Griffin, Timothy J.

ABSTRACT Mass spectrometry-based metaproteomics, the identification and quantification of thousands of proteins expressed by complex microbial communities, has become pivotal for unraveling functional interactions within microbiomes. However, metaproteomics data analysis encounters many challenges, including the search of tandem mass spectra against a protein sequence database using proteomics database search algorithms. We used a ground-truth dataset to assess a spectral library searching method against established database searching approaches. Mass spectrometry data collected by data-dependent acquisition (DDA-MS) was analyzed using database searching approaches (MaxQuant and FragPipe), as well as using Scribe with Prosit predicted spectral libraries. We used FASTA databases that included protein sequences from microbial species present in the ground-truth dataset along with background protein sequences, to estimate error rates and assess the effects on detection, peptide-spectral match quality, and quantification. Using the Scribe search engine resulted in more proteins detected at a 1% false discovery rate (FDR) compared to MaxQuant or FragPipe, while FragPipe detected more peptides verified by PepQuery. Scribe was able to detect more low-abundance proteins in the microbiome dataset and was more accurate in quantifying the microbial community composition. This research provides insights and guidance for metaproteomics researchers aiming to optimize results in their analysis of DDA-MS data.

Abstract Rationale Chronic obstructive pulmonary disease is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that lung stromal cells activate pathological gene expression programs that support oncogenesis. Objectives To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer. Methods The study included subjects with and without lung cancer across a spectrum of lung-function values. We conducted a multiomics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome. Measurements and Main Results Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation modulating protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms that drove these cancer-associated translation programs differed based on lung function. In subjects with normal to mildly impaired lung function, the mammalian target of rapamycin (mTOR) pathway served as an upstream driver, whereas in subjects with severe airflow obstruction, pathways downstream of pathological extracellular matrix emerged. Consistent with a role during cancer initiation, both the mTOR and extracellular matrix gene expression programs paralleled the activation of previously identified procancer secretomes. Furthermore, an in situ examination of lung tissue showed that stromal fibroblasts expressed cancer-associated proteins from two procancer secretomes: one that included IL-6 (in cases of mild or no airflow obstruction), and one that included BMP1 (in cases of severe airflow obstruction). Conclusions Two distinct stromal gene expression programs that promote cancer initiation are activated in patients with lung cancer depending on lung function. Our work has implications both for screening strategies and for personalized approaches to cancer treatment.

Abstract Rationale Chronic obstructive pulmonary disease (COPD) is an independent risk factor for lung cancer, but the underlying molecular mechanisms are unknown. We hypothesized that stromal cells comprising the COPD lung contain pathological gene expression programs supporting oncogenesis. Objectives To identify molecular mechanisms operating in the lung stroma that support the development of lung cancer. Methods Study subjects included patients with COPD with (n = 30) or without (n = 30) lung cancer across a spectrum of COPD severity. We conducted multi-omics analysis of nonmalignant lung tissue to quantify the transcriptome, translatome, and proteome. Results Cancer-associated gene expression changes predominantly manifested as alterations in the efficiency of mRNA translation impacting protein levels in the absence of corresponding changes in mRNA levels. The molecular mechanisms driving these procancer translation programs differed on the basis of COPD severity. In mild or no COPD, the mammalian target of rapamycin pathway served as an upstream driver; whereas in advanced COPD, pathways downstream of pathological extracellular matrix emerged. Both the mammalian target of rapamycin and extracellular matrix gene expression programs paralleled activation of previously identified secretomes that can be independently induced and promote cancer initiation in mouse models. These included the senescence secretome in mild to no COPD and the ETS2 (erythroblast transformation-specific 2) secretome in moderate to severe COPD. Furthermore, in situ examination of COPD lung tissue documented stromal fibroblasts expressing key cancer-associated proteins from procancer secretomes including IL-6 (senescence secretome) in mild COPD and bone morphogenetic protein-1 (ETS2 secretome) in advanced COPD. Conclusions Two distinct stromal gene expression programs promoting cancer initiation are activated in patients with lung cancer depending on the severity of COPD as measured by lung function. Our work has implications both for screening strategies and personalized approaches to prevent cancer in COPD.

Rationale Use of matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOFMS) to monitor serum and urine samples for endogenous monoclonal immunoglobulins. MALDI‐TOFMS is faster, fully automatable, and provides superior specificity compared to protein gel electrophoresis (PEL). Methods Samples were enriched for immunoglobulins in 5 min using Melon Gel™ followed by reduction with dithiothreitol for 15 min to separate immunoglobulin light chains and heavy chains. Samples were then desalted using C4 ZipTips, mixed with sinapinic acid matrix, and analyzed on a Bruker Biflex III MALDI‐TOF mass spectrometer. Results Monoclonal immunoglobulin light chains were identified in serum and urine samples from patients with a known monoclonal gammopathy using MALDI‐TOFMS with minimal sample preparation. Conclusions MALDI‐TOFMS can identify a monoclonal immunoglobulin in serum and urine samples. The molecular mass of the monoclonal immunoglobulin light chain is obtained providing unprecedented specificity compared to PEL. In addition, the methodology can be automated, making it a practical alternative to PEL. Copyright © 2015 John Wiley & Sons, Ltd.