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As a naturally occurring inhibitor of mTOR, accumulated evidence has suggested that DEPTOR plays a pivotal role in suppressing the progression of human malignances. However, the function of DEPTOR in the development of esophageal squamous cell carcinoma (ESCC) is still unclear. Here we report that the expression of DEPTOR is significantly reduced in tumor tissues derived from human patients with ESCC, and the downregulation of DEPTOR predicts a poor prognosis of ESCC patients. In addition, we found that the expression of DEPTOR negatively regulates the tumorigenic activities of ESCC cell lines (KYSE150, KYSE510 and KYSE190). Furthermore, ectopic DEPTOR expression caused a significant suppression of the cellular proliferation, migration and invasion of KYSE150 cells, which has the lowest expression level of DEPTOR in the three cell lines. Meanwhile, CRISPR/Cas9 mediated knockout of DEPTOR in KYSE-510 cells significantly promoted cellular proliferation, migration and invasion. In addition, in vivo assays further revealed that tumor growth was significantly inhibited in xenografts with ectopic DEPTOR expression as compared to untreated KYSE150 cells, and was markedly enhanced in DEPTOR knockout KYSE-510 cells. Biochemical studies revealed that overexpression of DEPTOR led to the suppression of AKT/mTOR pathway as evidenced by reduced phosphorylation of AKT, mTOR and downstream SGK1, indicating DEPTOR might control the progression of ESCC through AKT/mTOR signaling pathway. Thus, these findings, for the first time, demonstrated that DEPTOR inhibits the tumorigenesis of ESCC cells and might serve as a potential therapeutic target or prognostic marker for human patients with ESCC.

Microbiome composition profiles generated from 16S rRNA sequencing have been extensively studied for their usefulness in phenotype trait prediction, including for complex diseases such as diabetes and obesity. These microbiome compositions have typically been quantified in the form of Operational Taxonomic Unit (OTU) count matrices. However, alternate approaches such as Amplicon Sequence Variants (ASV) have been used, as well as the direct use of k-mer sequence counts. The overall effect of these different types of predictors when used in concert with various machine learning methods has been difficult to assess, due to varied combinations described in the literature. Here we provide an in-depth investigation of more than 1,000 combinations of these three clustering/counting methods, in combination with varied choices for normalization and filtering, grouping at various taxonomic levels, and the use of more than ten commonly used machine learning methods for phenotype prediction. The use of short k-mers, which have computational advantages and conceptual simplicity, is shown to be effective as a source for microbiome-based prediction. Among machine-learning approaches, tree-based methods show consistent, though modest, advantages in prediction accuracy. We describe the various advantages and disadvantages of combinations in analysis approaches, and provide general observations to serve as a useful guide for future trait-prediction explorations using microbiome data.

While the COVID-19 pandemic presents a global challenge, the U.S. response places substantial responsibility for both decision-making and communication on local health authorities. To better support counties and municipalities, we integrated baseline data on relevant community vulnerabilities with dynamic data on local infection rates and interventions into a Pandemic Vulnerability Index (PVI). The PVI presents a visual synthesis of county-level vulnerability indicators that can be compared in a regional, state, or nationwide context. We describe use of the PVI, supporting epidemiological modeling and machine-learning forecasts, and deployment of an interactive, web Dashboard. The Dashboard facilitates decision-making and communication among government officials, scientists, community leaders, and the public to enable more effective and coordinated action to combat the pandemic.

Autophagy, a form of lysosomal degradation capable of eliminating dysfunctional proteins and organelles, is a cellular process associated with homeostasis. Autophagy functions in cell survival by breaking down proteins and organelles and recycling them to meet metabolic demands. However, aberrant up regulation of autophagy can function as an alternative to apoptosis. The duality of autophagy, and its regulation over cell survival/death, intimately links it with human disease. Non-coding RNAs regulate mRNA levels and elicit diverse effects on mammalian protein expression. The most studied non-coding RNAs to-date are microRNAs (miRNA). MicroRNAs function in post-transcriptional regulation, causing profound changes in protein levels, and affect many biological processes and diseases. The role and regulation of autophagy, whether it is beneficial or harmful, is a controversial topic in cardiovascular disease. A number of recent studies have identified miRNAs that target autophagy-related proteins and influence the development, progression, or treatment of cardiovascular disease. Understanding the mechanisms by which these miRNAs work can provide promising insight and potential progress towards the development of therapeutic treatments in cardiovascular disease.

The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

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