Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

SCARF2 is a target for chronic obstructive pulmonary disease: Evidence from multi-omics research and cohort validation

Age-related chronic inflammatory lung diseases impose a threat on public health, including idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). However, their etiology and potential targets have not been clarified. We performed genome-wide meta-analysis for IPF with the largest sample size (2883 cases and 741,929 controls) and leveraged the summary statistics of COPD (17,547 cases and 617,598 controls). Transcriptome-wide and proteome-wide Mendelian randomization (MR) designs, together with genetic colocalization, were implemented to find robust targets. The mediation effect was assessed using leukocyte telomere length (LTL). The single-cell transcriptome analysis was performed to link targets with cell types. Individual-level data from UK Biobank (UKB) were used to validate our findings. Sixteen genetically predicted plasma proteins were causally associated with the risk of IPF and 6 proteins were causally associated with COPD. Therein, genetically-elevated plasma level of SCARF2 protein should reduce the risk of both IPF (odds ratio, OR = 0.9974 [0.9970, 0.9978]) and COPD (OR = 0.7431 [0.6253, 0.8831]) and such effects were not mediated by LTL. Genetic colocalization further corroborated these MR results of SCARF2. The transcriptome-wide MR confirmed that higher expression level of SCARF2 was associated with a reduced risk of both. However, the single-cell RNA analysis indicated that SCARF2 expression level was only relatively lower in epithelial cells of COPD lung tissue compared to normal lung tissue. UKB data implicated an inverse association of serum SCARF2 protein with COPD (hazard ratio, HR = 1.215 [1.106, 1.335]). The SCARF2 gene should be a novel target for COP.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders

N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.

Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data

OBJECTIVE

This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data.

METHODS

We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction.

RESULTS

We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues.

CONCLUSION

This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.

Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation, senescence, and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is largely unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. Here, we explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are mainly involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with the RNA-binding protein ELAVL1 and the selective autophagy receptor SQSTM1. We also found that YPEL2 localizes stress granules in response to sodium arsenite, an oxidative stress inducer, which suggests that YPEL2 participates in stress granule-related processes. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.

Rare copy-number variants as modulators of common disease susceptibility

BACKGROUND

Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described.

METHODS

Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank.

RESULTS

We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS.

CONCLUSIONS

Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.

Contribution of genetic variants to congenital heart defects in both singleton and twin fetuses: a Chinese cohort study

BACKGROUND

The contribution of genetic variants to congenital heart defects (CHDs) has been investigated in many postnatal cohorts but described in few prenatal fetus cohorts. Overall, specific genetic variants especially copy number variants (CNVs) leading to CHDs are somewhat diverse among different prenatal cohort studies. In this study, a total of 1118 fetuses with confirmed CHDs were recruited from three units over a 5-year period, composing 961 of singleton pregnancies and 157 of twin pregnancies. We performed chromosomal microarray analysis on all cases to detect numerical chromosomal abnormalities (NCAs) and pathogenic/likely pathogenic CNVs (P/LP CNVs) and employed whole-exome sequencing for some cases without NCAs and P/LP CNVs to detect P/LP sequence variants (P/LP SVs).

RESULTS

Overall, NCAs and P/LP CNVs were identified in 17.6% (197/1118) of cases, with NCA accounting for 9.1% (102/1118) and P/LP CNV for 8.5% (95/1118). Nonisolated CHDs showed a significantly higher frequency of NCA than isolated CHD (27.3% vs. 4.4%, p < 0.001), but there was no significant difference in the frequency of P/LP CNVs between isolated and nonisolated CHD (11.7% vs. 7.7%). A total of 109 P/LP CNVs were identified in 95 fetuses, consisting of 97 (89.0%) de novo, 6 (5.5%) parental inherited and 6 (5.5%) with unavailable parental information. The 16p11.2 proximal BP4-BP5 deletion was detected in 0.9% (10/1118) of all cases, second only to the most common 22q11.21 proximal A-D deletion (2.1%, 23/1118). Most of the 16p11.2 deletions (8/10) detected were de novo, and were enriched in CHD cases compared with a control cohort from a previous study. Additionally, SV was identified in 12.9% (8/62) of cases without NCA and P/LP CNV, most of which were de novo with autosomal dominant inheritance.

CONCLUSIONS

Our cohort study provides a deep profile of the contribution of genetic variants to CHDs in both singleton and twin fetuses; NCA and P/LP CNV contribute to 9.1% and 8.5% of CHD in fetuses, respectively. We confirmed the 16p11.2 deletion as a CHD-associated hotspot CNV, second only to the 22q11.21 deletion in frequency. Most 16p11.2 deletions detected were de novo. Additionally, P/LP SV was identified in 12.9% (8/62) of fetuses without NCA or P/LP CNV.

Diversity of Clinical and Molecular Characteristics in Korean Patients with 16p11.2 Microdeletion Syndrome

16p11.2 copy number variations (CNVs) are increasingly recognized as one of the most frequent genomic disorders, and the 16p11.2 microdeletion exhibits broad phenotypic variability and a diverse clinical phenotype. We describe the neurodevelopmental course and discordant clinical phenotypes observed within and between individuals with identical 16p11.2 microdeletions. An analysis with the CytoScan Dx Assay was conducted on a GeneChip System 3000Dx, and the sample signals were then compared to a reference set using the Chromosome Analysis Suite software version 3.1. Ten patients from six separate families were identified with 16p11.2 microdeletions. Nine breakpoints (BPs) 4-5 and one BP2-5 of the 16p11.2 microdeletion were identified. All patients with 16p11.2 microdeletions exhibited developmental delay and/or intellectual disability. Sixty percent of patients presented with neonatal hypotonia, but muscle weakness improved with age. Benign infantile epilepsy manifested between the ages of 7-10 months (a median of 8 months) in six patients (60%). Vertebral dysplasia was observed in two patients (20%), and mild scoliosis was noted in three patients. Sixty percent of patients were overweight. We present six unrelated Korean families, among which identical 16p11.2 microdeletions resulted in diverse developmental trajectories and discordant phenotypes. The clinical variability and incomplete penetrance observed in individuals with 16p11.2 microdeletions remain unclear, posing challenges to accurate clinical interpretation and diagnosis.

oFlowSeq: a quantitative approach to identify protein coding mutations affecting cell type enrichment using mosaic CRISPR-Cas9 edited cerebral organoids

Cerebral organoids are comprised of diverse cell types found in the developing human brain, and can be leveraged in the identification of critical cell types perturbed by genetic risk variants in common, neuropsychiatric disorders. There is great interest in developing high-throughput technologies to associate genetic variants with cell types. Here, we describe a high-throughput, quantitative approach (oFlowSeq) by utilizing CRISPR-Cas9, FACS sorting, and next-generation sequencing. Using oFlowSeq, we found that deleterious mutations in autism-associated gene KCTD13 resulted in increased proportions of Nestin+ cells and decreased proportions of TRA-1-60+ cells within mosaic cerebral organoids. We further identified that a locus-wide CRISPR-Cas9 survey of another 18 genes in the 16p11.2 locus resulted in most genes with > 2% maximum editing efficiencies for short and long indels, suggesting a high feasibility for an unbiased, locus-wide experiment using oFlowSeq. Our approach presents a novel method to identify genotype-to-cell type imbalances in an unbiased, high-throughput, quantitative manner.

Exploring scavenger receptor class F member 2 and the importance of scavenger receptor family in prediagnostic diseases

Scavenger Receptor Class F Member 2 (SCARF2), also known as the Type F Scavenger Receptor Family gene, encodes for Scavenger Receptor Expressed by Endothelial Cells 2 (SREC-II). This protein is a crucial component of the scavenger receptor family and is vital in protecting mammals from infectious diseases. Although research on SCARF2 is limited, mutations in this protein have been shown to cause skeletal abnormalities in both SCARF2-deficient mice and individuals with Van den Ende-Gupta syndrome (VDEGS), which is also associated with SCARF2 mutations. In contrast, other scavenger receptors have demonstrated versatile responses and have been found to aid in pathogen elimination, lipid transportation, intracellular cargo transportation, and work in tandem with various coreceptors. This review will concentrate on recent progress in comprehending SCARF2 and the functions played by members of the Scavenger Receptor Family in pre-diagnostic diseases.

