18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

Global Proteomics for Identifying the Alteration Pathway of Niemann-Pick Disease Type C Using Hepatic Cell Models

Niemann-Pick disease type C (NPC) is an autosomal recessive disorder with progressive neurodegeneration. Although the causative genes were previously identified, NPC has unclear pathophysiological aspects, and patients with NPC present various symptoms and onset ages. However, various novel biomarkers and metabolic alterations have been investigated; at present, few comprehensive proteomic alterations have been reported in relation to NPC. In this study, we aimed to elucidate proteomic alterations in NPC and perform a global proteomics analysis for NPC model cells. First, we developed two NPC cell models by knocking out NPC1 using CRISPR/Cas9 (KO1 and KO2). Second, we performed a label-free (LF) global proteomics analysis. Using the LF approach, more than 300 proteins, defined as differentially expressed proteins (DEPs), changed in the KO1 and/or KO2 cells, while the two models shared 35 DEPs. As a bioinformatics analysis, the construction of a protein-protein interaction (PPI) network and an enrichment analysis showed that common characteristic pathways such as ferroptosis and mitophagy were identified in the two model cells. There are few reports of the involvement of NPC in ferroptosis, and this study presents ferroptosis as an altered pathway in NPC. On the other hand, many other pathways and DEPs were previously suggested to be associated with NPC, supporting the link between the proteome analyzed here and NPC. Therapeutic research based on these results is expected in the future.

Biomarkers in exhaled breath condensate as fingerprints of asthma, chronic obstructive pulmonary disease and asthma-chronic obstructive pulmonary disease overlap: a critical review

Asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap are the third leading cause of mortality around the world. They share some common features, which can lead to misdiagnosis. To properly manage these conditions, reliable markers for early and accurate diagnosis are needed. Over the past 20 years, many molecules have been investigated in the exhaled breath condensate to better understand inflammation pathways and mechanisms related to these disorders. Recently, more advanced techniques, such as sensitive metabolomic and proteomic profiling, have been used to obtain a more comprehensive understanding. This article reviews the use of targeted and untargeted metabolomic methodology to study asthma, COPD and asthma-COPD overlap.

The effect of mitochondria-targeted slow hydrogen sulfide releasing donor AP39-treatment on airway inflammation

Mitochondrial dysfunction has been shown to contribute to the pathophysiology of airway diseases. Therefore, mitochondria are targeted in the development of new therapeutic approaches. Hydrogen sulfide (H₂S) has been shown to be involved in the pathophysiological processes of airway inflammation. We aimed to evaluate the effect of mitochondria-targeted slow H₂S releasing donor AP39 [(10-oxo-10-(4-(3-thioxo-3H-1,2-dithiol5yl)phenoxy)decyl)triphenylphosphoniumbromide)] on lipopolysaccharide (LPS)-induced airway inflammation in mice. LPS was applied to female Balb/c mice by intranasal (i.n.) route to induce airway inflammation and the subgroups of mice were treated with i.n. AP39 (250-1000 nmol/kg). 48 h after LPS administration airway reactivity was evaluated in vivo, then bronchoalveolar lavage (BAL) fluid and lungs were collected. LPS application led to bronchial hyperreactivity and neutrophil infiltration into the lung tissues along with increased TNF-α, IL-1β and IL-6 levels in BAL fluid. LPS also induced an increase in the rate of glycolysis, glycogenolysis and Krebs-cycle. AP39 treatment prevented the LPS-induced bronchial hyperreactivity and reversed the increase in TNF-α and IL-6 levels in BAL fluid. The increase in neutrophil numbers in BAL fluid was also prevented by AP39 treatment at the highest dose. Our results indicate that AP39 can prevent bronchial hyperreactivity and decrease airway inflammation. Targeting H₂S to the mitochondria may be a new therapeutic approach in airway inflammation.

