Combined analysis reveals a core set of cycling genes

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

SUMMARY

High-throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), a Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lasseignelab/CoSIA.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

A feature-based integrated scoring scheme for cell cycle-regulated genes prioritization

Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle. The reason being that the current availability of a total of 12 datasets, produced by 6 independent groups using 4 different synchronization methods, permits a re-analysis and re-consideration of this problem in a more reliable and extensive data domain. Notably, budding yeast is a model organism for studying cancer and testing new drugs. Here we propose a novel multi-feature score (called PERLA, PERiodicity, Regulation and Lag-Autocorrelation) that integrates different features of cell cycle-regulated gene expression time-profiles. We obtained increased performances on a wide range of benchmarks and, most importantly, a substantially increased overlap of the top ranking genes among different datasets, thus proving the effectiveness of the proposed prioritization algorithm. Examples on how to use PERLA to gain new insight into the biology of the cell cycle, are provided in a final dedicated section.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

Scaling single-cell genomics from phenomenology to mechanism

Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.

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.

Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue

To evaluate the taste characteristics of Chinese rice wine, wine samples sourced from different vintage years were analyzed using liquid chromatographic analysis, sensory evaluation, and an electronic tongue. Six organic acids and seventeen amino acids were measured using high performance liquid chromatography (HPLC). Five monosaccharides were measured using anion-exchange chromatography. The global taste attributes were analyzed using an electronic tongue (E-tongue). The correlations between the 28 taste-active compounds and the sensory attributes, and the correlations between the E-tongue response and the sensory attributes were established via partial least square discriminant analysis (PLSDA). E-tongue response data combined with linear discriminant analysis (LDA) were used to discriminate the Chinese rice wine samples sourced from different vintage years. Sensory evaluation indicated significant differences in the Chinese rice wine samples sourced from 2003, 2005, 2008, and 2010 vintage years in the sensory attributes of harmony and mellow. The PLSDA model for the taste-active compounds and the sensory attributes showed that proline, fucose, arabinose, lactic acid, glutamic acid, arginine, isoleucine, valine, threonine, and lysine had an influence on the taste characteristic of Chinese rice wine. The Chinese rice wine samples were all correctly classified using the E-tongue and LDA. The electronic tongue was an effective tool for rapid discrimination of Chinese rice wine.

Meta-analysis of bivariate P values

AIM: To propose a new meta-analysis method for bivariate P value which account for the paired structure.

METHODS: Studies that look to test two different features from the same sample gives rise to bivariate P value. A relevant example of this is testing for periodicity as well expression from time-course gene expression studies. Kocak et al (2010) uses George and Mudholkar’ (1983) “Difference of Two Logit-Sums” method to pool bivariate P value across independent experiments, assuming independence within a pair. As bivariate P value need not to be independent within a given study, we propose a new meta-analysis approach for pooling bivariate P value across independent experiments, which accounts for potential correlation between paired P-values. We compare the “Difference of Two Logit Sums”method with our novel approach in terms of their sensitivity and specificity through extensive simulations by generating P value samples from most commonly used tests namely, Z test, t test, chi-square test, and F test, with varying sample sizes and correlation structure.

RESULTS: The simulations results showed that our new meta-analysis approach for correlated and uncorrelated bivariate P value has much more desirable sensitivity and specificity features compared to the existing method, which treats each member of the paired P value as independent. We also compare these meta-analysis approaches on bivariate P value from periodicity and expression tests of 4936 S.Pombe genes from 10 independent time-course experiments and we showed that our new approach ranks the periodic, conserved, and cycling genes significantly higher, and detects many more periodic, “conserved” and “cycling” genes among the top 100 genes, compared to the ‘Difference of Two Logit-Sums’ method. Finally, we used our meta-analytic approach to compare the relative evidence in the association of pre-term birth with preschool wheezing versus pre-school asthma.

CONCLUSION: The new meta-analysis method has much better sensitivity and specific characteristics compared to the “Difference of Two-Logit Sums” method and it is not computationally more expensive.

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.

Identifying novel cell cycle proteins in Apicomplexa parasites through co-expression decision analysis

Hypothetical proteins comprise roughly half of the predicted gene complement of Toxoplasma gondii and Plasmodium falciparum and represent the largest class of uniquely functioning proteins in these parasites. Following the idea that functional relationships can be informed by the timing of gene expression, we devised a strategy to identify the core set of apicomplexan cell division cycling genes with important roles in parasite division, which includes many uncharacterized proteins. We assembled an expanded list of orthologs from the T. gondii and P. falciparum genome sequences (2781 putative orthologs), compared their mRNA profiles during synchronous replication, and sorted the resulting set of dual cell cycle regulated orthologs (744 total) into protein pairs conserved across many eukaryotic families versus those unique to the Apicomplexa. The analysis identified more than 100 ortholog gene pairs with unknown function in T. gondii and P. falciparum that displayed co-conserved mRNA abundance, dynamics of cyclical expression and similar peak timing that spanned the complete division cycle in each parasite. The unknown cyclical mRNAs encoded a diverse set of proteins with a wide range of mass and showed a remarkable conservation in the internal organization of ordered versus disordered structural domains. A representative sample of cyclical unknown genes (16 total) was epitope tagged in T. gondii tachyzoites yielding the discovery of new protein constituents of the parasite inner membrane complex, key mitotic structures and invasion organelles. These results demonstrate the utility of using gene expression timing and dynamic profile to identify proteins with unique roles in Apicomplexa biology.

Regulation of a transcription factor network by Cdk1 coordinates late cell cycle gene expression

To maintain genome stability, regulators of chromosome segregation must be expressed in coordination with mitotic events. Expression of these late cell cycle genes is regulated by cyclin-dependent kinase (Cdk1), which phosphorylates a network of conserved transcription factors (TFs). However, the effects of Cdk1 phosphorylation on many key TFs are not known. We find that elimination of Cdk1-mediated phosphorylation of four S-phase TFs decreases expression of many late cell cycle genes, delays mitotic progression, and reduces fitness in budding yeast. Blocking phosphorylation impairs degradation of all four TFs. Consequently, phosphorylation-deficient mutants of the repressors Yox1 and Yhp1 exhibit increased promoter occupancy and decreased expression of their target genes. Interestingly, although phosphorylation of the transcriptional activator Hcm1 on its N-terminus promotes its degradation, phosphorylation on its C-terminus is required for its activity, indicating that Cdk1 both activates and inhibits a single TF. We conclude that Cdk1 promotes gene expression by both activating transcriptional activators and inactivating transcriptional repressors. Furthermore, our data suggest that coordinated regulation of the TF network by Cdk1 is necessary for faithful cell division.

The transcription factor FOXM1 (Forkhead box M1): proliferation-specific expression, transcription factor function, target genes, mouse models, and normal biological roles

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor, which stimulates cell proliferation and exhibits a proliferation-specific expression pattern. Accordingly, both the expression and the transcriptional activity of FOXM1 are increased by proliferation signals, but decreased by antiproliferation signals, including the positive and negative regulation by protooncoproteins or tumor suppressors, respectively. FOXM1 stimulates cell cycle progression by promoting the entry into S-phase and M-phase. Moreover, FOXM1 is required for proper execution of mitosis. Accordingly, FOXM1 regulates the expression of genes, whose products control G1/S-transition, S-phase progression, G2/M-transition, and M-phase progression. Additionally, FOXM1 target genes encode proteins with functions in the execution of DNA replication and mitosis. FOXM1 is a transcriptional activator with a forkhead domain as DNA binding domain and with a very strong acidic transactivation domain. However, wild-type FOXM1 is (almost) inactive because the transactivation domain is repressed by three inhibitory domains. Inactive FOXM1 can be converted into a very potent transactivator by activating signals, which release the transactivation domain from its inhibition by the inhibitory domains. FOXM1 is essential for embryonic development and the foxm1 knockout is embryonically lethal. In adults, FOXM1 is important for tissue repair after injury. FOXM1 prevents premature senescence and interferes with contact inhibition. FOXM1 plays a role for maintenance of stem cell pluripotency and for self-renewal capacity of stem cells. The functions of FOXM1 in prevention of polyploidy and aneuploidy and in homologous recombination repair of DNA-double-strand breaks suggest an importance of FOXM1 for the maintenance of genomic stability and chromosomal integrity.

Mitotic exit and separation of mother and daughter cells

Productive cell proliferation involves efficient and accurate splitting of the dividing cell into two separate entities. This orderly process reflects coordination of diverse cytological events by regulatory systems that drive the cell from mitosis into G1. In the budding yeast Saccharomyces cerevisiae, separation of mother and daughter cells involves coordinated actomyosin ring contraction and septum synthesis, followed by septum destruction. These events occur in precise and rapid sequence once chromosomes are segregated and are linked with spindle organization and mitotic progress by intricate cell cycle control machinery. Additionally, critical paarts of the mother/daughter separation process are asymmetric, reflecting a form of fate specification that occurs in every cell division. This chapter describes central events of budding yeast cell separation, as well as the control pathways that integrate them and link them with the cell cycle.

Bck2 acts through the MADS box protein Mcm1 to activate cell-cycle-regulated genes in budding yeast

The Bck2 protein is a potent genetic regulator of cell-cycle-dependent gene expression in budding yeast. To date, most experiments have focused on assessing a potential role for Bck2 in activation of the G1/S-specific transcription factors SBF (Swi4, Swi6) and MBF (Mbp1, Swi6), yet the mechanism of gene activation by Bck2 has remained obscure. We performed a yeast two-hybrid screen using a truncated version of Bck2 and discovered six novel Bck2-binding partners including Mcm1, an essential protein that binds to and activates M/G1 promoters through Early Cell cycle Box (ECB) elements as well as to G2/M promoters. At M/G1 promoters Mcm1 is inhibited by association with two repressors, Yox1 or Yhp1, and gene activation ensues once repression is relieved by an unknown activating signal. Here, we show that Bck2 interacts physically with Mcm1 to activate genes during G1 phase. We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes. The Bck2-Mcm1 interaction requires valine 69 on Mcm1, a residue known to be required for interaction with Yox1. Overexpression of BCK2 decreases Yox1 localization to the early G1-specific CLN3 promoter and rescues the lethality caused by overexpression of YOX1. Our data suggest that Yox1 and Bck2 may compete for access to the Mcm1-ECB scaffold to ensure appropriate activation of the initial suite of genes required for cell cycle commitment.

A novel method for cross-species gene expression analysis

Background

Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.

Results

In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.

Conclusions

The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

An empirical Bayes approach for analysis of diverse periodic trends in time-course gene expression data

MOTIVATION

There is a substantial body of works in the biology literature that seeks to characterize the cyclic behavior of genes during cell division. Gene expression microarrays made it possible to measure the expression profiles of thousands of genes simultaneously in time-course experiments to assess changes in the expression levels of genes over time. In this context, the commonly used procedures for testing include the permutation test by de Lichtenberg et al. and the Fisher's G-test, both of which are designed to evaluate periodicity against noise. However, it is possible that a gene of interest may have expression that is neither cyclic nor just noise. Thus, there is a need for a new test for periodicity that can identify cyclic patterns against not only noise but also other non-cyclic patterns such as linear, quadratic or higher order polynomial patterns.

RESULTS

To address this weakness, we have introduced an empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the permutation test and Fisher's G-test through extensive simulations and by application to a set of time-course experiments on the Schizosaccharomyces pombe cell-cycle gene expression. We use 'conserved' and 'cycling' genes by Lu et al. to assess the sensitivity and CESR genes by Chenet al. to assess the specificity of our new empirical Bayes method.

AVAILABILITY AND IMPLEMENTATION

The SAS Macro for our empirical Bayes test for periodicity is included in the supplementary materials along with a sample run of the MACRO program.

CONTACT

mkocak1@uthsc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

Stochastic simulation for the inference of transcriptional control network of yeast cyclins genes

Cell cycle is controlled by the activity of protein family of cyclins and cyclin-dependent kinases that are periodically expressed during cell cycle and that are conserved among different species. Genome-wide location analysis found that cyclins are controlled by a small number of transcription factors that form closed network of genes controlling each other. To investigate gene expression dynamics of this network, we developed a general procedure for stochastic simulation of gene expression process. Using the binding data, we simulated gene expression of all genes of the network for all possible combinations of regulatory interactions and by statistical comparison with experimentally measured time series excluded those interactions that formed gene expression temporal profiles significantly different from the measured ones. These experiments led to a new definition of the cyclins regulatory network coherent with the binding experiments which are kinetically plausible. Level of influence of individual regulators in control of the regulated genes is defined. Simulation results indicate particular mechanism of regulatory activity of protein complexes involved in the control of cyclins.

Coordination of cell growth and division by the ubiquitin-proteasome system

The coupling of cellular growth and division is crucial for a cell to make an accurate copy of itself. Regulated protein degradation by the ubiquitin-proteasome system (UPS) plays an important role in the coordination of these two processes. Many ubiquitin ligases, in particular the Skp1-Cullin-F-box (SCF) family and the Anaphase-Promoting Complex (APC), couple growth and division by targeting cell cycle and metabolic regulators for degradation. However, many regulatory proteins are targeted by multiple ubiquitin ligases. As a result, we are only just beginning to understand the complexities of the proteolytic regulatory network that connects cell growth and the cell cycle.

