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The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses. [ABSTRACT FROM AUTHOR]

Motivation Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. Results Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. Availability and implementation PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. Results: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. Conclusions: PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download. [ABSTRACT FROM AUTHOR]

Our phase I Hepatic Immunotherapy for Metastases (HITM) trial tested the safety of chimeric antigen receptor-modified T-cell (CAR-T) hepatic artery infusions (HAI) for unresectable carcinoembryonic antigen (CEA)+ liver metastases (LM). High neutrophil:lymphocyte ratios (NLR) predict poor outcome in cancer patients and we hypothesized that NLR changes would correlate with early responses to CAR-T HAI. Six patients completed the protocol. Three patients received CAR-T HAI in dose escalation (1 × 108, 1 × 109 and 1 × 1010 cells) and the remainder received three doses (1 × 1010 cells) with interleukin (IL)2 support. Serum cytokines and NLR were measured at multiple time points. The mean NLR for all patients was 13.9 (range 4.8-38.1). NLR increased in four patients following treatment with a mean fold change of 1.9. Serum IL6 levels and NLR fold changes demonstrated a trend towards a positive correlation (r=0.77, P=0.10). Patients with poor CEA responses were significantly more likely to have higher NLR level increases (P=0.048). Increased NLR levels were associated with poor responses following CAR-T HAI. NLR variations and associated cytokine changes may be useful surrogates of response to CAR-T HAI. [ABSTRACT FROM AUTHOR]

Provides information on a study of a class of antibacterials that have been discovered to inhibit the growth of Gram-positive pathogenic bacteria. Methodology and materials used to conduct the study; Results of the study.