Additional file 1 of The influence of spaceflight on the astronaut salivary microbiome and the search for a microbiome biomarker for viral reactivation
Creators: name: Urbaniak, Camilla
name: Hernan Lorenzi
name: Thissen, James
name: Jaing, Crystal
name: Crucian, Brian
name: Sams, Clarence
name: Pierson, Duane
name: Kasthuri Venkateswaran
name: Mehta, Satish
Publisher: name: figshare
Year: 2020
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Ecology
subject: 20199 Astronomical and Space Sciences not elsewhere classified
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: 60506 Virology

description: Additional file 1: Figure S1. Alpha diversity of Streptococcus sequences in the saliva. Saliva samples were collected from 10 astronauts at various timepoints before flying to the ISS (pre-flight), while on the ISS (flight) and upon return to Earth (post-flight). Streptococcal diversity was measured using Shannon’s diversity index (A) and richness was measured by counting the number of unique Staphylococcus amplicon sequence variants within each sample (B) Reads were rarified to different counts (x- axis) and the values for each rarefied read count plotted (y-axis). Shannon’s diversity was lowest during flight compared to pre-flight and post-flight samples. Richness, on the other hand, was lowest during post-flight, with pre-flight and in-flight having similar number of observed Streptococcus sequences. Figure S2. Heatmap of read counts of genera detected in the saliva. ASVs were summarized, based on taxonomy, to the genus level and all those that could be assigned to a genus were included in this heatmap. Multiple samples were collected from each astronaut but were averaged for a given flight status. The data was then clr transformed. Clr values that are positive are higher than the geometric mean (and thus can be considered more abundant) and those that are negative are lower than the geometric mean (and can be considered less abundant). The heatmap is separated by subject and by flight status (i.e. pre-flight, in-flight or post-flight samples). In the heatmap, red represents the highest clr value and light green the lowest. Figure S3.Streptococcus strains can predict viral status. Results from the random forest classifier showing the Streptococcus sequences that are the most discriminatory between viral positive (qPCR detection of EBV or HSC-1) and viral negative samples, in descending order. OOB error rate was 19% and LOOCV accuracy was 81%. Figure S4. Microbiome profiles in subjects with a history of viral reactivation vs those that never had viral shedding. (A) &amp; (B) Boxplots of Veillonella and Haemophilus showing the relative abundances of each genus in (i) samples that were negative for virus but from subjects with a history of viral shedding (“history”) and (ii) samples from subjects that never had a reactivation episode “never”. These taxa were statistically significantly different (p &lt; 0.05) between the groups. (C) Results from the random forest classifier showing the bacterial genera that are the most discriminatory between the “never” and “history” groups, in descending order. OOB error rate was 9% and LOOCV accuracy was 91%. Veillonella was the most predictive.