Profiling Of Small Molecules Research Articles

Particulate Air Pollution Exposure during Pregnancy and Mitochondrial-Associated Plasma Metabolites in Mothers at 48 Months Postpartum: A Pilot Study

BackgroundPregnancy and early postpartum life are periods of major metabolic change in women and may be critical windows of susceptibility for longer-term metabolic changes induced by inflammatory exposures, such as PM2.5 (ambient particulate matter <2.5 µm in diameter).HypothesisAs mitochondria are sensitive to inflammation, we hypothesized that gestational/early postpartum PM2.5 exposure would predict metabolomic profiles of small molecules in mitochondrial-associated pathways measured years after pregnancy.MethodsIn a pilot study, we randomly selected 50 mothers from the Programming Research on Obesity, GRowth, Environment and Social Stress (PROGRESS) cohort based in Mexico City. We estimated PM2.5 exposure using a satellite-based spatiotemporally-resolved prediction model. In maternal plasma collected at 48 months postpartum, we assessed 110 metabolites in mitochondrial pathways (TCA cycle, fatty acid β-oxidation, and branched chain amino acid catabolism) using Metabolon’s Discovery HD4™ platform.ResultsIn robust regression models adjusted for age, secondhand smoke exposure, socioeconomic status, alcohol consumption, and parity, average PM2.5 during the first year postpartum was associated with 17 metabolites (p<0.05). The majority were long chain fatty acids and polyunsaturated fatty acids, which were negatively associated with PM2.5. No associations were observed for these fatty acids with average PM2.5 during pregnancy. Further, fewer metabolites overall were associated with PM2.5 during pregnancy (two at p<0.05), suggesting results vary by time of exposure.ConclusionIn this pilot study, we found that PM2.5 exposure during the first year postpartum was associated with altered fatty acid metabolism in plasma from mothers at 48 months postpartum. The postpartum period may be a susceptibility window for PM2.5 exposure with regard to metabolic dysregulation, particularly lipids. We plan to replicate these findings in the full sample and in independent cohorts.

Ectopic Expression of a Thellungiella salsuginea Aquaporin Gene, TsPIP1;1, Increased the Salt Tolerance of Rice.

Aquaporins play important regulatory roles in the transport of water and small molecules in plants. In this study, a Thellungiella salsuginea TsPIP1;1 aquaporin was transformed into Kitaake rice, and three transgenic lines were evaluated by profiling the changes of the physiological metabolism, osmotic potential, and differentially expressed genes under salt stress. The TsPIP1;1 protein contains six transmembrane domains and is localized in the cytoplasm membrane. Overexpression of the TsPIP1;1 gene not only increased the accumulation of prolines, soluble sugars and chlorophyll, but also lowered the osmotic potential and malondialdehyde content in rice under salt stress, and alleviated the amount of salt damage done to rice organs by regulating the distribution of Na/K ions, thereby promoting photosynthetic rates. Transcriptome sequencing confirmed that the differentially expressed genes that are up-regulated in rice positively respond to salt stimulus, the photosynthetic metabolic process, and the accumulation profiles of small molecules and Na/K ions. The co-expressed Rubisco and LHCA4 genes in rice were remarkably up-regulated under salt stress. This data suggests that overexpression of the TsPIP1;1 gene is involved in the regulation of water transport, the accumulation of Na/K ions, and the translocation of photosynthetic metabolites, thus conferring enhanced salt tolerance to rice.

Multiparameter Phenotypic Profiling in MCF-7 Cells for Assessing the Toxicity and Estrogenic Activity of Whole Environmental Water.

Multi-parameter phenotypic profiling of small molecules is a powerful approach to their toxicity assessment and identifying potential mechanisms of actions. The present study demonstrates the application of image-based multi-parameter phenotypic profiling in MCF-7 cells to assess the overall toxicity and estrogenic activity of whole environmental water. Phenotypic profiling of 30 reference compounds and their complex mixtures was evaluated to investigate the cellular morphological outcomes to targeted biological pathways. Overall toxicity and estrogenic activity of environmental water samples were then evaluated by phenotypic analysis comparing with conventional bioassays and chemical analysis by multivariate analysis. The phenotypic analysis for reference compounds demonstrated that size and structure of cells related to biological processes like cell growth, death, and communication. The phenotypic alteration and nuclei intensity were selected as potential biomarkers to evaluate overall toxicity and estrogenic activities, respectively. The phenotypic profiles were associated with the chemical structure profiles in environmental water samples. Since the phenotypic parameters revealed multiple toxicity endpoints, it could provide more information that is relevant to assessing the toxicity of environmental water samples in compare with conventional bioassays. In conclusion, the image-based multi-parameters phenotypic analysis with MCF-7 cells provides a rapid and information-rich tool for toxicity evaluation and identification in whole water samples.

In vitro secondary pharmacological profiling: An IQ-DruSafe industry survey on current practices

IntroductionIn 2015, IQ DruSafe conducted a survey of its membership to identify industry practices related to in vitro off target pharmacological profiling of small molecules. MethodsAn anonymous survey of 20 questions was submitted to IQ-DruSafe representatives. Questions were designed to explore screening strategies, methods employed and experience of regulatory interactions related to in vitro secondary pharmacology profiling. ResultsThe pharmaceutical industry routinely utilizes panels of in vitro assays to detect undesirable off-target interactions of new chemical entities that are deployed at all stages of drug discovery and early development. The formats, approaches and size of panels vary between companies, in particular i) choice of assay technology; ii) test concentration (single vs. multiple concentrations) iii) rationale for targets and panels selection (taking into account organizational experience, primary target, therapeutic area, availability at service providers) iv) threshold level for significant interaction with a target and v) data interpretation. Data are generated during the early phases of drug discovery, principally before in vivo GLP studies (i.e., hit-to-lead, lead optimization, development candidate selection) and used to contextualize in vivo non-clinical and clinical findings. Data were included in regulatory documents, and around half of respondents experienced regulatory questions about the significance of the results. ConclusionWhile it seems that in vitro secondary pharmacological profiling is generally considered valuable across the industry, particularly as a tool in early phases of drug discovery for small molecules, there is only loose consensus on testing paradigm, the required interpretation and suitable follow up strategies to fully understand potential risk.

Metabolomic aging and its association with depression in a UK cohort

Introduction Depression is associated with both reduced lifespan and cardiovascular disease risk. Accelerated biological aging may underlie this association. Modern ‘omics’ platforms provide new opportunities for the systematic assessment of biological aging. Genome-wide DNA methylation data has been used to predict chronological age with high accuracy, and ‘age acceleration’ - the difference between DNA methylation age and chronological age - is predictive of mortality. Few studies have employed metabolomics (small molecule profiling) to model the aging process. We have employed untargeted metabolomics across multiple nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) based platforms, to develop a highly predictive model of age, within a large sample from the Airwave Health Monitoring Study (AIRWAVE). We investigate the correlation between metabolomic age and DNA methylation age and show that metabolic age acceleration is associated with depression and other disease risk factors. Methods Full metabolomic data was acquired for 2238 participants (age range: 19.2–65.2 years, 61% male) in the AIRWAVE cohort of employees of police forces from across Great Britain. Nine metabolomic analyses were applied: NMR, hydrophilic interaction chromatography-MS and reversed-phase chromatography-MS (both positive and negative modes) in both serum and urine, and NMR-based Bruker IVDr Lipoprotein Subclass Analysis in serum only. NMR profiles were aligned and interpolated onto a common 20,000-point grid while MS data was processed into retention time-m/z pairs. All platforms were combined into one dataset providing 98,824 metabolic features. A metabolomic age prediction model was constructed using elastic net modelling, with penalisation parameters found following 10-fold cross validation, in a training portion of the data (80% of participants) and then validated in a test set (remaining 20%). Metabolic age acceleration (AA) was defined as the difference between predicted metabolomic and chronological age. We computed the ‘intrinsic’ AA, defined as the residuals from the linear regression of AA with chronological age, hereafter referred to as ‘metAA’. DNA methylation was measured in leukocytes in 1102 participants using the Illumina 850 K methylationEPIC array and Hannum's DNA methylation age calculated. Intrinsic DNA methylation AA was calculated as for metAA, additionally accounting for blood cell proportion, and referred to as ‘DNAmethAA’. Depression was assessed through the Patient Health Questionnaire (PHQ-9) and associations with metAA and DNA methAA were assessed through linear regression. Results Metabolomic age, based on 472 features, was highly correlated both with chronological age in the independent validation set (Pearson's correlation r = 0.85) and with DNA methylation age ( r = 0.85). There was no relationship between DNAmethAA and metAA ( r = 0.02). In analyses adjusted for sex, disease risk factors and diet, metAA was associated with both having some depressive symptoms [β, interpretable as years of increase in metabolic age, = 0.70, 95% confidence interval (CI): 0.38, 1.01] and being a depression case (0.54, 95% CI: 0.08, 0.99) compared to those with no symptoms of depression. Obesity (1.35, 95% CI: 0.95, 1.69); low income (0.33, 95% CI: 0.00, 0.65); and heavy drinking (0.72, 95% CI: 0.08, 1.36) were also associated with metAA. Only being male was significantly associated with DNAmethAA (0.89, 95% CI: 0.47, 1.30). Participants with low income had higher DNAmethAA than those with high income, although this was not statistically significant [0.41 (−0.12, 0.94) after adjustment for sex]. Conclusion Metabolomic analysis can be used to predict age with high accuracy. Accelerated metabolic aging appears to capture a different dimension of the aging process to DNA methylation age acceleration, and may contribute to associations between depression and mortality.

