Analysis of Horizontal Gene Transfers of Potential Relevance to Microbial Virulence Fiona Brinkman Simon Fraser University, Greater Vancouver, British Columbia, Canada

Overview 1.High pathogen-host protein similarities: detecting horizontal gene transfer 2.Characteristics of proteins/genes putatively horizontally acquired by bacterial pathogens 3.Implications 4.Proposal: How we should be combating bacterial pathogens

Yersinia Type III secretion system

Approach Idea: Could we identify novel virulence factors by identifying bacterial pathogen proteins more similar to host proteins than you would expect? 1. Primary sequence similarity approach – identifies possible horizontal gene transfer (2. Structural similarity approach)

For each complete bacterial and eukaryote genome: BLASTP (and MSP Crunch) analysis of all deduced proteins, searched against non-redundant SWALL database Overlay NCBI taxonomy information Query database for bacterial proteins who’s top BLASTP scoring “hit” is eukaryotic (and eukaryotic proteins who’s top hit is bacterial) Initial Assumption: Three Domains of life (Bacteria, Eukarya, and Archaea) are so divergent that top hits to another Domain are rare Unusual similarities between Bacteria & Eukaryote genes: Sequence similarity-based approach

Problem: If a gene transfer occurs from a eukaryote to an ancestor of closely related bacteria  top hit will be to other bacteria Therefore, perform similar query, but filtering different taxonomic groups from the analysis… Unusual similarities between Bacteria & Eukaryote genes: Sequence similarity-based approach Bacteria 1 closely related Bacteria 2bacteria (same species, family, etc) Eukaryote

BAE-watch Database: Bacterial proteins with unusual similarity with Eukaryotic proteins

Problem: Proteins highly conserved in the three domains of life Top hit to a protein from another domain may occur by chance. “StepRatio” score helps detect these. Example: Glucose-6- Phosphate Reductase

Example of a case with a high StepRatio: Enoyl ACP reductase

PhyloBLAST – a tool for analysis Brinkman et al. (2001) Bioinformatics 17:

BAE-watch: Analysis of Haemophilus influenzae Rd-KW20 proteins for unusual eukaryotic protein similarities

Genome data for… AnthraxNecrotizing fasciitis Cat scratch diseaseParatyphoid/enteric fever Chancroid Peptic ulcers and gastritis Chlamydia Periodontal disease CholeraPlague Dental cariesPneumonia Diarrhea (E. coli etc.)Salmonellosis DiphtheriaScarlet fever Epidemic typhusShigellosis Mediterranean feverStrep throat Gastroenteritis Syphilis GonorrheaToxic shock syndrome Legionnaires' disease Tuberculosis LeprosyTularemia Leptospirosis Typhoid fever Listeriosis Urethritis Lyme disease Urinary Tract Infections Meliodosis Whooping cough Meningitis +Hospital-acquired infections

Bacterial Pathogens Chlamydophila psittaci Respiratory disease, primarily in birds Mycoplasma mycoides Contagious bovine pleuropneumonia Mycoplasma hyopneumoniae Pneumonia in pigs Pasteurella haemolytica Cattle shipping fever Pasteurella multicoda Cattle septicemia, pig rhinitis Ralstonia solanacearum Plant bacterial wilt Xanthomonas citri Citrus canker Xylella fastidiosa Pierce’s Disease - grapevines Bacterial wilt

Trends in this Sequence-based Analysis Identifies the strongest cases of lateral gene transfer between bacteria and eukaryotes Most common: Bacteria   Unicellular Eukaryote  Makes sense: Bacteria to Multicellular eukaryote must involve germline Eukaryote to Bacteria must not involve introns

Trends in this Sequence-based Analysis Identifies nuclear genes with potential organelle origins A control: Method identifies all previously reported Chlamydia trachomatis “plant-like” genes.

