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Early detection of doxorubicin-induced cardiotoxicity in rats by its cardiac metabolic signature assessed with hyperpolarized MRI
<jats:title>Abstract</jats:title><jats:p>Doxorubicin (DOX) is a widely used chemotherapeutic agent that can cause serious cardiotoxic side effects culminating in congestive heart failure (HF). There are currently no clinical imaging techniques or biomarkers available to detect DOX-cardiotoxicity before functional decline. Mitochondrial dysfunction is thought to be a key factor driving functional decline, though real-time metabolic fluxes have never been assessed in DOX-cardiotoxicity. Hyperpolarized magnetic resonance imaging (MRI) can assess real-time metabolic fluxes in vivo. Here we show that cardiac functional decline in a clinically relevant rat-model of DOX-HF is preceded by a change in oxidative mitochondrial carbohydrate metabolism, measured by hyperpolarized MRI. The decreased metabolic fluxes were predominantly due to mitochondrial loss and additional mitochondrial dysfunction, and not, as widely assumed hitherto, to oxidative stress. Since hyperpolarized MRI has been successfully translated into clinical trials this opens up the potential to test cancer patients receiving DOX for early signs of cardiotoxicity.</jats:p>
Oxidation of Protein Kinase A Regulatory Subunit PKARIα Protects Against Myocardial Ischemia-Reperfusion Injury by Inhibiting Lysosomal-Triggered Calcium Release.
Background: Kinase oxidation is a critical signalling mechanism through which changes in the intracellular redox state alter cardiac function. In the myocardium, type-1 protein kinase A (PKARIα) can be reversibly oxidized, forming interprotein disulfide bonds within the holoenzyme complex. However, the effect of PKARIα disulfide formation on downstream signaling in the heart, particularly under states of oxidative stress such as ischemia and reperfusion (I/R), remains unexplored. Methods: Atrial tissue obtained from patients before and after cardiopulmonary bypass and reperfusion and left ventricular (LV) tissue from mice subjected to I/R or sham surgery were used to assess PKARIα disulfide formation by immunoblot. To determine the impact of disulfide formation on PKARIα catalytic activity and sub-cellular localization, live-cell fluorescence imaging and stimulated emission depletion super-resolution microscopy were performed in prkar1 knock-out mouse embryonic fibroblasts, neonatal myocytes or adult LV myocytes isolated from 'redox dead' (Cys17Ser) PKARIα knock-in mice and their wild-type littermates. Comparison of intracellular calcium dynamics between genotypes was assessed in fura2-loaded LV myocytes whereas I/R-injury was assessed ex vivo. Results: In both humans and mice, myocardial PKARIα disulfide formation was found to be significantly increased (2-fold in humans, p=0.023; 2.4-fold in mice, p<0.001) in response to I/R in vivo. In mouse LV cardiomyocytes, disulfide-containing PKARIα was not found to impact catalytic activity, but instead led to enhanced A-kinase-anchoring protein (AKAP) binding with preferential localization of the holoenzyme to the lysosome. Redox-dependent regulation of lysosomal two pore channels (TPC) by PKARIα was sufficient to prevent global calcium release from the sarcoplasmic reticulum in LV myocytes, without affecting intrinsic ryanodine receptor leak or phosphorylation. Absence of I/R-induced PKARIα disulfide formation in "redox dead" knock-in mouse hearts resulted in larger infarcts (2-fold, p<0.001) and a concomitant reduction in LV contractile recovery (1.6-fold, p<0.001), which was prevented by administering the lysosomal TPC inhibitor Ned-19 at the time of reperfusion. Conclusions: Disulfide-modification targets PKARIα to the lysosome where it acts as a gatekeeper for TPC-mediated triggering of global calcium release. In the post-ischemic heart, this regulatory mechanism is critical for protecting from extensive injury and offers a novel target for the design of cardioprotective therapeutics.
Off-target effects of SGLT2 blockers: empagliflozin does not inhibit Na+/H+ exchanger-1 or lower [Na+]i in the heart.
AIMS: Empagliflozin (EMPA) is a potent inhibitor of the renal sodium-glucose cotransporter 2 (SGLT2) and an effective treatment for type-2 diabetes. In patients with diabetes and heart failure, EMPA has cardioprotective effects independent of improved glycaemic control, despite SGLT2 not being expressed in the heart. A number of non-canonical mechanisms have been proposed to explain these cardiac effects, most notably an inhibitory action on cardiac Na+/H+ exchanger 1 (NHE1), causing a reduction in intracellular [Na+] ([Na+]i). However, at resting intracellular pH (pHi), NHE1 activity is very low and its pharmacological inhibition is not expected to meaningfully alter steady-state [Na+]i. We re-evaluate this putative EMPA target by measuring cardiac NHE1 activity. METHODS AND RESULTS: The effect of EMPA on NHE1 activity was tested in isolated rat ventricular cardiomyocytes from measurements of pHi recovery following an ammonium pre-pulse manoeuvre, using cSNARF1 fluorescence imaging. Whereas 10 µM cariporide produced near-complete inhibition, there was no evidence for NHE1 inhibition with EMPA treatment (1, 3, 10 or 30 µM). Intracellular acidification by acetate-superfusion evoked NHE1 activity and raised [Na+]i, reported by sodium binding benzofuran isophthalate (SBFI) fluorescence, but EMPA did not ablate this rise. EMPA (10 µM) also had no significant effect on the rate of cytoplasmic [Na+]i-rise upon superfusion of Na+-depleted cells with Na+-containing buffers. In Langendorff-perfused mouse, rat and guinea pig hearts, EMPA did not affect [Na+]i at baseline nor pHi recovery following acute acidosis, as measured by 23Na triple quantum filtered NMR and 31P NMR, respectively. CONCLUSIONS: Our findings indicate that cardiac NHE1 activity is not inhibited by EMPA (or other SGLT2i's) and EMPA has no effect on [Na+]i over a wide range of concentrations, including the therapeutic dose. Thus, the beneficial effects of SGLT2i's in failing hearts should not be interpreted in terms of actions on myocardial NHE1 or intracellular [Na+]. TRANSLATIONAL PERSPECTIVE: Heart failure remains a huge clinical burden. Clinical trials of SGLT2 inhibitors in patients with diabetes and heart failure have reported highly significant cardiovascular benefit that appears independent of improved glycaemic control. As SGLT2 is not expressed in the heart, the mechanism by which SGLT2 inhibitors are cardioprotective remains unknown. Understanding this mechanism is clearly essential as the use of SGLT2 inhibitors in non-diabetics is increasing and a better understanding may allow refinement of therapeutic approaches in both HFpEF and HFrEF. One suggested mechanism that has received significant attention, inhibition of cardiac Na+/H+ exchanger, is investigated here.
Doxorubicin is a commonly used chemotherapeutic agent for the treatment of a range of cancers, but despite its success in improving cancer survival rates, doxorubicin is cardiotoxic and can lead to congestive heart failure. Therapeutic options for this patient group are limited to standard heart failure medications with the only drug specific for doxorubicin cardiotoxicity to reach FDA approval being dexrazoxane, an iron-chelating agent targeting oxidative stress. However, dexrazoxane has failed to live up to its expectations from preclinical studies while also bringing up concerns about its safety. Despite decades of research, the molecular mechanisms of doxorubicin cardiotoxicity are still poorly understood and oxidative stress is no longer considered to be the sole evil. Mitochondrial impairment, increased apoptosis, dysregulated autophagy and increased fibrosis have also been shown to be crucial players in doxorubicin cardiotoxicity. These cellular processes are all linked by one highly conserved intracellular kinase: adenosine monophosphate-activated protein kinase (AMPK). AMPK regulates mitochondrial biogenesis via PGC1α signalling, increases oxidative mitochondrial metabolism, decreases apoptosis through inhibition of mTOR signalling, increases autophagy through ULK1 and decreases fibrosis through inhibition of TGFβ signalling. AMPK therefore sits at the control point of many mechanisms shown to be involved in doxorubicin cardiotoxicity and cardiac AMPK signalling itself has been shown to be impaired by doxorubicin. In this review, we introduce different agents known to activate AMPK (metformin, statins, resveratrol, thiazolidinediones, AICAR, specific AMPK activators) as well as exercise and dietary restriction, and we discuss the existing evidence for their potential role in cardioprotection from doxorubicin cardiotoxicity.
Cardiovascular disease is the leading cause of death world-wide. It is increasingly recognised that cardiac pathologies show, or may even be caused by, changes in metabolism, leading to impaired cardiac energetics. The heart turns over 15 times its own weight in ATP every day and thus relies heavily on the availability of substrates and on efficient oxidation to generate this ATP. A number of old and emerging drugs that target different aspects of metabolism are showing promising results with regard to improved cardiac outcomes in patients. A non-invasive imaging technique that could assess the role of different aspects of metabolism in heart disease, as well as measure changes in cardiac energetics due to treatment, would be valuable in the routine clinical care of cardiac patients. Hyperpolarised magnetic resonance spectroscopy and imaging have revolutionised metabolic imaging, allowing real-time metabolic flux assessment in vivo for the first time. In this review we summarise metabolism in the healthy and diseased heart, give an introduction to the hyperpolarisation technique, 'dynamic nuclear polarisation' (DNP), and review the preclinical studies that have thus far explored healthy cardiac metabolism and different models of human heart disease. We furthermore show what advances have been made to translate this technique into the clinic, what technical challenges still remain and what unmet clinical needs and unexplored metabolic substrates still need to be assessed by researchers in this exciting and fast-moving field.
Anaemia and coagulopathy are common in critically ill patients and are associated with poor outcomes, including increased risk of mortality, myocardial infarction, failure to be liberated from mechanical ventilation and poor physical recovery. Transfusion of blood and blood products remains the corner stone of anaemia and coagulopathy treatment in critical care. However, determining when the benefits of transfusion outweigh the risks of anaemia may be challenging in some critically ill patients. Therefore, the European Society of Intensive Care Medicine prioritised the development of a clinical practice guideline to address anaemia and coagulopathy in non-bleeding critically ill patients. The aims of this article are to: (1) review the evolution of transfusion practice in critical care and the direction for future developments in this important area of transfusion medicine and (2) to provide a brief synopsis of the guideline development process and recommendations in a format designed for busy clinicians and blood bank staff. These clinical practice guidelines provide recommendations to clinicians on how best to manage non-bleeding critically ill patients at the bedside. More research is needed on alternative transfusion targets, use of transfusions in special populations (e.g., acute neurological injury, acute coronary syndromes), use of anaemia prevention strategies and point-of-care interventions to guide transfusion strategies.
Many theories of brain function assume that information is encoded and behaviour is controlled through sparse, distributed patterns of activity. It is therefore crucial to place a lower bound on the amount of neural activity that can drive behaviour and to understand how neuronal networks operate within these constraints. We use an all-optical approach to test this lower limit by driving behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while using two-photon calcium imaging to record the impact on the local network. By precisely titrating the number of neurons in activated ensembles we demonstrate that the lower bound for detection of cortical activity is ~14 pyramidal neurons. We show that there is a very steep sigmoidal relationship between the number of activated neurons and behavioural output, saturating at only ~37 neurons, and that this relationship can shift with learning. By simultaneously measuring activity in the local network, we show that the activation of stimulated ensembles is balanced by the suppression of neighbouring neurons. This surprising behavioural sensitivity in the face of potent network suppression supports the sparse coding hypothesis and suggests that perception of cortical activity balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.
Groupwise functional analysis of gene variants is becoming standard in next-generation sequencing studies. As the function of many genes is unknown and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data. Such data types--including protein-protein interactions and gene co-expression networks--are used to examine the interrelations of the implicated genes. Statistical significance is assessed by comparing the interconnectedness of the mutated genes with that of random gene sets. However, interconnectedness can be affected by confounding bias, potentially resulting in false positive findings. We show that genes implicated through de novo sequence variants are biased in their coding-sequence length and longer genes tend to cluster together, which leads to exaggerated p-values in functional studies; we present here an integrative method that addresses these bias. To discern molecular pathways relevant to complex disease, we have inferred functional associations between human genes from diverse data types and assessed them with a novel phenotype-based method. Examining the functional association between de novo gene variants, we control for the heretofore unexplored confounding bias in coding-sequence length. We test different data types and networks and find that the disease-associated genes cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. We present a tool of superior power to identify functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the suitability of this method to functionally cluster variant genes underlying polygenic disorders.
Clusters of functionally related genes can be disrupted by a single copy number variant (CNV). We demonstrate that the simultaneous disruption of multiple functionally related genes is a frequent and significant characteristic of de novo CNVs in patients with developmental disorders (P = 1 × 10(-3)). Using three different functional networks, we identified unexpectedly large numbers of functionally related genes within de novo CNVs from two large independent cohorts of individuals with developmental disorders. The presence of multiple functionally related genes was a significant predictor of a CNV's pathogenicity when compared to CNVs from apparently healthy individuals and a better predictor than the presence of known disease or haploinsufficient genes for larger CNVs. The functionally related genes found in the de novo CNVs belonged to 70% of all clusters of functionally related genes found across the genome. De novo CNVs were more likely to affect functional clusters and affect them to a greater extent than benign CNVs (P = 6 × 10(-4)). Furthermore, such clusters of functionally related genes are phenotypically informative: Different patients possessing CNVs that affect the same cluster of functionally related genes exhibit more similar phenotypes than expected (P < 0.05). The spanning of multiple functionally similar genes by single CNVs contributes substantially to how these variants exert their pathogenic effects.
The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.
Gene networks underlying convergent and pleiotropic phenotypes in a large and systematically-phenotyped cohort with heterogeneous developmental disorders.
Readily-accessible and standardised capture of genotypic variation has revolutionised our understanding of the genetic contribution to disease. Unfortunately, the corresponding systematic capture of patient phenotypic variation needed to fully interpret the impact of genetic variation has lagged far behind. Exploiting deep and systematic phenotyping of a cohort of 197 patients presenting with heterogeneous developmental disorders and whose genomes harbour de novo CNVs, we systematically applied a range of commonly-used functional genomics approaches to identify the underlying molecular perturbations and their phenotypic impact. Grouping patients into 408 non-exclusive patient-phenotype groups, we identified a functional association amongst the genes disrupted in 209 (51%) groups. We find evidence for a significant number of molecular interactions amongst the association-contributing genes, including a single highly-interconnected network disrupted in 20% of patients with intellectual disability, and show using microcephaly how these molecular networks can be used as baits to identify additional members whose genes are variant in other patients with the same phenotype. Exploiting the systematic phenotyping of this cohort, we observe phenotypic concordance amongst patients whose variant genes contribute to the same functional association but note that (i) this relationship shows significant variation across the different approaches used to infer a commonly perturbed molecular pathway, and (ii) that the phenotypic similarities detected amongst patients who share the same inferred pathway perturbation result from these patients sharing many distinct phenotypes, rather than sharing a more specific phenotype, inferring that these pathways are best characterized by their pleiotropic effects.
Duplications in ADHD patients harbour neurobehavioural genes that are co-expressed with genes associated with hyperactivity in the mouse.