Abstract 954: Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles

Abstract Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and which patients to give them to based on the genetic or pathological features of their disease. However, progress towards this goal has been hindered by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts. We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the cell-viability profiles of single-agent small molecules, might be leveraged to nominate specific synergistic drug combinations and identify mechanisms of drug resistance, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy. To test this notion, we analyzed nearly 400,000 drug-sensitivity profiles in >800 cancer cell lines to identify candidate compound-gene pairs. We identified over 100 examples where outlier expression of a single transcript was correlated with resistance to a small molecule. Of these gene/drug pairs, 9 genes represented imminently druggable targets, including established clinically-relevant relationships between the alkylating agent temozolomide and MGMT expression, and between a subset of chemotherapeutics including paclitaxel and the efflux pump ABCB1. Inhibition of candidate “co-targets”, which included 3 previously characterized relationships and 6 novel relationships, resulted in cell-line-specific synergistic cell killing across multiple cell-line models. For validated compound-gene pairs, exogenous expression of the “co-target” was sufficient to confer resistance. For example, we found that high expression of MGLL, encoding monoglyceride lipase, was uniquely associated with lack of response to the histone lysine demethylase inhibitor GSK-J4. Endogenous or exogenous MGLL expression conferred resistance to GSK-J4, while MGLL-proficient cell lines could be sensitized to GSK-J4 up to 50-fold by co-treatment with an irreversible MGLL inhibitor. These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations. Citation Format: Matthew G. Rees, Lisa Brenan, Patrick Duggan, Cory M. Johannessen. Predicting synergistic drug combinations and resistance mechanisms from genomic features and single-agent response profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 954.

Abstract 3940: Small molecule profiling uncovers the landscape uncovers the landscape of combinational PI3K inhibitor responses in HNSCC

Abstract Recent sequencing studies of head and neck squamous cell carcinomas (HNSCCs) have identified the phosphatidylinositol 3-kinase (PI3K) pathway as the most frequently mutated, oncogenic pathway in this cancer type. Despite the frequency of activating mutations or amplification in PIK3CA (the gene encoding the catalytic subunit of PI3K), targeted inhibitors of PI3K have shown limited clinical efficacy as monotherapies. To identify factors that might predict sensitivity and resistance to PI3K inhibitors, we tested a panel of more than 20 patient-derived HNSCC cell lines and observed varying sensitivity to PI3K inhibitors. While all cell lines were much more sensitive than fibroblasts to these drugs, responses varied widely; for pan-PI3K inhibitor BKM120, IC50 values ranged from 0.4 to 7.4 uM across the OSCC cell line panel. Responses to BKM120 not correlate with genetic or phenotypic features, including: PIK3CA mutation status copy number, RNA expression, p110a protein levels, and AKT phosphorylation at baseline and after inhibitor treatment. In order to characterize potential mechanisms of PI3K inhibitor resistance in HNSCC, we have also developed and optimized an unbiased, high-throughput screening approach. We have used this assay to test ~1400 inhibitors as monotherapies and in combination with PI3K inhibitors HS-173 and BKM120 in several HNSCC models. Our initial screening data suggested that the combination of PI3K inhibitor and EGFR/HER2 inhibitor afatinib is synergistic in a subset of cell lines. After testing this combination in additional models, we identified some cell lines that were responsive to the combination of HS-173 and afatinib, but not HS-173 and either EGFR inhibitor gefitinib or HER2 inhibitor CP-724714. In Detroit 562 cells, combination treatment with 0.25 uM HS-173 and 2.5 uM afatinib resulted in 35% more trypan positive cells than either drug alone. This also induced significantly more trypan positivity than combinations of 0.25 uM HS-173 with 5 uM gefitinib or 5 uM CP-724714. Likewise, the same HS-173 + afatinib treatment resulted in 35% or greater increases in Annexin V positivity compared to gefitinib and CP-724714 combinations. Western blot analysis demonstrated that these doses of gefitinib, CP-724714 and afatinib all inhibited target after 20 minute treatment in this cell line. Future studies will also examine additional factors that may predict PI3K inhibitor responses and will seek to better understand promising combination treatments and advance them for effective use in HNSCC patients. Citation Format: Nicole Lynn Michmerhuizen, Chloe Matovina, Elizabeth Leonard, Micah Harris, Caitlin Heenan, Gabrielle Herbst, Susan K. Foltin, Aditi Kulkarni, Thomas E. Carey, Carol R. Bradford, Hui Jiang, Chad Brenner. Small molecule profiling uncovers the landscape uncovers the landscape of combinational PI3K inhibitor responses in HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3940.

Metabonomic Profiling of Chicken Eggs during Storage Using High-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry.

Metabonomic techniques have been used to discover subtle differences in the small-molecule profiles of chicken eggs, which could help to combat fraud within the egg industry. High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-ToF-MS) was used to obtain profiles of the small molecules present in the yolks of chicken eggs stored for different lengths of time. Statistical analysis, including the use of XCMS Online and further exploratory statistics, was able to uncover differences in the abundances of several of the small molecules found in these egg yolks. One of these small molecules was identified through the use of METLIN and MS/MS analysis as choline. A targeted study was then carried out over a longer storage period, using the same instrumentation and analytical techniques, in order to observe how the concentration of choline in egg yolk changes over a longer period of time.

Recent advances of metabolomics techniques for diagnosis and treatment of kidney disease

Kidney disease(KD) is a major challenge for global public healthcare systems. Traditional biomarkers (such as serum creatinine and urea) for measuring kidney function are not sufficient sensitivity and specificity. They are not only affected by many factors, but also were only increase significantly in severe KD. Therefore, more sensitive biomarkers for evaluation of KD is essential. Metabolomics is a promising tool that identify non-targeted, global small-molecule metabolite profiles of complex samples, such as biofluids and tissues extract. The application of metabolomics in KD studies has developed rapidly in the past years. Many benefits have been shown from the use of metabolomics in identifying biomarkers for KD. In particular, metabolomic approaches have the potential to diagnose KD with a higher accuracy than traditional diagnostic methods. Metabolomics in KD research has been expanded from experimental study into clinical applications.(Chin J Lab Med, 2018, 41: 246-250) Key words: Kidney disease; Biomarker; Metabonome/metabolome; Metabonomics/metabolomics

Multiplex Immunoassay Profiling of Hormones Involved in Metabolic Regulation.

Multiplex immunoassays are used for rapid profiling of biomarker proteins and small molecules in biological fluids. The advantages over single immunoassays include lower sample consumption, cost, and labor. This chapter details a protocol to develop a 5-plex assay for glucagon-like peptide 1, growth hormone, insulin, leptin, and thyroid-stimulating hormone on the Luminex® platform. The results of the analysis of insulin in normal control subjects are given due to the important role of this hormone in nutritional programming diseases.

Metabolic Regulation of Pre- and Postnatal Growth.

Growth characteristics during periods of early developmental plasticity are linked with later health outcomes and with disease risks. Infant growth is modulated by genetic and exogenous factors including nutrition. We try to explore their underlying mechanisms using targeted metabolomic profiling of small molecules in biological samples using high-performance liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) to quantify hundreds of molecules in small biosamples, e.g., 50 µL plasma. In the large German LISA birth cohort study, cord blood lysophosphatidylcholines and fatty acids were closely associated with infant birth weight, with a nonsignificant trend towards an association with infant weight gain and later BMI. Studies in infants randomized to different protein intakes in the European CHOP Study show conventional high protein intakes to markedly increase plasma-indispensable amino acids (AA), particularly branched-chain AA (BCAA), while exceeding the infant's capacity of BCAA breakdown, and an increase in the dispensable AA tyrosine previously associated with insulin resistance. In a path model analysis of the relationship of infant plasma AA, growth factors, and infant growth, AA were generally found to induce a stronger response of insulin than IGF-I although effects of individual AA were very different. We conclude that targeted improvement in nutrient supply in pregnancy and infancy may offer large opportunities for promoting desirable child growth patterns and long-term health.

Abstract 30: Small molecule profiling uncovers the landscape of combinatorial PI3K inhibitor responses in HNSCC

Abstract Recent sequencing studies of head and neck squamous cell carcinomas (HNSCCs) have identified the phosphatidylinositol 3-kinase (PI3K) pathway as the most frequently mutated oncogenic pathway in this cancer type. Although activating mutations or amplification in PIK3CA (the gene encoding the catalytic subunit of PI3K) are frequently observed in HNSCC, monotherapies targeting PI3K have shown limited clinical efficacy. We tested a panel of more than 20 patient-derived oral cavity squamous cell carcinoma (OCSCC) cell lines, which were profiled by Nimblegen V3 exome sequencing. Despite PIK3CA copy number amplification and/or mutations, we observed resistance to PI3K inhibitors in these models. Of six inhibitors tested, the only agents with marginal effects were alpha-isoform selective and pan-PI3K agents; these inhibitors, despite on-target and downstream activity, did not cause an appreciable reduction in cell viability when administered at submicromolar concentrations. We hypothesized that other oncogenic pathways, such as the Ras-MEK-ERK pathway, might still be functional in the presence of PI3K inhibitors and serve as compensatory mechanisms of resistance to these drugs. In order to more fully characterize other novel co-dependent pathway in PI3K inhibitor resistant HNSCC models, we have developed a high-throughput screening approach that utilizes a resazurin cell viability assay. This approach is an unbiased means of testing ~1400 inhibitors as monotherapies and in combination with alpha-isoform selective PI3K inhibitor HS-173 and pan-PI3K inhibitor BKM120 and has been utilized in several in vitro models of OCSCC to date. Testing of additional cellular models and validation of “hits” from this screen, which are drugs that are effective in combination but not as monotherapies, may identify additional resistance mechanisms and are currently ongoing. Developing a better understanding of these combination therapies and the patients in which they might be most effective may lead to improved prognosis for this disease. Citation Format: Nicole L. Michmerhuizen, Elizabeth Leonard, Micah Harris, Susan K. Foltin, Aditi Kulkarni, Thomas E. Carey, Carol R. Bradford, Jiang Hui, J. Chad Brenner. Small molecule profiling uncovers the landscape of combinatorial PI3K inhibitor responses in HNSCC [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 23-25, 2017; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(23_Suppl):Abstract nr 30.

Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics

Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPLS-DA, PLS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data.

Metabolomics analyses identify platelet activating factors and heme breakdown products as Lassa fever biomarkers

Lassa fever afflicts tens of thousands of people in West Africa annually. The rapid progression of patients from febrile illness to fulminant syndrome and death provides incentive for development of clinical prognostic markers that can guide case management. The small molecule profile of serum from febrile patients triaged to the Viral Hemorrhagic Fever Ward at Kenema Government Hospital in Sierra Leone was assessed using untargeted Ultra High Performance Liquid Chromatography Mass Spectrometry. Physiological dysregulation resulting from Lassa virus (LASV) infection occurs at the small molecule level. Effects of LASV infection on pathways mediating blood coagulation, and lipid, amino acid, nucleic acid metabolism are manifest in changes in the levels of numerous metabolites in the circulation. Several compounds, including platelet activating factor (PAF), PAF-like molecules and products of heme breakdown emerged as candidates that may prove useful in diagnostic assays to inform better care of Lassa fever patients.

In-depth Profiling of MvfR-Regulated Small Molecules in Pseudomonas aeruginosa after Quorum Sensing Inhibitor Treatment.

Pseudomonas aeruginosa is a Gram-negative bacterium, which causes opportunistic infections in immuno-compromised individuals. Due to its multiple resistances toward antibiotics, the development of new drugs is required. Interfering with Quorum Sensing (QS), a cell-to-cell communication system, has shown to be highly efficient in reducing P. aeruginosa pathogenicity. One of its QS systems employs Pseudomonas Quinolone Signal (PQS) and 4-hydroxy-2-heptylquinoline (HHQ) as signal molecules. Both activate the transcriptional regulator MvfR (Multiple Virulence Factor Regulator), also called PqsR, driving the production of QS molecules as well as toxins and biofilm formation. The aim of this work was to elucidate the effects of QS inhibitors (QSIs), such as MvfR antagonists and PqsBC inhibitors, on the biosynthesis of the MvfR-regulated small molecules 2′-aminoacetophenone (2-AA), dihydroxyquinoline (DHQ), HHQ, PQS, and 4-hydroxy-2-heptylquinoline-N-oxide (HQNO). The employed synthetic MvfR antagonist fully inhibited pqs small molecule formation showing expected sigmoidal dose-response curves for 2-AA, HQNO, HHQ and PQS. Surprisingly, DHQ levels were enhanced at lower antagonist concentrations followed by a full suppression at higher QSI amounts. This particular bi-phasic profile hinted at the accumulation of a biosynthetic intermediate resulting in the observed overproduction of the shunt product DHQ. Additionally, investigations on PqsBC inhibitors showed a reduction of MvfR natural ligands, while increased 2-AA, DHQ and HQNO levels compared to the untreated cells were detected. Moreover, PqsBC inhibitors did not show any significant effect in PA14 pqsC mutant demonstrating their target selectivity. As 2-AA is important for antibacterial tolerance, the QSIs were evaluated in their capability to attenuate persistence. Indeed, persister cells were reduced along with 2-AA inhibition resulting from MvfR antagonism, but not from PqsBC inhibition. In conclusion, antagonizing MvfR using a dosage capable of fully suppressing this QS system will lead to a favorable therapeutic outcome as DHQ overproduction is avoided and bacterial persistence is reduced.

Blood metabolomic fingerprint is distinct in healthy coronary and in stenosing or microvascular ischemic heart disease

BackgroundThe endothelium is a key variable in the pathogenesis of atherosclerosis and its complications, particularly coronary artery disease (CAD). Current evidence suggests that the endothelial status can be regarded as an integrated index of individual atherogenic and anti-atherogenic properties, and that the interaction between circulating factors and the arterial wall might be critical for atherogenesis. In organism-level investigations, a functional view is provided by metabolomics, the study of the metabolic profile of small molecules. We sought to verify whether metabolomic analysis can reveal the presence of coronary microenvironment peculiarities associated with distinct manifestations of CAD.MethodsThirty-two coronary blood samples were analyzed using 1H-NMR-based metabolomics. Samples collected from patients with evidence of myocardial ischemia formed the case group, and were further divided into the stenotic-disease (SD) group (N = 13) and absence of stenosis (microvascular disease; “Micro”) group (N = 8); specimens of patients presenting no evidence of ischemic heart disease (dilated cardiomyopathy, valvular diseases) constituted the control group (N = 11).ResultsApplication of an orthogonal partial least squares discriminant analysis (OPLS-DA) model to the entire dataset clearly separated the samples into 3 groups, indicating 3 distinct metabolic fingerprints. Relative to control-group members, Micro patients showed a higher content of 2-hydroxybutirate, alanine, leucine, isoleucine, and N-acetyl groups and lower levels of creatine/phosphocreatine, creatinine, and glucose, whereas SD patients showed higher levels of 3-hydroxybutirate and acetate and a lower content of 2-hydroxybutirate. Moreover, relative to SD patients, Micro patients showed higher levels of 2-hydroxybutirate, alanine, leucine, and N-acetyl groups and lower levels of 3-hydroxybutirate and acetate.ConclusionsSpecific coronary microenvironments are likely associated with distinct development and pathological expression of CAD.

