Hao Wang

Analyzing brain morphological changes across individuals with varying genetic risk scores may reveal patterns of brain alterations from health to disease. This study investigates gray matter structural alterations in individuals with clinical diagnoses compared with those with genetic risk alone. UK Biobank MRI and genotypes (N = 34,720) were used to derive brain measures and polygenic risk scores, creating genetic risk brain maps for 14 neuropsychiatric disorders. Eight disorders from ENIGMA were used to construct disease brain maps. Brain maps of genetic risk and clinical diagnosis show overall alignment for ADHD, schizophrenia, bipolar disorder, and autism. Other conditions, including Alzheimer’s disease, show specific brain regions linked to genetic risk aligning with established patient patterns. Incomplete data for some conditions limit analyses. ADHD and PTSD polygenic burden was associated with smaller global brain sizes, while Parkinson’s disease was linked to larger brain volume. Mendelian randomization analyses revealed unidirectional relationships where the brain influences ADHD and Parkinson’s disease, while a bidirectional causal association was observed for schizophrenia. Focusing on schizophrenia and bipolar disorder, we found that individuals with high genetic risk combined with smaller brain structures were more likely to have these diagnoses. Overall, the study demonstrates marked similarities in brain changes between clinical diagnoses and genetic risk for several disorders, albeit with mild effect sizes in the latter. These findings underscore the importance of genetic risk in influencing brain anatomy and the progression of neuropsychiatric disorders.

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for ‘actionable’ genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C—two conditions presenting with overlapping symptoms—with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.

Although Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) share some clinical manifestations, their cardiovascular outcomes are different, and this may be reflected at the level of the endothelial cell (EC). We performed RNA-seq on cultured ECs incubated with pre-treatment sera from KD (n = 5), MIS-C (n = 7), and healthy controls (n = 3). We conducted a weighted gene co-expression network analysis (WGCNA) using 935 transcripts differentially expressed between MIS-C and KD using relaxed filtering (unadjusted p < 0.05, >1.1-fold difference). We found seven gene modules in MIS-C, annotated as an increased TNFα/NFκB pathway, decreased EC homeostasis, anti-inflammation and immune response, translation, and glucocorticoid responsive genes and endothelial–mesenchymal transition (EndoMT). To further understand the difference in the EC response between MIS-C and KD, stringent filtering was applied to identify 41 differentially expressed genes (DEGs) between MIS-C and KD (adjusted p < 0.05, >2-fold-difference). Again, in MIS-C, NFκB pathway genes, including nine pro-survival genes, were upregulated. The expression levels were higher in the genes influencing autophagy (UBD, EBI3, and SQSTM1). Other DEGs also supported the finding by WGCNA. Compared to KD, ECs in MIS-C had increased pro-survival transcripts but reduced transcripts related to EndoMT and EC homeostasis. These differences in the EC response may influence the different cardiovascular outcomes in these two diseases.

Integration of multi-omics data with molecular interaction networks enables elucidation of the pathophysiology of Alzheimer’s disease (AD). Using the latest genome-wide association studies (GWAS) including proxy cases and the STRING interactome, we identified an AD network of 142 risk genes and 646 network-proximal genes, many of which were linked to synaptic functions annotated by mouse knockout data. The proximal genes were confirmed to be enriched in a replication GWAS of autopsy-documented cases. By integrating the AD gene network with transcriptomic data of AD and healthy temporal cortices, we identified 17 gene clusters of pathways, such as up-regulated complement activation and lipid metabolism, down-regulated cholinergic activity, and dysregulated RNA metabolism and proteostasis. The relationships among these pathways were further organized by a hierarchy of the AD network pinpointing major parent nodes in graph structure including endocytosis and immune reaction. Control analyses were performed using transcriptomics from cerebellum and a brain-specific interactome. Further integration with cell-specific RNA sequencing data demonstrated genes in our clusters of immunoregulation and complement activation were highly expressed in microglia.

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca’s area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

According to the WHO 2009 classification, dengue with warning signs is at the risk of developing severe form of dengue disease. One of the most important warning signs is plasma leakage, which can be a serious complication associated with higher morbidity and mortality. We report that the frequency of CD4⁺CD8⁺ double-positive (DP) T cells is significantly increased in patients at risk of developing plasma leakage. Transcriptomic analysis demonstrated that CD4⁺CD8⁺ DP cells were distinct from CD4⁺ Single Positive (SP) T cells but co-clustered with CD8⁺ SP cells, indicating a largely similar transcriptional profile. Twenty significant differentially expressed (DE) genes were identified between CD4⁺CD8⁺ DP and CD8⁺ SP cells. These genes encode OX40 and CCR4 proteins as well as other molecules associated with cell signaling on the cell surface (NT5E, MXRA8, and PTPRK). While comparing the profile of gene expression in CD4⁺CD8⁺ DP cells from patients with and without warning signs of plasma leakage, similar expression profile was observed, implying a role of CD4⁺CD8⁺ DP cells in plasma leakage through a quantitative increase rather than functional alteration. This study provided novel insight into the host immune response during the acute febrile phase of DENV infection and the role of CD4⁺CD8⁺ DP T cells in the pathogenesis of plasma leakage.

Introduction: Sex‐dependent risk factors may underlie sex differences in Alzheimer's disease (AD). Methods: Using sex‐stratified genome‐wide association studies (GWAS) of AD, we evaluated associations of 12 traits with AD through polygenic risk scores (PRS) and Mendelian randomization (MR), and explored joint genetic architecture among significant traits by genomic structural equation modeling and network analysis. Results: AD was associated with lower PRS for premorbid cognitive performance, intelligence, and educational attainment. MR showed a causal role for the cognition‐related traits in AD, particularly among females. Their joint genetic components encompassed RNA processing, neuron projection development, and cell cycle pathways that overlap with cellular senescence. Cholesterol and C‐reactive protein showed pleiotropy but no causality with AD. Discussion: Lower cognitive reserve is causally related to AD. The stronger causal link between cognitive performance and AD in females, despite similar PRS between sexes, suggest these differences may result from gene–environmental interactions accumulated over the lifespan.