First case: Bacterium Eukaryote Lateral Transfer 0.1 Bacillus subtilis Escherichia coli Salmonella typhimurium Staphylococcus aureus Clostridium perfringens Clostridium difficile Trichomonas vaginalis Haemophilus influenzae Acinetobacillus actinomycetemcomitans Pasteurella multocida N-acetylneuraminate lyase (NanA) of the protozoan Trichomonas vaginalis is 92-95% similar to NanA of Pasteurellaceae bacteria. de Koning et al. (2000) Mol Biol Evol 17: Pasteurellaceae

N-acetylneuraminate lyase – role in pathogenicity? Pasteurellaceae Mucosal pathogens of the respiratory tract T. vaginalis Mucosal pathogen, causative agent of the STD Trichomonas

N-acetylneuraminate lyase (sialic acid lyase, NanA) Involved in sialic acid metabolism Role in Bacteria: Proposed to parasitize the mucous membranes of animals for nutritional purposes Role in Trichomonas: ? Hydrolysis of glycosidic linkages of terminal sialic residues in glycoproteins, glycolipids Sialidase Free sialic acid Transporter Free sialic acid NanA N-acetyl-D-mannosamine + pyruvate

Another case: A Sensor Histidine Kinase for a Two-component Regulation System Signal Transduction “In General” Histidine kinases more common in bacteria Ser/Thr/Tyr kinases more common in eukaryotes However, a histidine kinase was recently identified in fungi, including pathogens Fusarium solani and Candida albicans How did it get there? Candida

Neurospora crassa NIK-1 Fusarium solani FIK2 Streptomyces coelicolor SC4G10.06c Candida albicans CaNIK1 Escherichia coli RcsC Erwinia carotovora RpfA / ExpS Escherichia coli BarA Salmonella typhimurium BarA Pseudomonas aeruginosa GacS Pseudomonas fluorescens GacS / ApdA Pseudomonas tolaasii RtpA / PheN Pseudomonas syringae GacS / LemA Pseudomonas viridiflava RepA Azotobacter vinelandii GacS 0.1 Streptomyces coelicolor SC7C7.03 Xanthomonas campestris RpfC Vibrio cholerae TorS Escherichia coli TorS Fusarium solani FIK1 Fungi Pseudomonas aeruginosa PhoQ Streptomyces Histidine Kinase. The Missing Link? Virulence Factor ( ) in every organism examined to date Brinkman et al. (2001) Infection and Immunity 69:

“Plant-like” genes in Chlamydia Proteins: Unusually high number most similar to plant proteins Previous proposal: Obtained genes from a plant-like amoebal host? (A relative of Chlamydiaceae infects Acanthamoeba. Chlamydiaceae: Obligate intracellular pathogens) However Acanthamoeba relationship to plants very controversial

“Plant-like” genes in Chlamydia NCBI GIProtein descriptionSubcellular localization in plants Glycyl tRNA SynthetaseChloroplast c ADP/ATP TranslocaseChloroplast c Glycogen HydrolaseChloroplast GTP Cyclohydratase & DHBP SynthaseChloroplast c Beta-Ketoacyl-ACP SynthaseChloroplast c Enoy-Acyl-Carrier ReductaseChloroplast c Thioredoxin ReductaseChloroplast Metal Transport P-type ATPaseChloroplast Similar to NA+/H+ AntiporterChloroplast c Phosphate PermeaseChloroplast GcpE proteinChloroplast Tyrosyl tRNA SynthetaseChloroplast c Malate DehydrogenaseChloroplast GTP Binding proteinChloroplast c ADP/ATP TranslocaseChloroplast Phosphoglycerate MutaseChloroplast c Glycerol-3-Phosphate AcyltransferaseChloroplast ABC Transporter ATPaseChloroplast d Deoxyoctulonosic Acid SynthetaseChloroplast e Sugar Nucleotide PhosphorylaseChloroplast c Shikimate 5-DehydrogenaseChloroplast Geranyl TransferaseChloroplast Deoxyxylulose 5-Phosphate ReductoisomeraseChloroplast

“Plant-like” genes in Chlamydia rRNA MethytransferaseChloroplast HSP60Chloroplast c Phosphoribosylanthranilate IsomeraseChloroplast c Aspartate AminotransferaseChloroplast f c Polyribonucleotide NucleotidyltransferaseChloroplast f Putative D-Amino Acid DehydrogenaseChloroplast g Cytosine DeaminaseChloroplast? h Lipoate-Protein Ligase AMitochondrial Glycogen SynthaseN/A i c Dihydropteroate SynthaseN/A i c Inorganic PyrophosphataseN/A i Uridine 5’-Monophosphate SynthaseN/A i c UDP-Glucose PyrophosphorylaseN/A i GutQ/Kpsf Family Sugar-Phosphate IsomeraseMitochondrial? j

“Plant-like” genes in Chlamydia Endosymbiotic theory Rickettsia: Many eukaryotic-like genes Synechocystis: Many plant-like genes Does Chlamydia share an ancient ancestral relationship with the ancestor of the Chloroplast?

Chlamydiaceae share an ancestral relationship with Cyanobacteria and Chloroplast 0.1 Pyrococcus furiosus (Archaea) Thermotoga maritima Aquifex pyrophilus Bacillus subtilis Chlamydophila pneumoniae Chlamydophila psittaci Chlamydia muridarum Chlamydia trachomatis Chlamydomonas reinhardtii Klebsormidium flaccidum Zea mays Nicotiana tabacum Synechococcus PCC6301 Synechocystis PCC6803 Microcystis viridis Escherichia coli Zea mays mitochondrion Rickettsia prowazekii Caulobacter crescentus Chloroplasts Cyanobacteria Chlamydiaceae 16S rRNA

Chlamydiaceae share an ancestral relationship with Cyanobacteria and Chloroplast L3L4 L23 L2 S19L22 S3 L16 L29 S17L14L24 L5 S14 S8 L6 L18 S5 L30L15 S10 Escherichia Bacillus Thermatoga Synechocystis Chlamydia Unique shared-derived characters unite Chlamydiaceae and Synechocystis

Chlamydiaceae “plant-like” genes reflect an ancestral relationship with Cyanobacteria and the Chloroplast Chlamydiaceae do not appear to be exchanging DNA with their hosts Existing knowledge of Cyanobacteria may stimulate ideas about the function and control of pathogenic Chlamydia? Brinkman et al. (2002) Genome Research 12:

Overview 1.High pathogen-host protein similarities: detecting horizontal gene transfer 2.Characteristics of proteins/genes putatively horizontally acquired by bacterial pathogens 3.Implications 4.Proposal: How we should be combating bacterial pathogens

Horizontal Gene Transfer and Bacterial Pathogenicity Transposons: ST enterotoxin genes in E. coli Prophages: Shiga-like toxins in EHEC Diptheria toxin gene, Cholera toxin Botulinum toxins Plasmids: Shigella, Salmonella, Yersinia Pathogenicity Islands: Uro/Entero-pathogenic E. coli Salmonella typhimurium Yersinia spp. Helicobacter pylori Vibrio cholerae

Pathogenicity Islands Associated with –Atypical %G+C –tRNA sequences –Transposases, Integrases and other mobility genes –Flanking repeats

IslandPath: Aiding identification of Pathogenicity Islands and other Genomic Islands Yellow circle = high %G+C Pink circle = low %G+C Region of unusual dinucleotide bias tRNA gene lies between the two dots rRNA gene lies between the two dots Both tRNA and rRNA lie between the two dots Dot is named a transposase Dot is named an integrase _ Hsiao et al. (2003) Bioinformatics 19:

Dinucleotide bias analysis Genome divided into “ORF-clusters” of 6 consecutive ORFs For each ORF cluster, the average absolute dinucleotide relative abundance difference is where f (fragment) is derived from sequences in an ORF-cluster g (genome) is derived from all predicted ORFs in the genome Dinucleotide relative abundance is  * XY = f* XY /f* X f* Y where f* X denotes the frequency of the mononucleotide X f* XY the frequency of the dinucleotide XY See Hsiao et al. (2003) Bioinformatics 19: and Karlin, S. and Burge, C (1995). Trends in Genetics : for review

