Comparative gene expression between two yeast species

Using genome-wide expression compendia to study microorganisms

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.

Complete Genome Sequencing and Comparative Analysis of the Clinically-Derived Apiotrichum mycotoxinivorans Strain GMU1709

Over the past decade, Apiotrichum mycotoxinivorans has been recognized globally as a source of opportunistic infections. It is a yeast-like fungus, and its association as an uncommon pulmonary pathogen with cystic fibrosis patients has been previously reported. Immunocompromised patients are at the highest risk of A. mycotoxinivorans infections. Therefore, to investigate the genetic basis for the pathogenicity of A. mycotoxinivorans, we performed whole-genome sequencing and comparative genomic analysis of A. mycotoxinivorans GMU1709 that was isolated from sputum specimens of a pneumonia patient receiving cardiac repair surgery. The assembly of Oxford Nanopore reads from the GMU1709 strain and its subsequent correction using Illumina paired-end reads yielded a high-quality complete genome with a genome size of 30.5 Mb in length, which comprised six chromosomes and one mitochondrion. Subsequently, 8,066 protein-coding genes were predicted based on multiple pieces of evidence, including transcriptomes. Phylogenomic analysis indicated that A. mycotoxinivorans exhibited the closest evolutionary affinity to A. veenhuisii, and both the A. mycotoxinivorans strains and the formerly Trichosporon cutaneum ACCC 20271 strain occupied the same phylogenetic position. Further comparative analysis supported that the ACCC 20271 strain belonged to A. mycotoxinivorans. Comparisons of three A. mycotoxinivorans strains indicated that the differences between clinical and non-clinical strains in pathogenicity and drug resistance may be little or none. Based on the comparisons with strains of other species in the Trichosporonaceae family, we identified potential key genetic factors associated with A. mycotoxinivorans infection or pathogenicity. In addition, we also deduced that A. mycotoxinivorans had great potential to inactivate some antibiotics (e.g., tetracycline), which may affect the efficacy of these drugs in co-infection. In general, our analyses provide a better understanding of the classification and phylogeny of the Trichosporonaceae family, uncover the underlying genetic basis of A. mycotoxinivorans infections and associated drug resistance, and provide clues into potential targets for further research and the therapeutic intervention of infections.

Network-based protein-protein interaction prediction method maps perturbations of cancer interactome

The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.

Accurate differential analysis of transcription factor activity from gene expression

MOTIVATION

Activity of transcriptional regulators is crucial in elucidating the mechanism of phenotypes. However regulatory activity hypotheses are difficult to experimentally test. Therefore, we need accurate and reliable computational methods for regulator activity inference. There is extensive work in this area, however, current methods have difficulty with one or more of the following: resolving activity of TFs with overlapping regulons, reflecting known regulatory relationships, or flexible modeling of TF activity over the regulon.

RESULTS

We present Effector and Perturbation Estimation Engine (EPEE), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses each of these principal challenges in the field. Firstly, EPEE collectively models all TF activity in a single multivariate model, thereby accounting for the intrinsic coupling among TFs that share targets, which is highly frequent. Secondly, EPEE incorporates context-specific TF-gene regulatory networks and therefore adapts the analysis to each biological context. Finally, EPEE can flexibly reflect different regulatory activity of a single TF among its potential targets. This allows the flexibility to implicitly recover other regulatory influences such as co-activators or repressors. We comparatively validated EPEE in 15 datasets from three well-studied contexts, namely immunology, cancer, and hematopoiesis. We show that addressing the aforementioned challenges enable EPEE to outperform alternative methods and reliably produce accurate results.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Cobanoglu-Lab/EPEE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ICGEC: a comparative method for measuring epigenetic conservation of genes via the integrated signal from multiple histone modifications between cell types

Background

Histone post-translational modifications play crucial roles in epigenetic regulation of gene expression and are known to be associated with the phenotypic differences of different cell types. Therefore, it is of fundamental importance to dissect the genes and pathways involved in such a phenotypic variation at the level of epigenetics. However, the existing comparative approaches are largely based on the differences, especially the absolute difference in the levels of individual histone modifications of genes under contrasting conditions. Thus, a method for measuring the overall change in the epigenetic circumstance of each gene underpinned by multiple types of histone modifications between cell types is lacking.

Results

To address this challenge, we developed ICGEC, a new method for estimating the degree of epigenetic conservation of genes between two cell lines. Different from existing comparative methods, ICGEC provides a reliable score for measuring the relative change in the epigenetic context of corresponding gene between two conditions and simultaneously produces a score for each histone mark. The application of ICGEC to the human embryonic stem cell line H1 and four H1-derived cell lines with available epigenomic data for the same 16 types of histone modifications indicated high robustness and reliability of ICGEC. Furthermore, the analysis of the epigenetically dynamic and conserved genes which were defined based on the ICGEC output results demonstrated that ICGEC can deepen our understanding of the biological processes of cell differentiation to overcome the limitations of traditional expression analysis. Specifically, the ICGEC-derived differentiation-direction-specific genes were shown to have putative functions that are well-matched with cell identity. Additionally, H3K79me1 and H3K27ac were found to be the main histone marks accounting for whether an epigenetically dynamic gene was differentially expressed between two cell lines.

