Molecular BioSystems最新文献

Methylglyoxal (MG) is a highly reactive dicarbonyl known to be elevated under the hyperglycemic conditions of diabetes and is implicated in the development of diabetic complications. Therefore, the current study investigates the role of MG in exacerbating insulin resistance at the insulin signaling level, as well as its effect on the global proteomic level. By using insulin sensitive rat muscle cells (L6) and Chinese hamster ovary (CHO) cells stably expressing the insulin receptor (IR) and a glucose transporter fused with green fluorescent protein (GLUT4-GFP), we have observed that MG impairs insulin signaling, inhibits GLUT4 translocation and reduces glucose uptake. SWATH MS analysis, a label-free quantitative mass spectrometric approach, showed altered expression of 99 proteins out of 2404 identified in response to MG treatment. These proteins are mainly involved in stress response, protein folding and proteolysis. Some of the deregulated proteins such as thioredoxin 2, glutathione S transferase, T complex protein 1 subunit β (tcbp1), heat shock protein 90 and E3 ubiquitin ligase were previously reported to be associated with either diabetes or insulin resistance. Interestingly, aminoguanidine (AMG), a potent dicarbonyl scavenger, restored the deleterious effects of MG. For the first time, we report that MG induces downregulation of enzymes involved in cholesterol biosynthesis such as acetyl-CoA acetyltransferase, hydroxymethylglutaryl-CoA synthase, farnesyl pyrophosphate synthetase, squalene monooxygenase, and lanosterol synthase. GC MS analysis for sterol metabolites corroborated the proteomic results; MG significantly reduced cholesterol production whereas AMG treatment restored cholesterol production to levels similar to the control. Thus, MG leads to primary defects in insulin signaling and cellular abnormalities at the proteomic and metabolic levels, both of which may contribute to the development of insulin resistance.

Hedgehog signaling (Hh) has been shown to be hyper-activated in several cancers. However, active Hh signaling can promote or inhibit tumor growth; thus identification of markers beyond main canonical Hh target genes is needed to improve patient selection and clinical outcome in response to Hh inhibitors. Cancer-associated fibroblasts (CAFs) have been linked with tumor progression and beneficial response to Hh inhibitors. Thus, we hypothesized that genes associated with Hh-activated CAFs can be used for stratification of tumors that will benefit from Hh inhibitors. In this work, we evaluated a 15-gene fingerprint that combines Hh and mesenchymal genes associated with CAF phenotype to profile breast cancer sub-types based on gene expression patterns among clustered groups. About 3800 cancer samples were evaluated using random forest models and linear discriminant analysis to sort breast cancer by subtypes and therapeutic approach. The results showed that the Hh-mesenchyme gene fingerprint has a highly sensitive and differential expression pattern among basal and luminal A sub-groups. Basal samples with high levels of Hh target genes had better prognosis than luminal A samples. Luminal A samples with a tendency towards Hh signaling suppression had higher overall and disease-free survival rates particularly if deprived of hormone therapy. Hh transcriptional repressor GLI3 and signaling activator SMO were the top 2 genes for discriminating among samples with active Hh signaling in human breast cancer subtypes and Hh-inhibitor resistant tumors. Caveolin-1 (CAV1), a gene with low expression in CAFs, shows strong correlation with active Hh signaling and discrimination among survival curves in luminal A patients with active or inactive Hh signaling. Our data suggest that CAV1 is an important gene for monitoring Hh inhibition in tumors and support further stratification by hormone therapy status prior to use of Hh inhibitors.

Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease–miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic. In this study, we take advantage of known disease–miRNA associations combined with a large number of experimentally validated miRNA–target associations, and further develop a novel disease–miRNA–target heterogeneous network for identifying disease-related miRNAs. The leave-one-out cross validation experiment and several statistical measures demonstrate that our method can effectively identify potential disease-related miRNAs. Furthermore, the good predictive performance of 15 common diseases and the manually confirmed analyses of the top 30 candidates of hepatocellular carcinoma, ovarian neoplasms and breast neoplasms further provide convincing evidence of the practical ability of our method. The source code implemented by our method is freely available at: https://github.com/USTC-HIlab/DMTHNDM.

