Advanced Molecular Detection

Transcription

1 Advanced Molecular Detection CAPHLD /10/18 Lafayette, CA Duncan MacCannell National Center for Emerging and Zoonotic Infectious Diseases Office of Advanced Molecular Detection

2 Nucleotides per $1,000 The Impact of NGS on Life Sciences 100,000,000 Sanger Sequencing Next-Generation Sequencing 10,000,000 1,000, ,000 10,000 1,000 Moore s Law Adapted from NHGRI (

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4 Library Data Info. ACAATTTGTGCATAACATGTGGACAGTTTTAATCACATGTGGGTAAATAGTTGTCCACATTTGCTTTTTT TGTCGAAAACCCTATCTCATATACAAACGACGTTTTTAGGTTTTAAAATACGTTTCGTATAAATATACAT TTTATATTTATTAGGTTGTACATTTGTTGCGCAACCTTATTCTTTTACCATCTTAGTAAAGGAGGGACAC CTTTGGAAAATATCTCTGATTTATGGAATAGTGCCTTAAAAGAATTAGAAAAAAAGGTAAGCAAGCCTAG TTATGAAACATGGTTAAAATCAACAACGGCTCATAACTTGAAGAAAGACGTATTAACGATTACAGCTCCA AATGAATTTGCTCGTGACTGGCTAGAATCTCATTACTCAGAACTTATTTCGGAAACACTATACGATTTAA CAGGGGCAAAATTAGCAATTCGCTTTATTATTCCCCAAAGTCAATCGGAAGAGGACATTGATCTTCCTCC AGTTAAGCGGAATCCAGCACAAGATGATTCAGCTCATTTACCACAGAGCATGTTAAATCCAAAATATACA TTTGATACATTTGTTATCGGCTCTGGTAACCGTTTTGCCCATGCAGCTTCATTAGCTGTAGCCGAGGCGC CAGCTAAAGCGTATAATCCACTCTTTATTTATGGGGGAGTTGGGCTTGGAAAGACGCATTTAATGCACGC AATTGGTCATTATGTAATTGAACATAATCCAAATGCAAAAGTTGTATATTTATCATCAGAAAAATTCACG AATGAATTTATTAACTCTATTCGTGATAATAAAGCTGTTGATTTTCGTAATAAATATCGCAACGTAGATG Input: DNA/RNA Source: Genomic Amplicon Whole sample Host/vector/ pathogen/ environment NGS Workflow: Platforms Chemistry Perf. char. Labor/TaT Expertise Cost Increasingly Universal Workflows Working to establish standardized sequencing workflows for a wide range of pathogens. Sample intake Prep/staging Extraction Conversion Library prep Sequencing Bioinformatics Workflow: Hardware/software Specialized skillsets Algorithms/pipelines Pathogen databases Data analysis/interpret/ Integration/visualization A Moving Target Rapidly evolving technology space. Changing hardware and COTS/OSS capabilities. Lots of choice, but lack of consistent standards. BIG DATA. New workforce and skillset is required. File hashes/versioning QA/QC Security Validated methods/databases Skills/proficiency Process logging/audit Standards Reporting Pathogen- and application-specific, standard and/or compliant assays Output: Information From Sequence Data Comparative Genomics Identification High res Straintyping/Subtyping Cluster identification Molecular evolution Genotypic characterization Virulence, AR, signatures Functional annotation Diagnostic dev/validation Minor populations, quasispecies Host/pathogen expression Metagenomics Pathogen identification/discovery Culture-independent diagnostics Microbial ecology/diversity Many results from a single dataset. Faster and cheaper than serial tests.

5 Advanced Molecular Detection (AMD) 2011: Bioinformatics Blue Ribbon Panel 2014: Advanced Molecular Detection and Response to Infectious Disease Outbreaks (AMD) initiative approved by Congress $30M/yr public health modernization and innovation program Five key objectives of the AMD initiative: 1. Improve pathogen detection and characterization 2. Enable new diagnostic methods to meet public health needs 3. Support genomic and bioinformatics needs in the US public health system 4. Implement enhanced, sustainable, integrated information systems 5. Develop tools for prediction, modeling and early recognition of emerging infectious threats

6 AMD: Areas of Emphasis [1] Advanced laboratory technologies and approaches Coordinate and fund the rollout of new technologies NGS for pathogen (meta)genomics and (meta) transcriptomics. Platforms: Illumina, PacBio, IonTorrent, Oxford Nanopore, others Other tech: MALDI-ToF, Optical Mapping, microfluidics, etc. CDC facilities; state/local public health laboratories. Field. Information technology IT infrastructure: storage, networking, high-performance computing, cloud. Bioinformatics and scientific computing core support; High Performance Computing Center of Excellence Lab/epi data aggregation, integration, visualization and reporting.

