Fengping Wang’s research while affiliated with Shanghai Jiao Tong University and other places

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Publications (210)


AArfs form a clade in the Arf GTPase family
a The role of inward budding in the establishment of eukaryotic endomembrane system. Inward membrane budding refers to the bending of membrane towards the cytosol, either from the surface of organelles or from the plasma membrane. Inward budding supports endomembrane organelle biogenesis either by forming organelle precursors, such as in peroxisome biogenesis, or by forming transport vesicles that supply organelle building blocks. Outward budding refers to the projecting of membrane away from the cytosol, either into the extracellular space or into the lumen of organelles. Outward budding is often employed for disposal of materials to the outside or into the lumen of endocytic/lytic organelles. b Phylogeny of AArfs in relation to other small GTPases. Maximum likelihood phylogenetic tree of small GTPases based on aligned amino acid sequences (179 positions) using IQ-TREE with the LG + R10 model. Branch support values were assessed with the Ultrafast bootstrap algorithm and the SH-aLRT test (both 1000 replicates). c Structural similarity between AArfs and Arf. Structures of AArfs were generated using AlphaFold2. An alignment of yeast Arf1 (PDB: 2K5U) and two AArfs is shown on top. d The G-box motifs are conserved in AArfs. Shown are sequence logos of G-boxes in eukaryotic Arfs, AArfs, and eukaryotic Rabs. e Switch regions are conserved in AArfs. Shown are sequence logos of switch I, II and inter-switch regions in eukaryotic Arfs, AArfs, and eukaryotic Rabs. f Key surface regions, including the guanine nucleotide binding pocket and the effector interacting surface (denoted by yellow circle), are conserved in AArfs. Surface fill models of AArf and eukaryotic Arf are colored by the level of sequence conservation.
GTP dependent membrane targeting of AArfs
a Heim12F4_9-5 displayed membrane targeting when expressed in yeast, and the targeting was dependent on GTP binding and its N-terminal helixe. Representative live cell fluorescent microscopy images are shown. DIC, differential interference contrast. b Four other AArfs displayed GTP dependent membrane targeting in yeast. Representative live cell fluorescent microscopy images are shown. c Heim12F4_9-5 and GerdYT_6_3-5 displayed GTP dependent membrane targeting in Hela cells. Cells were mildly fixed. Representative confocal microcopy images are shown. d Principle of liposome floatation assay. Liposomes float together with associated proteins to the top in a density gradient upon centrifugation. Proteins alone do not float. e, f Heim12F4_9-5-His-MBP floated to top fractions with liposomes of both archaeal and eukaryotic compositions. Heim12F4_9-5-His-MBP was purified from E. coli. Archaeal lipid was extracted from cultured Haloferax volcanii. Eukaryotic composition consisted of synthetic phospholipids and sterol. e representative immunoblots. f quantification of protein levels. Mean ± standard deviation, n = 3. a.u., arbitrary unit. g–k Heim12F4_9-5 association with liposomes was dependent on GTP loading, and independent of myristoylation. Purified full length Heim12F4_9-5, Heim12F4_9-5 lacking amphipathic helix (Heim12F4_9-5ΔN), Sar1, and Arf6 were subject to liposome floatation assay using liposomes of eukaryotic composition. g representative silver stained gels. h–k, quantification of protein levels. Mean ± standard deviation, n = 3. a.u., arbitrary unit. White scale bar, 2 μm. Yellow scale bar, 20 μm. Source data are provided in the Source Data.
Phylogenetic profile of AArfs
