Environmental factors may affect gene expression through epigenetic modifications of histones and transcription factors. Here, we report that cellular uptake of sorbate, a common food preservative, induces lysine sorb...
Environmental factors may affect gene expression through epigenetic modifications of histones and transcription factors. Here, we report that cellular uptake of sorbate, a common food preservative, induces lysine sorbylation (Ksor) in mammalian cells and tissue mediated by the noncanonical activities of class I histone deacetylases (HDAC1-3). We demonstrated that HDAC1-3 catalyze sorbylation upon sorbate uptake and desorbylation in the absence of sorbate both in vitro and in cells. Sorbate uptake in mice livers significantly induced histone Ksor, correlating with decreased expressions of inflammation-response genes. Accordingly, sorbate treatment in macrophage RAW264.7 cells upon lipopolysaccharide (LPS) stimulation dose-dependently down-regulated proinflammatory gene expressions and nitric oxide production. Proteomic profiling identified RelA, a component of the NF-κB complex, and its interacting proteins as bona fide Ksor targets and sorbate treatment significantly decreased NF-κB transcriptional activities in response to LPS stimulation in RAW264.7 cells. Together, our study demonstrated a noncanonical mechanism of sorbate uptake in regulating epigenetic histone modifications and inflammatory gene expression.
The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict ...
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Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeut...
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Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson's correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model's input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein's contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs' actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs.
We propose a novel approach for clustering the vertices of a graph. The method, Vertex-Frequency Clustering (VFC), considers the local harmonic content of one or many graph signals, forming partitions based on spectra...
We propose a novel approach for clustering the vertices of a graph. The method, Vertex-Frequency Clustering (VFC), considers the local harmonic content of one or many graph signals, forming partitions based on spectral features in the input signal. The method can be related to spectral clustering, and the length scale over which frequencies are considered is tunable. This allows one to cluster data based on intrinsic graph geometry in the context of signal dynamics. VFC is useful for unravelling active regions in a signal, collecting sets of similar observations, or detecting anomalies. We demonstrate the utility of VFC in synthetic and biological data, and show how VFC can be used to identify observations with similar feature sets and signal profiles.
Numerous experimental and computational studies show that continuous hopper flows of granular materials obey the Beverloo equation that relates the volume flow rate Q and the orifice width w: Q ∼ (w/σavg − k)β, whe...
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Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbi...
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*equal contribution, # co-corresponding authors Although the JAK2V617F mutation is the most common MPN phenotypic driver mutation, the precise consequences of the mutation on the behavior of individual human hematopoi...
*equal contribution, # co-corresponding authors Although the JAK2V617F mutation is the most common MPN phenotypic driver mutation, the precise consequences of the mutation on the behavior of individual human hematopoietic stem cells (HSCs) in vivo remains unknown. We used whole genome sequencing and single-cell profiling of hematopoietic stem and progenitor cells (HSPCs) to quantify the impact of JAK2V617F on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual newly diagnosed MPN patients. We reconstructed the lineage history of individual HSCs obtained from patients with newly diagnosed essential thrombocythemia (ET), using the pattern of spontaneous somatic mutations accrued in their genomes over decades (Figure 1). Intriguingly, our analysis indicates that the JAK2V617F mutation occurred in a single HSC many years before MPN diagnosis - at age 9±2 years in a 34 year-old patient, and at age 19±3 years in a 63 year-old patient. In each patient, we inferred the number of mutated HSCs over the years and computed their fitness. After escaping stochastic extinction, the population of mutated HSCs grew exponentially by 63±15% and 44±13% every year in the two patients respectively. To contrast the differentiation trajectories of the JAK2 -mutant HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single HSPCs in the two ET patients in whom we had performed whole genome sequencing and in one additional ET patient (N=3 total) and also in patients with polycythemia vera (PV) (N=3). We observed, at the time of MPN diagnosis, a consistent lineage bias of JAK2 -mutant HSPCs toward megakaryocyte-erythrocyte fate, across ET and PV patients. Exploiting our ability to discriminate JAK2 -mutant cells from JAK2 wild-type cells within individual MPN patients, we identified genes involved in antigen presentation and inflammation as differentially up-regulated in JAK2 -mutant HS
Experimental data on the compressive strength σmax versus strain rate Ε eng for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasse...
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Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models t...
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