A neuronal enhancer network upstream of MEF2C is compromised in patients with Rett-like characteristics

Systematic assessment of structural variant annotation tools for genomic interpretation

Structural variants (SVs) over 50 base pairs play a significant role in phenotypic diversity and are associated with various diseases, but their analysis is complex and resource-intensive. Numerous computational tools have been developed for SV prioritization, yet their effectiveness in biomedicine remains unclear. Here we benchmarked eight widely used SV prioritization tools, categorized into knowledge-driven (AnnotSV, ClassifyCNV) and data-driven (CADD-SV, dbCNV, StrVCTVRE, SVScore, TADA, XCNV) groups in accordance with the ACMG guidelines. We assessed their accuracy, robustness, and usability across diverse genomic contexts, biological mechanisms and computational efficiency using seven carefully curated independent datasets. Our results revealed that both groups of methods exhibit comparable effectiveness in predicting SV pathogenicity, although performance varies among tools, emphasizing the importance of selecting the appropriate tool based on specific research purposes. Furthermore, we pinpointed the potential improvement of expanding these tools for future applications. Our benchmarking framework provides a crucial evaluation method for SV analysis tools, offering practical guidance for biomedical research and facilitating the advancement of better genomic research tools.

Navigating an Uninformative Genomic Test Result: A Practical Guide

BACKGROUND

Although genomic testing is increasingly standard of care for diagnosing children and adults with genetic conditions, over 50% of genomic testing will return an uninformative result.

AIM

To provide a practical guide for paediatricians on how to approach an uninformative genomic test result, and the pathways which may be available to uncover a genetic diagnosis for their patients.

METHODS

Input from multiple genetics healthcare professionals including genetic counsellors, the literature, and a general paediatrician were used to construct this guide. We also provide a hypothetical case vignette, to further demonstrate the various options for a patient after receiving an uninformative result.

RESULTS

There are several reasons why an underlying genetic diagnosis may not be diagnosed with current testing, including incomplete phenotyping, a different underlying genetic mechanism requiring specialised testing, and limitations in knowledge at the time of the test.

CONCLUSION

Uninformative results are very common, and it is important to understand these results in the context of the limitations of genomic testing. General paediatricians play an important role in supporting families through their diagnostic odyssey, as well as reassessing the phenotype, referring for sub-specialty inputs, and discussion with local genetics services for consideration of alternative testing options or enrolment into research pathways.

Identification and Copy Number Variant Analysis of Enhancer Regions of Genes Causing Spinocerebellar Ataxia

Currently, routine diagnostics for spinocerebellar ataxia (SCA) look for polyQ repeat expansions and conventional variations affecting the proteins encoded by known SCA genes. However, ~40% of the patients still remain without a genetic diagnosis after routine tests. Increasing evidence suggests that variations in the enhancer regions of genes involved in neurodegenerative disorders can also cause disease. Since the enhancers of SCA genes are not yet known, it remains to be determined whether variations in these regions are a cause of SCA. In this pilot project, we aimed to identify the enhancers of the SCA genes ATXN1, ATXN3, TBP and ITPR1 in the human cerebellum using 4C-seq, publicly available datasets, reciprocal 4C-seq, and luciferase assays. We then screened these enhancers for copy number variants (CNVs) in a cohort of genetically undiagnosed SCA patients. We identified two active enhancers for each of the four SCA genes. CNV analysis did not reveal any CNVs in the enhancers of the four SCA genes in the genetically undiagnosed SCA patients. However, in one patient, we noted a CNV deletion with an unknown clinical significance near one of the ITPR1 enhancers. These results not only reveal elements involved in SCA gene regulation but can also lead to the discovery of novel SCA-causing genetic variants. As enhancer variations are being increasingly recognized as a cause of brain disorders, screening the enhancers of ATXN1, ATXN3, TBP and ITPR1 for variations other than CNVs and identifying and screening enhancers of other SCA genes might elucidate the genetic cause in undiagnosed patients.