7 AMD: Areas of Emphasis [2] Strategic coordination and program support Funding of 30+ CDC AMD project activities/year Establishment of standard platforms, methods and shared resources. Coordination of state-based activities and initiatives. Coordination with federal (FDA, NIH, NIST, DoD, DHS), NGOs, academic centers, int l consortia. Workforce Development Training, recruitment, administrative, fellowships Collaboration, openness, partnerships and innovation.

8 Highest Impact Areas in Near Future Domain Bacterial foodborne pathogens (PulseNet, 85+ labs) Objective Move from PFGE to WGS Address the threat from culture-independent diagnostic tests (CIDT) Tuberculosis Move from MIRU/VNTR typing to WGS Develop means of WGS directly from sample Influenza Move from traditional typing of isolates to WGS directly from samples Develop cloud-based information system Hospital-acquired and antimicrobial resistant organisms Develop standards for typing Develop methods for assessing relatedness in outbreaks Develop microbiome disruption index Move GC typing from traditional phenotyping to WGS

9 Foodborne Disease Surveillance ~48 million foodborne illnesses occur in the U.S. each year, resulting in ~128,000 hospital admissions and 3,000 deaths Although most cases occur as part of unrecognized outbreaks, >1,000 outbreaks are reported each year PulseNet, a national network of public health and food regulatory agency laboratories, has played a key role in identifying and stopping outbreaks of foodborne illness Pathogens/foods linked to most foodborne illnesses Campylobacter (poultry) E. coli O157 (ground beef, leafy greens, raw milk) Listeria (deli meats, unpasteurized soft cheeses, produce) Salmonella (eggs, poultry, meat, produce) Vibrio (raw oysters) Norovirus (common in many foods) Toxoplasma (meats)

10 Salmonella Heidelberg (May-July 2013) WGS-based analysis of putative multistate cluster of PFGE-indistinguishable Salmonella Heidelberg (XbaI: JF6X ). Contemporary timeframe, similar presentations, possible link Reference-NC T-3110-M947-AL T-3109-M947-AL T-3042-M947-AL T-3040-AL T-3059-AL T-3111-AL Food T-3112-M947-AL T-3050-AL T-3041-M947-AL T-3058-M947-AL AM-0487-NYC AM-0488-NYC AM-0486-NYC 2013K-1067-NE K-1036-OH K-1038-M02163-CO K-1040-CO K-1039-CO 2013K-1037_NC ALABAMA (Funeral) 6-13 SNP differences NEW YORK (Childcare) SNP differences COLORADO (Family Gathering) 6-9 SNP differences 10

11 WGS Challenges: A Data Deluge For PulseNet USA alone: >80,000 isolates/year x 1 to 5 GB raw sequence/isolate terabytes of raw data/year Transmission, management, storage and archiving? Data architectures and analytics? Metadata? Warehousing? Distributed processing? Data compression? Integration? Is full WGS the right approach for large-scale surveillance?

12 PulseNet NG: Dataflow STATE/LOCAL PHL refid metadata pass/fail QC correct ID Sequencing Lab refid metadata Reference DB (Local) Surveillance DB (Local) * Does not imply endorsement by CDC or HHS. Taxa Identification (ANI) Basic QC (raw reads, denovo) Presumptive serotype, AST, etc Straintype (wgmlst) Assembly / Genotype Straintype (wgmlst) Assembly RAW READS MIN METADATA Allele/ Nomenclature DB refid fastq CEStore Temp storage and staging of incoming sequence data (fastq.gz) Calculation Engine (HPC) Average Nuc Ident (ANI) Read/run QC De Novo Assembly Straintype (wgmlst/snp) Surveillance DB (National)

13 Nationwide Listeriosis Surveillance Using WGS Collaboration with: Federal Agencies FDA, NCBI, USDA All state public health laboratories France, UK, Denmark, Australia, and Canada Analysis of all ~1000 annual U.S. clinical cases of Listeria monocytogenes infection and some food/environmental sources Provides foundational infrastructure and methods for next generation PulseNet and a generalizable platform for genome-scale molecular epidemiologic surveillance. Harmonization with FDA GenomeTrackr. 9,592 isolates, as of October 17, ftp://ftp.ncbi.nlm.nih.gov/pathogen/results/listeria/latest

