Molecular BioSystems最新文献

Extracellular microRNAs (miRNAs) carried by exosomes can play a key role in cell-to-cell communication. Deregulation of miRNA expression and exosome secretion have been related to pathological conditions such as cancer. While it is known that circulating miRNAs can alter gene expression in recipient cells, it remains unclear how significant the dynamical impact of these extracellular miRNAs is. To shed light on this issue, we propose a model for the spatio-temporal evolution of the protein expression in a cell tissue altered by abnormal miRNA expression in a donor cell. This results in a nonhomogeneous cellular response in the tissue, which we quantify by studying the range of action of the donor cell on the surrounding cells. Key model parameters that control the range of action are identified. Based on a model for a heterogeneous cell population, we show that the dynamics of gene expression in the tissue is robust to random changes of the parameter values. Furthermore, we study the propagation of gene expression oscillations in a tissue induced by extracellular miRNAs. In the donor cell, the miRNA inhibits its own transcription which can give rise to local oscillations in gene expression. The resulting oscillations of the concentration of extracellular miRNA induce oscillations of the protein concentration in recipient cells. We analyse the nonmonotonic spatial evolution of the oscillation amplitude of the protein concentration in the tissue which may have implications for the propagation of oscillations in biological rhythms such as the circadian clock.

Cysteine S-sulfenylation is a major type of posttranslational modification that contributes to protein structure and function regulation in many cellular processes. Experimental identification of S-sulfenylation sites is challenging, due to the low abundance of proteins and the inefficient experimental methods. Computational identification of S-sulfenylation sites is an alternative strategy to annotate the S-sulfenylated proteome. In this study, a novel computational predictor SulCysSite was developed for accurate prediction of S-sulfenylation sites based on multiple sequence features, including amino acid index properties, binary amino acid codes, position specific scoring matrix, and compositions of profile-based amino acids. To learn the prediction model of SulCysSite, a random forest classifier was applied. The final SulCysSite achieved an AUC value of 0.819 in a 10-fold cross-validation test. It also exhibited higher performance than other existing computational predictors. In addition, the hidden and complex mechanisms were extracted from the predictive model of SulCysSite to investigate the understandable rules (i.e. feature combination) of S-sulfenylation sites. The SulCysSite is a useful computational resource for prediction of S-sulfenylation sites. The online interface and datasets are publicly available at http://kurata14.bio.kyutech.ac.jp/SulCysSite/.

Diabetic kidney disease (DKD) is the leading cause of ESRD; however, early intervention can greatly prevent the progression of DKD; thus, sensitive biomarkers for DKD are still required. This study was aimed at the identification of potential biomarkers and revelation of underlying pathways in DKD patients by non-targeted metabolomics. Gas chromatography-mass spectrometry was used to analyze urine obtained from the control and type 2 diabetes mellitus (T2DM) and DKD patients, and the renal histological changes in DKD patients were assessed. The DKD group showed increased level of uric acid, 1,5-anhydroglucitol, hippuric acid, stearic acid, and palmitic acid and reduced level of uracil, glycine, aconitic acid, isocitric acid, 4-hydroxybutyrate, 2-deoxyerythritol, and glycolic acid as compared to the control and T2DM groups. Further analysis indicated that many of the changed metabolites were involved in mitochondrial and fatty acid (FA) metabolism, and combined mitochondrial and FA metabolites showed better diagnosis values for DKD. Histological results confirmed that renal expression of key proteins was reduced in DKD patients with respect to mitochondrial biogenesis (PGC-1α, p-AMPK) and FA oxidation (PPAR-α, CPT-1) as compared to that in the control and T2DM groups. This study highlighted that both mitochondrial and FA metabolism were disturbed in DKD, and thus, they could serve as combined biomarkers for the prediction of DKD.

Changes of the extracellular milieu could affect cellular crowding. To prevent detrimental effects, cells use adaptation mechanisms to react to such conditions. Using fluorescent crowding sensors, we show that the initial response to osmotic stress is fast but imperfect, while the slow response renders cells more tolerant to stress, particularly in the presence of osmolytes.

Mannose-modified lectin-binding peptides were obtained from an α-helical-designed peptide phage library. Concanavalin A (ConA) was used as a representative target protein for the lectin family. The identified glycopeptides could selectively bind to ConA with micromolar affinity. With these results, the methodologies described in this study will enhance the selection of saccharide-modified ligands through the synergistic effects of sugar and peptide units, with better specificity and affinity towards lectin proteins.

