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Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but their predictive power has been modest compared to established stroke risk factors. Here, using a meta-scoring approach, we develop a metaGRS for ischaemic stroke (IS) and analyse this score in the UK Biobank ( n  = 395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22–1.31 per metaGRS standard deviation) doubles that of a previous GRS, identifying a subset of individuals at monogenic levels of risk: the top 0.25% of metaGRS have three-fold risk of IS. The metaGRS is similarly or more predictive compared to several risk factors, such as family history, blood pressure, body mass index, and smoking. We estimate the reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving risk factor levels recommended by current guidelines may be insufficient to mitigate risk. Stroke risk is influenced by genetic and lifestyle factors and previously a genomic risk score (GRS) for stroke was proposed, albeit with limited predictive power. Here, Abraham et al. develop a metaGRS that is composed of several stroke-related GRSs and demonstrate improved predictive power compared with individual GRS or classic risk factors.

All eukaryotes require iron. Replication, detoxification, and a cancer-protective form of regulated cell death termed ferroptosis, all depend on iron metabolism. Ferrous iron accumulates over adult lifetime in Caenorhabditis elegans. Here, we show that glutathione depletion is coupled to ferrous iron elevation in these animals, and that both occur in late life to prime cells for ferroptosis. We demonstrate that blocking ferroptosis, either by inhibition of lipid peroxidation or by limiting iron retention, mitigates age-related cell death and markedly increases lifespan and healthspan. Temporal scaling of lifespan is not evident when ferroptosis is inhibited, consistent with this cell death process acting at specific life phases to induce organismal frailty, rather than contributing to a constant aging rate. Because excess age-related iron elevation in somatic tissue, particularly in brain, is thought to contribute to degenerative disease, post-developmental interventions to limit ferroptosis may promote healthy aging.

The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes 1 . Although T cells are the predominant TIL population 2 , the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population. We demonstrate that BCs with a high number of TILs contained CD8 + T cells with features of tissue-resident memory T (T RM ) cell differentiation and that these CD8 + T RM cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8 + T RM gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple-negative breast cancer (TNBC) and provided better prognostication than CD8 expression alone. Our data suggest that CD8 + T RM cells contribute to BC immunosurveillance and are the key targets of modulation by immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of T RM cells will be crucial for successful immunotherapeutic development in BC. Extensive, high-dimensional characterization of T cells in breast cancer reveals activated T RM population and a gene signature associated with improved prognosis.

Urbanization towards the expansion of the city area causes urban sprawl and changes in space use. Furthermore, urban agglomeration towards urban spatial integration causes a decrease in environmental quality. This study aims to analyze (1) land-use change and urban sprawl work as determinants of environmental quality degradation in suburban areas. (2) The effect of urban sprawl, urban agglomeration, land-use change, urban activity systems, and transportation systems on environmental quality degradation in suburban areas. A combination of quantitative and qualitative approaches is used sequentially in this study. Data obtained through observation, surveys, and documentation. The results showed that the expansion of the Makassar City area to the suburbs had an impact on spatial dynamics, spatial segregation, and environmental degradation. Furthermore, urban sprawl, land-use change, urban agglomeration, activity systems, and transportation systems have a positive correlation to environmental quality degradation with a determination coefficient of 85.9%. This study recommends the handling of urban sprawl, land-use change, and urban agglomeration to be considered in the formulation of development policies towards the sustainability of natural resources and the environment of Makassar City, Indonesia.

Gene-editing technology and large-scale proteomics are used to provide insights into the modular assembly of the human mitochondrial respiratory chain complex I, as well as identifying new assembly factors. Assembly of human mitochondrial complex I Respiratory chain complexes, including complex I, generate the cellular energy molecule ATP, and their dysfunction is associated with various disorders including Parkinson's disease and ageing. As well as the 14 core subunits that are essential for its enzymatic function, human complex I carries 30 accessory subunits, which are actively added to the core subunits by assembly factors. Combining genome-editing technology with large-scale proteomics, Michael Ryan and colleagues study the requirement for the different accessory subunits in human cells. Their data provide insights into the modular assembly of complex I as well as identifying new assembly factors. Complex I (NADH:ubiquinone oxidoreductase) is the first enzyme of the mitochondrial respiratory chain and is composed of 45 subunits in humans, making it one of the largest known multi-subunit membrane protein complexes 1 . Complex I exists in supercomplex forms with respiratory chain complexes III and IV, which are together required for the generation of a transmembrane proton gradient used for the synthesis of ATP 2 . Complex I is also a major source of damaging reactive oxygen species and its dysfunction is associated with mitochondrial disease, Parkinson’s disease and ageing 3 , 4 , 5 . Bacterial and human complex I share 14 core subunits that are essential for enzymatic function; however, the role and necessity of the remaining 31 human accessory subunits is unclear 1 , 6 . The incorporation of accessory subunits into the complex increases the cellular energetic cost and has necessitated the involvement of numerous assembly factors for complex I biogenesis. Here we use gene editing to generate human knockout cell lines for each accessory subunit. We show that 25 subunits are strictly required for assembly of a functional complex and 1 subunit is essential for cell viability. Quantitative proteomic analysis of cell lines revealed that loss of each subunit affects the stability of other subunits residing in the same structural module. Analysis of proteomic changes after the loss of specific modules revealed that ATP5SL and DMAC1 are required for assembly of the distal portion of the complex I membrane arm. Our results demonstrate the broad importance of accessory subunits in the structure and function of human complex I. Coupling gene-editing technology with proteomics represents a powerful tool for dissecting large multi-subunit complexes and enables the study of complex dysfunction at a cellular level.

