Networks of coevolving sites in structural and functional domains of serpin proteins

A Genome-Wide Analysis of Serine Protease Inhibitors in Cydia pomonella Provides Insights into Their Evolution and Expression Pattern

Serine protease inhibitors (serpins) appear to be ubiquitous in almost all living organisms, with a conserved structure and varying functions. Serpins can modulate immune responses by negatively regulating serine protease activities strictly and precisely. The codling moth, Cydia pomonella (L.), a major invasive pest in China, can cause serious economic losses. However, knowledge of serpin genes in this insect remain largely unknown. In this study, we performed a systematic analysis of the serpin genes in C. pomonella, obtaining 26 serpins from the C. pomonella genome. Subsequently, their sequence features, evolutionary relationship, and expression pattern were characterized. Comparative analysis revealed the evolution of a number of serpin genes in Lepidoptera. Importantly, the evolutionary relationship and putative roles of serpin genes in C. pomonella were revealed. Additionally, selective pressure analysis found amino acid sites with strong evidence of positive selection. Interestingly, the serpin1 gene possessed at least six splicing isoforms with distinct reactive-center loops, and these isoforms were experimentally validated. Furthermore, we observed a subclade expansion of serpins, and these genes showed high expression in multiple tissues, suggesting their important roles in C. pomonella. Overall, this study will enrich our knowledge of the immunity of C. pomonella and help to elucidate the role of serpins in the immune response.

Reactive centre loop dynamics and serpin specificity

Serine proteinase inhibitors (serpins), typically fold to a metastable native state and undergo a major conformational change in order to inhibit target proteases. However, conformational lability of the native serpin fold renders them susceptible to misfolding and aggregation, and underlies misfolding diseases such as α₁-antitrypsin deficiency. Serpin specificity towards its protease target is dictated by its flexible and solvent exposed reactive centre loop (RCL), which forms the initial interaction with the target protease during inhibition. Previous studies have attempted to alter the specificity by mutating the RCL to that of a target serpin, but the rules governing specificity are not understood well enough yet to enable specificity to be engineered at will. In this paper, we use conserpin, a synthetic, thermostable serpin, as a model protein with which to investigate the determinants of serpin specificity by engineering its RCL. Replacing the RCL sequence with that from α1-antitrypsin fails to restore specificity against trypsin or human neutrophil elastase. Structural determination of the RCL-engineered conserpin and molecular dynamics simulations indicate that, although the RCL sequence may partially dictate specificity, local electrostatics and RCL dynamics may dictate the rate of insertion during protease inhibition, and thus whether it behaves as an inhibitor or a substrate. Engineering serpin specificity is therefore substantially more complex than solely manipulating the RCL sequence, and will require a more thorough understanding of how conformational dynamics achieves the delicate balance between stability, folding and function required by the exquisite serpin mechanism of action.

Reactive Center Loop Insertion in α-1-Antitrypsin Captured by Accelerated Molecular Dynamics Simulation

Protease inhibition by metastable serine protease inhibitors (serpins) is mediated by one of the largest functional intradomain conformational changes known in biology. In this extensive structural rearrangement, protease-serpin complex formation triggers cleavage of the serpin reactive center loop (RCL), its subsequent insertion into central β-sheet A, and covalent trapping of the target protease. In this study, we present the first detailed accelerated molecular dynamics simulation of the insertion of the fully cleaved RCL in α-1-antitrypsin (α₁AT), the archetypal member of the family of human serpins. Our results reveal internal water pathways that allow the initial incorporation of side chains of RCL residues into the protein interior. We observed structural plasticity of the helix F (hF) element that blocks the RCL path in the native state, which is in excellent agreement with previous experimental reports. Furthermore, the simulation suggested a novel role of hF and the connected turn (thFs3A) as chaperones that support the insertion process by reducing the conformational space available to the RCL. Transient electrostatic interactions of RCL residues potentially fine-tune the serpin inhibitory activity. On the basis of our simulation, we generated the α₁AT mutants K168E, E346K, and K168E/E346K and analyzed their inhibitory activity along with their intrinsic stability and heat-induced polymerization. Remarkably, the E346K mutation exhibited enhanced inhibitory activity along with an increased rate of premature structural collapse (polymerization), suggesting a significant role of E346 in the gatekeeping of the strain in the metastable native state.

Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores

As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; "central" positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints-detectable by divergent algorithms--that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions.

Bi-factor analysis based on noise-reduction (BIFANR): a new algorithm for detecting coevolving amino acid sites in proteins

Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.

Coevolution analyses illuminate the dependencies between amino acid sites in the chaperonin system GroES-L

BACKGROUND

GroESL is a heat-shock protein ubiquitous in bacteria and eukaryotic organelles. This evolutionarily conserved protein is involved in the folding of a wide variety of other proteins in the cytosol, being essential to the cell. The folding activity proceeds through strong conformational changes mediated by the co-chaperonin GroES and ATP. Functions alternative to folding have been previously described for GroEL in different bacterial groups, supporting enormous functional and structural plasticity for this molecule and the existence of a hidden combinatorial code in the protein sequence enabling such functions. Describing this plasticity can shed light on the functional diversity of GroEL. We hypothesize that different overlapping sets of amino acids coevolve within GroEL, GroES and between both these proteins. Shifts in these coevolutionary relationships may inevitably lead to evolution of alternative functions.

RESULTS

We conducted the first coevolution analyses in an extensive bacterial phylogeny, revealing complex networks of evolutionary dependencies between residues in GroESL. These networks differed among bacterial groups and involved amino acid sites with functional importance and others with previously unsuspected functional potential. Coevolutionary networks formed statistically independent units among bacterial groups and map to structurally continuous regions in the protein, suggesting their functional link. Sites involved in coevolution fell within narrow structural regions, supporting dynamic combinatorial functional links involving similar protein domains. Moreover, coevolving sites within a bacterial group mapped to regions previously identified as involved in folding-unrelated functions, and thus, coevolution may mediate alternative functions.

CONCLUSIONS

Our results highlight the evolutionary plasticity of GroEL across the entire bacterial phylogeny. Evidence on the functional importance of coevolving sites illuminates the as yet unappreciated functional diversity of proteins.

Using the theory of coevolution to predict protein-protein interactions in non-small cell lung cancer

Systems biology has become an effective approach for understanding the molecular mechanisms underlying the development of lung cancer. In this study, sequences of 100 non-small cell lung cancer (NSCLC)-related proteins were downloaded from the National Center for Biotechnology Information (NCBI) databases. The Theory of Coevolution was then used to build a protein-protein interaction (PPI) network of NSCLC. Adopting the reverse thinking approach, we analyzed the NSCLC proteins one at a time. Fifteen key proteins were identified and categorized into a special protein family F (K), which included Cyclin D1 (CCND1), E-cadherin (CDH1), Cyclin-dependent kinase inhibitor 2A (CDKN2A), chemokine (C-X-C motif) ligand 12 (CXCL12), epidermal growth factor (EGF), epidermal growth factor receptor (EGFR), TNF receptor superfamily, member 6 (FAS), FK506 binding protein 12-rapamycin associated protein 1 (FRAP1), O-6-methylguanine-DNA methyltransferase (MGMT), parkinson protein 2, E3 Ubiquitin protein ligase (PARK2), phosphatase and tensin homolog (PTEN), calcium channel voltage-dependent alpha 2/delta subunit 2 (CACNA2D2), tubulin beta class I (TUBB), SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 2 (SMARCA2), and wingless-type MMTV integration site family, member 7A (WNT7A). Seven key nodes of the sub-network were identified, which included PARK2, WNT7A, SMARCA2, FRAP1, CDKN2A, CCND1, and EGFR. The PPI predictions of EGFR-EGF, PARK2-FAS, PTEN-FAS, and CACNA2D2-CDH1 were confirmed experimentally by retrieving the Biological General Repository for Interaction Datasets (BioGRID) and PubMed databases. We proposed that the 7 proteins could serve as potential diagnostic molecular markers for NSCLC. In accordance with the developmental mode of lung cancer established by Sekine et al., we assumed that the occurrence and development of lung cancer were linked not only to gene loss in the 3p region (WNT7A, 3p25) and genetic mutations in the 9p region but also to similar events in the regions of 1p36.2 (FRAP1), 6q25.2-q27 (PARK2), and 11q13 (CCND1). Lastly, the invasion or metastasis of lung cancer happened.

Structural constraints identified with covariation analysis in ribosomal RNA

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab’s new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab’s Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair.

A coevolutionary residue network at the site of a functionally important conformational change in a phosphohexomutase enzyme family

Coevolution analyses identify residues that co-vary with each other during evolution, revealing sequence relationships unobservable from traditional multiple sequence alignments. Here we describe a coevolutionary analysis of phosphomannomutase/phosphoglucomutase (PMM/PGM), a widespread and diverse enzyme family involved in carbohydrate biosynthesis. Mutual information and graph theory were utilized to identify a network of highly connected residues with high significance. An examination of the most tightly connected regions of the coevolutionary network reveals that most of the involved residues are localized near an interdomain interface of this enzyme, known to be the site of a functionally important conformational change. The roles of four interface residues found in this network were examined via site-directed mutagenesis and kinetic characterization. For three of these residues, mutation to alanine reduces enzyme specificity to ∼10% or less of wild-type, while the other has ∼45% activity of wild-type enzyme. An additional mutant of an interface residue that is not densely connected in the coevolutionary network was also characterized, and shows no change in activity relative to wild-type enzyme. The results of these studies are interpreted in the context of structural and functional data on PMM/PGM. Together, they demonstrate that a network of coevolving residues links the highly conserved active site with the interdomain conformational change necessary for the multi-step catalytic reaction. This work adds to our understanding of the functional roles of coevolving residue networks, and has implications for the definition of catalytically important residues.

Conserved and variable correlated mutations in the plant MADS protein network

BACKGROUND

Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data.

RESULTS

Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins.

CONCLUSION

Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.

Functionally compensating coevolving positions are neither homoplasic nor conserved in clades

We demonstrated that a pair of positions in phosphoglycerate kinase that score highly by three nonparametric covariation measures are important for function even though the positions can be occupied by aliphatic, aromatic, or charged residues. Examination of these pairs suggested that the majority of the covariation scores could be explained by within-clade conservation. However, an analysis of diversity showed that the conservation within clades of covarying pairs was indistinguishable from pairs of positions that do not covary, thus ruling out both clade conservation and extensive homoplasy as means to identify covarying positions. Mutagenesis showed that the residues in the covarying pair were epistatic, with the type of epistasis being dependent on the initial pair. The results show that nonconserved covarying positions that affect protein function can be identified with high precision.

Protein sectors: evolutionary units of three-dimensional structure

Proteins display a hierarchy of structural features at primary, secondary, tertiary, and higher-order levels, an organization that guides our current understanding of their biological properties and evolutionary origins. Here, we reveal a structural organization distinct from this traditional hierarchy by statistical analysis of correlated evolution between amino acids. Applied to the S1A serine proteases, the analysis indicates a decomposition of the protein into three quasi-independent groups of correlated amino acids that we term "protein sectors." Each sector is physically connected in the tertiary structure, has a distinct functional role, and constitutes an independent mode of sequence divergence in the protein family. Functionally relevant sectors are evident in other protein families as well, suggesting that they may be general features of proteins. We propose that sectors represent a structural organization of proteins that reflects their evolutionary histories.

Coevolution in defining the functional specificity

Covariation between sites can arise due to a common evolutionary history. At the same time, structure and function of proteins play significant role in evolvability of different sites that are not directly connected with the common ancestry. The nature of forces which cause residues to coevolve is still not thoroughly understood, it is especially not clear how coevolutionary processes are related to functional diversification within protein families. We analyzed both functional and structural factors that might cause covariation of specificity determinants and showed that they more often participate in coevolutionary relationships with each other and other sites compared with functional sites and those sites that are not under strong functional constraints. We also found that protein sites with higher number of coevolutionary connections with other sites have a tendency to evolve slower. Our results indicate that in some cases coevolutionary connections exist between specificity sites that are located far away in space but are under similar functional constraints. Such correlated changes and compensations can be realized through the stepwise coevolutionary processes which in turn can shed light on the mechanisms of functional diversification.

