Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MOTIVATION

Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

RESULTS

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

AVAILABILITY

Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.

Vof16-miR-185-5p-GAP43 network improves the outcomes following spinal cord injury via enhancing self-repair and promoting axonal growth

INTRODUCTION

Self-repair of spinal cord injury (SCI) has been found in humans and experimental animals with partial recovery of neurological functions. However, the regulatory mechanisms underlying the spontaneous locomotion recovery after SCI are elusive.

AIMS

This study was aimed at evaluating the pathological changes in injured spinal cord and exploring the possible mechanism related to the spontaneous recovery.

RESULTS

Immunofluorescence staining was performed to detect GAP43 expression in lesion site after spinal cord transection (SCT) in rats. Then RNA sequencing and gene ontology (GO) analysis were employed to predict lncRNA that correlates with GAP43. LncRNA smart-silencing was applied to verify the function of lncRNA vof16 in vitro, and knockout rats were used to evaluate its role in neurobehavioral functions after SCT. MicroRNA sequencing, target scan, and RNA22 prediction were performed to further explore the underlying regulatory mechanisms, and miR-185-5p stands out. A miR-185-5p site-regulated relationship with GAP43 and vof16 was determined by luciferase activity analysis. GAP43-silencing, miR-185-5p-mimic/inhibitor, and miR-185-5p knockout rats were also applied to elucidate their effects on spinal cord neurite growth and neurobehavioral function after SCT. We found that a time-dependent increase of GAP43 corresponded with the limited neurological recovery in rats with SCT. CRNA chip and GO analysis revealed lncRNA vof16 was the most functional in targeting GAP43 in SCT rats. Additionally, silencing vof16 suppressed neurite growth and attenuated the motor dysfunction in SCT rats. Luciferase reporter assay showed that miR-185-5p competitively bound the same regulatory region of vof16 and GAP43.

CONCLUSIONS

Our data indicated miR-185-5p could be a detrimental factor in SCT, and vof16 may function as a ceRNA by competitively binding miR-185-5p to modulate GAP43 in the process of self-recovery after SCT. Our study revealed a novel vof16-miR-185-5p-GAP43 regulatory network in neurological self-repair after SCT and may underlie the potential treatment target for SCI.

Whole genome discovery of regulatory genes responsible for the response of chicken to heat stress

Long noncoding RNAs (lncRNAs) are functional bridges connecting the genome with phenotypes by interacting with DNA, mRNA, and proteins. Using publically available acute heat stress (AHS)-related RNA-seq data, we discovered novel lncRNAs and tested their association with AHS along with ~ 8800 known lncRNAs and ~ 28,000 mRNA transcripts. Our pipeline discovered a total of 145 potentially novel-lncRNAs. One of them (Fishcomb_p-value = 0.06) along with another novel transcript (annotated as protein-coding; Fishcomb_p-value = 0.03) were identified as significantly associated with AHS. We found five known-lncRNAs and 134 mRNAs transcripts that were significantly associated with AHS. Four novel lncRNAs interact cis-regulated with 12 mRNA transcripts and are targeted by 11 miRNAs. Also six meta-lncRNAs associate with 134 meta-mRNAs through trans-acting co-expression, each targeted by 15 and 216 miRNAs, respectively. Three of the known-lncRNAs significantly co-expressed with almost 97 of the significant mRNAs (Pearson correlation p-value < 0.05). We report the mentioned three known-lncRNAs (ENSGALT00000099876, ENSGALT00000107573, and ENSGALT00000106323) as the most, significantly regulatory elements of AHS in chicken. It can be concluded that in order to alleviate the adverse effects of AHS on chicken, the manipulation of the three regulatory lncRNAs could lead to a more desirable result than the manipulation of the most significant mRNAs.

Genome-Wide Identification and Characterization of Long Non-Coding RNAs Associated with Floral Scent Formation in Jasmine (Jasminum sambac)

Long non-coding RNAs (lncRNAs) have emerged as curial regulators of diverse biological processes in plants. Jasmine (Jasminum sambac) is a world-renowned ornamental plant for its attractive and exceptional flower fragrance. However, to date, no systematic screening of lncRNAs and their regulatory roles in the production of the floral fragrance of jasmine flowers has been reported. In this study, we identified a total of 31,079 novel lncRNAs based on an analysis of strand-specific RNA-Seq data from J. sambac flowers at different stages. The lncRNAs identified in jasmine flowers exhibited distinct characteristics compared with protein-coding genes (PCGs), including lower expression levels, shorter transcript lengths, and fewer exons. Certain jasmine lncRNAs possess detectable sequence conservation with other species. Expression analysis identified 2752 differentially expressed lncRNAs (DE_lncRNAs) and 8002 DE_PCGs in flowers at the full-blooming stage. DE_lncRNAs could potentially cis- and trans-regulate PCGs, among which DE_lincRNAs and their targets showed significant opposite expression patterns. The flowers at the full-blooming stage are specifically enriched with abundant phenylpropanoids and terpenoids potentially contributed by DE_lncRNA cis-regulated PCGs. Notably, we found that many cis-regulated DE_lncRNAs may be involved in terpenoid and phenylpropanoid/benzenoid biosynthesis pathways, which potentially contribute to the production of jasmine floral scents. Our study reports numerous jasmine lncRNAs and identifies floral-scent-biosynthesis-related lncRNAs, which highlights their potential functions in regulating the floral scent formation of jasmine and lays the foundations for future molecular breeding.

Maternal blood transcriptome as a sensor of fetal organ maturation at the end of organogenesis in cattle†

Harnessing information from the maternal blood to predict fetal growth is attractive yet scarcely explored in livestock. The objectives were to determine the transcriptomic modifications in maternal blood and fetal liver, gonads, and heart according to fetal weight and to model a molecular signature based on the fetal organs allowing the prediction of fetal weight from the maternal blood transcriptome in cattle. In addition to a contemporaneous maternal blood sample, organ samples were collected from 10 male fetuses at 42 days of gestation for RNA-sequencing. Fetal weight ranged from 1.25 to 1.69 g (mean = 1.44 ± 0.15 g). Clustering data analysis revealed clusters of co-expressed genes positively correlated with fetal weight and enriching ontological terms biologically relevant for the organ. For the heart, the 1346 co-expressed genes were involved in energy generation and protein synthesis. For the gonads, the 1042 co-expressed genes enriched seminiferous tubule development. The 459 co-expressed genes identified in the liver were associated with lipid synthesis and metabolism. Finally, the cluster of 571 co-expressed genes determined in maternal blood enriched oxidative phosphorylation and thermogenesis. Next, data from the fetal organs were used to train a regression model of fetal weight, which was predicted with the maternal blood data. The best prediction was achieved when the model was trained with 35 co-expressed genes overlapping between heart and maternal blood (root-mean-square error = 0.04, R2 = 0.93). In conclusion, linking transcriptomic information from maternal blood with that from the fetal heart unveiled maternal blood as a predictor of fetal development.

Prediction of lncRNA functions using deep neural networks based on multiple networks

BACKGROUND

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

RESULTS

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

CONCLUSIONS

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

Exploring lncRNAs associated with human pancreatic islet cell death induced by transfer of adoptive lymphocytes in a humanized mouse model

Background

Long noncoding RNA (lncRNA)-mediated posttranscriptional and epigenetic landscapes of gene regulation are associated with numerous human diseases. However, the regulatory mechanisms governing human β-cell function and survival remain unknown. Owing to technical and ethical constraints, studying the direct role of lncRNAs in β-cell function and survival in humans in vivo is difficult. Therefore, we utilized humanized mice with human islets to investigate lncRNA expression using whole transcriptome shotgun sequencing. Our study aimed to characterize lncRNAs that may be crucial for human islet cell function and survival.

Methods

Human β-cell death was induced in humanized mice engrafted with functional human islets. Using these humanized mice harboring human islets with induced β-cell death, we investigated lncRNA expression through whole transcriptome shotgun sequencing. Additionally, we systematically identified, characterized, and explored the regulatory functions of lncRNAs that are potentially important for human pancreatic islet cell function and survival.

Results

Human islet cell death was induced in humanized mice engrafted with functional human islets. RNA sequencing analysis of isolated human islets, islet grafts from humanized mice with and without induced cell death, revealed aberrant expression of a distinct set of lncRNAs that are associated with the deregulated mRNAs important for cellular processes and molecular pathways related to β-cell function and survival. A total of 10 lncRNA isoforms (SCYL1-1:22, POLG2-1:1, CTRB1-1:1, SRPK1-1:1, GTF3C5-1:1, PPY-1:1, CTRB1-1:5, CPA5-1:1, BCAR1-2:1, and CTRB1-1:4) were identified as highly enriched and specific to human islets. These lncRNAs were deregulated in human islets from donors with different BMIs and with type 2 diabetes (T2D), as well as in cultured human islets with glucose stimulation and induced cell death induced by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the medium used to culture islets with cytokines.

Conclusion

Islet-enriched and specific human lncRNAs are deregulated in human islet grafts and cultured human islets with induced cell death. These lncRNAs may be crucial for human β-cell function and survival and could have an impact on identifying biomarkers for β-cell loss and discovering novel therapeutic targets to enhance β-cell function and survival.

Genome-Wide Identification and Involvement in Response to Biotic and Abiotic Stresses of lncRNAs in Turbot (Scophthalmus maximus)

Long non-coding RNAs (lncRNAs) play crucial roles in a variety of biological processes, including stress response. However, the number, characteristics and stress-related expression of lncRNAs in turbot are still largely unknown. In this study, a total of 12,999 lncRNAs were identified at the genome-wide level of turbot for the first time using 24 RNA-seq datasets. Sequence characteristic analyses of transcripts showed that lncRNA transcripts were shorter in average length, lower in average GC content and in average expression level as compared to the coding genes. Expression pattern analyses of lncRNAs in 12 distinct tissues showed that lncRNAs, especially lincRNA, exhibited stronger tissue-specific expression than coding genes. Moreover, 612, 1351, 1060, 875, 420 and 1689 differentially expressed (DE) lncRNAs under Vibrio anguillarum, Enteromyxum scophthalmi, and Megalocytivirus infection and heat, oxygen, and salinity stress conditions were identified, respectively. Among them, 151 and 62 lncRNAs showed differential expression under various abiotic and biotic stresses, respectively, and 11 lncRNAs differentially expressed under both abiotic and biotic stresses were selected as comprehensive stress-responsive lncRNA candidates. Furthermore, expression pattern analysis and qPCR validation both verified the comprehensive stress-responsive functions of these 11 lncRNAs. In addition, 497 significantly co-expressed target genes (correlation coefficient (R) > 0.7 and q-value < 0.05) for these 11 comprehensive stress-responsive lncRNA candidates were identified. Finally, GO and KEGG enrichment analyses indicated that these target genes were enriched mainly in molecular function, such as cytokine activity and active transmembrane transporter activity, in biological processes, such as response to stimulus and immune response, and in pathways, such as protein families: signaling and cellular processes, transporters and metabolism. These findings not only provide valuable reference resources for further research on the molecular basis and function of lncRNAs in turbot but also help to accelerate the progress of molecularly selective breeding of stress-resistant turbot strains or varieties.

Ellagic acid inhibits human colon cancer HCT-116 cells by regulating long noncoding RNAs

The natural phenolic compound ellagic acid exerts anti-cancer effects, including activity against colorectal cancer (CRC). Previously, we reported that ellagic acid can inhibit the proliferation of CRC, and can induce cell cycle arrest and apoptosis. This study investigated ellagic acid-mediated anticancer effects using the human colon cancer HCT-116 cell line. After 72 h of ellagic acid treatment, a total of 206 long noncoding RNAs (lncRNAs) with differential expression greater than 1.5-fold were identified (115 down-regulated and 91 up-regulated). Furthermore, the co-expression network analysis of differentially expressed lncRNA and mRNA showed that differential expressed lncRNA might be the target of ellagic acid activity in inhibiting CRC.

Isoform-resolved transcriptome of the human preimplantation embryo

Human preimplantation development involves extensive remodeling of RNA expression and splicing. However, its transcriptome has been compiled using short-read sequencing data, which fails to capture most full-length mRNAs. Here, we generate an isoform-resolved transcriptome of early human development by performing long- and short-read RNA sequencing on 73 embryos spanning the zygote to blastocyst stages. We identify 110,212 unannotated isoforms transcribed from known genes, including highly conserved protein-coding loci and key developmental regulators. We further identify 17,964 isoforms from 5,239 unannotated genes, which are largely non-coding, primate-specific, and highly associated with transposable elements. These isoforms are widely supported by the integration of published multi-omics datasets, including single-cell 8CLC and blastoid studies. Alternative splicing and gene co-expression network analyses further reveal that embryonic genome activation is associated with splicing disruption and transient upregulation of gene modules. Together, these findings show that the human embryo transcriptome is far more complex than currently known, and will act as a valuable resource to empower future studies exploring development.

Comprehensive transcriptomic analysis of long non-coding RNAs in bovine ovarian follicles and early embryos

Long non-coding RNAs (lncRNAs) have been the subject of numerous studies over the past decade. First thought to come from aberrant transcriptional events, lncRNAs are now considered a crucial component of the genome with roles in multiple cellular functions. However, the functional annotation and characterization of bovine lncRNAs during early development remain limited. In this comprehensive analysis, we review lncRNAs expression in bovine ovarian follicles and early embryos, based on a unique database comprising 468 microarray hybridizations from a single platform designed to target 7,724 lncRNA transcripts, of which 5,272 are intergenic (lincRNA), 958 are intronic, and 1,524 are antisense (lncNAT). Compared to translated mRNA, lncRNAs have been shown to be more tissue-specific and expressed in low copy numbers. This analysis revealed that protein-coding genes and lncRNAs are both expressed more in oocytes. Differences between the oocyte and the 2-cell embryo are also more apparent in terms of lncRNAs than mRNAs. Co-expression network analysis using WGCNA generated 25 modules with differing proportions of lncRNAs. The modules exhibiting a higher proportion of lncRNAs were found to be associated with fewer annotated mRNAs and housekeeping functions. Functional annotation of co-expressed mRNAs allowed attribution of lncRNAs to a wide array of key cellular events such as meiosis, translation initiation, immune response, and mitochondrial related functions. We thus provide evidence that lncRNAs play diverse physiological roles that are tissue-specific and associated with key cellular functions alongside mRNAs in bovine ovarian follicles and early embryos. This contributes to add lncRNAs as active molecules in the complex regulatory networks driving folliculogenesis, oogenesis and early embryogenesis all of which are necessary for reproductive success.

Identification of Differentially Expressed lncRNAs in Response to Blue Light and Expression Pattern Analysis of Populus tomentosa Hybrid Poplar 741

Poplar is an important shelterbelt, timber stand, and city tree species that has been the focus of forestry research. The regulatory role of the long non-coding RNA molecule (lncRNA; length > 200 nt) has been a research hotspot in plants. In this study, seedlings of 741 poplar were irradiated with LED blue and white light, and the Illumina HiSeq 2000 sequencing platform was used to identify lncRNAs. |logFC| > 1 and p < 0.05 were considered to indicate differentially expressed lncRNAs, and nine differentially expressed lncRNAs were screened, the target genes of which were predicted, and three functionally annotated target genes were obtained. The differentially expressed lncRNAs were identified as miRNA targets. Six lncRNAs were determined to be target sites for twelve mRNAs in six miRNA families. LncRNAs and their target genes, including lncRNA MSTRG.20413.1-ptc-miR396e-5p-GRF9, were verified using quantitative real-time polymerase chain reaction analysis, and the expression patterns were analyzed. The analysis showed that the ptc-miR396e-5p expression was downregulated, while lncRNA MSTRG.20413.1 and GRF9 expression was upregulated, after blue light exposure. These results indicate that lncRNAs interact with miRNAs to regulate gene expression and affect plant growth and development.

Integrated agronomic, physiological, microstructure, and whole-transcriptome analyses reveal the role of biomass accumulation and quality formation during Se biofortification in alfalfa

Se-biofortified agricultural products receive considerable interest due to the worldwide severity of selenium (Se) deficiency. Alfalfa (Medicago sativa L.), the king of forage, has a large biomass, a high protein content, and a high level of adaptability, making it a good resource for Se biofortification. Analyses of agronomic, quality, physiological, and microstructure results indicated the mechanism of biomass increase and quality development in alfalfa during Se treatment. Se treatment effectively increased Se content, biomass accumulation, and protein levels in alfalfa. The enhancement of antioxidant capacity contributes to the maintenance of low levels of reactive oxygen species (ROS), which, in turn, serves to increase alfalfa's stress resistance and the stability of its intracellular environment. An increase in the rate of photosynthesis contributes to the accumulation of biomass in alfalfa. To conduct a more comprehensive investigation of the regulatory networks induced by Se treatment, the transcriptome sequencing of non-coding RNA (ncRNA) was employed to compare 100 mg/kg Se treatment and control groups. The analysis identified 1,414, 62, and 5 genes as DE-long non-coding RNAs (DE-lncRNA), DE-microRNAs (DE-miRNA), and DE-circular RNA (DE-circRNA), respectively. The function of miRNA-related regulatory networks during Se biofortification in alfalfa was investigated. Subsequent enrichment analysis revealed significant involvement of transcription factors, DNA replication and repair mechanisms, photosynthesis, carbohydrate metabolism, and protein processing. The antioxidant capacity and protein accumulation of alfalfa were regulated by the modulation of signal transduction, the glyoxalase pathway, proteostasis, and circRNA/lncRNA-related regulatory networks. The findings offer new perspectives on the regulatory mechanisms of Se in plant growth, biomass accumulation, and stress responses, and propose potential strategies for enhancing its utilization in the agricultural sector.

Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes. Our approach for gene function prediction was reliable (>90% balanced accuracy) for 486 gene-sets. PubMed abstract mining and CRISPR screens supported the inferred association of genes with biological functions, for which our method had high accuracy. Further analysis revealed that TF-binding patterns at promoters have high predictive strength for multiple functions. TF-binding patterns at the promoter add an unexplored dimension of explainable regulatory aspects of genes and their functions. Therefore, we performed a comprehensive analysis for the functional-specificity of TF-binding patterns at promoters and used them for clustering functions to reveal many latent groups of gene-sets involved in common major cellular processes. We also showed how our approach could be used to infer the functions of non-coding genes using the CRISPR screens of coding genes, which were validated using a long non-coding RNA CRISPR screen. Thus our results demonstrated the generality of our approach by using gene-sets from CRISPR screens. Overall, our approach opens an avenue for predicting the involvement of non-coding genes in various functions.

The long noncoding RNA THBS1-AS1 promotes cardiac fibroblast activation in cardiac fibrosis by regulating TGFBR1

Cardiac fibrosis is associated with an adverse prognosis in cardiovascular disease that results in a decreased cardiac compliance and, ultimately, heart failure. Recent studies have identified the role of long noncoding RNA (lncRNA) in cardiac fibrosis. However, the functions of many lncRNAs in cardiac fibrosis remain to be characterized. Through a whole-transcriptome sequencing and bioinformatics analysis on a mouse model of pressure overload-induced cardiac fibrosis, we screened a key lncRNA termed thrombospondin 1 antisense 1 (THBS1-AS1), which was positively associated with cardiac fibrosis. In vitro functional studies demonstrated that the silencing of THBS1-AS1 ameliorated TGF-β1 effects on cardiac fibroblast (CF) activation, and the overexpression of THBS1-AS1 displayed the opposite effect. A mechanistic study revealed that THBS1-AS1 could sponge miR-221/222 to regulate the expression of TGFBR1. Moreover, under TGF-β1 stimulation, the forced expression of miR-221/222 or the knockdown TGFBR1 significantly reversed the THBS1-AS1 overexpression induced by further CF activation. In vivo, specific knockdown of THBS1-AS1 in activated CFs significantly alleviated transverse aorta constriction-induced (TAC-induced) cardiac fibrosis in mice. Finally, we demonstrated that the human THBS1-AS1 can also affect the activation of CFs by regulating TGFBR1. In conclusion, this study reveals that lncRNA THBS1-AS1 is a potentially novel regulator of cardiac fibrosis and may serve as a target for the treatment of cardiac fibrosis.

Sox2ot /miR-9 /Cthrc1 Promote Proliferation and Migration of Schwann Cells Following Nerve Injury

The effects of traditional treatments for peripheral nerve injury (PNI) are not ideal, which has prompted the identification of new therapeutic strategies. As unique glial cells in the peripheral nervous system, Schwann cells (SCs) play an important role in the repair of PNI. Recent studies have demonstrated that long noncoding RNAs (lncRNAs) are involved in the regulation of nerve repair after PNI. In this study, we used microarray technology to detect mRNA and lncRNA expression profiles at different time points after PNI and identified lncRNA Sox2ot-miR-9-Cthrc1 as a competitive endogenous RNA (ceRNA) for further investigation. Expression of lncRNA Sox2ot was increased after PNI, and overexpression of Sox2ot promoted SCs migration and proliferation. Mechanistic analyses confirmed that Sox2ot can regulate the expression of Cthrc1 through competitive adsorption of miR-9 in SCs, ultimately affecting SCs migration and proliferation. Our findings reveal the key role of lncRNA Sox2ot in nerve regeneration and provide a new direction for PNI treatment.

Single-cell analyses reveal distinct expression patterns and roles of long non-coding RNAs during hESC differentiation into pancreatic progenitors

BACKGROUND

Deep understanding the differentiation process of human embryonic stem cells (hESCs) is essential for developing cell-based therapeutic strategy. Substantial efforts have been made to investigate protein-coding genes, yet it remains lacking comprehensive characterization of long non-coding RNAs (lncRNAs) during this process.

METHODS

hESCs were passaged every 5-6 days and had maintained stable karyotype even until the 50th generation. Pancreatic progenitor specification of in vitro differentiation from hESCs was performed and modified. The nuclei were stained with 4,6-Diamidino-2-phenylindole (DAPI). Droplet-based platform (10X Genomics) was applied to generate the single-cell RNA sequencing (scRNA-seq) data. The quality of the filtered read pairs was evaluated by using FastQC. Batch effects were removed using the size factor method. Dimension reduction and unsupervised clustering analyses were performed using Seurat R package. The Monocle 2 and MetaCell algorithms were used to order single cells on a pseudotime course and partition the scRNA-seq data into metacells, respectively. Co-expression network was constructed using WGCNA. Module- and hub-based methods were adopted to predict the functions of lncRNAs.

RESULTS

A total of 77,382 cells during the differentiation process of hESCs toward pancreatic progenitors were sequenced. According to the single-cell map, the cells from different time points were authenticated to constitute a relatively homogeneous population, in which a total of 7382 lncRNAs could be detected. Through further analyzing the time course data, conserved and specific expression features of lncRNAs during hESC differentiation were revealed. Based upon pseudotime analysis, 52 pseudotime-associated lncRNAs that grouped into three distinct expression patterns were identified. We also implemented MetaCell algorithm and network-based methods to explore the functional mechanisms of these lncRNAs. Totally, 464 lncRNAs, including 49 pseudotime-associated lncRNAs were functionally annotated by either module-based or hub-based methods. Most importantly, we demonstrated that the lncRNA HOTAIRM1, which co-localized and co-expressed with several HOX genes, may play crucial role in the generation of pancreatic progenitors through regulation of exocytosis and retinoic acid receptor signaling pathway.

CONCLUSIONS

Our single-cell analyses provide valuable data resources for biological researchers and novel insights into hESC differentiation processes, which will guide future endeavors to further elucidate the roles of lncRNAs.

Effects of Maternal High-Fructose Diet on Long Non-Coding RNAs and Anxiety-like Behaviors in Offspring

Increased fructose intake is an international issue. A maternal high-fructose diet during gestation and lactation could affect nervous system development in offspring. Long non-coding RNA (lncRNA) plays an important role in brain biology. However, the mechanism whereby maternal high-fructose diets influence offspring brain development by affecting lncRNAs is still unclear. Here, we administered 13% and 40% fructose water to establish a maternal high-fructose diet model during gestation and lactation. To determine lncRNAs and their target genes, full-length RNA sequencing was performed using the Oxford Nanopore Technologies platform, and 882 lncRNAs were identified. Moreover, the 13% fructose group and the 40% fructose group had differentially expressed lncRNA genes compared with the control group. Enrichment analyses and co-expression analyses were performed to investigate the changes in biological function. Furthermore, enrichment analyses, behavioral science experiments, and molecular biology experiments all indicated that the fructose group offspring showed anxiety-like behaviors. In summary, this study provides insight into the molecular mechanisms underlying maternal high-fructose diet-induced lncRNA expression and co-expression of lncRNA and mRNA.

Inferring cell developmental stage-specific lncRNA regulation in the developing human neocortex with CDSlncR

Noncoding RNAs (ncRNAs) occupy ~98% of the transcriptome in human, and are usually not translated into proteins. Among ncRNAs, long non-coding RNAs (lncRNAs, >200 nucleotides) are important regulators to modulate gene expression, and are involved in many biological processes (e.g., cell development). To study lncRNA regulation, many computational approaches or tools have been proposed by using bulk transcriptomics data. Nevertheless, previous bulk data-driven methods are mostly limited to explore the lncRNA regulation regarding all of cells, instead of the lncRNA regulation specific to cell developmental stages. Fortunately, recent advance in single-cell sequencing data has provided a way to investigate cell developmental stage-specific lncRNA regulation. In this work, we present a novel computational method, CDSlncR (Cell Developmental Stage-specific lncRNA regulation), which combines putative lncRNA-target binding information with single-cell transcriptomics data to infer cell developmental stage-specific lncRNA regulation. For each cell developmental stage, CDSlncR constructs a cell developmental stage-specific lncRNA regulatory network in the cell developmental stage. To illustrate the effectiveness of CDSlncR, we apply CDSlncR into single-cell transcriptomics data of the developing human neocortex for exploring lncRNA regulation across different human neocortex developmental stages. Network analysis shows that the lncRNA regulation is unique in each developmental stage of human neocortex. As a case study, we also perform particular analysis on the cell developmental stage-specific lncRNA regulation related to 18 known lncRNA biomarkers in autism spectrum disorder. Finally, the comparison result indicates that CDSlncR is an effective method for predicting cell developmental stage-specific lncRNA targets. CDSlncR is available at https://github.com/linxi159/CDSlncR.

LncPLAAT3-AS Regulates PLAAT3-Mediated Adipocyte Differentiation and Lipogenesis in Pigs through miR-503-5p

Animal fat deposition has a significant impact on meat flavor and texture. However, the molecular mechanisms of fat deposition are not well understood. LncPLAAT3-AS is a naturally occurring transcript that is abundant in porcine adipose tissue. Here, we focus on the regulatory role of lncPLAAT3-AS in promoting preadipocyte proliferation and adipocyte differentiation. By overexpressing or repressing lncPLAAT3 expression, we found that lncPLAAT3-AS promoted the transcription of its host gene PLAAT3, a regulator of adipocyte differentiation. In addition, we predicted the region of lncPLAAT3-AS that binds to miR-503-5p and showed by dual luciferase assay that lncPLAAT3-AS acts as a sponge to absorb miR-503-5p. Interestingly, miR-503-5p also targets and represses PLAAT3 expression and helps regulate porcine preadipocyte proliferation and differentiation. Taken together, these results show that lncPLAAT3-AS upregulates PLAAT3 expression by absorbing miR-503-5p, suggesting a potential regulatory mechanism based on competing endogenous RNAs. Finally, we explored lncPLAAT3-AS and PLAAT3 expression in adipose tissue and found that both molecules were expressed at significantly higher levels in fatty pig breeds compared to lean pig breeds. In summary, we identified the mechanism by which lncPLAAT3-AS regulates porcine preadipocyte proliferation and differentiation, contributing to our understanding of the molecular mechanisms of lipid deposition in pigs.

Integrated analysis of lncRNAs, mRNAs, and TFs to identify network modules underlying diterpenoid biosynthesis in Salvia miltiorrhiza

Long non-coding RNAs (lncRNAs) are transcripts of more than 200 nucleotides (nt) in length, with minimal or no protein-coding capacity. Increasing evidence indicates that lncRNAs play important roles in the regulation of gene expression including in the biosynthesis of secondary metabolites. Salvia miltiorrhiza Bunge is an important medicinal plant in China. Diterpenoid tanshinones are one of the main active components of S. miltiorrhiza. To better understand the role of lncRNAs in regulating diterpenoid biosynthesis in S. miltiorrhiza, we integrated analysis of lncRNAs, mRNAs, and transcription factors (TFs) to identify network modules underlying diterpenoid biosynthesis based on transcriptomic data. In transcriptomic data, we obtained 6,651 candidate lncRNAs, 46 diterpenoid biosynthetic pathway genes, and 11 TFs involved in diterpenoid biosynthesis. Combining the co-expression and genomic location analysis, we obtained 23 candidate lncRNA-mRNA/TF pairs that were both co-expressed and co-located. To further observe the expression patterns of these 23 candidate gene pairs, we analyzed the time-series expression of S. miltiorrhiza induced by methyl jasmonate (MeJA). The results showed that 19 genes were differentially expressed at least a time-point, and four lncRNAs, two mRNAs, and two TFs formed three lncRNA-mRNA and/or TF network modules. This study revealed the relationship among lncRNAs, mRNAs, and TFs and provided new insight into the regulation of the biosynthetic pathway of S. miltiorrhiza diterpenoids.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

Genome-wide identification and characterization of lncRNAs in sunflower endosperm

BACKGROUND

Long non-coding RNAs (lncRNAs), as important regulators, play important roles in plant growth and development. The expression and epigenetic regulation of lncRNAs remain uncharacterized generally in plant seeds, especially in the transient endosperm of the dicotyledons.

RESULTS

In this study, we identified 11,840 candidate lncRNAs in 12 day-after-pollination sunflower endosperm by analyzing RNA-seq data. These lncRNAs were evenly distributed in all chromosomes and had specific features that were distinct from mRNAs including tissue-specificity expression, shorter and fewer exons. By GO analysis of protein coding genes showing strong correlation with the lncRNAs, we revealed that these lncRNAs potential function in many biological processes of seed development. Additionally, genome-wide DNA methylation analyses revealed that the level of DNA methylation at the transcription start sites was negatively correlated with gene expression levels in lncRNAs. Finally, 36 imprinted lncRNAs were identified including 32 maternally expressed lncRNAs and four paternally expressed lncRNAs. In CG and CHG context, DNA methylation levels of imprinted lncRNAs in the upstream and gene body regions were slightly lower in the endosperm than that in embryo tissues, which indicated that the maternal demethylation potentially induce the paternally bias expression of imprinted lncRNAs in sunflower endosperm.

CONCLUSION

Our findings not only identified and characterized lncRNAs on a genome-wide scale in the development of sunflower endosperm, but also provide novel insights into the parental effects and epigenetic regulation of lncRNAs in dicotyledonous seeds.

Long noncoding RNA Pvt1 promotes the proliferation and migration of Schwann cells by sponging microRNA-214 and targeting c-Jun following peripheral nerve injury

Research has shown that long-chain noncoding RNAs (lncRNAs) are involved in the regulation of a variety of biological processes, including peripheral nerve regeneration, in part by acting as competing endogenous RNAs. c-Jun plays a key role in the repair of peripheral nerve injury. However, the precise underlying mechanism of c-Jun remains unclear. In this study, we performed microarray and bioinformatics analysis of mouse crush-injured sciatic nerves and found that the lncRNA Pvt1 was overexpressed in Schwann cells after peripheral nerve injury. Mechanistic studies revealed that Pvt1 increased c-Jun expression through sponging miRNA-214. We overexpressed Pvt1 in Schwann cells cultured in vitro and found that the proliferation and migration of Schwann cells were enhanced, and overexpression of miRNA-214 counteracted the effects of Pvt1 overexpression on Schwann cell proliferation and migration. We conducted in vivo analyses and injected Schwann cells overexpressing Pvt1 into injured sciatic nerves of mice. Schwann cells overexpressing Pvt1 enhanced the regeneration of injured sciatic nerves following peripheral nerve injury and the locomotor function of mice was improved. Our findings reveal the role of lncRNAs in the repair of peripheral nerve injury and highlight lncRNA Pvt1 as a novel potential treatment target for peripheral nerve injury.

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening

BACKGROUND

Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction.

METHODS

In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS).

RESULTS

First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration.

CONCLUSIONS

Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.

