Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

Combination of deep XLMS with deep learning reveals an ordered rearrangement and assembly of a major protein component of the vaccinia virion

Vaccinia virus, the prototypical poxvirus and smallpox/monkeypox vaccine, has proven a challenging entity for structural biology, defying many of the approaches leading to molecular and atomic models for other viruses. Via a combination of deep learning and cross-linking mass spectrometry, we have developed an atomic-level model and an integrated processing/assembly pathway for a structural component of the vaccinia virion, protein P4a. Within the pathway, proteolytic separation of the C-terminal P4a-3 segment of P4a triggers a massive conformational rotation within the N-terminal P4a-1 segment that becomes fixed by disulfide-locking while removing a steric block to trimerization of the processing intermediate P4a-1+2. These events trigger the proteolytic separation of P4a-2, allowing the assembly of P4a-1 into a hexagonal lattice that encloses the nascent virion core

Metabolomic analyses of Leishmania reveal multiple species differences and large differences in amino acid metabolism

Comparative genomic analyses of Leishmania species have revealed relatively minor heterogeneity amongst recognised housekeeping genes and yet the species cause distinct infections and pathogenesis in their mammalian hosts. To gain greater information on the biochemical variation between species, and insights into possible metabolic mechanisms underpinning visceral and cutaneous leishmaniasis, we have undertaken in this study a comparative analysis of the metabolomes of promastigotes of L. donovani, L. major and L. mexicana. The analysis revealed 64 metabolites with confirmed identity differing 3-fold or more between the cell extracts of species, with 161 putatively identified metabolites differing similarly. Analysis of the media from cultures revealed an at least 3-fold difference in use or excretion of 43 metabolites of confirmed identity and 87 putatively identified metabolites that differed to a similar extent. Strikingly large differences were detected in their extent of amino acid use and metabolism, especially for tryptophan, aspartate, arginine and proline. Major pathways of tryptophan and arginine catabolism were shown to be to indole-3-lactate and arginic acid, respectively, which were excreted. The data presented provide clear evidence on the value of global metabolomic analyses in detecting species-specific metabolic features, thus application of this technology should be a major contributor to gaining greater understanding of how pathogens are adapted to infecting their hosts

Metabolic characterisation of disturbances in the APOC3/triglyceride-rich lipoprotein pathway through sample-based recall by genotype

IntroductionHigh plasma triacylglyceride levels are known to be associated with increased risk of atherosclerotic cardiovascular disease. Apolipoprotein C-III (apoC-III) is a key regulator of plasma triacylglyceride levels and is associated with hypertriglyceridemia via a number of pathways. There is consistent evidence for an association of cardiovascular events with blood apoC-III level, with support from human genetic studies of APOC3 variants. As such, apoC-III has been recognised as a potential therapeutic target for patients with severe hypertriglyceridaemia with one of the most promising apoC-III-targeting drugs, volanesorsen, having recently progressed through Phase III trials.ObjectivesTo exploit a rare loss of function variant in APOC3 (rs138326449) to characterise the potential long-term treatment effects of apoC-III targeting interventions on the metabolome.MethodsIn a recall-by-genotype study, 115 plasma samples were analysed by UHPLC-MS to acquire non-targeted metabolomics data. The study included samples from 57 adolescents and 33 adults. Overall, 12 985 metabolic features were tested for an association with APOC3 genotype.Results161 uniquely annotated metabolites were found to be associated with rs138326449(APOC3). The highest proportion of associated metabolites belonged to the acyl-acyl glycerophospholipid and triacylglyceride metabolite classes. In addition to the anticipated (on-target) reduction of metabolites in the triacylglyceride and related classes, carriers of the rare variant exhibited previously unreported increases in levels of a number of metabolites from the acyl-alkyl glycerophospholipid class.ConclusionOverall, our results suggest that therapies targeting apoC-III may potentially achieve a broad shift in lipid profile that favours better metabolic health

An optimized SP3 sample processing workflow for in-depth and re-producible phosphoproteomics

Protein phosphorylation is a ubiquitous post-translational modification (PTM) found across the kingdoms of life, and is critical for the regulation of protein function in health &amp; disease. Advances in high-throughput mass spectrometry have transformed our ability to interrogate the phosphoproteome. However, sample preparation methodologies optimized for phosphoproteomics have not kept pace, compromising the ability to fully exploit these technological advances. In this study, we present an optimized phosphoproteomics workflow using carboxylated SP3 magnetic beads which have simplified proteomics sample preparation. By employing a washing step with 8 M urea and omitting the conventional C18 SPE clean-up, we demonstrate a significant improvement in phosphopeptide identifications, with application of this refined protocol to HEK-293T cell extracts increasing the number nearly 2-fold compared to standard SP3 techniques (7908 cf. 4129). We also observed a substantial improvement in the detection of multiply phosphorylated peptides. Our findings suggest that the complexity of PTM crosstalk using current peptide-based proteomics workflows is currently under-represented and underscores the necessity of methodological innovations to better capture the intricacies of the phosphoproteome landscape.</jats:p

