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Series GSE251671 Query DataSets for GSE251671
Status Public on Oct 11, 2024
Title Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics
Organism Pseudomonas aeruginosa UCBPP-PA14
Experiment type Expression profiling by high throughput sequencing
Summary We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether small molecule or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by mapping their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition maps to those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.
 
Overall design We obtained RNAseq data from Pseudomonas aeurginosa (PA14) under various drug treatments. The majority of the data were taken at 90 minutes after drug exposure, but a subset of the data include 30, 60, 90, and 120 minute time points. There are at least three replicates per condition. RNAseq data from CRISPRi strains are also included, at 270 minutes after arabiniose induction.
 
Contributor(s) Romano K, Bagnall J, Warrier T, Sullivan J, Ferrara K, Orzechowski M, Nguyen PH, Raines K, Livny J, Shoresh N, Hung DT
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NIH grant(s)
Grant ID Grant title Affiliation Name
K08 AI148581 Targeting lipopolysaccharide transport machinery in Pseudomonas aeruginosa BRIGHAM AND WOMEN'S HOSPITAL Keith Patrick Romano
U19 AI142780 Innovative technologies to transform antibiotic discovery. THE BROAD INSTITUTE, INC. Deborah T Hung
Submission date Dec 20, 2023
Last update date Oct 11, 2024
Contact name Keith Romano
Organization name Massachusetts General Hospital
Department Molecular Biology
Lab Hung Lab
Street address 185 Cambridge St.
City Boston
ZIP/Postal code 02114
Country USA
 
Platforms (1)
GPL27892 Illumina NovaSeq 6000 (Pseudomonas aeruginosa UCBPP-PA14)
Samples (1059)
GSM7985000 MOC_1430:ami:4.50000uM:120min:PA14:MOC_1430_1:r1
GSM7985001 MOC_1430:ami:4.50000uM:120min:PA14:MOC_1430_1:r2
GSM7985002 MOC_1430:ami:4.50000uM:120min:PA14:MOC_1430_1:r3
Relations
BioProject PRJNA1055047

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Supplementary file Size Download File type/resource
GSE251671_brd5750_modzscore.csv.gz 50.5 Kb (ftp)(http) CSV
GSE251671_crispri_counts_240515.csv.gz 1.1 Mb (ftp)(http) CSV
GSE251671_crispri_nonref_modzscore.csv.gz 466.8 Kb (ftp)(http) CSV
GSE251671_crispri_ref_set_modzscore.csv.gz 352.2 Kb (ftp)(http) CSV
GSE251671_moc1430_1779_0066_0110_2kd_counts_manuscript.csv.gz 6.6 Mb (ftp)(http) CSV
GSE251671_moc1430_1779_0066_0110_2kd_modzscore_ref_set.csv.gz 3.7 Mb (ftp)(http) CSV
GSE251671_moc1430_1779_0066_0110_2kd_weak_signal_removed_from_ref_counts_240515.csv.gz 1.1 Mb (ftp)(http) CSV
GSE251671_pa0918_modzscore.csv.gz 85.8 Kb (ftp)(http) CSV
GSE251671_pa69180_oprl_hypo_mod_nzscore.csv.gz 52.4 Kb (ftp)(http) CSV
GSE251671_weak_signal_removed_from_antimicrobial_reference_modzscore.csv.gz 690.3 Kb (ftp)(http) CSV
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