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1.1D: Lipids in Archea, Prokaryotes and Eukaryotes - Biology

1.1D: Lipids in Archea, Prokaryotes and Eukaryotes - Biology



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Do all organisms use the same lipid building blocks to construct bilayers? It turns out they don't. Yet they differ significantly in genetic structure and in their metabolic pathways.

Figure: Phylogenetic Tree of Life

This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license

Archea often have very unique chemistries. Members of this domain can use not only carbohydrates and fats as sources of energy, but they can also use inorganic species such as ammonium, hydrogen, and metal ions as well as organic molecules such as methane. Some (methanogens) actually make methane. Archea were once thought to be found only in extreme environments (hence they were also called extremophiles), but in actuality they inhabit many environmental niches, including the oceans and soil. Since many do live in extreme environments, you would expect them to have evolved to synthesize stable, structural molecules. Archea use phospholipids in the membrane bilayers, but the lipids differ in three very important ways. Instead of fatty acid chains, they use isoprenoid chains as the nonpolar chains. Instead of using an ester link, the isoprenoids are covalently attached to the glycerol backbone with an ether link, which is obviously more stable than an ester bond used in the phospholipids discussed above. Finally, the stereochemistry of the phospholipids is based on sn-glycerol-1-phosphate and not sn-glycerol-3-phosphate.


Learn about the similarities and differences between eukaryote and prokaryote cells

All living things, from the smallest to the largest, are made up of cells. Some organisms, like bacteria, are composed of just one cell, while others, like the giant sequoia, are made up of billions of cells.

Organisms can be divided into two main groups based on fundamental differences in their cell structure. Animals, plants, fungi, and protists are eukaryotes. Bacteria and archaea are prokaryotes. All prokaryotes are unicellular while eukaryotes may be single-celled or multicellular.

Both prokaryote and eukaryote cells have a cell membrane. This is a lipid bilayer that keeps the contents of the cell in and keeps unwanted substances out. The membrane controls the movement of substances into and out of the cell.

The material inside both types of cells is called the cytoplasm.

All cells contain DNA. In eukaryotes, DNA resides in a membrane-bound structure called the nucleus. But in prokaryotes, DNA is circular and floats freely within the cytoplasm.

Finally, both types of cells contain ribosomes. Ribosomes play a key role in assembling proteins. Think of them as the factories of the cell.

Eukaryotic cells also have other membrane-bound structures within them. These structures are called organelles. Prokaryotic cells lack organelles.

Some cells also have a structure called a cell wall. Most prokaryotes have a cell wall. Animal cells do not have a cell wall but plants do. However, plant cell walls and prokaryotic cell walls are not made up of the same materials.

The cell walls of plants are primarily made of cellulose which helps give them their form and structure - from the tiniest leaves to the massive trunks of trees like the giant sequoia.


Similarities and differences in the glycosylation mechanisms in prokaryotes and eukaryotes

Recent years have witnessed a rapid growth in the number and diversity of prokaryotic proteins shown to carry N- and/or O-glycans, with protein glycosylation now considered as fundamental to the biology of these organisms as it is in eukaryotic systems. This article overviews the major glycosylation pathways that are known to exist in eukarya, bacteria and archaea. These are (i) oligosaccharyltransferase (OST)-mediated N-glycosylation which is abundant in eukarya and archaea, but is restricted to a limited range of bacteria (ii) stepwise cytoplasmic N-glycosylation that has so far only been confirmed in the bacterial domain (iii) OST-mediated O-glycosylation which appears to be characteristic of bacteria and (iv) stepwise O-glycosylation which is common in eukarya and bacteria. A key aim of the review is to integrate information from the three domains of life in order to highlight commonalities in glycosylation processes. We show how the OST-mediated N- and O-glycosylation pathways share cytoplasmic assembly of lipid-linked oligosaccharides, flipping across the ER/periplasmic/cytoplasmic membranes, and transferring "en bloc" to the protein acceptor. Moreover these hallmarks are mirrored in lipopolysaccharide biosynthesis. Like in eukaryotes, stepwise O-glycosylation occurs on diverse bacterial proteins including flagellins, adhesins, autotransporters and lipoproteins, with O-glycosylation chain extension often coupled with secretory mechanisms.

Figures

This figure highlights similarities between…

This figure highlights similarities between the biosynthetic pathways of N-linked glycosylation in archaea…

This figure depicts key steps…

This figure depicts key steps in the LOS and LPS biosynthetic pathway in…

Structures of Dolichol phosphate and…

Structures of Dolichol phosphate and Undecaprenol phosphate.

Structures of representative examples of…

Structures of representative examples of bacterial and archaeal N-glycans.

This figure shows key steps…

This figure shows key steps in the O-oligosaccharyltransferase-mediated O-glycosylation pathways in Neisseria and…

Comparison of mucin-like sequences in…

Comparison of mucin-like sequences in bacteria with mammalian mucins. Partial sequences of Fap1…

Sequence of the passenger domain…

Sequence of the passenger domain of the autotransporter protein Ag43 from E. coli…


Affiliations

MRC-Laboratory of Molecular Cell Biology and the Institute for the Physics of Living Systems, UCL, Gower Street, London, WC1E6BT, UK

University of Wisconsin – Madison, 430 Lincoln Drive, Madison, WI, 53726, USA

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Contributions

The authors co-wrote, read and approved the final manuscript.

