Most "dark matter" transcripts are associated with known genes

A quantitative prediction method utilizing whole omics data for biosensing

Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker. Our benchmark test using a transcriptome dataset revealed that OmicSense achieves accurate and robust prediction against background noise without overfitting. Weighted gene co-expression network analysis revealed that OmicSense preferentially utilized hub nodes of the network, indicating the interpretability of the method. Application of OmicSense to single-cell transcriptome, metabolome, and microbiome datasets confirmed high prediction performance (r > 0.8), suggesting applicability to diverse scientific fields. Given the recent rapidly expanding availability of omics data, the developed prediction tool OmicSense, can accelerate the use of omics data as a "biosensor" based on an assemblage of potential biomarkers.

Host long noncoding RNAs in bacterial infections

Bacterial infections remain a significant global health concern, necessitating a comprehensive understanding of the intricate host-pathogen interactions that play a critical role in the outcome of infectious diseases. Recent investigations have revealed that noncoding RNAs (ncRNAs) are key regulators of these complex interactions. Among them, long noncoding RNAs (lncRNAs) have gained significant attention because of their diverse regulatory roles in gene expression, cellular processes and the production of cytokines and chemokines in response to bacterial infections. The host utilizes lncRNAs as a defense mechanism to limit microbial pathogen invasion and replication. On the other hand, some host lncRNAs contribute to the establishment and maintenance of bacterial pathogen reservoirs within the host by promoting bacterial pathogen survival, replication, and dissemination. However, our understanding of host lncRNAs in the context of bacterial infections remains limited. This review focuses on the impact of host lncRNAs in shaping host-pathogen interactions, shedding light on their multifaceted functions in both host defense and bacterial survival, and paving the way for future research aimed at harnessing their regulatory potential for clinical applications.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50 nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

Transcriptional determinism and stochasticity contribute to the complexity of autism-associated SHANK family genes

Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3-mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We apply an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in humans and mice. We unexpectedly discover an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts are altered in Shank3-mutant mice and postmortem brain tissues from individuals with autism spectrum disorder. The enhanced SHANK3 transcriptome significantly improves the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest that both deterministic and stochastic transcription of the genome is associated with SHANK family genes.

The Potential Links between lncRNAs and Drug Tolerance in Lung Adenocarcinoma

Lung cancer patients treated with targeted therapies frequently respond well but invariably relapse due to the development of drug resistance. Drug resistance is in part mediated by a subset of cancer cells termed "drug-tolerant persisters" (DTPs), which enter a dormant, slow-cycling state that enables them to survive drug exposure. DTPs also exhibit stem cell-like characteristics, broad epigenetic reprogramming, altered metabolism, and a mutagenic phenotype mediated by adaptive mutability. While several studies have characterised the transcriptional changes that lead to the altered phenotypes exhibited in DTPs, these studies have focused predominantly on protein coding changes. As long non-coding RNAs (lncRNAs) are also implicated in the phenotypes altered in DTPs, it is likely that they play a role in the biology of drug tolerance. In this review, we outline how lncRNAs may contribute to the key characteristics of DTPs, their potential roles in tolerance to targeted therapies, and the emergence of genetic resistance in lung adenocarcinoma.

Long-read RNA-Seq for the discovery of long noncoding and antisense RNAs in plant organelles

Plant organelle transcription has been studied for decades. As techniques advanced, so did the fields of mitochondrial and plastid transcriptomics. The current view is that organelle genomes are pervasively transcribed, irrespective of their size, content, structure, and taxonomic origin. However, little is known about the nature of organelle noncoding transcriptomes, including pervasively transcribed noncoding RNAs (ncRNAs). Next-generation sequencing data have uncovered small ncRNAs in the organelles of plants and other organisms, but long ncRNAs remain poorly understood. Here, we argue that publicly available third-generation long-read RNA sequencing data from plants can provide a fine-tuned picture of long ncRNAs within organelles. Indeed, given their bloated architectures, plant mitochondrial genomes are well suited for studying pervasive transcription of ncRNAs. Ultimately, we hope to showcase this new avenue of plant research while also underlining the limitations of the proposed approach.

Hidden regulators: the emerging roles of lncRNAs in brain development and disease

Long non-coding RNAs (lncRNAs) have emerged as critical players in brain development and disease. These non-coding transcripts, which once considered as "transcriptional junk," are now known for their regulatory roles in gene expression. In brain development, lncRNAs participate in many processes, including neurogenesis, neuronal differentiation, and synaptogenesis. They employ their effect through a wide variety of transcriptional and post-transcriptional regulatory mechanisms through interactions with chromatin modifiers, transcription factors, and other regulatory molecules. Dysregulation of lncRNAs has been associated with certain brain diseases, including Alzheimer's disease, Parkinson's disease, cancer, and neurodevelopmental disorders. Altered expression and function of specific lncRNAs have been implicated with disrupted neuronal connectivity, impaired synaptic plasticity, and aberrant gene expression pattern, highlighting the functional importance of this subclass of brain-enriched RNAs. Moreover, lncRNAs have been identified as potential biomarkers and therapeutic targets for neurological diseases. Here, we give a comprehensive review of the existing knowledge of lncRNAs. Our aim is to provide a better understanding of the diversity of lncRNA structure and functions in brain development and disease. This holds promise for unravelling the complexity of neurodevelopmental and neurodegenerative disorders, paving the way for the development of novel biomarkers and therapeutic targets for improved diagnosis and treatment.

