A complex-centric view of protein network evolution

The recent availability of protein–protein interaction networks for several species makes it possible to study protein complexes in an evolutionary context. In this article, we present a novel network-based framework for reconstructing the evolutionary history of protein complexes. Our analysis is based on generalizing evolutionary measures for single proteins to the level of whole subnetworks, comprehensively considering a broad set of computationally derived complexes and accounting for both sequence and interaction changes. Specifically, we compute sets of orthologous complexes across species, and use these to derive evolutionary rate and age measures for protein complexes. We observe significant correlations between the evolutionary properties of a complex and those of its member proteins, suggesting that protein complexes form early in evolution and evolve as coherent units. Additionally, our approach enables us to directly quantify the extent to which gene duplication has played a role in the evolution of complexes. We find that about one quarter of the sets of orthologous complexes have originated from evolutionary cores of homodimers that underwent duplication and divergence, testifying to the important role of gene duplication in protein complex evolution.     

An algebraic view of bacterial genome evolution

Rearrangements of bacterial chromosomes can be studied mathematically at several levels, most prominently at a local, or sequence level, as well as at a topological level. The biological changes involved locally are inversions, deletions, and transpositions, while topologically they are knotting and catenation. These two modelling approaches share some surprising algebraic features related to braid groups and Coxeter groups. The structural approach that is at the core of algebra has long found applications in sciences such as physics and analytical chemistry, but only in a small number of ways so far in biology. And yet there are examples where an algebraic viewpoint may capture a deeper structure behind biological phenomena. This article discusses a family of biological problems in bacterial genome evolution for which this may be the case, and raises the prospect that the tools developed by algebraists over the last century might provide insight to this area of evolutionary biology.     

An integrated view of aromatase and its inhibition

Aromatase inhibition has become a major treatment strategy for postmenopausal women with oestrogen-dependent breast cancer. Its optimal application is, however, dependent upon (i) the accurate identification of cancers which are ultimately dependent upon the activity of the aromatase enzyme, (ii) the use of the best method/inhibitor by which to blockade aromatase activity.

The single best predictor of response to aromatase inhibitors is the presence of tumour oestrogen receptors; receptor-negative cancers rarely respond whereas those with high levels seem particularly likely to benefit. However, there is a need for additional discriminatory markers. The use of microarray technology coupled with neoadjuvant therapy is likely to yield promising candidate genes. The finding that, amongst peripheral tissues, the tumour itself may have high activity has led to the suggestion that the tumour aromatase measurements may be predictive; however, in situ studies and the lack of robust assays for tumour aromatase suggest that tumour aromatase may not be an influential marker.

Whilst drugs such as anastrozole, exemestane, formestane and letrozole are all effective and specific inhibitors of aromatase, they differ in structure, potency and mechanism of action. Thus, differential sensitivity of tissues/tumours and non-cross resistance mean inhibitors are not equivalent and individual agents may have differing roles according to the setting in which they will be used. Aromatase inhibitors have evolved as key endocrine agents in the treatment of breast cancer. They offer the promise of rational treatment management based on the accurate identification of individual cohorts of tumours responsive to specific drugs.     

An integrated view of cyclin E function and regulation

Cancers of diverse cell lineages express high levels of cyclin E, and in various studies, cyclin E overexpression correlates with increased tumor aggression. One way that normal control of cyclin E expression is disabled in cancer cells is via loss-of-function mutations sustained by FBXW7. This gene encodes the Fbw7 tumor suppressor protein that provides substrate specificity for a ubiquitin ligase complex that targets multiple oncoproteins for degradation. Numerous other mechanisms besides Fbw7 mutations can deregulate cyclin E expression and activity in cancer cells. Recent reports demonstrate that inappropriate cyclin E expression may have far-reaching biological consequences for cell physiology, including altering gene expression programs governing proliferation, differentiation, survival and senescence. In this Perspective, we discuss the function of mammalian cyclin E in the context of these new data as well as the complex network that connects cyclin E functions to the cellular controls regulating its expression and activity.Key words: cell cycle, cyclin E, Cdk2, Fbw7, E2F, p21, p27, regulatory network     

An alternative view of protein fold space

Comparing and subsequently classifying protein structures information has received significant attention concurrent with the increase in the number of experimentally derived 3-dimensional structures. Classification schemes have focused on biological function found within protein domains and on structure classification based on topology. Here an alternative view is presented that groups substructures. Substructures are long (50-150 residue) highly repetitive near-contiguous pieces of polypeptide chain that occur frequently in a set of proteins from the PDB defined as structurally non-redundant over the complete polypeptide chain. The substructure classification is based on a previously reported Combinatorial Extension (CE) algorithm that provides a significantly different set of structure alignments than those previously described, having, for example, only a 40% overlap with FSSP. Qualitatively the algorithm provides longer contiguous aligned segments at the price of a slightly higher root-mean-square deviation (rmsd). Clustering these alignments gives a discreet and highly repetitive set of substructures not detectable by sequence similarity alone. In some cases different substructures represent all or different parts of well known folds indicative of the Russian doll effect--the continuity of protein fold space. In other cases they fall into different structure and functional classifications. It is too early to determine whether these newly classified substructures represent new insights into the evolution of a structural framework important to many proteins. What is apparent from on-going work is that these substructures have the potential to be useful probes in finding remote sequence homology and in structure prediction studies. The characteristics of the complete all-by-all comparison of the polypeptide chains present in the PDB and details of the filtering procedure by pair-wise structure alignment that led to the emergent substructure gallery are discussed. Substructure classification, alignments, and tools to analyze them are available at http://cl.sdsc.edu/ce.html.     

An engineer's view on genetic information and biological evolution

We develop ideas on genome replication introduced in Battail [Europhys. Lett. 40 (1997) 343]. Starting with the hypothesis that the genome replication process uses error-correcting means, and the auxiliary one that nested codes are used to this end, we first review the concepts of redundancy and error-correcting codes. Then we show that these hypotheses imply that: distinct species exist with a hierarchical taxonomy, there is a trend of evolution towards complexity, and evolution proceeds by discrete jumps. At least the first two features above may be considered as biological facts so, in the absence of direct evidence, they provide an indirect proof in favour of the hypothesized error-correction system. The very high redundancy of genomes makes it possible. In order to explain how it is implemented, we suggest that soft codes and replication decoding, to be briefly described, are plausible candidates. Experimentally proven properties of long-range correlation of the DNA message substantiate this claim.     

An integrated view of the correlations between genomic and phenomic variables

Genome sequencing opened the flood gate of ＂-omics＂ studies, among which the research about correlations between genomic and phenomic variables is an important part. With the development of functional genomics and systems biology, genome-wide investigation of the correlations between many genomic and phenomic variables became possible. In this review, five genomic variables, such as evolution rate （or ＂age＂ of the gene）, the length of intron and ORF （protein length） in one gene, the biases of amino acid composition and codon usage, along with the phenomic variables related to expression patterns （level and breadth） are focused on. In most cases, genes with higher mRNA/protein expression level tend to evolve slowly, have less intronic DNA, code for smaller proteins, and have higher biases of amino acid composition and codon usage. In addition, broadly expressed proteins evolve more slowly and are shorter than tissue-specific proteins. Studies in this field are helpful for deeper understanding the signatures of selection mediated by the features of gene expression and are of great significance to enrich the evolution theory.     

