Hydrophilic interaction chromatography–mass spectrometry for anionic metabolic profiling of urine from antibiotic-treated rats

Hydrophilic interaction chromatography–mass spectrometry (HILIC–MS) was used for anionic metabolic profiling of urine from antibiotic-treated rats to study microbial–host co-metabolism. Rats were treated with the antibiotics penicillin G and streptomycin sulfate for four or eight days and compared to a control group. Urine samples were collected at day zero, four and eight, and analyzed by HILIC–MS. Multivariate data analysis was applied to the urinary metabolic profiles to identify biochemical variation between the treatment groups. Principal component analysis found a clear distinction between those animals receiving antibiotics and the control animals, with twenty-nine discriminatory compounds of which twenty were down-regulated and nine up-regulated upon treatment. In the treatment group receiving antibiotics for four days, a recovery effect was observed for seven compounds after cessation of antibiotic administration. Thirteen discriminatory compounds could be putatively identified based on their accurate mass, including aconitic acid, benzenediol sulfate, ferulic acid sulfate, hippuric acid, indoxyl sulfate, penicillin G, phenol and vanillin 4-sulfate. The rat urine samples had previously been analyzed by capillary electrophoresis (CE) with MS detection and proton nuclear magnetic resonance (1H NMR) spectroscopy. Using CE–MS and 1H NMR spectroscopy seventeen and twenty-five discriminatory compounds were found, respectively. Both hippuric acid and indoxyl sulfate were detected across all three platforms. Additionally, eight compounds were observed with both HILIC–MS and CE–MS. Overall, HILIC–MS appears to be highly complementary to CE–MS and 1H NMR spectroscopy, identifying additional compounds that discriminate the urine samples from antibiotic-treated and control rats.

Identification of potential drug targets in Salmonella Typhimurium using metabolic modelling and experimental validation

almonella enterica serovar Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of Salmonella Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in energy demand, while growing in glucose minimal medium. By grouping reactions with similar flux responses, a sub-network of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions, that when removed from the genome-scale model interfered with energy and biomass generation. 11 such sets were found to be essential for the production of biomass precursors. Experimental investigation of 7 of these showed that knock-outs of the associated genes resulted in attenuated growth for 4 pairs of reactions, while 3 single reactions were shown to be essential for growth.

Alterations in predicted regulatory and coding regions of the sterol 14α-demethylase gene (CYP51) confer decreased azole sensitivity in the oilseed rape pathogen Pyrenopeziza brassicae

The incidence and severity of light leaf spot epidemics caused by the ascomycete fungus Pyrenopeziza brassicae on UK oilseed rape crops is increasing. The disease is currently controlled by a combination of host resistance, cultural practices and fungicide applications. We report decreases in sensitivities of modern UK P. brassicae isolates to the azole (imidazole and triazole) class of fungicides. By cloning and sequencing the P. brassicae CYP51 (PbCYP51) gene, encoding the azole target sterol 14α-demethylase, we identified two non-synonymous mutations encoding substitutions G460S and S508T associated with reduced azole sensitivity. We confirmed the impact of the encoded PbCYP51 changes on azole sensitivity and protein activity by heterologous expression in a Saccharomyces cerevisiae mutant YUG37::erg11 carrying a controllable promoter of native CYP51 expression. In addition, we identified insertions in the predicted regulatory regions of PbCYP51 in isolates with reduced azole sensitivity. The presence of these insertions was associated with enhanced transcription of PbCYP51 in response to sub-inhibitory concentrations of the azole fungicide tebuconazole. Genetic analysis of in vitro crosses of sensitive and resistant isolates confirmed the impact of PbCYP51 alterations in coding and regulatory sequences on a reduced sensitivity phenotype, as well as identifying a second major gene at another locus contributing to resistance in some isolates. The least sensitive field isolates carry combinations of upstream insertions and non-synonymous mutations, suggesting PbCYP51 evolution is on-going and the progressive decline in azole sensitivity of UK P. brassicae populations will continue. The implications for the future control of light leaf spot are discussed.

Reliability comparison of transmit/receive diversity and error control coding in low-power medium access control protocols

Low-power medium access control (MAC) protocols used for communication of energy constraint wireless embedded devices do not cope well with situations where transmission channels are highly erroneous. Existing MAC protocols discard corrupted messages which lead to costly retransmissions. To improve transmission performance, it is possible to include an error correction scheme and transmit/receive diversity. It is possible to add redundant information to transmitted packets in order to recover data from corrupted packets. It is also possible to make use of transmit/receive diversity via multiple antennas to improve error resiliency of transmissions. Both schemes may be used in conjunction to further improve the performance. In this study, the authors show how an error correction scheme and transmit/receive diversity can be integrated in low-power MAC protocols. Furthermore, the authors investigate the achievable performance gains of both methods. This is important as both methods have associated costs (processing requirements; additional antennas and power) and for a given communication situation it must be decided which methods should be employed. The authors’ results show that, in many practical situations, error control coding outperforms transmission diversity; however, if very high reliability is required, it is useful to employ both schemes together.

The intelligence paradox; will ET get the metabolic syndrome? Lessons from and for Earth

Mankind is facing an unprecedented health challenge in the current pandemic of obesity and diabetes. We propose that this is the inevitable (and predictable) consequence of the evolution of intelligence, which itself could be an expression of life being an information system driven by entropy. Because of its ability to make life more adaptable and robust, intelligence evolved as an efficient adaptive response to the stresses arising from an ever-changing environment. These adaptive responses are encapsulated by the epiphenomena of “hormesis”, a phenomenon we believe to be central to the evolution of intelligence and essential for the maintenance of optimal physiological function and health. Thus, as intelligence evolved, it would eventually reach a cognitive level with the ability to control its environment through technology and have the ability remove all stressors. In effect, it would act to remove the very hormetic factors that had driven its evolution. Mankind may have reached this point, creating an environmental utopia that has reduced the very stimuli necessary for optimal health and the evolution of intelligence – “the intelligence paradox”. One of the hallmarks of this paradox is of course the rising incidence in obesity, diabetes and the metabolic syndrome. This leads to the conclusion that wherever life evolves, here on earth or in another part of the galaxy, the “intelligence paradox’” would be the inevitable side-effect of the evolution of intelligence. ET may not need to just “phone home” but may also need to “phone the local gym”. This suggests another possible reason to explain Fermi’s paradox; Enrico Fermi, the famous physicist, suggested in the 1950s that if extra-terrestrial intelligence was so prevalent, which was a common belief at the time, then where was it? Our suggestion is that if advanced life has got going elsewhere in our galaxy, it can’t afford to explore the galaxy because it has to pay its healthcare costs.

Development of personalised functional foods needs metabolic profiling

Purpose of review There is growing interest in applying metabolic profiling technologies to food science as this approach is now embedded into the foodomics toolbox. This review aims at exploring how metabolic profiling can be applied to the development of functional foods. Recent findings One of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide that must suit high inter-individual variability driven by complex gene-nutrient-environment interactions. Although a number of functional foods are now proposed in support of a healthy diet, a one-size-fits-all approach to nutrition is inappropriate and new personalised functional foods are necessary. Metabolic profiling technologies can assist at various levels of the development of functional foods, from screening for food composition to identification of new biomarkers of food intake to support diet intervention and epidemiological studies. Summary Modern ‘omics’ technologies, including metabolic profiling, will support the development of new personalised functional foods of high relevance to twenty-first-century medical challenges such as controlling the worldwide spread of metabolic disorders and ensuring healthy ageing.

Nutrimetabonomics: nutritional applications of metabolic profiling

An individual's metabolic phenotype, and ultimately health, is significantly influenced by complex interactions between their genes and the diet. Studying these associations and their downstream biochemical consequences has proven extremely challenging using traditional hypothesis-led strategies. Metabonomics, a systems biology approach, allows the global metabolic response of biological systems to stimuli to be characterised. Through the application of this approach to nutritional-based research, nutrimetabonomics, the biochemical response to dietary inputs is being investigated at greater levels of resolution. This has allowed novel insights to be gained regarding intricate diet-gene interactions and their consequences for health and disease. In this review, we present some of the latest research exploring how nutrimetabonomics can assist in the elucidation of novel biomarkers of dietary behaviour and provide new perspectives on diet-health relationships. The use of this approach to study the metabolic interplay between the gut microbiota and the host is also explored.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Metabolic theory predicts whole-ecosystem properties