Automated cell type identification is a key computational challenge in single‐cell RNA‐sequencing (scRNA‐seq) data. To capitalise on the large collection of well‐annotated scRNA‐seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single‐cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state‐of‐the‐art methodology in automated cell type identification from scRNA‐seq data. Synopsis scClassify is a multiscale classification framework based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. scClassify performs multiscale cell type classification based on cell type hierarchies constructed from single or multiple reference datasets. It implements a post‐hoc clustering procedure for discovering novel cell types from cells that are unassigned due to the absence of their types in the reference data. It enables the estimation of the number of cells required in a reference dataset to accurately discriminate a given cell type in a cell type hierarchy. Application to large atlas datasets such as Tabula Muris demonstrates its ability to refine cell types and identify cells from sub‐populations. Graphical Abstract scClassify is a multiscale classification framework based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.

The association between rate of kidney function decline and age-of-onset or duration of diabetes has not been well investigated. We aimed to examine whether rates of estimated glomerular filtration rate (eGFR) decline differ by age-of-onset or duration in people with type 2 diabetes. Using the Action to Control Cardiovascular Risk in Diabetes study which included those with HbA1c ≥ 7.5% and who were at high risk of cardiovascular events,, rates of eGFR decline were calculated and were compared among groups defined by the known age-of-onset (0–39, 40–49, 50–59, 60–69 and > 70 years) and 5-year diabetes duration intervals. Changes in renal function were evaluated using median of 6 (interquartile range 3–10) eGFR measurements per person. eGFR decline was the slowest in those with known age-at-diagnosis of 50–59 years or those with duration of diabetes < 5 years. The rates of eGFR decline were significantly greater in those with known age-of-onset < 40 years or those with duration of diabetes > 20 years compared to those diagnosed at 50–59 or those with duration of diabetes < 5 years (− 1.98 vs − 1.61 mL/min/year; − 1.82 vs − 1.52 mL/min/year; respectively (p < 0.001). Those with youngest age-of-onset or longer duration of diabetes had more rapid declines in eGFR compared to those diagnosed at middle age or those with shorter duration of diabetes.

Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4 + T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis -eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis -eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis -eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2 , HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases. Some immune-mediated diseases may originate in early childhood. The authors mapped eQTLs and response eQTLs to various stimuli in neonatal myeloid cells and T cells, and revealed their potential role in immune-mediated diseases using colocalisation and Mendelian randomisation.

Stroke is the third most common cause of disability and the second most common cause of death globally. Between 1990 and 2019, its prevalence increased by 70%. The prevalence of stroke increased from 7% in 2013 to 10.9% in 2018 in Indonesia. Because antiplatelet medication does not improve platelet aggregation, ischemic stroke patients have significant death and disability rates. The efficacy of cilostazol, clopidogrel, and aspirin in ischemic stroke patients is compared in this study. 205 patients who satisfied the inclusion criteria from 919 stroke cases at RSUD Margono Soekarjo in 2023 were the subjects of a cross-sectional retrospective design using purposive sampling. The Kruskal-Wallis test and mean difference tests were used to examine the results of the therapy. Outperforming aspirin and cilostazol, clopidogrel (75 mg) were the most widely used medication (38.6%) and produced the best results, considerably enhancing PT (1 s), APTT (0.872 s), and lowering leukocytes (2.956) and platelets (3.035). Significant variations in leukocyte (p=0.045) and platelet (p=0.040) levels were found using statistical tests. When it came to enhancing coagulation markers and lowering inflammation, clopidogrel proved to be a more effective therapy option for ischemic stroke.

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how in-silico approaches can minimize their impact. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. Using the novel Removing Unwanted Variations-III-Negative Binomial (RUV-III-NB), we identified several known experimental factors, specifically storage conditions and freeze–thaw cycles, as likely major sources of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performances of different correction methods, including ComBat, ComBat-seq, RUVg, RUVs, and RUV-III-NB. While RUV-III-NB performed consistently robust across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally. Our analyses suggest that a careful consideration of possible technical confounders is critical during experimental design of microbiome studies, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.