Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks

To access publisher's full text version of this article click on the hyperlink belowThe temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.European Research Council United States Department of Energy NHLBI, National Institutes of Healt

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscop

Bridging the Gap from Enterotypes to Personalized Dietary Recommendations: A Metabolomics Perspective on Microbiome Research

Advances in high-throughput DNA sequencing have propelled research into the human microbiome and its link to metabolic health. We explore microbiome analysis methods, specifically emphasizing metabolomics, how dietary choices impact the production of microbial metabolites, providing an overview of studies examining the connection between enterotypes and diet, and thus, improvement of personalized dietary recommendations. Acetate, propionate, and butyrate constitute more than 95% of the collective pool of short-chain fatty acids. Conflicting data on acetate’s effects may result from its dynamic signaling, which can vary depending on physiological conditions and metabolic phenotypes. Human studies suggest that propionate has overall anti-obesity effects due to its well-documented chemistry, cellular signaling mechanisms, and various clinical benefits. Butyrate, similar to propionate, has the ability to reduce obesity by stimulating the release of appetite-suppressing hormones and promoting the synthesis of leptin. Tryptophan affects systemic hormone secretion, with indole stimulating the release of GLP-1, which impacts insulin secretion, appetite suppression, and gastric emptying. Bile acids, synthesized from cholesterol in the liver and subsequently modified by gut bacteria, play an essential role in the digestion and absorption of dietary fats and fat-soluble vitamins, but they also interact directly with intestinal microbiota and their metabolites. One study using statistical methods identified primarily two groupings of enterotypes Bacteroides and Ruminococcus. The Prevotella-dominated enterotype, P-type, in humans correlates with vegetarians, high-fiber and carbohydrate-rich diets, and traditional diets. Conversely, individuals who consume diets rich in animal fats and proteins, typical in Western-style diets, often exhibit the Bacteroides-dominated, B-type, enterotype. The P-type showcases efficient hydrolytic enzymes for plant fiber degradation but has limited lipid and protein fermentation capacity. Conversely, the B-type features specialized enzymes tailored for the degradation of animal-derived carbohydrates and proteins, showcasing an enhanced saccharolytic and proteolytic potential. Generally, models excel at predictions but often struggle to fully elucidate why certain substances yield varied responses. These studies provide valuable insights into the potential for personalized dietary recommendations based on enterotypes

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research.Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores.Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan.Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy

ENDOMET database – A means to identify novel diagnostic and prognostic tools for endometriosis

Endometriosis is a common benign hormone reliant inflammatory gynecological disease that affects fertile aged women and has a considerable economic impact on healthcare systems. Symptoms include intense menstrual pain, persistent pelvic pain, and infertility. It is defined by the existence of endometrium-like tissue developing in ectopic locations outside the uterine cavity and inflammation in the peritoneal cavity. Endometriosis presents with multifactorial etiology, and despite extensive research the etiology is still poorly understood. Diagnostic delay from the onset of the disease to when a conclusive diagnosis is reached is between 7–12 years. There is no known cure, although symptoms can be improved with hormonal medications (which often have multiple side effects and prevent pregnancy), or through surgery which carries its own risk. Current non-invasive tools for diagnosis are not sufficiently dependable, and a definite diagnosis is achieved through laparoscopy or laparotomy. This study was based on two prospective cohorts: The ENDOMET study, including 137 endometriosis patients scheduled for surgery and 62 healthy women, and PROENDO that included 138 endometriosis patients and 33 healthy women. Our long-term goal with the current study was to support the discovery of innovative new tools for efficient diagnosis of endometriosis as well as tools to further understand the etiology and pathogenesis of the disease. We set about achieving this goal by creating a database, EndometDB, based on a relational data model, implemented with PostgreSQL programming language. The database allows e.g., for the exploration of global genome-wide expression patterns in the peritoneum, endometrium, and in endometriosis lesions of endometriosis patients as well as in the peritoneum and endometrium of healthy control women of reproductive age. The data collected in the EndometDB was also used for the development and validation of a symptom and biomarker-based predictive model designed for risk evaluation and early prediction of endometriosis without invasive diagnostic methods. Using the data in the EndometDB we discovered that compared with the eutopic endometrium, the WNT- signaling pathway is one of the molecular pathways that undergo strong changes in endometriosis. We then evaluated the potential role for secreted frizzled-related protein 2 (SFRP-2, a WNT-signaling pathway modulator), in improving endometriosis lesion border detection. The SFRP-2 expression visualizes the lesion better than previously used markers and can be used to better define lesion size and that the surgical excision of the lesions is complete.ENDOMET tietokanta – Keino tunnistaa uusi diagnostinen ja ennustava työkalu endometrioosille Endometrioosi on yleinen hyvänlaatuinen, hormoneista riippuvainen tulehduksellinen lisääntymisikäisten naisten gynekologinen sairaus, joka kuormittaa terveydenhuoltojärjestelmää merkittävästi. Endometrioositaudin oireita ovat mm. voimakas kuukautiskipu, jatkuva lantion alueen kipu ja hedelmättömyys. Sairaus määritellään kohdun limakalvon kaltaisen kudoksen esiintymisenä kohdun ulkopuolella sekä siihen liittyvänä vatsakalvon tulehduksena. Endometrioosin etiologia on monitahoinen, ja laajasta tutkimuksesta huolimatta edelleen huonosti tunnettu. Kesto taudin puhkeamisesta lopullisen diagnoosin saamiseen on usein jopa 7–12 vuotta. Sairauteen ei tunneta parannuskeinoa, mutta oireita voidaan lievittää esimerkiksi hormonaalisilla lääkkeillä (joilla on usein monia sivuvaikutuksia ja jotka estävät raskauden) tai leikkauksella, johon liittyy omat tunnetut riskit. Nykyiset ei-invasiiviset diagnoosityökalut eivät ole riittävän luotettavia sairauden tunnistamiseen, ja varma endometrioosin diagnoosi saavutetaan laparoskopian tai laparotomian avulla. Tämä tutkimus perustui kahteen prospektiiviseen kohorttiin: ENDOMET-tutkimuk-seen, johon osallistui 137 endometrioosipotilasta ja 62 terveellistä naista, sekä PROENDO-tutkimukseen, johon osallistui 138 endometrioosipotilasta ja 33 terveellistä naista. Tässä tutkimuksessa pitkän aikavälin tavoitteemme oli löytää uusia työkalujen endometrioosin diagnosointiin, sekä ymmärtää endometrioosin etiologiaa ja patogeneesiä. Ensimmäisessä vaiheessa loimme EndometDB –tietokannan PostgreSQL-ohjelmointi-kielellä. Tämän osittain avoimeen käyttöön vapautetun tietokannan avulla voidaan tutkia genomin, esimerkiksi kaikkien tunnettujen geenien ilmentymistä peritoneumissa, endo-metriumissa ja endometrioosipotilaiden endometrioosileesioissa EndometDB-tietokantaan kerättyjä tietoja käytettiin oireiden ja biomarkkeripohjaisen ennustemallin kehittämiseen ja validointiin. Malli tuottaa riskinarvioinnin endometrioositaudin varhaiseen ennustamiseen ilman laparoskopiaa. Käyttäen EndometDB-tietokannan tietoja havaitsimme, että endo-metrioositautikudoksessa tapahtui voimakkaita geeni-ilmentymisen muutoksia erityisesti geeneissä, jotka liittyvät WNT-signalointireitin säätelyyn. Keskeisin löydös oli, että SFRP-2 proteiinin ilmentyminen oli huomattavasti koholla endometrioosikudoksessa ja SFRP-2 proteiinin immunohistokemiallinen värjäys erottaa endometrioosin tautikudoksen terveestä kudoksesta aiempia merkkiaineita paremmin. Löydetyllä menetelmällä voidaan siten selvittää tautikudoksen laajuus ja tarvittaessa osoittaa, että leikkauksella on kyetty poistamaan koko sairas kudos

NEW FRONTIERS OF THE RESEARCH IN BUFFALO BREEDING

The interest around buffalo breeding in Italy has noticeably augmented during the last years, as demonstrated from the increase of buffalo population that passed from about 200,000 heads in 2000 to more than 400,000 in 2020. This is due to the interest around the main product derived from buffalo milk: the mozzarella cheese, also known as Mozzarella di Bufala Campana, that received in 1996 the Protected Designation of Origin (PDO). In Italy buffalo breeding is carried out in intensive and semi-intensive systems, similar to those utilized in dairy cattle: thus, similar issues in terms of productive and reproductive efficiency have been risen in this species. Furthermore, in the Mediterranean region, buffaloes show annual fluctuations in reproduction with distinct breeding and non-breeding seasons. As buffaloes are short-day breeders, the annual peak in fertility coincides with decreasing day length from autumn to winter. Therefore, in order to guarantee milk production throughout the year for mozzarella cheese production, some strategies are applied to increase the reproductive efficiency out of the breeding season, when a greater incidence of anestrus, a decline in the function of the corpus luteum (CL), and an increase in embryonic mortality are usually recorded. Furthermore, natural mating is often preferred to assisted reproduction (estrus synchronization and artificial insemination), because of some peculiarities of buffalo species, such as low estrus behavior and large variability in estrus duration, which makes difficult the individuation of the optimal moment for artificial insemination (AI). In this scenario, the purpose of this project was to improve reproductive efficiency and management in buffaloes through the application of new methodological approaches, such as synchronization of estrus, eco-color Doppler and metabolomics. In particular, a great attention has been paid to the improvement of AI in buffalo, in both heifers and pluriparous buffaloes. In the first experiment (Chapter 5), the efficiency of two synchronization protocols for oestrus synchronization and the influence of live body weight (LBW) and age on reproductive performance was evaluated in buffalo heifers. The animals were synchronized by Ovsynch-TAI Program (OVS; n=72) or double prostaglandin administered 12 days apart (PGF; n=74) and all the buffaloes were inseminated twice (24 days apart). Follicle dimensions and ovulation rate (OR) were assessed by ultrasound 24 and 48 h post-insemination. Pregnancy was assessed on day 25, 45 and 90 post-insemination and the incidence of late embryonic (LEM) and fetal (FM) mortality were respectively recorded. Data were analyzed by ANOVA, Chi-square test and multiple logistic regression. In the second experiment (Chapter 6), a deep study of CL development was carried out in buffaloes out of breeding season through the application of eco-color Doppler technique. Adult Mediterranean buffaloes (n=29) were synchronized by Ovsynch-TAI Program and artificially inseminated. CL B-mode/color Doppler ultrasonography examinations were performed daily from Days 5 to 10 post-synchronization, recording CL dimensions and blood flow parameters. Blood samples were collected on the same days for the progesterone (P4) assay. Data were grouped into pregnant or non-pregnant and retrospectively analyzed. In the third experiment (Chapter 7) a metabolomic approach on milk was used on 10 pregnant and 10 non-pregnant buffaloes in order to identify potential biomarkers of early pregnancy. The study was carried out on 10 pregnant and 10 non-pregnant buffaloes that were synchronized by Ovsynch-TAI Program and have undergone the first AI. Milk samples were individually collected ten days before AI (the start of the synchronization treatment), on the day of AI, day 7 and 18 after AI, and were analyzed by LC–MS. Data were analyzed retrospectively by dividing pregnant and non-pregnant subjects. Statistical analysis was carried out by using Mass Profile Professional. In the fourth experiment (Chapter 8) an advanced GC-MS and metabolite identification approach was also utilized to characterize the metabolome of buffalo milk and mozzarella cheese in a robust and repeatable technology platform. The study utilized eleven dairies located in a protected designation of origin (PDO) region and nine dairies located in non-PDO region in Italy. Samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy and maintained at -80°C until analysis. Metabolomic assay was carried out through gas-chromatography and mass spectroscopy and differently expressed metabolites were identified. Statistical analysis of the results was carried out by ANOVA. The results of the first experiment showed that the LBW was significantly (P<0.05) higher in inseminated animals, compared to those that did not respond to the treatments (450.0±3.2 vs. 423.2±9.6 