Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum.

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

MULTISCALE ESTIMATION OF MORPHOMETRICITY FOR REVEALING NEUROANATOMICAL BASIS OF COGNITIVE TRAITS.

Morphometricity examines the global statistical association between brain morphology and an observable trait, and is defined as the proportion of the trait variation attributable to brain morphology. In this work, we propose an accurate morphometricity estimator based on the generalized random effects (GRE) model, and perform morphometricity analyses on five cognitive traits in an Alzheimer's study. Our empirical study shows that the proposed GRE model outperforms the widely used LME model on both simulation and real data. In addition, we extend morphometricity estimation from the whole brain to the focal-brain level, and examine and quantify both global and regional neuroanatomical signatures of the cognitive traits. Our global analysis reveals 1) a relatively strong anatomical basis for ADAS13, 2) intermediate ones for MMSE, CDRSB and FAQ, and 3) a relatively weak one for RAVLT.learning. The top associations identified from our regional morphometricity analysis include those between all five cognitive traits and multiple regions such as hippocampus, amygdala, and inferior lateral ventricles. As expected, the identified regional associations are weaker than the global ones. While the whole brain analysis is more powerful in identifying higher morphometricity, the regional analysis could localize the neuroanatomical signatures of the studied cognitive traits and thus provide valuable information in imaging and cognitive biomarker discovery for normal and/or disordered brain research.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

BACKGROUND: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aß) profiles, and to predicted progression to Alzheimer's disease (AD). METHODS: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). FINDINGS: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. INTERPRETATION: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. FUNDING: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Preparation of Zirconium-Based MOF-Derived Phosphide on GO/MXene Double Substrates for High-Performance Asymmetric Supercapacitors.

At present, it is very necessary to select and prepare suitable positive and negative electrode materials to fabricate high-performance asymmetric supercapacitors. Metal-organic frameworks (MOFs) have garnered significant attention in the energy storage field due to their high conductivity. As a branch, the zirconium organic framework (UIO-66) is a promising porous material due to its large specific surface area and abundant Zr centers. Graphene oxide (GO) and MXene are very suitable as substrate materials for conducting an MOF due to their abundant active sites and adjustable interlayer distance. The GO/MXene@NiZrP prepared through an in situ composite of GO and Mxene with the hydrothermal method and calcining method showed excellent electrochemical performance. Compared with the precursor UIO-66, the specific capacitance of the final product GO/MXene@NiZrP increases more than ten times, mainly because of its special layered porous structure, and GO/MXene@NiZrP has a larger specific surface area, pore volume, and surface defects caused by unstable Zr4+ than those of UIO-66. Using GO/MXene@NiZrP as the positive electrode and biochar (BC) as the negative electrode, an asymmetric supercapacitor, BC//GO/MXene@NiZrP, is assembled. After 10,000 cycles at a current density of 10 A g-1, the capacitance retention remains at 83.3%, showing excellent cycle stability.

Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control.

Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Proteomic analyses reveal plasma EFEMP1 and CXCL12 as biomarkers and determinants of neurodegeneration.

INTRODUCTION: Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes. METHODS: We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk. RESULTS: Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization. DISCUSSION: Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration. HIGHLIGHTS: Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

Reduced estrogenic risks of a sunscreen additive: Theoretical design and evaluation of functionally improved salicylates.

Salicylic esters (SEs), the widely used ultraviolet (UV) absorbers in sunscreen products, have been found to have health risks such as skin sensitization and estrogenic effects. This study aims to design SE substitutes that maintain high UV absorbance while reducing estrogenicity. Using molecular docking and Gaussian09 software for initial assessments and further application of a combination of two-dimensional and three-dimensional quantitative structure-activity relationships (2D-QSAR and 3D-QSAR, respectively) models, we designed 73 substitutes. The best-performing molecules, ethylhexyl salicylate (EHS)-5 and EHS-15, significantly reduced estrogenicity (44.54 % and 17.60 %, respectively) and enhanced UV absorbance (249.56 % and 46.94 %, respectively). Through screening for human health risks, we found that EHS-5 and EHS-15 were free from skin sensitivity and eye irritation and exhibited reduced skin permeability compared with EHS. Furthermore, the photolysis and synthetic pathways of EHS-5 and EHS-15 were deduced, demonstrating their good photodegradability and potential synthesizability. In addition, we analyzed the mechanisms underlying the changes in estrogenic effects and UV absorption properties. We identified covalent hydrogen bond basicity and acidity Propgen value for atomic molecular properties and the highest occupied molecular orbital eigenvalue as the main factors affecting the estrogenic effect and UV absorbance of SEs, respectively. This study focuses on the design and screening of SEs, exhibiting enhanced functionality, reduced health risks, and synthetic feasibility.

The genetic architecture of biological age in nine human organ systems.

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

Toxicity assessment of dioxins and their transformation by-products from inferred degradation pathways.

