Evaluating the Contribution of Cell Type-Specific Alternative Splicing to Variation in Lipid Levels.

BACKGROUND: Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest. METHODS: We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs). RESULTS: We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone. CONCLUSIONS: These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure.

Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.

ColocQuiaL: a QTL-GWAS colocalization pipeline.

SUMMARY: Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data. AVAILABILITY AND IMPLEMENTATION: ColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders.

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations.

BACKGROUND: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. OBJECTIVES: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. METHODS: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). RESULTS: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. CONCLUSIONS: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Multi-trait association studies discover pleiotropic loci between Alzheimer's disease and cardiometabolic traits.

BACKGROUND: Identification of genetic risk factors that are shared between Alzheimer's disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Previous epidemiological correlations observed between cardiometabolic traits and AD led us to assess the pleiotropy between these traits. METHODS: We performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. For each of these loci, we performed colocalization with Genotype-Tissue Expression (GTEx) project expression quantitative trait loci (eQTL) to identify candidate causal genes. RESULTS: We identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the gene DOCK4 and is potentially implicated in its alternative splicing. Colocalization with GTEx eQTL data identified additional candidate genes for the loci we detected, including ACE, the target of the hypertensive drug class of ACE inhibitors. We found that the allele associated with decreased ACE expression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic. CONCLUSION: Our results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.

Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci.

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.

Publisher Correction: Regulation of cellular sterol homeostasis by the oxygen responsive noncoding RNA lincNORS.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Regulation of cellular sterol homeostasis by the oxygen responsive noncoding RNA lincNORS.

We hereby provide the initial portrait of lincNORS, a spliced lincRNA generated by the MIR193BHG locus, entirely distinct from the previously described miR-193b-365a tandem. While inducible by low O2 in a variety of cells and associated with hypoxia in vivo, our studies show that lincNORS is subject to multiple regulatory inputs, including estrogen signals. Biochemically, this lincRNA fine-tunes cellular sterol/steroid biosynthesis by repressing the expression of multiple pathway components. Mechanistically, the function of lincNORS requires the presence of RALY, an RNA-binding protein recently found to be implicated in cholesterol homeostasis. We also noticed the proximity between this locus and naturally occurring genetic variations highly significant for sterol/steroid-related phenotypes, in particular the age of sexual maturation. An integrative analysis of these variants provided a more formal link between these phenotypes and lincNORS, further strengthening the case for its biological relevance.

Natural human genetic variation determines basal and inducible expression of PM20D1, an obesity-associated gene.

PM20D1 is a candidate thermogenic enzyme in mouse fat, with its expression cold-induced and enriched in brown versus white adipocytes. Thiazolidinedione (TZD) antidiabetic drugs, which activate the peroxisome proliferator-activated receptor-γ (PPARγ) nuclear receptor, are potent stimuli for adipocyte browning yet fail to induce Pm20d1 expression in mouse adipocytes. In contrast, PM20D1 is one of the most strongly TZD-induced transcripts in human adipocytes, although not in cells from all individuals. Two putative PPARγ binding sites exist near the gene's transcription start site (TSS) in human but not mouse adipocytes. The -4 kb upstream site falls in a segmental duplication of a nearly identical intronic region +2.5 kb downstream of the TSS, and this duplication occurred in the primate lineage and not in other mammals, like mice. PPARγ binding and gene activation occur via this upstream duplicated site, thus explaining the species difference. Furthermore, this functional upstream PPARγ site exhibits genetic variation among people, with 1 SNP allele disrupting a PPAR response element and giving less activation by PPARγ and TZDs. In addition to this upstream variant that determines PPARγ regulation of PM20D1 in adipocytes, distinct variants downstream of the TSS have strong effects on PM20D1 expression in human fat as well as other tissues. A haplotype of 7 tightly linked downstream SNP alleles is associated with very low PMD201 expression and correspondingly high DNA methylation at the TSS. These PM20D1 low-expression variants may account for human genetic associations in this region with obesity as well as neurodegenerative diseases.

Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network.

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.

Defining Disease, Diagnosis, and Translational Medicine within a Homeostatic Perturbation Paradigm: The National Institutes of Health Undiagnosed Diseases Program Experience.

Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research.

Biallelic SCN10A mutations in neuromuscular disease and epileptic encephalopathy.

OBJECTIVES: Two consanguineous families, one of Sudanese ethnicity presenting progressive neuromuscular disease, severe cognitive impairment, muscle weakness, upper motor neuron lesion, anhydrosis, facial dysmorphism, and recurrent seizures and the other of Egyptian ethnicity presenting with neonatal hypotonia, bradycardia, and recurrent seizures, were evaluated for the causative gene mutation. METHODS AND RESULTS: Homozygosity mapping and whole exome sequencing (WES) identified damaging homozygous variants in SCN10A, namely c.4514C>T; p.Thr1505Met in the first family and c.4735C>T; p.Arg1579* in the second family. A third family, of Western European descent, included a child with febrile infection-related epilepsy syndrome (FIRES) who also had compound heterozygous missense mutations in SCN10A, namely, c.3482T>C; p.Met1161Thr and c.4709C>A; p.Thr1570Lys. A search for SCN10A variants in three consortia datasets (EuroEPINOMICS, Epi4K/EPGP, Autism/dbGaP) identified an additional five individuals with compound heterozygous variants. A Hispanic male with infantile spasms [c.2842G>C; p.Val948Leu and c.1453C>T; p.Arg485Cys], and a Caucasian female with Lennox-Gastaut syndrome [c.1529C>T; p.Pro510Leu and c.4984G>A; p.Gly1662Ser] in the epilepsy databases and three in the autism databases with [c.4009T>A; p.Ser1337Thr and c.1141A>G; p.Ile381Val], [c.2972C>T; p.Pro991Leu and c.2470C>T; p.His824Tyr], and [c.4009T>A; p.Ser1337Thr and c.2052G>A; p.Met684Ile]. INTERPRETATION: SCN10A is a member of the voltage-gated sodium channel (VGSC) gene family. Sodium channels are responsible for the instigation and proliferation of action potentials in central and peripheral nervous systems. Heterozygous mutations in VGSC genes cause a wide range of epileptic and peripheral nervous system disorders. This report presents autosomal recessive mutations in SCN10A that may be linked to epilepsy-related phenotypes, Lennox-Gastaut syndrome, infantile spasms, and Autism Spectrum Disorder.

Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience.

The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities. The team, therefore, built the NIH UDP integrated collaboration system (UDPICS) to form virtual collaborative multidisciplinary research networks or communities. UDPICS supports these communities through integrated process management, ontology-based phenotyping, biospecimen management, cloud-based genomic analysis, and an electronic laboratory notebook. UDPICS provided a mechanism for efficient, transparent, and scalable translational research and thereby addressed many of the complex and diverse research and logistical problems of the NIH UDP. Full definition of the strengths and deficiencies of UDPICS will require formal qualitative and quantitative usability and process improvement measurement.

Explorations to improve the completeness of exome sequencing.

BACKGROUND: Exome sequencing has advanced to clinical practice and proven useful for obtaining molecular diagnoses in rare diseases. In approximately 75 % of cases, however, a clinical exome study does not produce a definitive molecular diagnosis. These residual cases comprise a new diagnostic challenge for the genetics community. The Undiagnosed Diseases Program of the National Institutes of Health routinely utilizes exome sequencing for refractory clinical cases. Our preliminary data suggest that disease-causing variants may be missed by current standard-of-care clinical exome analysis. Such false negatives reflect limitations in experimental design, technical performance, and data analysis. RESULTS: We present examples from our datasets to quantify the analytical performance associated with current practices, and explore strategies to improve the completeness of data analysis. In particular, we focus on patient ascertainment, exome capture, inclusion of intronic variants, and evaluation of medium-sized structural variants. CONCLUSIONS: The strategies we present may recover previously-missed, disease causing variants in second-pass exome analysis. Understanding the limitations of the current clinical exome search space provides a rational basis to improve methods for disease variant detection using genome-scale sequencing techniques.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

PURPOSE: Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles. METHODS: Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease-gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein-protein association neighbors. RESULTS: Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease-gene associations and ranked the correct seeded variant in up to 87% when detectable disease-gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation. CONCLUSION: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608-617.

Disruption of Golgi morphology and altered protein glycosylation in PLA2G6-associated neurodegeneration.

BACKGROUND: Mutations in PLA2G6, which encodes the calcium-independent phospholipase A2 group VI, cause neurodegeneration and diffuse cortical Lewy body formation by a yet undefined mechanism. We assessed whether altered protein glycosylation due to abnormal Golgi morphology might be a factor in the pathology of this disease. METHODS: Three patients presented with PLA2G6-associated neurodegeneration (PLAN); two had infantile neuroaxonal dystrophy (INAD) and one had adult-onset dystonia-parkinsonism. We analysed protein N-linked and O-linked glycosylation in cerebrospinal fluid, plasma, urine, and cultured skin fibroblasts using high performance liquid chromatography (HPLC) and matrix-assisted laser desorption ionization--time of flight/mass spectrometry (MALDI-TOF/MS). We also assessed sialylation and Golgi morphology in cultured fibroblasts by immunofluorescence and performed rescue experiments using a lentiviral vector. RESULTS: The patients with INAD had PLA2G6 mutations NM_003560.2: c.[950G>T];[426-1077dup] and c.[1799G>A];[2221C>T] and the patient with dystonia-parkinsonism had PLA2G6 mutations NM_003560.2: c.[609G>A];[2222G>A]. All three patients had altered Golgi morphology and abnormalities of protein O-linked glycosylation and sialylation in cultured fibroblasts that were rescued by lentiviral overexpression of wild type PLA2G6. CONCLUSIONS: Our findings add altered Golgi morphology, O-linked glycosylation and sialylation defects to the phenotypical spectrum of PLAN; these pathways are essential for correct processing and distribution of proteins. Lewy body and Tau pathology, two neuropathological features of PLAN, could emerge from these defects. Therefore, Golgi morphology, O-linked glycosylation and sialylation may play a role in the pathogenesis of PLAN and perhaps other neurodegenerative disorders.

Next-generation diagnostics and disease-gene discovery with the Exomiser.

Exomiser is an application that prioritizes genes and variants in next-generation sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian disease. Exomiser comprises a suite of algorithms for prioritizing exome sequences using random-walk analysis of protein interaction networks, clinical relevance and cross-species phenotype comparisons, as well as a wide range of other computational filters for variant frequency, predicted pathogenicity and pedigree analysis. In this protocol, we provide a detailed explanation of how to install Exomiser and use it to prioritize exome sequences in a number of scenarios. Exomiser requires ∼3 GB of RAM and roughly 15-90 s of computing time on a standard desktop computer to analyze a variant call format (VCF) file. Exomiser is freely available for academic use from http://www.sanger.ac.uk/science/tools/exomiser.

PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases.

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.

Impaired osteoblast and osteoclast function characterize the osteoporosis of Snyder - Robinson syndrome.

BACKGROUND: Snyder-Robinson Syndrome (SRS) is an X-linked intellectual disability disorder also characterized by osteoporosis, scoliosis, and dysmorphic facial features. It is caused by mutations in SMS, a ubiquitously expressed gene encoding the polyamine biosynthetic enzyme spermine synthase. We hypothesized that the tissue specificity of SRS arises from differential sensitivity to spermidine toxicity or spermine deficiency. METHODS: We performed detailed clinical, endocrine, histopathologic, and morphometric studies on two affected brothers with a spermine synthase loss of function mutation (NM_004595.4:c.443A > G, p.Gln148Arg). We also measured spermine and spermidine levels in cultured human bone marrow stromal cells (hBMSCs) and fibroblasts using the Biochrom 30 polyamine protocol and assessed the osteogenic potential of hBMSCs. RESULTS: In addition to the known tissue-specific features of SRS, the propositi manifested retinal pigmentary changes, recurrent episodes of hyper- and hypoglycemia, nephrocalcinosis, renal cysts, and frequent respiratory infections. Bone histopathology and morphometry identified a profound depletion of osteoblasts and osteoclasts, absence of a trabecular meshwork, a low bone volume and a thin cortex. Comparison of cultured fibroblasts from affected and unaffected individuals showed relatively small changes in polyamine content, whereas comparison of cultured osteoblasts identified marked differences in spermidine and spermine content. Osteogenic differentiation of the SRS-derived hBMSCs identified a severe deficiency of calcium phosphate mineralization. CONCLUSIONS: Our findings support the hypothesis that cell specific alterations in polyamine metabolism contribute to the tissue specificity of SRS features, and that the low bone density arises from a failure of mineralization.