Mass spectrometry imaging identifies altered hepatic lipid signatures during experimental Leishmania donovani infection.

Introduction: Spatial analysis of lipids in inflammatory microenvironments is key to understand the pathogenesis of infectious disease. Granulomatous inflammation is a hallmark of leishmaniasis and changes in host and parasite lipid metabolism have been observed at the bulk tissue level in various infection models. Here, mass spectrometry imaging (MSI) is applied to spatially map hepatic lipid composition following infection with Leishmania donovani, an experimental mouse model of visceral leishmaniasis. Methods: Livers from naïve and L. donovani-infected C57BL/6 mice were harvested at 14- and 20-days post-infection (n=5 per time point). 12 µm transverse sections were cut and covered with norhamane, prior to lipid analysis using MALDI-MSI. MALDI-MSI was performed in negative mode on a Rapiflex (Bruker Daltonics) at 5 and 50 µm spatial resolution and data-dependent analysis (DDA) on an Orbitrap-Elite (Thermo-Scientific) at 50 µm spatial resolution for structural identification analysis of lipids. Results: Aberrant lipid abundances were observed in a heterogeneous distribution across infected mouse livers compared to naïve mouse liver. Distinctive localized correlated lipid masses were found in granulomas and surrounding parenchymal tissue. Structural identification revealed 40 different lipids common to naïve and d14/d20 infected mouse livers, whereas 15 identified lipids were only detected in infected mouse livers. For pathology-guided MSI imaging, we deduced lipids from manually annotated granulomatous and parenchyma regions of interests (ROIs), identifying 34 lipids that showed significantly different intensities between parenchyma and granulomas across all infected livers. Discussion: Our results identify specific lipids that spatially correlate to the major histopathological feature of Leishmania donovani infection in the liver, viz. hepatic granulomas. In addition, we identified a three-fold increase in the number of unique phosphatidylglycerols (PGs) in infected liver tissue and provide direct evidence that arachidonic acid-containing phospholipids are localized with hepatic granulomas. These phospholipids may serve as important precursors for downstream oxylipin generation with consequences for the regulation of the inflammatory cascade. This study provides the first description of the use of MSI to define spatial-temporal lipid changes at local sites of infection induced by Leishmania donovani in mice.

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression.

Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.

Evaluation of cyclooxygenase oxylipins as potential biomarker for obesity-associated adipose tissue inflammation and type 2 diabetes using targeted multiple reaction monitoring mass spectrometry.

INTRODUCTION: Obesity is associated with adipose tissue inflammation which in turn drives insulin resistance and the development of type 2 diabetes. Oxylipins are a collection of lipid metabolites, subdivided in different classes, which are involved in inflammatory cascades. They play important roles in regulating adipose tissue homeostasis and inflammation and are therefore putative biomarkers for obesity-associated adipose tissue inflammation and the subsequent risk of type 2 diabetes onset. The objective for this study is to design an assay for a specific oxylipin class and evaluate these as potential prognostic biomarker for obesity-associated adipose tissue inflammation and type 2 diabetes. METHODS: An optimized workflow was developed to extract oxylipins from plasma using solid-phase extraction followed by analysis using ultra-high performance liquid chromatography coupled to a triple quadrupole mass spectrometer in multiple reaction monitoring mode. This workflow was applied to clinical plasma samples obtained from obese-type 2 diabetes patients and from lean and obese control subjects. RESULTS: The assay was analytically validated and enabled reproducible analyses of oxylipins extracted from plasma with acceptable sensitivities. Analysis of clinical samples revealed discriminative values for four oxylipins between the type 2 diabetes patients and the lean and obese control subjects, viz. PGF2α, PGE2, 15-keto-PGE2 and 13,14-dihydro-15-keto-PGE2. The combination of PGF2α and 15-keto-PGE2 had the most predictive value to discriminate type 2 diabetic patients from lean and obese controls. CONCLUSIONS: This proof-of-principle study demonstrates the potential value of oxylipins as biomarkers to discriminate obese individuals from obese-type 2 diabetes patients.

Affimers as an alternative to antibodies for protein biomarker enrichment.