Due to the significant POPs characteristics, dioxins caused concern in public health and environmental protection. Evaluating the toxicity risk of dioxin degradation pathways is critical. OCDD, 1,2,3,4,6,7,8-HpCDD, and 1,2,3,4,6,7,8-HpCDF, which are highly abundant in the environment and have strong biodegradation capabilities, were selected as precursor molecules in this study. Firstly, their transformation pathways were deduced during the metabolism of biometabolism, microbial aerobic, microbial anaerobic, and photodegradation pathways, and density function theory (DFT) was used to calculate the Gibbs free energy to infer the possibility of the occurrence of the transformation pathway. Secondly, the carcinogenic potential of the precursors and their degradation products was evaluated using the TOPKAT modeling method. With the help of the positive indicator (0-1) normalization method and heat map analysis, a significant increase in the toxic effect of some of the transformation products was found, and it was inferred that it was related to the structure of the transformation products. Meanwhile, the strength of the endocrine disrupting effect of dioxin transformation products was quantitatively assessed using molecular docking and subjective assignment methods, and it was found that dioxin transformation products with a higher content of chlorine atoms and molecules similar to those of thyroid hormones exhibited a higher risk of endocrine disruption. Finally, the environmental health risks caused by each degradation pathway were comprehensively assessed with the help of the negative indicator (1-2) standardization method, which provides a theoretical basis for avoiding the toxicity risks caused by dioxin degradation transformation. In addition, the 3D-QSAR model was used to verify the necessity and rationality of this study. This paper provides theoretical support and reference significance for the toxicity assessment of dioxin degradation by-products from inferred degradation pathways.

Identification and Mechanistic Analysis of Toxic Degradation Products in the Advanced Oxidation Pathways of Fluoroquinolone Antibiotics.

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.

The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Accumulation characteristics of liquid crystal monomers in plants: A multidimensional analysis.

Liquid crystal monomers (LCMs), identified as emerging contaminations, have been detected in soils and plants, but their accumulation characteristics in plants haven't been studied. Therefore, this study systematically investigated the accumulation characteristics of LCMs in plants from four dimensions (i.e., plant fruit species, soil types, plant growth stages, and LCMs categories) for the first time. The LCMs concentrations (9.96 × 10-4 to 114.608 ng/g) in 22 plant fruits were predicted by the partition-limited model. Grains with the highest lipid content showed the highest LCMs accumulation propensity. Plants grown in paddy soil showed a strong LCMs accumulation capacity. Results showed that the LCMs accumulation capacity in plants from soils decreased when the soil organic matter content increased. A preferential accumulation of LCMs in plant root systems during growth was found by the molecular dynamics simulations. Compared to polychlorinated biphenyls (as the reference contaminants of LCMs), LCMs exhibit higher accumulation in plant roots and lower translocation to shoots. For the fourth dimension, lipophilicity was found to be the main reason of LCMs accumulation by intergraded stepwise linear regression with sensitivity analysis. This is the inaugural research concentrating on LCMs accumulation in plants, providing insights and theoretical guidance for future LCMs management strategies multidimensionally.

ZIF-derived sulfides with tremella-like core-shell structure for high performance supercapacitors.

Metal-organic frameworks (MOFs) have emerged as promising active electrode materials in supercapacitors for its controllable porous structure and excellent physio-chemical properties. However, the poor conductivities keep it from achieving its full capacitance potential, which greatly limits its practical application. Here, a facile pathway is reported to fabricate the GO/Ni2ZnS4@NiCo2S4 composite with large specific surface area and favorable electrical conductivity. Thanks to the novel tremella-like core-shell structure and high-efficient synergistic effects among multi-components, the designed GO/Ni2ZnS4@NiCo2S4 electrode shows a high specific capacitance of 2284 F/g at 1 A/g. Furthermore, the asymmetric supercapacitor fabricated by coupling GO/Ni2ZnS4@NiCo2S4 positive electrode with biological carbon negative electrode achieves a remarkable energy density of 120 Wh kg-1 at a power density of 750 W kg-1.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

The Genetic Architecture of Biological Age in Nine Human Organ Systems.

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.

Properties of Dietary Flavone Glycosides, Aglycones, and Metabolites on the Catalysis of Human Endoplasmic Reticulum Uridine Diphosphate Glucuronosyltransferase 2B7 (UGT2B7).

Flavone glycosides, their aglycones, and metabolites are the major phytochemicals in dietary intake. However, there are still many unknowns about the cellular utilization and active sites of these natural products. Uridine diphosphate glucuronosyltransferases (UGTs) in the endoplasmic reticulum have gene polymorphism distribution in the population and widely mediate the absorption and metabolism of endogenous and exogenous compounds by catalyzing the covalent addition of glucuronic acid and various lipophilic chemicals. Firstly, we found that rutin, a typical flavone O-glycoside, has a stronger UGT2B7 binding effect than its metabolites. After testing a larger number of flavonoids with different aglycones, their aglycones, and metabolites, we demonstrated that typical dietary flavone O-glycosides generally have high binding affinities towards UGT2B7 protein, but the flavone C-glycosides and the phenolic acid metabolites of flavones had no significant effect on this. With the disposition of 4-methylumbelliferone examined by HPLC assay, we determined that 10 µM rutin and nicotifiorin could significantly inhibit the activity of recombinant UGT2B7 protein, which is stronger than isovitexin, vitexin, 3-hydroxyphenylacetic acid and 3,4-dihydroxyphenylacetic acid. In addition, in vitro experiments showed that in normal and doxorubicin-induced lipid composition, both flavone O-glycosides rutin and flavone C-glycosides isovitexin at 10 µM had no significant effect on the expression of UGT1A1, UGT2B4, UGT2B7, and UGT2B15 genes for 24 h exposure. The obtained results enrich the regulatory properties of dietary flavone glycosides, aglycones, and metabolites towards the catalysis of UGTs and will contribute to the establishment of a precise nutritional intervention system based on lipid bilayers and theories of nutrients on endoplasmic reticulum and mitochondria communication.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.