Influence of redox potential on bacterial protein evolution (2023)

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

Thermodynamic model for the relationship between carbon oxidation state of reference proteomes and redox potential (Figure 1)

orp16S_1()

Z_C of reference proteomes compared with oxygen tolerance and with metaproteomes (Figure 2)

orp16S_2()

## [1] "64 genera not matched to RefSeq"
## [1] "Archaeal genera: Methanobrevibacter Methanosphaera"

Data sources: List of strictly anaerobic and aerotolerant genera (Million and Raoult, 2018), Metaproteomes: Manus Basin Inactive Chimney (Meier et al., 2019), Manus Basin Active Chimneys (Pjevac et al., 2018), Soda Lake Biomats (Kleiner et al., 2017), Mock Communities (Kleiner et al., 2017), Saanich Inlet (Hawley et al., 2017).

Methods overview and chemical depth profiles in Winogradsky columns (Figure 3)

orp16S_3()

Data sources: (b) ORP and pH (Diez-Ercilla et al., 2019) to calculate Eh7. (c) 16S rRNA gene sequences (Rundell et al., 2014) to calculate Z_C.

Sample locations on world map and Eh-pH diagram (Figure 4)

orp16S_4()

Data sources: (a) coastlineWorld dataset in R package oce (Kelley and Richards, 2022); shapefiles for North American Great Lakes (USGS, 2010). (b) Outline is from Baas Becking et al. (1960).

Associations between Eh7 and Z_C at local scales (Figure 5)

orp16S_5()

Data sources: (a) Daya Bay (Wu et al., 2021), Bay of Biscay (Lanzén et al., 2021), Hunan Province (Meng et al., 2019). (c) 1 acidic and 2 circumneutral to alkaline New Zealand hot springs (Power et al., 2018), 3 Eastern Tibetan Plateau (Guo et al., 2020), 4 Uzon Caldera (Peltek et al., 2020), 5 Southern Tibetan Plateau (Ma et al., 2021).

Tally of regression slopes and binomial p-values (Table 1a)

colnames <- c("Ntot", "Npos", "Nneg", "p")
kable(orp16S_T1(), col.names = rep(colnames, 2)) %>%
  add_header_above(c(" " = 1, "Bacteria" = 4, "Archaea" = 4))

	Bacteria				Archaea
	Ntot	Npos	Nneg	p	Ntot	Npos	Nneg	p
River & Seawater	10	6	4	0.37700	4	4	0	0.06200
Lake & Pond	9	6	3	0.25400	3	2	1	0.50000
Geothermal	5	5	0	0.03100	4	1	3	0.93800
Hyperalkaline	6	5	1	0.10900	3	2	1	0.50000
Groundwater	10	7	3	0.17200	6	2	4	0.89100
Sediment	16	11	5	0.10500	6	2	4	0.89100
Soil	12	10	2	0.01900	5	2	3	0.81200
Total	68	50	18	0.00007	31	15	16	0.63995

Tally of regression slopes with same sign of minimum and maximum values in 95% confidence interval (Table 1b)

colnames <- c("Ntot", "Npos", "Nneg", "p")
kable(orp16S_T1(samesign = TRUE), col.names = rep(colnames, 2)) %>%
  add_header_above(c(" " = 1, "Bacteria" = 4, "Archaea" = 4))

	Bacteria				Archaea
	Ntot	Npos	Nneg	p	Ntot	Npos	Nneg	p
River & Seawater	3	3	0	0.12500	2	2	0	0.25000
Lake & Pond	5	3	2	0.50000	1	1	0	0.50000
Geothermal	3	3	0	0.12500	0	0	0	NA
Hyperalkaline	3	3	0	0.12500	3	2	1	0.50000
Groundwater	3	3	0	0.12500	2	0	2	1.00000
Sediment	10	7	3	0.17200	3	0	3	1.00000
Soil	9	9	0	0.00200	3	1	2	0.87500
Total	36	31	5	0.00001	14	6	8	0.78802

Associations between Eh7 and Z_C at a global scale (Figure 6)

global.slopes <- orp16S_6()

Comparison of Eh7, Eh, and O₂ as predictors of carbon oxidation state (Figure S2)

orp16S_S2()

Global analysis including only datasets using 515F/806R primers from the Earth Microbiome Project (EMP) (Figure S3)

orp16S_6(EMP_primers = TRUE)

Genus-level RDP classifications mapped to NCBI taxonomy (values mentioned in text)

# Read Dataset S3 (created by orp16S_D3())
dat <- read.csv(system.file("extdata/orp16S/Dataset_S3.csv", package = "JMDplots"))
perc_bac <- round(mean(subset(dat, lineage == "Bacteria")$mapperc))
perc_arc <- round(mean(subset(dat, lineage == "Archaea")$mapperc))
message(paste0(perc_bac, "% for bacteria, ", perc_arc, "% for archaea"))

## 86% for bacteria, 77% for archaea

References

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