New genomic signals underlying the emergence of human proto-genes

De novo genes are novel genes which emerge from non-coding DNA. Until now, little is known about de novo genes properties, correlated to their age and mechanisms of emergence. In this study, we investigate four properties: introns, upstream regulatory motifs, 5 prime UTRs and protein domains, in 23135 human proto-genes. We found that proto-genes contain introns, whose number and position correlates with the genomic position of proto-gene emergence. The origin of these introns is debated, as our result suggest that 41% proto-genes might have captured existing introns, as well as the fact that 13.7% of them do not splice the ORF. We show that proto-genes which emerged via overprinting tend to be more enriched in core promotor motifs, while intergenic and intronic ones are more enriched in enhancers, even if the motif TATA is most expressed upstream these genes. Intergenic and intronic 5 prime UTRs of proto-genes have a lower potential to stabilise mRNA structures than exonic proto-genes and established human genes. Finally, we confirm that proto-genes gain new putative domains with age. Overall, we find that regulatory motifs inducing transcription and translation of previously non-coding sequences may facilitate proto-gene emergence. Our paper demonstrates that introns, 5 prime UTRs, and domains have specific properties in proto-genes. We also show the importance of studying proto-genes in relation to their genomic position, as it strongly impacts these properties.

Novel Genes Potentially Involved in Fibroblasts of Diabetic Wound

Fibroblasts are the essential cell type of skin, highly involved in the wound regeneration process. In this study, we sought to screen out the novel genes which act important roles in diabetic fibroblasts through bioinformatic methods. A total of 811 and 490 differentially expressed genes (DEGs) between diabetic and normal fibroblasts were screened out in GSE49566 and GSE78891, respectively. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in type 2 diabetes were retrieved from miRWalk. Consequently, the integrated bioinformatic analyses revealed the shared KEGG pathways between DEG-identified and diabetes-related pathways were functionally enriched in the MAPK signaling pathway, and the MAPKAPK3, HSPA2, TGFBR1, and p53 signaling pathways were involved. Finally, ETV4 and NPE2 were identified as the targeted transcript factors of MAPKAPK3, HSPA2, and TGFBR1. Our findings may throw novel sight in elucidating the molecular mechanisms of fibroblast pathologies in patients with diabetic wounds and targeting new factors to advance diabetic wound treatment in clinic.

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease

Background Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development. Results We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes. Conclusions Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

The invaluable role of consanguinity in providing insight into paediatric endocrine conditions: Lessons learned from congenital hyperinsulinism, monogenic diabetes, and short stature

Consanguineous families have often played a role in the discovery of novel genes, especially in paediatric endocrinology. At this time, it has been estimated that over 8.5% of all children worldwide have consanguineous parents. Consanguinity is linked to demographic, cultural and religious practises and is more common in some areas around the world than others. In children with endocrine conditions from consanguineous families, there is a greater probability that a single gene condition with autosomal recessive inheritance is causative. From 1966 and the first description of Laron syndrome, through the discovery of the first KATP channel genes ABCC8 and KCNJ11 causing congenital hyperinsulinism in the 1990s, to recent discoveries of mutations in YIPF5 as the first cause of monogenic diabetes due to the disruption of the endoplasmic reticulum (ER)-to-Golgi trafficking in the β-cell and increased ER stress; positive genetic findings in children from consanguinity have been important in elucidating novel genes and mechanisms of disease, thereby expanding knowledge into disease pathophysiology. The aim of this narrative review is to shed light on the lessons learned from consanguineous pedigrees with the help of three fundamental endocrine conditions that represent an evolving spectrum of pathophysiological complexity – from congenital hyperinsulinism, a typically single cell condition, to monogenic diabetes which presents with uniform biochemical parameters (hyperglycaemia and glycosuria), despite varying aetiologies, up to the genetic regulation of human growth – the most complex developmental phenomenon.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.

Discovering Novel Genes in Non-Model Fly Accessory Glands Using De Novo Nanopore Transcriptomics

BackgroundOxford Nanopore Technologies (ONT) long-read transcriptomes offer many advantages including long reads (>10kbp), end-to-end transcripts, structural variants, isoform-level resolution of genes and expression. However, uptake of ONT transcriptomics is still low, largely due to high error rates (2 to 13%) and reliance on reference databases that are unavailable for many non-model species. Additionally, bioinformatics tools and pipelines for de novo ONT transcriptomics are still in early stages of development. ResultsHere, we use de novo ONT GridION transcriptomics to discover novel genes from the male accessory glands (AG) of a widespread, non-model dung fly, Sepsis punctum. Insect AGs are of particular interest for this as they are hotspots for rapid evolution of novel reproductive genes, and they synthesize seminal fluid proteins that lack homology to any other known proteins. We implement a completely de novo ONT GridION transcriptome pipeline, incorporating quality-filtering and rigorous error-correction procedures, to characterize this novel gene set and to quantify their expression. Specifically, we compare these ONT genes and their expression against de novo lllumina HiSeq transcriptome data. We find 40 high-quality and high-confidence ONT genes that cross-verify against Illumina genes; twenty-six of which are novel and specific to S. punctum. Read count based expression quantification in ONT samples is highly congruent with Illumina’s Transcript per Million (TPM), both in overall pattern and within functional categories. Novel genes account for an average of 81% of total gene expression underscoring their functional importance in S. punctum AGs. Eighty percentage of these genes are secretory in nature, responsible for 74% total gene expression. Notably, median sequence similarities of ONT nucleotide and protein sequences match within-Illumina sequence similarities indicating that our de novo ONT transcriptome pipeline successfully mitigated sequencing errors. ConclusionsThis is the first study to adapt ONT transcriptomics for completely de novo characterization of novel genes in animals. Our study demonstrates that ONT long-reads, constituting a quarter of the number of bases sequenced at less than a third the cost of Illumina reads, can be a resource-friendly and cost-effective solution for end-to-end sequencing of unknown genes even in the absence of a reference database.