A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Bidirectional promoter activity from expression cassettes can drive off-target repression of neighboring gene translation.

Targeted selection-based genome-editing approaches have enabled many fundamental discoveries and are used routinely with high precision. We found, however, that replacement of DBP1 with a common selection cassette in budding yeast led to reduced expression and function for the adjacent gene, MRP51, despite all MRP51 coding and regulatory sequences remaining intact. Cassette-induced repression of MRP51 drove all mutant phenotypes detected in cells deleted for DBP1. This behavior resembled the 'neighboring gene effect' (NGE), a phenomenon of unknown mechanism whereby cassette insertion at one locus reduces the expression of a neighboring gene. Here, we leveraged strong off-target mutant phenotypes resulting from cassette replacement of DBP1 to provide mechanistic insight into the NGE. We found that the inherent bidirectionality of promoters, including those in expression cassettes, drives a divergent transcript that represses MRP51 through combined transcriptional interference and translational repression mediated by production of a long undecoded transcript isoform (LUTI). Divergent transcript production driving this off-target effect is general to yeast expression cassettes and occurs ubiquitously with insertion. Despite this, off-target effects are often naturally prevented by local sequence features, such as those that terminate divergent transcripts between the site of cassette insertion and the neighboring gene. Thus, cassette-induced off-target effects can be eliminated by the insertion of transcription terminator sequences into the cassette, flanking the promoter. Because the driving features of this off-target effect are broadly conserved, our study suggests it should be considered in the design and interpretation of experiments using integrated expression cassettes in other eukaryotic systems, including human cells.

Identifying Predictors of Taxane-Induced Peripheral Neuropathy Using Mass Spectrometry-Based Proteomics Technology.

Major advances in early detection and therapy have significantly increased the survival of breast cancer patients. Unfortunately, most cancer therapies are known to carry a substantial risk of adverse long-term treatment-related effects. Little is known about patient susceptibility to severe side effects after chemotherapy. Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of taxanes. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. On the other hand, recent advances in proteomic technologies incorporating mass spectrometry (MS) for biomarker discovery show great promise to provide clinically relevant protein biomarkers. In this study, we evaluated the association between protein content in serum exosomes and severity of CIPN. Women with early stage breast cancer receiving adjuvant taxane chemotherapy were assessed with the FACT-Ntx score and serum was collected before and after the taxane treatment. Based on the change in FACT-Ntx score from baseline to 12 month follow-up, we separated patients into two groups: those who had no change (Group 1, N = 9) and those who had a ≥20% worsening (Group 1, N = 8). MS-based proteomics technology was used to identify proteins present in serum exosomes to determine potential biomarkers. Mann-Whitney-Wilcoxon analysis was applied and maximum FDR was controlled at 20%. From the serum exosomes derived from this cohort, we identified over 700 proteins known to be in different subcellular locations and have different functions. Statistical analysis revealed a 12-protein signature that resulted in a distinct separation between baseline serum samples of both groups (q<0.2) suggesting that the baseline samples can predict subsequent neurotoxicity. These toxicity-associated biomarkers can be further validated in larger retrospective cohorts for their utility in identifying patients at high risk for CIPN.