Non-coding RNA detection methods combined to improve usability, reproducibility and precision

Cytokine and chemokine profiles in women with endometriosis, polycystic ovary syndrome, and unexplained infertility

Numerous factors (including immunological, congenital, hormonal, and morphological disorders) can lead to infertility. In this regard, 3 specific diseases associated with infertility are discussed in this review study (i.e., polycystic ovary syndrome [PCOS], endometriosis [EMS], and unexplained infertility [UI]). PCOS is a common endocrine disorder characterized by chronic low-grade inflammation, and EMS is a benign disease characterized by the presence of ectopic endometrial tissue. UI refers to couples who are unable to conceive for no known reason. Conception and pregnancy are significantly affected by the immune system; in this regard, chemokines and cytokines play important roles in the regulation of immune responses. Patients with PCOS, EMS, and UI have altered cytokine and chemokine profiles, suggesting that dysregulation of these molecules may contribute to infertility in these conditions. Accordingly, the issue of infertility is addressed in this review study, a condition that affects approximately 16% of couples worldwide.

Genetics, Structure, and Function of Group A Streptococcal Pili

Streptococcus pyogenes (Group A Streptococcus; GAS) is an exclusively human pathogen. This bacterial species is responsible for a large variety of infections, ranging from purulent but mostly self-limiting oropharynx/skin diseases to streptococcal sequelae, including glomerulonephritis and rheumatic fever, as well as life-threatening streptococcal toxic-shock syndrome. GAS displays a wide array of surface proteins, with antigenicity of the M protein and pili utilized for M- and T-serotyping, respectively. Since the discovery of GAS pili in 2005, their genetic features, including regulation of expression, and structural features, including assembly mechanisms and protein conformation, as well as their functional role in GAS pathogenesis have been intensively examined. Moreover, their potential as vaccine antigens has been studied in detail. Pilus biogenesis-related genes are located in a discrete section of the GAS genome encoding fibronectin and collagen binding proteins and trypsin-resistant antigens (FCT region). Based on the heterogeneity of genetic composition and DNA sequences, this region is currently classified into nine distinguishable forms. Pili and fibronectin-binding proteins encoded in the FCT region are known to be correlated with infection sites, such as the skin and throat, possibly contributing to tissue tropism. As also found for pili of other Gram-positive bacterial pathogens, GAS pilin proteins polymerize via isopeptide bonds, while intramolecular isopeptide bonds present in the pilin provide increased resistance to degradation by proteases. As supported by findings showing that the main subunit is primarily responsible for T-serotyping antigenicity, pilus functions and gene expression modes are divergent. GAS pili serve as adhesins for tonsillar tissues and keratinocyte cell lines. Of note, a minor subunit is considered to have a harpoon function by which covalent thioester bonds with host ligands are formed. Additionally, GAS pili participate in biofilm formation and evasion of the immune system in a serotype/strain-specific manner. These multiple functions highlight crucial roles of pili during the onset of GAS infection. This review summarizes the current state of the art regarding GAS pili, including a new mode of host-GAS interaction mediated by pili, along with insights into pilus expression in terms of tissue tropism.

Conserved and specific features of Streptococcus pyogenes and Streptococcus agalactiae transcriptional landscapes

BACKGROUND

The human pathogen Streptococcus pyogenes, or group A Streptococcus, is responsible for mild infections to life-threatening diseases. To facilitate the characterization of regulatory networks involved in the adaptation of this pathogen to its different environments and their evolution, we have determined the primary transcriptome of a serotype M1 S. pyogenes strain at single-nucleotide resolution and compared it with that of Streptococcus agalactiae, also from the pyogenic group of streptococci.

