The human transcriptome: an unfinished story

Determining structures of RNA conformers using AFM and deep neural networks

Much of the human genome is transcribed into RNAs1, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded2-are conformationally heterogeneous and flexible, which is a prerequisite for function3,4, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for structure elucidation. Moreover, owing to the lack of a large RNA structure database, and no clear correlation between sequence and structure, approaches such as AlphaFold5 for protein structure prediction do not apply to RNA. Therefore, determining the structures of heterogeneous RNAs remains an unmet challenge. Here we report holistic RNA structure determination method using atomic force microscopy, unsupervised machine learning and deep neural networks (HORNET), a novel method for determining three-dimensional topological structures of RNA using atomic force microscopy images of individual molecules in solution. Owing to the high signal-to-noise ratio of atomic force microscopy, this method is ideal for capturing structures of large RNA molecules in distinct conformations. In addition to six benchmark cases, we demonstrate the utility of HORNET by determining multiple heterogeneous structures of RNase P RNA and the HIV-1 Rev response element (RRE) RNA. Thus, our method addresses one of the major challenges in determining heterogeneous structures of large and flexible RNA molecules, and contributes to the fundamental understanding of RNA structural biology.

lncRNAs'p potential roles in the pathogenesis of cancer via interacting with signaling pathways; special focus on lncRNA-mediated signaling dysregulation in lung cancer

Lung cancer ranks among the most lethal types of cancer globally, with a high occurrence and fatality rate. The spread of cancer to other parts of the body, known as metastasis, is the primary cause of treatment failure and death in lung cancer cases. Current approaches for treating advanced lung cancer typically involve a combination of chemotherapy and targeted therapy. However, the majority of patients ultimately develop resistance to these treatments, leading to a worsened prognosis. In recent years, cancer biology research has predominantly focused on the role of protein-encoding genes in cancer development. Long non-coding RNAs (lncRNAs) are transcripts over 200 nucleotides in length that do not encode proteins but are crucial RNA molecules involved in numerous biological functions. While many functions of lncRNAs remain unknown, some have been linked to human diseases, including cancer. Studies have demonstrated that lncRNAs interact with other large molecules in the cell, such as proteins, DNA, and RNA, influencing various critical aspects of cancer. LncRNAs play a significant role in regulating gene expression and have a crucial function in the transcriptional regulation of cancer cells. They mediate various biological and clinical processes such as invasion, metastasis, apoptosis, and cell proliferation. Dysregulation of lncRNAs has been found to impact the process of carcinogenesis through advanced technologies like RNA sequencing and microarrays. Collectively, these long non-coding RNAs hold promise as potential biomarkers and therapeutic targets for human cancers. In this segment, we provide a comprehensive summary of the literature on the characteristics and formation of lncRNAs, along with an overview of their current known roles in lung cancer.

Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects

Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.

RNA-Binding Small Molecules in Drug Discovery and Delivery: An Overview from Fundamentals

RNA molecules, similar to proteins, fold into complex structures to confer diverse functions in cells. The intertwining of functions with RNA structures offers a new therapeutic opportunity for small molecules to bind and manipulate disease-relevant RNA pathways, thus creating a therapeutic realm of RNA-binding small molecules. The ongoing interest in RNA targeting and subsequent screening campaigns have led to the identification of numerous compounds that can regulate RNAs from splicing, degradation to malfunctions, with therapeutic benefits for a variety of diseases. Moreover, along with the rise of RNA-based therapeutics, RNA-binding small molecules have expanded their application to the modification, regulation, and delivery of RNA drugs, leading to the burgeoning interest in this field. This Perspective overviews the emerging roles of RNA-binding small molecules in drug discovery and delivery, covering aspects from their action fundamentals to therapeutic applications, which may inspire researchers to advance the field.

RNA-mediated double-strand break repair by end-joining mechanisms

Double-strand breaks (DSBs) in DNA are challenging to repair. Cells employ at least three DSB-repair mechanisms, with a preference for non-homologous end joining (NHEJ) over homologous recombination (HR) and microhomology-mediated end joining (MMEJ). While most eukaryotic DNA is transcribed into RNA, providing complementary genetic information, much remains unknown about the direct impact of RNA on DSB-repair outcomes and its role in DSB-repair via end joining. Here, we show that both sense and antisense-transcript RNAs impact DSB repair in a sequence-specific manner in wild-type human and yeast cells. Depending on its sequence complementarity with the broken DNA ends, a transcript RNA can promote repair of a DSB or a double-strand gap in its DNA gene via NHEJ or MMEJ, independently from DNA synthesis. The results demonstrate a role of transcript RNA in directing the way DSBs are repaired in DNA, suggesting that RNA may directly modulate genome stability and evolution.

SVA Regulation of Transposable Element Clustered Transcription within the Major Histocompatibility Complex Genomic Class II Region of the Parkinson's Progression Markers Initiative

SINE-VNTR-Alu (SVA) retrotransposons can regulate expression quantitative trait loci (eQTL) of coding and noncoding genes including transposable elements (TEs) distributed throughout the human genome. Previously, we reported that expressed SVAs and human leucocyte antigen (HLA) class II genotypes on chromosome 6 were associated significantly with Parkinson's disease (PD). Here, our aim was to follow-up our previous study and evaluate the SVA associations and their regulatory effects on the transcription of TEs within the HLA class II genomic region. We reanalyzed the transcriptome data of peripheral blood cells from the Parkinson's Progression Markers Initiative (PPMI) for 1530 subjects for TE and gene RNAs with publicly available computing packages. Four structurally polymorphic SVAs regulate the transcription of 20 distinct clusters of 235 TE loci represented by LINES (37%), SINES (28%), LTR/ERVs (23%), and ancient transposon DNA elements (12%) that are located in close proximity to HLA genes. The transcribed TEs were mostly short length, with an average size of 389 nucleotides. The numbers, types and profiles of positive and negative regulation of TE transcription varied markedly between the four regulatory SVAs. The expressed SVA and TE RNAs in blood cells appear to be enhancer-like elements that are coordinated differentially in the regulation of HLA class II genes. Future work on the mechanisms underlying their regulation and potential impact is essential for elucidating their roles in normal cellular processes and disease pathogenesis.

