Vahap Eldem

Genome-wide identification of miRNAs responsive to drought in peach (Prunus persica) by high-throughput deep sequencing.

Peach (Prunus persica L.) is one of the most important worldwide fresh fruits. Since fruit growth largely depends on adequate water supply, drought stress is considered as the most important abiotic stress limiting fleshy fruit production and quality in peach. Plant responses to drought stress are regulated both at transcriptional and post-transcriptional level. As post-transcriptional gene regulators, miRNAs (miRNAs) are small (19–25 nucleotides in length), endogenous, non-coding RNAs. Recent studies indicate that miRNAs are involved in plant responses to drought. Therefore, Illumina deep sequencing technology was used for genome-wide identification of miRNAs and their expression profile in response to drought in peach. In this study, four sRNA libraries were constructed from leaf control (LC), leaf stress (LS), root control (RC) and root stress (RS) samples. We identified a total of 531, 471, 535 and 487 known mature miRNAs in LC, LS, RC and RS libraries, respectively. The expression level of 262 (104 up-regulated, 158 down-regulated) of the 453 miRNAs changed significantly in leaf tissue, whereas 368 (221 up-regulated, 147 down-regulated) of the 465 miRNAs had expression levels that changed significantly in root tissue upon drought stress. Additionally, a total of 197, 221, 238 and 265 novel miRNA precursor candidates were identified from LC, LS, RC and RS libraries, respectively. Target transcripts (137 for LC, 133 for LS, 148 for RC and 153 for RS) generated significant Gene Ontology (GO) terms related to DNA binding and catalytic activites. Genome-wide miRNA expression analysis of peach by deep sequencing approach helped to expand our understanding of miRNA function in response to drought stress in peach and Rosaceae. A set of differentially expressed miRNAs could pave the way for developing new strategies to alleviate the adverse effects of drought stress on plant growth and development.

Genome-wide analysis of the bZIP transcription factors in cucumber.

bZIP proteins are one of the largest transcriptional regulators playing crucial roles in plant development, physiological processes, and biotic/abiotic stress responses. Despite the availability of recently published draft genome sequence of Cucumis sativus, no comprehensive investigation of these family members has been presented for cucumber. We have identified 64 bZIP transcription factor-encoding genes in the cucumber genome. Based on structural features of their encoded proteins, CsbZIP genes could be classified into 6 groups. Cucumber bZIP genes were expanded mainly by segmental duplication rather than tandem duplication. Although segmental duplication rate of the CsbZIP genes was lower than that of Arabidopsis, rice and sorghum, it was observed as a common expansion mechanism. Some orthologous relationships and chromosomal rearrangements were observed according to comparative mapping analysis with other species. Genome-wide expression analysis of bZIP genes indicated that 64 CsbZIP genes were differentially expressed in at least one of the ten sampled tissues. A total of 4 CsbZIP genes displayed higher expression values in leaf, flowers and root tissues. The in silico micro-RNA (miRNA) and target transcript analyses identified that a total of 21 CsbZIP genes were targeted by 38 plant miRNAs. CsbZIP20 and CsbZIP22 are the most targeted by miR165 and miR166 family members, respectively. We also analyzed the expression of ten CsbZIP genes in the root and leaf tissues of drought-stressed cucumber using quantitative RT-PCR. All of the selected CsbZIP genes were measured as increased in root tissue at 24th h upon PEG treatment. Contrarily, the down-regulation was observed in leaf tissues of all analyzed CsbZIP genes. CsbZIP12 and CsbZIP44 genes showed gradual induction of expression in root tissues during time points. This genome-wide identification and expression profiling provides new opportunities for cloning and functional analyses, which may be used in further studies for improving stress tolerance in plants.

