Methods to detect transcribed pseudogenes: RNA-Seq discovery allows learning through features

The World of Pseudogenes: New Diagnostic and Therapeutic Targets in Cancers or Still Mystery Molecules?

Pseudogenes were once considered as “junk DNA”, due to loss of their functions as a result of the accumulation of mutations, such as frameshift and presence of premature stop-codons and relocation of genes to inactive heterochromatin regions of the genome. Pseudogenes are divided into two large groups, processed and unprocessed, according to their primary structure and origin. Only 10% of all pseudogenes are transcribed into RNAs and participate in the regulation of parental gene expression at both transcriptional and translational levels through senseRNA (sRNA) and antisense RNA (asRNA). In this review, about 150 pseudogenes in the different types of cancers were analyzed. Part of these pseudogenes seem to be useful in molecular diagnostics and can be detected in various types of biological material including tissue as well as biological fluids (liquid biopsy) using different detection methods. The number of pseudogenes, as well as their function in the human genome, is still unknown. However, thanks to the development of various technologies and bioinformatic tools, it was revealed so far that pseudogenes are involved in the development and progression of certain diseases, especially in cancer.

Using metatranscriptomics to estimate the diversity and composition of zooplankton communities

DNA metabarcoding is a rapid, high‐resolution tool used for biomonitoring complex zooplankton communities. However, diversity estimates derived with this approach can be biased by the co‐detection of sequences from environmental DNA (eDNA), nuclear‐encoded mitochondrial (NUMT) pseudogene contamination, and taxon‐specific PCR primer affinity differences. To avoid these methodological uncertainties, we tested the use of metatranscriptomics as an alternative approach for characterizing zooplankton communities. Specifically, we compared metatranscriptomics with PCR‐based methods using genomic (gDNA) and complementary DNA (cDNA) amplicons, and morphology‐based data for estimating species diversity and composition for both mock communities and field‐collected samples. Mock community analyses showed that the use of gDNA mitochondrial cytochrome c oxidase I (mtCO1) amplicons inflates species richness due to the co‐detection of extra‐organismal eDNA. Significantly more amplicon sequence variants, nucleotide diversity, and indels were observed with gDNA amplicons than with cDNA, indicating the presence of putative NUMT pseudogenes. Moreover, PCR‐based methods failed to detect the most abundant species in mock communities due to priming site mismatch. Overall, metatranscriptomics provided estimates of species richness and composition that closely resembled those derived from morphological data. The use of metatranscriptomics was further tested using field‐collected samples, with the results showing consistent species diversity estimates among biological and technical replicates. Additionally, temporal zooplankton species composition changes could be monitored using different mitochondrial markers. These findings demonstrate the advantages of metatranscriptomics as an effective tool for monitoring diversity in zooplankton research.

Pseudogenes as Biomarkers and Therapeutic Targets in Human Cancers

Pseudogenes are commonly labeled as "junk DNA" given their perceived nonfunctional status. However, the advent of large-scale genomics projects prompted a revisit of pseudogene biology, highlighting their key functional and regulatory roles in numerous diseases, including cancers. Integrative analyses of cancer data have shown that pseudogenes can be transcribed and even translated, and that pseudogenic DNA, RNA, and proteins can interfere with the activity and function of key protein coding genes, acting as regulators of oncogenes and tumor suppressors. Capitalizing on the available clinical research, we are able to get an insight into the spread and variety of pseudogene biomarker and therapeutic potential. In this chapter, we describe pseudogenes that fulfill their role as diagnostic or prognostic biomarkers, both as unique elements and in collaboration with other genes or pseudogenes. We also report that the majority of prognostic pseudogenes are overexpressed and exert an oncogenic role in colorectal, liver, lung, and gastric cancers. Finally, we highlight a number of pseudogenes that can establish future therapeutic avenues.

Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes

We studied the transcriptome landscape of skin cutaneous melanoma (SKCM) using 103 primary tumor samples from TCGA, and measured the expression levels of both protein coding genes and non-coding RNAs (ncRNAs). In particular, we emphasized pseudogenes potentially relevant to this cancer. While cataloguing the profiles based on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of significant biotypes. We thus designed an approach to reconcile the profiles from all methods following a simple strategy: we selected genes that were confirmed as differentially expressed by the ensemble predictions obtained in a regression model. The main advantages of this approach are: 1) Selection of a high-confidence gene set identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict an averaged profile; 3) Method-specific assessment of prediction power and significance. Furthermore, the approach can be generalized to any biological system for which noisy RNA-Seq profiles are computed. As our analyses concerned bio-annotations of both high-quality protein coding genes and ncRNAs, we considered the associations between pseudogenes and parental genes (targets). Among the candidate targets that were validated, we identified PINK1, which is studied in patients with Parkinson and cancer (especially melanoma).

Current Research on Non-Coding Ribonucleic Acid (RNA)

Non-coding ribonucleic acid (RNA) has without a doubt captured the interest of biomedical researchers. The ability to screen the entire human genome with high-throughput sequencing technology has greatly enhanced the identification, annotation and prediction of the functionality of non-coding RNAs. In this review, we discuss the current landscape of non-coding RNA research and quantitative analysis. Non-coding RNA will be categorized into two major groups by size: long non-coding RNAs and small RNAs. In long non-coding RNA, we discuss regular long non-coding RNA, pseudogenes and circular RNA. In small RNA, we discuss miRNA, transfer RNA, piwi-interacting RNA, small nucleolar RNA, small nuclear RNA, Y RNA, single recognition particle RNA, and 7SK RNA. We elaborate on the origin, detection method, and potential association with disease, putative functional mechanisms, and public resources for these non-coding RNAs. We aim to provide readers with a complete overview of non-coding RNAs and incite additional interest in non-coding RNA research.

Pseudogenes in Human Cancer

Recent advances in the analysis of RNA sequencing data have shown that pseudogenes are highly specific markers of cell identity and can be used as diagnostic and prognostic markers. Furthermore, genetically engineered mouse models have recently provided compelling support for a causal link between altered pseudogene expression and cancer. In this review, we discuss the most recent milestones reached in the pseudogene field and the use of pseudogenes as cancer classifiers.

Pseudogene-derived lncRNAs: emerging regulators of gene expression

In the more than one decade since the completion of the Human Genome Project, the prevalence of non-protein-coding functional elements in the human genome has emerged as a key revelation in post-genomic biology. Highlighted by the ENCODE (Encyclopedia of DNA Elements) and FANTOM (Functional Annotation of Mammals) consortia, these elements include tens of thousands of pseudogenes, as well as comparably numerous long non-coding RNA (lncRNA) genes. Pseudogene transcription and function remain insufficiently understood. However, the field is of great importance for human disease due to the high sequence similarity between pseudogenes and their parental protein-coding genes, which generates the potential for sequence-specific regulation. Recent case studies have established essential and coordinated roles of both pseudogenes and lncRNAs in development and disease in metazoan systems, including functional impacts of lncRNA transcription at pseudogene loci on the regulation of the pseudogenes’ parental genes. This review synthesizes the nascent evidence for regulatory modalities jointly exerted by lncRNAs and pseudogenes in human disease, and for recent evolutionary origins of these systems.

RNA-Seq Data: A Complexity Journey

A paragraph from the highlights of “Transcriptomics: Throwing light on dark matter” by L. Flintoft (Nature Reviews Genetics 11, 455, 2010), says: “Reports over the past few years of extensive transcription throughout eukaryotic genomes have led to considerable excitement. However, doubts have been raised about the methods that have detected this pervasive transcription and about how much of it is functional.” Since the appearance of the ENCODE project and due to follow-up work, a shift from the pervasive transcription observed from RNA-Seq data to its functional validation is gradually occurring. However, much less attention has been turned to the problem of deciphering the complexity of transcriptome data, which determines uncertainty with regard to identification, quantification and differential expression of genes and non-coding RNAs. The aim of this mini-review is to emphasize transcriptome-related problems of direct and inverse nature for which novel inference approaches are needed.