Brain exposure to SARS-CoV-2 virions perturbs synaptic homeostasis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with short- and long-term neurological complications. The variety of symptoms makes it difficult to unravel molecular mechanisms underlying neurological sequalae after coronavirus disease 2019 (COVID-19). Here we show that SARS-CoV-2 triggers the up-regulation of synaptic components and perturbs local electrical field potential. Using cerebral organoids, organotypic culture of human brain explants from individuals without COVID-19 and post-mortem brain samples from individuals with COVID-19, we find that neural cells are permissive to SARS-CoV-2 to a low extent. SARS-CoV-2 induces aberrant presynaptic morphology and increases expression of the synaptic components Bassoon, latrophilin-3 (LPHN3) and fibronectin leucine-rich transmembrane protein-3 (FLRT3). Furthermore, we find that LPHN3-agonist treatment with Stachel partially restored organoid electrical activity and reverted SARS-CoV-2-induced aberrant presynaptic morphology. Finally, we observe accumulation of relatively static virions at LPHN3-FLRT3 synapses, suggesting that local hindrance can contribute to synaptic perturbations. Together, our study provides molecular insights into SARS-CoV-2-brain interactions, which may contribute to COVID-19-related neurological disorders.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model.

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

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.

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.

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.

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.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry.

Monitoring of host cell proteins (HCPs) during the manufacturing of monoclonal antibodies (mAb) has become a critical requirement to provide effective and safe drug products. Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities. However, this technique has several limitations and does, among others, not enable the precise identification of proteins. In this context, mass spectrometry (MS) became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs. However, in order to be routinely implemented in biopharmaceutical companies, liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification. Here, we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard, the HCP Profiler solution, with a spectral library-based data-independent acquisition (DIA) method and strict data validation criteria. The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process. While we also explored spectral library-free DIA interpretation, the spectral library-based approach still showed highest accuracy and reproducibility (coefficients of variation < 10%) with a sensitivity down to the sub-ng/mg mAb level. Thus, this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

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.

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/.

Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma.

Imputing missing values is a common practice in label-free quantitative proteomics. Imputation replaces a missing value by a user-defined one. However, the imputation itself is not optimally considered downstream of the imputation process. In particular, imputed datasets are considered as if they had always been complete. The uncertainty due to the imputation is not properly taken into account. Hence, the mi4p package provides a more accurate statistical analysis of multiple-imputed datasets. A rigorous multiple imputation methodology is implemented, leading to a less biased estimation of parameters and their 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.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry / 药物分析学报

Monitoring of host cell proteins(HCPs)during the manufacturing of monoclonal antibodies(mAb)has become a critical requirement to provide effective and safe drug products.Enzyme-linked immunosor-bent assays are still the gold standard methods for the quantification of protein impurities.However,this technique has several limitations and does,among others,not enable the precise identification of pro-teins.In this context,mass spectrometry(MS)became an alternative and orthogonal method that de-livers qualitative and quantitative information on all identified HCPs.However,in order to be routinely implemented in biopharmaceutical companies,liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification.Here,we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard,the HCP Profiler solution,with a spectral library-based data-independent acquisition(DIA)method and strict data validation criteria.The performances of the HCP Profiler solution were compared to more con-ventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process.While we also explored spectral library-free DIA interpretation,the spectral library-based approach still showed highest accuracy and reproducibility(coefficients of variation＜10％)with a sensitivity down to the sub-ng/mg mAb level.Thus,this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

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.

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.

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort.

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 "critical" (in the intensive care unit under mechanical ventilation) and 25 "non-critical" (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Biodesulfurization Induces Reprogramming of Sulfur Metabolism in Rhodococcus qingshengii IGTS8: Proteomics and Untargeted Metabolomics.

Sulfur metabolism in fuel-biodesulfurizing bacteria and the underlying physiological adaptations are not understood, which has impeded the development of a commercially viable bioprocess for fuel desulfurization. To fill these knowledge gaps, we performed comparative proteomics and untargeted metabolomics in cultures of the biodesulfurization reference strain Rhodococcus qingshengii IGTS8 grown on either inorganic sulfate or the diesel-borne organosulfur compound dibenzothiophene as a sole sulfur source. Dibenzothiophene significantly altered the biosynthesis of many sulfur metabolism proteins and metabolites in a growth phase-dependent manner, which enabled us to reconstruct the first experimental model for sulfur metabolism in a fuel-biodesulfurizing bacterium. All key pathways related to assimilatory sulfur metabolism were represented in the sulfur proteome, including uptake of the sulfur sources, sulfur acquisition, and assimilatory sulfate reduction, in addition to biosynthesis of key sulfur-containing metabolites such as S-adenosylmethionine, coenzyme A, biotin, thiamin, molybdenum cofactor, mycothiol, and ergothioneine (low-molecular weight thiols). Fifty-two proteins exhibited significantly different abundance during at least one growth phase. Sixteen proteins were uniquely detected and 47 proteins were significantly more abundant in the dibenzothiophene culture during at least one growth phase. The sulfate-free dibenzothiophene-containing culture reacted to sulfate starvation by restricting sulfur assimilation, enforcing sulfur-sparing, and maintaining redox homeostasis. Biodesulfurization triggered alternative pathways for sulfur assimilation different from those operating in the inorganic sulfate culture. Sulfur metabolism reprogramming and metabolic switches in the dibenzothiophene culture were manifested in limiting sulfite reduction and biosynthesis of cysteine, while boosting the production of methionine via the cobalamin-independent pathway, as well as the biosynthesis of the redox buffers mycothiol and ergothioneine. The omics data underscore the key role of sulfur metabolism in shaping the biodesulfurization phenotype and highlight potential targets for improving the biodesulfurization catalytic activity via metabolic engineering. IMPORTANCE For many decades, research on biodesulfurization of fossil fuels was conducted amid a large gap in knowledge of sulfur metabolism and its regulation in fuel-biodesulfurizing bacteria, which has impeded the development of a commercially viable bioprocess. In addition, lack of understanding of biodesulfurization-associated metabolic and physiological adaptations prohibited the development of efficient biodesulfurizers. Our integrated omics-based findings reveal the assimilatory sulfur metabolism in the biodesulfurization reference strain Rhodococcus qingshengii IGTS8 and show how sulfur metabolism and oxidative stress response were remodeled and orchestrated to shape the biodesulfurization phenotype. Our findings not only explain the frequently encountered low catalytic activity of native fuel-biodesulfurizing bacteria but also uncover unprecedented potential targets in sulfur metabolism that could be exploited via metabolic engineering to boost the biodesulfurization catalytic activity, a prerequisite for commercial application.

