The construction of powerful cell factories requires intensive and extensive remodelling of microbial genomes. Considering the rapidly increasing number of these synthetic biology endeavors, there is an increasing need for DNA watermarking strategies that enable the discrimination between synthetic and native gene copies. While it is well documented that codon usage can affect translation, and most likely mRNA stability in eukaryotes, remarkably few quantitative studies explore the impact of watermarking on transcription, protein expression, and physiology in the popular model and industrial yeast Saccharomyces cerevisiae. The present study, using S. cerevisiae as eukaryotic paradigm, designed, implemented, and experimentally validated a systematic strategy to watermark DNA with minimal alteration of yeast physiology. The 13 genes encoding proteins involved in the major pathway for sugar utilization (i.e., glycolysis and alcoholic fermentation) were simultaneously watermarked in a yeast strain using the previously published pathway swapping strategy. Carefully swapping codons of these naturally codon optimized, highly expressed genes, did not affect yeast physiology and did not alter transcript abundance, protein abundance, and protein activity besides a mild effect on Gpm1. The markerQuant bioinformatics method could reliably discriminate native from watermarked genes and transcripts. Furthermore, presence of watermarks enabled selective CRISPR/Cas genome editing, specifically targeting the native gene copy while leaving the synthetic, watermarked variant intact. This study offers a validated strategy to simply watermark genes in S. cerevisiae.
Malaria is perhaps one of the most lethal infectious diseases that have exterminated nearly half a billion population of the world.Growing resistance of malarial strains to artemisinin therapy, the only available viable cure for malaria, has made the situation grimmer.Hence, identifying new cure for malaria is the need of the hour.Our commentary discusses the advent and contribution of diversity oriented synthesis (DOS) in the drug discovery efforts towards developing cure for malaria.DOS based on chemical genetics focusses on design and synthesis of molecular libraries which covers large tracts of biologically relevant chemical space.Herein, we will discuss the applications, advantages, disadvantages and future directions of DOS with respect to malaria.
Synthetic biology is currently one of the most debated emerging biotechnologies. The societal assessment of this technology is primarily based on contributions by scientists and policy makers, who focus mainly on technical challenges and possible risks. While public dialogue is given, it is yet rather limited. This study explores public debates concerning synthetic biology based on a focus group study with citizens from Austria and Germany and contextualises the analysed public views with content from policy reports and previous empirical studies on public engagement. The findings suggest that discussants favoured a gradual implementation process of synthetic biology, which is receptive to questions about the distribution of possible benefits. The discussed topics correspond in many ways with content from policy reports and former investigations, yet the emphasis of the discussions was different for many aspects.
Brucellaphage Gadvasu (BpG) is a lytic phage infecting Brucella spp. Brucellaphages contain dsDNA as genetic material and are short-tailed particles with host-specificity. Here, we report the challenges on annotation in the complete genome sequence of BpG when compared with that of a recent broad host-range brucellaphage Pr, an original reference genome. The extracted DNA was subjected to genome sequencing with Illumina technology and assembled using SSAKE/Velvet. A significant number of genes were found to be similar between the phages with sequence analysis revealing conserved open reading frames that correspond to 33 gene ontology classifiers, transcriptional terminators and a few putative transcriptional promoters. The analyses revealed that the genome constitutes 1269 contigs and 275 genes encoding 260 proteins. The sequence comparison from the reference data indicated that the genome shares an approximately 70 % nucleotide similarity and differs mainly in the region encoding proteins. We bring this commentary providing an overview of how this exemplar genome can allow us to understand these known unknown regions in brucellaphages.
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.
MicroRNAs are a ~22 nucleotide small non-coding RNAs found in animals, plants and viruses. They regulate key cellular processes by enhancing, degrading or silencing protein coding targets. Currently most of the data on miRNA is available from Drosophila . Given their important post-transcriptional role in several organisms, there is a need to understand the miRNA mediated processes in normal and abnormal conditions. Here we report four novel microRNAs ast - mir - 2502, ast - mir - 2559, ast - mir - 3868 and ast - mir - 9891 in Anopheles stephensi identified from a set of 3,052 transcriptome sequences, showing average minimum free energy of −31.8 kcal/mol of duplex formation with mRNA indicating their functional relevance. Phylogenetic study shows conservation of sequence signatures within the Class Insecta. Furthermore, 26 potential targets of these four miRNAs have been predicted that play an important role in the mosquito life-cycle. This work leads to novel leads and experimental possibilities for improved understanding of gene regulatory processes in mosquito.
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.
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.
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.
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.
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.
Transforming growth factor-betas (TGF-βs) and their family members that include bone morphogenic proteins and activins have been implicated in the regulation of proliferation, hibernation, quiescence and differentiation of hematopoietic stem cells (HSCs). Increasing evidence suggests that the superfamily of TGF-βs play an integral role in the intercellular cross-talk between the stem cells and their microenvironment as well as within the cells at an intracellular level. Active sites of hematopoiesis, such as fetal liver and bone marrow are known to have abundant presence of TGF-β indicating their importance in the maintenance and regulation of hematopoiesis. One of the striking features of TGF-β superfamily is the variety of effects they evoke, contingent on the developing history of the responding cells. In the present review, we discuss the Smad-dependent and Smad-independent TGF-β signaling pathways in order to understand and underscore their role in the regulation of HSCs.
Systems biology addresses challenges in the analysis of genomics data, especially for complex genes and protein interactions using Meta data approach on various signaling pathways. In this paper, we report systems biology and biological circuits approach to construct pathway and identify early gene and protein interactions for predicting GPR142 responses in Type 2 diabetes. The information regarding genes, proteins and other molecules involved in Type 2 diabetes were retrieved from literature and kinetic simulation of GPR142 was carried out in order to determine the dynamic interactions. The major objective of this work was to design a GPR142 biochemical pathway using both systems biology as well as biological circuits synthetically. The term 'synthetically' refers to building biological circuits for cell signaling pathway especially for hormonal pathway disease. The focus of the paper is on logical components and logical circuits whereby using these applications users can create complex virtual circuits. Logic gates process represents only true or false and investigates whether biological regulatory circuits are active or inactive. The basic gates used are AND, NAND, OR, XOR and NOT gates and Integrated circuit composition of many such basic gates and some derived gates. Biological circuits may have a futuristic application in biomedical sciences which may involve placing a micro chip in human cells to modulate the down or up regulation of hormonal disease.
Serpentine receptors with G-protein coupled receptor like seven transmembrane (7 TM) topology are identified in Plasmodium. A class of 7 TM receptors known as purinergic receptors binds to purines such as ADP, ATP and UTP and mediates important physiological functions including regulation of calcium signaling. Here we performed in silico analysis of Plasmodium falciparum serpentine receptors and found that one of the P. falciparum serpentine receptors, PfSR12 possess nucleotide binding consensus P-loop sequence in addition to seven transmembrane domains. The presence of conserved seven transmembrane domains and a consensus nucleotide binding sequence (P-loop) suggest that PfSR12 is a putative purinergic receptor. On further analysis using docking programmes we found four active binding residues Asn149, Lys150, Asn151 and Gly152 in P-loop of PfSR12, interact with ATP. This work gives insights into the interactions between putative purinergic receptor PfSR12 and its ligand ATP which can be explored in structure based drug designing against malaria.
Malaria, a leading parasitic killer, is caused by Plasmodium spp. The pathology of the disease starts when Plasmodium merozoites infect erythrocytes to form rings, that matures through a large trophozoite form and develop into schizonts containing multiple merozoites. The number of intra-erythrocytic merozoites is a key-determining factor for multiplication rate of the parasite. Counting of intraerythrocytic merozoites by classical 2-D microscopy method is error prone due to insufficient representation of merozoite in one optical plane of a schizont. Here, we report an alternative 3-D microscopy based automated method for counting of intraerythrocytic merozoites in entire volume of schizont. This method offers a considerable amount of advantages in terms of both, ease and accuracy.
Malaria a global pandemic has engulfed nearly 0.63 million people globally. It is high time that a cure for malaria is required to stop its ever increasing menace. Our commentary discusses the advent and contribution of genetic algorithm (GA) in the drug discovery efforts towards developing cure for malaria. GAs are computational models of Darwinian evolution, ideally capture and mimic the principles of genetic variation and natural selection to evolve good solutions through multiple iterations on the space of all possible candidate solutions, called the search space, to a given optimization problem. Herein we will discuss the applications, advantages, disadvantages and future directions of GA with respect to malaria.
Brain cancers demonstrate a complex metabolic behavior so as to adapt the external hypoxic environment and internal stress generated by reactive oxygen species. To survive in these stringent conditions, glioblastoma cells develop an antagonistic metabolic phenotype as compared to their predecessors, the astrocytes, thereby quenching the resources expected for nourishing the neurons. The complexity and cumulative effect of the large scale metabolic functioning of glioblastoma is mostly unexplored. In this study, we reconstruct a metabolic network comprising of pathways that are known to be deregulated in glioblastoma cells as compared to the astrocytes. The network, consisted of 147 genes encoding for enzymes performing 247 reactions distributed across five distinct model compartments, was then studied using constrained-based modeling approach by recreating the scenarios for astrocytes and glioblastoma, and validated with available experimental evidences. From our analysis, we predict that glycine requirement of the astrocytes are mostly fulfilled by the internal glycine–serine metabolism, whereas glioblastoma cells demand an external uptake of glycine to utilize it for glutathione production. Also, cystine and glucose were identified to be the major contributors to glioblastoma growth. We also proposed an extensive set of single and double lethal reaction knockouts, which were further perturbed to ascertain their role as probable chemotherapeutic targets. These simulation results suggested that, apart from targeting the reactions of central carbon metabolism, knockout of reactions belonging to the glycine–serine metabolism effectively reduce glioblastoma growth. The combinatorial targeting of glycine transporter with any other reaction belonging to glycine–serine metabolism proved lethal to glioblastoma growth.
Synthetic biology is a recent scientific approach towards engineering biological systems from both pre-existing and novel parts. The aim is to introduce computational aided design approach in biology leading to rapid delivery of useful applications. Though the term reprogramming has been frequently used in the synthetic biology community, currently the technological sophistication only allows for a probabilistic approach instead of a precise engineering approach. Recently, several human health applications have emerged that suggest increased usage of synthetic biology approach in developing novel drugs. This mini review discusses recent translational developments in the field and tries to identify some of the upcoming future developments.
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.
