Non-coding yet non-trivial: a review on the computational genomics of lincRNAs

Long noncoding RNAs in ubiquitination, protein degradation, and human diseases

Protein stability and turnover is critical in normal cellular and physiological process and their misregulation may contribute to accumulation of unwanted proteins causing cellular malfunction, neurodegeneration, mitochondrial malfunction, and disrupted metabolism. Signaling mechanism associated with protein degradation is complex and is extensively studied. Many protein and enzyme machineries have been implicated in regulation of protein degradation. Despite these insights, our understanding of protein degradation mechanisms remains limited. Emerging studies suggest that long non-coding RNAs (lncRNAs) play critical roles in various cellular and physiological processes including metabolism, cellular homeostasis, and protein turnover. LncRNAs, being large nucleic acids (>200 nt long) can interact with various proteins and other nucleic acids and modulate protein structure and function leading to regulation of cell signaling processes. LncRNAs are widely distributed across cell types and may exhibit tissue specific expression. They are detected in body fluids including blood and urine. Their expressions are also altered in various human diseases including cancer, neurological disorders, immune disorder, and others. LncRNAs are being recognized as novel biomarkers and therapeutic targets. This review article focuses on the emerging role of noncoding RNAs (ncRNAs), particularly long noncoding RNAs (lncRNAs), in the regulation of protein polyubiquitination and proteasomal degradation.

The Role of Long Intergenic Noncoding RNA in Fetal Development

The role of long intergenic noncoding RNAs (lincRNAs) in fetal development has emerged as a significant area of study, challenging the traditional protein-centric view of gene expression. While messenger RNAs (mRNAs) have long been recognized for their role in encoding proteins, recent advances have illuminated the critical functions of lincRNAs in various biological processes. Initially identified through high-throughput sequencing technologies, lincRNAs are transcribed from intergenic regions between protein-coding genes and exhibit unique regulatory functions. Unlike mRNAs, lincRNAs are involved in complex interactions with chromatin and chromatin-modifying complexes, influencing gene expression and chromatin structure. LincRNAs are pivotal in regulating tissue-specific development and embryogenesis. For example, they are crucial for proper cardiac, neural, and reproductive system development, with specific lincRNAs being associated with organogenesis and differentiation processes. Their roles in embryonic development include regulating transcription factors and modulating chromatin states, which are essential for maintaining developmental programs and cellular identity. Studies using RNA sequencing and genetic knockout models have highlighted the importance of lincRNAs in processes such as cell differentiation, tissue patterning, and organ development. Despite their functional significance, the comprehensive annotation and understanding of lincRNAs remain limited. Ongoing research aims to elucidate their mechanisms of action and potential applications in disease diagnostics and therapeutics. This review summarizes current knowledge on the functional roles of lincRNAs in fetal development, emphasizing their contributions to tissue-specific gene regulation and developmental processes.

Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty

Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long noncoding RNAs (lncRNAs) have been proposed as a new class of potential biomarkers for cancer diagnosis. The reported correlation between the presence of tumors and abnormal levels of lncRNAs in the blood of cancer patients has notably triggered a worldwide interest among clinicians and oncologists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved ("the Good") and challenges encountered ("the Bad") in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the diagnostic performance of more than 50 different circulating lncRNAs and emphasize their numerous potential clinical applications ("the Beauty") including therapeutic targets and agents, on top of diagnostic and prognostic capabilities. This review also summarizes the best methods of investigation and provides useful guidelines for clinicians and scientists who desire conducting their own clinical studies on circulating lncRNAs in cancer patients via RT-qPCR or Next Generation Sequencing (NGS).

EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models

lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.

Identification and expression analysis of ten novel small non-coding RNAs (sncRNAs) in cancer cells using a high-throughput sequencing approach

Non-coding RNAs are characterized as RNA molecules, which lack the capacity to encode protein structures and appear to include a level of internal signals. Moreover, they control various stages of gene expression, thus controlling the cell physiology and development. In this study, we implemented a high-throughput sequencing approach based on the primary semi-conductor technology and computational tools, in order to identity novel small non-coding RNAs. Fourteen human cancer cell lines were cultured, and RNA samples were enriched for small RNAs following semi-conductor next generation sequencing (NGS). Bioinformatics analysis of NGS data revealed the existence of several classes of ncRNAs using the miRDeep* and CPSS 2.0 software. To investigate the existence of the predicted non-coding RNA sequences in cDNA pools of cell lines, a developed qPCR-based assay was implemented. The structure of each novel small ncRNA was visualized, using the RNAfold algorithm. Our results support the existence of twenty (20) putative new small ncRNAs, ten (10) of which have had their expression experimentally validated and presented differential profiles in cancerous and normal cells. A deeper comprehension of the ncRNAs interactive network and its role in cancer can therefore be translated into a wide range of clinical applications. Despite this progress, further scientific research from different perspectives and in different fields is needed, so that the riddle of the human transcriptome can be solved.

A head-to-head comparison of ribodepletion and polyA selection approaches for C. elegans low input RNA-sequencing libraries

A recent and powerful technique is to obtain transcriptomes from rare cell populations, such as single neurons in Caenorhabditis elegans, by enriching dissociated cells using fluorescent sorting. However, these cell samples often have low yields of RNA that present challenges in library preparation. This can lead to PCR duplicates, noisy gene expression for lowly expressed genes, and other issues that limit endpoint analysis. Furthermore, some common resources, such as sequence-specific kits for removing ribosomal RNA, are not optimized for nonmammalian samples. To advance library construction for such challenging samples, we compared two approaches for building RNAseq libraries from less than 10 nanograms of C. elegans RNA: SMARTSeq V4 (Takara), a widely used kit for selecting poly-adenylated transcripts; and SoLo Ovation (Tecan Genomics), a newly developed ribodepletion-based approach. For ribodepletion, we used a custom kit of 200 probes designed to match C. elegans rRNA gene sequences. We found that SoLo Ovation, in combination with our custom C. elegans probe set for rRNA depletion, detects an expanded set of noncoding RNAs, shows reduced noise in lowly expressed genes, and more accurately counts expression of long genes. The approach described here should be broadly useful for similar efforts to analyze transcriptomics when RNA is limiting.

Performance comparison: exome sequencing as a single test replacing Sanger sequencing

Next generation sequencing tests are used routinely as first-choice tests in the clinic. However, systematic performance comparing the results of exome sequencing as a single test replacing Sanger sequencing of targeted gene(s) is still lacking. Performance comparison data are critically important for clinical case management. In this study, we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger sequencing was completed for all exons, including flanking ± 8 bp regions. For the 258 genes, NGS mean coverage was > 20 × for > 98 and > 91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger sequencing and 97-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger sequencing and NGS performances. Both methods demonstrated false-positive and false-negative calls. High clinical suspicion for a specific diagnosis should, therefore, override negative results of either Sanger sequencing or NGS.

SIK1-LNC represses the proliferative, migrative, and invasive abilities of lung cancer cells

Background

Discussions regarding the correlations between long non-coding RNAs (lncRNAs) and cancers have dominated research in recent years. SIK1-LNC, a type of lncRNA and adjacent to salt-inducible kinases 1 (SIK1), has been found aberrantly expressed in lung cancer. However, its functional role in lung cancer remains largely unknown.

Purpose

In this study, we aimed to explore the association between SIK1-LNC expression and SIK1 in lung cancer cells and further identify the impact of SIK1-LNC on the proliferation, migration invasion of lung cancer cells.

Patients and methods

Of the 30 patients with non-small-cell lung carcinoma from Zhongnan Hospital of Wuhan University, RT-qPCR was performed to detect SIK1 and SIK1-LNC expressions in patients’ samples. Overexpression and knockdown experiments were conducted to analyze the SIK1 and SIK1-LNC expressions in lung cancer cell lines. CCK-8, Brdu, scratch wound-healing, and Transwell assays were respectively employed to evaluate the proliferative, migrative, and invasive abilities of lung cancer cells.

