Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this Review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes) presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with the disease etiology.

Probing Energy-Funneling Kinetics in Nanocrystal Sublattices for Superior X-Ray Imaging.

Long-lasting radioluminescence scintillators have recently attracted substantial attention from both research and industrial communities, primarily due to their distinctive capabilities of converting and storing X-ray energy. However, determination of energy-conversion kinetics in these nanocrystals remains unexplored. Here we present a strategy to probe and unveil energy-funneling kinetics in NaLuF4:Mn2+/Gd3+ nanocrystal sublattices through Gd3+-driven microenvironment engineering and Mn2+-mediated radioluminescence profiling. Our photophysical studies reveal effective control of energy-funneling kinetics and demonstrate the tunability of electron trap depth ranging from 0.66 to 0.96 eV, with the corresponding trap density varying between 2.38×105 and 1.34×107 cm-3. This enables controlled release of captured electrons over durations spanning from seconds to 30 days. It allows tailorable emission wavelength within the range of 520-580 nm and fine-tuning of thermally-stimulated temperature between 313-403 K. We further utilize these scintillators to fabricate high-density, large-area scintillation screens that exhibit a 6-fold improvement in X-ray sensitivity, 22 lp/mm high-resolution X-ray imaging, and a 30-day-long optical memory. This enables high-contrast imaging of injured mice through fast thermally-stimulated radioluminescence readout. These findings offer new insights into the correlation of radioluminescence dynamics with energy-funneling kinetics, thereby contributing to the advancement of high-energy nanophotonic applications.

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 .

Anlotinib Inhibits Ovarian Cancer and Enhances Cisplatinum Sensitivity via Suppressing NOTCH2 Expression and Stemness.

BACKGROUND/AIM: The prognosis of ovarian cancer (OC) patients is especially poor for patients with chemotherapy resistance. Anlotinib, a novel multi-targeted tyrosine kinase inhibitor, has shown encouraging clinical efficacy in several tumor types. The aim of the present study was to examine the inhibitory efficacy and mechanism of anlotinib on the proliferation and chemosensitivity of OC cells. MATERIALS AND METHODS: The inhibitory effects of Anlotinib on SKOV3 and OVCAR3 OC cells were examined using CCK-8 cell-viability, colony-formation, flow-cytometry, transwell-migration and sphere-formation assays. A xenograft mouse model was used for in vivo studies. RT-qPCR and western blotting were used to detect gene expression. RESULTS: Molecular targets of anlotinib were elevated in OC patient tumors. Anlotinib significantly inhibited ovarian cancer cell proliferation and migration in vitro. Anlotinib enhanced the sensitivity of ovarian cancer cells to cisplatinum both in vitro and in vivo. Anlotinib suppressed sphere formation and the stemness phenotype of OC cells by inhibiting NOTCH2 expression. CONCLUSION: Anlotinib inhibits ovarian cancer and enhances cisplatinum sensitivity, suggesting its future clinical promise.

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.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

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.

Palladium-Catalyzed Synthesis of Indanone via C-H Annulation Reaction of Aldehydes with Norbornenes.

A novel methodology for the synthesis of indanone derivates has been developed. The palladium-catalyzed annulation reaction of o-bromobenzaldehydes with norbornene derivatives is achieved through extremely concise reaction processes. The indanone skeleton was established directly via C-H activation of the aldehyde group under a mild reaction condition. This method is simple and practical, which simplified the traditional synthesis method for the rapid construction of indanone.

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.

A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (SPArc-particle swarm).

Objective.Delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (ELs) ascending switches. This study aims to develop a new algorithm to mitigate the need for EL ascending via water equivalent thickness (WET) sector selection followed by particle swarm optimization (SPArc-particle swarm).Approach.SPArc-particle swarmdivided the full arc trajectory into the optimal sectors based on K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, optimizing the energy selection integrated with the EL delivery sequence and relationship. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer) were selected for testing purposes. Two kinds of plans were generated: SPArc_seq and SPArc-particle swarm. The plan quality and delivery efficiency were evaluated.Main results. With a similar plan quality, the delivery efficiency was significantly improved using SPArc-particle swarmcompared to SPArc_seq. More specifically, it reduces the number of ELs ascending switching compared to the SPArc_seq (from 21 to 7 in the brain, from 21 to 5 in the prostate, from 21 to 6 in the liver), leading to a 16%-26% reduction of the beam delivery time (BDT) in the SPArc treatment.Significance. A novel planning framework, SPArc-particle swarm, could significantly improve the delivery efficiency, which paves the roadmap towards routine clinical implementation.