Enzymes: An integrated view of structure, dynamics and function

Microbes utilize enzymes to perform a variety of functions. Enzymes are biocatalysts working as highly efficient machines at the molecular level. In the past, enzymes have been viewed as static entities and their function has been explained on the basis of direct structural interactions between the enzyme and the substrate. A variety of experimental and computational techniques, however, continue to reveal that proteins are dynamically active machines, with various parts exhibiting internal motions at a wide range of time-scales. Increasing evidence also indicates that these internal protein motions play a role in promoting protein function such as enzyme catalysis. Moreover, the thermodynamical fluctuations of the solvent, surrounding the protein, have an impact on internal protein motions and, therefore, on enzyme function. In this review, we describe recent biochemical and theoretical investigations of internal protein dynamics linked to enzyme catalysis. In the enzyme cyclophilin A, investigations have lead to the discovery of a network of protein vibrations promoting catalysis. Cyclophilin A catalyzes peptidyl-prolyl cis/trans isomerization in a variety of peptide and protein substrates. Recent studies of cyclophilin A are discussed in detail and other enzymes (dihydrofolate reductase and liver alcohol dehydrogenase) where similar discoveries have been reported are also briefly discussed. The detailed characterization of the discovered networks indicates that protein dynamics plays a role in rate-enhancement achieved by enzymes. An integrated view of enzyme structure, dynamics and function have wide implications in understanding allosteric and co-operative effects, as well as protein engineering of more efficient enzymes and novel drug design.     

An integrated approach to life-cycle evolution using selective landscapes

A model which defines fitness in terms of the intrinsic rate of increase of phenotypes is used to analyse which life cycles are appropriate to which ecological circumstances. The following predictions are made for asexual animals and those sexual animals producing on average more than one daughter per brood. If there are no behavioural or physiological interactions between variables, then number of offspring per breeding should be maximized, survival until first/next breeding should be maximized, and time to first/next breeding should be minimized. If interactions occur such that altering one life-cycle variable affects another, then there are trade-offs between variables and the optimum trade-off will maximize fitness.Number of offspring per breeding will generally affect adult survivorship until next breeding. Given certain reasonable assumptions about this trade-off, high juvenile survivorship selects towards semelparity (many offspring per brood), low juvenile survivorship selects towards iteroparity (few offspring per brood). If juvenile survival depends on adult feeding, as in altricial birds, then juvenile survivorship declines as clutch size is increased. Optimal clutch size maximizes the number of surviving offspring per brood.Two trade-offs involve parental care. If parents guard their offspring they should take more risks if brood size is larger. The amount that parents feed their offspring should depend on how effective feeding is in enhancing growth. Growth may also be enhanced by taking risks, in juveniles or adults. The extent of risk-taking should depend on how effective risk-taking is in enhancing growth.If the number of offspring per brood is related to growing conditions for offspring, the prediction is that more offspring per brood should be produced if growing conditions for offspring are better. If the adult can protect the offspring, for example by encapsulating them, the amount of protection provided should depend on how effective the protection is in increasing offspring survivorship.     

Morphological evolution in Tropidurinae squamates: an integrated view along a continuum of ecological settings

Variation in squamate foot morphology is likely relevant during evolutionary processes of habitat colonization because distinct surfaces differ in energetic and functional demands for locomotion. We combined new foot morphological data with published information of limb and tail lengths to investigate evolutionary changes possibly associated with the differential usage of ecological settings by Tropidurinae species. Several traits exhibited significant phylogenetic signal, and we performed conventional and phylogenetic regressions of PC scores (retained from Principal Components Analyses of morphometric traits) on continuous ecological indices. Tropidurines from sandy habitats exhibit larger foot soles, opposite to the evolution of narrow feet in species that use branches and rocks. Also, species that usually move along trunks present longer femora. This study provides evidence for morphological adaptations associated with substrate usage in Tropidurinae, and suggests that opposite morphological profiles might evolve associated with the use of surfaces energetically and functionally contrasting, possibly leading to trade‐offs.     

An integrated view of quantitative trait variation using tomato interspecific introgression lines

Resolving natural phenotypic variation into genetic and molecular components is a major objective in biology. Over the past decade, tomato interspecific introgression lines (ILs), each carrying a single 'exotic' chromosome segment from a wild species, have exposed thousands of quantitative trait loci (QTL) affecting plant adaptation, morphology, yield, metabolism, and gene expression. QTL for fruit size and sugar composition were isolated by map-based cloning, while others were successfully implemented in marker-assisted breeding programs. More recently, integrating the multitude of IL-QTL into a single database has unraveled some unifying principles about the architecture of complex traits in plants.     