According to GLOBOCAN 2012, the incidence and the mortality rate of colorectal, stomach and liver cancers are the highest among the total gastrointestinal (GI) cancers. Here we aimed to find the common genes and pathways that are simultaneously deregulated in these three malignancies using systems biology approaches. Here we conducted a differential expression analysis on high-quality gene expression datasets of gastric cancer (GC), colorectal cancer (CRC) and hepatocellular carcinoma (HCC). To address the inter gene correlations that were neglected in differential expression studies, we also applied differential co-expression analysis on the understudied datasets. The common significant differentially expressed genes (DEGs) among the three cancers were used for further regulatory and PPI network construction. In parallel the regulatory roles of miRNAs and lncRNAs in the common DEGs were investigated. 23 common DEGs were detected between GC, CRC and HCC. Two cases of potential feed forward loops were identified in the constructed TF–target regulatory network, indicating the probable cross-talk between biological pathways. The result of a vulnerability test on the common PPI network resulted in the finding of three candidates, the simultaneous targeting of which will disintegrate the main parts of the network. The results of the differential co-expression study led to the identification of respectively 7 and 1 common differentially co-expressed pairs of genes between GC and CRC and between CRC and HCC. The results of the differential expression study introduced new common players in CRC, GC and HCC and provided better insights into the molecular characteristics of these GI malignancies. Moreover, we concluded that differential co-expression studies are an essential complement for differential expression studies that just take single differentially expressed genes into account.

The emergence of antibiotic resistance caused by β-lactamases, including serine β-lactamases (SβLs) and metallo-β-lactamases (MβLs), is a global public health threat. L1, a B3 subclass MβL, hydrolyzes almost all of known β-lactam antibiotics. We report a simple and straightforward UV-Vis approach for real-time activity assays of β-lactamases inside living bacterial cells, and this method has been exemplified by choosing antibiotics, L1 enzyme, Escherichia coli expressing L1 (L1 E. coli), Escherichia coli expressing extended-spectrum β-lactamases (ESBL-E. coli), clinical bacterial strains, and reported MβL and SβL inhibitors. The cell-based studies demonstrated that cefazolin was hydrolyzed by L1 E. coli and clinical strains, and confirmed the hydrolysis to be inhibited by two known L1 inhibitors EDTA and azolylthioacetamide (ATAA), with an IC₅₀ value of 1.6 and 18.9 μM, respectively. Also, it has been confirmed that the breakdown of cefazolin caused by ESBL-E. coli was inhibited by clavulanic acid, the first SβL inhibitor approved by FDA. The data gained through this approach are closely related to the biological function of the target enzyme in its physiological environment. The UV-Vis method proposed here can be applied to target-based whole-cell screening to search for potent β-lactamase inhibitors, and to assays of reactions in complex biological systems, for instance in medical assays.

Recently, substantial evidence has demonstrated that long non-coding RNAs (lncRNAs) play critical roles in multiple cancers including colorectal cancer (CRC). Utilizing publicly available lncRNA-expression-profiling data from the Gene Expression Omnibus (GEO) dataset GSE21510, we screened SNHG17 as a new candidate lncRNA associated with CRC development and progression. We further demonstrated that SNHG17 was upregulated in CRC tissues, and that its overexpression was significantly correlated with tumor size, TNM stage, and lymph node metastasis in CRC patients. Moreover, SNHG17 knockdown significantly inhibited the proliferation of CRC cells, and induced cell cycle G1/G0 phase arrest and cell apoptosis. Consistent with these findings, SNHG17 silencing inhibited tumor growth in vivo. Mechanistic studies revealed the capability of lncRNA SNHG17 to epigenetically suppress P57 by binding to enhancer of zeste homolog 2 (a key component of polycomb repressive complex 2) in CRC cells, and quantitative real-time polymerase chain reaction assays demonstrated that SNHG17 expression levels were inversely correlated with those of P57 in CRC tissues. Furthermore, rescue experiments confirmed that SNHG17 exerted oncogenic functions partly through regulating P57 expression. These findings represent the first reporting of the roles and mechanisms associated with SNHG17 in CRC progression, highlighting SNHG17 as a potential therapeutic target for CRC patients.

