Computational Identification of piRNAs Using Features Based on RNA Sequence, Structure, Thermodynamic and Physicochemical Properties

Databases and computational methods for the identification of piRNA-related molecules: A survey

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.

Identifying quantitative sncRNAs signature using global sequencing as a potential biomarker for tuberculosis diagnosis and their role in regulating host response

OBJECTIVES

The study aimed to identify a quantitative signature of circulating small non-coding RNAs (sncRNAs) as a biomarker for pulmonary tuberculosis disease (active-TB/ATB) and explore their regulatory roles in host-pathogen interactions and disease progression.

METHODS

We conducted a cross-sectional study recruiting subjects diagnosed with active-TB (drug-sensitive and drug-resistant) and healthy controls. Sera samples were collected and utilized for preparing small RNA libraries. Quantitative patterns of circulating sncRNAs (miRNAs, piRNAs and tRFs) were identified via high-throughput sequencing and DeSeq2 analysis and validated in independent active-TB cohorts. Functional knockdown for two selected miRNAs were also performed.

RESULTS

A diagnostic signature of four sncRNAs for both drug-sensitive and drug-resistant active-TB cases was validated, exhibiting an AUC of 0.96 (95% CI: 0.937-0.996, p < 0.001) with 86.7% sensitivity (95% CI: 0.775-0.932) and 91.7% specificity (95% CI: 0.730-0.990) in ROC analysis. Functional knockdown demonstrated regulatory roles of hsa-miR-223-5p and hsa-miR-10b-5p in Mycobacterium tuberculosis (Mtb) growth and pro-inflammatory cytokine expression (IL-6 and IL-8).

CONCLUSION

The study identified a diagnostic tool utilizing a signature of four sncRNAs with high specificity and sensitivity, enhancing our understanding of sncRNAs as ATB diagnostic biomarker. Additionally, hsa-miR-223-5p and hsa-miR-10b-5p demonstrated potential roles in Mtb pathogenesis and host-response to infection.

Harnessing male germline epigenomics for the genetic improvement in cattle

Sperm is essential for successful artificial insemination in dairy cattle, and its quality can be influenced by both epigenetic modification and epigenetic inheritance. The bovine germline differentiation is characterized by epigenetic reprogramming, while intergenerational and transgenerational epigenetic inheritance can influence the offspring's development through the transmission of epigenetic features to the offspring via the germline. Therefore, the selection of bulls with superior sperm quality for the production and fertility traits requires a better understanding of the epigenetic mechanism and more accurate identifications of epigenetic biomarkers. We have comprehensively reviewed the current progress in the studies of bovine sperm epigenome in terms of both resources and biological discovery in order to provide perspectives on how to harness this valuable information for genetic improvement in the cattle breeding industry.

Missing Causality and Heritability of Autoimmune Hepatitis

BACKGROUND

Autoimmune hepatitis has an unknown cause and genetic associations that are not disease-specific or always present. Clarification of its missing causality and heritability could improve prevention and management strategies.

AIMS

Describe the key epigenetic and genetic mechanisms that could account for missing causality and heritability in autoimmune hepatitis; indicate the prospects of these mechanisms as pivotal factors; and encourage investigations of their pathogenic role and therapeutic potential.

METHODS

English abstracts were identified in PubMed using multiple key search phases. Several hundred abstracts and 210 full-length articles were reviewed.

RESULTS

Environmental induction of epigenetic changes is the prime candidate for explaining the missing causality of autoimmune hepatitis. Environmental factors (diet, toxic exposures) can alter chromatin structure and the production of micro-ribonucleic acids that affect gene expression. Epistatic interaction between unsuspected genes is the prime candidate for explaining the missing heritability. The non-additive, interactive effects of multiple genes could enhance their impact on the propensity and phenotype of autoimmune hepatitis. Transgenerational inheritance of acquired epigenetic marks constitutes another mechanism of transmitting parental adaptations that could affect susceptibility. Management strategies could range from lifestyle adjustments and nutritional supplements to precision editing of the epigenetic landscape.

CONCLUSIONS

Autoimmune hepatitis has a missing causality that might be explained by epigenetic changes induced by environmental factors and a missing heritability that might reflect epistatic gene interactions or transgenerational transmission of acquired epigenetic marks. These unassessed or under-evaluated areas warrant investigation.

The long and short: Non-coding RNAs in the mammalian inner ear

Non-coding RNAs (ncRNAs) play a critical role in the entire body, and their mis-regulation is often associated with disease. In parallel with the advances in high-throughput sequencing technologies, there is a great deal of focus on this broad class of RNAs. Although these molecules are not translated into proteins, they are now well established as significant regulatory components in many biological pathways and pathological conditions. ncRNAs can be roughly divided into two main sub-groups based on the length of the transcript, with both the small and long non-coding RNAs having diverse regulatory functions. The smaller length group includes ribosomal RNAs (rRNA), transfer RNAs (tRNA), small nuclear RNAs (snRNA), small nucleolar RNAs (snoRNA), microRNAs (miRNA), small interfering RNAs (siRNA), and PIWI-associated RNAs (piRNA). The longer length group includes linear long non-coding RNAs (lncRNA) and circular RNAs (circRNA). This review is designed to present the different classes of small and long ncRNA molecules and describe some of their known roles in physiological and pathological conditions, as well as methods used to assess the validity and function of miRNAs and lncRNAs, with a focus on their role and functions in the inner ear, hearing and deafness.

Respiratory syncytial virus infection changes the piwi-interacting RNA content of airway epithelial cells

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs (sncRNAs) of about 26–32 nucleotides in length and represent the largest class of sncRNA molecules expressed in animal cells. piRNAs have been shown to play a crucial role to safeguard the genome, maintaining genome complexity and integrity, as they suppress the insertional mutations caused by transposable elements. However, there is growing evidence for the role of piRNAs in controlling gene expression in somatic cells as well. Little is known about changes in piRNA expression and possible function occurring in response to viral infections. In this study, we investigated the piRNA expression profile, using a human piRNA microarray, in human small airway epithelial (SAE) cells infected with respiratory syncytial virus (RSV), a leading cause of acute respiratory tract infections in children. We found a time-dependent increase in piRNAs differentially expressed in RSV-infected SAE cells. We validated the top piRNAs upregulated and downregulated at 24 h post-infection by RT-qPCR and identified potential targets. We then used Gene Ontology (GO) tool to predict the biological processes of the predicted targets of the most represented piRNAs in infected cells over the time course of RSV infection. We found that the most significant groups of targets of regulated piRNAs are related to cytoskeletal or Golgi organization and nucleic acid/nucleotide binding at 15 and 24 h p.i. To identify common patterns of time-dependent responses to infection, we clustered the significantly regulated expression profiles. Each of the clusters of temporal profiles have a distinct set of potential targets of the piRNAs in the cluster Understanding changes in piRNA expression in RSV-infected airway epithelial cells will increase our knowledge of the piRNA role in viral infection and might identify novel therapeutic targets for viral lung-mediated diseases.

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

PIWI-interacting RNAs in human diseases: databases and computational models

PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.

Evaluation of exosomal non-coding RNAs in cancer using high-throughput sequencing

Clinical oncologists need more reliable and non-invasive diagnostic and prognostic biomarkers to follow-up cancer patients. However, the existing biomarkers are often invasive and costly, emphasizing the need for the development of biomarkers to provide convenient and precise detection. Extracellular vesicles especially exosomes have recently been the focus of translational research to develop non-invasive and reliable biomarkers for several diseases such as cancers, suggesting as a valuable source of tumor markers. Exosomes are nano-sized extracellular vesicles secreted by various living cells that can be found in all body fluids including serum, urine, saliva, cerebrospinal fluid, and ascites. Different molecular and genetic contents of their origin such as nucleic acids, proteins, lipids, and glycans in a stable form make exosomes a promising approach for various cancers' diagnoses, prediction, and follow-up in a minimally invasive manner. Since exosomes are used by cancer cells for intercellular communication, they play a critical role in the disease process, highlighting the importance of their use as clinically relevant biomarkers. However, regardless of the advantages that exosome-based diagnostics have, they suffer from problems regarding their isolation, detection, and characterization of their contents. This study reviews the history and biogenesis of exosomes and discusses non-coding RNAs (ncRNAs) and their potential as tumor markers in different types of cancer, with a focus on next generation sequencing (NGS) as a detection method. Moreover, the advantages and challenges associated with exosome-based diagnostics are also presented.

