The document discusses the discovery of Lokiarchaeota, a new archaeal lineage related to the TACK superphylum. Phylogenomic analyses place Lokiarchaeota as the nearest relative of eukaryotes. Interestingly, the genome of Lokiarchaeota encodes many eukaryotic-specific features, such as distant homologs of actin and tubulin, ESCRT complex proteins involved in cell division, and proteins related to transcription and translation. This provides unique insights into the emergence of cellular complexity in eukaryotes and suggests the archaeal ancestor was more complex than previously known archaeal lineages. The genome content of Lokiarchaeota holds clues about the nature of the archaeal ancestor
EveMicrobial Phylogenomics (EVE161) Class 9Jonathan Eisen
Microbial Phylogenomics (EVE161) at UC Davis Spring 2016. Co-taught by Jonathan Eisen and Holly Ganz.
Class 9:
Era II: rRNA Case Study: Built Environment Metaanalysis
Slides from a Comparative Genomics and Visualisation course (part 2) presented at the University of Dundee, 11th March 2014. Other materials are available at GitHub (https://github.com/widdowquinn/Teaching)
A systematic approach to Genotype-Phenotype correlationsfisherp
It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. Here we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region.
Molecular pathology in microbiology and metagenomicsCharithRanatunga
INTRODUCTION
HISTORY
Steps
Analysis
Metagenomic Process
Sequence-based analysis
Function-based analysis
Application of metagenomics
Future Directions of metagenomics
Examples for metagenomics projects
Modeling evolution in the classroom: The case of Fukushima’s mutant butterfliesAmyLark
Science education in the United States is evolving. New standards and reform recommendations spanning grades K-16 focus on a limited set of key scientific concepts from each discipline that all students should know but emphasize integrating these with science practices so that students learn not only the “what” of science but also the “how” and “why”. In line with this approach, we present an exercise that models the integration of fundamental evolutionary concepts with science practices. Students use Avida-ED digital evolution software to test claims from a study on mutated butterflies in the vicinity of the compromised Fukushima Daiichi Nuclear Power Plant complex subsequent to the Great East Japan Earthquake of 2011 (Hiyama et al., Scientific Reports 2 Article 570, 2012) to determine the effects of mutation rate on the genomes of individual organisms. This exercise is appropriate for use in both high school and undergraduate biology classrooms.
Is microbial ecology driven by roaming genes?beiko
Microbial ecology often makes assumptions about the relationship between phylogeny and function, but these assumptions can be invalidated by lateral gene transfer. We need to take a broader view of relationships between genes and genomes in order to make better sense out of microbes.
Introduction to Modern Biosystemaics for Fungal ClassificationMrinal Vashisth
This is a more specific version of the slide-set "Major Characteristics Used in Microbial Classification". A presentation I could not deliver for some reasons yet turned out to be pretty nice. I hope to deliver it some day, but for the time being I am making it public. I hope it would be of some use. :)
Microbiology has experienced a transformation during the last 25 years that has altered microbiologists' view of microorganisms and how to study them. The realization that most microorganisms cannot be grown readily in pure culture forced microbiologists to question their belief that the microbial world had been conquered. We were forced to replace this belief with an acknowledgment of the extent of our ignorance about the range of metabolic and organismal diversity.
this presentation depicts the usefulness of single cell profiling in crop plant for identifying novel gene sources which can be used for crop improvement
EveMicrobial Phylogenomics (EVE161) Class 9Jonathan Eisen
Microbial Phylogenomics (EVE161) at UC Davis Spring 2016. Co-taught by Jonathan Eisen and Holly Ganz.
Class 9:
Era II: rRNA Case Study: Built Environment Metaanalysis
Slides from a Comparative Genomics and Visualisation course (part 2) presented at the University of Dundee, 11th March 2014. Other materials are available at GitHub (https://github.com/widdowquinn/Teaching)
A systematic approach to Genotype-Phenotype correlationsfisherp
It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. Here we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region.
