Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs.

Whereas 16p11.2 BP4-5 copy-number variants (CNVs) represent one of the most pleiotropic etiologies of genomic syndromes in both clinical and population cohorts, the mechanisms leading to such pleiotropy remain understudied. Identifying 73 deletion and 89 duplication carrier individuals among unrelated White British UK Biobank participants, we performed a phenome-wide association study (PheWAS) between the region's copy number and 117 complex traits and diseases, mimicking four dosage models. Forty-six phenotypes (39%) were affected by 16p11.2 BP4-5 CNVs, with the deletion-only, mirror, U-shape, and duplication-only models being the best fit for 30, 10, 4, and 2 phenotypes, respectively, aligning with the stronger deleteriousness of the deletion. Upon individually adjusting CNV effects for either body mass index (BMI), height, or educational attainment (EA), we found that sixteen testable deletion-driven associations-primarily with cardiovascular and metabolic traits-were BMI dependent, with EA playing a more subtle role and no association depending on height. Bidirectional Mendelian randomization supported that 13 out of these 16 associations were secondary consequences of the CNV's impact on BMI. For the 23 traits that remained significantly associated upon individual adjustment for mediators, matched-control analyses found that 10 phenotypes, including musculoskeletal traits, liver enzymes, fluid intelligence, platelet count, and pneumonia and acute kidney injury risk, remained associated under strict Bonferroni correction, with 10 additional nominally significant associations. These results paint a complex picture of 16p11.2 BP4-5's pleiotropic pattern that involves direct effects on multiple physiological systems and indirect co-morbidities consequential to the CNV's impact on BMI and EA, acting through trait-specific dosage mechanisms.

Robust multivariable Mendelian randomization based on constrained maximum likelihood.

Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.

Mediation analysis in longitudinal study with high-dimensional methylation mediators.

Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

Precursor-mediated in situ growth of hierarchical N-doped graphene nanofibers confining nickel single atoms for CO2 electroreduction.

Despite the various strategies for achieving metal-nitrogen-carbon (M-N-C) single-atom catalysts (SACs) with different microenvironments for electrochemical carbon dioxide reduction reaction (CO2RR), the synthesis-structure-performance correlation remains elusive due to the lack of well-controlled synthetic approaches. Here, we employed Ni nanoparticles as starting materials for the direct synthesis of nickel (Ni) SACs in one spot through harvesting the interaction between metallic Ni and N atoms in the precursor during the chemical vapor deposition growth of hierarchical N-doped graphene fibers. By combining with first-principle calculations, we found that the Ni-N configuration is closely correlated to the N contents in the precursor, in which the acetonitrile with a high N/C ratio favors the formation of Ni-N3, while the pyridine with a low N/C ratio is more likely to promote the evolution of Ni-N2. Moreover, we revealed that the presence of N favors the formation of H-terminated edge of sp2 carbon and consequently leads to the formation of graphene fibers consisting of vertically stacked graphene flakes, instead of the traditional growth of carbon nanotubes on Ni nanoparticles. With a high capability in balancing the *COOH formation and *CO desorption, the as-prepared hierarchical N-doped graphene nanofibers with Ni-N3 sites exhibit a superior CO2RR performance compared to that with Ni-N2 and Ni-N4 ones.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.

Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.

Genetic control of RNA editing in neurodegenerative disease.

A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variants. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership-Alzheimer's Disease and Parkinson's Progression Markers Initiative. The large-scale and genome-wide identification of 95 198 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA-binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56 and DHX9, since their regulations on multiple RNA-editing events were probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of 3 proteins, expressions of 277 genes and splicing of 449 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.

Longitudinal association of depressive symptoms with cognition and neuroimaging biomarkers in cognitively unimpaired older adults, mild cognitive impairment, and Alzheimer's disease.