Computing highly correlated positions using mutual information and graph theory for G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a superfamily of seven transmembrane-spanning proteins involved in a wide array of physiological functions and are the most common targets of pharmaceuticals. This study aims to identify a cohort or clique of positions that share high mutual information. Using a multiple sequence alignment of the transmembrane (TM) domains, we calculated the mutual information between all inter-TM pairs of aligned positions and ranked the pairs by mutual information. A mutual information graph was constructed with vertices that corresponded to TM positions and edges between vertices were drawn if the mutual information exceeded a threshold of statistical significance. Positions with high degree (i.e. had significant mutual information with a large number of other positions) were found to line a well defined inter-TM ligand binding cavity for class A as well as class C GPCRs. Although the natural ligands of class C receptors bind to their extracellular N-terminal domains, the possibility of modulating their activity through ligands that bind to their helical bundle has been reported. Such positions were not found for class B GPCRs, in agreement with the observation that there are not known ligands that bind within their TM helical bundle. All identified key positions formed a clique within the MI graph of interest. For a subset of class A receptors we also considered the alignment of a portion of the second extracellular loop, and found that the two positions adjacent to the conserved Cys that bridges the loop with the TM3 qualified as key positions. Our algorithm may be useful for localizing topologically conserved regions in other protein families.

Rewiring the specificity of two-component signal transduction systems

Two-component signal transduction systems are the predominant means by which bacteria sense and respond to environmental stimuli. Bacteria often employ tens or hundreds of these paralogous signaling systems, comprised of histidine kinases (HKs) and their cognate response regulators (RRs). Faithful transmission of information through these signaling pathways and avoidance of detrimental crosstalk demand exquisite specificity of HK-RR interactions. To identify the determinants of two-component signaling specificity, we examined patterns of amino acid coevolution in large, multiple sequence alignments of cognate kinase-regulator pairs. Guided by these results, we demonstrate that a subset of the coevolving residues is sufficient, when mutated, to completely switch the substrate specificity of the kinase EnvZ. Our results shed light on the basis of molecular discrimination in two-component signaling pathways, provide a general approach for the rational rewiring of these pathways, and suggest that analyses of coevolution may facilitate the reprogramming of other signaling systems and protein-protein interactions.

Reducing the false positive rate in the non-parametric analysis of molecular coevolution

Background

The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA). Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods.

Results

Here we test the effect that variations on the MSA properties have over the sensitivity of non-parametric methods to detect coevolution. We test the effect that the size of the MSA (number of sequences), mean pairwise amino acid distance per site and the strength of the coevolution signal have on the ability of non-parametric methods to detect coevolution. Our results indicate that all three factors have significant effects on the accuracy of non-parametric methods. Further, introducing statistical filters improves the sensitivity and increases the statistical power of the methods to detect functional coevolution. Statistical analysis of the physico-chemical properties of amino acid sites in the context of the protein structure reveals striking dependencies among amino acid sites. Results indicate a covariation trend in the hydrophobicities and molecular weight characteristics of amino acid sites when analysing a non-redundant set of 8000 protein structures. Using this biological information as filter in coevolutionary analyses minimises the false positive rate of these methods. Application of these filters to three different proteins with known functional domains supports the importance of using biological filters to detect coevolution.

Conclusion

Coevolutionary analyses using non-parametric methods have proved difficult and highly prone to provide spurious results depending on the properties of MSAs and on the strength of coevolution between amino acid sites. The application of statistical filters to the number of pairs detected as coevolving reduces significantly the number of artifactual results. Analysis of the physico-chemical properties of amino acid sites in the protein structure context reveals their structure-dependent covariation. The application of this known biological information to the analysis of covariation greatly enhances the functional coevolutionary signal and removes historical covariation. Simultaneous use of statistical and biological data is instrumental in the detection of functional amino acid sites dependencies and compensatory changes at the protein level.

Why Should We Care about Molecular Coevolution?

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and non-parametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations.

