This document discusses using machine learning models to predict pathogen phenotypes like drug resistance and virulence from genomic data. It presents results from models trained to predict HIV drug resistance from protease sequences with good performance. The models captured the temporal emergence of resistance after new drug approvals. The approach aims to establish genomic surveillance pipelines for pathogens by computing a "risk factor" from sequence data to help monitor zoonotic pathogens. Challenges include mapping genotypes to phenotypes given epistasis and lack of data. The vision is to assay pathogen populations to build genotype-phenotype datasets to train models that can quantify risk profiles and inform interventions.

1. 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 PhenotypeHT Assay
HA
NA
receptor affinity
inhibitor resistance
a(2,6) binding
a(2,6) cleavage
antigenic distanceHA, 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