Restriction of histone gene transcription to S phase by phosphorylation of a chromatin boundary protein

The cell cycle-regulated expression of core histone genes is required for DNA replication and proper cell cycle progression in eukaryotic cells. Although some factors involved in histone gene transcription are known, the molecular mechanisms that ensure proper induction of histone gene expression during S phase remain enigmatic. Here we demonstrate that S-phase transcription of the model histone gene HTA1 in yeast is regulated by a novel attach-release mechanism involving phosphorylation of the conserved chromatin boundary protein Yta7 by both cyclin-dependent kinase 1 (Cdk1) and casein kinase 2 (CK2). Outside S phase, integrity of the AAA-ATPase domain is required for Yta7 boundary function, as defined by correct positioning of the histone chaperone Rtt106 and the chromatin remodeling complex RSC. Conversely, in S phase, Yta7 is hyperphosphorylated, causing its release from HTA1 chromatin and productive transcription. Most importantly, abrogation of Yta7 phosphorylation results in constitutive attachment of Yta7 to HTA1 chromatin, preventing efficient transcription post-recruitment of RNA polymerase II (RNAPII). Our study identified the chromatin boundary protein Yta7 as a key regulator that links S-phase kinases with RNAPII function at cell cycle-regulated histone gene promoters.

TEMPORAL GRAPHICAL MODELS FOR CROSS-SPECIES GENE REGULATORY NETWORK DISCOVERY

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L1penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.

Conserved toxic responses across divergent phylogenetic lineages: a meta-analysis of the neurotoxic effects of RDX among multiple species using toxicogenomics

At military training sites, a variety of pollutants such as hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), may contaminate the area originating from used munitions. Studies investigating the mechanism of toxicity of RDX have shown that it affects the central nervous system causing seizures in humans and animals. Environmental pollutants such as RDX have the potential to affect many different species, therefore it is important to establish how phylogenetically distant species may respond to these types of emerging pollutants. In this paper, we have used a transcriptional network approach to compare and contrast the neurotoxic effects of RDX among five phylogenetically disparate species: rat (Sprague-Dawley), Northern bobwhite quail (Colinus virginianus), fathead minnow (Pimephales promelas), earthworm (Eisenia fetida), and coral (Acropora formosa). Pathway enrichment analysis indicated a conservation of RDX impacts on pathways related to neuronal function in rat, Northern bobwhite quail, fathead minnows and earthworm, but not in coral. As evolutionary distance increased common responses decreased with impacts on energy and metabolism dominating effects in coral. A neurotransmission related transcriptional network based on whole rat brain responses to RDX exposure was used to identify functionally related modules of genes, components of which were conserved across species depending upon evolutionary distance. Overall, the meta-analysis using genomic data of the effects of RDX on several species suggested a common and conserved mode of action of the chemical throughout phylogenetically remote organisms.

Circadian control of global gene expression patterns

An internal time-keeping mechanism has been observed in almost every organism studied from archaea to humans. This circadian clock provides a competitive advantage in fitness and survival ( 18, 30, 95, 129, 137 ). Researchers have uncovered the molecular composition of this internal clock by combining enzymology, molecular biology, genetics, and modeling approaches. However, understanding the mechanistic link between the clock and output responses has been elusive. In three model organisms, Arabidopsis thaliana, Drosophila melanogaster, and Mus musculus, whole-genome expression arrays have enabled researchers to investigate how maintaining a time-keeping mechanism connects to an adaptive advantage. Here, we review the impacts transcriptomics have had on our understanding of the clock and how this molecular clock connects with system-level circadian responses. We explore the discoveries made possible by high-throughput RNA assays, the network approaches used to investigate these large transcript datasets, and potential future directions.

Large scale comparison of global gene expression patterns in human and mouse

Background

It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress.

Results

In a principal component analysis (PCA) of combined data from human and mouse samples merged on orthologous probesets, samples largely form distinctive clusters based on their tissue sources when projected onto the top principal components. The most prominent groups are the nervous system, muscle/heart tissues, liver and cell lines. Despite the great differences in sample characteristics and experiment conditions, the overall patterns of these prominent clusters are strikingly similar for human and mouse. We further analyzed data for each tissue separately and found that the most variable genes in each tissue are highly enriched with human-mouse tissue-specific orthologs and the least variable genes in each tissue are enriched with human-mouse housekeeping orthologs.

Conclusions

The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse. The expression of groups of orthologous genes co-varies in the two species, both for the most variable genes and the most ubiquitously expressed genes.

Cross species expression analysis of innate immune response

The innate immune response is the first line of host defense against infections. This system employs a number of different types of cells, which in turn activate different sets of genes. Microarray studies of human and mouse cells infected with various pathogens identified hundreds of differentially expressed genes. However, combining these datasets to identify common and unique response patterns remained a challenge. We developed methods based on probabilistic graphical models to combine expression experiments across species, cells, and pathogens. Our method analyzes homologous genes in different species concurrently overcoming problems related to noise and orthology assignments. Using our method, we identified both core immune response genes and genes that are activated in macrophages in both human and mouse but not in dendritic cells, and vice versa. Our results shed light on immune response mechanisms and on the differences between various types of cells that are used to fight infecting bacteria. For supporting website, see www.cs.cmu.edu/-lyongu/pub/immune/.

Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast

BACKGROUND

Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction.

RESULTS

Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes.

CONCLUSION

A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.

Budding yeast SSD1-V regulates transcript levels of many longevity genes and extends chronological life span in purified quiescent cells

Ssd1 is an RNA-binding protein that affects literally hundreds of different processes and is polymorphic in both wild and lab yeast strains. We have used transcript microarrays to compare mRNA levels in an isogenic pair of mutant (ssd1-d) and wild-type (SSD1-V) cells across the cell cycle. We find that 15% of transcripts are differentially expressed, but there is no correlation with those mRNAs bound by Ssd1. About 20% of cell cycle regulated transcripts are affected, and most show sharper amplitudes of oscillation in SSD1-V cells. Many transcripts whose gene products influence longevity are also affected, the largest class of which is involved in translation. Ribosomal protein mRNAs are globally down-regulated by SSD1-V. SSD1-V has been shown to increase replicative life span currency and we show that SSD1-V also dramatically increases chronological life span (CLS). Using a new assay of CLS in pure populations of quiescent prototrophs, we find that the CLS for SSD1-V cells is twice that of ssd1-d cells.

Cross species analysis of microarray expression data

MOTIVATION

Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied.

RESULTS

In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them.

Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata

Comparative transcriptomics of Saccharomyces cerevisiae and Candida glabrata revealed a remarkable conservation of response to drug-induced stress, despite underlying differences in the regulatory networks.

Background

Recent technical and methodological advances have placed microbial models at the forefront of evolutionary and environmental genomics. To better understand the logic of genetic network evolution, we combined comparative transcriptomics, a differential clustering algorithm and promoter analyses in a study of the evolution of transcriptional networks responding to an antifungal agent in two yeast species: the free-living model organism Saccharomyces cerevisiae and the human pathogen Candida glabrata.

Results

We found that although the gene expression patterns characterizing the response to drugs were remarkably conserved between the two species, part of the underlying regulatory networks differed. In particular, the roles of the oxidative stress response transcription factors ScYap1p (in S. cerevisiae) and Cgap1p (in C. glabrata) had diverged. The sets of genes whose benomyl response depends on these factors are significantly different. Also, the DNA motifs targeted by ScYap1p and Cgap1p are differently represented in the promoters of these genes, suggesting that the DNA binding properties of the two proteins are slightly different. Experimental assays of ScYap1p and Cgap1p activities in vivo were in accordance with this last observation.

Conclusions

Based on these results and recently published data, we suggest that the robustness of environmental stress responses among related species contrasts with the rapid evolution of regulatory sequences, and depends on both the coevolution of transcription factor binding properties and the versatility of regulatory associations within transcriptional networks.

Circular reasoning rather than cyclic expression

A response to Combined analysis reveals a core set of cycling genes by Y Lu, S Mahony, PV Benos, R Rosenfeld, I Simon, LL Breeden and Z Bar-Joseph. Genome Biol 2007, 8:R146.

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into "single-cell" expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.

Cyclebase.org--a comprehensive multi-organism online database of cell-cycle experiments

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase.org, for viewing and downloading these data. The user interface facilitates searches for genes of interest as well as downloads of genome-wide results. Individual genes are displayed with graphs of expression profiles throughout the cell cycle from all available experiments. These expression profiles are normalized to a common timescale to enable inspection of the combined experimental evidence. Furthermore, state-of-the-art computational analyses provide key information on both individual experiments and combined datasets such as whether or not a gene is periodically expressed and, if so, the time of peak expression. Cyclebase is available at http://www.cyclebase.org.