A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04 469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 -Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. This study was supported by the European Commission through project SHIKIFACTORY100 -Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408). The submitted manuscript has been created by UChicago Argonne, LLC as Operator of Argonne National Laboratory (`Argonne') under Contract No. DE-AC02-06CH11357 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.info:eu-repo/semantics/publishedVersio

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana.

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments

Novel Methods for Metagenomic Analysis

By sampling the genetic content of microbes at the nucleotide level, metagenomics has rapidly established itself as the standard in characterizing the taxonomic diversity and functional capacity of microbial populations throughout nature. The decreasing cost of sequencing technologies and the simultaneous increase of throughput per run has given scientists the ability to deeply sample highly diverse communities on a reasonable budget. The Human Microbiome Project is representative of the flood of sequence data that will arrive in the coming years. Despite these advancements, there remains the significant challenge of analyzing massive metagenomic datasets to make appropriate biological conclusions. This dissertation is a collection of novel methods developed for improved analysis of metagenomic data: (1) We begin with Figaro, a statistical algorithm that quickly and accurately infers and trims vector sequence from large Sanger-based read sets without prior knowledge of the vector used in library construction. (2) Next, we perform a rigorous evaluation of methodologies used to cluster environmental 16S rRNA sequences into species-level operational taxonomic units, and discover that many published studies utilize highly stringent parameters, resulting in overestimation of microbial diversity. (3) To assist in comparative metagenomics studies, we have created Metastats, a robust statistical methodology for comparing large-scale clinical datasets with up to thousands of subjects. Given a collection of annotated metagenomic features (e.g. taxa, COGs, or pathways), Metastats determines which features are differentially abundant between two populations. (4) Finally, we report on a new methodology that employs the generalized Lotka-Volterra model to infer microbe-microbe interactions from longitudinal 16S rRNA data. It is our hope that these methods will enhance standard metagenomic analysis techniques to provide better insight into the human microbiome and microbial communities throughout our world. To assist metagenomics researchers and those developing methods, all software described in this thesis is open-source and available online

Proteome characterizations of microbial systems using MS-based experimental and informatics approaches to examine key metabolic pathways, proteins of unknown function, and phenotypic adaptation

Microbes express complex phenotypes and coordinate activities to build microbial communities. Recent work has focused on understanding the ability of microbial systems to efficiently utilize cellulosic biomass to produce bioenergy-related products. In order to maximize the yield of these bioenergy-related products from a microbial system, it is necessary to understand the molecular mechanisms.The ability of mass spectrometry to precisely identify thousands of proteins from a bacterial source has established mass spectrometry-based proteomics as an indispensable tool for various biological disciplines. This dissertation developed and optimized various proteomics experimental and informatic protocols, and integrated the resulting data with metabolomics, transcriptomics, and genomics in order to understand the systems biology of bio-energy relevant organisms. Integration of these various omics technologies led to an improved understanding of microbial cell-to-cell communication in response to external stimuli, microbial adaptation during deconstruction of lignocellulosic biomass and proteome diversity when an organism is subjected to different growth conditions.Integrated omics revealed Clostridium thermocellum\u27s accumulate long-chain, branched fatty acids over time in response to cytotoxic inhibitors released during the deconstruction and utilization of switchgrass. A striking feature implies a restructuring of C. thermocellum\u27s cellular membrane as the culture progresses. The membrane remodulation was further examined in a study involving the swarming and swimming phenotypes of Paenibacillus polymyxa. The possible roles of phospholipids, hydrolytic enzymes, surfactin, flagellar assembly, chemotaxis and glycerol metabolism in swarming motility were investigated by integrating lipidomics with proteomics.Extracellular proteome analysis of Caldicellulosiruptor bescii revealed secretome plasticity based on the complexity (mono-/disaccharides vs. polysaccharides) and type of carbon (C5 vs. C6) available to the microorganism. This study further opened the avenue for research to characterize proteins of unknown function (PUFs) specific to growth conditions.To gain a better understanding of the possible functions of PUFs in C. thermocellum, a time course analysis of C. thermocellum was conducted. Based on the concept of guilt-by-association, protein intensities and their co-expressions were used to tease out the functional aspect of PUFs. Clustering trends and network analysis were used to infer potential functions of PUFs. Selected PUFs were further interrogated by the use of phylogeny and structural modeling

MOLECULAR AND ECOLOGICAL ASPECTS OF THE INTERACTIONS BETWEEN \u3ci\u3eAUREOCOCCUS ANOPHAGEFFERENS\u3c/i\u3e AND ITS GIANT VIRUS

Viruses are increasingly being recognized as an important biotic component of all ecosystems including agents that control the rapid ecological events that are harmful algal blooms (HABS). Aureococcus anophagefferens is a pelagophyte which causes recurrent ecosystem devastating brown tide blooms along the east coast of the USA and has recently spread to China and South Africa. It has been suggested that a large virus (AaV) is possibly an important agent for demise of brown tide blooms. This observation is consistent with the recognition of a number of other giant viruses modulating algal blooms in marine systems. In this dissertation, we investigated both the molecular underpinnings of Aureococcus-AaV interactions and the dynamics of AaV and the associated viral community in situ. We determined the genome sequence and phylogenetic history of AaV using high throughput sequencing approach and revealed it’s intertwined evolutionary history with the host and other organisms. Building upon the available genome of AaV and its host, we took an RNA-seq approach to provide insights on the physiological state of the AaV-infected Aureococcus ‘virocell’ that is geared towards virus production. In situ activity of AaV was detected by targeted amplicon and high throughput community RNA sequencing (metatranscriptomics) from Quantuck Bay, NY, a site with recurrent brown tide blooms. AaV and associated giant algal viruses in the Mimiviridae clade were found to respond to environmental changes, indicating that this newly recognized phylogenetic group is an important contributor to the eukaryotic phytoplankton dynamics. Analyzing time series metatranscriptomics from two distinct coastal sites recovered diverse viruses infecting microeukaryotes (including AaV) as part of interacting networks of viruses and microeukaryotes. Results from these studies testify AaV as an important factor for brown tide bloom demise, reveals the molecular underpinnings of AaV-host interactions and establishes the ecological relevance of Mimivirus-like algal viruses. We also provide foundation for using metatranscriptomics as an important tool in marine virus ecology – capable of recovering associations among coexisting marine microeukaryotes and viruses

Predicting Cellular Growth from Gene Expression Signatures

Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate

On the effects of alternative optima in context-specific metabolic model predictions

Recent methodological developments have facilitated the integration of high-throughput data into genome-scale models to obtain context-specific metabolic reconstructions. A unique solution to this data integration problem often may not be guaranteed, leading to a multitude of context-specific predictions equally concordant with the integrated data. Yet, little attention has been paid to the alternative optima resulting from the integration of context-specific data. Here we present computational approaches to analyze alternative optima for different context-specific data integration instances. By using these approaches on metabolic reconstructions for the leaf of Arabidopsis thaliana and the human liver, we show that the analysis of alternative optima is key to adequately evaluating the specificity of the predictions in particular cellular contexts. While we provide several ways to reduce the ambiguity in the context-specific predictions, our findings indicate that the existence of alternative optimal solutions warrant caution in detailed context-specific analyses of metabolism