A maximum likelihood phylogenetic tree is shown at the center. The tree was inferred in IQ-TREE with LG + R5 model automatically selected. Brach support was evaluated by ultrafast bootstrap algorithm (1000 replicates). Each AArf protein is also denoted by a bar surrounding the tree, with bar length proportional to protein length, colored by species lineage. Predicted structures of select AArfs are shown close to the corresponding bars. Cyan, core Arf GTPase structure; gray, additional non-Arf domains present in certain AArf lineages. Stars denote AArfs experimentally tested in yeast; red stars, those that displayed prominent membrane association; green starts, the rest.
AArfs are potential hubs in a protein network functioning in organelle dynamics
a AArfs can be regulated by Arf GEFs. Overexpression of several Arf GEFs altered the subcellular distribution of AArfs. Shown are representative live cell fluorescent microscopy images of yeast cells expressing Heim12F4_9-5-GFP or GerdYT_6_3-5-GFP before and 4 h after galactose-induced GEF overexpression. Fluorescent intensity profiles of cropped areas are presented at the side, with positions of plasma membrane marked by red arrows. Schematic AArf subcellular distribution patterns are shown at the bottom. b, c AArfs can interact with a protein network regulating organelle dynamics. b GO term enrichment in GerdYT_6_3-5 interactome in yeast. Membrane and organelle related terms are colored blue. Data analyzed by two-sided t-test with no adjustment for multiple comparisons. Significance threshold (s0), 0; false discovery rate (FDR), 0.05. c A sub-network of GerdYT_6_3-5 interacting proteins, consisting of those mediating intracellular protein transport, organelle function, and lipid metabolism. Lines and dashed lines denote known protein-protein interactions in STRNG. White scale bar, 2 μm.
AArf can regulate a eukaryotic endomembrane system
a, b Expression of GerdYT_6_3-5 in yeast triggered the emergence of a massively expanded endomembrane network. a representative transmission electron micrographs of yeast cells expressing wild-type or GDP-bound mutant of GerdYT_6_3-5. b schematic representation of yeast endoplasmic reticulum morphology. Cyan lines denote the endoplasmic reticulum. c, d Expression of GerdYT_6_3-5 in yeast triggered the proliferation of endomembrane organelles. Yeast cells expressing red fluorescent protein tagged organelle markers in combination with GFP alone or GFP tagged wild type or GDP-bound mutant of GerdYT_6_3-5 were observed by live cell fluorescent microscopy. c representative microscopy images of cells carrying ER and Golgi markers. See Fig. S7C for other organelle markers. d quantification of the number of punctate organelles per cell. Mean ± standard deviation, n = 3 independent repeats (>50 cells quantified in each sample of each repeat). Analyzed by two-way ANOVA with Tukey test. Exact P values (from left to right): <0.01, 0.95, <0.01, 0.99, <0.01, 0.52, 0.07, 0.92, <0.01, 0.54. e GerdYT_6_3-5 could interact with Sec23, a component of COPII coat, in a GTP dependent manner. GTP (Q87L) or GDP-bound (T47N) variants of GerdYT_6_3-5-GTP were co-expressed with Sec23-8V5 in yeast. The interaction between GerdYT_6_3-5 and Sec23 was evaluated by co-immunoprecipitation. Representative immunoblots are shown. f, g Structural comparison between Sec23 and Asgard Sec23-like proteins. f Alignment of Sec23 (PDB: 1M2O) and predicted structures of Asgard Sec23-like proteins. g Asgard Sec23-like proteins lack a gelsolin-domain critical for Sar1 interaction and catalytic activity. Sec23-Sar1 complex structure rendered from PDB 1M2O. h Status of Asgard archaea in the evolution of inward membrane budding capacity. Asgard archaea possess AArfs that function as molecular switches in membrane related processes. Sec23-like coat ancestor proteins are present, which possess all but one of the Sec23 constituent domains. Subsequent emergence of a functional coat in a single species would complete the evolution of a prototypic inward membrane budding machinery, promoting endomembrane organelle biogenesis. White scale bar, 2 μm. Source data are provided in the Source Data.
Asgard Arf GTPases can act as membrane-associating molecular switches with the potential to function in organelle biogenesis
  • Article
  • Full-text available

