This document discusses the importance of animal microbiomes. It notes that animals are covered in clouds of microbes that form their microbiome, which is likely involved in many important animal phenotypes. Reasons for the growing focus on animal microbiomes include advances in culture-independent techniques and sequencing that have revealed greater microbial diversity than previously appreciated. Understanding animal microbiomes could provide insights into processes like health, disease transmission, and mutualistic relationships with microbes.
This document summarizes a lecture given by Dr. Larry Smarr on his research exploring the human microbiome. Some key points:
- Dr. Smarr has been studying the microbial universe inside the human body for 40 years, since microbiology began as a frontier science.
- Advances in DNA sequencing have enabled the sequencing of human and microbial genomes, revolutionizing our understanding of the microbiome.
- The human microbiome is essential to health and disease, with 99% of our genes located in microbes rather than human cells.
- Dr. Smarr's own microbiome was studied before and after colonoscopy and colon surgery, showing dramatic shifts and recovery periods.
- Fecal microbiota trans
Unit 9: Human Microbiome
LECTURE LEARNING GOALS
1. Describe the human microbiome: how many microbes there are, how you get your microbiome, who’s there, and how it changes over time and by region.
2. Describe the domain eukarya. List the five superkingdoms and a few notable species.
3. Explain how the human microbiome is related to health and disease.
The document summarizes a lecture on the human microbiome. It discusses how humans are colonized by vast numbers of microbes, forming complex microbial ecosystems. There is enormous variation in microbiome composition both within and between individuals, and this variation is associated with health states and phenotypes. Research has identified some possible causes of microbiome variation and suggests it may be possible to alter or restore microbiome composition.
The Rise of the Microbiome - talk by Jonathan Eisen for AHCJ15Jonathan Eisen
This document discusses the rise of research on the human microbiome. It provides five reasons for the recent increased interest: 1) increased appreciation of microbial diversity, 2) seeking new areas of research after the human genome was sequenced, 3) advances in DNA sequencing and analysis techniques, 4) understanding the functions of microbes in and on the human body, 5) sequencing costs decreasing drastically. It also outlines some of the major challenges in microbiome research, like complexity from host and environmental factors, and gaining public understanding. Finally, it discusses opportunities in the field, such as improving reference databases, analysis methods, model systems like rice, whole systems approaches, education, citizen science, and more.
Jonathan Eisen Talk for #UCDavis #HostMicrobe on Phylogeny & MicrobiomesJonathan Eisen
The document discusses approaches to studying microbiome diversity using phylogeny. It begins by showing the rise in publications on the microbiome over time. It then discusses how phylogeny-driven approaches can be used to study microbiome diversity at various sites in the human body. The approaches involve constructing phylogenetic trees of microbial sequences from samples to determine diversity and relationships between microbes.
Jonathan Eisen talk at #UCDavis 10/19/15 on "Microbiomes in Food and Agricult...Jonathan Eisen
Slides for talk on "Microbiomes in Food and Agriculture" by JonathanEisen - note - not all slides were used in talk. These were there to stimulate discussion ...
This document summarizes a lecture given by Dr. Larry Smarr on his research exploring the human microbiome. Some key points:
- Dr. Smarr has been studying the microbial universe inside the human body for 40 years, since microbiology began as a frontier science.
- Advances in DNA sequencing have enabled the sequencing of human and microbial genomes, revolutionizing our understanding of the microbiome.
- The human microbiome is essential to health and disease, with 99% of our genes located in microbes rather than human cells.
- Dr. Smarr's own microbiome was studied before and after colonoscopy and colon surgery, showing dramatic shifts and recovery periods.
- Fecal microbiota trans
Unit 9: Human Microbiome
LECTURE LEARNING GOALS
1. Describe the human microbiome: how many microbes there are, how you get your microbiome, who’s there, and how it changes over time and by region.
2. Describe the domain eukarya. List the five superkingdoms and a few notable species.
3. Explain how the human microbiome is related to health and disease.
The document summarizes a lecture on the human microbiome. It discusses how humans are colonized by vast numbers of microbes, forming complex microbial ecosystems. There is enormous variation in microbiome composition both within and between individuals, and this variation is associated with health states and phenotypes. Research has identified some possible causes of microbiome variation and suggests it may be possible to alter or restore microbiome composition.
The Rise of the Microbiome - talk by Jonathan Eisen for AHCJ15Jonathan Eisen
This document discusses the rise of research on the human microbiome. It provides five reasons for the recent increased interest: 1) increased appreciation of microbial diversity, 2) seeking new areas of research after the human genome was sequenced, 3) advances in DNA sequencing and analysis techniques, 4) understanding the functions of microbes in and on the human body, 5) sequencing costs decreasing drastically. It also outlines some of the major challenges in microbiome research, like complexity from host and environmental factors, and gaining public understanding. Finally, it discusses opportunities in the field, such as improving reference databases, analysis methods, model systems like rice, whole systems approaches, education, citizen science, and more.
Jonathan Eisen Talk for #UCDavis #HostMicrobe on Phylogeny & MicrobiomesJonathan Eisen
The document discusses approaches to studying microbiome diversity using phylogeny. It begins by showing the rise in publications on the microbiome over time. It then discusses how phylogeny-driven approaches can be used to study microbiome diversity at various sites in the human body. The approaches involve constructing phylogenetic trees of microbial sequences from samples to determine diversity and relationships between microbes.
Jonathan Eisen talk at #UCDavis 10/19/15 on "Microbiomes in Food and Agricult...Jonathan Eisen
Slides for talk on "Microbiomes in Food and Agriculture" by JonathanEisen - note - not all slides were used in talk. These were there to stimulate discussion ...
The document summarizes microbiome biomarker data from the American Gut Project. It describes the project's goal of characterizing participants' gut, skin, and oral bacteria to better understand relationships between microbiome and lifestyle/health factors. Over 4,600 samples from 3,624 participants have been sequenced and analyzed. The analyzed samples included some from patients that provided multiple sample types (mouth & skin, mouth & stool). T-Bioinfo then performed analyses including mapping samples to identify bacteria, generating abundance tables, and using machine learning methods to identify correlations between bacterial species from different body sites. Preliminary conclusions identified outstanding samples and correlated oral and stool bacteria.
UC Davis EVE161 Lecture 17 by @phylogenomicsJonathan Eisen
This document contains slides from a lecture on metagenomics given by Jonathan Eisen at UC Davis in winter 2014. The lecture discusses shotgun metagenomics and analyzing metagenomic functions and gene content from environmental samples without genome assemblies. It provides an example of a comparative metagenomics study of various microbial communities that identified habitat-specific genes and metabolic profiles reflecting the different environments. The slides include figures and references from a 2005 Science paper on this topic. Problem set 4 for the class involves selecting a relevant paper for presentation the following week.
Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characteri...QIAGEN
High-throughput sequencing, combined with high-resolution metagenomic analysis, provides a powerful diagnostic tool for clinical management of enteric disease. Forty-five patient samples of known and unknown disease etiology and 20 samples from health individuals were subjected to next-generation sequencing. Subsequent metagenomic analysis identified all microorganisms (bacteria, viruses, fungi and parasites) in the samples, including the expected pathogens in the samples of known etiology. Multiple pathogens were detected in the individual samples, providing evidence for polymicrobial infection. Patients were clearly differentiated from healthy individuals based on microorganism abundance and diversity. The speed, accuracy and actionable features of CosmosID bioinformatics and curated GenBook® databases, implemented in the QIAGEN Microbial Genomics Pro Suite, and the functional analysis, leveraging the QIAGEN functional metagenomics workflow, provide a powerful tool contributing to the revolution in clinical diagnostics, prophylactics and therapeutics that is now in progress globally.
Marine Host-Microbiome Interactions: Challenges and OpportunitiesJonathan Eisen
This document summarizes a talk given by Jonathan Eisen on marine host-microbiome interactions. It discusses various topics researched in Eisen's lab, including phylogenomic methods and tools, microbial phylogenomics and evolvability, reference data resources, communication in science, and model systems. Specific projects are mentioned, such as automated genome trees, phylogenetic marker genes, the GEBA project, and dark matter microbes. The document then introduces the concept of the host-microbiome stress triangle and gives examples of stress types including nutrient acquisition, pathogens, and environmental change. It concludes by discussing a potential project on seagrass microbiomes in collaboration with Jay Stachowicz's lab.
The Human Microbiome and the Revolution in Digital HealthLarry Smarr
2014.01.22
Calit2 Director Larry Smarr speaks as part of the Pensacola Evening Lecture Series, organized by the Florida Institute for Human and Machine Cognition, in Pensacola, FL.
"The Quest for A field Guide to the Microbes" talk by Jonathan Eisen February...Jonathan Eisen
The document discusses the author's quest to create a field guide to microbes. It describes the challenges in doing so given microbes' small size and high diversity. The author discusses using DNA sequencing and phylogenetic trees to identify microbes and determine their functions and relationships. Examples are given of using DNA to study human microbiomes, forensic analysis, and microbial communities. The need for a comprehensive field guide is argued to better understand the roles and identification of microbes.
Unit 1. How to measure diversity
LECTURE LEARNING GOALS
1. Describe the abundance and diversity of microbes, the “unseen majority”, in all natural and manufactured environments.
2. Explain the common measures of microbial diversity, and how diversity is measured.
3. What is the purpose of diversity?
This document summarizes Jonathan Eisen's presentation on seagrass as a model system for plant microbiome studies. It describes how Eisen initially knew little about seagrasses but connected with colleague Jay Stachowicz, a seagrass expert, to learn more. They collaborated on a proposal to study the microbiomes of seagrasses. Initial studies found the microbial communities varied by tissue type, with more variation below ground. A global study by Eisen's group using the Zostera Experimental Network sampled seagrass microbiomes from sites around the world. The study found seagrass leaf microbiomes resembled local water, while roots had microbial communities enriched in sulfur metabolism.
American Gut Project presentation at Masaryk Universitymcdonadt
The document discusses the microbiome and how microbes outnumber human cells in the human body. It provides several references from scientific studies published between 2001-2015 that examine the microbiome composition in different human and environmental populations using genetic sequencing and analysis techniques. It also discusses challenges and variations in microbiome analysis methods and highlights some key researchers and projects investigating the human microbiome.
This document discusses using ancient DNA analysis to study archaeological remains. It notes that ancient DNA is typically fragmented into small pieces 100-500 base pairs long. Contamination from other sources is also a major issue. However, ancient DNA analysis can be used to study species phylogenies, hominin evolution, past diets and behaviors, origins of domestication, and population histories. As a case study, the document discusses analyzing ancient DNA from pygmy hippopotamus remains on Cyprus to learn about population dynamics and what caused their extinction 12,000 years ago alongside human arrival and climate change. Stable isotope analysis of bones and teeth can also provide clues about past diets.
Exploring Our Inner Universe Using Supercomputers and Gene SequencersLarry Smarr
This document summarizes a talk given by Dr. Larry Smarr on his research exploring the human microbiome using supercomputers and gene sequencers. He began by researching astrophysics but has recently applied those methods to study the microbes within the human body. Through deep genome sequencing of his own stool samples over time and large-scale computational analysis, he was able to map changes in his gut microbiome that provided insights into an undiagnosed autoimmune disease. His research demonstrates how quantitative analysis of the microbiome using advanced technologies can lead to new understandings of health and disease.
