Examining DNA Breathing with pyDNA-EPBD.

Motivation: The two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion.This dynamics results in transient openings in the double helix and is referred to as "DNA breathing" or "DNA bubbles." The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding.

Impaired cardiac glycolysis and glycogen depletion are linked to poor myocardial outcomes in juvenile male swine with metabolic syndrome and ischemia.

Obesity continues to rise in the juveniles and obese children are more likely to develop metabolic syndrome (MetS) and related cardiovascular disease. Unfortunately, effective prevention and long-term treatment options remain limited. We determined the juvenile cardiac response to MetS in a swine model.

Uncovering novel mutational signatures by extraction with SigProfilerExtractor.

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures.

Quantum annealing algorithms for Boolean tensor networks.

Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks.

The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients.

Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients.

Improved Protein Decoy Selection via Non-Negative Matrix Factorization.

A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. Research on decoy selection is active.

Metabolomics and the pig model reveal aberrant cardiac energy metabolism in metabolic syndrome.

Although metabolic syndrome (MS) is a significant risk of cardiovascular disease (CVD), the cardiac response (MR) to MS remains unclear due to traditional MS models' narrow scope around a limited number of cell-cycle regulation biomarkers and drawbacks of limited human tissue samples. To date, we developed the most comprehensive platform studying MR to MS in a pig model tightly related to human MS criteria. By incorporating comparative metabolomic, transcriptomic, functional analyses, and unsupervised machine learning (UML), we can discover unknown metabolic pathways connections and links on numerous biomarkers across the MS-associated issues in the heart.

Robust effect of metabolic syndrome on major metabolic pathways in the myocardium.

Although the high-fat-diet-induced metabolic syndrome (MetS) is a precursor of human cardiac pathology, the myocardial metabolic state in MetS is far from clear. The discrepancies in metabolite handling between human and small animal models and the difficulties inherent in obtaining human tissue complicate the identification of the myocardium-specific metabolic response in patients. Here we use the large animal model of swine that develops the hallmark criteria of human MetS.

Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture.

Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable of decomposing and categorizing the evolution of thousands if not millions of lipids involved in the phenomenon. The interpretation and analysis of molecular dynamics (MD) simulations representing temporal and spatial changes in such systems are still a challenging task.

Nonnegative/Binary matrix factorization with a D-Wave quantum annealer.

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful.

Nonnegative tensor factorization for contaminant source identification.

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources.

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored.

Contaminant source identification using semi-supervised machine learning.

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data.

Evaluating the role of coherent delocalized phonon-like modes in DNA cyclization.

The innate flexibility of a DNA sequence is quantified by the Jacobson-Stockmayer's J-factor, which measures the propensity for DNA loop formation. Recent studies of ultra-short DNA sequences revealed a discrepancy of up to six orders of magnitude between experimentally measured and theoretically predicted J-factors. These large differences suggest that, in addition to the elastic moduli of the double helix, other factors contribute to loop formation.

Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions.

We present a dynamical model of drug accumulation in bacteria. The model captures key features in experimental time courses on ofloxacin accumulation: initial uptake; two-phase response; and long-term acclimation. In combination with experimental data, the model provides estimates of import and export rates in each phase, the time of entry into the second phase, and the decrease of internal drug during acclimation.

A rare functional noncoding variant at the GWAS-implicated MIR137/MIR2682 locus might confer risk to schizophrenia and bipolar disorder.

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.

Specificity and heterogeneity of terahertz radiation effect on gene expression in mouse mesenchymal stem cells.

We report that terahertz (THz) irradiation of mouse mesenchymal stem cells (mMSCs) with a single-frequency (SF) 2.52 THz laser or pulsed broadband (centered at 10 THz) source results in irradiation specific heterogenic changes in gene expression. The THz effect depends on irradiation parameters such as the duration and type of THz source, and on the degree of stem cell differentiation.

Binding of nucleoid-associated protein fis to DNA is regulated by DNA breathing dynamics.

Physicochemical properties of DNA, such as shape, affect protein-DNA recognition. However, the properties of DNA that are most relevant for predicting the binding sites of particular transcription factors (TFs) or classes of TFs have yet to be fully understood. Here, using a model that accurately captures the melting behavior and breathing dynamics (spontaneous local openings of the double helix) of double-stranded DNA, we simulated the dynamics of known binding sites of the TF and nucleoid-associated protein Fis in Escherichia coli.

DNA breathing dynamics distinguish binding from nonbinding consensus sites for transcription factor YY1 in cells.

The genome-wide mapping of the major gene expression regulators, the transcription factors (TFs) and their DNA binding sites, is of great importance for describing cellular behavior and phenotypic diversity. Presently, the methods for prediction of genomic TF binding produce a large number of false positives, most likely due to insufficient description of the physiochemical mechanisms of protein-DNA binding. Growing evidence suggests that, in the cell, the double-stranded DNA (dsDNA) is subject to local transient strands separations (breathing) that contribute to genomic functions.

Non-thermal effects of terahertz radiation on gene expression in mouse stem cells.

In recent years, terahertz radiation sources are increasingly being exploited in military and civil applications. However, only a few studies have so far been conducted to examine the biological effects associated with terahertz radiation. In this study, we evaluated the cellular response of mesenchymal mouse stem cells exposed to THz radiation.

Mammalian stem cells reprogramming in response to terahertz radiation.

We report that extended exposure to broad-spectrum terahertz radiation results in specific changes in cellular functions that are closely related to DNA-directed gene transcription. Our gene chip survey of gene expression shows that whereas 89% of the protein coding genes in mouse stem cells do not respond to the applied terahertz radiation, certain genes are activated, while other are repressed. RT-PCR experiments with selected gene probes corresponding to transcripts in the three groups of genes detail the gene specific effect.