maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.     

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.     

Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics

We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line–specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways.     

The current and potential role of urban metabolism studies to analyze the role of food in urban sustainability

Food supply chains are essential for urban sustainability. To reflect on the state of knowledge on urban food flows in urban metabolism research, and the actual and potential role of urban metabolism studies to tackle food sustainability in cities, we systematically review scientific research on food from an urban metabolism perspective and apply statistical and thematic analyses. The analysis of 89 studies provides insights as to the relation between food supply and (environmental and social dimensions of) urban sustainability. First, food is an important contributor to urban environmental impacts, if a consumption-based approach is adopted. Secondly, the social impacts of urban food supply remain scarcely studied in urban metabolism research, but emerging results on public health, malnutrition, and food waste appear promising. In parallel, we find that the findings of the studies fail to engage with debates present in the broader literature, such as that of food justice. Our analysis shows that most studies focus on large cities in high-income, data-rich countries. This limits our understanding of global urban food supply. Existing studies use innovative mixed-methods to produce robust accounts of urban food flows in data-scarce contexts; expanding these accounts is necessary to get a better understanding of how urban food supply and its diverse impacts in terms of environmental and social sustainability may vary across cities, a necessary step for the urban metabolism literature to contribute to current debates around food sustainability and justice.     

Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.     

Ammonia Assimilation in Rumen Bacteria: A Review

In the rumen bacteria, ammonia as the end product of nitrogen is incorporated into carbon skeleton (α-ketoglutarate) to yield glutamine and glutamate which are important nitrogen donors in nitrogenous compounds metabolism in cells. The enzymes glutamine synthetase, glutamate synthetase, and glutamate dehydrogenase are involved in these processes. Some experimental results have proven that the global nitrogen regulation system may participate in the regulation of assimilation of ammonia in rumen bacteria. This review offers a current perspective on the pathways and key enzymes of ammonia assimilation in rumen bacteria with the possible molecular regulation strategy, while points out the further research direction.     

Analysis of the Arabidopsis cytosolic proteome highlights subcellular partitioning of central plant metabolism

The plant cell cytosol is a dynamic and complex intracellular matrix that, by definition, contains no compartmentalization. Nonetheless, it maintains a wide variety of biochemical networks and often links metabolic pathways across multiple organelles. There have been numerous detailed proteomic studies of organelles in the model plant Arabidopsis thaliana, although no such analysis has been undertaken on the cytosol. The cytosolic protein fraction from cell suspensions of Arabidopsis thaliana was isolated and analyzed using offline strong cation exchange liquid chromatography and LC-MS/MS. This generated a robust set of 1071 cytosolic proteins. Functional annotation of this set revealed major activities in protein synthesis and degradation, RNA metabolism and basic sugar metabolism. This included an array of important cytosol-related functions, specifically the ribosome, the set of tRNA catabolic enzymes, the ubiquitin-proteasome pathway, glycolysis and associated sugar metabolism pathways, phenylpropanoid biosynthesis, vitamin metabolism, nucleotide metabolism, an array of signaling and stress-responsive molecules, and NDP-sugar biosynthesis. This set of cytosolic proteins provides for the first time an extensive analysis of enzymes responsible for the myriad of reactions in the Arabidopsis cytosol and defines an experimental set of plant protein sequences that are not targeted to subcellular locations following translation and folding in the cytosol.     

A short fragment of 23S rRNA containing the binding sites for two ribosomal proteins, L24 and L4, is a key element for rRNA folding during early assembly

Previously we described an in vitro selection variant abbreviated SERF (in vitro selection from random rRNA fragments) that identifies protein binding sites within large RNAs. With this method, a small rRNA fragment derived from the 23S rRNA was isolated that binds simultaneously and independently the ribosomal proteins L4 and L24 from Escherichia coli. Until now the rRNA structure within the ternary complex L24-rRNA-L4 could not be studied due to the lack of an appropriate experimental strategy. Here we tackle the issue by separating the various complexes via native gel-electrophoresis and analyzing the rRNA structure by in-gel iodine cleavage of phosphorothioated RNA. The results demonstrate that during the transition from either the L4 or L24 binary complex to the ternary complex the structure of the rRNA fragment changes significantly. The identified protein binding sites are in excellent agreement with the recently reported crystal structure of the 50S subunit. Because both proteins play a prominent role in early assembly of the large subunit, the results suggest that the identified rRNA fragment is a key element for the folding of the 23S RNA during early assembly. The introduced in-gel cleavage method should be useful when an RNA structure within mixed populations of different but related complexes should be studied.     

Comparative genomics of the vitamin B12 metabolism and regulation in prokaryotes

Using comparative analysis of genes, operons, and regulatory elements, we describe the cobalamin (vitamin B12) biosynthetic pathway in available prokaryotic genomes. Here we found a highly conserved RNA secondary structure, the regulatory B12 element, which is widely distributed in the upstream regions of cobalamin biosynthetic/transport genes in eubacteria. In addition, the binding signal (CBL-box) for a hypothetical B12 regulator was identified in some archaea. A search for B12 elements and CBL-boxes and positional analysis identified a large number of new candidate B12-regulated genes in various prokaryotes. Among newly assigned functions associated with the cobalamin biosynthesis, there are several new types of cobalt transporters, ChlI and ChlD subunits of the CobN-dependent cobaltochelatase complex, cobalt reductase BluB, adenosyltransferase PduO, several new proteins linked to the lower ligand assembly pathway, l-threonine kinase PduX, and a large number of other hypothetical proteins. Most missing genes detected within the cobalamin biosynthetic pathways of various bacteria were identified as nonorthologous substitutes. The variable parts of the cobalamin metabolism appear to be the cobalt transport and insertion, the CobG/CbiG- and CobF/CbiD-catalyzed reactions, and the lower ligand synthesis pathway. The most interesting result of analysis of B12 elements is that B12-independent isozymes of the methionine synthase and ribonucleotide reductase are regulated by B12 elements in bacteria that have both B12-dependent and B12-independent isozymes. Moreover, B12 regulons of various bacteria are thought to include enzymes from known B12-dependent or alternative pathways.     

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.     

Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control

Cybernetic modeling strives to uncover the inbuilt regulatory programs of biological systems and leverage them toward computational prediction of metabolic dynamics. Because of its focus on incorporating the global aims of metabolism, cybernetic modeling provides a systems-oriented approach for describing regulatory inputs and inferring the impact of regulation within biochemical networks. Combining cybernetic control laws with concepts from metabolic pathway analysis has culminated in a systematic strategy for constructing cybernetic models, which was previously lacking. The newly devised framework relies upon the simultaneous application of local controls that maximize the net flux through each elementary flux mode and global controls that modulate the activities of these modes to optimize the overall nutritional state of the cell. The modeling concepts are illustrated using a simple linear pathway and a larger network representing anaerobic E. coli central metabolism. The E. coli model successfully describes the metabolic shift that occurs upon deleting the pta-ackA operon that is responsible for fermentative acetate production. The model also furnishes predictions that are consistent with experimental results obtained from additional knockout strains as well as strains expressing heterologous genes. Because of the stabilizing influence of the included control variables, the resulting cybernetic models are more robust and reliable than their predecessors in simulating the network response to imposed genetic and environmental perturbations.