This document summarizes a study that used PCR and cloning to analyze the 16S rRNA genes present in a natural marine bacterioplankton population from the Sargasso Sea. Researchers constructed a library of 51 small-subunit rRNA genes and sequenced five unique genes. In addition to genes from known marine Synechococcus and SAR11 lineages, they identified two new classes of genes belonging to alpha- and gamma-proteobacteria, confirming that many planktonic bacteria have not been previously recognized by microbiologists.
Comparative analysis of genome sequences from six strains of Streptococcus agalactiae (Group B Streptococcus; GBS), representing the five major disease-causing serotypes, and two previously sequenced genomes suggests that a bacterial species can be described by its "pan-genome". The pan-genome includes a core genome of genes present in all strains and a dispensable genome of strain-specific and partially shared genes. While 80% of any single genome is shared among all isolates (core genome), sequencing additional strains revealed unique genes, and extrapolation predicts more unique genes will be found with further sequencing. Multiple independent genome sequences are thus required to fully understand the genomic complexity of a bacterial species.
UC Davis EVE161 Lecture 14 by @phylogenomicsJonathan Eisen
This document contains slides from a lecture on metagenomics and microbial phylogenomics. The lecture discusses the history and development of metagenomics, which involves studying the collective genomes of microbes in an environment. It reviews key papers on metagenomics and the discovery of proteorhodopsin and the SAR11 lineage of bacteria from environmental samples. The slides also discuss previous findings on marine microbes from rRNA studies and introduce two new lineages of alpha- and gamma-proteobacteria identified from an analysis of 16S rRNA genes cloned from Sargasso Sea bacterioplankton DNA.
UC Davis EVE161 Lecture 9 by @phylogenomicsJonathan Eisen
This document summarizes a lecture about a case study analyzing microbial communities in dust samples from various spaces in a university building using rRNA sequencing. The study found indoor bacterial communities were highly diverse but dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural characteristics like space type, building layout, ventilation sources, and human occupancy patterns significantly influenced the structure of bacterial communities between spaces. Restrooms in particular contained very distinct microbial communities. The study demonstrates how human activities and building design can shape the indoor microbiome.
Jonathan Eisen - History of Lake Arrowhead Microbial Genomes meeting #LAMG14Jonathan Eisen
This document appears to be notes from the Lake Arrowhead Microbial Genomes meeting in 2014. It references Jeffrey Miller numerous times and contains many quotes attributed to him about microbial genomes and microbiomes. The notes include discussions of topics over time at the meetings from 1998 to 2016 and potential talk topics for future meetings.
Eisen Lecture for Ian Korf genomics courseJonathan Eisen
The document discusses four eras in the use of DNA sequencing for microbial diversity studies:
1) Era I focused on using rRNA sequencing to construct the tree of life and identify the three domains of life - Bacteria, Archaea, and Eukaryota.
2) Era II involved using rRNA sequencing to study microbial communities in environmental samples, revealing far more microbial diversity than previously imagined.
3) Advances in sequencing technologies are revolutionizing the study of microbial diversity across all four eras.
The document summarizes microbiome biomarker data from the American Gut Project. It describes the project's goal of characterizing participants' gut, skin, and oral bacteria to better understand relationships between microbiome and lifestyle/health factors. Over 4,600 samples from 3,624 participants have been sequenced and analyzed. The analyzed samples included some from patients that provided multiple sample types (mouth & skin, mouth & stool). T-Bioinfo then performed analyses including mapping samples to identify bacteria, generating abundance tables, and using machine learning methods to identify correlations between bacterial species from different body sites. Preliminary conclusions identified outstanding samples and correlated oral and stool bacteria.
UC Davis EVE161 Lecture 17 by @phylogenomicsJonathan Eisen
This document contains slides from a lecture on metagenomics given by Jonathan Eisen at UC Davis in winter 2014. The lecture discusses shotgun metagenomics and analyzing metagenomic functions and gene content from environmental samples without genome assemblies. It provides an example of a comparative metagenomics study of various microbial communities that identified habitat-specific genes and metabolic profiles reflecting the different environments. The slides include figures and references from a 2005 Science paper on this topic. Problem set 4 for the class involves selecting a relevant paper for presentation the following week.
Clinical Metagenomics for Rapid Detection of Enteric Pathogens and Characteri...QIAGEN
High-throughput sequencing, combined with high-resolution metagenomic analysis, provides a powerful diagnostic tool for clinical management of enteric disease. Forty-five patient samples of known and unknown disease etiology and 20 samples from health individuals were subjected to next-generation sequencing. Subsequent metagenomic analysis identified all microorganisms (bacteria, viruses, fungi and parasites) in the samples, including the expected pathogens in the samples of known etiology. Multiple pathogens were detected in the individual samples, providing evidence for polymicrobial infection. Patients were clearly differentiated from healthy individuals based on microorganism abundance and diversity. The speed, accuracy and actionable features of CosmosID bioinformatics and curated GenBook® databases, implemented in the QIAGEN Microbial Genomics Pro Suite, and the functional analysis, leveraging the QIAGEN functional metagenomics workflow, provide a powerful tool contributing to the revolution in clinical diagnostics, prophylactics and therapeutics that is now in progress globally.
Marine Host-Microbiome Interactions: Challenges and OpportunitiesJonathan Eisen
This document summarizes a talk given by Jonathan Eisen on marine host-microbiome interactions. It discusses various topics researched in Eisen's lab, including phylogenomic methods and tools, microbial phylogenomics and evolvability, reference data resources, communication in science, and model systems. Specific projects are mentioned, such as automated genome trees, phylogenetic marker genes, the GEBA project, and dark matter microbes. The document then introduces the concept of the host-microbiome stress triangle and gives examples of stress types including nutrient acquisition, pathogens, and environmental change. It concludes by discussing a potential project on seagrass microbiomes in collaboration with Jay Stachowicz's lab.
The Human Microbiome and the Revolution in Digital HealthLarry Smarr
2014.01.22
Calit2 Director Larry Smarr speaks as part of the Pensacola Evening Lecture Series, organized by the Florida Institute for Human and Machine Cognition, in Pensacola, FL.
"The Quest for A field Guide to the Microbes" talk by Jonathan Eisen February...Jonathan Eisen
The document discusses the author's quest to create a field guide to microbes. It describes the challenges in doing so given microbes' small size and high diversity. The author discusses using DNA sequencing and phylogenetic trees to identify microbes and determine their functions and relationships. Examples are given of using DNA to study human microbiomes, forensic analysis, and microbial communities. The need for a comprehensive field guide is argued to better understand the roles and identification of microbes.
Unit 1. How to measure diversity
LECTURE LEARNING GOALS
1. Describe the abundance and diversity of microbes, the “unseen majority”, in all natural and manufactured environments.
2. Explain the common measures of microbial diversity, and how diversity is measured.
3. What is the purpose of diversity?
This document summarizes Jonathan Eisen's presentation on seagrass as a model system for plant microbiome studies. It describes how Eisen initially knew little about seagrasses but connected with colleague Jay Stachowicz, a seagrass expert, to learn more. They collaborated on a proposal to study the microbiomes of seagrasses. Initial studies found the microbial communities varied by tissue type, with more variation below ground. A global study by Eisen's group using the Zostera Experimental Network sampled seagrass microbiomes from sites around the world. The study found seagrass leaf microbiomes resembled local water, while roots had microbial communities enriched in sulfur metabolism.
American Gut Project presentation at Masaryk Universitymcdonadt
The document discusses the microbiome and how microbes outnumber human cells in the human body. It provides several references from scientific studies published between 2001-2015 that examine the microbiome composition in different human and environmental populations using genetic sequencing and analysis techniques. It also discusses challenges and variations in microbiome analysis methods and highlights some key researchers and projects investigating the human microbiome.
This document discusses using ancient DNA analysis to study archaeological remains. It notes that ancient DNA is typically fragmented into small pieces 100-500 base pairs long. Contamination from other sources is also a major issue. However, ancient DNA analysis can be used to study species phylogenies, hominin evolution, past diets and behaviors, origins of domestication, and population histories. As a case study, the document discusses analyzing ancient DNA from pygmy hippopotamus remains on Cyprus to learn about population dynamics and what caused their extinction 12,000 years ago alongside human arrival and climate change. Stable isotope analysis of bones and teeth can also provide clues about past diets.
Exploring Our Inner Universe Using Supercomputers and Gene SequencersLarry Smarr
This document summarizes a talk given by Dr. Larry Smarr on his research exploring the human microbiome using supercomputers and gene sequencers. He began by researching astrophysics but has recently applied those methods to study the microbes within the human body. Through deep genome sequencing of his own stool samples over time and large-scale computational analysis, he was able to map changes in his gut microbiome that provided insights into an undiagnosed autoimmune disease. His research demonstrates how quantitative analysis of the microbiome using advanced technologies can lead to new understandings of health and disease.
This document summarizes a study that used PCR and cloning to analyze the 16S rRNA genes present in a natural marine bacterioplankton population from the Sargasso Sea. Researchers constructed a library of 51 small-subunit rRNA genes and sequenced five unique genes. In addition to genes from known marine Synechococcus and SAR11 lineages, they identified two new classes of genes belonging to alpha- and gamma-proteobacteria, confirming that many planktonic bacteria have not been previously recognized by microbiologists.
Comparative analysis of genome sequences from six strains of Streptococcus agalactiae (Group B Streptococcus; GBS), representing the five major disease-causing serotypes, and two previously sequenced genomes suggests that a bacterial species can be described by its "pan-genome". The pan-genome includes a core genome of genes present in all strains and a dispensable genome of strain-specific and partially shared genes. While 80% of any single genome is shared among all isolates (core genome), sequencing additional strains revealed unique genes, and extrapolation predicts more unique genes will be found with further sequencing. Multiple independent genome sequences are thus required to fully understand the genomic complexity of a bacterial species.
UC Davis EVE161 Lecture 14 by @phylogenomicsJonathan Eisen
This document contains slides from a lecture on metagenomics and microbial phylogenomics. The lecture discusses the history and development of metagenomics, which involves studying the collective genomes of microbes in an environment. It reviews key papers on metagenomics and the discovery of proteorhodopsin and the SAR11 lineage of bacteria from environmental samples. The slides also discuss previous findings on marine microbes from rRNA studies and introduce two new lineages of alpha- and gamma-proteobacteria identified from an analysis of 16S rRNA genes cloned from Sargasso Sea bacterioplankton DNA.
UC Davis EVE161 Lecture 9 by @phylogenomicsJonathan Eisen
This document summarizes a lecture about a case study analyzing microbial communities in dust samples from various spaces in a university building using rRNA sequencing. The study found indoor bacterial communities were highly diverse but dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural characteristics like space type, building layout, ventilation sources, and human occupancy patterns significantly influenced the structure of bacterial communities between spaces. Restrooms in particular contained very distinct microbial communities. The study demonstrates how human activities and building design can shape the indoor microbiome.