Characterizing nascent transcription patterns of PROMPTs, eRNAs, and readthrough transcripts in the ENCODE4 deeply profiled cell lines

Arising as co-products of canonical gene expression, transcription-associated lincRNAs, such as promoter upstream transcripts (PROMPTs), enhancer RNAs (eRNAs), and readthrough (RT) transcripts, are often regarded as byproducts of transcription, although they may be important for the expression of nearby genes. We identified regions of nascent expression of these lincRNA in 16 human cell lines using Bru-seq techniques, and found distinctly regulated patterns of PROMPT, eRNA, and RT transcription using the diverse biochemical approaches in the ENCODE4 deeply profiled cell lines collection. Transcription of these lincRNAs was influenced by sequence-specific features and the local or 3D chromatin landscape. However, these sequence and chromatin features do not describe the full spectrum of lincRNA expression variability we identify, highlighting the complexity of their regulation. This may suggest that transcription-associated lincRNAs are not merely byproducts, but rather that the transcript itself, or the act of its transcription, is important for genomic function.

Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes

Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.

A review of plant epigenetics through the lens of almond

While genomes were originally seen as static entities that stably held and organized genetic information, recent advances in sequencing have uncovered the dynamic nature of the genome. New conceptualizations of the genome include complex relationships between the environment and gene expression that must be maintained, regulated, and sometimes even transmitted over generations. The discovery of epigenetic mechanisms has allowed researchers to understand how traits like phenology, plasticity, and fitness can be altered without changing the underlying deoxyribonucleic acid sequence. While many discoveries were first made in animal systems, plants provide a particularly complex set of epigenetic mechanisms due to unique aspects of their biology and interactions with human selective breeding and cultivation. In the plant kingdom, annual plants have received the most attention; however, perennial plants endure and respond to their environment and human management in distinct ways. Perennials include crops such as almond, for which epigenetic effects have long been linked to phenomena and even considered relevant for breeding. Recent discoveries have elucidated epigenetic phenomena that influence traits such as dormancy and self-compatibility, as well as disorders like noninfectious bud failure, which are known to be triggered by the environment and influenced by inherent aspects of the plant. Thus, epigenetics represents fertile ground to further understand almond biology and production and optimize its breeding. Here, we provide our current understanding of epigenetic regulation in plants and use almond as an example of how advances in epigenetics research can be used to understand biological fitness and agricultural performance in crop plants.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

Identification of G-quadruplex structures in MALAT1 lncRNA that interact with nucleolin and nucleophosmin

Nuclear-retained long non-coding RNAs (lncRNAs) including MALAT1 have emerged as critical regulators of many molecular processes including transcription, alternative splicing and chromatin organization. Here, we report the presence of three conserved and thermodynamically stable RNA G-quadruplexes (rG4s) located in the 3' region of MALAT1. Using rG4 domain-specific RNA pull-down followed by mass spectrometry and RNA immunoprecipitation, we demonstrated that the MALAT1 rG4 structures are specifically bound by two nucleolar proteins, Nucleolin (NCL) and Nucleophosmin (NPM). Using imaging, we found that the MALAT1 rG4s facilitate the localization of both NCL and NPM to nuclear speckles, and specific G-to-A mutations that disrupt the rG4 structures compromised the localization of both NCL and NPM in speckles. In vitro biophysical studies established that a truncated version of NCL (ΔNCL) binds tightly to all three rG4s. Overall, our study revealed new rG4s within MALAT1, established that they are specifically recognized by NCL and NPM, and showed that disrupting the rG4s abolished localization of these proteins to nuclear speckles.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

Long non-coding RNAs: definitions, functions, challenges and recommendations

Genes specifying long non-coding RNAs (lncRNAs) occupy a large fraction of the genomes of complex organisms. The term 'lncRNAs' encompasses RNA polymerase I (Pol I), Pol II and Pol III transcribed RNAs, and RNAs from processed introns. The various functions of lncRNAs and their many isoforms and interleaved relationships with other genes make lncRNA classification and annotation difficult. Most lncRNAs evolve more rapidly than protein-coding sequences, are cell type specific and regulate many aspects of cell differentiation and development and other physiological processes. Many lncRNAs associate with chromatin-modifying complexes, are transcribed from enhancers and nucleate phase separation of nuclear condensates and domains, indicating an intimate link between lncRNA expression and the spatial control of gene expression during development. lncRNAs also have important roles in the cytoplasm and beyond, including in the regulation of translation, metabolism and signalling. lncRNAs often have a modular structure and are rich in repeats, which are increasingly being shown to be relevant to their function. In this Consensus Statement, we address the definition and nomenclature of lncRNAs and their conservation, expression, phenotypic visibility, structure and functions. We also discuss research challenges and provide recommendations to advance the understanding of the roles of lncRNAs in development, cell biology and disease.

RNA out of the mist

RNA has long been regarded primarily as the intermediate between genes and proteins. It was a surprise then to discover that eukaryotic genes are mosaics of mRNA sequences interrupted by large tracts of transcribed but untranslated sequences, and that multicellular organisms also express many long 'intergenic' and antisense noncoding RNAs (lncRNAs). The identification of small RNAs that regulate mRNA translation and half-life did not disturb the prevailing view that animals and plant genomes are full of evolutionary debris and that their development is mainly supervised by transcription factors. Gathering evidence to the contrary involved addressing the low conservation, expression, and genetic visibility of lncRNAs, demonstrating their cell-specific roles in cell and developmental biology, and their association with chromatin-modifying complexes and phase-separated domains. The emerging picture is that most lncRNAs are the products of genetic loci termed 'enhancers', which marshal generic effector proteins to their sites of action to control cell fate decisions during development.

A review on the role of LINC00173 in human cancers

Long intergenic non-protein coding RNA 173 (LINC00173) is a long non-coding RNA with especial function in the tumorigenic process. Studies in different types of cancers support an oncogenic role for LINC00173 except for studies in B-cell precursor acute lymphoblastic leukemia, cervical cancer, pancreatic cancer and gastric cancer. In breast and lung cancers, both oncogenic and tumor suppressor roles have been reported for LINC00173. LINC00173 can serve as a molecular sponge for miRNAs. miR-218/Etk, miR-511-5p/VEGFA, miR-182-5p/AGER, miR-765/NUTF2, miR-765/PLP2, miR-182-5p/FBXW7, miR-338-3p/Rab25, miR‑641/RAB14 and miR-1275/BCL2 are examples of the miRNA/mRNA axes being regulated by LINC00173 in the context of cancer. The current review provides a summary of different studies on the role of LINC00173 in these cancers.

