Protein complexity, gene duplicability and gene dispensability in the yeast genome

Using functional genomic and protein structural data we studied the effects of protein complexity (here defined as the number of subunit types in a protein) on gene dispensability and gene duplicability. We found that in terms of gene duplicability the major distinction in protein complexity is between hetero-complexes, each of which includes at least two different types of subunits (polypeptides), and homo-complexes, which include monomers and complexes that consist of only subunits of one polypeptide type. However, gene dispensability decreases only gradually as the number of subunit types in a protein complex increases. These observations suggest that the dosage balance hypothesis can explain well gene duplicability of complex proteins, but cannot completely explain the difference in dispensabilities between hetero-complex subunits. It is likely that knocking out a gene coding for a hetero-complex subunit would disrupt the function of the whole complex, so that the deletion effect on fitness would increase with protein complexity. We also found that multi-domain polypeptide genes are less dispensable but more duplicable than single-domain polypeptide genes. Duplicate genes derived from the whole genome duplication event in yeast are more dispensable (except for ribosomal protein genes) than other duplicate genes. Further, we found that subunits of the same protein complex tend to have similar expression levels and similar effects of gene deletion on fitness. Finally, we estimated that in yeast the contribution of duplicate genes to genetic robustness against null mutation is approximately 9%, smaller than previously estimated. In yeast, protein complexity may serve as a better indicator of gene dispensability than do duplicate genes.     

Genome Reduction Promotes Increase in Protein Functional Complexity in Bacteria

Obligate pathogenic and endosymbiotic bacteria typically experience gene loss due to functional redundancy, asexuality, and genetic drift. We hypothesize that reduced genomes increase their functional complexity through protein multitasking, in which many genes adopt new roles to counteract gene loss. Comparisons of interaction networks among six bacteria that have varied genome sizes (Mycoplasma pneumoniae, Treponema pallidum, Helicobacter pylori, Campylobacter jejuni, Synechocystis sp., and Mycobacterium tuberculosis) reveal that proteins in small genomes interact with proteins from a wider range of functions than do their orthologs in larger genomes. This suggests that surviving proteins form increasingly complex functional relationships to compensate for genes that are lost.     

Gene Duplicability-Connectivity-Complexity across Organisms and a Neutral Evolutionary Explanation

Gene duplication has long been acknowledged by biologists as a major evolutionary force shaping genomic architectures and characteristics across the Tree of Life. Major research has been conducting on elucidating the fate of duplicated genes in a variety of organisms, as well as factors that affect a gene’s duplicability–that is, the tendency of certain genes to retain more duplicates than others. In particular, two studies have looked at the correlation between gene duplicability and its degree in a protein-protein interaction network in yeast, mouse, and human, and another has looked at the correlation between gene duplicability and its complexity (length, number of domains, etc.) in yeast. In this paper, we extend these studies to six species, and two trends emerge. There is an increase in the duplicability-connectivity correlation that agrees with the increase in the genome size as well as the phylogenetic relationship of the species. Further, the duplicability-complexity correlation seems to be constant across the species. We argue that the observed correlations can be explained by neutral evolutionary forces acting on the genomic regions containing the genes. For the duplicability-connectivity correlation, we show through simulations that an increasing trend can be obtained by adjusting parameters to approximate genomic characteristics of the respective species. Our results call for more research into factors, adaptive and non-adaptive alike, that determine a gene’s duplicability.     

Relationship between gene duplicability and diversifiability in the topology of biochemical networks

Background

Selective gene duplicability, the extensive expansion of a small number of gene families, is universal. Quantitatively, the number of genes (P_(K)) with K duplicates in a genome decreases precipitously as K increases, and often follows a power law (P_(k)∝k^-α). Functional diversification, either neo- or sub-functionalization, is a major evolution route for duplicate genes.

Results

Using three lines of genomic datasets, we studied the relationship between gene duplicability and diversifiability in the topology of biochemical networks. First, we explored scenario where two pathways in the biochemical networks antagonize each other. Synthetic knockout of respective genes for the two pathways rescues the phenotypic defects of each individual knockout. We identified duplicate gene pairs with sufficient divergences that represent this antagonism relationship in the yeast S. cerevisiae. Such pairs overwhelmingly belong to large gene families, thus tend to have high duplicability. Second, we used distances between proteins of duplicate genes in the protein interaction network as a metric of their diversification. The higher a gene’s duplicate count, the further the proteins of this gene and its duplicates drift away from one another in the networks, which is especially true for genetically antagonizing duplicate genes. Third, we computed a sequence-homology-based clustering coefficient to quantify sequence diversifiability among duplicate genes – the lower the coefficient, the more the sequences have diverged. Duplicate count (K) of a gene is negatively correlated to the clustering coefficient of its duplicates, suggesting that gene duplicability is related to the extent of sequence divergence within the duplicate gene family.

