Fast Prediction of DNA Melting Bubbles Using DNA Thermodynamic Stability

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

Multiple plasmid origin-of-transfer regions might aid the spread of antimicrobial resistance to human pathogens

Antimicrobial resistance poses a great danger to humanity, in part due to the widespread horizontal gene transfer of plasmids via conjugation. Modeling of plasmid transfer is essential to uncovering the fundamentals of resistance transfer and for the development of predictive measures to limit the spread of resistance. However, a major limitation in the current understanding of plasmids is the incomplete characterization of the conjugative DNA transfer mechanisms, which conceals the actual potential for plasmid transfer in nature. Here, we consider that the plasmid-borne origin-of-transfer substrates encode specific DNA structural properties that can facilitate finding these regions in large datasets and develop a DNA structure-based alignment procedure for typing the transfer substrates that outperforms sequence-based approaches. Thousands of putative DNA transfer substrates are identified, showing that plasmid mobility can be twofold higher and span almost twofold more host species than is currently known. Over half of all putative mobile plasmids contain the means for mobilization by conjugation systems belonging to different mobility groups, which can hypothetically link previously confined host ranges across ecological habitats into a robust plasmid transfer network. This hypothetical network is found to facilitate the transfer of antimicrobial resistance from environmental genetic reservoirs to human pathogens, which might be an important driver of the observed rapid resistance development in humans and thus an important point of focus for future prevention measures.

DNA structure at the plasmid origin-of-transfer indicates its potential transfer range

Horizontal gene transfer via plasmid conjugation enables antimicrobial resistance (AMR) to spread among bacteria and is a major health concern. The range of potential transfer hosts of a particular conjugative plasmid is characterised by its mobility (MOB) group, which is currently determined based on the amino acid sequence of the plasmid-encoded relaxase. To facilitate prediction of plasmid MOB groups, we have developed a bioinformatic procedure based on analysis of the origin-of-transfer (oriT), a merely 230 bp long non-coding plasmid DNA region that is the enzymatic substrate for the relaxase. By computationally interpreting conformational and physicochemical properties of the oriT region, which facilitate relaxase-oriT recognition and initiation of nicking, MOB groups can be resolved with over 99% accuracy. We have shown that oriT structural properties are highly conserved and can be used to discriminate among MOB groups more efficiently than the oriT nucleotide sequence. The procedure for prediction of MOB groups and potential transfer range of plasmids was implemented using published data and is available at http://dnatools.eu/MOB/plasmid.html.

Bridge-Induced Translocation between NUP145 and TOP2 Yeast Genes Models the Genetic Fusion between the Human Orthologs Associated With Acute Myeloid Leukemia

In mammalian organisms liquid tumors such as acute myeloid leukemia (AML) are related to spontaneous chromosomal translocations ensuing in gene fusions. We previously developed a system named bridge-induced translocation (BIT) that allows linking together two different chromosomes exploiting the strong endogenous homologous recombination system of the yeast Saccharomyces cerevisiae. The BIT system generates a heterogeneous population of cells with different aneuploidies and severe aberrant phenotypes reminiscent of a cancerogenic transformation. In this work, thanks to a complex pop-out methodology of the marker used for the selection of translocants, we succeeded by BIT technology to precisely reproduce in yeast the peculiar chromosome translocation that has been associated with AML, characterized by the fusion between the human genes NUP98 and TOP2B. To shed light on the origin of the DNA fragility within NUP98, an extensive analysis of the curvature, bending, thermostability, and B-Z transition aptitude of the breakpoint region of NUP98 and of its yeast ortholog NUP145 has been performed. On this basis, a DNA cassette carrying homologous tails to the two genes was amplified by PCR and allowed the targeted fusion between NUP145 and TOP2, leading to reproduce the chimeric transcript in a diploid strain of S. cerevisiae. The resulting translocated yeast obtained through BIT appears characterized by abnormal spherical bodies of nearly 500 nm of diameter, absence of external membrane and defined cytoplasmic localization. Since Nup98 is a well-known regulator of the post-transcriptional modification of P53 target genes, and P53 mutations are occasionally reported in AML, this translocant yeast strain can be used as a model to test the constitutive expression of human P53. Although the abnormal phenotype of the translocant yeast was never rescued by its expression, an exogenous P53 was recognized to confer increased vitality to the translocants, in spite of its usual and well-documented toxicity to wild-type yeast strains. These results obtained in yeast could provide new grounds for the interpretation of past observations made in leukemic patients indicating a possible involvement of P53 in cell transformation toward AML.