Multi-Omics Studies towards Novel Modulators of Influenza A Virus-Host Interaction

Söderholm, Sandra; Fu, Yu; Gaelings, Lana; Belanov, Sergei; Yetukuri, Laxmana; Berlinkov, Mikhail V.; Cheltov, Anton V.; Anders, Simon; Aittokallio, Tero; Nyman, Tuula A.; Matikainen, Sampsa; Kainov, Denis E.

doi:10.20944/preprints201608.0054.v1

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…In order to extend our findings beyond SARS-CoV-2, we tested IFNa2a-based combinations against FluAV. Through completion of a literature review, we identified several drugs that could be combined with IFNa2a to inhibit FluAV infection in vitro [10, 33, 34]. We tested emetine, flavopiridol, camostat, obatoclax, SNS-032, gemcitabine, monensin, cycloheximide and pimodivir against human influenza A/Udorn/307/1972 in A549 cells.…”

Section: Resultsmentioning

confidence: 99%

Interferon alpha-based combinations suppress SARS-CoV-2 infection in vitro and in vivo

Ianevski

Yao

Žusinaite

et al. 2021

Preprint

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There is an urgent need for new antivirals with powerful therapeutic potential and tolerable side effects. In the present study, we found that recombinant human interferon-alpha (IFNa) triggered cell intrinsic and extrinsic antiviral responses and reduced replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human lung epithelial Calu-3 cells. However, IFNa alone was insufficient to completely abolish SARS-CoV-2 replication. Combinations of IFNa with camostat, remdesivir, EIDD-2801, cycloheximide or convalescent serum showed strong synergy and effectively inhibited SARS-CoV-2 infection. Additionally, we demonstrated synergistic antiviral activity of IFNa2a with pimodivir against influenza A virus (FluAV) infection in human lung epithelial A549 cells, as well as of IFNa2a with lamivudine against human immunodeficiency virus 1 (HIV-1) infection in human TZM-bl cells. Our results indicate that IFNa2a-based combinational therapies help to reduce drug dose and improve efficacy in comparison with monotherapies, making them attractive targets for further pre-clinical and clinical development.

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Section: Resultsmentioning

confidence: 99%

Interferon alpha-based combinations suppress SARS-CoV-2 infection in vitro and in vivo

Ianevski

Yao

Žusinaite

et al. 2021

Preprint

Self Cite

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“…Integrative analyses that use information across these data modalities promise to deliver more comprehensive insights into the biological systems under study. Motivated by this, multi‐omics profiling is increasingly applied across biological domains, including cancer biology (Gerstung et al , ; Iorio et al , ; Mertins et al , ; Cancer Genome Atlas Research Network, ), regulatory genomics (Chen et al , ), microbiology (Kim et al , ) or host‐pathogen biology (Soderholm et al , ). Most recent technological advances have also enabled performing multi‐omics analyses at the single‐cell level (Macaulay et al , ; Angermueller et al , ; Guo et al , ; Clark et al , ; Colomé‐Tatché & Theis, ).…”

Section: Introductionmentioning

confidence: 99%

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

Argelaguet

Velten

Arnol

et al. 2018

Molecular Systems Biology

836

804

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Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

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“…We also applied PTMProphet to Dataset #3, a phosphopeptide enriched dataset derived from human cells. We selected and reanalyzed the PRIDE repository identifier PXD001620, as originally presented and described in detail by Söderholm et al 46 . Briefly, the dataset is from human macrophage cells infected with influenza A virus.…”

Section: Resultsmentioning

confidence: 99%

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

et al. 2019

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Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms and demonstrate its performance on ground-truth synthetic peptide reference datasets, one previously published small dataset, one new larger dataset, and also on a previously published phosphoenriched dataset where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.

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Multi-Omics Studies towards Novel Modulators of Influenza A Virus-Host Interaction

Cited by 8 publications

References 19 publications

Interferon alpha-based combinations suppress SARS-CoV-2 infection in vitro and in vivo

Interferon alpha-based combinations suppress SARS-CoV-2 infection in vitro and in vivo

Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

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