Radhakrishnan Sabarinathan

Nucleotide excision repair is impaired by binding of transcription factors to DNA

Somatic mutations are the driving force of cancer genome evolution. The rate of somatic mutations appears to be greatly variable across the genome due to variations in chromatin organization, DNA accessibility and replication timing. However, other variables that may influence the mutation rate locally are unknown, such as a role for DNA-binding proteins, for example. Here we demonstrate that the rate of somatic mutations in melanomas is highly increased at active transcription factor binding sites and nucleosome embedded DNA, compared to their flanking regions. Using recently available excision-repair sequencing (XR-seq) data, we show that the higher mutation rate at these sites is caused by a decrease of the levels of nucleotide excision repair (NER) activity. Our work demonstrates that DNA-bound proteins interfere with the NER machinery, which results in an increased rate of DNA mutations at the protein binding sites. This finding has important implications for our understanding of mutational and DNA repair processes and in the identification of cancer driver mutations.

RNAsnp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNPs

Structural characteristics are essential for the functioning of many noncoding RNAs and cis‐regulatory elements of mRNAs. SNPs may disrupt these structures, interfere with their molecular function, and hence cause a phenotypic effect. RNA folding algorithms can provide detailed insights into structural effects of SNPs. The global measures employed so far suffer from limited accuracy of folding programs on large RNAs and are computationally too demanding for genome‐wide applications. Here, we present a strategy that focuses on the local regions of maximal structural change between mutant and wild‐type. These local regions are approximated in a “screening mode” that is intended for genome‐wide applications. Furthermore, localized regions are identified as those with maximal discrepancy. The mutation effects are quantified in terms of empirical P values. To this end, the RNAsnp software uses extensive precomputed tables of the distribution of SNP effects as function of length and GC content. RNAsnp thus achieves both a noise reduction and speed‐up of several orders of magnitude over shuffling‐based approaches. On a data set comprising 501 SNPs associated with human‐inherited diseases, we predict 54 to have significant local structural effect in the untranslated region of mRNAs. RNAsnp is available at http://rth.dk/resources/rnasnp.

Profiling of Ribose Methylations in RNA by High‐Throughput Sequencing

Ribose methylations are the most abundant chemical modifications of ribosomal RNA and are critical for ribosome assembly and fidelity of translation. Many aspects of ribose methylations have been difficult to study due to lack of efficient mapping methods. Here, we present a sequencing-based method (RiboMeth-seq) and its application to yeast ribosomes, presently the best-studied eukaryotic model system. We demonstrate detection of the known as well as new modifications, reveal partial modifications and unexpected communication between modification events, and determine the order of modification at several sites during ribosome biogenesis. Surprisingly, the method also provides information on a subset of other modifications. Hence, RiboMeth-seq enables a detailed evaluation of the importance of RNA modifications in the cells most sophisticated molecular machine. RiboMeth-seq can be adapted to other RNA classes, for example, mRNA, to reveal new biology involving RNA modifications.

The RNAsnp web server: predicting SNP effects on local RNA secondary structure

The function of many non-coding RNA genes and cis-regulatory elements of messenger RNA largely depends on the structure, which is in turn determined by their sequence. Single nucleotide polymorphisms (SNPs) and other mutations may disrupt the RNA structure, interfere with the molecular function and hence cause a phenotypic effect. RNAsnp is an efficient method to predict the effect of SNPs on local RNA secondary structure based on the RNA folding algorithms implemented in the Vienna RNA package. The SNP effects are quantified in terms of empirical P-values, which, for computational efficiency, are derived from extensive pre-computed tables of distributions of substitution effects as a function of gene length and GC content. Here, we present a web service that not only provides an interface for RNAsnp but also features a graphical output representation. In addition, the web server is connected to a local mirror of the UCSC genome browser database that enables the users to select the genomic sequences for analysis and visualize the results directly in the UCSC genome browser. The RNAsnp web server is freely available at: http://rth.dk/resources/rnasnp/.