Small non-coding RNA transcriptomic profiling in adult and fetal human brain

Small non-coding RNAs (sncRNAs) make up ~1% of the transcriptome; nevertheless, they play significant roles in regulating cellular processes. Given the complexity of the central nervous system, sncRNAs likely hold particular importance in the human brain. In this study, we provide sncRNA transcriptomic profiles in a range of adult and prenatal brain regions, with a focus on piRNAs, due to their underexplored expression in somatic cells and tissue-specific nature. Using the WIND workflow, which combines two detection methods, we found 1333 (731 miRNAs, 249 piRNAs, 285 snoRNAs, and 68 other sncRNAs) and 1445 unique sncRNAs (770 miRNAs, 307 piRNAs, 289 snoRNAs, and 79 other sncRNAs) in developing and adult brains, respectively. Significant variations were found upon comparison of fetal and adult brain groups, with 82 miRNAs, 17 piRNAs, and 70 snoRNAs enriched in fetal brains and 22 miRNAs, 11 piRNAs in adult brains. This dataset represents a valuable resource for exploring the sncRNA roles in brain function, their involvement in neurological diseases, and the molecular mechanisms behind brain region interactions.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

Synthetic reversed sequences reveal default genomic states

Pervasive transcriptional activity is observed across diverse species. The genomes of extant organisms have undergone billions of years of evolution, making it unclear whether these genomic activities represent effects of selection or 'noise'1-4. Characterizing default genome states could help understand whether pervasive transcriptional activity has biological meaning. Here we addressed this question by introducing a synthetic 101-kb locus into the genomes of Saccharomyces cerevisiae and Mus musculus and characterizing genomic activity. The locus was designed by reversing but not complementing human HPRT1, including its flanking regions, thus retaining basic features of the natural sequence but ablating evolved coding or regulatory information. We observed widespread activity of both reversed and native HPRT1 loci in yeast, despite the lack of evolved yeast promoters. By contrast, the reversed locus displayed no activity at all in mouse embryonic stem cells, and instead exhibited repressive chromatin signatures. The repressive signature was alleviated in a locus variant lacking CpG dinucleotides; nevertheless, this variant was also transcriptionally inactive. These results show that synthetic genomic sequences that lack coding information are active in yeast, but inactive in mouse embryonic stem cells, consistent with a major difference in 'default genomic states' between these two divergent eukaryotic cell types, with implications for understanding pervasive transcription, horizontal transfer of genetic information and the birth of new genes.

Application of omics technologies in cariology research: A critical review with bibliometric analysis

OBJECTIVES

To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges.

DATA & SOURCES

Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review.

STUDY SELECTION

Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %).

CONCLUSION

This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research.

CLINICAL SIGNIFICANCE

This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.

An assessment of alterations to human sperm methylation patterns in coronavirus disease 2019 infected and healthy control males

OBJECTIVE

To determine whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects male reproductive health, considering the many potential factors that contribute to declines in male fertility on a semiglobal scale.

DESIGN

In total, 64 human semen samples-32 treatment and 32 control-were laboratory processed and bioinformatically analyzed to assess differences in DNA methylation patterns. Implementing multiple bioinformatic tools, the analyses conducted will elicit between-group differences with respect to epigenetic age, epigenetic instability, semiglobal, and regional methylation, in addition to methylation patterns as a function of time since infection.

SETTING

University hospital.

PATIENTS

The study cohort of 64 individuals was drawn from a larger population of 94 volunteer participants recruited at the Human Reproduction Center at the Clinical Hospital of the Ribeirao Preto Medical School-University of São Paulo between June 2021 and January 2022 as well as in accordance with the ethical guidelines established by the Declaration of Helsinki.

INTERVENTION

Exposure to SARS-CoV-2.

MAIN OUTCOME MEASURE(S)

Effects on male reproductive health were reported as differences in DNA methylation measured using an array. Mean β values at key regulatory loci for human spermatocytes were analyzed and compared between groups. Further analysis of β values using epigenetic age, instability, semiglobal, and regional methylation tools provided an analysis with substantial breadth and depth.

RESULTS

In all analyses, there were no differences between groups. Considering these results, it can be inferred that infection with SARS-CoV-2 does not alter the epigenome of human spermatocytes in significant and/or persistent ways. Tangentially, these data also suggest that human male reproductive health is minimally altered by the virus, or that it is altered in a way that is independent of epigenetic programming.

CONCLUSION

Infection with SARS-CoV-2 has been reportedly associated with alterations in male fertility. This study asserts that such alterations do not have an epigenetic basis but are likely a result of concomitant symptomatology, i.e., fever and inflammation. Across the multiple bioinformatic analyses conducted, the results of this test did not detect any differences in DNA methylation patterns between coronavirus disease 2019 and noncoronavirus disease semen donor groups.

Immunosuppression for older liver transplant recipients

Older liver transplant recipients have a lower risk of acute rejection than younger patients (9% for patients aged ≥65 years versus 23% for those aged 18-34 years) and are more vulnerable to immunosuppression-related complications. The number of liver transplant recipients ≥65 years has risen to 22% in Europe and the US, but limited information is available on the optimal immunosuppressive regimen for these patients. In this review, we discuss the appropriate management of immunosuppressive agents in older adults to minimize adverse events while avoiding acute rejection. The way the body processes drugs greatly depends on age. In the case of calcineurin inhibitor drugs, aging reduces hepatic metabolism, leading to changes in their pharmacokinetics. Corticosteroids also show decreased clearance as the patient ages. In severe cases of hypoalbuminemia, dose adjustment of mycophenolate acid derivatives may be necessary. However, the pharmacokinetic profiles of the mammalian target of rapamycin inhibitors, basiliximab, and rabbit anti-thymocyte globulin remain unaffected by age. Furthermore, age-related frailty may impact drug metabolism and require tailored interventions and closer follow-up. Although there is limited research, elderly liver transplant recipients require less immunosuppression with double or triple-agent regimens, lower exposure to calcineurin inhibitors, and a shorter course of corticosteroids. The usage of mammalian target of rapamycin inhibitors in older transplant populations has not been specifically investigated, and thus their usage should align with indications for younger patient groups.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Protocol for transcriptome assembly by the TransBorrow algorithm

High-throughput RNA-seq enables comprehensive analysis of the transcriptome for various purposes. However, this technology generally generates massive amounts of sequencing reads with a shorter read length. Consequently, fast, accurate, and flexible tools are needed for assembling raw RNA-seq data into full-length transcripts and quantifying their expression levels. In this protocol, we report TransBorrow, a novel transcriptome assembly software specifically designed for short RNA-seq reads. TransBorrow is employed in conjunction with a splice-aware alignment tool (e.g. Hisat2 and Star) and some other transcriptome assembly tools (e.g. StringTie, Cufflinks, and Scallop). The protocol encompasses all necessary steps, starting from downloading and processing raw sequencing data to assembling the full-length transcripts and quantifying their expressed abundances. The execution time of the protocol may vary depending on the sizes of processed datasets and computational platforms.