March 2025

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59 Reads

Jing Zhu

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Qiaoying Ren

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Inward membrane budding, i.e., the bending of membrane towards the cytosol, is essential for forming and maintaining eukaryotic organelles. In eukaryotes, Arf GTPases initiate this inward budding. Our research shows that Asgard archaea genomes encode putative Arf proteins (AArfs). AArfs possess structural elements characteristic of their eukaryotic counterparts. When expressed in yeast and mammalian cells, some AArfs displayed GTP-dependent membrane targeting. In vitro, AArf associated with both eukaryotic and archaeal membranes. In yeast, AArfs interacted with and were regulated by key organelle biogenesis players. Expressing an AArf led to a massive proliferation of endomembrane organelles including the endoplasmic reticulum and Golgi. This AArf interacted with Sec23, a COPII vesicle coat component, in a GTP-dependent manner. These findings suggest certain AArfs are membrane-associating molecular switches with the functional potential to initiate organelle biogenesis, and the evolution of a functional coat could be the next critical step towards establishing eukaryotic cell architecture.

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Multi-round smHCR-FISH for archaeal gene expression
a Schematic of multi-round smHCR-FISH for labeling and imaging mRNA in archaeal cells across various growth stages. In each round, mRNAs from three genes are hybridized with primary probes, whose signals are read through the polymerization of two amplifier probes. After imaging, the targeted probes are cleared by DNase I. b Methylotrophic methanogenesis pathway in Methanococcoides orientis (LMO-1), with non-conventional pathway components in gray. Fmd, formylmethanofuran dehydrogenase; Ftr, formylmethanofuran-H4MPT formyltransferase; Mch, methenyl-H4MPT cyclohydrolase; Mtd, F420-dependent methylene H4MPT dehydrogenase; Mer, F420-dependent methylene-H4MPT reductase; Mtr, Na⁺-translocating methyl-H4MPT-coenzyme-M-methyltransferase; Mcr, methyl-coenzyme M reductase; Fpo, F420H2 dehydrogenase; Hdr, heterodisulfide reductase; MmcA, multi-haem C-type cytochrome; Rnf, Na⁺-translocating ferredoxin-NAD oxidoreductase; F420H2, reduced coenzyme F420; H4MPT, tetrahydromethanopterin; CoB, coenzyme B; CoM, coenzyme M; CoM-S-S-CoB, coenzyme B-coenzyme M heterodisulfide; Fd, ferredoxin, a two-electron carrier; red, reduced; ox, oxidized. c Five-round smHCR-FISH applied to LMO-1 for labeling thirteen protein-coding genes and 16S rRNA. Top: for each round, raw fluorescence data (upper) identify mRNA spots for each gene (lower). Bottom: spots from different genes are overlaid for composite imaging. Each pseudocolor denotes a gene. Scale bars, 2 μm. d Intensity histogram of mcrA smHCR-FISH spots (4438 spots). The typical intensity corresponding to a single mRNA molecule is determined by fitting the histogram to a sum of Gaussians. Inset, smHCR-FISH image of a representative cell. Scale bar, 1 μm. A.u., arbitrary unit. e Average copy number of mcrA mRNA per cell (mean ± s.t.d.) across different growth phases measured by smHCR-FISH and RT-qPCR (n = 3666, 6742, 6375, 7118, 6476, 4329, and 3909 cells per time point from three biological replicates). Error bars, s.t.d. Insets, mcrA signals in different growth phases. Scale bars, 2 μm. f DNA, total RNA, and 16S rRNA signals in log- and stationary-phase cells. The 16S rRNA signal is merged with the identified mRNA spots. Each pseudocolor denotes a gene. Scale bars, 2 μm. c, d, f Experiments were repeated twice with similar results. Source data are provided as a Source Data file.
Methanogenic and ETC gene expression patterns match their functional order
a–c Cumulative (blue) and rate (red) curves (mean ± s.e.m.) for cell growth (a), CH4 production (b), and methyl consumption (c) over time under the optimal growth condition, respectively. Data from three biological replicates. Shadings indicate s.e.m. d Representative image of LMO-1 cells grown in the optimal condition (15 hpi), with seven methanogenic enzyme genes and five ETC genes labeled, each represented by a unique color. Scale bar, 2 μm. Experiments were repeated twice with similar results. e, f Temporal expression patterns (mean ± s.e.m.) of methanogenic genes (e) and ETC genes (f), respectively. Left: simplified pathway diagrams, with inactive components in gray. Middle: For each gene, mRNA level (y-axis) is plotted as a function of time. Red arrows indicate expression peaks. Log-phase data were from two biological replicates with ≥10 fields of view (FOVs). Shadings indicate s.e.m. calculated from the means of each field of view. n = 8787, 15,150, 14,001, 15,835, 23,096, 4707, 5346, 2568, 3575, 3017, 3009, and 3054 cells at time points from early to late. Right: smHCR-FISH signals for each gene, corresponding to the white box in (d). Scale bars, 2 μm. Source data are provided as a Source Data file.
Fe(III) amendment enhances CH4 production with rearranged gene expression
a–d Cumulative (blue) and rate (red) curves (mean ± s.e.m.) for cell growth (a), Fe(II) production (b), CH4 production (c), and methyl consumption (d) over time under optimal (−Fe(III), dim color) and ferrihydrite-amended (+Fe(III), bright color) conditions, respectively. The gray line in (b) represents the background Fe(II) level in the ferrihydrite-amended medium without biomass. Shadings indicate s.e.m. e, f Cellular ROS level (e, mean ± s.e.m.) and katG expression level (f, mean ± s.e.m.) under optimal (−Fe(III)) and ferrihydrite-amended (+Fe(III)) conditions (17 hpi). a.u. arbitrary unit. g Representative image of LMO-1 cells grown in ferrihydrite-amended condition (17 hpi), with seven methanogenic enzyme genes and five ETC genes labeled, each represented by a unique color. The background signal is the brightfield image. Scale bar, 2 μm. Inset, ferrihydrite mineral particle observed under scanning electron microscopy (SEM). Scale bar, 2 μm. Experiments were repeated twice with similar results. h, i Temporal expression patterns (mean ± s.e.m.) of methanogenic genes (h) and ETC genes (i), respectively. Left: simplified pathway diagrams involving the Fenton reaction, with inactive components in gray. Middle: For each gene, mRNA levels (y-axis) with (n = 953, 3953, 7205, 7219, 9794, 255, 468, 1545, 953, and 904 cells at time points from early to late) and without (n = 8787, 15,150, 14,001, 15,835, 23,096, 4707, 5346, 2568, 3575, 3017, 3009, and 3054 cells at time points from early to late) Fe(III) addition are plotted as a function of time, respectively. Log-phase data were from two biological replicates with ≥5 FOVs. Shadings indicate s.e.m. calculated from the means of each field of view. Right: smHCR-FISH signals for each gene, corresponding to the white box in (g). Scale bars, 2 μm. j Stable carbon isotope values (mean ± s.e.m.) of CH4 production under optimal (−Fe(III)) and ferrihydrite-amended (+Fe(III)) conditions (31 hpi). k Abiotic CH4 production rate (mean ± s.e.m.) in cell-free culture media amended with Fe(II) and H2O2, with bare culture medium and Fe(III)-amended cell culture serving as negative and positive controls, respectively. a–e, j, k Data from three biological replicates. f For each growth condition, cells are from two biological replicates with five FOVs. n = 3879 and 1412 cells for optimal (−Fe(III)) and ferrihydrite-amended (+Fe(III)) conditions, respectively. S.e.m., calculated from the means of each field of view. c, e, f, j, k Statistical analysis was performed using two-sided t-test. Source data are provided as a Source Data file.
Single-cell analysis reveals dynamic clusters of methanogenic/ETC gene expression
a, b UMAP analysis of methanogenic and ETC gene expression from all time points under optimal (−Fe(III), a) and ferrihydrite-amended (+Fe(III), b) conditions. Each dot represents a single cell. Identified clusters are shown in different colors, with a Sankey diagram relating clusters in two growth conditions. Each cluster’s gene expression profile is displayed on the right. c, d Cell distribution in each cluster as a function of time for optimal (−Fe(III), c) and ferrihydrite-amended (+Fe(III), d) conditions. a, cn = 32,885 cells from 11 time points. b, dn = 6408 cells from nine time points. Source data are provided as a Source Data file.
Kinetic modeling reveals the transcriptional regulation mechanism of methanogenesis
a Fano factor as a function of the mean expression level for seven methanogenic enzyme genes, five ETC genes, and a housekeeping gene under optimal (−Fe(III), circles, n = 7609 cells) and ferrihydrite-amended (+Fe(III), triangles, n = 2283 cells) conditions. Each gene is represented by a unique color. Solid line, theoretical prediction for consistent mRNA production. Inset, cell-to-cell variability in mcrA signals. Scale bar, 2 μm. b Histograms of mcrA mRNA copy number per cell at various growth stages under optimal (−Fe(III), left, n = 1357, 625, 2862, 2765, 4707, 5346, 3575, 3017, and 3054 cells at time points from early to late) and ferrihydrite-amended (+Fe(III), right, n = 315, 304, 678, 986, 255, 468, 1545, 953, and 904 cells at time points from early to late) conditions. The experimental data (gray bars) were fitted to a two-state model (solid lines). c Schematic of the two-state transcription model with stochastic ON/OFF switching, mRNA synthesis, and degradation. d, e Temporal patterns of kON and 1/kOFF, in units of kD and 1/kD, for different genes under optimal (−Fe(III), d, k values estimated from n = 1357, 625, 2862, 2765, 4707, 5346, 2568, 3575, 3017, 3009, and 3054 cells for each time point) and ferrihydrite-amended (+Fe(III), e, k values estimated from n = 315, 304, 678, 986, 255, 468, 1545, 953, and 904 cells for each time point) conditions. Background shadings indicate pattern groups. f, g Conceptual diagrams of methanogenic transcriptional regulation under optimal (−Fe(III), f) and ferrihydrite-amended (+Fe(III), g) conditions. kON and 1/kOFF for different genes are regulated by multiple factors. Fe(III) addition significantly modifies the regulatory scheme. Source data are provided as a Source Data file.
Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression

March 2025

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113 Reads

Archaeal methanogenesis is a dynamic process regulated by various cellular and environmental signals. However, understanding this regulation is technically challenging due to the difficulty of measuring gene expression dynamics in individual archaeal cells. Here, we develop a multi-round hybridization chain reaction (HCR)-assisted single-molecule fluorescence in situ hybridization (FISH) method to quantify the transcriptional dynamics of 12 genes involved in methanogenesis in individual cells of Methanococcoides orientis. Under optimal growth condition, most of these genes appear to be expressed in a temporal order matching metabolic reaction order. Interestingly, an important environmental factor, Fe(III), stimulates cellular methane production without upregulating methanogenic gene expression, likely through a Fenton-reaction-triggered mechanism. Through single-cell clustering and kinetic analyses, we associate these gene expression patterns to a dynamic mixture of distinct cellular states, potentially regulated by a set of shared factors. Our work provides a quantitative framework for uncovering the mechanisms of metabolic regulation in archaea.


Fig. 1. Phylogenetic tree based on 16S rRNA gene. The analysis was conducted via the DSMZ single-gene phylogeny server [32], accessible through the GGDC website (http://ggdc.dsmz.de/). The numbers above the branches are support values above 60% from maximum likelihood (left) and maximum parsimony (right) on 1000 bootstrapping. The accession numbers of the 16S rRNA gene sequences are presented in parentheses.
Fig. 3. Cells of M. cohabitans LMO-2 T visualized by scanning electron microscopy.
Descriptive and catabolic features of M. cohabitans LMO-2 T with other strains in the Methanococcoides genus* Strain/species: 1. M. cohabitans LMO-2 T (=CGMCC 1.18051 T =KTCC 25774 T ); 2. M. orientis LMO-1 T [17]; 3. M. methylutens strain MM1[47]; 4. M. methylutens strain TMA-10 T [15]; 5. M. alaskense strain AK-5 T [16]; 6. M. burtonii strain ACE-M T [18]; 7. M. vulcani SLH33 T [19]. nd, no data.
Methanococcoides cohabitans sp. nov., a marine methylotrophic methanogen isolated from an anaerobic methane-oxidizing enrichment culture

March 2025

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91 Reads

International Journal of Systematic and Evolutionary Microbiology

Enrichment cultures of archaea and bacteria performing the anaerobic oxidation of methane (AOM) regularly contain persistent methanogens. Here, we isolated the marine methanogen Methanococcoides cohabitans sp. nov. strain LMO-2 T from a long-term AOM enrichment culture from the Northern Gulf of Mexico. Strain LMO-2 T is Gram-stain-negative, irregular 0.5–1 µm coccus without flagella. It utilizes a variety of methylated compounds including methanol, monomethylamine, dimethylamine and trimethylamine for growth and methanogenesis. However, it does not grow on formate, acetate, dimethyl sulphate, H 2 /CO 2 , betaine and choline. The optimal conditions for growth were observed within a temperature range of 30–35 °C, a pH range of 7.0–8.0 and a salinity range of 2–4% NaCl. Based on the similarity and phylogeny of the 16S rRNA gene and genomic sequence, strain LMO-2 T is classified within the genus Methanococcoides . Among the isolated type strains of the genus, strain LMO-2 T exhibited the highest 16S rRNA gene sequence identity with Methanococcoides vulcani SLH33 T (99.4%). The digital DNA–DNA hybridization and average nucleotide identity based on genome sequence showed that strain LMO-2 T shared the highest similarity with Methanococcoides orientis LMO-1 T , with values of 27.3% and 83.4%, respectively. In conclusion, we isolated a methylotrophic methanogen from an AOM culture, and the isolated strain LMO-2 T represented a novel species of the genus Methanococcoides , for which the name Methanococcoides cohabitans sp. nov. is proposed. The type strain is LMO-2 T (=CGMCC 1.18051 T =KCTC 25774 T ).


Fig. 1.The phylogeny of prokaryotic SMC protein. A) The archaeal and cyanobacterial SMC proteins were identified from the genomes sampled at the genus level in GTDB r207. SMC proteins in Bacteria (excluding Cyanobacteria) were identified from all available genomes in GTDB r207, and highly homologous sequences were removed using CD-HIT v4.8.1 to facilitate tractable phylogenetic inference. The phylogenetic tree was constructed using IQ-TREE 2 with the parameters -alrt 1000 -bb 1000 -m LG + F + R10. This analysis supports the HGT of the smc gene from Halobacteriota to Cyanobacteria. B) The genomes from Halobacteriota and Cyanobacteria were carefully selected to ensure even taxon sampling. To pinpoint the exact donor lineage within phylum Halobacteriota, noncyanobacterial bacterial genomes were excluded due to computational limitations. The phylogenetic tree was reconstructed using IQ-TREE 2 under parameters -alrt 1000 -bb 1000 -m LG + F + R8. The clades outside of Cyanobacteria and Halobacteriota are collapsed for clarity.
Fig. 2.Average abundance of oxygen-tolerant enzyme families involved in the elimination of oxygen/ROS and repair of oxidative damages. The COG information was retrieved from the COG database. All available genomes of phyla Cyanobacteria, Margulisbacteria, Halobacteriota, Methanobacteriota, and Methanobacteriota_A in GTDB r207 were collected. Functional annotations were performed using eggNOG-mapper v2 with default parameters. For the calculation of enzyme family abundance, only the primary root eggNOG_OGs annotation for each sequence was retained.
Fig. 3.Hypothesized evolutionary scenarios of oxygen-tolerant enzymes within Class II methanogens. A) The depicted enzymes are posited to have derived from a common ancestral origin shared among Class II methanogens. B) An alternative scenario suggests that these enzymes emerged subsequent to the divergence of the three primary clades observed within Class II methanogens. C) Additionally, it is proposed that certain enzymes might have been acquired through recent HGT into particular lineages following their evolutionary radiation. In each panel, the timeline incorporates schematic representations marked by bars to denote the period during which hypothesized stem-group oxyphototrophs are thought to have emerged, thereby contextualizing the temporal framework of these proposed evolutionary scenarios.
Fig. 4.Hypothetical evolutionary scenarios of oxygenic photosynthesis in prokaryotes. A) Hypothetical evolutionary pathways of oxygenic photosynthesis. B) Hypothetical single origin of PSs/RCs. C) Hypothetical multiple originations of PSs/RCs. Gene gain, loss, and transfer events are labeled with the capital letters “G,” “L,” and “T,” respectively. The hypothetical extinct lineages are depicted in dashed branches, and the capital letter “X” denotes extinction.
Fig. 5.Divergence time estimations of Class II methanogens and other Halobacteriota lineages under different root settings. Phylogenomic trees were reconstructed using IQ-TREE 2 under parameters -alrt 1000 -bb 1000 -m LG + R10 + C60, based on the concatenated alignments of SMC and 37 conserved proteins. Node dates were inferred using MCMCTree in paml v4.8. The substitution rate was calculated using 3.46 Ga as the root node age. The root node, representing the beginning of the archaeal domain radiation, was calibrated to 3.46–4.29 Ga. The MRCA of the crown-group oxygenic Cyanobacteria was calibrated to 2.50–3.00 Ga. The divergence between families Nostocaceae and Chroococcidiopsidaceae was calibrated to 0.80–2.00 Ga. The 95% highest posterior density intervals were indicated by flanking horizontal bars. The last common ancestor of Class II methanogens is marked with a star. The geological timescale follows the ICS International Chronostratigraphic Chart (v 2023/06). The visualization was accomplished using the R package ggtree. A) The root was set between the superphylum DPANN and the rest of archaeal lineages. B) The root was set between the superphyla DPANN, TACK, Asgard and the rest archaeal lineages. C) The root was set between the superphylum TACK and other archaeal lineages, after excluding DPANN species.
Oxidative adaptations in prokaryotes imply the oxygenic photosynthesis before crown-group Cyanobacteria