A review of the cognitive impact of neurodevelopmental and neuropsychiatric associated copy number variants

The heritability of intelligence or general cognitive ability is estimated at 41% and 66% in children and adults respectively. Many rare copy number variants are associated with neurodevelopmental and neuropsychiatric conditions (ND-CNV), including schizophrenia and autism spectrum disorders, and may contribute to the observed variability in cognitive ability. Here, we reviewed studies of intelligence quotient or cognitive function in ND-CNV carriers, from both general population and clinical cohorts, to understand the cognitive impact of ND-CNV in both contexts and identify potential genotype-specific cognitive phenotypes. We reviewed aggregate studies of sets ND-CNV broadly linked to neurodevelopmental and neuropsychiatric conditions, and genotype-first studies of a subset of 12 ND-CNV robustly associated with schizophrenia and autism. Cognitive impacts were observed across ND-CNV in both general population and clinical cohorts, with reports of phenotypic heterogeneity. Evidence for ND-CNV-specific impacts were limited by a small number of studies and samples sizes. A comprehensive understanding of the cognitive impact of ND-CNVs would be clinically informative and could identify potential educational needs for ND-CNV carriers. This could improve genetic counselling for families impacted by ND-CNV, and clinical outcomes for those with complex needs.

Copy-number variation in congenital heart disease

Genomic copy-number variants (CNVs) contribute to as many congenital heart disease (CHD) cases (10-15%) as chromosomal aberrations or single-gene mutations and influence clinical outcomes. CNVs in a few genomic hotspots (1q21.1, 2q13, 8p23.1, 11q24, 15q11.2, 16p11.2, and 22q11.2) are recurrently enriched in CHD cohorts and affect dosage-sensitive transcriptional regulators that are required for cardiac development. Reduced penetrance and pleiotropic effects on brain and heart development are common features of these CNVs. Therefore, additional genetic 'hits,' such as a second CNV or gene mutation, are probably required to cause CHD in most cases. Integrative analysis of CNVs, genome sequence, epigenetic alterations, and gene function will be required to delineate the complete genetic landscape of CHD.

Shared genetics between classes of obesity and psychiatric disorders: A large-scale genome-wide cross-trait analysis

AIMS

Epidemiological studies demonstrate an association between classes of obesity and psychiatric disorders, although little is known about shared genetics and causality of association. Thus, we aimed to investigate shared genetics and causal link between different classes of obesity and psychiatric disorders.

METHODS

We used genome-wide association study (GWAS) summary data range from 9725 to 500,199 sample sizes of European descent, conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between the classes of obesity and anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. We conducted transcriptome-wide association study analysis (TWAS) to identified variants regulated gene expression in those related disorders. Finally, pathway enrichment analysis to identified major pathways.

RESULTS

In the combined analysis, we replicated 211 previously reported loci and discovered 58 novel independent loci that were associated with all three classes of obesity and related psychiatric disorders. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, circulatory, adipose, digestive, respiratory, and nervous systems, such as DCC, NEGR1, INO80E. Mendelian randomization analyses suggested that there may be a two-way or one-way causal relationship between obesity and psychiatric disorders.

CONCLUSION

This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between classes of obesity and psychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, anorexia nervosa, major depressive disorder, schizophrenia, and obsessive-compulsive disorder). Such shared genetics suggests potential new biological functions in common among them.

Tissue- and cell-type-specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models

Chromosome 16p11.2 reciprocal genomic disorder, resulting from recurrent copy-number variants (CNVs), involves intellectual disability, autism spectrum disorder (ASD), and schizophrenia, but the responsible mechanisms are not known. To systemically dissect molecular effects, we performed transcriptome profiling of 350 libraries from six tissues (cortex, cerebellum, striatum, liver, brown fat, and white fat) in mouse models harboring CNVs of the syntenic 7qF3 region, as well as cellular, transcriptional, and single-cell analyses in 54 isogenic neural stem cell, induced neuron, and cerebral organoid models of CRISPR-engineered 16p11.2 CNVs. Transcriptome-wide differentially expressed genes were largely tissue-, cell-type-, and dosage-specific, although more effects were shared between deletion and duplication and across tissue than expected by chance. The broadest effects were observed in the cerebellum (2,163 differentially expressed genes), and the greatest enrichments were associated with synaptic pathways in mouse cerebellum and human induced neurons. Pathway and co-expression analyses identified energy and RNA metabolism as shared processes and enrichment for ASD-associated, loss-of-function constraint, and fragile X messenger ribonucleoprotein target gene sets. Intriguingly, reciprocal 16p11.2 dosage changes resulted in consistent decrements in neurite and electrophysiological features, and single-cell profiling of organoids showed reciprocal alterations to the proportions of excitatory and inhibitory GABAergic neurons. Changes both in neuronal ratios and in gene expression in our organoid analyses point most directly to calretinin GABAergic inhibitory neurons and the excitatory/inhibitory balance as targets of disruption that might contribute to changes in neurodevelopmental and cognitive function in 16p11.2 carriers. Collectively, our data indicate the genomic disorder involves disruption of multiple contributing biological processes and that this disruption has relative impacts that are context specific.

Prediction of Synaptically Localized RNAs in Human Neurons Using Developmental Brain Gene Expression Data

In the nervous system, synapses are special and pervasive structures between axonal and dendritic terminals, which facilitate electrical and chemical communications among neurons. Extensive studies have been conducted in mice and rats to explore the RNA pool at synapses and investigate RNA transport, local protein synthesis, and synaptic plasticity. However, owing to the experimental difficulties of studying human synaptic transcriptomes, the full pool of human synaptic RNAs remains largely unclear. We developed a new machine learning method, called PredSynRNA, to predict the synaptic localization of human RNAs. Training instances of dendritically localized RNAs were compiled from previous rodent studies, overcoming the shortage of empirical instances of human synaptic RNAs. Using RNA sequence and gene expression data as features, various models with different learning algorithms were constructed and evaluated. Strikingly, the models using the developmental brain gene expression features achieved superior performance for predicting synaptically localized RNAs. We examined the relevant expression features learned by PredSynRNA and used an independent test dataset to further validate the model performance. PredSynRNA models were then applied to the prediction and prioritization of candidate RNAs localized to human synapses, providing valuable targets for experimental investigations into neuronal mechanisms and brain disorders.

Targeted Exome Sequencing of Genes Involved in Rare CNVs in Early-Onset Severe Obesity

Context: Rare copy number variants (CNVs) have been associated with the development of severe obesity. However, the potential disease-causing contribution of individual genes within the region of CNVs is often not known.

Objective: Screening of rare variants in genes involved in CNVs in Finnish patients with severe early-onset obesity to find candidate genes linked to severe obesity.

Methods: Custom-made targeted exome sequencing panel to search for rare (minor allele frequency <0.1%) variants in genes affected by previously identified CNVs in 92 subjects (median age 14 years) with early-onset severe obesity (median body mass index (BMI) Z-score + 4.0).

Results: We identified thirteen rare heterozygous variants of unknown significance in eleven subjects in twelve of the CNV genes. Two rare missense variants (p.Pro405Arg and p.Tyr232Cys) were found in SORCS1, a gene highly expressed in the brain and previously linked to diabetes risk. Four rare variants were in genes in the proximal 16p11.2 region (a frameshift variant in TAOK2 and missense variants in SEZ6L2, ALDOA and KIF22) and three rare missense variants were in genes in the 22q11.21 region (AIFM3, ARVCF and KLHL22).

Conclusion: We report several rare variants in CNV genes in subjects with childhood obesity. However, the role of the individual genes in the previously identified rare CNVs to development of obesity remains uncertain. More studies are needed to understand the potential role of the specific genes within obesity associated CNVs.