Decoding the Effect of Isobaric Substitutions on Identifying Missing Proteins and Variant Peptides in Human Proteome

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9–40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2–4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions

Decoding the Effect of Isobaric Substitutions on Identifying Missing Proteins and Variant Peptides in Human Proteome

To confirm the existence of missing proteins, we need to identify at least two unique peptides with length of 9–40 amino acids of a missing protein in bottom-up mass-spectrometry-based proteomic experiments. However, an identified unique peptide of the missing protein, even identified with high level of confidence, could possibly coincide with a peptide of a commonly observed protein due to isobaric substitutions, mass modifications, alternative splice isoforms, or single amino acid variants (SAAVs). Besides unique peptides of missing proteins, identified variant peptides (SAAV-containing peptides) could also alternatively map to peptides of other proteins due to the aforementioned issues. Therefore, we conducted a thorough comparative analysis on data sets in PeptideAtlas Tiered Human Integrated Search Proteome (THISP, 2017-03 release), including neXtProt (2017-01 release), to systematically investigate the possibility of unique peptides in missing proteins (PE2–4), unique peptides in dubious proteins, and variant peptides affected by isobaric substitutions, causing doubtful identification results. In this study, we considered 11 isobaric substitutions. From our analysis, we found <5% of the unique peptides of missing proteins and >6% of variant peptides became shared with peptides of PE1 proteins after isobaric substitutions

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein–protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks