How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype--phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to identifying disease-associated genes using computational approaches is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene--disease associations. We find that only mouse phenotypes can accurately predict human gene--disease associations. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

Recurrence eigenvalues of movements from brain signals

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

Exploring Aβ Proteotoxicity and Therapeutic Candidates Using Drosophila melanogaster

Alzheimer’s disease is a widespread and devastating neurological disorder associated with proteotoxic events caused by the misfolding and aggregation of the amyloid-β peptide. To find therapeutic strategies to combat this disease, Drosophila melanogaster has proved to be an excellent model organism that is able to uncover anti-proteotoxic candidates due to its outstanding genetic toolbox and resemblance to human disease genes. In this review, we highlight the use of Drosophila melanogaster to both study the proteotoxicity of the amyloid-β peptide and to screen for drug candidates. Expanding the knowledge of how the etiology of Alzheimer’s disease is related to proteotoxicity and how drugs can be used to block disease progression will hopefully shed further light on the field in the search for disease-modifying treatments.

A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

Pre-mRNA splicing is a key controller of human gene expression. Disturbances in splicing due to mutation lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of splicing modulator compounds (SMCs) have been recently identified and establish that pre-mRNA splicing represents a target for therapy. We describe herein the identification of BPN-15477, a SMC that restores correct splicing of ELP1 exon 20. Using transcriptome sequencing from treated fibroblast cells and a machine learning approach, we identify BPN-15477 responsive sequence signatures. We then leverage this model to discover 155 human disease genes harboring ClinVar mutations predicted to alter pre-mRNA splicing as targets for BPN-15477. Splicing assays confirm successful correction of splicing defects caused by mutations in CFTR, LIPA, MLH1 and MAPT. Subsequent validations in two disease-relevant cellular models demonstrate that BPN-15477 increases functional protein, confirming the clinical potential of our predictions.

Using Drosophila melanogaster To Discover Human Disease Genes: An Educational Primer for Use with “Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal the Phospholipase D Pathway as a Potential Therapeutic Target”

Since the dawn of the 20th century, the fruit fly Drosophila melanogaster has been used as a model organism to understand the nature of genes and how they control development, behavior, and physiology. One of the most powerful experimental approaches employed in Drosophila is the forward genetic screen. In the 21st century, genome-wide screens have become popular tools for identifying evolutionarily conserved genes involved in complex human diseases. In the accompanying article “Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal the Phospholipase D Pathway as a Potential Therapeutic Target,” Kankel and colleagues describe a forward genetic modifier screen to discover factors that contribute to the severe neurodegenerative disease amyotrophic lateral sclerosis (ALS). This primer briefly traces the history of genetic screens in Drosophila and introduces students to ALS. We then provide a set of guided reading questions to help students work through the data presented in the research article. Finally, several ideas for literature-based research projects are offered as opportunities for students to expand their appreciation of the potential scope of genetic screens. The primer is intended to help students and instructors thoroughly examine a current study that uses forward genetics in Drosophila to identify human disease genes.

CoCoCoNet: conserved and comparative co-expression across a diverse set of species

Co-expression analysis has provided insight into gene function in organisms from Arabidopsis to zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.

CoCoCoNet: Conserved and Comparative Co-expression Across a Diverse Set of Species

ABSTRACTCo-expression analysis has provided insight into gene function in organisms from Arabidopsis to Zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties, or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold-standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups, and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.

The Experimentally Obtained Functional Impact Assessments of 5' Splice Site GT>GC Variants Differ Markedly from Those Predicted

Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described “in vivo” dataset of 45 known disease-causing +2T>C variants and the “in vitro” dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay. The third dataset comprised 12 BRCA1 +2T>C variants that were recently analyzed by saturation genome editing. Results: Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Conclusion: Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.

RNA secondary structure mediated by Alu insertion as a novel disease-causing mechanism

ABSTRACTWe have recently reported a homozygous Alu insertion variant (termed Alu_Ins) within the 3’-untranslated region (3’-UTR) of the SPINK1 gene as the cause of a new pediatric disease entity. Although Alu-Ins has been shown, by means of a full-length gene expression assay (FLGEA), to result in the complete loss of SPINK1 mRNA expression, the precise underlying mechanism(s) has remained elusive. Herein, we filled this knowledge gap by adopting a hypothesis-driven approach. Employing RepeatMasker, we identified two Alu elements (termed Alu1 and Alu2) within the SPINK1 locus; both are located deep within intron 3 and, most importantly, reside in the opposite orientation to Alu-Ins. Using FLGEA, we provide convincing evidence that Alu-Ins disrupts splicing by forming RNA secondary structures with Alu1 in the pre-mRNA sequence. Our findings reveal a previously undescribed disease-causing mechanism, resulting from an Alu insertion variant, which has implications for Alu detection and interpretation in human disease genes.