Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level.

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Updated MS²PIP web server supports cutting-edge proteomics applications.

Interest in the use of machine learning for peptide fragmentation spectrum prediction has been strongly on the rise over the past years, especially for applications in challenging proteomics identification workflows such as immunopeptidomics and the full-proteome identification of data independent acquisition spectra. Since its inception, the MS²PIP peptide spectrum predictor has been widely used for various downstream applications, mostly thanks to its accuracy, ease-of-use, and broad applicability. We here present a thoroughly updated version of the MS²PIP web server, which includes new and more performant prediction models for both tryptic- and non-tryptic peptides, for immunopeptides, and for CID-fragmented TMT-labeled peptides. Additionally, we have also added new functionality to greatly facilitate the generation of proteome-wide predicted spectral libraries, requiring only a FASTA protein file as input. These libraries also include retention time predictions from DeepLC. Moreover, we now provide pre-built and ready-to-download spectral libraries for various model organisms in multiple DIA-compatible spectral library formats. Besides upgrading the back-end models, the user experience on the MS²PIP web server is thus also greatly enhanced, extending its applicability to new domains, including immunopeptidomics and MS3-based TMT quantification experiments. MS²PIP is freely available at https://iomics.ugent.be/ms2pip/.

N 2-methylguanosine modifications on human tRNAs and snRNA U6 are important for cell proliferation, protein translation and pre-mRNA splicing.

Modified nucleotides in non-coding RNAs, such as tRNAs and snRNAs, represent an important layer of gene expression regulation through their ability to fine-tune mRNA maturation and translation. Dysregulation of such modifications and the enzymes installing them have been linked to various human pathologies including neurodevelopmental disorders and cancers. Several methyltransferases (MTases) are regulated allosterically by human TRMT112 (Trm112 in Saccharomyces cerevisiae), but the interactome of this regulator and targets of its interacting MTases remain incompletely characterized. Here, we have investigated the interaction network of human TRMT112 in intact cells and identify three poorly characterized putative MTases (TRMT11, THUMPD3 and THUMPD2) as direct partners. We demonstrate that these three proteins are active N2-methylguanosine (m2G) MTases and that TRMT11 and THUMPD3 methylate positions 10 and 6 of tRNAs, respectively. For THUMPD2, we discovered that it directly associates with the U6 snRNA, a core component of the catalytic spliceosome, and is required for the formation of m2G, the last 'orphan' modification in U6 snRNA. Furthermore, our data reveal the combined importance of TRMT11 and THUMPD3 for optimal protein synthesis and cell proliferation as well as a role for THUMPD2 in fine-tuning pre-mRNA splicing.

Improved characterization of trastuzumab deruxtecan with PTCR and internal fragments implemented in middle-down MS workflows.

Antibody-drug conjugates (ADCs) are highly complex proteins mainly due to the structural microvariability of the mAb, along with the additional heterogeneity afforded by the bioconjugation process. Top-down (TD) and middle-down (MD) strategies allow the straightforward fragmentation of proteins to elucidate the conjugated amino acid residues. Nevertheless, these spectra are very crowded with multiple overlapping and unassigned ion fragments. Here we report on the use of dedicated software (ClipsMS) and application of proton transfer charge reduction (PTCR), to respectively expand the fragment ion search space to internal fragments and improve the separation of overlapping fragment ions for a more comprehensive characterization of a recently approved ADC, trastuzumab deruxtecan (T-DXd). Subunit fragmentation allowed between 70 and 90% of sequence coverage to be obtained. Upon addition of internal fragment assignment, the three subunits were fully sequenced, although internal fragments did not contribute significantly to the localization of the payloads. Finally, the use of PTCR after subunit fragmentation provided a moderate sequence coverage increase between 2 and 13%. The reaction efficiently decluttered the fragmentation spectra allowing increasing the number of fragment ions characteristic of the conjugation site by 1.5- to 2.5-fold. Altogether, these results show the interest in the implementation of internal fragment ion searches and more particularly the use of PTCR reactions to increase the number of signature ions to elucidate the conjugation sites and enhance the overall sequence coverage of ADCs, making this approach particularly appealing for its implementation in R&D laboratories.

MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates.

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS2PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS2PIP was tailored toward tryptic peptides, we have here retrained MS2PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS2PIP models, DeepLC, and Percolator in one software package, MS2Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS2Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS2Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

Advanced mass spectrometry workflows for accurate quantification of trace-level host cell proteins in drug products: Benefits of FAIMS separation and gas-phase fractionation DIA.

Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.

Mechanistic insights into sulfur source-driven physiological responses and metabolic reorganization in the fuel-biodesulfurizing Rhodococcus qingshengii IGTS8.

Comparative proteomics and untargeted metabolomics were combined to study the physiological and metabolic adaptations of Rhodococcus qingshengii IGTS8 under biodesulfurization conditions. After growth in a chemically defined medium with either dibenzothiophene (DBT) or MgSO4 as the sulfur source, many differentially produced proteins and metabolites associated with several metabolic and physiological processes were detected including the metabolism of carbohydrates, amino acids, lipids, nucleotides, vitamins, protein synthesis, transcriptional regulation, cell envelope biogenesis, and cell division. Increased production of the redox cofactor mycofactocin and associated proteins was one of the most striking adaptations under biodesulfurization conditions. While most central metabolic enzymes were less abundant in the presence of DBT, a key enzyme of the glyoxylate shunt, isocitrate lyase, was up to 26-fold more abundant. Several C1 metabolism and oligotrophy-related enzymes were significantly more abundant in the biodesulfurizing culture. R. qingshengii IGTS8 exhibited oligotrophic growth in liquid and solid media under carbon starvation. Moreover, the oligotrophic growth was faster on the solid medium in the presence of DBT compared to MgSO4 cultures. In the DBT culture, the cell envelope and phospholipids were remodeled, with lower levels of phosphatidylethanolamine and unsaturated and short-chain fatty acids being the most prominent changes. Biodesulfurization increased the biosynthesis of osmoprotectants (ectoine and mannosylglycerate) as well as glutamate and induced the stringent response. Our findings reveal highly diverse and overlapping stress responses that could protect the biodesulfurizing culture not only from the associated sulfate limitation but also from chemical, oxidative, and osmotic stress, allowing efficient resource management. IMPORTANCE Despite decades of research, a commercially viable bioprocess for fuel desulfurization has not been developed yet. This is mainly due to lack of knowledge of the physiology and metabolism of fuel-biodesulfurizing bacteria. Being a stressful condition, biodesulfurization could provoke several stress responses that are not understood. This is particularly important because a thorough understanding of the microbial stress response is essential for the development of environmentally friendly and industrially efficient microbial biocatalysts. Our comparative systems biology studies provide a mechanistic understanding of the biology of biodesulfurization, which is crucial for informed developments through the rational design of recombinant biodesulfurizers and optimization of the bioprocess conditions. Our findings enhance the understanding of the physiology, metabolism, and stress response not only in biodesulfurizing bacteria but also in rhodococci, a precious group of biotechnologically important bacteria.

Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.

Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPAR R package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.

The catalytic activity of the translation termination factor methyltransferase Mtq2-Trm112 complex is required for large ribosomal subunit biogenesis.

The Mtq2-Trm112 methyltransferase modifies the eukaryotic translation termination factor eRF1 on the glutamine side chain of a universally conserved GGQ motif that is essential for release of newly synthesized peptides. Although this modification is found in the three domains of life, its exact role in eukaryotes remains unknown. As the deletion of MTQ2 leads to severe growth impairment in yeast, we have investigated its role further and tested its putative involvement in ribosome biogenesis. We found that Mtq2 is associated with nuclear 60S subunit precursors, and we demonstrate that its catalytic activity is required for nucleolar release of pre-60S and for efficient production of mature 5.8S and 25S rRNAs. Thus, we identify Mtq2 as a novel ribosome assembly factor important for large ribosomal subunit formation. We propose that Mtq2-Trm112 might modify eRF1 in the nucleus as part of a quality control mechanism aimed at proof-reading the peptidyl transferase center, where it will subsequently bind during translation termination.

Combining label-free and label-based accurate quantifications with SWATH-MS: Comparison with SRM and PRM for the evaluation of bovine muscle type effects.

