Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

Tailored machine learning models for functional RNA detection in genome-wide screens

The in silico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

Comparative Transcriptome Profiling of mRNA and lncRNA of Mouse Spleens Inoculated with the Group ACYW135 Meningococcal Polysaccharide Vaccine

The Group ACYW135 meningococcal polysaccharide vaccine (MPV-ACYW135) is a classical common vaccine used to prevent Neisseria meningitidis serogroups A, C, Y, and W135, but studies on the vaccine at the transcriptional level are still limited. In the present study, mRNAs and lncRNAs related to immunity were screened from the spleens of mice inoculated with MPV-ACYW135 and compared with the control group to identify differentially expressed mRNAs and lncRNAs in the immune response. The result revealed 34375 lncRNAs and 41321 mRNAs, including 405 differentially expressed (DE) lncRNAs and 52 DE mRNAs between the MPV group and the control group. Results of GO and KEGG enrichment analysis turned out that the main pathways related to the immunity of target genes of those DE mRNAs and DE lncRNAs were largely associated with positive regulation of T cell activation, CD8-positive immunoglobulin production in mucosal tissue, alpha-beta T cell proliferation, negative regulation of CD4-positive, and negative regulation of interleukin-17 production, suggesting that the antigens of MPV-ACYW135 capsular polysaccharide might activate T cell related immune reaction in the vaccine inoculation. In addition, it was noted that Bach2 (BTB and CNC homolog 2), the target gene of lncRNA MSTRG.17645, was involved in the regulation of immune response in MPV-ACYW135 vaccination. This study provided a preliminary catalog of both mRNAs and lncRNAs associated with the proliferation and differentiation of body immune cells, which was worthy of further research to enhance the understanding of the biological immune process regulated by MPV-ACYW135.

Analysis of Long Non-Coding RNA-Mediated Regulatory Networks of Plutella xylostella in Response to Metarhizium anisopliae Infection

Long non-coding RNAs (lncRNAs) represent a diverse class of RNAs that are structurally similar to messenger RNAs (mRNAs) but do not encode proteins. Growing evidence suggests that in response to biotic and abiotic stresses, the lncRNAs play crucial regulatory roles in plants and animals. However, the potential role of lncRNAs during fungal infection has yet to be characterized in Plutella xylostella, a devastating pest of cruciferous crops. In the current study, we performed a strand-specific RNA sequencing of Metarhizium anisopliae-infected (Px36hT, Px72hT) and uninfected (Px36hCK, Px72hCK) P. xylostella fat body tissues. Comprehensive bioinformatic analysis revealed a total of 5665 and 4941 lncRNAs at 36 and 72-h post-infection (hpi), including 563 (Px36hT), 532 (Px72hT) known and 5102 (Px36hT), 4409 (Px72hT) novel lncRNA transcripts. These lncRNAs shared structural similarities with their counterparts in other species, including shorter exon and intron length, fewer exon numbers, and a lower expression profile than mRNAs. LncRNAs regulate the expression of neighboring protein-coding genes by acting in a cis and trans manner. Functional annotation and pathway analysis of cis-acting lncRNAs revealed their role in several immune-related genes, including Toll, serpin, transferrin, βGRP etc. Furthermore, we identified multiple lncRNAs acting as microRNA (miRNA) precursors. These miRNAs can potentially regulate the expression of mRNAs involved in immunity and development, suggesting a crucial lncRNA-miRNA-mRNA complex. Our findings will provide a genetic resource for future functional studies of lncRNAs involved in P. xylostella immune responses to M. anisopliae infection and shed light on understanding insect host-pathogen interactions.

Whole transcriptome RNA Sequencing Reveals the Global Molecular Responses and circRNA/lncRNA-miRNA-mRNA ceRNA Regulatory Network in Chicken Fat Deposition