Relationship in Broccoli Between Genetic Mapping and Small Molecule Profiling Using NMR Spectroscopy

Broccoli was analyzed using both genetic mapping and small molecule profiling by NMR spectroscopy. 125 separate families of a broccoli population were utilized for the analysis. Broccoli families were analyzed using NMR spectroscopy to obtain small molecule profiles. These profiles were generated by quantifying a number of compounds in over 600 broccoli spectra. The profiles were compared to genetic mapping data generated for each broccoli family to determine possible QTLs (quantitative trait loci) associated with variation in the small molecules detected. Variation in some sugars and amino acids ranging up to 15% could be accounted for by genetic variation and traced to certain QTLs.

Predicting the release profile of small molecules from within the ordered nanostructured lipidic bicontinuous cubic phase using translational diffusion coefficients determined by PFG-NMR.

The ordered nanostructured lipidic bicontinuous cubic phase has demonstrated potential as a drug release material, due to its ability to encapsulate a wide variety of compounds, which may undergo sustained, diffusion controlled release over time. Control of drug release has been shown to depend on the nanostructural parameters of the lipid mesophase. Herein, the diffusion and release of two amino acids, encapsulated within a range of different lipidic cubic mesophases are investigated. Pulsed-field gradient NMR was used to determine the diffusion coefficient of the encapsulated amino acid, which was found to be correlated with the nanoscale diameter of the water channels within the cubic mesophase. This information was used to predict the release profiles of encapsulated compounds from within the cubic mesophase, which was verified by directly measuring the release of each amino acid in vitro. Predicted release profiles tracked reasonably close to the measured release profiles, indicating that NMR determined diffusion measurements can be used to predict release profiles.

A Platform to Enable the Pharmacological Profiling of Small Molecules in Gel-Based Electrophoretic Mobility Shift Assays

We describe a polyacrylamide gel casting cassette that overcomes limitations of commercially available gel electrophoresis equipment. This apparatus molds a single polyacrylamide gel that can evaluate more than 200 samples in parallel, is loaded with a multichannel pipettor, and is flexible with respect to composition of the separating matrix. We demonstrate its use to characterize inhibitors of enzymes that modify protein and nucleic acid substrates. Throughputs of greater than 1000 samples per day were achieved when this system was paired with a quantitative laser-based imaging system, yielding data of remarkable quality.

Metabolite and Lipid Profiling of Biobank Plasma Samples Collected Prior to Onset of Rheumatoid Arthritis.

ObjectiveThe early diagnosis of rheumatoid arthritis (RA) is desirable to install treatment to prevent disease progression and joint destruction. Autoantibodies and immunological markers pre-date the onset of symptoms by years albeit not all patients will present these factors, even at disease onset. Additional biomarkers would be of high value to improve early diagnosis and understanding of the process, leading to disease development.MethodsPlasma samples donated before the onset of RA were identified in the Biobank of Northern Sweden, a collection within national health survey programs. Thirty samples from pre-symptomatic individuals and nineteen from controls were subjected to liquid chromatography-mass spectrometry (LCMS) metabolite and lipid profiling. Lipid and metabolite profiles discriminating samples from pre-symptomatic individuals from controls were identified after univariate and multivariate OPLS-DA based analyses.ResultsThe OPLS-DA models including pre-symptomatic individuals and controls identified profiles differentiating between the groups that was characterized by lower levels of acyl-carnitines and fatty acids, with higher levels of lysophospatidylcholines (LPCs) and metabolites from tryptophan metabolism in pre-symptomatic individuals compared with controls. Lipid profiling showed that the majority of phospholipids and sphingomyelins were at higher levels in pre-symptomatic individuals in comparison with controls.ConclusionsOur LCMS based approach demonstrated that there are changes in small molecule and lipid profiles detectable in plasma samples collected from the pre-symptomatic individuals who subsequently developed RA, which point to an up-regulation of levels of lysophospatidylcholines, and of tryptophan metabolism, perturbation of fatty acid beta-oxidation and increased oxidative stress in pre-symptomatic individuals’ years before onset of symptoms.