Metabolic Fingerprinting Approach Research Articles

Metabolic Fingerprinting of Serum and Seminal Plasma of Testicular Cancer Patients Using Raman Spectroscopy: A Pilot Study

Background: Testicular cancer (TC) is a relatively rare type of cancer in men. Early diagnosis of TC remains challenging. Metabolomics holds promise in offering valuable insights in this regard. In this study, a metabolic fingerprinting approach was employed to identify potential biomarkers in both serum and seminal plasma of TC patients.
 Methods: A total of 9 patients with testicular cancer and 10 controls were included in the study. The metabolic fingerprinting approach was utilized as a rapid diagnostic tool to analyze the metabolome in serum and seminal plasma of TC patients in comparison to fertile men. Raman spectroscopy was applied for the analysis of metabolites in these biological samples.
 Results: Principal component analysis (PCA) and functional group analysis showed that the differentiation between serum samples from healthy men and TC patients was not possible. However, when analyzing seminal plasma, a significant difference was found between the two groups (p<0.05). Functional group analysis of serum only showed an increase in tryptophan concentration ratio in TC patients as compared to healthy men (p=0.03). In contrast, in seminal plasma of TC patients, this increase was observed in all analyzed compounds, including phenylalanine, tyrosine, lipids, proteins, phenols (p<0.001).
 Conclusion: Our study highlights the potential of metabolic fingerprinting as a fast diagnostic tool for screening TC patients, with seminal plasma serving as a valuable biological sample. Furthermore, several potential biomarkers, particularly phenylalanine, were identified in seminal plasma. This research contributes to our understanding of TC pathogenesis and has the potential to pave the way for early detection and personalized treatment approaches.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics.

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Optical photothermal infrared spectroscopy: A novel solution for rapid identification of antimicrobial resistance at the single-cell level via deuterium isotope labeling.

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C-D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C-D signature peak at 2,163 cm-1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.

Molecular Metabolic Fingerprinting Approach to Investigate the Metabolic Alterations in Heart, Aorta and Renal Tissues of Nitric Oxide Deficient Hypertensive Rats

Hypertension is the leading cause of cardiovascular diseases (CVD) globally. It is well known that the incidence of several CVDs, including stroke, coronary heart disease, and peripheral artery disease, is closely linking to hypertension. Nitric oxide is the imperative regulator of the vascular system and its deficiency leads to elevated blood pressure and metabolic alterations in the tissues such as liver, kidney heart and aorta. Fourier transform infrared spectroscopy (FTIR) is a vibrational spectroscopic technique that uses infrared radiation to vibrate molecular bonds with in the sample that absorbs it and different vibrational spectra can be used to explore the qualitative and quantitative constituent of macromolecules. The aim of this study was to compare molecular changes in the tissues of normal control rats with nitric oxide deficient hypertensive rats. Hypertension was induced in male albino Wistar rats by oral administration of L-NAME (40mg/kg body weight) dissolved in drinking water daily for four weeks. Results indicate that FTIR can successfully identify the molecular changes in hypertensive groups. Overall, the findings demonstrate that in International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 06 Issue: 12 | December - 2022 Impact Factor: 7.185 ISSN: 2582-3930 © 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM17171 | Page 2 nitric oxide-deficient animals, the heart, kidney and aortic tissues metabolic programs were altered through an increase in structural modifications in proteins and triglycerides and a quantitative alteration in proteins lipids and transcription factors. Key words: Hypertension; metabolites; FTIR spectroscopy; nitric oxide

Molecular, Morphological and Chemical Diversity of Two New Species of Antarctic Diatoms, Craspedostauros ineffabilis sp. nov. and Craspedostauros zucchellii sp. nov.