Understanding the effects of individual organisms on material cycles and energy fluxes within ecosystems is central to predicting the impacts of human-caused changes on climate, land use, and biodiversity. Here we present a theory that integrates metabolic (organism-based bottom-up) and systems (ecosystem-based top-down) approaches to characterize how the metabolism of individuals affects the flows and stores of materials and energy in ecosystems. The theory predicts how the average residence time of carbon molecules, total system throughflow (TST), and amount of recycling vary with the body size and temperature of the organisms and with trophic organization. We evaluate the theory by comparing theoretical predictions with outputs of numerical models designed to simulate diverse ecosystem types and with empirical data for real ecosystems. Although residence times within different ecosystems vary by orders of magnitude—from weeks in warm pelagic oceans with minute phytoplankton producers to centuries in cold forests with large tree producers—as predicted, all ecosystems fall along a single line: residence time increases linearly with slope = 1.0 with the ratio of whole-ecosystem biomass to primary productivity (B/P). TST was affected predominantly by primary productivity and recycling by the transfer of energy from microbial decomposers to animal consumers. The theory provides a robust basis for estimating the flux and storage of energy, carbon, and other materials in terrestrial, marine, and freshwater ecosystems and for quantifying the roles of different kinds of organisms and environments at scales from local ecosystems to the biosphere.

Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype

The postnatal environment, including factors such as weaning and acquisition of the gut microbiota, has been causally linked to the development of later immunological diseases such as allergy and autoimmunity, and has also been associated with a predisposition to metabolic disorders. We show that the very early-life environment influences the development of both the gut microbiota and host metabolic phenotype in a porcine model of human infants. Farmpiglets were nursed by their mothers for 1 day, before removal to highly controlled, individual isolators where they received formula milk until weaning at 21 days. The experiment was repeated, to create two batches, which differed only in minor environmental fluctuations during the first day. At day 1 after birth, metabolic profiling of serum by 1H nuclear magnetic resonance spectroscopy demonstrated significant, systemic, inter-batch variation which persisted until weaning. However, the urinary metabolic profiles demonstrated that significant inter-batch effects on 3-hydroxyisovalerate, trimethylamine-N-oxide and mannitol persisted beyond weaning to at least 35 days. Batch effects were linked to significant differences in the composition of colonic microbiota at 35 days, determined by 16 S pyrosequencing. Different weaning diets modulated both the microbiota and metabolic phenotype independently of the persistent batch effects. We demonstrate that the environment during the first day of life influences development of the microbiota and metabolic phenotype and thus should be taken into account when interrogating experimental outcomes. In addition, we suggest that intervention at this early time could provide ‘metabolic rescue’ for at-risk infants who have undergone aberrant patterns of initial intestinal colonisation.

New insights into the impact of Lactobacillus population on host-bacteria metabolic interplay

We aimed at evaluating the association between intestinal Lactobacillus sp. composition and their metabolic activity with the host metabolism in adult and elderly individuals. Faecal and plasma metabolites were measured and correlated to the Lactobacillus species distribution in healthy Estonian cohorts of adult (n=16; <48 y) and elderly (n=33; >65 y). Total cholesterol, LDL, C-reactive protein and glycated hemoglobin were statistically higher in elderly, while platelets, white blood cells and urinary creatinine were higher in adults. Aging was associated with the presence of L. paracasei and L. plantarum and the absence of L. salivarius and L. helveticus. High levels of intestinal Lactobacillus sp. were positively associated with increased concentrations of faecal short chain fatty acids, lactate and essential amino acids. In adults, high red blood cell distribution width was positively associated with presence of L. helveticus and absence of L. ruminis. L. helveticus was correlated to lactate and butyrate in faecal waters. This indicates a strong relationship between the composition of the gut Lactobacillus sp. and host metabolism. Our results confirm that aging is associated with modulations of blood biomarkers and intestinal Lactobacillus species composition. We identified specific Lactobacillus contributions to gut metabolic environment and related those to blood biomarkers. Such associations may prove useful to decipher the biological mechanisms underlying host-gut microbial metabolic interactions in an ageing population.

Blockade of ActRIIB signaling triggers muscle fatigability and metabolic myopathy

Myostatin regulates skeletal muscle size via the activin receptor IIB (ActRIIB). However, its effect on muscle energy metabolism and energy dependent muscle function remains largely unexplored. This question needs to be solved urgently since various therapies for neuromuscular diseases based on blockade of ActRIIB signaling are being developed. Here we show in mice that four months of pharmacological abrogation of ActRIIB signaling by treatment with soluble ActRIIB-Fc triggers extreme muscle fatigability. This is associated with elevated serum lactate levels and a severe metabolic myopathy in the mdx mouse, an animal model of Duchenne muscular dystrophy. Blockade of ActRIIB signaling down-regulates Porin, a crucial ADP/ATP shuttle between cytosol and mitochondrial matrix leading to a consecutive deficiency of oxidative phosphorylation as measured by in vivo Phophorus Magnetic Resonance Spectroscopy (31P-MRS). Further, ActRIIB blockade reduces muscle capillarization, which further compounds the metabolic stress. We show that ActRIIB regulates key determinants of muscle metabolism, such as Pparβ, Pgc1α, and Pdk4 thereby optimizing different components of muscle energy metabolism. In conclusion, ActRIIB signaling endows skeletal muscle with high oxidative capacity and low fatigability. The severe metabolic side effects following ActRIIB blockade caution against deploying this strategy, at least in isolation, for treatment of neuromuscular disorders.

Insulin resistance determines a differential response to changes in dietary fat modification on metabolic syndrome risk factors: the LIPGENE study

Background: Previous data support the benefits of reducing dietary saturated fatty acids (SFAs) on insulin resistance (IR) and other metabolic risk factors. However, whether the IR status of those suffering from metabolic syndrome (MetS) affects this response is not established. OBJECTIVE: Our objective was to determine whether the degree of IR influences the effect of substituting high-saturated fatty acid (HSFA) diets by isoenergetic alterations in the quality and quantity of dietary fat on MetS risk factors. DESIGN: In this single-blind, parallel, controlled, dietary intervention study, MetS subjects (n = 472) from 8 European countries classified by different IR levels according to homeostasis model assessment of insulin resistance (HOMA-IR) were randomly assigned to 4 diets: an HSFA diet; a high-monounsaturated fatty acid (HMUFA) diet; a low-fat, high-complex carbohydrate (LFHCC) diet supplemented with long-chain n-3 polyunsaturated fatty acids (1.2 g/d); or an LFHCC diet supplemented with placebo for 12 wk (control). Anthropometric, lipid, inflammatory, and IR markers were determined. RESULTS: Insulin-resistant MetS subjects with the highest HOMA-IR improved IR, with reduced insulin and HOMA-IR concentrations after consumption of the HMUFA and LFHCC n-3 diets (P < 0.05). In contrast, subjects with lower HOMA-IR showed reduced body mass index and waist circumference after consumption of the LFHCC control and LFHCC n-3 diets and increased HDL cholesterol concentrations after consumption of the HMUFA and HSFA diets (P < 0.05). MetS subjects with a low to medium HOMA-IR exhibited reduced blood pressure, triglyceride, and LDL cholesterol levels after the LFHCC n-3 diet and increased apolipoprotein A-I concentrations after consumption of the HMUFA and HSFA diets (all P < 0.05). CONCLUSIONS: Insulin-resistant MetS subjects with more metabolic complications responded differently to dietary fat modification, being more susceptible to a health effect from the substitution of SFAs in the HMUFA and LFHCC n-3 diets. Conversely, MetS subjects without IR may be more sensitive to the detrimental effects of HSFA intake. The metabolic phenotype of subjects clearly determines response to the quantity and quality of dietary fat on MetS risk factors, which suggests that targeted and personalized dietary therapies may be of value for its different metabolic features.