Protein carbonylation is one of the most important biomarkers of oxidative protein damage and such protein damage is linked to various diseases and aging. It is thus vital that carbonylation sites are identified accurately. In this study, CarSite, a novel bioinformatics tool, was established to identify carbonylation sites in human proteins. The one-sided selection (OSS) resampling method was used to establish balanced training datasets and this resampling method is demonstrated to perform better than a Monte Carlo resampling method via 10-fold cross-validation tests on the Jia dataset. Moreover, the hybrid combination of position-specific amino acid propensity (PSAAP), composition of k-spaced amino acid pairs (CKSAAP), amino acid composition (AAC), and composition of hydrophobic and hydrophilic amino acids (CHHAA) was selected to optimize the performance of the predictor. On 10-fold cross-validation of the Jia dataset, CarSite obtained rates of sensitivity corresponding to K/P/R/T-type peptides of ～21%, 22%, 19%, or 18% higher than those obtained by iCar-PseCp, respectively, which was previously considered as the best predictor for identifying carbonylation sites in human proteins. Furthermore, compared with other existing predictors, CarSite obtained much higher sensitivity and accuracy when tested on the same dataset.

Adhesive interactions between molecules on tumor cells and those on target organs play a key role in organ specific metastasis. Poly-N-acetyl-lactosamine (polyLacNAc) substituted N-oligosaccharides on melanoma cell surface glycoproteins promote lung specific metastasis via galectin-3 by facilitating their arrest and extravasation. This study reports the identification and characterization of galectin-3 interacting proteins using a combination of galectin-3 sepharose affinity and leucoagglutinating phytohemagglutinin (L-PHA) columns. A total of 83 proteins were identified as galectin-3 interacting glycoproteins, of which 35 were constituents of the L-PHA bound fraction, suggesting that these proteins carry polyLacNAc substituted β1,6 branched N-glycans. The identities of some of these proteins, like LAMP-1, LAMP-3, basigin, embigin, and α5 and β1 Integrin, have been confirmed by western blotting, and functional relevance with respect to metastatic properties has been established.

Chirality is a ubiquitous basic attribute of nature, which inseparably relates to the life activity of living organisms. However, enantiomeric differences have still failed to arouse enough attention during the biological evaluation and practical application of chiral substances, and this poses a large threat to human health. In the current study, we explore the enantioselective biorecognition of a chiral compound by an asymmetric biomolecule, and then decipher the molecular basis of such a biological phenomenon on the static and, in particular, the dynamic scale. In light of the wet experiments, in silico docking results revealed that the orientation of the latter part of the optical isomer structures in the recognition domain can be greatly affected by the chiral carbon center in a model ligand molecule, and this event may induce large disparities between the static chiral bioreaction modes and noncovalent interactions (especially hydrogen bonding). Dynamic stereoselective biorecognition assays indicated that the conformational stability of the protein–(S)-diclofop system is clearly greater than the protein–(R)-diclofop adduct; and moreover, the conformational alterations of the diclofop enantiomers in the dynamic process will directly influence the conformational flexibility of the key residues found in the biorecognition region. These points enable the changing trends of biopolymer structural flexibility and free energy to exhibit significant distinctions when proteins sterically recognize the (R)-/(S)-stereoisomers. The outcomes of the energy decomposition further showed that the van der Waals’ energy has roughly the same contribution to the chiral recognition biosystems, whereas the contribution of electrostatic energy to the protein–(R)-diclofop complex is notably smaller than to the protein–(S)-diclofop bioconjugate. This proves that differences in the noncovalent bonds would have a serious impact on the stereoselective biorecognition between a biomacromolecule and chiral ligand. The present scenario is expected to attract more interest from both researchers and administrative agencies, since in a chiral environment, enantioselectivity exists in all of the biochemical processes of a chiral chemical, and this might finally elicit the disparate biological activities of (R)-/(S)-enantiomers.