Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone

Synthetic associative learning in engineered multicellular consortia

Associative learning is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was early recognized to be a general trait of complex multicellular organisms but also found in "simpler" ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display associative learning responses to two classes of stimuli and displaying long and short-term memory (i. e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.Comment: 5 figure

New synthetic biology tools for metabolic control

In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies

Complexity of evolutionary equilibria in static fitness landscapes

A fitness landscape is a genetic space -- with two genotypes adjacent if they differ in a single locus -- and a fitness function. Evolutionary dynamics produce a flow on this landscape from lower fitness to higher; reaching equilibrium only if a local fitness peak is found. I use computational complexity to question the common assumption that evolution on static fitness landscapes can quickly reach a local fitness peak. I do this by showing that the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2; the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are NK fitness landscapes where every adaptive path from some vertices is of exponential length. Alternatively -- under the standard complexity theoretic assumption that there are problems in PLS not solvable in polynomial time -- this means that there are no evolutionary dynamics (known, or to be discovered, and not necessarily following adaptive paths) that can converge to a local fitness peak on all NK landscapes with K = 2. Applying results from the analysis of simplex algorithms, I show that there exist single-peaked landscapes with no reciprocal sign epistasis where the expected length of an adaptive path following strong selection weak mutation dynamics is $e^{O(n^{1/3})}$ even though an adaptive path to the optimum of length less than n is available from every vertex. The technical results are written to be accessible to mathematical biologists without a computer science background, and the biological literature is summarized for the convenience of non-biologists with the aim to open a constructive dialogue between the two disciplines.Comment: 14 pages, 3 figure

Characterization of the ftsYEX operon of Escherichia coli

The ftsYEX operon of Escherichia coli historically has been described as a cluster of essential cell division genes. Filamentation temperature-sensitive (fts) designates a class of mutants that displays normal cell morphology at the permissive temperature of 30°C, but filaments and dies at the restrictive temperature of 42°C. The first gene, ftsY, is known to encode an essential component of a protein localization system first described in eukaryotes, the signal recognition particle (SRP) pathway. The function of the following genes, ftsE and ftsX, are much less understood. It has been shown that FtsE and FtsX comprise a two-component ATP-binding cassette (ABC) transporter, but the substrate is unknown. Constructing a null mutant of ftsE, I have demonstrated that ftsE is not an essential gene, although viability depends upon the concentration of NaCl or other compounds in the growth medium. In the characterization of this ftsE mutant, it was found that several predominant proteins were missing in this mutant when compared to wild-type protein profiles. Contrary to previous speculation, we have shown that the cell division septation machinery appears to be intact in an ftsE null mutant and it remains capable of forming septa. Regulation of the ftsYEX operon was investigated with the use of gene fusions. We have also constructed a plasmid shuffling system for the isolation of new mutants to further study the function of this operon. E. coli strains exhibiting a salt-dependent temperature-sensitive phenotype were also identified and investigated. This dissertation summarizes the scientific characterization of the ftsYEX operon of E. coli

Bacteriophage therapy: a novel method of lytic phage delivery

The widespread emergence of multi-antibiotic resistant bacteria has increased the need for alternatives to conventional antibiotic therapy. Accordingly, a significant amount of effort has been made to investigate the potential use of bacteriophages as prophylactic and therapeutic agents for bacterial infections. In this study, molecular biological techniques were applied to construct a lysogen of lytic bacteriophage lambda in an attempt to combat with multi-antibiotic resistant bacteria by a novel method of lytic phage delivery;To accomplish this goal, two plasmid-based site-specific recombination (SSR) systems for integration and recovery of DNA constructs from Escherichia coli and Salmonella typhimurium chromosomes were developed. The two systems are mediated by SSR machineries of bacteriophages lambda of E. coli and P22 of S. typhimurium. These systems utilize plasmid vectors with conditional replicating origin of replication and provide stable chromosomal integration of genes at specific bacteriophage attachment sites without disruption of any host gene or a need for antibiotic selection. E. coli contains attachment sites for both bacteriophages. When the two systems are applied consecutively, two different genes can be integrated at two specific locations. The integrated plasmids of both systems can also be completely excised and recovered from the host chromosomes to observe any genetic changes, e.g. by DNA sequencing. Both systems are also very applicable in construction of bacterial strains as well as live E. coli and S. typhimurium recombinant vaccines expressing foreign genes of interest;To construct a lysogen of lytic bacteriophage lambda, both SSR systems were applied. A lytic mutant (cI-) of bacteriophage lambda was marked with an antibiotic resistant gene cassette to facilitate a lysogen selection. The P22 SSR system helped integrate functional lambda repressor gene (cI) into a non-pathogenic E. coli strain and the marked lytic lambda phage lysogenized in the presence of the helper plasmid of the lambda SSR system. The lysogen demonstrated its efficacy in decreasing number of lambda sensitive E. coli. This lytic phage lysogen construction strategy can be applied for other bacteriophages. A pool of different lysogens infects a wider range of bacteria and could be utilized as alternatives to the use of antibiotics to control bacterial infections

Using bayesian networks to construct gene regulatory networks from microarray data

In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. At the end of this research, the gene networks constructed for Saccharomyces cerevisiae not only have achieved high True Positive Rate (more than 90%), but the networks constructed also have discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using Bayesian networks in this research is proved to be better because it can reveal more gene relationships

Structural network analysis of biological networks for assessment of potential disease model organisms

AbstractModel organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach

PROTEIN FUNCTION, DIVERISTY AND FUNCTIONAL INTERPLAY

Functional annotations of novel or unknown proteins is one of the central problems in post-genomics bioinformatics research. With the vast expansion of genomic and proteomic data and technologies over the last decade, development of automated function prediction (AFP) methods for large-scale identification of protein function has be-come imperative in many aspects. In this research, we address two important divergences from the “one protein – one function” concept on which all existing AFP methods are developed

Heterogeneity in pure microbial systems: experimental measurements and modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale