Advances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure–activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R2 values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array.
glycan array; PLS; QSAR
The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.
A consensus genetic map of tetraploid cotton was constructed using six high-density maps and after the integration of a sequence-based marker redundancy check. Public cotton SSR libraries (17,343 markers) were curated for sequence redundancy using 90% as a similarity cutoff. As a result, 20% of the markers (3,410) could be considered as redundant with some other markers. The marker redundancy information had been a crucial part of the map integration process, in which the six most informative interspecific Gossypium hirsutum×G. barbadense genetic maps were used for assembling a high density consensus (HDC) map for tetraploid cotton. With redundant markers being removed, the HDC map could be constructed thanks to the sufficient number of collinear non-redundant markers in common between the component maps. The HDC map consists of 8,254 loci, originating from 6,669 markers, and spans 4,070 cM, with an average of 2 loci per cM. The HDC map presents a high rate of locus duplications, as 1,292 markers among the 6,669 were mapped in more than one locus. Two thirds of the duplications are bridging homoeologous AT and DT chromosomes constitutive of allopolyploid cotton genome, with an average of 64 duplications per AT/DT chromosome pair. Sequences of 4,744 mapped markers were used for a mutual blast alignment (BBMH) with the 13 major scaffolds of the recently released Gossypium raimondii genome indicating high level of homology between the diploid D genome and the tetraploid cotton genetic map, with only a few minor possible structural rearrangements. Overall, the HDC map will serve as a valuable resource for trait QTL comparative mapping, map-based cloning of important genes, and better understanding of the genome structure and evolution of tetraploid cotton.
Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.
Objective: Retrovirus has been suggested as one of agents involved in the development of schizophrenia. In the present study, we examined the role of the human endogenous retrovirus W family (HERV-W) env gene in the etiopathogenesis of recent-onset schizophrenia, using molecular and epidemiological approaches. Methods: Nested RT-PCR was used to detect the messenger RNA (mRNA) of the HERV-w env gene in plasmas. Quantitative real-time polymerase chain reaction (PCR) was employed to detect the viral reverse transcriptase activity in human sera. Human U251 glioma cells were used to study the potential role of the HERV-W env gene in the etiopathogenesis of recent-onset schizophrenia. Results: We identified genes with mRNA sequences homologous to HERV-W env gene from plasmas of 42 out of 118 individuals with recent-onset schizophrenia but not from any of 106 normal persons (P < .01, t test). Quantitative real-time PCR showed a significantly increase in the reverse transcriptase activity in the sera of patients (by 35.59%) compared with controls (by 2.83%) (P < .05, t test). Overexpression of HERV-w env in human U251 glioma cells upregulated brain-derived neurotrophic factor (BDNF), an important schizophrenia-associated gene, neurotrophic tyrosine kinase receptor type 2 (NTRK2, also called TrkB), and dopamine receptor D3 and increased the phosphorylation of cyclic adenosine monophosphate response element–binding (CREB) protein. BDNF promoter reporter gene assays showed that the HERV-W env triggers BDNF production in human U251 glioma cells. Using gene knockdown, we found that CREB is required for the expression of BDNF that is regulated by env. Conclusion: Our data revealed that the transcriptional activation of HERV is associated with the development of schizophrenia in some patients and indicated that HERV-W env regulates the expression of schizophrenia-associated genes. This report is the first to elucidate the signaling pathway responsible for the upregulation of HERV-W env–triggered BDNF. Our study provides new evidence for the involvement of HERV-W in the central nervous system, which will benefit the diagnosis and treatment of the devastating schizophrenia and related disorders.
schizophrenia; HERV-W; env; Human U251 glioma cells; DRD3; BDNF; siRNA
Protein synthetic lethal genetic interactions are useful to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions remains unclear.
In this study we used the clusters of short polypeptide sequences, which are typically shorter than the classically defined protein domains, to characterize the functionalities of proteins. We developed a framework to identify significant short polypeptide clusters from yeast protein sequences, and then used these short polypeptide clusters as features to predict yeast synthetic lethal genetic interactions. The short polypeptide clusters based approach provides much higher coverage for predicting yeast synthetic lethal genetic interactions. Evaluation using experimental data sets showed that the short polypeptide clusters based approach is superior to the previous protein domain based one.
