A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

Fast Screening of Tuberculosis Patients Based on Analysis of Plasma by Infrared Spectroscopy Coupled with Machine Learning Approaches

Prompt diagnosis of tuberculosis (TB) enables timely treatment, limiting spread and improving public health for this disease. Currently, a rapid, sensitive, accurate, and cost-effective detection of TB still remains a challenge. For this purpose, we engaged a transmission skill and an attenuated total reflectance (ATR) technique coupled with Fourier-transform infrared spectrometry (FTIR) to study the IR spectra of the plasma samples from TB patients (n = 10) and healthy individuals (n = 10). To ensure high-quality spectral data, spectra were collected in both transmission and ATR modes, with each measurement consisting of 256 scans at a resolution of 8 cm-1. For the transmission mode, measurements were repeated five times per sample, while ATR-FTIR measurements were repeated three times per sample. These parameters were carefully optimized through rigorous testing to achieve the highest possible signal-to-noise ratio for patient sample analysis. Using this method, we obtained a total of 100 spectra from 20 samples in the transmission mode and 60 spectra in the ATR-FTIR mode, ensuring sufficient data for robust spectral analysis. Further, we applied machine learning techniques to analyze and classify the IR spectra; by this means, we differentiated those spectra between TB patients and healthy ones. In this work, we modified the transmission-FTIR setup to improve the absorption sensitivity by focusing the IR light on the interface of the sample; while, we used a high-refractive-index crystal ZnSe as a medium to reflect the signals in ATR scheme. Routinely, we compared the spectra obtained from both methods; in their second derivative curves, we notified that there had distinct spectral differences in protein and lipid regions (3500-3000, 2900-2800, and 1700-1500 cm-1) between TB and healthy groups. Using three machine learning classifiers-Logistic Regression (LR), Random Forest (RF), and XGBoost (Xg)-we found that the Xg achieved an accuracy of 0.749, precision of 0.703, recall of 0.901, F1 score of 0.790, and an AUC of the ROC curve of 0.82 for absorption spectra in the 3500-2700 cm-1 region; additionally, the machine learning practice showed that ATR data possessed performance parameters of ∼ 80% in accuracy. We randomly assigned participants (rather than individual scans) to 80% training and 20% test sets to train and validate three machine learning models (LR, RF, and Xg). Based on the results, we concluded that the absorption spectroscopic method demonstrated its superior performance in TB diagnosis. Thus, we have showed that absorption-FTIR spectroscopy is a valuable tool for sorting the TB disease from patients. The spectral IR analysis of plasmas can complement clinical evidence and provides a rapid and accurate diagnosis of TB in clinic.

Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis

Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.

Candidate serum protein biomarkers for active pulmonary tuberculosis diagnosis in tuberculosis endemic settings

BACKGROUND

Identification of non-sputum diagnostic markers for tuberculosis (TB) is urgently needed. This exploratory study aimed to discover potential serum protein biomarkers for the diagnosis of active pulmonary TB (PTB).

METHOD

We employed Proximity Extension Assay (PEA) to measure levels of 92 protein biomarkers related to inflammation in serum samples from three patient groups: 30 patients with active PTB, 29 patients with other respiratory diseases with latent TB (ORD with LTBI+), and 29 patients with other respiratory diseases without latent TB (ORD with LTBI-). To understand the functional mechanisms associated with differentially expressed proteins, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify potential TB diagnostic protein biomarkers. Network interactions among the identified candidate diagnostic markers were then analyzed, and their diagnostic performance was evaluated using logistic regression and receiver operating characteristic (ROC) analysis.

RESULT

The analysis revealed 37 differentially expressed proteins (DEPs) in the active PTB group compared to both ORD with LTBI + and ORD with LTBI- groups. Gene Ontology analysis indicated that these DEPs were primarily involved in the inflammatory response, while KEGG enrichment analysis highlighted the cytokine-cytokine receptor interaction pathway as the top significant hit. LASSO regression identified eight promising candidate protein biomarkers: IFN-gamma, LIF, uPA, CSF-1, SCF, SIRT2, 4E-BP1, and GDNF. The combined set of these eight proteins yielded an AUC of 0.943 for differentiating active PTB from ORD with LTBI+, and an AUC of 0.927 for distinguishing PTB from ORD with LTBI-.