The current study focuses on the biological diversity of two strains of Antarctic diatoms (strains IMA082A and IMA088A) collected and isolated from the Ross Sea (Antarctica) during the XXXIV Italian Antarctic Expedition. Both species presented the typical morphological characters of the genus Craspedostauros: cribrate areolae, two “fore-and-aft” chloroplasts and a narrow “stauros”. This classification is congruent with the molecular phylogeny based on the concatenated 18S rDNA-rbcL-psbC alignment, which showed that these algae formed a monophyletic lineage including six taxonomically accepted species of Craspedostauros. Since the study of the evolution of this genus and of others raphe-bearing diatoms with a “stauros” is particularly challenging and their phylogeny is still debated, we tested alternative tree topologies to evaluate the relationships among these taxa. The metabolic fingerprinting approach was implemented for the assessment of the chemical diversity of IMA082A and IMA088A. In conclusion, combining (1) traditional morphological features used in diatoms identification, (2) phylogenetic analyses of the small subunit rDNA (18S rDNA), rbcL and psbC genes, and (3) metabolic fingerprint, we described the strains IMA082A and IMA088A as Craspedostauros ineffabilis sp. nov. and Craspedostauros zucchellii sp. nov. as new species, respectively.

Metabolic Fingerprint Analysis of Cytochrome b 5-producing E. coli N4830-1 Using FT-IR Spectroscopy.

Optimization of recombinant protein expression in bacteria is an important task in order to increase protein yield while maintaining the structural fidelity of the product. In this study, we employ Fourier transform infrared (FT-IR) spectroscopy as a high throughput metabolic fingerprinting approach to optimize and monitor cytochrome b5 (CYT b5) production in Escherichia coli N4830-1, as the heterologous host. Cyt b5 was introduced as a plasmid with between 0 and 6 copies under a strong promoter. The FT-IR spectroscopy results combined with multivariate chemometric analysis illustrated discriminations among culture conditions as well as revealing features that correlated to the different cytb5 gene copy numbers. The second derivative of the FT-IR spectral data allowed for the quantitative detection of Cyt b5 directly inside the intact cells without the need for extraction, and highlighted changes in protein secondary structure that was directly correlated to the cytb5 gene copy number and protein content, and was in complete agreement with quantitative findings of standard traditional techniques such as SDS–PAGE and western blot analysis.

GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer

Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.

Metabolomics, lipidomics and proteomics profiling of myoblasts infected with Trypanosoma cruzi after treatment with different drugs against Chagas disease.

Chagas disease, the most important parasitic infection in Latin America, is caused by the intracellular protozoan Trypanosoma cruzi. To treat this disease, only two nitroheterocyclic compounds with toxic side effects exist and frequent treatment failures are reported. Hence there is an urgent need to develop new drugs. Recently, metabolomics has become an efficient and cost-effective strategy for dissecting drug mode of action, which has been applied to bacteria as well as parasites, such as different Trypanosome species and forms. We assessed if the metabolomics approach can be applied to study drug action of the intracellular amastigote form of T. cruzi in a parasite-host cell system. We applied a metabolic fingerprinting approach (DI-MS and NMR) to evaluate metabolic changes induced by six different (candidate) drugs in a parasite-host cell system. In a second part of our study, we analyzed the impact of two drugs on polar metabolites, lipid and proteins to evaluate if affected pathways can be identified. Metabolic signatures, obtained by the fingerprinting approach, resulted in three different clusters. Two can be explained by already known of mode actions, whereas the three experimental drugs formed a separate cluster. Significant changes induced by drug action were observed in all the three metabolic fractions (polar metabolites, lipids and proteins). We identified a general impact on the TCA cycle, but no specific pathways could be attributed to drug action, which might be caused by a high percentage of common metabolome between a eukaryotic host cell and a eukaryotic parasite. Additionally, ion suppression effects due to differences in abundance between host cells and parasites may have occurred. We validated the metabolic fingerprinting approach to a complex host-cell parasite system. This technique can potentially be applied in the early stage of drug discovery and could help to prioritize early leads or reconfirmed hits for further development.

