Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci.
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ABSTRACT:
SUBMITTER: Wang L
PROVIDER: S-EPMC3610942 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPhylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.ResultsWe develop suitable statistical resampling schemes that can incorporate these two potential sources of correlation into a single inferential framework. To illustrate our approach we apply it to protein interaction data in yeast and investigate whether the phylogenetic trees of interacting proteins in a panel of yeast species are more similar than would be expected by chance.ConclusionsWhile we find only negligible evidence for such increased levels of similarities, our statistical approach allows us to resolve the previously reported contradictory results on the levels of co-evolution induced by protein-protein interactions. We conclude with a discussion as to how we may employ the statistical framework developed here in further functional and evolutionary analyses of biological networks and systems.
| S-EPMC2955699 | biostudies-literature
Project description:Directed evolution methodologies benefit from read-outs quantitatively linking genotype to phenotype. We therefore devised a method that couples protein-peptide interactions to the dynamic read-out provided by an engineered DNA polymerase. Fusion of a processivity clamp protein to a thermostable nucleic acid polymerase enables polymerase activity and DNA amplification in otherwise prohibitive high-salt buffers. Here, we recapitulate this phenotype by indirectly coupling the Sso7d processivity clamp to Taq DNA polymerase via respective fusion to a high affinity and thermostable interacting protein-peptide pair. Escherichia coli cells co-expressing protein-peptide pairs can directly be used in polymerase chain reactions to determine relative interaction strengths by the measurement of amplicon yields. Conditional polymerase activity is further used to link genotype to phenotype of interacting protein-peptide pairs co-expressed in E. coli using the compartmentalized self-replication directed evolution platform. We validate this approach, termed compartmentalized two-hybrid replication, by selecting for high-affinity peptides that bind two model protein partners: SpyCatcher and the large fragment of NanoLuc luciferase. We further demonstrate directed co-evolution by randomizing both protein and peptide components of the SpyCatcher-SpyTag pair and co-selecting for functionally interacting variants.
| S-EPMC7736784 | biostudies-literature
Project description:BackgroundBreast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data).ResultsOur key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites' co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites' locations are significantly different from the genome-wide CG sites' locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns.ConclusionsOur findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.
| S-EPMC9011975 | biostudies-literature
Project description:BACKGROUND: Coordinated cell growth and development requires that cells regulate the expression of large sets of genes in an appropriate manner, and one of the most complex and metabolically demanding pathways that cells must manage is that of ribosome biogenesis. Ribosome biosynthesis depends upon the activity of hundreds of gene products, and it is subject to extensive regulation in response to changing cellular conditions. We previously described an unusual property of the genes that are involved in ribosome biogenesis in yeast; a significant fraction of the genes exist on the chromosomes as immediately adjacent gene pairs. The incidence of gene pairing can be as high as 24% in some species, and the gene pairs are found in all of the possible tandem, divergent, and convergent orientations. RESULTS: We investigated co-regulated gene sets in S. cerevisiae beyond those related to ribosome biogenesis, and found that a number of these regulons, including those involved in DNA metabolism, heat shock, and the response to cellular stressors were also significantly enriched for adjacent gene pairs. We found that as a whole, adjacent gene pairs were more tightly co-regulated than unpaired genes, and that the specific gene pairing relationships that were most widely conserved across divergent fungal lineages were correlated with those genes that exhibited the highest levels of transcription. Finally, we investigated the gene positions of ribosome related genes across a widely divergent set of eukaryotes, and found a significant level of adjacent gene pairing well beyond yeast species. CONCLUSION: While it has long been understood that there are connections between genomic organization and transcriptional regulation, this study reveals that the strategy of organizing genes from related, co-regulated pathways into pairs of immediately adjacent genes is widespread, evolutionarily conserved, and functionally significant.
| S-EPMC3500266 | biostudies-literature
Project description:Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.
| S-EPMC5689832 | biostudies-literature
Project description:During the evolution of multicellular eukaryotes, gene duplication occurs frequently to generate new genes and/or functions. A duplicated gene may have a similar function to its ancestral gene. Therefore, it may be expected that duplicated genes are less likely to be critical for the survival of an organism, since there are multiple copies of the gene rendering each individual copy redundant. In this study, we explored the developmental expression patterns of duplicate gene pairs and the relationship between development co-expression and phenotypes resulting from the knockout of duplicate genes in the mouse. We define genes that generate lethal phenotypes in single gene knockout experiments as essential genes. We found that duplicate gene pairs comprised of two essential genes tend to be expressed at different stages of development, compared to duplicate gene pairs with at least one non-essential member, showing that the timing of developmental expression affects the ability of one paralogue to compensate for the loss of the other. Gene essentiality, developmental expression and gene duplication are thus closely linked.
