Whole-genome sequencing in autism identifies hot spots for de novo germline mutation.

De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

Targeting the coronavirus nucleocapsid protein through GSK-3 inhibition.

The coronaviruses responsible for severe acute respiratory syndrome (SARS-CoV), COVID-19 (SARS-CoV-2), Middle East respiratory syndrome-CoV, and other coronavirus infections express a nucleocapsid protein (N) that is essential for viral replication, transcription, and virion assembly. Phosphorylation of N from SARS-CoV by glycogen synthase kinase 3 (GSK-3) is required for its function and inhibition of GSK-3 with lithium impairs N phosphorylation, viral transcription, and replication. Here we report that the SARS-CoV-2 N protein contains GSK-3 consensus sequences and that this motif is conserved in diverse coronaviruses, raising the possibility that SARS-CoV-2 may be sensitive to GSK-3 inhibitors, including lithium. We conducted a retrospective analysis of lithium use in patients from three major health systems who were PCR-tested for SARS-CoV-2. We found that patients taking lithium have a significantly reduced risk of COVID-19 (odds ratio = 0.51 [0.35-0.74], P = 0.005). We also show that the SARS-CoV-2 N protein is phosphorylated by GSK-3. Knockout of GSK3A and GSK3B demonstrates that GSK-3 is essential for N phosphorylation. Alternative GSK-3 inhibitors block N phosphorylation and impair replication in SARS-CoV-2 infected lung epithelial cells in a cell-type-dependent manner. Targeting GSK-3 may therefore provide an approach to treat COVID-19 and future coronavirus outbreaks.

The combination of autism and exceptional cognitive ability is associated with suicidal ideation.

Autism with co-occurring exceptional cognitive ability is often accompanied by severe internalizing symptoms and feelings of inadequacy. Whether cognitive ability also translates into greater risk for suicidal ideation is unclear. To investigate this urgent question, we examined two samples of high-ability autistic individuals for factors that were predictive of suicidal ideation. In the first sample (N = 1,074 individuals seen at a clinic specializing in gifted/talented youth), we observed a striking excess of parent-reported suicidal ideation in autistic individuals with IQ ≥ 120 (Odds Ratio = 5.9, p=0.0007). In a separate sample of SPARK participants, we confirmed higher rates of suicidal thoughts compared to non-autistic children from the ABCD cohort (combined N = 16,049, Odds Ratio = 6.8, p<2.2e-16), and further that autistic children with suicidal thoughts had significantly higher cognitive ability (p<2.2e-16) than those without. Elevated polygenic scores (PGS) for cognitive performance were associated with increased suicidal thoughts (N = 1,983, Z=2.16,p=0.03), with PGS for educational attainment trending in the same direction (Z=1.4,p=0.17). Notably, similar results were found in parents of these autistic youth, where higher PGS for educational attainment was associated with increasing thoughts of suicide (N = 736, Z=2.28,p=0.02). Taken together, these results suggest that on a phenotypic and genetic level, increasing cognitive ability is an unexpected risk factor for suicidal ideation in individuals diagnosed with, or at risk for autism.

Language and reading impairments are associated with increased prevalence of non-right-handedness.

Handedness has been studied for association with language-related disorders because of its link with language hemispheric dominance. No clear pattern has emerged, possibly because of small samples, publication bias, and heterogeneous criteria across studies. Non-right-handedness (NRH) frequency was assessed in N = 2503 cases with reading and/or language impairment and N = 4316 sex-matched controls identified from 10 distinct cohorts (age range 6-19 years old; European ethnicity) using a priori set criteria. A meta-analysis (Ncases  = 1994) showed elevated NRH % in individuals with language/reading impairment compared with controls (OR = 1.21, CI = 1.06-1.39, p = .01). The association between reading/language impairments and NRH could result from shared pathways underlying brain lateralization, handedness, and cognitive functions.

The CBP KIX domain regulates long-term memory and circadian activity.

