Data science for pathogen genomic surveillance: predicting quantitative phenotype from genotype Eric J. Ma, Islam T. M. Hussein, Jonathan A. Runstadler Department of Biological Engineering, MIT Analysis: HIV Drug Resistance Cross-Validated Prediction Performance • Good prediction performance: high correlation, low error. Global Drug Resistance Prediction • Model predictions are largely concordant with one another. Important Amino Acids • Match between expert-identified important positions and model predictions. Position 10 33 88 84 47 46 54 Rel. Impt 47% 12% 5% 3% 2% 2% 2% Database Y Y Y Y Y Y Y Temporal Emergence of Drug Resistance • FDA approval dates - IDV (indinavir): 1996, FPV (fosamprenavir): 2003, DRV (duranivir): 2006 (arrows) • Drug resistance emerged a few years after approval • FPV and DRV have similar chemical structures • Goal: Establish example pipeline for genomic surveillance • Input: HIV protease sequence & drug resistance profile Conclusions & Future Work • Machine learning models can predict drug resistance from protein sequence. • Genomic surveillance able to capture temporal rise of drug resistance • Applicable to other pathogens, with high quality genotype-phenotype data Genomic Surveillance • Zoonotic pathogens circulating in wild may affect livestock and human health. • Given sequence information, can we compute a pathogen “risk factor”? • Given a computed risk, can we do preventative surveillance of zoonotic pathogens? Influenza Influenza Genome Structure 1 PB2 2.4 kb 2 PB1 2.4 kb 3 PA 2.2 kb 4 HA 1.8 kb 5 NP 1.6 kb 6 NA 1.5 kb 7 M 1.0 kb 8 NS 0.9 kb Reassortment • Influenza is a zoonotic pathogen that has a broad tropic range. • Segmented genome allows reassortment, accelerating viral evolution. • A high polymerase mutation rate rapidly generates novel sequence diversity. Introduction Difficulties • Necessity: The presence of a point mutation may “enhance” phenotype, but not necessarily cause “dangerouse” phenotype levels (right). • Epistasis: Mapping from genotype to phenotype. • Experiments: Require assays to measure biochemical phenotype relevant to pathogenesis without infecting humans. • Data: Lack genotype-phenotype data. • Biology: Novel sequence diversity generated through error-prone polymerase. Gene(s) Genotypes Phenotype HT Assay HA NA receptor affinity inhibitor resistance a(2,6) binding a(2,6) cleavage antigenic distance HA, NA hemagglutinin/ neuraminidase inhibition Pol replication activity polymerase assay Significance infection potential treatability disease burden immunity training data for ML ML predicts phenotype Risk Prediction risk phenotype MERI... MKAK... risk phenotype risk phenotype MNPN... risk phenotype MKAK... MNPN... Application Risk Profile Informed Intervention NS1 IFN-β production dampening innate immunity risk phenotype MDSN... immunity Vision • Assay: Biochemical, quantitative measure relevant to pathogenesis • Characterize: population diversity • Machine learning: learn non-linear mapping from genotype to phenotype • Model: quantitative risk profile Experimentation Plan Rational Library ATGGTAACCA PacBio Sequencing Polymerase Assay Genotype- Phenotype Sequence PEU Machine Learning Web Query MERIKEL MERIREL MDRIKEL MERIKNL 26 10 9 15 • Rational sampling to cover polymorphic diversity. • High throughput library construction & verification. • Safe, scalable, standardized assay of RNA replication rate. • Matched phenotype to genotype • Machine learning models to predict RNA replication rate. • Open data release via web interface & API

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Data science for pathogen genomic surveillance: predicting quantitative phenotype from genotype

Eric J. Ma, Islam T. M. Hussein, Jonathan A. RunstadlerDepartment of Biological Engineering, MIT

Analysis: HIV Drug Resistance

Cross-Validated Prediction Performance

Good prediction performance: high correlation, low error.

Global Drug Resistance Prediction

Model predictions are largely concordant with one another.

Important Amino Acids

Match between expert-identified important positions and model predictions.

Position10338884474654

Rel. Impt47%12%5%3%2%2%2%

DatabaseYYYYYYY

Temporal Emergence of Drug Resistance

FDA approval dates - IDV (indinavir): 1996, FPV (fosamprenavir): 2003, DRV (duranivir): 2006 (arrows)

Drug resistance emerged a few years after approval FPV and DRV have similar chemical structures

Goal: Establish example pipeline for genomic surveillance Input: HIV protease sequence & drug resistance profile

Conclusions & Future Work Machine learning models can predict drug resistance from protein sequence. Genomic surveillance able to capture temporal rise of drug resistance Applicable to other pathogens, with high quality genotype-phenotype data

Genomic Surveillance Zoonotic pathogens circulating in wild may affect livestock and human health. Given sequence information, can we compute a pathogen risk factor? Given a computed risk, can we do preventative surveillance of zoonotic pathogens?

InfluenzaInuenza Genome Structure

1 PB2 2.4 kb2 PB1 2.4 kb3 PA 2.2 kb4 HA 1.8 kb5 NP 1.6 kb6 NA 1.5 kb7 M 1.0 kb8 NS 0.9 kb

Reassortment

Influenza is a zoonotic pathogen that has a broad tropic range. Segmented genome allows reassortment, accelerating viral evolution. A high polymerase mutation rate rapidly generates novel sequence diversity.

Introduction

Difficulties Necessity: The presence of a point mutation may

enhance phenotype, but not necessarily cause dangerouse phenotype levels (right).

Epistasis: Mapping from genotype to phenotype. Experiments: Require assays to measure

biochemical phenotype relevant to pathogenesis without infecting humans.

Data: Lack genotype-phenotype data. Biology: Novel sequence diversity generated

through error-prone polymerase.

Gene(s)Genotypes PhenotypeHT Assay

HA

NA

receptor anity

inhibitor resistance

a(2,6) binding

a(2,6) cleavage

antigenic distanceHA, NAhemagglutinin/neuraminidase

inhibition

Pol replication activitypolymeraseassay

Signicance

infectionpotential

treatability

diseaseburden

immunity

training data for ML ML predicts phenotype

Risk Prediction

risk

phenotypeMERI...

MKAK... risk

phenotype

risk

phenotypeMNPN...

risk

phenotype

MKAK...MNPN...

Application

Risk Prole

InformedIntervention

NS1 IFN-productiondampening

innate immunity ris

k

phenotypeMDSN... immunity

Vision

Assay: Biochemical, quantitative measure relevant to pathogenesis Characterize: population diversity Machine learning: learn non-linear mapping from genotype to phenotype Model: quantitative risk profile

Experimentation PlanRational Library

ATGGTAACCA

PacBioSequencing

PolymeraseAssay

Genotype-Phenotype

Sequence PEU

MachineLearning

Web Query

MERIKELMERIRELMDRIKELMERIKNL

2610915

Rational sampling to cover polymorphic diversity. High throughput library construction & verification. Safe, scalable, standardized assay of RNA replication rate. Matched phenotype to genotype Machine learning models to predict RNA replication rate. Open data release via web interface & API