We aimed to longitudinally examine the relationship between depression and cognitive function and investigate the mediating effects of imaging indicators in this relationship. 2,251 subjects with longitudinal assessment of geriatric depression scale, Mini-Mental State Examination, Montreal Cognitive Assessment, Clinical Dementia Rating-Sum of Boxes (CDRSB), Alzheimer's Disease Assessment Scale11, Alzheimer's Disease Assessment Scale13 and imaging of 3DT1, diffusion tensor imaging, fluid-attenuated inversion recovery, arterial spin labeling, fluorodeoxyglucose positron emission tomography, 18F-AV45-PET, and 18F-AV1451-PET were included from the Alzheimer's Disease Neuroimaging Initiative database. The multivariate mixed-effects models were employed to analyze the correlation between geriatric depression scale scores, cognitive function, and imaging indicators. The sgmediation software package was utilized to analyze the mediating effects of imaging indicators. The geriatric depression scale was negatively correlated with Mini-Mental State Examination and Montreal Cognitive Assessment, and positively correlated with CDRSB, Alzheimer's Disease Assessment Scale11, and Alzheimer's Disease Assessment Scale13 when the subjects were not grouped. The geriatric depression scale was negatively correlated with Montreal Cognitive Assessment and positively correlated with Alzheimer's Disease Assessment Scal13 in groups with baseline diagnosis of early mild cognitive impairment and late mild cognitive impairment. Furthermore, depression was associated with regional imaging indicators, while cognitive function was linked to broad imaging indicators. Some of these indicators were related to both depression and cognitive function, playing a mediating role in their relationship. Depression was related to cognitive function, especially in subjects with mild cognitive impairment. Some imaging indicators may represent the underlying basis for the association between depression and cognitive function.

Exploring causal effects and potential mediating mechanisms of genetically linked environmental senses with intracerebral hemorrhage.

The occurrence mechanism of intracerebral hemorrhage remains unclear. Several recent studies have highlighted the close relationship between environmental senses and intracerebral hemorrhage, but the mechanisms of causal mediation are inconclusive. We aimed to investigate the causal relationships and potential mechanisms between environmental senses and intracerebral hemorrhage. Multiple Mendelian randomization methods were used to identify a causal relationship between environmental senses and intracerebral hemorrhage. Gut microbiota and brain imaging phenotypes were used to find possible mediators. Enrichment and molecular interaction analyses were used to identify potential mediators and molecular targets. No causal relationship between temperature and visual perception with intracerebral hemorrhage was found, whereas long-term noise was identified as a risk factor for intracerebral hemorrhage (OR 2.95, 95% CI: 1.25 to 6.93, PIVW = 0.01). The gut microbiota belonging to the class Negativicutes and the order Selenomonadales and the brain image-derived phenotypes ICA100 node 54, edge 803, edge 1149, and edge 1323 played mediating roles. "Regulation of signaling and function in synaptic organization" is the primary biological pathway of noise-induced intracerebral hemorrhage, and ARHGAP22 may be the critical gene. This study emphasized the importance of environmental noise in the prevention, disease management, and underlying biological mechanisms of intracerebral hemorrhage.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging.

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction.

Personality traits are commonly regarded as relatively stable, whereas life satisfaction can fluctuate with time and circumstances, shaped by external influences and personal encounters. The correlation between personality traits and life satisfaction is well-established, yet the underlying neural mechanisms of the myelin-based microstructural brain network connecting them remain unclear. Here, we constructed individual-level whole-brain myelin microstructural networks from the MRI data of 1,043 healthy adults and performed correlation analysis to detect significant personality trait-related and life satisfaction-related subnetworks. A mediation analysis was used to verify whether the shared structural basis of personality traits and life satisfaction significantly mediated their association. The results showed that agreeableness positively correlated with life satisfaction. We identified a shared structural basis of the personality trait of agreeableness and life satisfaction. The regions comprising this overlapping network include the superior parietal lobule, inferior parietal lobule, and temporoparietal junction. Moreover, the shared microstructural connections mediate the association between the personality trait of agreeableness and life satisfaction. This large-scale neuroimaging investigation substantiates a mediation framework for understanding the microstructural connections between personality and life satisfaction, offering potential targets for assessment and interventions to promote human well-being.

Causal relationship between primary headache mediated by circulating cytokines and cerebral cortex structure: a mediation Mendelian randomization study.

