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Category: Environmental Microbiology; Applied and Industrial Microbiology
Comparative Microbial Genomics and Forensics, Page 1 of 2
< Previous page | Next page > /docserver/preview/fulltext/10.1128/9781555818852/9781555818852_Chap11-1.gif /docserver/preview/fulltext/10.1128/9781555818852/9781555818852_Chap11-2.gifAbstract:
Microbes are phylogenetically diverse and are comprised of viruses, archaea, bacteria, fungi, protozoa, microalgae, and microscopic metazoa. These organisms are invisible to the naked eye, but all contain unique genome DNA sequences (or RNA in the case of some viruses). The genomes of microbes range in size from ∼13 kb for the smallest RNA viruses and up to several hundred megabases in the case of microscopic planarians. Thus, each genome contains a large amount of sequence information that may allow the classification and characterization of these microbes, which often have few morphological characters to allow them to be distinguished. We are now living in an era whereby complete genome sequences can be generated by individual researchers and may be analyzed using computational techniques, revealing a range of insights into the lifestyle, phylogenetic origin, degree of genetic modification, and pathogenic mechanism of the microbes concerned. Such comparison of the complete genome sequences of microbes is termed comparative microbial genomics.
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Metagenomic fingerprints and their applications. (A) Example of a microbial meta-metabolomic network generated from human skin. Meta-metabolomic networks represent a novel way of interpreting metagenomic data, and certain microhabitats may have characteristic “fingerprints” reflected in the metabolism of the microbial community, irrespective of the particular taxonomic composition. Thus, such a profile can in principle reveal the origin of the sample, using biochemical considerations and comparison with reference datasets. The methodology for construction of the network was as follows. DNA was isolated from microbes isolated from the left retroauricular crease (behind the ear). This was then subjected to shotgun sequencing using the Illumina platform. The sequences were downloaded from the Human Microbiome Project webpage (http://www.hmpdacc.org/HMASM/; identification number SRS ID SRS013258). These were then used to query the NCBI nonredundant database using BLAST. Significant hits were mapped to KEGG K numbers ( 98 ), representing the respective biochemical reaction that each protein is involved in, and then superimposed onto a network of central metabolism using iPath2.0 ( 99 ).
Metagenomic fingerprints and their applications. (A) Example of a microbial meta-metabolomic network generated from human skin. Meta-metabolomic networks represent a novel way of interpreting metagenomic data, and certain microhabitats may have characteristic “fingerprints” reflected in the metabolism of the microbial community, irrespective of the particular taxonomic composition. Thus, such a profile can in principle reveal the origin of the sample, using biochemical considerations and comparison with reference datasets. The methodology for construction of the network was as follows. DNA was isolated from microbes isolated from the left retroauricular crease (behind the ear). This was then subjected to shotgun sequencing using the Illumina platform. The sequences were downloaded from the Human Microbiome Project webpage (http://www.hmpdacc.org/HMASM/; identification number SRS ID SRS013258). These were then used to query the NCBI nonredundant database using BLAST. Significant hits were mapped to KEGG K numbers ( 98 ), representing the respective biochemical reaction that each protein is involved in, and then superimposed onto a network of central metabolism using iPath2.0 ( 99 ).
Metagenomic fingerprints and their applications. (B) Comparative metagenomics of soil microbial communities. A comparative metagenomics analysis of bacterial communities present in different soil types is shown. Community profiles were generated from each sample using NGS of 16S rRNA PCR products generated from environmental DNA extracted from each habitat. Then, the different profiles were compared using principal-component analysis. Bacterial communities present in soil obtained from hot deserts, cold deserts, and forests cluster separately. Reproduced from reference 100 .
Metagenomic fingerprints and their applications. (B) Comparative metagenomics of soil microbial communities. A comparative metagenomics analysis of bacterial communities present in different soil types is shown. Community profiles were generated from each sample using NGS of 16S rRNA PCR products generated from environmental DNA extracted from each habitat. Then, the different profiles were compared using principal-component analysis. Bacterial communities present in soil obtained from hot deserts, cold deserts, and forests cluster separately. Reproduced from reference 100 .
Plotting the phylogenetics of an infectious outbreak. (A) Phylogenetic network of E. coli strain O104:H4 and related genomes. A minimum spanning tree of E. coli strain O104:H4 was created using allelic profiles of the core genome, which contains 1,144 genes. Each node represents a complete E. coli genome; numbers on the lines connecting nodes represent numbers of alleles that differ between genomes. Different colors represent different pathovars (enterohemorrhagic E. coli [EHEC], enteroaggregative E. coli [EAEC], extraintestinal pathogenic E. coli [ExPEC], enteropathogenic E. coli [EPEC], enterotoxigenic E. coli [ETEC], and commensals). A hypothetical O104:H4 progenitor (green) was created in silico by ancestral sequence reconstruction. Reproduced from reference 101 .
Plotting the phylogenetics of an infectious outbreak. (A) Phylogenetic network of E. coli strain O104:H4 and related genomes. A minimum spanning tree of E. coli strain O104:H4 was created using allelic profiles of the core genome, which contains 1,144 genes. Each node represents a complete E. coli genome; numbers on the lines connecting nodes represent numbers of alleles that differ between genomes. Different colors represent different pathovars (enterohemorrhagic E. coli [EHEC], enteroaggregative E. coli [EAEC], extraintestinal pathogenic E. coli [ExPEC], enteropathogenic E. coli [EPEC], enterotoxigenic E. coli [ETEC], and commensals). A hypothetical O104:H4 progenitor (green) was created in silico by ancestral sequence reconstruction. Reproduced from reference 101 .
Plotting the phylogenetics of an infectious outbreak. (B) Phylogenetic tree of HIV gp120 using sequences from the criminal case State of Washington v Anthony Eugene Whitfield ( 102 ). Six case individuals were included in the analysis, WA01 to WA06. WA04 (red) sequences were obtained from the defendant; WA01 to WA03, WA05, and WA06 were from the other case individuals, and black indicates outgroup sequences obtained from the GenBank database. Color gradients represent events of transmission from WA04 to other individuals. The red circle represents the most recent common ancestor of the WA04 sequences. Branch numbers represent statistical support (Bayesian posterior probability/maximum likelihood bootstrap proportion). Values of <0.5 are indicated by a dash or are not shown.
Plotting the phylogenetics of an infectious outbreak. (B) Phylogenetic tree of HIV gp120 using sequences from the criminal case State of Washington v Anthony Eugene Whitfield ( 102 ). Six case individuals were included in the analysis, WA01 to WA06. WA04 (red) sequences were obtained from the defendant; WA01 to WA03, WA05, and WA06 were from the other case individuals, and black indicates outgroup sequences obtained from the GenBank database. Color gradients represent events of transmission from WA04 to other individuals. The red circle represents the most recent common ancestor of the WA04 sequences. Branch numbers represent statistical support (Bayesian posterior probability/maximum likelihood bootstrap proportion). Values of <0.5 are indicated by a dash or are not shown.
Genome of E. coli strain O104:H4. The nine circles show different strains of E. coli O104:H4, 559589 being the original isolate. Annotations on the outside of the circles show positions of transposons, while three plasmids, pTY1 to pTY3, are also included. pTY2 is an aggregative plasmid carrying a fimbria gene that is responsible for the enteroaggregative phenotype of the strain and has been linked to virulence. In green is a variable region, also associated with pathogenicity. Reproduced from reference 103 .
Genome of E. coli strain O104:H4. The nine circles show different strains of E. coli O104:H4, 559589 being the original isolate. Annotations on the outside of the circles show positions of transposons, while three plasmids, pTY1 to pTY3, are also included. pTY2 is an aggregative plasmid carrying a fimbria gene that is responsible for the enteroaggregative phenotype of the strain and has been linked to virulence. In green is a variable region, also associated with pathogenicity. Reproduced from reference 103 .
Pandemics of the past: the Black Death and Spanish flu. Two microbial genomes of pathogens involved in severe historical pandemics have been recovered and sequenced. These are of the Yersinia pestis bacterial strain that caused the Black Death and the influenza virus strain that caused the Spanish flu. The severe effects of these epidemics are illustrated by these photos. (A) Plague pit from medieval Venice (photo reproduced with kind permission of Michel Drancourt, Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, Université de la Méditerranée, Marseille, France). Bodies were unceremoniously thrown into the pit, indicating the severity with which the epidemic struck the community. PCR was used to amplify Y. pestis sequences from ancient DNA obtained from the pit ( 68 ).
Pandemics of the past: the Black Death and Spanish flu. Two microbial genomes of pathogens involved in severe historical pandemics have been recovered and sequenced. These are of the Yersinia pestis bacterial strain that caused the Black Death and the influenza virus strain that caused the Spanish flu. The severe effects of these epidemics are illustrated by these photos. (A) Plague pit from medieval Venice (photo reproduced with kind permission of Michel Drancourt, Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, Université de la Méditerranée, Marseille, France). Bodies were unceremoniously thrown into the pit, indicating the severity with which the epidemic struck the community. PCR was used to amplify Y. pestis sequences from ancient DNA obtained from the pit ( 68 ).
Pandemics of the past: the Black Death and Spanish flu. Two microbial genomes of pathogens involved in severe historical pandemics have been recovered and sequenced. These are of the Yersinia pestis bacterial strain that caused the Black Death and the influenza virus strain that caused the Spanish flu. The severe effects of these epidemics are illustrated by these photos. (B) Treatment center for victims of the Spanish flu in the main drill hall of the Naval Training Center, San Francisco, CA, 1918, showing the scale of the epidemic (U.S. Naval History and Heritage Command photograph).
Pandemics of the past: the Black Death and Spanish flu. Two microbial genomes of pathogens involved in severe historical pandemics have been recovered and sequenced. These are of the Yersinia pestis bacterial strain that caused the Black Death and the influenza virus strain that caused the Spanish flu. The severe effects of these epidemics are illustrated by these photos. (B) Treatment center for victims of the Spanish flu in the main drill hall of the Naval Training Center, San Francisco, CA, 1918, showing the scale of the epidemic (U.S. Naval History and Heritage Command photograph).