Systems and Synthetic Biology最新文献

Clustered regularly interspaced short palindromic repeats (CRISPRs) are direct features of the prokaryotic genomes involved in resistance to their bacterial viruses and phages. Herein, we have identified CRISPR loci together with CRISPR-associated sequences (CAS) genes to reveal their immunity against genome invaders in the thermophilic archaea and bacteria. Genomic survey of this study implied that genomic distribution of CRISPR-CAS systems was varied from strain to strain, which was determined by the degree of invading mobiloms. Direct repeats found to be equal in some extent in many thermopiles, but their spacers were differed in each strain. Phylogenetic analyses of CAS superfamily revealed that genes cmr, csh, csx11, HD domain, devR were belonged to the subtypes of cas gene family. The members in cas gene family of thermophiles were functionally diverged within closely related genomes and may contribute to develop several defense strategies. Nevertheless, genome dynamics, geological variation and host defense mechanism were contributed to share their molecular functions across the thermophiles. A thermophilic archaean, Thermococcus gammotolerans and thermophilic bacteria, Petrotoga mobilis and Thermotoga lettingae have shown superoperons-like appearance to cluster cas genes, which were typically evolved for their defense pathways. A cmr operon was identified with a specific promoter in a thermophilic archaean, Caldivirga maquilingensis. Overall, we concluded that knowledge-based genomic survey and phylogeny-based functional assignment have suggested for designing a reliable genetic regulatory circuit naturally from CRISPR-CAS systems, acquired defense pathways, to thermophiles in future synthetic biology.

Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.

Small RNAs (sRNAs) are genetic tools for the efficient and specific tuning of target genes expression in bacteria. Inspired by naturally occurring sRNAs, recent works proposed the use of artificial sRNAs in synthetic biology for predictable repression of the desired genes. Their potential was demonstrated in several application fields, such as metabolic engineering and bacterial physiology studies. Guidelines for the rational design of novel sRNAs have been recently proposed. According to these guidelines, in this work synthetic sRNAs were designed, constructed and quantitatively characterized in Escherichia coli. An sRNA targeting the reporter gene RFP was tested by measuring the specific gene silencing when RFP was expressed at different transcription levels, under the control of different promoters, in different strains, and in single-gene or operon architecture. The sRNA level was tuned by using plasmids maintained at different copy numbers. Results demonstrated that RFP silencing worked as expected in an sRNA and mRNA expression-dependent fashion. A mathematical model was used to support sRNA characterization and to estimate an efficiency-related parameter that can be used to compare the performance of the designed sRNA. Gene silencing was also successful when RFP was placed in a two-gene synthetic operon, while the non-target gene (GFP) in the operon was not considerably affected. Finally, silencing was evaluated for another designed sRNA targeting the endogenous lactate dehydrogenase gene. The quantitative study performed in this work elucidated interesting performance-related and context-dependent features of synthetic sRNAs that will strongly support predictable gene silencing in disparate basic or applied research studies.

Studies on chemotaxis of Escherichia coli have shown that modulation of tumble frequency causes a net drift up the gradient of attractants. Recently, it has been demonstrated that the bacteria is also capable of varying its runs speed in uniform concentration of attractant. In this study, we investigate the role of swimming speed on the chemotactic migration of bacteria. To this end, cells are exposed to gradients of a non-metabolizable analogue of glucose which are sensed via the Trg sensor. When exposed to a gradient, the cells modulate their tumble duration, which is accompanied with variation in swimming speed leading to drift velocities that are much higher than those achieved through the modulation of the tumble duration alone. We use an existing intra-cellular model developed for the Tar receptor and incorporate the variation of the swimming speed along with modulation of tumble frequency to predict drift velocities close to the measured values. The main implication of our study is that E. coli not only modulates the tumble frequency, but may also vary the swimming speed to affect chemotaxis and thereby efficiently sample its nutritionally rich environment.

Flagellar assembly in Salmonella is controlled by an intricate genetic and biochemical network. This network comprises of a number of inter-connected feedback loops, which control the assembly process dynamically. Critical among these are the FliA-FlgM feedback, FliZ-mediated positive feedback, and FliT-mediated negative feedback. In this work, we develop a mathematical model to track the dynamics of flagellar gene expression in Salmonella. Analysis of our model demonstrates that the network is wired to not only control the transition of the cell from a non-flagellated to a flagellated state, but to also control dynamics of gene expression during cell division. Further, we predict that FliZ encoded in the flagellar regulon acts as a critical secretion-dependent molecular link between flagella and Salmonella Pathogenicity Island 1 gene expression. Sensitivity analysis of the model demonstrates that the flagellar regulatory network architecture is extremely robust to mutations.

