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Preston, R.

Publications and source records attributed to Preston, R..

3 recordsLinked to original sources

Ageing impacts extracellular matrix turnover and remodelling in the kidney

At least 10% of the global population is impacted by chronic kidney disease (CKD) and ageing is a key risk factor. CKD is characterised by the build-up of extracellular matrix and a loss of functional nephrons. However, the mechanisms that maintain matrix homeostasis across the physiological lifespan remain elusive. Using {superscript 1}3C-lysine metabolic labelling, we quantified kidney matrix protein turnover in healthy mice at four timepoints (8, 22, 52, and 78 weeks). We found that basement membrane components, including collagen IV, laminin-521, nidogens and perlecan, were more long-lived over age, with collagen IV half-lives extending from weeks in young kidneys to years in aged kidneys, suggesting a reduced capacity for basement membrane renewal. The half-lives of fibrillar collagens I and III also increased over age up to forty-fold, which is consistent with minimal degradation. In contrast, collagen XV retained rapid turnover despite increased abundance, indicating a persistent role in tissue remodelling. Using peptide location fingerprinting to predict structural alterations and proteolytic processing we identified age-dependent meprin oligomerisation and altered nidogen-laminin interaction states. We predicted structural alterations within assembly domains of collagen VI and reduced accessibility of integrin-binding regions, suggesting altered microfibril organisation and cell-surface binding. Collagen XV had predicted structural changes across the NC1 domain encoding the matrikine restin, consistent with altered protease accessibility and matrikine release during ageing. These findings indicate that age-related kidney fibrosis is primarily caused by impaired matrix degradation, with protease accessibility and altered matrix interactions likely playing key roles in this remodeling process.

systems biology↗

Different approaches to estimate benthic metazoan diversity associated with free-living macroalgae (Fucus vesiculosus) on shallow soft sediments

Habitat complexity can boost biodiversity by providing a wide range of niches allowing species co-existence. Baltic Sea benthic communities are characterised by low species diversity. Thus the occurrence of the habitat forming macroalga Fucus vesiculosus may influence benthic communities and promote diversity. Here we obtain biodiversity estimates through conventional and eDNA approaches for the benthic assemblages associated with free-living Fucus and the adjacent bare-sediment habitats at six sites from the Northern Baltic Proper and the Gulf of Finland. Free-living F. vesiculosus habitats are heterogeneous with biodiversity estimates varying considerably among sites. The additional habitat complexity provided by F. vesiculosus tends to improve taxa richness as a result of additional epifauna assemblages, although infaunal taxa richness and abundance is often reduced. Consequently the complex habitats provided by free-living F. vesiculosus often improves biodiversity, yet alters the composition of assemblages in soft sediment habitats and consequential ecosystem functioning. We emphasise the disparity in biodiversity estimates achieved when employing different biodiversity approaches. Biodiversity estimates were more similar within approaches compared to between habitat types, with each approach detecting exclusive taxa. We suggest that biodiversity estimates benefit from a multi-approach design where both conventional and eDNA approaches are employed in complement.

ecology↗

Predicting neurodevelopmental outcomes in children with perinatal HIV using a novel machine learning algorithm

BackgroundA subset of children with perinatal HIV (pHIV) experience long-term neurocognitive symptoms despite treatment with antiretroviral therapy. However, predictors of neurocognitive outcomes remain elusive, particularly for children with pHIV residing in low-to-middle income countries. The present study utilized a novel data analytic approach to identify clinically-relevant predictors of neurocognitive development in children with pHIV.\n\nMethodsAnalyses were conducted on a large repository of longitudinal data from 285 children with pHIV in Thailand (n=170) and Cambodia (n=115). Participants were designated as neurocognitively resilient (i.e., positive slope; n=143) or at risk (i.e., negative slope; n=142) according to annual performances on the Beery-Buktenica Developmental Test of Visual-Motor Integration over an average of 5.4 years. Gradient-boosted multivariate regression (GBM) with 5-fold cross validation was utilized to identify the optimal combination of demographic, HIV disease, blood markers, and emotional health indices that predicted classification into the two neurocognitive subgroups. Model performance was assessed using Receiver Operator Curves and sensitivity/specificity.\n\nResultsThe analytic approach distinguished neurocognitive subgroups with high accuracy (93%; sensitivity and specificity each > 90%). Dynamic change indices and interactions between mental health and biological indices emerged as key predictors.\n\nConclusionMachine learning-based regression defined a unique explanatory model of neurocognitive outcomes among children with pHIV. The predictive algorithm included a combination of HIV, physical health, and mental health indices extracted from readily available clinical measures. Studies are needed to explore the clinical relevance of the data-driven explanatory model, including potential to inform targeted interventions aimed at modifiable risk factors.

neuroscience↗