Variant interpretation through Bayesian fusion of frequency and genomic knowledge

Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation

Identification of a functional genetic variant driving racially dimorphic platelet gene expression of the thrombin receptor regulator, PCTP.

Platelet activation in response to stimulation of the Protease Activated Receptor 4 (PAR4) receptor differs by race. One factor that contributes to this difference is the expression level of Phosphatidylcholine Transfer Protein (PCTP), a regulator of platelet PAR4 function. We have conducted an expression Quantitative Trait Locus (eQTL) analysis that identifies single nucleotide polymorphisms (SNPs) linked to the expression level of platelet genes. This analysis revealed 26 SNPs associated with the expression level of PCTP at genome-wide significance (p \u3c 5×10(-8)). Using annotation from ENCODE and other public data we prioritised one of these SNPs, rs2912553, for functional testing. The allelic frequency of rs2912553 is racially-dimorphic, in concordance with the racially differential expression of PCTP. Reporter gene assays confirmed that the single nucleotide change caused by rs2912553 altered the transcriptional potency of the surrounding genomic locus. Electromobility shift assays, luciferase assays, and overexpression studies indicated a role for the megakaryocytic transcription factor GATA1. In summary, we have integrated multi-omic data to identify and functionalise an eQTL. This, along with the previously described relationship between PCTP and PAR4 function, allows us to characterise a genotype-phenotype relationship through the mechanism of gene expression

Induction of the HIV-1 Tat co-factor cyclin T1 during monocyte differentiation is required for the regulated expression of a large portion of cellular mRNAs

BACKGROUND: P-TEFb, a general RNA polymerase II elongation factor, is composed of CDK9 (cyclin-dependent kinase 9) as a catalytic unit and either cyclin T1, T2 or K as a regulatory subunit. The cyclin T1/P-TEFb complex is targeted by HIV to mediate Tat transactivation. Cyclin T1 protein expression is induced during early macrophage differentiation, suggesting a role in regulation of mRNA expression during the differentiation process. To study the functional significance of cyclin T1 induction during differentiation, we utilized the human Mono Mac 6 (MM6) monocytic cell line. RESULTS: We found that cyclin T1 protein expression is induced by a post-transcriptional mechanism following PMA treatment of MM6 cells, similar to its induction in primary monocytes and macrophages. Also in agreement with findings in primary cells, cyclin T2a is present at relatively high levels in MM6 cells and is not induced by PMA. Although the knock-down of cyclin T1 in MM6 cells by shRNA inhibited HIV-1 Tat transactivation, MM6 cell growth was not affected by the depletion of cyclin T1. Using DNA microarray technology, we found that more than 20% of genes induced by PMA require cyclin T1 for their normal level of induction, and approximately 15% of genes repressed by PMA require cyclin T1 for their normal level of repression. Gene ontology analysis indicates that many of these cyclin T1-dependent genes are related to immune response and signal transduction. CONCLUSION: These results suggest that cyclin T1 serves a critical role in the program of macrophage differentiation, and this raises questions about the feasibility of cyclin T1 serving as an antiviral therapeutic target

Bayesian modelling of high-throughput sequencing assays with malacoda.

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell\u27s output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations

Parent of Origin, Mosaicism, and Recurrence Risk: Probabilistic Modeling Explains the Broken Symmetry of Transmission Genetics

Most new mutations are observed to arise in fathers, and increasing paternal age positively correlates with the risk of new variants. Interestingly, new mutations in X-linked recessive disease show elevated familial recurrence rates. In male offspring, these mutations must be inherited from mothers. We previously developed a simulation model to consider parental mosaicism as a source of transmitted mutations. In this paper, we extend and formalize the model to provide analytical results and flexible formulas. The results implicate parent of origin and parental mosaicism as central variables in recurrence risk. Consistent with empirical data, our model predicts that more transmitted mutations arise in fathers and that this tendency increases as fathers age. Notably, the lack of expansion later in the male germline determines relatively lower variance in the proportion of mutants, which decreases with paternal age. Subsequently, observation of a transmitted mutation has less impact on the expected risk for future offspring. Conversely, for the female germline, which arrests after clonal expansion in early development, variance in the mutant proportion is higher, and observation of a transmitted mutation dramatically increases the expected risk of recurrence in another pregnancy. Parental somatic mosaicism considerably elevates risk for both parents. These findings have important implications for genetic counseling and for understanding patterns of recurrence in transmission genetics. We provide a convenient online tool and source code implementing our analytical results. These tools permit varying the underlying parameters that influence recurrence risk and could be useful for analyzing risk in diverse family structures

