Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DR hi CD11c hi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DR lo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.

Across a variety of Mendelian disorders, ∼50–75% of patients do not receive a genetic diagnosis by exome sequencing indicating disease-causing variants in non-coding regions. Although genome sequencing in principle reveals all genetic variants, their sizeable number and poorer annotation make prioritization challenging. Here, we demonstrate the power of transcriptome sequencing to molecularly diagnose 10% (5 of 48) of mitochondriopathy patients and identify candidate genes for the remainder. We find a median of one aberrantly expressed gene, five aberrant splicing events and six mono-allelically expressed rare variants in patient-derived fibroblasts and establish disease-causing roles for each kind. Private exons often arise from cryptic splice sites providing an important clue for variant prioritization. One such event is found in the complex I assembly factor TIMMDC1 establishing a novel disease-associated gene. In conclusion, our study expands the diagnostic tools for detecting non-exonic variants and provides examples of intronic loss-of-function variants with pathological relevance.

Highlights d SARS-CoV2 infection elicits dynamic changes of circulating cells in the blood d Severe COVID-19 is characterized by increased metabolically active plasmablasts d Elevation of IFN-activated megakaryocytes and erythroid cells in severe COVID-19 d Cell-type-specific expression signatures are associated with a fatal COVID-19 outcome

44Across a large variety of Mendelian disorders, ~50-75% of patients do not receive a 45 genetic diagnosis by whole exome sequencing indicative of underlying disease-causing 46 variants in non-coding regions. In contrast, whole genome sequencing facilitates the 47 discovery of all genetic variants, but their sizeable number, coupled with a poor 48 understanding of the non-coding genome, makes their prioritization challenging. Here, we 49 demonstrate the power of transcriptome sequencing to provide a confirmed genetic 50 diagnosis for 10% (5 of 48) of undiagnosed mitochondrial disease patients and identify 51 strong candidate genes for patients remaining without diagnosis. We found a median of 1 52 aberrantly expressed gene, 5 aberrant splicing events, and 6 mono-allelically expressed 53 rare variants in patient-derived fibroblasts and established disease-causing roles for each 54 kind. Private exons often arose from sites that are weakly spliced in other individuals, 55providing an important clue for future variant prioritization. One such intronic exon-56 creating variant was found in three unrelated families in the complex I assembly factor 57 TIMMDC1, which we consequently established as a novel disease-associated gene. In 58 conclusion, our study expands the diagnostic tools for detecting non-exonic variants of 59Mendelian disorders and provides examples of intronic loss-of-function variants with 60 pathological relevance. 61Despite the revolutionizing impact of whole exome sequencing (WES) on the molecular 62 genetics of Mendelian disorders, ~50-75% of the patients do not receive a genetic diagnosis after 63 WES [1][2][3][4][5][6] . The disease-causing variants might be detected by WES but remain as variants of 64 unknown significance (VUS, Methods) or they are missed due to the inability to prioritize them. 65Many of these VUS are synonymous or non-coding variants that may affect RNA abundance or 66 isoform but cannot be prioritized due to the poor understanding of regulatory sequence to date 67 compared to coding sequence. Furthermore, WES covers only the 2% exonic regions of the 68 genome. Accordingly, it is mostly blind to regulatory variants in non-coding regions that could 69 affect RNA sequence and abundance. While the limitation of genome coverage is overcome by 70 whole genome sequencing (WGS), prioritization and interpretation of variants identified by 71 WGS is in turn limited by their amount [7][8][9] . 72With RNA sequencing (RNA-seq), limitations of the sole genetic information can be 73 complemented by directly probing variations in RNA abundance and in RNA sequence, 74 including allele-specific expression and splice isoforms. At least three extreme situations can be 75 directly interpreted to prioritize candidate disease-causing genes for a rare disorder. First, the 76 expression level of a gene can lie outside its physiological range. Genes with expression outside 77 their physical range can be identified as expression outliers, often using a stringent cutoff on 78 expression variat...

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (Outlier in RNA-Seq Finder), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read-count expectations according to the gene covariation resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best recall of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of controlling for covariation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a dataset, for identifying outlier samples with too many aberrantly expressed genes, and for detecting aberrant gene expression on the basis of false-discovery-rate-adjusted p values. Overall, OUTRIDER provides an end-to-end solution for identifying aberrantly expressed genes and is suitable for use by rare-disease diagnostic platforms.

Background Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. Methods We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. Results We detected on average 12,500 genes per sample including around 60% of all disease genes—a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. Conclusion Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

Aberrant splicing is a major cause of rare diseases.  However, its prediction from genome sequence alone remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we develop FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1 causing mucolipidosis. FRASER automatically controls for latent confounders, which are widespread and affect sensitivity substantially. Moreover, FRASER is based on a count distribution and multiple testing correction, thus reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor loss of sensitivity. Applying FRASER to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.

MDH2 encodes mitochondrial malate dehydrogenase (MDH), which is essential for the conversion of malate to oxaloacetate as part of the proper functioning of the Krebs cycle. We report bi-allelic pathogenic mutations in MDH2 in three unrelated subjects presenting with early-onset generalized hypotonia, psychomotor delay, refractory epilepsy, and elevated lactate in the blood and cerebrospinal fluid. Functional studies in fibroblasts from affected subjects showed both an apparently complete loss of MDH2 levels and MDH2 enzymatic activity close to null. Metabolomics analyses demonstrated a significant concomitant accumulation of the MDH substrate, malate, and fumarate, its immediate precursor in the Krebs cycle, in affected subjects' fibroblasts. Lentiviral complementation with wild-type MDH2 cDNA restored MDH2 levels and mitochondrial MDH activity. Additionally, introduction of the three missense mutations from the affected subjects into Saccharomyces cerevisiae provided functional evidence to support their pathogenicity. Disruption of the Krebs cycle is a hallmark of cancer, and MDH2 has been recently identified as a novel pheochromocytoma and paraganglioma susceptibility gene. We show that loss-of-function mutations in MDH2 are also associated with severe neurological clinical presentations in children.