Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Advances in understanding immune homeostasis in latent tuberculosis infection

Nearly one-fourth of the global population is infected by Mycobacterium tuberculosis (Mtb), and approximately 90%-95% remain asymptomatic as latent tuberculosis infection (LTBI), an estimated 5%-10% of those with latent infections will eventually progress to active tuberculosis (ATB). Although it is widely accepted that LTBI transitioning to ATB results from a disruption of host immune balance and a weakening of protective immune responses, the exact underlying immunological mechanisms that promote this conversion are not well characterized. Thus, it is difficult to accurately predict tuberculosis (TB) progression in advance, leaving the LTBI population as a significant threat to TB prevention and control. This article systematically explores three aspects related to the immunoregulatory mechanisms and translational research about LTBI: (1) the distinct immunocytological characteristics of LTBI and ATB, (2) LTBI diagnostic markers discovery related to host anti-TB immunity and metabolic pathways, and (3) vaccine development focus on LTBI. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology Infectious Diseases > Genetics/Genomics/Epigenetics Immune System Diseases > Genetics/Genomics/Epigenetics.

Lysophosphatidylcholine Acetyltransferase 2 (LPCAT2) Influences the Gene Expression of the Lipopolysaccharide Receptor Complex in Infected RAW264.7 Macrophages, Depending on the E. coli Lipopolysaccharide Serotype

Escherichia coli (E. coli) is a frequent gram-negative bacterium that causes nosocomial infections, affecting more than 100 million patients annually worldwide. Bacterial lipopolysaccharide (LPS) from E. coli binds to toll-like receptor 4 (TLR4) and its co-receptor's cluster of differentiation protein 14 (CD14) and myeloid differentiation factor 2 (MD2), collectively known as the LPS receptor complex. LPCAT2 participates in lipid-raft assembly by phospholipid remodelling. Previous research has proven that LPCAT2 co-localises in lipid rafts with TLR4 and regulates macrophage inflammatory response. However, no published evidence exists of the influence of LPCAT2 on the gene expression of the LPS receptor complex induced by smooth or rough bacterial serotypes. We used RAW264.7-a commonly used experimental murine macrophage model-to study the effects of LPCAT2 on the LPS receptor complex by transiently silencing the LPCAT2 gene, infecting the macrophages with either smooth or rough LPS, and quantifying gene expression. LPCAT2 only significantly affected the gene expression of the LPS receptor complex in macrophages infected with smooth LPS. This study provides novel evidence that the influence of LPCAT2 on macrophage inflammatory response to bacterial infection depends on the LPS serotype, and it supports previous evidence that LPCAT2 regulates inflammatory response by modulating protein translocation to lipid rafts.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Characteristics of plasma exosomes in drug-resistant tuberculosis patients

BACKGROUND

Increasing prevalence of drug-resistant tuberculosis (DR-TB) poses a major challenge to the early detection and effective control of tuberculosis (TB). Exosomes carrying proteins and nucleic acid mediate intercellular communication between host and pathogen including Mycobacterium tuberculosis. However, molecular events of exosomes indicating the status and development of DR-TB remain unknown. This study determined the proteomics of exosome in DR-TB and explored the potential pathogenesis of DR-TB.

METHODS

Plasma samples were collected from 17 DR-TB patients and 33 non-drug-resistant tuberculosis (NDR-TB) patients using grouped case-control study design. After exosomes of plasma were isolated and confirmed by compositional and morphological measurement for exosomal characteristics, a label-free quantitative proteomics of exosomes was performed and differential protein components were determined via bioinformatics analysis.

RESULTS

Compared with the NDR-TB group, we identified 16 up-regulated proteins and 10 down-regulated proteins in the DR-TB group. The down-regulated proteins were mainly apolipoproteins and mainly enriched in cholesterol metabolism-related pathways. Apolipoproteins family including APOA1, APOB, APOC1 were key proteins in protein-protein interaction network.

CONCLUSION

Differentially expressed proteins in the exosomes may indicate the status of DR-TB from NDR-TB. Apolipoproteins family including APOA1, APOB, APOC1 may be involved in the pathogenesis of DR-TB by regulating cholesterol metabolism via exosomes.

Lipid class-dependent alterations of Caenorhabditis elegans under harmane exposure

Altered lipid patterns in Caenorhabditis elegans (C. elegans) resulting from exposure to harmane remain to be explored. In this study, untargeted lipidomics was carried out to elucidate the effects of acute exposure to harmane on the lipidome of C. elegans. Exposure to the compound was evaluated based on the reproduction ability of the worms at 0.1 and 1 μg/mL. No significant effects of harmane were observed at these concentrations. Furthermore, we found that the modulatory effects of harmane on the lipidome of C. elegans at 1 μg/mL were lipid class dependent. In particular, harmane-treated worms were enriched in triglycerides and fatty acids, regardless of the degree of saturation. Glycerophospholipids were generally down-regulated. Furthermore, functional analyses suggested that there was a reduction in lipid membrane bilayer-related terms, and in some related to the mitochondria, and endoplasmic reticulum of C. elegans when treated with harmane. Lipid droplets and storage appeared to be up-regulated. In conclusion, our findings suggest that harmane exposure affects the lipidome of C. elegans in a sophisticated manner. Further investigations are required to elucidate the molecular mechanisms underlying these lipid pattern changes.

Reduced Cholesterol Levels during Acute Human Babesiosis

BACKGROUND

Babesiosis, an intra-erythrocytic protozoan disease, is an emerging zoonotic parasitic disease worldwide. Cholesterol levels are correlated with severe infections, such as sepsis and COVID-19, and anecdotal reports suggest that high-density lipoprotein (HDL) cholesterol declines during acute babesiosis. Our aim was to describe the cholesterol levels in patients with acute babesiosis diagnosed in an endemic area in New York, hypothesizing that HDL levels correlate with the severity of infection.

METHODS

We reviewed the medical records of adult patients with babesiosis diagnosed by identification of Babesia parasites on a thin blood smear and confirmed by polymerase chain reaction from 2013 to 2018, who also had available a lipid profile drawn at the time of clinical presentation. Additional lipid profile levels were considered as "baseline" if they were drawn within 2 months before or after the infection as part of routine care.

RESULTS

A total of 39 patients with babesiosis had a lipid profile drawn on presentation. The patients were divided into two groups for comparison based on the treating physician's clinical decision: 33 patients who were admitted to the hospital and 8 patients who were evaluated as outpatients. A history of hypertension was more common in admitted patients (37% vs. 17%, p = 0.02). The median levels of low-density lipoprotein (LDL) and HDL were significantly reduced in admitted patients compared to non-admitted patients (46 vs. 76 mg/dL, p = 0.04; and 9 vs. 28.5 mg/dL, p = 0.03, respectively). In addition, LDL and HDL levels returned to baseline values following resolution of acute babesiosis.

CONCLUSION

LDL and HDL levels are significantly reduced during acute babesiosis, suggesting that cholesterol depletion may predict disease severity. Pathogen and host factors may contribute to a reduction in serum cholesterol levels during acute babesiosis.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Advancing personalized medicine for tuberculosis through the application of immune profiling

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.