Jonathan Eisen - History of Lake Arrowhead Microbial Genomes meeting #LAMG14Jonathan Eisen
This document appears to be notes from the Lake Arrowhead Microbial Genomes meeting in 2014. It references Jeffrey Miller numerous times and contains many quotes attributed to him about microbial genomes and microbiomes. The notes include discussions of topics over time at the meetings from 1998 to 2016 and potential talk topics for future meetings.
Eisen Lecture for Ian Korf genomics courseJonathan Eisen
The document discusses four eras in the use of DNA sequencing for microbial diversity studies:
1) Era I focused on using rRNA sequencing to construct the tree of life and identify the three domains of life - Bacteria, Archaea, and Eukaryota.
2) Era II involved using rRNA sequencing to study microbial communities in environmental samples, revealing far more microbial diversity than previously imagined.
3) Advances in sequencing technologies are revolutionizing the study of microbial diversity across all four eras.
GenomeTrakr: Whole-Genome Sequencing for Food Safety and A New Way Forward in...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
GenomeTrakr: Whole-Genome Sequencing for Food Safety and A New Way Forward in the Microbiological Testing & Traceability for Foodborne Pathogens. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management -23-25 May 2016, Rome, Italy.
Chomsky argues that behaviorism cannot fully explain children's language acquisition for three reasons: 1) Children learn more about their language's structure than what they are directly exposed to, 2) Children learn to distinguish grammatical and ungrammatical sentences despite exposure to errors, and 3) Language develops without systematic instruction. Chomsky proposes an innate Language Acquisition Device containing Universal Grammar principles that are triggered by language exposure to help children learn the structures of their native language. Evidence for this innatist view includes that language develops similarly in all children and separately from other cognitive skills.
Linguistic oriented theories,behaviorism and innatismHina Honey
The document summarizes three main theories of first language acquisition: behaviorism, innatism, and cognitivism. Behaviorism, proposed by Skinner, views language learning as a process of habit formation through imitation, repetition, and reinforcement. Innatism, proposed by Chomsky, posits that children are born with an innate language acquisition device and universal grammar that allows them to learn the rules of any human language. Cognitivism incorporates aspects of both by recognizing the importance of cognitive processes and environmental influences in language development.
The document discusses microbiology and microorganisms. It defines microbiology as the study of organisms too small to see without magnification, including bacteria, viruses, fungi, protozoa, and helminthes. It notes that bacteria are very tiny, around 1-2 micrometers in size. The document outlines some key figures in the field, including Antonie van Leeuwenhoek who first observed microbes under magnification, and Louis Pasteur who demonstrated germ theory and developed pasteurization. It also discusses how microorganisms are identified, measured, and classified, and notes that while some are pathogens and cause disease, many are beneficial and help with processes like nutrient production and decomposition.
1) Describe the genetic code in your own words, and the three coding.pdfarhamnighty
1) Describe the genetic code in your own words, and the three coding systems in which DNA
affects phenotype.Describe each process separetly .
2) Describe, in your own words, evolution and how it produces and redistributes variation?
explian each of the four merchanisms of evolution separately.
3) Explain (from an evoluionary point of view) why tuberculosis was not eradicated after the
discovery of antibiotics. What is the current status of tuberculosis treatment? and why are there
increasing problems with treating tuberculosis in different regions of the worldd?
Solution
1.Like every operating things have there specific sequences,our DNA too has a specific sequence
which are called as \'code\'.It is said as \'genetic code\' because it contains all the infoormation of
our genes(characterstics) of our ancestors.These genetic codes are further translated into proteins
by living cells in our body.
Three coding systems in which DNA affects phenotype are:
(i)Mutation-The mutations are called mismatches beacause they are positions ehere the
nucleotide that is inserted into the daughter polynucleotide doesnot match,by base pairing the
nucleotide at corresponding position in the template DNA.If in case the mismatch is obtained
inside the daughter double helix then one of its granddaughter molecules produced during the
next round of DNAreplication will carry a permanent double stranded version of the mutation.
(ii)DNA repair-Every day the genomes face a lot of damages and that doubles when the errors
that occur when the genome replicates.These damages need efficient repair systems.The repair
systems will help the genome to maintain its essentiality of the cellular functions.
(iii)Recombination-The genomes will undergo very slight changes,if the recombination wont
occur.The several accumulation of mutations over a span of time will result in small scale
alteration in the nucleotide sequence of the genome.But the role of recombination is to
restructing the genome.
2.A period or process by which a living organism goes through different variations since ages is
called as evolution.
Production is something that is produced to come in use of human.The nature starts producing its
raw material into an useful way so that humans can sustain in the present environment.
Redistribution occurs in evolution to achieve greater social equality.This particular thing helps in
distributing all types of genes in order to sustain in the environment.
THe four mechanisms are:
(i)Natural selection-Natural selection is the idea that only the strong can survive.This means that
each generation must pass on anew trait.For example giraff having longer neck. This makes it
easier to reach to the leaves on the top of the tree.But before,the ancestors of giraff were not
having longer necks.
(ii)Geographical isolation-ex-storm,The isolation of the species may led to adapting to different
environments due to geographical isolation.
(iii)Mutation-In whicch a particular phenotype is favour.
This document provides an overview of Chapter 31 from the textbook Biology. It discusses the key characteristics and traits of fungi, including their heterotrophic nutrition, absorption of nutrients, diverse lifestyles as decomposers, parasites or mutualists, and multicellular filamentous or unicellular yeast body structures. The chapter also examines fungal life cycles of sexual and asexual reproduction through spores, the evolutionary origin of fungi as unicellular aquatic organisms, and the radiation of major fungal lineages including chytrids, zygomycetes, glomeromycetes, ascomycetes, and basidiomycetes.
Discovering the 100 Trillion Bacteria Living Within Each of UsLarry Smarr
This document provides a summary of a lecture on the human microbiome given by Dr. Larry Smarr. Some key points:
- The human microbiome refers to the trillions of bacteria that live within the human body. Each person contains 100 trillion bacteria, outnumbering human cells.
- Research into the microbiome is a rapidly growing field that provides insights into health and disease. The microbiome plays a role in processes like drug metabolism and immunity.
- The microbiome is established early in life and influenced by factors like birth method and antibiotic use in the first years. This early development can impact future health.
- Microbiome composition and function can change with health status, diet, medications and other
Discovering the 100 Trillion Bacteria Living Within Each of UsLarry Smarr
This document provides a summary of a lecture on the human microbiome given by Dr. Larry Smarr. Some key points:
- The human microbiome refers to the trillions of bacteria that live within the human body. Each person contains 100 trillion bacteria, outnumbering human cells.
- Research into the microbiome is a rapidly growing field that provides insights into health and disease. The microbiome plays a role in processes like drug metabolism and immunity.
- The microbiome is established early in life and influenced by factors like birth method and antibiotic use in the first years. This early development can impact future health.
- Microbiome imbalances are linked to diseases like inflammatory bowel disease. New treatments are
Microbiology is the study of microorganisms too small to be seen without a microscope. Microbes are found everywhere and play important roles in ecosystems and human bodies. While most microbes are harmless or beneficial, some can cause disease. Key figures like van Leeuwenhoek first observed microbes, Pasteur disproved spontaneous generation and established germ theory, Koch linked specific microbes to diseases, Jenner developed the first vaccine, Fleming discovered penicillin, and advances now help detect, treat, and prevent infectious diseases.
Space Microbiology: Modern Research and Advantages for Human Colonization on ...AnuragSingh1049
Astromicrobiology or exomicrobiology, is the study of microorganisms in outer space. Microorganisms in outer space are most wide spread form of life on Earth, and are capable of colonising any environment, this article usually focus on microbial life in the field of astrobiology. Microorganisms exhibit high adaptability to extreme environments of outer space via phenotypic and genetic changes. These changes may affect astronauts in the space environment as well as on earth because mutant microbes will inevitably return with the spacecraft. In this article, the advantages and disadvantages of microbes in outer space are discussed. We all know that outer space is extreme and very complex environment, microorganisms readily adapt to changes in environmental variables, such as weightlessness, cosmic radiation, temperature, pressure and nutrient levels, and these microorganisms exhibit a variety of morphological and physiological changes. Space conditions may significantly increase the mutation frequency of certain genes in microorganisms, which could allow the cultivation of the bacterial mutants, followed by screening of the bacteria for large scale production. Also we can extract microbial secondary metabolites as medicine, flavouring and nutritional drugs. This article provides the planetary exploration and also provides the microbial observatory program on ISS. The aim of this article will also help us to determine the benefits of bacteria and other microorganisms in case of “Human colonization on Mars”.
Microbiology is the study of microscopic organisms. The document provides an overview of the topics covered in microbiology including the scope, importance, characteristics, and history of microorganisms. It discusses the early discoveries of microbes through microscopes in the 1600s and 1700s. It also summarizes the theories of spontaneous generation and biogenesis, and how experiments by Pasteur and Koch helped prove that microbes cause disease rather than spontaneous generation.
This document provides an overview of prokaryotes and viruses from the Apologia Biology course. It begins with a brief history of microscopy and the development of modern taxonomy. It then discusses the domains of life, focusing on archaea and bacteria. Various shapes, habitats, and modes of feeding are described for bacteria. The document also discusses classification, with keys being used to identify organisms. It concludes with information about protists and different types of algae.
Microbiomes in Agriculture, Food, Health and the EnvironmentJonathan Eisen
The document outlines an agenda for a meeting on microbiomes in agriculture, food, health and the environment. The meeting will include four panels discussing the impacts of human and animal microbiomes on food and health, the impacts of microbiomes on plants and agriculture, and the impacts of microbiomes on the environment. It also includes background information on microbiomes and their importance in various contexts.
BIS2C. Biodiversity and the Tree of Life. 2014. L14. FungiJonathan Eisen
The document is a set of lecture slides about fungi. It discusses several types of fungi, including microsporidia, chytrids, zygospore fungi, glomeromycota, and dikarya. It provides details on the characteristics, life cycles, and evolution of these groups. It specifically examines the life cycles of sac fungi, noting they have a haploid stage, form a dikaryotic mycelium through plasmogamy, undergo karyogamy and meiosis within ascocarps to produce haploid ascospores.
BIS2C. Biodiversity and the Tree of Life. 2014. L12. Symbioses and the Human ...Jonathan Eisen
This document contains lecture slides about symbiosis and the human microbiome. It discusses the evolution of the human microbiome and how history is important for understanding ecosystems. It also summarizes some of the key functions of the microbiome, including digestion, immune system management, and vitamin production. Finally, it outlines different types of symbiotic relationships and provides examples of pathogenic bacteria and eukaryotes.
The document provides an overview of the human gut microbiome and its role in immunity. It discusses how the gut microbiome interacts with and helps train the immune system. The gut microbiome contains nearly 10 times more microbial cells than human cells, and plays important roles like producing compounds that regulate immune cells and preventing pathogenic organisms from taking hold. The document also summarizes methods that scientists use to study the human microbiome, such as 16S rRNA sequencing and multi-omic computational analyses.
