Biology subjects

Davis, M. J.

Publications and source records attributed to Davis, M. J..

35 records · Page 2Linked to original sources

bioRxiv2022

Response to immune checkpoint blockade improved in pre-clinical model of breast cancer after bariatric surgery

Bariatric surgery is becoming more prevalent as a sustainable weight loss approach, with vertical sleeve gastrectomy (VSG) being the first line of surgical intervention. We and others have shown that obesity exacerbates tumor growth while diet-induced weight loss impairs obesity-driven progression. It remains unknown how bariatric surgery-induced weight loss impacts cancer progression or alters responses to therapy. Using a pre-clinical model of diet induced obesity followed by VSG or diet-induced weight loss, breast cancer progression and immune checkpoint blockade therapy was investigated. Weight loss by bariatric surgery or weight matched dietary intervention before tumor engraftment protected against obesity-exacerbated tumor progression. However, VSG was not as effective as dietary intervention in reducing tumor burden despite achieving a similar extent of weight and adiposity loss. Circulating leptin did not associate with changes in tumor burden. Uniquely, tumors in mice that received VSG displayed elevated inflammation and immune checkpoint ligand, PD-L1. Further, mice that received VSG had reduced tumor infiltrating T lymphocytes and cytolysis suggesting an ineffective anti-tumor microenvironment. VSG-associated elevation of PD-L1 prompted us to next investigate the efficacy of immune checkpoint blockade in lean, obese, and formerly obese mice that lost weight by VSG or weight matched controls. While obese mice were resistant to immune checkpoint blockade, anti-PD-L1 potently impaired tumor progression after VSG through improved anti-tumor immunity. Thus, in formerly obese mice, surgical weight loss followed by immunotherapy reduced breast cancer burden.

cancer biology↗

bioRxiv2022

vissE: A versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissEs versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, removing investigator bias from molecular discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/483195v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1c384b0org.highwire.dtl.DTLVardef@13c6bd3org.highwire.dtl.DTLVardef@1cb1d91org.highwire.dtl.DTLVardef@24cbf_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

An Inducible Cre Mouse with Preferential Activity in Vascular Smooth Muscle Evades a Previously Lethal Intestinal Phenotype

All smooth muscle cell (SMC) restricted Cre mice recombine floxed alleles in vascular and visceral SMCs. We generated a new tamoxifen-inducible CreERT2 mouse, Itga8-CreERT2, and compared its activity to the widely used Myh11-CreERT2 mouse. Both CreERT2 mice showed similar activity in vascular SMCs; however, Itga8-CreERT2 displayed limited activity in visceral SMC-containing tissues (e.g., intestine). Myh11-CreERT2 (but not Itga8-CreERT2) mice displayed high levels of CreERT2 protein, tamoxifen-independent activity, and an altered transcriptome. Whereas Myh11-CreERT2-mediated knockout of Srf resulted in a lethal intestinal phenotype, loss of Srf with Itga8-CreERT2 (SrfItga8) revealed viable mice with attenuated vascular SMC contractile gene expression, but no evidence of intestinal pathology. Male and female SrfItga8 mice presented with vascular contractile incompetence; however, only male SrfItga8 mice showed systemic changes in blood pressure. These results establish the Itga8-CreERT2 mouse as an alternative to existing SMC Cre strains, including Myh11-CreERT2, where SMC gene loss results in visceral myopathies that obfuscate accurate phenotyping in vascular SMCs.

genetics↗

bioRxiv2021

PIPP: Improving peptide identity propagation using neural networks

Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate.

bioinformatics↗

bioRxiv2021

Label-free characterisation of amyloids and alpha-Synuclein polymorphs by exploiting their intrinsic fluorescence property