Corresponding authors


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Opinion: Archaea Is Our Evolutionary Sister, Not Mother

Morgan Gaia , Violette Da Cunha , and Patrick Forterre
Jun 1, 2018

© ISTOCK.COM/NANOSTOCKK In 1977, biologist Carl Woese discovered that microbes living in anaerobic conditions and producing methane had a genetic imprint very different from known bacteria species. He and his colleagues eventually suggested that researchers stop referring to such methanogens and related microorganisms as bacteria, classifying them instead as members of a new domain in a tripartite division of the living world, alongside Bacteria and Eukarya.

Woese named this domain Archaea (from the Greek archaio, meaning ancient or original) because the microbes he studied seemed to thrive in extreme conditions akin to those of early Earth. Later on, scientists observed archaea in more-diverse environments, from oceanic water and deep sediments to forest soil and the surface of human skin. Recently, a new archaeal group named after its discoverer, Woesearchaeota, was even detected in human lungs.
Although archaea superficially resemble bacteria in terms of size and cellular organization (members.

Many new archaea species discovered in the past decade exhibit additional eukaryotic features, such as components of the cytoskeleton, but many of these are only present in one or a few archaeal subgroups. This indicates that these features were probably all present in the ancestor common to Archaea and Eukarya before being lost in some archaeal lineages. These ancient eukaryotic features were potentially replaced by bacterial ones over time in some archaeal lineages from frequent lateral gene transfer between archaea and bacteria living in the same environments.

If this model is correct, the common ancestor of Archaea resembled eukaryotes more closely than any modern archaeon, and combining all eukaryotic features presently dispersed in Archaea should allow researchers to reconstruct the ancestor’s phylogenomic profile. Assuming that these shared archaeal/eukaryotic features were present in the common ancestor of these two domains, the profile would provide a starting point to picture how eukaryotes originated and evolved. Screening for new archaeal lineages with additional eukaryotic features is therefore crucial to get more information about our origin.

Researchers are also seeking to understand the origin of the unique eukaryotic features missing in Archaea. One possibility is that some of them originated in the many lineages of large DNA viruses that coevolved with the ancestors of eukaryotes after their separation from the archaeal lineage. We suggested, for instance, that the nucleus evolved from nucleus-like factories that these viruses built in the cytoplasm of infected cells to protect their genomes (Curr Opin Micro­biol, 31:44-49, 2016).

Over time, several researchers have proposed alternative evolutionary scenarios in which the eukaryotic features actually appeared and accumulated in some archaeal lineages before Eukarya eventually originated from a specific archaeal branch. These scenarios, which include the archaea ancestor hypothesis where an ancient archaeon merged with a bacterium, have recently been supported by universal trees of life (See THE EOCYTE TREE on next page) with Eukarya branching from Asgard archaea, which contain many eukaryotic features. (See “Archaea Family Tree blossoms, Thanks to Genomics,” here.) These models posit Archaea as the mother of eukaryotes, rather than a sister.

Our data come to a different conclusion. Phylogenetic trees are built using universal proteins, which are conserved in the three domains of life. Recently, we showed that the results of such analyses are strongly dependent on the sets of proteins and species used. Avoiding artifact-prone elements from our analyses, we obtained a robust universal tree that did not support the archaeal ancestry of Eukarya but the sisterhood of the two domains instead (PLOS Genet, 13:e1006810, 2017 PLOS Genet, 14:e1007215, 2018).

In our view, scenarios in which Archaea gave birth to Eukarya raise several difficult questions. For one, they imply that thousands of archaeal lineages remained similar to their ancestors during the last 3 billion years, whereas one was dramatically transformed into a new domain, the Eukarya. This seems unlikely because Eukarya exhibits many unique features absent in the two other domains. For instance, large DNA viruses that infect eukaryotes have no direct ancestors that infect archaea. Moreover, a few eukaryotic features, such as the nature of their lipids, remain more similar to those of bacteria. Deriving all these features from archaea requires proposing ad hoc scenarios that seem far from parsimonious, such as getting all of those characters directly or indirectly from the bacterium that engaged in the original endosymbiotic union.

Considering archaea as eukaryotes’ ancestors also reproduces the common confusion of ignoring the evolution that takes place in two lineages after their divergence. This would be akin to considering chimps as humans’ direct ancestors. Humans and chimps share a common great ape ancestor that was neither one nor the other. Similarly, the last common ancestor of Archaea and Eukarya was most likely different from all modern organisms.

Interestingly, if archaea are indeed our sisters and not our mothers, one could imagine that some common features present in Bacteria and Eukarya have been inherited from the last universal common ancestor (LUCA) of all life and subsequently lost in Archaea. The identification of these features in already known organisms or in lineages of Bacteria and Eukarya yet to be discovered would be another important step in the reconstruction of LUCA, crucial to truly understanding the origin of life itself.

