Biology subjects

Lord, J. M.

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

8 recordsLinked to original sources

bioRxiv2025

Increased Quadriceps Intermuscular Adipose Tissue in Chronic Liver Disease is Associated with an Altered Muscle Transcriptome compared with Healthy Age Matched Controls.

Muscle fat infiltration (i.e., myosteatosis) is an important index of muscle health and pathology in disease states, such as chronic liver disease (CLD). However, it is unknown whether myosteatosis differs due to anatomical location. Further, the mechanistic implications of divergent intermuscular adipose tissue (IMAT) deposition remains unknown in patients with CLD. 33 patients with CLD (55.0{+/-}10.5years) and 17 healthy controls (HC) (49.6{+/-}15.4years) were recruited. Quadriceps IMAT was estimated between 20-80% of muscle length and psoas IMAT at L3 via MRI. Body composition, muscle strength and habitual activity were assessed. Fasted vastus lateralis muscle biopsies were collected and subjected to RNA sequencing analysis. https://ClinicalTrials.gov identifier: NCT04734496. IMAT was greater in CLD compared with HC in both quadriceps and psoas (P<0.0001). Quadriceps IMAT positively correlated with BMI (r=0.62), body fat (r=0.65), age (r=0.36) and negatively correlated with maximal knee extensor strength (r=-0.45) and habitual physical activity (r=-0.50) in CLD and HC. 169 differentially expressed genes (DEGs) were identified in low IMAT CLD vs HC, and 178 DEGs were identified in high IMAT CLD vs HC. CLD patients with high and low IMAT exhibited defined expression profiles, with only 39 DEGs (representing 12.7%) in common. Pathway analysis of DEGs revealed enrichment of atrophic and pro-inflammatory pathways in the high IMAT group. IMAT is greater in patients with CLD compared to HC, irrespective of anatomical location and is associated with reductions in muscle function and may be exacerbated by physical inactivity. IMAT appears to alter the muscle transcriptome in COLD, potentially exacerbating muscle loss.

physiology↗

bioRxiv2025

Systemic drivers and molecular mechanisms of sarcopenia in aetiology-specific end-stage liver disease

IntroductionPatients with end-stage liver disease (ESLD) often present with sarcopenia, defined as loss of skeletal muscle mass and quality, which is associated with reduced quality of life and increased mortality. However, the molecular mechanisms driving sarcopenia in ESLD are not fully understood and there are currently no therapeutic interventions. This study aimed to identify potential circulating factors contributing to sarcopenia progression in ESLD by assessing their role in driving transcriptomic alterations in skeletal muscle. MethodsQuadriceps muscle tissue, plasma and serum were obtained from ESLD patients (n=24) and age/sex-matched healthy controls (HC; n=18) (clinical trial ID: NCT04734496, ethical approval 18/WM/0167). Total RNA from snap-frozen vastus lateralis muscle biopsies underwent RNA sequencing (Illumina). Serum concentrations of 60 cytokines were profiled by Luminex and ELISA, with comparisons made both between ESLD and HC, and across ESLD aetiologies (alcohol-related, NAFLD, viral hepatitis, other). In vitro, primary human myotubes (from non-ESLD aged donors, NRES #16/SS/0172) were treated with 10% ESLD or HC plasma (24 h, n=6 per group) followed by RNA sequencing (BGI Genomics). Differentially expressed genes (p<0.05, fold-change >1.5) were identified via Qlucore and DESeq2, and pathway analysis performed using Ingenuity (Qiagen). The impact of physiological concentrations of candidate cytokines (IL-1, GDF-15, HGF) on myotube thickness, differentiation and mitochondrial function was assessed by immunofluorescence microscopy, RT-qPCR and metabolic flux assays. ResultsIn ESLD muscle, 387 and 225 genes were significantly up- and downregulated compared to HC respectively, with cellular senescence identified as a top dysregulated function. Upstream regulator analysis predicted activation of hepatocyte growth factor (HGF) and interleukin-1 signalling. Subgroup analysis revealed distinct transcriptomic profiles based on disease aetiology. Serum profiling identified 15 cytokines significantly elevated (p<0.05) and 5 reduced (p<0.05) in ESLD, including increased HGF and reduced interleukin-1 receptor antagonist. Stratified analysis also revealed aetiology specific cytokine profiles, with only GDF-15 significantly (P<0.0001) elevated in all groups. 24h ESLD plasma treatment induced 423 differentially expressed genes in human myotubes, which were again associated with significant activation of senescence pathways, with IL-1 identified as a key upstream driver. In vitro, IL-1, GDF-15, and HGF significantly reduced myotube thickness, nuclear fusion index and perturbed metabolism (Increased glycolysis, impaired oxidative phosphorylation). ConclusionsCollectively, these findings suggest that sarcopenia in ESLD is driven by aetiology-specific mechanisms, highlighting the potential for targeted therapies to improve muscle mass and function.

molecular biology↗

bioRxiv2025

Floral temperatures of the southern-most flowering Amorphophallus titanum and seedling growth following self-pollination

Amorphophallus titanum (Becc.) Becc. (Araceae) is native to the rainforests of Sumatra, and famously produces a single, massive, thermogenic and highly odoriferous inflorescence. Despite having both male and female flowers in close proximity, A. titanum does not readily self-pollinate as it is strictly dichogamous, with female flowers receptive on the first night of flowering and pollen released on the second night. We report on external and internal temperatures reached by the most southerly plant in cultivation which flowered in February 2018 in Dunedin, New Zealand. We also report on successful self-pollination of the same plant in January 2021 with pollen collected in 2018 that had been stored at -80{degrees}C. External spathe and internal spadix temperatures exceeded ambient temperatures on both flowering nights in 2018. Self-pollination in 2021 resulted in copious fruits that matured in 8 months and showed 75% germination success. More than a fifth of all seedings to germinate were achlorophyllous and died within four months, which is consistent with inbreeding depression. Seedling height, leaf production, length of active growth period and corm size varied markedly among the surviving chlorophyllous seedlings, with some producing two leaves in their first year. While we would not recommend the use of self-pollination for propagation of this species due to the likely presence of inbreeding depression, our study does prove that pollen can retain its viability at -80{degrees}C for at least several years which is an important step towards a pedigree-informed breeding programme among plants in cultivation.

plant biology↗

bioRxiv2024

Physical activity modifies the metabolic profile of CD4+ and CD8+ T cell subtypes at rest and upon activation in older adults

Aging reduces the functional competence of T cells. T cell metabolism regulates their function, with mitochondrial defects in mice resulting in aged phenotypes, including accelerated senescence. Physical activity (PA) maintains T cell function in older adults, although the mechanisms underlying this effect are poorly understood. This study examined the effects of aging on the metabolic profile of T cell subsets and investigated whether PA could improve metabolic function in T cells from older donors. We recruited nine young adults (23 {+/-} 3y) and 19 healthy older adults who had high PA (HPA, N=9, 75.5 {+/-} 4.7y) or low PA levels (LPA, N=10, 76.4 {+/-} 2.1y), based on their moderate-to-vigorous PA scores. We investigated the metabolic profiles of CD4+ and CD8+ T cells at rest and post-activation (PMA and ionomycin), via SCENITH flow cytometry. Compared to young adults, older adults had higher mitochondrial dependence (MD) in unstimulated CD4+ and CD8+ naive, effector memory (EM) and central memory (CM); and higher protein synthesis in CD4+ EM, CD4+ naive, CD8+ EM, suggesting higher energetic demand in T cells with aging. Upon activation there was a lower reduction in MD of CD4+ EMRA and CD8+ EMRA; and a greater increase in IL-6 and TNF expression in CD8+ cells of older than young adults, indicative of impaired metabolic flexibility with aging. PA effects were more prominent in unstimulated CD8+ cells, where HPA had lower glucose dependence (GD) for overall CD8+, CD8+EM and a trend to higher MD in CD8+ CM than LPA. Upon activation, HPA had a lower increase in CD4+ TNF expression and trended to have a higher reduction in MD of overall CD4+ and a higher reduction in GD of CD4+ EMRA, than LPA. This suggests a lower metabolic demand in CD4+ T cells of HPA. We concluded that PA could modify T cell metabolic profile at rest, and following activation, in older adults, which may explain the better T cell function in physically active older individuals.

immunology↗

bioRxiv2023

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. In this study, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm data to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We then apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve our understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

bioinformatics↗

bioRxiv2022

Different factors drive community assembly of rare and common ectomycorrhizal fungi

1.Understanding what drives community assembly processes and how communities respond to environmental gradients are fundamental goals in community ecology. Ectomycorrhizal fungi support major forest systems across the globe, but the diversity, distribution and environmental controls affecting ectomycorrhizal community composition are unknown in many regions, particularly in the southern hemisphere. Here we investigate the assembly of ectomycorrhizal fungal communities based on eDNA samples from 81 Nothofagus forests across New Zealands South Island. We apply zeta diversity analysis and multi-site generalised dissimilarity modelling (MS-GDM) to investigate assembly patterns and quantify the effects of 43 biotic and environmental variables on community turnover. The zeta diversity MS-GDM framework differentiates between the environmental factors driving turnover of rare and common species, so provides a more complete picture of community dynamics than traditional beta diversity analyses. Results showed that community assembly was dominated by deterministic rather than stochastic processes. Soil variables were important drivers across the full range of rare, intermediate and common species. Ground cover variables, forest patch size and rainfall had greater effects on turnover of rare species, whereas temperature variables and host tree size had greater effects on common species turnover. Applying these methods for the first time to fungi demonstrates that there are distinct differences in the ecological processes affecting different aspects of the ectomycorrhizal community, which has important implications for understanding the functional effects of community responses to environmental change.

ecology↗

bioRxiv2021

Consistent predictors of microbial community composition across scales in grasslands reveal low context-dependency

Environmental circumstances shaping soil microbial communities have been studied extensively, but due to disparate study designs it has been difficult to resolve whether a globally consistent set of predictors exists, or context-dependency prevails. Here, we used a network of 18 grassland sites (11 sampled across regional plant productivity gradients) to examine i) if the same abiotic or biotic factors predict both large- and regional-scale patterns in bacterial and fungal community composition, and ii) if microbial community composition differs consistently with regional plant productivity (low vs high) across different sites. We found that there is high congruence between predictors of microbial community composition across spatial scales; bacteria were predominantly associated with soil properties and fungi with plant community composition. Moreover, there was a microbial community signal that clearly distinguished high and low productivity soils that was shared across worldwide distributed grasslands suggesting that microbial assemblages vary predictably depending on grassland productivity.

ecology↗

bioRxiv2020

SIMON: open-source knowledge discovery platform

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

systems biology↗