Conventional in vitro aggregation assays often involve tagging with extrinsic fluorophores which can interfere with aggregation. We propose the use of intrinsic amyloid fluorescence lifetime probed using two-photon excitation and represented by model-free phasor plots, as a label-free assay to characterise amyloid structure. Intrinsic amyloid fluorescence arises from structured packing of {beta}-sheets in amyloids and is independent of aromatic-based fluorescence. We show that different amyloids (i.e., -Synuclein (S), {beta}-Lactoglobulin and TasA) and different polymorphic populations of S (induced by aggregation in salt-free and salt buffers mimicking the intra-/extracellular environments) can be differentiated by their unique fluorescence lifetimes. Moreover, we observe that disaggregation of pre-formed fibrils of S and {beta}LG leads to increased fluorescence lifetimes, distinct to those of their fibrillar counterpart. Our assay presents a medium-throughput method for rapid classification of amyloids and their polymorphs (the latter of which recent studies have shown lead to different disease pathology), and for testing small molecule inhibitory compounds.

molecular biology↗

bioRxiv2021

Modelling the coupling of the M-clock and C-clock in lymphatic muscle cells

Lymphoedema develops due to chronic dysfunction of the lymphatic vascular system which results in fluid accumulation between cells. The condition is commonly acquired secondary to diseases such as cancer or the therapies associated with it. The primary driving force for fluid return through the lymphatic vasculature is provided by contractions of the muscularized lymphatic collecting vessels, driven by electrical oscillations. However, there is an incomplete understanding of the molecular and bioelectric mechanisms involved in lymphatic muscle cell excitation, hampering the development and use of pharmacological therapies. Modelling in silico has contributed greatly to understanding the contributions of specific ion channels to the cardiac action potential, but modelling of these processes in lymphatic muscle remains limited. Here, we propose a model of oscillations in the membrane voltage (M-clock) and intracellular calcium concentrations (C-clock) of lymphatic muscle cells. We modify a model by Imtiaz and colleagues to enable the M-clock to drive the C-clock oscillations. This approach differs from typical models of calcium oscillators in lymphatic and related cell types, but is required to fit recent experimental data. We include an additional voltage dependence in the gating variable control for the L-type calcium channel, enabling the M-clock to oscillate independently of the C-clock. We use phase-plane analysis to show that these M-clock oscillations are qualitatively similar to those of a generalised FitzHugh-Nagumo model. We also provide phase plane analysis to understand the interaction of the M-clock and C-clock oscillations. The model and methods have the potential to help determine mechanisms and find targets for pharmacological treatment of lymphoedema.

biophysics↗

bioRxiv2020

Ex vivo Demonstration of Functional Deficiencies in Popliteal Lymphatic Vessels from TNF-Tg Mice with Inflammatory Arthritis

BackgroundRheumatoid arthritis (RA) is a progressive immune-mediated inflammatory disease characterized by intermittent episodes of pain and inflammation in affected joints, or flares. Recent studies demonstrated lymphangiogenesis and expansion of draining lymph nodes during chronic inflammatory arthritis, and lymphatic dysfunction associated with collapse of draining lymph nodes in RA patients and TNF-transgenic (TNF-Tg) mice experiencing arthritic flare. As the intrinsic differences between lymphatic vessels afferent to healthy, expanding, and collapsed draining lymph nodes are unknown, we characterized the ex vivo behavior of popliteal lymphatic vessels (PLVs) from WT and TNF-Tg mice. We also interrogated the mechanisms of lymphatic dysfunction through inhibition of nitric oxide synthase (NOS). MethodsPopliteal lymph nodes (PLNs) in TNF-Tg mice were phenotyped as Expanding or Collapsed by in vivo ultrasound and age-matched to WT littermate controls. The PLVs were harvested and cannulated for ex vivo functional analysis over a relatively wide range of hydrostatic pressures (0.5 to 10 cmH2O) to quantify the end diastolic diameter (EDD), tone, amplitude (AMP), ejection fraction (EF), contraction frequency (FREQ) and fractional pump flow (FPF) with or without NOS inhibitors Data was analyzed using repeated measures two-way ANOVA with Bonferronis post hoc test. ResultsReal time videos of the cannulated PLVs demonstrated the predicted phenotypes of robust versus weak contractions of the WT versus TNF-Tg PLV, respectively. Quantitative analyses confirmed that TNF-Tg PLVs had significantly decreased AMP, EF and FPF versus WT (p<0.05). EF and FPF were recovered by NOS inhibition, while the reduction in AMP was NOS independent. No differences in EDD, tone, or FREQ were observed between WT and TNF-Tg PLVs, nor between Expanding versus Collapsed PLVs. ConclusionThese findings support the concept that chronic inflammatory arthritis leads to NOS dependent and independent draining lymphatic vessel dysfunction that exacerbates disease, and may trigger arthritic flare due to decreased egress of inflammatory cells and soluble factors from affected joints.