The function of Mef2c toward the development of excitatory and inhibitory cortical neurons

Neurodevelopmental disorders (NDDs) are caused by abnormal brain development, leading to altered brain function and affecting cognition, learning, self-control, memory, and emotion. NDDs are often demarcated as discrete entities for diagnosis, but empirical evidence indicates that NDDs share a great deal of overlap, including genetics, core symptoms, and biomarkers. Many NDDs also share a primary sensitive period for disease, specifically the last trimester of pregnancy in humans, which corresponds to the neonatal period in mice. This period is notable for cortical circuit assembly, suggesting that deficits in the establishment of brain connectivity are likely a leading cause of brain dysfunction across different NDDs. Regulators of gene programs that underlie neurodevelopment represent a point of convergence for NDDs. Here, we review how the transcription factor MEF2C, a risk factor for various NDDs, impacts cortical development. Cortical activity requires a precise balance of various types of excitatory and inhibitory neuron types. We use MEF2C loss-of-function as a study case to illustrate how brain dysfunction and altered behavior may derive from the dysfunction of specific cortical circuits at specific developmental times.

Chromosomal structural rearrangements implicate long non-coding RNAs in rare germline disorders

In recent years, there has been increased focus on exploring the role the non-protein-coding genome plays in Mendelian disorders. One class of particular interest is long non-coding RNAs (lncRNAs), which has recently been implicated in the regulation of diverse molecular processes. However, because lncRNAs do not encode protein, there is uncertainty regarding what constitutes a pathogenic lncRNA variant, and thus annotating such elements is challenging. The Developmental Genome Anatomy Project (DGAP) and similar projects recruit individuals with apparently balanced chromosomal abnormalities (BCAs) that disrupt or dysregulate genes in order to annotate the human genome. We hypothesized that rearrangements disrupting lncRNAs could be the underlying genetic etiology for the phenotypes of a subset of these individuals. Thus, we assessed 279 cases with BCAs and selected 191 cases with simple BCAs (breakpoints at only two genomic locations) for further analysis of lncRNA disruptions. From these, we identified 66 cases in which the chromosomal rearrangements directly disrupt lncRNAs. In 30 cases, no genes of any other class aside from lncRNAs are directly disrupted, consistent with the hypothesis that lncRNA disruptions could underly the phenotypes of these individuals. Strikingly, the lncRNAs MEF2C-AS1 and ENSG00000257522 are each disrupted in two unrelated cases. Furthermore, we experimentally tested the lncRNAs TBX2-AS1 and MEF2C-AS1 and found that knockdown of these lncRNAs resulted in decreased expression of the neighboring transcription factors TBX2 and MEF2C, respectively. To showcase the power of this genomic approach for annotating lncRNAs, here we focus on clinical reports and genetic analysis of seven individuals with likely developmental etiologies due to lncRNA disruptions.

Rare germline disorders implicate long non-coding RNAs disrupted by chromosomal structural rearrangements

In recent years, there has been increased focus on exploring the role the non-protein-coding genome plays in Mendelian disorders. One class of particular interest is long non-coding RNAs (lncRNAs), which has recently been implicated in the regulation of diverse molecular processes. However, because lncRNAs do not encode protein, there is uncertainty regarding what constitutes a pathogenic lncRNA variant, and thus annotating such elements is challenging. The Developmental Genome Anatomy Project (DGAP) and similar projects recruit individuals with apparently balanced chromosomal abnormalities (BCAs) that disrupt or dysregulate genes in order to annotate the human genome. We hypothesized that rearrangements disrupting lncRNAs could be the underlying genetic etiology for the phenotypes of a subset of these individuals. Thus, we assessed 279 cases with BCAs and selected 191 cases with simple BCAs (breakpoints at only two genomic locations) for further analysis of lncRNA disruptions. From these, we identified 66 cases in which the chromosomal rearrangements directly disrupt lncRNAs. Strikingly, the lncRNAs MEF2C-AS1 and ENSG00000257522 are each disrupted in two unrelated cases. Furthermore, in 30 cases, no genes of any other class aside from lncRNAs are directly disrupted, consistent with the hypothesis that lncRNA disruptions could underly the phenotypes of these individuals. To showcase the power of this genomic approach for annotating lncRNAs, here we focus on clinical reports and genetic analysis of two individuals with BCAs and additionally highlight six individuals with likely developmental etiologies due to lncRNA disruptions.

Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks

During neurogenesis, the generation and differentiation of neuronal progenitors into inhibitory gamma-aminobutyric acid-containing interneurons is dependent on the combinatorial activity of transcription factors (TFs) and their corresponding regulatory elements (REs). However, the roles of neuronal TFs and their target REs in inhibitory interneuron progenitors are not fully elucidated. Here, we developed a deep-learning-based framework to identify enriched TF motifs in gene REs (eMotif-RE), such as poised/repressed enhancers and putative silencers. Using epigenetic datasets (e.g., ATAC-seq and H3K27ac/me3 ChIP-seq) from cultured interneuron-like progenitors, we distinguished between active enhancer sequences (open chromatin with H3K27ac) and non-active enhancer sequences (open chromatin without H3K27ac). Using our eMotif-RE framework, we discovered enriched motifs of TFs such as ASCL1, SOX4, and SOX11 in the active enhancer set suggesting a cooperativity function for ASCL1 and SOX4/11 in active enhancers of neuronal progenitors. In addition, we found enriched ZEB1 and CTCF motifs in the non-active set. Using an in vivo enhancer assay, we showed that most of the tested putative REs from the non-active enhancer set have no enhancer activity. Two of the eight REs (25%) showed function as poised enhancers in the neuronal system. Moreover, mutated REs for ZEB1 and CTCF motifs increased their in vivo activity as enhancers indicating a repressive effect of ZEB1 and CTCF on these REs that likely function as repressed enhancers or silencers. Overall, our work integrates a novel framework based on deep learning together with a functional assay that elucidated novel functions of TFs and their corresponding REs. Our approach can be applied to better understand gene regulation not only in inhibitory interneuron differentiation but in other tissue and cell types.

The non-coding genome in genetic brain disorders: new targets for therapy?

The non-coding genome, consisting of more than 98% of all genetic information in humans and once judged as ‘Junk DNA’, is increasingly moving into the spotlight in the field of human genetics. Non-coding regulatory elements (NCREs) are crucial to ensure correct spatio-temporal gene expression. Technological advancements have allowed to identify NCREs on a large scale, and mechanistic studies have helped to understand the biological mechanisms underlying their function. It is increasingly becoming clear that genetic alterations of NCREs can cause genetic disorders, including brain diseases. In this review, we concisely discuss mechanisms of gene regulation and how to investigate them, and give examples of non-coding alterations of NCREs that give rise to human brain disorders. The cross-talk between basic and clinical studies enhances the understanding of normal and pathological function of NCREs, allowing better interpretation of already existing and novel data. Improved functional annotation of NCREs will not only benefit diagnostics for patients, but might also lead to novel areas of investigations for targeted therapies, applicable to a wide panel of genetic disorders. The intrinsic complexity and precision of the gene regulation process can be turned to the advantage of highly specific treatments. We further discuss this exciting new field of ‘enhancer therapy’ based on recent examples.

Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance

BACKGROUND

Non-coding regulatory elements (NCREs), such as enhancers, play a crucial role in gene regulation, and genetic aberrations in NCREs can lead to human disease, including brain disorders. The human brain is a complex organ that is susceptible to numerous disorders; many of these are caused by genetic changes, but a multitude remain currently unexplained. Understanding NCREs acting during brain development has the potential to shed light on previously unrecognized genetic causes of human brain disease. Despite immense community-wide efforts to understand the role of the non-coding genome and NCREs, annotating functional NCREs remains challenging.

METHODS

Here we performed an integrative computational analysis of virtually all currently available epigenome data sets related to human fetal brain.

