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 understanding 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-throughput 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 reference-guided or de novo. Once transcripts are assembled, downstream analysis such as expression profiling, gene ontology, and pathway enrichment analyses can give more insight into gene regulation. This chapter describes the significance of RNASeq study over array-based 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

Occurrence of Temporomandibular Disorder in Subjects with Low Back Pain and Spinal Postural Deformities: An Observational Study

Purpose: Back pain and temporomandibular disorder (TMD) are two predominant illnesses that affect the human motor system. Literature has stated significant associations between chronic low back pain (CLBP) and TMD. Global postural deviations cause body adaptation and realignment, which may interfere with the function of TMJ. However, the possibility of TMD in subjects with CLBP associated with spinal postural deformities has yet to be completely explored. Method: This was an observational study carried out among 65 people having CLBP with co-presence of any spinal deformities. Forward head posture (FHP) was assessed using the On-Protractor application and thoracic kyphosis and lumbar lordosis were assessed using flexicurve. Those with co-occurrence of LBP and spinal deformity were further evaluated for the presence of TMD using Fonseca’s questionnaire. The prevalence of TMD in LBP along with spinal deformities was analyzed and the variables were compared based on gender, age categories, and type of LBP (specific and non-specific). Results: The overall prevalence of TMD (mild, moderate, and severe) was 89.2% (n=58) in participants with low back pain and spinal postural abnormalities. The severity of FHP was more in specific LBP than in non-specific LBP, while the occurrence of TMD was equal. The severity of TMD was higher in females than males. Conclusion: The occurrence of TMD is highly prevalent in patients with low back pain and spinal postural deformities. The findings of the study imply that individuals with low back pain and spinal postural deformity should also be evaluated for TMJ dysfunction and initiate early intervention

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

Oncostatin-M Differentially Regulates Mesenchymal and Proneural Signature Genes in Gliomas via STAT3 Signaling

Glioblastoma (GBM), the most malignant of the brain tumors is classified on the basis of molecular signature genes using TCGA data into four subtypes- classical, mesenchymal, proneural and neural. The mesenchymal phenotype is associated with greater aggressiveness and low survival in contrast to GBMs enriched with proneural genes. The proinflammatory cytokines secreted in the microenvironment of gliomas play a key role in tumor progression. The study focused on the role of Oncostatin-M (OSM), an IL-6 family cytokine in inducing mesenchymal properties in GBM. Analysis of TCGA and REMBRANDT data revealed that expression of OSMR but not IL-6R or LIFR is upregulated in GBM and has negative correlation with survival. Amongst the GBM subtypes, OSMR level was in the order of mesenchymal > classical > neural > proneural. TCGA data and RT-PCR analysis in primary cultures of low and high grade gliomas showed a positive correlation between OSMR and mesenchymal signature genes-YKL40/CHI3L1, fibronectin and vimentin and a negative correlation with proneural signature genes-DLL3, Olig2 and BCAN. OSM enhanced transcript and protein level of fibronectin and YKL-40 and reduced the expression of Olig2 and DLL3 in GBM cells. OSM-regulated mesenchymal phenotype was associated with enhanced MMP-9 activity, increased cell migration and invasion. Importantly, OSM induced mesenchymal markers and reduced proneural genes even in primary cultures of grade-III glioma cells. We conclude that OSM-mediated signaling contributes to aggressive nature associated with mesenchymal features via STAT3 signaling in glioma cells. The data suggest that OSMR can be explored as potential target for therapeutic intervention

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

<div><p>Introduction</p><p>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.</p><p>Results</p><p>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).</p><p>Conclusions</p><p>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.</p></div

Comparative performance of our prediction workflows with VEGA v1.08.

<p>Panel A: Molecules of challenge set-1 processed by our prediction workflows (= 74) and VEGA v1.08 (= 69) used for computation; Panel B: 69 molecules of challenge set-1 processed by our prediction workflows as well as VEGA v1.08 were used for computation; Panel C: Molecules of challenge set-2 processed by our prediction workflows (= 77) and VEGA v1.08 (= 68) used for computation; Panel D: 68 molecules of challenge set-2 processed by our prediction workflows as well as VEGA v1.08 were used for computation. VEGA v1.08: orange bars; PW-1: blue bars; PW-2: green bars. CCR: Correct Classification Rate.</p

Percent prediction accuracy of short-listed variants of models.

<p>Color-coded scale from green to red indicates decreasing prediction accuracy. RTS and Challenge-1 sets are expanded to show the prediction accuracy for each category of sensitizers and non-sensitizers. Internal: Internal test set; RTS: Representative test set; Challenge-1: Challenge set-1; Both: Internal & RTS; X: Extreme; St: Strong; S: Sensitizer with unknown potency; M: Moderate; W: Weak; N: Non-sensitizer.</p