The document discusses major geological drivers of evolution including tectonic plate movement, vulcanism, climate change, and meteorite impacts. Tectonic plate movement has caused continental drift and formation of supercontinents like Pangaea, affecting species distributions. Vulcanism causes both local and global climate changes through emission of gases and particles and formation of new land barriers and islands. Climate changes over geological timescales have also impacted evolution. Meteorite impacts have precipitated mass extinctions. These geological forces alter Earth's conditions and drive evolution through large-scale migrations, speciation events, mass extinctions, and adaptive radiations.
[Bio1] ch 1 evolution the themes of biology and scientific inquiryRandomDude4
1. The document summarizes key themes and theories of biology presented in a lecture, including the diversity of life, cellular organization, properties of life, energy flow, homeostasis, classification, levels of organization, and major theories of biology such as cell theory, gene theory, heredity, and evolution.
2. It discusses how evolution occurs through natural selection, where traits that increase survival and reproduction are passed on, using examples like peppered moths adapting to environmental changes.
3. The gene theory holds that DNA contains genes which code for traits and are passed from parents to offspring, though gene expression can produce different tissues.
Fungal Biotechnology Chapt The course material for fungal bitotechnolog cour...tadilodessie614
Fungal biotechnology refers to the utilization of fungi for industrial, agricultural, pharmaceutical, and environmental applications. It involves harnessing the metabolic capabilities of fungi to produce valuable products, enzymes, bioactive compounds, and to perform tasks like bioremediation and biocontrol. Fungi are diverse eukaryotic organisms that include yeasts, molds, and mushrooms. They are found in various environments where they play important roles in nutrient cycling and decomposition. Fungi have several characteristics including obtaining nutrients from dead or living organic matter through absorption, growing as multicellular mycelium, and reproducing both sexually and asexually.
A renewed need for a genomic field guide to microbesJonathan Eisen
This document discusses the need for a genomic field guide to microbes. It outlines several challenges to creating such a guide, including the small size and diversity of microbes, as well as difficulties observing and collecting data on them in natural environments. Potential solutions proposed include advances in DNA sequencing technologies that have enabled large-scale cataloging and identification of microbes. Components suggested for inclusion in a field guide are phylogenetic catalogs, functional profiles, biogeography data, identification methods, and information on applications like pathogen detection. Citizen science initiatives are also presented as a way to engage the public in microbiology. The talk concludes by advocating the creation of a comprehensive genomic field guide to microbes.
First year SBC174 Evolution course - week 2
1. NeoDarwinism/ModernSynthesis
2. Major transitions in Evolution
3. Geological Timescales
4. Some drivers of evolution
The word MICROBIOLOGY describes exactly what the discipline is: the study of small living things. MICRO = small, BIO = living, and LOGY = to study. Microbiology (or specifically, bacteriology) is still a very young science and not yet completely understood.
Similar to Nonhumans: Don't Neglect Their Microbiomes (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
Talk by Jonathan Eisen for LAMG2022 meetingJonathan Eisen
The document discusses the history of the Lake Arrowhead Microbial Genomes (LAMG) conference. It reveals that LAMG2020 was cancelled due to a secret plan by organizers who formed an "anti-karyote society" that hates eukaryotes. The meeting was to be renamed the "Big, Large, Enormous" meeting of the Lake Arrowhead Big Large Enormous Anti-Karyote Society. The document also hints that several past LAMG speakers have made cryptic comments indicating involvement in a conspiracy surrounding the conference.
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
Phylogenetic and Phylogenomic Approaches to the Study of Microbes and Microbi...Jonathan Eisen
The document discusses Jonathan Eisen's work as a microbiology professor at UC Davis. It provides an overview of his research topics, which include microbial phylogenomics and evolvability, phylogenetic methods and tools, and using phylogenomics to study microbial communities and interactions between microbes and hosts under stress. The document also acknowledges collaborators and funding sources for Eisen's research over the years.
This document summarizes a class on detecting, quantifying, and tracking variations of SARS-CoV-2 RNA from COVID-19 samples. It discusses using quantitative RT-PCR (qRT-PCR) to detect and measure viral RNA levels in samples. Sequencing is used to identify variations in the viral genome over time, and online tools like Nextstrain allow viewing the evolution and global transmission of variants. Genotyping assays are also described that can rapidly screen samples for known single nucleotide variations during PCR.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
EVE198 Winter2020 Class 8 - COVID RNA DetectionJonathan Eisen
This document summarizes a class on SARS-CoV-2 RNA detection, quantification, and variation. It discusses how qRT-PCR is used to detect and quantify the virus by amplifying and detecting viral RNA. It also covers sequencing to identify variants, how variants evolve over time, and genotyping assays that can screen samples for known single nucleotide variations. Nextstrain and other online tools are presented that use sequencing data to analyze viral phylogenies, track variant distributions globally, and visualize genetic variations across the SARS-CoV-2 genome.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
EVE198 Winter2020 Class 5 - COVID VaccinesJonathan Eisen
The document discusses a class on COVID-19 vaccines. It covers topics like vaccine development, current candidates, delivery challenges, and comparisons between vaccines. Moderna and Pfizer mRNA vaccines are highlighted as being similar but having some differences in mRNA region, nanoparticle structure/synthesis, dosage amount, and storage temperature requirements. Other vaccines discussed include Novavax using spike protein nanoparticles, and AstraZeneca and Johnson & Johnson using DNA for spike protein delivered by a modified virus.
EVE198 Winter2020 Class 9 - COVID TransmissionJonathan Eisen
This document discusses modes of SARS-CoV-2 transmission including droplets, aerosols, and surfaces. It emphasizes that surfaces are not as big a risk as initially thought. It provides guidance on limiting transmission from different modes such as distancing, masks, washing hands, cleaning surfaces, and improving ventilation. The focus in 2021 is on droplets and aerosols rather than surfaces.
EVE198 Fall2020 "Covid Mass Testing" Class 8 VaccinesJonathan Eisen
This document discusses a class on vaccines for COVID-19. It covers topics like vaccine development, current candidate vaccines, challenges with vaccine distribution, and how vaccines are being assessed for safety, effectiveness, costs and production feasibility. Over 100 vaccine candidates are in development using platforms like DNA, RNA, viral vectors and inactivated viruses. Efforts like Operation Warp Speed are coordinating development of nucleic acid, viral vector and protein subunit vaccines. Distribution challenges include vaccine production, storage and logistics, number of doses required, and overcoming vaccine nationalism and hesitancy.
EVE198 Fall2020 "Covid Mass Testing" Class 2: Viruses, COIVD and TestingJonathan Eisen
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
EVE198 Fall2020 "Covid Mass Testing" Class 1 IntroductionJonathan Eisen
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Signatures of wave erosion in Titan’s coastsSérgio Sacani
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
1. Slides by Jonathan Eisen for BIS2C at UC Davis Spring 2014 1
Don’t Neglect Their Microbiomes
Jonathan A. Eisen
@phylogenomics
November 17, 2014
Talk for Nonhumans Meeting
38. • Animals are covered in a cloud of microbes
!26
The Rise of the Microbiome
39. • Animals are covered in a cloud of microbes
• This “microbiome” likely is involved in
many important animal phenotypes
!27
The Rise of the Microbiome
40. • Animals are covered in a cloud of microbes
• This “microbiome” LIKELY is involved in
many important animal phenotypes
!28
The Rise of the Microbiome
41. • Animals are covered in a cloud of microbes
• This “microbiome” LIKELY is INVOLVED in
many important animal phenotypes
!29
The Rise of the Microbiome
70. Model Animal Microbiomes
!4141
Both natural surveys and laboratory experiments indicate that
host diet plays a major role in shaping the Drosophila bacterial
microbiome.
Laboratory strains provide only a limited model of natural host–
microbe interactions
83. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
84. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
85. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
86. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
87. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
88. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
89. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
90. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
91. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
Eukaryotes
92. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria
93. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????
94. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????Archaebacteria
95. Woese: Classification of Cultured Taxa by rRNA
!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????ArchaebacteriaArchaea
96. Culture Independent rRNA PCR: One Taxon
• v
DNA
ACTGC
ACCTAT
CGTTCG
ACTGC
ACCTAT
CGTTCG
ACTGC
ACCTAT
CGTTCG
Taxa Characters
B1 ACTGCACCTATCGTTCG
B2 ACTCCACCTATCGTTCG
E1 ACTCCAGCTATCGATCG
E2 ACTCCAGGTATCGATCG
A1 ACCCCAGCTCTCGCTCG
A2 ACCCCAGCTCTGGCTCG
New1 ACTGCACCTATCGTTCG
EukaryotesBacteria Archaea
!48
Many
sequences
from one
sample all
point to the
same branch
on the tree
103. Culture Independent “Metagenomics”
DNA DNADNA
!53
Taxa Characters
B1 ACTGCACCTATCGTTCG
B2 ACTCCACCTATCGTTCG
E1 ACTCCAGCTATCGATCG
E2 ACTCCAGGTATCGATCG
A1 ACCCCAGCTCTCGCTCG
A2 ACCCCAGCTCTGGCTCG
New1 ACCCCAGCTCTGCCTCG
New2 AGGGGAGCTCTGCCTCG
New3 ACTCCAGCTATCGATCG
New4 ACTGCACCTATCGTTCG
RecA RecARecA
http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7
Genome Biology 2008, 9:R151
sequences are not conserved at the nucleotide level [29]. As a
result, the nr database does not actually contain many more
protein marker sequences that can be used as references than
those available from complete genome sequences.
Comparison of phylogeny-based and similarity-based phylotyping
Although our phylogeny-based phylotyping is fully auto-
mated, it still requires many more steps than, and is slower
than, similarity based phylotyping methods such as a
MEGAN [30]. Is it worth the trouble? Similarity based phylo-
typing works by searching a query sequence against a refer-
ence database such as NCBI nr and deriving taxonomic
information from the best matches or 'hits'. When species
that are closely related to the query sequence exist in the ref-
erence database, similarity-based phylotyping can work well.
However, if the reference database is a biased sample or if it
contains no closely related species to the query, then the top
hits returned could be misleading [31]. Furthermore, similar-
ity-based methods require an arbitrary similarity cut-off
value to define the top hits. Because individual bacterial
genomes and proteins can evolve at very different rates, a uni-
versal cut-off that works under all conditions does not exist.
As a result, the final results can be very subjective.
In contrast, our tree-based bracketing algorithm places the
query sequence within the context of a phylogenetic tree and
only assigns it to a taxonomic level if that level has adequate
sampling (see Materials and methods [below] for details of
the algorithm). With the well sampled species Prochlorococ-
cus marinus, for example, our method can distinguish closely
related organisms and make taxonomic identifications at the
species level. Our reanalysis of the Sargasso Sea data placed
672 sequences (3.6% of the total) within a P. marinus clade.
On the other hand, for sparsely sampled clades such as
Aquifex, assignments will be made only at the phylum level.
Thus, our phylogeny-based analysis is less susceptible to data
sampling bias than a similarity based approach, and it makes
Major phylotypes identified in Sargasso Sea metagenomic dataFigure 3
Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using
AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The
breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file 5.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Alphaproteobacteria
Betaproteobacteria
G
am
m
aproteobacteria
D
eltaproteobacteria
Epsilonproteobacteria
U
nclassified
proteobacteria
Bacteroidetes
C
hlam
ydiae
C
yanobacteria
Acidobacteria
Therm
otogae
Fusobacteria
ActinobacteriaAquificae
Planctom
ycetes
Spirochaetes
Firm
icutes
C
hloroflexiC
hlorobi
U
nclassified
bacteria
dnaG
frr
infC
nusA
pgk
pyrG
rplA
rplB
rplC
rplD
rplE
rplF
rplK
rplL
rplM
rplN
rplP
rplS
rplT
rpmA
rpoB
rpsB
rpsC
rpsE
rpsI
rpsJ
rpsK
rpsM
rpsS
smpB
tsf
Relativeabundance
RpoB RpoBRpoB
Rpl4 Rpl4Rpl4 rRNA rRNArRNA
Hsp70 Hsp70Hsp70
EFTu EFTuEFTu
Many other genes
better than rRNA
108. Biogeography
!57
a broader range of Proteobacteria, but yielded similar results
(Fig. S1 and Tables S2 and S3).
Across all samples, we identified 4,931 quality Nitrosomadales
sequences, which grouped into 176 OTUs (operational taxo-
nomic units) using an arbitrary 99% sequence similarity cutoff.
This cutoff retained a high amount of sequence diversity, but
minimized the chance of including diversity because of se-
quencing or PCR errors. Most (95%) of the sequences appear
closely related either to the marine Nitrosospira-like clade,
known to be abundant in estuarine sediments (e.g., ref. 19) or to
marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2).
Pairwise community similarity between the samples was calcu-
lated based on the presence or absence of each OTU using
a rarefied Sørensen’s index (4). Community similarity using this
incidence index was highly correlated with the abundance-based
Sørensen index (Mantel test: ρ = 0.9239; P = 0.0001) (21).
A plot of community similarity versus geographic distance for
each pairwise set of samples revealed that the Nitrosomonadales
display a significant, negative distance-decay curve (slope = −0.08,
P < 0.0001) (Fig. 2). Furthermore, the slope of this curve varied
significantly among the three spatial scales. The distance-decay
slope within marshes was significantly shallower than the overall
slope (slope = −0.04; P < 0.0334) and steeper across marshes within
a region than the overall slope (slope = −0.27, P < 0.0007) (Fig. 2).
In contrast, at the continental scale, the distance-decay curve did
not differ from zero (P = 0.0953). Thus, there is no evidence that
somonadales community similarity. Geographic distance con-
tributed the largest partial regression coefficient (b = 0.40,
P < 0.0001), with sediment moisture, nitrate concentration, plant
cover, salinity, and air and water temperature contributing to
smaller, but significant, partial regression coefficients (b = 0.09–
0.17, P < 0.05) (Table 1). Because salt marsh bacteria may be
dispersing through ocean currents, we also used a global ocean
circulation model (23), as applied previously (24), to estimate
relative dispersal times of hypothetical microbial cells between
each sampling location. Dispersal times between sampling points
did not explain more variability in bacterial community similarity
(ln dispersal time: b = 0.06, P = −0.0799; with dispersal R2
= 0.47
vs. without 0.46). Therefore, in the remaining analyses we use
geographic distance rather than dispersal time.
As hypothesized, the relative importance of environmental
factors versus geographic distance to Nitrosomadales community
similarity differed across the three spatial scales. Contrary to our
expectations, however, geographic distance had a strong effect
Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com-
pared with one another within regions are circled. (Inset) The arrangement
of sampling points within marshes. Six points were sampled along a 100-m
transect, and a seventh point was sampled ∼1 km away. Two marshes in the
Northeast United States (outlined stars) were sampled more intensively,
along four 100-m transects in a grid pattern.
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.
ECOLOGY
ults
ales
xo-
off.
but
se-
om-
ent
0-m
the
ely,
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.
109. Biogeography
!57
a broader range of Proteobacteria, but yielded similar results
(Fig. S1 and Tables S2 and S3).
Across all samples, we identified 4,931 quality Nitrosomadales
sequences, which grouped into 176 OTUs (operational taxo-
nomic units) using an arbitrary 99% sequence similarity cutoff.
This cutoff retained a high amount of sequence diversity, but
minimized the chance of including diversity because of se-
quencing or PCR errors. Most (95%) of the sequences appear
closely related either to the marine Nitrosospira-like clade,
known to be abundant in estuarine sediments (e.g., ref. 19) or to
marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2).
Pairwise community similarity between the samples was calcu-
lated based on the presence or absence of each OTU using
a rarefied Sørensen’s index (4). Community similarity using this
incidence index was highly correlated with the abundance-based
Sørensen index (Mantel test: ρ = 0.9239; P = 0.0001) (21).
A plot of community similarity versus geographic distance for
each pairwise set of samples revealed that the Nitrosomonadales
display a significant, negative distance-decay curve (slope = −0.08,
P < 0.0001) (Fig. 2). Furthermore, the slope of this curve varied
significantly among the three spatial scales. The distance-decay
slope within marshes was significantly shallower than the overall
slope (slope = −0.04; P < 0.0334) and steeper across marshes within
a region than the overall slope (slope = −0.27, P < 0.0007) (Fig. 2).
In contrast, at the continental scale, the distance-decay curve did
not differ from zero (P = 0.0953). Thus, there is no evidence that
somonadales community similarity. Geographic distance con-
tributed the largest partial regression coefficient (b = 0.40,
P < 0.0001), with sediment moisture, nitrate concentration, plant
cover, salinity, and air and water temperature contributing to
smaller, but significant, partial regression coefficients (b = 0.09–
0.17, P < 0.05) (Table 1). Because salt marsh bacteria may be
dispersing through ocean currents, we also used a global ocean
circulation model (23), as applied previously (24), to estimate
relative dispersal times of hypothetical microbial cells between
each sampling location. Dispersal times between sampling points
did not explain more variability in bacterial community similarity
(ln dispersal time: b = 0.06, P = −0.0799; with dispersal R2
= 0.47
vs. without 0.46). Therefore, in the remaining analyses we use
geographic distance rather than dispersal time.
As hypothesized, the relative importance of environmental
factors versus geographic distance to Nitrosomadales community
similarity differed across the three spatial scales. Contrary to our
expectations, however, geographic distance had a strong effect
Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com-
pared with one another within regions are circled. (Inset) The arrangement
of sampling points within marshes. Six points were sampled along a 100-m
transect, and a seventh point was sampled ∼1 km away. Two marshes in the
Northeast United States (outlined stars) were sampled more intensively,
along four 100-m transects in a grid pattern.
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.
ECOLOGY
ults
ales
xo-
off.
but
se-
om-
ent
0-m
the
ely,
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.
110. !58Huttenhower et al. 2012.
Population Variability
!58Morgan et al. Genome Biology 2012, 13:R79
MJ Blaser et al. ISMEJ 2012
US Amerindian
Actinobacteria
(Propionibacteria)
Firmicutes
(Staphylococcus)
Relativeabundance
Actinobacteria dominates in the US
Boulder NY Platanillal A Platanillal B
Proteobacteria
Between Countries
Age
Vaginal Microbiome
Corn
at
Different
Locations
Individuals
124. Captivity and Conservation
!61
Research article
Captivity results in disparate loss of gut microbial
diversity in closely related hosts
Kevin D. Kohl1*, Michele M. Skopec2 and M. Denise Dearing1
2
*Corresponding author: +
The gastrointestinal tracts of animals contain diverse communities of microbes that provide a number of services to their
hosts. There is recent concern that these communities may be lost as animals enter captive breeding programmes, due to
changes in diet and/or exposure to environmental sources. However, empirical evidence documenting the effects of captivity
and captive birth on gut communities is lacking. We conducted three studies to advance our knowledge in this area. First, we
compared changes in microbial diversity of the gut communities of two species of woodrats (Neotoma albigula, a dietary gen-
eralist, and Neotoma stephensi, which specializes on juniper) before and after 6–9 months in captivity. Second, we investi-
gated whether reintroduction of the natural diet of N. stephensi could restore microbial diversity. Third, we compared the
microbial communities between offspring born in captivity and their mothers. We found that the dietary specialist, N. ste-
phensi, lost a greater proportion of its native gut microbiota and overall diversity in response to captivity compared with N.
albigula. Addition of the natural diet increased the proportion of the original microbiota but did not restore overall diversity
in N. stephensi. Offspring of N. albigula more closely resembled their mothers compared with offspring–mother pairs of
N. stephensi.Thisresearchsuggeststhatthemicrobiotaofdietaryspecialistsmaybemoresusceptibletocaptivity.Furthermore,
this work highlights the need for further studies investigating the mechanisms underlying how loss of microbial diversity may
vary between hosts and what an acceptable level of diversity loss may be to a host.This knowledge will aid conservation biolo-
gists in designing captive breeding programmes effective at maintaining microbial diversity.
Sequence Accession Numbers: NCBI’s Sequence Read Archive (SRA) – SRP033616
Key words: Neotoma
Editor:
Cite as: Conserv
Physiol
Introduction
The gut microbial communities of animals are hyperdiverse
and influence many aspects of their physiology, such as nutri-
tion, immune development and even behaviour (Amato,
2013). The preservation of the microbial diversity present in
resulting in microbial communities that are more susceptible
to invasion or by altering host immune function (Blaser and
Falkow, 2009). Additionally, gut microbes serve as sources of
novel gene products, such as enzymes for biomass degradation
(Hess et al., 2011) or bioremediation (Verma et al., 2006).
byguestonNovember16,2014http://conphys.oxfordjournals.org/Downloadedfrom
Zoos and Shelters
132. History Important Too
Genera that cross the divide. Another way to visualize
the vertebrate gut–environment dichotomy is by using a
network diagram that displays, in addition to the clus-
tering of hosts with similar microbiotas, the bacterial
genera they share. In this representation of the data, the
vertebrate gut samples are more connected to one another
than to the environmental samples (FIG. 4a,b). As in the
UniFrac-based analysis, the non-gut human samples also
occupy an intermediate position between the free-living
andthegutcommunities. FIGURE 5 showsthephylogenetic
classification of operational taxonomic units (OTUs) that
are shared between samples: among humans, an over-
whelming number of these are from the Firmicutes, with
a smaller number from the Bacteroidetes. By contrast, the
free-living communities share OTUs from a wider range
of phyla. Samples from the guts of obese humans cluster
away from the samples of healthy subjects, and most of
theirsharedOTUsarefoundintheFirmicutes.Thisobser-
vation is consistent with the finding that samples from
obese individuals have a higher number of OTUs
family of the gammaproteobacteria class. This fam-
ily contained OTUs from both the vertebrate gut and
free-living communities in saline and non-saline
habitats. Members of the Enterobacteriales order (also
from the gammaproteobacteria) were detected in the
vertebrate gut, termite gut and other invertebrates, as
well as in a surface soil sample and anoxic saline water.
Staphylococcaceae family members (from the phylum
Firmicutes and class Bacilli) were common in the ver-
tebrate gut samples, but were also detected in soil and
cultures derived from freshwater and saline habitats.