Molecular pathology in microbiology and metagenomicsCharithRanatunga
INTRODUCTION
HISTORY
Steps
Analysis
Metagenomic Process
Sequence-based analysis
Function-based analysis
Application of metagenomics
Future Directions of metagenomics
Examples for metagenomics projects
Modeling evolution in the classroom: The case of Fukushima’s mutant butterfliesAmyLark
Science education in the United States is evolving. New standards and reform recommendations spanning grades K-16 focus on a limited set of key scientific concepts from each discipline that all students should know but emphasize integrating these with science practices so that students learn not only the “what” of science but also the “how” and “why”. In line with this approach, we present an exercise that models the integration of fundamental evolutionary concepts with science practices. Students use Avida-ED digital evolution software to test claims from a study on mutated butterflies in the vicinity of the compromised Fukushima Daiichi Nuclear Power Plant complex subsequent to the Great East Japan Earthquake of 2011 (Hiyama et al., Scientific Reports 2 Article 570, 2012) to determine the effects of mutation rate on the genomes of individual organisms. This exercise is appropriate for use in both high school and undergraduate biology classrooms.
Is microbial ecology driven by roaming genes?beiko
Microbial ecology often makes assumptions about the relationship between phylogeny and function, but these assumptions can be invalidated by lateral gene transfer. We need to take a broader view of relationships between genes and genomes in order to make better sense out of microbes.
Introduction to Modern Biosystemaics for Fungal ClassificationMrinal Vashisth
This is a more specific version of the slide-set "Major Characteristics Used in Microbial Classification". A presentation I could not deliver for some reasons yet turned out to be pretty nice. I hope to deliver it some day, but for the time being I am making it public. I hope it would be of some use. :)
Microbiology has experienced a transformation during the last 25 years that has altered microbiologists' view of microorganisms and how to study them. The realization that most microorganisms cannot be grown readily in pure culture forced microbiologists to question their belief that the microbial world had been conquered. We were forced to replace this belief with an acknowledgment of the extent of our ignorance about the range of metabolic and organismal diversity.
this presentation depicts the usefulness of single cell profiling in crop plant for identifying novel gene sources which can be used for crop improvement
Compare and contrast eukaryotic and prokaryotic cells. Why do you th.pdfseasonsnu
Compare and contrast eukaryotic and prokaryotic cells. Why do you think their endomembrane
systems are different, is evolution involved? Paragraph format please, be very detailed and the
answer is expected to be long!
Solution
Prokaryote,relatively simple unicellular organism lacking a nucleus and other features found in
the more complex cells of all other organisms,called eukaryotes.In 1938 American biologist
Herbert Copeland proposed that unicellular organisms lacking nuclei be classified in their own
kingdom, Monera, also called Kingdom Prokaryotae.All bacteria were categorized in this newly
established kingdom. This scheme was the first to establish separate kingdoms for prokaryotes
(organisms without nuclei) and eukaryotes (organisms with nuclei).In 1990 American
microbiologist Carl Woese proposed that bacteria be divided into two groups,the archaea,or
archaebacteria,and bacteria, based on their structural and physiological differences.In some
classification systems,the archaea are considered prokaryotes;in others, they are classified in
their own domain, the archaea.Archaebacteria consist of a small group of primitive anaerobes
(organisms that do not require oxygen).They are found in a narrow range of habitats–often in
extreme environments with high temperature,high salt,or high acidity.In contrast, bacteria live in
a wide range of environments with or without oxygen, at various temperatures, and at various
levels of acidity.
In both prokaryotes and eukaryotes,the plasma membrane is composed of two layers of
phospholipid molecules interspersed with proteins,and regulates the traffic that flows in andout
of the cell.The prokaryotic plasma membrane,however,carries out additional functions.It
participates in replication of deoxyribonucleic acid (DNA) for cell division andsynthesis of
adenosine triphosphate (ATP),an energy molecule.In some prokaryotes,the plasma membrane is
essential for photosynthesis,the process that uses light energy to convert carbon dioxide and
water to glucose.
