Synthetic regulatory tools for microbial engineering

Microbial engineering requires accurate information about cellular metabolic networks and a set of molecular tools that can be predictably applied to the efficient redesign of such networks. Recent advances in the field of metabolic engineering and synthetic biology, particularly the development of molecular tools for synthetic regulation in the static and dynamic control of gene expression, have increased our ability to efficiently balance the expression of genes in various biological systems. It would accelerate the creation of synthetic pathways and genetic programs capable of adapting to environmental changes in real time to perform the programmed cellular behavior. In this paper, we review current developments in the field of synthetic regulatory tools for static and dynamic control of microbial gene expression.     

Graph-based analysis and visualization of experimental results with ONDEX

MOTIVATION: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. RESULTS: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function.     

Using transgenic modulation of protein synthesis and accumulation to probe protein signaling networks in Arabidopsis thaliana

Deployment of new model species in the plant biology community requires the development and/or improvement of numerous genetic tools. Sequencing of the Arabidopsis thaliana genome opened up a new challenge of assigning biological function to each gene. As many genes exhibit spatiotemporal or other conditional regulation of biological processes, probing for gene function necessitates applications that can be geared toward temporal, spatial and quantitative functional analysis in vivo. The continuing quest to establish new platforms to examine plant gene function has resulted in the availability of numerous genomic and proteomic tools. Classical and more recent genome-wide experimental approaches include conventional mutagenesis, tagged DNA insertional mutagenesis, ectopic expression of transgenes, activation tagging, RNA interference and two-component transactivation systems. The utilization of these molecular tools has resulted in conclusive evidence for the existence of many genes, and expanded knowledge on gene structure and function. This review covers several molecular tools that have become increasingly useful in basic plant research. We discuss their advantages and limitations for probing cellular protein function while emphasizing the contributions made to lay the fundamental groundwork for genetic manipulation of crops using plant biotechnology.Key words: enhancer trap, RNAi, plant transformation, transactivation, transgenic     

MOTS‐c: A Mitochondrial‐Encoded Regulator of the Nucleus

Mitochondria are increasingly being recognized as information hubs that sense cellular changes and transmit messages to other cellular components, such as the nucleus, the endoplasmic reticulum (ER), the Golgi apparatus, and lysosomes. Nonetheless, the interaction between mitochondria and the nucleus is of special interest because they both host part of the cellular genome. Thus, the communication between genome‐bearing organelles would likely include gene expression regulation. Multiple nuclear‐encoded proteins have been known to regulate mitochondrial gene expression. On the contrary, no mitochondrial‐encoded factors are known to actively regulate nuclear gene expression. MOTS‐c (mitochondrial open reading frame of the 12S ribosomal RNA type‐c) is a recently identified peptide encoded within the mitochondrial 12S ribosomal RNA gene that has metabolic functions. Notably, MOTS‐c can translocate to the nucleus upon metabolic stress (e.g., glucose restriction and oxidative stress) and directly regulate adaptive nuclear gene expression to promote cellular homeostasis. It is hypothesized that cellular fitness requires the coevolved mitonuclear genomes to coordinate adaptive responses using gene‐encoded factors that cross‐regulate the opposite genome. This suggests that cellular gene expression requires the bipartite split genomes to operate as a unified system, rather than the nucleus being the sole master regulator.     

Bioinformatics in microbial biotechnology – a mini review

The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics – sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics – identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.     

Representing and analysing molecular and cellular function using the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.     

Stress‐associated H3K4 methylation accumulates during postnatal development and aging of rhesus macaque brain

Epigenetic modifications are critical determinants of cellular and developmental states. Epigenetic changes, such as decreased H3K27me3 histone methylation on insulin/IGF1 genes, have been previously shown to modulate lifespan through gene expression regulation. However, global epigenetic changes during aging and their biological functions, if any, remain elusive. Here, we examined the histone modification H3K4 dimethylation (H3K4me2) in the prefrontal cortex of individual rhesus macaques at different ages by chromatin immunoprecipitation, followed by deep sequencing (ChIP‐seq) at the whole genome level. Through integrative analysis of the ChIP‐seq profiles with gene expression data, we found that H3K4me2 increased at promoters and enhancers globally during postnatal development and aging, and those that correspond to gene expression changes in cis are enriched for stress responses, such as the DNA damage response. This suggests that metabolic and environmental stresses experienced by an organism are associated with the progressive opening of chromatin. In support of this, we also observed increased expression of two H3K4 methyltransferases, SETD7 and DPY30, in aged macaque brain.     

Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.     

Gene expression analysis in LLC-PK1 renal tubular cells by atrial natriuretic peptide (ANP): correlation of homologous human genes with renal response

We used human DNA microarray to explore the differential gene expression profiling of atrial natriuretic peptide (ANP)-stimulated renal tubular epithelial kidney cells (LLC-PK1) in order to understand the biological effect of ANP on renal kidney cell's response. Gene expression profiling revealed 807 differentially expressed genes, consisting of 483 up-regulated and 324 down-regulated genes. The bioinformatics tool was used to gain a better understanding of differentially expressed genes in porcine genome homologous with human genome and to search the gene ontology and category classification, such as cellular component, molecular function and biological process. Four up-regulated genes of ATP1B1, H3F3A, ITGB1 and RHO that were typically validated by real-time quantitative PCR (RT-qPCR) analysis serve important roles in the alleviation of renal hypertrophy as well as other related effects. Therefore, the human array can be used for gene expression analysis in pig kidney cells and we believe that our findings of differentially expressed genes served as genetic markers and biological functions can lead to a better understanding of ANP action on the renal protective system and may be used for further therapeutic application.