A systems approach to mapping DNA damage response pathways

ChIP-exo and CRISPRi/a illuminate the role of Pdr1 and Yap1 in acetic acid tolerance in Saccharomyces cerevisiae

Budding yeast Saccharomyces cerevisiae has great potential as a host organism for various biorefinery applications. Nevertheless, the utilization of renewable plant biomass as feedstock for yeast in industrial applications remains a bottleneck, largely due to the presence of inhibitory substances such as acetic acid that are released in the biomass pretreatment processes. Exposure to acetic acid leads to different cellular stress mechanisms, several of which are directed by transcription factors. In this work, the role of the transcription factors Pdr1 and Yap1 in acetic acid tolerance was investigated using ChIP-exo and CRISPR interference/activation (CRISPRi/a). Pdr1 is the main regulator of the pleiotropic drug response, whereas Yap1 governs the oxidative stress response. CRISPRa targeting YAP1 for overexpression conferred a higher specific growth rate of S. cerevisiae, whereas CRISPRi-based downregulation of PDR1 proved to be beneficial for growth in medium containing acetic acid. ChIP-exo experiments showed increased binding of Pdr1 or Yap1 to their target promoters in the presence of acetic acid, and a large number of promoters were bound by either transcription factor. Promoters of genes involved in amino acid synthesis or encoding ABC transporters had the highest level of binding enrichment in the presence of acetic acid. The results highlight the potential for developing more acetic acid-tolerant yeast by altering the expression of transcription factor-encoding genes and demonstrate how expression can be fine-tuned by CRISPRi/a.IMPORTANCEBiotechnological conversion of plant biomass into a variety of commodity chemicals and specialty molecules is an important step towards a bioeconomy. This study highlights the importance of two transcription factors, Pdr1 and Yap1, in the tolerance of Saccharomyces cerevisiae to acetic acid, a common inhibitor in bioprocesses using lignocellulosic biomass. CRISPR interference/activation and ChIP-exo were used to manipulate the expression and binding of these transcription factors in response to acetic acid stress. The study provides new insights into adaptation to acetic acid and suggests ways to improve yeast performance in industrial applications.

The yeast checkpoint kinase Dun1p represses transcription of RNR genes independently of catalytic activity or Rad53p during respiratory growth

One of the key events in DNA damage response is activation of checkpoint kinases leading to activation of ribonucleotide reductase (RNR) and increased synthesis of deoxyribonucleotide triphosphates (dNTPs) required for DNA repair. Among other mechanisms, the activation of dNTP synthesis is driven by derepression of genes encoding RNR subunits RNR2, RNR3, and RNR4, following checkpoint activation and checkpoint kinase Dun1p-mediated phosphorylation and inactivation of transcriptional repressor Crt1p. We report here that in the absence of genotoxic stress during respiratory growth on nonfermentable carbon source acetate, inactivation of checkpoint kinases results in significant growth defect and alters transcriptional regulation of RNR2-4 genes and genes encoding enzymes of the tricarboxylic acid and glyoxylate cycles and gluconeogenesis. Dun1p, independently of its kinase activity or signaling from the upstream checkpoint kinase Rad53p, represses RNR2, RNR3, and RNR4 genes by maintaining Crt1p occupancy in the corresponding promoters. Consistently with the role of dNTPs in the regulation of mitochondrial DNA copy number, DUN1 inactivation elevates mitochondrial DNA copy number in acetate-grown cells. Together, our data reveal an unexpected role for Dun1p in transcriptional regulation of RNR2-4 and metabolic genes during growth on nonfermentable carbon source and suggest that Dun1p contributes to transcription regulation independently of its kinase activity as a structural component by binding to protein(s) involved in gene regulation.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Activity of the pleiotropic drug resistance transcription factors Pdr1p and Pdr3p is modulated by binding site flanking sequences

The transcription factors Pdr1p and Pdr3p regulate pleiotropic drug resistance (PDR) in Saccharomyces cerevisiae via the PDR responsive elements (PDREs) to modulate gene expression. However, the exact mechanisms underlying the differences in their regulons remain unclear. Employing genomic occupancy profiling (CUT&RUN), binding assays, and transcription studies, we characterized the differences in sequence specificity between transcription factors. Findings reveal distinct preferences for core PDRE sequences and the flanking sequences for both proteins. While flanking sequences moderately alter DNA binding affinity, they significantly impact Pdr1/3p transcriptional activity. Notably, both proteins demonstrated the ability to bind half sites, showing potential enhancement of transcription from adjacent PDREs. This insight sheds light on ways Pdr1/3p can differentially regulate PDR.

Unraveling the Half and Full Site Sequence Specificity of the Saccharomyces cerevisiae Pdr1p and Pdr3p Transcription Factors

The transcription factors Pdr1p and Pdr3p regulate pleotropic drug resistance (PDR) in Saccharomyces cerevisiae , via the PDR responsive elements (PDREs) to modulate gene expression. However, the exact mechanisms underlying the differences in their regulons remain unclear. Employing genomic occupancy profiling (CUT&RUN), binding assays, and transcription studies, we characterized the differences in sequence specificity between transcription factors. Findings reveal distinct preferences for core PDRE sequences and the flanking sequences for both proteins. While flanking sequences moderately alter DNA binding affinity, they significantly impact Pdr1/3p transcriptional activity. Notably, both proteins demonstrated the ability to bind half sites, showing potential enhancement of transcription from adjacent PDREs. This insight sheds light on ways Pdr1/3 can differentially regulate PDR.

SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid

The nuclear envelope (NE) is important in maintaining genome organization. The role of lipids in communication between the NE and telomere regulation was investigated, including how changes in lipid composition impact gene expression and overall nuclear architecture. Yeast was treated with the non-metabolizable lysophosphatidylcholine analog edelfosine, known to accumulate at the perinuclear ER. Edelfosine induced NE deformation and disrupted telomere clustering but not anchoring. Additionally, the association of Sir4 at telomeres decreased. RNA-seq analysis showed altered expression of Sir-dependent genes located at sub-telomeric (0-10 kb) regions, consistent with Sir4 dispersion. Transcriptomic analysis revealed that two lipid metabolic circuits were activated in response to edelfosine, one mediated by the membrane sensing transcription factors, Spt23/Mga2, and the other by a transcriptional repressor, Opi1. Activation of these transcriptional programs resulted in higher levels of unsaturated fatty acids and the formation of nuclear lipid droplets. Interestingly, cells lacking Sir proteins displayed resistance to unsaturated-fatty acids and edelfosine, and this phenotype was connected to Rap1.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Effects of modified clay on the formation of Phaeocystis globosa colony revealed by physiological and transcriptomic analyses

The harmful algal bloom (HAB) species Phaeocystis globosa is commonly observed in global temperate and tropical oceans, and colonies of P. globosa exhibit a dominant morphotype during blooms. The use of polyaluminium chloride modified clay (PAC-MC) is an effective mitigation strategy for P. globosa blooms. Although previous studies have found that PAC-MC can stimulate P. globosa colony formation at low concentrations and inhibit it at higher concentrations, the underlying mechanisms of these effects are poorly understood. Here, we comprehensively compared the physiochemical indices and transcriptomic response of residual P. globosa cells after treatment with two concentrations of PAC-MC. The results showed that PAC-MC induced oxidative stress, photosynthetic inhibition, and DNA damage in residual cells. Moreover, it could activate antioxidant responses and enhance the repair of photosynthetic structure and DNA damage in cells. The biosynthesis of polysaccharides was enhanced and genes associated with cell motility were down-regulated after treatment with PAC-MC, resulting in the accumulation of colonial matrixes. After treatment with a low concentration of PAC-MC (0.1 g/L), the residual cells were slightly stressed, including physical damage, oxidative stress and other damage, and polysaccharide synthesis was enhanced to promote colony formation to alleviate environmental stress. Moreover, the damage to residual cells was slight; thus, normal cell function provided abundant energy and matter for colony formation. After treatment with a high concentration of PAC-MC (0.5 g/L), the residual cells suffered severe damage, which disrupted normal physiological processes and inhibited cell proliferation and colony formation. The present study elucidated the concentration-dependent mechanism of PAC-MC affecting the formation of P. globosa colonies and provided a reference for the application of PAC-MC to control P. globosa blooms.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Learning causal networks using inducible transcription factors and transcriptome-wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

