SETD1A variant-associated psychosis: A systematic review of the clinical literature and description of two new cases

OBJECTIVE

SETD1A encodes a histone methyltransferase involved in various cell cycle regulatory processes. Loss-of-function SETD1A variants have been associated with numerous neurodevelopmental phenotypes, including intellectual disability and schizophrenia. While the association between rare coding variants in SETD1A and schizophrenia has achieved genome-wide significance by rare variant burden testing, only a few studies have described the psychiatric phenomenology of such individuals in detail. This systematic review and case report aims to characterize the neurodevelopmental and psychiatric phenotypes of SETD1A variant-associated schizophrenia.

METHODS

A PubMed search was completed in July 2022 and updated in May 2023. Only studies that reported individuals with a SETD1A variant as well as a primary psychotic disorder were ultimately included. Additionally, another two previously unpublished cases of SETD1A variant-associated psychosis from our own sequencing cohort are described.

RESULTS

The search yielded 32 articles. While 15 articles met inclusion criteria, only five provided case descriptions. In total, phenotypic information was available for 11 individuals, in addition to our own two unpublished cases. Our findings suggest that although individuals with SETD1A variant-associated schizophrenia may share a number of common features, phenotypic variability nonetheless exists. Moreover, although such individuals may exhibit numerous other neurodevelopmental features suggestive of the syndrome, their psychiatric presentations appear to be similar to those of general schizophrenia populations.

CONCLUSIONS

Loss-of-function SETD1A variants may underlie the development of psychosis in a small percentage of individuals with schizophrenia. Identifying such individuals may become increasingly important, given the potential for advances in precision medicine treatment approaches.

ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases and has played a significant role in rare disease gene discovery. Although this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients (affected individuals) can help resolve such medical mysteries and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including nonprofit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

The Canadian Rare Diseases Models and Mechanisms (RDMM) Network: Connecting Understudied Genes to Model Organisms

Advances in genomics have transformed our ability to identify the genetic causes of rare diseases (RDs), yet we have a limited understanding of the mechanistic roles of most genes in health and disease. When a novel RD gene is first discovered, there is minimal insight into its biological function, the pathogenic mechanisms of disease-causing variants, and how therapy might be approached. To address this gap, the Canadian Rare Diseases Models and Mechanisms (RDMM) Network was established to connect clinicians discovering new disease genes with Canadian scientists able to study equivalent genes and pathways in model organisms (MOs). The Network is built around a registry of more than 500 Canadian MO scientists, representing expertise for over 7,500 human genes. RDMM uses a committee process to identify and evaluate clinician-MO scientist collaborations and approve 25,000 Canadian dollars in catalyst funding. To date, we have made 85 clinician-MO scientist connections and funded 105 projects. These collaborations help confirm variant pathogenicity and unravel the molecular mechanisms of RD, and also test novel therapies and lead to long-term collaborations. To expand the impact and reach of this model, we made the RDMM Registry open-source, portable, and customizable, and we freely share our committee structures and processes. We are currently working with emerging networks in Europe, Australia, and Japan to link international RDMM networks and registries and enable matches across borders. We will continue to create meaningful collaborations, generate knowledge, and advance RD research locally and globally for the benefit of patients and families living with RD.

VariCarta: A Comprehensive Database of Harmonized Genomic Variants Found in Autism Spectrum Disorder Sequencing Studies

Recent years have seen a boom in the application of the next-generation sequencing technology to the study of human disorders, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation. Here we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling, and consistently cataloging literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps, and documents data provenance. The harmonization aspect is especially important since it prevents the potential double counting of variants, which can lead to inflation of gene-based evidence for ASD-association. The database currently contains 170,416 variant events from 10,893 subjects, collected across 61 publications, and reconciles 16,202 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca. Autism Res 2019, 12: 1728-1736. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The search for genetic factors underlying Autism Spectrum Disorder (ASD) yielded numerous studies reporting potentially causative genomic variants found in ASD individuals. However, methodological differences and subject overlap across studies complicate the assembly of these data, diminishing its utility and accessibility. We developed VariCarta, a web-based database that aggregates carefully curated, annotated, and harmonized literature-derived variants identified in individuals with ASD using ongoing semi-manual curation.

