Systems-level analyses identify extensive coupling among gene expression machines

Localization and Functional Roles of Components of the Translation Apparatus in the Eukaryotic Cell Nucleus

Components of the translation apparatus, including ribosomal proteins, have been found in cell nuclei in various organisms. Components of the translation apparatus are involved in various nuclear processes, particularly those associated with genome integrity control and the nuclear stages of gene expression, such as transcription, mRNA processing, and mRNA export. Components of the translation apparatus control intranuclear trafficking; the nuclear import and export of RNA and proteins; and regulate the activity, stability, and functional recruitment of nuclear proteins. The nuclear translocation of these components is often involved in the cell response to stimulation and stress, in addition to playing critical roles in oncogenesis and viral infection. Many components of the translation apparatus are moonlighting proteins, involved in integral cell stress response and coupling of gene expression subprocesses. Thus, this phenomenon represents a significant interest for both basic and applied molecular biology. Here, we provide an overview of the current data regarding the molecular functions of translation factors and ribosomal proteins in the cell nucleus.

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms

BACKGROUND

In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary.

METHODS

Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs).

RESULTS

The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome.

CONCLUSION

In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

Many studies are aimed at identifying dense clusters/subgraphs from protein-protein interaction (PPI) networks for protein function prediction. However, the prediction performance based on the dense clusters is actually worse than a simple guilt-by-association method using neighbor counting ideas. This indicates that the local topological structures and properties of PPI networks are still open to new theoretical investigation and empirical exploration. We introduce a novel topological structure called k-partite cliques of protein interactions-a functionally coherent but not-necessarily dense subgraph topology in PPI networks-to study PPI networks. A k-partite protein clique is a maximal k-partite clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI's maximal k-partite cliques, we propose to transform PPI networks into induced K-partite graphs where edges exist only between the partites. Then, we present a maximal k-partite clique mining (MaCMik) algorithm to enumerate maximal k-partite cliques from K-partite graphs. Our MaCMik algorithm is then applied to a yeast PPI network. We observed interesting and unusually high functional coherence in k-partite protein cliques-the majority of the proteins in k-partite protein cliques, especially those in the same partites, share the same functions, although k-partite protein cliques are not restricted to be dense compared with dense subgraph patterns or (quasi-)cliques. The idea of k-partite protein cliques provides a novel approach of characterizing PPI networks, and so it will help function prediction for unknown proteins.

Semantically predicting protein functions based on protein functional connectivity

BACKGROUND

The current availability of public protein-protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.

RESULTS

An approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.

CONCLUSION

Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.

Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties

Background

With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.

Methodology/Principal Findings

Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.

Conclusions/Significance

The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.

Cross-talk in transcription, splicing and chromatin: who makes the first call?

The complex processes of mRNA transcription and splicing were traditionally studied in isolation. In vitro studies showed that splicing could occur independently of transcription and the perceived wisdom was that, to a large extent, it probably did. However, there is now abundant evidence for functional interactions between transcription and splicing, with important consequences for splicing regulation. In the present paper, we summarize the evidence that transcription affects splicing and vice versa, and the more recent indications of epigenetic effects on splicing, through chromatin modifications. We end by discussing the potential for a systems biology approach to obtain better insight into how these processes affect each other.

Molecular function prediction using neighborhood features

The recent advent of high-throughput methods has generated large amounts of gene interaction data. This has allowed the construction of genomewide networks. A significant number of genes in such networks remain uncharacterized and predicting the molecular function of these genes remains a major challenge. A number of existing techniques assume that genes with similar functions are topologically close in the network. Our hypothesis is that genes with similar functions observe similar annotation patterns in their neighborhood, regardless of the distance between them in the interaction network. We thus predict molecular functions of uncharacterized genes by comparing their functional neighborhoods to genes of known function. We propose a two-phase approach. First, we extract functional neighborhood features of a gene using Random Walks with Restarts. We then employ a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features. We perform leave-one-out validation experiments on two S. cerevisiae interaction networks and show significant improvements over previous techniques. Our technique provides a natural control of the trade-off between accuracy and coverage of prediction. We further propose and evaluate prediction in sparse genomes by exploiting features from well-annotated genomes.