Conclusions

The use of ICGEC creates a convenient and robust way to measure the overall epigenetic conservation of individual genes and marks between two conditions. Thus, it provides a basis for exploring the epigenotype-phenotype relationship. ICGEC can be deemed a state-of-the-art method tailored for comparative epigenomic analysis of changes in cell dynamics.

Transposon insertional mutagenesis in Saccharomyces uvarum reveals trans-acting effects influencing species-dependent essential genes

To understand how complex genetic networks perform and regulate diverse cellular processes, the function of each individual component must be defined. Comprehensive phenotypic studies of mutant alleles have been successful in model organisms in determining what processes depend on the normal function of a gene. These results are often ported to newly sequenced genomes by using sequence homology. However, sequence similarity does not always mean identical function or phenotype, suggesting that new methods are required to functionally annotate newly sequenced species. We have implemented comparative analysis by high-throughput experimental testing of gene dispensability in Saccharomyces uvarum, a sister species of Saccharomyces cerevisiae. We created haploid and heterozygous diploid Tn7 insertional mutagenesis libraries in S. uvarum to identify species-dependent essential genes, with the goal of detecting genes with divergent functions and/or different genetic interactions. Comprehensive gene dispensability comparisons with S. cerevisiae predicted diverged dispensability at 12% of conserved orthologs, and validation experiments confirmed 22 differentially essential genes. Despite their differences in essentiality, these genes were capable of cross-species complementation, demonstrating that trans-acting factors that are background-dependent contribute to differential gene essentiality. This study shows that direct experimental testing of gene disruption phenotypes across species can inform comparative genomic analyses and improve gene annotations. Our method can be widely applied in microorganisms to further our understanding of genome evolution.

Genome-wide copy number variation (CNV) detection in Nelore cattle reveals highly frequent variants in genome regions harboring QTLs affecting production traits

Background

Copy number variations (CNVs) have been shown to account for substantial portions of observed genomic variation and have been associated with qualitative and quantitative traits and the onset of disease in a number of species. Information from high-resolution studies to detect, characterize and estimate population-specific variant frequencies will facilitate the incorporation of CNVs in genomic studies to identify genes affecting traits of importance.

Results

Genome-wide CNVs were detected in high-density single nucleotide polymorphism (SNP) genotyping data from 1,717 Nelore (Bos indicus) cattle, and in NGS data from eight key ancestral bulls. A total of 68,007 and 12,786 distinct CNVs were observed, respectively. Cross-comparisons of results obtained for the eight resequenced animals revealed that 92 % of the CNVs were observed in both datasets, while 62 % of all detected CNVs were observed to overlap with previously validated cattle copy number variant regions (CNVRs). Observed CNVs were used for obtaining breed-specific CNV frequencies and identification of CNVRs, which were subsequently used for gene annotation. A total of 688 of the detected CNVRs were observed to overlap with 286 non-redundant QTLs associated with important production traits in cattle. All of 34 CNVs previously reported to be associated with milk production traits in Holsteins were also observed in Nelore cattle. Comparisons of estimated frequencies of these CNVs in the two breeds revealed 14, 13, 6 and 14 regions in high (>20 %), low (<20 %) and divergent (NEL > HOL, NEL < HOL) frequencies, respectively.

Conclusions

Obtained results significantly enriched the bovine CNV map and enabled the identification of variants that are potentially associated with traits under selection in Nelore cattle, particularly in genome regions harboring QTLs affecting production traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2752-9) contains supplementary material, which is available to authorized users.

Identifying Network Perturbation in Cancer

We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project.

Differences in environmental stress response among yeasts is consistent with species-specific lifestyles

Defining how organisms respond to environmental change has always been an important step toward understanding their adaptive capacity and physiology. Variation in transcription during stress has been widely described in model species, especially in the yeast Saccharomyces cerevisiae, which helped to shape general rules regarding how cells cope with environmental constraints, as well as to decipher the functions of many genes. Comparison of the environmental stress response (ESR) across species is essential to obtaining better insight into the common and species-specific features of stress defense. In this context, we explored the transcriptional landscape of the yeast Lachancea kluyveri (formerly Saccharomyces kluyveri) in response to diverse stresses, using RNA sequencing. We investigated variation in gene expression and observed a link between genetic plasticity and environmental sensitivity. We identified the ESR genes in this species and compared them to those already found in S. cerevisiae We observed common features between the two species, as well as divergence in the regulatory networks involved. Of interest, some changes were related to differences in species lifestyle. Thus we were able to decipher how adaptation to stress has evolved among different yeast species. Finally, by analyzing patterns of coexpression, we were able to propose potential biological functions for 42% of genes and also annotate 301 genes for which no function could be assigned by homology. This large data set allowed for the characterization of the evolution of gene regulation and provides an efficient tool for assessing gene function.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

