Identification of MicroRNAs controlling human ovarian cell proliferation and apoptosis

Previous studies have shown that microRNAs (miRNAs) can control steroidogenesis in cultured granulosa cells. In this study we wanted to determine if miRNAs can also affect proliferation and apoptosis in human ovarian cells. The effect of transfection of cultured primary ovarian granulosa cells with 80 different constructs encoding human pre‐miRNAs on the expression of the proliferation marker, PCNA, and the apoptosis marker, Bax was evaluated by immunocytochemistry. Eleven out of 80 tested miRNA constructs resulted in stimulation, and 53 miRNAs inhibited expression of PCNA. Furthermore, 11 of the 80 miRNAs tested promoted accumulation of Bax, while 46 miRNAs caused a reduction in Bax in human ovarian cells. In addition, two selected antisense constructs that block the corresponding miRNAs mir‐15a and mir‐188 were evaluated for their effects on expression of PCNA. An antisense construct inhibiting mir‐15a (which precursor suppressed PCNA) increased PCNA, whereas an antisense construct for mir‐188 (which precursor did not change PCNA) did not affect PCNA expression. Verification of effects of selected pre‐mir‐10a, mir‐105, and mir‐182 by using other markers of proliferation (cyclin B1) and apoptosis (TdT and caspase 3) confirmed specificity of miRNAs effects on these processes. This is the first direct demonstration of the involvement of miRNAs in controlling both proliferation and apoptosis by ovarian granulose cells, as well as the identification of miRNAs promoting and suppressing these processes utilizing a genome‐wide miRNA screen. J. Cell. Physiol. 223: 49–56, 2010. © 2009 Wiley‐Liss, Inc.     

A genome‐scale screen reveals context‐dependent ovarian cancer sensitivity to miRNA overexpression

Large‐scale molecular annotation of epithelial ovarian cancer (EOC) indicates remarkable heterogeneity in the etiology of that disease. This diversity presents a significant obstacle against intervention target discovery. However, inactivation of miRNA biogenesis is commonly associated with advanced disease. Thus, restoration of miRNA activity may represent a common vulnerability among diverse EOC oncogenotypes. To test this, we employed genome‐scale, gain‐of‐function, miRNA mimic toxicity screens in a large, diverse spectrum of EOC cell lines. We found that all cell lines responded to at least some miRNA mimics, but that the nature of the miRNA mimics provoking a response was highly selective within the panel. These selective toxicity profiles were leveraged to define modes of action and molecular response indicators for miRNA mimics with tumor‐suppressive characteristics in vivo. A mechanistic principle emerging from this analysis was sensitivity of EOC to miRNA‐mediated release of cell fate specification programs, loss of which may be a prerequisite for development of this disease.     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.     

Microbial laboratory evolution in the era of genome‐scale science

Laboratory evolution studies provide fundamental biological insight through direct observation of the evolution process. They not only enable testing of evolutionary theory and principles, but also have applications to metabolic engineering and human health. Genome‐scale tools are revolutionizing studies of laboratory evolution by providing complete determination of the genetic basis of adaptation and the changes in the organism's gene expression state. Here, we review studies centered on four central themes of laboratory evolution studies: (1) the genetic basis of adaptation; (2) the importance of mutations to genes that encode regulatory hubs; (3) the view of adaptive evolution as an optimization process; and (4) the dynamics with which laboratory populations evolve.     

A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.     

Large‐scale molecular genetic analysis in plant‐pathogenic fungi: a decade of genome‐wide functional analysis

Plant‐pathogenic fungi cause diseases to all major crop plants world‐wide and threaten global food security. Underpinning fungal diseases are virulence genes facilitating plant host colonization that often marks pathogenesis and crop failures, as well as an increase in staple food prices. Fungal molecular genetics is therefore the cornerstone to the sustainable prevention of disease outbreaks. Pathogenicity studies using mutant collections provide immense function‐based information regarding virulence genes of economically relevant fungi. These collections are rich in potential targets for existing and new biological control agents. They contribute to host resistance breeding against fungal pathogens and are instrumental in searching for novel resistance genes through the identification of fungal effectors. Therefore, functional analyses of mutant collections propel gene discovery and characterization, and may be incorporated into disease management strategies. In the light of these attributes, mutant collections enhance the development of practical solutions to confront modern agricultural constraints. Here, a critical review of mutant collections constructed by various laboratories during the past decade is provided. We used Magnaporthe oryzae and Fusarium graminearum studies to show how mutant screens contribute to bridge existing knowledge gaps in pathogenicity and fungal–host interactions.     

