ProtPhylo: identification of protein-phenotype and protein-protein functional associations via phylogenetic profiling

PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes

Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data. IMPORTANCE This study developed PPNet, the first tool that can be used to infer large-scale bacterial functional association networks of a single species. PPNet includes a method for assigning the uniqueness of a bacterial strain using the average nucleotide identity and the average nucleotide coverage. PPNet collected 81 binary similarity and distance measures for phylogenetic profiling and then evaluated and divided them into four groups. PPNet can effectively capture gene networks that are functionally related to phenotype from publicly prokaryotic genomes, as well as provide valuable results for downstream analysis and experiment testing.

Identifying protein function and functional links based on large-scale co-occurrence patterns

Objective

The vast majority of known proteins have not been experimentally tested even at the level of measuring their expression, and the function of many proteins remains unknown. In order to decipher protein function and examine functional associations, we developed "Cliquely", a software tool based on the exploration of co-occurrence patterns.

Computational model

Using a set of more than 23 million proteins divided into 404,947 orthologous clusters, we explored the co-occurrence graph of 4,742 fully sequenced genomes from the three domains of life. Edge weights in this graph represent co-occurrence probabilities. We use the Bron–Kerbosch algorithm to detect maximal cliques in this graph, fully-connected subgraphs that represent meaningful biological networks from different functional categories.

Main results

We demonstrate that Cliquely can successfully identify known networks from various pathways, including nitrogen fixation, glycolysis, methanogenesis, mevalonate and ribosome proteins. Identifying the virulence-associated type III secretion system (T3SS) network, Cliquely also added 13 previously uncharacterized novel proteins to the T3SS network, demonstrating the strength of this approach. Cliquely is freely available and open source. Users can employ the tool to explore co-occurrence networks using a protein of interest and a customizable level of stringency, either for the entire dataset or for a one of the three domains—Archaea, Bacteria, or Eukarya.

GFICLEE: ultrafast tree-based phylogenetic profile method inferring gene function at the genomic-wide level

Background

Phylogenetic profiling is widely used to predict novel members of large protein complexes and biological pathways. Although methods combined with phylogenetic trees have significantly improved prediction accuracy, computational efficiency is still an issue that limits its genome-wise application.

Results

Here we introduce a new tree-based phylogenetic profiling algorithm named GFICLEE, which infers common single and continuous loss (SCL) events in the evolutionary patterns. We validated our algorithm with human pathways from three databases and compared the computational efficiency with current tree-based with 10 different scales genome dataset. Our algorithm has a better predictive performance with high computational efficiency.

Conclusions

The GFICLEE is a new method to infers genome-wide gene function. The accuracy and computational efficiency of GFICLEE make it possible to explore gene functions at the genome-wide level on a personal computer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08070-7.

CladeOScope: functional interactions through the prism of clade-wise co-evolution

Mapping co-evolved genes via phylogenetic profiling (PP) is a powerful approach to uncover functional interactions between genes and to associate them with pathways. Despite many successful endeavors, the understanding of co-evolutionary signals in eukaryotes remains partial. Our hypothesis is that 'Clades', branches of the tree of life (e.g. primates and mammals), encompass signals that cannot be detected by PP using all eukaryotes. As such, integrating information from different clades should reveal local co-evolution signals and improve function prediction. Accordingly, we analyzed 1028 genomes in 66 clades and demonstrated that the co-evolutionary signal was scattered across clades. We showed that functionally related genes are frequently co-evolved in only parts of the eukaryotic tree and that clades are complementary in detecting functional interactions within pathways. We examined the non-homologous end joining pathway and the UFM1 ubiquitin-like protein pathway and showed that both demonstrated distinguished co-evolution patterns in specific clades. Our research offers a different way to look at co-evolution across eukaryotes and points to the importance of modular co-evolution analysis. We developed the 'CladeOScope' PP method to integrate information from 16 clades across over 1000 eukaryotic genomes and is accessible via an easy to use web server at http://cladeoscope.cs.huji.ac.il.