Serpins in plants and green algae

Control of proteolysis is important for plant growth, development, responses to stress, and defence against insects and pathogens. Members of the serpin protein family are likely to play a critical role in this control through irreversible inhibition of endogenous and exogenous target proteinases. Serpins have been found in diverse species of the plant kingdom and represent a distinct clade among serpins in multicellular organisms. Serpins are also found in green algae, but the evolutionary relationship between these serpins and those of plants remains unknown. Plant serpins are potent inhibitors of mammalian serine proteinases of the chymotrypsin family in vitro but, intriguingly, plants and green algae lack endogenous members of this proteinase family, the most common targets for animal serpins. An Arabidopsis serpin with a conserved reactive centre is now known to be capable of inhibiting an endogenous cysteine proteinase. Here, knowledge of plant serpins in terms of sequence diversity, inhibitory specificity, gene expression and function is reviewed. This was advanced through a phylogenetic analysis of amino acid sequences of expressed plant serpins, delineation of plant serpin gene structures and prediction of inhibitory specificities based on identification of reactive centres. The review is intended to encourage elucidation of plant serpin functions.

Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation

Background

Some amino acid residues functionally interact with each other. This interaction will result in an evolutionary co-variation between these residues – coevolution. Our goal is to find these coevolving residues.

Results

We present six new methods for detecting coevolving residues. Among other things, we suggest measures that are variants of Mutual Information, and measures that use a multidimensional representation of each residue in order to capture the physico-chemical similarities between amino acids. We created a benchmarking system, in silico, able to evaluate these methods through a wide range of realistic conditions. Finally, we use the combination of different methods as a way of improving performance.

Conclusion

Our best method (Row and Column Weighed Mutual Information) has an estimated accuracy increase of 63% over Mutual Information. Furthermore, we show that the combination of different methods is efficient, and that the methods are quite sensitive to the different conditions tested.

A simple model of co-evolutionary dynamics caused by epistatic selection

Epistasis is the dependency of the effect of a mutation on the genetic background in which it occurs. Epistasis has been widely documented and implicated in the evolution of species barriers and the evolution of genetic architecture. Here we propose a simple model to formalize the idea that epistasis can also lead to co-evolutionary patterns in molecular evolution of interacting genes. This model epistasis is represented by the influence of one gene substitution on the fitness rank of the resident allele at another locus. We assume that increasing or decreasing fitness rank occur equally likely. In simulations we show that this form of epistasis leads to co-evolution in the sense that the length of an adaptive walk between interacting loci is highly correlated. This effect is caused by episodes of elevated rate of evolution in both loci simultaneously. We find that the influence of epistasis on these measures of co-evolutionary dynamics is relatively robust to the details of the model. The main factor influencing the correlation in evolutionary rates is the probability that a substitution will have an epistatic effect, but the strength of epistasis or the asymmetry of the initial fitness ranks of the alleles have only a minor effect. We suggest that covariance in rates of evolution among loci could be used to detect epistasis among loci.

Leveraging hierarchical population structure in discrete association studies

Population structure can confound the identification of correlations in biological data. Such confounding has been recognized in multiple biological disciplines, resulting in a disparate collection of proposed solutions. We examine several methods that correct for confounding on discrete data with hierarchical population structure and identify two distinct confounding processes, which we call coevolution and conditional influence. We describe these processes in terms of generative models and show that these generative models can be used to correct for the confounding effects. Finally, we apply the models to three applications: identification of escape mutations in HIV-1 in response to specific HLA-mediated immune pressure, prediction of coevolving residues in an HIV-1 peptide, and a search for genotypes that are associated with bacterial resistance traits in Arabidopsis thaliana. We show that coevolution is a better description of confounding in some applications and conditional influence is better in others. That is, we show that no single method is best for addressing all forms of confounding. Analysis tools based on these models are available on the internet as both web based applications and downloadable source code at http://atom.research.microsoft.com/bio/phylod.aspx.

Shopping for plastids

Recent suggestions that endosymbionts in a diatom and an amoeba represent independent origins of plastids from those in plants and algae raise again the question of how many times plastids have evolved. In this Opinion article, we review the evidence for a single origin or multiple origins of primary plastids. Although the data are widely taken as supporting a single origin, we stress the assumptions underlying that view, and argue for a more cautious interpretation. We also suggest that the implicit view of plastids being acquired from single ancestors at a single point (or points) in time is an over-simplification.