pathology↗

bioRxiv2020

Offline Memory Reactivation Promotes the ConsolidationOf Spatially Unbiased Long-Term Cognitive Maps

Spatial memories which can last a lifetime are thought to be encoded during online periods of exploration and subsequently consolidated into stable cognitive maps through their offline reactivation1-5. However, the mechanisms and computational principles by which offline reactivation stabilize long-lasting spatial representations remain poorly understood. Here we employed simultaneous fast calcium imaging and electrophysiology to track hippocampal place cells over weeks of online spatial reward learning behavior and offline resting. We describe that recruitment to persistent network-level offline reactivation of spatial experiences predicts cells future multi-day representational stability. Moreover, while representations of reward-adjacent locations are generally more stable across days, reactivation-related stabilization is, conversely, most prominent for reward-distal locations. Thus, while occurring on millisecond time-scales, offline reactivation counter-balances the observed multi-day representational reward-adjacency bias, promoting the stabilization of cognitive maps which comprehensively reflect entire underlying spatial contexts. These findings suggest that post-learning offline-related memory consolidation plays complimentary and computationally distinct role in learning as compared to online encoding.

neuroscience↗

bioRxiv2020

MSImpute: Imputation of label-free mass spectrometry peptides by low-rank approximation

Recent developments in mass spectrometry (MS) instruments and data acquisition modes have aided multiplexed, fast, reproducible and quantitative analysis of proteome profiles, yet missing values remain a formidable challenge for proteomics data analysis. The stochastic nature of sampling in Data Dependent Acquisition (DDA), suboptimal preprocessing of Data Independent Acquisition (DIA) runs and dynamic range limitation of MS instruments impedes the reproducibility and accuracy of peptide quantification and can introduce systematic patterns of missingness that impact downstream analyses. Thus, imputation of missing values becomes an important element of data analysis. We introduce msImpute, an imputation method based on low-rank approximation, and compare it to six alternative imputation methods using public DDA and DIA datasets. We evaluate the performance of methods by determining the error of imputed values and accuracy of detection of differential expression. We also measure the post-imputation preservation of structures in the data at different levels of granularity. We develop a visual diagnostic to determine the nature of missingness in datasets based on peptides with high biological dropout rate and introduce a method to identify such peptides. Our findings demonstrate that msImpute performs well when data are missing at random and highlights the importance of prior knowledge about nature of missing values in a dataset when selecting an imputation technique.

bioinformatics↗

bioRxiv2020

Stable gene expression for normalisation and single-sample scoring

BackgroundTranscriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient samples. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to samples typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context. ResultsGenes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue samples, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple samples to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of samples, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore, which quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these "stably-expressed genes". ConclusionWe show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

bioinformatics↗

bioRxiv2020

RASA1-driven cellular export of collagen IV is required for the development of lymphovenous and venous valves in mice

