decodeRNA- predicting non-coding RNA functions using guilt-by-association

Transcriptome and Animal Model Integration Reveals Inhibition of Calcium Homeostasis-Associated Gene ITPKB Alleviates Amyloid Plaque Deposition

Alzheimer's disease (AD) is a severe neurological illness that causes memory loss and is a global problem. The calcium hypothesis recently steadily evolved in AD. The prospective targets for calcium homeostasis therapy, however, are limited, and gene expression-level research connected to calcium homeostasis in AD remains hazy. In this study, we analyzed the microarray dataset (GSE132903) taken from the Gene Expression Omnibus (GEO) database to investigate calcium homeostasis-related genes for AD. Using immunoblot analysis, we examined the association of ITPKB with inflammation in AD. Additionally, the immunofluorescence technique was employed to assess the impact of pharmacological inhibition of ITPKB on the amyloid-β (Aβ) plaque deposition in APP/PS1 mice. This article's further exploration of calcium homeostasis-related genes has propelled the validation of the calcium homeostasis theory in AD.

CYP51-mediated cholesterol biosynthesis is required for the proliferation of CD4+ T cells in Sjogren's syndrome

CYtochrome P450, family 51 (CYP51) is an important enzyme for de novo cholesterol synthesis in mammalian cells. In the present study, we found that the expression of CYP51 positively correlated with CD4+ T cell activation both in vivo and in vitro. The addition of ketoconazole, a pharmacological inhibitor of CYP51, prevented the proliferation and activation of anti-CD3/CD28-expanded mouse CD4+ T cells in a dose-dependent fashion. Liquid chromatography-tandem mass spectrometry indicated an increase in levels of lanosterol in T cells treated with ketoconazole during activation. Ketoconazole-induced blockade of the cholesterol synthesis pathway also caused Sterol regulatory element binding protein 2 (SREBP2) activation in CD4+ T cells. Additionally, ketoconazole treatment elicited an integrated stress response in T cells that up-regulated activating transcription factor 4 (ATF4) and DNA-damage inducible transcript 3 (DDIT3/CHOP) at the translational level. Furthermore, treatment with ketoconazole significantly decreased the amount of CD4+ T cells infiltrating lesions in the submandibular glands of NOD/Ltj mice. In summary, our results suggest that CYP51 plays an essential role in the proliferation and survival of CD4+ T cells, which makes ketoconazole an inhibitor of CD4+ T cell proliferation and of the SS-like autoimmune response through regulating the biosynthesis of cholesterol and inducing the integrated stress response.

Bioinformatics analysis reveals potential biomarkers associated with the occurrence of intracranial aneurysms

To better understand the molecular mechanisms of intracranial aneurysm (IA) pathogenesis, we used gene coexpression networks to identify hub genes and functional pathways associated with IA onset. Two Gene Expression Omnibus (GEO) datasets encompassing intracranial aneurysm tissue samples and cerebral artery control samples were included. To discover functional pathways and potential biomarkers, weighted gene coexpression network analysis was employed. Next, single-gene gene set enrichment analysis was employed to investigate the putative biological roles of the chosen genes. We also used receiver operating characteristic analysis to confirm the diagnostic results. Finally, we used a rat model to confirm the hub genes in the module of interest. The module of interest, which was designated the green module and included 115 hub genes, was the key module that was most strongly and negatively associated with IA formation. According to gene set variation analysis results, 15 immune-related pathways were significantly activated in the IA group, whereas 7 metabolic pathways were suppressed. In two GEO datasets, SLC2A12 could distinguish IAs from control samples. Twenty-nine hub genes in the green module might be biomarkers for the occurrence of cerebral aneurysms. SLC2A12 expression was significantly downregulated in both human and rat IA tissue. In the present study, we identified 115 hub genes related to the pathogenesis of IA onset and deduced their potential roles in various molecular pathways; this new information may contribute to the diagnosis and treatment of IAs. By external validation, the SLC2A12 gene may play an important role. The molecular function of SLC2A12 in the process of IA occurrence can be further studied in a rat model.

A lncRNA signature associated with tumor immune heterogeneity predicts distant metastasis in locoregionally advanced nasopharyngeal carcinoma

Increasing evidence has revealed the roles of long noncoding RNAs (lncRNAs) as tumor biomarkers. Here, we introduce an immune-associated nine-lncRNA signature for predicting distant metastasis in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The nine lncRNAs are identified through microarray profiling, followed by RT-qPCR validation and selection using a machine learning method in the training cohort (n = 177). This nine-lncRNA signature classifies patients into high and low risk groups, which have significantly different distant metastasis-free survival. Validations in the Guangzhou internal (n = 177) and Guilin external (n = 150) cohorts yield similar results, confirming that the signature is an independent risk factor for distant metastasis and outperforms anatomy-based metrics in identifying patients with high metastatic risk. Integrative analyses show that this nine-lncRNA signature correlates with immune activity and lymphocyte infiltration, which is validated by digital pathology. Our results suggest that the immune-associated nine-lncRNA signature can serve as a promising biomarker for metastasis prediction in LA-NPC.

Developing Tumor Radiosensitivity Signatures Using LncRNAs

Long non-coding RNAs (lncRNAs) are involved in diverse biological processes, including DNA damage repair, and are of interest as potential biomarkers of radiosensitivity. We investigated whether lncRNA radiosensitivity signatures could be derived for use in cancer patients treated with radiotherapy. Signature development involved radiosensitivity measurements for cell lines and primary tumor samples, and patient outcome after radiotherapy. A 10-lncRNA signature trained on radiosensitivity measurements in bladder cell lines showed a trend towards independent validation. In multivariable analyses, patients with tumors classified as radioresistant by the lncRNA signature had poorer local relapse-free survival (P = 0.065) in 151 patients with muscle-invasive bladder cancer who underwent radiotherapy. An mRNA-based radiosensitivity index signature performed similarly to the lncRNA bladder signature for local relapse-free survival (P = 0.055). Pathway analysis showed the lncRNA signature associated with molecular processes involved in radiation responses. Knockdown of one of the lncRNAs in the signature showed a modest increase in radiosensitivity in one cell line. An alternative approach involved training on primary cervical tumor radiosensitivity or local control after radiotherapy. Both approaches failed to generate a cervix lncRNA radiosensitivity signature, which was attributed to the age of samples in our cohorts. Our work highlights challenges in validating lncRNA signatures as biomarkers in archival tissue from radiotherapy cohorts, but supports continued investigation of lncRNAs for a role in radiosensitivity.

Dynamic Expression of Long Non-Coding RNAs Throughout Parasite Sexual and Neural Maturation in Schistosoma japonicum

Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering

BACKGROUND

The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks (i.e., consisting of only one type of objects and relationships) or can exploit only a small subset of the features of biological entities, such as the presence of a particular binding domain, enzymatic properties or their involvement in specific diseases.

RESULTS

To overcome the limitations of existing approaches, we propose the system LP-HCLUS, which exploits a multi-type hierarchical clustering method to predict possibly unknown ncRNA-disease relationships. In particular, LP-HCLUS analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. The extracted clusters are overlapping, hierarchically organized, involve entities of different types, and allow LP-HCLUS to catch multiple roles of ncRNAs in diseases at different levels of granularity. Our experimental evaluation, performed on heterogeneous attributed networks consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that LP-HCLUS is able to obtain better results with respect to existing approaches. The biological relevance of the obtained results was evaluated according to both quantitative (i.e., TPR@k, Areas Under the TPR@k, ROC and Precision-Recall curves) and qualitative (i.e., according to the consultation of the existing literature) criteria.

CONCLUSIONS

The obtained results prove the utility of LP-HCLUS to conduct robust predictive studies on the biological role of ncRNAs in human diseases. The produced predictions can therefore be reliably considered as new, previously unknown, relationships among ncRNAs and diseases.

Bioinformatics Research Methodology of Non-coding RNAs in Cardiovascular Diseases

The transcriptional complexity generated by the human genomic output is within the core of cell and organ physiology, but also could be in the origin of pathologies. In cardiovascular diseases, the role of specific families of RNA transcripts belonging to the group of the non-coding RNAs started to be unveiled in the last two decades. The knowledge of the functional rules and roles of non-coding RNAs in the context of cardiovascular diseases is an important factor to derive new diagnostic methods, but also to design targeted therapeutic strategies. The characterization and analysis of ncRNA function requires a deep knowledge of the regulatory mechanism of these RNA species that often relies on intricated interaction networks. The use of specific bioinformatic tools to interrogate biological data and to derive functional implications is particularly relevant and needs to be extended to the general practice of translational researchers. This chapter briefly summarizes the bioinformatic tools and strategies that could be used for the characterization and functional analysis of non-coding RNAs, with special emphasis in their applications to the cardiovascular field.

Novel Breast-Specific Long Non-coding RNA LINC00993 Acts as a Tumor Suppressor in Triple-Negative Breast Cancer

Background: Triple-negative breast cancer (TNBC) was characterized by breast cancers that do not express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor (HER)-2 genes. TNBC patients are associated with a shorter median time to relapse and death for the lack of available treatment targets. Long non-coding RNAs (LncRNAs) have been reported to play an important role in the development of TNBC. We identified a novel breast-specific long non-coding RNA LINC00993, but less was known about its expression pattern and functional role in TNBC.

Methods: LINC00993 RNA expression was detected across different types of clinical breast cancer samples by using qRT-PCR. Bioinformatic methods “guilt by association” and gene set enrichment analysis (GSEA) were used to predict LINC00993 functions. Subcellular localization of LINC00993 in cells was detected by RNA fluorescence in situ hybridization (FISH). Effect of LINC00993 on cell growth was measured by plate colony formation assays, typical growth curve, and an in vivo tumor model. Cell cycle analysis was done by flow cytometry analysis. Key cell cycle regulators were detected by Western blot.

Results: LINC00993 was largely downregulated in TNBC, and higher expression indicated better outcome. LINC00993 located mainly in the nucleus. LINC00993 suppressed TNBC growth both in vitro and in vivo. LINC00993 was predicted to be involved in cell cycle pathways by using “guilt by association” and GSEA methods. Key cell cycle regulators like p16^INK4A, p14^ARF, p53, and p21 were affected by LINC00993 overexpression.

Conclusions: A new breast-specific lincRNA LINC00993 was identified with a tumor-suppressive feature and with prognostic value. This is the first research on LINC00993 function. Our results suggest that controlling LINC00993 level may be beneficial for breast cancer treatment.

Long non-coding RNAs and latent HIV - A search for novel targets for latency reversal

The latent cellular reservoir of HIV is recognized as the major barrier to cure from HIV infection. Long non-coding RNAs (lncRNAs) are more tissue and cell type-specific than protein coding genes, and may represent targets of choice for HIV latency reversal. Using two in vitro primary T-cell models, we identified lncRNAs dysregulated in latency. PVT1 and RP11-347C18.3 were up-regulated in common between the two models, and RP11-539L10.2 was down-regulated. The major component of the latent HIV reservoir, memory CD4+ T-cells, had higher expression of these lncRNAs, compared to naïve T-cells. Guilt-by-association analysis demonstrated that lncRNAs dysregulated in latency were associated with several cellular pathways implicated in HIV latency establishment and maintenance: proteasome, spliceosome, p53 signaling, and mammalian target of rapamycin (MTOR). PVT1, RP11-347C18.3, and RP11-539L10.2 were down-regulated by latency reversing agents, suberoylanilide hydroxamic acid and Romidepsin, suggesting that modulation of lncRNAs is a possible secondary mechanism of action of these compounds. These results will facilitate prioritization of lncRNAs for evaluation as targets for HIV latency reversal. Importantly, our study provides insights into regulatory function of lncRNA during latent HIV infection.

Reverse Engineering Cancer: Inferring Transcriptional Gene Signatures from Copy Number Aberrations with ICAro

The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process from in vitro characterization results to human validation is a difficult journey. Here, we present a simple algorithm to extract mRNA signatures from cancer datasets, where a particular gene has been deleted at the genomic level, ICAro. The process is implemented as a two-step workflow. The first one employs several filters in order to select the two patient subsets: the inactivated one, where the target gene is deleted, and the control one, where large genomic rearrangements should be absent. The second step performs a signature extraction via a Differential Expression analysis and a complementary Random Forest approach to provide an additional gene ranking in terms of information loss. We benchmarked the system robustness on a panel of genes frequently deleted in cancers, where we validated the downregulation of target genes and found a correlation with signatures extracted with the L1000 tool, outperforming random sampling for two out of six L1000 classes. Furthermore, we present a use case correlation with a published transcriptomic experiment. In conclusion, deciphering the complex interactions of the tumor environment is a challenge that requires the integration of several experimental techniques in order to create reproducible results. We implemented a tool which could be of use when trying to find mRNA signatures related to a gene loss event to better understand its function or for a gene-loss associated biomarker research.

Landscape of tumor suppressor long noncoding RNAs in breast cancer

BACKGROUND

The landscape and biological functions of tumor suppressor long noncoding RNAs in breast cancer are still unknown.

METHODS

Data from whole transcriptome sequencing of 33 breast specimens in the Harbin Medical University Cancer Center cohort and The Cancer Genome Atlas was applied to identify and validate the landscape of tumor suppressor long noncoding RNAs, which was further validated by The Cancer Genome Atlas pancancer data including 33 cancer types and 12,839 patients. Next, the expression model, prognostic roles, potential biological functions and epigenetic regulation of tumor suppressor long noncoding RNAs were investigated and validated in the breast cancer and pancancer cohorts. Finally, EPB41L4A-AS2 was selected to validate our novel finding, and the tumor suppressive roles of EPB41L4A-AS2 in breast cancer were examined.

RESULTS

We identified and validated the landscape of tumor suppressor long noncoding RNAs in breast cancer. The expression of the identified long noncoding RNAs was downregulated in cancer tissue samples compared with normal tissue samples, and these long noncoding RNAs correlated with a favorable prognosis in breast cancer patients and the patients in the pancancer cohort. Multiple carcinogenesis-associated biological functions were predicted to be regulated negatively by these long noncoding RNAs. Moreover, these long noncoding RNAs were transcriptionally regulated by epigenetic modification, including DNA methylation and histone methylation modification. Finally, EPB41L4A-AS2 inhibited breast cancer cell proliferation, migration and invasion and induced cell apoptosis in vitro. Mechanistically, EPB41L4A-AS2, acting at least in part as a tumor suppressor, upregulated tumor suppressor gene expression. Moreover, ZNF217 recruited EZH2 to the EPB41L4A-AS2 locus and suppressed the expression of EPB41L4A-AS2 by epigenetically increasing H3K27me3 enrichment.

CONCLUSIONS

This work enlarges the functional landscape of known long noncoding RNAs in human cancer and provides novel insights into the suppressive roles of these long noncoding RNAs.

Long noncoding RNA expression profiling in cancer: Challenges and opportunities

In recent years, technological advances in transcriptome profiling revealed that the repertoire of human RNA molecules is more diverse and extended than originally thought. This diversity and complexity mainly derive from a large ensemble of noncoding RNAs. Because of their key roles in cellular processes important for normal development and physiology, disruption of noncoding RNA expression is intrinsically linked to human disease, including cancer. Therefore, studying the noncoding portion of the transcriptome offers the prospect of identifying novel therapeutic and diagnostic targets. Although evidence of the relevance of noncoding RNAs in cancer is accumulating, we still face many challenges when it comes to accurately profiling their expression levels. Some of these challenges are inherent to the technologies employed, whereas others are associated with characteristics of the noncoding RNAs themselves. In this review, we discuss the challenges related to long noncoding RNA expression profiling, highlight how cancer long noncoding RNAs provide new opportunities for cancer diagnosis and treatment, and reflect on future developments.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Long non-coding RNAs in hematological malignancies: translating basic techniques into diagnostic and therapeutic strategies

Recent studies have revealed that non-coding regions comprise the vast majority of the human genome and long non-coding RNAs (lncRNAs) are a diverse class of non-coding RNAs that has been implicated in a variety of biological processes. Abnormal expression of lncRNAs has also been linked to different human diseases including cancers, yet the regulatory mechanisms and functional effects of lncRNAs are still ambiguous, and the molecular details also need to be confirmed. Unlike protein-coding gene, it is much more challenging to unravel the roles of lncRNAs owing to their unique and complex features such as functional diversity and low conservation among species, which greatly hamper their experimental characterization. In this review, we summarize and discuss both conventional and advanced approaches for the identification and functional characterization of lncRNAs related to hematological malignancies. In particular, the utility and advancement of clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system as gene-editing tools are envisioned to facilitate the molecular dissection of lncRNAs via different knock-in/out strategies. Besides experimental considerations specific to lncRNAs, the roles of lncRNAs in the pathogenesis and progression of leukemia are also highlighted in the review. We expect that these insights may ultimately lead to clinical applications including development of biomarkers and novel therapeutic approaches targeting lncRNAs.