RESUMEN
Stimbiotic supplementation may provide an innovative feed additive solution to accelerate the proliferation of beneficial fiber-degrading bacteria in the distal intestine and the utilization of dietary fiber. Optimal utilization of dietary fiber has multiple benefits for gut health and nutrient utilization. This study was conducted to evaluate the late gestation and lactation performance, the plasma, colostrum, and milk immunoglobulin (IgA, IgG, and IgM) concentrations, and the anti-inflammatory and antioxidant biomarkers in plasma of sows fed with or without a stimbiotic during the late gestation and lactation phase. A total of 40 sows were allocated to two treatment groups: control (CT) with no supplementation or 100 mg/kg stimbiotic (VP), with 20 sows per treatment. Sows were fed the treatment diets from d 85 of gestation to d 28 of lactation. In the results, the average daily weight gain of piglets during lactation was greater from sows fed in the VP group compared to that in the CT group (p < 0.05). The plasma concentrations of IgM at farrowing and IgG at weaning of the sows fed the diet with the stimbiotic supplementation were much higher than those in the CT sows (p < 0.05), respectively. In addition, the dietary stimbiotic increased the concentrations of IgM in the colostrum and of IgA and IgM in the milk at d 14 of lactation (p < 0.05). Plasma concentrations of malondialdehyde (MDA) on d 0 and d 28 of lactation tended to be lower in sows fed the VP diets compared with those of the sows fed the CT diets. Thus, our study indicated that stimbiotic supplementation could improve the daily weight gain of piglets and the immune function of sows in lactation.
RESUMEN
β-Elemene is an effective anti-cancer ingredient extracted from the genus Curcuma (Zingiberaceae familiy). In the present study, we demonstrated that β-elemene inhibited the proliferation of colorectal cancer cells and induced cell cycle arrest in the G2/M phase. In addition, β-elemene induced nuclear chromatin condensation and cell membrane phosphatidylserine eversion, decreased cell mitochondrial membrane potential, and promoted the cleavage of caspase-3, caspase-9 and PARP proteins, indicating apoptosis in colorectal cancer cells. At the same time, β-elemene induced autophagy response, and the treated cells showed autophagic vesicle bilayer membrane structure, which was accompanied by up-regulation of the expression of LC3B and SQSTM1. Furthermore, β-elemene increased ROS levels in colorectal cancer cells, promoted phosphorylation of AMPK protein, and inhibited mTOR protein phosphorylation. In the experiments in vivo, β-elemene inhibited the tumor size and induced apoptosis and autophagy in nude mice. In summary, β-elemene inhibited the occurrence and development of colon cancer xenografts in nude mice, and significantly induced apoptosis and autophagy in colorectal cancer cells in vitro. These effects were associated with regulation of the ROS/AMPK/mTOR signaling. We offered a molecular basis for the development of β-elemene as a promising anti-tumor drug candidate for colorectal cancer.
Asunto(s)
Animales , Humanos , Ratones , Proteínas Quinasas Activadas por AMP/genética , Apoptosis , Autofagia , Línea Celular Tumoral , Neoplasias Colorrectales/genética , Ratones Desnudos , Especies Reactivas de Oxígeno , Sesquiterpenos , Serina-Treonina Quinasas TOR/genéticaRESUMEN
One of the major bottle-necks in current LC-MS-based metabolomic investigations is metabolite identification. An often-used approach is to first look up metabolites from databases through peak mass, followed by verification of the obtained putative identifications using MS/MS data. However, the mass-based search may provide inappropriate putative identifications when the observed peak is from isotopes, fragments, or adducts. In addition, a large fraction of peaks is often left with multiple putative identifications. To differentiate these putative identifications, manual verification of metabolites through comparison between biological samples and authentic compounds is necessary. However, such experiments are laborious, especially when multiple putative identifications are encountered. It is desirable to use computational approaches to obtain more reliable putative identifications and prioritize them before performing experimental verification of the metabolites. In this article, a computational pipeline is proposed to assist metabolite identification with improved metabolome coverage and prioritization capability. Multiple publicly available software tools and databases, along with in-house developed algorithms, are utilized to fully exploit the information acquired from LC-MS/MS experiments. The pipeline is successfully applied to identify metabolites on the basis of LC-MS as well as MS/MS data. Using accurate masses, retention time values, MS/MS spectra, and metabolic pathways/networks, more appropriate putative identifications are retrieved and prioritized to guide subsequent metabolite verification experiments.
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Cromatografía Liquida/métodos , Metaboloma , Metabolómica/métodos , Espectrometría de Masas en Tándem/métodos , Biomarcadores de Tumor/sangre , Biomarcadores de Tumor/metabolismo , Análisis por Conglomerados , Bases de Datos Factuales , Humanos , Redes y Vías Metabólicas , Neoplasias/sangre , Neoplasias/metabolismoRESUMEN
Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3-5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3ß, 6ß-dihydroxy-5ß-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC-MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.
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Biomarcadores de Tumor/sangre , Carcinoma Hepatocelular/diagnóstico , Cromatografía Liquida/métodos , Neoplasias Hepáticas/diagnóstico , Metabolómica/métodos , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Carcinoma Hepatocelular/metabolismo , Estudios de Casos y Controles , Biología Computacional/métodos , Egipto , Femenino , Humanos , Cirrosis Hepática/diagnóstico , Cirrosis Hepática/metabolismo , Neoplasias Hepáticas/metabolismo , Masculino , Metaboloma , Persona de Mediana Edad , Estadificación de Neoplasias/métodosRESUMEN
Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.
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Biomarcadores/sangre , Carcinoma Hepatocelular/sangre , Carcinoma Hepatocelular/complicaciones , Detección Precoz del Cáncer/métodos , Cirrosis Hepática/sangre , Cirrosis Hepática/complicaciones , Neoplasias Hepáticas/diagnóstico , Metabolómica , Carcinoma Hepatocelular/diagnóstico , Pruebas de Química Clínica/economía , Humanos , Neoplasias Hepáticas/sangreRESUMEN
Metabolomics aims at detection and quantitation of all metabolites in biological samples. The presence of metabolites with a wide variety of physicochemical properties and different levels of abundance challenges existing analytical platforms used for identification and quantitation of metabolites. Significant efforts have been made to improve analytical and computational methods for metabolomics studies.This review focuses on the use of liquid chromatography with tandem mass spectrometry (LC-MS/MS) for quantitative and qualitative metabolomics studies. It illustrates recent developments in computational methods for metabolite identification, including ion annotation, spectral interpretation and spectral matching. We also review selected reaction monitoring and high-resolution MS for metabolite quantitation. We discuss current challenges in metabolite identification and quantitation as well as potential solutions.
RESUMEN
BACKGROUND: Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. RESULTS: The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose. CONCLUSIONS: The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.
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Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.