RESULTS

Our in-depth analysis unravels 39,709 differentially active enhancers (DAEs) that show dynamic epigenomic rearrangement during early stages of human brain development, indicating likely biological function. Many of these DAEs are linked to clinically relevant genes, and functional validation of selected DAEs in cell models and zebrafish confirms their role in gene regulation. Compared to enhancers without dynamic epigenomic rearrangement, DAEs are subjected to higher sequence constraints in humans, have distinct sequence characteristics and are bound by a distinct transcription factor landscape. DAEs are enriched for GWAS loci for brain-related traits and for genetic variation found in individuals with neurodevelopmental disorders, including autism.

CONCLUSION

This compendium of high-confidence enhancers will assist in deciphering the mechanism behind developmental genetics of human brain and will be relevant to uncover missing heritability in human genetic brain disorders.

Identification of neural oscillations and epileptiform changes in human brain organoids

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

Clinical genetic testing of protein-coding regions identifies a likely causative variant in only around half of developmental disorder (DD) cases. The contribution of regulatory variation in non-coding regions to rare disease, including DD, remains very poorly understood. We screened 9,858 probands from the Deciphering Developmental Disorders (DDD) study for de novo mutations in the 5' untranslated regions (5' UTRs) of genes within which variants have previously been shown to cause DD through a dominant haploinsufficient mechanism. We identified four single-nucleotide variants and two copy-number variants upstream of MEF2C in a total of ten individual probands. We developed multiple bespoke and orthogonal experimental approaches to demonstrate that these variants cause DD through three distinct loss-of-function mechanisms, disrupting transcription, translation, and/or protein function. These non-coding region variants represent 23% of likely diagnoses identified in MEF2C in the DDD cohort, but these would all be missed in standard clinical genetics approaches. Nonetheless, these variants are readily detectable in exome sequence data, with 30.7% of 5' UTR bases across all genes well covered in the DDD dataset. Our analyses show that non-coding variants upstream of genes within which coding variants are known to cause DD are an important cause of severe disease and demonstrate that analyzing 5' UTRs can increase diagnostic yield. We also show how non-coding variants can help inform both the disease-causing mechanism underlying protein-coding variants and dosage tolerance of the gene.

Interpreting the impact of noncoding structural variation in neurodevelopmental disorders

The emergence of novel sequencing technologies has greatly improved the identification of structural variation, revealing that a human genome harbors tens of thousands of structural variants (SVs). Since these SVs primarily impact noncoding DNA sequences, the next challenge is one of interpretation, not least to improve our understanding of human disease etiology. However, this task is severely complicated by the intricacy of the gene regulatory landscapes embedded within these noncoding regions, their incomplete annotation, as well as their dependence on the three-dimensional (3D) conformation of the genome. Also in the context of neurodevelopmental disorders (NDDs), reports of putatively causal, noncoding SVs are accumulating and understanding their impact on transcriptional regulation is presenting itself as the next step toward improved genetic diagnosis.

Anxiolytic and Anxiogenic? How the Transcription Factor MEF2 Might Explain the Manifold Behavioral Effects of Oxytocin

The neuromodulator oxytocin, since its first synthesis by du Vigneaud in 1953, has mainly been associated with beneficial physiological effects, as well as positive social and emotional behaviors. This overall positive picture of oxytocin as the "love-, cuddle-, or bonding-hormone" has repeatedly been challenged since then. Oxytocin-induced effects that would be perceived as negative by the individual, such as increased anxiety or potentiation of stress-induced ACTH release, as well as the regulation of negative approach-related emotions, such as envy and schadenfreude (gloating) have been described. The general consent is that oxytocin, instead of acting unidirectional, induces changes in the salience network to shift the emphasis of emotional contexts, and therefore can, e.g., produce both anxiolytic as well as anxiogenic behavioral outcomes. However, the underlying mechanisms leading to alterations in the salience network are still unclear. With the aim to understand the manifold effects of oxytocin on a cellular/molecular level, a set of oxytocin receptor-coupled signaling cascades and downstream effectors regulating transcription and translation has been identified. Those oxytocin-driven effectors, such as MEF2 and CREB, are known modulators of the neuronal and glial cytoarchitecture. We hypothesize that, by determining cellular morphology and connectivity, MEF2 is one of the key factors that might contribute to the diverse behavioral effects of oxytocin.