RESULTS

By using a combination of differential RNA-sequencing and oriented RNA-sequencing we have identified 892 transcription start sites (TSS) and 885 promoters in the S. pyogenes M1 strain S119. 8.6% of S. pyogenes mRNAs were leaderless, among which 81% were also classified as leaderless in S. agalactiae. 26% of S. pyogenes transcript 5' untranslated regions (UTRs) were longer than 60 nt. Conservation of long 5' UTRs with S. agalactiae allowed us to predict new potential regulatory sequences. In addition, based on the mapping of 643 transcript ends in the S. pyogenes strain S119, we constructed an operon map of 401 monocistrons and 349 operons covering 81.5% of the genome. One hundred fifty-six operons and 254 monocistrons retained the same organization, despite multiple genomic reorganizations between S. pyogenes and S. agalactiae. Genomic reorganization was found to more often go along with variable promoter sequences and 5' UTR lengths. Finally, we identified 117 putative regulatory RNAs, among which nine were regulated in response to magnesium concentration.

CONCLUSIONS

Our data provide insights into transcriptome evolution in pyogenic streptococci and will facilitate the analysis of genetic polymorphisms identified by comparative genomics in S. pyogenes.

RNA sequencing uncovers antisense RNAs and novel small RNAs in Streptococcus pyogenes

Streptococcus pyogenes is a human pathogen responsible for a wide spectrum of diseases ranging from mild to life-threatening infections. During the infectious process, the temporal and spatial expression of pathogenicity factors is tightly controlled by a complex network of protein and RNA regulators acting in response to various environmental signals. Here, we focus on the class of small RNA regulators (sRNAs) and present the first complete analysis of sRNA sequencing data in S. pyogenes. In the SF370 clinical isolate (M1 serotype), we identified 197 and 428 putative regulatory RNAs by visual inspection and bioinformatics screening of the sequencing data, respectively. Only 35 from the 197 candidates identified by visual screening were assigned a predicted function (T-boxes, ribosomal protein leaders, characterized riboswitches or sRNAs), indicating how little is known about sRNA regulation in S. pyogenes. By comparing our list of predicted sRNAs with previous S. pyogenes sRNA screens using bioinformatics or microarrays, 92 novel sRNAs were revealed, including antisense RNAs that are for the first time shown to be expressed in this pathogen. We experimentally validated the expression of 30 novel sRNAs and antisense RNAs. We show that the expression profile of 9 sRNAs including 2 predicted regulatory elements is affected by the endoribonucleases RNase III and/or RNase Y, highlighting the critical role of these enzymes in sRNA regulation.

LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

Computational Detection of piRNA in Human Using Support Vector Machine

BACKGROUND

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs (ncRNAs), with a length of about 24-32 nucleotides, which have been discovered recently. These ncRNAs play an important role in germline development, transposon silencing, epigenetic regulation, protecting the genome from invasive transposable elements, and the pathophysiology of diseases such as cancer. piRNA identification is challenging due to the lack of conserved piRNA sequences and structural elements.

METHODS

To detect piRNAs, an appropriate feature set, including 8 diverse feature groups to encode each RNA was applied. In addition, a Support Vector Machine (SVM) classifier was used with optimized parameters for RNA classification. According to the obtained results, the classification performance using the optimized feature subsets was much higher than the one in previously published studies.

RESULTS

Our results revealed 98% accuracy, Mathew' correlation coefficient of 98% and 99% specificity in discriminating piRNAs from the other RNAs. Also, the obtained results show that the proposed method outperforms its competitors.

CONCLUSION

In this paper, a prediction method was proposed to identify piRNA in human. Also, 48 heterogeneous features (sequence and structural features) were used to encode RNAs. To assess the performance of the method, a benchmark dataset containing 515 piRNAs and 1206 types of other RNAs was constructed. Our method reached the accuracy of 99% on the benchmark dataset. Also, our analysis revealed that the structural features are the most contributing features in piRNA prediction.