Bacterial diversity in the aquatic system in India based on metagenome analysis-a critical review

Microbial analysis has become one of the most critical areas in aquatic ecology and a crucial component for assessing the contribution of microbes in food web dynamics and biogeochemical processes. Initial research was focused on estimating the abundance and distribution of the microbes using microscopy and culture-based analysis, which are undoubtedly complex tasks. Over the past few decades, microbiologists have endeavored to apply and extend molecular techniques to address pertinent questions related to the function and metabolism of microbes in aquatic ecology. Metagenomics analysis has revolutionized aquatic ecology studies involving the investigation of the genome of a mixed community of organisms in an ecosystem to identify microorganisms, their functionality, and the discovery of novel proteins. This review discusses the metagenomics analysis of bacterial diversity in and around different aquatic systems in India.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

ChEAP: ChIP-exo analysis pipeline and the investigation of Escherichia coli RpoN protein-DNA interactions

Genome-scale studies of the bacterial regulatory network have been leveraged by declining sequencing cost and advances in ChIP (chromatin immunoprecipitation) methods. Of which, ChIP-exo has proven competent with its near-single base-pair resolution. While several algorithms and programs have been developed for different analytical steps in ChIP-exo data processing, there is a lack of effort in incorporating them into a convenient bioinformatics pipeline that is intuitive and publicly available. In this paper, we developed ChIP-exo Analysis Pipeline (ChEAP) that executes the one-step process, starting from trimming and aligning raw sequencing reads to visualization of ChIP-exo results. The pipeline was implemented on the interactive web-based Python development environment - Jupyter Notebook, which is compatible with the Google Colab cloud platform to facilitate the sharing of codes and collaboration among researchers. Additionally, users could exploit the free GPU and CPU resources allocated by Colab to carry out computing tasks regardless of the performance of their local machines. The utility of ChEAP was demonstrated with the ChIP-exo datasets of RpoN sigma factor in E. coli K-12 MG1655. To analyze two raw data files, ChEAP runtime was 2 min and 25 s. Subsequent analyses identified 113 RpoN binding sites showing a conserved RpoN binding pattern in the motif search. ChEAP application in ChIP-exo data analysis is extensive and flexible for the parallel processing of data from various organisms.

ortho2align: a sensitive approach for searching for orthologues of novel lncRNAs

Background

Many novel long noncoding RNAs have been discovered in recent years due to advances in high-throughput sequencing experiments. Finding orthologues of these novel lncRNAs might facilitate clarification of their functional role in living organisms. However, lncRNAs exhibit low sequence conservation, so specific methods for enhancing the signal-to-noise ratio were developed. Nevertheless, current methods such as transcriptomes comparison approaches or searches for conserved secondary structures are not applicable to novel, previously unannotated lncRNAs by design.

Results

We present ortho2align—a versatile sensitive synteny-based lncRNA orthologue search tool with statistical assessment of sequence conservation. This tool allows control of the specificity of the search process and optional annotation of found orthologues. ortho2align shows similar performance in terms of sensitivity and resource usage as the state-of-the-art method for aligning orthologous lncRNAs but also enables scientists to predict unannotated orthologous sequences for lncRNAs in question. Using ortho2align, we predicted orthologues of three distinct classes of novel human lncRNAs in six Vertebrata species to estimate their degree of conservation.

Conclusions

Being designed for the discovery of unannotated orthologues of novel lncRNAs in distant species, ortho2align is a versatile tool applicable to any genomic regions, especially weakly conserved ones. A small amount of input files makes ortho2align easy to use in orthology studies as a single tool or in bundle with other steps that researchers will consider sensible. ortho2align is available as an Anaconda package with its source code hosted at https://github.com/dmitrymyl/ortho2align.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04929-y.

Navigating the genomic instability mine field of osteosarcoma to better understand implications of non-coding RNAs

Osteosarcoma is one of the most genomically complex cancers and as result, it has been difficult to assign genomic aberrations that contribute to disease progression and patient outcome consistently across samples. One potential source for correlating osteosarcoma and genomic biomarkers is within the non-coding regions of RNA that are differentially expressed. However, it is unsurprising that a cancer classification that is fraught with genomic instability is likely to have numerous studies correlating non-coding RNA expression and function have been published on the subject. This review undertakes the formidable task of evaluating the published literature of noncoding RNAs in osteosarcoma. This is not the first review on this topic and will certainly not be the last. The review is organized with an introduction into osteosarcoma and the epigenetic control of gene expression before reviewing the molecular function and expression of long non-coding RNAs, circular RNAs, and short non-coding RNAs such as microRNAs, piwi RNAs, and short-interfering RNAs. The review concludes with a review of the literature and how the biology of non-coding RNAs can be used therapeutically to treat cancers, especially osteosarcoma. We conclude that non-coding RNA expression and function in osteosarcoma is equally complex to understanding the expression differences and function of coding RNA and proteins; however, with the added lens of both coding and non-coding genomic sequence, researchers can begin to identify the patterns that consistently associate with aggressive osteosarcoma.