Interactive Exploration, Analysis, and Visualization of Complex Phenome-Genome Datasets with ASPIREdb

Identifying variants causal for complex genetic disorders is challenging. With the advent of whole-exome and whole-genome sequencing, computational tools are needed to explore and analyze the list of variants for further validation. Correlating genetic variants with subject phenotype is crucial for the interpretation of the disease-causing mutations. Often such work is done by teams of researchers who need to share information and coordinate activities. To this end, we have developed a powerful, easy to use Web application, ASPIREdb, which allows researchers to search, organize, analyze, and visualize variants and phenotypes associated with a set of human subjects. Investigators can annotate variants using publicly available reference databases and build powerful queries to identify subjects or variants of interest. Functional information and phenotypic associations of these genes are made accessible as well. Burden analysis and additional reporting tools allow investigation of variant properties and phenotype characteristics. Projects can be shared, allowing researchers to work collaboratively to build queries and annotate the data. We demonstrate ASPIREdb's functionality using publicly available data sets, showing how the software can be used to accomplish goals that might otherwise require specialized bioinformatics expertise. ASPIREdb is available at http://aspiredb.chibi.ubc.ca.

Meta-Analysis of Gene Expression Patterns in Animal Models of Prenatal Alcohol Exposure Suggests Role for Protein Synthesis Inhibition and Chromatin Remodeling

BACKGROUND

Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioral, and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models; however, there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets.

METHODS

We assembled 10 microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol (EtOH)-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and postnatal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures.

RESULTS

For each subanalysis, we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute EtOH treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over representation of genes involved in protein synthesis, mRNA splicing, and chromatin organization.

CONCLUSIONS

Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of fetal alcohol spectrum disorders.

Transcriptome sequencing of the anterior cingulate in bipolar disorder: dysregulation of G protein-coupled receptors

OBJECTIVE

Gene expression dysregulation in the brain has been associated with bipolar disorder through candidate gene and microarray expression studies, but questions remain about isoform-specific dysregulation and the role of noncoding RNAs whose importance in the brain has been suggested recently but not yet characterized for bipolar disorder.

METHOD

The authors used RNA sequencing, a powerful technique that captures the complexity of gene expression, in postmortem tissue from the anterior cingulate cortex from 13 bipolar disorder case subjects and 13 matched comparison subjects. Differential expression was computed, and a global pattern of downregulation was detected, with 10 transcripts significant at a false discovery rate ≤5%. Importantly, all 10 genes were also replicated in an independent RNA sequencing data set (N=61) from the anterior cingulate cortex.

RESULTS

Among the most significant results were genes coding for class A G protein-coupled receptors: SSTR2 (somatostatin receptor 2), CHRM2 (cholinergic receptor, muscarinic 2), and RXFP1 (relaxin/insulin-like family peptide receptor 1). A gene ontology analysis of the entire set of differentially expressed genes pointed to an overrepresentation of genes involved in G protein-coupled receptor regulation. The top genes were followed up by querying the effect of treatment with mood stabilizers commonly prescribed in bipolar disorder, which showed that these drugs modulate expression of the candidate genes.

CONCLUSIONS

By using RNA sequencing in the postmortem bipolar disorder brain, an interesting profile of G protein-coupled receptor dysregulation was identified, several new bipolar disorder genes were indicated, and the noncoding transcriptome in bipolar disorder was characterized. These findings have important implications with regard to fine-tuning our understanding of the bipolar disorder brain, as well as for identifying potential new drug target pathways.

Meta-Analysis of Gene Expression in Autism Spectrum Disorder

Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. In general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. A considerable number of transcriptome analyses have been performed in attempts to address this question, but their findings lack a clear consensus. As a result, each of these individual studies has not led to any significant advance in understanding the autistic phenotype as a whole. Here, we report a meta-analysis of more than 1000 microarrays across twelve independent studies on expression changes in ASD compared to unaffected individuals, in both blood and brain tissues. We identified a number of known and novel genes that are consistently differentially expressed across three studies of the brain (71 samples in total). A subset of the highly ranked genes is suggestive of effects on mitochondrial function. In blood, consistent changes were more difficult to identify, despite individual studies tending to exhibit larger effects than the brain studies. Our results are the strongest evidence to date of a common transcriptome signature in the brains of individuals with ASD.

Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma

PURPOSE

Our aim was to reliably identify patients with advanced-stage classical Hodgkin lymphoma (cHL) at increased risk of death by developing a robust predictor of overall survival (OS) using gene expression measured in routinely available formalin-fixed paraffin-embedded tissue (FFPET).

METHODS

Expression levels of 259 genes, including those previously reported to be associated with outcome in cHL, were determined by digital expression profiling of pretreatment FFPET biopsies from 290 patients enrolled onto the E2496 Intergroup trial comparing doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) and Stanford V regimens in locally extensive and advanced-stage cHL. A model for OS separating patients into low- and high-risk groups was produced using penalized Cox regression. The model was tested in an independent cohort of 78 patients enriched for treatment failure but otherwise similar to patients in a population-based registry of patients treated with ABVD. Weighted analysis methods generated unbiased estimates of predictor performance in the population-based registry.

RESULTS

A 23-gene outcome predictor was generated. The model identified a population at increased risk of death in the validation cohort. There was a 29% absolute difference in 5-year OS between the high- and low-risk groups (63% v 92%, respectively; log-rank P < .001; hazard ratio, 6.7; 95% CI, 2.6 to 17.4). The predictor was superior to the International Prognostic Score and CD68 immunohistochemistry in multivariate analyses.

CONCLUSION

A gene expression-based predictor, developed in and applicable to routinely available FFPET biopsies, identifies patients with advanced-stage cHL at increased risk of death when treated with standard-intensity up-front regimens.

Concurrent expression of MYC and BCL2 in diffuse large B-cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone

PURPOSE

Diffuse large B-cell lymphoma (DLBCL) is curable in 60% of patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). MYC translocations, with or without BCL2 translocations, have been associated with inferior survival in DLBCL. We investigated whether expression of MYC protein, with or without BCL2 protein expression, could risk-stratify patients at diagnosis.

PATIENTS AND METHODS

We determined the correlation between presence of MYC and BCL2 proteins by immunohistochemistry (IHC) with survival in two independent cohorts of patients with DLBCL treated with R-CHOP. We further determined if MYC protein expression correlated with high MYC mRNA and/or presence of MYC translocation.

RESULTS

In the training cohort (n = 167), MYC and BCL2 proteins were detected in 29% and 44% of patients, respectively. Concurrent expression (MYC positive/BCL2 positive) was present in 21% of patients. MYC protein correlated with presence of high MYC mRNA and MYC translocation (both P < .001), but the latter was less frequent (both 11%). MYC protein expression was only associated with inferior overall and progression-free survival when BCL2 protein was coexpressed (P < .001). Importantly, the poor prognostic effect of MYC positive/BCL2 positive was validated in an independent cohort of 140 patients with DLBCL and remained significant (P < .05) after adjusting for presence of high-risk features in a multivariable model that included elevated international prognostic index score, activated B-cell molecular subtype, and presence of concurrent MYC and BCL2 translocations.

CONCLUSION

Assessment of MYC and BCL2 expression by IHC represents a robust, rapid, and inexpensive approach to risk-stratify patients with DLBCL at diagnosis.

TBL1XR1/TP63: a novel recurrent gene fusion in B-cell non-Hodgkin lymphoma

Recently, the landscape of single base mutations in diffuse large B-cell lymphoma (DLBCL) was described. Here we report the discovery of a gene fusion between TBL1XR1 and TP63, the only recurrent somatic novel gene fusion identified in our analysis of transcriptome data from 96 DLBCL cases. Based on this cohort and a further 157 DLBCL cases analyzed by FISH, the incidence in de novo germinal center B cell-like (GCB) DLBCL is 5% (6 of 115). The fusion appears exclusive to GCB and was not seen in 138 non-GCB cases examined (P = .008, Fisher exact test) but was present at low incidence in follicular lymphoma (1 of 81). In all 7 cases identified, the 3' end of the fusion consists of exons 4 and onwards of TP63. The recurrence, subtype enrichment, and the remarkably conserved nature of the TP63 portion of the fusion suggest an important functional role in the lymphomas that harbor this event.

Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma

Mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma, is characterized by the hallmark translocation t(11;14)(q13;q32) and the resulting overexpression of cyclin D1 (CCND1). Our current knowledge of this disease encompasses frequent secondary cytogenetic aberrations and the recurrent mutation of a handful of genes, such as TP53, ATM, and CCND1. However, these findings insufficiently explain the biologic underpinnings of MCL. Here, we performed whole transcriptome sequencing on a discovery cohort of 18 primary tissue MCL samples and 2 cell lines. We found recurrent mutations in NOTCH1, a finding that we confirmed in an extension cohort of 108 clinical samples and 8 cell lines. In total, 12% of clinical samples and 20% of cell lines harbored somatic NOTCH1 coding sequence mutations that clustered in the PEST domain and predominantly consisted of truncating mutations or small frame-shifting indels. NOTCH1 mutations were associated with poor overall survival (P = .003). Furthermore, we showed that inhibition of the NOTCH pathway reduced proliferation and induced apoptosis in 2 MCL cell lines. In summary, we have identified recurrent NOTCH1 mutations that provide the preclinical rationale for therapeutic inhibition of the NOTCH pathway in a subset of patients with MCL.

SNP analysis of minimally evolved t(14;18)(q32;q21)-positive follicular lymphomas reveals a common copy-neutral loss of heterozygosity pattern

Follicular lymphoma (FL) cases with a t(14;18)(q32;q21) and minimal or no additional karyotypic alterations, such as copy number gains and losses and/or chromosomal rearrangements, may exhibit pathologic features and a clinical behavior similar to those with more complex karyotypes. This study sought to investigate whether the copy-neutral loss of heterozygosity (cnLOH) profiles of these minimally evolved t(14;18)(q32;q21)-positive follicular lymphoma (MEV-FL) cases are similar to or different from the majority of FL cases with more karyotypic alterations. Affymetrix SNP 6.0 array analysis was applied to the tumor genomes of 23 MEV-FL biopsy samples to assess for the presence of cnLOH. These cases carried either a single or no chromosomal abnormality in addition to t(14;18)(q32;q21) as determined by karyotyping. We found that, although these MEV-FL cases had simple karyotypes, they showed very similar cnLOH profiles as compared to cytogenetically complex cases. The most frequent regions affected by cnLOH were 1p (17%), 6p (17%), 12q (13%) and 16p (13%). Our study suggests that cnLOH alterations may serve as important contributors to the pathological and clinical manifestations of FL.

Meta-analysis of kindling-induced gene expression changes in the rat hippocampus

Numerous studies have been performed to examine gene expression patterns in the rodent hippocampus in the kindling model of epilepsy. However, recent reviews of this literature have revealed limited agreement among studies. Because this conclusion was based on retrospective comparison of reported “hit lists” from individual studies, we hypothesized that re-analysis of the original expression data would help address this concern. In this paper, we reanalyzed four genome-wide expression studies of excitotoxin-induced kindling in rat and performed a statistical meta-analysis. The meta-analysis revealed over 800 genes which show significant change in expression 24 h after initial seizure induction, and 59 genes altered after 10 days. To evaluate our results in light of previous work, we assembled a reference list of genes formed from a consensus of the published literature. Our profiles include most of the genes in this reference list, and most of the additional genes are from pathways or biological processes previously recognized to be altered in kindling. In addition our results emphasized expression changes in lipid metabolism and protein degradation pathways. We conclude that a cautious re-analysis of published expression data can help illuminate genes and pathways underling kindling. Supplementary Material is available at http://www.chibi.ubc.ca/faculty/pavlidis/meta-analysis-of-brain-kindling/

Correlation between the secondary structure of pre-mRNA introns and the efficiency of splicing in Saccharomyces cerevisiae

BACKGROUND

Secondary structure interactions within introns have been shown to be essential for efficient splicing of several yeast genes. The nature of these base-pairing interactions and their effect on splicing efficiency were most extensively studied in ribosomal protein gene RPS17B (previously known as RP51B). It was determined that complementary pairing between two sequence segments located downstream of the 5' splice site and upstream of the branchpoint sequence promotes efficient splicing of the RPS17B pre-mRNA, presumably by shortening the branchpoint distance. However, no attempts were made to compute a shortened, 'structural' branchpoint distance and thus the functional relationship between this distance and the splicing efficiency remains unknown.

RESULTS

In this paper we use computational RNA secondary structure prediction to analyze the secondary structure of the RPS17B intron. We show that it is necessary to consider suboptimal structure predictions and to compute the structural branchpoint distances in order to explain previously published splicing efficiency results. Our study reveals that there is a tight correlation between this distance and splicing efficiency levels of intron mutants described in the literature. We experimentally test this correlation on additional RPS17B mutants and intron mutants within two other yeast genes.

CONCLUSION

The proposed model of secondary structure requirements for efficient splicing is the first attempt to specify the functional relationship between pre-mRNA secondary structure and splicing. Our findings provide further insights into the role of pre-mRNA secondary structure in gene splicing in yeast and also offer basis for improvement of computational methods for splice site identification and gene-finding.

GeneComber: combining outputs of gene prediction programs for improved results

We recently demonstrated that combining the output from Genscan and HMMgene can provide increased accuracy of gene predictions. We have created a robust software system that runs algorithms previously described on DNA sequences and provides a public web interface to the system for use by the biological community worldwide. The GeneComber system performs ab initio gene prediction by first taking a user inputted DNA sequence and running Genscan and HMMgene. The outputs of Genscan and HMMgene are then integrated using the EUI, GI and EUI_frame algorithms. All results are then stored into a relational database management system (RDBMS) and can then be retrieved through a web interface. The web interface provides a unified view of the GeneComber predictions by graphically overlaying outputs from Genscan, HMMgene, EUI, GI and EUI_frame. Outputs can also be retrieved in general feature format (GFF) or FASTA format. The software is written in the Perl programming language and is both dependent on and interoperable with the Bioperl toolkit. It includes high-level application programming interfaces (APIs) to run Genscan, HMMgene and a database API to insert prediction results into an RDBMS. The APIs are assembled into the genecomber script which is executed by the web interface or can be run directly from the Unix command line. The web interface is written in PHP and is structured so as to be easily modified for viewing data from any database that stores gene structures.

AVAILABILITY

The GeneComber public web interface and supplementary information is located at http://bioinformatics.ubc.ca/genecomber The source code is released under the GNU General Public License and is available at ftp://ftp.bioinformatics.ubc.ca/pub/genecomber/software.

Improving gene recognition accuracy by combining predictions from two gene-finding programs

MOTIVATION

Despite constant improvements in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with desired correctness. The current programs can identify up to 75% of exons correctly and less than 50% of predicted gene structures correspond to actual genes. New approaches to computational gene-finding are clearly needed.

RESULTS

In this paper we have explored the benefits of combining predictions from already existing gene prediction programs. We have introduced three novel methods for combining predictions from programs Genscan and HMMgene. The methods primarily aim to improve exon level accuracy of gene-finding by identifying more probable exon boundaries and by eliminating false positive exon predictions. This approach results in improved accuracy at both the nucleotide and exon level, especially the latter, where the average improvement on the newly assembled dataset is 7.9% compared to the best result obtained by Genscan and HMMgene. When tested on a long genomic multi-gene sequence, our method that maintains reading frame consistency improved nucleotide level specificity by 21.0% and exon level specificity by 32.5% compared to the best result obtained by either of the two programs individually.

AVAILABILITY

The scripts implementing our methods are available from http://www.cs.ubc.ca/labs/beta/genefinding/

Evaluation of gene-finding programs on mammalian sequences

We present an independent comparative analysis of seven recently developed gene-finding programs: FGENES, GeneMark.hmm, Genie, Genescan, HMMgene, Morgan, and MZEF. For evaluation purposes we developed a new, thoroughly filtered, and biologically validated dataset of mammalian genomic sequences that does not overlap with the training sets of the programs analyzed. Our analysis shows that the new generation of programs has substantially better results than the programs analyzed in previous studies. The accuracy of the programs was also examined as a function of various sequence and prediction features, such as G + C content of the sequence, length and type of exons, signal type, and score of the exon prediction. This approach pinpoints the strengths and weaknesses of each individual program as well as those of computational gene-finding in general. The dataset used in this analysis (HMR195) as well as the tables with the complete results are available at http://www.cs.ubc.ca/~rogic/evaluation/.

A physical map of the human genome

The human genome is by far the largest genome to be sequenced, and its size and complexity present many challenges for sequence assembly. The International Human Genome Sequencing Consortium constructed a map of the whole genome to enable the selection of clones for sequencing and for the accurate assembly of the genome sequence. Here we report the construction of the whole-genome bacterial artificial chromosome (BAC) map and its integration with previous landmark maps and information from mapping efforts focused on specific chromosomal regions. We also describe the integration of sequence data with the map.