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The physical tissue microenvironment regulates cell state and behaviour. How mechanical confinement rewires the subcellular localisation of organelles and affects cellular metabolism is largely unknown. In this study, proteomics analysis revealed that cellular confinement induced a strong enrichment of mitochondrial proteins in the nuclear fraction. Quantitative live cell microscopy confirmed that mechanical cell confinement leads to a rapid re-localisation of mitochondria to the nuclear periphery in vitro, reflecting a physiologically relevant phenomenon in patient-derived tumours. This nucleus-mitochondria proximity is mediated by an endoplasmic reticulum-based net that entraps the mitochondria in an actin-dependent manner. Functionally, the nucleus-mitochondria proximity results in a nuclear ATP surge, which can be regulated by the genetic and pharmacological modulation of mitochondrial ATP production or via alterations of the actin cytoskeleton. The confinement-induced nuclear ATP surge has physiologically significant long-term effects on cell fitness, driven by changes in chromatin state, enhanced DNA damage repair, and cell cycle progression during mechanical cell deformation. Together, our data describe a confinement-induced metabolic adaptation that is required to enable prompt DNA damage repair and cell proliferation under mechanical confinement stress by facilitating chromatin state transitions. The authors uncover a mechano-metabolic adaptation where confinement induces rapid mitochondrial relocalization to the nuclear periphery, generating localized nuclear ATP surges that support chromatin remodeling, DNA repair, and cell cycle progression.

IntroductionType 2 diabetes is a risk factor for dementia and Parkinson’s disease (PD). Drug treatments for diabetes, such as metformin, could be used as novel treatments for these neurological conditions. Using electronic health records from the USA (OPTUM EHR) we aimed to assess the association of metformin with all-cause dementia, dementia subtypes and PD compared with sulfonylureas.Research design and methodsA new user comparator study design was conducted in patients ≥50 years old with diabetes who were new users of metformin or sulfonylureas between 2006 and 2018. Primary outcomes were all-cause dementia and PD. Secondary outcomes were Alzheimer’s disease (AD), vascular dementia (VD) and mild cognitive impairment (MCI). Cox proportional hazards models with inverse probability of treatment weighting (IPTW) were used to estimate the HRs. Subanalyses included stratification by age, race, renal function, and glycemic control.ResultsWe identified 96 140 and 16 451 new users of metformin and sulfonylureas, respectively. Mean age was 66.4±8.2 years (48% male, 83% Caucasian). Over the 5-year follow-up, 3207 patients developed all-cause dementia (2256 (2.3%) metformin, 951 (5.8%) sulfonylurea users) and 760 patients developed PD (625 (0.7%) metformin, 135 (0.8%) sulfonylurea users). After IPTW, HRs for all-cause dementia and PD were 0.80 (95% CI 0.73 to 0.88) and 1.00 (95% CI 0.79 to 1.28). HRs for AD, VD and MCI were 0.81 (0.70–0.94), 0.79 (0.63–1.00) and 0.91 (0.79–1.04). Stronger associations were observed in patients who were younger (<75 years old), Caucasian, and with moderate renal function.ConclusionsMetformin users compared with sulfonylurea users were associated with a lower risk of all-cause dementia, AD and VD but not with PD or MCI. Age and renal function modified risk reduction. Our findings support the hypothesis that metformin provides more neuroprotection for dementia than sulfonylureas but not for PD, but further work is required to assess causality.

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer’s disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831 respectively. The identified proteins were enriched in 5 clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase–protein kinase B/Akt signaling pathway. Conclusions: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

Background Hypertension is a well‐established risk factor for cognitive impairment, brain atrophy, and dementia. However, the relationship of other types of hypertensions, such as isolated hypertension on brain health and its comparison to systolic‐diastolic hypertension (where systolic and diastolic measures are high), is still relatively unknown. Due to its increased prevalence, it is important to investigate the impact of isolated hypertension to help understand its potential impact on cognitive decline and future dementia risk. In this study, we compared a variety of global brain measures between participants with isolated hypertension to those with normal blood pressure (BP) or systolic‐diastolic hypertension using the largest cohort of healthy individuals. Methods Using the UK Biobank cohort, we carried out a cross‐sectional study using 29,775 participants (mean age 63 years, 53% female) with BP measurements and brain magnetic resonance imaging (MRI) data. We used linear regression models adjusted for multiple confounders to compare a variety of global, subcortical, and white matter brain measures. We compared participants with either isolated systolic or diastolic hypertension with normotensives and then with participants with systolic‐diastolic hypertension. Results The results showed that participants with isolated systolic or diastolic hypertension taking BP medications had smaller gray matter but larger white matter microstructures and macrostructures compared to normotensives. Isolated systolic hypertensives had larger total gray matter and smaller white matter traits when comparing these regions with participants with systolic‐diastolic hypertension. Conclusions These results provide support to investigate possible preventative strategies that target isolated hypertension as well as systolic‐diastolic hypertension to maintain brain health and/or reduce dementia risk earlier in life particularly in white matter regions. The relationship between brain health and different types of hypertensions such as isolated hypertension is unknown. Using UK Biobank, we showed that participants with isolated hypertension taking BP medications had smaller gray matter but larger white matter microstructures and macrostructures compared to normotensives. Isolated systolic hypertensives had larger total gray matter and smaller white matter traits when comparing these regions with participants with systolic‐diastolic hypertension. These results provide support to investigate possible preventative strategies that target isolated hypertension.

Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank ( n  = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r  = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China ( n  = 3,977) and Finland ( n  = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r  = 0.92 and r  = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations. A proteomic aging clock, developed using data from the UK Biobank and validated using data from the China Kadoorie Biobank and FinnGen, predicts the incidence of 18 major chronic diseases, as well as multimorbidity and all-cause mortality.

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as \\(25\\) million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: tbd.

The physical microenvironment regulates cell behaviour. However, whether physical confinement rewires the subcellular localisation of organelles and affect metabolism is unknown. Proteomics analysis revealed that cellular confinement induces a strong enrichment of mitochondrial proteins within the nuclear compartment. High-resolution microscopy confirmed that mechanical cell confinement leads to a rapid re-localisation of mitochondria to the nuclear periphery. This nuclear-mitochondrial proximity is mediated by an endoplasmic reticulum-based net that entraps the mitochondria in an actin-dependent manner. Functionally, the mitochondrial proximity results in a nuclear ATP surge, which can be reverted by the pharmacological inhibition of mitochondrial ATP production or via actin depolymerisation. Inhibition of the confinement-derived nuclear ATP surge reveals long-term effects on cell fitness which arise from alterations of chromatin states, delayed DNA damage repair, and impaired cell cycle progression. Together, our data describe a confinement-induced metabolic adaptation that is required to enable prompt DNA damage repair and cell cycle progression by allowing chromatin state transitions.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/Skourtis/Rito_Fabio* https://github.com/SdelciLab/CINAPS

Metastasis is responsible for nearly 90% of all cancer-related deaths. Despite global efforts to prevent aggressive tumours, cancers such as pancreatic ductal adenocarcinoma (PDAC) are poorly diagnosed in the primary stage, resulting in lethal metastatic disease. RAS mutations are known to promote tumour spread, with mutant KRAS present in up to 90% of cases. Until recently, mutant KRAS remained untargeted and, despite the recent development of inhibitors, results show that tumour cells develop resistance. Another strategy for targeting mutant KRAS-dependent PDAC proliferation and metastasis may come from targeting the downstream effectors of KRAS. One such axis, which controls tumour proliferation, invasiveness and immune evasion, is represented by ARF6-ASAP1. Here we show that targeting ARF6 results in adaptive rewiring that can restore proliferation and invasion potential over time. Using time-series RNA and ATAC sequencing approaches, we identified TLR-dependent NFκB, TNFα and hypoxia signalling as key drivers of adaptation in ARF6-depleted KRAS-dependent PDAC. Using in vitro and in vivo assays, we show that knocking down TLR2 with ARF6 significantly reduces proliferation, migration and invasion. Taken together, our data shed light on a novel co-targeting strategy with the therapeutic potential to counteract PDAC proliferation and metastasis.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/SdelciLab/arfAdapt