PARSING BRANCHES: In the eocyte tree, the various features shared by Archaea and Eukarya (circles) appeared and accumulated progressively during the diversification and complexification (black arrow) of Archaea. Many eukaryotic-specific features originated after the separation of Eukarya from other Archaea. In this scenario, Eukarya evolved from a subgroup of Archaea beside other archaeal phyla such as Euryarchaea, Crenarchaea, Thaumarchaea, and Asgards. In the Woese tree, which our research supports, the various features shared by Archaea and Eukarya appeared in the branch leading from the last universal common ancestor (LUCA) to the last arkaryal common ancestor (LARCA). After separation of the branches leading to Archaea and Eukarya, the former progressively lost some of these features (gray arrow), whereas new features accumulated in the branch leading to Eukarya (black arrow). In both scenarios, Bacteria and Eukarya evolved other features (squares) in parallel. MODIFIED FIGURE COURTESY OF PATRICK FORTERRE


Archaea Characteristics

Being prokaryotes, they have no membrane-bound organelles within their cells, as you and I do.

This means no nucleus, no mitochondria, no chloroplasts, etc.

Their DNA is normally a single molecule, circular in shape. Whereas our DNA comes in linear form, in several or many molecules.

Their ribosomes are of the 70S type (ours are of the 80S type – except in mitochondria) and chloroplasts and plasmids are relatively common. Lastly, they have no microtubule cytoskeleton.

The Archaea have a diverse variety of shapes and exist not only as rods and dots (cocci), like bacteria – but also as triangles, discs, plates and cup-shapes.


Eukaryotic Cells: Cell and Plasma Membrane

Plasma membrane (De Robertis, 1965), Plasma-lemma (J.Q. Plowe, 1931), Unit membrane (Rorbertson, 1959).

The term cell membrane was originally used by C. Nageli and C. Cramer (1855). Plasma Membrane of neuron (nerve cell) is called neurolemma while that of haemolysed RBC is called red cell ghost.

The plasma membrane of muscle cell along with based lamina is called sarcolemma.

Plasma membrane is a living, ultrathin, dynamic elastic semipermeable membrane that encloses the protoplasm of a cell.

It is the outermost boundary of all living cells. But prokaryotes and plant cells generally have an additional cell wall outside the plasma membrane. In addition to Plasma membrane, eukaryotic cells contain intracellular membrane surrounding, the vacuole and organelles. The plasma membrane and the intracellular membranes together called as biological membranes of bio-membranes.

Chemically plasma membrane is a molecular assembly of lipids (20- 40%), proteins (60-75%) and carbohydrates (1-5%). The carbohydrates found in the form of glycoproteins or glycolipids and restricted only to the outer surface of plasma membrane. The lipids and proteins are held together by non-covalent interactions.

The membrane lipids of plasma membrane are of 3 major types:

All of them are amphipathic or amphiatic molecules because they possess both hydrophilic (polar) and hydrophobic (non-polar) ends. Majority (80%) of the phospholipids are neutral (e.g. phosphatidylcholine, phosphatidylethalamine and spingomyelin) and the rest phospholipid s are acidic or negatively charged (e.g. phophatidylionositol, phosphotidylserine etc.). The glycolipids may be cerebroside or ganglioside. Sterols found in the membrane may be cholesterol (in animals), stigmasterol, β-sterol (in plants) and ergosterol (in microbes). All lipids are symbolically represented with a polar head and two fatty acid tails.

The Membrane proteins are two types: integral or intrinsic (

70%) and peripheral or extrinsic (-30%). Nearly all known integral proteins span the lipid bilayer, while peripheral proteins are superficially attached by electrostatic and hydrogen bond interactions. Membrane proteins have various roles-mechanical, transport, enzymatic etc.

Carbohydrates are mainly branched or un-branched oligosaccharides present only on the outer face of plasma membrane. In many protists and animal cells they form a cell coat (= glycocalyx) on the outer face of plasma membrane which protect the underline plasma membrane.

Structural Models of Plasma Membrane:

1. Lipid bilayer Model (Gorter and Grendell, 1926):

The plasma membrane of erythrocyte is a continuous lipid bilayer structure.

2. Sandwich model or ‘Protein-Lipid-Protein’ model (Danielli and Davson, 1935): According to this model plasma membrane is a trilamellar structure with a middle lipid bilayer sandwiched between two continuous layers of protein (Fig. 3.5).

3. Unit membrane model (Robertson, 1959):

This model is the interpretation of electron on microscopic image on myelin along the line of Danielli- Davson model.

According to this model all biological membranes have the same basic structure:

(a) The average thickness is about 7.5 nm (75A).

(b) They have a characteristic trilamellar (3-layered) structure,

(c) The three layers include a central lipid bilayer (3.5 nm) sandwiched between 2 protein layers (each-7.5 nm) (Fig. 3.6).

4. Fluid mosaic model (Singer and Nicolson, 1972):

The authors described the model as “protein icebergs in a two dimensional lipid sea”.

