Krista's Presentation at the 2019 SFAF Meeting
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This study aims to evaluate pathogen transmission rates under various simulated healthcare conditions using both traditional culturing and culture-independent metagenomic methods. Researchers designed experiments to directly and indirectly transmit pathogens between surfaces and hands at different concentrations, with and without washing. Preliminary results found direct transmission was more efficient than indirect transmission, and washing reduced transmission most on cotton. Further bioinformatics analysis will characterize detection limits and identify pathogen genes.
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Krista's Presentation at the 2019 SFAF Meeting
- 1. Validating Metagenomic Analyses through Simulated Direct and Indirect Healthcare- Related Pathogen Transmissions Krista Ternus Katharina Weber, Nicolette Albright, Gene Godbold, Veena Palsikar, Danielle LeSassier, Nicole Westfall, Kathleen Schulte, Curt Hewitt SFAF Meeting • 23 May 2019
- 2. Funding Acknowledgement 2 This work was supported by the Centers of Disease Control and Prevention’s investments to combat antibiotic resistance under award number 200-2018- 75D30118 C02922
- 3. Research Study Objectives 3 • Evaluate how healthcare-associated pathogen transfer rates change under a variety of simulated conditions • Compare traditional culturing methods with the culture-independent methods of shotgun metagenomics and metranscriptomics for pathogen detection and characterization • Assess how pathogen abundance impacts detection of virulence and antimicrobial resistance genes • Identify potential signatures of pathogen viability www.cdc.gov/cdiff/index.html stock/123RF.com
- 4. 4 Two Types of Culture-Independent Methods • Funding is incremental and reactive • Funding is upfront and proactive • Works at inflexible, known locations • Works at virtually any location • Strong signal in specific circumstances • Weak signals can be lost • Know your goal ahead of time • Decide your goal in the moment • Limited utility • Broad utility • Currently less expensive if already in place • Very adaptable to emerging, new use cases https://upload.wikimedia.org/wikipedia/commons/2/2e/2015-03- 16_14_11_28_Old_phone_booth_at_the_Northeastern_Nevada_Museum_in_Elko%2C_Nevada.JPG Methods developed for specific pathogens (e.g., amplicons) Pathogen agnostic methods (e.g., metagenomics) https://pixabay.com/p-2464968/?no_redirect
- 5. 5 Things that Change… Funding to maintain reference databases Funding to detect specific pathogens https://upload.wikimedia.org/wi kipedia/commons/e/eb/Zika- information-virus.jpg Emerging antimicrobial resistance Reference database content “Unique” regions of microbial genomes Synthetic biology techniques https://www.arl.army.mil/www/articles/ 2903/image.2.large.jpg Pathogen detection platforms and chemistries Pathogen threat lists http://www.cell.com/pb-assets/journals/research/cell-host- microbe/online-now/S1931-3128(17)30554-1.pdf SNPs underlying primer sites
- 7. Bacterial Isolates 7 • Enterococcus faecium • Staphylococcus aureus • Klebsiella pneumoniae • Acinetobacter baumannii • Pseudomonas aeruginosa • Enterobacter species • Klebsiella (Enterobacter) aerogenes • Enterobacter cloacae • Clostridioides difficile • Eight background isolates representative of skin microbiome • Sources: Plot Generated with Krona
- 8. 8 Analysis of Bacterial Isolate Sequences • Illumina MiSeq 2x75bp Nextera XT DNA sequencing of eight skin microbiome “background” bacterial isolates and two pathogens • Raw reads came from isolates sequenced in house and from previous studies, as indicated by sequence accession numbers in CDC AR Isolate Bank • Reads were processed with Trimmomatic to achieve high quality scores and visualized with FastQC/MultiQC • Trimmed reads were assembled with SPAdes, evaluated with QUAST statistics and reference alignments, and predicted gene content with prokka to verify strain identification • Custom curated databases, along with SRST2 and ABRicate default databases, will be used to evaluate the presence of antimicrobial and virulence genes Plots Generated with FastQC and MultiQC