Impaired complex I repair causes recessive Leber's hereditary optic neuropathy

Leber’s hereditary optic neuropathy (LHON) is the most frequent mitochondrial disease and was the first to be genetically defined by a point mutation in mitochondrial DNA (mtDNA). A molecular diagnosis is achieved in up to 95% of cases, the vast majority of which are accounted for by 3 mutations within mitochondrial complex I subunit–encoding genes in the mtDNA (mtLHON). Here, we resolve the enigma of LHON in the absence of pathogenic mtDNA mutations. We describe biallelic mutations in a nuclear encoded gene, DNAJC30, in 33 unsolved patients from 29 families and establish an autosomal recessive mode of inheritance for LHON (arLHON), which to date has been a prime example of a maternally inherited disorder. Remarkably, all hallmarks of mtLHON were recapitulated, including incomplete penetrance, male predominance, and significant idebenone responsivity. Moreover, by tracking protein turnover in patient-derived cell lines and a DNAJC30-knockout cellular model, we measured reduced turnover of specific complex I N-module subunits and a resultant impairment of complex I function. These results demonstrate that DNAJC30 is a chaperone protein needed for the efficient exchange of complex I subunits exposed to reactive oxygen species and integral to a mitochondrial complex I repair mechanism, thereby providing the first example to our knowledge of a disease resulting from impaired exchange of assembled respiratory chain subunits.

Molecular machinery regulating mitochondrial calcium levels: The nuts and bolts of mitochondrial calcium dynamics

Mitochondria play vital role in regulating the cellular energetics and metabolism. Further, it is a signaling hub for cell survival and apoptotic pathways. One of the key determinants that calibrate both cellular energetics and survival functions is mitochondrial calcium (Ca2+) dynamics. Mitochondrial Ca2+ regulates three Ca2+-sensitive dehydrogenase enzymes involved in tricarboxylic acid cycle (TCA) cycle thereby directly controlling ATP synthesis. On the other hand, excessive Ca2+ concentration within the mitochondrial matrix elevates mitochondrial reactive oxygen species (mROS) levels and causes mitochondrial membrane depolarization. This leads to opening of the mitochondrial permeability transition pore (mPTP) and release of cytochrome c into cytosol eventually triggering apoptosis. Therefore, it is critical for cell to maintain mitochondrial Ca2+ concentration. Since cells can neither synthesize nor metabolize Ca2+, it is the dynamic interplay of Ca2+ handling proteins involved in mitochondrial Ca2+ influx and efflux that take the center stage. In this review we would discuss the key molecular machinery regulating mitochondrial Ca2+ concentration. We would focus on the channel complex involved in bringing Ca2+ into mitochondrial matrix i.e. Mitochondrial Ca2+ Uniporter (MCU) and its key regulators Mitochondrial Ca2+ Uptake proteins (MICU1, 2 and 3), MCU regulatory subunit b (MCUb), Essential MCU Regulator (EMRE) and Mitochondrial Ca2+ Uniporter Regulator 1 (MCUR1). Further, we would deliberate on major mitochondrial Ca2+ efflux proteins i.e. Mitochondrial Na+/Ca2+/Li+ exchanger (NCLX) and Leucine zipper EF hand-containing transmembrane1 (Letm1). Moreover, we would highlight the physiological functions of these proteins and discuss their relevance in human pathophysiology. Finally, we would highlight key outstanding questions in the field.

Towards a systems-level understanding of mitochondrial biology

Human mitochondria are complex and highly dynamic biological systems, comprised of over a thousand parts and evolved to fully integrate into the specialized intracellular signaling networks and metabolic requirements of each cell and organ. Over the last two decades, several complementary, top-down computational and experimental approaches have been developed to identify, characterize and modulate the human mitochondrial system, demonstrating the power of integrating classical reductionist and discovery-driven analyses in order to de-orphanize hitherto unknown molecular components of mitochondrial machineries and pathways. To this goal, systematic, multiomics-based surveys of proteome composition, protein networks, and phenotype-to-pathway associations at the tissue, cell and organellar level have been largely exploited to predict the full complement of mitochondrial proteins and their functional interactions, therefore catalyzing data-driven hypotheses. Collectively, these multidisciplinary and integrative research approaches hold the potential to propel our understanding of mitochondrial biology and provide a systems-level framework to unraveling mitochondria-mediated and disease-spanning pathomechanisms.

Leveraging computational genomics to understand the molecular basis of metal homeostasis

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network

Aims

To develop a tool that can annotate subcellular localization of human proteins.

Background

With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research.

Objective

To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision.

Methods

In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset.

Results

The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit).

Conclusion

In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

Transmembrane BAX Inhibitor-1 Motif Containing Protein 5 (TMBIM5) Sustains Mitochondrial Structure, Shape, and Function by Impacting the Mitochondrial Protein Synthesis Machinery

The Transmembrane Bax Inhibitor-1 motif (TMBIM)-containing protein family is evolutionarily conserved and has been implicated in cell death susceptibility. The only member with a mitochondrial localization is TMBIM5 (also known as GHITM or MICS1), which affects cristae organization and associates with the Parkinson's disease-associated protein CHCHD2 in the inner mitochondrial membrane. We here used CRISPR-Cas9-mediated knockout HAP1 cells to shed further light on the function of TMBIM5 in physiology and cell death susceptibility. We found that compared to wild type, TMBIM5-knockout cells were smaller and had a slower proliferation rate. In these cells, mitochondria were more fragmented with a vacuolar cristae structure. In addition, the mitochondrial membrane potential was reduced and respiration was attenuated, leading to a reduced mitochondrial ATP generation. TMBIM5 did not associate with Mic10 and Mic60, which are proteins of the mitochondrial contact site and cristae organizing system (MICOS), nor did TMBIM5 knockout affect their expression levels. TMBIM5-knockout cells were more sensitive to apoptosis elicited by staurosporine and BH3 mimetic inhibitors of Bcl-2 and Bcl-XL. An unbiased proteomic comparison identified a dramatic downregulation of proteins involved in the mitochondrial protein synthesis machinery in TMBIM5-knockout cells. We conclude that TMBIM5 is important to maintain the mitochondrial structure and function possibly through the control of mitochondrial biogenesis.

MICU1 Confers Protection from MCU-Dependent Manganese Toxicity

The mitochondrial calcium uniporter is a highly selective ion channel composed of species- and tissue-specific subunits. However, the functional role of each component still remains unclear. Here, we establish a synthetic biology approach to dissect the interdependence between the pore-forming subunit MCU and the calcium-sensing regulator MICU1. Correlated evolutionary patterns across 247 eukaryotes indicate that their co-occurrence may have conferred a positive fitness advantage. We find that, while the heterologous reconstitution of MCU and EMRE in vivo in yeast enhances manganese stress, this is prevented by co-expression of MICU1. Accordingly, MICU1 deletion sensitizes human cells to manganese-dependent cell death by disinhibiting MCU-mediated manganese uptake. As a result, manganese overload increases oxidative stress, which can be effectively prevented by NAC treatment. Our study identifies a critical contribution of MICU1 to the uniporter selectivity, with important implications for patients with MICU1 deficiency, as well as neurological disorders arising upon chronic manganese exposure.

Electrophysiological Characterization of Calcium-Permeable Channels Using Planar Lipid Bilayer

Numerous researchers tried to identify the key players of calcium signaling in mitochondria using molecular and cell biology techniques for more than five decades. However, only an integrated approach involving also electrophysiological techniques has finally allowed to define the components of the protein complex responsible for the uptake of this ion into mitochondria.Here we describe the protocol used for the electrophysiological characterization of the mitochondrial calcium uniporter (MCU) complex: the following outline indicates step-by-step the setup of planar lipid bilayer experiments.

A Comprehensive Computational Analysis of Mycobacterium Genomes Pinpoints the Genes Co-occurring with YczE, a Membrane Protein Coding Gene Under the Putative Control of a MocR, and Predicts its Function

Bacterial proteins belonging to the YczE family are predicted to be membrane proteins of yet unknown function. In many bacterial species, the yczE gene coding for the YczE protein is divergently transcribed with respect to an adjacent transcriptional regulator of the MocR family. According to in silico predictions, proteins named YczR are supposed to regulate the expression of yczE genes. These regulators linked to the yczE genes are predicted to constitute a subfamily within the MocR family. To put forward hypotheses amenable to experimental testing about the possible function of the YczE proteins, a phylogenetic profile strategy was applied. This strategy consists in searching for those genes that, within a set of genomes, co-occur exclusively with a certain gene of interest. Co-occurrence can be suggestive of a functional link. A set of 30 mycobacterial complete proteomes were collected. Of these, only 16 contained YczE proteins. Interestingly, in all cases each yczE gene was divergently transcribed with respect to a yczR gene. Two orthology clustering procedures were applied to find proteins co-occurring exclusively with the YczE proteins. The reported results suggest that YczE may be involved in the membrane translocation and metabolism of sulfur-containing compounds mostly in rapidly growing, low pathogenicity mycobacterial species. These observations may hint at potential targets for therapies to treat the emerging opportunistic infections provoked by the widespread environmental mycobacterial species and may contribute to the delineation of the genomic and physiological differences between the pathogenic and non-pathogenic mycobacterial species.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.

Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.

Mitochondrial Calcium Handling in Physiology and Disease

Calcium (Ca²⁺) accumulation inside mitochondria represents a pleiotropic signal controlling a wide range of cellular functions, including key metabolic pathways and life/death decisions. This phenomenon has been first described in the 1960s, but the identity of the molecules controlling this process remained a mystery until just few years ago, when both mitochondrial Ca²⁺ uptake and release systems were genetically dissected. This finally opened the possibility to develop genetic models to directly test the contribution of mitochondrial Ca²⁺ homeostasis to cellular functions. Here we summarize our current understanding of the molecular machinery that controls mitochondrial Ca²⁺ handling and critically evaluate the physiopathological role of mitochondrial Ca²⁺ signaling, based on recent evidences obtained through in vitro and in vivo models.

