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Topologically associating domains, or TADs, play important roles in genome organization and gene regulation; however, they are often altered in diseases. High-throughput chromatin conformation capturing assays, such as Hi-C, can capture domains of increased interactions, and TADs and boundaries can be identified using well-established analytical tools. However, generating Hi-C data is expensive. In our study, we addressed the relationship between multi-omics data and higher-order chromatin structures using a newly developed machine-learning model called PredTAD. Our tool uses already-available and cost-effective datatypes such as transcription factor and histone modification ChIPseq data. Specifically, PredTAD utilizes both epigenetic and genetic features as well as neighboring information to classify the entire human genome as boundary or non-boundary regions. Our tool can predict boundary changes between normal and breast cancer genomes. Among the most important features for predicting boundary alterations were CTCF, subunits of cohesin (RAD21 and SMC3), and chromosome number, suggesting their roles in conserved and dynamic boundaries formation. Upon further analysis, we observed that genes near altered TAD boundaries were found to be involved in several important breast cancer signaling pathways such as Ras, Jak-STAT, and estrogen signaling pathways. We also discovered a TAD boundary alteration that contributes to RET oncogene overexpression. PredTAD can also successfully predict TAD boundary changes in other conditions and diseases. In conclusion, our newly developed machine learning tool allowed for a more complete understanding of the dynamic 3D chromatin structures involved in signaling pathway activation, altered gene expression, and disease state in breast cancer cells.The combination of several closely spaced DNA lesions, which can be induced by a single radical hit, constitutes a hallmark in the DNA damage landscape and radiation chemistry. The occurrence of such a tandem base lesion gives rise to a strong coupling with the double helix degrees of freedom and induces important structural deformations, in contrast to DNA strands containing a single oxidized nucleobase. Although such complex lesions are known to be refractory to repair by DNA glycosylases, there is still a lack of structural evidence to rationalize these phenomena. In this contribution, we explore, by numerical modeling and molecular simulations, the behavior of the bacterial glycosylase responsible for base excision repair (MutM), specialized in excising oxidatively-damaged defects such as 7,8-dihydro-8-oxoguanine (8-oxoG). The difference in lesion recognition between a simple damage and a tandem lesion featuring an additional abasic site is assessed at atomistic resolution owing to microsecond molecular dynamics simulations and machine learning postprocessing, allowing to extensively pinpoint crucial differences in the interaction patterns of the damaged bases. Our results reveal substantial changes in the interaction network surrounding the 8-oxoG upon addition of an adjacent abasic site, leading to the perturbation of the intercalation triad which is crucial for lesion recognition and processing. The recognition process might also be impacted by a more constrained MutM-DNA binding upon tandem damage, as shown by the machine learning post-processing. This work advocates for the use of such high throughput numerical simulations for exploring the complex combinatorial chemistry of tandem DNA lesions repair and more generally local multiple damaged sites of the utmost significance in radiation chemistry.Ribosome profiling (RiboSeq) has emerged as a powerful technique for studying the genome-wide regulation of translation in various cells. Several steps in the biological protocol have been improved, but the bioinformatics part of RiboSeq suffers from a lack of standardization, preventing the straightforward and complete reproduction of published results. Too many published studies provide insufficient detail about the bioinformatics pipeline used. The broad range of questions that can be asked with RiboSeq makes it difficult to use a single bioinformatics tool. Indeed, many scripts have been published for addressing diverse questions. Here (https//github.com/equipeGST/RiboDoc), we propose a unique tool (for use with multiple operating systems, OS) to standardize the general steps that must be performed systematically in RiboSeq analysis, together with the statistical analysis and quality control of the sample. The data generated can then be exploited with more specific tools. We hope that this tool will help to standardize bioinformatics analyses pipelines in the field of translation.Pathogenic DNA secondary structures have been identified as a common and causative factor for expansion in trinucleotide, hexanucleotide, and other simple sequence repeats. These expansions underlie about fifty neurological and neuromuscular disorders known as “anticipation diseases”. Cell toxicity and death have been linked to the pathogenic conformations and functional changes of the RNA transcripts, of DNA itself and, when trinucleotides are present in exons, of the translated proteins. We review some of our results for the conformations and dynamics of pathogenic structures for both RNA and DNA, which include mismatched homoduplexes formed by trinucleotide repeats CAG and GAC; CCG and CGG; CTG(CUG) and GTC(GUC); the dynamics of DNA CAG hairpins; mismatched homoduplexes formed by hexanucleotide repeats (GGGGCC) and (GGCCCC); and G-quadruplexes formed by (GGGGCC) and (GGGCCT). NSC 178886 nmr We also discuss the dynamics of strand slippage in DNA hairpins formed by CAG repeats as observed with single-molecule Fluorescence Resonance Energy Transfer. This review focuses on the rich behavior exhibited by the mismatches associated with these simple sequence repeat noncanonical structures.The extracellular matrix (ECM) is an intricate megastructure made by bacterial cells to form architecturally complex biostructures called biofilms. Protection of cells, modulation of cell-to-cell signalling, cell differentiation and environmental sensing are functions of the ECM that reflect its diverse chemical composition. Proteins, polysaccharides and eDNA have specific functionalities while cooperatively interacting to sustain the architecture and biological relevance of the ECM. The accumulated evidence on the chemical heterogeneity and specific functionalities of ECM components has attracted attention because of their potential biotechnological applications, from agriculture to the water and food industries. This review compiles information on the most relevant bacterial ECM components, the biophysical and chemical features responsible for their biological roles, and their potential to be further translated into biotechnological applications.