- 9. 9 “Ideal Mixtures” of DNA and RNA Sequences • Represents the best case scenario of 100% transfer and recovery • Microbial concentrations: – Background organisms at equal amounts of 105 CFU/mL – Pathogens at equal amounts of 106 CFU/mL • These are the “high” pathogen concentrations for subsequent transfer events • Lab methods: – All eight background microorganisms and eight pathogens were independently cultured and pooled, followed by sample processing and library prep – For RNA samples, ribosomal RNA depletion was performed with MICROBExpress™ Bacterial mRNA Enrichment Kit (Thermo), then reverse transcribed to cDNA and converted to double stranded DNA using the SuperScript™ Double-Stranded cDNA Synthesis Kit – Both mixtures were sequenced on the MiSeq with Nextera XT 2x75bp CDC/Janice Carr stock/123RF.com https://microbewiki.kenyon.edu/inde x.php/File:Brevibacteriumlinens.jpg
- 10. 10 Number of Reads from Ideal Mixtures Mapped to Isolate Assemblies* 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 9,000,000 Ideal DNA Mix Ideal cDNA Mix NumberofReads Data Generated with BWA and SAMtools *Note: These mappings are not to unique genomic regions
- 11. 11 Isolate Genomes Contained in Ideal Mixtures MashScreenIdentity Data Generated with Mash Screen 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Ideal DNA Mix Ideal cDNA Mix
- 12. Simulated Direct Transmission Scenarios 12
- 13. 13 Simulated Direct Transmission Scenarios https://www.ims-usa.com VITRO Skin
- 14. 14 Preliminary Results from Direct Transmission Scenarios • The simulated hand washing did not have a strong effect on pathogen presence or viability • Possibly because the force of water was not included in our simulation • Pathogen spike-in levels had an impact on CFU count and the amount of detectable pathogen genomes available • More bioinformatics analyses will be performed to further evaluate LoD and gene-based detection and characterization • Microbial concentrations in direct and indirect scenarios: • High = Pathogens at equal amounts of 106 CFU/mL • Low = Pathogens at equal amounts of 102 CFU/mL • Background organisms at equal amounts of 106 CFU/mL
- 15. 15 Direct Contact Scenario with High Spike-In and No Wash 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 2000 4000 6000 8000 10000 12000 14000 MashScreenIdentityMetric CFU/mL Direct Contact Scenario with High Spike-In and Wash 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 1000 2000 3000 4000 5000 6000 MashScreenIdentityMetric CFU/mL
- 16. 16 Direct Contact Scenario with Low Spike-In and No Wash 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 1000 2000 3000 4000 5000 6000 MashScreenIdentityMetric CFU/mL Direct Contact Scenario with Low Spike-In and Wash 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 1000 2000 3000 4000 5000 6000 MashScreenIdentityMetric CFU/mL
- 17. Simulated Indirect Transmission Scenarios 17
- 18. 18 Simulated Indirect Transmission Scenarios
- 19. 19 Preliminary Results from Indirect Transmission Scenarios • Direct transfer is more efficient than indirect, especially at high pathogen concentrations • Cotton transferred the least and was most impacted by washing (i.e., simulated laundry) • Nitrile and stainless steel showed slightly higher pathogen transmission rates than cotton
- 20. 20 NCBI BioProject ID: 530203 Data will be publically available here after all sequencing experiments are completed
- 22. Isolate DNA Sequences from Background Organisms 22 Sequence Type Organism Name Source Raw Reads Trimmed Reads Expected Genome Size (bp)* Estimated Coverage Exp. GC%* Obs. GC% Illumina 2 x 75bp Brevibacterium linens ATCC 9172 1,316,989 1,201,403 3,891,210 23x 64.5% 63% Illumina 2 x 75bp Corynebacterium matruchotii ATCC 14265 1,805,023 1,667,157 2,867,410 44x 57.1% 56% Illumina 2 x 75bp Cutibacterium acnes ATCC 11827 1,290,401 1,136,529 2,502,120 34x 60.1% 58% Illumina 2 x 75bp Escherichia coli ATCC 9637 1,235,775 1,147,439 5,140,860 17x 50.6% 50% Illumina 2 x 75bp Lactobacillus gasseri ATCC 33323 1,952,715 1,845,844 1,930,440 72x 34.9% 37% Illumina 2 x 75bp Micrococcus luteus ATCC 4698 745,923 623,878 2,494,700 19x 73.0% 71% Illumina 2 x 75bp Staphylococcus epidermidis ATCC 12228 1,577,379 1,481,150 2,518,190 44x 32.0% 34% Illumina 2 x 75bp Streptococcus pyogenes ATCC 19615 2,078,285 1,947,273 1,831,320 80x 38.5% 39% *Expected genome sizes and GC% were of isolates were obtained from www.ncbi.nlm.nih.gov/genome/