Annotation and functional characterization of long noncoding RNAs deregulated in pancreatic adenocarcinoma

PURPOSE

Transcriptome analysis of pancreatic ductal adenocarcinoma (PDAC) has been useful to identify gene expression changes that sustain malignant phenotypes. Yet, most studies examined only tumor tissues and focused on protein-coding genes, leaving long non-coding RNAs (lncRNAs) largely underexplored.

METHODS

We generated total RNA-Seq data from patient-matched tumor and nonmalignant pancreatic tissues and implemented a computational pipeline to survey known and novel lncRNAs. siRNA-mediated knockdown in tumor cell lines was performed to assess the contribution of PDAC-associated lncRNAs to malignant phenotypes. Gene co-expression network and functional enrichment analyses were used to assign deregulated lncRNAs to biological processes and molecular pathways.

RESULTS

We detected 9,032 GENCODE lncRNAs as well as 523 unannotated lncRNAs, including transcripts significantly associated with patient outcome. Aberrant expression of a subset of novel and known lncRNAs was confirmed in patient samples and cell lines. siRNA-mediated knockdown of a subset of these lncRNAs (LINC01559, LINC01133, CCAT1, LINC00920 and UCA1) reduced cell proliferation, migration and invasion. Gene co-expression network analysis associated PDAC-deregulated lncRNAs with diverse biological processes, such as cell adhesion, protein glycosylation and DNA repair. Furthermore, UCA1 knockdown was shown to specifically deregulate co-expressed genes involved in DNA repair and to negatively impact DNA repair following damage induced by ionizing radiation.

CONCLUSIONS

Our study expands the repertoire of lncRNAs deregulated in PDAC, thereby revealing novel candidate biomarkers for patient risk stratification. It also provides a roadmap for functional assays aimed to characterize novel mechanisms of action of lncRNAs in pancreatic cancer, which could be explored for therapeutic development.

Downregulation of CRTC1 Is Involved in CUMS-Induced Depression-Like Behavior in the Hippocampus and Its RNA Sequencing Analysis

Chronic stress is an important risk factor for mood disorders including depression. The decreased level of CREB (cAMP-responsive element binding)-regulated transcription coactivator 1 (CRTC1) expression in hippocampus may be involved in depression-like behavior in some stress-induced depression models. But the mechanism of CRTC1 in mediating depression-like behavior remains unknown. In this study, chronic unpredictable mild stress (CUMS)-treated mice showed depression-like behavior accompanied by the downregulation of CRTC1 in the hippocampus. Adeno-associated virus (AAV)-CRTC1-mediated overexpression of CRTC1 in the hippocampus by stereotactic brain injection could significantly prevent depression-like behavior in CUMS-treated mice. The above data reveal that the downregulation of hippocampal CRTC1 expression participates in CUMS-induced depression-like behavior. In order to explore the key targets regulated by CRTC1, AAV-mediated CRTC1 short hairpin (shRNA) was constructed to achieve knockdown of CRTC1 in the hippocampus, and then the hippocampi were collected for RNA-sequencing (RNA-seq). The RNA-seq data show that upregulated genes were enriched in stress and immune system-associated GO terms and pathways such as response to stress and external stimulus and regulation of immune response and that downregulated genes were enriched in neural activity such as synaptic transmission and cognitive behavior. We further provided RT-qPCR data that the inflammation-related factors including Gpr84, Tlr2, Lyz2, and Icam1 were significantly upregulated in the hippocampus of both CUMS- and CRTC1 shRNA-induced models, some of them were also validated in protein levels by Western blotting. We propose a hypothesis that CUMS induces downregulation of CRTC1, which might lead to depression-like behavior via neuroinflammation pathway. This study provides new explanation for the inflammatory hypothesis of depression and some clues for exploring the molecular mechanism of CRTC1 regulation.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

Non-Coding RNAs Are Brokers in Breast Cancer Interactome Networks and Add Discrimination Power between Subtypes

Despite the power of high-throughput genomics, most non-coding RNA (ncRNA) biotypes remain hard to identify, characterize, and validate. This is a clear indication that intensive next-generation sequencing research has led to great efficiency and accuracy in detecting ncRNAs, but not in their functionalization. Computational scientists continue to support the discovery process by spotting significant data features (expression or mutational profiles), elucidating phenotype uncertainty, and delineating complex regulation landscapes for biological pathways and pathophysiological processes. With reference to transcriptome regulation dynamics in cancer, this work introduces a novel network-driven inference approach designed to reveal the potential role of computationally identified ncRNAs in discriminating between breast cancer (BC) subtypes beyond the traditional gene expression signatures. As heterogeneity cast in the subtypes is a characteristic of most cancers, the proposed approach is generalizable beyond BC. Expression profiles of a wide transcriptome spectrum were obtained for a number of BC patients (and controls) listed in TCGA and processed with RNA-Seq. The well-known PAM50 subtype signature was available for the samples and used to move from differentially expressed transcript profiles to subtype-specific biclusters associating gene patterns with patients. Co-expressed gene networks were then generated and annotations were provided, focusing on the biclusters with basal and luminal signatures. These were used to build template maps, i.e., networks in which to embed the ncRNAs and contextually functionalize them based on their interactors. This inference approach is able to assess the influence of ncRNAs at the level of BC subtype. Network topology was considered through the brokerage measure to account for disruptiveness effects induced by the removal of nodes corresponding to ncRNAs. Equivalently, it is shown that ncRNAs can act as brokers of network interactome dynamics, and removing them allows the refinement of subtype-related characteristics previously obtained by gene signatures only. The results of the study elucidate the role of pseudogenes in two major BC subtypes, considering the contextual annotations. Put into a wider perspective, ncRNA brokers may help predictive functionalization studies targeted to new disease phenotypes, for instance those linked to the tumor microenvironment or metabolism, or those specifically involving metastasis. Overall, the approach may represent an in silico prioritization strategy toward the systems identification of new diagnostic and prognostic biomarkers.

GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation

Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.

Comprehensive Analysis of RNA-Seq in Endometriosis Reveals Competing Endogenous RNA Network Composed of circRNA, lncRNA and mRNA

Although long non coding RNAs (lncRNAs) and circular RNAs (circRNAs) play important roles in the pathogenesis of diseases, endometriosis related lncRNAs and circRNAs are still rarely reported. This study focused on the potential molecular mechanism of endometriosis related competitive endogenous RNA (ceRNA) composed of lncRNAs and circRNAs. We performed high-throughout sequencing of six normal endometria, six eutopic endometria and six ectopic endometria for the first time to describe and analyze the expression profile of lncRNA, circRNA and mRNA. Our results showed that 140 lncRNAs, 107 circRNAs and 1,206 mRNAs were differentially expressed in the ectopic group, compared with the normal and eutopic groups. We established an lncRNA/circRNA-mRNA co-expression network using pearson correlation test. Meanwhile, the results of Gene set enrichment analysis analysis showed that the 569 up-regulated differentially expressed mRNA (DEmRNA) were mainly related to the epithelial-mesenchymal transition, regulation of immune system process and immune effector process. Subsequently, we established a DElncRNA-miRNA and DEcircRNA-miRNA network using the starbase database, identified the common miRNAs and constructed DElncRNA/DEcircRNA-miRNA pairs. miRDB, Targetscan, miRwalk and circRNA/lncRNA-mRNA pairs jointly determined the miRNA-mRNA portion of the circRNA/lncRNA-miRNA-mRNA co-expression network. RT-qPCR results of 15 control samples and 25 ectopic samples confirmed that circGLIS2, circFN1, LINC02381, IGFL2-AS1, CD84, LYPD1 and FAM163A were significantly overexpressed in ectopic tissues. In conclusion, this is the first study to illustrate ceRNA composed of differentially expressed circRNA, lncRNA and mRNA in endometriosis. We also found that lncRNA and circRNA exerted a pivotal function on the pathogenesis of endometriosis, which can provide new insights for further exploring the pathogenesis of endometriosis and identifying new targets.

