Biology subjects

Mahoney, J. M.

Publications and source records attributed to Mahoney, J. M..

24 records · Page 2Linked to original sources

bioRxiv2021

Genetic Analysis of Bphse: a Novel Gene Complementing Resistance to Bordetella pertussis-Induced Histamine Sensitization

AO_SCPLOWBSTRACTC_SCPLOWHistamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a [~]5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

genetics↗

bioRxiv2021

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

genetics↗

bioRxiv2019

Network-based functional prediction augments genetic association to predict candidate genes for histamine hypersensitivity in mice

Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for computationally prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Hhs). Hhs is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Hhs risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Hhs locus by predicting functional association with multiple Hhs-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Hhs. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in highly challenging cases involving large QTLs and extreme trait complexity.

genetics↗

bioRxiv2019

Genetic interactions affect lung function in patients with systemic sclerosis

Scleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprising WNT5A, RBMS3, and MSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin.\n\nAuthor summarySystemic sclerosis (SSc), or scleroderma, is a devastating autoimmune disease. Patients experience progressive fibrosis of their skin and internal organs, reduced quality of life, and increased risk of death. Lung disease associated with SSc is particularly dangerous and is currently the leading cause of death in SSc patients. There are no specific treatments for SSc or SSc-related lung disease, but promising work in the genetics of this disease has identified more than 200 genetic variants that influence SSc [1]. Piecing together how genetic variants interact with each other to influence disease may provide clues for targeted therapies. Here we present a novel analytical approach for identifying genetic interactions in a human disease cohort. In this approach we first filtered SNPs to those that are most likely to interact to influence the disease traits. We then applied the Combined Analysis of Pleiotropy and Epistasis (CAPE), which combines information across multiple traits to increase power to detect genetic interactions. Using this approach, we identified a three-gene network among MSI2, WNT5A, and RBMS3 that influenced autoantibody status and lung function in a cohort of 416 SSc patients. Gene expression data suggest that this interaction network is tissue- and disease-specific, and may thus provide a specific target for SSc therapy.

genetics↗

bioRxiv2017

Environmental Enrichment Normalizes Hippocampal Timing Coding in a Malformed Hippocampus

Neurodevelopmental insults such as malformations of cortical development (MCD) are a common cause of psychiatric disorders, learning impairments and epilepsy. Animals with MCDs have impairments in spatial cognition that, remarkably, are improved by post-weaning environmental enrichment (EE). To establish the network-level mechanisms responsible for these impacts, hippocampal in vivo single unit recordings were performed in freely moving animals in an open arena. We took a generalized linear modeling approach to extract fine spike timing (FST) characteristics and related these to place cell fidelity used as a surrogate of spatial cognition. We find that MCDs disrupt FST and place-modulated rate coding in hippocampal CA1 and that EE restores both to normal. Moreover, FST parameters predict spatial coherence of neurons, suggesting that mechanisms determining FST are critical for cognition. This suggests that FST parameters could represent a therapeutic target to improve cognition even in the context of a structurally abnormal brain.\n\nHIGHLIGHTSO_LIEnvironmental enrichment (EE) in rats with cortical malformations improves cognition.\nC_LIO_LIEE resolves impaired rate and timing coding of hippocampal pyramidal neurons.\nC_LIO_LITaken together, circuit-level dynamics directly affect quality of the cognitive map.\nC_LI\n\nRESEARCH IN CONTEXTInsults during neurodevelopment, particularly those that result in physical malformations in the brain, lead to cognitive impairment, psychiatric disorders and epilepsy. Environmental enrichment (EE) improves cognitive outcome in patients and animal models with brain malformations. Understanding how EE can improve cognition at the level of neural networks can lead to new treatment targets. Remarkably, using an approach that mathematically models neuron firing we show that firing is mistimed in animals with malformations and that EE improves this abnormality. Importantly, timing abnormalities predict abnormalities in cognition at the single neuron level, suggesting that restoring timing could improve learning and memory deficits.

neuroscience↗

bioRxiv2016

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

genomics↗