Out of the dark: the emerging roles of lncRNAs in pain.

The dark genome, the nonprotein-coding part of the genome, is replete with long noncoding RNAs (lncRNAs). These functionally versatile transcripts, with specific temporal and spatial expression patterns, are critical gene regulators that play essential roles in health and disease. In recent years, FAAH-OUT was identified as the first lncRNA associated with an inherited human pain insensitivity disorder. Several other lncRNAs have also been studied for their contribution to chronic pain and genome-wide association studies are frequently identifying single nucleotide polymorphisms that map to lncRNAs. For a long time overlooked, lncRNAs are coming out of the dark and into the light as major players in human pain pathways and as potential targets for new RNA-based analgesic medicines.

Ribosomal profiling of human endogenous retroviruses in healthy tissues.

Human endogenous retroviruses (HERVs) are the germline embedded proviral fragments of ancient retroviral infections that make up roughly 8% of the human genome. Our understanding of HERVs in physiology primarily surrounds their non-coding functions, while their protein coding capacity remains virtually uncharacterized. Therefore, we applied the bioinformatic pipeline "hervQuant" to high-resolution ribosomal profiling of healthy tissues to provide a comprehensive overview of translationally active HERVs. We find that HERVs account for 0.1-0.4% of all translation in distinct tissue-specific profiles. Collectively, our study further supports claims that HERVs are actively translated throughout healthy tissues to provide sequences of retroviral origin to the human proteome.

A tale of a hidden family of genetic immigrants.

Though not usual for the editors of a scientific journal to ask that a story be told to its readers, this special issue is offering an opportunity to pay tribute to all those who have made it possible for a long scientific journey to open up many research avenues, to access the discoveries of what was not known and to the understanding of what was unveiled in the field of human endogenous retroviruses. In particular, and beyond a simple fortuitous association, to show their pathogenic involvement in certain diseases whose causality has been the subject of numerous and variable hypotheses.

Resolving intra-repeat variation in medically relevant VNTRs from short-read sequencing data using the cardiovascular risk gene LPA as a model.

BACKGROUND: Variable number tandem repeats (VNTRs) are highly polymorphic DNA regions harboring many potentially disease-causing variants. However, VNTRs often appear unresolved ("dark") in variation databases due to their repetitive nature. One particularly complex and medically relevant VNTR is the KIV-2 VNTR located in the cardiovascular disease gene LPA which encompasses up to 70% of the coding sequence. RESULTS: Using the highly complex LPA gene as a model, we develop a computational approach to resolve intra-repeat variation in VNTRs from largely available short-read sequencing data. We apply the approach to six protein-coding VNTRs in 2504 samples from the 1000 Genomes Project and developed an optimized method for the LPA KIV-2 VNTR that discriminates the confounding KIV-2 subtypes upfront. This results in an F1-score improvement of up to 2.1-fold compared to previously published strategies. Finally, we analyze the LPA VNTR in > 199,000 UK Biobank samples, detecting > 700 KIV-2 mutations. This approach successfully reveals new strong Lp(a)-lowering effects for KIV-2 variants, with protective effect against coronary artery disease, and also validated previous findings based on tagging SNPs. CONCLUSIONS: Our approach paves the way for reliable variant detection in VNTRs at scale and we show that it is transferable to other dark regions, which will help unlock medical information hidden in VNTRs.

PINNED: identifying characteristics of druggable human proteins using an interpretable neural network.

The identification of human proteins that are amenable to pharmacologic modulation without significant off-target effects remains an important unsolved challenge. Computational methods have been devised to identify features which distinguish between "druggable" and "undruggable" proteins, finding that protein sequence, tissue and cellular localization, biological role, and position in the protein-protein interaction network are all important discriminant factors. However, many prior efforts to automate the assessment of protein druggability suffer from low performance or poor interpretability. We developed a neural network-based machine learning model capable of generating druggability sub-scores based on each of four distinct categories, combining them to form an overall druggability score. The model achieves an excellent performance in separating drugged and undrugged proteins in the human proteome, with an area under the receiver operating characteristic (AUC) of 0.95. Our use of multiple sub-scores allows the assessment of potential protein targets of interest based on distinct contributors to druggability, leading to a more interpretable and holistic model to identify novel targets.

Searching the Dark Genome for Alzheimer's Disease Risk Variants.

Sporadic Alzheimer's disease (AD) is a complex genetic disease, and the leading cause of dementia worldwide. Over the past 3 decades, extensive pioneering research has discovered more than 70 common and rare genetic risk variants. These discoveries have contributed massively to our understanding of the pathogenesis of AD but approximately half of the heritability for AD remains unaccounted for. There are regions of the genome that are not assayed by mainstream genotype and sequencing technology. These regions, known as the Dark Genome, often harbour large structural DNA variants that are likely relevant to disease risk. Here, we describe the dark genome and review current technological and bioinformatics advances that will enable researchers to shed light on these hidden regions of the genome. We highlight the potential importance of the hidden genome in complex disease and how these strategies will assist in identifying the missing heritability of AD. Identification of novel protein-coding structural variation that increases risk of AD will open new avenues for translational research and new drug targets that have the potential for clinical benefit to delay or even prevent clinical symptoms of disease.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

The reproductive life cycle of cancer: Hypotheses of cell of origin, TP53 drivers and stem cell conversions in the light of the atavistic cancer cell theory.

Polyploid giant cancer cells (PGCCs) found in different solid cancers are reproductive cyst-like structures surrounded by an actin envelop. They give rise by hyper-polyploidisation to numerous progeny (microcells, neotic cells) that start a primitive multi-lined lineage and generate subsequent PGCCs by asymmetric cell division and cyclic differentiation. This cancer cell life cycle has multiple similarities with the life cycle of lower eukaryotes (protists) substantiating the atavistic theory of cancer. The primitive cancer life cycle contains several cell types including primary cancer stem cells, somatic cells, as well as reproductive cells, that differentiate new atavistic cyst like structures (aCLSs, PGCCs). Accordingly, cancer stem cells are not transformed normal stem cells (hSCs). Similarities between CSCs and normal hSCs arise from the evolutionary common origin of primitive eukaryotes and more highly evolved eukaryotic cells (stemness evolution). The cell of origin of cancer, as postulated here is a deregulated human cell that has lost, not only relevant control mechanisms and mitotic capacity, but also its normal human p53 network becoming useless for the atavistic life cycle. We believe that this protoprecursor of cancer reactivates an ancient primitive TP53 network originating from the common eukaryotic ancestor. This atavistic p53 helpes to repair genotoxic DNA damages of reproductive cancer cells including CSCs but not DNA damages of somatic cancer cells exposed to genotoxic stress.