Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and ...Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.
Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a ...cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.
Display omitted
•Computational method to predict alternative splicing effects on molecular interactions•The method leverages a machine learning approach and shows superior accuracy•Large-scale application to study transcriptome of type 2 diabetes in mice•Easy-to-use Docker container and python script interface to use the method
Narykov et al. develops a sequence-based machine learning method that determines whether a protein-protein interaction can be disrupted by the alternatively spliced version of an interacting protein. Accurate identification of disrupted interactions due to alternative splicing can provide new insights into molecular mechanisms governing functional changes associated with a disease.
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA ...microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.
Ultra‐high molecular weight polyethylene (UHMWPE) is one of the most prominent high‐performance thermoplastics for biomedical, leisure, and coating applications. Large‐scale recycling of UHMWPE is ...extremely difficult due to the high melt viscosity of the material as well as its exceptional chemical resistance and impact strength. There is a need for a commercially scalable methodology that can process the waste feedstock for mechanical recycling while sustaining the outstanding physical properties of the material. Solid‐state shear pulverization (SSSP) is a continuous, twin‐screw extruder‐based processing technique in which the low‐temperature application of shear and compressive forces impart changes in structure at different length scales to overcome the challenges of difficult‐to‐recycle polymers. This paper investigates the use of SSSP in mechanically recycling post‐industrial scrap UHMWPE (rUHMWPE) material from a local ski and snowboard manufacturer. The SSSP‐processed particles are flat, micron‐scale flakes with enhanced surface area, which can sinter very quickly when compression molded. The molded rUHMWPE samples in turn exhibit enhanced ductility and toughness compared to the as‐received scrap material, based on the tunable mechanochemical modification of the ethylene chains.
Solid‐state shear pulverization (SSSP) is a modified twin screw extrusion technique that can process high melt‐viscosity materials in effective, continuous, and sustainable ways. Mechanical recycling of post‐industrial ultrahigh molecular weight polyethylene (UHMWPE) is facilitated by the SSSP process, which can be tuned to affect the particle size/shape, molecular weight, and entanglement density. The SSSP‐processed materials can be compression‐molded like typical polyolefins.
Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. Analysis of scRNA-seq data routinely involves machine ...learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amount of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. To address this, we developed a new hybrid approach, deep unsupervised single-cell clustering (DUSC), which integrates feature generation based on a deep learning architecture by using a new technique to estimate the number of latent features, with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to a single-cell transcriptomics data set obtained from a triple-negative breast cancer tumor to identify potential cancer subclones accentuated by copy-number variation and investigate the role of clonal heterogeneity. Our method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.
Fast-scan cyclic voltammetry permits robust subsecond measurements of in vivo neurotransmitter dynamics, resulting in its established use in elucidating these species’ roles in the actions of ...behaving animals. However, the technique’s limitations, namely the need for digital background subtraction for analytical signal resolution, have restricted the information obtainable largely to that about phasic neurotransmitter release on the second-to-minute time scale. The study of basal levels of neurotransmitters and their dynamics requires a means of isolating the portion of the background current arising from neurotransmitter redox reactions. Previously, we reported on the use of a convolution-based method for prediction of the resistive-capacitive portion of the carbon-fiber microelectrode background signal, to improve the information content of background-subtracted data. Here we evaluated this approach for direct analytical signal isolation. First, protocol modifications (i.e., applied waveform and carbon-fiber type) were optimized to permit simplification of the interfering background current to components that are convolution-predictable. It was found that the use of holding potentials of at least 0.0 V, as well as the use of pitch-based carbon fibers, improved the agreement between convolution predictions and the observed background. Subsequently, it was shown that measurements of basal dopamine concentrations are possible with careful control of the electrode state. Successful use of this approach for measurement of in vivo basal dopamine levels is demonstrated, suggesting the approach may serve as a useful tool in expanding the capabilities of fast-scan cyclic voltammetry.