BACKGROUND: CREB-dependent transcription necessary for long-term memory is driven by interactions with CREB-binding protein (CBP), a multi-domain protein that binds numerous transcription factors potentially affecting expression of thousands of genes. Identifying specific domain functions for multi-domain proteins is essential to understand processes such as cognitive function and circadian clocks. We investigated the function of the CBP KIX domain in hippocampal memory and gene expression using CBPKIX/KIX mice with mutations that prevent phospho-CREB (Ser133) binding. RESULTS: We found that CBPKIX/KIX mice were impaired in long-term memory, but not learning acquisition or short-term memory for the Morris water maze. Using an unbiased analysis of gene expression in the dorsal hippocampus after training in the Morris water maze or contextual fear conditioning, we discovered dysregulation of CREB, CLOCK, and BMAL1 target genes and downregulation of circadian genes in CBPKIX/KIX mice. Given our finding that the CBP KIX domain was important for transcription of circadian genes, we profiled circadian activity and phase resetting in CBPKIX/KIX mice. CBPKIX/KIX mice exhibited delayed activity peaks after light offset and longer free-running periods in constant dark. Interestingly, CBPKIX/KIX mice displayed phase delays and advances in response to photic stimulation comparable to wildtype littermates. Thus, this work delineates site-specific regulation of the circadian clock by a multi-domain protein. CONCLUSIONS: These studies provide insight into the significance of the CBP KIX domain by defining targets of CBP transcriptional co-activation in memory and the role of the CBP KIX domain in vivo on circadian rhythms.

Genetic Intersections of Language and Neuropsychiatric Conditions.

PURPOSE OF REVIEW: To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes implicated in both language and neuropsychiatric disorders, as they reveal biological domains likely to underpin the processes controlling both. RECENT FINDINGS: Abnormal language and communication are common manifestations of neuropsychiatric conditions, and children with impaired language are more likely to develop psychiatric disorders than their peers. Major themes in the genetics of both language and psychiatry include master transcriptional regulators, like FOXP2; key developmental regulators, like AUTS2; and mediators of neurotransmission, like GRIN2A and CACNA1C.

TiSAn: estimating tissue-specific effects of coding and non-coding variants.

Motivation: Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations. Results: Herein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine. Availability and implementation: Software and vignettes are available at http://github.com/kevinVervier/TiSAn. Supplementary information: Supplementary data are available at Bioinformatics online.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis.

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

cerebroViz: an R package for anatomical visualization of spatiotemporal brain data.

Summary: Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease's age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics. Availability and Implementation: cerebroViz is freely available through GitHub ( https://github.com/ethanbahl/cerebroViz ). The tutorial is available at http://ethanbahl.github.io/cerebroViz/. Contacts: ethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Differential relationship of DNA replication timing to different forms of human mutation and variation.

Human genetic variation is distributed nonrandomly across the genome, though the principles governing its distribution are only partially known. DNA replication creates opportunities for mutation, and the timing of DNA replication correlates with the density of SNPs across the human genome. To enable deeper investigation of how DNA replication timing relates to human mutation and variation, we generated a high-resolution map of the human genome's replication timing program and analyzed its relationship to point mutations, copy number variations, and the meiotic recombination hotspots utilized by males and females. DNA replication timing associated with point mutations far more strongly than predicted from earlier analyses and showed a stronger relationship to transversion than transition mutations. Structural mutations arising from recombination-based mechanisms and recombination hotspots used more extensively by females were enriched in early-replicating parts of the genome, though these relationships appeared to relate more strongly to the genomic distribution of causative sequence features. These results indicate differential and sex-specific relationship of DNA replication timing to different forms of mutation and recombination.

forestSV: structural variant discovery through statistical learning.

Detecting genomic structural variants from high-throughput sequencing data is a complex and unresolved challenge. We have developed a statistical learning approach, based on Random Forests, that integrates prior knowledge about the characteristics of structural variants and leads to improved discovery in high-throughput sequencing data. The implementation of this technique, forestSV, offers high sensitivity and specificity coupled with the flexibility of a data-driven approach.

Lingo: an automated, web-based deep phenotyping platform for language ability.

Background: Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Methods: Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. Results: We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Conclusions: Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.

Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity.

Background: Gender-diverse individuals are at increased risk for mental health problems, but it is unclear whether this is due to shared environmental or genetic factors. Methods: In two SPARK samples, we tested for associations of 16 polygenic scores (PGSs) with quantitative measures of gender diversity and mental health. In study 1, 639 independent adults (59% autistic) reported their mental health with the Adult Self-Report and their gender diversity with the Gender Self-Report (GSR). The GSR has 2 dimensions: binary (degree of identification with the gender opposite that implied by sex designated at birth) and nonbinary (degree of identification with a gender that is neither male nor female). In study 2 (N = 5165), we used a categorical measure of gender identity. Results: In study 1, neuropsychiatric PGSs were positively associated with Adult Self-Report scores: externalizing was positively associated with the attention-deficit/hyperactivity disorder PGS (ß = 0.10 [0.03-0.17]), and internalizing was positively associated with the PGSs for depression (ß = 0.07 [0-0.14]) and neuroticism (ß = 0.10 [0.03-0.17]). Interestingly, GSR scores were not significantly associated with any neuropsychiatric PGS. However, GSR nonbinary was positively associated with the cognitive performance PGS (ß = 0.11 [0.05-0.18]), with the effect size comparable in magnitude to the associations of the neuropsychiatric PGSs with the Adult Self-Report. Additionally, GSR binary was positively associated with the nonheterosexual sexual behavior PGS (ß = 0.07 [0-0.14]). In study 2, the cognitive performance PGS effect replicated; transgender and nonbinary individuals had higher PGSs (t316 = 4.16). Conclusions: We showed that while gender diversity is phenotypically positively associated with mental health problems, the strongest PGS associations with gender diversity were with the cognitive performance PGS, not the neuropsychiatric PGSs.

This research explores the connection between gender diversity, mental health, and genetic factors. It reveals that gender-diverse individuals often experience more mental health issues. Interestingly, rather than finding evidence linking these mental health challenges to genetic risk factors, the study discovered a replicable positive correlation between gender diversity and genetic markers for higher cognitive performance. This suggests that gender-diverse individuals typically have more of these cognitive performance gene variants. Finally, the study presents some early evidence suggesting that interactions between the environment (e.g., stigma) and genetic risk explain some of the elevated risk to mental health in gender-diverse individuals.

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem.

IMPORTANCE: This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. OBJECTIVES: The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. METHODS: A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. RESULTS: To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. CONCLUSION: The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, defining learning-responsive gene expression across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to elucidate transcriptome-wide changes in gene expression in the hippocampus following learning, enabling us to define molecular signatures unique to each hippocampal subregion. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. Although the CA1 region exhibited increased expression of genes related to transcriptional regulation, the DG showed upregulation of genes associated with protein folding. We demonstrate the functional relevance of subregion-specific gene expression by genetic manipulation of a transcription factor selectively in the CA1 hippocampal subregion, leading to long-term memory deficits. This work demonstrates the power of using spatial molecular approaches to reveal transcriptional events during memory consolidation.

Activity-induced gene expression in the human brain.

Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain.

The Gender Self-Report: A multidimensional gender characterization tool for gender-diverse and cisgender youth and adults.

Gender identity is a core component of human experience, critical to account for in broad health, development, psychosocial research, and clinical practice. Yet, the psychometric characterization of gender has been impeded due to challenges in modeling the myriad gender self-descriptors, statistical power limitations related to multigroup analyses, and equity-related concerns regarding the accessibility of complex gender terminology. Therefore, this initiative employed an iterative multi-community-driven process to develop the Gender Self-Report (GSR), a multidimensional gender characterization tool, accessible to youth and adults, nonautistic and autistic people, and gender-diverse and cisgender individuals. In Study 1, the GSR was administered to 1,654 individuals, sampled through seven diversified recruitments to be representative across age (10-77 years), gender and sexuality diversity (∼33% each gender diverse, cisgender sexual minority, cisgender heterosexual), and autism status (> 33% autistic). A random half-split subsample was subjected to exploratory factor analytics, followed by confirmatory analytics in the full sample. Two stable factors emerged: Nonbinary Gender Diversity and Female-Male Continuum (FMC). FMC was transformed to Binary Gender Diversity based on designated sex at birth to reduce collinearity with designated sex at birth. Differential item functioning by age and autism status was employed to reduce item-response bias. Factors were internally reliable. Study 2 demonstrated the construct, convergent, and ecological validity of GSR factors. Of the 30 hypothesized validation comparisons, 26 were confirmed. The GSR provides a community-developed gender advocacy tool with 30 self-report items that avoid complex gender-related "insider" language and characterize diverse populations across continuous multidimensional binary and nonbinary gender traits. (PsycInfo Database Record (c) 2023 APA, all rights reserved).