Hypothalamic dysfunction is a key factor in depression; increasing evidence highlights neuroinflammation abnormalities as well as imbalances in neurotransmitters and the purinergic system in the pathophysiology of depression. However, little is known about the metabolomic changes in the hypothalamus of depressed patients with neuroinflammation. Herein, taking advantage of the well-established lipopolysaccharide (LPS)-induced depression mouse model, we measured metabolic changes in the hypothalamus using gas chromatography-mass spectrometry (GC-MS). Sucrose preference test (SPT), open field test (OFT), forced swimming test (FST), and tail suspension test (TST) were conducted to assess our depressive model. To better understand the metabolic disturbances occurring in the hypothalamus of depressed mice, multivariate statistics were applied to analyse the clinical significance of differentially expressed metabolites in the hypothalamus of mice with LPS-induced depression. Bioinformatic analysis was conducted to detect potential relationships among the changed metabolites. The data confirmed that mice with LPS-induced depression were good mimics of depression patients in some characteristic symptoms such as decreased sucrose intake and increased immobility. In our study, 27 differentially expressed metabolites were identified in the hypothalamus of mice with LPS-induced depression. Herein, seventeen of these metabolites decreased, whereas 10 metabolites increased. These molecular changes were closely related to perturbations in the amino acid and purine metabolisms. Our data indicate that dysfunction of amino acid and purine metabolisms is one of main characteristics of inflammation-mediated depression. These results provide new insights into the mechanisms underlying depression, which may shed some light on the role of the hypothalamus in the pathogenesis of inflammation-mediated depression.

Correction for 'Dynamic properties of dipeptidyl peptidase III from Bacteroides thetaiotaomicron and the structural basis for its substrate specificity - a computational study' by M. Tomin et al., Mol. BioSyst., 2017, 13, 2407-2417.

A cationic terminal extension or tail is a common feature of many DNA-binding proteins. We show that a particular type of tail rich in proline, alanine and lysine belongs to the class of 'flexible disorder' and consists of characteristic pentapeptide repeats. Our designed peptides, (AAKKA)1-4 and (PAKKA)1-4, represent the tails of several bacterial DNA-binding proteins. Enhanced conformational sampling of these representative peptides using accelerated molecular dynamic simulations supported by circular dichroism spectroscopy and nuclear magnetic resonance studies demonstrates the role of frequent and interspersed prolines in augmenting conformational heterogeneity of the peptide backbone. Analysis of circular variance of backbone dihedral angles indicates alternating regions of relative rigidity and flexibility along the peptide sequence due to prolines. Preferred placement of lysines in the regions of higher backbone flexibility might improve DNA-binding by conformational selection. Our results could be relevant for rational de novo design of disordered peptides.

In prokaryotes, the RecA protein catalyzes the repair and strand exchange of double-stranded DNA. RecA binds to single-stranded DNA (ssDNA) and forms a presynaptic complex in which the protein polymerizes around the ssDNA to form a right-handed helical nucleoprotein filament structure. In the present work, the mechanism for the formation of the RecA-ssDNA filament structure is modeled using coarse-grained molecular dynamics simulations. Information from the X-ray structure was used to model the protein itself but not its interactions; the interactions between the protein and the ssDNA were modeled solely by electrostatic, aromatic, and repulsive energies. For the present study, the monomeric, dimeric, and trimeric units of RecA and 4, 8, and 11 NT-long ssDNA, respectively, were studied. Our results indicate that monomeric RecA is not sufficient for nucleoprotein filament formation; rather, dimeric RecA is the elementary binding unit, with higher multimeric units of RecA facilitating filament formation. Our results reveal that loop region flexibility at the primary binding site of RecA is essential for it to bind the incoming ssDNA, that the aromatic residues present in the loop region play an important role in ssDNA binding, and that ATP may play a role in guiding the ssDNA by changing the electrostatic potential of the RecA protein.

