Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis

Abstract

Rationale: The monitoring of TB treatments in clinical practice and clinical trials relies on traditional sputum-based culture status indicators at specific time points. Accurate, predictive, blood-based protein markers would provide a simpler and more informative view of patient health and response to treatment.

Objective: We utilized sensitive, high throughput multiplexed ion mobility-mass spectrometry (IM-MS) to characterize the serum proteome of TB patients at the start of and at 8 weeks of rifamycin-based treatment. We sought to identify treatment specific signatures within patients as well as correlate the proteome signatures to various clinical markers of treatment efficacy.

Methods: Serum samples were collected from 289 subjects enrolled in CDC TB Trials Consortium Study 29 at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). Serum proteins were immunoaffinity-depleted of high abundant components, digested to peptides and analyzed for data acquisition utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS). Linear mixed models were utilized to identify serum protein changes in the host response to antibiotic treatment as well as correlations with culture status end points.

Results: A total of 10,137 peptides corresponding to 872 proteins were identified, quantified, and used for statistical analysis across the longitudinal patient cohort. In response to TB treatment, 244 proteins were significantly altered. Pathway/network comparisons helped visualize the interconnected proteins, identifying up regulated (lipid transport, coagulation cascade, endopeptidase activity) and down regulated (acute phase) processes and pathways in addition to other cross regulated networks (inflammation, cell adhesion, extracellular matrix). Detection of possible lung injury serum proteins such as HPSE, significantly downregulated upon treatment. Analyses of microbiologic data over time identified a core set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2) which change in response to treatment and also strongly correlate with culture status. A similar set of proteins at baseline were found to be predictive of week 6 and 8 culture status.

Conclusion: A comprehensive host serum protein dataset reflective of TB treatment effect is defined. A repeating set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2, among others) were found to change significantly in response to treatment, to strongly correlate with culture status, and at baseline to be predictive of future culture conversion. If validated in cohorts with long term follow-up to capture failure and relapse of TB, these protein markers could be developed for monitoring of treatment in clinical trials and in patient care.

Keywords: Antibiotic treatment; Ion mobility spectrometry; Proteomics; Tuberculosis.

Fig. 1.

Schematic workflow of the experimental design and analysis procedure. Utilization of the LC-IM-MS platform generated peptide level peak intensity data for each of the 578 samples (289 baseline samples and 289 8-week samples) from which protein level data was inferred. Protein level data was the basis to investigate the dynamic changes in the serum proteome after 8 weeks of treatment.

Fig. 2.

Visual representation of the serum proteins differential abundant due to 8 weeks of antibiotic treatment. A) Volcano plot of all proteins analyzed via linear mixed model to determine differential abundance due to antibiotic treatment at 8 weeks. Red lines correspond to thresholds of acceptance (pval < 0.05 and > ±0.1 log2 fold change). B) Heat map of 244 proteins representing quantitative changes in protein abundances between baseline and 8 weeks from 289 patients. Red magnitude representing increase in abundance with treatment, blue magnitude representing reduced serum abundance upon treatment. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3.

Protein based pathway mapping of the patient response in the serum to antibiotic treatment. The GO annotations of 244 significantly altered proteins were extracted from DAVID and after corrected p-value filtering was visualized in Cytoscape to create the interconnected pathway network. Yellow squares: Pathways, spatial proximity indicates similarity in pathways; Red circles: proteins upregulated following 8 weeks of therapy; Blue circles: proteins downregulated following 8 weeks of therapy. Color intensity represents degree of fold change, darker signifying larger fold change. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4.

Plots and diagram of proteins significantly altered based upon culture status at week 6 and week 8 treatment. Volcano plot showing log2 values calculated from the difference of fold change from baseline to week 8 for culture positive (culture+) patients compared to culture negative (culture-) patients at (a) Week 6 culture status and B) Week 8 culture status based upon a linear mixed model. Proteins labeled are those most significant and found in common across both week 6 and week 8 results. C) Venn diagram representing overlap between the 244 treatment serum proteins and discriminatory proteins for week 6 and week 8 culture status stratification.

Fig. 5.

Plots of 6 serum proteins measured at baseline with highest significance in discriminating subsequent culture status across all culture time points. Black dots represent the average log2 protein abundance for that protein, red error bars (standard deviation) represent positive culture status patients, where green error bars represent patients with negative culture status. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)