Analysis of Urinary Eicosanoids by LC–MS/MS Reveals Alterations in the Metabolic Profile after Smoking Cessation

A preceding untargeted metabolic fingerprinting approach in our lab followed by targeted fatty acid analysis revealed alterations in arachidonic acid metabolism in samples derived from a diet-controlled smoking cessation study in which compliant subjects ( N = 39) quit smoking at baseline and were followed over the course of 3 months. Consequently, urinary eicosanoids were evaluated by means of a validated LC-MS/MS method. A significant decrease was obtained for the prostaglandins PGF2α, 8-iso-PGF2α, thromboxane 2,3-d-TXB2, and leukotriene E4 upon quitting smoking. These findings indicate a partial recovery of smoking-induced alterations in the eicosanoid profile due to a reduction in oxidative stress and the inflammatory response.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H3O+, NO+ and O2+ as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90–100%) and prediction accuracy (96.70–100% accuracy for cross validation and 97.30–100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases).

Gamma radiation treatment activates glucomoringin synthesis in Moringa oleifera

Plants are a very rich source of pharmacologically relevant metabolites. However, the relative concentrations of these compounds are subject to the genetic make-up, the physiological state of the plant as well as environmental effects. Recently, metabolic perturbations through the use of abiotic stressors have proven to be a valuable strategy for increasing the levels of these compounds. Oxidative stress-associated stressors, including ionizing radiation, have also been reported to induce metabolites with various biological activities in plants. Hence, the aim of the current study was to investigate the effect of gamma radiation on the induction of purported anti-cancerous metabolites, glucomoringin and its derivatives, in Moringa oleifera Lam., Moringaceae. Here, an UHPLC-qTOF-MS-based targeted metabolic fingerprinting approach was used to evaluate the effect of gamma radiation treatment on the afore-mentioned health-beneficial secondary metabolites of M. oleifera. Following radiation, an increase in glucomoringin and three acylated derivatives was noted. As such, these molecules can be regarded as components of the inducible defense mechanism of M. oleifera as opposed to being constitutive components as it has previously been assumed. This might be an indication of a possible, yet unexplored role of moringin against the effects of oxidative stress in M. oleifera plants. The results also suggest that plants undergoing photo-oxidative stress could accumulate higher amounts of glucomoringin and related molecules.

Chemical comparison of coat and kernel of mung bean by nuclear magnetic resonance-based metabolic fingerprinting approach

ABSTRACTTo utilize the wasted coat resources, which are usually discarded in the process of mung bean derived food, and understand the traditional use of mung bean, nuclear magnetic resonance-based metabolic fingerprinting approach was used to compare the chemical composition between the coat and kernel. The results showed that the flavonoids, isovitexin and vitexin, were only present in the coat, while the kernel contained higher levels of amino acids and sugars. In addition, the coat showed much stronger 2,2-diphenyl-1-picrylhydrazyl scavenging activity than the kernel, which was in accordance with the analysis result from the biplot of partial least squares.

The impact of stress on the prevalence of prednisolone in bovine urine: A metabolic fingerprinting approach

Recent studies support the hypothesis that the glucocorticoid prednisolone can be formed from cortisol under influence of stress. To evaluate this hypothesis, urine samples of supposedly non-stressed bovines (at the farm) and bovines subjected to two different forms of stress, i.e. upon slaughter (natural stress) or following administration of a synthetic analog of the adrenocorticotropic hormone (pharmacologically-induced stress) were analysed, and their urinary cortisol and prednisolone levels evaluated. At the farm, none of the examined samples exhibited urinary prednisolone levels higher than the CCα (0.09μgL−1). Upon slaughter or following synthetically induced stress, significantly positive correlations between cortisol and prednisolone could be demonstrated, 0.52 and 0.69, respectively. Of all prednisolone-positive urine samples (n=84), only one showed a prednisolone levels (i.e. 6.45μgL−1) above the threshold level of 5μgL−1 suggested by the European Reference Laboratories. Subsequently, an untargeted analysis was performed (metabolic fingerprinting) to characterize the urinary metabolite patterns related to the three different cattle groups. In this context, multivariate statistics assigned a total of 169 differentiating metabolites as playing a key role in the urinary pattern in response to stress. Three of these ions were defined as steroids using an in-house created database. As a result, the metabolic fingerprinting approach proved to be a powerful tool to classify unknown bovine urine samples that tested positive for prednisolone, while providing information about the stress status of the animal.

The effects of mineral nitrogen limitation, competition, arbuscular mycorrhiza, and their respective interactions, on morphological and chemical plant traits of Plantago lanceolata

Plants are sessile organisms that suffer from a multitude of challenges such as abiotic stress or the interactions with competitors, antagonists and symbionts, which influence their performance as well as their eco-physiological and biochemical responses in complex ways. In particular, the combination of different stressors and their impact on plant biomass production and the plant’s ability to metabolically adjust to these challenges are less well understood. To study the effects of mineral nitrogen (N) availability, interspecific competition and the association with arbuscular mycorrhizal fungi (AMF) on biomass production, biomass allocation patterns (root/shoot ratio, specific leaf area) and metabolic responses, we chose the model organism Plantago lanceolata L. (Plantaginaceae). Plants were grown in a full factorial experiment. Biomass production and its allocation patterns were assessed at harvest, and the influence of the different treatments and their interactions on the plant metabolome were analysed using a metabolic fingerprinting approach with ultra-high performance liquid chromatography coupled with time-of-flight-mass spectrometry. Limited supply of mineral N caused the most pronounced changes with respect to plant biomass and biomass allocation patterns, and altered the concentrations of more than one third of the polar plant metabolome. Competition also impaired plant biomass production, yet affected the plant metabolome to a much lesser extent than limited mineral N supply. The interaction of competition and limited mineral N supply often caused additive changes on several traits. The association with AMF did not enhance biomass production, but altered biomass allocation patterns such as the root/shoot ratio and the specific leaf area. Interestingly, we did not find significant changes in the plant metabolome caused by AMF. A targeted analysis revealed that only limited mineral N supply reduced the concentrations of one of the main target defence compounds of P. lanceolata, the iridoid glycoside catalpol. In general, the interaction of competition and limited mineral N supply led to additive changes, while the association with AMF in any case alleviated the observed stress responses. Our results show that the joint analysis of biomass/allocation patterns and metabolic traits allows a more comprehensive interpretation of plant responses to different biotic and abiotic challenges; specifically, when multiple stresses interact.

Metabolic Profiling of Geobacter sulfurreducens during Industrial Bioprocess Scale-Up.

During the industrial scale-up of bioprocesses it is important to establish that the biological system has not changed significantly when moving from small laboratory-scale shake flasks or culturing bottles to an industrially relevant production level. Therefore, during upscaling of biomass production for a range of metal transformations, including the production of biogenic magnetite nanoparticles by Geobacter sulfurreducens, from 100-ml bench-scale to 5-liter fermentors, we applied Fourier transform infrared (FTIR) spectroscopy as a metabolic fingerprinting approach followed by the analysis of bacterial cell extracts by gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. FTIR results clearly differentiated between the phenotypic changes associated with different growth phases as well as the two culturing conditions. Furthermore, the clustering patterns displayed by multivariate analysis were in agreement with the turbidimetric measurements, which displayed an extended lag phase for cells grown in a 5-liter bioreactor (24 h) compared to those grown in 100-ml serum bottles (6 h). GC-MS analysis of the cell extracts demonstrated an overall accumulation of fumarate during the lag phase under both culturing conditions, coinciding with the detected concentrations of oxaloacetate, pyruvate, nicotinamide, and glycerol-3-phosphate being at their lowest levels compared to other growth phases. These metabolites were overlaid onto a metabolic network of G. sulfurreducens, and taking into account the levels of these metabolites throughout the fermentation process, the limited availability of oxaloacetate and nicotinamide would seem to be the main metabolic bottleneck resulting from this scale-up process. Additional metabolite-feeding experiments were carried out to validate the above hypothesis. Nicotinamide supplementation (1 mM) did not display any significant effects on the lag phase of G. sulfurreducens cells grown in the 100-ml serum bottles. However, it significantly improved the growth behavior of cells grown in the 5-liter bioreactor by reducing the lag phase from 24 h to 6 h, while providing higher yield than in the 100-ml serum bottles.