Create, activate, destroy, repeat: Cdk1 controls proliferation by limiting transcription factor activity

Progression through the cell cycle is controlled by a network of transcription factors that coordinate gene expression with cell-cycle events. One transcriptional activator in this network in budding yeast is the forkhead protein Hcm1, which controls the expression of genes that are transcribed during S-phase. Hcm1 activity is coordinated with the cell cycle via its regulation by cyclin-dependent kinase (Cdk1), which both activates Hcm1 and targets it for degradation, through phosphorylation of distinct sites. The mechanisms controlling the differential phosphorylation timing of the activating and destabilizing phosphosites are not clear. However, a recent study shows that the phosphatase calcineurin specifically removes activating phosphates from Hcm1 when cells are exposed to environmental stress, thus extinguishing its activity and slowing proliferation under unfavorable growth conditions. This regulatory mechanism, whereby a phosphatase actively alters the distribution of phosphosites on a cell cycle-regulatory transcription factor to elicit a change in cellular proliferation, adds an additional layer of complexity to the regulatory network controlling the cell cycle. Furthermore, this regulatory paradigm is likely to be a conserved mode of phosphoregulation that controls the cell cycle in diverse systems.

Evolution of intraspecific transcriptomic landscapes in yeasts

Variations in gene expression have been widely explored in order to obtain an accurate overview of the changes in regulatory networks that underlie phenotypic diversity. Numerous studies have characterized differences in genomic expression between large numbers of individuals of model organisms such as Saccharomyces cerevisiae. To more broadly survey the evolution of the transcriptomic landscape across species, we measured whole-genome expression in a large collection of another yeast species: Lachancea kluyveri (formerly Saccharomyces kluyveri), using RNAseq. Interestingly, this species diverged from the S. cerevisiae lineage prior to its ancestral whole genome duplication. Moreover, L. kluyveri harbors a chromosome-scale compositional heterogeneity due to a 1-Mb ancestral introgressed region as well as a large set of unique unannotated genes. In this context, our comparative transcriptomic analysis clearly showed a link between gene evolutionary history and expression behavior. Indeed, genes that have been recently acquired or under function relaxation tend to be less transcribed show a higher intraspecific variation (plasticity) and are less involved in network (connectivity). Moreover, utilizing this approach in L. kluyveri also highlighted specific regulatory network signatures in aerobic respiration, amino-acid biosynthesis and glycosylation, presumably due to its different lifestyle. Our data set sheds an important light on the evolution of intraspecific transcriptomic variation across distant species.

Topology and control of the cell-cycle-regulated transcriptional circuitry

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls.

Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

Background

The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigated preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data.

Results

The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles.

Conclusions

Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional annotation. We identified R. capsulatus modules enriched with genes for ribosomal proteins, porphyrin and bacteriochlorophyll anabolism, and biosynthesis of secondary metabolites to be preserved in R. sphaeroides whereas modules related to RcGTA production and signalling showed lack of preservation in R. sphaeroides. In addition, we demonstrated that network statistics may also be applied within-species to identify congruence between mRNA expression and protein abundance data for which simple correlation measurements have previously had mixed results.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-730) contains supplementary material, which is available to authorized users.

Conservation and divergence of gene expression plasticity following c. 140 million years of evolution in lodgepole pine (Pinus contorta) and interior spruce (Picea glauca×Picea engelmannii)

Species respond to environmental stress through a combination of genetic adaptation and phenotypic plasticity, both of which may be important for survival in the face of climatic change. By characterizing the molecular basis of plastic responses and comparing patterns among species, it is possible to identify how such traits evolve. Here, we used de novo transcriptome assembly and RNAseq to explore how patterns of gene expression differ in response to temperature, moisture, and light regime treatments in lodgepole pine (Pinus contorta) and interior spruce (a natural hybrid population of Picea glauca and Picea engelmannii). We found wide evidence for an effect of treatment on expression within each species, with 6413 and 11,658 differentially expressed genes identified in spruce and pine, respectively. Comparing patterns of expression among these species, we found that 74% of all orthologs with differential expression had a pattern that was conserved in both species, despite 140 million yr of evolution. We also found that the specific treatments driving expression patterns differed between genes with conserved versus diverged patterns of expression. We conclude that natural selection has probably played a role in shaping plastic responses to environment in these species.

A new system for comparative functional genomics of Saccharomyces yeasts

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.