piRNAQuest V.2: an updated resource for searching through the piRNAome of multiple species

PIWI interacting RNAs (piRNAs) have emerged as important gene regulators in recent times. Since the release of our first version of piRNAQuest in 2014, lots of novel piRNAs have been annotated in different species other than human, mouse and rat. Such new developments in piRNA research have led us to develop an updated database piRNAQuest V.2. It consists of 92,77,689 piRNA entries for 25 new species of different phylum along with human, mouse and rat. Besides providing primary piRNA features which include their genomic location, with further information on piRNAs overlapping with repeat elements, pseudogenes and syntenic regions, etc., the novel features of this version includes (i) density based cluster prediction, (ii) piRNA expression profile across various healthy and disease systems and (iii) piRNA target prediction. The concept of density-based piRNA cluster identification is robust as it does not consider parametric distribution in its model. The piRNA expression profile for 21 disease systems including cancer have been hosted in addition to 32 tissue specific piRNA expression profile for various species. Further, the piRNA target prediction section includes both predicted and curated piRNA targets within eight disease systems and developmental stages of mouse testis. Further, users can visualize the piRNA-target duplex structure and the ping-pong signature pattern for all the ping-pong piRNA partners in different species. Overall, piRNAQuest V.2 is an updated user-friendly database which will serve as a useful resource to survey, search and retrieve information on piRNAs for multiple species. This freely accessible database is available at http://dibresources.jcbose.ac.in/zhumur/pirnaquest2.

A Review of Discovery Profiling of PIWI-Interacting RNAs and Their Diverse Functions in Metazoans

PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs (sncRNAs) that perform crucial biological functions in metazoans and defend against transposable elements (TEs) in germ lines. Recently, ubiquitously expressed piRNAs were discovered in soma and germ lines using small RNA sequencing (sRNA-seq) in humans and animals, providing new insights into the diverse functions of piRNAs. However, the role of piRNAs has not yet been fully elucidated, and sRNA-seq studies continue to reveal different piRNA activities in the genome. In this review, we summarize a set of simplified processes for piRNA analysis in order to provide a useful guide for researchers to perform piRNA research suitable for their study objectives. These processes can help expand the functional research on piRNAs from previously reported sRNA-seq results in metazoans. Ubiquitously expressed piRNAs have been discovered in the soma and germ lines in Annelida, Cnidaria, Echinodermata, Crustacea, Arthropoda, and Mollusca, but they are limited to germ lines in Chordata. The roles of piRNAs in TE silencing, gene expression regulation, epigenetic regulation, embryonic development, immune response, and associated diseases will continue to be discovered via sRNA-seq.

Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements

Simple Summary

Transposable elements (TEs) are DNA sequences that are, or were, able to move (transpose) within the genome of a single cell. They were first discovered by Barbara McClintock while working on maize, and they make up a large fraction of the genome. Transpositions can result in mutations and they can alter the genome size. Cells regulate the activity of TEs using a variety of mechanisms, such as chemical modifications of DNA and small RNAs. Machine learning (ML) is an interdisciplinary subject that studies computer algorithms that can improve through experience and by the use of data. ML has been successfully applied to a variety of problems in bioinformatics and has exhibited favorable precision and speed. Here, we provide a systematic and guided review on the ML and bioinformatic methods and tools that are used for the analysis of the regulation of TEs.

Abstract

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

Computational Methods and Online Resources for Identification of piRNA-Related Molecules

piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA-disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.