Boron Stress Responsive MicroRNAs and Their Targets in Barley

Boron stress is an environmental factor affecting plant development and production. Recently, microRNAs (miRNAs) have been found to be involved in several plant processes such as growth regulation and stress responses. In this study, miRNAs associated with boron stress were identified and characterized in barley. miRNA profiles were also comparatively analyzed between root and leave samples. A total of 31 known and 3 new miRNAs were identified in barley; 25 of them were found to respond to boron treatment. Several miRNAs were expressed in a tissue specific manner; for example, miR156d, miR171a, miR397, and miR444a were only detected in leaves. Additionally, a total of 934 barley transcripts were found to be specifically targeted and degraded by miRNAs. In silico analysis of miRNA target genes demonstrated that many miRNA targets are conserved transcription factors such as Squamosa promoter-binding protein, Auxin response factor (ARF), and the MYB transcription factor family. A majority of these targets were responsible for plant growth and response to environmental changes. We also propose that some of the miRNAs in barley such as miRNA408 might play critical roles against boron exposure. In conclusion, barley may use several pathways and cellular processes targeted by miRNAs to cope with boron stress.

Global Transcriptome Analysis Reveals Differences in Gene Expression Patterns Between Nonhyperhydric and Hyperhydric Peach Leaves

Hyperhydricity is a morphophysiological disorder of plants in tissue culture RNA‐seq showed more than 300 transcripts notably responsive to hyperhydricity Expression of Laccase 3 involved in lignin polymerization related to hyperhydricity

Classification of RNA-Seq Data via Bagging Support Vector Machines

Background RNA sequencing (RNA-Seq) is a powerful technique for transcriptome profiling of the organisms that uses the capabilities of next-generation sequencing (NGS) technologies. Recent advances in NGS let to measure the expression levels of tens to thousands of transcripts simultaneously. Using such information, developing expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of disease. Here, we present the bagging support vector machines (bagSVM), a machine learning approach and bagged ensembles of support vector machines (SVM), for classification of RNA-Seq data. The bagSVM basically uses bootstrap technique and trains each single SVM separately; next it combines the results of each SVM model using majority-voting technique. Results We demonstrate the performance of the bagSVM on simulated and real datasets. Simulated datasets are generated from negative binomial distribution under different scenarios and real datasets are obtained from publicly available resources. A deseq normalization and variance stabilizing transformation (vst) were applied to all datasets. We compared the results with several classifiers including Poisson linear discriminant analysis (PLDA), single SVM, classification and regression trees (CART), and random forests (RF). In slightly overdispersed data, all methods, except CART algorithm, performed well. Performance of PLDA seemed to be best and RF as second best for very slightly and substantially overdispersed datasets. While data become more spread, bagSVM turned out to be the best classifier. In overall results, bagSVM and PLDA had the highest accuracies. Conclusions According to our results, bagSVM algorithm after vst transformation can be a good choice of classifier for RNA-Seq datasets mostly for overdispersed ones. Thus, we recommend researchers to use bagSVM algorithm for the purpose of classification of RNA-Seq data. PLDA algorithm should be a method of choice for slight and moderately overdispersed datasets. An R/BIOCONDUCTOR package MLSeq with a vignette is freely available at http://www.bioconductor.org/packages/2.14/bioc/html/MLSeq.html

Theory of room temperature ferromagnetism in Cr modified DNA nanowire

We investigated the magnetic properties of Cr3+ (J < 0) ion-modified DNA (M-DNA) nanowire (1000 base) at room temperature under a uniform magnetic field (~100 Oe) for different doping concentrations. A Monte Carlo simulation method-based Metropolis algorithm is used to figure out the thermodynamic quantities of nanowire formed by Cr M-DNA followed by analysing the dependency of the ferromagnetic behaviour of the M-DNA to dopant concentration. It is understood that ion density/base and ion density/helical of Cr3+ ions can be a tuning parameter, herewith the dopant ratio has an actual importance on the magnetic characterization of M-DNA nanowire (3%–20%). We propose the source of magnetism as an exchange interaction between Cr and DNA helical atoms indicated in the Heisenberg Hamiltonian.

De novo assembly and comprehensive characterization of the skeletal muscle transcriptomes of the European anchovy (Engraulis encrasicolus).