14 Listeria Cluster Metrics Pre/Post WGS Pre-WGS (Sept 2012 Aug 2013) WGS Year 1 (Sept 2013 Aug 2014) WGS Year 2 (Sept 2014 Aug 2015) No. of clusters detected No. of clusters detected sooner or only by WGS No. of outbreaks solved (food source identified) Median no. of cases per cluster 16 No. of cases linked to food source Jackson et al. 2016

15 PulseNet Sequencing by States (FY16) Pathogen Type Isolates Sequenced STEC 3,115 Salmonella 3,207 Campylobacter 787 Listeria 394 Vibrio 18 TOTAL: 7,525 isolates Over 150% FY16 target. Yersinia 4 27 labs in 24 states are now PulseNet WGS certified. 1 PulseNet International site.

16 NCBI Metadata Submission Immediate public release (to NCBI when the sequence is uploaded) Unique sample ID (WGS_ID) Organism genus and species (e.g., Listeria monocytogenes) Organism source (clinical, environmental) Country of origin (USA) Organism DNA sequence Delayed public release (6 months delay) Isolate source (e.g., blood, cerebral spinal fluid, stool, ground beef, etc.) Organism serotype Collection date (month and year) Geographic location (Region of the US for the case-patient) Age category (in years, 0-4, 5-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+)

17 Antibiotic Resistance in the United States Each year, AR pathogens Sicken >2 million people Kill at least 23,000, plus 14,000 from C. difficile Cause >$20B in healthcare costs Comprehensive set of new federal actions to combat the rise of AR bacteria released in Sept 2014 (CARB report) Sets goals for reducing major AR threats over next 5 years Companion AR Action Plan March threat-report-2013/ 1. Working with partners to establish curated reference sets of genomes with attendant susceptibility profiles 2. Working to develop standards and interpretive criteria for genotypic prediction of functional AST. 3. Working to develop NGS data standards for reference AST genotype data sharing. 4. Leveraging laboratory and bioinformatic capacity for ARLN /docs/carb_national_strategy.pdf

18 Mycobacterium tuberculosis Cluster, State X Phylogenetic tree based on conventional genotyping (spoligotyping/miru-vntr) Phylogenetic tree based on WGS ~ 100 Patients

19 Fold Genome Coverage Recovery of MTb gdna from Simulated Sputum Metagenomic Background % Mtb DNA in Sputum H37Rv Genome Position Gene: rrs (16S) Gene: rrl (23S) Gene: rrf (5S) Average Read Depth Post Enrichment % Mtb in Original Sample 99.4% Genome Coverage, Approaching culture sensitivity. Selective capture of Mycobacterium tuberculosis genome, using custom NimbleGen SeqCap probeset. Unpublished data, 2015: Posey, Hopkins, Tyagi, Burns

20 Clutter Mitigation Strategies NUCLEIC ACID EXTRACTION DNA RNA TNA POST-EXTRACTION AND LIBRARY CONSTRUCTION LIBRARY SEQUENCING Differential cell lysis Filtering, Concentration Separation/Pulldown Direct amplification Laser capture, microfluidics Nucleases (RNAse/DNAse) cdna conversion rrna depletion Bind/degrade CpG methylated DNA Preferential separation (mass, seq, chem) Genome/transcriptome amplification Sequencing platform selection Library method and parameters Size selection New platforms/approaches Multiplexing and pooling Bioinformatic strategies Many variables impact sequence yield, quality and bias.

21 AMD Impact on Influenza Vaccine Strain Selection CDC Influenza division is an important player in surveillance and epidemiology of influenza Serves as US National Influenza Center and WHO Collaborating Center for Surveillance, Epidemiology and Control of Influenza Analyses 8,000 12,000 influenza samples/year in support of surveillance and selection of vaccine strains o o Vaccine is produced in a just in time fashion 150 Million vaccine doses/year in the US Evolution of influenza is very rapid Critical to find variants quickly Antigenic drift Reassortment AMD improves characterization High throughput NGS sequencing for influenza surveillance Antigenic inference

