Plant-associated microbes are important for the growth and health of their hosts. As a result of numerous prior studies, we know that host genotypes and abiotic factors influence the composition of ...plant microbiomes. However, the high complexity of these communities challenges detailed studies to define experimentally the mechanisms underlying the dynamics of community assembly and the beneficial effects of such microbiomes on plant hosts. In this work, from the distinctive microbiota assembled by maize roots, through host-mediated selection, we obtained a greatly simplified synthetic bacterial community consisting of seven strains (Enterobacter cloacae, Stenotrophomonas maltophilia, Ochrobactrum pituitosum, Herbaspirillum frisingense, Pseudomonas putida, Curtobacterium pusillum, and Chryseobacterium indologenes) representing three of the four most dominant phyla found in maize roots. By using a selective culture-dependent method to track the abundance of each strain, we investigated the role that each plays in community assembly on roots of axenic maize seedlings. Only the removal of E. cloacae led to the complete loss of the community, and C. pusillum took over. This result suggests that E. cloacae plays the role of keystone species in this model ecosystem. In planta and in vitro, this model community inhibited the phytopathogenic fungus Fusarium verticillioides, indicating a clear benefit to the host. Thus, combined with the selective culture-dependent quantification method, our synthetic seven-species community representing the root microbiome has the potential to serve as a useful system to explore how bacterial interspecies interactions affect root microbiome assembly and to dissect the beneficial effects of the root microbiota on hosts under laboratory conditions in the future.
Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed ...towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data.
We first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line-drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into "sensitive" and "resistant" groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising.
CDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To pursue the sensitive and efficient detection of informative biomolecules for bioanalysis and disease diagnosis, a series of signal amplification techniques have been put forward. Among them, ...hybridization chain reaction (HCR) is an isothermal and enzyme-free process where the cascade reaction of hybridization events is initiated by a target analyte, yielding a long nicked dsDNA molecule analogous to alternating copolymers. Compared with conventional polymerase chain reaction (PCR) that can proceed only with the aid of polymerases and complicated thermal cycling, HCR has attracted increasing attention because it can occur under mild conditions without using enzymes. As a powerful signal amplification tool, HCR has been employed to construct various simple, sensitive and economic biosensors for detecting nucleic acids, small molecules, cells, and proteins. Moreover, HCR has also been applied to assemble complex nanostructures, some of which even act as the carriers to execute the targeted delivery of anticancer drugs. Recently, HCR has engendered tremendous progress in RNA imaging applications, which can not only achieve endogenous RNA imaging in living cells or even living animals but also implement imaging-guided photodynamic therapy, paving a promising path to promote the development of theranostics. In this review, we begin with the fundamentals of HCR and then focus on summarizing the recent advances in HCR-based biosensors for biosensing and RNA imaging strategies. Further, the challenges and future perspective of HCR-based signal amplification in biosensing and theranostic application are discussed.
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•The basic principle and features of hybridization chain reaction (HCR), especially strategies for improving HCR assay.•Recent advances in HCR assay of disease-related biomarkers, including intracellular nucleic acids and proteins.•The latest HCR-based researches focusing on the intracellular and even in vivo RNA imaging.•Challenges and future perspective of HCR-based signal amplification, especially in improving assay and imaging ability.
is an opportunistic pathogen that can cause fatal bacterial infections. MurD catalyzes the formation of peptide bond between UDP-
-acetylehyl-l-alanine and d-glutamic acid, which plays an important ...role in the synthesis of peptidoglycan and the formation of cell wall by
. Because
is resistant to most existing antibiotics, it is necessary to develop new inhibitors. In this study, Schrodinger 11.5 Prime homology modeling was selected to prepare the protein model of MurD enzyme, and its structure was optimized. We used a virtual screening program and similarity screening to screen 47163 compounds from three marine natural product libraries to explore new inhibitors of
. ADME provides analysis of the physicochemical properties of the best performing compounds during the screening process. To determine the stability of the docking effect, a 100 ns molecular dynamics was performed to verify how tightly the compound was bound to the protein. By docking analysis and molecular dynamics analysis, both 46604 and 46608 have strong interaction with the docking pocket, have good pharmacological properties, and maintain stable conformation with the target protein, so they have a chance to become drugs for
. Through virtual screening, similarity screening, ADME study and molecular dynamics simulation, 46604 and 46608 were selected as potential drug candidates for
.
The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to ...predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Programmed DNA recombination in mammalian cells occurs predominantly in a directional manner. While random DNA breaks are typically repaired both by deletion and by inversion at approximately equal ...proportions, V(D)J and class switch recombination (CSR) of immunoglobulin heavy chain gene overwhelmingly delete intervening sequences to yield productive rearrangement. What factors channel chromatin breaks to deletional CSR in lymphocytes is unknown. Integrating CRISPR knockout and chemical perturbation screening we here identify the Snf2-family helicase-like ERCC6L2 as one such factor. We show that ERCC6L2 promotes double-strand break end-joining and facilitates optimal CSR in mice. At the cellular levels, ERCC6L2 rapidly engages in DNA repair through its C-terminal domains. Mechanistically, ERCC6L2 interacts with other end-joining factors and plays a functionally redundant role with the XLF end-joining factor in V(D)J recombination. Strikingly, ERCC6L2 controls orientation-specific joining of broken ends during CSR, which relies on its helicase activity. Thus, ERCC6L2 facilitates programmed recombination through directional repair of distant breaks.
An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, ...but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.
Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).
Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.
These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Tumor-infiltrating B cells are an important component in the microenvironment but have unclear anti-tumor effects. We enhanced our previous computational algorithm TRUST to extract the B cell ...immunoglobulin hypervariable regions from bulk tumor RNA-sequencing data. TRUST assembled more than 30 million complementarity-determining region 3 sequences of the B cell heavy chain (IgH) from The Cancer Genome Atlas. Widespread B cell clonal expansions and immunoglobulin subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations in the MICA and MICB genes related to antibody-dependent cell-mediated cytotoxicity were identified in tumors with elevated B cell activity. The IgG3-1 subclass switch interacts with B cell-receptor affinity maturation and defects in the antibody-dependent cell-mediated cytotoxicity pathway. Comprehensive pancancer analyses of tumor-infiltrating B cell-receptor repertoires identified novel tumor immune evasion mechanisms through genetic alterations. The IgH sequences identified here are potentially useful resources for future development of immunotherapies.
A novel method for visible-light photoredox-catalyzed difluoromethylation of electron-rich N-, O-, and S-containingheteroarenes under mild reaction conditions is developed. Mechanistic investigation ...indicates that the net C–H difluoromethylation proceeds through an electrophilic radical-type pathway.
Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a ...few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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