Attention deficit/hyperactivity disorder (ADHD) is a childhood onset disorder, prevalent in 5.3% of children and 1-4% of adults. ADHD is highly heritable, with a burden of large (>500 Kb) copy number variants (CNVs) identified among individuals with ADHD. However, how such CNVs exert their effects is poorly understood. We examined the genes affected by 71 large, rare, and predominantly inherited CNVs identified among 902 individuals with ADHD. We applied both mouse-knockout functional enrichment analyses, exploiting behavioral phenotypes arising from the determined disruption of 1:1 mouse orthologues, and human brain-specific spatio-temporal expression data to uncover molecular pathways common among genes contributing to enriched phenotypes. Twenty-two percent of genes duplicated in individuals with ADHD that had mouse phenotypic information were associated with abnormal learning/memory/conditioning ("l/m/c") phenotypes. Although not observed in a second ADHD-cohort, we identified a similar enrichment among genes duplicated by eight de novo CNVs present in eight individuals with Hyperactivity and/or Short attention span ("Hyperactivity/SAS", the ontologically-derived phenotypic components of ADHD). In the brain, genes duplicated in patients with ADHD and Hyperactivity/SAS and whose orthologues' disruption yields l/m/c phenotypes in mouse ("candidate-genes"), were co-expressed with one another and with genes whose orthologues' mouse models exhibit hyperactivity. Moreover, genes associated with hyperactivity in the mouse were significantly more co-expressed with ADHD candidate-genes than with similarly identified genes from individuals with intellectual disability. Our findings support an etiology for ADHD distinct from intellectual disability, and mechanistically related to genes associated with hyperactivity phenotypes in other mammalian species.
Any given human individual carries multiple genetic variants that disrupt protein-coding genes, through structural variation, as well as nucleotide variants and indels. Predicting the phenotypic consequences of a gene disruption remains a significant challenge. Current approaches employ information from a range of biological networks to predict which human genes are haploinsufficient (meaning two copies are required for normal function) or essential (meaning at least one copy is required for viability). Using recently available study gene sets, we show that these approaches are strongly biased towards providing accurate predictions for well-studied genes. By contrast, we derive a haploinsufficiency score from a combination of unbiased large-scale high-throughput datasets, including gene co-expression and genetic variation in over 6000 human exomes. Our approach provides a haploinsufficiency prediction for over twice as many genes currently unassociated with papers listed in Pubmed as three commonly-used approaches, and outperforms these approaches for predicting haploinsufficiency for less-studied genes. We also show that fine-tuning the predictor on a set of well-studied 'gold standard' haploinsufficient genes does not improve the prediction for less-studied genes. This new score can readily be used to prioritize gene disruptions resulting from any genetic variant, including copy number variants, indels and single-nucleotide variants.
GeneNet Toolbox for MATLAB: a flexible platform for the analysis of gene connectivity in biological networks.
SUMMARY: We present GeneNet Toolbox for MATLAB (also available as a set of standalone applications for Linux). The toolbox, available as command-line or with a graphical user interface, enables biologists to assess connectivity among a set of genes of interest ('seed-genes') within a biological network of their choosing. Two methods are implemented for calculating the significance of connectivity among seed-genes: 'seed randomization' and 'network permutation'. Options include restricting analyses to a specified subnetwork of the primary biological network, and calculating connectivity from the seed-genes to a second set of interesting genes. Pre-analysis tools help the user choose the best connectivity-analysis algorithm for their network. The toolbox also enables visualization of the connections among seed-genes. GeneNet Toolbox functions execute in reasonable time for very large networks (∼10 million edges) on a desktop computer. AVAILABILITY AND IMPLEMENTATION: GeneNet Toolbox is open source and freely available from http://avigailtaylor.github.io/gntat14. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: email@example.com.
GeneNet Toolbox for MATLAB: A flexible platform for the analysis of gene connectivity in biological networks
© The Author 2014. Published by Oxford University Press. All rights reserved. We present GeneNet Toolbox for MATLAB (also available as a set of standalone applications for Linux). The toolbox, available as command-line or with a graphical user interface, enables biologists to assess connectivity among a set of genes of interest ('seed-genes') within a biological network of their choosing. Two methods are implemented for calculating the significance of connectivity among seed-genes: 'seed randomization' and 'network permutation'. Options include restricting analyses to a specified subnetwork of the primary biological network, and calculating connectivity from the seed-genes to a second set of interesting genes. Pre-analysis tools help the user choose the best connectivity-analysis algorithm for their network. The toolbox also enables visualization of the connections among seed-genes. GeneNet Toolbox functions execute in reasonable time for very large networks (∼10 million edges) on a desktop computer.
Migraine is a debilitating neurological disorder affecting around one in seven people worldwide, but its molecular mechanisms remain poorly understood. There is some debate about whether migraine is a disease of vascular dysfunction or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we carried out a genetic study of migraine on 59,674 affected subjects and 316,078 controls from 22 GWA studies. We identified 44 independent single-nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 × 10(-8)) that mapped to 38 distinct genomic loci, including 28 loci not previously reported and a locus that to our knowledge is the first to be identified on chromosome X. In subsequent computational analyses, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies.
Parkinson's disease (PD) is the most common neurodegenerative movement disorder, affecting 1% of the population over 65 years characterized clinically by both motor and non-motor symptoms accompanied by the preferential loss of dopamine neurons in the substantia nigra pars compacta. Here, we sequenced the exomes of 244 Parkinson's patients selected from the Oxford Parkinson's Disease Centre Discovery Cohort and, after quality control, 228 exomes were available for analyses. The PD patient exomes were compared to 884 control exomes selected from the UK10K datasets. No single non-synonymous (NS) single nucleotide variant (SNV) nor any gene carrying a higher burden of NS SNVs was significantly associated with PD status after multiple-testing correction. However, significant enrichments of genes whose proteins have roles in the extracellular matrix were amongst the top 300 genes with the most significantly associated NS SNVs, while regions associated with PD by a recent Genome Wide Association (GWA) study were enriched in genes containing PD-associated NS SNVs. By examining genes within GWA regions possessing rare PD-associated SNVs, we identified RAD51B. The protein-product of RAD51B interacts with that of its paralogue RAD51, which is associated with congenital mirror movements phenotypes, a phenotype also comorbid with PD.
Transcriptomic profiling of purified patient-derived dopamine neurons identifies convergent perturbations and therapeutics for Parkinson's disease.
While induced pluripotent stem cell (iPSC) technologies enable the study of inaccessible patient cell types, cellular heterogeneity can confound the comparison of gene expression profiles between iPSC-derived cell lines. Here, we purified iPSC-derived human dopaminergic neurons (DaNs) using the intracellular marker, tyrosine hydroxylase. Once purified, the transcriptomic profiles of iPSC-derived DaNs appear remarkably similar to profiles obtained from mature post-mortem DaNs. Comparison of the profiles of purified iPSC-derived DaNs derived from Parkinson's disease (PD) patients carrying LRRK2 G2019S variants to controls identified significant functional convergence amongst differentially-expressed (DE) genes. The PD LRRK2-G2019S associated profile was positively matched with expression changes induced by the Parkinsonian neurotoxin rotenone and opposed by those induced by clioquinol, a compound with demonstrated therapeutic efficacy in multiple PD models. No functional convergence amongst DE genes was observed following a similar comparison using non-purified iPSC-derived DaN-containing populations, with cellular heterogeneity appearing a greater confound than genotypic background.
Systematic Phenomics Analysis Deconvolutes Genes Mutated in Intellectual Disability into Biologically Coherent Modules.
Intellectual disability (ID) disorders are genetically and phenotypically extremely heterogeneous. Can this complexity be depicted in a comprehensive way as a means of facilitating the understanding of ID disorders and their underlying biology? We provide a curated database of 746 currently known genes, mutations in which cause ID (ID-associated genes [ID-AGs]), classified according to ID manifestation and associated clinical features. Using this integrated resource, we show that ID-AGs are substantially enriched with co-expression, protein-protein interactions, and specific biological functions. Systematic identification of highly enriched functional themes and phenotypes revealed typical phenotype combinations characterizing process-defined groups of ID disorders, such as chromatin-related disorders and deficiencies in DNA repair. Strikingly, phenotype classification efficiently breaks down ID-AGs into subsets with significantly elevated biological coherence and predictive power. Custom-made functional Drosophila datasets revealed further characteristic phenotypes among ID-AGs and specific clinical classes. Our study and resource provide systematic insights into the molecular and clinical landscape of ID disorders, represent a significant step toward overcoming current limitations in ID research, and prove the utility of systematic human and cross-species phenomics analyses in highly heterogeneous genetic disorders.