Transcriptional regulation of protein complexes in yeast

PCfun: a hybrid computational framework for systematic characterization of protein complex function

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

Coexpressed subunits of dual genetic origin define a conserved supercomplex mediating essential protein import into chloroplasts

In photosynthetic eukaryotes, thousands of proteins are translated in the cytosol and imported into the chloroplast through the concerted action of two translocons-termed TOC and TIC-located in the outer and inner membranes of the chloroplast envelope, respectively. The degree to which the molecular composition of the TOC and TIC complexes is conserved over phylogenetic distances has remained controversial. Here, we combine transcriptomic, biochemical, and genetic tools in the green alga Chlamydomonas (Chlamydomonas reinhardtii) to demonstrate that, despite a lack of evident sequence conservation for some of its components, the algal TIC complex mirrors the molecular composition of a TIC complex from Arabidopsis thaliana. The Chlamydomonas TIC complex contains three nuclear-encoded subunits, Tic20, Tic56, and Tic100, and one chloroplast-encoded subunit, Tic214, and interacts with the TOC complex, as well as with several uncharacterized proteins to form a stable supercomplex (TIC-TOC), indicating that protein import across both envelope membranes is mechanistically coupled. Expression of the nuclear and chloroplast genes encoding both known and uncharacterized TIC-TOC components is highly coordinated, suggesting that a mechanism for regulating its biogenesis across compartmental boundaries must exist. Conditional repression of Tic214, the only chloroplast-encoded subunit in the TIC-TOC complex, impairs the import of chloroplast proteins with essential roles in chloroplast ribosome biogenesis and protein folding and induces a pleiotropic stress response, including several proteins involved in the chloroplast unfolded protein response. These findings underscore the functional importance of the TIC-TOC supercomplex in maintaining chloroplast proteostasis.

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.

Exploiting multi-layered information to iteratively predict protein functions

BACKGROUND

Similarity based computational methods are a useful tool for predicting protein functions from protein-protein interaction (PPI) datasets. Although various similarity-based prediction algorithms have been proposed, unsatisfactory prediction results have occurred on many occasions. The purpose of this type of algorithm is to predict functions of an unannotated protein from the functions of those proteins that are similar to the unannotated protein. Therefore, the prediction quality largely depends on how to select a set of proper proteins (i.e., a prediction domain) from which the functions of an unannotated protein are predicted, and how to measure the similarity between proteins. Another issue with existing algorithms is they only believe the function prediction is a one-off procedure, ignoring the fact that interactions amongst proteins are mutual and dynamic in terms of similarity when predicting functions. How to resolve these major issues to increase prediction quality remains a challenge in computational biology.

RESULTS

In this paper, we propose an innovative approach to predict protein functions of unannotated proteins iteratively from a PPI dataset. The iterative approach takes into account the mutual and dynamic features of protein interactions when predicting functions, and addresses the issues of protein similarity measurement and prediction domain selection by introducing into the prediction algorithm a new semantic protein similarity and a method of selecting the multi-layer prediction domain. The new protein similarity is based on the multi-layered information carried by protein functions. The evaluations conducted on real protein interaction datasets demonstrated that the proposed iterative function prediction method outperformed other similar or non-iterative methods, and provided better prediction results.

CONCLUSIONS

The new protein similarity derived from multi-layered information of protein functions more reasonably reflects the intrinsic relationships among proteins, and significant improvement to the prediction quality can occur through incorporation of mutual and dynamic features of protein interactions into the prediction algorithm.

An integrated view of molecular coevolution in protein-protein interactions

Protein-protein interactions effectively mediate molecular function. They are the result of specific interactions between protein interfaces and are maintained by the action of evolutionary pressure on the regions of the interacting proteins that contribute to binding. For the most part, selection restricts amino acid replacements, accounting for the conservation of binding interfaces. However, in some cases, change in one protein will be mitigated by compensatory change in its binding partner, maintaining function in the face of evolutionary change. There have been several attempts to use correlations in sequence evolution to predict interactions of proteins. Most commonly, these approaches use the entire sequence to identify correlations and so infer probable binding. However, other factors such as shared evolutionary history and similarities in the rates of evolution confound these whole-sequence-based approaches. Here, we discuss recent work on this topic and argue that both site-specific coevolutionary change and whole-sequence evolution contribute to evolutionary signals in sets of interacting proteins. We discuss the relative effects of both types of selection and how they might be identified. This permits an integrated view of protein-protein interactions, their evolution, and coevolution.

Composite functional module inference: detecting cooperation between transcriptional regulation and protein interaction by mantel test

Background

Functional modules are basic units of cell function, and exploring them is important for understanding the organization, regulation and execution of cell processes. Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies. Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes.

Results

We aimed to detect composite functional modules containing co-transcriptional regulation interaction, and protein-protein interaction, in our pre-constructed integrated network of Saccharomyces cerevisiae. We computationally extracted 15 composite functional modules, and found structural consistency between co-transcriptional regulation interaction sub-network and protein-protein interaction sub-network that was well correlated with their functional hierarchy. This type of composite functional modules was compact in structure, and was found to participate in essential cell processes such as oxidative phosphorylation and RNA splicing.