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.

High-Confidentiality X-Ray Imaging Encryption Using Prolonged Imperceptible Radioluminescence Memory Scintillators.

X-ray imaging plays an increasingly crucial role in clinical radiography, industrial inspection, and military applications. However, current X-ray imaging technologies have difficulty in protecting against information leakage caused by brute force attacks via trial-and-error. Here high-confidentiality X-ray imaging encryption by fabricating ultralong radioluminescence memory films composed of lanthanide-activated nanoscintillators (NaLuF4 : Gd3+ or Ce3+ ) with imperceptible purely-ultraviolet (UV) emission is reported. Mechanistic investigations unveil that ultralong X-ray memory is attributed to the long-lived trapping of thermalized charge carriers within Frenkel defect states and subsequent slow release in the form of imperceptible radioluminescence. The encrypted X-ray imaging can be securely stored in the memory film for more than 7 days and optically decoded by perovskite nanocrystal. Importantly, this encryption strategy can protect X-ray imaging information against brute force trial-and-error attacks through the perception of lifetime change in the persistent radioluminescence. It is further demonstrated that the as-fabricated flexible memory film enables achieving of 3D X-ray imaging encryption of curved objects with a high spatial resolution of 20 lp/mm and excellent recyclability. This study provides valuable insights into the fundamental understanding of X-ray-to-UV conversion in nanocrystal lattices and opens up a new avenue toward the development of high-confidential 3D X-ray imaging encryption technologies.

Risk Factor Analysis and Intervention Study for Unspecific Functional Enteropathy in a Maritime Environment: A Cross-Sectional Analytical Survey.

Background: The study aimed to investigate the risk factors and interventions for unspecific functional bowel disorders (U-FBDs) in military personnel under maritime environment. Methods: This cross-sectional analytical survey used the Rome III questionnaire for surveying 1018 military personnel involved in overseas humanitarian medical services from June 2013 to January 2016. Individuals diagnosed with U-FBDs were included in the U-FBDs group, while those without FBDs or other diseases were considered the control group. The psychological and sleep conditions of military personnel with U-FBDs were assessed using the SCL-90 scale and the Pittsburgh Sleep Quality Index scale, respectively. Health education and treatment were provided to individuals diagnosed with U-FBDs, and the improvements were evaluated after three months. Results: Among 923 qualified questionnaires, 243 subjects was included in U-FBDs group and 240 in the control group. Smoking, alcohol consumption, and multiple seafaring missions were identified as risk factors for U-FBDs in military personnel on ocean-going missions. The U-FBDs group had significantly worse sleep quality, sleep efficiency, daytime dysfunction score, and total PSQI score compared to the control group (P < 0.05). Additionally, 10 factor scores of SCL-90 and the total score in the U-FBDs group were significantly higher than those in the control group (P < 0.01). Patients with U-FBDs also reported the highest rate of somatic symptoms (P < 0.01). Conclusion: The onset of U-FBDs among military personnel on long-haul maritime may be closely related to mental, psychological, and sleep factors. Health education and treatment may help improve the symptoms of U-FBDs.

Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability.

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.

GC-MS based comparative metabolomic analysis of human cancellous bone reveals the critical role of linoleic acid metabolism in femur head necrosis.

INTRODUCTION: Femur head necrosis (FHN) is a challenging clinical disease with unclear underlying mechanism, which pathologically is associated with disordered metabolism. However, the disordered metabolism in cancellous bone of FHN was never analyzed by gas chromatography-mass spectrometry (GC-MS). OBJECTIVES: To elucidate altered metabolism pathways in FHN and identify putative biomarkers for the detection of FHN. METHODS: We recruited 26 patients with femur head necrosis and 22 patients with femur neck fracture in this study. Cancellous bone tissues from the femoral heads were collected after the surgery and were analyzed by GC-MS based untargeted metabolomics approach. The resulting data were analyzed via uni- and multivariate statistical approaches. The changed metabolites were used for the pathway analysis and potential biomarker identification. RESULTS: Thirty-seven metabolites distinctly changed in FHN group were identified. Among them, 32 metabolites were upregulated and 5 were downregulated in FHN. The pathway analysis showed that linoleic acid metabolism were the most relevant to FHN pathology. On the basis of metabolites network, L-lysine, L-glutamine and L-serine were deemed as the junctions of the whole metabolites. Finally, 9,12-octadecadienoic acid, inosine, L-proline and octadecanoic acid were considered as the potential biomarkers of FHN. CONCLUSION: This study provides a new insight into the pathogenesis of FHN and confirms linoleic acid metabolism as the core.

Role of soil nutrient elements transport on Camellia oleifera yield under different soil types.

BACKGROUND: Most of Camellia oleifera forests have low fruit yield and poor oil quality that are largely associated with soil fertility. Soil physical and chemical properties interact with each other affecting soil fertility and C. oleifera growing under different soil conditions produced different yield and oil composition. Three main soil types were studied, and redundancy, correlation, and double-screening stepwise regression analysis were used for exploring the relationships between C. oleifera nutrients uptake and soil physical and chemical properties, shedding light on the transport law of nutrient elements from root, leaves, and kernel, and affecting the regulation of fruit yield and oil composition. RESULTS: In the present study, available soil elements content of C. oleifera forest were mainly regulated by water content, pH value, and total N, P and Fe contents. Seven elements (N, P, K, Mg, Cu, Mn and C) were key for kernel's growth and development, with N, P, K, Cu and Mn contents determining 74.0% the yield traits. The transport characteristics of these nutrients from root, leaves to the kernel had synergistic and antagonistic effects. Increasing oil production and unsaturated fatty acid content can be accomplished in two ways: one through increasing N, P, Mg, and Zn contents of leaves by applying corresponding N, P, Mg, Zn foliar fertilizers, while the other through maintaining proper soil moisture content by applying Zn fertilizer in the surface layer and Mg and Ca fertilizer in deep gully. CONCLUSION: Soil type controlled nutrient absorption by soil pH, water content and total N, P and Fe content. There were synergistic and antagonistic effects on the inter-organ transport of nutrient elements, ultimately affecting N, P, K, Cu and Mn contents in kernel, which determined the yield and oil composition of C. oleifera.

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

The Genetic Architecture of Multimodal Human Brain Age.

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). 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 showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

Hidden Genetic Regulation of Human Complex Traits via Brain Isoforms.

Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00100-6.

The ALOX5 inhibitor Zileuton regulates tumor-associated macrophage M2 polarization by JAK/STAT and inhibits pancreatic cancer invasion and metastasis.

5-lipoxygenase (encoded by ALOX5) plays an important role in immune regulation. Zileuton is currently the only approved ALOX5 inhibitor. However, the mechanisms of ALOX5 and Zileuton in progression of pancreatic cancer remain unclear. Therefore, we investigated the effects of Zileuton on tumor-associated macrophage M2 polarization and pancreatic cancer invasion and metastasis, both in vivo and in vitro. In bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) analyses, we found a significant association between elevated levels of ALOX5 and poor survival, adverse stages, M2 macrophage infiltration, and the activation of JAK/STAT pathways in macrophages. In clinical samples, immunofluorescence, quantitative real-time PCR and immunohistochemical results verified the high expression of ALOX5 in pancreatic cancer, primarily in macrophages. We constructed PANC-1 human pancreatic cancer cells and macrophages overexpressing ALOX5 using lentivirus. In PANC-1 pancreatic cancer cells, low-dose Zileuton inhibited PANC-1 cell invasion and migration by blocking ALOX5. In macrophages, ALOX5 induced the M2-like phenotype through the JAK/STAT pathway and promoted the chemotaxis of macrophages towards PANC-1 cells, while Zileuton can inhibit these effects. We constructed the nude mouse model of in situ transplantation tumor of pancreatic cancer. After treatment with Zileuton, the mice showed increased survival rates and reduced liver metastasis. These findings indicate that ALOX5 regulates tumor-associated macrophage M2 polarization via the JAK/STAT pathway and promotes invasion and metastasis in pancreatic cancer. Zileuton can inhibit these effects by inhibiting ALOX5. These results provide a theoretical basis for the potential use of Zileuton in the treatment of pancreatic cancer.