The metabolomics of psoriatic disease.

Metabolomics is an emerging new "omics" field involving the systematic analysis of the metabolites in a biologic system. These metabolites provide a molecular snapshot of cellular activity and are thus important for understanding the functional changes in metabolic pathways that drive disease. Recently, metabolomics has been used to study the local and systemic metabolic changes in psoriasis and its cardiometabolic comorbidities. Such studies have revealed novel insights into disease pathogenesis and suggest new biochemical signatures that may be used as a marker of psoriatic disease. This review will discuss common strategies in metabolomics analysis, current findings in the metabolomics of psoriasis, and emerging trends in psoriatic metabolomics

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

A Canadian Study of Cisplatin Metabolomics and Nephrotoxicity (ACCENT): A Clinical Research Protocol

Background: Cisplatin, a chemotherapy used to treat solid tumors, causes acute kidney injury (AKI), a known risk factor for chronic kidney disease and mortality. AKI diagnosis relies on biomarkers which are only measurable after kidney damage has occurred and functional impairment is apparent; this prevents timely AKI diagnosis and treatment. Metabolomics seeks to identify metabolite patterns involved in cell tissue metabolism related to disease or patient factors. The A Canadian study of Cisplatin mEtabolomics and NephroToxicity (ACCENT) team was established to harness the power of metabolomics to identify novel biomarkers that predict risk and discriminate for presence of cisplatin nephrotoxicity, so that early intervention strategies to mitigate onset and severity of AKI can be implemented. Objective: Describe the design and methods of the ACCENT study which aims to identify and validate metabolomic profiles in urine and serum associated with risk for cisplatin-mediated nephrotoxicity in children and adults. Design: Observational prospective cohort study. Setting: Six Canadian oncology centers (3 pediatric, 1 adult and 2 both). Patients: Three hundred adults and 300 children planned to receive cisplatin therapy. Measurements: During two cisplatin infusion cycles, serum and urine will be measured for creatinine and electrolytes to ascertain AKI. Many patient and disease variables will be collected prospectively at baseline and throughout therapy. Metabolomic analyses of serum and urine will be done using mass spectrometry. An untargeted metabolomics approach will be used to analyze serum and urine samples before and after cisplatin infusions to identify candidate biomarkers of cisplatin AKI. Candidate metabolites will be validated using an independent cohort. Methods: Patients will be recruited before their first cycle of cisplatin. Blood and urine will be collected at specified time points before and after cisplatin during the first infusion and an infusion later during cancer treatment. The primary outcome is AKI, defined using a traditional serum creatinine-based definition and an electrolyte abnormality-based definition. Chart review 3 months after cisplatin therapy end will be conducted to document kidney health and survival. Limitations: It may not be possible to adjust for all measured and unmeasured confounders when evaluating prediction of AKI using metabolite profiles. Collection of data across multiple sites will be a challenge. Conclusions: ACCENT is the largest study of children and adults treated with cisplatin and aims to reimagine the current model for AKI diagnoses using metabolomics. The identification of biomarkers predicting and detecting AKI in children and adults treated with cisplatin can greatly inform future clinical investigations and practices

On the Road to Accurate Biomarkers for Cardiometabolic Diseases by Integrating Precision and Gender Medicine Approaches

The need to facilitate the complex management of cardiometabolic diseases (CMD) has led to the detection of many biomarkers, however, there are no clear explanations of their role in the prevention, diagnosis or prognosis of these diseases. Molecules associated with disease pathways represent valid disease surrogates and well-fitted CMD biomarkers. To address this challenge, data from multi-omics types (genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and nutrigenomics), from human and animal models, have become available. However, individual omics types only provide data on a small part of molecules involved in the complex CMD mechanisms, whereas, here, we propose that their integration leads to multidimensional data. Such data provide a better understanding of molecules related to CMD mechanisms and, consequently, increase the possibility of identifying well-fitted biomarkers. In addition, the application of gender medicine also helps to identify accurate biomarkers according to gender, facilitating a differential CMD management. Accordingly, the impact of gender differences in CMD pathophysiology has been widely demonstrated, where gender is referred to the complex interrelation and integration of sex (as a biological and functional marker of the human body) and psychological and cultural behavior (due to ethnical, social, and religious background). In this review, all these aspects are described and discussed, as well as potential limitations and future directions in this incipient field

Identification of Plasma Metabolites Associated with Breast and Ovarian Cancer and Breast Cancer Prognosis

As two leading female cancers, breast cancer, especially metastatic breast cancer, and ovarian cancer, have brought an increasing health and economic burden globally. Biomarkers could improve patient outcomes and quality of life because they play vital roles in cancer screening, diagnosis, prognosis, and prediction. Metabolites are promising cancer biomarkers, as they represent the ultimate phenotypic alteration of the organism and are closely related to cancer. Plasma metabolites can be accessed with minimally invasive procedures. Using plasma metabolites as biomarkers for cancer and other diseases has been widely explored because of the possibility of repeated sampling and periodic monitoring of blood samples. However, metabolic studies are still in their infancy, and only a few studies with large sample sizes are available so far. In this thesis project, we explored the potential of metabolites as putative diagnostic and prognostic markers in breast and ovarian cancer. Plasma metabolite profiling and subsequent validation in primary breast cancer patients and healthy controls identified 18 metabolites that were significantly differentially represented (FDR < 0.05). Multivariate logistic regression analysis selected a panel of seven metabolites to discriminate primary breast cancer patients from healthy controls with an AUC of 0.80. If this panel of metabolites identified here could be verified in large prospective study cohorts, this panel, including Glu, Orn, Thr, Trp, Met-SO, C2, and C3, might add value to multi-molecular diagnostic marker sets for breast cancer early detection. The association of plasma metabolites with metastatic breast cancer was investigated as well. Metastatic breast cancer patients with high numbers of circulating tumor cells (termed CTC-positive) and those with low numbers or without CTCs (termed CTC-negative) were analyzed and compared to healthy controls as well as primary breast cancer patients. Lists of 19 and 12 metabolites were identified to significantly distinguish CTC-positive and CTC-negative samples from healthy controls, respectively. A panel comprising His, C4:0, C18:1, lysoPC a C18:2, PC aa C40:6, and PC ae C42:3 for CTC-positive patients with AUC = 0.92, and a combination of Asn, Glu, His, Thr, Trp, C16:0, C18:0, C18:1, C18:2, lysoPC a C18:2, and PC aa C40:6 for CTC-negative patients with AUC = 0.89 were selected to distinguish from healthy controls. Significantly different metabolites between CTC-positive/CTC-negative and primary breast cancer patients exhibited significant overlaps with those between CTC-positive/CTC-negative patients and healthy controls. We also investigated the prognostic value of metabolites in metastatic breast cancer patients. After successive analysis of the discovery and validation cohorts, four metabolites were found to be significantly negatively correlated with progression-free survival, while 12 metabolites were negatively correlated with overall survival. Amongst these metabolites associated with survival, LASSO Cox regression analysis selected a combination of PC ae C36:1 and PC ae C38:3 to predict progression-free survival, and a combination of lysoPC a C20:3, lysoPC a C20:4, PC aa C38:5, PC ae C38:3, and SM (OH) C22:2 to predict overall survival. Even though the proposed metabolic signatures showed a lower prognostic power than the CTC status, an FDA-approved prognostic marker, the combination of the Cox selected metabolites with the CTC status displayed a lower integrated prediction error than CTC status alone. Therefore, the identified metabolic markers might add prognostic value in combination with other biomarkers such as CTC status determination. The majority of the here identified metabolites have previously shown functional roles in cancer and metastasis development, thus laying a supposed mechanistic basis for their differential levels observed in plasma. Lastly, comparative profiling of plasma metabolites in ovarian cancer patients and healthy controls were applied to identify metabolites associated with ovarian cancer. Remarkably, 71 significantly differentially expressed metabolites were identified (FDR < 0.05). Most of them were down-regulated in ovarian cancer patients. A combination of seven metabolites, including His, Trp, C18:1, lysoPC a C18:2, PC aa C32:2, PC aa C34:4, PC ae C34:3, were identified to differentiate ovarian cancer cases from healthy controls with an AUC of 0.95. Furthermore, this panel could distinguish ovarian cancer from primary breast cancer patients with an AUC of 0.93. In conclusion, we identified specific signatures of plasma metabolites associated with primary breast cancer, metastatic breast cancer, and ovarian cancer. Further, we identified sets of metabolites correlated with the prognosis of metastatic breast cancer patients. If these identified metabolic marker signatures can be verified in large, multi-centric, prospective studies, they might add value to the development of blood-based diagnostic tests

Salivary biomarker development using genomic, proteomic and metabolomic approaches.

The use of saliva as a diagnostic sample provides a non-invasive, cost-efficient method of sample collection for disease screening without the need for highly trained professionals. Saliva collection is far more practical and safe compared with invasive methods of sample collection, because of the infection risk from contaminated needles during, for example, blood sampling. Furthermore, the use of saliva could increase the availability of accurate diagnostics for remote and impoverished regions. However, the development of salivary diagnostics has required technical innovation to allow stabilization and detection of analytes in the complex molecular mixture that is saliva. The recent development of cost-effective room temperature analyte stabilization methods, nucleic acid pre-amplification techniques and direct saliva transcriptomic analysis have allowed accurate detection and quantification of transcripts found in saliva. Novel protein stabilization methods have also facilitated improved proteomic analyses. Although candidate biomarkers have been discovered using epigenetic, transcriptomic, proteomic and metabolomic approaches, transcriptomic analyses have so far achieved the most progress in terms of sensitivity and specificity, and progress towards clinical implementation. Here, we review recent developments in salivary diagnostics that have been accomplished using genomic, transcriptomic, proteomic and metabolomic approaches