Sequence Alignment/Map format: a comprehensive review of approaches and applications

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

The role of microRNA-185 in the pathogenesis of human diseases: A focus on cancer

MicroRNAs (miRNAs) are a widely-studied class of non-coding RNAs characterized by their short length (18-25 nucleotides). The precise functions of miRNAs are not well-elucidated; however, an increasing number of studies suggest their involvement in various physiologic processes and deregulation in pathologic conditions. miRNA-185 (miR-185) is among the mostly-studied miRNAs in human diseases, which is found to play putative roles in conditions like metabolic disorders, asthma, frailty, schizophrenia, and hepatitis. Notably, many cancer studies report the downregulation of miR-185 in cell lines, tumor tissues, and plasma specimens of patients, while it demonstrates a suppressing role on the malignant properties of cancer cells in vitro and in vivo. Accordingly, miR-185 can be considered a tumor suppressor miRNA in human malignancies, while a few studies also report inconsistent findings. Being suggested as a prognostic/diagnostic biomarker, mi-185 is also found to offer clinical potentials, particularly for early diagnosis and prediction of the prognosis of cancer patients. In this review, we have outlined the studies that have evaluated the functions and clinical significance of miR-185 in different human diseases with a particular focus on cancer.

Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks

The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics1, even when they are structured and well-folded2, which limit the applicability of methods such as NMR, crystallography, or cryo-EM. Moreover, because of the lack of a large structure RNA database, and no clear correlation between sequence and structure, approaches like AlphaFold3 for protein structure prediction, do not apply to RNA. Therefore determining the structures of heterogeneous RNA is an unmet challenge. Here we report a novel method of determining RNA three-dimensional topological structures using deep neural networks and atomic force microscopy (AFM) images of individual RNA molecules in solution. Owing to the high signal-to-noise ratio of AFM, our method is ideal for capturing structures of individual conformationally heterogeneous RNA. We show that our method can determine 3D topological structures of any large folded RNA conformers, from ~ 200 to ~ 420 residues, the size range that most functional RNA structures or structural elements fall into. Thus our method addresses one of the major challenges in frontier RNA structural biology and may impact our fundamental understanding of RNA structure.

Air Pollution: Role of Extracellular Vesicles-Derived Non-Coding RNAs in Environmental Stress Response

Air pollution has increased over the years, causing a negative impact on society due to the many health-related problems it can contribute to. Although the type and extent of air pollutants are known, the molecular mechanisms underlying the induction of negative effects on the human body remain unclear. Emerging evidence suggests the crucial involvement of different molecular mediators in inflammation and oxidative stress in air pollution-induced disorders. Among these, non-coding RNAs (ncRNAs) carried by extracellular vesicles (EVs) may play an essential role in gene regulation of the cell stress response in pollutant-induced multiorgan disorders. This review highlights EV-transported ncRNAs' roles in physiological and pathological conditions, such as the development of cancer and respiratory, neurodegenerative, and cardiovascular diseases following exposure to various environmental stressors.

Long-Term Transcriptomic Changes and Cardiomyocyte Hyperpolyploidy after Lactose Intolerance in Neonatal Rats

Many cardiovascular diseases originate from growth retardation, inflammation, and malnutrition during early postnatal development. The nature of this phenomenon is not completely understood. Here we aimed to verify the hypothesis that systemic inflammation triggered by neonatal lactose intolerance (NLI) may exert long-term pathologic effects on cardiac developmental programs and cardiomyocyte transcriptome regulation. Using the rat model of NLI triggered by lactase overloading with lactose and the methods of cytophotometry, image analysis, and mRNA-seq, we evaluated cardiomyocyte ploidy, signs of DNA damage, and NLI-associated long-term transcriptomic changes of genes and gene modules that differed qualitatively (i.e., were switched on or switched off) in the experiment vs. the control. Our data indicated that NLI triggers the long-term animal growth retardation, cardiomyocyte hyperpolyploidy, and extensive transcriptomic rearrangements. Many of these rearrangements are known as manifestations of heart pathologies, including DNA and telomere instability, inflammation, fibrosis, and reactivation of fetal gene program. Moreover, bioinformatic analysis identified possible causes of these pathologic traits, including the impaired signaling via thyroid hormone, calcium, and glutathione. We also found transcriptomic manifestations of increased cardiomyocyte polyploidy, such as the induction of gene modules related to open chromatin, e.g., "negative regulation of chromosome organization", "transcription" and "ribosome biogenesis". These findings suggest that ploidy-related epigenetic alterations acquired in the neonatal period permanently rewire gene regulatory networks and alter cardiomyocyte transcriptome. Here we provided first evidence indicating that NLI can be an important trigger of developmental programming of adult cardiovascular disease. The obtained results can help to develop preventive strategies for reducing the NLI-associated adverse effects of inflammation on the developing cardiovascular system.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks

Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.

A genome-wide CRISPR activation screen identifies SCREEM a novel SNAI1 super-enhancer demarcated by eRNAs

The genome is pervasively transcribed to produce a vast array of non-coding RNAs (ncRNAs). Long non-coding RNAs (lncRNAs) are transcripts of >200 nucleotides and are best known for their ability to regulate gene expression. Enhancer RNAs (eRNAs) are subclass of lncRNAs that are synthesized from enhancer regions and have also been shown to coordinate gene expression. The biological function and significance of most lncRNAs and eRNAs remain to be determined. Epithelial to mesenchymal transition (EMT) is a ubiquitous cellular process that occurs during cellular migration, homeostasis, fibrosis, and cancer-cell metastasis. EMT-transcription factors, such as SNAI1 induce a complex transcriptional program that coordinates the morphological and molecular changes associated with EMT. Such complex transcriptional programs are often subject to coordination by networks of ncRNAs and thus can be leveraged to identify novel functional ncRNA loci. Here, using a genome-wide CRISPR activation (CRISPRa) screen targeting ∼10,000 lncRNA loci we identified ncRNA loci that could either promote or attenuate EMT. We discovered a novel locus that we named SCREEM (SNAI1 cis-regulatory eRNAs expressed in monocytes). The SCREEM locus contained a cluster of eRNAs that when activated using CRISPRa induced expression of the neighboring gene SNAI1, driving concomitant EMT. However, the SCREEM eRNA transcripts themselves appeared dispensable for the induction of SNAI1 expression. Interestingly, the SCREEM eRNAs and SNAI1 were co-expressed in activated monocytes, where the SCREEM locus demarcated a monocyte-specific super-enhancer. These findings suggest a potential role for SNAI1 in monocytes. Exploration of the SCREEM-SNAI axis could reveal novel aspects of monocyte biology.