22 Transforming Influenza Surveillance: Before NGS Sample Batching & Shipping Sequence First Strategy Viral culture (typically, 2-3 passages) Phenotyping, including antigenic typing N 10,000 Selection based on antigenic type Antigenic subtyping Sanger Sequencing N weeks (longer if international) 3-4 weeks After NGS Sample NGS Selection based on sequence Viral culture (typically, 2-3 passages) N > 10,000 N week 3-4 weeks Phenotyping, including antigenic typing Advantages Faster Cheaper More samples More data Better data Wentworth, Barnes et al. Unpublished

23 Beth Neuhaus (CDC/NCIRD/ID)

24 HCV Outbreak Investigations 18 cases 154,233 reads 33,767 unique sequences Each node is a unique sequence Different patients are shown in different colors Two sequences are linked if they differ at a single nucleotide position Network analysis using GHOST

25 Other AMD Project Areas FY2016 Diagnostics MicrobeNet ID of unusual bacterial pathogens Unexplained respiratory disease outbreaks HCV infection classification Digital PCR for hepatitis viral load Malaria Anti-malarial resistance in plasmodia Insecticide resistance in vectors Multiplicity of infection WGS for bacterial pathogen typing and inference of phenotype Pneumococcus, GAS, GBS Meningococcus, H. influenza Legionella B. anthracis: forensics B. pertussis: subtyping and investigation of emergence Genomic typing of eukaryotic pathogens Cyclospora, Cryptosporidium Naegleria Leishmania Coccidioides Diagnosis and typing of viral pathogens Viral vaccine-preventable diseases HIV diagnostics and network visualization HCV ( GHOST system) Dengue, chikungunya, & Zika Pathogen discovery Application of NGS Arboviruses Filoviruses

26 AMD: Challenges [1] Pace of innovation: incredible rate of change in technology platforms and capabilities. Lack of standardization: laboratory and bioinformatic methods for sequencing, data analysis, storage, and exchange remain under active development. Data volume and complexity: new technologies represent an increase of six or more orders of magnitude; integration and management challenges outweigh data transmission and storage issues

27 AMD: Challenges [2] Limited reference databases: need comprehensive and well-curated reference databases for context to understand diversity and pathogenic characteristics of new sequences Barriers to data sharing: how do we facilitate data sharing and open, reproducible research while maintaining necessary controls over proprietary and/or personally-identifiable healthcare info? Need for specialized systems and expertise: bioinformatics and data science are new disciplines in public health; technical and computational complexity of tasks can vary widely; turnkey, deployable solutions are needed to ensure sustainable impact across a public health system with differing levels of capacity and resources. Workforce change.

28 Bioinformatics PH Workforce Capacity Like microbiology and epidemiology, good bioinformatics is increasingly critical to good public health. Challenges with recruitment and retention Skilled bioinformaticians are a highly sought-after discipline. Career bioinformaticians with relevant public health/microbiology experience are an extremely rare breed. It is more common to find microbiologists with on-the-job bioinformatics experience -- expand/enable through training. Limited state/federal workforce support (series, competencies, training ) Training and Workforce Development Interdisciplinary communication is vital: translating between program scientific needs and bioinformatics technical specialists. How do we foster bioinformatics skills/specialists in public health? Expanding impact: Regional Training Networks, FSCoE, curricula and best practices, targeted training for epidemiologists/non-lab.

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31 SAS Access Excel EpiInfo for DOS SQL NoSQL Unstructured Data Genomic Data

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33 AMD Platform Standardization CDC ENTERPRISE IT ENVIRONMENT (OCIO) 4 x Oxford Nanopore MinION 32 x Illumina Mini/MiSeq/MiSeqDx STATE PHL AMD PORTA L INFLUENZA TB Etc. HPC CLUSTERS STORAGE ARRAY (3.6PB) OID Scientific Computing (SCICOMP) 3 x 1 x 3 x Illumina NextSeq 500 Illumina HiSeq 2500 Pacific BioSciences RS II CDC USER COMMUNITY 3 x ThermoFisher IonTorrent PGM

34 AMDPortal (Coming soon!)

35 Advanced Molecular Detection FOLLOW US ON THE WEB: FOLLOW US ON

36 STATE, COUNTY and LOCAL PUBLIC HEALTH DEPARTMENTS * Does not imply endorsement by CDC or HHS. AMD Partnerships and Collaboration

37 National Center for Emerging and Zoonotic Infectious Diseases Office of Advanced Molecular Detection Questions? For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA Telephone: CDC-INFO ( )/TTY: Web: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.