The epigenetics of aging and neurodegeneration

Epigenetics is a quickly growing field encompassing mechanisms regulating gene expression that do not involve changes in the genotype. Epigenetics is of increasing relevance to neuroscience, with epigenetic mechanisms being implicated in brain development and neuronal differentiation, as well as in more dynamic processes related to cognition. Epigenetic regulation covers multiple levels of gene expression; from direct modifications of the DNA and histone tails, regulating the level of transcription, to interactions with messenger RNAs, regulating the level of translation. Importantly, epigenetic dysregulation currently garners much attention as a pivotal player in aging and age-related neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and Huntington's disease, where it may mediate interactions between genetic and environmental risk factors, or directly interact with disease-specific pathological factors. We review current knowledge about the major epigenetic mechanisms, including DNA methylation and DNA demethylation, chromatin remodeling and non-coding RNAs, as well as the involvement of these mechanisms in normal aging and in the pathophysiology of the most common neurodegenerative diseases. Additionally, we examine the current state of epigenetics-based therapeutic strategies for these diseases, which either aim to restore the epigenetic homeostasis or skew it to a favorable direction to counter disease pathology. Finally, methodological challenges of epigenetic investigations and future perspectives are discussed.

Genome-wide analyses of small non-coding RNAs in streptococci

Streptococci represent a diverse group of Gram-positive bacteria, which colonize a wide range of hosts among animals and humans. Streptococcal species occur as commensal as well as pathogenic organisms. Many of the pathogenic species can cause severe, invasive infections in their hosts leading to a high morbidity and mortality. The consequence is a tremendous suffering on the part of men and livestock besides the significant financial burden in the agricultural and healthcare sectors. An environmentally stimulated and tightly controlled expression of virulence factor genes is of fundamental importance for streptococcal pathogenicity. Bacterial small non-coding RNAs (sRNAs) modulate the expression of genes involved in stress response, sugar metabolism, surface composition, and other properties that are related to bacterial virulence. Even though the regulatory character is shared by this class of RNAs, variation on the molecular level results in a high diversity of functional mechanisms. The knowledge about the role of sRNAs in streptococci is still limited, but in recent years, genome-wide screens for sRNAs have been conducted in an increasing number of species. Bioinformatics prediction approaches have been employed as well as expression analyses by classical array techniques or next generation sequencing. This review will give an overview of whole genome screens for sRNAs in streptococci with a focus on describing the different methods and comparing their outcome considering sRNA conservation among species, functional similarities, and relevance for streptococcal infection.

RNA-mediated regulation in Gram-positive pathogens: an overview punctuated with examples from the group A Streptococcus

RNA-based mechanisms of regulation represent a ubiquitous class of regulators that are associated with diverse processes including nutrient sensing, stress response, modulation of horizontal gene transfer, and virulence factor expression. While better studied in Gram-negative bacteria, the literature is replete with examples of the importance of RNA-mediated regulatory mechanisms to the virulence and fitness of Gram-positives. Regulatory RNAs are classified as cis-acting, e.g. riboswitches, which modulate the transcription, translation, or stability of co-transcribed RNA, or trans-acting, e.g. small regulatory RNAs, which target separate mRNAs or proteins. The group A Streptococcus (GAS, Streptococcus pyogenes) is a Gram-positive bacterial pathogen from which several regulatory RNA mechanisms have been characterized. The study of RNA-mediated regulation in GAS has uncovered novel concepts with respect to how small regulatory RNAs may positively regulate target mRNA stability, and to how CRISPR RNAs are processed from longer precursors. This review provides an overview of RNA-mediated regulation in Gram-positive bacteria, and is highlighted with specific examples from GAS research. The key roles that these systems play in regulating bacterial virulence are discussed and future perspectives outlined.

