MétaCan
Menu
Back to cohort
Record W3044778137 · doi:10.1016/j.celrep.2020.107927

Genetic and Chemical Screening in Human Blood Serum Reveals Unique Antibacterial Targets and Compounds against Klebsiella pneumoniae

2020· article· en· W3044778137 on OpenAlexafffund
Brent S. Weber, Aaron M. De Jong, Amelia Bing Ya Guo, Srinivas Dharavath, Shawn French, Aline Fiebig‐Comyn, Brian K. Coombes, Jakob Magolan, Eric D. Brown

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchMichael G. DeGroote Institute for Infectious Disease Research, McMaster UniversityCanada Research Chairs
KeywordsKlebsiella pneumoniaeAntibioticsBacteriaBiologyDrug discoveryMicrobiologyHuman pathogenGeneBiochemistryEscherichia coliGenetics

Abstract

fetched live from OpenAlex

Antibiotics halt the growth of bacteria by targeting core, essential physiology that is required for life on standard microbiological media. Many more biochemical and virulence processes, however, are required for bacteria to cause infection in a host. Indeed, chemical inhibitors of the latter processes are overlooked using conventional antibiotic drug discovery approaches. Here, we use human blood serum as an alternative growth medium to explore new targets and compounds. High-throughput screening of genetic and chemical libraries identified compounds targeting biological activities required by Klebsiella pneumoniae to grow in serum, such as nucleobase biosynthesis and iron acquisition, and showed that serum can chemically transform compounds to reveal cryptic antibacterial activity. One of these compounds, ruthenium red, was effective in a rat bloodstream infection model. Our data demonstrate that human serum is an effective tool to find new chemical matter to address the current antibiotic resistance crisis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.223
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicAntibiotic Resistance in BacteriaFrench-language works237,207