Molecular Signatures of Proliferation and Quiescence in Hematopoietic Stem Cells

Stem cells resident in adult tissues are principally quiescent, yet harbor enormous capacity for proliferation to achieve self renewal and to replenish their tissue constituents. Although a single hematopoietic stem cell (HSC) can generate sufficient primitive progeny to repopulate many recipients, little is known about the molecular mechanisms that maintain their potency or regulate their self renewal. Here we have examined the gene expression changes that occur over a time course when HSCs are induced to proliferate and return to quiescence in vivo. These data were compared to data representing differences between naturally proliferating fetal HSCs and their quiescent adult counterparts. Bioinformatic strategies were used to group time-ordered gene expression profiles generated from microarrays into signatures of quiescent and dividing stem cells. A novel method for calculating statistically significant enrichments in Gene Ontology groupings for our gene lists revealed elemental subgroups within the signatures that underlie HSC behavior, and allowed us to build a molecular model of the HSC activation cycle. Initially, quiescent HSCs evince a state of readiness. The proliferative signal induces a preparative state, which is followed by active proliferation divisible into early and late phases. Re-induction of quiescence involves changes in migratory molecule expression, prior to reestablishment of homeostasis. We also identified two genes that increase in both gene and protein expression during activation, and potentially represent new markers for proliferating stem cells. These data will be of use in attempts to recapitulate the HSC self renewal process for therapeutic expansion of stem cells, and our model may correlate with acquisition of self renewal characteristics by cancer stem cells

Evidence for Diversity in Transcriptional Profiles of Single Hematopoietic Stem Cells

Hematopoietic stem cells replenish all the cells of the blood throughout the lifetime of an animal. Although thousands of stem cells reside in the bone marrow, only a few contribute to blood production at any given time. Nothing is known about the differences between individual stem cells that dictate their particular state of activation readiness. To examine such differences between individual stem cells, we determined the global gene expression profile of 12 single stem cells using microarrays. We showed that at least half of the genetic expression variability between 12 single cells profiled was due to biological variation in 44% of the genes analyzed. We also identified specific genes with high biological variance that are candidates for influencing the state of readiness of individual hematopoietic stem cells, and confirmed the variability of a subset of these genes using single-cell real-time PCR. Because apparent variation of some genes is likely due to technical factors, we estimated the degree of biological versus technical variation for each gene using identical RNA samples containing an RNA amount equivalent to that of single cells. This enabled us to identify a large cohort of genes with low technical variability whose expression can be reliably measured on the arrays at the single-cell level. These data have established that gene expression of individual stem cells varies widely, despite extremely high phenotypic homogeneity. Some of this variation is in key regulators of stem cell activity, which could account for the differential responses of particular stem cells to exogenous stimuli. The capacity to accurately interrogate individual cells for global gene expression will facilitate a systems approach to biological processes at a single-cell level

Regulatory Pathway Analysis by High-Throughput In Situ Hybridization

Automated in situ hybridization enables the construction of comprehensive atlases of gene expression patterns in mammals. Such atlases can become Web-searchable digital expression maps of individual genes and thus offer an entryway to elucidate genetic interactions and signaling pathways. Towards this end, an atlas housing ∼1,000 spatial gene expression patterns of the midgestation mouse embryo was generated. Patterns were textually annotated using a controlled vocabulary comprising >90 anatomical features. Hierarchical clustering of annotations was carried out using distance scores calculated from the similarity between pairs of patterns across all anatomical structures. This process ordered hundreds of complex expression patterns into a matrix that reflects the embryonic architecture and the relatedness of patterns of expression. Clustering yielded 12 distinct groups of expression patterns. Because of the similarity of expression patterns within a group, members of each group may be components of regulatory cascades. We focused on the group containing Pax6, an evolutionary conserved transcriptional master mediator of development. Seventeen of the 82 genes in this group showed a change of expression in the developing neocortex of Pax6-deficient embryos. Electromobility shift assays were used to test for the presence of Pax6-paired domain binding sites. This led to the identification of 12 genes not previously known as potential targets of Pax6 regulation. These findings suggest that cluster analysis of annotated gene expression patterns obtained by automated in situ hybridization is a novel approach for identifying components of signaling cascades

Sphingosine induces the aggregation of imine-containing peroxidized vesicles

AbstractLipid peroxidation plays a central role in the pathogenesis of many diseases like atherosclerosis and multiple sclerosis. We have analyzed the interaction of sphingosine with peroxidized bilayers in model membranes. Cu2+ induced peroxidation was checked following UV absorbance at 245nm, and also using the novel Avanti snoopers®. Mass spectrometry confirms the oxidation of phospholipid unsaturated chains. Our results show that sphingosine causes aggregation of Cu2+-peroxidized vesicles. We observed that aggregation is facilitated by the presence of negatively-charged phospholipids in the membrane, and inhibited by anti-oxidants e.g. BHT. Interestingly, long-chain alkylamines (C18, C16) but not their short-chain analogues (C10, C6, C1) can substitute sphingosine as promoters of vesicle aggregation. Furthermore, sphinganine but not sphingosine-1-phosphate can mimic this effect. Formation of imines in the membrane upon peroxidation was detected by 1H-NMR and it appeared to be necessary for the aggregation effect. 31P-NMR spectroscopy reveals that sphingosine facilitates formation of non-lamellar phase in parallel with vesicle aggregation. The data might suggest a role for sphingosine in the pathogenesis of atherosclerosis

Functionalization of CD36 Cardiovascular Disease and Expression Associated Variants by Interdisciplinary High Throughput Analysis.

CD36 is a platelet membrane glycoprotein whose engagement with oxidized low-density lipoprotein (oxLDL) results in platelet activation. The CD36 gene has been associated with platelet count, platelet volume, as well as lipid levels and CVD risk by genome-wide association studies. Platelet CD36 expression levels have been shown to be associated with both the platelet oxLDL response and an elevated risk of thrombo-embolism. Several genomic variants have been identified as associated with platelet CD36 levels, however none have been conclusively demonstrated to be causative. We screened 81 expression quantitative trait loci (eQTL) single nucleotide polymorphisms (SNPs) associated with platelet CD36 expression by a Massively Parallel Reporter Assay (MPRA) and analyzed the results with a novel Bayesian statistical method. Ten eQTLs located 13kb to 55kb upstream of the CD36 transcriptional start site of transcript ENST00000309881 and 49kb to 92kb upstream of transcript ENST00000447544, demonstrated significant transcription shifts between their minor and major allele in the MPRA assay. Of these, rs2366739 and rs1194196, separated by only 20bp, were confirmed by luciferase assay to alter transcriptional regulation. In addition, electromobility shift assays demonstrated differential DNA:protein complex formation between the two alleles of this locus. Furthermore, deletion of the genomic locus by CRISPR/Cas9 in K562 and Meg-01 cells results in upregulation of CD36 transcription. These data indicate that we have identified a variant that regulates expression of CD36, which in turn affects platelet function. To assess the clinical relevance of our findings we used the PhenoScanner tool, which aggregates large scale GWAS findings; the results reinforce the clinical relevance of our variants and the utility of the MPRA assay. The study demonstrates a generalizable paradigm for functional testing of genetic variants to inform mechanistic studies, support patient management and develop precision therapies