Distinct patterns of expression and evolution of intronless and intron-containing mammalian genes

Comparison of expression levels and breadth and evolutionary rates of intronless and intron-containing mammalian genes shows that intronless genes are expressed at lower levels, tend to be tissue specific, and evolve significantly faster than spliced genes. By contrast, monomorphic spliced genes that are not subject to detectable alternative splicing and polymorphic alternatively spliced genes show similar statistically indistinguishable patterns of expression and evolution. Alternative splicing is most common in ancient genes, whereas intronless genes appear to have relatively recent origins. These results imply tight coupling between different stages of gene expression, in particular, transcription, splicing, and nucleocytosolic transport of transcripts, and suggest that formation of intronless genes is an important route of evolution of novel tissue-specific functions in animals.

Evidence that the nonsense-mediated mRNA decay pathway participates in X chromosome dosage compensation in mammals

Current models of X chromosome dosage compensation are usually framed by reference to how regulation in transcriptional level elevates the gene expression of the active X chromosome. This framework, however, might be oversimplified because regulation of gene expression can also act at the post-transcriptional level. Here, after a genome-wide survey, we find that autosomal genes are more likely subject to nonsense-mediated mRNA decay (NMD) than X-linked genes. Furthermore, we demonstrate that after NMD inhibition, balanced gene expression between X chromosome and autosomes is corrupted such that the global mean X/autosome gene expression ratio is decreased by 10-15%. Our results identify NMD as a post-transcription-level regulatory mechanism that contributes to the observed fine-tuning of X chromosome dosage compensation in mammals.

"Genes"

In order to describe a cell at molecular level, a notion of a “gene” is neither necessary nor helpful. It is sufficient to consider the molecules (i.e., chromosomes, transcripts, proteins) and their interactions to describe cellular processes. The downside of the resulting high resolution is that it becomes very tedious to address features on the organismal and phenotypic levels with a language based on molecular terms. Looking for the missing link between biological disciplines dealing with different levels of biological organization, we suggest to return to the original intent behind the term “gene”. To this end, we propose to investigate whether a useful notion of “gene” can be constructed based on an underlying notion of function, and whether this can serve as the necessary link and embed the various distinct gene concepts of biological (sub)disciplines in a coherent theoretical framework. In reply to the Genon Theory recently put forward by Klaus Scherrer and Jürgen Jost in this journal, we shall discuss a general approach to assess a gene definition that should then be tested for its expressiveness and potential cross-disciplinary relevance.

The influence of genetic variation on gene expression

The view that changes to the control of gene expression rather than alterations to protein sequence are central to the evolution of organisms has become something of a truism in molecular biology. In reality, the direct evidence for this is limited, and only recently have we had the ability to look more globally at how genetic variation influences gene expression, focusing upon inter-individual variation in gene expression and using microarrays to test for differences in mRNA levels. Here, we review the scope of these experimental analyses, what they are designed to tell us about genetic variation, and what are their limitations from both a technical and a conceptual viewpoint. We conclude that while we are starting to understand the impact of this class of genetic variation upon steady-state mRNA levels, we are still far from identifying the potential phenotypic and evolutionary outcomes.

A simple principle concerning the robustness of protein complex activity to changes in gene expression

Background

The functions of a eukaryotic cell are largely performed by multi-subunit protein complexes that act as molecular machines or information processing modules in cellular networks. An important problem in systems biology is to understand how, in general, these molecular machines respond to perturbations.

Results

In yeast, genes that inhibit growth when their expression is reduced are strongly enriched amongst the subunits of multi-subunit protein complexes. This applies to both the core and peripheral subunits of protein complexes, and the subunits of each complex normally have the same loss-of-function phenotypes. In contrast, genes that inhibit growth when their expression is increased are not enriched amongst the core or peripheral subunits of protein complexes, and the behaviour of one subunit of a complex is not predictive for the other subunits with respect to over-expression phenotypes.

