Biology subjects

Freeman, T. C.

Publications and source records attributed to Freeman, T. C..

11 recordsLinked to original sources

bioRxiv2023

Mitochondrial dysfunction and mitophagy blockade contribute to renal osteodystrophy in chronic kidney disease-mineral bone disorder

Chronic kidney disease-mineral and bone disorder (CKD-MBD) presents with extra-skeletal calcification and renal osteodystrophy (ROD). The origins of ROD likely lie with elevated uremic toxins and/or an altered hormonal profile but the cellular events responsible remain unclear. Here, we report that stalled mitophagy contributes to mitochondrial dysfunction in bones of a CKD-MBD mouse model, and also human CKD-MBD patients. RNA-seq analysis exposed an altered expression of genes associated with mitophagy and mitochondrial function in tibia of CKD-MBD mice. The accumulation of damaged osteocyte mitochondria and the expression of mitophagy regulators, p62/SQSTM1, ATG7 and LC3 was inconsistent with functional mitophagy, and in mito-QC reporter mice with CKD-MBD, there was a 2.3-fold increase in osteocyte mitolysosomes. Altered expression of mitophagy regulators in human CKD-MBD bones was also observed. To determine if uremic toxins were possibly responsible for these observations, indoxyl sulfate treatment of osteoblasts revealed mitochondria with distorted morphology and whose membrane potential and oxidative phosphorylation were decreased, and oxygen-free radical production increased. The altered p62/SQSTM1 and LC3-II expression was consistent with impaired mitophagy machinery and the effects of indoxyl sulfate were reversible by rapamycin. In conclusion, mitolysosome accumulation from impaired clearance of damaged mitochondria may contribute to the skeletal complications, characteristic of ROD. Targeting mitochondria and the mitophagy process may therefore offer novel routes for intervention to preserve bone health in patients with ROD. Such approaches would be timely as our current armamentarium of anti-fracture medications has not been developed for, or adequately studied in, patients with severe CKD-MBD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/573355v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1f23caforg.highwire.dtl.DTLVardef@6e3e94org.highwire.dtl.DTLVardef@1ce6adeorg.highwire.dtl.DTLVardef@ca5558_HPS_FORMAT_FIGEXP M_FIG C_FIG TRANSLATIONAL STATEMENTRenal osteodystrophy (ROD) remains the major skeletal complication of chronic kidney disease-mineral and bone disorder (CKD-MBD). As a disease characterised by biochemical and hormone abnormalities, ROD is exacerbated by osteocyte mitochondrial dysfunction. Advances in our understanding of the mitophagy pathway are vital to improving the clinical management of ROD. The dysregulation of mitophagy in murine and human CKD-MBD bone provided evidence of delayed clearance of damaged mitochondria, which was also observed in uremic toxin-treated-osteoblasts but reversible upon rapamycin treatment. This study reveals the therapeutic potential of managing ROD by restoring defective mitophagy in osteocytes.

genomics↗

bioRxiv2023

Cellular heterogeneity of the developing worker honey bee (Apis mellifera) pupa: a single cell transcriptomics analysis

It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behaviour, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15), and sought to determine their gene signatures and thereby provide potential functional annotations for as yet poorly characterized genes. To identify cell type populations we examined the cell-to-cell network based on the similarity of the single-cells transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 15 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene co-expression analysis. We combined this analysis with literature mining, the honey bee protein atlas and Gene Ontology analysis to determine cell cluster identity. Of the cell clusters identified, 9 were related to the nervous system, 7 to the fat body, 14 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology of at the cellular level.

genomics↗

bioRxiv2021

A systems-level analysis of dynamic total-body PET data reveals complex skeletal energy metabolism networks in vivo

Bone is now regarded to be a key regulator of a number of metabolic processes, in addition to the regulation of mineral metabolism. However, our understanding of complex bone metabolic interactions at a systems level remains rudimentary, limiting our ability to assess systemic mechanisms underlying diseases and develop novel therapeutics. In vitro molecular biology and bioinformatics approaches have frequently been used to understand the mechanistic changes underlying disease at the cell level, however, these approaches lack the capability to interrogate dynamic multi-bone metabolic interactions in vivo. Here we present a novel and integrative approach to understand complex bone metabolic interactions in vivo using total-body positron emission tomography (PET) network analysis of murine 18F-FDG scans, as a biomarker of glucose metabolism signature in bones. In this report we show that different bones within the skeleton have a unique glucose metabolism and form a complex metabolic network. These data could have important therapeutic implications in the management of the metabolic syndrome and skeletal disease. The application of our approach to clinical and preclinical total-body PET studies promises to reveal further physiological and pathological tissue interactions, which simplistic PET standard uptake values analysis fail to interrogate, extending beyond skeletal metabolism, due to the diversity of PET radiotracers available and under development as well as the advent of clinical total-body PET systems. One Sentence SummaryBones form a complex metabolic network.

physiology↗

bioRxiv2020

Monocyte-derived Prostaglandin E2 inhibits antigen-specific cutaneous immunity during ageing

Ageing results in a decline in immune function. We showed previously that healthy older humans (>65 years old) have reduced antigen-specific cutaneous immunity to varicella zoster virus (VZV) antigen challenge. This was associated with p38 MAP kinase driven inflammation that was induced by mild tissue injury caused by the injection of the antigen itself. Here we show that non-specific injury induced by injection of air or saline into the skin of older adults recruits CCR2+CD14+ monocytes by CCL2 produced by senescent fibroblasts. These monocytes reduced TRM proliferation via secretion of prostaglandin E2 (PGE2). Pre-treatment with a p38-MAPK inhibitor (Losmapimod) in older adults in vivo significantly decreased CCL2 expression, recruitment of monocyte into the skin, COX2 expression and PGE2 production. This enhanced the VZV response in the skin. Therefore, local inflammation arising from interaction between senescent cells and monocytes leads to immune decline in the skin during ageing, a process that can be reversed. SummaryInflammation resulting from tissue injury blocks antigen-specific cutaneous immunity during ageing. Monocytes recruited to the skin inhibit TRM function through COX2-derived prostaglandin E2 production. Blocking inflammation and resulting prostaglandin E2 production with a p38-MAP kinase inhibitor significantly enhances cutaneous antigen-specific responses.

immunology↗

bioRxiv2019

The transcriptional signature associated with human motile cilia

Cilia are complex microtubule-based organelles implicated in the aetiology of numerous diseases. Accordingly, many cilia-associated proteins have been described, while those distinguishing cilia subtypes are poorly defined. Here, we characterise the gene signature associated with human motile cilia that captures both known and unknown components of this class of cilia. To define the signature, we performed network deconvolution of transcriptomics data derived from tissues possessing motile ciliated cell populations. For each tissue, genes coexpressed with the motile cilia-associated transcriptional factor, FOXJ1, were identified. The consensus across tissues provided a transcriptional signature of 248 genes. For validation, we examined the literature, databases, single cell RNA-Seq data, and the localisation of mRNA and proteins in motile ciliated cells. To validate some of the many poorly characterised genes, we performed new localisation experiments on ARMC3, EFCAB6, FAM183A, MYCBPAP, RIBC2 and VWA3A. In summary, we report a highly validated set of motile cilia-associated genes that helps shape our understanding of these complex cellular organelles.\n\nSummaryThis work defines a conserved transcriptional signature associated with human motile cilia, including many genes with little or no previous association with these structures. These genes were compared with existing resources and a number of poorly characterised genes validated.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC=\"FIGDIR/small/817072v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (74K):\norg.highwire.dtl.DTLVardef@152f5d6org.highwire.dtl.DTLVardef@189eba6org.highwire.dtl.DTLVardef@1cde94forg.highwire.dtl.DTLVardef@1906450_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2019

A detailed graphical and computational model of the mammalian renal circadian clock

Here we describe the construction of a detailed graphical and computational model of the mammalian circadian clock. We use it to simulate the clock activity within the kidney, where it plays a pivotal role in regulating blood pressure. First, we assembled a network-based process diagram, which includes all known components of the clock and the interactions between them. Parameterisation of the model for Petri net-based simulation experiments used mRNA levels in the kidney to define initial conditions. With empirical testing, model parameterisation was further refined such that the simulated activity of core genes closely matched their measured activity. Furthermore, virtual knockout experiments performed on the model were shown to reflect experimental gene knockout data. It also identified points at which canonical clock genes may integrate with downstream genes likely to affect blood pressure and other aspects of kidney function. We believe that the model provides new insights into the complexity and function of this most central of physiological pathways and provides a valuable resource for the research community.

systems biology↗

bioRxiv2018

Network analysis of canine brain morphometry links tumour risk to oestrogen deficiency and accelerated brain ageing

BackgroundStructural brain age is a valuable but complex biomarker for several brain disorders. The dog is an unrivalled comparator for neurological disease modeling, however brain phenotypic diversity among pedigrees creates computational and statistical challenges.\n\nMethodsWe applied unbiased network correlation analysis in dogs to explore complex interactions between brain morphometrics, patient metadata, and neurological disease. Twenty-four parameters measured from each of 286 brain magnetic resonance imaging scans generated 9,438 data points that were used to cluster canine patients according to their brain morphometry profiles. The network was then explored for statistically significant enrichments within breed, sex, age, and diagnostic categories.\n\nFindingsMorphometric comparisons revealed an advanced aged-brain profile in the Boxer breed, consisting of a small brain length, width, and volume, combined with ventriculomegaly. Key features of this profile were paralleled in neutered female dogs which, relative to un-neutered females, had an 11-fold greater risk of developing primary brain tumours. Enrichment analysis confirmed that Boxers and geriatric individuals were enriched for brain tumour diagnoses, despite a lack of geriatric Boxers within the cohort.\n\nInterpretationThese findings suggest that accelerated brain ageing might contribute to tumour risk in Boxers and may be influenced by oestrogen deficiency -- a risk factor for dementia and brain tumours in humans. We propose that morphometric features of brain ageing in dogs, like humans, might better predict neurological disease risk than a patients chronological age.\n\nFundingWellcome Trust Integrated Training Fellowship for Veterinarians (096409/Z/11/Z to N.M.R) and an MSD Animal Health Connect Bursary (to O.M.S.).

neuroscience↗

bioRxiv2018

Visualisation and analysis of RNA-Seq assembly graphs

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging.\n\nHere we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

bioinformatics↗

bioRxiv2018

Characterisation of the UK honey bee (Apis mellifera) metagenome.

The European honey bee (Apis mellifera) plays a major role in pollination and food production, but is under threat from emerging pathogens and agro-environmental insults. As with other organisms, honey bee health is a complex product of environment, host genetics and associated microbes (commensal, opportunistic and pathogenic). Improved understanding of bee genetics and their molecular ecology can help manage modern challenges to bee health and production. Sampling bee and cobiont genomes, we characterised the metagenome of 19 honey bee colonies across Britain. Low heterozygosity was observed in bees from many Scottish colonies, sharing high similarity to the native dark bee, A. mellifera mellifera. Apiaries exhibited high diversity in the composition and relative abundance of individual microbiome taxa. Most non-bee sequences derived from known honey bee commensal bacteria or known pathogens, e.g. Lotmaria passim (Trypanosomatidae), and Nosema spp. (Microsporidia). However, DNA was also detected from numerous additional bacterial, plant (food source), protozoan and metazoan organisms. To classify sequences from cobionts lacking genomic information, we developed a novel network analysis approach clustering orphan contigs, allowing the identification of a pathogenic gregarine. Our analyses demonstrate the power of high-throughput, directed metagenomics in agroecosystems identifying potential threats to honey bees present in their microbiota.

genomics↗

bioRxiv2018

Assembly of a Parts List of the Human Mitotic Cell Cycle Machinery

The set of proteins required for mitotic division remains poorly characterised. Here, an extensive series of correlation analyses of human and mouse transcriptomics data was performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, a list of 701 cell cycle-associated genes was defined and shown that whilst many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterised. Among these, 27 were screened for subcellular localisation revealing many to be nuclear localised and at least four to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins.

genomics↗

bioRxiv2017

Continuous Biomarker Assessment by Exhaustive Survival Analysis

Publicly available high-throughput molecular data can enable biomarker identification and evaluation in a meta-analysis. However, a continuous biomarkers underlying distribution and/or potential confounding factors associated with outcome will inevitably vary between cohorts and is often ignored. The survivALL R package (https://CRAN.R-project.org/package=survivALL) allows researchers to generate visual and numerical comparisons of all possible points-of-separation, enabling quantitative biomarkers to be reliably evaluated within and across datasets, independent of compositional variation. Here, we demonstrate survivALLs ability to robustly and reproducibly determine an applicable level of gene expression for patient prognostic classification, in datasets of similar and dissimilar compositions. We believe survivALL represents a significant improvement over existing methodologies in stratifying patients and determining quantitative biomarker(s) cut-points for public and novel datasets.

bioinformatics↗