Addressing variability in iPSC-derived models of human disease: guidelines to promote reproducibility

Induced pluripotent stem cell (iPSC) technologies have provided in vitro models of inaccessible human cell types, yielding new insights into disease mechanisms especially for neurological disorders. However, without due consideration, the thousands of new human iPSC lines generated in the past decade will inevitably affect the reproducibility of iPSC-based experiments. Differences between donor individuals, genetic stability and experimental variability contribute to iPSC model variation by impacting differentiation potency, cellular heterogeneity, morphology, and transcript and protein abundance. Such effects will confound reproducible disease modelling in the absence of appropriate strategies. In this Review, we explore the causes and effects of iPSC heterogeneity, and propose approaches to detect and account for experimental variation between studies, or even exploit it for deeper biological insight

Phenotype ontologies and cross-species analysis for translational research

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

Unbiased functional clustering of gene variants with a phenotypic-linkage network

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

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

Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network

Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry

Haploinsufficiency predictions without study bias

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

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

Headache and type 2 diabetes association: a US national ambulatory case-control study

Objective We investigate the joint observation between type 2 diabetes and headache using a case-control study of a US ambulatory dataset. Background Recent whole-population cohort studies propose that type 2 diabetes may have a protective effect against headache prevalence. With headaches ranked as a leading cause of disability, headache-associated comorbidities could help identify shared molecular mechanisms. Methods We performed a case-control study using the US National Ambulatory Medical Care Survey, 2009, on the joint observation between headache and specific comorbidities, namely type 2 diabetes, hypertension and anxiety, for all patients between 18 and 65 years of age. The odds ratio of having a headache and a comorbidity were calculated using conditional logistic regression, controlling for gender and age over a study population of 3,327,947 electronic health records in the absence of prescription medication data. Results We observed estimated odds ratio of 0.89 (95% CI: 0.83-0.95) of having a headache and a record of type 2 diabetes over the population, and 0.83 (95% CI: 2.02-2.57) and 0.89 (95% CI: 3.00-3.49) for male and female, respectively. Conclusions We find that patients with type 2 diabetes are less likely to present a recorded headache indication. Patients with hypertension are almost twice as likely of having a headache indication and patients with an anxiety disorder are almost three times as likely. Given the possibility of confounding indications and prescribed medications, additional studies are recommended

Human‐specific transcriptome of ventral and dorsal midbrain dopamine neurons

Objective Neuronal loss in the substantia nigra pars compacta (SNpc) in Parkinson's disease (PD) is not uniform as dopamine neurons from the ventral tier are lost more rapidly than those of the dorsal tier. Identifying the intrinsic differences that account for this differential vulnerability may provide a key for developing new treatments for PD. Method Here we compared the RNA‐sequenced transcriptomes of ~100 laser captured micro‐dissected SNpc neurons from each tier from seven healthy controls. Results Expression levels of dopaminergic markers were similar across the tiers while markers specific to the neighbouring ventral tegmental area were virtually undetected. After accounting for unwanted sources of variation, we identified one hundred and six differentially expressed genes (DEGs) between the SNpc tiers. The genes higher in the dorsal/resistant SNpc tier neurons displayed coordinated patterns of expression across the human brain, their protein products had more interactions than expected by chance and they demonstrated evidence of functional convergence. No significant shared functionality was found for genes higher in the ventral/vulnerable SNpc tier. Surprisingly but importantly, none of the identified DEGs were among the familial PD genes or genome‐wide associated loci. Finally, we found some DEGs in opposite tier orientation between human and analogous mouse populations. Interpretation Our results highlight functional enrichments of vesicular trafficking, ion transport/homeostasis and oxidative stress genes showing higher expression in the resistant neurons of the SNpc dorsal tier. Furthermore, the comparison of gene expression variation in human and mouse SNpc populations strongly argues for the need of human‐focused Omics studies

Pro-maturational effects of human iPSC-derived cortical astrocytes upon iPSC-derived cortical neurons

Astrocytes influence neuronal maturation and function by providing trophic support, regulating the extracellular environment, andmodulating signaling at synapses. The emergence of induced pluripotent stem cell (iPSC) technology offers a human system with whichto validate and re-evaluate insights from animal studies. Here, we set out to examine interactions between human astrocytes and neuronsderived from a common cortical progenitor pool, thereby recapitulating aspects ofin vivocortical development. We show that the corticaliPSC-derived astrocytesexhibit many of the molecular and functional hallmarks of astrocytes. Furthermore, optogenetic and electrophys-iological co-culture experiments reveal that the iPSC-astrocytes can actively modulate ongoing synaptic transmission and exertpro-maturational effects upon developing networks of iPSC-derived cortical neurons. Finally, transcriptomic analyses implicate syn-apse-associated extracellular signaling in the astrocytes’ pro-maturational effects upon the iPSC-derived neurons. This work helps laythe foundation for future investigations into astrocyte-to-neuron interactions in human health and disease