A Quartet Network Analysis Identifying Mechanically Responsive Long Noncoding RNAs in Bone Remodeling

Mechanical force, being so ubiquitous that it is often taken for granted and overlooked, is now gaining the spotlight for reams of evidence corroborating their crucial roles in the living body. The bone, particularly, experiences manifold extraneous force like strain and compression, as well as intrinsic cues like fluid shear stress and physical properties of the microenvironment. Though sparkled in diversified background, long noncoding RNAs (lncRNAs) concerning the mechanotransduction process that bone undergoes are not yet detailed in a systematic way. Our principal goal in this research is to highlight the potential lncRNA-focused mechanical signaling systems which may be adapted by bone-related cells for biophysical environment response. Based on credible lists of force-sensitive mRNAs and miRNAs, we constructed a force-responsive competing endogenous RNA network for lncRNA identification. To elucidate the underlying mechanism, we then illustrated the possible crosstalk between lncRNAs and mRNAs as well as transcriptional factors and mapped lncRNAs to known signaling pathways involved in bone remodeling and mechanotransduction. Last, we developed combinative analysis between predicted and established lncRNAs, constructing a pathway–lncRNA network which suggests interactive relationships and new roles of known factors such as H19. In conclusion, our work provided a systematic quartet network analysis, uncovered candidate force-related lncRNAs, and highlighted both the upstream and downstream processes that are possibly involved. A new mode of bioinformatic analysis integrating sequencing data, literature retrieval, and computational algorithm was also introduced. Hopefully, our work would provide a moment of clarity against the multiplicity and complexity of the lncRNA world confronting mechanical input.

Genome-Wide Identification of mRNAs, lncRNAs, and Proteins, and Their Relationship With Sheep Fecundity

The exploration of multiple birth-related genes has always been a significant focus in sheep breeding. This study aimed to find more genes and proteins related to the litter size in sheep. Ovarian specimens of Small Tail Han sheep (multiple births) and Xinji Fine Wool sheep (singleton) were collected during the natural estrus cycle. Transcriptome and proteome of ovarian specimens were analyzed. The transcriptome results showed that "steroid hormone biosynthesis" and "ovarian steroidogenesis" were significantly enriched, in which HSD17B1 played an important role. The proteome data also confirmed that the differentially expressed proteins (DEPs) were enriched in the ovarian steroidogenesis pathway, and the CYP17A1 was the candidate DEP. Furthermore, lncRNA MSTRG.28645 was highly expressed in Small Tailed Han sheep but lowly expressed in Xinji fine wool sheep. In addition, MSTRG.28645, a hub gene in the co-expression network between mRNAs and lncRNAs, was selected as one of the candidate genes for subsequent verification. Expectedly, the overexpression and interference of HSD17B1 and MSTRG.28645 showed a significant effect on hormone secretion in granulosa cells. Therefore, this study confirmed that HSD17B1 and MSTRG.28645 might be potential genes related to the fecundity of sheep. It was concluded that both HSD17B1 and MSTRG.28645 were critical regulators in the secretion of hormones that affect the fecundity of the sheep.

A seven-lncRNA signature for predicting prognosis in breast carcinoma

Background

Long non-coding RNAs (lncRNAs) play an important part in tumorigenesis and cancer metastasis and can serve as a potential biosignature for cancer prognosis. However, the use of lncRNA signatures to predict survival in breast carcinoma is yet unreported.

Methods

The lncRNA expression profiles and homologous clinical data of 913 breast carcinoma samples from the Cancer Genome Atlas (TCGA), were analyzed to obtain 2,547 differentially expressed lncRNAs. Univariate Cox proportional risk regression was applied to both the training and testing datasets to screen the common prognostic lncRNAs. Potential prognostic LncRNAs were screened by multivariate Cox proportional risk regression in the training data set of the selected LncRNAs.

Results

Seven lncRNAs (LINC02037, MAPT-AS1, RP1-37C10.3, RP11-344E13.4, RP11-454P21.1, RP11-616M22.1, SPACA6P-AS) were prominently associated with overall survival. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves indicated that these indicators were sensitive and specific for survival prediction. The areas under the ROC curve of the seven-lncRNA signature in predicting 3- and 5-year survival rates were 0.771 and 0.780 respectively in the combined cohort. Furthermore, enrichment analysis revealed that these seven lncRNAs might participate multiple pathways related to tumorigenesis and prognosis.

Conclusions

The proposed seven-lncRNA signature could serve as a latent prognostic biomarker for survival prediction in patients with breast carcinoma.

Serum-Based lncRNA ANRIL, TUG1, UCA1, and HIT Expressions in Breast Cancer Patients

Breast cancer is a heterogeneous disease and is the most common and prevalent form of malignancy diagnosed in women. lncRNAs are found to be frequently dysregulated in cancer, and its expression plays a critical role in tumorigenesis. The study included 100 histopathologically confirmed, newly diagnosed untreated patients of invasive ductal carcinoma (IDC) of breast cancer patients and 100 healthy subjects. After blood collection, the serum was separated and total RNA was extracted, cDNA was synthesized using 100 ng of total RNA, and lncRNA (ANRIL, TUG1, UCA1, and HIT) expression was analyzed. Increased ANRIL (3.83-fold), TUG1 (7.64-fold), UCA1 (7.82-fold), and HIT (3.31-fold) expressions were observed in breast cancer patients compared to healthy controls. Relative expression of lncRNAs UCA-1 (p = 0.010) and HIT-1 (p < 0.0001) was significantly elevated in patients with advanced breast cancer stage compared to those with early-stage disease. While lncRNA TUG-1 expression was found to be higher in patients with early-stage tumors than those with advanced-stage tumors (p = 0.06), lncRNA ANRIL showed increased expression in patients with PR positive status (p = 0.04). However, we found a significant difference in lncRNA HIT expression in HER-2 positive breast cancer patients compared to HER-2 negative breast cancer patients (p = 0.005). An increase in the expression of serum lncRNAs ANRIL (p < 0.0001), UCA-1 (p = 0.004), and HIT (p < 0.0001) was observed in the distant organ metastatic breast cancer patients. In the ROC curve concerning lymph node involvement, the sensitivity and specificity of lncRNA HIT were 68% and 58%, respectively (p value = 0.007). In the ROC curve w.r.t. stages of disease, the sensitivity and specificity of lncRNA HIT were 80% and 50%, respectively (p value < 0.0001). Better sensitivity and specificity were observed for lncRNA HIT (sensitivity 91% and specificity 78%; p value < 0.0001) and ANRIL (sensitivity 70% and specificity 60%; p value < 0.0001) w.r.t distant organ metastases.

Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

Interference With ACSL1 Gene in Bovine Adipocytes: Transcriptome Profiling of mRNA and lncRNA Related to Unsaturated Fatty Acid Synthesis

The enzyme long-chain acyl-CoA synthetase 1 (ACSL1) is essential for lipid metabolism. The ACSL1 gene controls unsaturated fatty acid (UFA) synthesis as well as the formation of lipid droplets in bovine adipocytes. Here, we used RNA-Seq to determine lncRNA and mRNA that regulate UFA synthesis in bovine adipocytes using RNA interference and non-interference with ACSL1. The corresponding target genes of differentially expressed (DE) lncRNAs and the DE mRNAs were found to be enriched in lipid and FA metabolism-related pathways, according to GO and KEGG analyses. The differentially expressed lncRNA- differentially expressed mRNA (DEL-DEM) interaction network indicated that some DELs, such as TCONS_00069661, TCONS_00040771, TCONS_ 00035606, TCONS_00048301, TCONS_001309018, and TCONS_00122946, were critical for UFA synthesis. These findings assist our understanding of the regulation of UFA synthesis by lncRNAs and mRNAs in bovine adipocytes.

A Heterogeneous Information Network Model for Long Non-Coding RNA Function Prediction

Exciting information on the functional roles played by long non-coding RNA (lncRNA) has drawn substantial research attention these days. With the advent of techniques such as RNA-Seq, thousands of lncRNAs are identified in very short time spans. However, due to the poor annotation rate, only a few of them are functionally characterised. The wet lab experiments to elucidate lncRNA functions are challenging, slow progressing and sometimes prohibitively expensive. This work attempts to solve the crucial problem of developing computational methods to predict lncRNA functions. The model presented here, predicts the functions of lncRNAs by making use of a meta-path based measure, AvgSim on a Heterogeneous Information Network (HIN). The network is constructed from existing protein and function association data of lncRNAs, lncRNA co-expression data and protein protein interaction data. Out of the 2,758 lncRNA considered for the experiment, the proposed method predicts possible functions for 2,695 lncRNAs with an accuracy of 73.68 percent and found to perform better than the other state-of-the-art approaches for an independent test set. A case study of two well-known lncRNAs (HOTAIR and H19) is conducted and the associated functions are identified. The results were validated using experimental evidence from the literature. The script and data used for the implementation of the model is freely available at: http://bdbl.nitc.ac.in/LncFunPred/index.html.

