Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Dressler, Frederick A.; Brägelmann, Johannes; Reischl, Markus; Perner, Sven

doi:10.1016/j.mcpro.2022.100269

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…Furthermore, it modifies the cellular protein expression in general, which is also reflected in the immune epitopes presented by the cells [ 51 ]. While IFNγ treatment induces a plethora of pro-inflammatory proteins, the majority of proteins are assumed to be non-differentially expressed [ 52 ]. This is also true for A549 cells, where most proteins are not differentially expressed, as shown here by the proteome analysis.…”

Section: Discussionmentioning

confidence: 99%

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

Schmalen,

Kammerl,

Meiners

et al. 2023

Biomolecules

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The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.

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Section: Discussionmentioning

confidence: 99%

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

Schmalen,

Kammerl,

Meiners

et al. 2023

Biomolecules

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“…The raw data was filtered for proteins quantified in at least 50% of all samples. Data was normalized using Normics median 49 based on the top 100 invariant proteins. Significance for differential expression was calculated with Mann–Whitney-U tests (unadjusted due to comparison to orthogonal unsupervised evaluation).…”

Section: Methodsmentioning

confidence: 99%

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Pasternack,

Polzer,

Gemoll

et al. 2023

Sci Rep

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Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI–MS/MS) on a total of 38 FFPE (formalin‐fixed and paraffin‐embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

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“…Data from MS analyses are susceptible to systematic, dependent or independent biases (e.g., different handling, equipment calibration) on the measured peptide/protein abundances [ 102 ]. Therefore, a key step is to normalize the data to take the bias into account, allowing the data to be comparable and downstream analyses reliable [ 103 , 104 , 105 ]. Advanced analysis pipeline frameworks are therefore needed for data normalization but also for protein inference and data analysis [ 88 , 89 , 103 , 104 , 106 ].…”

Section: Key Considerations For Successful Proteomicsmentioning

confidence: 99%

“…Therefore, a key step is to normalize the data to take the bias into account, allowing the data to be comparable and downstream analyses reliable [ 103 , 104 , 105 ]. Advanced analysis pipeline frameworks are therefore needed for data normalization but also for protein inference and data analysis [ 88 , 89 , 103 , 104 , 106 ]. The latter has been extensively reviewed by Schessner et al [ 90 ] in their guide to interpreting and generate visual representation of bottom-up proteomics data.…”

Section: Key Considerations For Successful Proteomicsmentioning

confidence: 99%

Key Proteomics Tools for Fundamental and Applied Microalgal Research

Plouviez,

Dubreucq

2024

Proteomes

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Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

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Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Cited by 6 publications

References 48 publications

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Key Proteomics Tools for Fundamental and Applied Microalgal Research

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