Iron(III) complexes using NNS reduced Schiff bases and NNOS coordinating tetradentate ligands: Synthesis, structure and catecholase activity

The orange-red colored complexes of the type [Fe(L_SB)Cl₃], 1, have been synthesized in excellent yields by reacting FeCl₃·6H₂O with L_SB in methanol. Here, L_SB is (2-(ethylthio)-N-(pyridin-2-ylmethyl)ethanamine), (L_SB¹) and (2-(benzylthio)-N-(pyridin-2-ylmethyl)ethanamine) (L_SB²). Similarly, FeCl₃·6H₂O reacted with 2-(((2-(ethylthio) ethyl) (pyridin-2-ylmethyl)amino)methyl)phenol (HL¹), 2-(((2-(ethylthio)ethyl)(pyridin-2 ylmethyl)amino)methyl)-4-nitrophenol (HL²), 4-chloro-2-(((2-(ethylthio)ethyl)(pyridin-2-ylmethyl)amino)methyl)phenol (HL³), 2-(((2-(benzylthio)ethyl)(pyridin-2-ylmethyl) amino)methyl)phenol (HL⁴), 2-(((2-(benzylthio)ethyl)(pyridin-2-ylmethyl)amino)methyl) -4-nitrophenol (HL⁵), and 4-chloro-2-(((2-(benzylthio)ethyl)(pyridin-2-ylmethyl)amino) methyl)phenol (HL⁶) to give dichloro complexes of the type [Fe(L)Cl₂], 2. The solid and solution structure of the complexes, as well as their properties, were probed using X-ray diffraction, spectroscopic and electrochemical methods. The Mössbauer spectral study at 80 K for complexes reveals the existence of (III) oxidation state and high-spin state of the metal center in the complex. Dioxygenase activity of the complexes has been studied and both 1 and 2 have been found to display the intradiol-cleaving pathway. However, no extradiol cleavage products have been isolated.     

Site-directed mutagenesis of the Thauera aromatica strain T1 tutE tutFDGH gene cluster

Benzylsuccinate synthase, encoded by the tutF, tutD, and tutG genes of Thauera aromatica strain T1, is responsible for the first step of anaerobic toluene metabolism. Previous work has shown that these genes are part of the tutE tutFDGH gene cluster and strains carrying a mutation in the tutE, tutF, tutD, or tutG genes are unable to metabolize toluene. In this study, we performed site-directed mutagenesis of the tutE, tutF, and tutG genes and determined that the cysteines at position 72 and 79 of TutE are likely to be critical for the radical activation of benzylsuccinate synthase, while the cysteine alanine at positions 9 and 10 of TutF, and the cysteine at position 29 of TutG are also essential for toluene metabolism. Additionally, we report that the tutH gene is necessary for toluene metabolism and the glycine lysine serine (part of the putative ATP/GTP binding domain) at positions 52-54 of the TutH protein is essential for toluene metabolism.     

IgA Anti-β2-Glycoprotein I Autoantibodies Are Associated with an Increased Risk of Thromboembolic Events in Patients with Systemic Lupus Erythematosus

Background

The clinical utility of testing for antiphospholipid antibodies (aPL) of IgA isotype remains controversial.

Methodology/Principal Findings

To address this issue, we reasoned that if IgA aPL contribute to the clinical manifestations of the antiphospholipid syndrome, then an association with thromboembolic events should manifest in patients whose only aPL is of IgA isotype. We performed a retrospective chart review of 56 patients (31 with systemic lupus erythematosus [SLE] and 25 without SLE) whose only positive aPL was IgA anti-β2-glycoprotein I (isolated IgA anti-β2GPI) and compared their clinical features with 56 individually matched control patients without any aPL. Patients with isolated IgA anti-β2GPI had a significantly increased number of thromboembolic events, as compared to controls. When patients were stratified into those with and without SLE, the association between isolated IgA anti-β2GPI and thromboembolic events persisted for patients with SLE, but was lost for those without SLE. Titers of IgA anti-β2GPI were significantly higher in SLE patients who suffered a thromboembolic event. Among patients with isolated IgA anti-β2GPI, there was an increased prevalence of diseases or morbidities involving organs of mucosal immunity (i.e., gastrointestinal system, pulmonary system, and skin).