Comparison of Volatile Components between Raw and Vinegar Baked Radix Bupleuri by GC-MS Based Metabolic Fingerprinting Approach.

Radix Bupleuri (RB), also named Chaihu in Chinese, is a commonly used herbal drug in traditional Chinese medicine (TCM), and the processing of RB with vinegar to prepare vinegar-baked Radix Bupleuri (VBRB) has a long history in the clinic of TCM. In the present study, GC-MS coupled with multivariate data analysis was applied to compare the volatile components between crude and two vinegar processed RBs. After vinegar baking, the oil yields were decreased significantly, and the chemical compositions were also changed greatly. The chemical changes included the disappearance or appearance, as well as the content increase or decrease of some volatile compounds. The oil yields of two different VBRBs showed no significant difference but differed markedly in their chemical compositions, suggesting that the type of vinegar exerted great impacts on the vinegar-baking process. Thus, the effect of different vinegars on processing should be further investigated to ensure the therapeutic effect and safety of VBRB in clinic.

Metabolic fingerprint of Gestational Diabetes Mellitus

Gestational Diabetes (GDM) is causing severe short- and long-term complications for mother, fetus or neonate. As yet, the metabolic alterations that are specific for the development of GDM have not been fully determined, which also precludes the early diagnosis and prognosis of this pathology. In this pilot study, we determine the metabolic fingerprint, using a multiplatform LC-QTOF/MS, GC-Q/MS and CE-TOF/MS system, of plasma and urine samples of 20 women with GDM and 20 with normal glucose tolerance in the second trimester of pregnancy. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls. In particular, lysoglycerophospholipids showed a close association with the glycemic state of the women. In addition, we identified some metabolites with a strong discriminative power, such as LPE(20:1), (20:2), (22:4); LPC(18:2), (20:4), (20:5); LPI(18:2), (20:4); LPS(20:0) and LPA(18:2), as well as taurine-bile acids and long-chain polyunsaturated fatty acid derivatives. Finally, we provide evidence for the implication of these compounds in metabolic routes, indicative of low-grade inflammation and altered redox-balance, that may be related with the specific pathophysiological context of the genesis of GDM. This highlights their potential use as prognostic markers for the identification of women at risk to develop severe glucose intolerance during pregnancy. Gestational Diabetes Mellitus (GDM) is increasing worldwide and, although diabetes usually remits after pregnancy, women with GDM have a high risk of developing postpartum type 2-diabetes, particularly when accompanied by obesity. Therefore, understanding the pathophysiology of GDM, as well as the identification of potentially modifiable risk factors and early diagnostic markers for GDM are relevant issues. In the present study, we devised a multiplatform metabolic fingerprinting approach to obtain a comprehensive picture of the early metabolic alternations that occur in GDM, and may reflect on the specific pathophysiological context of the disease. Future studies at later stages of gestation will allow us to validate the discriminant power of the identified metabolites.

Surface-enhanced Raman spectroscopy of blood plasma and serum using Ag and Au nanoparticles: a systematic study

Surface-enhanced Raman spectroscopy (SERS) is a good candidate for the development of fast and easy-to-use diagnostic tools, possibly used on biofluids in point-of-care or screening tests. In particular, label-free SERS spectra of blood serum and plasma, two biofluids widely used in diagnostics, could be used as a metabolic fingerprinting approach for biomarker discovery. This study aims at a systematic evaluation of SERS spectra of blood serum and plasma, using various Ag and Au aqueous colloids, as SERS substrates, in combination with three excitation lasers of different wavelengths, ranging from the visible to the near-infrared. The analysis of the SERS spectra collected from 20 healthy subjects under a variety of experimental conditions revealed that intense and repeatable spectra are quickly obtained only if proteins are filtered out from samples, and an excitation in the near-infrared is used in combination with Ag colloids. Moreover, common plasma anticoagulants such as EDTA and citrate are found to interfere with SERS spectra; accordingly, filtered serum or heparin plasma are the samples of choice, having identical SERS spectra. Most bands observed in SERS spectra of these biofluids are assigned to uric acid, a metabolite whose blood concentration depends on factors such as sex, age, therapeutic treatments, and various pathological conditions, suggesting that, even when the right experimental conditions are chosen, great care must be taken in designing studies with the purpose of developing diagnostic tests.

Role of plant β-glucosidases in the dual defense system of iridoid glycosides and their hydrolyzing enzymes in Plantago lanceolata and Plantago major

The typical defense compounds of Plantaginaceae are the iridoid glycosides, which retard growth and/or enhance mortality of non-adapted herbivores. In plants, glycosidic defense compounds and hydrolytic enzymes often form a dual defense system, in which the glycosides are activated by the enzymes to exert biological effects. Yet, little is known about the activating enzymes in iridoid glycoside-containing plants. To examine the role of plant-derived β-glucosidases in the dual defense system of two common plantain species, Plantago lanceolata and Plantago major, we determined the concentration of iridoid glycosides as well as the β-glucosidase activity in leaves of different age. To investigate the presence of other leaf metabolites potentially involved in plant defense, we used a metabolic fingerprinting approach with ultra-high performance liquid chromatography coupled with time-of-flight-mass spectrometry. According to the optimal defense hypothesis, more valuable parts such as young leaves should be better protected than less valuable parts. Therefore, we expected that both, the concentrations of defense compounds as well as the β-glucosidase activity, should be highest in younger leaves and decrease with increasing leaf age. Both species possessed β-glucosidase activity, which hydrolyzed aucubin, one of the two most abundant iridoid glycosides in both plant species, with high activity. In line with the optimal defense hypothesis, the β-glucosidase activity in both Plantago species as well as the concentration of defense-related metabolites such as iridoid glycosides correlated negatively to leaf age. When leaf extracts were incubated with bovine serum albumin and aucubin, SDS-PAGE revealed a protein-denaturing effect of the leaf extracts of both plantain species, suggesting that iridoid glycosides and plant β-glucosidase interact in a dual defense system.

Metabolic fingerprinting of high-fat plasma samples processed by centrifugation- and filtration-based protein precipitation delineates significant differences in metabolite information coverage

Metabolomics and metabolic fingerprinting are being extensively employed for improved understanding of biological changes induced by endogenous or exogenous factors. Blood serum or plasma samples are often employed for metabolomics studies. Plasma protein precipitation (PPP) is currently performed in most laboratories before LC–MS analysis. However, the impact of fat content in plasma samples on metabolite coverage has not previously been investigated. Here, we have studied whether PPP procedures influence coverage of plasma metabolites from high-fat plasma samples. An optimized UPLC-QTOF/MS metabolic fingerprinting approach and multivariate modeling (PCA and OPLS-DA) were utilized for finding characteristic metabolite changes induced by two PPP procedures; centrifugation and filtration. We used 12-h fasting samples and postprandial samples collected at 2h after a standardized high-fat protein-rich meal in obese non-diabetic subjects recruited in a dietary intervention. The two PPP procedures as well as external and internal standards (ISs) were used to track errors in response normalization and quantification. Remarkably and sometimes uniquely, the fPPP, but not the cPPP approach, recovered not only high molecular weight (HMW) lipophilic metabolites, but also small molecular weight (SMW) relatively polar metabolites. Characteristic SMW markers of postprandial samples were aromatic and branched-chain amino acids that were elevated (p<0.001) as a consequence of the protein challenge. In contrast, some HMW lipophilic species, e.g. acylcarnitines, were moderately lower (p<0.001) in postprandial samples. LysoPCs were largely unaffected. In conclusion, the fPPP procedure is recommended for processing high-fat plasma samples in metabolomics studies. While method improvements presented here were clear, use of several ISs revealed substantial challenges to untargeted metabolomics due to large and variable matrix effects.