Results

Both SIK1-LNC and SIK1 expression levels were evidently downregulated in 30 lung cancer tissues. SIK1-LNC expression was bound up with clinicopathologic features, including lymph node metastasis and distant metastasis. SIK1 expression showed a positive tendency with SIK1-LNC expression in lung cancer cells. SIK1-LNC exerted a significant repression on cell proliferatiive, miogrative and invasive abilities of lung cancer cells.

Conclusion

Our findings suggested that SIK1-LNC may act as a novel biomarker and therapeutic target for lung cancer.

Genome-wide discovery of lincRNAs with spatiotemporal expression patterns in the skin of goat during the cashmere growth cycle

Background

Long intergenic noncoding RNAs (lincRNAs) have been recognized in recent years as key regulators of biological processes. However, lincRNAs in goat remain poorly characterized both across various tissues and during different developmental stages in goat (Capra hircus).

Results

We performed the genome-wide discovery of the lincRNAs in goat by combining the RNA-seq dataset that were generated from 28 cashmere goat skin samples and the 12 datasets of goat tissues downloaded from the NCBI database. We identified a total of 5546 potential lincRNA transcripts that overlapped 3641 lincRNA genes. These lincRNAs exhibited a tissue-specific pattern. Specifically, there are 584 lincRNAs expressed exclusively in only one tissue, and 91 were highly expressed in hair follicle (HF). In addition, 2350 protein-coding genes and 492 lincRNAs were differentially expressed in the skin of goat. The majority exhibited the remarkable differential expression during the transition of the goat skin from the May–June to August–October time point, which covered the different seasons. Fundamental biological processes, such as skin development, were significantly enriched in these genes. Furthermore, we identified several lincRNAs highly expressed in the HF, which exhibited not only the co-expression pattern with the key factors to the HF development but also the activated expression in the August to October time point. Intriguingly, one of spatiotemporal lincRNAs, linc-chig1598 could be a potential regulator of distal-less homeobox 3 expression during the secondary hair follicle growth.

Conclusions

This study will facilitate future studies aimed at unravelling the function of lincRNAs in hair follicle development.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4864-x) contains supplementary material, which is available to authorized users.

Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA

Long intergenic non-coding RNAs have been shown to play important roles in cancer. However, because lincRNAs are a relatively new class of RNAs compared to protein-coding mRNAs, the mutational landscape of lincRNAs has not been as extensively studied. Here we characterize expressed somatic nucleotide variants within lincRNAs using 12 cancer RNA-Seq datasets in TCGA. We build machine-learning models to discriminate somatic variants from germline variants within lincRNA regions (AUC 0.987). We build another model to differentiate lincRNA somatic mutations from background regions (AUC 0.72) and find several molecular features that are strongly associated with lincRNA mutations, including copy number variation, conservation, substitution type and histone marker features.

Deep Illumina sequencing reveals differential expression of long non-coding RNAs in hyperoxia induced bronchopulmonary dysplasia in a rat model

BACKGROUND

Bronchopulmonary dysplasia (BPD) in premature infants is a predominantly secondary occurrence to intrauterine inflammation/infection and postpartum mechanical ventilation; The purpose of this study is to explore the biological roles of lincRNA in the pathogenesis of BPD.

METHODS

Newborn rats were randomly assigned to hyperoxia (85% O2) or the control group: the normoxia group (21% O2). Lung tissues were collected on days 1-14. The BPD animal model was validated using HE staining, Masson staining, and real-time RT-PCR. Deep Illumina sequencing was used to reveal the differential expression of long non-coding RNAs in hyperoxia bronchopulmonary dysplasia rat models. KEGG and GO functions were predicted. Nine possible BPD-related target lincRNAs were verified by RTq-PCR.

