Illuminating eukaryotic transcription start sites

Machine learning in cardiology: Clinical application and basic research

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increasingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.

TP53BP1, a dual-coding gene, uses promoter switching and translational reinitiation to express a smORF protein

The complexity of the metazoan proteome is significantly increased by the expression of small proteins (<100 aa) derived from smORFs within lncRNAs, uORFs, 3' UTRs and, reading frames overlapping the CDS. These smORF encoded proteins (SEPs) have diverse roles, ranging from the regulation of cellular physiological to essential developmental functions. We report the characterization of a new member of this protein family, SEP53BP1, derived from a small internal ORF that overlaps the CDS encoding 53BP1. Its expression is coupled to the utilization of an alternative, cell-type specific promoter coupled to translational reinitiation events mediated by a uORF in the alternative 5' TL of the mRNA. This uORF-mediated reinitiation at an internal ORF is also observed in zebrafish. Interactome studies indicate that the human SEP53BP1 associates with components of the protein turnover pathway including the proteasome, and the TRiC/CCT chaperonin complex, suggesting that it may play a role in cellular proteostasis.

High-throughput identification of viral termini and packaging mechanisms in virome datasets using PhageTermVirome

Viruses that infect bacteria (phages) are increasingly recognized for their importance in diverse ecosystems but identifying and annotating them in large-scale sequence datasets is still challenging. Although efficient scalable virus identification tools are emerging, defining the exact ends (termini) of phage genomes is still particularly difficult. The proper identification of termini is crucial, as it helps in characterizing the packaging mechanism of bacteriophages and provides information on various aspects of phage biology. Here, we introduce PhageTermVirome (PTV) as a tool for the easy and rapid high-throughput determination of phage termini and packaging mechanisms using modern large-scale metagenomics datasets. We successfully tested the PTV algorithm on a mock virome dataset and then used it on two real virome datasets to achieve the rapid identification of more than 100 phage termini and packaging mechanisms, with just a few hours of computing time. Because PTV allows the identification of free fully formed viral particles (by recognition of termini present only in encapsidated DNA), it can also complement other virus identification softwares to predict the true viral origin of contigs in viral metagenomics datasets. PTV is a novel and unique tool for high-throughput characterization of phage genomes, including phage termini identification and characterization of genome packaging mechanisms. This software should help researchers better visualize, map and study the virosphere. PTV is freely available for downloading and installation at https://gitlab.pasteur.fr/vlegrand/ptv.

Differential Expression Profile of lncRNAs from Primary Human Hepatocytes Following DEET and Fipronil Exposure

While the synthesis and use of new chemical compounds is at an all-time high, the study of their potential impact on human health is quickly falling behind, and new methods are needed to assess their impact. We chose to examine the effects of two common environmental chemicals, the insect repellent N,N-diethyl-m-toluamide (DEET) and the insecticide fluocyanobenpyrazole (fipronil), on transcript levels of long non-protein coding RNAs (lncRNAs) in primary human hepatocytes using a global RNA-Seq approach. While lncRNAs are believed to play a critical role in numerous important biological processes, many still remain uncharacterized, and their functions and modes of action remain largely unclear, especially in relation to environmental chemicals. RNA-Seq showed that 100 µM DEET significantly increased transcript levels for 2 lncRNAs and lowered transcript levels for 18 lncRNAs, while fipronil at 10 µM increased transcript levels for 76 lncRNAs and decreased levels for 193 lncRNAs. A mixture of 100 µM DEET and 10 µM fipronil increased transcript levels for 75 lncRNAs and lowered transcript levels for 258 lncRNAs. This indicates a more-than-additive effect on lncRNA transcript expression when the two chemicals were presented in combination versus each chemical alone. Differentially expressed lncRNA genes were mapped to chromosomes, analyzed by proximity to neighboring protein-coding genes, and functionally characterized via gene ontology and molecular mapping algorithms. While further testing is required to assess the organismal impact of changes in transcript levels, this initial analysis links several of the dysregulated lncRNAs to processes and pathways critical to proper cellular function, such as the innate and adaptive immune response and the p53 signaling pathway.

What Is the Impact of mRNA 5' TL Heterogeneity on Translational Start Site Selection and the Mammalian Cellular Phenotype?

A major determinant in the efficiency of ribosome loading onto mRNAs is the 5′ TL (transcript leader or 5′ UTR). In addition, elements within this region also impact on start site selection demonstrating that it can modulate the protein readout at both quantitative and qualitative levels. With the increasing wealth of data generated by the mining of the mammalian transcriptome, it has become evident that a genes 5′ TL is not homogeneous but actually exhibits significant heterogeneity. This arises due to the utilization of alternative promoters, and is further compounded by significant variability with regards to the precise transcriptional start sites of each (not to mention alternative splicing). Consequently, the transcript for a protein coding gene is not a unique mRNA, but in-fact a complexed quasi-species of variants whose composition may respond to the changing physiological environment of the cell. Here we examine the potential impact of these events with regards to the protein readout.

Machine learning applications in genetics and genomics

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

The core promoter: At the heart of gene expression

The identities of different cells and tissues in multicellular organisms are determined by tightly controlled transcriptional programs that enable accurate gene expression. The mechanisms that regulate gene expression comprise diverse multiplayer molecular circuits of multiple dedicated components. The RNA polymerase II (Pol II) core promoter establishes the center of this spatiotemporally orchestrated molecular machine. Here, we discuss transcription initiation, diversity in core promoter composition, interactions of the basal transcription machinery with the core promoter, enhancer-promoter specificity, core promoter-preferential activation, enhancer RNAs, Pol II pausing, transcription termination, Pol II recycling and translation. We further discuss recent findings indicating that promoters and enhancers share similar features and may not substantially differ from each other, as previously assumed. Taken together, we review a broad spectrum of studies that highlight the importance of the core promoter and its pivotal role in the regulation of metazoan gene expression and suggest future research directions and challenges.

Nucleosome positioning and kinetics near transcription-start-site barriers are controlled by interplay between active remodeling and DNA sequence

We investigate how DNA sequence, ATP-dependent chromatin remodeling and nucleosome-depleted ‘barriers’ co-operate to determine the kinetics of nucleosome organization, in a stochastic model of nucleosome positioning and dynamics. We find that ‘statistical’ positioning of nucleosomes against ‘barriers’, hypothesized to control chromatin structure near transcription start sites, requires active remodeling and therefore cannot be described using equilibrium statistical mechanics. We show that, unlike steady-state occupancy, DNA site exposure kinetics near a barrier is dominated by DNA sequence rather than by proximity to the barrier itself. The timescale for formation of positioning patterns near barriers is proportional to the timescale for active nucleosome eviction. We also show that there are strong gene-to-gene variations in nucleosome positioning near barriers, which are eliminated by averaging over many genes. Our results suggest that measurement of nucleosome kinetics can reveal information about sequence-dependent regulation that is not apparent in steady-state nucleosome occupancy.

Familial Alzheimer's disease coding mutations reduce Presenilin-1 expression in a novel genomic locus reporter model

We have generated a physiologically relevant bacterial artificial chromosome (BAC)-based genomic DNA expression model to study PS1 gene expression and function. The PS1-WT-BAC construct restored γ-secretase function, whereas the mutant PS1 BACs demonstrated partial to complete loss of enzymatic activity when stably expressed in a PS double knock-out clonal cell line. We then engineered WT and mutant human PS1-BAC-Luciferase whole genomic locus reporter transgenes, which we transiently transduced in mouse and human non-neuronal and neuronal-like cells, respectively. PS1 ΔE9 and C410Y FAD were found to lower PS1 gene expression in both cell lines, whereas PS1-M146V showed a neuron-specific effect. The nonclinical γ-secretase inactive PS1-D257A mutation did not alter gene expression in either cell line. This is the first time that pathogenic coding mutations in the PS1 gene have been shown to lower PS1 gene expression. These findings may represent a pathologic mechanism for PS1 FAD mutations independent of their effects on γ-secretase activity and demonstrate how dominant PS1 mutations may exert their pathogenic effects by a loss-of-function mechanism.

Identification and analysis of an outer-seed-coat-specific promoter from Arabidopsis thaliana

Differentiation of the Arabidopsis thaliana (Arabidopsis) seed coat epidermal cells involves pronounced changes highlighted by the synthesis and secretion of copious amounts of dispensable, pectinaceous mucilage followed by a thick cellulosic secondary cell wall. This cell type, therefore, represents an excellent molecular-genetic model to study the biosynthesis and modification of cell wall components, particularly pectin. To support such research, we sought to identify a promoter that drives expression specifically in the Arabidopsis seed coat epidermis. Arabidopsis seed coat microarray data was analysed for genes expressed in the wild type seed coat but not the seed coat of the apetala2 mutant where the epidermal cells fail to differentiate. Of 14 candidate genes, 9 showed a seed-specific expression pattern by reverse transcriptase-PCR. Transcriptional regulatory region-β-glucuronidase (GUS) reporter gene fusions introduced into Arabidopsis identified one promoter, that of the DIRIGENT PROTEIN1 (DP1) gene, as seed coat specific. The specificity of the expression was confirmed using a second reporter gene, Citrine YFP. Expression of both reporter genes was limited to the epidermal and palisade cell layers of the seed coat. Quantitative PCR data using wild type seed coat RNA suggested that the promoter is particularly active at 7 days post anthesis. The DP1 promoter was able to direct transcription of GUS in a similar pattern in the Brassica napus seed coat. Thus, in addition to its application in studying the plant cell wall, this promoter will provide an experimental tool for expressing high-valued recombinant proteins as well as modifying seed coat traits in economically important crops.

Multiple isoforms of the translation initiation factor eIF4GII are generated via use of alternative promoters, splice sites and a non-canonical initiation codon

During the initiation stage of eukaryotic mRNA translation, the eIF4G (eukaryotic initiation factor 4G) proteins act as an aggregation point for recruiting the small ribosomal subunit to an mRNA. We previously used RNAi (RNA interference) to reduce expression of endogenous eIF4GI proteins, resulting in reduced protein synthesis rates and alterations in the morphology of cells. Expression of EIF4G1 cDNAs, encoding different isoforms (f–a) which arise through selection of alternative initiation codons, rescued translation to different extents. Furthermore, overexpression of the eIF4GII paralogue in the eIF4GI-knockdown background was unable to restore translation to the same extent as eIF4GIf/e isoforms, suggesting that translation events governed by this protein are different. In the present study we show that multiple isoforms of eIF4GII exist in mammalian cells, arising from multiple promoters and alternative splicing events, and have identified a non-canonical CUG initiation codon which extends the eIF4GII N-terminus. We further show that the rescue of translation in eIF4GI/eIF4GII double-knockdown cells by our novel isoforms of eIF4GII is as robust as that observed with either eIF4GIf or eIF4GIe, and more than that observed with the original eIF4GII. As the novel eIF4GII sequence diverges from eIF4GI, these data suggest that the eIF4GII N-terminus plays an alternative role in initiation factor assembly.