Dinucleotide bias analysis ORF-clusters sampled in an overlapping manner (shift by one ORF at a time) The mean is calculated by averaging the results from all ORF-clusters in the genome Regions with greater than 1 standard deviation away from the mean are marked on the IslandPath graphical display with strikethrough lines Why did we use 6 ORFs per cluster? - Not enough bp in a single ORF to get a good estimate - 4.5kb (corresponding to approximately 6-8 ORFs) is required for “reliable estimation of nucleotide composition” (Lawrence and Ochman, J Mol Evolution :383-97)

II I V IV III VI VII VIII IX X 32 Boxes: Known islands in the Salmonella typhi genome

What features best predict Islands? Examined prevalence of features in over 200 known islands 94% of islands contain >25% dinucleotide bias (majority have >75% dinucleotide bias coverage) Mobility genes identified in >75% (but ID recently improved) Atypical %G+C (above cutoff used in Brinkman et al., 2002) not over 50% coverage on average, and tRNA genes not observed with >50% of known islands

II I V IV III VI VII VIII IX X Boxes: “Insertions” in the Salmonella typhi genome verses Salmonella typhimurium

Properties of genes in these islands? Defined a “putative island” as –8 or more genes in a row with dinucleotide bias Functional category analysis  Any difference for genes in islands verses genome?

Hypothetical genes are more common in putative islands vs the genome (Paired T test P= 6.8E-19) Genome Put. Islands Analysis 1: COG functional category analysis

Analysis 2: SUPERFAMILY HMM search results SUPERFAMILY: a set of HMMs built from SCOP superfamilies Fewer ORFs in the putative islands were assigned to a SUPERFAMILY class Genome Put Islands Paired T test P= 3.3E-14

Analysis 3: Gene size in Putative Islands vs. “Non-Islands” ORFans (genes with no homologs among 60 microbial genomes) tend to be shorter genes Are genes in putative islands shorter as well on average? In most cases, average ORF length in putative islands is shorter Non Island Put. Islands Paired T test P= 7.1E-34

Analysis 4: COG analysis after removing ORFs <300 bp Genes may be less well predicted in such island/atypical dinucleotide bias regions Some genomes still show marked increase % hypothetical genes in islands verses genome Hypothetical genes more common in islands? Paired T test P=

Summary: Bacteria gene transfer analysis No cases identified in our database to date of clear, recent horizontal gene transfer between bacteria and a multicellular eukaryote (involving >80% sequence similarity) The pathogens studied are not commonly acquiring genes from their hosts, or vice versa Bacterial and eukaryotic pathogens may have exchanged genes Overall increased prevalence of hypothetical genes in putative bacterial genomic islands?  Cautionary note about gene prediction accuracy

Overview 1.High pathogen-host protein similarities: detecting horizontal gene transfer 2.Characteristics of proteins/genes putatively horizontally acquired by bacterial pathogens 3.Implications 4.Proposal: How we should be combating bacterial pathogens

Implications: Evolution of Pathogenicity Pathogen mimicry of their host: Convergent evolution or genes selectively maintained Gene exchange between pathogens: “Arms Deals”

Pathogens and “The Art of War” “What is of supreme importance in war is to attack the enemy's strategy. Next best is to disrupt his alliances by diplomacy. The next best is to attack his army. And the worst policy is to attack cities.”

FPMI INDUSTRY Anigenics Canada Inimex Pharma Inc ACADEMIA VIDO, U Sask UBC, SFU, BCGSC GOVERNMENT Genome Canada Genome Prairie Genome BC Govt of Saskatchewan Functional Pathogenomics of Mucosal Immunity

BC Pathogenomics group Ann M. Rose, Yossef Av-Gay, David L. Baillie, Fiona S. L. Brinkman, Robert Brunham, Artem Cherkasov, Rachel C. Fernandez, B. Brett Finlay, Hans Greberg, Robert E.W. Hancock, Steven J. Jones, Patrick Keeling, Audrey de Koning, Don G. Moerman, Sarah P. Otto, B. Francis Ouellette, Nancy Price, William Hsiao. Jeff Blanchard (NCGR, New Mexico) and Olof Emanuelsson (Stockholm Bioinformatics Center) Peter Wall Institute for Advanced Studies, Genome Canada