Chemical links between redox conditions and community reference proteomes (2023)

This vignette runs the code to make the plots from the following paper first published by Springer Nature:

This vignette was compiled on 2024-03-11 with JMDplots 1.2.19-11 and chem16S 1.0.0-16.

Distinct chemical parameters of reference proteomes for major taxonomic groups (Figure 1)

Table_S5 <- geo16S1()

Data source: NCBI Reference Sequence (RefSeq) database (O’Leary et al., 2016). Numbered symbols: (1) Methanococci, (2) Archaeoglobi, (3) Thermococci, (4) Halobacteria, (5) Clostridia.

Specific values mentioned in the text

Z_C for reference proteomes of genera that are abundant in produced fluids of shale gas wells:

datadir <- system.file("extdata/RefDB/RefSeq", package = "JMDplots")
taxon_metrics <- read.csv(file.path(datadir, "taxon_metrics.csv.xz"), as.is = TRUE)
subset(taxon_metrics, group %in% c("Halanaerobium", "Thermoanaerobacter"))

##      rank              group ntaxa                  parent      nH2O        Zc
## 337 genus Thermoanaerobacter    15 Thermoanaerobacteraceae -0.695134 -0.227056
## 483 genus      Halanaerobium     9        Halanaerobiaceae -0.716348 -0.198702
##           nC
## 337 5.105583
## 483 5.080626

Z_C for reference proteomes of Halanaerobium species (numeric names are NCBI taxids):

datadir <- system.file("extdata/RefDB/RefSeq", package = "JMDplots")
refseq <- read.csv(file.path(datadir, "genome_AA.csv.xz"))
Zc.refseq <- Zc(refseq)
names(Zc.refseq) <- refseq$organism

names <- read.csv(file.path(datadir, "taxonomy.csv.xz"))
is.Halanaerobium <- names$genus %in% "Halanaerobium" & !is.na(names$species)
(Zc.Halanaerobium <- round(Zc.refseq[is.Halanaerobium], 3))

##    2331   29563   43595   54121   56779  656519 1653064 2183913 2183914 
##  -0.214  -0.207  -0.191  -0.191  -0.194  -0.196  -0.202  -0.202  -0.194

range(Zc.Halanaerobium)

## [1] -0.214 -0.191

Estimated community proteomes from different environments have distinct chemical signatures (Figure 2)

Table_S6 <- geo16S2()

Data sources: Guerrero Negro mat (Harris et al., 2013), Yellowstone hot springs (Bowen De León et al., 2013), Baltic Sea water (Herlemann et al., 2016), Lake Fryxell mat (Jungblut et al., 2016), Tibetan Plateau lakes (Zhong et al., 2016), Manus Basin vents (Meier et al., 2017), Qarhan Salt Lake soils (Xie et al., 2017), Black Sea water (Sollai et al., 2019).

Lower carbon oxidation state is tied to oxygen depletion in water columns (Figure 3)

Table_S7 <- geo16S3()

Data sources: Black Sea (Sollai et al., 2019), Swiss lakes (Lake Zug and Lake Lugano) (Mayr et al., 2020), Eastern Tropical North Pacific (ETNP) (Ganesh et al., 2015), Sansha Yongle Blue Hole (He et al., 2020), Ursu Lake (Baricz et al., 2021).

Common trends of carbon oxidation state of estimated community proteomes for shale gas wells and hydrothermal systems (Figure 4)

Table_S8 <- geo16S4()

Data sources: Northwestern Pennsylvania stream water and sediment (Ulrich et al., 2018), Pennsylvania State Forests stream water in spring and fall (Mumford et al., 2020), Marcellus Shale (Cluff et al., 2014), Denver–Julesburg Basin (Hull et al., 2018), Duvernay Formation (Zhong et al., 2019).

Comparison of protein Z_C from metagenomic or metatranscriptomic data with estimates from 16S and reference sequences (Figure 5)

Table_S9 <- geo16S5()

Data sources: A. Guerrero Negro mat metagenome (Kunin et al., 2008), 16S (Harris et al., 2013); Bison Pool metagenome (Havig et al., 2011), 16S (Swingley et al., 2012); Eastern Tropical North Pacific metagenome (Glass et al., 2015), metatranscriptome and 16S (Ganesh et al., 2015); Mono Lake metatranscriptome (Edwardson and Hollibaugh, 2017), 16S (Edwardson and Hollibaugh, 2018). B. Marcellus Shale metagenome (Daly et al., 2016), 16S (Cluff et al., 2014). C. Manus Basin vents (Meier et al., 2017), Black Sea metagenome (Villanueva et al., 2021), 16S (Sollai et al., 2019). D. Human Microbiome Project (The Human Microbiome Project Consortium, 2012). E. Soils (Fierer et al., 2012); mammalian guts (Muegge et al., 2011).

RefSeq and 16S rRNA data processing outline (Figure S1)

geo16S_S1()

Scatterplots of Z_C and n_H₂O for bacterial and archaeal genera vs higher taxonomic levels (Figure S2)

geo16S_S2()

n_H₂O-Z_C plots for major phyla and their genera (Figure S3)

geo16S_S3()

Venn diagrams for phylum and genus names in the RefSeq (NCBI), RDP, and SILVA taxonomies (Figure S4)

Table_S10 <- geo16S_S4()

Data sources: RefSeq (NCBI): Names of taxa with protein sequences in RefSeq as listed in system.file("extdata/RefDB/RefSeq/taxonomy.csv.xz", package = "JMDplots"); RDP: trainset18_062020_speciesrank.fa in https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/RDPClassifier_16S_trainsetNo18_rawtrainingdata.zip; SILVA: https://www.arb-silva.de/fileadmin/silva_databases/release_138_1/Exports/SILVA_138.1_SSURef_NR99_tax_silva.fasta.gz.

Correlations between Z_C estimated from metagenomes and 16S rRNA sequences (Figure S5)

geo16S_S5()

Correlation of Z_C with GC content of metagenomic and 16S amplicon reads (Figure S6)

geo16S_S6()

Data source: https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR*******, where SRR******* is the SRA Run accession for metagenomic or 16S amplicon sequences.

Supplementary Table files

This code shows how the files for each of the Supplementary Tables is saved. The dat* objects are created by running the code blocks above, but the following code block is not run in this vignette in order to avoid cluttering the working directory.

write.csv(Table_S5, "Table_S5.csv", row.names = FALSE, quote = FALSE)
write.csv(Table_S6, "Table_S6.csv", row.names = FALSE, quote = FALSE)
write.csv(Table_S7, "Table_S7.csv", row.names = FALSE, quote = FALSE)
write.csv(Table_S8, "Table_S8.csv", row.names = FALSE, quote = FALSE)
write.csv(Table_S9, "Table_S9.csv", row.names = FALSE, quote = FALSE)
write.csv(Table_S10, "Table_S10.csv", row.names = FALSE, quote = FALSE)

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