Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

To control the spread of Corona Virus Disease , screening large numbers of suspected cases for appropriate quarantine and treatment is a priority.Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false negative results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we aimed to develop a deep learning method that could extract COVID-19's graphical features in order to provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control.Methods：We collected 1,119 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results:The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Conclusion:These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis.

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a broadly expressed lncRNA involved in many aspects of cellular processes. To further delineate the underlying molecular mechanism, we employed a high-throughput strategy to characterize the interacting proteins of MALAT1 by combining RNA pull-down, quantitative proteomics, bioinformatics, and experimental validation. Our approach identified 127 potential MALAT1-interacting proteins and established a highly connected MALAT1 interactome network consisting of 788 connections. Gene ontology annotation and network analysis showed that MALAT1 was highly involved in five biological processes: RNA processing; gene transcription; ribosomal proteins; protein degradation; and metabolism regulation. The interaction between MALAT1 and depleted in breast cancer 1 (DBC1) was validated using RNA pull-down and RNA immunoprecipitation. Further mechanistic studies reveal that MALAT1 binding competes with the interaction between sirtuin1 (SIRT1) and DBC1, which then releases SIRT1 and enhances its deacetylation activity. Consequently, the deacetylation of p53 reduces the transcription of a spectrum of its downstream target genes, promotes cell proliferation and inhibits cell apoptosis. Our results uncover a novel mechanism by which MALAT1 regulates the activity of p53 through the lncRNA–protein interaction.

Interacting with proteins is a crucial way for long noncoding RNAs (lncRNAs) to exert their biological responses. Here we report a high throughput strategy to characterize lncRNA interacting proteins in vivo by combining tobramycin affinity purification and mass spectrometric analysis (TOBAP-MS). Using this method, we identify 140 candidate binding proteins for lncRNA highly upregulated in liver cancer (HULC). Intriguingly, HULC directly binds to two glycolytic enzymes, lactate dehydrogenase A (LDHA) and pyruvate kinase M2 (PKM2). Mechanistic study suggests that HULC functions as an adaptor molecule that enhances the binding of LDHA and PKM2 to fibroblast growth factor receptor type 1 (FGFR1), leading to elevated phosphorylation of these two enzymes and consequently promoting glycolysis. This study provides a convenient method to study lncRNA interactome in vivo and reveals a unique mechanism by which HULC promotes Warburg effect by orchestrating the enzymatic activities of glycolytic enzymes.

Temperature changes influence the reaction rates of all biological processes, which can pose dramatic challenges to cold-blooded organisms, and the capability to adapt to temperature fluctuations is crucial for the survival of these animals. In order to understand the roles that neuropeptides play in the temperature stress response, we employed a mass spectrometry-based approach to investigate the neuropeptide changes associated with acute temperature elevation in three neural tissues from the Jonah crab Cancer borealis. At high temperature, members from two neuropeptide families, including RFamide and RYamide, were observed to be significantly reduced in one of the neuroendocrine structures, the pericardial organ, while several orcokinin peptides were detected to be decreased in another major neuroendocrine organ, the sinus gland. These results implicate that the observed neuropeptides may be involved with temperature perturbation response via hormonal regulation. Furthermore, a temperature stress marker peptide with the primary sequence of SFRRMGGKAQ (m/z 1137.7) was detected and de novo sequenced in the circulating fluid (hemolymph) from animals under thermal perturbation.

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The Y-box-binding protein 1 (YBX1) plays a critical role in tumorigenesis by promoting cell proliferation, overriding cell-cycle check points, and enhancing genomic instability. In this study, the interactome of YBX1 in renal cell carcinoma (RCC) was analyzed by coimmunoprecipitation and mass spectrometry to better understand its function and regulatory mechanism. A total of 129 proteins were identified as potential YBX1 binding partners. The interaction between the complement component 1, q subcomponent binding protein (C1QBP), and YBX1 was further confirmed by immunoprecipitation and Western blotting. Knockdown of C1QBP enhanced the phosphorylation of YBX1and its nuclear translocation, indicating that C1QBP negatively regulated YBX1 activation. The clinical significance of these two proteins was analyzed in the tissues from 52 RCC patients by immunohistochemistry. Expression of YBX1 was markedly elevated in the carcinoma tissues, and its nuclear expression was associated with histological T stage and metastasis. Meanwhile, the level of C1QBP in the carcinoma tissues was significantly lower than that in the adjacent healthy tissues, which was negatively correlated with the nuclear localization of YBX1 in the RCC tissues (P = 0.011). These data suggest that C1QBP is a novel regulator of YBX1, and the expression of C1QBP and the nuclear expression of YBX1 could both be used as independent prognostic makers for cancer progression in the RCC patients. The proteomics data have been deposited to the ProteomeXchange with identifier PXD001493.

The human intestine hosts various complex microbial communities that are closely associated with multiple health and disease processes. Determining the composition and function of these microbial communities is critical to unveil disease mechanisms and promote human health. Recently, meta-omic strategies have been developed that use high-throughput techniques to provide a wealth of information, thus accelerating the study of gut microbes. Metaproteomics is a newly emerged analytical approach that aims to identify proteins on a large scale in complex environmental microbial communities (e.g., the gut microbiota). This review introduces the recent analytical strategies and applications of metaproteomics, with a focus on advances in gut microbiota research, including a discussion of the limitations and challenges of these approaches.