Transcriptome-wide analysis uncovers regulatory elements of the antennal transcriptome repertoire of bumblebee at different life stages

Bumblebees are crucial pollinators, providing essential ecosystem services and global food production. The success of pollination services relies on the interaction between sensory organs and the environment. The antenna functions as a versatile multi-sensory organ, pivotal in mediating chemosensory/olfactory information, and governs adaptive responses to environmental changes. Despite an increasing number of RNA-sequencing studies on insect antenna, comprehensive antennal transcriptome studies at the different life stages were not elucidated systematically. Here, we quantified the expression profile and dynamics of coding/microRNA genes of larval head and antennal tissues from early- and late-stage pupa to the adult of Bombus terrestris as suitable model organism among pollinators. We further performed Pearson correlation analyses on the gene expression profiles of the antennal transcriptome from larval head tissue to adult stages, exploring both positive and negative expression trends. The positively correlated coding genes were primarily enriched in sensory perception of chemical stimuli, ion transport, transmembrane transport processes and olfactory receptor activity. Negatively correlated genes were mainly enriched in organic substance biosynthesis and regulatory mechanisms underlying larval body patterning and the formation of juvenile antennal structures. As post-transcriptional regulators, miR-1000-5p, miR-13b-3p, miR-263-5p and miR-252-5p showed positive correlations, whereas miR-315-5p, miR-92b-3p, miR-137-3p, miR-11-3p and miR-10-3p exhibited negative correlations in antennal tissue. Notably, based on the inverse expression relationship, positively and negatively correlated microRNA (miRNA)-mRNA target pairs revealed that differentially expressed miRNAs predictively targeted genes involved in antennal development, shaping antennal structures and regulating antenna-specific functions. Our data serve as a foundation for understanding stage-specific antennal transcriptomes and large-scale comparative analysis of transcriptomes in different insects.

Deciphering the host genetic factors conferring susceptibility to severe COVID-19 using exome sequencing

The COVID-19 pandemic remains a significant public health concern despite the new vaccines and therapeutics. The clinical course of acute SARS-CoV-2 infection is highly variable and influenced by several factors related to the virus and the host. Numerous genetic studies, including candidate gene, exome, and genome sequencing studies, genome-wide association studies, and other omics efforts, have proposed various Mendelian and non-Mendelian associations with COVID-19 course. In this study, we conducted whole-exome sequencing on 90 unvaccinated patients from Turkey with no known comorbidities associated with severe COVID-19. Of these patients, 30 had severe, 30 had moderate, and 30 had mild/asymptomatic disease. We identified rare variants in genes associated with SARS-CoV-2 susceptibility and pathogenesis, with an emphasis on genes related to the regulation of inflammation, and discussed these in the context of the clinical course of the patients. In addition, we compared the frequencies of common variants between each group. Even though no variant remained statistically significant after correction for multiple testing, we observed that certain previously associated genes and variants showed significant associations before correction. Our study contributes to the existing literature regarding the genetic susceptibility to SARS-CoV-2. Future studies would be beneficial characterizing the host genetic properties in different populations.

The effects of dietary microbial 6-phytase on growth parameters, intestinal morphometric properties and selected intestinal genes expression in rainbow trout (Oncorhynchus mykiss, Walbaum 1876)

This study investigated the effects of dietary 6-phytase, produced by a genetically modified Komagataella phaffii, on growth performance, feed utilisation, flesh quality, villus morphometric properties, and intestinal mRNA expression in rainbow trout. Six iso-nitrogenous, iso-lipidic, and iso-caloric diets were formulated and fed to triplicate groups of juvenile rainbow trout weighing 32.57 ± 0.36 g (mean ± SD) for 90 days. The dietary treatments included two positive controls (PC), one formulated with 400 g/kg of fish meal named T1, and the other formulated with 170 g/kg of fish meal plus 1% avP derived from monocalcium phosphate named T2. The remaining dietary treatments consisted of a negative control (NC) formulated with 170 g/kg of fish meal (T3), NC+ 750, NC+ 1500, and NC+ 3000 OTU/kg levels of phytase designated as T4, T5, and T6 diets respectively. Compared to T1, weight gain (WG) increased by 16.29, 13.71 and 11.66% in T4, T5 and T6, respectively (p < 0.05). Feed conversion ratio (FCR) was lowered by 3.2 and 0.8% in T4 and T5 compared to T1 (p < 0.05). WG, feed intake (FI), FCR, final body length, bone ash, bone ash P, and intestinal morphometry were negatively affected in T3 fed fish (p < 0.05). Whole-body fish nutrient, bone ash, bone ash phosphorus (P) compositions and mucosal villus morphometric properties improved in rainbow trout fed diets supplemented with phytase dose ranging from 750-3000 OTU. Bone ash increased by 6.12% in T5 compared to T1 (p < 0.05). Phytase inclusion enhanced the profitability of feeding juvenile rainbow trout such diets as it reduced the feed price and economic conversion rate. Dietary inclusion of phytase down-regulated mRNA expression of genes responsible for fatty acid synthesis and lipogenesis in juvenile rainbow trout. Dietary phytase up-regulated the mRNA expression of genes (SLC4A11 and ATP1A3A) responsible for nutrient uptake and down-regulated intestinal expression of MUCIN 5AC-like genes (mucus secreting genes) in juvenile rainbow trout. Along with improving performance parameters, the inclusion of phytase in rainbow trout diet containing plant-based protein sources, can preserve intestinal morphology by regulating the mRNA expression of genes responsible for fatty acid synthesis, lipogenesis and nutrient uptake and transport.

