The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

Temperature perception and signal transduction in plants

Plants can show remarkable responses to small changes in temperature, yet one of the great unknowns in plant science is how that temperature signal is perceived. The identity of the early components of the temperature signal transduction pathway also remains a mystery. To understand the consequences of anthropogenic environmental change we will have to learn much more about the basic biology of how plants sense temperature. Recent advances show that many known plant-temperature responses share common signalling components, and suggest ways in which these might be linked to form a plant temperature signalling network.

Mathematical modeling and synthetic biology

Synthetic biology is an engineering discipline that builds on our mechanistic understanding of molecular biology to program microbes to carry out new functions. Such predictable manipulation of a cell requires modeling and experimental techniques to work together. The modeling component of synthetic biology allows one to design biological circuits and analyze its expected behavior. The experimental component merges models with real systems by providing quantitative data and sets of available biological 'parts' that can be used to construct circuits. Sufficient progress has been made in the combined use of modeling and experimental methods, which reinforces the idea of being able to use engineered microbes as a technological platform.

Microenvironments Engineered by Inkjet Bioprinting Spatially Direct Adult Stem Cells Toward Muscle- and Bone-Like Subpopulations

In vivo, growth factors exist both as soluble and as solid-phase molecules, immobilized to cell surfaces and within the extracellular matrix. We used this rationale to develop more biologically relevant approaches to study stem cell behaviors. We engineered stem cell microenvironments using inkjet bioprinting technology to create spatially defined patterns of immobilized growth factors. Using this approach, we engineered cell fate toward the osteogenic lineage in register to printed patterns of bone morphogenetic protein (BMP) 2 contained within a population of primary muscle-derived stem cells (MDSCs) isolated from adult mice. This patterning approach was conducive to patterning the MDSCs into subpopulations of osteogenic or myogenic cells simultaneously on the same chip. When cells were cultured under myogenic conditions on BMP-2 patterns, cells on pattern differentiated toward the osteogenic lineage, whereas cells off pattern differentiated toward the myogenic lineage. Time-lapse microscopy was used to visualize the formation of multinucleated myotubes, and immunocytochemistry was used to demonstrate expression of myosin heavy chain (fast) in cells off BMP-2 pattern. This work provides proof-of-concept for engineering spatially controlled multilineage differentiation of stem cells using patterns of immobilized growth factors. This approach may be useful for understanding cell behaviors to immobilized biological patterns and could have potential applications for regenerative medicine.

AtPID: Arabidopsis thaliana protein interactome database--an integrative platform for plant systems biology

Arabidopsis thaliana Protein Interactome Database (AtPID) is an object database that integrates data from several bioinformatics prediction methods and manually collected information from the literature. It contains data relevant to protein-protein interaction, protein subcellular location, ortholog maps, domain attributes and gene regulation. The predicted protein interaction data were obtained from ortholog interactome, microarray profiles, GO annotation, and conserved domain and genome contexts. This database holds 28,062 protein-protein interaction pairs with 23,396 pairs generated from prediction methods. Among the rest 4666 pairs, 3866 pairs of them involving 1875 proteins were manually curated from the literature and 800 pairs were from enzyme complexes in KEGG. In addition, subcellular location information of 5562 proteins is available. AtPID was built via an intuitive query interface that provides easy access to the important features of proteins. Through the incorporation of both experimental and computational methods, AtPID is a rich source of information for system-level understanding of gene function and biological processes in A. thaliana. Public access to the AtPID database is available at http://atpid.biosino.org/.

Designability and disease

Structural designability is the number of ways it is possible to encode for structure. A protein's designability has been equated with the size of sequence space encoding for the protein's structure, a measure that reflects the structure's robustness to mutation. Current evidence suggests that designability is fundamental to our understanding of the evolvability and distribution of structures in nature and is a significant factor associated with human disease. Here, we describe definitions and principles underlying the concept of designability and discuss its relation to disease.