kg in inseminated and not inseminated heifers, respectively). Total OR was similar between groups, although OR at 24 h tended to be higher (P=0.06) in OVS (86.7 vs. 72.9% in OVS and PGF, respectively). A (P<0.01) higher LBW was observed in ovulated heifers of PGF, while no differences were recorded in OVS. LBW affected OR (odds ratio = 1,032; P < 0.05) only in PGF, while no effects were recorded in OVS. Total pregnancy rate, LEM and FM were similar between groups. In conclusion, the LBW would be considered before including buffalo heifers in a synchronization program and both synchronization treatments can be useful. In the second experiment, the total pregnancy rate was 50.0% (13/26) on Day 45. A significant difference between CL average area in pregnant and non-pregnant buffaloes was recorded only on Day 10. Pregnant buffaloes showed a significantly higher mean P4 concentration and higher mean time average medium velocity (TAMV) values from Day 5 to Day 10 compared to non-pregnant buffaloes. Linear regression analysis showed a significant relationship between P4 levels and TAMV. Multiple logistic regression highlighted a significant influence of TAMV on pregnancy outcome, particularly on Day 8. This is probably due to the strong relationship between TAMV and P4 production. Both TAMV and P4 could be used to predict pregnancy starting on Day 6, although a more reliable result was obtained at Day 10. Thus, the period between Days 5 and 10 is critical for CL development and pregnancy maintenance during the transitional period in buffalo. During the third experiment, Metabolomic analysis revealed the presence of several metabolites differentially expressed between pregnant and non-pregnant buffaloes. Among these, a total of five metabolites were identified by comparison with an online database and a standard compound as acetylcarnitine (3-Acetoxy-4-(trimethylammonio)butanoate), arginine-succinic acid hydrate, 5'-O-{[3-({4-[(3aminopropyl)amino]butyl}amino)propyl]carbamoyl}-2'-deoxyadenosine, N-(1-Hydroxy-2-hexadecanyl)pentadecanamide, and N-[2,3-Bis(dodecyloxy)propyl]-L-lysinamide). Interestingly, acetylcarnitine was dominant in milk samples collected from non-pregnant buffaloes. The results obtained from milk metabolic profile and hierarchical clustering analysis revealed significant differences between pregnant and non-pregnant buffaloes, as well as in the metabolite expression. Overall, the findings indicate the effectiveness of the metabolomic analysis for the identification of novel potential biomarkers in early prediction of pregnancy in buffaloes after AI, and these findings would give breeders the opportunity to rebreed animals at the next estrus event, saving most of the days as open. Finally, during the fourth experiment a total of 185 metabolites were consistently detected in both milk and mozzarella cheese. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples. For milk samples, it was possible to divide metabolites into two classes according to region: those with lower concentrations in PDO samples (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with lower concentrations in non-PDO samples (talopyranose, pantothenic acid, mannobiose, etc.). The same was observed for mozzarella samples with the proportion of some metabolites (talopyranose, 2, 3-dihydroxypropyl icosanoate, etc.,) higher in PDO samples while others (tagatose, lactic acid dimer, ribitol, etc.,) higher in non-PDO samples. The findings establish the utility of GC-MS together with mass spectral libraries as a powerful technology platform to determine the authenticity, and create market protection, for "Mozzarella di Bufala Campana" PDO

Precision Nutrition and Metabolic Syndrome Management

Precision nutrition is an emerging concept encompassing an integrated action considering not only the genetic/epigenetic makeup and ethnic aspects of individuals, but other personalized phenotypical features, such as family and individual clinical issues, previous diseases and therapeutic treatments, perinatal nutrition, food likes/dislikes, allergies/intolerances, lifestyle attitudes and patterns, social and cultural circumstances or religious beliefs, etc. In this context, chronic disease prevalence is a global public health problem itself, which is also accompanied by a number of complications, including insulin resistance, hypertension, hypercholesterolemia, fatty liver, inflammation, oxidative status and immunocompetence disturbances, and other adverse manifestations related to metabolic syndrome, which may need individualized nutritional approaches. Therefore, the current Special Issue attempts to provide specific nutritional strategies to prevent or treat the complications associated with metabolic syndrome features concerning diabetes, vascular events, liver diseases, dyslipemia, and cancer with a precision nutrition scope

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds