Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury

Neurochemical Research - High-throughput and bioinformatics technology have been broadly applied to demonstrate the key molecules involved in traumatic brain injury (TBI), while no study has...     

A risk signature-based on metastasis-associated genes to predict survival of patients with osteosarcoma

Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis-associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis-associated genes were identified by limma package. Further, a protein-protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three-gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low- and high-risk groups were significantly different in the training set and validation set. Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin-6 production. Therefore, the three-gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets.     

Retracted: The promoting effect of MMP13 on mediating the development of HFLS-RA by the target of miR-19a through IL-17 signaling pathway

By investigating the expression profiles of miR-19a and metalloproteinases (MMP13) in human fibroblast-like synoviocytes-rheumatoid arthritis (HFLS-RA) and HFL cells lines, this study intends to confirm the directly target connection between them and reveal the effect of suppressing MMP13 on HLFS-RA migration, invasion and apoptosis. After screening the abnormal expressed messenger RNAs and microRNAs in synovial tissues of patients with RA, the underlying pathway was determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The HFLS-RA cell line was transfected for the following experiments with pcDNA3.1(+) served as vector. The directly target association between miR-19a and MMP13 was confirmed by Luciferase reporter assay. Microarray analysis suggested that MMP13 was upregulated while miR-19a was downregulated in HFLS of RA tissues compared with the healthy control group. MMP13 was related to many proteins in protein-protein interaction network, which might be the main influencing factor of RA. KEGG pathway analysis identified that interleukin (IL)-17 pathway was activated in the regulation of MMP13 in the development of RA. Through observing the alteration of luciferase activity, miR-19a could indeed bind to the 3′UTR of the downstream of MMP13, the target association was then confirmed. The proliferation and invasion of HFLS-RA were promoted by overexpressing MMP13 protein. miR-19a could function as a suppressor of MMP13 and thereby retard the severity of RA. The results showed that miR-19a could regulate the expression of MMP13 in HFLS-RA by mediating the proliferation and invasion of HFLS-RA through IL-17 signaling pathway, thereby participating in the degradation of chondrocytes in the progression of RA.     

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Synthetic microbial communities have become a focus of biotechnological research since they can overcome several of the limitations of single-specie cultures. A paradigmatic example is Clostridium cellulovorans DSM 743B, which can decompose lignocellulose but cannot produce butanol. Clostridium beijerinckii NCIMB 8052 however, is unable to use lignocellulose but can produce high amounts of butanol from simple sugars. In our previous studies, both organisms were cocultured to produce butanol by consolidated bioprocessing. However, such consolidated bioprocessing implementation strongly depends on pH regulation. Since low pH (pH 4.5–5.5) is required for butanol fermentation, C. cellulovorans cannot grow well and saccharify sufficient lignocellulose to feed both strains at a pH below 6.4. To overcome this bottleneck, this study engineered C. cellulovorans by adaptive laboratory evolution, inactivating cell wall lyases genes (Clocel_0798 and Clocel_2169), and overexpressing agmatine deiminase genes (augA, encoded by Cbei_1922) from C. beijerinckii NCIMB 8052. The generated strain WZQ36: 743B*6.0*3△lyt0798△lyt2169-(pXY1-P_thl-augA) can tolerate a pH of 5.5. Finally, the alcohol aldehyde dehydrogenase gene adhE1 from Clostridium acetobutylicum ATCC 824 was introduced into the strain to enable butanol production at low pH, in coordination with solvent fermentation of C. beijerinckii in consortium. The engineered consortium produced 3.94 g/L butanol without pH control within 83 hr, which is more than 5-fold of the level achieved by wild consortia under the same conditions. This exploration represents a proof of concept on how to combine metabolic and evolutionary engineering to coordinate coculture of a synthetic microbial community.     

Reprogramming of sugar transport pathways in Escherichia coli using a permeabilized SecY protein-translocation channel

In the initial step of sugar metabolism, sugar-specific transporters play a decisive role in the passage of sugars through plasma membranes into cytoplasm. The SecY complex (SecYEG) in bacteria forms a membrane channel responsible for protein translocation. The present work shows that permeabilized SecY channels can be used as nonspecific sugar transporters in Escherichia coli. SecY with the plug domain deleted allowed the passage of glucose, fructose, mannose, xylose, and arabinose, and, with additional pore-ring mutations, facilitated lactose transport, indicating that sugar passage via permeabilized SecY was independent of sugar stereospecificity. The engineered E. coli showed rapid growth on a wide spectrum of monosaccharides and benefited from the elimination of transport saturation, improvement in sugar tolerance, reduction in competitive inhibition, and prevention of carbon catabolite repression, which are usually encountered with native sugar uptake systems. The SecY channel is widespread in prokaryotes, so other bacteria may also be engineered to utilize this system for sugar uptake. The SecY channel thus provides a unique sugar passageway for future development of robust cell factories for biotechnological applications.     

Apoptosis detection via automated algorithms to analyze biomarker translocation in reporter cells

Rapid, efficient, and robust quantitative analyses of dynamic apoptotic events are essential in a high-throughput screening workflow. Currently used methods have several bottlenecks, specifically, limitations in available fluorophores for downstream assays and misinterpretation of statistical image data analysis. In this study, we developed cytochrome-C (Cyt-C) and caspase-3/-8 reporter cell lines using lung (PC9) and breast (T47D) cancer cells, and characterized them from the response to apoptotic stimuli. In these two reporter cell lines, the spatial fluorescent signal translocation patterns served as reporters of activations of apoptotic events, such as Cyt-C release and caspase-3/-8 activation. We also developed a vision-based, tunable, automated algorithm in MATLAB to implement the robust and accurate analysis of signal translocation in single or multiple cells. Construction of the reporter cell lines allows live monitoring of apoptotic events without the need for any other dyes or fixatives. Our algorithmic implementation forgoes the use of simple image statistics for more robust analytics. Our optimized algorithm can achieve a precision greater than 90% and a sensitivity higher than 85%. Combining our automated algorithm with reporter cells bearing a single-color dye/fluorophore, we expect our approach to become an integral component in the high-throughput drug screening workflow.     

Microhabitat contributes to microgeographic divergence in threespine stickleback

Since the New Synthesis, most migration-selection balance theory has predicted that there should be negligible differentiation over small spatial scales (relative to dispersal), because gene flow should erode any effect of divergent selection. Nevertheless, there are classic examples of microgeographic divergence, which theory suggests can arise under specific conditions: exceptionally strong selection, phenotypic plasticity in philopatric individuals, or nonrandom dispersal. Here, we present evidence of microgeographic morphological variation within lake and stream populations of threespine stickleback (Gasterosteus aculeatus). It seems reasonable to assume that a given lake or stream population of fish is well-mixed. However, we found this assumption to be untenable. We examined trap-to-trap variation in 34 morphological traits measured on stickleback from 16 lakes and 16 streams. Most traits varied appreciably among traps within populations. Both between-trap distance and microhabitat characteristics such as depth and substrate explained some of the within-population morphological variance. Microhabitat was also associated with genotype at particular loci but there was no genetic isolation by distance, implying that heritable habitat preferences may contribute to microgeographic variation. Our study adds to growing evidence that microgeographic divergence can occur across small spatial scales within individuals’ daily dispersal neighborhood where gene flow is expected to be strong.