Global expression pattern of genes containing positively selected sites in European anchovy (Engraulis encrasicolus L.) may shed light on teleost reproduction

European anchovy is a multiple-spawning and highly fecundate pelagic fish with high economic and ecological significance. Although fecundity is influenced by nutrition, temperature and weight of spawners, high reproductive capacity is related to molecular processes in the ovary. The ovary is an essential and complex reproductive organ composed of various somatic and germ cells, which interact to facilitate the development of the ovary and functional oocytes. Revealing the ovarian transcriptome profile of highly fecundate fishes provides insights into oocyte production in teleosts. Here we use a comprehensive tissue-specific RNA sequencing which yielded 102.3 billion clean bases to analyze the transcriptional profiles of the ovary compared with other organs (liver, kidney, ovary, testis, fin, cauda and gill) and juvenile tissues of European anchovy. We conducted a comparative transcriptome and positive selection analysis of seven teleost species with varying fecundity rates to identify genes potentially involved in oogenesis and oocyte development. Of the 2,272 single copies of orthologous genes found, up to 535 genes were under positive selection in European anchovy and these genes are associated with a wide spectrum of cellular and molecular functions, with enrichments such as RNA methylation and modification, ribosome biogenesis, DNA repair, cell cycle processing and peptide/amide biosynthesis. Of the 535 positively selected genes, 55 were upregulated, and 45 were downregulated in the ovary, most of which were related to RNA and DNA transferase, developmental transcription factors, protein kinases and replication factors. Overall, our analysis of the transcriptome level in the ovarian tissue of a teleost will provide further insights into molecular processes and deepen our genetic understanding of egg production in highly fecund fish.

Comprehensive analysis of circulating viral DNA in maternal plasma at population-scale using low-pass whole-genome sequencing

As the most readily adopted molecular screening test, low-pass WGS of maternal plasma cell-free DNA for aneuploidy detection generates a vast amount of genomic data. This large-scale method also allows for high-throughput virome screening. NIPT sequencing data, yielding 6.57 terabases of data from 187.8 billion reads, from 12,951 pregnant Turkish women was used to investigate the prevalence and abundance of viral DNA in plasma. Among the 22 virus sequences identified in 12% of participants were human papillomavirus, herpesvirus, betaherpesvirus and anellovirus. We observed a unique pattern of circulating viral DNA with a high prevalence of papillomaviruses. The prevalence of herpesviruses/anellovirus was similar among Turkish, European and Dutch populations. Hepatitis B prevalence was remarkably low in Dutch, European and Turkish populations, but higher in China. WGS data revealed that herpesvirus/anelloviruses are naturally found in European populations. This represents the first comprehensive research on the plasma virome of pregnant Turkish women.

Voomsom: Voom-Based Self-Organizing Maps For Clustering RNA-Sequencing Data

Background:

Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several cluster-ing approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed.

Objective:

In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering al-gorithms such as k-means, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering.

Methods:

We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm val-ues to get the codebook used in the downstream analysis. Next, we calculate the weighted dis-tance matrices using the sample quality weights obtained from voom transformation and code-books from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical cluster-ing algorithms to cluster samples.

Results:

The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similar-ly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in over-dispersed RNA-seq data.

Conclusion:

The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

Genome skimming approach reveals the gene arrangements in the chloroplast genomes of the highly endangered Crocus L. species: Crocus istanbulensis (B.Mathew) Rukšāns

Crocus istanbulensis (B.Mathew) Rukšāns is one of the most endangered Crocus species in the world and has an extremely limited distribution range in Istanbul. Our recent field work indicates that no more than one hundred individuals remain in the wild. In the present study, we used genome skimming to determine the complete chloroplast (cp) genome sequences of six C. istanbulensis individuals collected from the locus classicus. The cp genome of C. istanbulensis has 151,199 base pairs (bp), with a large single-copy (LSC) (81,197 bp), small single copy (SSC) (17,524 bp) and two inverted repeat (IR) regions of 26,236 bp each. The cp genome contains 132 genes, of which 86 are protein-coding (PCGs), 8 are rRNA and 38 are tRNA genes. Most of the repeats are found in intergenic spacers of Crocus species. Mononucleotide repeats were most abundant, accounting for over 80% of total repeats. The cp genome contained four palindrome repeats and one forward repeat. Comparative analyses among other Iridaceae species identified one inversion in the terminal positions of LSC region and three different gene (psbA, rps3 and rpl22) arrangements in C. istanbulensis that were not reported previously. To measure selective pressure in the exons of chloroplast coding sequences, we performed a sequence analysis of plastome-encoded genes. A total of seven genes (accD, rpoC2, psbK, rps12, ccsA, clpP and ycf2) were detected under positive selection in the cp genome. Alignment-free sequence comparison showed an extremely low sequence diversity across naturally occurring C. istanbulensis specimens. All six sequenced individuals shared the same cp haplotype. In summary, this study will aid further research on the molecular evolution and development of ex situ conservation strategies of C. istanbulensis.