Fat deposition is a vital factor affecting the economics of poultry production. Numerous studies on fat deposition have been done. However, the molecular regulatory mechanism is still unclear. In the present study, the whole-transcriptome RNA sequencing in abdominal fat, back skin, and liver both high- and low-abdominal fat groups was used to uncover the competitive endogenous RNA (ceRNA) regulation network related to chicken fat deposition. The results showed that differentially expressed (DE) genes in abdominal fat, back skin, liver were 1207(784 mRNAs, 330 lncRNAs, 41 circRNAs, 52 miRNAs), 860 (607 mRNAs, 166 lncRNAs, 26 circRNAs, 61 miRNAs), and 923 (501 mRNAs, 262 lncRNAs, 15 circRNAs, 145 miRNAs), respectively. The ceRNA regulatory network analysis indicated that the fatty acid metabolic process, monocarboxylic acid metabolic process, carboxylic acid metabolic process, glycerolipid metabolism, fatty acid metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathway took part in chicken fat deposition. Meanwhile, we scan the important genes, FADS2, HSD17B12, ELOVL5, AKR1E2, DGKQ, GPAM, PLIN2, which were regulated by gga-miR-460b-5p, gga-miR-199-5p, gga-miR-7470-3p, gga-miR-6595-5p, gga-miR-101-2-5p. While these miRNAs were competitive combined by lncRNAs including MSTRG.18043, MSTRG.7738, MSTRG.21310, MSTRG.19577, and circRNAs including novel_circ_PTPN2, novel_circ_CTNNA1, novel_circ_PTPRD. This finding provides new insights into the regulatory mechanism of mRNA, miRNA, lncRNA, and circRNA in chicken fat deposition.

Identification of Long Noncoding RNAs Involved in Eyelid Pigmentation of Hereford Cattle

Several ocular pathologies in cattle, such as ocular squamous cell carcinoma and infectious keratoconjunctivitis, have been associated with low pigmentation of the eyelids. The main objective of this study was to analyze the transcriptome of eyelid skin in Hereford cattle using strand-specific RNA sequencing technology to characterize and identify long noncoding RNAs (lncRNAs). We compared the expression of lncRNAs between pigmented and unpigmented eyelids and analyzed the interaction of lncRNAs and putative target genes to reveal the genetic basis underlying eyelid pigmentation in cattle. We predicted 4,937 putative lncRNAs mapped to the bovine reference genome, enriching the catalog of lncRNAs in Bos taurus. We found 27 differentially expressed lncRNAs between pigmented and unpigmented eyelids, suggesting their involvement in eyelid pigmentation. In addition, we revealed potential links between some significant differentially expressed lncRNAs and target mRNAs involved in the immune response and pigmentation. Overall, this study expands the catalog of lncRNAs in cattle and contributes to a better understanding of the biology of eyelid pigmentation.

Common Features in lncRNA Annotation and Classification: A Survey

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

A systematic evaluation of the computational tools for lncRNA identification

The computational identification of long non-coding RNAs (lncRNAs) is important to study lncRNAs and their functions. Despite the existence of many computation tools for lncRNA identification, to our knowledge, there is no systematic evaluation of these tools on common datasets and no consensus regarding their performance and the importance of the features used. To fill this gap, in this study, we assessed the performance of 17 tools on several common datasets. We also investigated the importance of the features used by the tools. We found that the deep learning-based tools have the best performance in terms of identifying lncRNAs, and the peptide features do not contribute much to the tool accuracy. Moreover, when the transcripts in a cell type were considered, the performance of all tools significantly dropped, and the deep learning-based tools were no longer as good as other tools. Our study will serve as an excellent starting point for selecting tools and features for lncRNA identification.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Are spliced ncRNA host genes distinct classes of lncRNAs?

Many small nucleolar RNAs and many of the hairpin precursors of miRNAs are processed from long non-protein-coding host genes. In contrast to their highly conserved and heavily structured payload, the host genes feature poorly conserved sequences. Nevertheless, there is mounting evidence that the host genes have biological functions beyond their primary task of carrying a ncRNA as payload. So far, no connections between the function of the host genes and the function of their payloads have been reported. Here we investigate whether there is evidence for an association of host gene function or mechanisms with the type of payload. To assess this hypothesis we test whether the miRNA host genes (MIRHGs), snoRNA host genes (SNHGs), and other lncRNA host genes can be distinguished based on sequence and/or structure features unrelated to their payload. A positive answer would imply a functional and mechanistic correlation between host genes and their payload, provided the classification does not depend on the presence and type of the payload. A negative answer would indicate that to the extent that secondary functions are acquired, they are not strongly constrained by the prior, primary function of the payload. We find that the three classes can be distinguished reliably when the classifier is allowed to extract features from the payloads. They become virtually indistinguishable, however, as soon as only sequence and structure of parts of the host gene distal from the snoRNAs or miRNA payload is used for classification. This indicates that the functions of MIRHGs and SNHGs are largely independent of the functions of their payloads. Furthermore, there is no evidence that the MIRHGs and SNHGs form coherent classes of long non-coding RNAs distinguished by features other than their payloads.

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges

Long noncoding RNAs (lncRNAs) make up a large proportion of transcriptome in eukaryotes, and have been revealed with many regulatory functions in various biological processes. When studying lncRNAs, the first step is to accurately and specifically distinguish them from the colossal transcriptome data with complicated composition, which contains mRNAs, lncRNAs, small RNAs and their primary transcripts. In the face of such a huge and progressively expanding transcriptome data, the in-silico approaches provide a practicable scheme for effectively and rapidly filtering out lncRNA targets, using machine learning and probability statistics. In this review, we mainly discussed the characteristics of algorithms and features on currently developed approaches. We also outlined the traits of some state-of-the-art tools for ease of operation. Finally, we pointed out the underlying challenges in lncRNA identification with the advent of new experimental data.

Long Noncoding RNA HOXA11-AS Modulates the Resistance of Nasopharyngeal Carcinoma Cells to Cisplatin via miR-454-3p/c-Met

To elucidate the mechanism of action of HOXA11-AS in modulating the cisplatin resistance of nasopharyngeal carcinoma (NPC) cells. HOXA11-AS and miR-454-3p expression in NPC tissue and cisplatin-resistant NPC cells were measured via quantitative reverse transcriptase polymerase chain reaction. NPC parental cells (C666-1 and HNE1) and cisplatin-resistant cells (C666-1/DDP and HNE1/DDP) were transfected and divided into different groups, after which the MTT method was used to determine the inhibitory concentration 50 (IC50) of cells treated with different concentrations of cisplatin. Additionally, a clone formation assay, flow cytometry and Western blotting were used to detect DDP-induced changes. Thereafter, xenograft mouse models were constructed to verify the in vitro results. Obviously elevated HOXA11-AS and reduced miR-454-3p were found in NPC tissue and cisplatin-resistant NPC cells. Compared to the control cells, cells in the si-HOXA11-AS group showed sharp decreases in cell viability and IC50, and these results were reversed in the miR-454-3p inhibitor group. Furthermore, HOXA11-AS targeted miR-454-3p, which further targeted c-Met. In comparison with cells in the control group, HNE1/DDP and C666-1/DDP cells in the si-HOXA11-AS group demonstrated fewer colonies, with an increase in the apoptotic rate, while the expression levels of c-Met, p-Akt/Akt and p-mTOR/mTOR decreased. Moreover, the si-HOXA11-AS-induced enhancement in sensitivity to cisplatin was abolished by miR-454-3p inhibitor transfection. The in vivo experiment showed that DDP in combination with si-HOXA11-AS treatment could inhibit the growth of xenograft tumors. Silencing HOXA11-AS can inhibit the c-Met/AKT/mTOR pathway by specifically upregulating miR-454-3p, thus promoting cell apoptosis and enhancing the sensitivity of cisplatin-resistant NPC cells to cisplatin.

LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers’ requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA–protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

The peptide encoded by a novel putative lncRNA HBVPTPAP inducing the apoptosis of hepatocellular carcinoma cells by modulating JAK/STAT signaling pathways

When the hepatitis B virus (HBV) enters target cells, there are complex trans-regulatory mechanisms involved in the interactions between the virus and the target cells. In the present study, a new gene screened from the hepatoblastoma cell line HepG2 using suppression subtractive hybridization, referred to as lncRNA HBVPTPAP, was used to study the trans-regulation of HBV DNA polymerase. According to the structural characteristics of the full-length sequences, it was classified as long non-coding RNA. However, a unique and complete open reading frame (ORF) was still present. Therefore, to further identify the lncRNA HBVPTPAP gene's encoding potential, this study used several online tools to analyze and verify its encoding polypeptide authenticity. On that basis, the effects of the lncRNA HBVPTPAP gene on the biological behaviors of HepG2 cells and its molecular regulatory mechanism were investigated. It was found that the lncRNA HBVPTPAP subcellular was mainly located in the cytoplasm, and possibly activated the downstream JAK/STAT signaling pathway through the interaction between the encoding polypeptide and PILRA intracellular domain. Then, the mitochondrial apoptosis pathway may have been initiated to induce apoptosis. These results provided a basis for further study of the biological functions of the lncRNA HBVPTPAP gene.

An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools

The CRISPR-Cas9 system has become the most promising and versatile tool for genetic manipulation applications. Albeit the technology has been broadly adopted by both academic and pharmaceutic societies, the activity (on-target) and specificity (off-target) of CRISPR-Cas9 are decisive factors for any application of the technology. Several in silico gRNA activity and specificity predicting models and web tools have been developed, making it much more convenient and precise for conducting CRISPR gene editing studies. In this review, we present an overview and comparative analysis of machine and deep learning (MDL)-based algorithms, which are believed to be the most effective and reliable methods for the prediction of CRISPR gRNA on- and off-target activities. As an increasing number of sequence features and characteristics are discovered and are incorporated into the MDL models, the prediction outcome is getting closer to experimental observations. We also introduced the basic principle of CRISPR activity and specificity and summarized the challenges they faced, aiming to facilitate the CRISPR communities to develop more accurate models for applying.

Transcriptome and Phytochemical Analyses Provide New Insights Into Long Non-Coding RNAs Modulating Characteristic Secondary Metabolites of Oolong Tea (Camellia sinensis) in Solar-Withering

Oolong tea is a popular and semi-fermented beverage. During the processing of tea leaves, withering is the first indispensable process for improving flavor. However, the roles of long non-coding RNAs (lncRNAs) and the characteristic secondary metabolites during the withering of oolong tea leaves remain unknown. In this study, phytochemical analyses indicated that total polyphenols, flavonoids, catechins, epigallocatechin (EGC), catechin gallate (CG), gallocatechin gallate (GCG), epicatechin gallate (ECG), and epigallocatechin gallate (EGCG) were all less abundant in the solar-withered leaves (SW) than in the fresh leaves (FL) and indoor-withered leaves (IW). In contrast, terpenoid, jasmonic acid (JA), and methyl jasmonate (MeJA) contents were higher in the SW than in the FL and IW. By analyzing the transcriptome data, we detected 32,036 lncRNAs. On the basis of the Kyoto Encyclopedia of Genes and Genomes analysis, the flavonoid metabolic pathway, the terpenoid metabolic pathway, and the JA/MeJA biosynthesis and signal transduction pathway were enriched pathways. Additionally, 63 differentially expressed lncRNAs (DE-lncRNAs) and 23 target genes were identified related to the three pathways. A comparison of the expression profiles of the DE-lncRNAs and their target genes between the SW and IW revealed four up-regulated genes (FLS, CCR, CAD, and HCT), seven up-regulated lncRNAs, four down-regulated genes (4CL, CHI, F3H, and F3'H), and three down-regulated lncRNAs related to flavonoid metabolism; nine up-regulated genes (DXS, CMK, HDS, HDR, AACT, MVK, PMK, GGPPS, and TPS), three up-regulated lncRNAs, and six down-regulated lncRNAs related to terpenoid metabolism; as well as six up-regulated genes (LOX, AOS, AOC, OPR, ACX, and MFP2), four up-regulated lncRNAs, and three down-regulated lncRNAs related to JA/MeJA biosynthesis and signal transduction. These results suggested that the expression of DE-lncRNAs and their targets involved in the three pathways may be related to the low abundance of the total polyphenols, flavonoids, and catechins (EGC, CG, GCG, ECG, and EGCG) and the high abundance of terpenoids in the SW. Moreover, solar irradiation, high JA and MeJA contents, and the endogenous target mimic (eTM)-related regulatory mechanism in the SW were also crucial for increasing the terpenoid levels. These findings provide new insights into the greater contribution of solar-withering to the high-quality flavor of oolong tea compared with the effects of indoor-withering.

A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network

Background

Long-chain non-coding RNA (lncRNA) is closely related to many biological activities. Since its sequence structure is similar to that of messenger RNA (mRNA), it is difficult to distinguish between the two based only on sequence biometrics. Therefore, it is particularly important to construct a model that can effectively identify lncRNA and mRNA.

