Constructing and interpreting a large-scale variant effect map for an ultrarare disease gene: Comprehensive prediction of the functional impact of PSAT1 genotypes.

Reduced activity of the enzymes encoded by PHGDH, PSAT1, and PSPH causes a set of ultrarare, autosomal recessive diseases known as serine biosynthesis defects. These diseases present in a broad phenotypic spectrum: at the severe end is Neu-Laxova syndrome, in the intermediate range are infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end is childhood disease with intellectual disability. However, L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms. Therefore, knowledge of pathogenic variants can improve clinical outcomes. Here, we use a yeast-based assay to individually measure the functional impact of 1,914 SNV-accessible amino acid substitutions in PSAT. Results of our assay agree well with clinical interpretations and protein structure-function relationships, supporting the inclusion of our data as functional evidence as part of the ACMG variant interpretation guidelines. We use existing ClinVar variants, disease alleles reported in the literature and variants present as homozygotes in the primAD database to define assay ranges that could aid clinical variant interpretation for up to 98% of the tested variants. In addition to measuring the functional impact of individual variants in yeast haploid cells, we also assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results from our diploid assay successfully distinguish the genotypes of affected individuals from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements to predict the biallelic function of ~1.8 million allele combinations corresponding to potential human genotypes. Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of ultrarare diseases.

Predicting the functional effect of compound heterozygous genotypes from large scale variant effect maps.

Background: Pathogenic variants in PHGDH, PSAT1 , and PSPH cause a set of rare, autosomal recessive diseases known as serine biosynthesis defects. Serine biosynthesis defects present in a broad phenotypic spectrum that includes, at the severe end, Neu-Laxova syndrome, a lethal multiple congenital anomaly disease, intermediately in the form of infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end, as childhood disease with intellectual disability. However, because L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is highly actionable. Methods: Recently, our laboratory established a yeast-based assay for human PSAT1 function. We have now applied it at scale to assay the functional impact of 1,914 SNV-accessible amino acid substitutions. In addition to assaying the functional impact of individual variants in yeast haploid cells, we can assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. Results: Results of our assays of individual variants (in haploid yeast cells) agree well with clinical interpretations and protein structure-function relationships, supporting the use of our data as functional evidence under the ACMG interpretation guidelines. Results from our diploid assay successfully distinguish patient genotypes from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements (in haploid yeast cells) to accurately predict the biallelic function (in diploid yeast cells) of ~ 1.8 million allele combinations corresponding to potential human genotypes. Conclusions: Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of a rare disease.

A method for quantifying sporulation efficiency and isolating meiotic progeny in non-GMO strains of Saccharomyces cerevisiae.

Meiotic mapping, a linkage-based method for analyzing the recombinant progeny of a cross, has long been a cornerstone of genetic research. The yeast Saccharomyces cerevisiae is a powerful system because it is possible to isolate and cultivate the four products (spores) of a single meiotic event. However, the throughput of this process has historically been limited by the process of identifying tetrads in a heterogeneous population of vegetative cells, tetrads, and dyads followed by manual separation (dissection) of the spores contained in a tetrad. To date, methods that facilitate high throughput characterization and isolation of meiotic progeny have relied on genetic engineering. Here, we characterize the ability of the fluorescent dye DiBAC4 (5) to stain yeast tetrads and dyads as well as to adhere to spores following bulk tetrad disruption. Applications include quantitative assays of sporulation rates and efficiency by flow cytometry as well as enrichment of intact tetrads, dyads, or disrupted spores by fluorescence-activated cell sorting  in strains that have not been genetically modified.

A Severe Asthma Disease Signature from Gene Expression Profiling of Peripheral Blood from U-BIOPRED Cohorts.