RESULTS

The histopathologic changes in lung tissues manifested as hyperaemia, edema, hemorrhage, and inflammation cell infiltration after continuous exposure to hyperoxia for 3 days, and became aggravated after 7 days of hyperoxic exposure. The above lung tissue inflammatory manifestations were alleviated and taken over by pulmonary interstitia hyperplasia and fibrocyte proliferation after 14 days of hyperoxic exposure. The expressions of lincRNA differed between the hyperoxia bronchopulmonary dysplasia model group and the normoxia group. 1175 different lincRNAs were detected in the hyperoxia group and the normoxia group, of which 544 were up-regulated and 631 were down-regulated. 673 moleculars related to GO functions were enriched, including cell location and biological process. Pathway enrichment analysis showed that lincRNA was involved in 257 KEGG pathways. 9 lincRNA were validated in the sample, and the difference was statistically significant.

CONCLUSION

LincRNAs were identified differently between the BPD model and the normoxia group. Many target genes were involved in the developmental process, including cell component biogenesis, biological regulation, transcription regulator, and translation regulator. The BPD might be caused by the activation of the pathways of the EMC-receptor interaction, cytokine-cytokine receptor interaction, cell cycle, and cell adhesion molecules. The present study provides new insight into the pathogenesis mechanism of BPD.

RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking

BACKGROUND

Cigarette smoking is the leading modifiable risk factor for disease and death worldwide. Previous studies quantifying gene-level expression have documented the effect of smoking on mRNA levels. Using RNA sequencing, it is possible to analyze the impact of smoking on complex regulatory phenomena (e.g. alternative splicing, differential isoform usage) leading to a more detailed understanding of the biology underlying smoking-related disease.

METHODS

We used whole-blood RNA sequencing to describe gene and exon-level expression differences between 229 current and 286 former smokers in the COPDGene study. We performed differential gene expression and differential exon usage analyses using the voom/limma and DEXseq R packages. Samples from current and former smokers were compared while controlling for age, gender, race, lifetime smoke exposure, cell counts, and technical covariates.

RESULTS

At an adjusted p-value <0.05, 171 genes were differentially expressed between current and former smokers. Differentially expressed genes included 7 long non-coding RNAs that have not been previously associated with smoking: LINC00599, LINC01362, LINC00824, LINC01624, RP11-563D10.1, RP11-98G13.1, AC004791.2. Secondary analysis of acute smoking (having smoked within 2-h) revealed 5 of the 171 smoking genes demonstrated an acute response above the baseline effect of chronic smoking. Exon-level analyses identified 9 exons from 8 genes with significant differential usage by smoking status, suggesting smoking-induced changes in isoform expression.

CONCLUSIONS

Transcriptomic changes at the gene and exon levels from whole blood can refine our understanding of the molecular mechanisms underlying the response to smoking.

Linc-DYNC2H1-4 promotes EMT and CSC phenotypes by acting as a sponge of miR-145 in pancreatic cancer cells

The acquisition of epithelial-mesenchymal transition (EMT) and/or existence of a sub-population of cancer stem-like cells (CSC) are associated with malignant behavior and chemoresistance. To identify which factor could promote EMT and CSC formation and uncover the mechanistic role of such factor is important for novel and targeted therapies. In the present study, we found that the long intergenic non-coding RNA linc-DYNC2H1-4 was upregulated in pancreatic cancer cell line BxPC-3-Gem with acquired gemcitabine resistance. Knockdown of linc-DYNC2H1-4 decreased the invasive behavior of BxPC-3-Gem cells while ectopic expression of linc-DYNC2H1-4 promoted the acquisition of EMT and stemness of the parental sensitive cells. Linc-DYNC2H1-4 upregulated ZEB1, the EMT key player, which led to upregulation and downregulation of its targets vimentin and E-cadherin respectively, as well as enhanced the expressions of CSC makers Lin28, Nanog, Sox2 and Oct4. Linc-DYNC2H1-4 is mainly located in the cytosol. Mechanically, it could sponge miR-145 that targets ZEB1, Lin28, Nanog, Sox2, Oct4 to restore these EMT and CSC-associated genes expressions. We proved that MMP3, the nearby gene of linc-DYNC2H1-4 in the sense strand, was also a target of miR-145. Downregulation of MMP3 by miR-145 was reverted by linc-DYNC2H1-4, indicating that competing with miR-145 is one of the mechanisms for linc-DYNC2H1-4 to regulate MMP3. In summary, our results explore the important role of linc-DYNC2H1-4 in the acquisition of EMT and CSC, and the impact it has on gemcitabine resistance in pancreatic cancer cells.