Results

First, the difference in the k-mer frequency distribution between lncRNA and mRNA sequences is considered in this paper, and they are transformed into the k-mer frequency matrix. Moreover, k-mers with more species are screened by relative entropy. The classification model of the lncRNA and mRNA sequences is then proposed by inputting the k-mer frequency matrix and training the convolutional neural network. Finally, the optimal k-mer combination of the classification model is determined and compared with other machine learning methods in humans, mice and chickens. The results indicate that the proposed model has the highest classification accuracy. Furthermore, the recognition ability of this model is verified to a single sequence.

Conclusion

We established a classification model for lncRNA and mRNA based on k-mers and the convolutional neural network. The classification accuracy of the model with 1-mers, 2-mers and 3-mers was the highest, with an accuracy of 0.9872 in humans, 0.8797 in mice and 0.9963 in chickens, which is better than those of the random forest, logistic regression, decision tree and support vector machine.

A novel long non-coding RNA regulates the immune response in MAC-T cells and contributes to bovine mastitis

The long non-coding RNAs (lncRNAs) are known to transcriptionally regulate a wide spectrum of diseases. Here, we screened for potentially functional lncRNAs in a mammary epithelial cell model of bovine mastitis by RNA-Seq technology and identified a class of previously undetected mastitis-related lncRNAs. A novel lncRNA was widely expressed in a variety of bovine tissues with diverse relative abundance and had a relatively low expression in mammary tissue. Given its predicted target gene is TUBA1C, we name it lncRNA-TUB. We found a higher expression of lncRNA-TUB in mammary epithelial cells that received a proinflammatory stimulus compared to normal cells. Knockout of lncRNA-TUB by the CRISPR/Cas9 system revealed that it plays crucial roles in the morphological shape, proliferation, migration and β-casein secretion of mammary epithelial cells. In addition, lncRNA-TUB mediates Escherichia coli-induced inflammatory factor secretion and Staphylococcus aureus adhesion to epithelial cells. Our results suggest that the lncRNAs identified here function in bovine mastitis, and that lncRNA-TUB affects the basic biological characteristics and functions of bovine mammary epithelial cells in inflammatory conditions, providing valuable insights into the mechanisms of bovine mastitis.

Comparative transcriptome analysis reveals the genetic basis of coat color variation in Pashmina goat

The genetics of coat color variation remains a classic area. Earlier studies have focused on a limited number of genes involved in color determination; however, the complete set of trait determinants are still not well known. In this study, we used high-throughput sequencing technology to identify and characterize intricate interactions between genes that cause complex coat color variation in Changthangi Pashmina goats, producer of finest and costly commercial animal fiber. We systematically identified differentially expressed mRNAs and lncRNAs from black, brown and white Pashmina goat skin samples by using RNA-sequencing technique. A pairwise comparison of black, white and brown skin samples yielded 2479 significantly dysregulated genes (2422 mRNA and 57 lncRNAs). Differentially expressed genes were enriched in melanin biosynthesis, melanocyte differentiation, developmental pigmentation, melanosome transport activities GO terms. Our analysis suggested the potential role of lncRNAs on color coding mRNAs in cis and trans configuration. We have also developed online data repository as a component of the study to provide a central location for data access, visualization and interpretation accessible through http://pcd.skuastk.org/.

Integrated analysis of lncRNA and mRNA repertoires in Marek's disease infected spleens identifies genes relevant to resistance

BACKGROUND

Marek's disease virus (MDV) is an oncogenic herpesvirus that can cause T-cell lymphomas in chicken. Long noncoding RNA (lncRNA) is strongly associated with various cancers and many other diseases. In chickens, lncRNAs have not been comprehensively identified. Here, we profiled mRNA and lncRNA repertoires in three groups of spleens from MDV-infected and non-infected chickens, including seven tumorous spleens (TS) from MDV-infected chickens, five spleens from the survivors (SS) without lesions after MDV infection, and five spleens from noninfected chickens (NS), to explore the underlying mechanism of host resistance in Marek's disease (MD).