Enjoy the Trip: Calcium in Mitochondria Back and Forth

In the last 5 years, most of the molecules that control mitochondrial Ca(2+) homeostasis have been finally identified. Mitochondrial Ca(2+) uptake is mediated by the Mitochondrial Calcium Uniporter (MCU) complex, a macromolecular structure that guarantees Ca(2+) accumulation inside mitochondrial matrix upon increases in cytosolic Ca(2+). Conversely, Ca(2+) release is under the control of the Na(+)/Ca(2+) exchanger, encoded by the NCLX gene, and of a H(+)/Ca(2+) antiporter, whose identity is still debated. The low affinity of the MCU complex, coupled to the activity of the efflux systems, protects cells from continuous futile cycles of Ca(2+) across the inner mitochondrial membrane and consequent massive energy dissipation. In this review, we discuss the basic principles that govern mitochondrial Ca(2+) homeostasis and the methods used to investigate the dynamics of Ca(2+) concentration within the organelles. We discuss the functional and structural role of the different molecules involved in mitochondrial Ca(2+) handling and their pathophysiological role.

PTIR: Predicted Tomato Interactome Resource

Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.

PhyloPro2.0: a database for the dynamic exploration of phylogenetically conserved proteins and their domain architectures across the Eukarya

PhyloPro is a database and accompanying web-based application for the construction and exploration of phylogenetic profiles across the Eukarya. In this update article, we present six major new developments in PhyloPro: (i) integration of Pfam-A domain predictions for all proteins; (ii) new summary heatmaps and detailed level views of domain conservation; (iii) an interactive, network-based visualization tool for exploration of domain architectures and their conservation; (iv) ability to browse based on protein functional categories (GOSlim); (v) improvements to the web interface to enhance drill down capability from the heatmap view; and (vi) improved coverage including 164 eukaryotes and 12 reference species. In addition, we provide improved support for downloading data and images in a variety of formats. Among the existing tools available for phylogenetic profiles, PhyloPro provides several innovative domain-based features including a novel domain adjacency visualization tool. These are designed to allow the user to identify and compare proteins with similar domain architectures across species and thus develop hypotheses about the evolution of lineage-specific trajectories. Database URL: http://www.compsysbio.org/phylopro/.

Topological characteristics of target genes regulated by abiotic-stress-responsible miRNAs in a rice interactome network

A great number of microRNAs (miRNAs) have been identified in responding and acting in gene regulatory networks associated with plant tolerance to abiotic stress conditions, such as drought, salinity, and high temperature. The topological exploration of target genes regulated by abiotic-stress-responsible miRNAs (ASRmiRs) in a network facilitates to discover the molecular basis of plant abiotic stress response. This study was based on the staple food rice (Oryza sativa) in which ASRmiRs were manually curated. After having compared the topological properties of target genes (stress-miR-targets) with those (non-stress-miR-targets) not regulated by ASRmiRs in a rice interactome network, we found that stress-miR-targets exhibited distinguishable topological properties. The interaction probability analysis and k-core decomposition showed that stress-miR-targets preferentially interacted with non-stress-miR-targets and located at the peripheral positions in the network. Our results indicated an obvious topological distinction between the two types of genes, reflecting the specific mechanisms of action of stress-miR-targets in rice abiotic stress response. Also, the results may provide valuable clues to elucidate molecular mechanisms of crop response to abiotic stress.

SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles

Summary: A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods.

Availability and implementation: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy

Contact:lars.juhl.jensen@cpr.ku.dk

Supplementary information:Supplementary data are available at Bioinformatics online.

Phylogenetic Profiling for Probing the Modular Architecture of the Human Genome

Information about functional connections between genes can be derived from patterns of coupled loss of their homologs across multiple species. This comparative approach, termed phylogenetic profiling, has been successfully used to infer genetic interactions in bacteria and eukaryotes. Rapid progress in sequencing eukaryotic species has enabled the recent phylogenetic profiling of the human genome, resulting in systematic functional predictions for uncharacterized human genes. Importantly, groups of co-evolving genes reveal widespread modularity in the underlying genetic network, facilitating experimental analyses in human cells as well as comparative studies of conserved functional modules across species. This strategy is particularly successful in identifying novel metabolic proteins and components of multi-protein complexes. The targeted sequencing of additional key eukaryotes and the incorporation of improved methods to generate and compare phylogenetic profiles will further boost the predictive power and utility of this evolutionary approach to the functional analysis of gene interaction networks.