| S-EPMC6397145 | biostudies-literature
Project description:The long-range interactions, required to the accurate predictions of tertiary structures of ?-sheet-containing proteins, are still difficult to simulate. To remedy this problem and to facilitate ?-sheet structure predictions, many efforts have been made by computational methods. However, known efforts on ?-sheets mainly focus on interresidue contacts or amino acid partners. In this study, to go one step further, we studied ?-sheets on the strand level, in which a statistical analysis was made on the terminal extensions of paired ?-strands. In most cases, the two paired ?-strands have different lengths, and terminal extensions exist. The terminal extensions are the extended part of the paired strands besides the common paired part. However, we found that the best pairing required a terminal alignment, and ?-strands tend to pair to make bigger common parts. As a result, 96.97%? of ?-strand pairs have a ratio of 25% of the paired common part to the whole length. Also 94.26% and 95.98%? of ?-strand pairs have a ratio of 40% of the paired common part to the length of the two ?-strands, respectively. Interstrand register predictions by searching interacting ?-strands from several alternative offsets should comply with this rule to reduce the computational searching space to improve the performances of algorithms.
| S-EPMC3569888 | biostudies-literature
Project description:BackgroundUsing motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes.ResultsUsing the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters. Using the STAMP web-server, 49 sequence motifs were classified into 7 motif families for which similarities with known cis-regulatory sequences were identified. All motifs were subjected to a footprintDB analysis to predict interacting DNA binding domains from plant TF families. Predictions were confirmed by using a yeast-one-hybrid approach to select interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions.ConclusionsThe present work demonstrates the successful integration of several bioinformatic resources with experimental approaches to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes.
| S-EPMC4234446 | biostudies-literature
Project description:Plant-insect interactions are common and important in basic and applied biology. Trait and genetic variation can affect the outcome and evolution of these interactions, but the relative contributions of plant and insect genetic variation and how these interact remain unclear and are rarely subject to assessment in the same experimental context. Here, we address this knowledge gap using a recent host-range expansion onto alfalfa by the Melissa blue butterfly. Common garden rearing experiments and genomic data show that caterpillar performance depends on plant and insect genetic variation, with insect genetics contributing to performance earlier in development and plant genetics later. Our models of performance based on caterpillar genetics retained predictive power when applied to a second common garden. Much of the plant genetic effect could be explained by heritable variation in plant phytochemicals, especially saponins, peptides, and phosphatidyl cholines, providing a possible mechanistic understanding of variation in the species interaction. We find evidence of polygenic, mostly additive effects within and between species, with consistent effects of plant genotype on growth and development across multiple butterfly species. Our results inform theories of plant-insect coevolution and the evolution of diet breadth in herbivorous insects and other host-specific parasites.
| S-EPMC9456756 | biostudies-literature
Project description:Homoeologs are pairs of genes or chromosomes in the same species that originated by speciation and were brought back together in the same genome by allopolyploidization. Bioinformatic methods for accurate homoeology inference are crucial for studying the evolutionary consequences of polyploidization, and homoeology is typically inferred on the basis of bidirectional best hit (BBH) and/or positional conservation (synteny). However, these methods neglect the fact that genes can duplicate and move, both prior to and after the allopolyploidization event. These duplications and movements can result in many-to-many and/or nonsyntenic homoeologs-which thus remain undetected and unstudied. Here, using the allotetraploid upland cotton (Gossypium hirsutum) as a case study, we show that conventional approaches indeed miss a substantial proportion of homoeologs. Additionally, we found that many of the missed pairs of homoeologs are broadly and highly expressed. A gene ontology analysis revealed a high proportion of the nonsyntenic and non-BBH homoeologs to be involved in protein translation and are likely to contribute to the functional repertoire of cotton. Thus, from an evolutionary and functional genomics standpoint, choosing a homoeolog inference method which does not solely rely on 1:1 relationship cardinality or synteny is crucial for not missing these potentially important homoeolog pairs.
| S-EPMC8214411 | biostudies-literature