RESULTS

By using a precise lncRNA identification pipeline, we identified 1315 putative lncRNAs and 1166 known lncRNAs in spleen tissue. Genomic features of putative lncRNAs were characterized. Differentially expressed (DE) mRNAs, putative lncRNAs, and known lncRNAs were profiled among three groups. We found that several specific intergroup differentially expressed genes were involved in important biological processes and pathways, including B cell activation and the Wnt signaling pathway; some of these genes were also found to be the hub genes in the co-expression network analyzed by WGCNA. Network analysis depicted both intergenic correlation and correlation between genes and MD traits. Five DE lncRNAs including MSTRG.360.1, MSTRG.6725.1, MSTRG.6754.1, MSTRG.15539.1, and MSTRG.7747.5 strongly correlated with MD-resistant candidate genes, such as IGF-I, CTLA4, HDAC9, SWAP70, CD72, JCHAIN, CXCL12, and CD8B, suggesting that lncRNAs may affect MD resistance and tumorigenesis in chicken spleens through their target genes.

CONCLUSIONS

Our results provide both transcriptomic and epigenetic insights on MD resistance and its pathological mechanism. The comprehensive lncRNA and mRNA transcriptomes in MDV-infected chicken spleens were profiled. Co-expression analysis identified integrated lncRNA-mRNA and gene-gene interaction networks, implying that hub genes or lncRNAs exert critical influence on MD resistance and tumorigenesis.

Transcriptome Analysis Suggests the Roles of Long Intergenic Non-coding RNAs in the Growth Performance of Weaned Piglets

Long intergenic non-coding RNAs (lincRNAs) have been considered to play a key regulatory role in various biological processes. An increasing number of studies have utilized transcriptome analysis to obtain lincRNAs with functions related to cancer, but lincRNAs affecting growth rates in weaned piglets are rarely described. Although lincRNAs have been systematically identified in various mouse tissues and cell lines, studies of lincRNA in pigs remain rare. Therefore, identifying and characterizing novel lincRNAs affecting the growth performance of weaned piglets is of great importance. Here, we reconstructed 101,988 lincRNA transcripts and identified 1,078 lincRNAs in two groups of longissimus dorsi muscle (LDM) and subcutaneous fat (SF) based on published RNA-seq datasets. These lincRNAs exhibit typical characteristics, such as shorter lengths and lower expression relative to protein-encoding genes. Gene ontology analysis revealed that some lincRNAs could be involved in weaned piglet related processes, such as insulin resistance and the AMPK signaling pathway. We also compared the positional relationship between differentially expressed lincRNAs (DELs) and quantitative trait loci (QTL) and found that some of DELs may play an important role in piglet growth and development. Our work details part of the lincRNAs that may affect the growth performance of weaned piglets and promotes future studies of lincRNAs for molecular-assisted development in weaned piglets.

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts

Background

In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.

Methods

The proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.

Results

This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.

Conclusions

The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4178-4) contains supplementary material, which is available to authorized users.

Identification of key lncRNAs in colorectal cancer progression based on associated protein-protein interaction analysis

BACKGROUND

Colorectal cancer (CRC) was one of the most commonly diagnosed malignancies. The molecular mechanisms involved in the progression of CRC remain unclear. Accumulating evidences showed that long noncoding RNAs (lncRNAs) played key roles in tumorigenesis, cancer progression, and metastasis. Therefore, we aimed to explore the roles of lncRNAs in the progression of CRC.

METHODS

In this study, we aimed to identify differentially expressed lncRNAs and messenger RNAs (mRNAs) in CRC by analyzing a cohort of previously published datasets: GSE64857. GO and KEGG pathway analyses were applied to give us insight in the functions of those lncRNAs and mRNAs in CRC.

RESULTS

Totally, 46 lncRNAs were identified as differentially expressed between stage II and stage III CRC for the first time screening by microarray. GO and KEGG pathway analyses showed that differentially expressed lncRNAs were involved in regulating signal transduction, cell adhesion, cell differentiation, focal adhesion, and cell adhesion molecules.

CONCLUSIONS

We found three lncRNAs (LOC100129973, PGM5-AS1, and TTTY10) widely co-expressed with differentially expressed mRNAs. We also constructed lncRNA-associated PPI in CRC and found that these lncRNAs may be associated with CRC progression. Moreover, we found that high PGM5-AS1 expression levels were associated with worse overall survival in CRC cancer. We believe that this study would provide novel potential therapeutic and prognostic targets for CRC.