Ghrelin intronic lncRNAs, lnc-GHRL-3:2 and lnc-GHRL-3:3, as novel biomarkers in type 2 diabetes mellitus

BACKGROUND

We aim to identify circulating lncRNAs located in the region of the ghrelin (GHRL) gene that play a role in the development of T2DM.

METHODS

Bioinformatic tool was used to identify candidates GHRL-lncRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to compare the expression levels of selected lncRNAs on diabetic patients and non-diabetic controls.Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory power of selected GHRL-lncRNAs.

RESULTS

The bioinformatic analysis predicted three antisense and eight sense-intronic GHRL- lncRNAs. Two differentially expressed GHRL-lncRNAs were detected in diabetic patients. The expression levels of lnc-GHRL-3:2, lnc-GHRL-3:3, and the GHRL mRNA were significantly (p ≤ .0001) lower in the diabetic patients. ROC analysis showed that the area under the curve (AUC) value was 0.93 for lnc-GHRL-3:2 and 0.90 for lnc-GHRL-3:3.

CONCLUSION

lnc-GHRL-3:2 and lnc-GHRL-3:3 are novel biomarkers and might play a regulatory role in T2DM pathogenesis.

The discovery and development of RNA-based therapies for treatment of HIV-1 infection

INTRODUCTION

Long-term control of HIV-1 infection can potentially be achieved using autologous stem cell transplants with gene-modified cells. Non-coding RNAs represent a diverse class of therapeutic agents including ribozymes, RNA aptamers and decoys, small interfering RNAs, short hairpin RNAs, and U1 interference RNAs that can be designed to inhibit HIV-1 replication. They have been engineered for delivery as drugs to complement current HIV-1 therapies and as gene therapies for a potential HIV-1 functional cure.

AREAS COVERED

This review surveys the past three decades of development of these RNA technologies with a focus on their efficacy and safety for treating HIV-1 infections. We describe the mechanisms of each RNA-based agent, targets they have been developed against, efforts to enhance their stability and efficacy, and we evaluate their performance in past and ongoing preclinical and clinical trials.

EXPERT OPINION

RNA-based technologies are among the top candidates for gene therapies where they can be stably expressed for long-term suppression of HIV-1. Advances in both gene and drug delivery strategies and improvements to non-coding RNA stability and antiviral properties will cooperatively drive forward progress in improving drug therapy and engineering HIV-1 resistant cells.

Placental transcriptomic signatures of prenatal exposure to Hydroxy-Polycyclic aromatic hydrocarbons

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants originating from petrogenic and pyrogenic sources. PAH compounds can cross the placenta, and prenatal PAH exposure is linked to adverse infant and childhood health outcomes.

OBJECTIVE

In this first human transcriptomic assessment of PAHs in the placenta, we examined associations between prenatal PAH exposure and placental gene expression to gain insight into mechanisms by which PAHs may disrupt placental function.

METHODS

The ECHO PATHWAYS Consortium quantified prenatal PAH exposure and the placental transcriptome from 629 pregnant participants enrolled in the CANDLE study. Concentrations of 12 monohydroxy-PAH (OH-PAH) metabolites were measured in mid-pregnancy urine using high performance liquid chromatography tandem mass spectrometry. Placental transcriptomic data were obtained using paired-end RNA sequencing. Linear models were fitted to estimate covariate-adjusted associations between maternal urinary OH-PAHs and placental gene expression. We performed sex-stratified analyses to evaluate whether associations varied by fetal sex. Selected PAH/gene expression analyses were validated by treating HTR-8/SVneo cells with phenanthrene, and quantifying expression via qPCR.

RESULTS

Urinary concentrations of 6 OH-PAHs were associated with placental expression of 8 genes. Three biological pathways were associated with 4 OH-PAHs. Placental expression of SGF29 and TRIP13 as well as the vitamin digestion and absorption pathway were positively associated with multiple metabolites. HTR-8/SVneo cells treated with phenanthrene also exhibited 23 % increased TRIP13 expression compared to vehicle controls (p = 0.04). Fetal sex may modify the relationship between prenatal OH-PAHs and placental gene expression, as more associations were identified in females than males (45 vs 28 associations).

DISCUSSION

Our study highlights novel genes whose placental expression may be disrupted by OH-PAHs. Increased expression of DNA damage repair gene TRIP13 may represent a response to double-stranded DNA breaks. Increased expression of genes involved in vitamin digestion and metabolism may reflect dietary exposures or represent a compensatory mechanism to combat damage related to OH-PAH toxicity. Further work is needed to study the role of these genes in placental function and their links to perinatal outcomes and lifelong health.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

DHNLDA: A Novel Deep Hierarchical Network Based Method for Predicting lncRNA-Disease Associations

Recent studies have found that lncRNA (long non-coding RNA) in ncRNA (non-coding RNA) is not only involved in many biological processes, but also abnormally expressed in many complex diseases. Identification of lncRNA-disease associations accurately is of great significance for understanding the function of lncRNA and disease mechanism. In this paper, a deep learning framework consisting of stacked autoencoder(SAE), multi-scale ResNet and stacked ensemble module, named DHNLDA, was constructed to predict lncRNA-disease associations, which integrates multiple biological data sources and constructing feature matrices. Among them, the biological data including the similarity and the interaction of lncRNAs, diseases and miRNAs are integrated. The feature matrices are obtained by node2vec embedding and feature extraction respectively. Then, the SAE and the multi-scale ResNet are used to learn the complementary information between nodes, and the high-level features of node attributes are obtained. Finally, the fusion of high-level feature is input into the stacked ensemble module to obtain the prediction results of lncRNA-disease associations. The experimental results of five-fold cross-validation show that the AUC of DHNLDA reaches 0.975 better than the existing methods. Case studies of stomach cancer, breast cancer and lung cancer have shown the great ability of DHNLDA to discover the potential lncRNA-disease associations.

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N⁶-methyladenosine (m⁶A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.

Pan-Cancer Gene Analysis of m6A Modification and Immune Infiltration in Uterine Corpus Endometrial Carcinoma

Objective

This investigation was to test the potential role of m6A-related long non-coding RNAs (lncRNAs) and immune infiltration as crucial factors in the diagnosis and treatment of uterine corpus endometrial cancer (UCEC).

Method

The UCEC RNA-seq data were downloaded in the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). There were 587 samples totally, containing 543 UCEC cases and 35 healthy cases. The clinical information of UCEC cases included survival time, survival status, gender, age, stage, and TMN stage. Twenty-three m6A-related genes were found in published journals. The RNA-seq documents of UCEC were downloaded in the Cancer Genome Atlas (TCGA). The hub gene data of UCEC were downloaded from GEPIA2 database. The different packages of R language were applied to calculate and analyze in this research.

Results

Among 587 cases in our study, we discovered 3039 lncRNAs in the TCGA-UCEC database. After the differential analysis, 23 m6A-associated genetics were screened and twenty-one m6A-associated differential genetics were found. In the end, we obtained 20 m6A-related lncRNAs. LNCTAM34A was considered as a predictive gene through univariate and multivariate Cox regression analysis. In addition to the above, patients with high LNCTAM34A expression had better outcomes than those with low LNCTAM34A expression. The high-risk cohort had greater scores of activated dendritic cells (aDCs), B cells, and T cell regulatory (Tregs) than low-risk cohort; in the meanwhile, high-risk cohort had lower scores of DCs and iDCs. Then, the high-risk cohort displayed greater scores in the immune functions of MHC class I, para-inflammation, and type I IFN response than those of low-risk cohort. Among 27 immune-inducible genes, the level of CD244, KIR3DLI, NRP1, PDCD1LG2, and TNFRSF8 was reduced in UCEC samples and the level of CD27, CD28, CD70, CD80, CD86, HAVCR2, ICOS, IDO1, LAIR1, PDCD1, TIGIT, TNFRSF18, -25, -9, -14, and VTCN1 was increased in UCEC samples.

Conclusion

The key role of M6A-related lncRNAs in immune microenvironment in high-risk patients of UCEC. The patients with strong expression of LNCTAM34A have a good prognosis, and LNCTAM34A can be used as a prognostic gene for UCEC. m6A-related lncRNAs can be used as a potential treatment for UCEC. Our observations can be used as a hypothetical basis for future in vitro and animal experiments.

The Role of microRNAs in the Gonocyte Theory as Target of Malignancy: Looking for Potential Diagnostic Biomarkers

Some pediatric patients with cryptorchidism preserve cells with gonocyte characteristics beyond their differentiation period, which could support the theory of the gonocyte as a target for malignancy in the development of testicular neoplasia. One of the key molecules in gonocyte malignancy is represented by microRNAs (miRNAs). The goal of this review is to give an overview of miRNAs, a class of small non-coding RNAs that participate in the regulation of gene expression. We also aim to review the crucial role of several miRNAs that have been further described in the regulation of gonocyte differentiation to spermatogonia, which, when transformed, could give rise to germ cell neoplasia in situ, a precursor lesion to testicular germ cell tumors. Finally, the potential use of miRNAs as diagnostic and prognostic biomarkers in testicular neoplasia is addressed, due to their specificity and sensitivity compared to conventional markers, as well as their applications in therapeutics.

Long non-coding RNA NR2F2-AS1: its expanding oncogenic roles in tumor progression

Long non-coding RNA (LncRNA) is a new type of non-coding RNA whose transcription is more than 200 nucleotides in length and can be up to 100 kb. The crucial regulatory function of lncRNAs in different cellular processes is now notable in many human diseases, especially in different steps of tumorigenesis, making them clinically significant. This research tried to collect all evidence obtained so far regarding Nuclear Receptor subfamily 2 group F member 2 Antisense RNA 1 (NR2F2-AS1) to explore its role in carcinogenesis and molecular mechanism in several cancers. Collecting evidence value an oncogenic role for NR2F2-AS1, whose dysregulation changes the status for cancerous cells to gain the supremacy toward cellular proliferation, dissemination, and ultimately migration. The NR2F2-AS1 acts as competitive endogenous RNA (ceRNA) and contains several microRNA response elements (MREs) for different microRNAs involved in various pathways such as PI3K/AKT, Wnt/β-catenin, and TGF-β. This clinically makes NR2F2-AS1 a remarkable lncRNA which contributes to cancer progression and invasion and perhaps could be a candidate as a prognostic marker or even a therapeutic target.

Aspirin sensitivity of PIK3CA-mutated Colorectal Cancer: potential mechanisms revisited

PIK3CA mutations are amongst the most prevalent somatic mutations in cancer and are associated with resistance to first-line treatment along with low survival rates in a variety of malignancies. There is evidence that patients carrying PIK3CA mutations may benefit from treatment with acetylsalicylic acid, commonly known as aspirin, particularly in the setting of colorectal cancer. In this regard, it has been clarified that Class IA Phosphatidylinositol 3-kinases (PI3K), whose catalytic subunit p110α is encoded by the PIK3CA gene, are involved in signal transduction that regulates cell cycle, cell growth, and metabolism and, if disturbed, induces carcinogenic effects. Although PI3K is associated with pro-inflammatory cyclooxygenase-2 (COX-2) expression and signaling, and COX-2 is among the best-studied targets of aspirin, the mechanisms behind this clinically relevant phenomenon are still unclear. Indeed, there is further evidence that the protective, anti-carcinogenic effect of aspirin in this setting may be mediated in a COX-independent manner. However, until now the understanding of aspirin's prostaglandin-independent mode of action is poor. This review will provide an overview of the current literature on this topic and aims to analyze possible mechanisms and targets behind the aspirin sensitivity of PIK3CA-mutated cancers.

