Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression

Global early replication disrupts gene expression and chromatin conformation in a single cell cycle

Background

The early embryonic divisions of many organisms, including fish, flies, and frogs, are characterized by a very rapid S-phase caused by high rates of replication initiation. In somatic cells, S-phase is much longer due to both a reduction in the total number of initiation events and the imposition of a temporal order of origin activation. The physiological importance of changes in the rate and timing of replication initiation in S-phase remains unclear.

Results

Here we assess the importance of the temporal control of replication initiation using a conditional system in budding yeast to drive the early replication of the majority of origins in a single cell cycle. We show that global early replication disrupts the expression of over a quarter of all genes. By deleting individual origins, we show that delaying replication is sufficient to restore normal gene expression, directly implicating origin firing control in this regulation. Global early replication disrupts nucleosome positioning and transcription factor binding during S-phase, suggesting that the rate of S-phase is important to regulate the chromatin landscape.

Conclusions

Together, these data provide new insight into the role of the temporal control of origin firing during S-phase for coordinating replication, gene expression, and chromatin establishment as occurs in the early embryo.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02788-7.

Point centromere activity requires an optimal level of centromeric noncoding RNA

Budding yeast harbors a simple point centromere, which is originally believed to be sequence dependent without much epigenetic regulation and is transcription incompatible, as inserting a strong promoter upstream inactivates the centromere completely. Here, we demonstrate that an optimal level centromeric noncoding RNA is required for budding yeast centromere activity. Centromeric transcription is induced in S phase, coinciding with the assembly of new centromeric proteins. Too much or too little centromeric noncoding RNA leads to centromere malfunction. Overexpression of centromeric noncoding RNA reduces the protein levels and chromatin localization of inner centromere and kinetochore proteins, such as CENP-A, CENP-C, and the chromosome passenger complex. This work shows that point centromere is epigenetically regulated by noncoding RNA.

In budding yeast, which possesses simple point centromeres, we discovered that all of its centromeres express long noncoding RNAs (cenRNAs), especially in S phase. Induction of cenRNAs coincides with CENP-A^Cse4 loading time and is dependent on DNA replication. Centromeric transcription is repressed by centromere-binding factor Cbf1 and histone H2A variant H2A.Z^Htz1. Deletion of CBF1 and H2A.Z^HTZ1 results in an up-regulation of cenRNAs; an increased loss of a minichromosome; elevated aneuploidy; a down-regulation of the protein levels of centromeric proteins CENP-A^Cse4, CENP-A chaperone HJURP^Scm3, CENP-C^Mif2, Survivin^Bir1, and INCENP^Sli15; and a reduced chromatin localization of CENP-A^Cse4, CENP-C^Mif2, and Aurora B^Ipl1. When the RNA interference system was introduced to knock down all cenRNAs from the endogenous chromosomes, but not the cenRNA from the circular minichromosome, an increase in minichromosome loss was still observed, suggesting that cenRNA functions in trans to regulate centromere activity. CenRNA knockdown partially alleviates minichromosome loss in cbf1Δ, htz1Δ, and cbf1Δ htz1Δ in a dose-dependent manner, demonstrating that cenRNA level is tightly regulated to epigenetically control point centromere function.

DNA replication timing influences gene expression level

Eukaryotic genomes are replicated in a reproducible temporal order whose physiological significance is poorly understood. Müller and Nieduszynski compare the temporal order of genome replication in phylogenetically diverse yeast species and identify genes for which conserved replication timing contributes to maximal expression.

Eukaryotic genomes are replicated in a reproducible temporal order; however, the physiological significance is poorly understood. We compared replication timing in divergent yeast species and identified genomic features with conserved replication times. Histone genes were among the earliest replicating loci in all species. We specifically delayed the replication of HTA1-HTB1 and discovered that this halved the expression of these histone genes. Finally, we showed that histone and cell cycle genes in general are exempt from Rtt109-dependent dosage compensation, suggesting the existence of pathways excluding specific loci from dosage compensation mechanisms. Thus, we have uncovered one of the first physiological requirements for regulated replication time and demonstrated a direct link between replication timing and gene expression.

Tensor factorization toward precision medicine

Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability.

Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

We use the generalized singular value decomposition (GSVD), formulated as a comparative spectral decomposition, to model patient-matched grades III and II, i.e., lower-grade astrocytoma (LGA) brain tumor and normal DNA copy-number profiles. A genome-wide tumor-exclusive pattern of DNA copy-number alterations (CNAs) is revealed, encompassed in that previously uncovered in glioblastoma (GBM), i.e., grade IV astrocytoma, where GBM-specific CNAs encode for enhanced opportunities for transformation and proliferation via growth and developmental signaling pathways in GBM relative to LGA. The GSVD separates the LGA pattern from other sources of biological and experimental variation, common to both, or exclusive to one of the tumor and normal datasets. We find, first, and computationally validate, that the LGA pattern is correlated with a patient's survival and response to treatment. Second, the GBM pattern identifies among the LGA patients a subtype, statistically indistinguishable from that among the GBM patients, where the CNA genotype is correlated with an approximately one-year survival phenotype. Third, cross-platform classification of the Affymetrix-measured LGA and GBM profiles by using the Agilent-derived GBM pattern shows that the GBM pattern is a platform-independent predictor of astrocytoma outcome. Statistically, the pattern is a better predictor (corresponding to greater median survival time difference, proportional hazard ratio, and concordance index) than the patient's age and the tumor's grade, which are the best indicators of astrocytoma currently in clinical use, and laboratory tests. The pattern is also statistically independent of these indicators, and, combined with either one, is an even better predictor of astrocytoma outcome. Recurring DNA CNAs have been observed in astrocytoma tumors' genomes for decades, however, copy-number subtypes that are predictive of patients' outcomes were not identified before. This is despite the growing number of datasets recording different aspects of the disease, and due to an existing fundamental need for mathematical frameworks that can simultaneously find similarities and dissimilarities across the datasets. This illustrates the ability of comparative spectral decompositions to find what other methods miss.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Genome-wide replication landscape of Candida glabrata

Background

The opportunistic pathogen Candida glabrata is a member of the Saccharomycetaceae yeasts. Like its close relative Saccharomyces cerevisiae, it underwent a whole-genome duplication followed by an extensive loss of genes. Its genome contains a large number of very long tandem repeats, called megasatellites. In order to determine the whole replication program of the C. glabrata genome and its general chromosomal organization, we used deep-sequencing and chromosome conformation capture experiments.

Results

We identified 253 replication fork origins, genome wide. Centromeres, HML and HMR loci, and most histone genes are replicated early, whereas natural chromosomal breakpoints are located in late-replicating regions. In addition, 275 autonomously replicating sequences (ARS) were identified during ARS-capture experiments, and their relative fitness was determined during growth competition. Analysis of ARSs allowed us to identify a 17-bp consensus, similar to the S. cerevisiae ARS consensus sequence but slightly more constrained. Megasatellites are not in close proximity to replication origins or termini. Using chromosome conformation capture, we also show that early origins tend to cluster whereas non-subtelomeric megasatellites do not cluster in the yeast nucleus.

Conclusions

Despite a shorter cell cycle, the C. glabrata replication program shares unexpected striking similarities to S. cerevisiae, in spite of their large evolutionary distance and the presence of highly repetitive large tandem repeats in C. glabrata. No correlation could be found between the replication program and megasatellites, suggesting that their formation and propagation might not be directly caused by replication fork initiation or termination.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-015-0177-6) contains supplementary material, which is available to authorized users.

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

OBJECTIVE

Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.

METHODS

The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria.

RESULTS AND CONCLUSION

SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.

Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival

The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient’s prognosis, is independent of the tumor’s stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding CDKN1A and p38-encoding MAPK14 and amplification of RAD51AP1 and KRAS encode for human cell transformation, and are correlated with a cell’s immortality, and a patient’s shorter survival time. In 7p, RPA3 deletion and POLD2 amplification are correlated with DNA stability, and a longer survival. In Xq, PABPC5 deletion and BCAP31 amplification are correlated with a cellular immune response, and a longer survival.

SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism

To search for evolutionary forces that might act upon transcript length, we use the singular value decomposition (SVD) to identify the length distribution functions of sets and subsets of human and yeast transcripts from profiles of mRNA abundance levels across gel electrophoresis migration distances that were previously measured by DNA microarrays. We show that the SVD identifies the transcript length distribution functions as “asymmetric generalized coherent states” from the DNA microarray data and with no a-priori assumptions. Comparing subsets of human and yeast transcripts of the same gene ontology annotations, we find that in both disparate eukaryotes, transcripts involved in protein synthesis or mitochondrial metabolism are significantly shorter than typical, and in particular, significantly shorter than those involved in glucose metabolism. Comparing the subsets of human transcripts that are overexpressed in glioblastoma multiforme (GBM) or normal brain tissue samples from The Cancer Genome Atlas, we find that GBM maintains normal brain overexpression of significantly short transcripts, enriched in transcripts that are involved in protein synthesis or mitochondrial metabolism, but suppresses normal overexpression of significantly longer transcripts, enriched in transcripts that are involved in glucose metabolism and brain activity. These global relations among transcript length, cellular metabolism and tumor development suggest a previously unrecognized physical mode for tumor and normal cells to differentially regulate metabolism in a transcript length-dependent manner. The identified distribution functions support a previous hypothesis from mathematical modeling of evolutionary forces that act upon transcript length in the manner of the restoring force of the harmonic oscillator.

