Metabolomics: building on a century of biochemistry to guide human health

Medical diagnosis and treatment efficacy will improve significantly when a more personalized system for health assessment is implemented. This system will require diagnostics that provide sufficiently detailed information about the metabolic status of individuals such that assay results will be able to guide food, drug and lifestyle choices to maintain or improve distinct aspects of health without compromising others. Achieving this goal will use the new science of metabolomics – comprehensive metabolic profiling of individuals linked to the biological understanding of human integrative metabolism. Candidate technologies to accomplish this goal are largely available, yet they have not been brought into practice for this purpose. Metabolomic technologies must be sufficiently rapid, accurate and affordable to be routinely accessible to both healthy and acutely ill individuals. The use of metabolomic data to predict the health trajectories of individuals will require bioinformatic tools and quantitative reference databases. These databases containing metabolite profiles from the population must be built, stored and indexed according to metabolic and health status. Building and annotating these databases with the knowledge to predict how a specific metabolic pattern from an individual can be adjusted with diet, drugs and lifestyle to improve health represents a logical application of the biochemistry knowledge that the life sciences have produced over the past 100 years.     

The tumor suppressor p53 can reduce stable transfection in the presence of irradiation

The tumor suppressor p53 is believed to play an essential role in maintaining genome stability. Although it is currently unknown how p53 is involved in this important biological safeguard, several previous publications indicate that p53 can help to maintain genome integrity through the recombination-mediated DNA repair process. The integration of linearized plasmid DNA into the host chromosome utilizes the same repair process, and the frequency can be measured by clonogenic assays in which cells that were stably transfected by plasmid integration can be scored by their colony-forming abilities. To gain insight into whether p53 has a direct role in plasmid integration into the host chromosome, we determined the frequency of stable transfection with CHO cells expressing either wild-type or mutant p53 in the presence and absence of irradiation. We found that low-dose irradiation (50 to 100 cGy) increased stable transfection frequencies in CHO cells regardless of their p53 status. However, the increase of transfection frequency was significantly lower in CHO cells expressing wild-type p53. Our data thus suggest that wild-type p53 can suppress plasmid DNA integration into the host genome. This p53 function may play a direct and significant role in maintaining genome stability.     

Molecular classification as prognostic factor and guide for treatment decision of pancreatic cancer

Clinico-pathological factors fail to consistently predict the outcome after pancreatic resection for pancreatic ductal adenocarcinoma (PDAC). PDACs show a high level of inter- and intra- tumor genetic heterogeneity. A molecular classification should help sort patients into less heterogeneous and more appropriate groups regarding the metastatic risk and the therapeutic response, with the consequences of better predicting evolution and better orienting the treatment. PDAC can be classified based on mutational subtypes and 18gene alterations. Whole-genome sequencing identified mutational signatures, mutational burden and hyper-mutated tumors with specific DNA repair defects. Their overlap/similarities allow the definition of molecular subtypes. DNA and RNA classifications can be used in prognosis assessment. They are useful in therapeutic choice for they allow the design of approaches that can predict the respective drug sensitivity of each molecular subtype. This review provides a comprehensive analysis of available molecular classifications in PDAC and how this can help guide clinical decisions.     

Host-specific factors determine the persistence of IncP-1 plasmids

Conjugative plasmids play a very important role in bacterial adaptation through the dissemination of useful traits. Incompatibility group P-1 (IncP-1) plasmids exhibit an extreme broad-host-range among Gram-negative bacteria and known to be one of the major agents to disseminate various phenotypic traits such as antibiotic resistance and xenobiotic degradation. Although the plasmids are believed to be very stable in most Gram-negative bacteria, little is known about the factors that affect their stability in various hosts, allowing their persistence in bacterial population. Here we show that the stability of the cryptic IncP-1β plasmid pBP136 differed greatly in four different Escherichia coli K12 host backgrounds (MG1655, DH5α, EC100, and JM109), whereas the closely related plasmid pB10 was stable in all four strains. The supply of the kleF gene, which is involved in the stability of IncP-1 plasmids but absent in pBP136, did not improve the stability of the plasmid. Our findings suggest that persistence of IncP-1 plasmids in the absence of selection is affected by strain-specific factors.     

Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X = 0.223, R2Y = 0.82, Q2 = 0.562; p < 0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p = 0.01; CI: 0.75–1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.