Heterodera glycines, commonly referred to as the soybean cyst nematode (SCN), is an obligatory and sedentary plant parasite that causes over a billion-dollar yield loss to soybean production ...annually. Although there are genetic determinants that render soybean plants resistant to certain nematode genotypes, resistant soybean cultivars are increasingly ineffective because their multi-year usage has selected for virulent H. glycines populations. The parasitic success of H. glycines relies on the comprehensive re-engineering of an infection site into a syncytium, as well as the long-term suppression of host defense to ensure syncytial viability. At the forefront of these complex molecular interactions are effectors, the proteins secreted by H. glycines into host root tissues. The mechanisms of effector acquisition, diversification, and selection need to be understood before effective control strategies can be developed, but the lack of an annotated genome has been a major roadblock.
Here, we use PacBio long-read technology to assemble a H. glycines genome of 738 contigs into 123 Mb with annotations for 29,769 genes. The genome contains significant numbers of repeats (34%), tandem duplicates (18.7 Mb), and horizontal gene transfer events (151 genes). A large number of putative effectors (431 genes) were identified in the genome, many of which were found in transposons.
This advance provides a glimpse into the host and parasite interplay by revealing a diversity of mechanisms that give rise to virulence genes in the soybean cyst nematode, including: tandem duplications containing over a fifth of the total gene count, virulence genes hitchhiking in transposons, and 107 horizontal gene transfers not reported in other plant parasitic nematodes thus far. Through extensive characterization of the H. glycines genome, we provide new insights into H. glycines biology and shed light onto the mystery underlying complex host-parasite interactions. This genome sequence is an important prerequisite to enable work towards generating new resistance or control measures against H. glycines.
The dopamine system has been implicated in decision-making particularly when associated with effortful behavior. We examined acute optogenetic stimulation of dopamine cells in the ventral tegmental ...area (VTA) as mice engaged in an effort-based decision-making task. Tyrosine hydroxylase-Cre mice were injected with Cre-dependent ChR2 or eYFP control virus in the VTA. While eYFP control mice showed effortful discounting, stimulation of dopamine cells in ChR2 mice disrupted effort-based decision-making by reducing choice toward the lever associated with a preferred outcome and greater effort. Surprisingly, disruptions in effortful discounting were observed in subsequent test sessions conducted in the absence of optogenetic stimulation, however during these sessions ChR2 mice displayed enhanced high choice responding across trial blocks. These findings suggest increases in VTA dopamine cell activity can disrupt effort-based decision-making in distinct ways dependent on the timing of optogenetic stimulation.
Fluid bolus therapy (FBT) is fundamental to the management of circulatory shock in critical care but balancing the benefits and toxicities of FBT has proven challenging in individual patients. ...Improved predictors of the hemodynamic response to a fluid bolus, commonly referred to as a fluid challenge, are needed to limit non-beneficial fluid administration and to enable automated clinical decision support and patient-specific precision critical care management. In this study we retrospectively analyzed data from 394 fluid boluses from 58 pigs subjected to either hemorrhagic or distributive shock. All animals had continuous blood pressure and cardiac output monitored throughout the study. Using this data, we developed a machine learning (ML) model to predict the hemodynamic response to a fluid challenge using only arterial blood pressure waveform data as the input. A Random Forest binary classifier referred to as the ML fluid responsiveness algorithm (MLFRA) was trained to detect fluid responsiveness (FR), defined as a ≥ 15% change in cardiac stroke volume after a fluid challenge. We then compared its performance to pulse pressure variation, a commonly used metric of FR. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. Across multiple train/test splits and feature selection methods designed to assess performance in the setting of small sample size conditions typical of large animal experiments, the MLFRA achieved an average AUROC, recall (sensitivity), specificity, and precision of 0.82, 0.86, 0.62. and 0.76, respectively. In the same datasets, pulse pressure variation had an AUROC, recall, specificity, and precision of 0.73, 0.91, 0.49, and 0.71, respectively. The MLFRA was generally well-calibrated across its range of predicted probabilities and appeared to perform equally well across physiologic conditions. These results suggest that ML, using only inputs from arterial blood pressure monitoring, may substantially improve the accuracy of predicting FR compared to the use of pulse pressure variation. If generalizable, these methods may enable more effective, automated precision management of critically ill patients with circulatory shock.
PDF