Viruses as Living Systems—A Metacybernetic View

The debate over whether viruses are living organisms tends to be paradigmatically determined. The metabolic paradigm denies that they are, while new research evidences the opposite. The purpose of this paper is to deliver a generic model for viral contexts that explains why viruses are alive. It will take a systems biology approach, with a qualitative part (using metacybernetics) to provide deeper explanations of viral contexts, and a quantitative part (using Fisher Information deriving from the variational principle of Extreme Physical Information) which is in principle able to take measurements and predict outcomes. The modelling process provides an extended view of the epigenetic processes of viruses. The generic systems biology model will depict viruses as autonomous entities with metaphysical processes of autopoietic self-organisation and adaptation, enabling them to maintain their physical viability and hence, within their populations, mutate and evolve. The autopoietic epigenetic processes are shown to describe their capability to change, and these are both qualitatively and quantitatively explored, the latter providing an approach to make measurements of physical phenomena under uncertainty. Viruses maintain their fitness when they are able to maintain their stability, and this is indicated by information flow efficacy. A brief case study is presented on the COVID-19 virus from the perspective that it is a living system, and this includes outcome predictions given Fisher Information conditions for known contexts.

Sbk2, a Newly Discovered Atrium-Enriched Regulator of Sarcomere Integrity

BACKGROUND

Heart development relies on tight spatiotemporal control of cardiac gene expression. Genes involved in this intricate process have been identified using animals and pluripotent stem cell-based models of cardio(myo)genesis. Recently, the repertoire of cardiomyocyte differentiation models has been expanded with iAM-1, a monoclonal line of conditionally immortalized neonatal rat atrial myocytes (NRAMs), which allows toggling between proliferative and differentiated (ie, excitable and contractile) phenotypes in a synchronized and homogenous manner.

METHODS

In this study, the unique properties of conditionally immortalized NRAMs (iAMs) were exploited to identify and characterize (lowly expressed) genes with an as-of-yet uncharacterized role in cardiomyocyte differentiation.

RESULTS

Transcriptome analysis of iAM-1 cells at different stages during one cycle of differentiation and subsequent dedifferentiation identified ≈13 000 transcripts, of which the dynamic changes in expression upon cardiomyogenic differentiation mostly opposed those during dedifferentiation. Among the genes whose expression increased during differentiation and decreased during dedifferentiation were many with known (lineage-specific) functions in cardiac muscle formation. Filtering for cardiac-enriched low-abundance transcripts, identified multiple genes with an uncharacterized role during cardio(myo)genesis including Sbk2 (SH3 domain binding kinase family member 2). Sbk2 encodes an evolutionarily conserved putative serine/threonine protein kinase, whose expression is strongly up- and downregulated during iAM-1 cell differentiation and dedifferentiation, respectively. In neonatal and adult rats, the protein is muscle-specific, highly atrium-enriched, and localized around the A-band of cardiac sarcomeres. Knockdown of Sbk2 expression caused loss of sarcomeric organization in NRAMs, iAMs and their human counterparts, consistent with a decrease in sarcomeric gene expression as evinced by transcriptome and proteome analyses. Interestingly, co-immunoprecipitation using Sbk2 as bait identified possible interaction partners with diverse cellular functions (translation, intracellular trafficking, cytoskeletal organization, chromatin modification, sarcomere formation).

CONCLUSIONS

iAM-1 cells are a relevant and suitable model to identify (lowly expressed) genes with a hitherto unidentified role in cardiomyocyte differentiation as exemplified by Sbk2: a regulator of atrial sarcomerogenesis.

Placenta-specific lncRNA 1600012P17Rik is expressed in spongiotrophoblast and glycogen trophoblast cells of mouse placenta

A few long noncoding RNAs (long ncRNAs, lncRNAs) exhibit trophoblast cell type-specific expression patterns and functional roles in mouse placenta. However, the cell- and stage-specific expression patterns and functions of most placenta-derived lncRNAs remain unclear. In this study, we explored mouse placenta-associated lncRNAs using a combined bioinformatic and experimental approach. We used the FANTOM5 database to survey lncRNA expression in mouse placenta and found that 1600012P17Rik (MGI: 1919275, designated P17Rik), a long intergenic ncRNA, was the most highly expressed lncRNA at gestational day 17. Polymerase chain reaction analysis confirmed that P17Rik was exclusively expressed in placenta and not in any of the adult organs examined in this study. In situ hybridization analysis revealed that it was highly expressed in spongiotrophoblast cells and to a lesser extent in glycogen trophoblast cells, including migratory glycogen trophoblast cells invading the decidua. Moreover, we found that it is a polyadenylated lncRNA localized mainly to the cytoplasm of these trophoblast cells. As these trophoblast cells also expressed the neighboring protein-coding gene, pappalysin 2 (Pappa2), we investigated the effects of P17Rik on Pappa2 expression using Pappa2-expressing MC3T3-E1 cells and found that P17Rik transfection increased the messenger RNA (mRNA) and protein levels of Pappa2. These results indicate that mouse placenta-specific lncRNA P17Rik modulates the expression of the neighboring protein-coding gene Pappa2 in spongiotrophoblast and glycogen trophoblast cells of mouse placenta during late gestation.

Long Non-Coding RNAs in the Pathogenesis of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is one of the major microvascular complications of diabetes mellitus, with relatively high morbidity and mortality globally but still in short therapeutic options. Over the decades, a large body of data has demonstrated that oxidative stress, inflammatory responses, and hemodynamic disorders might exert critical influence in the initiation and development of DKD, whereas the delicate pathogenesis of DKD remains profoundly elusive. Recently, long non-coding RNAs (lncRNAs), extensively studied in the field of cancer, are attracting increasing attentions on the development of diabetes mellitus and its complications including DKD, diabetic retinopathy, and diabetic cardiomyopathy. In this review, we chiefly focused on abnormal expression and function of lncRNAs in major resident cells (mesangial cell, endothelial cell, podocyte, and tubular epithelial cell) in the kidney, summarized the critical roles of lncRNAs in the pathogenesis of DKD, and elaborated their potential therapeutic significance, in order to advance our knowledge in this field, which might help in future research and clinical treatment for the disease.

The RNA helicases DDX5 and DDX17 facilitate neural differentiation of human pluripotent stem cells NTERA2

AIMS

Understanding human neurogenesis is critical toward regenerative medicine for neurodegeneration. However, little is known how neural differentiation is regulated by DEAD box-containing RNA helicases, which comprise a diverse class of RNA remodeling enzymes.

