DOI: 10.5061/dryad.z612jm6n2
title: Human adenovirus serotype 5 infection dysregulates cysteine, purine, and unsaturated fatty acid metabolism in fibroblasts

description: Viral infection causes cellular dysregulation of metabolic chemical
 reactions. Viruses alter host metabolism for their replication needs. How
 viruses impact specific metabolic pathways is not well-understood, even in
 a well-studied virus like human adenovirus. Adenoviral infection is known
 to influence cellular glycolysis and respiration, however, global effects
 on overall cellular metabolism in response to infection are unclear.
 Further, few studies have employed an untargeted approach, combining
 emphasis on viral dosage and of infection. To address this, we employed
 untargeted metabolomics to quantify the dynamic metabolic shifts in
 fibroblasts infected with human adenovirus serotype 5 (HAdV-5) at 3
 dosages (0.5, 1.0, and 2.0 multiplicity of infection [MOI]) and across 4
 time points (6, 12, 24, and 36 hours post-infection [HPI]). The greatest
 differences in individual metabolites were observed at 6- and 12-hours
 post-infection, correlating to the early phase of the HAdV-5 infection
 cycle. In addition to effects on glycolysis and respiration, adenoviral
 infection downregulated cysteine and unsaturated fatty acid metabolism,
 while upregulating aspects of purine metabolism. These results reveal
 specific metabolic pathways dysregulated by adenoviral infection and the
 associated, dynamic shifts in metabolism suggesting that viral infections
 alter energetics via profound changes in lipid, nucleic acid, and protein
 metabolism. The results reveal previously unconsidered metabolic pathways
 disrupted by HAdV-5 that can alter cellular metabolism, thereby prompting
 further investigation into HAdV mechanisms and antiviral targeting.
description: The experimental steps (controls, treatments, pooled samples)
 were conducted at UC Merced by Bailey Sanchez. The preparation and running
 of samples on the metabolomic platform (GC-TOF-MS) were performed by the
 members of Oliver Fiehn's lab at UC Davis, according to their
 published/established protocol
 (https://doi.org/10.1002/0471142727.mb3004s114). The preparation of the
 raw data uploaded to this repository was performed also by the FIehn lab,
 including derivatization. Data analysis was performed by Bailey Sanchez
 from UC Merced. 
description: ## **Human adenovirus serotype 5 infection dysregulates cysteine, purine,
 and unsaturated fatty acid metabolism in fibroblasts** ####
 **GC-TOF-MS\_RAW primary carbon data\_SANCHEZ\_B\_HAdV5\_HEK293**
 [https://doi.org/10.5061/dryad.z612jm6n2](https://doi.org/10.5061/dryad.z612jm6n2) ## Description of the data and file structure ## Code/software Description of metabolomics data file: The data presented in this file was generated by the West Coast Metabolomics Center (WCMC) at UC Davis. Samples were sent here and the samples were ran on a Leco Pegasus IV mass spectrometer coupled with a Restek corporation Rtx-5Sil MS column. Fatty acid methyl ester (FAME) was used as the internal standard in the instrument run. Automatic liner exchanges were performed after each set of 10 instrument injections. Peak heights for the quantification ion (mz value) are the indicated values in the data set, which were vector normalized by taking the sum of all peak heights for all of the **identified** metabolites for each sample (mTIC). If mTIC averages were significantly different by a p &lt; 0.05, data were normalized to the average of each group, if not the data was normalized to the total average mTIC. The tabs in the dataset correspond to 4 tabs for each time point and their associated metabolite data plus one tab for the metadata associated with the metabolites in the study. The variables in the **time point tabs** from the dataset include: * Treatment (categorical): this column corresponds to the treatment to the HEK293 cells (dosage of HAdV-5 in terms of multiplicity of infection (MOI)). * Sample (categorical): this column corresponds to the replicates for each treatment (x3 for noninfected, x6 for infected samples). * The following columns (number) correspond to each individual metabolite's numerical data as described previously. - NA denotes missing data for unidentified metabolites for the 24HPI samples. Data from unidentified metabolites were not used in the study. Because some of the 24HPI replicates for the 0.5 and 1.0 were sent in separate batches, metabolite data for the some of the unidentified metabolites were not registered, but are still available for the identified metabolites. - As indicated in the 'metabolite metadata' tab, the red highlighted '2.0MOI E' replicate failed injection and was not used in the data analysis. The variables in the **metabolite metadata** tab from the dataset includes: * BinBase name (categorical/number): denotes the name of the metabolite, if the peak has been identified. Identification is based on the mass spectrum peak processing performed by the WCMC at UC Davis, which includes deconvolution using the Fiehn library. * ret.index (number): target retention index in the BinBase database system * quant mz (number): m/z value that was used to quantify the peak height of a BinBase entry * BB id (number): unique identifier for the GCTOFMS platform, given for both identified and unidentified metabolites in the same manner * mass spec (number): complete mass spectrum of the metabolite given as mz: intensity values, separated by spaces * PubChem (number): unique identifier of a metabolite in the PubChem database * KEGG (categorical): unique identifier associated with an identified metabolite in the KEGG community database (BLANK cells indicate that the metabolite does not have a KEGG-associated identifier) * InChIKey (categorical): unique chemical identifier defined by the IUPAC and NIST consortia The raw metabolomic data can be opened using common platforms including but not limited to: Microsoft excel, R and/or Python. Statistical generation, PCA/PLS-DA plots, and heatmaps were generated using Rstudio (R version 4.1.2 (2021-11-01)). R packages used to perform these downstream analyses included: DESeq2, reshape, ggplot2, ggrepel, DEGreport, RColorBrewer, pheatmap, dplyr, rlang, mixomics, ggpubr. Network maps were generated using the statistical and metadata associated with the metabolites identified in this study. These data were input into MetaMAPP online software, in which the data featured metadata associated with each metabolite and the p-value and fold change values. [https://metamapp.fiehnlab.ucdavis.edu/ocpu/library/MetaMapp2020/www/](https://metamapp.fiehnlab.ucdavis.edu/ocpu/library/MetaMapp2020/www/) Cystoscope software was used to open the MetaMAPP network file and associated Node Attribute File.  Data associated with the heatmap made originated from the ChemRICH online software. The data featured metadata associated with each metabolite and the p-value and fold change values [https://chemrich.fiehnlab.ucdavis.edu/](https://chemrich.fiehnlab.ucdavis.edu/)