Abhay Jere

HIV-1 reverse transcriptase connection subdomain mutations reduce template RNA degradation and enhance AZT excision

We previously proposed that mutations in the connection subdomain (cn) of HIV-1 reverse transcriptase increase AZT resistance by altering the balance between nucleotide excision and template RNA degradation. To test the predictions of this model, we analyzed the effects of previously identified cn mutations in combination with thymidine analog mutations (D67N, K70R, T215Y, and K219Q) on in vitro RNase H activity and AZT monophosphate (AZTMP) excision. We found that cn mutations G335C/D, N348I, A360I/V, V365I, and A376S decreased primary and secondary RNase H cleavages. The patient-derived cns increased ATP- and PPi-mediated AZTMP excision on an RNA template compared with a DNA template. One of 5 cns caused an increase in ATP-mediated AZTMP excision on a DNA template, whereas three cns showed a higher ratio of ATP- to PPi-mediated excision, indicating that some cn mutations also affect excision on a DNA substrate. Overall, the results strongly support the model that cn mutations increase AZT resistance by reducing template RNA degradation, thereby providing additional time for RT to excise AZTMP.

Conservation Patterns of HIV-1 RT Connection and RNase H Domains: Identification of New Mutations in NRTI-Treated Patients

Background Although extensive HIV drug resistance information is available for the first 400 amino acids of its reverse transcriptase, the impact of antiretroviral treatment in C-terminal domains of Pol (thumb, connection and RNase H) is poorly understood. Methods and Findings We wanted to characterize conserved regions in RT C-terminal domains among HIV-1 group M subtypes and CRF. Additionally, we wished to identify NRTI-related mutations in HIV-1 RT C-terminal domains. We sequenced 118 RNase H domains from clinical viral isolates in Brazil, and analyzed 510 thumb and connection domain and 450 RNase H domain sequences collected from public HIV sequence databases, together with their treatment status and histories. Drug-naïve and NRTI-treated datasets were compared for intra- and inter-group conservation, and differences were determined using Fishers exact tests. One third of RT C-terminal residues were found to be conserved among group M variants. Three mutations were found exclusively in NRTI-treated isolates. Nine mutations in the connection and 6 mutations in the RNase H were associated with NRTI treatment in subtype B. Some of them lay in or close to amino acid residues which contact nucleic acid or near the RNase H active site. Several of the residues pointed out herein have been recently associated to NRTI exposure or increase drug resistance to NRTI. Conclusions This is the first comprehensive genotypic analysis of a large sequence dataset that describes NRTI-related mutations in HIV-1 RT C-terminal domains in vivo. The findings into the conservation of RT C-terminal domains may pave the way to more rational drug design initiatives targeting those regions.

Identification of Optimum Sequencing Depth Especially for De Novo Genome Assembly of Small Genomes Using Next Generation Sequencing Data

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6–40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker’s or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow–tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow–tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems.

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules.

Introduction Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage. Results The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably). Conclusions Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

Transcriptomic Profiling Using Next Generation Sequencing - Advances, Advantages, and Challenges

Transcriptome, the functional element of the genome, is comprised of different kinds of RNA molecules such as mRNA, miRNA, ncRNA, rRNA, and tRNA to name a few. Each of these RNA molecules plays a vital role in the physiological response, and understand‐ ing the regulation of these molecules is extremely critical for the better understanding of the functional genome. RNA Sequencing (RNASeq) is one of the latest techniques applied to study genome-wide transcriptome characterization and profiling using high-through‐ put sequenced data. As compared to array-based methods, RNASeq provides in-depth and more precise information on transcriptome characterization and quantification. Based upon availability of reference genome, transcriptome assembly can be referenceguided or de novo. Once transcripts are assembled, downstream analysis such as expres‐ sion profiling, gene ontology, and pathway enrichment analyses can give more insight into gene regulation. This chapter describes the significance of RNASeq study over arraybased traditional methods, approach to analyze RNASeq data, available methods and tools, challenges associated with the data analysis, application areas, some of the recent advancement made in the area of transcriptome study and its application.

Next-Generation Sequencing for Cancer Genomics

In the last couple of decades, availability of high-throughput genomic technologies such as microarrays and next-generation sequencing (NGS) has provided unprecedented insights into the complexity of cancer genomics. In particular, NGS with its ability to provide an unbiased view of the genome is a very useful tool in studying the cancer genome which is characterized by de novo genetic aberrations. Using NGS, gene expression signatures, copy number variations, mutations, and epigenetic changes such as methylation as well as histone modifications can be identified which could point towards novel diagnostic and/or prognostic biomarkers. Comprehensive understanding of the cancer genomics could also provide mechanistic insights into cancer susceptibility, development, and progression. This chapter provides an overview of the studies that have applied NGS technologies to further our understanding of cancer.

CGDV: a webtool for circular visualization of genomics and transcriptomics data

BackgroundInterpretation of large-scale data is very challenging and currently there is scarcity of web tools which support automated visualization of a variety of high throughput genomics and transcriptomics data and for a wide variety of model organisms along with user defined karyotypes. Circular plot provides holistic visualization of high throughput large scale data but it is very complex and challenging to generate as most of the available tools need informatics expertise to install and run them.ResultWe have developed CGDV (Circos for Genomics and Transcriptomics Data Visualization), a webtool based on Circos, for seamless and automated visualization of a variety of large scale genomics and transcriptomics data. CGDV takes output of analyzed genomics or transcriptomics data of different formats, such as vcf, bed, xls, tab limited matrix text file, CNVnator raw output and Gene fusion raw output, to plot circular view of the sample data. CGDV take cares of generating intermediate files required for circos. CGDV is freely available at https://cgdv-upload.persistent.co.in/cgdv/.ConclusionThe circular plot for each data type is tailored to gain best biological insights into the data. The inter-relationship between data points, homologous sequences, genes involved in fusion events, differential expression pattern, sequencing depth, types and size of variations and enrichment of DNA binding proteins can be seen using CGDV. CGDV thus helps biologists and bioinformaticians to visualize a variety of genomics and transcriptomics data seamlessly.

Next-Generation Sequencing for Cancer Biomarker Discovery

Cancer is a genetic disorder that arises from gene mutations as well as changes in transcriptional and epigenetic profiles. These genetic changes can serve as valuable biomarkers for early detection, staging, and detailed molecular characterization of cancer for individualized therapy. Mutations in several known oncogenes (e.g., EGFR, HER2, KRAS) and tumor suppressor genes (e.g., TP53, PTEN, PI3K) are already being used as biomarkers to guide therapy in breast cancer, ovarian cancer, lung cancer, prostate cancer, etc. However, tumor heterogeneity and instability of cancer genomes poses a significant challenge to reliable and reproducible detection of biomarkers. Moreover, cancer is a multigene disorder and comprehensive knowledge of the mutational landscape is extremely important for the most effective therapeutic intervention.