MLSeq: Machine learning interface for RNA-sequencing data

BACKGROUND AND OBJECTIVE

In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network.

METHODS

Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq.

MATERIALS

Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set.

RESULTS

The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features.

CONCLUSION

MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.

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.

The Relationship Between Blood Pressure and Sleep Duration in Turkish Children: A Cross-Sectional Study

OBJECTIVE

As in adults, hypertension is also an important risk factor for cardiovascular disease in children. We aimed to evaluate the effect of sleep duration on blood pressure in normal weight Turkish children aged between 11-17 years.

METHODS

This cross-sectional study was conducted in the primary and secondary schools of the two central and ten outlying districts of Kayseri, Turkey. Subjects were 2860 children and adolescents (1385 boys, 1475 girls). Systolic and diastolic blood pressures were measured according to the recommendations of the Fourth Report of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. Sleep duration was classified as follows: ≤8 hours, 8.1-8.9 hours, 9.0-9.9 hours or ≥10 hours.

RESULTS

For short sleeper boys and girls (participants with a sleep duration ≤8 h) the prevalence of prehypertension and hypertension was 35.0% and 30.8%, respectively. In univariate binary logistic regression analyses (age-adjusted), each unit increment in sleep duration (hours) in boys and girls, decreased the prehypertension and hypertension risk by 0.89 [odds ratio (OR)] [confidance interval (CI); 0.82-0.98] and 0.88 (OR) (CI; 0.81-0.97), respectively (p<0.05). In multiple binary logistic regression analyses [age- and body mass index (BMI)-adjusted] the location of the school and sleep duration categories were shown to be the most important factors for prehypertension and hypertension in both genders, while household income was the most important factor, only in boys.

CONCLUSIONS

A sleep duration ≤8 h is an independent risk factor for prehypertension and hypertension in Turkish children aged 11-17 years.

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/.

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.

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 characterized morphologically by the presence of translucent, thick, curled, and fragile leaves as a result of excessive water intake. Since clonal propagation is a major in vitro technique for multiplying plants vegetatively, the emergence of hyperhydricity-related symptoms causes significant economic losses to agriculture and horticulture. Although numerous efforts have been hitherto devoted to the morphological and anatomical responses of plants to hyperhydricity, the underlying molecular mechanism remains largely unknown. Here, a genome-wide transcriptome analysis was performed to identify differentially expressed genes in hyperhydric and nonhyperhydric leaves of peach [ (L.) Batsch]. The RNA sequencing (RNA-Seq) analysis showed that the expression of >300 transcripts was altered between control and hyperhydric leaf cells. The top 30 differentially expressed transcripts (DETs) were related to the posttranscriptional regulators of organelle gene expression and photosynthesis, cellular elimination, plant cuticle development, and abiotic stress response processes. The expression of 10 DETs was also conformed by quantitative real-time polymerase chain reaction (RT-qPCR) in hyperhydric and nonhyperhydric leaves. As a complex biological process, hyperhydricity alters the expression of various transcripts including transcription factor (), RNA binding protein (pentatricopeptide, ), transporter protein (), and . Thus, this genome-wide transcriptome profiling study may help elucidate the molecular mechanism of hyperhydricity.

Complete mitochondrial genome of Alburnus istanbulensis (Teleostei, Cyprinidae)

Alburnus istanbulensis (Thracian shemaya) is an endemic fish species to the northwestern region of Turkey. Alburnus istanbulensis has a remarkably limited geographic distribution, and no genetic data have been available on this species until now. The mitogenome (16,612 bp) had the typical mitochondrial characteristics of other Leuciscinae fishes. This mitogenome sequence can help to further resolve phylogenetic relationships among Cyprinidae.

The complete mitochondrial genome of Alburnus tarichi (Teleostei, Cyprinidae)

Alburnus tarichi is the only vertebrate species that can survive in Lake Van, the largest soda lake on Earth, which is characterized by extremely high pH and salinity. The circular mitogenomes of A. tarichi was 16,602 base pairs in size, containing 13 protein-coding genes (PCGs), 2 rRNA genes, 22 tRNA genes, and 2 major non-coding control regions (D-Loop and O(L)). Congurence was observed between the Alburnus mtDNAs in terms of genome organization, base composition, gene arrangement, and tRNA structure. Mitogenome sequences may be useful for conservation efforts of this endangered species.

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.

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.