Conclusions

The structure of composite functional modules containing co-transcriptional regulation interaction, and protein-protein interaction reflected the cooperation of transcriptional regulation and protein function implementation, and was indicative of their important roles in essential cell functions. In addition, their structural and functional characteristics were closely related, and suggesting the complexity of the cell regulatory system.

Using RSAT oligo-analysis and dyad-analysis tools to discover regulatory signals in nucleic sequences

This protocol explains how to discover functional signals in genomic sequences by detecting over- or under-represented oligonucleotides (words) or spaced pairs thereof (dyads) with the Regulatory Sequence Analysis Tools (http://rsat.ulb.ac.be/rsat/). Two typical applications are presented: (i) predicting transcription factor-binding motifs in promoters of coregulated genes and (ii) discovering phylogenetic footprints in promoters of orthologous genes. The steps of this protocol include purging genomic sequences to discard redundant fragments, discovering over-represented patterns and assembling them to obtain degenerate motifs, scanning sequences and drawing feature maps. The main strength of the method is its statistical ground: the binomial significance provides an efficient control on the rate of false positives. In contrast with optimization-based pattern discovery algorithms, the method supports the detection of under- as well as over-represented motifs. Computation times vary from seconds (gene clusters) to minutes (whole genomes). The execution of the whole protocol should take approximately 1 h.

Origin of co-expression patterns in E. coli and S. cerevisiae emerging from reverse engineering algorithms

Background

The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of “physical” networks of interactions among genes or gene products.

Results

This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E.coli and S.cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor–binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E.coli.

Conclusion

The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E.coli to S.cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.

Embedding mRNA stability in correlation analysis of time-series gene expression data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R² that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R² for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R² value among genes coding for a transient complex.

Microarrays provide snapshots of the transcriptional state of the cell at some point in time. Multiple snapshots can be taken sequentially in time, thus providing insight into the dynamics of change. Since genome-wide expression data report on the abundance of mRNA, not on the underlying activity of genes, we developed a novel method to relate the expression pattern of genes, detected in a time-series experiment, using a similarity measure that incorporates mRNA decay and called lead-lag R². We used the lead-lag R² similarity measure to predict the presence of common transcription factors between gene pairs using an integrated dataset consisting of 13 yeast cell-cycles. The method was benchmarked against six well-established similarity measures and obtained the best true positive rate result, around 95%. We believe that the lead-lag analysis can be successfully used also to predict the presence of a common mechanism able to modulate the degradation rate of specific transcripts. Finally, we envisage the possibility to extend our analysis to different experimental conditions and organisms, thus providing a simple off-the-shelf computational tool to support the understanding of the transcriptional and post-transcriptional regulation layer and its role in many diseases, such as cancer.

Integrated approaches to uncovering transcription regulatory networks in mammalian cells

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.

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.

Transcriptional regulation of protein complexes within and across species

Yeast two-hybrid and coimmunoprecipitation experiments have defined large-scale protein-protein interaction networks for many model species. Separately, systematic chromatin immunoprecipitation experiments have enabled the assembly of large networks of transcriptional regulatory interactions. To investigate the functional interplay between these two interaction types, we combined both within a probabilistic framework that models the cell as a network of transcription factors regulating protein complexes. This framework identified 72 putative coregulated complexes in yeast and allowed the prediction of 120 previously uncharacterized transcriptional interactions. Several predictions were tested by new microarray profiles, yielding a confirmation rate (58%) comparable with that of direct immunoprecipitation experiments. Furthermore, we extended our framework to a cross-species setting, identifying 24 coregulated complexes that were conserved between yeast and fly. Analyses of these conserved complexes revealed different conservation levels of their regulators and provided suggestive evidence that protein-protein interaction networks may evolve more slowly than transcriptional interaction networks. Our results demonstrate how multiple molecular interaction types can be integrated toward a global wiring diagram of the cell, and they provide insights into the evolutionary dynamics of protein complex regulation.

Evaluation of clustering algorithms for protein-protein interaction networks

Background

Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions.

The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE).

Results

A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions.

Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes.

We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values.

We also evaluated their robustness to alterations of the test graph.

We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes.

Conclusion

This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions.

The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.

Characterization and prediction of protein-protein interactions within and between complexes

Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks

MOTIVATION

Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community.

RESULTS

We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules.