This postulates:

(i) That the biological membranes are quasi-fluid (semi-fluid) structures in which both lipids and integral proteins are free to move laterally as well as within the bilayer (Fig. 3.7).

(ii) That the lipids and proteins are arranged in a mosaic manner.

(iii) The integral or intrinsic proteins are embedded in the lipid bilayer while the extrinsic or peripheral proteins are superficially attached on both surface of the membrane.

(iv) The exoplasmic face (E-face) of the cell membrane often possesses carbohydrate chains or oligosaccharides. They are bound to both proteins and phospholipids producing glycoproteins and glycolipids respectively. The carbohydrate coat present on the E-face of plasma membrane constitute glycocalyx or cell coat. The oligosaccharides gives a negative charge to outer surface. They act as cell surface markers, receptors, blood grouping etc..

Evidences supporting Fluid-mosaic model:

(a) Branton (1968) conformed the mosaic nature of proteins by studying freeze-fracture electron microscopy of the plasma membrane that revealed randomly distributed pumps and depressions (Fig. 3.8).

(b) Frey and Edidin (1970) experimentally demonstrated the fluid nature of plasma membrane b) fusion of a mouse cell with a human cell to yield a hybrid cell called heterokaryon or cybrids. This cell fusion can be induced by agents called fusogen (e.g., Sendai virus, polyethyleneglycol, electric shock etc.) (Fig. 3.9).


Results and Discussion

Analysis of Large-Subunit (LSU) and Small-Subunit (SSU) rRNA Sequences.

Much of the disagreement over the origin of the eukaryotic nuclear lineage has been based on conflicting results from phylogenetic analyses of rRNA sequences (1, , 8, , 9, , 14). Here, we have analyzed rRNA data for 40 taxa spanning the 3 domains (Table S1 in SI Appendix). Analyses of combined LSU and SSU rRNA sequences using maximum parsimony or a composition homogeneous [general time-reversible (GTR)] model, implemented in either a Bayesian or maximum-likelihood (ML) framework, recovered archaebacteria and eukaryotes as separate groups (Fig. 2A). These results are consistent with the 3-domains theory of life. However, in violation of the assumptions of these methods, both datasets are markedly heterogeneous for their nucleotide compositions G+C content varies from 45% to 74% for variable positions in these sequences. Posterior predictive simulations of composition homogeneity revealed that SSU and LSU rRNA each required 2 composition vectors (CV) to model the data adequately using the NDCH model (Fig. 3A). When this was done, a topology consistent with the eocyte hypothesis was recovered (Fig. 2B). The CAT model analysis also supported the eocyte hypothesis (Fig. 2B). That the heterogeneous composition NCDH and CAT models provide a better fit to the data than the composition homogeneous model is indicated by comparison of Bayes factors (Fig. 3B) (22, , 23).

Phylogenetic analysis of combined LSU and SSU rRNA. Scale bars indicate number of substitutions per site. The dotted branches leading to eubacteria are arbitrary lengths. (A) Consensus tree of 16,000 trees obtained from the posterior distribution of an MCMC analysis with homogeneous composition across the tree ([GTR+Γ]x2) loge(Lm) = −23,960.00. Nodes highlighted with dots were supported by ≥95% PP. The 3 values indicate support for a monophyletic archaebacteria from homogeneous composition MCMC (73% PP, Fig. S52 in SI Appendix), equally weighted maximum parsimony (95% BS, Fig. S53 in SI Appendix) and ML (55% BS, SI Text and Fig. S54 in SI Appendix). (B) Consensus tree of 10,000 trees obtained from the posterior distribution of an MCMC analyses with heterogeneous composition across the tree (NDCH model: [GTR+Γ+2CV]x2, loge(Lm) = −23,507.36). The posterior predictive simulations of X 2 for the NCDH model were: SSU: original statistic = 468.06, P = 0.3810 (range of simulated stat under the model = 186.57–1,014.93, mean = 449.61), LSU: original statistic = 759.69, P = 0.7515 (range of simulated statistic under the model = 475.46–1,589.75, mean = 845.98). By contrast, posterior predictive simulations of X 2 for the homogeneous model were SSU: range = 29.55–210.79, mean = 70.49, and LSU: range = 27.03–151.45, mean = 63.13. Nodes highlighted with dots were supported by >95% PP. The 2 values indicating support for the eocyte tree are posterior probabilities for the NDCH analyses as described (75% PP, Fig. S55 in SI Appendix), and for an MCMC analysis with the CAT model (95% PP, +Γ, Fig. S56 in SI Appendix).

Composition fit and Bayes factor comparisons for the combined rRNA data. (A) Bayesian model composition fit assessed by posterior predictive simulations. Bars show the posterior distribution of X 2 for the composition homogeneous MCMC ([GTR+Γ]X2) model and the composition heterogeneous NDCH model ([GTR+Γ+2CV]X2) compared with the original data statistic. The simulated data for the NCDH model include the statistic from the original data, whereas the simulated data from the homogenous model do not. (B) Marginal likelihoods of the 4 MCMC analyses. Bayes factor comparisons between successive models are shown [2loge(BF): (marginal likelihood Model1/marginal likelihood Model0), marginal likelihoods were estimated as described in equation 16 in Newton and Raftery (22), i.e., the CAT model is favored by a 2loge (BF) of 3181.54 over the NDCH model, and both are favored over the homogeneous GTR model].