February 2025

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117 Reads

PNAS Nexus

The metabolic transition from anaerobic to aerobic in prokaryotes reflects adaptations to oxidative stress. Methanogen, one of the earliest life forms on Earth, has evolved into three major groups within the Euryarchaeota, exhibiting different phylogenetic affiliations and metabolic characters. In comparison with other strictly anaerobic methanogenic groups, the Class II methanogens possess a better capability to adapt to limited oxygen pressure. Cyanobacteria is considered the first and only prokaryote evolving oxygenic photosynthesis and is responsible for the Great Oxidation Event on Earth. However, the connection between oxygenic Cyanobacteria and evolutionary adaptations to oxidative stress in prokaryotes remains elusive. Here, through the gene encoding structural maintenance of chromosomes (SMC) protein, which was horizontally transferred from ancient Class II methanogens to the last common ancestor of the crown-group Cyanobacteria, we demonstrate that the origin of extant Cyanobacteria was undoubtedly posterior to the occurrence of oxygen-tolerant Class II methanogens. In addition, we found that certain prokaryotic lineages had evolved the tolerance mechanisms against oxidative stress before the origin of extant Cyanobacteria. The contradiction that oxidative adaptations in Class II methanogens and other prokaryotes predating the crown-group oxygenic Cyanobacteria implies the existence of more ancient biological oxygenesis. We propose that these potential oxygenic organisms might represent the extinct phototrophs and first emerge during the Paleoarchean, contributing to the oxidative adaptations in the prokaryotic tree of life and facilitating the dispersal of reaction centers across the bacterial domain.








Citations (57)


... A step-by-step protocol for multi-round smHCR-FISH and imaging has been deposited in the protocols.io repository 67 . Briefly, a 4-chamber glass-bottom dish (Cellvis, D35C4-20-1-N) was further divided into 12 wells using reagent barriers (FastWells, 70339-44) and bottom-coated with poly-L-Lysine (Sigma, P4707) 4 . ...

Reference:

Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression
Multi-round smHCR-FISH for archaea v1

... To reverse this inaction, a collective call from scientific societies, institutions, editors, and publishers has urged the global community and governments to take immediate and decisive emergency action, while proposing a clear and effective framework for deploying these technologies at scale (Peixoto et al., 2024). In response to this call, we review recent achievements in microbe-based solutions to mitigate global warming. ...

Microbial solutions must be deployed against climate catastrophe

The ISME Journal

... This would require allocating technical, financial, and human resources to develop and implement interventions at the primordial and primary prevention levels, focusing on underlying vulnerabilities and addressing the root causes of misinformation. Coordinated efforts and collaboration between various stakeholders, including civil society organizations, educational systems, media organizations, and community-based organizations, are essential for the consistent and comprehensive design and implementation of these proactive interventions (Peixoto et al., 2024). ...