Conclusion

Thus, a positive correlation exists between gene diversifiability and duplicability in the context of biochemical networks – an improvement of our understanding of gene duplicability.     

Co-clustering phenome�Cgenome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.     

Gene essentiality, gene duplicability and protein connectivity in human and mouse

It has previously been found that, in yeast, gene essentiality is positively correlated with protein connectivity (number of interaction partners) but negatively correlated with the existence of gene duplicates and that highly connected proteins tend to have a low gene duplicability. Using data from human and mouse, we show here that, in mammals, the first of these relationships holds true, but unlike the second relationship in yeast, highly connected mammalian proteins tend to have a high gene duplicability, and there is no correlation between gene essentiality and gene duplication in mammals.     

Modification of gene duplicability during the evolution of protein interaction network

Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human) that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.     

Complexity, connectivity, and duplicability as barriers to lateral gene transfer

Background

Lateral gene transfer is a major force in microbial evolution and a great source of genetic innovation in prokaryotes. Protein complexity has been claimed to be a barrier for gene transfer, due to either the inability of a new gene's encoded protein to become a subunit of an existing complex (lack of positive selection), or from a harmful effect exerted by the newcomer on native protein assemblages (negative selection).

Results

We tested these scenarios using data from the model prokaryote Escherichia coli. Surprisingly, the data did not support an inverse link between membership in protein complexes and gene transfer. As the complexity hypothesis, in its strictest sense, seemed valid only to essential complexes, we broadened its scope to include connectivity in general. Transferred genes are found to be less involved in protein-protein interactions, outside stable complexes, and this is especially true for genes recently transferred to the E. coli genome. Thus, subsequent to transfer, new genes probably integrate slowly into existing protein-interaction networks. We show that a low duplicability of a gene is linked to a lower chance of being horizontally transferred. Notably, many essential genes in E. coli are conserved as singletons across multiple related genomes, have high connectivity and a highly vertical phylogenetic signal.

Conclusion

High complexity and connectivity generally do not impede gene transfer. However, essential genes that exhibit low duplicability and high connectivity do exhibit mostly vertical descent.     

Gene complexity and gene duplicability

Eukaryotic genes are on average more complex than prokaryotic genes in terms of expression regulation, protein length, and protein-domain structure [1-5]. Eukaryotes are also known to have a higher rate of gene duplication than prokaryotes do [6, 7]. Because gene duplication is the primary source of new genes [], the average gene complexity in a genome may have been increased by gene duplication if complex genes are preferentially duplicated. Here, we test this "gene complexity and gene duplicability" hypothesis with yeast genomic data. We show that, on average, duplicate genes from either whole-genome or individual-gene duplication have longer protein sequences, more functional domains, and more cis-regulatory motifs than singleton genes. This phenomenon is not a by-product of previously known mechanisms, such as protein function [10-13], evolutionary rate [14, 15], dosage [11], and dosage balance [16], that influence gene duplicability. Rather, it appears to have resulted from the sub-neo-functionalization process in duplicate-gene evolution [11]. Under this process, complex genes are more likely to be retained after duplication because they are prone to subfunctionalization, and gene complexity is regained via subsequent neofunctionalization. Thus, gene duplication increases both gene number and gene complexity, two important factors in the origin of genomic and organismal complexity.     

Fine-Scale Dissection of Functional Protein Network Organization by Statistical Network Analysis

Revealing organizational principles of biological networks is an important goal of systems biology. In this study, we sought to analyze the dynamic organizational principles within the protein interaction network by studying the characteristics of individual neighborhoods of proteins within the network based on their gene expression as well as protein-protein interaction patterns. By clustering proteins into distinct groups based on their neighborhood gene expression characteristics, we identify several significant trends in the dynamic organization of the protein interaction network. We show that proteins with distinct neighborhood gene expression characteristics are positioned in specific localities in the protein interaction network thereby playing specific roles in the dynamic network connectivity. Remarkably, our analysis reveals a neighborhood characteristic that corresponds to the most centrally located group of proteins within the network. Further, we show that the connectivity pattern displayed by this group is consistent with the notion of “rich club connectivity” in complex networks. Importantly, our findings are largely reproducible in networks constructed using independent and different datasets.     