Compositional Heterogeneity Is a Common Feature of Molecular Data.

Although analyses of rRNA sequences that account for compositional heterogeneity favored a topology consistent with the eocyte hypothesis rather than the 3-domains tree, only the CAT model analysis was decisive, using the conventional 95% statistical significance criterion. To bring more data to bear on the question, we analyzed 51 proteins conserved across all 3 domains, including ribosomal proteins, elongation factors, and polymerases involved in nucleic acid replication, transcription, and translation (Table S2 in SI Appendix). Of the 51 proteins, 39 were identified as having heterogeneous compositions among lineages (2–9 CV required to fit Table S2 and Figs. S1–S51 in SI Appendix), confirming that compositional heterogeneity is a pervasive feature of these data. Only one tree, for the largest subunit of eukaryotic RNA polymerase I, recovered archaebacterial monophyly at the 95% level. The largest subunit of eukaryotic RNA polymerase III recovered archaebacterial monophyly more weakly [67% posterior probability (PP)], but the trees from the other 4 subunits of eukaryotic RNA polymerases I, II, or III did not recover a monophyletic archaebacteria. The other 35 trees depicted eukaryotes derived from within a poorly resolved paraphyletic archaebacteria 8 of these trees depicted the eocytes as the closest relatives of eukaryotes but not at the 95% level. In the remaining 14 trees, archaebacteria formed a polytomy with the eukaryote cluster. Thus, very few of the individual protein trees resolved the relationship between eukaryotes and archaebacteria. Part of the reason for the lack of resolution in these analyses is the short length of most alignments (average length, 160 sites range, 60–432 sites) when positions of dubious positional homology between domains were removed. Yutin et al. (24) also recently reported that individual proteins contained insufficient information to resolve the order of relationships among archaebacteria and eukaryotes but suggested there was a trend in their analyses favoring the 3-domains tree. It should be noted, however, that Yutin et al. (24) used only composition homogeneous models within their study, and they did not attempt concatenated protein analyses.

Phylogenetic Analyses of Concatenated Protein Sequences.

To increase the number of characters analyzed, we concatenated 45 proteins (Table S2 in SI Appendix), after eliminating multiple alignments containing paralogous genes for example, we removed the paralogous largest subunits of eukaryotic RNA polymerases II and III to make a combined protein dataset containing 5,521 amino acids. The 3-domains tree was recovered by maximum parsimony analyses of this dataset (Fig. S57 in SI Appendix), but the eocyte tree was preferred by a composition homogeneous model in both an ML [99% bootstrap support (BS)] and Bayesian (100% PP) framework (Figs. S58 and S59 in SI Appendix).

To reduce the observed compositional heterogeneity in the combined protein dataset, we recoded each amino acid according to the 6 “Dayhoff groups” of chemically related amino acids that commonly replace one another (25). This recoding is related to transversion analysis of DNA sequences and, like other “reduced alphabet” methods, can improve topological estimation when data show substitution saturation or compositional heterogeneity ( , 26). Recoding had an additional advantage of allowing us to estimate a GTR rate matrix specific to these data (4,248 characters). We carried out NDCH analyses on both the standard amino acid and Dayhoff-recoded data, progressively adding composition vectors to improve the fit of the model to the data. We added up to 26 composition vectors (standard amino acid data Fig. S60 in SI Appendix) or 14 composition vectors (Dayhoff-recoded data Fig. 4A) and obtained a markedly better fit of the model to the data compared with homogeneous analyses as measured by posterior predictive simulations and Bayes factors, although in neither case were we able to fit the model to the data at the 95% confidence level. The NDCH analysis recovered the eocyte topology (≥95% PP) with both datasets, irrespective of the number of composition vectors added. The CAT model on standard amino acid data recovered the eocyte topology (Fig. 4B) with maximum (100% PP) support. In the analyses of the Dayhoff-recoded data using CAT, Nanoarchaeum equitans branched (94% PP) at the base of the eocytes, and together they clustered with the eukaryotes (99% PP Fig. S61 in SI Appendix). The difficulties in determining a stable phylogenetic position for N. equitans, which is an obligate parasite with a highly reduced genome, have been reported (27).

Phylogenetic analysis of 45 concatenated proteins. Scale bars indicate substitutions per site. The dotted branches leading to eubacteria are arbitrary lengths. Nodes highlighted with dots were supported by ≥95% PP. The 2 values indicate support (PP) for the eocyte hypothesis. (A) Fifty percent majority-rule consensus tree of 10,000 trees sampled from the PP distribution of an MCMC with 14 across-tree composition vectors NDCH model (GTR+Γ+14CV) with Dayhoff-recoded data loge(Lm) = −119349.62 - X 2 original data = 1,585.02 posterior predictive simulations of X 2 : mean = 998.62, range = 612.27–1,472.57, P = 0.00. By contrast, in the homogeneous model simulations the X 2 test statistic ranged between 73.80 and 230.23 (mean = 125.64), demonstrating that the NCDH model provides a much better fit to the original data. (B) Fifty percent majority-rule consensus tree of 1,275 trees sampled from the PP distribution of an MCMC with the CAT model (+Γ) with standard amino acid coded data loge(Lm) = −252376.53, mode number of categories (k) = 200.86 (standard error ± 9.5).

Combined data analyses showed some unconventional or controversial relationships among the eukaryotes, such as the placement of the microsporidian Encephalitozoon toward the base of the eukaryotes (e.g., Fig. 4 A and B) as opposed to its widely accepted relationship with the fungi (12). These results may be due in part to relatively short internal branches and long terminal branches within the eukaryotes, a pattern that can lead to the spurious attraction of unrelated taxa by a phenomenon called long-branch attraction (LBA) ( , 28). This interpretation is supported by analyses of the eukaryote sequences alone, when more conventional relationships such as the Amoebozoa, Opisthokonts, and Plantae were all recovered (Fig. S62 in SI Appendix). Despite the presence of apparent phylogenetic artifacts affecting the placement of particular eukaryotes in some analyses, we obtained no evidence that the grouping of the eocytes and eukaryotes is the result of LBA. Indeed, as noted (14, , 17), it is the 3-domains tree that resembles a LBA artifact, whereby attraction between the long eubacterial and eukaryotic branches forces together the residual archaebacterial taxa, resulting in a misleading impression of archaebacterial monophyly. In our analyses, we only obtained the 3-domains tree with simpler models that are more sensitive to LBA ( , 20, , 29) the complex and better-fitting models consistently supported the eocyte tree.

Although we have modeled compositional heterogeneity in our analyses, we recognize that phylogenetic inference of ancient relationships is fraught with difficulty (30, , 31), and that other substitution patterns in molecular data can also lead to incorrect trees when the model is misspecified. For example, a failure to adequately accommodate across-tree site-rate variation, also called covarion shifts, has been shown to cause LBA at the base of the eukaryotic tree ( , 32). A covarion model was favored by Bayes factors over a homogeneous model for 11 proteins from our dataset, but it was favored over the optimal heterogeneous composition models for only 3 proteins (Table S3 in SI Appendix). Similarly, for the combined rRNA data a covarion model (Fig. S63 in SI Appendix) was favored over the homogeneous model (Fig. S52 in SI Appendix) but not over the optimal heterogeneous composition model (Fig. S55 in SI Appendix). This suggests that a covarion substitution pattern is evident for some genes and proteins, but it is typically not as strong a factor as heterogeneous composition patterns when modeling interdomain relationships. Bayesian analyses of the combined protein dataset using a covarion model recovered the eocyte topology with maximal support (100% PP Fig. S64 in SI Appendix).

Conclusions and Implications for Archaebacterial and Eukaryotic Evolution.

Of the 51 proteins we analyzed (Table S2 in SI Appendix), 39 are involved in DNA replication, transcription, or translation and are the products of so-called “informational” genes (33). The remaining 12 proteins are involved in biosynthesis and metabolism and are the products of what have been called “operational” genes ( , 33). Although many eukaryotic operational genes are thought to have been gained by lateral gene transfer from either the mitochondrial endosymbiont or diverse other eubacteria ( , 34, , 35), the 12 operational genes included in this analysis showed no evidence of such interdomain transfers. Eukaryotic informational genes are widely held to have been vertically inherited within the cell line ( , 2– , 4, , 36), because the encoded proteins perform highly integrated and fundamental tasks that makes their successful transfer less likely ( , 2, , 36). These genes have been called the “genealogy defining core” or “genetic core” of cells, and it has been claimed that their common history is congruent with the 3-domains tree ( , 2– , 4). By contrast, we show here that analyses designed to overcome compositional heterogeneity, something that is manifestly evident for these data, provide support for the eocyte tree, rather than the 3-domains tree.

It has been suggested (37) that archaebacterial monophyly is supported by the fragmentation in all archaebacteria of the gene (rpoA) for the largest subunit of RNA polymerase and the gene (gltB) for the large subunit of glutamate synthetase into 2 and 3 separate genes, respectively. Investigation of the conservation and stability of these characters among archaebacteria is hindered by the paucity of complete eocyte genomes. However, we note that a nonfragmented rpoA gene, like that found in eukaryotes, has now been found in the genomes of the eocytes Cenarchaeum symbiosum (38) and Nitrosopumilis maritimus (Joint Genome Institute, unpublished data GenBank accession no. CP000866), and that the history of gltB is complicated by lineage specific loss among archaebacteria and by lateral gene transfers between archaebacteria and eubacteria (39).