Cytomegalovirus replicon-based regulation of gene expression in vitro and in vivo

There is increasing evidence for a connection between DNA replication and the expression of adjacent genes. Therefore, this study addressed the question of whether a herpesvirus origin of replication can be used to activate or increase the expression of adjacent genes. Cell lines carrying an episomal vector, in which reporter genes are linked to the murine cytomegalovirus (MCMV) origin of lytic replication (oriLyt), were constructed. Reporter gene expression was silenced by a histone-deacetylase-dependent mechanism, but was resolved upon lytic infection with MCMV. Replication of the episome was observed subsequent to infection, leading to the induction of gene expression by more than 1000-fold. oriLyt-based regulation thus provided a unique opportunity for virus-induced conditional gene expression without the need for an additional induction mechanism. This principle was exploited to show effective late trans-complementation of the toxic viral protein M50 and the glycoprotein gO of MCMV. Moreover, the application of this principle for intracellular immunization against herpesvirus infection was demonstrated. The results of the present study show that viral infection specifically activated the expression of a dominant-negative transgene, which inhibited viral growth. This conditional system was operative in explant cultures of transgenic mice, but not in vivo. Several applications are discussed.

All herpesviruses show a precisely regulated gene expression profile, including true-late genes, which are turned on only after the onset of DNA replication. We used this intrinsic viral mechanism to generate a versatile conditional gene expression system that exploits the activity of the murine cytomegalovirus (MCMV) viral origin of lytic replication (oriLyt). Upon virus infection, replication of the viral genome also led to the replication and activation of the oriLyt-coupled episomal transgene. The oriLyt-based replicons were silenced in all stable cell lines and transgenic mice; however, virus infection liberated the plasmids from histone-deacetylase-induced inactivation. As maximum gene expression relied on relief from silencing via replication of the episomal constructs, very strong induction of the reporter gene was achieved. We showed that this system can be used for trans-complementation of late, toxic viral genes, to block virus production by activating dominant-negative (DN) transgenes, and to provide a new tool to study the principles of viral replication.

Maintaining replication origins in the face of genomic change

Origins of replication present a paradox to evolutionary biologists. As a collection, they are absolutely essential genomic features, but individually are highly redundant and nonessential. It is therefore difficult to predict to what extent and in what regard origins are conserved over evolutionary time. Here, through a comparative genomic analysis of replication origins and chromosomal replication patterns in the budding yeasts Saccharomyces cerevisiae and Lachancea waltii, we assess to what extent replication origins survived genomic change produced from 150 million years of evolution. We find that L. waltii origins exhibit a core consensus sequence and nucleosome occupancy pattern highly similar to those of S. cerevisiae origins. We further observe that the overall progression of chromosomal replication is similar between L. waltii and S. cerevisiae. Nevertheless, few origins show evidence of being conserved in location between the two species. Among the conserved origins are those surrounding centromeres and adjacent to histone genes, suggesting that proximity to an origin may be important for their regulation. We conclude that, over evolutionary time, origins maintain sequence, structure, and regulation, but are continually being created and destroyed, with the result that their locations are generally not conserved.

On a fundamental structure of gene networks in living cells

Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival

Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in >3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern.

A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms

The number of high-dimensional datasets recording multiple aspects of a single phenomenon is increasing in many areas of science, accompanied by a need for mathematical frameworks that can compare multiple large-scale matrices with different row dimensions. The only such framework to date, the generalized singular value decomposition (GSVD), is limited to two matrices. We mathematically define a higher-order GSVD (HO GSVD) for N≥2 matrices , each with full column rank. Each matrix is exactly factored as D_i = U_iΣ_iV^T, where V, identical in all factorizations, is obtained from the eigensystem SV = VΛ of the arithmetic mean S of all pairwise quotients of the matrices , i≠j. We prove that this decomposition extends to higher orders almost all of the mathematical properties of the GSVD. The matrix S is nondefective with V and Λ real. Its eigenvalues satisfy λ_k≥1. Equality holds if and only if the corresponding eigenvector v_k is a right basis vector of equal significance in all matrices D_i and D_j, that is σ_i,k/σ_j,k = 1 for all i and j, and the corresponding left basis vector u_i,k is orthogonal to all other vectors in U_i for all i. The eigenvalues λ_k = 1, therefore, define the “common HO GSVD subspace.” We illustrate the HO GSVD with a comparison of genome-scale cell-cycle mRNA expression from S. pombe, S. cerevisiae and human. Unlike existing algorithms, a mapping among the genes of these disparate organisms is not required. We find that the approximately common HO GSVD subspace represents the cell-cycle mRNA expression oscillations, which are similar among the datasets. Simultaneous reconstruction in the common subspace, therefore, removes the experimental artifacts, which are dissimilar, from the datasets. In the simultaneous sequence-independent classification of the genes of the three organisms in this common subspace, genes of highly conserved sequences but significantly different cell-cycle peak times are correctly classified.

OriDB, the DNA replication origin database updated and extended

OriDB (http://www.oridb.org/) is a database containing collated genome-wide mapping studies of confirmed and predicted replication origin sites. The original database collated and curated Saccharomyces cerevisiae origin mapping studies. Here, we report that the OriDB database and web site have been revamped to improve user accessibility to curated data sets, to greatly increase the number of curated origin mapping studies, and to include the collation of replication origin sites in the fission yeast Schizosaccharomyces pombe. The revised database structure underlies these improvements and will facilitate further expansion in the future. The updated OriDB for S. cerevisiae is available at http://cerevisiae.oridb.org/ and for S. pombe at http://pombe.oridb.org/.

Interactions between DNA damage, repair, and transcription

This review addresses a variety of mechanisms by which DNA repair interacts with transcription and vice versa. Blocking of transcriptional elongation is the best studied of these mechanisms. Transcription recovery after damage therefore has often been used as a surrogate marker of DNA repair in cells. However, it has become evident that relationships between DNA damage, repair, and transcription are more complex due to various indirect effects of DNA damage on gene transcription. These include inhibition of transcription by DNA repair intermediates as well as regulation of transcription and of the epigenetic status of the genes by DNA repair-related mechanisms. In addition, since transcription is emerging as an important endogenous source of DNA damage in cells, we briefly summarise recent advances in understanding the nature of co-transcriptionally induced DNA damage and the DNA repair pathways involved.

Cruciform structures are a common DNA feature important for regulating biological processes

DNA cruciforms play an important role in the regulation of natural processes involving DNA. These structures are formed by inverted repeats, and their stability is enhanced by DNA supercoiling. Cruciform structures are fundamentally important for a wide range of biological processes, including replication, regulation of gene expression, nucleosome structure and recombination. They also have been implicated in the evolution and development of diseases including cancer, Werner's syndrome and others.

Cruciform structures are targets for many architectural and regulatory proteins, such as histones H1 and H5, topoisomerase IIβ, HMG proteins, HU, p53, the proto-oncogene protein DEK and others. A number of DNA-binding proteins, such as the HMGB-box family members, Rad54, BRCA1 protein, as well as PARP-1 polymerase, possess weak sequence specific DNA binding yet bind preferentially to cruciform structures. Some of these proteins are, in fact, capable of inducing the formation of cruciform structures upon DNA binding. In this article, we review the protein families that are involved in interacting with and regulating cruciform structures, including (a) the junction-resolving enzymes, (b) DNA repair proteins and transcription factors, (c) proteins involved in replication and (d) chromatin-associated proteins. The prevalence of cruciform structures and their roles in protein interactions, epigenetic regulation and the maintenance of cell homeostasis are also discussed.

Defining the replication program through the chromatin landscape

DNA replication is an essential cell cycle event required for the accurate and timely duplication of the chromosomes. It is essential that the genome is replicated accurately and completely within the confines of S-phase. Failure to completely copy the genome has the potential to result in catastrophic genomic instability. Replication initiates in a coordinated manner from multiple locations, termed origins of replication, distributed across each of the chromosomes. The selection of these origins of replication is a dynamic process responding to both developmental and tissue-specific signals. In this review, we explore the role of the local chromatin environment in regulating the DNA replication program at the level of origin selection and activation. Finally, there is increasing molecular evidence that the DNA replication program itself affects the chromatin landscape, suggesting that DNA replication is critical for both genetic and epigenetic inheritance.

Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural motifs in ribosomal RNA

Evolutionary relationships among organisms are commonly described by using a hierarchy derived from comparisons of ribosomal RNA (rRNA) sequences. We propose that even on the level of a single rRNA molecule, an organism's evolution is composed of multiple pathways due to concurrent forces that act independently upon different rRNA degrees of freedom. Relationships among organisms are then compositions of coexisting pathway-dependent similarities and dissimilarities, which cannot be described by a single hierarchy. We computationally test this hypothesis in comparative analyses of 16S and 23S rRNA sequence alignments by using a tensor decomposition, i.e., a framework for modeling composite data. Each alignment is encoded in a cuboid, i.e., a third-order tensor, where nucleotides, positions and organisms, each represent a degree of freedom. A tensor mode-1 higher-order singular value decomposition (HOSVD) is formulated such that it separates each cuboid into combinations of patterns of nucleotide frequency variation across organisms and positions, i.e., "eigenpositions" and corresponding nucleotide-specific segments of "eigenorganisms," respectively, independent of a-priori knowledge of the taxonomic groups or rRNA structures. We find, in support of our hypothesis that, first, the significant eigenpositions reveal multiple similarities and dissimilarities among the taxonomic groups. Second, the corresponding eigenorganisms identify insertions or deletions of nucleotides exclusively conserved within the corresponding groups, that map out entire substructures and are enriched in adenosines, unpaired in the rRNA secondary structure, that participate in tertiary structure interactions. This demonstrates that structural motifs involved in rRNA folding and function are evolutionary degrees of freedom. Third, two previously unknown coexisting subgenic relationships between Microsporidia and Archaea are revealed in both the 16S and 23S rRNA alignments, a convergence and a divergence, conferred by insertions and deletions of these motifs, which cannot be described by a single hierarchy. This shows that mode-1 HOSVD modeling of rRNA alignments might be used to computationally predict evolutionary mechanisms.

Convergence of logic of cellular regulation in different premalignant cells by an information theoretic approach

BACKGROUND

Surprisal analysis is a thermodynamic-like molecular level approach that identifies biological constraints that prevents the entropy from reaching its maximum. To examine the significance of altered gene expression levels in tumorigenesis we apply surprisal analysis to the WI-38 model through its precancerous states. The constraints identified by the analysis are transcription patterns underlying the process of transformation. Each pattern highlights the role of a group of genes that act coherently to define a transformed phenotype.

RESULTS

We identify a major transcription pattern that represents a contraction of signaling networks accompanied by induction of cellular proliferation and protein metabolism, which is essential for full transformation. In addition, a more minor, "tumor signature" transcription pattern completes the transformation process. The variation with time of the importance of each transcription pattern is determined. Midway through the transformation, at the stage when cells switch from slow to fast growth rate, the major transcription pattern undergoes a total inversion of its weight while the more minor pattern does not contribute before that stage.

CONCLUSIONS

A similar network reorganization occurs in two very different cellular transformation models: WI-38 and the cervical cancer HF1 models. Our results suggest that despite differences in a list of transcripts expressed in different cancer models the rationale of the network reorganization remains essentially the same.

Coordinated progression through two subtranscriptomes underlies the tachyzoite cycle of Toxoplasma gondii

BACKGROUND

Apicomplexan parasites replicate by varied and unusual processes where the typically eukaryotic expansion of cellular components and chromosome cycle are coordinated with the biosynthesis of parasite-specific structures essential for transmission.

METHODOLOGY/PRINCIPAL FINDINGS

Here we describe the global cell cycle transcriptome of the tachyzoite stage of Toxoplasma gondii. In dividing tachyzoites, more than a third of the mRNAs exhibit significant cyclical profiles whose timing correlates with biosynthetic events that unfold during daughter parasite formation. These 2,833 mRNAs have a bimodal organization with peak expression occurring in one of two transcriptional waves that are bounded by the transition into S phase and cell cycle exit following cytokinesis. The G1-subtranscriptome is enriched for genes required for basal biosynthetic and metabolic functions, similar to most eukaryotes, while the S/M-subtranscriptome is characterized by the uniquely apicomplexan requirements of parasite maturation, development of specialized organelles, and egress of infectious daughter cells. Two dozen AP2 transcription factors form a series through the tachyzoite cycle with successive sharp peaks of protein expression in the same timeframes as their mRNA patterns, indicating that the mechanisms responsible for the timing of protein delivery might be mediated by AP2 domains with different promoter recognition specificities.

CONCLUSION/SIGNIFICANCE

Underlying each of the major events in apicomplexan cell cycles, and many more subordinate actions, are dynamic changes in parasite gene expression. The mechanisms responsible for cyclical gene expression timing are likely crucial to the efficiency of parasite replication and may provide new avenues for interfering with parasite growth.