European anchovy has considerable economic and ecological importance due to its high reproduction capacity and growth rate. As one of the largest source of wild marine protein, an increasing muscle mass strength has a major contribution to this growth rate during transition from subadult to adult stage. In the present study, using Illumina sequencing technology (HiSeq2000) accompanied with appropriate bioinformatics softwares; we have sequenced, assembled and annotated the transcriptome of wild subadult and adult anchovy muscles. A total of 131,081,776 high-quality reads were assembled into 125,506 contigs with an average length of 709.35 bp and N50 length of 1159 bp. Functional annotations of assembled contigs have been summarized according to 3325 GO terms, 3370 PFAM domains and 378 predicted KEGG metabolic pathways. About 11% of all contigs had at least one type of SSR motif in their sequences. According to the sequence homology analysis by BlasTN it was concluded that the assembled contigs include 16 skeletal muscle-expressed miRNAs, 14 ncRNAs and most of sarcomeric/myofibrillar genes. We hope that the sequence information regarding the muscle transcriptome of anchovy can provide some insight into the understanding of genome-wide transcriptome profile of teleost muscle tissue and give useful information in fish muscle development.

A comprehensive simulation study on classification of RNA-Seq data

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

The relationship between phylogenetic classification, virulence and antibiotic resistance of extraintestinal pathogenic Escherichia coli in İzmir province, Turkey

Background Extraintestinal pathogenic Escherichia coli (ExPEC) is an important bacterium and responsible for many bloodstream infections, including urinary tract infections and even fatal bacteremia. The aim of this research was to investigate whether ExPEC strains isolated from Turkish blood cultures have a relationship between 16S rRNA based phylogenetic clusters and antibiotic resistance profiles, virulence factors or clonal lineages. Methods Phenotypically identified ExPEC blood culture isolates (n = 104) were included in this study. The 16S rRNA partial sequence analysis was performed for genotypic identification of ExPEC isolates. Antibiotic susceptibility and Extended-Spectrum β-Lactamase testing of isolates were performed. Phylogenetic classification (A, B1, B2 and D), Multi Locus Sequence Typing analysis and virulence-associated genes were investigated. Results Based on 16S rRNA partial sequence analysis, 97 out of 104 (93.26%) ExPEC isolates were confirmed as E. coli. Ampicillin (74.22%) and cefuroxime axetil (65.97%) resistances had the highest frequencies among the ExPEC isolates. In terms of phylogenetic classification of ExPEC, D (38.14%, 37/97) was the most prevalent group after A (29.89%, 29/97), B2 (20.61%, 20/97), and B1 (11.34%, 11/97). The sequence types of the 20 ExPEC isolates belonging to the B2 phylogenetic group were analyzed by Multi Locus Sequence Typing. Ten isolates out of 20 (50.0%) were identified as ST131. The other STs were ST95 (n = 1), ST14 (n = 1), ST10 (n = 1), ST69 (n = 1), ST1722 (n = 2), ST141 (n = 1), ST88 (n = 1), ST80 (n = 1), and ST998 (n = 1). Of the ST131 strains, six (60%, 6/10) represented serogroup O25. The most common virulence factor genes were serum resistance factor gene, traT (55.7%) aerobactin siderophore receptor and yersiniabactin encoding genes iutA (45.3%) and fyuA (50.5%), respectively. In addition, PAI (41.2%), iroN (23.7%), hlyA (15.4%), kpsII (13.4%), ompT (13.4%), papG (12.4%), iss (9.3%), cnf1 (7.2%), ibeA (2.06%), and sfaS (2.06%) genes were present in the ExPEC isolates. Conclusion The 16S rRNA-based phylogenetic relationship tree analysis showed that a large cluster was present among 97 ExPEC isolates along with related reference strains. There were 21 main clusters with 32 closely related subclusters. Based on our findings, different clonal lineages of ExPEC can display different antibiotic susceptibilities and virulence properties. We also concluded that virulence factors were not distributed depending on phylogenetic groups (A, B1, B2, and D). The ExPEC isolates belonging to the same phylogenetic group and sequence type could display different resistance and virulence characteristics.

voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data

RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom’s precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.