Higher Gene Duplicabilities for Metabolic Proteins Than for Nonmetabolic Proteins in Yeast and <Emphasis Type="Italic">E. coli</Emphasis>

Although the evolutionary significance of gene duplication has long been appreciated, it remains unclear what factors determine gene duplicability. In this study we investigated whether metabolism is an important determinant of gene duplicability because cellular metabolism is crucial for the survival and reproduction of an organism. Using genomic data and metabolic pathway data from the yeast (Saccharomyces cerevisiae) and Escherichia coli, we found that metabolic proteins indeed tend to have higher gene duplicability than nonmetabolic proteins. Moreover, a detailed analysis of metabolic pathways in these two organisms revealed that genes in the central metabolic pathways and the catabolic pathways have, on average, higher gene duplicability than do other genes and that most genes in anabolic pathways are single-copy genes.Reviewing Editor: Dr. Rüdiger Cerff     

Protein networks, pleiotropy and the evolution of senescence

The number of interactions, or connectivity, among proteins in the yeast protein interaction network follows a power law. I compare patterns of connectivity for subsets of yeast proteins associated with senescence and with five other traits. I find that proteins associated with ageing have significantly higher connectivity than expected by chance, a pattern not seen for most other datasets. The pattern holds even when controlling for other factors also associated with connectivity, such as localization of protein expression within the cell. I suggest that these observations are consistent with the antagonistic pleiotropy theory for the evolution of senescence. In further support of this argument, I find that a protein's connectivity is positively correlated with the number of traits it influences or its degree of pleiotropy, and further show that the average degree of pleiotropy is greatest for proteins associated with senescence. I explain these results with a simple mathematical model combining assumptions of the antagonistic pleiotropy theory for the evolution of senescence with data on network topology. These findings integrate molecular and evolutionary models of senescence, and should aid in the search for new ageing genes.     

酵母蛋白质相互作用与基因表达谱和亚细胞定位的相关性

研究酵母（yeast）蛋白质相互作用与基因表达谱和蛋白质亚细胞定位的关系.首先,构建了蛋白质相互作用正样本集、负样本集、随机组对负样本集和混合样本集.然后,对于4个数据集中的所有蛋白质对,通过比较它们的基于距离的基因共表达的分布以及它们中具有已知亚细胞定位的蛋白质对的共定位出现率,实现了这些高通量数据的交叉量化分析.结果揭示,与非相互作用蛋白质对相比,相互作用蛋白质对的基因表达谱具有较高的相似性;相互作用蛋白质对更倾向于具有相同的亚细胞定位.结果还揭示出这些蛋白质特征相关的总体趋势.     

The Nucleolar PICT-1/GLTSCR2 Protein Forms Homo-Oligomers

The human “protein interacting with carboxyl terminus 1” (PICT-1), also designated as the “glioma tumor suppressor candidate region 2 gene product”, GLTSCR2, is a nucleolar protein whose activity is, as yet, unknown. Contradictory results regarding the role of PICT-1 in cancer have been reported, and PICT-1 has been suggested to function either as a tumor suppressor protein or as an oncogene. In this study, we demonstrate self-association of PICT-1. Through yeast two-hybrid assay, we identified PICT-1 as its own interaction partner. We confirmed the interaction of PICT-1 with itself by direct yeast two-hybrid assay and also showed self-association of PICT-1 in mammalian cells by co-immunoprecipitation and fluorescence resonance energy transfer assays. Furthermore, we confirmed direct self-association of PICT-1 by using in vitro microfluidic affinity binding assays. The later assay also identified the carboxy-terminal domain as mediating self-interaction of PICT-1. Glutaraldehyde cross-linking and gel-filtration assays suggest that PICT-1 forms dimers, though it may form higher-order complexes as well. Our findings add another layer of complexity in understanding the different functions of PICT-1 and may help provide insights regarding the activities of this protein.     

酵母双杂交筛选心脏cDNA文库中与人热激蛋白70相互作用的蛋白质

目的:利用酵母双杂交系统从人心肌cDNA文库中筛选与热激蛋白70(HSP70)相互作用的蛋白质。方法:从人心脏cDNA文库扩增Hsp70基因,克隆于pGBKT7载体上,酶切鉴定及序列分析,并检测pGBKT7-Hsp70酵母细胞AH109中的自激活活性;将构建的酵母表达诱饵质粒载体pGBKT7-Hsp70转化AH109酵母细胞,与转化有人心脏cDNA文库的酵母Yl87进行交配实验,筛选与HSP70相互作用的蛋白质,通过一对一的回复杂交实验排除假阳性,对阳性克隆进行序列测定和生物信息学分析。结果:构建了"诱饵"质粒栽体pGBKT7-Hsp70,并证明其在酵母双杂交系统中无自激活活性,筛选得到多个与Hsp70相互作用的阳性转化子,并最终得到HSP70的1个相互作用蛋白质HIP。结论:应用酵母双杂交系统筛选出与HSP70相互作用的1个蛋白质,它们的相互作用可能与HSP70发挥细胞分子伴侣作用有关。