RATIONALE: Stratification of asthma at the molecular level, especially using accessible biospecimens, could greatly enable patient selection for targeted therapy. OBJECTIVES: To determine the value of blood analysis to identify transcriptional differences between clinically defined asthma and nonasthma groups, identify potential patient subgroups based on gene expression, and explore biological pathways associated with identified differences. METHODS: Transcriptomic profiles were generated by microarray analysis of blood from 610 patients with asthma and control participants in the U-BIOPRED (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes) study. Differentially expressed genes (DEGs) were identified by analysis of variance, including covariates for RNA quality, sex, and clinical site, and Ingenuity Pathway Analysis was applied. Patient subgroups based on DEGs were created by hierarchical clustering and topological data analysis. MEASUREMENTS AND MAIN RESULTS: A total of 1,693 genes were differentially expressed between patients with severe asthma and participants without asthma. The differences from participants without asthma in the nonsmoking severe asthma and mild/moderate asthma subgroups were significantly related (r = 0.76), with a larger effect size in the severe asthma group. The majority of, but not all, differences were explained by differences in circulating immune cell populations. Pathway analysis showed an increase in chemotaxis, migration, and myeloid cell trafficking in patients with severe asthma, decreased B-lymphocyte development and hematopoietic progenitor cells, and lymphoid organ hypoplasia. Cluster analysis of DEGs led to the creation of subgroups among the patients with severe asthma who differed in molecular responses to oral corticosteroids. CONCLUSIONS: Blood gene expression differences between clinically defined subgroups of patients with asthma and individuals without asthma, as well as subgroups of patients with severe asthma defined by transcript profiles, show the value of blood analysis in stratifying patients with asthma and identifying molecular pathways for further study. Clinical trial registered with www.clinicaltrials.gov (NCT01982162).

Gene expression profiles normalized in psoriatic skin by treatment with brodalumab, a human anti-IL-17 receptor monoclonal antibody.

The IL-17 pathway is an established driver of psoriasis pathogenesis. We examined the detailed molecular and cellular effects of blockade of IL-17 signaling in human psoriatic skin before and following treatment with brodalumab, a competitive inhibitor of the IL-17 Receptor A subunit. Thousands of aberrantly expressed genes in lesional skin normalized within 2 weeks following brodalumab treatment, with conversion of the lesional psoriasis transcriptome to resemble that seen in nonlesional skin. Keratinocyte-expressed genes appeared to normalize rapidly, whereas T cell-specific normalization occurred over six weeks. The three IL-17 ligand genes that are upregulated in lesional skin, IL17A, IL17C, and IL17F, were all downregulated in a dose-dependent manner following brodalumab treatment. Cellular measures also showed a similar pattern with dramatic decreases in keratinocyte hyperplasia within one week, and decreases in infiltrating leukocytes occurred over a longer timescale. Individuals with the highest brodalumab exposure showed normalization of both IL-17-responsive genes and the psoriasis transcriptome, whereas subjects with lower exposures showed transient or incomplete molecular responses. Clinical and molecular response appeared dependent on the extent of brodalumab exposure relative to the expression of IL-17 ligand genes, and reduction of IL-17 signaling into the nonlesional range was strongly correlated with normalization of the psoriasis transcriptome. These data indicate that blockade of IL-17 signaling in psoriatic skin leads to rapid transcriptomal changes initially in keratinocyte-expressed genes, followed by normalization in the leukocyte abnormalities, and demonstrates the essential role of the IL-17R on keratinocytes in driving disease pathogenesis.

Cross-study homogeneity of psoriasis gene expression in skin across a large expression range.

BACKGROUND: In psoriasis, only limited overlap between sets of genes identified as differentially expressed (psoriatic lesional vs. psoriatic non-lesional) was found using statistical and fold-change cut-offs. To provide a framework for utilizing prior psoriasis data sets we sought to understand the consistency of those sets. METHODOLOGY/PRINCIPAL FINDINGS: Microarray expression profiling and qRT-PCR were used to characterize gene expression in PP and PN skin from psoriasis patients. cDNA (three new data sets) and cRNA hybridization (four existing data sets) data were compared using a common analysis pipeline. Agreement between data sets was assessed using varying qualitative and quantitative cut-offs to generate a DEG list in a source data set and then using other data sets to validate the list. Concordance increased from 67% across all probe sets to over 99% across more than 10,000 probe sets when statistical filters were employed. The fold-change behavior of individual genes tended to be consistent across the multiple data sets. We found that genes with <2-fold change values were quantitatively reproducible between pairs of data-sets. In a subset of transcripts with a role in inflammation changes detected by microarray were confirmed by qRT-PCR with high concordance. For transcripts with both PN and PP levels within the microarray dynamic range, microarray and qRT-PCR were quantitatively reproducible, including minimal fold-changes in IL13, TNFSF11, and TNFRSF11B and genes with >10-fold changes in either direction such as CHRM3, IL12B and IFNG. CONCLUSIONS/SIGNIFICANCE: Gene expression changes in psoriatic lesions were consistent across different studies, despite differences in patient selection, sample handling, and microarray platforms but between-study comparisons showed stronger agreement within than between platforms. We could use cut-offs as low as log10(ratio) = 0.1 (fold-change = 1.26), generating larger gene lists that validate on independent data sets. The reproducibility of PP signatures across data sets suggests that different sample sets can be productively compared.