Protein and Microbial Biomarkers in Sputum Discern Acute and Latent Tuberculosis in Investigation of Pastoral Ethiopian Cohort

Abstract

Differential diagnosis of tuberculosis (TB) and latent TB infection (LTBI) remains a public health priority in high TB burden countries. Pulmonary TB is diagnosed by sputum smear microscopy, chest X-rays, and PCR tests for distinct Mycobacterium tuberculosis (Mtb) genes. Clinical tests to diagnose LTBI rely on immune cell stimulation in blood plasma with TB-specific antigens followed by measurements of interferon-γ concentrations. The latter is an important cytokine for cellular immune responses against Mtb in infected lung tissues. Sputum smear microscopy and chest X-rays are not sufficiently sensitive while both PCR and interferon-γ release assays are expensive. Alternative biomarkers for the development of diagnostic tests to discern TB disease states are desirable. This study's objective was to discover sputum diagnostic biomarker candidates from the analysis of samples from 161 human subjects including TB patients, individuals with LTBI, negative community controls (NCC) from the province South Omo, a pastoral region in Ethiopia. We analyzed 16S rRNA gene-based bacterial taxonomies and proteomic profiles. The sputum microbiota did not reveal statistically significant differences in α-diversity comparing the cohorts. The genus Mycobacterium, representing Mtb, was only identified for the TB group which also featured reduced abundance of the genus Rothia in comparison with the LTBI and NCC groups. Rothia is a respiratory tract commensal and may be sensitive to the inflammatory milieu generated by infection with Mtb. Proteomic data supported innate immune responses against the pathogen in subjects with pulmonary TB. Ferritin, an iron storage protein released by damaged host cells, was markedly increased in abundance in TB sputum compared to the LTBI and NCC groups, along with the α-1-acid glycoproteins ORM1 and ORM2. These proteins are acute phase reactants and inhibit excessive neutrophil activation. Proteomic data highlight the effector roles of neutrophils in the anti-Mtb response which was not observed for LTBI cases. Less abundant in the sputum of the LTBI group, compared to the NCC group, were two immunomodulatory proteins, mitochondrial TSPO and the extracellular ribonuclease T2. If validated, these proteins are of interest as new biomarkers for diagnosis of LTBI.

Keywords: LTBI; Rothia; acute phase response; microbiome; protein biomarker; sputum; tuberculosis; α-1-acid glycoprotein.

Figure 1

Flow chart showing the three human subject groups and the specimens used for sputum proteomics and 16S rRNA gene sequencing analyses. Abbreviations used are denoted in the text.

Figure 2

Venn diagram with protein identifications derived from two studies of saliva proteomes and two studies of sputum proteomes. The Cao study included analysis of samples associated with asthma. The Wu study included samples associated with squamous epithelial cell carcinoma of the oral cavity. In all studies, at least 800 proteins were identified.

Figure 3

Principal Component Analysis (PCA) of sputum proteomes for PTB (red), LTBI (blue), and negative community controls (NCC, green). Two Principal Components explaining 15.8 and 14.1% of the variance among the groups are displayed. The data represent quantities of 432 human proteins. No separation of clusters is observed for the LTBI and NCC subjects while PTB datasets cluster separately.

Figure 4

Volcano plot depicting protein abundance differences for the comparison of PTB and LTBI groups. Data are derived from sputum shotgun proteomic analyses. The unequal variance Welch t-test with multiple testing corrections was used in the Perseus software, and 103 proteins (marked with UniProt short names) had differences with a P-value <0.01 and a fold change >2. Red and blue dots denote proteins increased and decreased in the PTB group, respectively.

Figure 5

Gene Ontology (GO) biological process enrichments based on differentially abundant proteins (PTB vs. LTBI). The top-10 enriched terms and their significances (P-values) are plotted. The numbers placed next to a bar indicate the proteins in our datasets belonging to that category. GO term analysis details are included in a worksheet of a Supplemental Dataset ( Supplementary File S5 ).

Figure 6

Protein network analysis and functional enrichment clusters. The network was built from 103 differentially abundant proteins comparing PTB and LTBI sample groups as input data and the String App in Cytoscape software. The score cut-off for interaction confidence was set to 0.4. Color coding is in accordance with the fold changes. Diamond shape depicts proteins associated with a response to stimulus. Protein clusters were annotated based on enrichment, a function embedded in Cytoscape.

Figure 7

Quantitative differences for the α-1-acid glycoproteins ORM1 and ORM2 in box plots comparing datasets for PTB vs LTBI as well as PTB vs NCC. The P-values were highly significant indicating the important role for the acute phase reactants in modulating the PTB pathology. LFQ values are based on summed MS1 peak integrations for all peptides assigned to protein of origin. n.s, not statistically significant.

Figure 8

Volcano plot depicting protein abundance differences comparing the LTBI and NCC groups. A P-value <0.01 and a fold change >2 were applied to identify the differentially abundant proteins (each denoted in green and marked with the UniProt short name).

Figure 9

Microbial taxonomy profiling performed at the phylum level denoting the differentially abundant genera based on sequence analysis of the V4 region of 16S bacterial rRNA on a MiSeq platform. The phyla are shown denoting the most abundant genera for each phylum by color codes. Streptococcus is the most abundant genus of Firmicutes and dominant in oral microbiota. Among Actinobacteria, Rothia and Atopobium were dominant with variations in abundance among LTBI, NCC, and PTB datasets. Mycobacterium, also an Actinobacterium, revealed low abundance so that it is not visualized in the segmented bars for this phylum. Haemophilus was the most abundant genus, followed by Neisseria, in the phylum Proteobacteria.

Figure 10

Quantitative differences for Rothia displayed in box plots comparing PTB datasets with those of the LTBI and NCC groups. As shown, the P-values were significant in both comparisons. ****P-value < 0.0001; **P-value < 0.01; ns, not statistically significant.