We were able to achieve higher performance in yeast synthetic lethal genetic interactions prediction using short polypeptide clusters as features. Our study suggests that the short polypeptide cluster may help better understand the functionalities of proteins.
Group A Streptococcus (GAS) has a rich evolutionary history of horizontal transfer among its core genes. Yet, despite extensive genetic mixing, GAS strains have discrete ecological phenotypes. To further our understanding of the molecular basis for ecological phenotypes, comparative genomic hybridization of a set of 97 diverse strains to a GAS pangenome microarray was undertaken, and the association of accessory genes with emm genotypes that define tissue tropisms for infection was determined. Of the 22 nonprophage accessory gene regions (AGRs) identified, only 3 account for all statistically significant linkage disequilibrium among strains having the genotypic biomarkers for throat versus skin infection specialists. Networked evolution and population structure analyses of loci representing each of the AGRs reveal that most strains with the skin specialist and generalist biomarkers form discrete clusters, whereas strains with the throat specialist biomarker are highly diverse. To identify coinherited and coselected accessory genes, the strength of genetic associations was determined for all possible pairwise combinations of accessory genes among the 97 GAS strains. Accessory genes showing very strong associations provide the basis for an evolutionary model, which reveals that a major transition between many throat and skin specialist haplotypes correlates with the gain or loss of genes encoding fibronectin-binding proteins. This study employs a novel synthesis of tools to help delineate the major genetic changes associated with key adaptive shifts in an extensively recombined bacterial species.
To explore the effects and mechanism of glycogen synthase kinase 3β (GSK-3β) inhibitor (2'Z,3'E)-6-bromo-indirubin-3'-oxime (BIO) on drug resistance in colon cancer cells.
The colon cancer SW480 and SW620 cells were treated with BIO, 5-fluorouracil (5-FU) and BIO/5-FU, separately. Cell cycle distribution, apoptosis level and efflux ability of rhodamine 123 (Rh123) were detected by flow cytometry. The protein expressions of P-glycoprotein (P-gp), multidrug resistance protein 2 (MRP2), thymidylate synthase (TS), β-catenin, E2F-1 and Bcl-2 were detected by Western blot. β-catenin and P-gp were stained with double immunofluorescence and observed under a confocal microscope.
BIO up-regulated β-catenin, P-gp, MRP2 and TS, enhanced the efflux ability of Rh123, decreased Bcl-2 protein and gave the opposite effect to E2F-1 protein in SW480 and SW620 cells. Furthermore, BIO significantly inhibited cell apoptosis, increased S and G2/M phase cells, and reduced the cell apoptosis induced by 5-FU in SW480 cells, whereas the effects were slight or not obvious in SW620 cells.
GSK-3β was involved in drug resistance regulation, and activation of β-catenin and inhibition of E2F-1 may be the most responsible for the enhancement of 5-FU chemotherapy resistance induced by GSK-3β inhibitor BIO in colon cancer.
Colorectal neoplasms; Drug resistance; Glycogen synthase kinase 3β; Fluorouracil; β-catenin; E2F-1
Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.
Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).
The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.
Ecological network; Random Matrix Theory; Microbial community; Microbiological ecology; Network interaction; Environmental changes
In this paper, a series of amphiphilic triblock copolymers based on polyethylene glycol–poly ɛ-caprolactone–polyethylenimine (mPEG–PCL-g–PEI) were successfully synthesized, and their application for codelivery of chemotherapeutic drugs and DNA simultaneously was investigated.
Methods and results
These copolymers could self-assemble into micelles with positive charges. The size and zeta potential of the micelles was measured, and the results indicate that temperature had a large effect on the micelles obtained. In vitro gene transfection evaluation in cancer cells indicated that the self-assembled micelles could serve as potential gene delivery vectors. In addition, hydrophobic drug entrapment efficiency and codelivery with the gene was also studied in vitro. The self-assembled micelles could load doxorubicin efficiently and increase cellular uptake in vitro, while maintaining high gene transfection efficiency.
The triblock copolymer mPEG–PCL-g–PEI could be a novel vector for codelivery of drug and gene therapy.
self-assembly; triblock copolymer; DNA; drug codelivery; gene transfection
Animal models have contributed significantly to our understanding of the underlying biological mechanisms of Alzheimer's disease (AD). As a result, over 300 interventions have been investigated and reported to mitigate pathological phenotypes or improve behavior in AD animal models or both. To date, however, very few of these findings have resulted in target validation in humans or successful translation to disease-modifying therapies. Challenges in translating preclinical studies to clinical trials include the inability of animal models to recapitulate the human disease, variations in breeding and colony maintenance, lack of standards in design, conduct and analysis of animal trials, and publication bias due to under-reporting of negative results in the scientific literature. The quality of animal model research on novel therapeutics can be improved by bringing the rigor of human clinical trials to animal studies. Research communities in several disease areas have developed recommendations for the conduct and reporting of preclinical studies in order to increase their validity, reproducibility, and predictive value. To address these issues in the AD community, the Alzheimer's Drug Discovery Foundation partnered with Charles River Discovery Services (Morrisville, NC, USA) and Cerebricon Ltd. (Kuopio, Finland) to convene an expert advisory panel of academic, industry, and government scientists to make recommendations on best practices for animal studies testing investigational AD therapies. The panel produced recommendations regarding the measurement, analysis, and reporting of relevant AD targets, th choice of animal model, quality control measures for breeding and colony maintenance, and preclinical animal study design. Major considerations to incorporate into preclinical study design include a priori hypotheses, pharmacokinetics-pharmacodynamics studies prior to proof-of-concept testing, biomarker measurements, sample size determination, and power analysis. The panel also recommended distinguishing between pilot 'exploratory' animal studies and more extensive 'therapeutic' studies to guide interpretation. Finally, the panel proposed infrastructure and resource development, such as the establishment of a public data repository in which both positive animal studies and negative ones could be reported. By promoting best practices, these recommendations can improve the methodological quality and predictive value of AD animal studies and make the translation to human clinical trials more efficient and reliable.
Poly (ethylene glycol)-poly (ɛ-caprolactone)-poly (ethylene glycol) (PEG-PCL-PEG, PECE) hydrogel has been demonstrated to be biocompatible and thermosensitive. In this study, its potential efficacy and mechanisms of preventing postsurgical abdominal adhesions were investigated.
PECE hydrogel was transformed into gel state from sol state in less than 20 seconds at 37°C. None of the animals treated with the hydrogel (n = 15) developed adhesions. In contrast, all untreated animals (n = 15) had adhesions that could only be separated by sharp dissection (P < 0.001). The hydrogel adhered to the peritoneal wounds, gradually disappeared from the wounds within 7 days, and transformed into viscous fluid, being completely absorbed within 12 days. The parietal and visceral peritoneum were remesothelialized in about 5 and 9 days, respectively. The hydrogel prevented the formation of fibrinous adhesion and the invasion of fibroblasts. Also, along with the hydrogel degradation, a temporary inflammatory cell barrier was formed which could effectively delay the invasion of fibroblasts during the critical period of mesothelial regeneration.
The results suggested that PECE hydrogel could effectively prevent postsurgical intra-abdominal adhesions, which possibly result from the prevention of the fibrinous adhesion formation and the fibroblast invasion, the promotion of the remesothelialization, and the hydroflotation effect.
anti-adhesion; thermosensitive; barrier; biocompatible
A three-strain mixture of Escherichia coli O157:H7 was inoculated into fresh dairy compost (ca. 107 CFU/g) with 40 or 50% moisture and was placed in an environmental chamber (ca. 70% humidity) that was programmed to ramp from room temperature to selected composting temperatures in 2 and 5 days to simulate the early composting phase. The surviving E. coli O157:H7 population was analyzed by direct plating and enrichment. Optimal and suboptimal compost mixes, with carbon/nitrogen (C/N) ratios of 25:1 and 16:1, respectively, were compared in this study. In the optimal compost mix, E. coli O157:H7 survived for 72, 48, and 24 h in compost with 40% moisture and for 72, 24, and 24 h with 50% moisture at 50, 55, and 60°C, respectively, following 2 days of come-up time (rate of heating up). However, in the suboptimal compost mix, the pathogen survived for 288, 72, and 48 h in compost with 40% moisture and for 240, 72, 24 h in compost with 50% moisture at the same temperatures, respectively. Pathogen survival was longer, with 5 days of come-up time compared with 2 days of come-up. Overall, E. coli O157:H7 was inactivated faster in the compost with 50% moisture than in the compost with 40% at 55 and 60°C. Both moisture and come-up time were significant factors affecting Weibull model parameters. Our results suggest that slow come-up time at the beginning of composting can extend pathogen survival during composting. Additionally, both the C/N ratio and the initial moisture level in the compost mix affect the rate of pathogen inactivation as well.
Polylactide (PLA) electrospun fibers have been reported as a scaffold for bone tissue engineering application, however, the great hydrophobicity limits its broad application. In this study, the hybrid amphiphilic poly(ethylene glycol) (PEG)/hydrophobic PLA fibrous scaffolds exhibited improved morphology with regular and continuous fibers compared to corresponding blank PLA fiber mats. The prepared PEG/PLA fibrous scaffolds favored mesenchymal stem cell (MSC) attachment and proliferation by providing an interconnected porous extracellular environment. Meanwhile, MSCs can penetrate into the fibrous scaffold through the interstitial pores and integrate well with the surrounding fibers, which is very important for favorable application in tissue engineering. More importantly, the electrospun hybrid PEG/PLA fibrous scaffolds can enhance MSCs to differentiate into bone-associated cells by comprehensively evaluating the representative markers of the osteogenic procedure with messenger ribonucleic acid quantitation and protein analysis. MSCs on the PEG/PLA fibrous scaffolds presented better differentiation potential with higher messenger ribonucleic acid expression of the earliest osteogenic marker Cbfa-1 and mid-stage osteogenic marker Col I. The significantly higher alkaline phosphatase activity of the PEG/PLA fibrous scaffolds indicated that these can enhance the differentiation of MSCs into osteoblast-like cells. Furthermore, the higher messenger ribonucleic acid level of the late osteogenic differentiation markers OCN (osteocalcin) and OPN (osteopontin), accompanied by the positive Alizarin red S staining, showed better maturation of osteogenic induction on the PEG/PLA fibrous scaffolds at the mineralization stage of differentiation. After transplantation into the thigh muscle pouches of rats, and evaluating the inflammatory cells surrounding the scaffolds and the physiological characteristics of the surrounding tissues, the PEG/PLA scaffolds presented good biocompatibility. Based on the good cellular response and excellent osteogenic potential in vitro, as well as the biocompatibility with the surrounding tissues in vivo, the electrospun PEG/PLA fibrous scaffolds could be one of the most promising candidates in bone tissue engineering.
electrospinning; fibrous scaffolds; poly(ethylene glycol)/polylactide; mesenchymal stem cells; bone tissue engineering
In the title compound, C16H20FN3O3S, the pyrimidine and benzene rings are oriented at a dihedral angle of 38.8 (3)°. An intramolecular C—H⋯O hydrogen bond occurs. The crystal structure is stabilized by O—H⋯N hydrogen bonds. In addition, C—H⋯O interactions are also present.
During wakefulness and in absence of performing tasks or sensory processing, the default-mode network (DMN), an intrinsic central nervous system (CNS) network, is in an active state. Non-human primate and human CNS imaging studies have identified the DMN in these two species. Clinical imaging studies have shown that the pattern of activity within the DMN is often modulated in various disease states (e.g., Alzheimer's, schizophrenia or chronic pain). However, whether the DMN exists in awake rodents has not been characterized. The current data provides evidence that awake rodents also possess ‘DMN-like’ functional connectivity, but only subsequent to habituation to what is initially a novel magnetic resonance imaging (MRI) environment as well as physical restraint. Specifically, the habituation process spanned across four separate scanning sessions (Day 2, 4, 6 and 8). At Day 8, significant (p<0.05) functional connectivity was observed amongst structures such as the anterior cingulate (seed region), retrosplenial, parietal, and hippocampal cortices. Prior to habituation (Day 2), functional connectivity was only detected (p<0.05) amongst CNS structures known to mediate anxiety (i.e., anterior cingulate (seed region), posterior hypothalamic area, amygdala and parabracial nucleus). In relating functional connectivity between cingulate-default-mode and cingulate-anxiety structures across Days 2-8, a significant inverse relationship (r = −0.65, p = 0.0004) was observed between these two functional interactions such that increased cingulate-DMN connectivity corresponded to decreased cingulate anxiety network connectivity. This investigation demonstrates that the cingulate is an important component of both the rodent DMN-like and anxiety networks.
Gene therapy is a promising approach to the treatment of a wide range of diseases. The development of efficient and adequate gene delivery systems could be one of the most important factors. Polyethyleneimine, a cationic polymer, is one of the most successful and widely used vectors for nonviral transfection in vitro and in vivo.
A novel biodegradable poly(ester amine) copolymer (PEA) was successfully prepared from low molecular weight polyethylenimine (PEI, 2000 Da) and poly(L-lactide) copolymers.
According to the results of agarose gel electrophoresis, particle size and zeta potential measurement, and transfection efficiency, the PEA copolymers showed a good ability to condense plasmid DNA effectively into nanocomplexes with a small particle size (≤150 nm) and moderate zeta potential (≥10 mV) at an appropriate polymeric carrier/DNA weight ratio. Compared with high molecular weight PEI (25kDa), the PEA obtained showed relatively high gene transfection efficiency as well as low cytotoxicity in vitro.
These results indicate that such PEA might have potential application as a gene delivery system.
polyethylenimine; poly(L-lactide); gene delivery; cytotoxicity; transfection efficiency
Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.
The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.
Synthetic lethal genetic interactions among proteins have been widely used to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions is still unclear.
In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions between domains of those proteins. The domain genetic interactions rarely overlap with the domain physical interactions from iPfam database and provide a complementary view about domain relationships. Moreover, we found that domains in multidomain yeast proteins contribute to their genetic interactions differently. The domain genetic interactions help more precisely define the function related to the synthetic lethal genetic interactions, and then help understand how domains contribute to different functionalities of multidomain proteins. Using the probabilities of domain genetic interactions, we were able to predict novel yeast synthetic lethal genetic interactions. Furthermore, we had also identified novel compensatory pathways from the predicted synthetic lethal genetic interactions.
The identification of domain genetic interactions helps the understanding of originality of functional relationship in SLGIs at domain level. Our study significantly improved the understanding of yeast mulitdomain proteins, the synthetic lethal genetic interactions and the functional relationships between proteins and pathways.
In the title molecule, C23H21NO, the dihedral angle between the planes of the indole ring and naphthalene ring system is 68.8 (5)°.
TRA-8 is a murine agonist monoclonal antibody to death receptor 5 (DR5), which is able to trigger apoptosis in DR5 positive human tumor cells without the aid of crosslinking. It has demonstrated cytotoxicity in vitro and in vivo antitumor efficacy to a wide range of solid tumors in murine xenograft models. Tigatuzumab is a humanized IgG1 monoclonal antibody derived from TRA-8.
A phase I trial of tigatuzumab in patients with relapsed/refractory carcinomas (n = 16) or lymphoma (n = 1) was designed to determine the maximal tolerated dose (MTD), pharmacokinetics, immunogenicity, and safety. Three to six (3–6) patients were enrolled in successive escalating cohorts at doses ranging from 1 to 8 mg/kg weekly.
Seventeen (17) patients enrolled, 9 in the 1-, 2-, and 4-mg/kg dose cohorts (3 in each cohort) and 8 in the 8-mg/kg dose cohort. Tigatuzumab was well tolerated with no DLTs observed, and the MTD was not reached. There were no study-drug–related grade 3 or 4, renal, hepatic, or hematologic toxicities. Plasma half-life was 6–10 days, and no anti-tigatuzumab responses were detected. Seven (7) patients had stable disease, with the duration of response ranging from 81 to 798 days.
