Integration of proteomics with genomics and transcriptomics increases the diagnostic rate of Mendelian disorders

Abstract

By lack of functional evidence, genome-based diagnostic rates cap at approximately 50% across diverse Mendelian diseases. Here we demonstrate the effectiveness of combining genomics, transcriptomics, and, for the first time, proteomics and phenotypic descriptors, in a systematic diagnostic approach to discover the genetic cause of mitochondrial diseases. On fibroblast cell lines from 145 individuals, tandem mass tag labelled proteomics detected approximately 8,000 proteins per sample and covered over 50% of all Mendelian disease-associated genes. By providing independent functional evidence, aberrant protein expression analysis allowed validation of candidate protein-destabilising variants and of variants leading to aberrant RNA expression. Overall, our integrative computational workflow led to genetic resolution for 21% of 121 genetically unsolved cases and to the discovery of two novel disease genes. With increasing democratization of high-throughput omics assays, our approach and code provide a blueprint for implementing multi-omics based Mendelian disease diagnostics in routine clinical practice.

Clinical implementation of RNA sequencing for Mendelian disease diagnostics

“…This procedure led to a genetic diagnosis of 33 unsolved cases, representing 16% (95%CI: 11%-22%) of the WES-inconclusive cohort (Table 1, fig. S1), seven of which were previously published (22,57), and ten described in a companion manuscript (58). Among the 47 causative variants in these 33 cases, 13 (28%) were already classified as pathogenic or likely pathogenic, 10 (21%) required functional validation, 10 (21%) were not prioritized during WES-analysis, and 14 (30%) were not captured by WES.…”

Diagnosing pediatric mitochondrial disease: lessons from 2,000 exomes

“…The copyright holder for this preprint this version posted June 25, 2021. ; https://doi.org/10.1101/2021.06.21.21259171 doi: medRxiv preprint an excellent resource for the study of many MDs, as they may be used for mitochondrial RCC measurement with results comparable to invasive muscle biopsy (Ogawa et al, 2017, Wortmann et al, 2017 and detection of impaired mitochondrial respiration by measurement of oxygen consumption rate or other biomarkers. Moreover, fibroblasts open the option for multi-omic studies, pioneered in MD, such as RNA sequencing and proteomics (Kremer et al, 2017, Kopajtich et al, 2021, Yepez et al, 2021. These A prime example was the diagnosis of 11 patients with pathogenic de novo dominant variants in MORC2 by reanalysis following a report expanding the phenotypic spectrum to include neurodevelopmental and metabolic abnormalities (Guillen Sacoto et al, 2020).…”

“…We were unable to provide a molecular diagnosis for just under half of the investigated patients. The possible reasons for this are manifold: (i) due to pitfalls in variant prioritization, such as single heterozygous de novo variants, given the majority of patients underwent singleton WES analysis, and pitfalls in variant interpretation (e.g., hypomorphic and synonymous variants causing abnormal splicing, and deep intronic and regulatory variants), a limitation beginning to be addressed by multi-omic integration (Kopajtich et al, 2021); (ii) due to certain genetic variants being undetectable by WES given insufficient depth of coverage, locus-specific features (e.g., GC-rich regions and homopolymeric repeats), sequencing biases, and genomic alterations (e.g., large deletions, insertions, chromosomal rearrangements, short tandem repeats), the inability to delineate some of these alterations may be overcome by WGS, specifically long-read sequencing; (iii) due to the high number of VUS found in genes clinically compatible with the patient's phenotype, the possibility to perform functional studies is fundamental to achieve a definitive diagnosis; and (iv) due to individuals with suspected MD carrying pathogenic variants in as yet unidentified disease-relevant genes, the discovery of which continues year-on-year (Supplemental Fig. 4b).…”

“…The copyright holder for this preprint this version posted June 25, 2021. ; https://doi.org/10.1101/2021.06.21.21259171 doi: medRxiv preprint 2021, Kopajtich et al, 2021, Bonora et al, 2021. This definition included 49 of 62 novel gene discoveries made in this cohort (Table S2).…”

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

“…For example, integrated genome, transcriptome, and proteome analyses allowed the validation of a rare variant causing aberrant gene expression of genes such as TIMMDC1 (Kremer et al, 2017), PTCD3 (Borna et al, 2019), or MRPS34 (Lake et al, 2018). More recently, Kopajtich et al (2021) demonstrated the effectiveness of integrating genomics, transcriptomics, and proteomics in a systematic diagnostic application to discover the genetic cause of 20% of unsolved patients with suspected mitochondrial disorders. These recent advancements in bioinformatic approaches help to combine these multi-omics data and enable their holistic analysis.…”