Microbial solutions must be deployed against climate catastrophe

FEMS Microbiology Ecology

... For instance, Aželytė et al. [17] showed that microbiota modification through a bird-administered anti-microbiota vaccine could affect Plasmodium development within mosquitoes, suggesting the potential for microbiota-based strategies to indirectly disrupt disease transmission. Peixoto et al. [27] emphasized the broader significance of leveraging microbial solutions, advocating for their integration into public health and ecological management strategies. Despite these promising findings, microbiota-targeted approaches remain largely experimental, with several challenges limiting their field application. ...

Microbial solutions must be deployed against climate catastrophe

Nature Reviews Microbiology

... Rather, this covers the health of humans, animals, insects, fish, and other life forms as well as applications to agriculture, aquaculture, and the overall ecosystem in which we humans live. A recent call to action supports this position (Peixoto et al. 2024). ...

Microbial solutions must be deployed against climate catastrophe

... Bacterial enzymes and metabolic activities have emerged as promising solutions as microbial strategies that can be developed and/or deployed at scale to tackle climate change (Mehrotra et al., 2021;Muras et al., 2021;Peixoto et al., 2024;Yong et al., 2021). In particular, the exploration of Microbially Induced Calcium Carbonate Precipitation (MICP), and the corresponding bacterial enzymes, is receiving attention for carbon dioxide capture applications (Taharia et al., 2024;Tamayo-Figueroa et al., 2019;Zhu et al., 2021), to sequester heavy metals and radionuclide in contaminated sites (Baidya et al., 2023;Han et al., 2020;Sanjurjo-Sánchez et al., 2024) and to preserve construction materials (Dong et al., 2022;Farajnia et al., 2022;Kim et al., 2021;Park et al., 2022). ...

Microbial solutions must be deployed against climate catastrophe

Nature Microbiology

... As a result, the great bulk of microbial diversity goes unexplored. Recent advances, such as metagenomic sequencing and diffusion-based integrative cultivation techniques, have revealed significantly greater microbial diversity and permitted the isolation of hitherto unknown taxa [10,11]. These contemporary techniques provide a more thorough understanding of microbial communities and their ecological significance, emphasizing the importance of moving beyond old methodologies in order to fully grasp microbial diversity in natural habitats. ...

A diffusion-based integrative approach for culturing previously uncultured bacteria from marine sediments

Marine Life Science & Technology

... Nonetheless, it has been suggested that the reduced availability of iron oxides under reducing conditions may diminish the significance of OC-Fe R in OC burial (Longman et al., 2022). Although recent work by Chen et al. (2024) observed the destabilization of OC-Fe R within the SMTZ, the extent of Fe R reduction, the transformation of iron oxides to iron sulfides, and the resultant influence on OC-Fe R was not detailed. To elucidate the elusive role of OC-Fe R in the long-term OC preservation in marine sediments, it is essential to investigate the stability of OC-Fe R during the conversion of iron oxides to iron sulfides. ...

Cycling and persistence of iron-bound organic carbon in subseafloor sediments

... There is a pressing need to inform the public about the safety and advantages of phage therapy and other biotechnological applications to foster understanding and acceptance [5,66,68]. Science projects that directly involve citizens can play a vital role in this effort by promoting scientific literacy and community engagement, which can accelerate the development of new therapies for treating resistant bacterial infections in humans, animals, and plants [69,70]. Educational initiatives to collaborate with society to raise awareness about phages as beneficial viruses that are potentially effective against multidrug-resistant bacteria can contribute to inspiring pre-university students to pursue careers in science [68,71,72], as well as to raising awareness among citizens about antimicrobial resistance. ...

A concept for international societally relevant microbiology education and microbiology knowledge promulgation in society

... MASH-Ocean ( https:// www.biosino.org/ mash-ocean/ ) integrates and analyzes oceanic microbiome and environmental data through the iMA C / iMA C + system, creating the comprehensive Microbiome Atlas / Sino-Hydrosphere for Ocean Ecosystems ( 45 ). It offers public access to datasets with unique features tailored to marine microorganisms, including depthspecific selection and comparative analysis between deep-sea and shallow-sea ecosystems, as well as specialized environments such as cold seeps and hydrothermal vents. ...

MASH‐Ocean 1.0: Interactive platform for investigating microbial diversity, function, and biogeography with marine metagenomic data