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

Background Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement. Methodology/Principal Findings Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis. Conclusions/Significance We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

RNAsnp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNPs: HUMAN MUTATION

The original article to which this Erratum refers was published in Human Mutation 34(4):546–556 (DOI 10.1002/humu.22273). In the article cited above, the symbols for Mik and ξ < i were inadvertently changed during the production process. The corrected text appears below. The Publisher sincerely regrets this error. At face value, this minimization is rather expensive because for each index pair u,v, the values of π i[u,v] need to be determined. The naı̈ve evaluation of Eq. (7) can be replaced by a recursive scheme (Supp. Fig. S1). Consider sequence positions k < i < l and denote by Mik and Nil, the probabilities that i has a pairing partner in the interval [k,i – 1] and the interval [i + 1,l], respectively. Clearly, these auxiliary variables satisfy Mik = Mi(k – 1) + Pki and Nil = Ni(l + 1) + Pil. Obviously, for all k < i < l, we have π i[k,l] = Mik + Nil, ξ < i [k, l] = Mik , and ξ > i [k, l] = Nil . Precomputing M and N thus allows the evaluation of distances and correlation coefficients in linear time for each sequence interval.

Simultaneous DNA and RNA mapping of somatic mitochondrial mutations across diverse human cancers.

Somatic mutations in the nuclear genome are required for tumor formation, but the functional consequences of somatic mitochondrial DNA (mtDNA) mutations are less understood. Here we identify somatic mtDNA mutations across 527 tumors and 14 cancer types, using an approach that takes advantage of evidence from both genomic and transcriptomic sequencing. We find that there is selective pressure against deleterious coding mutations, supporting that functional mitochondria are required in tumor cells, and also observe a strong mutational strand bias, compatible with endogenous replication-coupled errors as the major source of mutations. Interestingly, while allelic ratios in general were consistent in RNA compared to DNA, some mutations in tRNAs displayed strong allelic imbalances caused by accumulation of unprocessed tRNA precursors. The effect was explained by altered secondary structure, demonstrating that correct tRNA folding is a major determinant for processing of polycistronic mitochondrial transcripts. Additionally, the data suggest that tRNA clusters are preferably processed in the 3′ to 5′ direction. Our study gives insights into mtDNA function in cancer and answers questions regarding mitochondrial tRNA biogenesis that are difficult to address in controlled experimental systems.

Evolution, homology conservation, and identification of unique sequence signatures in GH19 family chitinases.

The discovery of GH (Glycoside Hydrolase) 19 chitinases in Streptomyces sp. raises the possibility of the presence of these proteins in other bacterial species, since they were initially thought to be confined to higher plants. The present study mainly concentrates on the phylogenetic distribution and homology conservation in GH19 family chitinases. Extensive database searches are performed to identify the presence of GH19 family chitinases in the three major super kingdoms of life. Multiple sequence alignment of all the identified GH19 chitinase family members resulted in the identification of globally conserved residues. We further identified conserved sequence motifs across the major sub groups within the family. Estimation of evolutionary distance between the various bacterial and plant chitinases are carried out to better understand the pattern of evolution. Our study also supports the horizontal gene transfer theory, which states that GH19 chitinase genes are transferred from higher plants to bacteria. Further, the present study sheds light on the phylogenetic distribution and identifies unique sequence signatures that define GH19 chitinase family of proteins. The identified motifs could be used as markers to delineate uncharacterized GH19 family chitinases. The estimation of evolutionary distance between chitinase identified in plants and bacteria shows that the flowering plants are more related to chitinase in actinobacteria than that of identified in purple bacteria. We propose a model to elucidate the natural history of GH19 family chitinases.

Transcriptome-wide analysis of UTRs in non-small cell lung cancer reveals cancer-related genes with SNV-induced changes on RNA secondary structure and miRNA target sites.

Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type KRAS (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fishers exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways.

Brief communication: ProSTRIP: A method to find similar structural repeats in three-dimensional protein structures

The occurrence of similar structural repeats in a protein structure has evolved through gene duplication. These repeats act as a structural building block and form more than one compact structural and functional unit called a repeat domain. The protein families comprising similar structural repeats are mainly involved in protein-protein interactions as well as binding to other ligand molecules. The identification of internal sequence repeats in the primary structure is not sufficient for the analysis of structural repeats. Thus, a new method called ProSTRIP has been developed using dynamic programming to find the similar structural repeats in a three-dimensional protein structure. The detection of these repeats is made by calculating the protein backbone Calpha angles. An internet computing server is also created by implementing this method and enables graphical visualization of the results. It can be freely accessed at http://cluster.physics.iisc.ernet.in/prostrip/.