The methyltransferase YfgB/RlmN is responsible for modification of adenosine 2503 in 23S rRNA

A2503 in 23S rRNA of the gram-negative bacterium Escherichia coli is located in a functionally important region of the ribosome, at the entrance to the nascent peptide exit tunnel. In E. coli, and likely in other species, this adenosine residue is post-transcriptionally modified to m2A. The enzyme responsible for this modification was previously unknown. We identified E. coli protein YfgB, which belongs to the radical SAM enzyme superfamily, as the methyltransferase that modifies A2503 of 23S rRNA to m2A. Inactivation of the yfgB gene in E. coli led to the loss of modification at nucleotide A2503 of 23S rRNA as revealed by primer extension analysis and thin layer chromatography. The A2503 modification was restored when YfgB protein was expressed in the yfgB knockout strain. A similar protein was shown to catalyze post-transcriptional modification of A2503 in 23S rRNA in gram-positive Staphylococcus aureus. The yfgB knockout strain loses in competition with wild type in a co-growth experiment, indicating functional importance of A2503 modification. The location of A2503 in the exit tunnel suggests its possible involvement in interaction with the nascent peptide and raises the possibility that its post-transcriptional modification may influence such an interaction.

A bifunctional locus (BIO3-BIO1) required for biotin biosynthesis in Arabidopsis

We identify here the Arabidopsis (Arabidopsis thaliana) gene encoding the third enzyme in the biotin biosynthetic pathway, dethiobiotin synthetase (BIO3; At5g57600). This gene is positioned immediately upstream of BIO1, which is known to be associated with the second reaction in the pathway. Reverse genetic analysis demonstrates that bio3 insertion mutants have a similar phenotype to the bio1 and bio2 auxotrophs identified using forward genetic screens for arrested embryos rescued on enriched nutrient medium. Unexpectedly, bio3 and bio1 mutants define a single genetic complementation group. Reverse transcription-polymerase chain reaction analysis demonstrates that separate BIO3 and BIO1 transcripts and two different types of chimeric BIO3-BIO1 transcripts are produced. Consistent with genetic data, one of the fused transcripts is monocistronic and encodes a bifunctional fusion protein. A splice variant is bicistronic, with distinct but overlapping reading frames. The dual functionality of the monocistronic transcript was confirmed by complementing the orthologous auxotrophs of Escherichia coli (bioD and bioA). BIO3-BIO1 transcripts from other plants provide further evidence for differential splicing, existence of a fusion protein, and localization of both enzymatic reactions to mitochondria. In contrast to most biosynthetic enzymes in eukaryotes, which are encoded by genes dispersed throughout the genome, biotin biosynthesis in Arabidopsis provides an intriguing example of a bifunctional locus that catalyzes two sequential reactions in the same metabolic pathway. This complex locus exhibits several unusual features that distinguish it from biotin operons in bacteria and from other genes known to encode bifunctional enzymes in plants.

Metabolite profiling analysis ofMethylobacterium extorquens AM1 by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry

Methylobacterium extorquens AM1 is a facultative methylotroph, which is a potential candidate to be used in commercial processes to convert simple one-carbon compounds to a variety of multicarbon chemicals and products. To better understand C(1) metabolism in M. extorquens AM1 at the systems level, metabolite profiling tools were developed and applied in this bacterium. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC x GC-TOFMS) was used to obtain metabolite profiles of M. extorquens AM1 (primarily organic acids) and to identify the metabolite differences between cells grown on methanol (C(1) substrate) and succinate (multicarbon substrate). In this study, a list of compounds that included amino acids and major intermediates of central C(1) and multicarbon metabolism were studied as target metabolites. For these, calibration curves were obtained for absolute quantification by spiking different amounts of standard mixtures to cell cultures. Parallel factor analysis (PARAFAC) was used for accurate peak quantification. Unknown chemical differences between cells grown on methanol and succinate were identified by applying Fisher ratio analysis at a selective mass channel (m/z 147). Thirty-six compounds were discovered to be statistically differentially expressed between C(1) and multicarbon metabolism. Among these, 13 were identified by matching to library mass spectra, and the rest were novel compounds that were not included in libraries. These differentially expressed compounds have provided clues to new pathways that are specifically linked to C(1) metabolism.

