ArticlebioRxiv : the preprint server for biology2024
Inferring the composition of a mixed culture of natural microbial isolates by deep sequencing.
Mark Voorhies, Bastian Joehnk, Jessie Uehling, Keith Walcott, Claire Dubin, Heather L Mead, Christina M Homer, John N Galgiani, Bridget M Barker, Rachel B Brem and 1 more
Abstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
11 authors.
Mark VoorhiesDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America.ORCID 0000-0001-8815-7384 Bastian JoehnkDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America.
Jessie UehlingDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America.
Keith WalcottDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America.
Claire DubinDepartment of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America.
Heather L MeadThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.
Christina M HomerDivision of Infectious Diseases, University of California San Francisco, San Francisco, California, United States of America.ORCID 0000-0003-4835-090X John N GalgianiValley Fever Center for Excellence, Department of Medicine, University of Arizona, Tucson, Arizona, United States of America.
Bridget M BarkerThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.ORCID 0000-0002-3439-4517 Rachel B BremDepartment of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America.ORCID 0000-0002-8587-9434 Anita SilDepartment of Microbiology and Immunology, University of California San Francisco, San Francisco, California, United States of America.ORCID 0000-0002-3987-7534 Funding
Virulence gene discovery in CoccidioidesU19AI166798 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Satya Dandekar · 2022 to 2026
$10.5MBIOLOGY OF INFECTIOUS DISEASES TRAINING PROGRAMT32AI007641 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MATTHEW G DORSEY · 2000 to 2026
$6.9MIllumina NovaSeq 6000 Sequencing SystemS10OD028511 · OD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHOW, ERIC D · 2020 to 2020
$583kInvestigation of key proteases in the parasitic phase of CoccidioidesR21AI172185 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIL, ANITA · 2022 to 2023
$444kNIAID NIH HHS R21 AI172185NIAID NIH HHS T32 AI007641NIAID NIH HHS U19 AI166798NIH HHS S10 OD028511
6 · The paper itselfAbstract
Next generation sequencing has unlocked a wealth of genotype information for microbial populations, but phenotyping remains a bottleneck for exploiting this information, particularly for pathogens that are difficult to manipulate. Here, we establish a method for high-throughput phenotyping of mixed cultures, in which the pattern of naturally occurring single-nucleotide polymorphisms in each isolate is used as intrinsic barcodes which can be read out by sequencing. We demonstrate that our method can correctly deconvolute strain proportions in simulated mixed-strain pools. As an experimental test of our method, we perform whole genome sequencing of 66 natural isolates of the thermally dimorphic pathogenic fungus
Identifiers
PMID39149389
PMCPMC11326141
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