Mass spectrometry has proven to be a valuable tool for the accurate quantification of proteins. In this study, the performances of three targeted approaches, namely selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS), to accurately quantify ten potential biomarkers of beef meat tenderness or marbling in a cohort of 64 muscle samples were evaluated. So as to get the most benefit out of the complete MS2 maps that are acquired in SWATH-MS, an original label-free quantification method to estimate protein amounts using an I-spline regression model was developed. Overall, SWATH-MS outperformed SRM in terms of sensitivity and dynamic range, while PRM still performed the best, and all three strategies showed similar quantification accuracies and precisions for the absolute quantification of targets of interest. This targeted picture was extended by 585 additional proteins for which amounts were estimated using the label-free approach on SWATH-MS; thus, offering a more global profiling of muscle proteomes and further insights into muscle type effect on candidate biomarkers of beef meat qualities as well as muscle metabolism.

Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products.

Host cell proteins (HCPs) are a major class of bioprocess-related impurities generated by the host organism and are generally present at low levels in purified biopharmaceutical products. The monitoring of these impurities is identified as an important critical quality attribute of monoclonal antibody (mAb) formulations not only due to the potential risk for the product stability and efficacy but also concerns linked to the immunogenicity of some of them. While overall HCP levels are usually monitored by enzyme-linked immunosorbent assay (ELISA), mass spectrometry (MS)-based approaches have been emerging as powerful and promising alternatives providing qualitative and quantitative information. However, a major challenge for liquid chromatography (LC)-MS-based methods is to deal with the wide dynamic range of drug products and the extreme sensitivity required to detect trace-level HCPs. In this study, we developed powerful and reproducible MS-based analytical workflows coupling optimized and efficient sample preparations, the library-free data-independent acquisition (DIA) method, and stringent validation criteria. The performances of several preparation protocols and DIA versus classical data-dependent acquisition (DDA) were evaluated using a series of four commercially available drug products. Depending on the selected protocols, the user has access to different information: on the one hand, a deep profiling of tens of identified HCPs and on the other hand, accurate and reproducible (coefficients of variation (CVs) < 12%) quantification of major HCPs. Overall, a final global HCP amount of a few tens of ng/mg mAb in these mAb samples was measured, while reaching a sensitivity down to the sub-ng/mg mAb level. Thus, this straightforward and robust approach can be intended as a routine quality control for any drug product analysis.

Proline: an efficient and user-friendly software suite for large-scale proteomics.

MOTIVATION: The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study. RESULTS: Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample. This assessment demonstrated Proline's ability to provide high quantification accuracy in a user-friendly interface for datasets of any size. AVAILABILITY AND IMPLEMENTATION: Proline is available for Windows and Linux under CECILL open-source license. It can be deployed in client-server mode or in standalone mode at http://proline.profiproteomics.fr/#downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Combined N-terminomics and Shotgun Proteomics Approach to Investigate the Responses of Human Cells to Rapamycin and Zinc at the Mitochondrial Level.

All but thirteen mammalian mitochondrial proteins are encoded by the nuclear genome, translated in the cytosol and then imported into the mitochondria. For a significant proportion of the mitochondrial proteins, import is coupled with the cleavage of a presequence called the transit peptide, and the formation of a new N-terminus. Determination of the neo N-termini has been investigated by proteomic approaches in several systems, but generally in a static way to compile as many N-termini as possible. In the present study, we have investigated how the mitochondrial proteome and N-terminome react to chemical stimuli that alter mitochondrial metabolism, namely zinc ions and rapamycin. To this end, we have used a strategy that analyzes both internal and N-terminal peptides in a single run, the dN-TOP approach. We used these two very different stressors to sort out what could be a generic response to stress and what is specific to each of these stressors. Rapamycin and zinc induced different changes in the mitochondrial proteome. However, convergent changes to key mitochondrial enzymatic activities such as pyruvate dehydrogenase, succinate dehydrogenase and citrate synthase were observed for both treatments. Other convergent changes were seen in components of the N-terminal processing system and mitochondrial proteases. Investigations into the generation of neo-N-termini in mitochondria showed that the processing system is robust, as indicated by the lack of change in neo N-termini under the conditions tested. Detailed analysis of the data revealed that zinc caused a slight reduction in the efficiency of the N-terminal trimming system and that both treatments increased the degradation of mitochondrial proteins. In conclusion, the use of this combined strategy allowed a detailed analysis of the dynamics of the mitochondrial N-terminome in response to treatments which impact the mitochondria.

Precise Alkoxyamine Design to Enable Automated Tandem Mass Spectrometry Sequencing of Digital Poly(phosphodiester)s.

A major step towards reliable reading of information coded in the sequence of long poly(phosphodiester)s was previously achieved by introducing an alkoxyamine spacer between information sub-segments. However, MS/MS decoding had to be performed manually to safely identify useful fragments of low abundance compared to side-products from the amide-based alkoxyamine used. Here, alternative alkoxyamines were designed to prevent side-reactions and enable automated MS/MS sequencing. Different styryl-TEMPO spacers were prepared to increase radical delocalization and stiffness of the structure. Their dissociation behavior was investigated by EPR and best results were obtained with spacers containing in-chain benzyl ring, with no side-reaction during synthesis or sequencing. Automated decoding of these polymers was performed using the MS-DECODER software, which interprets fragmentation data recorded for each sub-segment and re-align them in their original order based on location tags.

Optimal conditions for tandem mass spectrometric sequencing of information-containing nitrogen-substituted polyurethanes.

RATIONALE: To prevent solubility issues faced with sequence-defined polyurethanes, a new family of digital polyurethanes was conceived with the alkyl coding chain held by the carbamate nitrogen (N) atom and CH3 instead of OH as the ϖ termination. This led to different dissociation mechanisms that were explored prior to optimizing tandem mass spectrometric (MS/MS) sequencing. METHODS: N-Substituted polyurethanes (N-R PUs) were dissolved in methanol and subjected to collision-induced dissociation (CID) as deprotonated chains in the negative ion mode, and as ammonium and sodium adducts in the positive ion mode, using electrospray ionization (ESI) as the ionization technique. Their dissociation behavior was thoroughly investigated using a quadrupole time-of-flight (QTOF) instrument in order to provide accurate mass measurements to support proposed fragmentation mechanisms. RESULTS: While O-(CO) bonds were broken in N-H PUs, the CH2 -O linkage between repeating units was cleaved upon CID of N-R PUs. This main process occurred either from deprotonated molecules or from cationized chains but was followed by different rearrangements, producing up to four product ion series. Yet, MS/MS spectra could be drastically simplified for precursor ions having their acidic α group methylated, as was found to spontaneously occur upon storage in methanol. CONCLUSIONS: Using experimental conditions aimed at avoiding any reactive proton in precursor ions (no acidic end-groups and alkali adduction), full coverage sequence of N-R PUs was successfully achieved with the single ion series observed in MS/MS, opening a promising perspective for reading large amounts of information stored in these dyad-encoded polymers.

The siRNA suppressor RTL1 is redox-regulated through glutathionylation of a conserved cysteine in the double-stranded-RNA-binding domain.

RNase III enzymes cleave double stranded (ds)RNA. This is an essential step for regulating the processing of mRNA, rRNA, snoRNA and other small RNAs, including siRNA and miRNA. Arabidopsis thaliana encodes nine RNase III: four DICER-LIKE (DCL) and five RNASE THREE LIKE (RTL). To better understand the molecular functions of RNase III in plants we developed a biochemical assay using RTL1 as a model. We show that RTL1 does not degrade dsRNA randomly, but recognizes specific duplex sequences to direct accurate cleavage. Furthermore, we demonstrate that RNase III and dsRNA binding domains (dsRBD) are both required for dsRNA cleavage. Interestingly, the four DCL and the three RTL that carry dsRBD share a conserved cysteine (C230 in Arabidopsis RTL1) in their dsRBD. C230 is essential for RTL1 and DCL1 activities and is subjected to post-transcriptional modification. Indeed, under oxidizing conditions, glutathionylation of C230 inhibits RTL1 cleavage activity in a reversible manner involving glutaredoxins. We conclude that the redox state of the dsRBD ensures a fine-tune regulation of dsRNA processing by plant RNase III.

Data-Independent Acquisition Coupled to Visible Laser-Induced Dissociation at 473 nm (DIA-LID) for Peptide-Centric Specific Analysis of Cysteine-Containing Peptide Subset.

Thanks to comprehensive and unbiased sampling of all precursor ions, the interest to move toward bottom-up proteomic with data-independent acquisition (DIA) is continuously growing. DIA offers precision and reproducibility performances comparable to true targeted methods but has the advantage of enabling retrospective data testing with the hypothetical presence of new proteins of interest. Nonetheless, the chimeric nature of DIA MS/MS spectra inherent to concomitant transmission of a multiplicity of precursor ions makes the confident identification of peptides often challenging, even with spectral library-based extraction strategy. The introduction of specificity at the fragmentation step upon ultraviolet or visible laser-induced dissociation (LID) range targeting only the subset of cysteine-containing peptides (Cys-peptide) has been proposed as an option to streamline and reduce the search space. Here, we describe the first coupling between DIA and visible LID at 473 nm to test for the presence of Cys-peptides with a peptide-centric approach. As a test run, a spectral library was built for a pool of Cys-synthetic peptides used as surrogates of human kinases (1 peptide per protein). By extracting ion chromatograms of query standard and kinase peptides spiked at different concentration levels in an Escherichia coli proteome lysate, DIA-LID demonstrates a dynamic range of detection of at least 3 decades and coefficients of precision better than 20%. Finally, the spectral library was used to search for endogenous kinases in human cellular extract.

Dual Data-Independent Acquisition Approach Combining Global HCP Profiling and Absolute Quantification of Key Impurities during Bioprocess Development.

Host cell proteins (HCP) are a major class of impurities derived from recombinant protein production processes. While HCP are usually monitored by ELISA, mass spectrometry (MS)-based approaches are emerging as promising orthogonal methods. Here, we developed an original method relying on data-independent acquisition (DIA) coupling global HCP amount estimation (Top 3) and absolute quantification with isotope dilution (ID). The method named Top 3-ID-DIA was benchmarked against ELISA and a gold-standard selected reaction monitoring assay (ID-SRM). Various samples generated at different steps and conditions of the purification process, including different culture durations, harvest procedures, and purification protocols were used to compare the methods. Overall, HCP were quantified over 5 orders of magnitude and down to the sub-ppm level. The Top 3-ID-DIA strategy proved to be equivalent to the gold-standard ID-SRM in terms of sensitivity (1-10 ppm), accuracy, and precision. Moreover, 81% of the Top 3 estimations were accurate within a factor of 2 when compared to ID-SRM. Thus, our approach aggregates global HCP profiling for comprehensive process understanding with absolute quantification of key HCP within a single analysis and provides an improved support for bioprocess development and product purity assessment.

Multi-OMICS analyses unveil STAT1 as a potential modifier gene in mevalonate kinase deficiency.

OBJECTIVES: The objective of the present study was to explain why two siblings carrying both the same homozygous pathogenic mutation for the autoinflammatory disease hyper IgD syndrome, show opposite phenotypes, that is, the first being asymptomatic, the second presenting all classical characteristics of the disease. METHODS: Where single omics (mainly exome) analysis fails to identify culprit genes/mutations in human complex diseases, multiomics analyses may provide solutions, although this has been seldom used in a clinical setting. Here we combine exome, transcriptome and proteome analyses to decipher at a molecular level, the phenotypic differences between the two siblings. RESULTS: This multiomics approach led to the identification of a single gene-STAT1-which harboured a rare missense variant and showed a significant overexpression of both mRNA and protein in the symptomatic versus the asymptomatic sister. This variant was shown to be of gain of function nature, involved in an increased activation of the Janus kinase/signal transducer and activator of transcription signalling (JAK/STAT) pathway, known to play a critical role in inflammatory diseases and for which specific biotherapies presently exist. Pathway analyses based on information from differentially expressed transcripts and proteins confirmed the central role of STAT1 in the proposed regulatory network leading to an increased inflammatory phenotype in the symptomatic sibling. CONCLUSIONS: This study demonstrates the power of a multiomics approach to uncover potential clinically actionable targets for a personalised therapy. In more general terms, we provide a proteogenomics analysis pipeline that takes advantage of subject-specific genomic and transcriptomic information to improve protein identification and hence advance individualised medicine.