AVAILABILITY

The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Modularity of the transcriptional response of protein complexes in yeast

A comprehensive study is performed on the condition-dependent expression of genes coding for the components of hand curated multi-protein complexes of the yeast Saccharomyces cerevisiae, in order to identify coherent transcriptional modules within these complexes. Such modules are defined as groups of genes within complexes whose expression profiles under a common set of experimental conditions allow us to discriminate them from random sets of genes. Our analysis reveals that complexes such as the cytoplasmic ribosome, the proteasome and the respiration chain complexes previously characterized as "stable" or "permanent" represent transcriptional modules that are coherently up or down-regulated in many different conditions. Overall however, some level of coherent expression is detected only in 71 out of the total of 113 complexes with at least five different protein components that could be reliably analyzed. Of these, 26 behave as coherently expressed transcriptional modules encompassing all the components of the complex. In another 15, at least half of the components make up such modules and in ten, few or no modules are detected. In an additional 20 complexes coherent expression is detected, but in too few conditions to enable reliable module detection. Interestingly, the transcriptional modules, when detected, often correspond to one or more known sub-complexes with specific functions. Furthermore, detected modules are generally consistent with transcriptional modules identified on the basis of predicted cis-regulatory sequence motifs. Also, groups of genes shared between complexes that carry out related functions tend to be part of overlapping transcriptional modules identified in these complexes. Together these findings suggest that transcriptional modules may represent basic functional and evolutionary building blocs of protein complexes.

LOOKING BACK

My encounter with Jacques Monod has shaped my scientific career. After a short incursion in the biochemistry of strict anaerobes, and after elucidating the biosynthetic pathway leading from aspartate to threonine in Escherichia coli, I joined his laboratory. With him and Howard Rickenberg, I discovered the stereospecific permeability of galactosides and amino acids (permeases). After this intermezzo, I returned to the analysis of biosynthetic pathways and of their regulation by allosteric feedback inhibition and repression in E. coli. Among others, my studies led to the discovery of the tryptophan and methionine repressors, to the incorporation of amino acid analogues in proteins, including selenomethionine (which much later led to progress in protein crystallography), to the definition of isofunctional and multifunctional enzymes, and to the elucidation of the primary structure of most of the enzymes leading to threonine and methionine.

Motifs, themes and thematic maps of an integrated Saccharomyces cerevisiae interaction network

BACKGROUND

Large-scale studies have revealed networks of various biological interaction types, such as protein-protein interaction, genetic interaction, transcriptional regulation, sequence homology, and expression correlation. Recurring patterns of interconnection, or 'network motifs', have revealed biological insights for networks containing either one or two types of interaction.

RESULTS

To study more complex relationships involving multiple biological interaction types, we assembled an integrated Saccharomyces cerevisiae network in which nodes represent genes (or their protein products) and differently colored links represent the aforementioned five biological interaction types. We examined three- and four-node interconnection patterns containing multiple interaction types and found many enriched multi-color network motifs. Furthermore, we showed that most of the motifs form 'network themes' -- classes of higher-order recurring interconnection patterns that encompass multiple occurrences of network motifs. Network themes can be tied to specific biological phenomena and may represent more fundamental network design principles. Examples of network themes include a pair of protein complexes with many inter-complex genetic interactions -- the 'compensatory complexes' theme. Thematic maps -- networks rendered in terms of such themes -- can simplify an otherwise confusing tangle of biological relationships. We show this by mapping the S. cerevisiae network in terms of two specific network themes.

CONCLUSION

Significantly enriched motifs in an integrated S. cerevisiae interaction network are often signatures of network themes, higher-order network structures that correspond to biological phenomena. Representing networks in terms of network themes provides a useful simplification of complex biological relationships.

Predictive screening for regulators of conserved functional gene modules (gene batteries) in mammals

Background

The expression of gene batteries, genomic units of functionally linked genes which are activated by similar sets of cis- and trans-acting regulators, has been proposed as a major determinant of cell specialization in metazoans. We developed a predictive procedure to screen the mouse and human genomes and transcriptomes for cases of gene-battery-like regulation.

Results

In a screen that covered ~40 per cent of all annotated protein-coding genes, we identified 21 co-expressed gene clusters with statistically supported sharing of cis-regulatory sequence elements. 66 predicted cases of over-represented transcription factor binding motifs were validated against the literature and fell into three categories: (i) previously described cases of gene battery-like regulation, (ii) previously unreported cases of gene battery-like regulation with some support in a limited number of genes, and (iii) predicted cases that currently lack experimental support. The novel predictions include for example Sox 17 and RFX transcription factor binding sites that were detected in ~10% of all testis specific genes, and HNF-1 and 4 binding sites that were detected in ~30% of all kidney specific genes respectively. The results are publicly available at .

Conclusion

21 co-expressed gene clusters were enriched for a total of 66 shared cis-regulatory sequence elements. A majority of these predictions represent novel cases of potential co-regulation of functionally coupled proteins. Critical technical parameters were evaluated, and the results and the methods provide a valuable resource for future experimental design.

How biologically relevant are interaction-based modules in protein networks?

By applying a graph-based algorithm to yeast protein-interaction networks we have extracted modular structures and show that they can be validated using information from the phylogenetic conservation of the network components. We show that the module cores, the parts with the highest intramodular connectivity, are biologically relevant components of the networks. These constituents correlate only weakly with other levels of organization. We also discuss how such structures could be used for finding targets for antimicrobial drugs.