key: cord-0698182-gwi9gy4w authors: Rhoades, Nicholas S.; Pinski, Amanda N.; Monsibais, Alisha N.; Jankeel, Allen; Doratt, Brianna M.; Cinco, Isaac R.; Ibraim, Izabela; Messaoudi, Ilhem title: Acute SARS-CoV-2 infection is associated with an increased abundance of bacterial pathogens, including Pseudomonas aeruginosa in the nose date: 2021-08-13 journal: Cell Rep DOI: 10.1016/j.celrep.2021.109637 sha: 50ea5478212c194a9de5b950882e4cb28e9b6d96 doc_id: 698182 cord_uid: gwi9gy4w Research conducted on SARS-CoV-2 pathogenesis and COVID-19 has focused on the systemic host response, especially that generated by severely ill patients with few studies investigating the impact of acute SARS-CoV-2 at the site of infection. We show that the nasal microbiome of SARS-CoV-2 positive patients (CoV+, n=68) at the time of diagnosis was unique compared to CoV- healthcare workers (n=45) and CoV- outpatients (n=21). This shift was marked by an increased abundance of bacterial pathogens including Pseudomonas aeruginosa which was also positively associated with viral RNA load. Additionally, we observed a robust host transcriptional response in the nasal epithelia of CoV+ patients, indicative of an antiviral innate immune repones and neuronal damage. These data suggest that the inflammatory response caused by SARS-CoV-2 infection is associated with an increased abundance of bacterial pathogens in the nasal cavity that could contribute to increased incidence of secondary bacterial infections. Significance for panels C-E was determined using Kruskal Wallis non-parametric ANOVA (p-297 values inset at the bottom of each panel), with Dunn's multiple comparison * = p < 0.05, ** = p 298 <0.01, *** = p < 0.001, **** = p < 0.0001. (F) Bubble plots of bacterial genera > 1% average 299 abundance across the entire study population. The size of each circle indicates the average 300 relative abundance for each taxa and the color of each circle denotes bacterial Phyla. See also 301 Figure S1 and Table S1 . in the nasal microbiome between CoV-, HCW, and CoV+ individuals. Differential abundance 306 was determined using LEfSe (Log10 LDA score > 2). (B-H) Scatter plots of bacterial genera and 307 species of interest identified by LEfSe analysis plotted as log 10 relative abundance + 0.01. 308 Horizontal black lines represent the mean and whisker the SEM. Significance for panels B-H 309 J o u r n a l P r e -p r o o f was determined using Kruskal Wallis non-parametric ANOVA, with Dunn's multiple comparison 310 * = p < 0.05, ** = p <0.01, *** = p < 0.001, **** = p < 0.0001. See also Figure S2 and Table S2 . 19_nasal_microboime). Any additional information required to reanalyze the data reported in 346 this paper is available from the lead contact upon request. 347 In this study we utilized excess material from samples originally collected for diagnostic 350 purposes that would have otherwise been discarded. These samples were de-identified prior to 351 being used for research purposes. As such the UC Irvine IRB determined that the data included 352 in this manuscript did not qualify as human subject research and did not require IRB approval. DNA that co-eluted with extracted RNA was used as the template to amplify the 384 hypervariable V4 region of the 16S rRNA gene using PCR primers (515F/806R with the forward 385 primer containing a 12-bp barcode) in duplicate reactions containing: 12.5 ul GoTaq master mix, 386 9.5 ul nuclease-free H20, 1 ul template DNA, and 1 ul 10uM primer mix. Thermal cycling 387 parameters were 94°C for 3 minutes; 35 cycles of 94°C for 45 seconds, 50°C for 1 minute, and 388 J o u r n a l P r e -p r o o f 72°C for 1 minute and 30 seconds; followed by 72°C for 10 minutes. PCR products were 389 sequences not assigned to a known phyla were removed along with any sequence assigned to 402 mitochondria or chloroplast. Any Amplicon Sequencing Variant (ASV) that was found in >1% 403 abundance in either the extraction or PCR negative control and >1% average abundance in 404 samples was also removed. These ASVs were primarily assigned to two taxa which were highly 405 abundant in the extraction controls (Thermoanaerobacterium saccharolyticum and 406 Myxococcales 0319-6G20). We also found that Streptococcus was shared between the 407 extraction control and samples, but these sequences were not shared at the ASV level and 408 therefore included in the dataset (Supp. Fig. 1A) . Finally, we confirmed a lack of PCR or 409 sequencing bias using duplicate community standard samples (Supp. Fig. 1A) . 410 After clean up taxonomy was reassigned to sequence variants using q2-feature-classifier 411 against the expanded Human Oral Microbiome Database (eHOMD: release version 15.21) 412 (Escapa et al., 2018). This curated and site-specific database was used to improve species-413 level resolution. To prevent sequencing depth bias, samples were rarified to 8,000 sequences 414 per sample before α and β diversity analysis. QIIME 2 was also used to generate the following α 415 J o u r n a l P r e -p r o o f diversity metrics: richness (as observed taxonomic units), Shannon evenness, and phylogenetic 416 diversity. β diversity was determined in QIIME 2 using weighted and unweighted UniFrac 417 distances (Lozupone et al., 2011) . 418 To confirm that microbial DNA co-eluted in our was reflective of a typical nasal 419 microbiome community, we compared our data to a previously published nasal microbiome 420 dataset (De Boeck et al., 2017) . This study as it sampled a large number of healthy subjects 421 and used the same amplicon primer set as our current study. Nasal microbiome data from this 422 study was downloaded from (BioProject: PRJEB23057), merged with our dataset and analyzed 423 using the exact parameters described above. Due to low sequencing depth of some samples in 424 BioProject: PRJEB23057 after applying our analysis parameters, this combined dataset was 425 rarified to 3000 sequences per sample prior to calculating α and β diversity metrics. A full 426 analysis pipeline including parameters used for all commands can be accessed at 427 https://github.com/NickRhoades/COVID-19_nasal_microboime. 428 429 Quantity and quality of RNA extracted from 8 nasal swabs of (4 CoV+ and 4 HCW 431 chosen at random, Supp. Table 1 ) was determined using an Agilent 2100 BioAnalyzer. cDNA 432 libraries were constructed using the NEB Next Ultra II Directional RNA Library kit (Thermo 433 Fischer). Briefly, RNA was treated with RNAseH and DNase I after depletion of ribosomal rRNA. ATP1A1 ATP2A3 CD36 CD59 CD9 CLCN3 DNAH10 DNAH11 DNAH3 DNAH7 DNHD1 DYNC2H1 FER1L5 FZD6 HSF4 HSP90B1 ITGA3 ITGAV ITGB1 LAMA2 LAMB2 LAMB3 LAMC2 PROS1 SLC1A1 SLC39A6 SLC39A7 SLC44A4 SLC4A8 TSPAN8 WDR19 WDR35 -2 0 1 2 -1 Row Z-score BCL2 BCL3 C1QA C3AR1 CCL4 CCL5 CCR1 CD53 COX2 CSF1 DDIT3 FCER1G FCGR3B FGD2 HLA-DPA1 HLA-DPB1 IKZF1 IKZF3 IRF1 IRF7 JAK3 JUN LCK LCP1 LGALS1 LGALS9 NUPR1 MYD88 MYO1G RELB SLAMF7 SOCS1 STAT1 TNFSF13B ZAP70 1, B.1.1.7, B.1.351, B.1.427, B.1.429 ) and all genomes collected from Orange County, California January 2020 to December 2020. 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