A Phenotarget Approach for Identifying an Alkaloid Interacting with the Tuberculosis Protein Rv1466.

In recent years, there has been a revival of interest in phenotypic-based drug discovery (PDD) due to target-based drug discovery (TDD) falling below expectations. Both PDD and TDD have their unique advantages and should be used as complementary methods in drug discovery. The PhenoTarget approach combines the strengths of the PDD and TDD approaches. Phenotypic screening is conducted initially to detect cellular active components and the hits are then screened against a panel of putative targets. This PhenoTarget protocol can be equally applied to pure compound libraries as well as natural product fractions. Here we described the use of the PhenoTarget approach to identify an anti-tuberculosis lead compound. Fractions from Polycarpa aurata were identified with activity against Mycobacterium tuberculosis H37Rv. Native magnetic resonance mass spectrometry (MRMS) against a panel of 37 proteins from Mycobacterium proteomes showed that a fraction from a 95% ethanol re-extraction specifically formed a protein-ligand complex with Rv1466, a putative uncharacterized Mycobacterium tuberculosis protein. The natural product responsible was isolated and characterized to be polycarpine. The molecular weight of the ligand bound to Rv1466, 233 Da, was half the molecular weight of polycarpine less one proton, indicating that polycarpine formed a covalent bond with Rv1466.

Unravelling inulin molecules in food sources using a matrix-assisted laser desorption/ionization magnetic resonance mass spectrometry (MALDI-MRMS) pipeline.

Inulin, a polysaccharide characterized by a ß-2,1 fructosyl-fructose structure terminating in a glucosyl moiety, is naturally present in plant roots and tubers. Current methods provide average degrees of polymerization (DP) but lack information on the distribution and absolute concentration of each DP. To address this limitation, a reproducible (CV < 10 %) high throughput (<2 min/sample) MALDI-MRMS approach capable of characterizing and quantifying inulin molecules in plants using matched-matrix consisting of α-cyano-4-hydroxycinnamic acid butylamine salt (CHCA-BA), chicory inulin-12C and inulin-13C was developed. The method identified variation in chain lengths and concentration of inulin across various plant species. Globe artichoke hearts, yacón and elephant garlic yielded similar concentrations at 15.6 g/100 g dry weight (DW), 16.8 g/100 g DW and 17.7 g/100 g DW, respectively, for DP range between 9 and 22. In contrast, Jerusalem artichoke demonstrated the highest concentration (53.4 g/100 g DW) within the same DP ranges. Jerusalem artichoke (DPs 9-32) and globe artichoke (DPs 9-36) showed similar DP distributions, while yacón and elephant garlic displayed the narrowest and broadest DP ranges (DPs 9-19 and DPs 9-45, respectively). Additionally, qualitative measurement for all inulin across all plant samples was feasible using the peak intensities normalized to Inulin-13C, and showed that the ratio of yacón, elephant garlic and Jerusalem was approximately one, two and three times that of globe artichoke. This MALDI-MRMS approach provides comprehensive insights into the structure of inulin molecules, opening avenues for in-depth investigations into how DP and concentration of inulin influence gut health and the modulation of noncommunicable diseases, as well as shedding light on refining cultivation practices to elevate the beneficial health properties associated with specific DPs.

One-step integrated sample pretreatment technique by gas-liquid microextraction (GLME) to determine multi-class pesticide residues in plant-derived foods.

Gas-liquid microextraction technique (GLME) has been integrated with dispersive solid phase extraction to establish a one-step sample pretreatment approach for rapid analysis of multi-class pesticides in different plant-derived foods. A 50 µL of organic solvent plus 40 mg of PSA were required throughout the 5-minute pretreatment procedure. Good trueness (recoveries of 67.2 - 105.4%) and precision (RSD ≤ 18.9%) were demonstrated by the one-step GLME method, with MLOQs ranged from 0.001 to 0.011 mg kg-1. As high as 93.6% pesticides experienced low matrix effect through this method, and the overall matrix effects (ME%) were generally better or comparable to QuEChERS. This method successfully quantified 2-phenylphenol, quintozene, bifenthrin and permethrin in the range of 0.001 - 0.008 mg kg-1 in real food samples. The multiresidue analysis feature of GLME has been validated, which displays further potential for on-site determination of organic pollutants in order to safeguard food safety and human health.

Olive Oil Quality and Authenticity Assessment Aspects Employing FIA-MRMS and LC-Orbitrap MS Metabolomic Approaches.

Edible vegetable oils comprise integral components of humans' daily diet during the lifetime. Therefore, they constitute a central part of dietary-exposome, which among other factors regulates human health. In particular, the regular consumption of olive oil (OO) has been largely accepted as a healthy dietary pattern. Responsible for its recognition as a superior edible oil is its exceptional aroma and flavor. Its unique composition is characterized by high levels of monounsaturated fatty acids and the presence of minor constituents with important biological properties, such as the so-called OO polyphenols. Being a high added value product, OO suffers from extensive fraud and adulteration phenomena. However, its great chemical complexity, variability, and the plethora of parameters affecting OO composition hamper significantly the selection of the absolute criteria defining quality and authenticity, and a reliable and robust methodology is still unavailable. In the current study, Flow Injection Analysis-Magnetic Resonance Mass Spectrometry (FIA-MRMS) was investigated under a metabolic profiling concept for the analysis of Greek Extra Virgin Olive Oils (EVOO). More than 200 monovarietal (Koroneiki) EVOO samples were collected from the main Greek OO producing regions and investigated. Both intact oil and the corresponding polyphenols were analyzed in fast analysis time of 2 and 8 min, respectively. In parallel, an LC-Orbitrap MS platform was used to verify the efficiency of the method as well as a tool to increase the identification confidence of the proposed markers. Based on the results, with FIA-MRMS, comparable and improved projection and prediction models were generated in comparison to those of the more established LC-MS methodology. With FIA-MRMS more statistically significant compounds and chemical classes were identified as quality and authenticity markers, associated with specific parameters, i.e. geographical region, cultivation practice, and production procedure. Furthermore, it was possible to monitor both lipophilic and hydrophilic compounds with a single analysis. To our knowledge, this approach is among the few studies in which two FT-MS platforms combining LC and FIA methods were integrated to provide solutions to quality control aspects of OO. Moreover, both lipophilic and hydrophilic components are analyzed together, providing a holistic quality control workflow for OO.

Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers.

Dysregulation of miRNA-mRNA regulatory networks is very common phenomenon in any diseases including cancer. Altered expression of biomarkers leads to these gynecologic cancers. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies that the pathways associated with gynecologic cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered gynecologic cancer pathways. In this regard, an existing robust mutual information-based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in gynecologic cancer. A set of miRNA-mRNA modules are identified first than their association with gynecologic cancer are studied exhaustively. The effectiveness of the proposed approach is compared with the existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in gynecologic cancer.

Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions.

In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies. This literature review briefly discusses recent machine learning-based techniques applied in the discovery of miRNAs, prediction of miRNA targets, and inference of miRNA functions. We also discuss the limitations of how these approaches have been elucidated in previous studies.

Conformational restriction of G-proteins Coupled Receptors (GPCRs) upon complexation to G-proteins: a putative activation mode of GPCRs?

GPCRs undergo large conformational changes during their activation. Starting from existing X-ray structures, we used Normal Modes Analyses to study the collective motions of the agonist-bound ß2-adrenergic receptor both in its isolated "uncoupled" and G-protein "coupled" conformations. We interestingly observed that the receptor was able to adopt only one major motion in the protein:protein complex. This motion corresponded to an anti-symmetric rotation of both its extra- and intra-cellular parts, with a key role of previously identified highly conserved proline residues. Because this motion was also retrieved when performing NMA on 7 other GPCRs which structures were available, it is strongly suspected to possess a significant biological role, possibly being the "activation mode" of a GPCR when coupled to G-proteins.