Finally, members of the Fusobacterium genus were
detected in salt-water sediments, in addition to the
vertebrate gut. The cosmopolitan distribution of these
organisms might have made them particularly impor-
tant for introducing novel functions during evolution of
the gut microbiota, as they could bring new useful genes
from the global microbiome into the gut microbiome
through horizontal gene transfer. However, it should
be noted that some OTUs that are common in humans
Nature Reviews | Microbiology
16SribosomalRNAsequences(%)
0
20
40
60
80
100
Bacteroidetes (red)
Firmicutes (blue)
Vertebrate gut
Termite gut
Salt-water surface
Salt water
Subsurface, anoxic or sediment
Other human
Non-saline cultured
Insects or earthworms
Soils or freshwater sediments
Mixed water
Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample
thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown.
ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody
habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)).
SS
Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
133. History Important Too
Genera that cross the divide. Another way to visualize
the vertebrate gut–environment dichotomy is by using a
network diagram that displays, in addition to the clus-
tering of hosts with similar microbiotas, the bacterial
genera they share. In this representation of the data, the
vertebrate gut samples are more connected to one another
than to the environmental samples (FIG. 4a,b). As in the
UniFrac-based analysis, the non-gut human samples also
occupy an intermediate position between the free-living
andthegutcommunities. FIGURE 5 showsthephylogenetic
classification of operational taxonomic units (OTUs) that
are shared between samples: among humans, an over-
whelming number of these are from the Firmicutes, with
a smaller number from the Bacteroidetes. By contrast, the
free-living communities share OTUs from a wider range
of phyla. Samples from the guts of obese humans cluster
away from the samples of healthy subjects, and most of
theirsharedOTUsarefoundintheFirmicutes.Thisobser-
vation is consistent with the finding that samples from
obese individuals have a higher number of OTUs
family of the gammaproteobacteria class. This fam-
ily contained OTUs from both the vertebrate gut and
free-living communities in saline and non-saline
habitats. Members of the Enterobacteriales order (also
from the gammaproteobacteria) were detected in the
vertebrate gut, termite gut and other invertebrates, as
well as in a surface soil sample and anoxic saline water.
Staphylococcaceae family members (from the phylum
Firmicutes and class Bacilli) were common in the ver-
tebrate gut samples, but were also detected in soil and
cultures derived from freshwater and saline habitats.
Finally, members of the Fusobacterium genus were
detected in salt-water sediments, in addition to the
vertebrate gut. The cosmopolitan distribution of these
organisms might have made them particularly impor-
tant for introducing novel functions during evolution of
the gut microbiota, as they could bring new useful genes
from the global microbiome into the gut microbiome
through horizontal gene transfer. However, it should
be noted that some OTUs that are common in humans
Nature Reviews | Microbiology
16SribosomalRNAsequences(%)
0
20
40
60
80
100
Bacteroidetes (red)
Firmicutes (blue)
Vertebrate gut
Termite gut
Salt-water surface
Salt water
Subsurface, anoxic or sediment
Other human
Non-saline cultured
Insects or earthworms
Soils or freshwater sediments
Mixed water
Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample
thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown.
ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody
habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)).
SS
Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
134. History Important Too
Genera that cross the divide. Another way to visualize
the vertebrate gut–environment dichotomy is by using a
network diagram that displays, in addition to the clus-
tering of hosts with similar microbiotas, the bacterial
genera they share. In this representation of the data, the
vertebrate gut samples are more connected to one another
than to the environmental samples (FIG. 4a,b). As in the
UniFrac-based analysis, the non-gut human samples also
occupy an intermediate position between the free-living
andthegutcommunities. FIGURE 5 showsthephylogenetic
classification of operational taxonomic units (OTUs) that
are shared between samples: among humans, an over-
whelming number of these are from the Firmicutes, with
a smaller number from the Bacteroidetes. By contrast, the
free-living communities share OTUs from a wider range
of phyla. Samples from the guts of obese humans cluster
away from the samples of healthy subjects, and most of
theirsharedOTUsarefoundintheFirmicutes.Thisobser-
vation is consistent with the finding that samples from
obese individuals have a higher number of OTUs
family of the gammaproteobacteria class. This fam-
ily contained OTUs from both the vertebrate gut and
free-living communities in saline and non-saline
habitats. Members of the Enterobacteriales order (also
from the gammaproteobacteria) were detected in the
vertebrate gut, termite gut and other invertebrates, as
well as in a surface soil sample and anoxic saline water.
Staphylococcaceae family members (from the phylum
Firmicutes and class Bacilli) were common in the ver-
tebrate gut samples, but were also detected in soil and
cultures derived from freshwater and saline habitats.
Finally, members of the Fusobacterium genus were
detected in salt-water sediments, in addition to the
vertebrate gut. The cosmopolitan distribution of these
organisms might have made them particularly impor-
tant for introducing novel functions during evolution of
the gut microbiota, as they could bring new useful genes
from the global microbiome into the gut microbiome
through horizontal gene transfer. However, it should
be noted that some OTUs that are common in humans
Nature Reviews | Microbiology
16SribosomalRNAsequences(%)
0
20
40
60
80
100
Bacteroidetes (red)
Firmicutes (blue)
Vertebrate gut
Termite gut
Salt-water surface
Salt water
Subsurface, anoxic or sediment
Other human
Non-saline cultured
Insects or earthworms
Soils or freshwater sediments
Mixed water
Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample
thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown.
ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody
habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)).
SS
Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
135. History Important Too
Genera that cross the divide. Another way to visualize
the vertebrate gut–environment dichotomy is by using a
network diagram that displays, in addition to the clus-
tering of hosts with similar microbiotas, the bacterial
genera they share. In this representation of the data, the
vertebrate gut samples are more connected to one another
than to the environmental samples (FIG. 4a,b). As in the
UniFrac-based analysis, the non-gut human samples also
occupy an intermediate position between the free-living
andthegutcommunities. FIGURE 5 showsthephylogenetic
classification of operational taxonomic units (OTUs) that
are shared between samples: among humans, an over-
whelming number of these are from the Firmicutes, with
a smaller number from the Bacteroidetes. By contrast, the
free-living communities share OTUs from a wider range
of phyla. Samples from the guts of obese humans cluster
away from the samples of healthy subjects, and most of
theirsharedOTUsarefoundintheFirmicutes.Thisobser-
vation is consistent with the finding that samples from
obese individuals have a higher number of OTUs
family of the gammaproteobacteria class. This fam-
ily contained OTUs from both the vertebrate gut and
free-living communities in saline and non-saline
habitats. Members of the Enterobacteriales order (also
from the gammaproteobacteria) were detected in the
vertebrate gut, termite gut and other invertebrates, as
well as in a surface soil sample and anoxic saline water.
Staphylococcaceae family members (from the phylum
Firmicutes and class Bacilli) were common in the ver-
tebrate gut samples, but were also detected in soil and
cultures derived from freshwater and saline habitats.
Finally, members of the Fusobacterium genus were
detected in salt-water sediments, in addition to the
vertebrate gut. The cosmopolitan distribution of these
organisms might have made them particularly impor-
tant for introducing novel functions during evolution of
the gut microbiota, as they could bring new useful genes
from the global microbiome into the gut microbiome
through horizontal gene transfer. However, it should
be noted that some OTUs that are common in humans
Nature Reviews | Microbiology
16SribosomalRNAsequences(%)
0
20
40
60
80
100
Bacteroidetes (red)
Firmicutes (blue)
Vertebrate gut
Termite gut
Salt-water surface
Salt water
Subsurface, anoxic or sediment
Other human
Non-saline cultured
Insects or earthworms
Soils or freshwater sediments
Mixed water
Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample
thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown.
ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody
habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)).
SS
Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
136. Example: Behavior
!65
PERSPECTIVES
H
uman bodies house trillions of sym-
biotic microorganisms. The genes
in this human microbiome outnum-
ber human genes by 100 to 1, and their study
is providing profound insights into human
health. But humans are not the only ani-
mals with microbiomes, and microbiomes
do not just impact health. Recent research is
revealing surprising roles for microbiomes
in shaping behaviors across many animal
taxa—shedding light on how behaviors from
diet to social interactions affect the compo-
sition of host-associated microbial commu-
nities (1, 2), and how microbes in turn influ-
ence host behavior in dramatic ways (2–6).
Our understanding of interactions
between host behavior and microbes stems
largely from studies of pathogens. Animal
social and mating activities have profound
effects on pathogen transmission, and many
animals use behavioral strategies to avoid
or remove pathogens (7). Pathogens can
also manipulate host behavior in overt or
covert ways. However, given the diversity of
microbes in nature, it is important to expand
the view of behavior-microbe interactions to
include nonpathogens.
For diverse animals, including iguanas,
squids, and many insects, behavior plays a
central role in the establishment and regula-
tion of microbial associations (see the first
figure). For example, the Kudzu bug (Mega-
copta cribraria), an agricultural pest, is
born without any symbionts. After birth it
acquires a specific symbiont from bacterial
capsules left by its mother. If these capsules
fit of social living in many species may be
the transmission of beneficial microbes (9).
Koch and Schmid-Hempel have shown that
in the case of bumble bees (Bombus terres-
tris), either direct contact with nest mates or
feeding on feces of nest mates was neces-
sary for establishing the normal gut micro-
biota. Bees never exposed to feces had an
altered gut microbiota and were more sus-
ceptible to the parasite Crithidia bombi (1).
Once host-microbe associations are
established, microbes can influence host
behavior in ways that have far-reaching
implications for host ecology and evolution
(see the second figure). Sharon et al. recently
found that fruit flies (Drosophila melano-
gaster) strongly prefer to mate with individ-
uals reared on the same diet on which they
were reared. Antibiotic treatment abolished
the mating preference, and inoculation of
Animal Behavior and
the Microbiome
MICROBIOLOGY
Vanessa O. Ezenwa1
, Nicole M. Gerardo2
, David W. Inouye3,4
, Mónica Medina5
, Joao B. Xavier6
Feedbacks between microbiomes and their
hosts affect a range of animal behaviors.
Gut microbiota
Behaviorsimpactmicrobiomes
Juvenile iguanas eat soil
or feces to tailor the
microbiota to their current
diet
Animals may adjust
the microbiota at
different life-history
stages
Ishikawaella
capsulata
When born, bugs feed on
capsules of symbionts; if no
capsules are present, nymphs
wander in search of microbes
Behaviors shape
symbiont acquisition
Vibrio fischeri Squids eject bioluminescent
bacteria daily
Suggests animals
can actively control
their symbiont
populations
Green iguana
(Iguana iguana)
Bobtail squid
(Euprymna scolopes)
Kudzu bug
(Megacopta cribraria)
Animal Implication
Microbial species
or consortium
Interaction with
behavior
Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors
alter microbial acquisition and maintenance.
EUNIVERSITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY
onNovember21,2012www.sciencemag.orgDownloadedfrom
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es
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id
an
or
of
nd
to
as,
s a
la-
rst
a-
is
it
fit of social living in many species may be
the transmission of beneficial microbes (9).
Koch and Schmid-Hempel have shown that
in the case of bumble bees (Bombus terres-
tris), either direct contact with nest mates or
feeding on feces of nest mates was neces-
sary for establishing the normal gut micro-
biota. Bees never exposed to feces had an
Once host-microbe associations are
established, microbes can influence host
behavior in ways that have far-reaching
implications for host ecology and evolution
(see the second figure). Sharon et al. recently
found that fruit flies (Drosophila melano-
gaster) strongly prefer to mate with individ-
uals reared on the same diet on which they
r and
avid W. Inouye3,4
, Mónica Medina5
, Joao B. Xavier6
Feedbacks between microbiomes and their
hosts affect a range of animal behaviors.