Fossil records indicate that eukaryotes evolved from prokaryotes somewhere between 1.5 to 2
billion years ago.Two proposed pathways describe the invasion of prokaryote cells by two
smaller prokaryote cells.They subsequently became successfully included as part of a now much
larger cell with additional structures and capable of additional functions.They are Endosymbiosis
and Membrane infolding.ProkaryotesEukaryotessize is 0.1-5.0 micrometressize is 5-100
micrometresnucleus is absentnucleus made of nuclear
envelope,chromatin,nucleoplasm,nucleoliDNA is circularDNA is linearDNA is naked and not
assosciated with histonesDNA is associated with histonesplasmids may occurplasmids are
rare.cell membrane have in folding called as mesosomemesosome is absentmitochondria are
absentpresentribosomes are 70S80Sgolgi apparatus,ER,lysosomes,centrosome absentall are
presentsexual reproduction is absentsexual reproduction occurs.endocytosis and exocytosis are
absentthey occur in e.
Similar to MIB200A at UCDavis Module: Microbial Phylogeny; Class 4 (20)
Innovations in Sequencing & Bioinformatics
Talk for
Healthy Central Valley Together Research Workshop
Jonathan A. Eisen University of California, Davis
January 31, 2024 linktr.ee/jonathaneisen
Thoughts on UC Davis' COVID Current ActionsJonathan Eisen
Slides I used for a presentation to Chancellor May's leadership council about the current state of UC Davis' response to COVID and how it could be improved
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
15. Cellular life is currently classified into three domains: Bacteria, Archaea and
Eukarya. Whereas the cytological properties of Bacteria and Archaea are
relatively simple, eukaryotes are characterized by a high degree of cellular
complexity, which is hard to reconcile given that most hypotheses assume a
prokaryote-to-eukaryote transition1,2. In this context, it seems particularly
difficult to account for the suggested presence of the endomembrane system, the
nuclear pores, the spliceosome, the ubiquitin protein degradation system, the
RNAi machinery, the cytoskeletal motors and the phagocytotic machinery in the
last eukaryotic common ancestor (ref. 3 and references therein). Ever since the
recognition of the archaeal domain of life by Carl Woese and co-workers4,5,
Archaea have featured prominently in hypotheses for the origin of eukaryotes,
as eukaryotes and Archaea represented sister lineages in Woese’s ‘universal
tree’5. The evolutionary link between Archaea and eukaryotes was further
reinforced through stud- ies of the transcription machinery6 and the first
archaeal genomes7, revealing that many genes, including the core of the genetic
information-processing machineries of Archaea, were more similar to those of
eukaryotes8 rather than to Bacteria.
Introduction
16. Cellular life is currently classified into three domains: Bacteria, Archaea and
Eukarya. Whereas the cytological properties of Bacteria and Archaea are
relatively simple, eukaryotes are characterized by a high degree of cellular
complexity, which is hard to reconcile given that most hypotheses assume a
prokaryote-to-eukaryote transition1,2. In this context, it seems particularly
difficult to account for the suggested presence of the endomembrane system, the
nuclear pores, the spliceosome, the ubiquitin protein degradation system, the
RNAi machinery, the cytoskeletal motors and the phagocytotic machinery in the
last eukaryotic common ancestor (ref. 3 and references therein). Ever since the
recognition of the archaeal domain of life by Carl Woese and co-workers4,5,
Archaea have featured prominently in hypotheses for the origin of eukaryotes,
as eukaryotes and Archaea represented sister lineages in Woese’s ‘universal
tree’5. The evolutionary link between Archaea and eukaryotes was further
reinforced through stud- ies of the transcription machinery6 and the first
archaeal genomes7, revealing that many genes, including the core of the genetic
information-processing machineries of Archaea, were more similar to those of
eukaryotes8 rather than to Bacteria.
Introduction
17. During the early stages of the genomic era, it also became apparent that
eukaryotic genomes were chimaeric by nature8,9, comprising genes of
both archaeal and bacterial origin, in addition to genes specific to
eukaryotes. Yet, whereas many of the bacterial genes could be traced
back to the alphaproteobacterial progenitor of mitochondria, the nature of
the lineage from which the eukaryotic host evolved remained
obscure1,10–13. This lineage might either descend from a common
ancestor shared with Archaea (follow- ing Woese’s classical three-
domains-of-life tree5), or have emerged from within the archaeal domain
(so-called archaeal host or eocyte-like scenarios1,14–17). Recent
phylogenetic analyses of universal protein data sets have provided
increasing support for models in which eukaryotes emerge as sister to or
from within the archaeal ‘TACK’ superphylum18–22, a clade originally
comprising the archaeal phyla Thaumarchaeota, Aigarchaeota,
Crenarchaeota and Korarchaeota23.
Introduction
18. During the early stages of the genomic era, it also became apparent that
eukaryotic genomes were chimaeric by nature8,9, comprising genes of
both archaeal and bacterial origin, in addition to genes specific to
eukaryotes. Yet, whereas many of the bacterial genes could be traced
back to the alphaproteobacterial progenitor of mitochondria, the nature of
the lineage from which the eukaryotic host evolved remained
obscure1,10–13. This lineage might either descend from a common
ancestor shared with Archaea (follow- ing Woese’s classical three-
domains-of-life tree5), or have emerged from within the archaeal domain
(so-called archaeal host or eocyte-like scenarios1,14–17). Recent
phylogenetic analyses of universal protein data sets have provided
increasing support for models in which eukaryotes emerge as sister to or
from within the archaeal ‘TACK’ superphylum18–22, a clade originally
comprising the archaeal phyla Thaumarchaeota, Aigarchaeota,
Crenarchaeota and Korarchaeota23.
Introduction
19. In support of this relationship, comparative genomics analyses have revealed
several eukaryotic signature proteins (ESPs)24 in TACK lineages, including
distant archaeal homologues of actin25 and tubulin26, archaeal cell division
proteins related to the eukaryotic endosomal sorting complexes required for
transport (ESCRT)-III complex27, and several information-processing
proteins involved in transcription and translation2,17,23. These findings
suggest an archaeal ancestor of eukaryotes that might have been more
complex than the archaeal lineages identified thus far2,23,28. Yet, the
absence of missing links in the prokaryote-to-eukaryote transition currently
precludes detailed predictions about the nature and timing of events that
have driven the process of eukaryogenesis1,2,17,28. Here we describe the
discovery of a new archaeal lineage related to the TACK superphylum that
represents the nearest relative of eukaryotes in phylogenomic analyses, and
intriguingly, its genome encodes many eukaryote-specific features,
providing a unique insight in the emergence of cellular complexity in
eukaryotes.
Introduction
20. In support of this relationship, comparative genomics analyses have revealed
several eukaryotic signature proteins (ESPs)24 in TACK lineages, including
distant archaeal homologues of actin25 and tubulin26, archaeal cell division
proteins related to the eukaryotic endosomal sorting complexes required for
transport (ESCRT)-III complex27, and several information-processing
proteins involved in transcription and translation2,17,23. These findings
suggest an archaeal ancestor of eukaryotes that might have been more
complex than the archaeal lineages identified thus far2,23,28. Yet, the
absence of missing links in the prokaryote-to-eukaryote transition currently
precludes detailed predictions about the nature and timing of events that
have driven the process of eukaryogenesis1,2,17,28. Here we describe the
discovery of a new archaeal lineage related to the TACK superphylum that
represents the nearest relative of eukaryotes in phylogenomic analyses, and
intriguingly, its genome encodes many eukaryote-specific features,
providing a unique insight in the emergence of cellular complexity in
eukaryotes.
Introduction
23. Figure 2 | Metagenomic reconstruction and phylogenetic
analysis of Lokiarchaeum. a, Schematic overview of the
metagenomics approach. BI, Bayesian inference; ML,
maximum likelihood. b, Bayesian phylogeny of
concatenated alignments comprising 36 conserved
phylogenetic marker proteins using sophisticated models of
protein evolution (Methods), showing eukaryotes branching
within Lokiarchaeota. Numbers above and below branches
refer to Bayesian posterior probability and maximum-
likelihood bootstrap support values, respectively. Posterior
probability values above 0.7 and bootstrap support values
above 70 are shown. Scale indicates the number of
substitutions per site. c, Phylogenetic breakdown of the
Lokiarchaeum proteome, in comparison with proteomes of
Korarchaeota, Aigarchaeota (Caldiarchaeum) and
Miscellaneous Crenarchaeota Group (MCG) archaea.
Category ‘Other’ contains proteins assigned to the root of
cellular organisms, to viruses and to unclassified proteins
30. In eukaryotes, the ESCRT machinery represents an essential com-
ponent of the multivesicular endosome pathway for lysosomal
degradation of damaged or superfluous proteins, and it plays a role in
several budding processes including cytokinesis, autophagy and viral
budding44. The ESCRT machinery generally consists of the ESCRT-
I–III subcomplexes, as well as associated subunits45. The analysis of
the Lokiarchaeum genome revealed the presence of an ESCRT gene
cluster (Fig. 4a), as well as of several additional pro- teins
homologous to components of the eukaryotic multivesicular
endosome pathway.
31.
32. We have identified and characterized the genome of Lokiarchaeota, a novel, deeply
rooting clade of the archaeal TACK superphylum, which in phylogenomic analyses of
universal proteins forms a mono- phyletic group with eukaryotes. While the obtained
phylogenomic resolution testifies to a deep archaeal ancestry of eukaryotes, the
Lokiarchaeum genome content holds valuable clues about the nature of the archaeal
ancestor of eukaryotes, and about the process of eukaryogenesis. Many of the ESPs
previously identified in different TACK lineages are united in Lokiarchaeum, indicating
that the patchy distribution of ESPs amongst archaea is most likely the result of lineage-
specific losses2 (Fig. 5). Moreover, the Lokiarchaeum genome significantly expands the
total number of ESPs in Archaea, lending support to the observed phylogenetic
affiliation of Lokiarchaeota and eukaryotes. Finally, and importantly, sequence-based
functional pre- dictions for these new ESPs indicate a predominance of proteins that
play pivotal roles in various membrane remodelling and vesicular trafficking processes
in eukaryotes. It is also noteworthy that Lokiarchaeum appears to encode the most
‘eukaryotic-like’ ribosome identified in Archaea thus far (Supplementary Discussion 4),
including a putative homologue of eukaryotic ribosomal protein L22e (Fig. 5;
Supplementary Fig. 24 and Supplementary Tables 7 and 8).
33. Taken together, our data indicate that the archaeal ancestor of eukar- yotes was even more
complex than previously inferred2 and allow us to speculate on the timing and order of several
key events in the process of eukaryogenesis. For example, the identification of archaeal genes
involved in membrane remodelling and vesicular trafficking processes indicates that the
emergence of cellular complexity was already under- way before the acquisition of the
mitochondrial endosymbiont, which now appears to be a universal feature of all
eukaryotes28,37,50. Indeed, based upon our results it seems plausible that the archaeal ancestor
of eukaryotes had a dynamic actin cytoskeleton and potentially endo- and/or phagocytic
capabilities, which would have facilitated the invagination of the mitochondrial progenitor.
The present identification and genomic characterization of a novel archaeal group that shares a
common ancestry with eukaryotes indi- cates that the gap between prokaryotes and eukaryotes
might, to some extent, be a result of poor sampling of the existing archaeal diversity.
Environmental surveys have revealed the existence of a plethora of uncultured archaeal lineages,
and some of these likely represent even closer relatives of eukaryotes. Excitingly, the genomic
exploration of these archaeal lineages has now come within reach. Such endeavours, combined
with prospective studies focusing on uncovering metabolic, chemical and cell biological
properties of these lineages, will uncover further details about the identity and nature of the
archaeal ancestor of eukaryotes, shedding new light on the evolutionary dark ages of the
eukaryotic cell.