MATERIALS AND METHODS

ChIP-seq was utilized to identify binding sites of DDX5 and DDX17 in both human pluripotent stem cell (hPSC) line NTERA2 and their retinoic acid-induced neural derivatives. RNA-seq was used to elucidate genes differentially expressed upon depletion of DDX5 and DDX17. Neurosphere assay, flow cytometry, and immunofluorescence staining were performed to test the effect of depletion of the two RNA helicases in neural differentiation.

KEY FINDINGS

We show here that expression of DDX5 and DDX17 is abundant throughout neural differentiation of NTERA2, and is mostly localized within the nucleus. The two RNA helicases occupy chromatin genome-wide at regions associated with neurogenesis-related genes in both hPSCs and their neural derivatives. Further, both DDX5 and DDX17 are mutually required for controlling transcriptional expression of these genes, but are not important for maintenance of stem cell state of hPSCs. In contrast, they facilitate early neural differentiation of hPSCs, generation of neurospheres from the stem cells, and transcriptional expression of key neurogenic transcription factors such as SOX1 and PAX6 during neural differentiation. Importantly, DDX5 and DDX17 are critical for differentiation of hPSCs toward NESTIN- and TUBB3-positive cells, which represent neural progenitors and mature neurons, respectively.

SIGNIFICANCE

Collectively, our findings suggest the role of DDX5 and DDX17 in transcriptional regulation of genes involved in neurogenesis, and hence in neural differentiation of hPSCs.

Alternative splicing modulation mediated by G-quadruplex structures in MALAT1 lncRNA

MALAT1, an abundant lncRNA specifically localized to nuclear speckles, regulates alternative-splicing (AS). The molecular basis of its role in AS remains poorly understood. Here, we report three conserved, thermodynamically stable, parallel RNA-G-quadruplexes (rG4s) present in the 3' region of MALAT1 which regulates this function. Using rG4 domain-specific RNA-pull-down followed by mass-spectrometry, RNA-immuno-precipitation, and imaging, we demonstrate the rG4 dependent localization of Nucleolin (NCL) and Nucleophosmin (NPM) to nuclear speckles. Specific G-to-A mutations that abolish rG4 structures, result in the localization loss of both the proteins from speckles. Functionally, disruption of rG4 in MALAT1 phenocopies NCL knockdown resulting in altered pre-mRNA splicing of endogenous genes. These results reveal a central role of rG4s within the 3' region of MALAT1 orchestrating AS.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

LncRNAs in breast cancer: a link to future approaches

Breast cancer affects millions of women each year. Despite recent advances in targeted treatments breast cancer remains a significant threat to women's health. In recent years the development of high-throughput sequencing technologies has advanced the field of transcriptomics shedding light on the role of non-coding RNAs (ncRNAs), including long ncRNAs (lncRNAs), in human cellular function and disease. LncRNAs are classified as transcripts longer than 200nt with no coding potential. These transcripts constitute a diverse group of regulatory molecules essential to the modulation of crucial cellular processes, which dysregulation of leads to disease. LncRNAs exert their regulatory functions through their sequences and by forming complex secondary and tertiary structures that interact with other transcripts, chromatin and/or proteins. Numerous studies have provided evidence of the involvement of LncRNAs in tumor development and disease progression. They possess multiple characteristics that make them novel therapeutic and diagnostic targets. Indeed, the discovery of a novel mechanism by which lncRNAs associated with proteins can induce the formation of phase-separated droplets broadens our understanding of the spatiotemporal control of cellular processes and opens up developing a new treatment. Nevertheless, the role and the molecular mechanisms of many lncRNAs in the regulation of cellular processes and cancer still remain elusive. This is due to the absence of a thorough characterization of the regulatory role of their loci and the functional impact of their aberrations in cancer biology. Here, we present some of the latest advances concerning the role of LncRNAs in breast cancer.

Long non-coding RNA FAM66C regulates glioma growth via the miRNA/LATS1 signaling pathway

Glioma is one of the most common primary intracranial carcinomas and typically associated with a dismal prognosis and poor quality of life. The identification of novel oncogenes is clinically valuable for early screening and prevention. Recently, the studies have revealed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of cancers including glioma. The expression of lncRNA FAM66C is reduced in glioma cell lines and clinical samples compared to non-tumor samples. Knockdown of FAM66C in U87 and U251 cells significantly promoted cell proliferation and migration, respectively. Furthermore, the correlation between FAM66C and Hippo pathway regulators YAP1 and LATS1, along with the alteration of their protein expression level indicated that FAM66C regulated cell growth through this pathway. Moreover, luciferase assay demonstrated that another two noncoding RNAs, miR15a/miR15b, directly bonded to the 3'UTR of LATS1 to facilitated its transcriptional expression and inhibited cell growth. In addition, the luciferase activity of FAM66C was block by miR15a/miR15b, and the promotion of cell growth effects caused by FAM66C deficiency was attenuated by miR15a/miR15b mimics, further proved that FAM66C functioned as a competing endogenous RNA to regulate glioma growth via the miRNA/LATS1 signaling pathway.

What is beyond LncRNAs in breast cancer: A special focus on colon cancer-associated Transcript-1 (CCAT-1)

Long non-coding RNAs (LncRNAs) play a vital role in the process of malignant transformation. In breast cancer (BC), lncRNAs field is currently under intensive investigations. Yet, the role of lncRNAs as promising diagnostic and/or prognostic biomarkers and as therapeutic target/tool among BC patients still needs a special focus from the biomedical scientists. In BC, triple negative breast cancer patients (TNBC) are the unlucky group as they are always represented with the worst prognosis and the highest mortality rates. For that reason, a special focus on TNBC and associated lncRNAs was addressed in this review. Colon cancer-associated transcript 1 (CCAT-1) is a newly discovered oncogenic lncRNA that has been emerged as a vital biomarker for diagnosis, prognosis and therapeutic interventions in multiple malignancies and showed differential expression among TNBC patients. In this review, the authors shed the light onto the general role of lncRNAs in BC and the specific functional activities, molecular mechanisms, competing endogenous ncRNA role of CCAT-1 in TNBC.

