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Record W4310125627 · doi:10.1182/blood-2022-163538

The Bacterial Chaperone High-Temperature Protein G (HtpG) Decreases in Abundance with Induction Therapy in Children with Acute Lymphoblastic Leukemia

2022· article· en· W4310125627 on OpenAlexaffabout
Emma K. Macdonald, Katherine A. Dunn, Joseph P. Bielawski, Johan VanLimbergen, Ketan Kulkarni, Tamara MacDonald

Bibliographic record

VenueBlood · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsIzaak Walton Killam Health CentreDalhousie UniversityUniversity of Waterloo
Fundersnot available
KeywordsHsp90BiologyHeat shock proteinCellular adaptationCarcinogenesisChaperone (clinical)GeneCancer cellProtein qualityLeukemiaCancerCell biologyGeneticsBiochemistryMedicine

Abstract

fetched live from OpenAlex

Introduction: Heat shock proteins (HSPs) are ancient and highly conserved genes that participate in protein quality control in eukaryotes and bacteria. These proteins are produced in large quantities when cells are exposed to stress that causes denaturation of essential proteins. HSPs help maintain cellular homeostasis, and act as molecular chaperones, assisting with protein folding and stability, multi-protein assembly, intracellular protein trafficking and degrading damaged proteins. The Hsp90 family of proteins includes 4 genes, with 1 (HtpG) being a bacterial form. Members of the Hsp90 family interact with a diverse set of client proteins, some involved in cell signalling and proliferation. Many of these client proteins are involved in growth, survival and adaptation of cancer cells. Eukaryotic Hsp90 plays an important role in tumorigenesis and there has been much research into the use of Hsp90 inhibitors to inhibit growth of cancer cells, but little has been done to examine what role the other paralogues have in stabilizing cancer cells. In addition, no work to date has looked at whether bacterial HtpG paralogues might interact with cancer cells. While it is anticipated that bacterial stress and the behaviour of HtpG would correlate to the host response to stress, this has not been examined. The first step is to examine bacterial HtpG gene levels before and during induction to determine if there are changes in HtpG gene abundance. Methods: Forty-eight stool samples were collected from 29 pediatric patients with Acute Lymphoblastic Leukemia (ALL) being treated at the IWK health centre, Nova Scotia, Canada after informed consent. DNA was extracted and whole shotgun metagenome sequencing (Illumina, Nextera XT) was performed. Low quality and contaminated sequences (human and PhiX174) were removed using the kneaddata pipeline (Trimmomatic and Bowtie2). Paired cleaned sequences were concatenated and HUMAnN3 was used to assign gene families to reads using the Uniref90 database. Genes assigned to HtpG or "Hsp90-like" gene families were included and summed from each sample and corrected for sequencing depth. Samples were grouped by phase of treatment (13 pre-treatment samples from 13 patients and 35 induction samples from 23 patients) for analysis. Comparisons between pre-treatment (PT) and induction (IN) samples were performed using Wilcoxon-Mann-Whitney U. Fisher's exact test was used to compare age group and sex between PT and IN samples. The study was approved by the IWK ethics board. Results: Neither sex nor age category differed between PT and IN (p=1). HtpG gene counts among the 48 stool samples from 29 patients ranged from 0 to 329 (mean 119). The mean counts of HtpG genes were significantly decreased in samples during induction therapy compared to pre-treatment (p=0.002). Shannon diversity, a measure of microbial diversity, did not differ between PT and IN samples (p=0.2793), nor did counts of bacteria (Chao-1 p=0.47). Discussion: Studies have found that Hsp90 is generally over expressed in cancer patients with elevated levels, suggesting poorer overall survival. They have been proposed as a potential biomarker of poor prognosis. We had anticipated increased stress and protein damage to occur in the gut environment as a result of chemotherapy, rather than as a result of the underlying cancer. The anticipated increase in HtpG during induction did not occur but rather declined with cancer remission. One possibility is that the decline in HtpG genes was a result of a loss of bacterial diversity due to treatment. However, we did not see significant differences in diversity between PT and IN. The increase in HtpG genes during PT and reduction during induction therapy seems to follow a similar pattern to Hsp90 expression in host cells, suggesting that bacterial HtpG might also serve as a marker of disease remission. It also highlights that a better understanding of bacterial HtpG, a protein known to be present in outer membrane vesicles, which are crucial for host communication and have been implicated in contributing to carcinogenic potential is warranted. Conclusion: This study suggests that the bacterial chaperone HtpG is possibly related to degree of inflammation caused by leukemic cells rather than leukemia treatment. A larger study is needed to verify the potential of HtpG as a marker of cancer remission or treatment toxicity or cancer immune response.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.183
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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