CONCLUSION

We have identified eight protein markers that reliably differentiate active PTB from ORD irrespective of LTBI presence. Further large-scale validation and translation of these protein markers into a user-friendly and affordable point-of-care test hold the potential to significantly enhance TB control in high-burden regions.

Real-life hypoglycaemia partially blunts the inflammatory response to experimental hypoglycaemia in people with type 1 diabetes

AIM

To determine whether recent repeated exposure to real-life hypoglycaemia affects the pro-inflammatory response during a hypoglycemia episode.

MATERIALS AND METHODS

This was a post hoc analysis of a hyperinsulinaemic normoglycaemic-hypoglycaemic clamp study, involving 40 participants with type 1 diabetes. Glucose levels 1 week before the clamp were monitored using a Freestyle Libre 1. Blood was drawn during normoglycaemia and hypoglycaemia, and 24 hours after resolution of hypoglycaemia for measurements of inflammatory responses and counterregulatory hormone levels. We determined the relationship between the frequency and duration of spontaneous hypoglycaemia, and time below range (TBR) and the inflammatory response to experimental hypoglycaemia.

RESULTS

On average, participants experienced 0.79 (0.43, 1.14) hypoglycaemia episodes per day, with a duration of 78 (47, 110) minutes and TBR of 5.5% (2.8%, 8.5%). TBR and hypoglycaemia frequency were inversely associated with the increase in circulating granulocyte and lymphocyte counts during experimental hypoglycaemia (P < .05 for all). A protein network consisting of DNER, IF-R, uPA, Flt3L, FGF-5 and TWEAK was negatively associated with hypoglycaemia frequency (P < .05), but not with the adrenaline response. Neither other counterregulatory hormones, nor hypoglycaemia awareness status, was associated with any of the inflammatory parameters markers.

CONCLUSIONS

Repeated exposure to spontaneous hypoglycaemia is associated with blunted effects of subsequent experimental hypoglycaemia on circulating immune cells and the number of inflammatory proteins.

Diagnostic markers reflecting dysregulation of the host response in the transition to tuberculosis disease

Sustained control of Mycobacterium tuberculosis infection without evidence of disease is based on a finely tuned balance between pro- and anti-inflammatory responses. Loss of this balance leads to tuberculosis (TB) disease, in which exacerbated myeloid and neutrophil activation is common. Proteomic and transcriptomic assessment of the host response can detect increasing immune activation associated with TB disease progression several months before clinical disease. Future diagnostic methods based on measuring host response biomarkers that are able to detect this dysregulation could therefore be valuable in the early detection of TB disease progression.

Disease-specific plasma protein profiles in patients with fever after traveling to tropical areas

Fever is common among individuals seeking healthcare after traveling to tropical regions. Despite the association with potentially severe disease, the etiology is often not determined. Plasma protein patterns can be informative to understand the host response to infection and can potentially indicate the pathogen causing the disease. In this study, we measured 49 proteins in the plasma of 124 patients with fever after travel to tropical or subtropical regions. The patients had confirmed diagnoses of either malaria, dengue fever, influenza, bacterial respiratory tract infection, or bacterial gastroenteritis, representing the most common etiologies. We used multivariate and machine learning methods to identify combinations of proteins that contributed to distinguishing infected patients from healthy controls, and each other. Malaria displayed the most unique protein signature, indicating a strong immunoregulatory response with high levels of IL10, sTNFRI and II, and sCD25 but low levels of sCD40L. In contrast, bacterial gastroenteritis had high levels of sCD40L, APRIL, and IFN-γ, while dengue was the only infection with elevated IFN-α2. These results suggest that characterization of the inflammatory profile of individuals with fever can help to identify disease-specific host responses, which in turn can be used to guide future research on diagnostic strategies and therapeutic interventions.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Diagnosis of cerebral malaria: Tools to reduce Plasmodium falciparum associated mortality

Cerebral malaria (CM) is a major cause of mortality in Plasmodium falciparum (Pf) infection and is associated with the sequestration of parasitised erythrocytes in the microvasculature of the host's vital organs. Prompt diagnosis and treatment are key to a positive outcome in CM. However, current diagnostic tools remain inadequate to assess the degree of brain dysfunction associated with CM before the window for effective treatment closes. Several host and parasite factor-based biomarkers have been suggested as rapid diagnostic tools with potential for early CM diagnosis, however, no specific biomarker signature has been validated. Here, we provide an updated review on promising CM biomarker candidates and evaluate their applicability as point-of-care tools in malaria-endemic areas.