The presence of membrane lipids in archaebacteria that are based on a sn-glycerol-1-phosphate backbone (G1P), rather than the sn-glycerol-3-phosphate backbone (G3P) found in eubacteria and eukaryotes, does appear to be a unifying character for the group (40, , 41). Most of the enzymes involved in the archaebacterial pathway are common to eubacteria and eukaryotes, but the enzyme [geranylgeranylglycerol phosphate (GGGP) synthase] determining the chirality of archaebacterial lipids ( , 41) has not been detected in eukaryotes. Theories for eukaryote origins that are consistent with the eocyte tree, posit that eubacterial-like pathways replaced many of the ancestral archaebacterial pathways, including that for lipid biosynthesis, during eukaryogenesis ( , 42).

The 3-domains (1) and eocyte ( , 11) trees assume that the root is on the lineage immediately ancestral to extant eubacteria ( , Fig. 1), in accord with the results of published reciprocal rooting studies using ancient paralogous proteins (e.g., refs. , 5 and , 6). The position of the root of the universal tree is important because it provides polarity to the tree enabling hypotheses of monophyly and sister-group relationships to be determined. It has been suggested that the eubacterial root could be an artifact of phylogenetic reconstruction resulting from long-branch attraction, or other sequence analysis artifacts ( , 43– , 46). Because the published paralog-rooting studies used similar homogeneous phylogenetic models to those that we investigated here, it is possible that they suffered from the same poor fit to data that we observed. More recent studies have inferred a root by polarizing insertions and deletions in paralogous molecular sequences ( , 44) or by polarizing other rare changes in molecular characters ( , 47). These analyses concur in placing the root within the eubacteria, rather than on the ancestral lineage, but disagree on its precise position. Even if the root were subsequently shown to lie outside of the eubacteria, for example on the eukaryotic branch as some have suggested ( , 46), the eocyte topology is still fundamentally incompatible with the 3-domains tree because no rooting can rescue archaebacterial monophyly.

Our results impact on current theories for eukaryogenesis, because the origin of the eukaryotic “genetic machinery” has often been conflated with the origin of the eukaryotic nuclear lineage (2– , 4, , 8). Thus, the 3-domains tree has been used to support hypotheses that posit that the nuclear line of descent is as ancient as the archaebacterial line ( , 4) or that eukaryotes are a unique primordial lineage ( , 48). The rooted 3-domains tree is also consistent with the neomuran hypothesis, whereby archaebacteria and eukaryotes are posited to be sister groups derived from a eubacterial-derived neomuran common ancestor ( , 37). By contrast, the eocyte tree favored by our analyses, and rooted on the eubacterial branch or among eubacteria ( , 44, , 47) is not consistent with any of these hypotheses, because it suggests that essential components of the eukaryotic cell originated from within an already diversified archaebacteria.


Discussion

Lane (2015) and Lane and Martin (2010) have proposed a scenario for how the mitochondrion became established by a series of adaptive steps, arguing that the eukaryotic leap to increased gene number and cellular complexity, and a subsequent adaptive cascade of morphological diversification, ‘was strictly dependent on mitochondrial power'. However, the scaling of the costs of building and maintaining cells is inconsistent with an abrupt shift in volumetric bioenergetic capacity of eukaryotic cells, and although the absolute costs of biosynthesis, maintenance, and operation of individual genes are much greater in eukaryotes, the proportional costs are less (Lynch and Marinov, 2015). This means that evolutionary additions and modifications of genes are more easily accrued in eukaryotic genomes from a bioenergetics perspective, regardless of their downstream fitness effects.

The analyses presented here reveal a number of additional scaling features involving cellular bioenergetic capacity that appear to transcend the substantial morphological differences across the bacterial-eukaryotic divide. There is not a quantum leap in the surface area of bioenergetic membranes exploited in eukaryotes relative to what would be possible on the cell surface alone, nor is the idea that ATP synthesis is limited by total membrane surface area supported. Moreover, the numbers of both ribosomes and ATP synthase complexes per cell, which jointly serve as indicators of a cell’s capacity to convert energy into biomass, scale with cell size in a continuous fashion both within and between bacterial and eukaryotic groups. Although there is considerable room for further comparative analyses in this area, when one additionally considers the substantial cost of building mitochondria, it is difficult to accept the idea that the establishment of the mitochondrion led to a major advance in net bioenergetic capacity.

Most discussion of the origin of the mitochondrion by endosymbiosis starts (and often ends) with a consideration of the benefits gained by the host cell. This ignores the fact that the eukaryotic consortium consists of two participants. At least initially, the establishment of a stable symbiotic relationship requires that each member of the pair gain as much from the association as is lost by relinquishing independence. Under the scenario painted by Lane and Martin (2010), and earlier by Martin and Müller (1998), the original mitochondrial-host cell affiliation was one in which the intracellular occupant provided hydrogen by-product to fuel methanogenesis in the host cell, while eventually giving up access to external resources and thereby coming to rely entirely on the host cell for organic substrates. For such a consortium to be evolutionarily stable as a true mutualism, both partners would have to acquire more resources than would be possible by living alone, in which case this novel relationship would be more than the sum of its parts.