Thrombin activation of the factor XI dimer is a multistaged process for each subunit

Background: Factor (F)XI can be activated by proteases, including thrombin and FXIIa. The interactions of these enzymes with FXI are transient in nature and therefore difficult to study. Objectives: To identify the binding interface between thrombin and FXI and understand the dynamics underlying FXI activation. Methods: Crosslinking mass spectrometry was used to localize the binding interface of thrombin on FXI. Molecular dynamics simulations were applied to investigate conformational changes enabling thrombin-mediated FXI activation after binding. The proposed trajectory of activation was examined with nanobody 1C10, which was previously shown to inhibit thrombin-mediated activation of FXI. Results: We identified a binding interface of thrombin located on the light chain of FXI involving residue Pro520. After this initial interaction, FXI undergoes conformational changes driven by binding of thrombin to the apple 1 domain in a secondary step to allow migration toward the FXI cleavage site. The 1C10 binding site on the apple 1 domain supports this proposed trajectory of thrombin. We validated the results with known mutation sites on FXI. As Pro520 is conserved in prekallikrein (PK), we hypothesized and showed that thrombin can bind PK, even though it cannot activate PK. Conclusion: Our investigations show that the activation of FXI is a multistaged procedure. Thrombin first binds to Pro520 in FXI; thereafter, it migrates toward the activation site by engaging the apple 1 domain. This detailed analysis of the interaction between thrombin and FXI paves a way for future interventions for bleeding or thrombosis

Oxonium Ion-Guided Optimization of Ion Mobility-Assisted Glycoproteomics on the timsTOF Pro

Spatial separation of ions in the gas phase, providing information about their size as collisional cross-sections, can readily be achieved through ion mobility. The timsTOF Pro (Bruker Daltonics) series combines a trapped ion mobility device with a quadrupole, collision cell, and a time-of-flight analyzer to enable the analysis of ions at great speed. Here, we show that the timsTOF Pro is capable of physically separating N-glycopeptides from nonmodified peptides and producing high-quality fragmentation spectra, both beneficial for glycoproteomics analyses of complex samples. The glycan moieties enlarge the size of glycopeptides compared with nonmodified peptides, yielding a clear cluster in the mobilogram that, next to increased dynamic range from the physical separation of glycopeptides and nonmodified peptides, can be used to make an effective selection filter for directing the mass spectrometer to analytes of interest. We designed an approach where we (1) focused on a region of interest in the ion mobilogram and (2) applied stepped collision energies to obtain informative glycopeptide tandem mass spectra on the timsTOF Pro:glyco-polygon–stepped collision energy-parallel accumulation serial fragmentation. This method was applied to selected glycoproteins, human plasma– and neutrophil-derived glycopeptides. We show that the achieved physical separation in the region of interest allows for improved extraction of information from the samples, even at shorter liquid chromatography gradients of 15 min. We validated our approach on human neutrophil and plasma samples of known makeup, in which we captured the anticipated glycan heterogeneity (paucimannose, phosphomannose, high mannose, hybrid and complex glycans) from plasma and neutrophil samples at the expected abundances. As the method is compatible with off-the-shelve data acquisition routines and data analysis software, it can readily be applied by any laboratory with a timsTOF Pro and is reproducible as demonstrated by a comparison between two laboratories

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics

Serological identification and expression analysis of gastric cancer-associated genes

Serological identification of tumour antigens by recombinant expression cloning has proved to be an effective strategy for the identification of cancer-associated genes having a relevance to cancer aetiology and progression, and for defining possible targets for immunotherapeutic intervention. In the present study we applied this technique to identify immunogenic proteins for gastric cancer that resulted in isolation of 14 distinct serum-reactive antigens. In order to evaluate their role in tumourigenesis and assess the immunogenicity of the identified antigens, we characterised each cDNA clone by DNA sequence analysis, mRNA tissue distribution, comparison of mRNA levels in cancerous and adjacent non-cancerous tissues and the frequency of antibody responses in allogeneic patient and control sera. Previously unknown splice variants of TACC1 and an uncharacterised gene Ga50 were identified. The expression of a newly identified TACC1 isoform is restricted to brain and gastric cancer tissues. Comparison of mRNA levels by semi-quantitative RT–PCR revealed a relative overexpression of three genes in cancer tissues, including growth factor granulin and Tbdn-1 – an orthologue of the mouse acetyltransferase gene which is associated with blood vessel development. An unusual DNA polymorphism – a three-nucleotide deletion was found in NUCB2 cDNA but its mRNA level was consistently decreased in gastric tumours compared with that in the adjacent non-cancerous tissues. This study has revealed several new gastric cancer candidate genes; additional studies are required to gain a deeper insight into their role in the tumorigenesis and their potential as therapeutic targets

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies