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We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention. • Analysis of serial blood from 139 COVID-19 patients reveals immune coordination • A major immunological shift is seen between mild and moderate infection • Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients • Novel immune cell subsets emerge in moderate cases and increase with severity Using serial blood draws from COVID-19 patients, Su et al. present an extensive multi-omics dataset of plasma and single PBMCs covering the first week of infection following clinical diagnosis, which includes information on plasma proteins, metabolites, and on PBMC transcriptomic and surface-protein data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity. [ABSTRACT FROM AUTHOR]

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The principal role of acetylcholinesterase is termination of nerve impulse transmission at cholinergic synapses, by rapid hydrolysis of the neurotransmitter acetylcholine to acetate and choline. Its active site is buried at the bottom of a deep and narrow gorge, at the rim of which is found a second anionic site, the peripheral anionic site. The fact that the active site is so deeply buried has raised cogent questions as to how rapid traffic of substrate and products occurs in such a confined environment. Various theoretical and experimental approaches have been used to solve this problem. Here, multiple conventional molecular dynamics simulations have been performed to investigate the clearance of the product, thiocholine, from the active-site gorge of acetylcholinesterase. Our results indicate that thiocholine is released from the peripheral anionic site via random pathways, while three exit routes appear to be favored for its release from the active site, namely, along the axis of the active-site gorge, and through putative back- and side-doors. The back-door pathway is that via which thiocholine exits most frequently. Our results are in good agreement with kinetic and kinetic-crystallography studies. We propose the use of multiple molecular dynamics simulations as a fast yet accurate complementary tool in structural studies of enzymatic trafficking., (Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.)