Inflammation may be related to structural changes in the cerebral cortex. We aimed to explore whether cytokines mediate the link between these changes and primary headache. The summary statistics of genome-wide association study (GWAS) related to migraine and its subtypes, cluster headache were derived from the FinnGen Release 10 database, and tension-type headache data was from the GWAS Catalog. Ninety-one cytokines were obtained from genome-wide pQTL mapping data. GWAS data on cortical surface area (SA) and thickness (TH) came from the ENIGMA Consortium. The methods of Mendelian randomization (MR) analysis included the inverse-variance-weighted (IVW), MR-Egger, and weighted median. Migraine reduces the SA of paracentral[ß = -1.3645, OR = 0.2555, 95%CI (0.0660, 0.9898)] by fibroblast growth factor-23(FGF-23), with an intermediate ratio (IR) of 38.13%. Migraine may reduce the TH of superior parietal[ß = -0.0029, OR = 0.9971, 95%CI (0.9943, 0.9999)] by interleukin (IL)-15RA, with an absolute IR of 11.11%. Migraine without aura may reduce the TH of rostral anterior cingulate[ß = -0.0005, OR = 0.9995, 95%CI (0.9991, 0.9999)] by IL-18R1, with an IR of 11.63%. FGF23 and IL-15RA are associated with reduced SA or TH in migraine, while IL-18R1 is associated with increased TH in migraine without aura.

The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). METHODS: We categorized census tracts at birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract including the percentage of low-income households, the CDC's social vulnerability index (SVI), and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through census tract-level mediators adjusting for individual-level covariates. MEASUREMENTS AND MAIN RESULTS: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6% and 13.2% resided in census tracts with a HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 1.01,1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for SVI and other tract-level variables. CONCLUSIONS: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

Biological Age, Chronological Age, and Survival in Pulmonary Fibrosis: A Causal Mediation Analysis.

Rationale: Accelerated biological aging has been implicated in the development of interstitial lung disease (ILD) and other diseases of aging but remains poorly understood. Objectives: To identify plasma proteins that mediate the relationship between chronological age and survival association in patients with ILD. Methods: Causal mediation analysis was performed to identify plasma proteins that mediated the chronological age-survival relationship in an idiopathic pulmonary fibrosis discovery cohort. Proteins mediating this relationship after adjustment for false discovery were advanced for testing in an independent ILD validation cohort and explored in a chronic obstructive pulmonary disease cohort. A proteomic-based measure of biological age was constructed and survival analysis performed, assessing the impact of biological age and peripheral blood telomere length on the chronological age-survival relationship. Measurements and Main Results: Twenty-two proteins mediated the chronological age-survival relationship after adjustment for false discovery in the idiopathic pulmonary fibrosis discovery cohort (n = 874), with 19 remaining significant mediators of this relationship in the ILD validation cohort (n = 983) and one mediating this relationship in the chronic obstructive pulmonary disease cohort. Latent transforming growth factor-ß binding protein 2 and ectodysplasin A2 receptor showed the strongest mediation across cohorts. A proteomic measure of biological age completely attenuated the chronological age-survival association and better discriminated survival than chronological age. Results were robust to adjustment for peripheral blood telomere length, which did not mediate the chronological age-survival relationship. Conclusions: Molecular measures of aging completely mediate the relationship between chronological age and survival, suggesting that chronological age has no direct effect on ILD survival.

Lower socioeconomic status may help explain racial disparities in asthma and atopic dermatitis prevalence: A mediation analysis.

BACKGROUND: Racial disparities in atopic disease (atopic dermatitis [AD], asthma, and allergies) prevalence are well documented. Despite strong associations between race and socioeconomic deprivation in the United States, and socioeconomic status (SES) and atopic diseases, the extent to which SES explains these disparities is not fully understood. OBJECTIVE: We sought to identify racial disparities in childhood atopic disease prevalence and determine what proportion of those disparities is mediated by SES. METHODS: This study used the National Health Interview Survey (2011-2018) to investigate AD, asthma, and respiratory allergy prevalence in Black and White children and the extent to which measures of SES explain any identified disparities. RESULTS: By race, prevalences were as follows: AD, White 11.8% (95% CI: 11.4%, 12.2%) and Black 17.4% (95% CI: 16.6%, 18.3%); asthma prevalence, White 7.4% (95% CI: 7.0%, 7.7%) and Black 14.3% (95% CI: 13.5%, 15.0%); respiratory allergy, White 11.4% (95% CI: 11.0%, 11.9%) and Black 10.9% (95% CI: 10.3%, 11.6%). The percentage of the disparity between racial groups and disease prevalence explained by a multivariable measure of SES was 25% (95% CI: 15%, 36%) for Black versus White children with AD and 47% (95% CI: 40%, 54%) for Black versus White children with asthma. CONCLUSIONS: In a nationally representative US population, Black children had higher prevalence of AD and asthma than White children did and similar prevalence of respiratory allergy; a multivariable SES measure explained a proportion of the association between Black versus White race and AD and a much larger proportion for asthma.

Key risk factors of asthma-like symptoms are mediated through infection burden in early childhood.

BACKGROUND: Risk factors of asthma-like symptoms in childhood may act through an increased infection burden because infections often trigger these symptoms. OBJECTIVE: We sought to investigate whether the effect of established risk factors of asthma-like episodes in early childhood is mediated through burden and subtypes of common infections. METHODS: The study included 662 children from the Copenhagen Prospective Studies on Asthma in Childhood 2010 mother-child cohort, in which infections were registered prospectively in daily diaries from age 0 to 3 years. The association between established risk factors of asthma-like episodes and infection burden was analyzed by quasi-Poisson regressions, and mediation analyses were performed for significant risk factors. RESULTS: In the first 3 years of life, the children experienced a median of 16 (interquartile range, 12-23) infectious episodes. We found that the infection burden significantly (PACME < .05) mediated the association of maternal asthma (36.6% mediated), antibiotics during pregnancy (47.3%), siblings at birth (57.7%), an asthma exacerbation polygenic risk score (30.6%), and a bacterial airway immune score (80.2%) with number of asthma-like episodes, whereas the higher number of episodes from male sex, low birth weight, low gestational age, and maternal antibiotic use after birth was not mediated through an increased infection burden. Subtypes of infections driving the mediation were primarily colds, pneumonia, gastroenteritis, and fever, but not acute otitis media or acute tonsillitis. CONCLUSIONS: Several risk factors of asthma-like symptoms in early childhood act through an increased infection burden in the first 3 years of life. Prevention of infectious episodes may therefore be beneficial to reduce the burden of asthma-like symptoms in early childhood.

Joint transcriptomic and cytometric study of children with peanut allergy reveals molecular and cellular cross talk in reaction thresholds.

BACKGROUND: Reaction thresholds in peanut allergy are highly variable. Elucidating causal relationships between molecular and cellular processes associated with variable thresholds could point to therapeutic pathways for raising thresholds. OBJECTIVE: The aim of this study was to characterize molecular and cellular systemic processes associated with reaction threshold in peanut allergy and causal relationships between them. METHODS: A total of 105 children aged 4 to 14 years with suspected peanut allergy underwent double-blind, placebo-controlled food challenge to peanut. The cumulative peanut protein quantity eliciting allergic symptoms was considered the reaction threshold for each child. Peripheral blood samples collected at 0, 2, and 4 hours after challenge start were used for RNA sequencing, whole blood staining, and cytometry. Statistical and network analyses were performed to identify associations and causal mediation between the molecular and cellular profiles and peanut reaction threshold. RESULTS: Within the cohort (N = 105), 81 children (77%) experienced allergic reactions after ingesting varying quantities of peanut, ranging from 43 to 9043 mg of cumulative peanut protein. Peripheral blood expression of transcripts (eg, IGF1R [false discovery rate (FDR) = 5.4e-5] and PADI4 [FDR = 5.4e-5]) and neutrophil abundance (FDR = 9.5e-4) were associated with peanut threshold. Coexpression network analyses revealed that the threshold-associated transcripts were enriched in modules for FcγR-mediated phagocytosis (FDR = 3.2e-3) and Toll-like receptor (FDR = 1.4e-3) signaling. Bayesian network, key driver, and causal mediation analyses identified key drivers (AP5B1, KLHL21, VASP, TPD52L2, and IGF2R) within these modules that are involved in bidirectional causal mediation relationships with neutrophil abundance. CONCLUSION: Key driver transcripts in FcγR-mediated phagocytosis and Toll-like receptor signaling interact bidirectionally with neutrophils in peripheral blood and are associated with reaction threshold in peanut allergy.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study.

AIMS/HYPOTHESIS: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. METHODS: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. RESULTS: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.