Missense meanderings in sequence space: a biophysical view of protein evolution

Proteins are finicky molecules; they are barely stable and are prone to aggregate, but they must function in a crowded environment that is full of degradative enzymes bent on their destruction. It is no surprise that many common diseases are due to missense mutations that affect protein stability and aggregation. Here we review the literature on biophysics as it relates to molecular evolution, focusing on how protein stability and aggregation affect organismal fitness. We then advance a biophysical model of protein evolution that helps us to understand phenomena that range from the dynamics of molecular adaptation to the clock-like rate of protein evolution.     

Neuronal damage and cognitive impairment associated with hypoglycemia: An integrated view

The aim of the present review is to offer a current perspective about the consequences of hypoglycemia and its impact on the diabetic disorder due to the increasing incidence of diabetes around the world. The main consequence of insulin treatment in type 1 diabetic patients is the occurrence of repetitive periods of hypoglycemia and even episodes of severe hypoglycemia leading to coma. In the latter, selective neuronal death is observed in brain vulnerable regions both in humans and animal models, such as the cortex and the hippocampus. Cognitive damage subsequent to hypoglycemic coma has been associated with neuronal death in the hippocampus. The mechanisms implicated in selective damage are not completely understood but many factors have been identified including excitotoxicity, oxidative stress, zinc release, PARP-1 activation and mitochondrial dysfunction. Importantly, the diabetic condition aggravates neuronal damage and cognitive failure induced by hypoglycemia. In the absence of coma prolonged and severe hypoglycemia leads to increased oxidative stress and discrete neuronal death mainly in the cerebral cortex. The mechanisms responsible for cell damage in this condition are still unknown. Recurrent moderate hypoglycemia is far more common in diabetic patients than severe hypoglycemia and currently important efforts are being done in order to elucidate the relationship between cognitive deficits and recurrent hypoglycemia in diabetics. Human studies suggest impaired performance mainly in memory and attention tasks in healthy and diabetic individuals under the hypoglycemic condition. Only scarce neuronal death has been observed under moderate repetitive hypoglycemia but studies suggest that impaired hippocampal synaptic function might be one of the causes of cognitive failure. Recent studies have also implicated altered mitochondrial function and mitochondrial oxidative stress.     

An empirical codon model for protein sequence evolution

In the past, 2 kinds of Markov models have been considered to describe protein sequence evolution. Codon-level models have been mechanistic with a small number of parameters designed to take into account features, such as transition-transversion bias, codon frequency bias, and synonymous-nonsynonymous amino acid substitution bias. Amino acid models have been empirical, attempting to summarize the replacement patterns observed in large quantities of data and not explicitly considering the distinct factors that shape protein evolution. We have estimated the first empirical codon model (ECM). Previous codon models assume that protein evolution proceeds only by successive single nucleotide substitutions, but our results indicate that model accuracy is significantly improved by incorporating instantaneous doublet and triplet changes. We also find that the affiliations between codons, the amino acid each encodes and the physicochemical properties of the amino acids are main factors driving the process of codon evolution. Neither multiple nucleotide changes nor the strong influence of the genetic code nor amino acids' physicochemical properties form a part of standard mechanistic models and their views of how codon evolution proceeds. We have implemented the ECM for likelihood-based phylogenetic analysis, and an assessment of its ability to describe protein evolution shows that it consistently outperforms comparable mechanistic codon models. We point out the biological interpretation of our ECM and possible consequences for studies of selection.     

An expanded view of the protein folding landscape of PDZ domains

Most protein domains fold in an apparently co-operative and two-state manner with only the native and denatured states significantly populated at any experimental condition. However, the protein folding energy landscape is often rugged and different transition states may be rate limiting for the folding reaction under different conditions, as seen for the PDZ protein domain family. We have here analyzed the folding kinetics of two PDZ domains and found that a previously undetected third transition state is rate limiting under conditions that stabilize the native state relative to the denatured state. In light of these results, we have re-analyzed previous folding data on PDZ domains and present a unified folding mechanism with three distinct transition states separated by two high-energy intermediates. Our data show that sequence composition tunes the relative stabilities of folding transition states within the PDZ family, while the overall mechanism is determined by topology. This model captures the kinetic folding mechanism of all PDZ domains studied to date.