Biology subjects

Burchfield, J. G.

Publications and source records attributed to Burchfield, J. G..

8 recordsLinked to original sources

bioRxiv2026

Systems genetics identifies ETS1 as a stress-dependent regulator of adipocyte insulin action and heme-iron homeostasis

White adipose tissue plays a central role in systemic energy homeostasis by buffering nutrient excess through insulin-stimulated glucose uptake and triglyceride storage. Despite its importance, the genetic and molecular mechanisms governing adipose tissue insulin action remain poorly defined because tissue-specific insulin responsiveness has been difficult to quantify at the scale required for genetic discovery. Here, we developed the first scalable platform for high-throughput genetic mapping of tissue-specific insulin action in adipose tissue, enabling systems genetic analysis across 559 genetically diverse Diversity Outbred Australia (DOz) mice. Genetic analysis accounting for adiposity identified 39 loci associated with adipose tissue insulin action, demonstrating that adipose insulin responsiveness is a genetically encoded trait that captures a dimension of metabolic health beyond adiposity. Among these, a strong diet-dependent locus on chromosome 9 encompassed the transcription factor Ets1. Functional studies demonstrated that Ets1 silencing selectively restored insulin-stimulated glucose uptake in insulin-resistant adipocytes. Proteomic profiling revealed that ETS1 orchestrates a stress-responsive program involving heme metabolism, iron handling and redox homeostasis. Consistent with this, ETS1 knockdown reduced cellular heme and labile iron levels and attenuated oxidative stress under insulin-resistant conditions. Collectively, these findings demonstrate the power of systems genetics to identify previously unrecognised regulators of adipose insulin action and establish the heme-iron axis as a critical determinant of adipocyte insulin responsiveness.

systems biology↗

bioRxiv2024

ThunderBolt: An interactive data sharing and analysis platform for large-omics experiments

SummaryMass-spectrometry (MS) datasets present a unique set of challenges that make in-depth bioinformatics analysis non-trivial, with analysis requiring both expertise and time. Often these datasets have unique structures that need to be dealt with on an individual basis. Currently, tools providing a fast, interactive and guided way of exploring and analysing these data sets are not readily available. To this end, we have developed ThunderBolt: a highly interactive, point-and-click web-based application providing both bioinformaticians and biologists with a platform for i) searching and comparing multiple omics datasets, ii) fast data exploration and quality control, iii) interactive visualization, iv) pre-processing, v) statistical analysis and vi) functional and network enrichment analysis of large proteomics datasets using the Shiny framework. AvailabilityThunderBolt is a shiny-application accessible at https://thunderbolt.sydney.edu.au/ Contactjames.burchfield@sydney.edu.au Supplementary information

systems biology↗

bioRxiv2023

Mitochondrial electron transport chain, ceramide and Coenzyme Q are linked in a pathway that drives insulin resistance in skeletal muscle

Insulin resistance (IR) is a complex metabolic disorder that underlies several human diseases, including type 2 diabetes and cardiovascular disease. Despite extensive research, the precise mechanisms underlying IR development remain poorly understood. Here, we provide new insights into the mechanistic connections between cellular alterations associated with IR, including increased ceramides, deficiency of coenzyme Q (CoQ), mitochondrial dysfunction, and oxidative stress. We demonstrate that elevated levels of ceramide in the mitochondria of skeletal muscle cells results in CoQ depletion and loss of mitochondrial respiratory chain components, leading to mitochondrial dysfunction and IR. Further, decreasing mitochondrial ceramide levels in vitro and in animal models (under chow and high fat diet) increased CoQ levels and was protective against IR. CoQ supplementation also rescued ceramide-associated IR. Examination of the mitochondrial proteome from human muscle biopsies revealed a strong correlation between the respirasome system and mitochondrial ceramide as key determinants of insulin sensitivity. Our findings highlight the mitochondrial Ceramide-CoQ-respiratory chain nexus as a potential foundation of an IR pathway that may also play a critical role in other conditions associated with ceramide accumulation and mitochondrial dysfunction, such as heart failure, cancer, and aging. These insights may have important clinical implications for the development of novel therapeutic strategies for the treatment of IR and related metabolic disorders.

cell biology↗

bioRxiv2022

Deep proteome profiling of white adipose tissue reveals marked conservation and distinct features between different anatomical depots

White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue (VAT), which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just 3% of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched in structural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Q biosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused reduction in lipid handling and glucose uptake pathways particularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the VAT, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment.

molecular biology↗

bioRxiv2022

Phosphoproteomics reveals rewiring of the insulin signaling network and multi-nodal defects in insulin resistance

The failure of metabolic tissues to appropriately respond to insulin ("insulin resistance") is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employed phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults triggering insulin resistance, we observed marked rewiring of the insulin signaling network. This included both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying signaling changes common to multiple insults revealed subnetworks likely containing causal drivers of insulin resistance. Focusing on defective GSK3 signaling initially observed in a relatively small subset of well-characterized substrates, we employed a pipeline for identifying context-specific kinase substrates. This facilitated robust identification of widespread dysregulated GSK3 signaling. Pharmacological inhibition of GSK3 partially reversed insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that encompasses dysregulated GSK3 activity.

molecular biology↗

bioRxiv2021

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a pseudo-positive learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data re-sampling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ( SnapKin) incorporating the above two learning strategies into a snapshot ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

bioinformatics↗

bioRxiv2020

Identifying a GSK3-TRARG1 signaling axis reveals BCL9L as a novel regulator of GLUT4 trafficking

Trafficking regulator of GLUT4-1, TRARG1, positively regulates insulin-stimulated GLUT4 trafficking and insulin sensitivity. However, the mechanism(s) by which this occurs remain(s) unclear. Using biochemical and mass spectrometry analyses we found that TRARG1 is dephosphorylated in response to insulin in a PI3K/Akt-dependent manner and is a novel substrate for GSK3. Priming phosphorylation of murine TRARG1 at serine 84 allows for GSK3-directed phosphorylation at serines 72, 76 and 80. A similar pattern of phosphorylation was observed in human TRARG1, suggesting that our findings are translatable to human TRARG1. Pharmacological inhibition of GSK3 increased cell surface GLUT4 in cells stimulated with a submaximal insulin dose, and this was impaired following Trarg1 knockdown, suggesting that TRARG1 acts as a GSK3-mediated regulator in GLUT4 trafficking. These data place TRARG1 within the insulin signaling network and provide insights into how GSK3 regulates GLUT4 trafficking in adipocytes.

cell biology↗

bioRxiv2019

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.\n\nResultsHere, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used.\n\nConclusionsOur results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble

bioinformatics↗