The prevalence of Staphylococcus aureus worldwide as a nosocomial infectious agent is recognized but the reason behind the spread of this bacterium has remained elusive. Here, we hypothesized that the communication of S. aureus might benefit from it blocking other bacteria from establishing themselves on the surface. This was found to be the case for several pathogens as the S. aureus supernatant curtailed their ability to form biofilms. Subsequent analyses using Acinetobacter baumannii as a model found this effect is primarily mediated by S. aureus' extracellular vesicles (EVs), which bound to the polystyrene surface. We found the EV-treated surfaces were significantly more hydrophilic after EV treatment, a condition that made it difficult for A. baumannii to initially adhere to the polystyrene surface and reduced its resulting biofilm by up to 93%. Subsequent tests found this also extended to several other bacterial pathogens, with a 40-70% decrease in their biofilm mass. The S. aureus EVs and their activity still remained after the surface was washed with 10% bleach, while the use of ethylenediaminetetraacetic acid (EDTA) removed both the EVs from the surface and their activity.

The protein arginine methyltransferase (PRMT) family of enzymes comprises nine family members in mammals. They catalyze arginine methylation, either monomethylation or symmetric/asymmetric dimethylation of histone and non-histone proteins. PRMT methylation of its substrate proteins modulates cellular processes such as signal transduction, transcription, and mRNA splicing. Recent studies have linked overexpression of PRMT5, a member of the PRMT superfamily, to oncogenesis, making it a potential target for cancer therapy. In this study, we developed a highly sensitive (Z' score = 0.7) robotic high throughput screening (HTS) platform to discover small molecule inhibitors of PRMT5 by adapting the AlphaLISA™ technology. Using biotinylated histone H4 as a substrate, and S-adenosyl-l-methionine as a methyl donor, PRMT5 symmetrically dimethylated H4 at arginine (R) 3. Highly specific acceptor beads for symmetrically dimethylated H4R3 and streptavidin-coated donor beads bound the substrate, emitting a signal that is proportional to the methyltransferase activity. Using this powerful approach, we identified specific PRMT5 inhibitors P1608K04 and P1618J22, and further validated their efficacy and specificity for inhibiting PRMT5. Importantly, these two compounds exhibited much more potent efficacy than the commercial PRMT5 inhibitor EPZ015666 in both pancreatic and colorectal cancer cells. Overall, our work highlights a novel, powerful, and sensitive approach to identify specific PRMT5 inhibitors. The general principle of this HTS screening method can not only be applied to PRMT5 and the PRMT superfamily, but may also be extended to other epigenetic targets. This approach allows us to identify compounds that inhibit the activity of their respective targets, and screening hits like P1608K04 and P1618J22 may serve as the basis for novel drug development to treat cancer and/or other diseases.

Host immune evasion is a key strategy for the continual survival of many microbial pathogens including Apicomplexan protozoan: Plasmodium spp., the causative agent of Malaria. The malaria parasite has evolved a variety of mechanisms to evade the host immune responses within its two hosts: the female Anopheles mosquito vector and vertebrate host. In this review, we will focus on the molecular mechanisms of the immune evasion strategies used by the Plasmodium parasite at the blood stage which is responsible for the clinical manifestations of human malaria. We also aim to provide some insights on the potential targets for malaria interventions through the recent advancement in understanding the molecular biology of the parasite.

Cone snails are predatory gastropods whose neurotoxic venom peptides (conotoxins) have been extensively studied for pharmacological probes, venom evolution mechanisms and potential therapeutics. Conotoxins have a wide range of structural and functional classes that continue to undergo accelerated evolution that underlies the rapid expansion of the genus over their short evolutionary history. A number of pharmacological classes, driven by separately evolved defensive and predatory venoms, have been hypothesised to facilitate shifts in prey that exemplify the adaptability of cone snails. Here we provide an overview of these pharmacological families and discuss their ecological roles and evolutionary impact.

Infectious diseases caused by bacterial pathogens pose a major concern to public health and, thus, greater attention must be given to providing insightful knowledge on host-pathogen interactions. There are several theories addressing the dynamics of complex mechanisms of host-pathogen interactions. The availability of an ample number of universally accepted model systems, including vertebrates, invertebrates, and mammalian cells, provides in-depth transcriptomics data to evaluate these complex mechanisms during host-pathogen interactions. Recent model system based proteomic studies have addressed the issues related to human diseases by establishing the protein profile of model animals that closely resemble the environment. As a result, model system based proteomics has been widely accepted as a powerful and effective approach to understand the highly complex host-pathogen interfaces at their protein levels. This review offers a snapshot of the contributions of selective model systems on host-bacterial pathogen interactions through proteomic approaches.

Endocrine disrupting chemicals (EDCs) are natural or synthetic exogenous substances affecting human health. Although present at low concentrations in the environment, they can cause a broad range of negative effects on the endocrine functions by mimicking the action of steroid hormones due to their structural similarity. Hormonal unbalance can play an important role in carcinogenesis at any stage of disease. In the case of the breast cancer, EDCs directly affect the transformation of normal breast cells into cancer cells by interfering with hormonal regulation and by inducing the alteration of factors that regulate gene expression. The principal aims of this work were to study the interaction networks of proteins modulated in breast cancer by either environmental EDCs or mycotoxins, and to identify the proteins with the strongest coordination role defined as hub nodes. Our studies evidenced the presence of seven and six hub proteins in two EDCs and mycotoxins networks, respectively. Then, by merging the two networks, we identified that three hub nodes (BCL2, ESR2 and CTNNB1) in the environmental EDCs network show direct interactions with three hub nodes (CASP8, RELA and MKI67) in the mycotoxins network. These data highlighted that two networks are linked through proteins involved in the apoptosis regulation and in processes related to cell proliferation and survival, and, thus, in breast cancer progression.

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects. Moreover, it should be implemented by efficient algorithms requiring the shortest possible execution time, to avoid enlarging excessively the time elapsing between data analysis and any subsequent experiment. Besides the features of their topological structure, the complexity of biological networks also refers to their dynamics, that is often non-linear and stiff. The stiffness is due to the presence of molecular species whose abundance fluctuates by many orders of magnitude. A fully stochastic simulation of a stiff system is computationally time-expensive. On the other hand, continuous models are less costly, but they fail to capture the stochastic behaviour of small populations of molecular species. We introduce a new efficient hybrid stochastic-deterministic computational model and the software tool MoBioS (MOlecular Biology Simulator) implementing it. The mathematical model of MoBioS uses continuous differential equations to describe the deterministic reactions and a Gillespie-like algorithm to describe the stochastic ones. Unlike the majority of current hybrid methods, the MoBioS algorithm divides the reactions' set into fast reactions, moderate reactions, and slow reactions and implements a hysteresis switching between the stochastic model and the deterministic model. Fast reactions are approximated as continuous-deterministic processes and modelled by deterministic rate equations. Moderate reactions are those whose reaction waiting time is greater than the fast reaction waiting time but smaller than the slow reaction waiting time. A moderate reaction is approximated as a stochastic (deterministic) process if it was classified as a stochastic (deterministic) process at the time at which it crosses the threshold of low (high) waiting time. A Gillespie First Reaction Method is implemented to select and execute the slow reactions. The performances of MoBios were tested on a typical example of hybrid dynamics: that is the DNA transcription regulation. The simulated dynamic profile of the reagents' abundance and the estimate of the error introduced by the fully deterministic approach were used to evaluate the consistency of the computational model and that of the software tool.

ADP-ribosylation is a protein post-translational modification catalyzed by ADP-ribose transferases (ARTs). ART activity is critical in mediating many cellular processes, and is required for DNA damage repair. All five histone proteins are extensively ADP-ribosylated by ARTs upon induction of DNA damage. However, how these modifications aid in repair processes is largely unknown, primarily due to lack of knowledge about where they site-specifically occur on histones. Here, we conduct a comprehensive analysis of histone Asp/Glu ADP-ribosylation sites upon DNA damage induced by dimethyl sulfate (DMS). We also demonstrate that incubation of cell nuclei with NAD+, as has been done previously in the literature, leads to spurious ADP-ribosylation levels of histone proteins. Altogether, we were able to identify 30 modification sites, 20 of which are novel. We also quantify the abundance of these modification sites during the course of DNA damage insult to identify which sites are critical for mediating repair. We found that every quantifiable site increases in abundance over time and that each identified ADP-ribosylation site is located on the surface of the nucleosome. Together, the data suggest specific Asp/Glu residues are unlikely to be critical for DNA damage repair and rather that this process is likely dependent on ADP-ribosylation of the nucleosomal surface in general.

An increasing amount of evidence indicates that microRNAs (miRNAs) are closely related to many important biological processes and play a significant role in various human diseases. More and more researchers have begun to seek effective methods to predict potential miRNA-disease associations. However, reliable computational methods to predict potential disease-related miRNAs are lacking. In this study, we developed a new miRNA-disease association prediction model called Negative-Aware and rating-based Recommendation algorithm for miRNA-Disease Association prediction (NARRMDA) based on the known miRNA-disease associations in the HMDD database, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. NARRMDA combined a rating-based recommendation algorithm and a negative-aware algorithm to score and rank miRNAs without known associations with investigated diseases. Furthermore, we used leave-one-out cross validation to evaluate the accuracy of NARRMDA and compared NARRMDA with four previous classical prediction models (RLSMDA, HDMP, RWRMDA and MCMDA). As it turned out, NARRMDA and the other four prediction models achieved AUCs of 0.8053, 0.6953, 0.7702, 0.7891 and 0.7718, respectively, which proved that NARRMDA has superior performance of prediction accuracy. Furthermore, we verified the prediction results associated with colon neoplasms, esophageal neoplasms, lymphoma and breast neoplasms by two different validation schemas. In these case studies, 92%, 84%, 92%, and 100% of the top 50 potential miRNAs for these four diseases were confirmed by experimental discoveries, respectively. These results further show that NARRMDA has reliable performance of prediction ability.

Expression level provides important clues about gene function. Previously, various efforts have been undertaken to profile human genes according to their expression level. Intrinsically disordered proteins (IDPs) do not adopt any rigid conformation under physiological conditions, however, are considered as an important functional class in all domains of life. Based on a human tissue-averaged gene expression level, previous studies showed that IDPs are expressed at a lower level than ordered globular proteins. Here, we examined the gene expression pattern of human ordered and disordered proteins in 32 normal tissues. We noticed that in most of the tissues, ordered and disordered proteins are expressed at a similar level. Moreover, in a number of tissues IDPs were found to be expressed at a higher level than ordered proteins. Rigorous statistical analyses suggested that the lower tissue-averaged gene expression level of IDPs (reported earlier) may be the consequence of their biased gene expression in some specific tissues and higher protein length. When we considered the gene repertory of each tissue we noticed that a number of human tissues (brain, testes, etc.) selectively express a higher fraction of disordered proteins, which help them to maintain higher protein connectivity by forming disordered binding motifs and to sustain their functional specificities. Our results demonstrated that the disordered proteins are indispensable in these tissues for their functional advantages.

In all organisms, DNA glycosylases initiate base excision repair pathways resulting in removal of aberrant bases from DNA. Human SMUG1 belongs to the superfamily of uracil-DNA glycosylases catalyzing the hydrolysis of the N-glycosidic bond of uridine and uridine lesions bearing oxidized groups at C5: 5-hydroxymethyluridine (5hmU), 5-formyluridine (5fU), and 5-hydroxyuridine (5hoU). An apurinic/apyrimidinic (AP) site formed as the product of an N-glycosylase reaction is tightly bound to hSMUG1, thus inhibiting the downstream action of AP-endonuclease APE1. The steady-state kinetic parameters (kcat and KM; obtained from the literature) correspond to the enzyme turnover process limited by the release of hSMUG1 from the complex with the AP-site. In the present study, our objective was to carry out a stopped-flow fluorescence analysis of the interaction of hSMUG1 with a DNA substrate containing a dU:dG base pair to follow the pre-steady-state kinetics of conformational changes in both molecules. A comparison of kinetic data obtained by means of Trp and 2-aminopurine fluorescence and Förster resonance energy transfer (FRET) detection allowed us to elucidate the stages of specific and nonspecific DNA binding, to propose the mechanism of damaged base recognition by hSMUG1, and to determine the true rate of the catalytic step. Our results shed light on the kinetic mechanism underlying the initiation of base excision repair by hSMUG1 using the "wedge" strategy for DNA lesion search.