Temporal multiomic modeling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia.

B-cell receptor (BCR) signaling is crucial for the pathophysiology of most mature B-cell lymphomas/leukemias and has emerged as a therapeutic target whose effectiveness remains limited by the occurrence of mutations. Therefore, deciphering the cellular program activated downstream this pathway has become of paramount importance for the development of innovative therapies. Using an original ex vivo model of BCR-induced proliferation of chronic lymphocytic leukemia cells, we generated 108 temporal transcriptional and proteomic profiles from 1 h up to 4 days after BCR activation. This dataset revealed a structured temporal response composed of 13,065 transcripts and 4027 proteins, comprising a leukemic proliferative signature consisting of 430 genes and 374 proteins. Mathematical modeling of this complex cellular response further highlighted a transcriptional network driven by 14 early genes linked to proteins involved in cell proliferation. This group includes expected genes (EGR1/2, NF-kB) and genes involved in NF-kB signaling modulation (TANK, ROHF) and immune evasion (KMO, IL4I1) that have not yet been associated with leukemic cells proliferation. Our study unveils the BCR-activated proliferative genetic program in primary leukemic cells. This approach combining temporal measurements with modeling allows identifying new putative targets for innovative therapy of lymphoid malignancies and also cancers dependent on ligand-receptor interactions.

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.

A proteomic view of cellular responses of macrophages to copper when added as ion or as copper-polyacrylate complex.

Copper is an essential metal for life, but is toxic at high concentrations. In mammalian cells, two copper transporters are known, CTR1 and CTR2. In order to gain insights on the possible influence of the import pathway on cellular responses to copper, two copper challenges were compared: one with copper ion, which is likely to use preferentially CTR1, and one with a copper-polyacrylate complex, which will be internalized via the endosomal pathway and is likely to use preferentially CTR2. A model system consisting in the J774A1 mouse macrophage system, with a strong endosomal/lysosomal pathway, was used. In order to gain wide insights into the cellular responses to copper, a proteomic approach was used. The proteomic results were validated by targeted experiments, and showed differential effects of the import mode on cellular physiology parameters. While the mitochondrial transmembrane potential was kept constant, a depletion in the free glutahione content was observed with copper (ion and polylacrylate complex). Both copper-polyacrylate and polyacrylate induced perturbations in the cytoskeleton and in phagocytosis. Inflammatory responses were also differently altered by copper ion and copper-polyacrylate. Copper-polyacrylate also perturbed several metabolic enzymes. Lastly, enzymes were used as a test set to assess the predictive value of proteomics. SIGNIFICANCE: Proteomic profiling provides an in depth analysis of the alterations induced on cells by copper under two different exposure modes to this metal, namely as the free ion or as a complex with polyacrylate. The cellular responses were substantially different between the two exposure modes, although some cellular effects are shared, such as the depletion in free glutathione. Targeted experiments were used to confirm the proteomic results. Some metabolic enzymes showed altered activities after exposure to the copper-polyacrylate complex. The basal inflammatory responses were different for copper ion and for the copper-polyacrylate complex, while the two forms of copper inhibited lipopolysaccharide-induced inflammatory responses.

Rab7-harboring vesicles are carriers of the transferrin receptor through the biosynthetic secretory pathway.

The biosynthetic secretory pathway is particularly challenging to investigate as it is underrepresented compared to the abundance of the other intracellular trafficking routes. Here, we combined the retention using selective hook (RUSH) to a CRISPR-Cas9 gene editing approach (eRUSH) and identified Rab7-harboring vesicles as an important intermediate compartment of the Golgi-to-plasma membrane transport of neosynthesized transferrin receptor (TfR). These vesicles did not exhibit degradative properties and were not associated to Rab6A-harboring vesicles. Rab7A was transiently associated to neosynthetic TfR-containing post-Golgi vesicles but dissociated before fusion with the plasma membrane. Together, our study reveals a role for Rab7 in the biosynthetic secretory pathway of the TfR, highlighting the diversity of the secretory vesicles' nature.