RASA1, a negative regulator of the Ras-mitogen-activated protein kinase (MAPK) signaling pathway, is essential for the development and maintenance of lymphatic vessel (LV) valves. However, whether RASA1 is required for the development and maintenance of lymphovenous valves (LVV) and venous valves (VV) is unknown. In this study we show that induced endothelial cell (EC)-specific disruption of Rasa1 in mid-gestation mouse embryos did not affect initial specification of LVV or central VV but did affect their continued development. Similarly, switch to expression of a catalytically inactive form of RASA1 resulted in impaired LVV and VV development. Blocked development of LVV in RASA1-deficient embryos was associated with accumulation of the basement membrane protein, collagen IV, in LVV-forming EC and could be partially or completely rescued by MAPK inhibitors and drugs that promote collagen IV folding. Disruption of Rasa1 in adult mice resulted in venous hypertension and impaired VV function that was associated with loss of EC from VV leaflets. In conclusion, RASA1 functions as a negative regulator of Ras signaling in EC that is necessary for EC export of collagen IV, thus permitting the development of LVV and the development and maintenance of VV.

developmental biology↗

bioRxiv2019

An Erg driven transcriptional program controls B-lymphopoiesis

Summary/AbstractB-cell development is initiated by the stepwise differentiation of hematopoietic stem cells into lineage committed progenitors, ultimately generating the mature B-cells that mediate protective immunity. This highly regulated process also generates clonal immunological diversity via recombination of immunoglobulin genes. While several transcription factors that control B-cell development and V(D)J recombination have been defined, how these processes are initiated and coordinated into a precise regulatory network remains poorly understood. Here, we show that the transcription factor ETS Related Gene (Erg) is essential for the earliest steps in B-cell differentiation. Erg initiates a transcriptional network involving the B-cell lineage defining genes, Ebf1 and Pax5, that directly promotes the expression of key genes involved in V(D)J recombination and formation of the B-cell receptor. Complementation of the Erg-deficiency with a productively rearranged immunoglobulin gene rescued B-cell development, demonstrating that Erg is an essential and exquisitely stage specific regulator of the gene regulatory network controlling B-lymphopoiesis.

immunology↗

bioRxiv2019

Snail induces epithelial cell extrusion through transcriptional control of RhoA contractile signalling and cell matrix adhesion

Cell extrusion is a morphogenetic process that is implicated in epithelial homeostasis and highly dependent on the cellular insult and context. Minorities of cells expressing HRasV12 or undergoing apoptosis are typically extruded apically in vertebrate cells. However, basal extrusion (delamination) predominates when mammalian cells express oncogenic KRas and during development in Drosophila. To explore if the morphogenetic transcription factor, Snail, induces extrusion, we inducibly expressed a metabolically stabilized Snail6SA transgene mosaically in confluent MCF-7 monolayers. We found that the morphogenetic impact of Snail6SA was critically influenced by the proportion of cells in which it was expressed. When expressed in small clusters (<3 cells) within confluent monolayers, Snail6SA expression induced apical cell extrusion. In contrast, confluent cultures of Snail6SA expressing cells were retained in the monolayer to eventually undergo basal extrusion (delamination). Transcriptomic profiling revealed that Snail6SA did not substantively alter the balance of epithelial:mesenchymal genes. However, we identified a gene transcriptional network that led to the upregulation of RhoA signalling, actomyosin contractility and reduced basal adhesion in Snail6SA expressing cells. We show that this was necessary to drive both apical extrusion and basal delamination. Thus, contractile upregulation by RhoA along with weakened basal adhesion provides a pathway for Snail to influence epithelial morphogenesis independently of classic Epithelial to Mesenchymal Transition (EMT).

cell biology↗

bioRxiv2018

A natural killer cell gene signature predicts melanoma patient survival

Animal models have demonstrated that natural killer (NK) cells can limit the metastatic dissemination of tumors, however their ability to combat established human tumors has been difficult to investigate.\n\nA number of computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several tools, then curated and expanded this list using recent reports from the literature. Using a gene set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA) we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of greater NK cell infiltration. Furthermore, these survival effects are enhanced in tumors which have a higher expression of NK cell stimuli such as IL-15, suggesting NK cells are part of a coordinated immune response within these patients. Using this signature we then examine transcriptomic data to identify tumor and stromal components which may influence the penetrance of NK cells into solid tumors.\n\nThese data support a role for NK cells in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Furthermore, our computational analysis identifies a number of potential targets which may help to unleash the anti-tumor potential of NK cells as we enter the age of immunotherapy.