- 23. Isolate DNA Sequences from Pathogens 23 Sequence Type Organism Name Source Raw Reads Trimmed Reads Expected Genome Size (bp)* Estimated Coverage Exp. GC%* Obs. GC% Illumina 2 x 75bp Enterococcus faecium CDC AR Bank #0579 1,972,771 1,782,677 2,935,330 46x 37.8% 39% Illumina 2 x 75bp Clostridioides difficile ATCC 43598 1,969,353 1,871,958 4,184,470 34x 28.6% 32% Illumina 2 x 101bp Klebsiella aerogenes (previously known as Enterobacter aerogenes) SRR3112300 (CDC AR Bank #0161) 1,802,174 1,473,019 5,280,350 28x 55.0% 52% Illumina 2 x 245bp Klebsiella pneumoniae SRR4025991 (CDC AR Bank #0139) 768,172 494,381 5,333,942 23x 57.2% 56% Illumina 2 x 300bp Klebsiella pneumoniae SRR4025991 (CDC AR Bank #0139) 875,558 549,306 5,333,942 31x 57.2% 56% Illumina 2 x 300bp Klebsiella pneumoniae SRR4025991 (CDC AR Bank #0139) 771,872 579,201 5,333,942 33x 57.2% 56% *Expected genome sizes and GC% were of isolates were obtained from www.ncbi.nlm.nih.gov/genome/
- 24. Isolate DNA Sequences from Pathogens 24 Sequence Type Organism Name Source Raw Reads Trimmed Reads Expected Genome Size (bp)* Estimated Coverage Exp. GC%* Obs. GC% Illumina 2 x 151bp Enterobacter hormaechei subsp. hoffmannii SRR6807647 (Enterobacter cloacae CDC AR Bank #0365) 4,763,893 3,089,482 4,847,226 96x 55.0% 54% Illumina 2 x 222bp Acinetobacter baumannii SRR4417583 (CDC AR Bank #0275) 1,443,179 802,810 4,335,793 41x 39% 39% Illumina 2 x 151bp Pseudomonas aeruginosa SRR6807660 (CDC AR Bank #0230) 7,197,057 5,215,874 6,264,404 126x 66.2% 62% Illumina 2 x 228bp Pseudomonas aeruginosa SRR6807660 (CDC AR Bank #0230) 474,301 395,208 6,264,404 14x 66.2% 65% Illumina 2 x 151bp Staphylococcus aureus SRR6985639 (CDC AR Bank #0219) 5,064,731 3,979,026 2,821,361 213x 32.7% 32% Illumina 2 x 225bp Staphylococcus aureus SRR4417447 (CDC AR Bank #0219) 400,414 337,141 2,821,361 27x 32.7% 32% *Expected genome sizes and GC% were of isolates were obtained from www.ncbi.nlm.nih.gov/genome/
- 25. 25 https://wwwn.cdc.gov/ARIsolateBank/Panel/IsolateDetail?IsolateID=365 CDC AR Bank #0365 (SRR6807647)
- 26. SRR6807647 = Enterobacter hormaechei subsp. hoffmannii 26
- 27. Ideal DNA and RNA Mixtures 27 Category Sample Source Raw Reads Trimmed Reads Expected GC% Observed GC% 8 Background + 8 Pathogens DNA Mixture ATCC and CDC 7,229,710 6,882,852 48% 50% 8 Background + 8 Pathogens RNA Mixture ATCC and CDC 5,645,200 5,313,848 48% 52%
Editor's Notes
- Advantages of amplicon sequencing: After initial development costs have been invested, it is more cost effective to run per reaction Lower limits of detection than shotgun sequencing Advantages of shotgun metagenomics and metatranscriptomics: - Reduces dependence on inflexible, pathogen-specific methods and reference databases - No prior knowledge needed about microbial targets - Potential to identify co-infections and unexpected disease causes or virulence/AMR genes - Could shorten time to answer if the pathogens take a long time to culture New technologies are likely to improve agnostic pathogen detection methods, eventually eliminating the need for amplicon-based detection. False negatives currently are a challenge for shotgun metagenomics because pathogen or gene-specific sequences may not always be present at high enough quantities in environmental samples to confidently determine that a pathogen or antimicrobial resistance gene is present. This study aims to better understand the limits of detection in metagenomics by evaluating the relationship between colony forming units and detectable pathogen and gene-specific sequences under a variety of environmental transmission scenarios.
- More agnostic pathogen detection methods will make changing with the times easier!
- Mash Screen “Identity” = Fraction of bases shared between the assembled isolate and the mixture, which is estimated from the fraction of their shared k-mers
- The simulated hand washing did not have a strong effect on pathogen presence or viability, possibly because the force of water was not included in our simulation.
- Pathogen spike-in levels had an impact on CFU count and the amount of detectable pathogen genomes available, with fewer viable pathogens in the low spike-in compared to the high.
- Some pathogens seem to be more easily transmitted than others, and this will be described in more detail in the future.
- This is an example of the CDC taxonomic assignment disagreeing with NCBI’s taxonomic assignment at the species level.
- This is an example of the CDC taxonomic assignment disagreeing with NCBI’s taxonomic assignment at the species level.