Long noncoding RNA (lincRNA), a new paradigm in gene expression control

Long intergenic non-coding RNAs (lincRNAs) are defined as RNA transcripts that are longer than 200 nucleotides. By definition, these RNAs must not have open reading frames that encode proteins. Many of these transcripts are encoded by RNA polymerase II, are spliced, and are poly-adenylated. This final fact indicates that there is a trove of information about lincRNAs in databases such as the Gene Expression Omnibus (GEO), which is a repository for RNAseq and microarray data. Recent experiments indicate that there are upwards of 15,000 lincRNAs encoded by the human genome. The term "intergenic" refers to the identification of these transcripts from regions of the genome that do not contain protein-encoding genes. These regions coincide with what was once labeled as the "junk DNA" portions of our genomes, which, upon careful examination by whole genome RNA sequencing experiments, clearly encode RNA transcripts. LincRNAs also contain promoter- or enhancer-associated RNAs that are gene proximal and can be either in the sense or antisense orientation, relative to the protein-coding gene with which they are associated. In this review, we describe the functions of lincRNAs playing roles in biological processes such as gene expression control, scaffold formation, and epigenetic control.

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

The emerging single-cell RNA-Seq (scRNA-Seq) technology holds the promise to revolutionize our understanding of diseases and associated biological processes at an unprecedented resolution. It opens the door to reveal intercellular heterogeneity and has been employed to a variety of applications, ranging from characterizing cancer cells subpopulations to elucidating tumor resistance mechanisms. Parallel to improving experimental protocols to deal with technological issues, deriving new analytical methods to interpret the complexity in scRNA-Seq data is just as challenging. Here, we review current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis, as well as addressing some critical analytical challenges that the field faces.

Genome-Wide Anaplasma phagocytophilum AnkA-DNA Interactions Are Enriched in Intergenic Regions and Gene Promoters and Correlate with Infection-Induced Differential Gene Expression

Anaplasma phagocytophilum, an obligate intracellular prokaryote, infects neutrophils, and alters cardinal functions via reprogrammed transcription. Large contiguous regions of neutrophil chromosomes are differentially expressed during infection. Secreted A. phagocytophilum effector AnkA transits into the neutrophil or granulocyte nucleus to complex with DNA in heterochromatin across all chromosomes. AnkA binds to gene promoters to dampen cis-transcription and also has features of matrix attachment region (MAR)-binding proteins that regulate three-dimensional chromatin architecture and coordinate transcriptional programs encoded in topologically-associated chromatin domains. We hypothesize that identification of additional AnkA binding sites will better delineate how A. phagocytophilum infection results in reprogramming of the neutrophil genome. Using AnkA-binding ChIP-seq, we showed that AnkA binds broadly throughout all chromosomes in a reproducible pattern, especially at: (i) intergenic regions predicted to be MARs; (ii) within predicted lamina-associated domains; and (iii) at promoters ≤ 3000 bp upstream of transcriptional start sites. These findings provide genome-wide support for AnkA as a regulator of cis-gene transcription. Moreover, the dominant mark of AnkA in distal intergenic regions known to be AT-enriched, coupled with frequent enrichment in the nuclear lamina, provides strong support for its role as a MAR-binding protein and genome “re-organizer.” AnkA must be considered a prime candidate to promote neutrophil reprogramming and subsequent functional changes that belie improved microbial fitness and pathogenicity.