Impact of rli87 gene deletion on response of Listeria monocytogenes to environmental stress

Listeria monocytogenes (LM) is a zoonotic pathogen that widely adapts to various environments. Recent studies have found that noncoding RNAs (ncRNAs) play regulatory roles in LM responses to environmental stress. To understand the role of ncRNA rli87 in the response regulation, a rli87 deletion strain LM-Δrli87 was constructed by homologous recombination and tested for stress responses to high temperature, low temperature, high osmotic pressure, alcohol, acidity, alkaline and oxidative environments, along with LM EGD-e strain (control). The results showed that compared with LM EGD-e, LM-Δrli87 grew faster (P < 0.05) at low temperature (30 °C), high temperature (42 °C), and in alkaline condition (pH = 9), similarly (P > 0.05) in acidic and high osmatic pressure (10% NaCl) conditions. When cultured in medium containing 3.8% ethanol, the growth was not significantly different between the two strains (P > 0.05). When cultured at pH 9, they had similar growth rates in the first 5 h (P > 0.05), but the rates were significantly different after 6 h (P < 0.05). The expression of rsbV, rsbW, hpt, clpP, and ctsR was upregulated in LM-∆rli87 compared with LM EGD-e at pH 9, indicating that the rli87 gene regulated the expression of the five genes in alkaline environment. Our results suggest that the rli87 gene has an important regulatory role in LM's response to temperature (30 and 42 °C), alkaline stresses.

Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm

To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.

Novel regulatory small RNAs in Streptococcus pyogenes

Streptococcus pyogenes (Group A Streptococcus or GAS) is a Gram-positive bacterial pathogen that has shown complex modes of regulation of its virulence factors to cause diverse diseases. Bacterial small RNAs are regarded as novel widespread regulators of gene expression in response to environmental signals. Recent studies have revealed that several small RNAs (sRNAs) have an important role in S. pyogenes physiology and pathogenesis by regulating gene expression at the translational level. To search for new sRNAs in S. pyogenes, we performed a genomewide analysis through computational prediction followed by experimental verification. To overcome the limitation of low accuracy in computational prediction, we employed a combination of three different computational algorithms (sRNAPredict, eQRNA and RNAz). A total of 45 candidates were chosen based on the computational analysis, and their transcription was analyzed by reverse-transcriptase PCR and Northern blot. Through this process, we discovered 7 putative novel trans-acting sRNAs. Their abundance varied between different growth phases, suggesting that their expression is influenced by environmental or internal signals. Further, to screen target mRNAs of an sRNA, we employed differential RNA sequencing analysis. This study provides a significant resource for future study of small RNAs and their roles in physiology and pathogenesis of S. pyogenes.

Identification of novel growth phase- and media-dependent small non-coding RNAs in Streptococcus pyogenes M49 using intergenic tiling arrays

BACKGROUND

Small non-coding RNAs (sRNAs) have attracted attention as a new class of gene regulators in both eukaryotes and bacteria. Genome-wide screening methods have been successfully applied in Gram-negative bacteria to identify sRNA regulators. Many sRNAs are well characterized, including their target mRNAs and mode of action. In comparison, little is known about sRNAs in Gram-positive pathogens. In this study, we identified novel sRNAs in the exclusively human pathogen Streptococcus pyogenes M49 (Group A Streptococcus, GAS M49), employing a whole genome intergenic tiling array approach. GAS is an important pathogen that causes diseases ranging from mild superficial infections of the skin and mucous membranes of the naso-pharynx, to severe toxic and invasive diseases.

RESULTS

We identified 55 putative sRNAs in GAS M49 that were expressed during growth. Of these, 42 were novel. Some of the newly-identified sRNAs belonged to one of the common non-coding RNA families described in the Rfam database. Comparison of the results of our screen with the outcome of two recently published bioinformatics tools showed a low level of overlap between putative sRNA genes. Previously, 40 potential sRNAs have been reported to be expressed in a GAS M1T1 serotype, as detected by a whole genome intergenic tiling array approach. Our screen detected 12 putative sRNA genes that were expressed in both strains. Twenty sRNA candidates appeared to be regulated in a medium-dependent fashion, while eight sRNA genes were regulated throughout growth in chemically defined medium. Expression of candidate genes was verified by reverse transcriptase-qPCR. For a subset of sRNAs, the transcriptional start was determined by 5' rapid amplification of cDNA ends-PCR (RACE-PCR) analysis.

