Sunday, April 28, 2024 4/28/2024

A priori adaptive evolution predictions for antibiotic resistance through genome-wide network analyses and machine learning

Award Number: R01AI148470
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Group Awards By Issue Date FY or Funding FY:

View Award Abstract

SUMMARY Adaptive evolution (AE) is both a “force of good” as it can help to optimize biological processes in industry, but it is also a “force of frustration” when infectious diseases exploit AE to escape the host immune system or become resistant to drugs. It has long been assumed close to impossible to make predictions on AE due to the presumed predominating influences of random forces and events. However, the observation that evolutionary repeatability across traits and species is far more common than previously thought, suggests that AE, with the right data and approach, may become (partially) predictable. Indeed, we found through experiments with the bacterial pathogen Streptococcus pneumoniae on its response to antibiotics and the emergence of antimicrobial resistance, that in order to make AE predictable a detailed understanding of at least two aspects of the bacterial system are required: 1.) the genetic constraints of the system (i.e. the architecture of the organismal network); and 2.) where and how in the system stress is experienced and processed. We showed that by mapping out ~25% of the bacterium's network, determining phenotypic and transcriptional antibiotic responses, applying network analyses to capture and quantify the responses in a network context, and exploiting experimental evolution to pin-point adaptive mutations in the genome it becomes possible, by means of machine learning, to uncover hidden patterns in the data that make AE predictions feasible. This means that the network in interaction with the environment shapes the adaptive landscape, it limits available solutions and makes some solutions more likely than others, thereby driving repeatability and enabling predictability. In this proposal we build on these exciting developments with the goal to map out the constraints of S. pneumoniae's entire network and develop a machine learning model that can forecast adaptive evolution a priori, and on a genome-wide scale. To accomplish this, we combine in aim 1 parts of Tn-Seq, dTn-Seq and Drop-Seq to finalize a new tool Tn-Seq^2 (Tn-Seq squared) that is able to map genetic-interactions in high-throughput and genome-wide. We use Tn-Seq^2 to reconstruct the first genome-wide genetic interaction network for S. pneumoniae in the presence of 20 antibiotics. In aim 2 we create 85 HA-tagged Transcription factor induction (TFI) strains and: a) Determine with ChIP-Seq the DNA-binding sites for all 85 TFs in S. pneumoniae; b) By overexpressing each TFI strain followed by RNA- Seq we determine each TFs regulatory signature; c) Use a Transcriptional Regulator Induced Phenotype screen in the presence of 20 antibiotics to untangle environment specific links between genetic and transcriptional perturbations and their phenotypic outcomes. Lastly, in aim 3, we train and test a variety of machine learning approaches to design an optimal model that predicts which genes in the genome are most likely to adapt in the presence of a specific antibiotic. The development of this predictive AE model, will not only be useful in predicting the emergence of antibiotic resistance, but the strategy should be valuable for most any biological field for which adaptive changes are important, ranging from biological engineering to cancer.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $445,000 )
2024	2024	CHILDREN'S HOSPITAL CORPORATION, THE	300 LONGWOOD AVE	BOSTON	MA	02115	SUFFOLK	USA	Allergy and Infectious Diseases Research	001	5	4/12/2024	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$445,000
2024	2023	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	000	4	4/12/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $445,000

Issue Date FY: 2023 ( Subtotal = $391,250 )
2023	2023	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	000	4	4/13/2023	NON-COMPETING CONTINUATION	$391,250
														Subtotal = $391,250

Issue Date FY: 2022 ( Subtotal = $391,250 )
2022	2022	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	000	3	6/9/2022	NON-COMPETING CONTINUATION	$391,250
2022	2022	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	001	3	7/29/2022	NON-COMPETING CONTINUATION	$0
														Subtotal = $391,250

Issue Date FY: 2021 ( Subtotal = $391,250 )
2021	2021	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	000	2	4/22/2021	NON-COMPETING CONTINUATION	$391,250
														Subtotal = $391,250

Issue Date FY: 2020 ( Subtotal = $391,250 )
2020	2020	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	000	1	5/1/2020	NEW	$391,250
2020	2020	TRUSTEES OF BOSTON COLLEGE	140 COMMONWEALTH AVE	CHESTNUT HILL	MA	02467	MIDDLESEX	USA	Allergy and Infectious Diseases Research	001	1	6/12/2020	NEW	$0
														Subtotal = $391,250

Grand Total All Awards = $2,010,000

Top

All Categories

About

Search

Reports

Data Submission

Award Information

A priori adaptive evolution predictions for antibiotic resistance through genome-wide network analyses and machine learning

Award Number: R01AI148470

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer