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Record W6981967067

Genome-wide and locus-specific approaches to characterize hepatic gene regulation in cancer cachexia

2018· dissertation· en· W6981967067 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsMcGill Genome Centre
Fundersnot available
KeywordsCachexiaWastingCancerEpigeneticsTranscription factorAdipose tissueTranscriptional regulationRegulation of gene expressionGene expressionMyelopoiesis
DOInot available

Abstract

fetched live from OpenAlex

Cancer-associated cachexia affects 80% of cancer patients and causes 30% of cancer deaths.Many studies have focused on the molecular mechanism of muscle wasting and adipose tissue loss in rodents or human subjects with cancer cachexia.Although the liver is a central player in body energy metabolism, its role in cachexia has rarely been studied.By investigating the gene expression profile of cachectic liver in the C26-induced cachexia mouse model, we aim to discover how this tissue contributes to energy wasting and metabolic regulation in cancer cachexia.The transcriptional profile of cachectic liver shows upregulated expression of genes involved in cholesterol biosynthesis and repressed expression of genes involved in ketogenesis and TCA cycle.Moreover, chromatin immunoprecipitation (ChIP) coupled with next generation sequencing (ChIP-seq) provides opportunities to scrutinize the genome-wide epigenetic status of the cachectic liver and identify transcriptional regulatory programs that determine this.The bioinformatic analysis of these active DNA elements could reveal the transcription factors involved in the hepatic response to cachexia.In addition to the well-known cytokine regulators implicated in cancer cachexia (such as TNFα and IL-6), the search for circulating cachectic factors has proceeded slowly.It is necessary to build a comprehensive catalogue of cachectic factors, whose contribution to energy wasting can be evaluated in different organs.We performed a time-series gene expression analysis of cachectic cells C26 and tumor tissue after xenograft transplantation.The bioinformatic analysis has identified a list of cachectic factors, which showed high similarity with Lewis Lung Carcinoma (LLC), another cachectogenic cells.To better characterize the transcriptional regulators of cholesterol biosynthesis, which is a major metabolic feature of cachectic liver, we developed a novel technology (TALE-AP) to study the DNA locus-specific transcription factor complex by repurposing TALE.We showed that TALE-AP identify SREBP1 as a transcription factor specifically binding to SQLE promoter, which is confirmed by ENCODE SummaryPrevious studies have characterized the gene expression profile of cachectic liver, but the underlying TF network mediating these transcription changes remains unknown.In this chapter, we adopted an epigenomic approach to systematically identify active DNA elements genome-wide and used bioinformatics analysis to uncover the TFs involved.To identify the active DNA regions in cachectic liver, we performed ChIP-seq against three commonly used histone modification marks (H3K4me1, H3K4me3 and H3K27ac), CTD-phosphorylated RNA polymerase II and CBP in cachectic and sham-injected liver.Pathway enrichment analysis indicated that the active genes were enriched for cytokine-cytokine receptor interaction, the insulin response pathway and JAK-STAT signaling.Motif analysis suggested that STAT3, FOX and ETS family TFs were involved in reprogramming hepatic gene expression during cachexia. 4.1

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.247
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreOther

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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Citations0
Published2018
Admission routes1
Has abstractyes

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