Synthetic Metabolism: Engineering Biology at the Protein and Pathway Scales

Biocatalysis has become a powerful tool for the synthesis of high-value compounds, particularly so in the case of highly functionalized and/or stereoactive products. Nature has supplied thousands of enzymes and assembled them into numerous metabolic pathways. Although these native pathways can be use to produce natural bioproducts, there are many valuable and useful compounds that have no known natural biochemical route. Consequently, there is a need for both unnatural metabolic pathways and novel enzymatic activities upon which these pathways can be built. Here, we review the theoretical and experimental strategies for engineering synthetic metabolic pathways at the protein and pathway scales, and highlight the challenges that this subfield of synthetic biology currently faces.Synthetic Biology Engineering Research CenterNational Science Foundation (Grant no. 0540879

Context-driven discovery of gene cassettes in mobile integrons using a computational grammar

<p>Abstract</p> <p>Background</p> <p>Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies.</p> <p>Results</p> <p>We discovered two new putative gene cassettes using the method, from 276 integron features and 978 GenBank sequences. The system achieved <it>κ </it>= 0.972 annotation agreement with an expert gold standard of 300 sequences. In rediscovery experiments, we deleted 789,196 cassette instances over 2030 experiments and correctly relabelled 85.6% (<it>α </it>≥ 95%, <it>E </it>≤ 1%, mean sensitivity = 0.86, specificity = 1, F-score = 0.93), with no false positives.</p> <p>Error analysis demonstrated that for 72,338 missed deletions, two adjacent deleted cassettes were labeled as a single cassette, increasing performance to 94.8% (mean sensitivity = 0.92, specificity = 1, F-score = 0.96).</p> <p>Conclusion</p> <p>Using grammars we were able to represent heuristic background knowledge about large and complex structures in DNA. Importantly, we were also able to use the context embedded in the model to discover new putative antibiotic resistance gene cassettes. The method is complementary to existing automatic annotation systems which operate at the sequence level.</p

The Phosphatomes of the Multicellular Myxobacteria Myxococcus xanthus and Sorangium cellulosum in Comparison with Other Prokaryotic Genomes

BACKGROUND: Analysis of the complete genomes from the multicellular myxobacteria Myxococcus xanthus and Sorangium cellulosum identified the highest number of eukaryotic-like protein kinases (ELKs) compared to all other genomes analyzed. High numbers of protein phosphatases (PPs) could therefore be anticipated, as reversible protein phosphorylation is a major regulation mechanism of fundamental biological processes. METHODOLOGY: Here we report an intensive analysis of the phosphatomes of M. xanthus and S. cellulosum in which we constructed phylogenetic trees to position these sequences relative to PPs from other prokaryotic organisms. PRINCIPAL FINDINGS: PREDOMINANT OBSERVATIONS WERE: (i) M. xanthus and S. cellulosum possess predominantly Ser/Thr PPs; (ii) S. cellulosum encodes the highest number of PP2c-type phosphatases so far reported for a prokaryotic organism; (iii) in contrast to M. xanthus only S. cellulosum encodes high numbers of SpoIIE-like PPs; (iv) there is a significant lack of synteny among M. xanthus and S. cellulosum, and (v) the degree of co-organization between kinase and phosphatase genes is extremely low in these myxobacterial genomes. CONCLUSIONS: We conclude that there has been a greater expansion of ELKs than PPs in multicellular myxobacteria

IgTM: An algorithm to predict transmembrane domains and topology in proteins

<p>Abstract</p> <p>Background</p> <p>Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.</p> <p>Results</p> <p>We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: <url>http://www.dsic.upv.es/users/tlcc/bio/bio.html</url></p> <p>Conclusion</p> <p>We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.</p

Diabetes Alters Intracellular Calcium Transients in Cardiac Endothelial Cells

Diabetic cardiomyopathy (DCM) is a diabetic complication, which results in myocardial dysfunction independent of other etiological factors. Abnormal intracellular calcium ([Ca2+]i) homeostasis has been implicated in DCM and may precede clinical manifestation. Studies in cardiomyocytes have shown that diabetes results in impaired [Ca2+]i homeostasis due to altered sarcoplasmic reticulum Ca2+ ATPase (SERCA) and sodium-calcium exchanger (NCX) activity. Importantly, altered calcium homeostasis may also be involved in diabetes-associated endothelial dysfunction, including impaired endothelium-dependent relaxation and a diminished capacity to generate nitric oxide (NO), elevated cell adhesion molecules, and decreased angiogenic growth factors. However, the effect of diabetes on Ca2+ regulatory mechanisms in cardiac endothelial cells (CECs) remains unknown. The objective of this study was to determine the effect of diabetes on [Ca2+]i homeostasis in CECs in the rat model (streptozotocin-induced) of DCM. DCM-associated cardiac fibrosis was confirmed using picrosirius red staining of the myocardium. CECs isolated from the myocardium of diabetic and wild-type rats were loaded with Fura-2, and UTP-evoked [Ca2+]i transients were compared under various combinations of SERCA, sarcoplasmic reticulum Ca2+ ATPase (PMCA) and NCX inhibitors. Diabetes resulted in significant alterations in SERCA and NCX activities in CECs during [Ca2+]i sequestration and efflux, respectively, while no difference in PMCA activity between diabetic and wild-type cells was observed. These results improve our understanding of how diabetes affects calcium regulation in CECs, and may contribute to the development of new therapies for DCM treatment

Systematic Planning of Genome-Scale Experiments in Poorly Studied Species

Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms

The Roots of Bioinformatics in Theoretical Biology

From the late 1980s onward, the term “bioinformatics” mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, I will trace this early history (from a personal point of view) and I will argue that the original meaning of the term is re-emerging

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases

Oxidant-NO dependent gene regulation in dogs with type I diabetes: impact on cardiac function and metabolism

<p>Abstract</p> <p>Background</p> <p>The mechanisms responsible for the cardiovascular mortality in type I diabetes (DM) have not been defined completely. We have shown in conscious dogs with DM that: <it>1</it>) baseline coronary blood flow (CBF) was significantly decreased, <it>2</it>) endothelium-dependent (ACh) coronary vasodilation was impaired, and <it>3</it>) reflex cholinergic NO-dependent coronary vasodilation was selectively depressed. The most likely mechanism responsible for the depressed reflex cholinergic NO-dependent coronary vasodilation was the decreased bioactivity of NO from the vascular endothelium. The goal of this study was to investigate changes in cardiac gene expression in a canine model of alloxan-induced type 1 diabetes.</p> <p>Methods</p> <p>Mongrel dogs were chronically instrumented and the dogs were divided into two groups: one normal and the other diabetic. In the diabetic group, the dogs were injected with alloxan monohydrate (40-60 mg/kg iv) over 1 min. The global changes in cardiac gene expression in dogs with alloxan-induced diabetes were studied using Affymetrix Canine Array. Cardiac RNA was extracted from the control and DM (n = 4).</p> <p>Results</p> <p>The array data revealed that 797 genes were differentially expressed (P < 0.01; fold change of at least ±2). 150 genes were expressed at significantly greater levels in diabetic dogs and 647 were significantly reduced. There was no change in eNOS mRNA. There was up regulation of some components of the NADPH oxidase subunits (gp91 by 2.2 fold, P < 0.03), and down-regulation of SOD1 (3 fold, P < 0.001) and decrease (4 - 40 fold) in a large number of genes encoding mitochondrial enzymes. In addition, there was down-regulation of Ca²⁺cycling genes (ryanodine receptor; SERCA2 Calcium ATPase), structural proteins (actin alpha). Of particular interests are genes involved in glutathione metabolism (glutathione peroxidase 1, glutathione reductase and glutathione S-transferase), which were markedly down regulated.</p> <p>Conclusion</p> <p>our findings suggest that type I diabetes might have a direct effect on the heart by impairing NO bioavailability through oxidative stress and perhaps lipid peroxidases.</p