Mammalian genome mapping: lessons and prospects.

The recent emphasis on human genome mapping has stimulated the development of gene maps in close to thirty mammalian species. Animal gene maps provide an invaluable resource for genetic analysis and manipulation of phenotypic characters, as well as a retrospective glimpse at the patterns and processes of genome evolution. An empirical strategy for developing new gene maps in mammals by emphasizing two important classes of index or anchor marker loci is presented.     

Construction of an E. Coli genome-scale atom mapping model for MFA calculations

Metabolic flux analysis (MFA) has so far been restricted to lumped networks lacking many important pathways, partly due to the difficulty in automatically generating isotope mapping matrices for genome-scale metabolic networks. Here we introduce a procedure that uses a compound matching algorithm based on the graph theoretical concept of pattern recognition along with relevant reaction information to automatically generate genome-scale atom mappings which trace the path of atoms from reactants to products for every reaction. The procedure is applied to the iAF1260 metabolic reconstruction of Escherichia coli yielding the genome-scale isotope mapping model imPR90068. This model maps 90,068 non-hydrogen atoms that span all 2,077 reactions present in iAF1260 (previous largest mapping model included 238 reactions). The expanded scope of the isotope mapping model allows the complete tracking of labeled atoms through pathways such as cofactor and prosthetic group biosynthesis and histidine metabolism. An EMU representation of imPR90068 is also constructed and made available.     

Low temperature dynamic mapping reveals unexpected order and disorder in troponin

Troponin is a pivotal regulatory protein that binds Ca(2+) reversibly to act as the muscle contraction on-off switch. To understand troponin function, the dynamic behavior of the Ca(2+)-saturated cardiac troponin core domain was mapped in detail at 10 °C, using H/D exchange-mass spectrometry. The low temperature conditions of the present study greatly enhanced the dynamic map compared with previous work. Approximately 70% of assessable peptide bond hydrogens were protected from exchange sufficiently for dynamic measurement. This allowed the first characterization by this method of many regions of regulatory importance. Most of the TnI COOH terminus was protected from H/D exchange, implying an intrinsically folded structure. This region is critical to the troponin inhibitory function and has been implicated in thin filament activation. Other new findings include unprotected behavior, suggesting high mobility, for the residues linking the two domains of TnC, as well as for the inhibitory peptide residues preceding the TnI switch helix. These data indicate that, in solution, the regulatory subdomain of cardiac troponin is mobile relative to the remainder of troponin. Relatively dynamic properties were observed for the interacting TnI switch helix and TnC NH(2)-domain, contrasting with stable, highly protected properties for the interacting TnI helix 1 and TnC COOH-domain. Overall, exchange protection via protein folding was relatively weak or for a majority of peptide bond hydrogens. Several regions of TnT and TnI were unfolded even at low temperature, suggesting intrinsic disorder. Finally, change in temperature prominently altered local folding stability, suggesting that troponin is an unusually mobile protein under physiological conditions.     

Yeast artificial chromosome-based genome mapping: some lessons from Xq24-q28.

Yeast artificial chromosomes (YACs) have recently provided a potential route to long-range coverage of complex genomes in contiguous cloned DNA. In a pilot project for 50 Mb (1.5% of the human genome), a variety of techniques have been applied to assemble Xq24-q28 YAC contigs up to 8 Mb in length and assess their quality. The results indicate the relative strength of several approaches and support the adequacy of YAC-based methods for mapping the human genome.     

Plant genome-scale metabolic reconstruction and modelling

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Highlights► Recent advances have been made on plant genome-scale metabolic reconstruction. ► Cellular compartmentation makes plant genome-scale reconstruction challenging. ► Current reconstructions capture important features of plant metabolism. ► The models have been used to study isolated tissues and tissue interaction. ► We review the challenges and potential of plant reconstruction and modelling.     

Yeast artificial chromosome-based genome mapping: Some lessons from Xq24–q28

Yeast artificial chromosomes (YACs) have recently provided a potential route to long-range coverage of complex genomes in contiguous cloned DNA. In a pilot project for 50 Mb (1.5% of the human genome), a variety of techniques have been applied to assemble Xq24–q28 YAC contigs up to 8 Mb in length and assess their quality. The results indicate the relative strength of several approaches and support the adequacy of YAC-based methods for mapping the human genome.     

Knowledge based identification of essential signaling from genome-scale siRNA experiments

Background  

A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.     

On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus

The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research.     

Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses

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Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints

Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H₂ production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design “fine-tuned” metabolic engineering strategies in silico that can be implemented directly with available genomic tools.     

Actin lessons from pathogens

Salmonella typhimurium secretes proteins that co-opt the host actin cytoskeleton to induce membrane ruffling, leading to the uptake of the bacterium. New information about the biochemical activities of the Salmonella protein SipA suggests that this protein might inhibit host cell actin dynamics by competing with ADF/cofilin and gelsolin, two key proteins that promote the turnover of actin filaments.     

Deep learning allows genome-scale prediction of Michaelis constants from structural features

The Michaelis constant K_M describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of K_M are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts K_M values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale K_M predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.

To understand the action of an enzyme, we need to know its affinity for its substrates, quantified by Michaelis constants, but these are difficult to measure experimentally. This study shows that a deep learning model that can predict them from structural features of the enzyme and substrate, providing KM predictions for all enzymes across 47 model organisms.