MapMaker and PathTracer for tracking carbon in genome‐scale metabolic models

Constraint‐based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome‐scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint‐based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO₂ fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint‐based models to identify feasible, active, and high flux paths between metabolites.     

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering.     

A chromosome‐scale reference genome and genome‐wide genetic variations elucidate adaptation in yak

Yak is an important livestock animal for the people indigenous to the harsh, oxygen‐limited Qinghai‐Tibetan Plateau and Hindu Kush ranges of the Himalayas. The yak genome was sequenced in 2012, but its assembly was fragmented because of the inherent limitations of the Illumina sequencing technology used to analyse it. An accurate and complete reference genome is essential for the study of genetic variations in this species. Long‐read sequences are more complete than their short‐read counterparts and have been successfully applied towards high‐quality genome assembly for various species. In this study, we present a high‐quality chromosome‐scale yak genome assembly (BosGru_PB_v1.0) constructed with long‐read sequencing and chromatin interaction technologies. Compared to an existing yak genome assembly (BosGru_v2.0), BosGru_PB_v1.0 shows substantially improved chromosome sequence continuity, reduced repetitive structure ambiguity, and gene model completeness. To characterize genetic variation in yak, we generated de novo genome assemblies based on Illumina short reads for seven recognized domestic yak breeds in Tibet and Sichuan and one wild yak from Hoh Xil. We compared these eight assemblies to the BosGru_PB_v1.0 genome, obtained a comprehensive map of yak genetic diversity at the whole‐genome level, and identified several protein‐coding genes absent from the BosGru_PB_v1.0 assembly. Despite the genetic bottleneck experienced by wild yak, their diversity was nonetheless higher than that of domestic yak. Here, we identified breed‐specific sequences and genes by whole‐genome alignment, which may facilitate yak breed identification.     

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.     

Integrative genome‐scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery

Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re‐sequenced and fully re‐annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome‐scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost‐effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole‐cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.     

A genome‐wide RNA interference screen identifies two novel components of the metazoan secretory pathway

Genetic screens in the yeast Saccharomyces cerevisiae have identified many proteins involved in the secretory pathway, most of which have orthologues in higher eukaryotes. To investigate whether there are additional proteins that are required for secretion in metazoans but are absent from yeast, we used genome‐wide RNA interference (RNAi) to look for genes required for secretion of recombinant luciferase from Drosophila S2 cells. This identified two novel components of the secretory pathway that are conserved from humans to plants. Gryzun is distantly related to, but distinct from, the Trs130 subunit of the TRAPP complex but is absent from S. cerevisiae. RNAi of human Gryzun (C4orf41) blocks Golgi exit. Kish is a small membrane protein with a previously uncharacterised orthologue in yeast. The screen also identified Drosophila orthologues of almost 60% of the yeast genes essential for secretion. Given this coverage, the small number of novel components suggests that contrary to previous indications the number of essential core components of the secretory pathway is not much greater in metazoans than in yeasts.     

Elucidation of metabolism in hybridoma cells grown in fed‐batch culture by genome‐scale modeling

Genome‐scale modeling of mouse hybridoma cells producing monoclonal antibodies (mAb) was performed to elucidate their physiological and metabolic states during fed‐batch cell culture. Initially, feed media nutrients were monitored to identify key components among carbon sources and amino acids with significant impact on the desired outcome, for example, cell growth and antibody production. The monitored profiles indicated rapid assimilation of glucose and glutamine during the exponential growth phase. Significant increase in mAb concentration was also observed when glutamine concentration was controlled at 0.5 mM as a feeding strategy. Based on the reconstructed genome‐scale metabolic network of mouse hybridoma cells and fed‐batch profiles, flux analysis was then implemented to investigate the cellular behavior and changes in internal fluxes during the cell culture. The simulated profile of the cell growth was consistent with experimentally measured specific growth rate. The in silico simulation results indicated (i) predominant utilization of glycolytic pathway for ATP production, (ii) importance of pyruvate node in metabolic shifting, and (iii) characteristic pattern in lactate to glucose ratio during the exponential phase. In future, experimental and in silico analyses can serve as a promising approach to identifying optimal feeding strategies and potential cell engineering targets as well as facilitate media optimization for the enhanced production of mAb or recombinant proteins in mammalian cells. Biotechnol. Bioeng. 2009;102: 1494–1504. © 2008 Wiley Periodicals, Inc.     

Basic and applied uses of genome‐scale metabolic network reconstructions of Escherichia coli

The genome‐scale model (GEM) of metabolism in the bacterium Escherichia coli K‐12 has been in development for over a decade and is now in wide use. GEM‐enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model‐driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome‐scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large‐scale network models with sufficient accuracy.