The social network of a cell: recent advances in interactome mapping

Proteins very rarely act in isolation. Biomolecular interactions are central to all biological functions. In human, for example, interference with biomolecular networks often lead to disease. Protein-protein and protein-metabolite interactions have traditionally been studied one by one. Recently, significant progresses have been made in adapting suitable tools for the global analysis of biomolecular interactions. Here we review this suite of powerful technologies that enable an exponentially growing number of large-scale interaction datasets. These new technologies have already contributed to a more comprehensive cartography of several pathways relevant to human pathologies, offering a broader choice for therapeutic targets. Genome-wide scale analyses in model organisms reveal general organizational principles of eukaryotic proteomes. We also review the biochemical approaches that have been used in the past on a smaller scale for the quantification of the binding constant and the thermodynamics parameters governing biomolecular interaction. The adaptation of these technologies to the large-scale measurement of biomolecular interactions in (semi-)quantitative terms represents an important challenge.

Systems approaches to identifying gene regulatory networks in plants

Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.

Similarity classifier in diagnosis of bladder cancer

In this article a similarity classifier's performance is studied in the diagnosis of bladder cancer. It is demonstrated that good classification results in diagnosis of bladder cancer are already achieved with a very small amount of data in the training set with the use of similarity classifier. When a new disease is initially discovered, the amount of samples are always quite limited (due to a fact that amount of patients is few), and this situation makes clinical work very difficult. A similarity classifier is a fast and accurate tool for medical diagnosis and it is capable of accurate performance already with a limited amount of data. This is quite important because there is a very limited amount of techniques available even to deal with such small sample sizes and especially in the diagnosis of cancer, high diagnosis accuracy is most important. In this study similarity classifier is used in diagnosis of bladder cancer. A good accuracy (of 100%) is already achieved with very small amount of samples in training the classifier. Here only four samples (two persons with bladder cancer and two persons without bladder cancer) were needed to train classifier managing the diagnosis of bladder cancer with 100% accuracy.

Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

Modelling reaction kinetics inside cells

In the past decade, advances in molecular biology such as the development of non-invasive single molecule imaging techniques have given us a window into the intricate biochemical activities that occur inside cells. In this chapter we review four distinct theoretical and simulation frameworks: (i) non-spatial and deterministic, (ii) spatial and deterministic, (iii) non-spatial and stochastic and (iv) spatial and stochastic. Each framework can be suited to modelling and interpreting intracellular reaction kinetics. By estimating the fundamental length scales, one can roughly determine which models are best suited for the particular reaction pathway under study. We discuss differences in prediction between the four modelling methodologies. In particular we show that taking into account noise and space does not simply add quantitative predictive accuracy but may also lead to qualitatively different physiological predictions, unaccounted for by classical deterministic models.

Quantitative imaging for discovery and assembly of the metabo-regulome

Little is known about regulatory networks that control metabolic flux in plant cells. Detailed understanding of regulation is crucial for synthetic biology. The difficulty of measuring metabolites with cellular and subcellular precision is a major roadblock. New tools have been developed for monitoring extracellular, cytosolic, organellar and vacuolar ion and metabolite concentrations with a time resolution of milliseconds to hours. Genetically encoded sensors allow quantitative measurement of steady-state concentrations of ions, signaling molecules and metabolites and their respective changes over time. Fluorescence resonance energy transfer (FRET) sensors exploit conformational changes in polypeptides as a proxy for analyte concentrations. Subtle effects of analyte binding on the conformation of the recognition element are translated into a FRET change between two fused green fluorescent protein (GFP) variants, enabling simple monitoring of analyte concentrations using fluorimetry or fluorescence microscopy. Fluorimetry provides information averaged over cell populations, while microscopy detects differences between cells or populations of cells. The genetically encoded sensors can be targeted to subcellular compartments or the cell surface. Confocal microscopy ultimately permits observation of gradients or local differences within a compartment. The FRET assays can be adapted to high-throughput analysis to screen mutant populations in order to systematically identify signaling networks that control individual steps in metabolic flux.