Conclusions/Significance

The presence of isolated IgA anti-β2GPI is associated with an increased risk of thromboembolic events, especially among patients with SLE. IgA anti-β2GPI is associated with an increased prevalence of morbidities involving organs of mucosal immunity.     

Combining Machine Learning and Homology-Based Approaches to Accurately Predict Subcellular Localization in Arabidopsis

A complete map of the Arabidopsis (Arabidopsis thaliana) proteome is clearly a major goal for the plant research community in terms of determining the function and regulation of each encoded protein. Developing genome-wide prediction tools such as for localizing gene products at the subcellular level will substantially advance Arabidopsis gene annotation. To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (for Arabidopsis subcellular localization predictor) that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews correlation coefficient values of 90.9% and 0.89, respectively. Benchmarking AtSubP on two independent data sets, one from Swiss-Prot and another containing green fluorescent protein- and mass spectrometry-determined proteins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some widely used “general” tools such as TargetP, LOCtree, PA-SUB, MultiLoc, WoLF PSORT, Plant-PLoc, and our newly created All-Plant method. Cross-comparison of AtSubP on six nontrained eukaryotic organisms (rice [Oryza sativa], soybean [Glycine max], human [Homo sapiens], yeast [Saccharomyces cerevisiae], fruit fly [Drosophila melanogaster], and worm [Caenorhabditis elegans]) revealed inferior predictions. AtSubP significantly outperformed all the prediction tools being currently used for Arabidopsis proteome annotation and, therefore, may serve as a better complement for the plant research community. A supplemental Web site that hosts all the training/testing data sets and whole proteome predictions is available at http://bioinfo3.noble.org/AtSubP/.Subcellular proteomics has gained tremendous attention of late, owing to the role played by organelles in carrying out defined cellular processes. Several experimental efforts have been made to catalog the complete subcellular proteomes of various organisms (Michaud and Snyder, 2002; Huh et al., 2003; Taylor et al., 2003; Andersen and Mann, 2006), with the aim being to improve our understanding of defined cellular processes at the organellar and cellular levels. Although such efforts have generated valuable information, cataloging all subcellular proteomes is far from complete, as experimental methods are expensive and more time consuming. Alternatively, computational prediction systems provide fast, economic (mostly free), automatic, and reasonably accurate assignment of subcellular location to a protein, especially for high-throughput analysis of large-scale genome sequences, ultimately giving the right direction to design cost-effective wet-lab experiments.The existing bioinformatics localization predictors in the literature can be broadly grouped into three categories: (1) amino acid composition based; (2) N-terminal sorting signals based; and (3) homology based (e.g. those based on domain or motif co-occurrence). These methods have previously been reviewed in detail (Mott et al., 2002; Scott et al., 2004). However, in bioinformatics in general, and in subcellular localization prediction in particular, it is often debated whether predictions should be done over broad systematic groups such as all eukaryotes or all plants, or over narrower groups such as dicots, or even at the single-species level. On the one hand, species-specific features of sorting signals and amino acid composition could make the prediction better if trained on the particular species where it is going to be used; on the other hand, the smaller data set available for a single species could make the single-species predictor less accurate. How to strike the balance between these two concerns is an important question, which has received far too little attention until now. In this study, we have investigated this important question by conducting a systematic species-specific case study on predicting subcellular localization in Arabidopsis (Arabidopsis thaliana). Although some recent reviews/advances in the prediction of protein-targeting signals have stressed the need for “species-specific” prediction tools (Schneider and Fechner, 2004; Chou and Shen, 2007a), very few have been developed/reported in the literature. The PSLT method (Scott et al., 2004), a Bayesian framework that uses a combination of InterPro motifs, signaling peptides, and transmembrane domains, was developed for predicting genome-wide subcellular localization of human proteins. Two others, HSLpred (Garg et al., 2005) and Hum-PLoc (Chou and Shen, 2006), were also developed specifically for human proteins; another species-specific system, TBpred, was developed for Mycobacterium tuberculosis (Rashid et al., 2007). However, none of these methods have rigorously tested whether their species-specific methods were actually better than the “general” ones.In plants, some widely used prediction tools are TargetP (Emanuelsson et al., 2000), LOCtree (Nair and Rost, 2005), PA-SUB (Lu et al., 2004), MultiLoc (Höglund et al., 2006), WoLF PSORT (updated version of PSORT II; Horton et al., 2007), and Plant-PLoc (Chou and Shen, 2007b), all having good accuracy (greater than 70%). A recent computational effort was made in developing a plant species-specific prediction system, RSLpred, for genome-wide subcellular localization annotations of rice (Oryza sativa) proteins (Kaundal and Raghava, 2009). However, although Arabidopsis was the first model plant that was completely sequenced back in the year 2000, there is still no efficient prediction method available for accurately annotating its proteome at the subcellular level. To date, we only know the subcellular localization of about 6,000 proteins that are experimentally proven (e.g. using GFP fusions, mass spectrometry [MS], or other approaches) out of the total 27,379 protein-coding genes as predicted by The Arabidopsis Information Resource (TAIR) release 9 (www.arabidopsis.org). To narrow this huge gap between the large number of predicted genes in the Arabidopsis genome and the limited experimental characterization of their corresponding proteins, a fully automatic and reliable prediction system for complete subcellular annotation of the Arabidopsis proteome would be very useful.This article presents AtSubP (for Arabidopsis subcellular localization predictor), an integrative system that addresses the aforementioned issues and problems. In this study, we develop this species-specific predictor and rigorously compare its performance with some of the widely used general tools, including the one being currently used by TAIR (Rhee et al., 2003), and discuss if species-specific predictors are more suitable for individual proteome-wide annotations. AtSubP uses the combinatorial presence of diverse features of a protein sequence, such as its amino acid composition, residue order-based dipeptide composition, N- and C-terminal composition, similarity search-based Position-Specific Iterated (PSI)-BLAST information, and the Position-Specific Scoring Matrix (PSSM), as its evolutionary information in a statistically coherent manner. Under five major classification approaches, we devised 15 different possible techniques to develop 105 different classifiers for each of the seven subcellular compartments under study (chloroplast, cytoplasm, Golgi apparatus, mitochondrion, extracellular, nucleus, and plasma membrane). The performance of these models was systematically evaluated based on a 5-fold cross-validation test and two diverse independent tests: one from Swiss-Prot and the other containing MS/GFP-proven sequences as an experimental test data set from the SUBcellular location database for Arabidopsis (SUBA; http://suba.plantenergy.uwa.edu.au/) and the eukaryotic Subcellular Localization DataBase (eSLDB; http://gpcr.biocomp.unibo.it/esldb/). Our novel approach of combining some diverse protein features into a smart hybrid technique led to the best classifier that achieved an outstanding accuracy level of 91%, with a high-confidence precision and Matthews correlation coefficient (MCC) of 90.9% and 0.89, respectively. The similarity search-based PSI-BLAST module alone performed moderately, achieving an overall accuracy of 78%, suggesting the advantages of machine learning-based classifiers.To expand on the application and data-mining aspects of the method, we cross-matched the AtSubP’s predictions with the available Swiss-Prot and TAIR annotations as well as compared its performance with some of the widely used general tools on both independent test sets. To explore the species-specific effects, a new All-Plant classifier was developed from a mixture of plant proteins using the same location definitions and encoding schemes as in AtSubP, and their performances were compared in an independent testing. As another benchmark, the performance of an Arabidopsis-specific classifier was cross-checked on six other eukaryotic organisms (rice [Oryza sativa], soybean [Glycine max], human [Homo sapiens], yeast [Saccharomyces cerevisiae], fruit fly [Drosophila melanogaster], and worm [Caenorhabditis elegans]). The basic purpose of all these diverse tests was to explore the advantages of developing a species-specific predictor(s), if any. To further test this hypothesis, we also analyzed the variation in amino acid composition across various eukaryotic organisms and compared with Arabidopsis, both at the sequence level and in the signal peptide-containing regions.Finally, AtSubP was used to annotate all 27,379 Arabidopsis proteins contained in TAIR release 9; among them, 21,649 (79.1%) proteins were predicated with their localization information, 7,982 (29.2%) sequences being predicted with high confidence. A user-friendly Web server, available at http://bioinfo3.noble.org/AtSubP/, was also developed to host all the training/testing data sets, whole proteome annotations, and options for annotating the query sequences using five diverse prediction modules based on user selection of protein feature(s).     

10th European Conference on Rare Diseases & Orphan Products (ECRD 2020)

The diagnosis of rare diseases poses a particular challenge to clinicians. This study analyzes whether patients’ pain drawings (PDs) help in the differentiation of two pain-associated rare diseases, Ehlers-Danlos Syndrome (EDS) and Guillain-Barré Syndrome (GBS). The study was designed as a prospective, observational, single-center study. The sample comprised 60 patients with EDS (3 male, 52 female, 5 without gender information; 39.2 ± 11.4 years) and 32 patients with GBS (10 male, 20 female, 2 without gender information; 50.5 ± 13.7 years). Patients marked areas afflicted by pain on a sketch of a human body with anterior, posterior, and lateral views. PDs were electronically scanned and processed. Each PD was classified based on the Ružička similarity to the EDS and the GBS averaged image (pain profile) in a leave-one-out cross validation approach. A receiver operating characteristic (ROC) curve was plotted. 60–80% of EDS patients marked the vertebral column with the neck and the tailbone and the knee joints as pain areas, 40–50% the shoulder-region, the elbows and the thumb saddle joint. 60–70% of GBS patients marked the dorsal and plantar side of the feet as pain areas, 40–50% the palmar side of the fingertips, the dorsal side of the left palm and the tailbone. 86% of the EDS patients and 96% of the GBS patients were correctly identified by computing the Ružička similarity. The ROC curve yielded an excellent area under the curve value of 0.95. PDs are a useful and economic tool to differentiate between GBS and EDS. Further studies should investigate its usefulness in the diagnosis of other pain-associated rare diseases. This study was registered in the German Clinical Trials Register, No. DRKS00014777 (Deutsches Register klinischer Studien, DRKS), on 01.06.2018.     

How do plants make mitochondria?

Plant mitochondria can differ in size, shape, number and protein content across different tissue types and over development. These differences are a result of signaling and regulatory processes that ensure mitochondrial function is tuned in a cell-specific manner to support proper plant growth and development. In the last decade, the processes involved in mitochondrial biogenesis are becoming clearer, including; how dormant seeds transition from empty promitochondria to fully functional mitochondria with extensive cristae structures and various biochemical activities, the regulation of nuclear genes encoding mitochondrial proteins via regulators of the diurnal cycle in plants, the mitochondrial stress response, the targeting of proteins to mitochondria and other organelles and connections between the respiratory chain and protein import complexes. All these findings indicate that mitochondrial function is a part of an integrated cellular network, and communication between mitochondria and other cellular processes extends beyond the known exchange or transport of metabolites. Our current knowledge now needs to be used to gain more insight into the molecular components at various levels of this hierarchical and complex regulatory and communication network, so that mitochondrial function can be predicted and modified in a rational manner.     

Biochemical and molecular profiling of micropropagated and conventionally grown Kaempferia galanga

Kaempferia galanga L., family Zingiberaceae, is used extensively in the preparation of both traditional and modern medicines. Buds of rhizomes of K. galanga were incubated on Murashige and Skoog (MS) medium supplemented with 1 mg/l benzyladenine and 0.5 mg/l indole-3-acetic acid (IAA) to induce shoot proliferation. Micropropagated plantlets subjected to random amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) marker-based molecular profiling revealed uniform banding patterns similar to those of the mother plants. After 2 years of culture in vitro, plantlets were transplanted to the field, evaluated for different agronomic traits, and their rhizomes were subjected to biochemical profiling using quantitative and qualitative assays of essential oils. Gas chromatography and mass spectroscopy analysis of rhizome oil of micropropagated plants showed the presence of 10 major components which were similar to those detected in the mother plants, and accounted for 95.5% of the total compounds. The compound ethyl-p-methoxy cinnamate accounted for 59.5% of the total compounds detected, followed by ethyl cinnamate, 3-carene, pentadecane, borneol, bornyl acetate, delta-selinene, camphor, alpha-piene and immidazole, 5-carbonylvinyl-4-nitro. Biochemical and molecular profiling of micropropagated clones revealed that an in vitro micropropagation protocol could be effectively used for commercial propagation of true-to-type K. galanga for a stable supply of the medicinal compounds present in this plant species.