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

Dipeptidyl peptidase III (DPP III) from the human gut symbiont Bacteroides thetaiotaomicron (Bt) is the first identified prokaryotic DPP III orthologue. It has low sequence similarity to the thoroughly studied human DPP III, and differently from eukaryotic orthologues it has a cysteine (Cys450) residue in the zinc-binding motif HEXXGH (HECLGH). The recently determined crystal structure of BtDPP III showed that its 3D structure, similar to the structure of the human DPP III, consists of two domains with a wide cleft in between. Although such a striking similarity of the 3D structures of orthologues with low sequence similarity is not surprising, it is no guarantee for similarity of their dynamic properties and the catalytic performance. Here, we report the results of the molecular modelling study of BtDPP III, wild type and its C450S mutant, as well as their complexes with characteristic DPP III substrates Arg–Arg–2-naphthylamide (RRNA) and Lys–Ala–2-naphtylamide (KANA). During several hundred nanoseconds of all-atom MD simulations of the wild type protein, the long range conformational changes, which can be described as protein ‘closing’, have been traced. We have determined a similar conformational change for the human orthologue as well. However, the amplitude of the change is lower for BtDPP III than for the human DPP III. The MD simulations have been performed using ff03, ff12SB and ff14SB force fields wherein the results of the last two better fit to the experimental results. The hydrogen bond analysis indicates reasons for higher substrate specificity of BtDPP III towards RRNA than KANA as well as for the decrease of the RRNA hydrolysis rate induced by the Cys450 to Ser mutation. The obtained results are in line with the experimental data.

Promoter binding specificity is one of the important characteristics of transcription by Mycobacterium tuberculosis (Mtb) sigma (σ) factors, which remains unexplored due to limited structural evidence. Our previous study on the structural features of Mtb-SigH, consisting of three alpha helices, and its interaction with core RNA polymerase has been extended herein to determine the little known DNA sequence recognition pattern involving its cognate promoters. Herein, high resolution X-ray crystallographic structures of the protein–DNA complexes were inspected to determine the tentative DNA-binding helix of the σ factor. The binding interface in the available crystal structures is found to be populated mainly with specific residues such as Arg, Asn, Lys, Gln, and Ser. We uncovered the helix 3 of Mtb-SigH containing most of these amino acids, which ranged from Arg 64 to Arg 75, forming the predicted active site. The complex of Mtb-SigH:DNA is modelled with 20 promoter sequences. The binding affinity is predicted by scoring these protein–DNA complexes through proximity and interaction parameters obtained by molecular dynamics simulations. The promoters are ranked considering hydrogen bonding, energy of interaction, buried surface area, and distance between centers of masses in interaction with the protein. The ranking is validated through in vitro transcription assays. The trends of these selected promoter interactions have shown variations parallel to the experimental evaluation, emphasizing the success of the active site determination along with screening of the promoter strength. The promoter interaction of Mtb-SigH can be highly beneficial for understanding the regulation of gene expression of a pathogen and also extends a solid platform to predict promoters for other bacterial σ factors.

The overarching goal of delineating molecular principles underlying differentiation of the activation mechanisms in cyclin-dependent kinases (CDKs) is important for understanding regulatory divergences among closely related kinases which can be exploited in drug discovery of targeted and allosteric inhibitors. To systematically characterize dynamic, energetic and network signatures of the activation mechanisms, we combined atomistic simulations and elastic network modeling with the analysis of the residue interaction networks and rigidity decomposition of the CDK2-cyclin A and CDK4-cyclin D1/D3 complexes. The results of this study show that divergences in the activation mechanisms of CDK2 and CDK4 may be determined by differences in stabilization and allosteric cooperativity of the regulatory regions. We show that differential stabilization of the kinase lobes in the CDK4-cyclin D complexes caused by the elevated mobility of the N-lobe residues can weaken allosteric interactions between regulatory regions and compromise cooperativity of the inter-lobe motions that is required to trigger activating transitions. Network modelling and percolation analysis were used to emulate thermal unfolding and perform decomposition of rigid and flexible regions in the CDK2 and CDK4 complexes. These simulations showed that the percolation phase transition in the CDK2-cyclin A complexes is highly cooperative and driven by allosteric coupling between functional regions from both kinase lobes. In contrast, the imbalances in the distribution of rigid and flexible regions for the CDK4-cyclin D complexes, which are manifested by the intrinsic instability of the N-lobe, may weaken allosteric interactions and preclude productive activation. The results of this integrative computational study offer a simple and robust network-based model that explains regulatory divergences between CDK2 and CDK4 kinases.

Correction for ‘An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis’ by Taiyi Wang et al., Mol. BioSyst., 2017, 13, 1018–1030.