Exploring Evidence of Non-coding RNA Translation With Trips-Viz and GWIPS-Viz Browsers

Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.

Genome-Wide SNP Markers for Genotypic and Phenotypic Differentiation of Melon (Cucumis melo L.) Varieties Using Genotyping-by-Sequencing

Melon (Cucumis melo L.) is an economically important horticultural crop with abundant morphological and genetic variability. Complex genetic variations exist even among melon varieties and remain unclear to date. Therefore, unraveling the genetic variability among the three different melon varieties, muskmelon (C. melo subsp. melo), makuwa (C. melo L. var. makuwa), and cantaloupes (C. melo subsp. melo var. cantalupensis), could provide a basis for evolutionary research. In this study, we attempted a systematic approach with genotyping-by-sequencing (GBS)-derived single nucleotide polymorphisms (SNPs) to reveal the genetic structure and diversity, haplotype differences, and marker-based varieties differentiation. A total of 6406 GBS-derived SNPs were selected for the diversity analysis, in which the muskmelon varieties showed higher heterozygote SNPs. Linkage disequilibrium (LD) decay varied significantly among the three melon varieties, in which more rapid LD decay was observed in muskmelon (r² = 0.25) varieties. The Bayesian phylogenetic tree provided the intraspecific relationships among the three melon varieties that formed, as expected, individual clusters exhibiting the greatest genetic distance based on the posterior probability. The haplotype analysis also supported the phylogeny result by generating three major networks for 48 haplotypes. Further investigation for varieties discrimination allowed us to detect a total of 52 SNP markers that discriminated muskmelon from makuwa varieties, of which two SNPs were converted into cleaved amplified polymorphic sequence markers for practical use. In addition to these markers, the genome-wide association study identified two SNPs located in the genes on chromosome 6, which were significantly associated with the phenotypic traits of melon seed. This study demonstrated that a systematic approach using GBS-derived SNPs could serve to efficiently classify and manage the melon varieties in the genebank.

Intergenic RNA mainly derives from nascent transcripts of known genes

BACKGROUND

Eukaryotic genomes undergo pervasive transcription, leading to the production of many types of stable and unstable RNAs. Transcription is not restricted to regions with annotated gene features but includes almost any genomic context. Currently, the source and function of most RNAs originating from intergenic regions in the human genome remain unclear.

RESULTS

We hypothesize that many intergenic RNAs can be ascribed to the presence of as-yet unannotated genes or the "fuzzy" transcription of known genes that extends beyond the annotated boundaries. To elucidate the contributions of these two sources, we assemble a dataset of more than 2.5 billion publicly available RNA-seq reads across 5 human cell lines and multiple cellular compartments to annotate transcriptional units in the human genome. About 80% of transcripts from unannotated intergenic regions can be attributed to the fuzzy transcription of existing genes; the remaining transcripts originate mainly from putative long non-coding RNA loci that are rarely spliced. We validate the transcriptional activity of these intergenic RNAs using independent measurements, including transcriptional start sites, chromatin signatures, and genomic occupancies of RNA polymerase II in various phosphorylation states. We also analyze the nuclear localization and sensitivities of intergenic transcripts to nucleases to illustrate that they tend to be rapidly degraded either on-chromatin by XRN2 or off-chromatin by the exosome.

CONCLUSIONS

We provide a curated atlas of intergenic RNAs that distinguishes between alternative processing of well-annotated genes from independent transcriptional units based on the combined analysis of chromatin signatures, nuclear RNA localization, and degradation pathways.

PVT1 Expression Is a Predictor for Poor Survival of Prostate Cancer Patients

Objective:

Dysregulation of long noncoding RNA is associated with a variety of cancers and LncRNA has anticancer or carcinogenic activities. PVT1, as a long noncoding RNA, plays an important role in the development of cancer.

Methods:

We use R to download and analyze the data in TCGA database. ROC curve is generated to evaluate the significance of PVT1 expression for the diagnosis of prostate cancer. Chi-square test is used to test correlation between PVT1 expression and clinical pathological features. Survival curve and univariate and multivariate cox regression analysis is performed to compare differences in the effect on the survival rate between PVT1 high expression and low expression.

Results:

The expression of PTV1 in tumor tissues was significantly higher than that in normal tissues(P<2.2e-16). The difference of PTV1 expression was observed according to vital status (P = 0.0051) and Gleason score (P = 0.0012). The expression of PTV1 is significantly associated with T classification (P < 0.0001), N classification (P = 0.0499), PSA (P = 0.0001), Gleason Score (P < 0.0001), targeted molecular therapy (P = 0.0264) and vital status(P = 0.0036). The area under the ROC curve (AUC) was 0.860, which revealed PTV1 expression has excellent diagnostic value in prostate cancer. Patients with high PVT1 expression had a worse prognosis.

Conclusions:

PVT1 expression may be a biomarker for the diagnosis and prognosis of prostate cancer.

Effects of transcriptional noise on estimates of gene and transcript expression in RNA sequencing experiments

RNA sequencing is widely used to measure gene expression across a vast range of animal and plant tissues and conditions. Most studies of computational methods for gene expression analysis use simulated data to evaluate the accuracy of these methods. These simulations typically include reads generated from known genes at varying levels of expression. Until now, simulations did not include reads from noisy transcripts, which might include erroneous transcription, erroneous splicing, and other processes that affect transcription in living cells. Here we examine the effects of realistic amounts of transcriptional noise on the ability of leading computational methods to assemble and quantify the genes and transcripts in an RNA sequencing experiment. We show that the inclusion of noise leads to systematic errors in the ability of these programs to measure expression, including systematic underestimates of transcript abundance levels and large increases in the number of false-positive genes and transcripts. Our results also suggest that alignment-free computational methods sometimes fail to detect transcripts expressed at relatively low levels.

A method for making alignments of related protein sequences that share very little similarity; shark interleukin 2 as an example

An optimized alignment of related protein sequences helps to see their important shared features and to deduce their phylogenetic relationships. At low levels of sequence similarity, there are no suitable computer programs for making the best possible alignment. This review summarizes some guidelines for how in such instances, nevertheless, insightful alignments can be made. The method involves, basically, the understanding of molecular family features at both the protein and intron-exon level, and the collection of many related sequences so that gradual differences may be observed. The method is exemplified by identifying and aligning interleukin 2 (IL-2) and related sequences in Elasmobranchii (sharks/rays) and coelacanth, as other authors have expressed difficulty with their identification. From the point of general immunology, it is interesting that the unusual long "leader" sequence of IL-15, already known in other species, is even more impressively conserved in cartilaginous fish. Furthermore, sequence comparisons suggest that IL-2 in cartilaginous fish has lost its ability to bind an IL-2Rα/15Rα receptor chain, which would prohibit the existence of a mechanism for regulatory T cell regulation identical to mammals.

Current challenges and best-practice protocols for microbiome analysis

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

Evolution of Genome-Organizing Long Non-coding RNAs in Metazoans

Long non-coding RNAs (lncRNAs) have important regulatory functions across eukarya. It is now clear that many of these functions are related to gene expression regulation through their capacity to recruit epigenetic modifiers and establish chromatin interactions. Several lncRNAs have been recently shown to participate in modulating chromatin within the spatial organization of the genome in the three-dimensional space of the nucleus. The identification of lncRNA candidates is challenging, as it is their functional characterization. Conservation signatures of lncRNAs are different from those of protein-coding genes, making identifying lncRNAs under selection a difficult task, and the homology between lncRNAs may not be readily apparent. Here, we review the evidence for these higher-order genome organization functions of lncRNAs in animals and the evolutionary signatures they display.

Non-Coding RNAs and Nucleosome Remodeling Complexes: An Intricate Regulatory Relationship

Eukaryotic genomes are pervasively transcribed, producing both coding and non-coding RNAs (ncRNAs). ncRNAs are diverse and a critical family of biological molecules, yet much remains unknown regarding their functions and mechanisms of regulation. ATP-dependent nucleosome remodeling complexes, in modifying chromatin structure, play an important role in transcriptional regulation. Recent findings show that ncRNAs regulate nucleosome remodeler activities at many levels and that ncRNAs are regulatory targets of nucleosome remodelers. Further, a series of recent screens indicate this network of regulatory interactions is more expansive than previously appreciated. Here, we discuss currently described regulatory interactions between ncRNAs and nucleosome remodelers and contextualize their biological functions.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.

Long noncoding RNAs and their link to cancer

The central dogma of molecular biology, developed from the study of simple organisms such as Escherichia coli, has up until recently been that RNA functions mainly as an information intermediate between a DNA sequence (gene), localized in the cell nucleus, serving as a template for the transcription of messenger RNAs, which in turn translocate into the cytoplasm and act as blueprints for the translation of their encoded proteins. There are a number of classes of non-protein coding RNAs (ncRNAs) which are essential for gene expression to function. The specific number of ncRNAs within the human genome is unknown. ncRNAs are classified on the basis of their size. Transcripts shorter than 200 nucleotides, referred to as ncRNAs, which group includes miRNAs, siRNAs, piRNAs, etc, have been extensively studied. Whilst transcripts with a length ranging between 200 nt up to 100 kilobases, referred to as lncRNAs, make up the second group, and are recently receiving growing concerns. LncRNAs play important roles in a variety of biological processes, regulating physiological functions of organisms, including epigenetic control of gene regulation, transcription and post-transcription, affecting various aspects of cellular homeostasis, including proliferation, survival, migration and genomic stability. LncRNAs are also capable of tuning gene expression and impact cellular signalling cascades, play crucial roles in promoter-specific gene regulation, and X-chromosome inactivation. Furthermore, it has been reported that lncRNAs interact with DNA, RNA, and/or protein molecules, and regulate chromatin organisation, transcriptional and post-transcriptional regulation. Consequently, they are differentially expressed in tumours, and they are directly linked to the transformation of healthy cells into tumour cells. As a result of their key functions in a wide range of biological processes, lncRNAs are becoming rising stars in biology and medicine, possessing potential active roles in various oncologic diseases, representing a gold mine of potential new biomarkers and drug targets.

Translation regulatory long non-coding RNA 1 represents a potential prognostic biomarker for colorectal cancer

Long non-coding RNAs (lncRNAs) have attracted a lot of attention for their role in the development, progression and prognosis of colorectal cancer (CRC). However, little is known on the clinical significance of the translation regulatory lncRNA 1 (TRERNA1) in CRC. The present study aimed to explore the clinical value of TRERNA1 in patients with CRC. A total of 89 cancer-associated lncRNA genes were analyzed using the RT² lncRNA PCR array Human Cancer PathwayFinder. Following the PCR array, reverse transcription-quantitative (RT-q)PCR was conducted to identify the differential expression of TRERNA1 between 130 CRC and corresponding non-tumorous adjacent tissues. Additionally, the association between TRERNA1 expression and clinical characteristics was analyzed. Furthermore, TRERNA1 expression was knocked down via small interfering RNAs. The results of the PCR array and RT-qPCR revealed that TRERNA1 expression was significantly upregulated in CRC tissues compared with in adjacent normal tissues. TRERNA1 upregulation was positively associated with distant metastasis, perineural invasion, TNM stage, node metastasis stage and tumor diameter. Multivariate analysis revealed that patients with higher TRERNA1 expression had a shorter overall survival (OS) time and a less favorable prognosis compared with those in the low TRERNA1 expression group. Knockdown of TRERNA1 inhibited invasion and metastasis of CRC cells via regulating Snail expression. In conclusion, TRERNA1 expression was upregulated in CRC tissues. High expression levels of TRERNA1 may be associated with poor OS times, a less favorable prognosis and lymph node metastasis in patients with CRC. TRERNA1 may therefore serve as a useful and novel biomarker for CRC lymph node metastasis and prognosis.