cancer biology↗

bioRxiv2018

Inferring Edge Function in Protein-Protein Interaction Networks

Motivation: Post-translational modifications (PTMs) regulate many key cellular processes. Numerous studies have linked the topology of protein-protein interaction (PPI) networks to many biological phenomena such as key regulatory processes and disease. However, these methods fail to give insight in the functional nature of these interactions. On the other hand, pathways are commonly used to gain biological insight into the function of PPIs in the context of cascading interactions, sacrificing the coverage of networks for rich functional annotations on each PPI. We present a machine learning approach that uses Gene Ontology, InterPro and Pfam annotations to infer the edge functions in PPI networks, allowing us to combine the high coverage of networks with the information richness of pathways.\n\nResults: An ensemble method with a combination Logistic Regression and Random Forest classifiers trained on a high-quality set of annotated interactions, with a total of 18 unique labels, achieves high a average F1 score 0.88 despite not taking advantage of multi-label dependencies. When applied to the human interactome, our method confidently classifies 62% of interactions at a probability of 0.7 or higher.\n\nAvailability: Software and data are available at https://github.com/DavisLaboratory/pyPPI\n\nContact: davis.m@wehi.edu.au\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Defining a landscape of molecular phenotypes using a simple single sample scoring method

BackgroundGene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition across a data set (e.g. varying numbers of samples for different cancer subtypes). To address these issues we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore.\n\nResultsWe have developed a rank-based single sample scoring method, implemented as a Bioconductor package. We use multiple cancer data sets to compare it against widely-used scoring methods, including GSVA, z-scores, PLAGE, and ssGSEA. Our approach does not depend upon background samples and thus the scores are stable regardless of the composition and number of samples in the gene expression data set. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (nsamples < 25). We show that the computational time for singscore is faster than current implementations of GSVA and ssGSEA, and is comparable with that of z-score and PLAGE. The singscore package also produces visualisations and interactive plots that enable exploration of molecular phenotypes.\n\nConclusionsThe single sample scoring method described here is independent of sample composition in gene expression data and thus it provides stable scores that are less likely to be influenced by unwanted variation across samples. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

bioinformatics↗

bioRxiv2017

Post-Transcriptional Control Of EMT Is Coordinated Through Combinatorial Targeting By Multiple microRNAs

Epithelial-mesenchymal transition (EMT) is a process whereby cells undergo reversible phenotypic change, losing epithelial characteristics and acquiring mesenchymal attributes. While EMT underlies normal, physiological programs in embryonic tissue development and adult wound healing, it also contributes to cancer progression by facilitating metastasis and altering drug sensitivity. Using a cell model of EMT (human mammary epithelial (HMLE) cells), we show that miRNAs act as an additional regulatory layer over and above the activity of the transcription factors with which they are closely associated. In this context, miRNAs serve to both enhance expression changes for genes with EMT function, whilst simultaneously reducing transcriptional noise in non-EMT genes. We find that members of the polycistronic miR-200c~141 and miR-183~182 clusters (which are decreased during HMLE cell EMT and are associated with epithelial gene expression in breast cancer patients) co-regulate common targets and pathways to enforce an epithelial phenotype. We demonstrate their combinatorial effects are apparent much closer to endogenous expression levels (and orders of magnitude lower than used in most studies). Importantly, the low levels of combinatorial miRNAs that are required to exert biological function ameliorate the \"off-target\" effects on gene expression that are a characteristic of supra-physiologic miRNA manipulation. We argue that high levels of over-expression characteristic of many miRNA functional studies have led to an over-estimation of the effect of many miRNAs in EMT regulation, with over 130 individual miRNAs directly implicated as drivers of EMT. We propose that the functional effects of co-regulated miRNAs that we demonstrate here more-accurately reflects the endogenous post-transcriptional regulation of pathways, networks and processes, and illustrates that the post-transcriptional miRNA regulatory network is fundamentally cooperative.

molecular biology↗