Gut microbiota
Behaviorsimpactmicrobiomes
Juvenile iguanas eat soil
or feces to tailor the
microbiota to their current
diet
Animals may adjust
the microbiota at
different life-history
stages
Ishikawaella
capsulata
When born, bugs feed on
capsules of symbionts; if no
capsules are present, nymphs
wander in search of microbes
Behaviors shape
symbiont acquisition
Vibrio fischeri Squids eject bioluminescent
bacteria daily
Suggests animals
can actively control
their symbiont
populations
Green iguana
(Iguana iguana)
Bobtail squid
(Euprymna scolopes)
Kudzu bug
(Megacopta cribraria)
Animal Implication
Microbial species
or consortium
Interaction with
behavior
Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors
alter microbial acquisition and maintenance.
SITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY
onNovember21,2012www.sciencemag.orgDownloadedfrom
Microbial effects on animal chemistry
also recently have been linked to changes
in predator-prey interactions (11) and feed-
ing behavior (12). Females of the African
malaria mosquito, Anopheles gambiae, use
chemical cues released from human skin
to locate hosts. By analyzing skin emana-
tions from 48 subjects, Verhulst et al. (12)
found that humans with higher microbial
diversity on their skin were less attractive
to these mosquitoes. High abundances of
Pseudomonas spp. and Variovorax spp.
were also associated with poor attractive-
ness to A. gambiae. These bacteria may pro-
duce chemicals that repel mosquitoes or
mask attractive volatiles emanating from
human skin. Given the importance of chem-
marine tubeworm Hydroides elegans. Bac-
terial biofilms play a key role in the settle-
ment behavior of many marine inverte-
brates, from corals to sea urchins. To study
the H. elegans system, the authors used
transposon mutagenesis to knock out a num-
ber of genes from the bacterium Pseudoal-
teromonas luteoviolacea, which is required
for larval settlement. Mutagenesis of four
genes related to cell adhesion and secretion
generated bacterial strains that altered worm
settlement behavior and metamorphosis (4).
It remains to be shown whether similar bac-
terial phenotypes drive this important life-
history transition across metazoans.
Some animal behaviors will be linked
to single microbial species, but many will
can
(5,
bac
swi
sho
acid
of t
betw
otic
mit
(5).
mo
bra
mo
beh
the
man
stan
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olo
und
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nut
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1.
2.
3.
4.
5.
6.
7.
8.
Animal
Microbiomesimpactbehaviors
Implication
Microbial species
or consortium
Interaction with
behavior
Human skin
microbiota
Skin microbes of humans
influence attraction to
mosquitoes
Differential attraction
could impact disease
spread
Lactobacillus
rhamnosus
The probiotic L. rhamnosus
decreases anxiety in mice
Suggests bacteria
can alter mood
Gut microbiota Diet-specific microbiota
influence mating
preferences
Microbes could drive
speciation
Mosquito
(Anopheles gambiae)
Mouse
(Mus musculus)
Fruit fly
(Drosophila melanogaster)
Microbiomes alter behaviors. In fruit flies (2), mosquitoes (12), and mice (5, 6), microbes alter mating,
feeding, and anxiety levels.
EASECONTROL;(MOUSE)G.SHUKLIN/WIKIMEDIACOMMONS;(FLIES)T.CHAPMAN/UNIVERSITYOFEASTANGLIA
www.sciencemag.org SCIENCE VOL 338 12 OCTOBER 2012
allowing characterization of microbiomes
beyond the few cultivable microbes (10, 13,
14). However, determining which animal
behaviors influence and are influenced by
microbial symbionts, and the mechanisms
underlying these interactions, will require
a combination of molecular and experimen-
tal approaches. For example, Huang et al.
have studied the settlement behavior in the
two.This requires manipulative experiments
and will be facilitated by studying the under-
lying mechanisms by which signals are sent
between hosts and microbes.
Recent experiments with mice, showing
that the gut microbiome can influence stress,
anxiety, and depression-related behavior via
effects on the host’s neuroendrocrine sys-
tem, provide insight into how information
Physiol. A Neuroethol. Sens. Neura
65 (1996).
Acknowledgments: This perspective
thanks to NSF meeting grant IOS 12294
Future of Research in Animal Behavior.”
A. Laughton, B. Wehrle, C. Fontaine, and
discussion and comments.
PHOTOCREDITSSECONDFIGURE:(M
10.1
Published by AAAS
137. Where You Reside / Spend Time Important
!66
ORIGINAL ARTICLE
Architectural design influences the diversity and
structure of the built environment microbiome
Steven W Kembel1
, Evan Jones1
, Jeff Kline1,2
, Dale Northcutt1,2
, Jason Stenson1,2
,
Ann M Womack1
, Brendan JM Bohannan1
, G Z Brown1,2
and Jessica L Green1,3
1
Biology and the Built Environment Center, Institute of Ecology and Evolution, Department of
Biology, University of Oregon, Eugene, OR, USA; 2
Energy Studies in Buildings Laboratory,
Department of Architecture, University of Oregon, Eugene, OR, USA and 3
Santa Fe Institute,
Santa Fe, NM, USA
Buildings are complex ecosystems that house trillions of microorganisms interacting with each
other, with humans and with their environment. Understanding the ecological and evolutionary
processes that determine the diversity and composition of the built environment microbiome—the
community of microorganisms that live indoors—is important for understanding the relationship
between building design, biodiversity and human health. In this study, we used high-throughput
sequencing of the bacterial 16S rRNA gene to quantify relationships between building attributes and
airborne bacterial communities at a health-care facility. We quantified airborne bacterial community
structure and environmental conditions in patient rooms exposed to mechanical or window
ventilation and in outdoor air. The phylogenetic diversity of airborne bacterial communities was
lower indoors than outdoors, and mechanically ventilated rooms contained less diverse microbial
communities than did window-ventilated rooms. Bacterial communities in indoor environments
contained many taxa that are absent or rare outdoors, including taxa closely related to potential
human pathogens. Building attributes, specifically the source of ventilation air, airflow rates, relative
humidity and temperature, were correlated with the diversity and composition of indoor bacterial
communities. The relative abundance of bacteria closely related to human pathogens was higher
indoors than outdoors, and higher in rooms with lower airflow rates and lower relative humidity.
The observed relationship between building design and airborne bacterial diversity suggests that
we can manage indoor environments, altering through building design and operation the community
of microbial species that potentially colonize the human microbiome during our time indoors.
The ISME Journal advance online publication, 26 January 2012; doi:10.1038/ismej.2011.211
Subject Category: microbial population and community ecology
Keywords: aeromicrobiology; bacteria; built environment microbiome; community ecology; dispersal;
environmental filtering
Introduction microbiome—includes human pathogens and com-
mensals interacting with each other and with their
The ISME Journal (2012), 1–11
& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12
www.nature.com/ismej
Microbial Biogeography of Public Restroom Surfaces
Gilberto E. Flores1
, Scott T. Bates1
, Dan Knights2
, Christian L. Lauber1
, Jesse Stombaugh3
, Rob Knight3,4
,
Noah Fierer1,5
*
1 Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, Colorado, United States of America, 2 Department of Computer Science,
University of Colorado, Boulder, Colorado, United States of America, 3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, United
States of America, 4 Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of America, 5 Department of Ecology and Evolutionary
Biology, University of Colorado, Boulder, Colorado, United States of America
Abstract
We spend the majority of our lives indoors where we are constantly exposed to bacteria residing on surfaces. However, the
diversity of these surface-associated communities is largely unknown. We explored the biogeographical patterns exhibited
by bacteria across ten surfaces within each of twelve public restrooms. Using high-throughput barcoded pyrosequencing of
the 16 S rRNA gene, we identified 19 bacterial phyla across all surfaces. Most sequences belonged to four phyla:
Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria. The communities clustered into three general categories: those
found on surfaces associated with toilets, those on the restroom floor, and those found on surfaces routinely touched with
hands. On toilet surfaces, gut-associated taxa were more prevalent, suggesting fecal contamination of these surfaces. Floor
surfaces were the most diverse of all communities and contained several taxa commonly found in soils. Skin-associated
bacteria, especially the Propionibacteriaceae, dominated surfaces routinely touched with our hands. Certain taxa were more
common in female than in male restrooms as vagina-associated Lactobacillaceae were widely distributed in female
restrooms, likely from urine contamination. Use of the SourceTracker algorithm confirmed many of our taxonomic
observations as human skin was the primary source of bacteria on restroom surfaces. Overall, these results demonstrate that
restroom surfaces host relatively diverse microbial communities dominated by human-associated bacteria with clear
linkages between communities on or in different body sites and those communities found on restroom surfaces. More
generally, this work is relevant to the public health field as we show that human-associated microbes are commonly found
on restroom surfaces suggesting that bacterial pathogens could readily be transmitted between individuals by the touching
of surfaces. Furthermore, we demonstrate that we can use high-throughput analyses of bacterial communities to determine
sources of bacteria on indoor surfaces, an approach which could be used to track pathogen transmission and test the
efficacy of hygiene practices.
Citation: Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, et al. (2011) Microbial Biogeography of Public Restroom Surfaces. PLoS ONE 6(11): e28132.
doi:10.1371/journal.pone.0028132
Editor: Mark R. Liles, Auburn University, United States of America
Received September 12, 2011; Accepted November 1, 2011; Published November 23, 2011
Copyright: ß 2011 Flores et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported with funding from the Alfred P. Sloan Foundation and their Indoor Environment program, and in part by the National
Institutes of Health and the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: noah.fierer@colorado.edu
Introduction
More than ever, individuals across the globe spend a large
portion of their lives indoors, yet relatively little is known about the
microbial diversity of indoor environments. Of the studies that
have examined microorganisms associated with indoor environ-
ments, most have relied upon cultivation-based techniques to
detect organisms residing on a variety of household surfaces [1–5].
Not surprisingly, these studies have identified surfaces in kitchens
and restrooms as being hot spots of bacterial contamination.
Because several pathogenic bacteria are known to survive on
surfaces for extended periods of time [6–8], these studies are of
obvious importance in preventing the spread of human disease.
However, it is now widely recognized that the majority of
communities and revealed a greater diversity of bacteria on
indoor surfaces than captured using cultivation-based techniques
[10–13]. Most of the organisms identified in these studies are
related to human commensals suggesting that the organisms are
not actively growing on the surfaces but rather were deposited
directly (i.e. touching) or indirectly (e.g. shedding of skin cells) by
humans. Despite these efforts, we still have an incomplete
understanding of bacterial communities associated with indoor
environments because limitations of traditional 16 S rRNA gene
cloning and sequencing techniques have made replicate sampling
and in-depth characterizations of the communities prohibitive.
With the advent of high-throughput sequencing techniques, we
can now investigate indoor microbial communities at an
unprecedented depth and begin to understand the relationship
the stall in), they were likely dispersed manually after women used
the toilet. Coupling these observations with those of the
distribution of gut-associated bacteria indicate that routine use of
toilets results in the dispersal of urine- and fecal-associated bacteria
throughout the restroom. While these results are not unexpected,
they do highlight the importance of hand-hygiene when using
public restrooms since these surfaces could also be potential
vehicles for the transmission of human pathogens. Unfortunately,
previous studies have documented that college students (who are
likely the most frequent users of the studied restrooms) are not
always the most diligent of hand-washers [42,43].
Results of SourceTracker analysis support the taxonomic
patterns highlighted above, indicating that human skin was the
primary source of bacteria on all public restroom surfaces
examined, while the human gut was an important source on or
around the toilet, and urine was an important source in women’s
restrooms (Figure 4, Table S4). Contrary to expectations (see
above), soil was not identified by the SourceTracker algorithm as
being a major source of bacteria on any of the surfaces, including
floors (Figure 4). Although the floor samples contained family-level
taxa that are common in soil, the SourceTracker algorithm
probably underestimates the relative importance of sources, like
Figure 3. Cartoon illustrations of the relative abundance of discriminating taxa on public restroom surfaces. Light blue indicates low
abundance while dark blue indicates high abundance of taxa. (A) Although skin-associated taxa (Propionibacteriaceae, Corynebacteriaceae,
Staphylococcaceae and Streptococcaceae) were abundant on all surfaces, they were relatively more abundant on surfaces routinely touched with
hands. (B) Gut-associated taxa (Clostridiales, Clostridiales group XI, Ruminococcaceae, Lachnospiraceae, Prevotellaceae and Bacteroidaceae) were most
abundant on toilet surfaces. (C) Although soil-associated taxa (Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were in low
abundance on all restroom surfaces, they were relatively more abundant on the floor of the restrooms we surveyed. Figure not drawn to scale.
doi:10.1371/journal.pone.0028132.g003
Bacteria of Public Restrooms
high diversity of floor communities is likely due to the frequency of
contact with the bottom of shoes, which would track in a diversity
of microorganisms from a variety of sources including soil, which is
known to be a highly-diverse microbial habitat [27,39]. Indeed,
bacteria commonly associated with soil (e.g. Rhodobacteraceae,
Rhizobiales, Microbacteriaceae and Nocardioidaceae) were, on average,
related differences in the relative abundances of s
some surfaces (Figure 1B, Table S2). Most notably
were clearly more abundant on certain surfaces
restrooms than male restrooms (Figure 1B). Some
family are the most common, and often most abun
found in the vagina of healthy reproductive age w
Figure 2. Relationship between bacterial communities associated with ten public restroom surfaces. Communities were
PCoA of the unweighted UniFrac distance matrix. Each point represents a single sample. Note that the floor (triangles) and toilet (as
form clusters distinct from surfaces touched with hands.
doi:10.1371/journal.pone.0028132.g002
Bacteria of P
time, the
un to take
of outside
om plants
ours after
ere shut
ortion of
e human
ck to pre-
which
26 Janu-
Journal,
hanically
had lower
y than ones with open win-
ility of fresh air translated
tions of microbes associ-
an body, and consequently,
pathogens. Although this
hat having natural airflow
Green says answering that
clinical data; she’s hoping
they move around. But to quantify those con-
tributions, Peccia’s team has had to develop
new methods to collect airborne bacteria and
extract their DNA, as the microbes are much
less abundant in air than on surfaces.
In one recent study, they used air filters
to sample airborne particles and microbes
in a classroom during 4 days during which
pant in indoor microbial
ecology research, Peccia
thinks that the field has
yet to gel. And the Sloan
Foundation’s Olsiewski
shares some of his con-
cern. “Everybody’s gen-
erating vast amounts of
data,” she says, but looking across data sets
can be difficult because groups choose dif-
ferent analytical tools. With Sloan support,
though, a data archive and integrated analyt-
ical tools are in the works.
To foster collaborations between micro-
biologists, architects, and building scientists,
the foundation also sponsored a symposium
100
80
60
40
20
0
Averagecontribution(%)
DoorinDoorout
StallinStallout
Faucethandles
SoapdispenserToiletseat
ToiletflushhandleToiletfloorSinkfloor
SOURCES
Soil
Water
Mouth
Urine
Gut
Skin
Bathroom biogeography. By
swabbing different surfaces in
public restrooms, researchers
determinedthatmicrobesvaryin
where they come from depend-
ing on the surface (chart).
February9,2012
139. !68
From Wu et al. 2009 Nature 462, 1056-1060
Challenge 1: Biological Dark Matter
140. Challenge 2: Function Prediction Difficult
Lateral Gene Transfer
Metagenomic Binning
Hypothetical
Proteins
141. Solution: Better Prediction Methods and Data
!70
Characterizing the niche-space distributions of components
Sites
North American East Coast_GS005_Embayment
North American East Coast_GS002_Coastal
North American East Coast_GS003_Coastal
North American East Coast_GS007_Coastal
North American East Coast_GS004_Coastal
North American East Coast_GS013_Coastal
North American East Coast_GS008_Coastal
North American East Coast_GS011_Estuary
North American East Coast_GS009_Coastal
Eastern Tropical Pacific_GS021_Coastal
North American East Coast_GS006_Estuary
North American East Coast_GS014_Coastal
Polynesia Archipelagos_GS051_Coral Reef Atoll
Galapagos Islands_GS036_Coastal
Galapagos Islands_GS028_Coastal
Indian Ocean_GS117a_Coastal sample
Galapagos Islands_GS031_Coastal upwelling
Galapagos Islands_GS029_Coastal
Galapagos Islands_GS030_Warm Seep
Galapagos Islands_GS035_Coastal
Sargasso Sea_GS001c_Open Ocean
Eastern Tropical Pacific_GS022_Open Ocean
Galapagos Islands_GS027_Coastal
Indian Ocean_GS149_Harbor
Indian Ocean_GS123_Open Ocean
Caribbean Sea_GS016_Coastal Sea
Indian Ocean_GS148_Fringing Reef
Indian Ocean_GS113_Open Ocean
Indian Ocean_GS112a_Open Ocean
Caribbean Sea_GS017_Open Ocean
Indian Ocean_GS121_Open Ocean
Indian Ocean_GS122a_Open Ocean
Galapagos Islands_GS034_Coastal
Caribbean Sea_GS018_Open Ocean
Indian Ocean_GS108a_Lagoon Reef
Indian Ocean_GS110a_Open Ocean
Eastern Tropical Pacific_GS023_Open Ocean
Indian Ocean_GS114_Open Ocean
Caribbean Sea_GS019_Coastal
Caribbean Sea_GS015_Coastal
Indian Ocean_GS119_Open Ocean
Galapagos Islands_GS026_Open Ocean
Polynesia Archipelagos_GS049_Coastal
Indian Ocean_GS120_Open Ocean
Polynesia Archipelagos_GS048a_Coral Reef
Component 1
Component 2
Component 3
Component 4
Component 5
0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.4 0.6 0.8 1.0
Salinity
SampleDepth
Chlorophyll
Temperature
Insolation
WaterDepth
General
High
Medium
Low
NA
High
Medium
Low
NA
Water depth
>4000m
2000!4000m
900!2000m
100!200m
20!100m
0!20m
>4000m
2000!4000m
900!2000m
100!200m
20!100m
0!20m
(a) (b) (c)
Figure 3: a) Niche-space distributions for our five components (HT
); b) the site-
similarity matrix ( ˆHT ˆH); c) environmental variables for the sites. The matrices are
aligned so that the same row corresponds to the same site in each matrix. Sites are
ordered by applying spectral reordering to the similarity matrix (see Materials and
Methods). Rows are aligned across the three matrices.
Figure 3a shows the estimated niche-space distribution for each of the five com-
ponents. Components 2 (Photosystem) and 4 (Unidentified) are broadly distributed;
Components 1 (Signalling) and 5 (Unidentified) are largely restricted to a handful of
sites; and component 3 shows an intermediate pattern. There is a great deal of overlap
between niche-space distributions for di erent components.
Figure 3b shows the pattern of filtered similarity between sites. We see clear pat-
terns of grouping, that do not emerge when we calculate functional distances without
filtering, or using PCA rather than NMF filtering (Figure 3 in Text S1). As with
the Pfams, we see clusters roughly associated with our components, but there is more
overlapping than with the Pfam clusters (Figure 2b).
Figure 3c shows the distribution of environmental variables measured at each site.
Inspection of Figure 3 reveals qualitative correspondence between environmental factors
Better Prediction Methods
More Reference Data
In Situ Function
A
B
C
Representative
Genomes
Extract
Protein
Annotation
All v. All
BLAST
Homology
Clustering
(MCL)
SFams
Align &
Build
HMMs
HMMs
Screen for
Homologs
New
Genomes
Extract
Protein
Annotation
Figure 1
142. Challenge 3: Systems are Complex
!71
• How distinguish
! Good vs. Bad
! Correlation
vs. Causation
• Solutions:
! More
controlled
ecological
studies
! Better
analysis tools
Good? Bad?
143. How define “bad” vs. “good” ecosystems
• Can (try to) define features that indicate whether an
ecosystem is good or bad
! Productivity
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of
ecosystem
!72
144. How define “bad” vs. “good” ecosystems
• Can (try to) define features that indicate whether a
microbiome is good or bad
! Productivity
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of a
microbiome
!73
145. How define “bad” vs. “good” ecosystems
• Can (try to) define features that indicate whether a
microbiome is good or bad
! Health of host
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of a
microbiome
!74
How do these relate to health?
146. • Idea of a healthy community vs. a unhealthy community is
very complex
• The enormous variation between people and over time
makes it VERY difficult and very risky to try and say what
is “normal”
!75
147. Challenge 4: Need More Reference Data
Historical
Collections
Global
Automated
Sampling
Filling
in the
Tree of Life
148. Acknowledgements
• GEBA:
• $$: DOE-JGI, DSMZ
• Eddy Rubin, Phil Hugenholtz, Hans-Peter Klenk, Nikos Kyrpides, Tanya Woyke, Dongying Wu, Aaron Darling,
Jenna Lang
• GEBA Cyanobacteria
• $$: DOE-JGI
• Cheryl Kerfeld, Dongying Wu, Patrick Shih
• Haloarchaea
• $$$ NSF
• Marc Facciotti, Aaron Darling, Erin Lynch,
• Phylosift
• $$$ DHS
• Aaron Darling, Erik Matsen, Holly Bik, Guillaume Jospin
• iSEEM:
• $$: GBMF
• Katie Pollard, Jessica Green, Martin Wu, Steven Kembel, Tom Sharpton, Morgan Langille, Guillaume Jospin,
Dongying Wu,
• aTOL
• $$: NSF
• Naomi Ward, Jonathan Badger, Frank Robb, Martin Wu, Dongying Wu
• Others (not mentioned in detail)
• $$: NSF, NIH, DOE, GBMF, DARPA, Sloan
• Frank Robb, Craig Venter, Doug Rusch, Shibu Yooseph, Nancy Moran, Colleen Cavanaugh, Josh Weitz
• EisenLab: Srijak Bhatnagar, Russell Neches, Lizzy Wilbanks, Holly Bik