Oncogenic Role of PVT1 and Therapeutic Implications

PVT1, a long non-coding RNA has been implicated in a variety of human cancers. Recent advancements have led to increasing discovery of the critical roles of PVT1 in cancer initiation and progression. Novel insight is emerging about PVT1's mechanism of action in different cancers. Identifying and understanding the variety of activities of PVT1 involved in cancers is a necessity for the development of PVT1 as a diagnostic biomarker or therapeutic target in cancers where PVT1 is dysregulated. PVT1's varied activities include overexpression, modulation of miRNA expression, protein interactions, targeting of regulatory genes, formation of fusion genes, functioning as a competing endogenous RNA (ceRNA), and interactions with MYC, among many others. Furthermore, bioinformatic analysis of PVT1 interactions in cancers has aided understanding of the numerous pathways involved in PVT1 contribution to carcinogenesis in a cancer type-specific manner. However, these recent findings show that there is much more to be learned to be able to fully exploit PVT1 for cancer prognostication and therapy. In this review, we summarize some of the latest findings on PVT1's oncogenic activities, signaling networks and how targeting these networks can be a strategy for cancer therapy.

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks.

Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs

Nascent transcription assays, such as global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), have uncovered a myriad of unstable RNAs being actively produced from numerous sites genome-wide. These transcripts provide a more complete and immediate picture of the impact of regulatory events. Transcription factors recruit RNA polymerase II, effectively initiating the process of transcription; repressors inhibit polymerase recruitment. Efficiency of recruitment is dictated by sequence elements in and around the RNA polymerase loading zone. A combination of sequence elements and RNA binding proteins subsequently influence the ultimate stability of the resulting transcript. Some of these transcripts are capable of providing feedback on the process, influencing subsequent transcription. By monitoring RNA polymerase activity, nascent assays provide insights into every step of the regulated process of transcription.

Long Noncoding RNA (lncRNA) FOXD2-AS1 Promotes Cell Proliferation and Metastasis in Hepatocellular Carcinoma by Regulating MiR-185/AKT Axis

BACKGROUND The aim of this study was to investigate the effects and mechanisms of long noncoding (lnc) RNA FOXD2-AS1 in hepatocellular carcinoma development. MATERIAL AND METHODS Collecting the 3 pairs of adjacent and hepatocellular carcinoma tissue and analysis by gene chip. Evaluating the FOXD2-AS1 expression by in situ hybridization assay. Evaluating the FOXD2-AS1 to Bel-7402 biological activity in vitro study by Cell Counting Kit-8, flow cytometry, Transwell and wound healing assay and correlation between miR-185 by dual-luciferase reporter assay. The relative proteins expressions were evaluated by western blot assay. RESULTS FOXD2-AS1 was significantly upregulation in hepatocellular carcinoma tissues. FOXD2-AS1 knockdown suppressed Bel-7401 cell biological activities (proliferation, invasion, and migration) with miR-185 overexpression and AKT depressing in cell expression. CONCLUSIONS LncRNA FOXD2-AS1 promoted hepatocellular carcinoma development by regulation miR-185/AKT axis.

Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires

BACKGROUND

Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.

METHODS

The dataset comprised of 1337 heifers genotyped using a GeneSeek® Genomic Profiler panel (74677 SNPs), representing the daughters from 78 sires. After performing marker quality control, 64800 SNPs were retained. Haplotypes carried by each sire at six previously identified QTL on BTAs 5, 14 and 18 for heifer pregnancy and BTAs 8, 11 and 22 for antral follicle count were constructed using findhap software. The significance of the contrasts between the effects of every two paternally-inherited haplotype alleles were used to identify sires that were heterozygous at each QTL. Whole-genome sequencing data localized to the haplotypes from six sires and 20 other ancestors were used to identify sequence variants that were concordant with the haplotype contrasts. Enrichment analyses were applied to these variants using KEGG and MeSH libraries.

RESULTS

A total of six (BTA 5), six (BTA 14) and five (BTA 18) sires were heterozygous for heifer pregnancy QTL whereas six (BTA 8), fourteen (BTA 11), and five (BTA 22) sires were heterozygous for number of antral follicles' QTL. Due to inadequate representation of many haplotype alleles in the sequenced animals, fine mapping analysis could only be reliably performed for the QTL on BTA 5 and 14, which had 641 and 3733 concordant candidate sequence variants, respectively. The KEGG "Circadian rhythm" and "Neurotrophin signaling pathway" were significantly associated with the genes in the QTL on BTA 5 whereas 32 MeSH terms were associated with the QTL on BTA 14. Among the concordant sequence variants, 0.2% and 0.3% were classified as missense variants for BTAs 5 and 14, respectively, highlighting the genes MTERF2, RTMB, ENSBTAG00000037306 (miRNA), ENSBTAG00000040351, PRKDC, and RGS20. The potential causal mutations found in the present study were associated with biological processes such as oocyte maturation, embryo development, placenta development and response to reproductive hormones.

CONCLUSIONS

The identification of heterozygous sires by positionally phasing SNP chip data and contrasting haplotype effects for previously detected QTL can be used for fine mapping to identify potential causal mutations and candidate genes. Genomic variants on genes MTERF2, RTBC, miRNA ENSBTAG00000037306, ENSBTAG00000040351, PRKDC, and RGS20, which are known to have influence on reproductive biological processes, were detected.