Although some scenario like this might have existed in the earliest stages of mitochondrial establishment, it is also possible that one member of the original consortium was a parasite rather than a benevolent partner (made plausible by the fact that many of the α -protobacteria to which mitochondria are most closely related are intracellular parasites). Despite its disadvantages, such a system could be rendered stable if one member of the pair (the primordial mitochondrion) experienced relocation of just a single self-essential gene to the other member’s genome, while the other lost a key function that was complemented by the presence of the endosymbiont. This scenario certainly applies today, as all mitochondria have relinquished virtually all genes for biosynthesis, replication, and maintenance, and as a consequence depend entirely on their host cells for these essential metabolic functions. In contrast, all eukaryotes have relocated membrane bioenergetics from the cell surface to mitochondrial membranes.

Such an outcome represents a possible grand example of the preservation of two ancestral components by complementary degenerative mutations (Force et al., 1999). Notably, this process of subfunctionalization is most likely to proceed in relatively small populations because the end state is slightly deleterious from the standpoint of mutational vulnerability, owing to the fact that the original set of tasks becomes reliant on a larger set of genes (Lynch et al., 2001). Thus, a plausible scenario is that the full eukaryotic cell plan emerged at least in part by initially nonadaptive processes made possible by a very strong and prolonged population bottleneck (Lynch, 2007 Koonin, 2015).

The origin of the mitochondrion was a singular event, and we may never know with certainty the early mechanisms involved in its establishment, nor the order of prior or subsequent events in the establishment of other eukaryotic cellular features (Koonin, 2015). However, the preceding observations suggest that if there was an energetic boost associated with the earliest stages of mitochondrial colonization, this has subsequently been offset by the loss of the use of the eukaryotic cell surface for bioenergetics and the resultant increase in costs associated with the construction of internal membranes. Rather than a major bioenergetic revolution being provoked by the origin of the mitochondrion, at best a zero-sum game is implied.

Thus, if the establishment of the mitochondrion was a key innovation in the adaptive radiation of eukaryotes, the causal connection does not appear to involve a boost in energy acquisition. Notably, a recent analysis suggests that the origin of the mitochondrion postdated the establishment of many aspects of eukaryotic cellular complexity (Pittis and Gabaldón, 2016). It is plausible, that phagocytosis was a late-comer in this series of events, made possible only after the movement of membrane bioenergetics to the mitochondrion, which would have eliminated the presumably disruptive effects of ingesting surface membranes containing the ETC and ATP synthase.


Sister act

"Archaea and eukaryotes are sister groups, sharing a common ancestor," said lead author Thijs Ettema, from Uppsala University in Sweden.

He told BBC News: "This has been a leading model for 20 years or so. What happened a few years ago is that the branch in the tree that had the eukaryotes jumped on to the archaea branch. More specifically, it was affiliating with a group known as the TACK archaea."

Lokiarchaeota fall within the TACK grouping and represent the closest prokaryotic organisms to the eukaryote state.

According to Dr Ettema, the similarities between them show that Lokiarchaeota shared a common ancestor with eukaryotes roughly two billion years ago, and that this ancestor possessed a "starter kit" of genes that supported the increase in cellular complexity seen in eukaryotes today.

He explained: "The fact that we have found these same genes in [Lokiarchaeota] does not mean that they have the same function as they do in eukaryotes.

"But what we need to do to find out what those genes do in Lokiarchaeota is to carry out experiments, and for that we need actual cells."

The team had to reconstruct the new organisms from genetic material found in the cold marine sediments. But the effort to isolate cells will be a challenge.

"Getting the samples is not easy, and the amount of nutrients in these harsh environments is extremely limited. So the number of cells in these sediments will be extremely low and in general life down there is very slow.

"Some people have made predictions about how often cells divide down there and they have come up with numbers like one division every 10 years. If you want to grow them in the lab, these are not timescales that are feasible."

But the researchers are looking for "Loki-like" organisms in other locations, including hot springs in Yellowstone National Park, in the US, and New Zealand.

"We might even find Loki-like organisms that have more recent ancestry with eukaryotes. We could try to reconstruct their genomes and find additional pieces of the puzzle of how complex life might have originated," said Dr Ettema.

A key event in the evolution of eukaryotes was the acquisition of mitochondria. Lokiarchaeota do not possess them - making this organism no different from any other prokaryote. So precisely when cells first merged with the ancestors of these cellular powerhouses remains an open question.

"The acquisition of mitochondria really got things started," said Dr Ettema, adding: "The genes we find in Loki provide some pointers."

One critically important gene in eukaryotes is that which encodes a protein called actin. This has many functions in eukaryotic cells, but one of them is "phagocytosis". This process enables cells to engulf other cells, "eating" them.

"In Loki we also find genes that are related to those that encode actin proteins. Although we don't know what they do in Loki, we can infer that the last common ancestor had these genes," said Thijs Ettema.

Commenting on the research in the latest edition of Nature, Newcastle University cell biologists Martin Embley and Tom Williams write: "The identification of Lokiarchaeota so early in the history of this nascent field suggests that more closely related archaeal relatives of eukaryotes will soon be discovered."