CONCLUSIONS

In accord with the results of previous studies, we found little overlap between different screening methods, which underlines the fact that a comprehensive analysis of sRNAs expressed by a given organism requires the complementary use of different methods and the investigation of several environmental conditions. Despite a high conservation of sRNA genes within streptococci, the expression of sRNAs appears to be strain specific.

Sensitive and label-free biosensing of RNA with predicted secondary structures by a triplex affinity capture method

A novel biosensing approach for the label-free detection of nucleic acid sequences of short and large lengths has been implemented, with special emphasis on targeting RNA sequences with secondary structures. The approach is based on selecting 8-aminoadenine-modified parallel-stranded DNA tail-clamps as affinity bioreceptors. These receptors have the ability of creating a stable triplex-stranded helix at neutral pH upon hybridization with the nucleic acid target. A surface plasmon resonance biosensor has been used for the detection. With this strategy, we have detected short DNA sequences (32-mer) and purified RNA (103-mer) at the femtomol level in a few minutes in an easy and level-free way. This approach is particularly suitable for the detection of RNA molecules with predicted secondary structures, reaching a limit of detection of 50 fmol without any label or amplification steps. Our methodology has shown a marked enhancement for the detection (18% for short DNA and 54% for RNA), when compared with the conventional duplex approach, highlighting the large difficulty of the duplex approach to detect nucleic acid sequences, especially those exhibiting stable secondary structures. We believe that our strategy could be of great interest to the RNA field.

Common regulators of virulence in streptococci

Streptococcal species are a diverse group of bacteria which can be found in animals and humans. Their interactions with host organisms can vary from commensal to pathogenic. Many of the pathogenic species are causative agents of severe, invasive infections in their hosts, accounting for a high burden of morbidity and mortality, associated with high economic costs in industry and health care. Among them, Streptococcus pyogenes, Streptococcus agalactiae, Streptococcus pneumoniae, and Streptococcus suis are discussed here. An environmentally stimulated and tightly controlled expression of their virulence factors is of utmost importance for their pathogenic potential. Thus, the most universal and widespread regulators from the classes of stand-alone transcriptional regulators, two-component signal transduction systems (TCS), eukaryotic-like serine/threonine kinases, and small noncoding RNAs are the topic of this chapter. The regulatory levels are reviewed with respect to function, activity, and their role in pathogenesis. Understanding of and interfering with transcriptional regulation mechanisms and networks is a promising basis for the development of novel anti-infective therapies.

The complete genome sequence of Pantoea ananatis AJ13355, an organism with great biotechnological potential

Pantoea ananatis AJ13355 is a newly identified member of the Enterobacteriaceae family with promising biotechnological applications. This bacterium is able to grow at an acidic pH and is resistant to saturating concentrations of L-glutamic acid, making this organism a suitable host for the production of L-glutamate. In the current study, the complete genomic sequence of P. ananatis AJ13355 was determined. The genome was found to consist of a single circular chromosome consisting of 4,555,536 bp [DDBJ: AP012032] and a circular plasmid, pEA320, of 321,744 bp [DDBJ: AP012033]. After automated annotation, 4,071 protein-coding sequences were identified in the P. ananatis AJ13355 genome. For 4,025 of these genes, functions were assigned based on homologies to known proteins. A high level of nucleotide sequence identity (99%) was revealed between the genome of P. ananatis AJ13355 and the previously published genome of P. ananatis LMG 20103. Short colinear regions, which are identical to DNA sequences in the Escherichia coli MG1655 chromosome, were found to be widely dispersed along the P. ananatis AJ13355 genome. Conjugal gene transfer from E. coli to P. ananatis, mediated by homologous recombination between short identical sequences, was also experimentally demonstrated. The determination of the genome sequence has paved the way for the directed metabolic engineering of P. ananatis to produce biotechnologically relevant compounds.