Conclusion

We propose the principle that the overall activity of a protein complex is in general robust to an increase, but not to a decrease in the expression of its subunits. This means that whereas phenotypes resulting from a decrease in gene expression can be predicted because they cluster on networks of protein complexes, over-expression phenotypes cannot be predicted in this way. We discuss the implications of these findings for understanding how cells are regulated, how they evolve, and how genetic perturbations connect to disease in humans.

Scale relativity theory and integrative systems biology: 1. Founding principles and scale laws

In these two companion papers, we provide an overview and a brief history of the multiple roots, current developments and recent advances of integrative systems biology and identify multiscale integration as its grand challenge. Then we introduce the fundamental principles and the successive steps that have been followed in the construction of the scale relativity theory, and discuss how scale laws of increasing complexity can be used to model and understand the behaviour of complex biological systems. In scale relativity theory, the geometry of space is considered to be continuous but non-differentiable, therefore fractal (i.e., explicitly scale-dependent). One writes the equations of motion in such a space as geodesics equations, under the constraint of the principle of relativity of all scales in nature. To this purpose, covariant derivatives are constructed that implement the various effects of the non-differentiable and fractal geometry. In this first review paper, the scale laws that describe the new dependence on resolutions of physical quantities are obtained as solutions of differential equations acting in the scale space. This leads to several possible levels of description for these laws, from the simplest scale invariant laws to generalized laws with variable fractal dimensions. Initial applications of these laws to the study of species evolution, embryogenesis and cell confinement are discussed.

Conceptual modeling in systems biology fosters empirical findings: the mRNA lifecycle

One of the main obstacles to understanding complex biological systems is the extent and rapid evolution of information, way beyond the capacity individuals to manage and comprehend. Current modeling approaches and tools lack adequate capacity to model concurrently structure and behavior of biological systems. Here we propose Object-Process Methodology (OPM), a holistic conceptual modeling paradigm, as a means to model both diagrammatically and textually biological systems formally and intuitively at any desired number of levels of detail. OPM combines objects, e.g., proteins, and processes, e.g., transcription, in a way that is simple and easily comprehensible to researchers and scholars. As a case in point, we modeled the yeast mRNA lifecycle. The mRNA lifecycle involves mRNA synthesis in the nucleus, mRNA transport to the cytoplasm, and its subsequent translation and degradation therein. Recent studies have identified specific cytoplasmic foci, termed processing bodies that contain large complexes of mRNAs and decay factors. Our OPM model of this cellular subsystem, presented here, led to the discovery of a new constituent of these complexes, the translation termination factor eRF3. Association of eRF3 with processing bodies is observed after a long-term starvation period. We suggest that OPM can eventually serve as a comprehensive evolvable model of the entire living cell system. The model would serve as a research and communication platform, highlighting unknown and uncertain aspects that can be addressed empirically and updated consequently while maintaining consistency.

A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality

BACKGROUND

Identifying all protein complexes in an organism is a major goal of systems biology. In the past 18 months, the results of two genome-scale tandem affinity purification-mass spectrometry (TAP-MS) assays in yeast have been published, along with corresponding complex maps. For most complexes, the published data sets were surprisingly uncorrelated. It is therefore useful to consider the raw data from each study and generate an accurate complex map from a high-confidence data set that integrates the results of these and earlier assays.

RESULTS

Using an unsupervised probabilistic scoring scheme, we assigned a confidence score to each interaction in the matrix-model interpretation of the large-scale yeast mass-spectrometry data sets. The scoring metric proved more accurate than the filtering schemes used in the original data sets. We then took a high-confidence subset of these interactions and derived a set of complexes using MCL. The complexes show high correlation with existing annotations. Hierarchical organization of some protein complexes is evident from inter-complex interactions.

CONCLUSION

We demonstrate that our scoring method can generate an integrated high-confidence subset of observed matrix-model interactions, which we subsequently used to derive an accurate map of yeast complexes. Our results indicate that essentiality is a product of the protein complex rather than the individual protein, and that we have achieved near saturation of the yeast high-abundance, rich-media-expressed "complex-ome."

Image-based multivariate profiling of drug responses from single cells

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.

Network-based prediction of protein function

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

How complete are current yeast and human protein-interaction networks?

How can protein-interaction networks can be made more complete?

We estimate the full yeast protein-protein interaction network to contain 37,800-75,500 interactions and the human network 154,000-369,000, but owing to a high false-positive rate, current maps are roughly only 50% and 10% complete, respectively. Paradoxically, releasing raw, unfiltered assay data might help separate true from false interactions.

Phosphorylation and functions of the RNA polymerase II CTD

The C-terminal repeat domain (CTD), an unusual extension appended to the C terminus of the largest subunit of RNA polymerase II, serves as a flexible binding scaffold for numerous nuclear factors; which factors bind is determined by the phosphorylation patterns on the CTD repeats. Changes in phosphorylation patterns, as polymerase transcribes a gene, are thought to orchestrate the association of different sets of factors with the transcriptase and strongly influence functional organization of the nucleus. In this review we appraise what is known, and what is not known, about patterns of phosphorylation on the CTD of RNA polymerases II at the beginning, the middle, and the end of genes; the proposal that doubly phosphorylated repeats are present on elongating polymerase is explored. We discuss briefly proteins known to associate with the phosphorylated CTD at the beginning and ends of genes; we explore in more detail proteins that are recruited to the body of genes, the diversity of their functions, and the potential consequences of tethering these functions to elongating RNA polymerase II. We also discuss accumulating structural information on phosphoCTD-binding proteins and how it illustrates the variety of binding domains and interaction modes, emphasizing the structural flexibility of the CTD. We end with a number of open questions that highlight the extent of what remains to be learned about the phosphorylation and functions of the CTD.

CellCircuits: a database of protein network models

CellCircuits () is an open-access database of molecular network models, designed to bridge the gap between databases of individual pairwise molecular interactions and databases of validated pathways. CellCircuits captures the output from an increasing number of approaches that screen molecular interaction networks to identify functional subnetworks, based on their correspondence with expression or phenotypic data, their internal structure or their conservation across species. This initial release catalogs 2019 computationally derived models drawn from 11 journal articles and spanning five organisms (yeast, worm, fly, Plasmodium falciparum and human). Models are available either as images or in machine-readable formats and can be queried by the names of proteins they contain or by their enriched biological functions. We envision CellCircuits as a clearinghouse in which theorists may distribute or revise models in need of validation and experimentalists may search for models or specific hypotheses relevant to their interests. We demonstrate how such a repository of network models is a novel systems biology resource by performing several meta-analyses not currently possible with existing databases.

Uneven size distribution of mammalian genes in the number of tissues expressed and in the number of co-expressed genes

Tissue specificity, the traditional predictor of gene function, has recently been used to interpret the selective pressure associated with gene architecture. In this work, we examine gene structures and their relation to the number of tissues expressed and to the number of co-expressed genes, using a recent atlas of microarray-based mouse gene expression in 55 normal tissues. We define tissue specificity and expression-pattern specificity according to the number of tissues expressed and the number of co-expressed genes, respectively. We find that, consistent with previous findings, tissue non-specific (housekeeping) genes are short in all gene regions (coding regions, intron, 5' and 3' untranslated regions). However, in contrast to previous suggestion that tissue-specific genes are long, the genes that are the most tissue-specific (expressed only in one tissue) are also short. We further show that both expression-pattern-specific and non-specific genes are long in coding and non-coding regions. The origins for short tissue-specific genes and long expression-pattern-specific genes are not clear. Genes with highly non-specific expression patterns (i.e. genes with a large number of co-expressed genes) are composed of genes that spread all tissues but are overwhelmingly enriched in the central nervous system (e.g. brain). Thus, the large sizes of these genes are possibly related to the functional complexity and/or accelerated evolutions of the central nervous system.