The present study is an attempt to make use of this attribute to identify regulatory regions in the Mycobacterium species followed by the development of a database, MycoRRdb. It consist the regulatory regions identified within the intergenic distances of 25 mycobacterial species. MycoRRdb allows to retrieve the identified intergenic regulatory elements in the mycobacterial genomes. In addition to the predicted motifs, it also allows user to retrieve the Reciprocal Best BLAST Hits across the mycobacterial genomes. It is a useful resource to understand the transcriptional regulatory mechanism of mycobacterial species. This database is first of its kind which specifically addresses cis-regulatory regions and also comprehensive to the mycobacterial species. Database URL: http://mycorrdb.uohbif.in.

NeMedPlant: a database of therapeutic applications and chemical constituents of medicinal plants from north-east region of India.

UNLABELLED: The North-East region of India is one of the twelve mega biodiversity region, containing many rare and endangered species. A curated database of medicinal and aromatic plants from the regions called NeMedPlant is developed. The database contains traditional, scientific and medicinal information about plants and their active constituents, obtained from scholarly literature and local sources. The database is cross-linked with major biochemical databases and analytical tools. The integrated database provides resource for investigations into hitherto unexplored medicinal plants and serves to speed up the discovery of natural productsbased drugs. AVAILABILITY: The database is available for free at http://bif.uohyd.ac.in/nemedplant/orhttp://202.41.85.11/nemedplant/