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Integrated top-down bottom-up proteomics combined with on-line digestion has great potential to improve the characterization of protein isoforms in biological systems and is amendable to high throughput proteomics experiments. Bottom-up proteomics ultimately provides the peptide sequences derived from the tandem MS analyses of peptides after the proteome has been digested. Top-down proteomics conversely entails the MS analyses of intact proteins for more effective characterization of genetic variations and/or post-translational modifications. Herein, we describe recent efforts toward efficient integration of bottom-up and top-down LC-MS-based proteomics strategies. Since most proteomics separations utilize acidic conditions, we exploited the compatibility of pepsin (where the optimal digestion conditions are at low pH) for integration into bottom-up and top-down proteomics work flows. Pressure-enhanced pepsin digestions were successfully performed and characterized with several standard proteins in either an off-line mode using a Barocycler or an on-line mode using a modified high pressure LC system referred to as a fast on-line digestion system (FOLDS). FOLDS was tested using pepsin and a whole microbial proteome, and the results were compared against traditional trypsin digestions on the same platform. Additionally, FOLDS was integrated with a RePlay configuration to demonstrate an ultrarapid integrated bottom-up top-down proteomics strategy using a standard mixture of proteins and a monkey pox virus proteome.

Understanding viral pathogenesis is challenging because of confounding factors, including nonabrasive access to infected tissues and high abundance of inflammatory mediators that may mask mechanistic details. In diseases such as influenza and smallpox where the primary cause of mortality results from complications in the lung, the characterization of lung fluid offers a unique opportunity to study host-pathogen interactions with minimal effect on infected animals. This investigation characterizes the global proteome response in the pulmonary fluid, bronchoalveolar lavage fluid, of macaques during upper respiratory infection by monkeypox virus (MPXV), a close relative of the causative agent of smallpox, variola virus. These results are compared and contrasted against infections by vaccinia virus (VV), a low pathogenic relative of MPXV, and with extracellular fluid from MPXV-infected HeLa cells. To identify changes in the pulmonary protein compartment, macaque lung fluid was sampled twice prior to infection, serving as base line, and up to six times following intrabronchial infection with either MPXV or VV. Increased expression of inflammatory proteins was observed in response to both viruses. Although the increased expression resolved for a subset of proteins, such as C-reactive protein, S100A8, and S100A9, high expression levels persisted for other proteins, including vitamin D-binding protein and fibrinogen γ. Structural and metabolic proteins were substantially decreased in lung fluid exclusively during MPXV and not VV infection. Decreases in structural and metabolic proteins were similarly observed in the extracellular fluid of MPXV-infected HeLa cells. Results from this study suggest that the host inflammatory response may not be the only facilitator of viral pathogenesis, but rather maintaining pulmonary structural integrity could be a key factor influencing disease progression and mortality.

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments.