RESUMEN
We performed long-read transcriptome and proteome profiling of pathogen-stimulated peripheral blood mononuclear cells (PBMCs) from healthy donors to discover new transcript and protein isoforms expressed during immune responses to diverse pathogens. Long-read transcriptome profiling reveals novel sequences and isoform switching induced upon pathogen stimulation, including transcripts that are difficult to detect using traditional short-read sequencing. Widespread loss of intron retention occurs as a common result of all pathogen stimulations. We highlight novel transcripts of NFKB1 and CASP1 that may indicate novel immunological mechanisms. RNA expression differences did not result in differences in the amounts of secreted proteins. Clustering analysis of secreted proteins revealed a correlation between chemokine (receptor) expression on the RNA and protein levels in C. albicans- and poly(I:C)-stimulated PBMCs. Isoform aware long-read sequencing of pathogen-stimulated immune cells highlights the potential of these methods to identify novel transcripts, revealing a more complex transcriptome landscape than previously appreciated.
RESUMEN
Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT's utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.
Asunto(s)
Programas Informáticos , Transcriptoma , Humanos , Anotación de Secuencia Molecular , Análisis de Secuencia de ARN/métodos , Exoma , Secuenciación de Nucleótidos de Alto RendimientoRESUMEN
Primary immunodeficiencies comprise a group of inborn errors of immunity that display significant clinical and genetic heterogeneity. Next-generation sequencing techniques and predominantly whole exome sequencing have revolutionized the understanding of the genetic and molecular basis of genetic diseases, thereby also leading to a sharp increase in the discovery of new genes associated with primary immunodeficiencies. In this review, we discuss the current diagnostic yield of this generic diagnostic approach by evaluating the studies that have employed next-generation sequencing techniques in cohorts of patients with primary immunodeficiencies. The average diagnostic yield for primary immunodeficiencies is determined to be 29% (range 10-79%) and 38% specifically for whole-exome sequencing (range 15-70%). The significant variation between studies is mainly the result of differences in clinical characteristics of the studied cohorts but is also influenced by varying sequencing approaches and (in silico) gene panel selection. We further discuss other factors contributing to the relatively low yield, including the inherent limitations of whole-exome sequencing, challenges in the interpretation of novel candidate genetic variants, and promises of exploring the non-coding part of the genome. We propose strategies to improve the diagnostic yield leading the way towards expanded personalized treatment in PIDs.