INTRODUCTION: Assessing the specificity of protein binders is an essential first step in protein biomarker assay development. Affimers are novel protein binders and can potentially replace antibodies in multiple protein capture-based assays. Affimers are selected for their high specificity against the target protein and have benefits over antibodies like batch-to-batch reproducibility and are stable across a wide range of chemical conditions. Here we mimicked a typical initial screening of affimers and commercially available monoclonal antibodies against two non-related proteins, IL-37b and proinsulin, to assess the potential of affimers as alternative to antibodies. METHODS: Binding specificity of anti-IL-37b and anti-proinsulin affimers and antibodies was investigated via magnetic bead-based capture of their recombinant protein targets in human plasma. Captured proteins were analyzed using SDS-PAGE, Coomassie blue staining, Western blotting and LC-MS/MS-based proteomics. RESULTS: All affimers and antibodies were able to bind their target protein in human plasma. Gel and LC-MS/MS analysis showed that both affimer and antibody-based captures resulted in co-purified background proteins. However, affimer-based captures showed the highest relative enrichment of IL-37b and proinsulin. CONCLUSIONS: For both proteins tested, affimers show higher specificity in purifying their target proteins from human plasma compared to monoclonal antibodies. These results indicate that affimers are promising antibody-replacement tools for protein biomarker assay development.

The future of protein biomarker research in type 2 diabetes mellitus.

INTRODUCTION: The onset of type 2 diabetes mellitus (T2DM) is strongly associated with obesity and subsequent perturbations in immuno-metabolic responses. To understand the complexity of these systemic changes and better monitor the health status of people at risk, validated clinical biomarkers are needed. Omics technologies are increasingly applied to measure the interplay of genes, proteins and metabolites in biological systems, which is imperative in understanding molecular mechanisms of disease and selecting the best possible molecular biomarkers for clinical use. Areas covered: This review describes the complex onset of T2DM, the contribution of obesity and adipose tissue inflammation to the T2DM disease mechanism, and the output of current biomarker strategies. A new biomarker approach is described that combines published and new self-generated data to merge multiple -omes (i.e. genome, proteome, metabolome etc.) toward understanding of mechanism of disease on the individual level and design multiparameter biomarker panels that drive significant impacts on personalized healthcare. Expert commentary: We here propose an approach to use cross-omics analyses to contextualize published biomarker data and better understand molecular mechanisms of health and disease. This will improve the current and future innovation gaps in translation of discovered putative biomarkers to clinically applicable biomarker tests.

Evaluation of chitotriosidase as a biomarker for adipose tissue inflammation in overweight individuals and type 2 diabetic patients.

BACKGROUND: Overweight and obesity can lead to adipose tissue inflammation, which causes insulin resistance and on the long-term type 2 diabetes mellitus (T2D). The inflammatory changes of obese-adipose tissue are characterized by macrophage infiltration and activation, but validated circulating biomarkers for adipose tissue inflammation for clinical use are still lacking. One of the most secreted enzymes by activated macrophages is chitotriosidase (CHIT1). OBJECTIVE: To test whether circulating CHIT1 enzymatic activity levels reflect adipose tissue inflammation. METHODS: Plasma and adipose tissue samples of 105 subjects (35 lean, 37 overweight, and 33 T2D patients) were investigated. CHIT1 mRNA levels were determined in adipose tissue-resident innate immune cells. CHIT1 mRNA levels, protein abundance, and plasma enzymatic activity were subsequently measured in adipose tissue biopsies and plasma of control subjects with varying levels of obesity and adipose tissue inflammation as well as in T2D patients. RESULTS: In adipose tissue, CHIT1 mRNA levels were higher in stromal vascular cells compared to adipocytes, and higher in adipose tissue-residing macrophages compared to circulating monocytes (p < 0.001). CHIT1 mRNA levels in adipose tissue were enhanced in overweightcompared to lean subjects and even more in T2D patients (p < 0.05). In contrast, plasma CHIT1 enzymatic activity did not differ between lean, overweight subjects and T2D patients. A mutation of the CHIT1 gene decreases plasma CHIT1 activity. CONCLUSIONS: CHIT1 is expressed by adipose tissue macrophages and expression is higher in overweight subjects and T2D patients, indicating its potential as tissue biomarker for adipose tissue inflammation. However, these differences do not translate into different plasma CHIT1 activity levels. Moreover, a common CHIT1 gene mutation causing loss of plasma CHIT1 activity interferes with its use as a biomarker of adipose tissue inflammation. These results indicate that plasma CHIT1 activity is of limited value as a circulating biomarker for adipose tissue inflammation in human subjects.