Biomarkers used in Alzheimer's disease diagnosis, treatment, and prevention

Alzheimer's disease (AD), being the number one in terms of dementia burden, is an insidious age-related neurodegenerative disease and is presently considered a global public health threat. Its main histological hallmarks are the Aβ senile plaques and the P-tau neurofibrillary tangles, while clinically it is marked by a progressive cognitive decline that reflects the underlying synaptic loss and neurodegeneration. Many of the drug therapies targeting the two pathological hallmarks namely Aβ and P-tau have been proven futile. This is probably attributed to the initiation of therapy at a stage where cognitive alterations are already obvious. In other words, the underlying neuropathological changes are at a stage where these drugs lack any therapeutic value in reversing the damage. Therefore, there is an urgent need to start treatment in the very early stage where these changes can be reversed, and hence, early diagnosis is of primordial importance. To this aim, the use of robust and informative biomarkers that could provide accurate diagnosis preferably at an earlier phase of the disease is of the essence. To date, several biomarkers have been established that, to a different extent, allow researchers and clinicians to evaluate, diagnose, and more specially exclude other related pathologies. In this study, we extensively reviewed data on the currently explored biomarkers in terms of AD pathology-specific and non-specific biomarkers and highlighted the recent developments in the diagnostic and theragnostic domains. In the end, we have presented a separate elaboration on aspects of future perspectives and concluding remarks.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Development of simultaneous quantitative analysis of tricarboxylic acid cycle metabolites to identify specific metabolites in cancer cells by targeted metabolomic approach

The tricarboxylic acid (TCA) cycle is one of the most important pathways of energy metabolism, and the profiles of its components are influenced by factors such as diseases and diets. Therefore, the differences in metabolic profile of TCA cycle between healthy and cancer cells have been the focus of studies to understand pathological conditions. In this study, we developed a quantitative method to measure TCA cycle metabolites using LC-MS/MS to obtain useful metabolic profiles for development of diagnostic and therapeutic methods for cancer. We successfully analyzed 11 TCA cycle metabolites by LC MS/MS with high reproducibility by using a PFP column with 0.5% formic acid as a mobile phase. Next, we analyzed the concentration of TCA cycle metabolites in human cell lines (HaCaT: normal skin keratinocytes; A431: skin squamous carcinoma cells; SW480: colorectal cancer cells). We observed reduced concentration of succinate and increased concentration of citrate, 2-hydroxyglutarate, and glutamine in A431 cells as compared with HaCaT cells. On the other hand, decreased concentration of isocitrate, fumarate, and α-ketoglutarate and increased concentration of malate, glutamine, and glutamate in A431 cells were observed in comparison with SW480 cells. These findings suggested the possibility of identifying disease-specific metabolites and/or organ-specific metabolites by using this targeted metabolomic analysis.

A line-broadening free real-time 31P pure shift NMR method for phosphometabolomic analysis

Phosphometabolomics by ³¹P NMR can be challenging, since overlapping multiplets of homonuclear coupled phosphorus nuclei complicate spectral analysis. Pure shift NMR allows to simplify such spectra by collapsing multiplets into singlets, but most pure shift methods require substantially elongated measurement times or cause disturbing spectral line broadening. Herein, we combine established pure shift NMR and artefact suppression techniques to record ³¹P pure shift NMR spectra without penalties in measurement time or line width. Examples are demonstrated in resolution of a mixture of nucleotide triphosphates and a biological sample of ¹⁸O labelled ATP isotopomers.

Amniotic fluid and urine metabolomic alterations associated with pregnant women with Down syndrome fetuses

BACKGROUND

Down syndrome (DS) is a chromosomal disorder caused by a third copy of all or part of chromosome 21. Clinical observations and preclinical studies both suggest that DS may be associated with significant metabolic and bioenergetic alterations. But the metabolic alterations in pregnant women carrying DS fetuses still remains unclear. In this study, we investigated the characteristic metabolomics and lipidomics changes during fetal development of DS.

METHODS

The AF and random urine specimens were selected from 20 pregnant women carrying DS fetuses and 20 pregnant women carrying healthy fetuses. The diagnosis of DS was screened according to chromosome karyotype analysis, and untargeted metabolomic and lipidomic analyses were performed.

RESULTS

Through the analyses of AF, 308 differential metabolites were selected between DS and controls. The metabolites with significant changes mainly involved lipid molecules, organic acids, nucleotides and carbon. Further analysis of lipidomics showed 64 differential metabolites, mainly involving glycerides, sphingolipids and glycerolipids. As for urine metabolomic and lipidomic analyses, there existed consistent metabolites with AF, but the number was much less.

CONCLUSIONS

Compared with the controls, carbon metabolism, amino acid metabolism, glyceride metabolism, sphingolipid metabolism and glycerophospholipid metabolism were significantly changed in DS cases. In addition, characterized biomarkers in AF and urine were screened for DS diagnosis, and these metabolites were mainly involved in energy metabolism and liver dysfunction. This finding may help improve the efficiency of prenatal screening for DS.