Identification and functional analysis of lncRNAs and mRNAs between tumorigenesis and metastasis in CRC

The role of long non-coding RNAs (lncRNAs) in colorectal cancer (CRC) tumorigenesis and metastasis remains poorly characterized. The aim of this study was to identify novel lncRNAs and their functions in CRC progression. Through microarray analysis of paired normal colorectal mucosa (NM), primary tumor (PT), and metastatic lymph node (MLN) tissues, lncRNA and mRNA expression patterns were identified. Further bioinformatic analyses were performed to compare the biological functions of lncRNAs between tumorigenesis and metastasis of CRC, which was further verified by TCGA-COAD and GSE82236. The expression of lncRNA MIR29B2CHG93 in paired CRC tissues was detected in a cohort of CRC patients. The effects of lncRNA MIR29B2CHG93 on proliferation, migration, and invasion were determined by in vitro experiments. We found that tumorigenesis-associated lncRNAs predominantly participated in the regulation of the EMT/P53/PI3K-Akt/KRAS signaling pathway as well as the processes related to cell cycle and cell mitosis, while metastasis-associated lncRNAs mainly regulated blood vessel morphogenesis and immune-related biological processes. Compared to the TCGA and GSE datasets, seven tumorigenesis-associated lncRNAs and eight metastasis-associated lncRNAs were identified. LncRNA MIR29B2CHG93 knockdown remarkably suppressed tumor growth and metastasis in vitro, which acted as a tumor promoter in CRC. The lncRNA MIR29B2CHG93 was significantly upregulated in CRC tissues and was indicator of unfavorable clinical outcome in CRC. These results revealed novel lncRNAs that provide new insights for an in-depth understanding of CRC progression. In particular, this study identified a novel lncRNA MIR29B2CHG93 in CRC progression, which might be a potential biomarker for diagnosis, prognosis and metastasis-prediction in CRC.

The long non-coding RNA landscape of Candida yeast pathogens

Long non-coding RNAs (lncRNAs) constitute a poorly studied class of transcripts with emerging roles in key cellular processes. Despite efforts to characterize lncRNAs across a wide range of species, these molecules remain largely unexplored in most eukaryotic microbes, including yeast pathogens of the Candida clade. Here, we analyze thousands of publicly available sequencing datasets to infer and characterize the lncRNA repertoires of five major Candida pathogens: Candida albicans, Candida tropicalis, Candida parapsilosis, Candida auris and Candida glabrata. Our results indicate that genomes of these species encode hundreds of lncRNAs that show levels of evolutionary constraint intermediate between those of intergenic genomic regions and protein-coding genes. Despite their low sequence conservation across the studied species, some lncRNAs are syntenic and are enriched in shared sequence motifs. We find co-expression of lncRNAs with certain protein-coding transcripts, hinting at potential functional associations. Finally, we identify lncRNAs that are differentially expressed during infection of human epithelial cells for four of the studied species. Our comprehensive bioinformatic analyses of Candida lncRNAs pave the way for future functional characterization of these transcripts.

Long non-coding RNAs (lncRNAs) play roles in key cellular processes, but remain largely unexplored in fungal pathogens such as Candida. Here, Hovhannisyan and Gabaldón analyze thousands of sequencing datasets to infer and characterize the lncRNA repertoires of five Candida species, paving the way for their future functional characterization.

Genome-wide analysis uncovers tomato leaf lncRNAs transcriptionally active upon Pseudomonas syringae pv. tomato challenge

Plants rely on (in)direct detection of bacterial pathogens through plasma membrane-localized and intracellular receptor proteins. Surface pattern-recognition receptors (PRRs) participate in the detection of microbe-associated molecular patterns (MAMPs) and are required for the activation of pattern-triggered immunity (PTI). Pathogenic bacteria, such as Pseudomonas syringae pv. tomato (Pst) deploys ~ 30 effector proteins into the plant cell that contribute to pathogenicity. Resistant plants are capable of detecting the presence or activity of effectors and mount another response termed effector-triggered immunity (ETI). In order to investigate the involvement of tomato’s long non-coding RNAs (lncRNAs) in the immune response against Pst, we used RNA-seq data to predict and characterize those that are transcriptionally active in leaves challenged with a large set of treatments. Our prediction strategy was validated by sequence comparison with tomato lncRNAs described in previous works and by an alternative approach (RT-qPCR). Early PTI (30 min), late PTI (6 h) and ETI (6 h) differentially expressed (DE) lncRNAs were identified and used to perform a co-expression analysis including neighboring (± 100 kb) DE protein-coding genes. Some of the described networks could represent key regulatory mechanisms of photosynthesis, PRR abundance at the cell surface and mitigation of oxidative stress, associated to tomato-Pst pathosystem.

Identification and Functional Analysis of lncRNAs Responsive to Hypoxia in Eospalax fontanierii

Subterranean rodents could maintain their normal activities in hypoxic environments underground. Eospalax fontanierii, as one kind of subterranean rodent found in China can survive very low oxygen concentration in labs. It has been demonstrated that long non-coding RNAs (lncRNAs) have important roles in gene expression regulations at different levels and some lncRNAs were found as hypoxia-regulated lncRNAs in cancers. We predicted thousands of lncRNAs in the liver and heart tissues by analyzing RNA-Seq data in Eospalax fontanierii. Those lncRNAs often have shorter lengths, lower expression levels, and lower GC contents than mRNAs. Majors of lncRNAs have expression peaks in hypoxia conditions. We found 1128 DE-lncRNAs (differential expressed lncRNAs) responding to hypoxia. To search the miRNA regulation network for lncRNAs, we predicted 471 and 92 DE-lncRNAs acting as potential miRNA target and target mimics, respectively. We also predicted the functions of DE-lncRNAs based on the co-expression networks of lncRNA-mRNA. The DE-lncRNAs participated in the functions of biological regulation, signaling, development, oxoacid metabolic process, lipid metabolic/biosynthetic process, and catalytic activity. As the first study of lncRNAs in Eospalax fontanierii, our results show that lncRNAs are popular in transcriptome widely and can participate in multiple biological processes in hypoxia responses.

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

BACKGROUND

Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms.

RESULTS

In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge.

CONCLUSION

In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Data integration and evolutionary analysis of long non-coding RNAs in 25 flowering plants

Background

Long non-coding RNAs (lncRNAs) play vital roles in many important biological processes in plants. Currently, a large fraction of plant lncRNA studies center at lncRNA identification and functional analysis. Only a few plant lncRNA studies focus on understanding their evolutionary history, which is crucial for an in-depth understanding of lncRNAs. Therefore, the integration of large volumes of plant lncRNA data is required to deeply investigate the evolution of lncRNAs.

Results

We present a large-scale evolutionary analysis of lncRNAs in 25 flowering plants. In total, we identified 199,796 high-confidence lncRNAs through data integration analysis, and grouped them into 5497 lncRNA orthologous families. Then, we divided the lncRNAs into groups based on the degree of sequence conservation, and quantified the various characteristics of 756 conserved Arabidopsis thaliana lncRNAs. We found that compared with non-conserved lncRNAs, conserved lncRNAs might have more exons, longer sequence length, higher expression levels, and lower tissue specificities. Functional annotation based on the A. thaliana coding-lncRNA gene co-expression network suggested potential functions of conserved lncRNAs including autophagy, locomotion, and cell cycle. Enrichment analysis revealed that the functions of conserved lncRNAs were closely related to the growth and development of the tissues in which they were specifically expressed.

Conclusions

Comprehensive integration of large-scale lncRNA data and construction of a phylogenetic tree with orthologous lncRNA families from 25 flowering plants was used to provide an oversight of the evolutionary history of plant lncRNAs including origin, conservation, and orthologous relationships. Further analysis revealed a differential characteristic profile for conserved lncRNAs in A. thaliana when compared with non-conserved lncRNAs. We also examined tissue specific expression and the potential functional roles of conserved lncRNAs. The results presented here will further our understanding of plant lncRNA evolution, and provide the basis for further in-depth studies of their functions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08047-6.

Prognostic signatures and potential pathogenesis of eRNAs-related genes in colon adenocarcinoma

Enhancer RNAs (eRNAs) are a subclass of long noncoding RNAs (lncRNAs) that have a wide effect in human tumors. However, the systematic analysis of potential functions of eRNAs-related genes (eRGs) in colon cancer (CC) remains unexplored. In this study, a total of 8231 eRGs including 6236 protein-coding genes and 1995 lncRNAs were identified in CC based on the multiple resources. These eRGs showed higher expression level and stability compared to other genes. What's more, the functions of these eRGs were closely related to cancer. Then a prognostic prediction model with 12 eRGs signatures were obtained for colon adenocarcinoma (COAD) patients. ROC curves showed the AUCs were 0.81, 0.77, and 0.78 for 1-, 3-, and 5-year survival prediction, respectively. And the prognostic model also manifested good performance in the validation datasets. Besides, the expression levels of two prognostic signatures, TMEM220 and LRRN2, were verified to be significantly lower in CC tissues than in adjacent noncancerous tissues (p < .05). Finally, the distinct molecular features were characterized between the high- and low-risk group through multiomics analysis including DNA mutation and methylation. Our results show eRGs signatures based prognostic model has high accuracy and may provide innovative biomarkers in COAD.

Extended mining of the oil biosynthesis pathway in biofuel plant Jatropha curcas by combined analysis of transcriptome and gene interactome data

BACKGROUND

Jatropha curcas L. is an important non-edible oilseed crop with a promising future in biodiesel production. However, little is known about the molecular biology of oil biosynthesis in this plant when compared with other established oilseed crops, resulting in the absence of agronomically improved varieties of Jatropha. To extensively discover the potentially novel genes and pathways associated with the oil biosynthesis in J. curcas, new strategy other than homology alignment is on the demand.

RESULTS

In this study, we proposed a multi-step computational framework that integrates transcriptome and gene interactome data to predict functional pathways in non-model organisms in an extended process, and applied it to study oil biosynthesis pathway in J. curcas. Using homologous mapping against Arabidopsis and transcriptome profile analysis, we first constructed protein-protein interaction (PPI) and co-expression networks in J. curcas. Then, using the homologs of Arabidopsis oil-biosynthesis-related genes as seeds, we respectively applied two algorithm models, random walk with restart (RWR) in PPI network and negative binomial distribution (NBD) in co-expression network, to further extend oil-biosynthesis-related pathways and genes in J. curcas. At last, using k-nearest neighbors (KNN) algorithm, the predicted genes were further classified into different sub-pathways according to their possible functional roles.

CONCLUSIONS

Our method exhibited a highly efficient way of mining the extended oil biosynthesis pathway of J. curcas. Overall, 27 novel oil-biosynthesis-related gene candidates were predicted and further assigned to 5 sub-pathways. These findings can help better understanding of the oil biosynthesis pathway of J. curcas, as well as paving the way for the following J. curcas breeding application.

Identification and characterization of early Fusarium wilt responsive mRNAs and long non-coding RNAs in banana root using high-throughput sequencing

Fusarium wilt disease, caused by Fusarium oxysporum f.sp. cubense (Foc), has been recognized as the most devastating disease to banana. The regulatory role of long non-coding RNAs (lncRNAs) in plant defense has been verified in many plant species. However, the understanding of their role during early FocTR4 (Foc tropical race 4) infection stage is very limited. In this study, lncRNA sequencing was used to reveal banana root transcriptome profile changes during early FocTR4 infection stages. Quantitative real time PCR (qRT-PCR) was performed to confirm the expression of eight differentially expressed (DE) lncRNAs (DELs) and their predicted target genes (DETs), and three DE genes (DEGs). Totally, 12,109 lncRNAs, 36,519 mRNAs and 2642 novel genes were obtained, of which 1398 (including 78 DELs, 1220 DE known genes and 100 DE novel genes) were identified as FocTR4 responsive DE transcripts. Gene function analysis revealed that most DEGs were involved in biosynthesis of secondary metabolites, plant–pathogen interaction, plant hormone signal transduction, phenylalanine metabolism, phenylpropanoid biosynthesis, alpha-linolenic acid metabolism and so on. Coincidently, many DETs have been identified as DEGs in previous transcriptome studies. Moreover, many DETs were found to be involved in ribosome, oxidative phosphorylation, lipoic acid metabolism, ubiquitin mediated proteolysis, N-glycan biosynthesis, protein processing in endoplasmic reticulum and DNA damage response pathways. QRT-PCR result showed the expression patterns of the selected transcripts were mostly consistent with our lncRNA sequencing data. Our present study showed the regulatory role of lncRNAs on known biotic and abiotic stress responsive genes and some new-found FocTR4 responsive genes, which can provide new insights into FocTR4-induced changes in the banana root transcriptome during the early pathogen infection stage.

Identification and Characterization of lncRNA and mRNA in Testes of Landrace and Hezuo Boars

Simple Summary

Precocious puberty is an excellent reproductive trait in domestic animals, which can generate higher breeding benefits in livestock production. However, regulators associated with this sexual maturation process remain largely unknown. Chinese Hezuo (HZ) boars are known for their early sexual maturity. In this work, the characteristics of precocious puberty in HZ pigs were confirmed by histological analysis, and some important long noncoding RNA (lncRNA) and mRNA were identified in the testes of immature (30-day-old) and mature (120-day-old) HZ boars, which could play a key role in precocious puberty. These results will provide a theoretical basis for further research on the regulatory mechanism of precocious puberty, which is important for accelerating the breeding process of highly fertile animals.

Abstract

Chinese HZ boars are typical plateau miniature boars characterized by precocious puberty, which is closely related to testicular development and spermatogenesis. Accumulating evidence indicates that lncRNA is involved in the testicular development and regulation of spermatogenesis. However, little is known about the lncRNA precocious regulation in testicular development and spermatogenesis on early sexual maturity of HZ boars. Thus, we investigated the expression and characterization of lncRNA and mRNA in 30-day-old and 120-day-old HZ boar testes using transcriptome to explore precocious puberty. Landrace (LC) boar was treated as the control. Histological analyses indicated that HZ boar underwent puberty development at an earlier stage than LC boar and had achieved sexual maturity at 120 days old. RNA-Seq yielded a total of 187 lncRNAs and 984 mRNAs; these molecules were identified as possible candidates for precocious puberty. GO terms and KEGG pathways enrichment analyses revealed that the differentially expressed lncRNA and their targeted genes were involved in metabolic pathways regulating testis development and spermatogenesis, such as the PI3K-Akt, TGF-beta and Wnt pathways. Further screening, some lncRNA (such as LOC102166140, LOC110259451, and MSTRG.15011.2), and mRNA (such as PDCL2, HSD17B4, SHCBP1L, CYP21A2, and SPATA3) were found to be possibly associated with precocious puberty, which would add to our understanding of the molecular regulatory mechanisms of precocious puberty. This study provided valuable information for further study of the role of lncRNA and mRNA in the process of precocious puberty.

ncFANs v2.0: an integrative platform for functional annotation of non-coding RNAs

Increasing evidence proves the essential regulatory roles of non-coding RNAs (ncRNAs) in biological processes. However, characterizing the specific functions of ncRNAs remains a challenging task, owing to the intensive consumption of the experimental approaches. Here, we present an online platform ncFANs v2.0 that is a significantly enhanced version of our previous ncFANs to provide multiple computational methods for ncRNA functional annotation. Specifically, ncFANs v2.0 was updated to embed three functional modules, including ncFANs-NET, ncFANs-eLnc and ncFANs-CHIP. ncFANs-NET is a new module designed for data-free functional annotation based on four kinds of pre-built networks, including the co-expression network, co-methylation network, long non-coding RNA (lncRNA)-centric regulatory network and random forest-based network. ncFANs-eLnc enables the one-stop identification of enhancer-derived lncRNAs from the de novo assembled transcriptome based on the user-defined or our pre-annotated enhancers. Moreover, ncFANs-CHIP inherits the original functions for microarray data-based functional annotation and supports more chip types. We believe that our ncFANs v2.0 carries sufficient convenience and practicability for biological researchers and facilitates unraveling the regulatory mechanisms of ncRNAs. The ncFANs v2.0 server is freely available at http://bioinfo.org/ncfans or http://ncfans.gene.ac.