Gene expression in cell lines from propionic acidemia patients, carrier parents, and controls
Introduction
Propionic acidemia (PA, OMIM #: 606054) is an intoxication-type inborn error of metabolism caused by dysfunction of propionyl CoA carboxylase (EC 6.4.1.3) in converting propionyl CoA to methylmalonyl CoA [1]. This dysfunction results in the diagnostic intermediates of methylcitrate, 3-hydroxypropionate and propionylcarnitine in urine and plasma. Newborn screening has increased the number of patients identified early in life with PA and improves outcomes [2]. Patients identified by newborn screening or clinically can have symptoms of metabolic decompensations during episodes of increased metabolic demands and catabolism, such as fever, vomiting, illness, and fasting [1].
Long term complications of PA include cardiomyopathy, skeletal myopathies and metabolic-stroke like episodes [2], [3], [4], [5]. In addition, individuals with PA also have increased risk for infection in that 30–65% have some dysfunction of their immune system [6]. Furthermore, previous studies in related intoxication type disorders such as urea cycle disorders indicate that metabolic decompensations are more related to infection than to dietary discretions [7] so immune function is important for overall health.
These studies examine gene expression in cell lines from PA patients, presumably carrier parents and controls. Assaying gene expression is a method to determine which RNAs are produced at a certain time under certain conditions and has been used in several seminal papers. A number of tissues from individuals with inborn errors of metabolism have differences in their overall gene expression profiles compared to healthy controls, including gene expression studies from the brain of a patient with Menkes [8], fibroblasts from patients with mucolipidosis [9], and brains from mice with neuronal ceroid lipofuscinosis [10], [11]. Each of these studies provides insight into underlying disease pathology, especially when the bioinformatic profile confirms known or identifies suspected and unsuspected pathway components [12]. There have not been extensive studies in gene expression in the intoxication metabolic disorders such as PA. Differences in gene expression may identify new approaches and insights into pathophysiology resulting in previously unrecognized therapeutic targets [13].
Our goal in this study was to characterize the gene expression from lymphoblastoid cell lines (LCLs) derived from PA patients, carrier parents, and sex- and age-matched healthy controls grown in low glucose (9 mg/dL) and normal glucose growth conditions (120 mg/dL). Decreased glucose was used to force the cell lines to use pathways other than glycolysis for energy production with the goal to model a catabolic state.
Section snippets
Lymphoblastoid cell lines (LCLs)
Studies were undertaken with LCLs from 12 clinically diagnosed PA patients, 15 parents of PA patients (presumed obligate carriers) and 12 age- and sex-matched control LCLs from the Centre d'Etude du Polymorphisme Humain (CEPH) HapMap families (Table 1). All the LCLs (including PA patients, parents, and controls) were commercially purchased from the Coriell Institute for Medical Research cell repository (http://ccr.coriell.org/). All the cell lines were anonymized before we received them and
Experimental controls and principal component analysis
This study set out to determine 1) whether LCLs from PA individuals were different than those from controls in terms of expression of genes in specific pathways under typical conditions and under stress situations (low glucose) and 2) whether LCLs from PA individuals had global expression differences in their response to low glucose stress compared to control LCLs.
Initially studies were analyzed to identify if the possible large confounding factors, including family, sex, and hybridization
Discussion
We examined gene expression broadly comparing the expression difference in over 33000 probes among cell lines from individuals with PA (affected), their parents (who are presumably carriers for one mutation in the genes encoding propionyl CoA carboxylase) and “normal controls” and found that there were differences between the expression levels of several genes in the controls' and PA patients' cell lines, but also among the controls', carriers' and PA patients' cell lines. To our knowledge,
Acknowledgments
K.A.C. was supported by a Medical Genetics Research Training Grant, 5-T32-GM-008638-11, to the University of Pennsylvania for the majority of this research. K.A.C. was also supported by NICHD Child Health Research Scholar Award (05K12-HD001399-12). Z.Z was supported by Intellectual and Developmental Disabilities Research Center, NIH/NICHD P30 HD026979. Research funded by a grant from the Propionic Acidemia Foundation. We are grateful to the University of Pennsylvania expression core laboratory
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2020, Molecular Genetics and MetabolismCitation Excerpt :Research in rare diseases has been impacted by the limited availability of robust in vitro experimental models capable of recapitulating metabolic responses associated with the diseases. Until recently, in vitro human models relied primarily on patient-derived fibroblasts or lymphoblastoid cell lines [4,18,24,33]. Although fibroblasts can be used to assess PCC and MMUT activity, these cells have considerably lower levels of activity of propionate pathways resulting in low metabolite levels and have little to no urea cycle activity or mitochondria function needed for in vitro disease modelling [5,7,57,69,70].
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2019, MicronCitation Excerpt :In addition, PPA represents the main mediator between nutrition, gut microbiota and brain physiology (MacFabe et al., 2007; Xu et al., 2016). However, excessive levels of PPA can produce adverse effects, including developmental delay, mitochondrial dysfunction, oxidative stress, other metabolic and immune reactions (Chapman et al., 2015; MacFabe et al., 2007). Moreover, PPA readily crosses the gut-brain barrier, and affects functional brain networks, provoking the changes in neurotransmitter synthesis, neurotransmission, brain signaling and mitochondrial function (De Almeida et al., 2006; El-Ansary et al., 2018; MacFabe, 2013).
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