Tigatuzumab is well tolerated, and the MTD was not reached. The high number of patients with stable disease suggests antitumor activity.
antibody; apoptosis; cancer; immunotherapy; targeted therapy
Fumarase catalyzes the reversible hydration of fumarate to L-malate and is a key enzyme in the tricarboxylic acid (TCA) cycle and in amino acid metabolism. Fumarase is also used for the industrial production of L-malate from the substrate fumarate. Thermostable and high-activity fumarases from organisms that inhabit extreme environments may have great potential in industry, biotechnology, and basic research. The marine environment is highly complex and considered one of the main reservoirs of microbial diversity on the planet. However, most of the microorganisms are inaccessible in nature and are not easily cultivated in the laboratory. Metagenomic approaches provide a powerful tool to isolate and identify enzymes with novel biocatalytic activities for various biotechnological applications.
A plasmid metagenomic library was constructed from uncultivated marine microorganisms within marine water samples. Through sequence-based screening of the DNA library, a gene encoding a novel fumarase (named FumF) was isolated. Amino acid sequence analysis revealed that the FumF protein shared the greatest homology with Class II fumarate hydratases from Bacteroides sp. 2_1_33B and Parabacteroides distasonis ATCC 8503 (26% identical and 43% similar). The putative fumarase gene was subcloned into pETBlue-2 vector and expressed in E. coli BL21(DE3)pLysS. The recombinant protein was purified to homogeneity. Functional characterization by high performance liquid chromatography confirmed that the recombinant FumF protein catalyzed the hydration of fumarate to form L-malate. The maximum activity for FumF protein occurred at pH 8.5 and 55°C in 5 mM Mg2+. The enzyme showed higher affinity and catalytic efficiency under optimal reaction conditions: Km= 0.48 mM, Vmax = 827 μM/min/mg, and kcat/Km = 1900 mM/s.
We isolated a novel fumarase gene, fumF, from a sequence-based screen of a plasmid metagenomic library from uncultivated marine microorganisms. The properties of FumF protein may be ideal for the industrial production of L-malate under higher temperature conditions. The identification of FumF underscores the potential of marine metagenome screening for novel biomolecules.
Physiological changes (such as heart rate and respiration rate) associated with strong pharmacological stimuli could change the blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) mapping signals, independent of neural activity.
This study investigates whether the physiological changes per se associated with systemic cocaine administration (1 mg/kg) contaminate the BOLD fMRI signals by measuring BOLD and cerebral blood flow (CBF) fMRI and estimating the cerebral metabolic rate of oxygen (CMRO2) changes.
Materials and methods
BOLD and CBF fMRI was performed, and changes in CMRO2 were estimated using the BOLD biophysical model.
After systemic cocaine administration, blood pressure, heart rate, and respiration rate increased, fMRI signals remained elevated after physiological parameters had returned to baseline. Cocaine induced changes in the BOLD signal within regions of the reward pathway that were heterogeneous and ranged from −1.2 to 5.4%, and negative changes in BOLD were observed along the cortical surface. Changes in CBF and estimated CMRO2 were heterogeneous and positive throughout the brain, ranging from 14 to 150% and 10 to 55%, respectively.
This study demonstrates a valuable tool to investigate the physiological and biophysical basis of drug action on the central nervous system, offering the means to distinguish the physiological from neural sources of the BOLD fMRI signal.
Multimodal imaging; CMRO2; BOLD; CBF; Pharmacological fMRI; Biophysical model; Arterial spin labeling; Hypercapnic calibration
Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO2 and ambient CO2 (aCO2) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO2 and aCO2, at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.
Microorganisms are the foundation of the Earth’s biosphere and play integral and unique roles in various ecosystem processes and functions. In an ecosystem, various microorganisms interact with each other to form complicated networks. Elucidating network interactions and their responses to environmental changes is difficult due to the lack of appropriate experimental data and an appropriate theoretical framework. This study provides a conceptual framework to construct interaction networks in microbial communities based on high-throughput functional gene array hybridization data. It also first documents that elevated carbon dioxide in the atmosphere dramatically alters the network interactions in soil microbial communities, which could have important implications in assessing the responses of ecosystems to climate change. The conceptual framework developed allows microbiologists to address research questions unapproachable previously by focusing on network interactions beyond the listing of, e.g., the number and abundance of species. Thus, this study could represent transformative research and a paradigm shift in microbial ecology.