Metabolic disorder in Alzheimer's disease

Alzheimer's disease (AD), a well known aging-induced neurodegenerative disease is related to amyloid proteinopathy. This proteinopathy occurs due to abnormalities in protein folding, structure and thereby its function in cells. The root cause of such kind of proteinopathy and its related neurodegeneration is a disorder in metabolism, rather metabolomics of the major as well as minor nutrients. Metabolomics is the most relevant "omics" platform that offers a great potential for the diagnosis and prognosis of neurodegenerative diseases as an individual's metabolome. In recent years, the research on such kinds of neurodegenerative diseases, especially aging-related disorders is broadened its scope towards metabolic function. Different neurotransmitter metabolisms are also involved with AD and its associated neurodegeneration. The genetic and epigenetic backgrounds are also noteworthy. In this review, the physiological changes of AD in relation to its corresponding biochemical, genetic and epigenetic involvements including its (AD) therapeutic aspects are discussed.

Gas chromatography-mass spectrometry based 18O stable isotope labeling of Krebs cycle intermediates

New technologies permit determining metabolomic profiles of human diseases by fingerprinting metabolites levels. However, to fully understand metabolomic phenotypes, metabolite levels and turnover rates are necessary to know. Krebs cycle is the major hub of energy metabolism and cell signaling. Traditionally, ¹³C stable isotope labeled substrates were used to track the carbon turnover rates in Krebs cycle metabolites. In this study, for the first time we introduce H₂[¹⁸O] based stable isotope marker that permit tracking oxygen exchange rates in separate segments of Krebs cycle. The chromatographic and non-chromatographic parameters were systematically tested on the effect of labeling ratio of Krebs cycle mediators to increase selectivity and sensitivity of the method. We have developed a rapid, precise, and robust GC-MS method for determining the percentage of ¹⁸O incorporation to Krebs cycle metabolites. The developed method was applied to track the cancer-induced shift in the Krebs cycle dynamics of Caco-2 cells as compared to the control FHC cells revealing Warburg effects in Caco-2 cells. We demonstrate that unique information could be obtained using this newly developed ¹⁸O-labeling analytical technology by following the oxygen exchange rates of Krebs cycle metabolites. Thus, ¹⁸O-labeling of Krebs cycle metabolites expands the arsenal of techniques for monitoring the dynamics of cellular metabolism. Moreover, the developed method will allow to apply the ¹⁸O-labeling technique to numerous other metabolic pathways where oxygen exchange with water takes place.

Metabolomics and proteomics profiles of some medicinal plants and correlation with BDNF activity

BACKGROUND

Identification of the low abundance of phytochemicals in plant extracts is very difficult. Pharmacological activity observed in such plants is not due to a single compound. In most cases, plant extracts show activity based on synergistic or antagonistic effects. Therefore, the idea of a holistic approach is more rational.

PURPOSE

This study was planned to compare the metabolomics and proteomics profiles of Valeriana officinalis L. (Valerianaceae), Melissa officinalis L. (Lamiaceae), Hypericum perforatum L. (Hypericaceae) and Passiflora incarnata L. (Passifloraceae) used in sedative anxiolytic and sleep disorders. Integrated omics analyses were used to provide a better understanding of the effect of plant extracts on the brain-derived neurotrophic factor (BDNF) expression levels on the SH-SY5Y cell line by a holistic approach.

METHODS

Metabolomic profiling of the plants was performed using the GC-MS and LC-qTOF-MS systems, and the proteomics analysis using the LC-qTOF-MS system after trypsin digestion. The Human BDNF Quantikine ELISA kit was utilized to test BDNF expression activity on the SH-SY5Y cell line.

RESULTS

The investigated plant extracts showed a significant increase in BDNF expression (p < 0.05). M. officinalis was found as the most active extract. According to the correlation analyses between BDNF activity and metabolomics or proteomics level, 94 metabolites had a positive correlation while 23 metabolites had a highly negative correlation; those for proteins are 24 and 6, respectively.

CONCLUSION

The multivariate data analysis revealed a similar metabolomics profile of H. perforatum and P. incarnata, which also had a similar activity profile. Remarkably, all the primary metabolites belonging to the Krebs Cycle (citric acid, fumaric acid, succinic acid, pyruvic acid, malic acid and citramalic acid, an analog of malic acid) were positively correlated with BDNF activity. Secondary metabolites with a high BDNF expression belonged to flavonoids, xanthone, coumarines, tannin, naphtalenes, terpenoids and carotenoid skeleton. Two proteins from the cytochrome P450 family (P450 71B11 and P450 94B3) were positively correlated with BDNF activity. Employing omics technologies in the plant research area will offer a better understanding of the role of plant extracts and may lead to the discovery of new compounds with specific activity.

GC-MS analysis of seven metabolites for the screening of pregnant women with Down Syndrome fetuses

Down Syndrome is a genetic disorder caused by the presence of all or part of a third copy of chromosome 21. Metabolomics is identification and quantification of small-molecule metabolites (molecular weight <1000 Da) in tissues, cells and physiological fluids within a certain period time. Metabolites are intermediate products of various types of biochemical reactions that participate in bonding metabolic pathways. In this study, metabolites such as 2-Hydroxybutyric acid, 3-Hydroxybutyric acid, β-Hydroxyisovaleric acid, Uracil, Glutamic acid, Maltose and Melezitose were chosen as the possible determinants/markers for the prenatal screening of Down Syndrome. Quantitative analysis of the metabolites conducted by GCMS method using 5 % phenyl / 95 % dimethylpolysiloxane (30 m ×0.25 mm, 0.25 μm film thickness) capillary column. The oven temperature was held constant at 60 °C for 1 min and ramped at 10 °C /min to 200 °C then ramped at 30 °C/min to 320 °C and hold for 6 min before cool-down, as helium mobile phase and flow rate of 2.8 mL/min and adding Myristic acid-d27 as an internal standard. Our method was validated by parameters of system suitability, stability, linearity, sensitivity, accuracy, precision, selectivity, robustness and ruggedness. The developed and validated method was applied to plasma samples taken from pregnant women with Down Syndrome (study group) and euploid fetuses (healthy group). The levels of these seven metabolites are statistically different (p < 0.05 for all) between the groups. It can be concluded that these relevant metabolites might be used for the prenatal screening of Down Syndrome.

Deep learning in pharmacogenomics: from gene regulation to patient stratification

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

Application of Metabolomics in Alzheimer's Disease

Progress toward the development of efficacious therapies for Alzheimer’s disease (AD) is halted by a lack of understanding early underlying pathological mechanisms. Systems biology encompasses several techniques including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Metabolomics is the newest omics platform that offers great potential for the diagnosis and prognosis of neurodegenerative diseases as an individual’s metabolome reflects alterations in genetic, transcript, and protein profiles and influences from the environment. Advancements in the field of metabolomics have demonstrated the complexity of dynamic changes associated with AD progression underscoring challenges with the development of efficacious therapeutic interventions. Defining systems-level alterations in AD could provide insights into disease mechanisms, reveal sex-specific changes, advance the development of biomarker panels, and aid in monitoring therapeutic efficacy, which should advance individualized medicine. Since metabolic pathways are largely conserved between species, metabolomics could improve the translation of preclinical research conducted in animal models of AD into humans. A summary of recent developments in the application of metabolomics to advance the AD field is provided below.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

New vasoactive peptides in cirrhosis: organ extraction and relation to the vasodilatory state

BACKGROUND

Patients with cirrhosis have substantial circulatory imbalance between vasoconstrictive and vasodilating forces. The study of circulatory vasoactive peptides may provide important pathophysiological information. This study aimed to assess concentrations, organ extraction and relations to haemodynamic changes in the pro-peptides copeptin, proadrenomedullin and pro-atrial natriuretic peptide (proANP) in patients with cirrhosis.

MATERIALS AND METHODS

Fifty-four cirrhotic patients and 15 controls were characterized haemodynamically during a liver vein catheterization. Copeptin, proadrenomedullin and proANP were measured in hepatic and renal veins and the femoral artery.

RESULTS

We found no differences in concentrations of copeptin and proadrenomedullin between patients and controls. ProANPs were higher in cirrhotic patients, median 138 pm (25/75 percentiles 101-194) compared with controls, median 91 pm (25/75 percentiles 82-153) P=0·02. ProANPs were higher in the femoral artery and renal vein, median 140 pm and 116 pm (25/75 percentiles 109-191 and 92-164, respectively), compared with controls, median 99 and 81 (25/75 percentiles 85-146 and 66-123) P=0·02 and P=0·007, respectively. We found no extraction of copeptin, proadrenomedullin or proANP over the liver. Copeptin correlated with portal pressure (R=0·50, P<0·001). Proadrenomedullin correlated with portal pressure (R=0·48, P<0·001) and heart rate (R=0·36, P<0·01). ProANP correlated with cardiac output (R=0·46, P<0·002) and portal pressure (R=0·32, P<0·02). All propeptides correlated with Child score (R>0·31, P<0·03).

CONCLUSIONS

Pro-atrial natriuretic peptide is elevated in cirrhosis. Copeptin, proadrenomedullin and proANP are related to portal pressure and seem associated with systemic haemodynamics. These propeptides may participate in development and perpetuation of vasodilatation and hyperdynamic circulation in cirrhosis.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several “-omic” layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.

Stem cell systems informatics for advanced clinical biodiagnostics: tracing molecular signatures from bench to bedside

Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several "-omic" layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.