Interactions between proteins largely govern cellular processes and this has led to numerous efforts culminating in enormous information related to the proteins, their interactions and the function which is determined by their interactions. The main concern of the present study is to present interface analysis of cardiovascular-disorder (CVD) related proteins to shed lights on details of interactions and to emphasize the importance of using structures in network studies. This study combines the network-centred approach with three dimensional studies to comprehend the fundamentals of biology. Interface properties were used as descriptors to classify the CVD associated proteins and non-CVD associated proteins. Machine learning algorithm was used to generate a classifier based on the training set which was then used to predict potential CVD related proteins from a set of polymorphic proteins which are not known to be involved in any disease. Among several classifying algorithms applied to generate models, best performance was achieved using Random Forest with an accuracy of 69.5 %. The tool named CARDIO-PRED, based on the prediction model is present at http://www.genomeinformatics.dce.edu/CARDIO-PRED/. The predicted CVD related proteins may not be the causing factor of particular disease but can be involved in pathways and reactions yet unknown to us thus permitting a more rational analysis of disease mechanism. Study of their interactions with other proteins can significantly improve our understanding of the molecular mechanism of diseases.

Cancer cells have upregulated DNA repair mechanisms, enabling them survive DNA damage induced during repeated rapid cell divisions and targeted chemotherapeutic treatments. Cancer cell proliferation and survival targeting via inhibition of DNA repair pathways is currently a very promiscuous anti-tumor approach. The deubiquitinating enzyme, USP1 is known to promote DNA repair via complexing with UAF1. The USP1/UAF1 complex is responsible for regulating DNA break repair pathways such as trans-lesion synthesis pathway, Fanconi anemia pathway and homologous recombination. Thus, USP1/UAF1 inhibition poses as an efficient anti-cancer strategy. The recently made available high throughput screen data for anti USP1/UAF1 activity prompted us to compute bioactivity predictive models that could help in screening for potential USP1/UAF1 inhibitors having anti-cancer properties. The current study utilizes publicly available high throughput screen data set of chemical compounds evaluated for their potential USP1/UAF1 inhibitory effect. A machine learning approach was devised for generation of computational models that could predict for potential anti USP1/UAF1 biological activity of novel anticancer compounds. Additional efficacy of active compounds was screened by applying SMARTS filter to eliminate molecules with non-drug like features. The structural fragment analysis was further performed to explore structural properties of the molecules. We demonstrated that modern machine learning approaches could be efficiently employed in building predictive computational models and their predictive performance is statistically accurate. The structure fragment analysis revealed the structures that could play an important role in identification of USP1/UAF1 inhibitors.

Peptides are increasingly used as inhibitors of various disease specific targets. Several naturally occurring and synthetically developed peptides are undergoing clinical trials. Our work explores the possibility of reusing the non-expressing DNA sequences to predict potential drug-target specific peptides. Recently, we experimentally demonstrated the artificial synthesis of novel proteins from non-coding regions of Escherichia coli genome. In this study, a library of synthetic peptides (Synpeps) was constructed from 2500 intergenic E. coli sequences and screened against Beta-secretase 1 protein, a known drug target for Alzheimer's disease (AD). Secondary and tertiary protein structure predictions followed by protein-protein docking studies were performed to identify the most promising enzyme inhibitors. Interacting residues and favorable binding poses of lead peptide inhibitors were studied. Though initial results are encouraging, experimental validation is required in future to develop efficient target specific inhibitors against AD.

Brucellosis is a zoonotic infection transmitted to humans from infected animals and is one of the widely spread zoonoses. Recently, six species were recognized within the genus Brucella wherein B. melitensis, B. suis and B. abortus are considered virulent for humans. While these species differ phenotypically by their pattern of metabolic activities, there has been an imperative need to understand pathogenesis of Brucella species. It has been foreseen that creating a human vaccine for Brucellosis would entail decreased dose of antibiotics. However the emerging role of Brucella pathogenesis still centers on isolation of the organism and various diagnostic tests thereby leading to varying strategies of treatment cycle. In view of disease heterogeneity, we focus systems and synthetic biology challenges that might improve our understanding the Brucella pathogenesis.