Long non-coding RNAs and microRNAs as crucial regulators in cardio-oncology

Cancer is one of the leading causes of morbidity and mortality worldwide. Significant improvements in the modern era of anticancer therapeutic strategies have increased the survival rate of cancer patients. Unfortunately, cancer survivors have an increased risk of cardiovascular diseases, which is believed to result from anticancer therapies. The emergence of cardiovascular diseases among cancer survivors has served as the basis for establishing a novel field termed cardio-oncology. Cardio-oncology primarily focuses on investigating the underlying molecular mechanisms by which anticancer treatments lead to cardiovascular dysfunction and the development of novel cardioprotective strategies to counteract cardiotoxic effects of cancer therapies. Advances in genome biology have revealed that most of the genome is transcribed into non-coding RNAs (ncRNAs), which are recognized as being instrumental in cancer, cardiovascular health, and disease. Emerging studies have demonstrated that alterations of these ncRNAs have pathophysiological roles in multiple diseases in humans. As it relates to cardio-oncology, though, there is limited knowledge of the role of ncRNAs. In the present review, we summarize the up-to-date knowledge regarding the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in cancer therapy-induced cardiotoxicities. Moreover, we also discuss prospective therapeutic strategies and the translational relevance of these ncRNAs.

Long Non-Coding RNAs Profiling Using Microarray in Papillary Thyroid Carcinoma

Long non-coding RNAs (lncRNAs) have been implicated in various cancers, including papillary thyroid carcinomas (PTCs). Genome-wide analysis (GWAS) of lncRNAs expression in PTC samples exhibited up and down regulation of lncRNAs, thus, acting as tumor promoting oncogenes or tumor suppressors in the pathogenesis of PTC by interacting with target genes. For example, lncRNAs such as HOTAIR, NEAT1, MALAT1, FAL1, HOXD-AS1, etc. are overexpressed in PTC in comparison to that of non-cancerous thyroid tissues, which stimulate the pathogenesis of PTC. On the other hand, lncRNAs such as MEG3, CASC2, PANDAR, LINC00271, NAMA, PTCSC3, etc. are down regulated in PTC tissues when compared to that of non-cancerous thyroid samples, suppressing formation of PTC. Also, several lncRNAs such as BANCR acts as oncogenic or tumor suppressor in PTC formation depending on which they are interacting with. In addition, lncRNAs expression in patients with PTC associated with clinicopathological parameters such as distance metastasis, lymph node metastasis, tumor size, pathological stage, and response to therapy. Thus, lncRNAs profiles could have the potential to be used as prognostic or predictive biomarker in patients with PTC. Therefore, we describe the microarray method to examine lncRNAs expression in PTC tissue samples, which could facilitate better management of patients with PTC. Furthermore, this method could be fabricated to examine lncRNAs expression in other biological and/or clinical samples.

Gene regulation by antisense transcription: A focus on neurological and cancer diseases

Advances in high-throughput sequencing over the past decades have led to the identification of thousands of non-coding RNAs (ncRNAs), which play a major role in regulating gene expression. One emerging class of ncRNAs is the natural antisense transcripts (NATs), the RNA molecules transcribed from the opposite strand of a protein-coding gene locus. NATs are known to concordantly and discordantly regulate gene expression in both cis and trans manners at the transcriptional, post-transcriptional, translational, and epigenetic levels. Aberrant expression of NATs can therefore cause dysregulation in many biological pathways and has been observed in many genetic diseases. This review outlines the involvements and mechanisms of NATs in the pathogenesis of various diseases, with a special emphasis on neurodegenerative diseases and cancer. We also summarize recent findings on NAT knockdown and/or overexpression experiments and discuss the potential of NATs as promising targets for future gene therapies.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Translational Applications of Linear and Circular Long Noncoding RNAs in Endometriosis

Endometriosis is a chronic gynecologic disease that negatively affects the quality of life of many women. Unfortunately, endometriosis does not have a cure. The current medical treatments involve hormonal manipulation with unwanted side effects and high recurrence rates after stopping the medication. Sadly, a definitive diagnosis for endometriosis requires invasive surgical procedures, with the risk of complications, additional surgeries in the future, and a high rate of recurrence. Both improved therapies and noninvasive diagnostic tests are needed. The unique molecular features of endometriosis have been studied at the coding gene level. While the molecular components of endometriosis at the small RNA level have been studied extensively, other noncoding RNAs, such as long intergenic noncoding RNAs and the more recently discovered subset of long noncoding RNAs called circular RNAs, have been studied more limitedly. This review describes the molecular formation of long noncoding and the unique circumstances of the formation of circular long noncoding RNAs, their expression and function in endometriosis, and promising preclinical studies. Continued translational research on long noncoding RNAs, including the more stable circular long noncoding RNAs, may lead to improved therapeutic and diagnostic opportunities.

Comparison of DNA and RNA sequencing of total nucleic acids from human cervix for metagenomics

Although metagenomics and metatranscriptomics are commonly used to identify bacteria and viruses in human samples, few studies directly compare these strategies. We wished to compare DNA and RNA sequencing of bacterial and viral metagenomes and metatranscriptomes in the human cervix. Total nucleic acids from six human cervical samples were subjected to DNA and RNA sequencing. The effect of DNase-treatment before reverse transcription to cDNA were also analyzed. Similarities and differences in the metagenomic findings with the three different sequencing approaches were evaluated. A higher proportion of human sequences were detected by DNA sequencing (93%) compared to RNA sequencing without (76%) and with prior DNase-treatment (11%). On the contrary, bacterial sequences increased 17 and 91 times. However, the number of detected bacterial genera were less by RNA sequencing, suggesting that only a few contribute to most of the bacterial transcripts. The viral sequences were less by RNA sequencing, still twice as many virus genera were detected, including some RNA viruses that were missed by DNA sequencing. Metatranscriptomics of total cDNA provided improved detection of mainly transcribed bacteria and viruses in cervical swabs as well as detection of RNA viruses, compared to metagenomics.

Regulatory role of long non coding RNAs (lncRNAs) in neurological disorders: From novel biomarkers to promising therapeutic strategies

Long non coding RNAs (lncRNAs) are non-protein or low-protein coding transcripts that contain more than 200 nucleotides. They representing a large share of the cell's transcriptional output, demonstrate functional attributes viz. tissue-specific expression, determination of cell fate, controlled expression, RNA processing and editing, dosage compensation, genomic imprinting, conserved evolutionary traits etc. These long non coding variants are well associated with pathogenicity of various diseases including the neurological disorders like Alzheimer's disease, schizophrenia, Huntington's disease, Parkinson's disease etc. Neurological disorders are widespread and there knowing the underlying mechanisms become crucial. The lncRNAs take part in the pathogenesis by a plethora of mechanisms like decoy, scaffold, mi-RNA sequestrator, histone modifiers and in transcriptional interference. Detailed knowledge of the role of lncRNAs can help to use them further as novel biomarkers for therapeutic aspects. Here, in this review we discuss regulation and functional roles of lncRNAs in eight neurological diseases and psychiatric disorders, and the mechanisms by which they act. With these, we try to establish their roles as potential markers and viable diagnostic tools in these disorders.

