Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders. Future Sci OA. 2016;2:FSO106. [PMID: 28031956 PMCID: PMC5137983 DOI: 10.4155/fsoa-2015-0014]

A systematic review: metabolomics-based identification of altered metabolites and pathways in the skin caused by internal and external factors

The skin's ability to function optimally is affected by many diverse factors. Metabolomics has a great potential to improve our understanding of the underlying metabolic changes and the affected pathways. Therefore, the objective of this study was to review the current state of the literature and to perform further metabolic pathway analysis on the obtained data. The aim was to gain an overview of the metabolic changes under altered conditions and to identify common and different patterns as a function of the investigated factors. A cross-study comparison of the extracted studies from different databases identified 364 metabolites, whose concentrations were considerably altered by the following factor groups: irradiation, xenobiotics, aging, and skin diseases (mainly psoriasis). Using metabolic databases and pathway analysis tools the individual metabolites were assigned to the corresponding metabolic pathways and the most strongly affected signaling pathways were identified. All factors induced oxidative stress. Thus, antioxidant defense systems, especially coenzyme Q₁₀  (aging) and the glutathione system (irradiation, aging, xenobiotics) were impacted. Lipid metabolism was also impacted by all factors studied. The carnitine shuttle as part of β-oxidation was activated by all factor groups except aging. Glycolysis, Krebs (TCA) cycle and purine metabolism were mainly affected by irradiation and xenobiotics. The pentose phosphate pathway was activated and Krebs cycle was downregulated in response to oxidative stress. In summary, it can be ascertained that mainly energy metabolism, lipid metabolism, antioxidative defense and DNA repair systems were impacted by the factors studied.

Mapping Salivary Proteases in Sjögren's Syndrome Patients Reveals Overexpression of Dipeptidyl Peptidase-4/CD26

Sjögren's Syndrome (SS) is an autoimmune exocrinopathy characterized by the progressive damage of salivary and lacrimal glands associated with lymphocytic infiltration. Identifying new non-invasive biomarkers for SS diagnosis remains a challenge, and alterations in saliva composition reported in patients turn this fluid into a source of potential biomarkers. Among these, proteases are promising candidates since they are involved in several key physio-pathological processes. This study evaluated differentially expressed proteases in SS individuals' saliva using synthetic fluorogenic substrates, zymography, ELISA, and proteomic approaches. Here we reported, for the first time, increased activity of the serine protease dipeptidyl peptidase-4/CD26 (DPP4/CD26) in pSS saliva, the expression level of which was corroborated by ELISA assay. Gelatin zymograms showed that metalloproteinase proteolytic band profiles differed significantly in intensity between control and SS groups. Focusing on matrix metalloproteinase-9 (MMP9) expression, an increased tendency in pSS saliva (p = 0.0527) was observed compared to the control group. Samples of control, pSS, and sSS were analyzed by mass spectrometry to reveal a general panorama of proteases in saliva. Forty-eight protein groups of proteases were identified, among which were the serine proteases cathepsin G (CTSG), neutrophil elastase (ELANE), myeloblastin (PRTN3), MMP9 and several protease inhibitors. This work paves the way for proteases to be explored in the future as biomarkers, emphasizing DPP4 by its association in several autoimmune and inflammatory diseases. Besides its proteolytic role, DPP4/CD26 acts as a cell surface receptor, signal transduction mediator, adhesion and costimulatory protein involved in T lymphocytes activation.

Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer

Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.

The frontiers of biomedical science and its application to animal science in addressing the major challenges facing Australasian dairy farming

Extraordinary advances are occurring in biomedical science that may revolutionise how we approach health and disease. Many have applications in the dairy industry. We have described one particular area of extracellular vesicles that have already proven to be of interest in diagnostics and prognostics for fertility and assessment of ‘transition’ cows (i.e. evaluation of the problems related to the risk of clinical diseases in dairy cows, such as mastitis and milk fever, during transition period). The addition of measurements of circulating RNA and DNA may prove of value in identifying dairy cows with higher risks of clinical diseases and potentially poor fertility. We describe the exciting opportunity provided by the possibility of generating exosomes to order as therapeutic agents to potentially enhance fertility. The even more radical concept of using exosomes to deliver a CRISPR-linked gene editing function is presented. Undoubtedly, the use of biomedical advances to assist the dairy industry is an obvious and practical approach that has significant merit.

HS-SPME-GC-MS approach for the analysis of volatile salivary metabolites and application in a case study for the indirect assessment of gut microbiota

In this work, a straightforward analytical approach based on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was developed for the analysis of salivary volatile organic compounds without any prior derivatization step. With a sample volume of 500 μL, optimal conditions were achieved by allowing the sample to equilibrate for 10 min at 50 °C and then extracting the samples for 10 min at the same temperature, using a carboxen/polydimethylsiloxane fibre. The method allowed the simultaneous identification and quantification of 20 compounds in sample headspace, including short-chain fatty acids and their derivatives which are commonly analysed after analyte derivatization. The proof of applicability of the methodology was performed with a case study regarding the analysis of the dynamics of volatile metabolites in saliva of a single subject undergoing 5-day treatment with rifaximin antibiotic. Non-stimulated saliva samples were collected over 3 weeks from a nominally healthy volunteer before, during, and after antibiotic treatment. The variations of some metabolites, known to be produced by the microbiota and by bacteria that are susceptible to antibiotics, suggest that the study of the dynamics of salivary metabolites can be an excellent indirect method for analysing the gut microbiota. This approach is novel from an analytical standpoint, and it encourages further studies combining saliva metabolite profiles and gut microbiota dynamics. Graphical abstract.

Cerumenogram: a new frontier in cancer diagnosis in humans

Cancer is the deadliest human disease and the development of new diagnosis methods is important to increase the chances of a cure. In this work it was developed a new method, named here for the first time as cerumenogram, using cerumen (earwax) as a new biomatrix for diagnosis. Earwax samples collected from cancer patients (cancer group) and cancer-free patients (control group) were analyzed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS), following with multivariate analysis steps to process the raw data generated. In total, 158 volatile organic metabolites (VOMs) were identified in the cerumen samples. The 27 selected as potential VOMs biomarkers for cancer provided 100% discrimination between the cancer and control groups. This new test can thus be routinely employed for cancer diagnoses that is non-invasive, fast, cheap, and highly accurate.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Identification of volatile metabolites in human saliva from patients with oral squamous cell carcinoma via zeolite-based thin-film microextraction coupled with GC-MS

In recent years, volatile organic compounds (VOCs) discharged from the human body, of which some compounds exhibit strong correlations with pathological conditions, have attracted attention as a new means of disease diagnosis technology. The aim of this study was to establish the salivary metabolomic profiles of oral squamous cell carcinoma (OSCC) patients and healthy volunteers (control group) and to investigate VOCs as potential biomarkers in the diagnosis of oral cancer. We have demonstrated a method combining thin-film microextraction based on a ZSM-5/polydimethylsiloxane hybrid film coupled with gas chromatography-mass spectrometry and carried out a comparative analysis of salivary VOC profiles between OSCC patients and healthy controls. The results depicted that 42 and 73 VOCs were detected and identified in samples from the healthy control group (n = 50) and oral cancer group (n = 24), respectively. Among them, twenty-seven VOCs (ten were decreased, seven disappeared, and ten were newly produced in the oral cancer group) depict significant differences between both the sample groups, and they have relevance as candidate biomarkers for OSCC. Twelve salivary VOCs that were characteristic of oral cancer patients were finally extracted and used for pattern recognition analyses for oral cancer diagnosis. The proposed TFME approach for analyzing human saliva on the basis of a ZSM-5-loaded PDMS hybrid thin film has been performed for the very first time in the field of dentistry.

Microbial markers in colorectal cancer detection and/or prognosis

Colorectal cancer (CRC) is the second leading cause of cancer worldwide. CRC is still associated with a poor prognosis among patients with advanced disease. On the contrary, due to its slow progression from detectable precancerous lesions, the prognosis for patients with early stages of CRC is encouraging. While most robust methods are invasive and costly, actual patient-friendly screening methods for CRC suffer of lack of sensitivity and specificity. Therefore, the development of sensitive, non-invasive and cost-effective methods for CRC detection and prognosis are necessary for increasing the chances of a cure. Beyond its beneficial functions for the host, increasing evidence suggests that the intestinal microbiota is a key factor associated with carcinogenesis. Many clinical studies have reported a disruption in the gut microbiota balance and an alteration in the faecal metabolome of CRC patients, suggesting the potential use of a microbial-based test as a non-invasive diagnostic and/or prognostic tool for CRC screening. This review aims to discuss the microbial signatures associated with CRC known to date, including dysbiosis and faecal metabolome alterations, and the potential use of microbial variation markers for non-invasive early diagnosis and/or prognostic assessment of CRC and advanced adenomas. We will finally discuss the possible use of these markers as predicators for treatment response and their limitations.

Growth of Malignant Non-CNS Tumors Alters Brain Metabolome

Cancer survivors experience numerous treatment side effects that negatively affect their quality of life. Cognitive side effects are especially insidious, as they affect memory, cognition, and learning. Neurocognitive deficits occur prior to cancer treatment, arising even before cancer diagnosis, and we refer to them as "tumor brain." Metabolomics is a new area of research that focuses on metabolome profiles and provides important mechanistic insights into various human diseases, including cancer, neurodegenerative diseases, and aging. Many neurological diseases and conditions affect metabolic processes in the brain. However, the tumor brain metabolome has never been analyzed. In our study we used direct flow injection/mass spectrometry (DI-MS) analysis to establish the effects of the growth of lung cancer, pancreatic cancer, and sarcoma on the brain metabolome of TumorGraft™ mice. We found that the growth of malignant non-CNS tumors impacted metabolic processes in the brain, affecting protein biosynthesis, and amino acid and sphingolipid metabolism. The observed metabolic changes were similar to those reported for neurodegenerative diseases and brain aging, and may have potential mechanistic value for future analysis of the tumor brain phenomenon.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.