Modeling and analyzing gene co-expression in hepatocellular carcinoma using actor-semiotic networks and centrality signatures

Primary hepatocellular carcinoma (HCC) is currently the fifth most common malignancy and the third most common cause of cancer mortality worldwide. Because of its high prevalence in developing nations, there have been numerous efforts made in the molecular characterization of primary HCC. However, a better understanding into the pathology of HCC required software-assisted network modeling and analysis. In this paper, the author presented his first attempt in exploring the biological implication of gene co-expression in HCC using actor-semiotic network modeling and analysis. The network was first constructed by integrating inter-actor relationships, e.g. gene co-expression, microRNA-to-gene, and protein interactions, with semiotic relationships, e.g. gene-to-Gene Ontology Process. Topological features that are highly discriminative of the HCC phenotype were identified by visual inspection. Finally, the author devised a graph signature-based analysis method to supplement the network exploration.

Predicting gene essentiality using genome-scale in silico models

Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.

Profiling of Escherichia coli Chromosome database

The Profiling of Escherichia coli Chromosome (PEC) database (http://www.shigen.nig.ac.jp/ecoli/pec/) is designed to allow E. coli researchers to efficiently access information from functional genomics studies. The database contains two principal types of data: gene essentiality and a large collection of E. coli genetic research resources. The essentiality data are based on data compilation from published single-gene essentiality studies and on cell growth studies of large-deletion mutants. Using the circular and linear viewers for both whole genomes and the minimal genome, users can not only gain an overview of the genome structure but also retrieve information on contigs, gene products, mutants, deletions, and so forth. In particular, genome-wide exhaustive mutants are an essential resource for studying E. coli gene functions. Although the genomic database was constructed independently from the genetic resources database, users may seamlessly access both types of data. In addition to these data, the PEC database also provides a summary of homologous genes of other bacterial genomes and of protein structure information, with a comprehensive interface. The PEC is thus a convenient and useful platform for contemporary E. coli researchers.

Systems biology: the case for a systems science approach to diabetes

The unprecedented accumulation of biological data in recent decades has underscored the need to organize and integrate the massive collection of information. In addition, there is rising agreement among biologists that a complete understanding of a single cell will not lead directly to a complete understanding of a system of cells. The success of a systems science approach in engineering and physics may be of great value in the evolution of biological science. This article reviews some examples that suggest the importance of a systems biology approach and, in addition, advance one specific systems science principle, the conservation of uncertainty, which may give insight into the emergent behavior of numerous biological and physiological phenomena.

Sensitivity Measures for Oscillating Systems: Application to Mammalian Circadian Gene Network

Vital physiological behaviors exhibited daily by bacteria, plants, and animals are governed by endogenous oscillators called circadian clocks. The most salient feature of the circadian clock is its ability to change its internal time (phase) to match that of the external environment. The circadian clock, like many oscillators in nature, is regulated at the cellular level by a complex network of interacting components. As a complementary approach to traditional biological investigation, we utilize mathematical models and systems theoretic tools to elucidate these mechanisms. The models are systems of ordinary differential equations exhibiting stable limit cycle behavior. To study the robustness of circadian phase behavior, we use sensitivity analysis. As the standard set of sensitivity tools are not suitable for the study of phase behavior, we introduce a novel tool, the parametric impulse phase response curve (pIPRC).

Quantitative imaging for discovery and assembly of the metabo-regulome

Little is known about regulatory networks that control metabolic flux in plant cells. Detailed understanding of regulation is crucial for synthetic biology. The difficulty of measuring metabolites with cellular and subcellular precision is a major roadblock. New tools have been developed for monitoring extracellular, cytosolic, organellar and vacuolar ion and metabolite concentrations with a time resolution of milliseconds to hours. Genetically encoded sensors allow quantitative measurement of steady-state concentrations of ions, signaling molecules and metabolites and their respective changes over time. Fluorescence resonance energy transfer (FRET) sensors exploit conformational changes in polypeptides as a proxy for analyte concentrations. Subtle effects of analyte binding on the conformation of the recognition element are translated into a FRET change between two fused green fluorescent protein (GFP) variants, enabling simple monitoring of analyte concentrations using fluorimetry or fluorescence microscopy. Fluorimetry provides information averaged over cell populations, while microscopy detects differences between cells or populations of cells. The genetically encoded sensors can be targeted to subcellular compartments or the cell surface. Confocal microscopy ultimately permits observation of gradients or local differences within a compartment. The FRET assays can be adapted to high-throughput analysis to screen mutant populations in order to systematically identify signaling networks that control individual steps in metabolic flux.

Bioinformatic tools for inferring functional information from plant microarray data II: Analysis beyond single gene

While it is possible to interpret microarray experiments a single gene at a time, most studies generate long lists of differentially expressed genes whose interpretation requires the integration of prior biological knowledge. This prior knowledge is stored in various public and private databases and covers several aspects of gene function and biological information. In this review, we will describe the tools and places where to find prior accurate biological information and how to process and incorporate them to interpret microarray data analyses. Here, we highlight selected tools and resources for gene class level ontology analysis (Section 2), gene coexpression analysis (Section 3), gene network analysis (Section 4), biological pathway analysis (Section 5), analysis of transcriptional regulation (Section 6), and omics data integration (Section 7). The overall goal of this review is to provide researchers with tools and information to facilitate the interpretation of microarray data.

Heritable stochastic switching revealed by single-cell genealogy

The partitioning and subsequent inheritance of cellular factors like proteins and RNAs is a ubiquitous feature of cell division. However, direct quantitative measures of how such nongenetic inheritance affects subsequent changes in gene expression have been lacking. We tracked families of the yeast Saccharomyces cerevisiae as they switch between two semi-stable epigenetic states. We found that long after two cells have divided, they continued to switch in a synchronized manner, whereas individual cells have exponentially distributed switching times. By comparing these results to a Poisson process, we show that the time evolution of an epigenetic state depends initially on inherited factors, with stochastic processes requiring several generations to decorrelate closely related cells. Finally, a simple stochastic model demonstrates that a single fluctuating regulatory protein that is synthesized in large bursts can explain the bulk of our results.

When cells divide, not only DNA but an entire pattern of gene expression can be passed from mother to daughter cell. Once cell division is complete, random processes cause this pattern to change, with closely related cells growing less similar over time. We measured inheritance of a dynamic gene-expression state in single yeast cells. We used an engineered network where individual cells switch between two semi-stable states (ON and OFF), even in a constant environment. Several generations after cells have physically separated, many pairs of closely related cells switch in near synchrony. We quantified this effect by measuring how likely a mother cell is to have switched given that the daughter cell has already switched. This yields a conditional probability distribution that is very different from the exponential one found in the entire population of switching cells. We measured the extent to which this correlation between switching cells persists by comparing our results with a model Poisson process. Together, these findings demonstrate the inheritance of a dynamic gene expression state whose post-division changes include both random factors arising from noise as well as correlated factors that originate in two related cells' shared history. Finally, we constructed a model that demonstrates that our major findings can be explained by burst-like fluctuations in the levels of a single regulatory protein.

When cells divide, each daughter cell inherits a share of the contents of the mother. If the contents include a regulatory system with a feedback loop, sister cells switch states in synchrony.

Study of thiazole orange in aptamer-based dye-displacement assays

We screened a series of RNA and DNA aptamers for their ability to serve in the dye displacement assays in which analytes compete with TO dye. We conclude that, while the performance of the TO dye displacement approach is not always predictable, it is still a simple and sensitive assay to detect binding between RNA aptamers and small molecules. In particular, we describe efficient assays for tobramycin and theophylline, with up to 90% displacement of TO observed, and we describe the first aptameric assay for cAMP.

The monofunctional glycosyltransferase of Escherichia coli localizes to the cell division site and interacts with penicillin-binding protein 3, FtsW, and FtsN

The monofunctional peptidoglycan glycosyltransferase (MtgA) catalyzes glycan chain elongation of the bacterial cell wall. Here we show that MtgA localizes at the division site of Escherichia coli cells that are deficient in PBP1b and produce a thermosensitive PBP1a and is able to interact with three constituents of the divisome, PBP3, FtsW, and FtsN, suggesting that MtgA may play a role in peptidoglycan assembly during the cell cycle in collaboration with other proteins.

Circadian rhythms: rho-related signals in time-specific light perception

A recent study shows that a small GTPase, LIF1, helps to coordinate the plant circadian clock with the daily light-dark cycle.