The Use of circRNAs as Biomarkers of Cancer

CircRNAs are a subclass of lncRNAs that have been found to be abundantly present in a wide range of species, including humans. CircRNAs are generally produced by a noncanonical splicing event called backsplicing that is dependent on the canonical splicing machinery, giving rise to circRNAs classified into three main categories: exonic circRNA, circular intronic RNA, and exon-intron circular RNA. Notably, circRNAs possess functional importance and display their functions through different mechanisms of action including sponging miRNAs, or even being translated into functional proteins. In addition, circRNAs also have great potential as biomarkers, particularly in cancer, thanks to their high stability, tissue type and developmental stage specificity, and their presence in biological fluids, which make them promising candidates as noninvasive biomarkers. In this chapter, we describe the most commonly used techniques for the study of circRNAs as cancer biomarkers, including high-throughput techniques such as RNA-Seq and microarrays, and other methods to analyze the presence of specific circRNAs in patient samples.

Pharmacology of Catechins in Ischemia-Reperfusion Injury of the Heart

Catechins represent a group of polyphenols that possesses various beneficial effects in the cardiovascular system, including protective effects in cardiac ischemia-reperfusion (I/R) injury, a major pathophysiology associated with ischemic heart disease, myocardial infarction, as well as with cardioplegic arrest during heart surgery. In particular, catechin, (−)-epicatechin, and epigallocatechin gallate (EGCG) have been reported to prevent cardiac myocytes from I/R-induced cell damage and I/R-associated molecular changes, finally, resulting in improved cell viability, reduced infarct size, and improved recovery of cardiac function after ischemic insult, which has been widely documented in experimental animal studies and cardiac-derived cell lines. Cardioprotective effects of catechins in I/R injury were mediated via multiple molecular mechanisms, including inhibition of apoptosis; activation of cardioprotective pathways, such as PI3K/Akt (RISK) pathway; and inhibition of stress-associated pathways, including JNK/p38-MAPK; preserving mitochondrial function; and/or modulating autophagy. Moreover, regulatory roles of several microRNAs, including miR-145, miR-384-5p, miR-30a, miR-92a, as well as lncRNA MIAT, were documented in effects of catechins in cardiac I/R. On the other hand, the majority of results come from cell-based experiments and healthy small animals, while studies in large animals and studies including comorbidities or co-medications are rare. Human studies are lacking completely. The dosages of compounds also vary in a broad scale, thus, pharmacological aspects of catechins usage in cardiac I/R are inconclusive so far. Therefore, the aim of this focused review is to summarize the most recent knowledge on the effects of catechins in cardiac I/R injury and bring deep insight into the molecular mechanisms involved and dosage-dependency of these effects, as well as to outline potential gaps for translation of catechin-based treatments into clinical practice.

Reference-based assembly of chloroplast genome from leaf transcriptome data of Pterocarpus santalinus

Chloroplast genome sequencing is an essential tool to understand genome evolution and phylogenetic relationship. The available methods for constructing chloroplast genome include chloroplast enrichment followed by long overlapping PCR or extraction and assembly of chloroplast-specific reads from whole-genome datasets. In the present study, we propose an alternate strategy of extraction and assembly of chloroplast-specific reads from leaf transcriptome data of Pterocarpus santalinus using bowtie2 aligner program. The assembled genome was compared with the published chloroplast genome of P. santalinus for genome size, number of predicted genes, microsatellite repeat motifs, and nucleotide repeats. A near-complete chloroplast genome was assembled from the transcriptome reads. The proposed method requires less computational time and know-how, limited virtual memory, and is cost-effective when compared to whole-genome sequencing. Assembly of Cp genome from transcriptome data will enhance the resolution of phylogenetic studies through comparative plastome analysis, facilitate accurate species/genotype discrimination and accelerate the development of transplastomic plants with enhanced biotic and abiotic tolerance.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02943-0.

Determination of Biological Age: Geriatric Assessment vs Biological Biomarkers

Purpose of Review

Biological age is the concept of using biophysiological measures to more accurately determine an individual’s age-related risk of adverse outcomes. Grading of the degree of frailty and measuring biomarkers are distinct methods of measuring biological age. This review compares these strategies for estimating biological age for clinical purposes.

Recent Findings

The degree of frailty predicts susceptibility to adverse outcomes independently of chronological age. The utility of this approach has been demonstrated across a range of clinical contexts. Biomarkers from various levels of the biological aging process are improving in accuracy, with the potential to identify aberrant aging trajectories before the onset of clinically manifest frailty.

Summary

Grading of frailty is a demonstrably, clinically, and research-relevant proxy estimate of biological age. Emerging biomarkers can supplement this approach by identifying accelerated aging before it is clinically apparent. Some biomarkers may even offer a means by which interventions to reduce the rate of aging can be developed.

Epigenetic modulation of the tumor microenvironment in head and neck cancer: Challenges and opportunities

Head and neck cancer is globally challenging due to the resistance to therapy and aggressive behavior leading to high rates of mortality. Recent findings show that the tumor microenvironment plays a role in the maintenance and progression of many solid tumors, including head and neck cancer. The mechanisms involved in the modulation and regulation of the tumor microenvironment remain poorly understood. Increasing evidence suggests that epigenetic events can modulate the crosstalk between neoplastic and non-neoplastic cells during tumor progression. In this review, we explore the current understanding of the involvement of epigenetic events in the modulation of the tumor microenvironment and its impact on head and neck cancer behavior. We also explore the latest therapeutic strategies that use epigenetic-modulating drugs to manage tumor growth and progression.

Non-coding RNAs and lipids mediate the function of extracellular vesicles in cancer cross-talk

Research on extracellular vesicles (EVs) has been expanded, especially in the field of cancer. The cargoes in EVs, especially those in small EVs such as exosomes include microRNAs (miRNAs), mRNA, proteins, and lipids, are assumed to work cooperatively in the tumor microenvironment. In 2007, it was reported that miRNAs were abundant among the non-coding RNAs present in exosomes. Since then, many studies have investigated the functions of miRNAs and have tried to apply these molecules to aid in the diagnosis of cancer. Accordingly, many reviews of non-coding RNAs in EVs have been published for miRNAs. This review focuses on relatively new cargoes, covering long noncoding (lnc) RNAs, circular RNAs, and repeat RNAs, among non-coding RNAs. These RNAs, regardless of EV or cell type, have newly emerged due to the innovation of sequencing technology. The poor conservation, low quantity, and technical difficulty in detecting these RNA types have made it difficult to elucidate their functions and expression patterns. We herein summarize a limited number of studies. Although lipids are major components of EVs, current research on EVs focuses on miRNA and protein biology, while the roles of lipids in exosomes have not drawn attention. However, several recent studies revealed that phospholipids, which are components of the EV membrane, play important roles in the intercommunication between cells and in the generation of lipid mediators. Here, we review the reported roles of these molecules, and describe their potential in cancer biology.

lncRNADetector: a bioinformatics pipeline for long non-coding RNA identification and MAPslnc: a repository of medicinal and aromatic plant lncRNAs

Long non-coding RNAs (lncRNAs) are an emerging class of non-coding RNAs and potent regulatory elements in the living cells. High throughput RNA sequencing analyses have generated a tremendous amount of transcript sequence data. A large proportion of these transcript sequences does not code for proteins and are known as non-coding RNAs. Among them, lncRNAs are a unique class of transcripts longer than 200 nucleotides with diverse biological functions and regulatory mechanisms. Recent emerging studies and next-generation sequencing technologies show a substantial amount of lncRNAs within the plant genome, which are yet to be identified. The computational identification of lncRNAs from these transcripts is a challenging task due to the involvement of a series of filtering steps. We have developed lncRNADetector, a bioinformatics pipeline for the identification of novel lncRNAs, especially from medicinal and aromatic plant (MAP) species. The lncRNADetector has been utilized to analyse and identify more than 88,459 lncRNAs from 21 species of MAPs. To provide a knowledge resource for the plant research community towards elucidating the diversity of biological roles of lncRNAs, the information generated about MAP lncRNAs (post-filtering steps) through lncRNADetector has been stored and organized in MAPslnc database (MAPslnc, https://lncrnapipe.cimap.res.in). The lncRNADetector web server and MAPslnc database have been developed in order to facilitate researchers for accurate identification of lncRNAs from the next-generation sequencing data of different organisms for downstream studies. To the best of our knowledge no such MAPslnc database is available till date.

Biomarkers of cholestasis

Cholestasis is a major pathological manifestation, often resulting in detrimental liver conditions, which occurs in a variety of indications collectively termed cholestatic liver diseases. The frequent asymptomatic character and complexity of cholestasis, together with the lack of a straightforward biomarker, hampers early detection and treatment of the condition. The 'omics' era, however, has resulted in a plethora of cholestatic indicators, yet a single clinically applicable biomarker for a given cholestatic disease remains missing. The criteria to fulfil as an ideal biomarker as well as the challenging molecular pathways in cholestatic liver diseases advocate for a scenario in which multiple biomarkers, originating from different domains, will be assessed concomitantly. This review gives an overview of classical clinical and novel molecular biomarkers in cholestasis, focusing on their benefits and drawbacks.

Transcriptome analysis of sinensetin-treated liver cancer cells guided by biological network analysis

Hepatocellular carcinoma is recognized as one of the most frequently occurring malignant types of liver cancer globally, making the identification of biomarkers critically important. The aim of the present study was to identify the genes involved in the anticancer effects of flavonoid compounds so that they may be used as targets for cancer treatment. Sinensetin (SIN), an isolated polymethoxyflavone monomer compound, possesses broad antitumor activities in vitro. Therefore, the identification of a transcriptome profile on the condition of cells treated with SIN may aid to better understand the genes involved and its mechanism of action. Genomic profiling studies of cancer are increasing rapidly in order to provide gene expression data that can reveal prognostic biomarkers to combat liver cancer. In the present study, high-throughput RNA sequencing (RNA-seq) was performed to reveal differential gene expression patterns between SIN-treated and SIN-untreated human liver cancer HepG2 cells. A total of 43 genes were identified to be differentially expressed (39 downregulated and 4 upregulated in the SIN-treated group compared with the SIN-untreated group). An extensive network analysis for these 43 genes resulted in the identification of 10 upregulated highly interconnected hub genes that contributed to the progression of cancer. Functional enrichment analysis of these 10 hub genes revealed their involvement in the regulation of apoptotic processes, immune response and tumor necrosis factor production. Additionally, the mRNA expression levels of these 10 genes were evaluated using reverse transcription-quantitative PCR, and the results were consistent with the RNA-seq data. Overall, the results of the present study revealed differentially expressed genes involved in cancer after SIN treatment in HepG2 cells and may help to develop strategies targeting these genes for treating liver cancer.

RNA Biogenesis Instructs Functional Inter-Chromosomal Genome Architecture

Three-dimensional (3D) genome organization has emerged as an important layer of gene regulation in development and disease. The functional properties of chromatin folding within individual chromosomes (i.e., intra-chromosomal or in cis) have been studied extensively. On the other hand, interactions across different chromosomes (i.e., inter-chromosomal or in trans) have received less attention, being often regarded as background noise or technical artifacts. This viewpoint has been challenged by emerging evidence of functional relationships between specific trans chromatin interactions and epigenetic control, transcription, and splicing. Therefore, it is an intriguing possibility that the key processes involved in the biogenesis of RNAs may both shape and be in turn influenced by inter-chromosomal genome architecture. Here I present the rationale behind this hypothesis, and discuss a potential experimental framework aimed at its formal testing. I present a specific example in the cardiac myocyte, a well-studied post-mitotic cell whose development and response to stress are associated with marked rearrangements of chromatin topology both in cis and in trans. I argue that RNA polymerase II clusters (i.e., transcription factories) and foci of the cardiac-specific splicing regulator RBM20 (i.e., splicing factories) exemplify the existence of trans-interacting chromatin domains (TIDs) with important roles in cellular homeostasis. Overall, I propose that inter-molecular 3D proximity between co-regulated nucleic acids may be a pervasive functional mechanism in biology.

Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies

The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host-microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.