DNA damage induces Yap5-dependent transcription of ECO1/CTF7 in Saccharomyces cerevisiae

Yeast Eco1 (ESCO2 in humans) acetyltransferase converts chromatin-bound cohesins to a DNA tethering state, thereby establishing sister chromatid cohesion. Eco1 establishes cohesion during DNA replication, after which Eco1 is targeted for degradation by SCF E3 ubiquitin ligase. SCF E3 ligase, and sequential phosphorylations that promote Eco1 ubiquitination and degradation, remain active throughout the M phase. In this way, Eco1 protein levels are high during S phase, but remain low throughout the remaining cell cycle. In response to DNA damage during M phase, however, Eco1 activity increases-providing for a new wave of cohesion establishment (termed Damage-Induced Cohesion, or DIC) which is critical for efficient DNA repair. To date, little evidence exists as to the mechanism through which Eco1 activity increases during M phase in response to DNA damage. Possibilities include that either the kinases or E3 ligase, that target Eco1 for degradation, are inhibited in response to DNA damage. Our results reveal instead that the degradation machinery remains fully active during M phase, despite the presence of DNA damage. In testing alternate models through which Eco1 activity increases in response to DNA damage, the results reveal that DNA damage induces new transcription of ECO1 and at a rate that exceeds the rate of Eco1 turnover, providing for rapid accumulation of Eco1 protein. We further show that DNA damage induction of ECO1 transcription is in part regulated by Yap5-a stress-induced transcription factor. Given the role for mutated ESCO2 (homolog of ECO1) in human birth defects, this study highlights the complex nature through which mutation of ESCO2, and defects in ESCO2 regulation, may promote developmental abnormalities and contribute to various diseases including cancer.

Cancer Fighting SiRNA-RRM2 Loaded Nanorobots

BACKGROUND

Silencing of several genes is critical for cancer therapy. These genes may be apoptotic gene, cell proliferation gene, DNA synthesis gene, etc. The two subunits of Ribonucleotide Reductase (RR), RRM1 and RRM2, are critical for DNA synthesis. Hence, targeting the blockage of DNA synthesis at tumor site can be a smart mode of cancer therapy. Specific targeting of blockage of RRM2 is done effectively by SiRNA. The drawbacks of siRNA delivery in the body include the poor uptake by all kinds of cells, questionable stability under physiological condition, non-target effect and ability to trigger the immune response. These obstacles may be overcome by target delivery of siRNA at the tumor site. This review presents a holistic overview regarding the role of RRM2 in controlling cancer progression. The nanoparticles are more effective due to specific characteristics like cell membrane penetration capacity, less toxicity, etc. RRM2 have been found to be elevated in different types of cancer and identified as the prognostic and predictive marker of the disease. Reductase RRM1 and RRM2 regulate the protein and gene expression of E2F, which is critical for protein expression and progression of cell cycle and cancer. The knockdown of RRM2 leads to apoptosis via Bcl2 in cancer. Both Bcl2 and E2F are critical in the progression of cancer, hence a gene that can affect both in regulating DNA replication is essential for cancer therapy.

AIM

The aim of the review is to identify the related gene whose silencing may inhibit cancer progression.

CONCLUSION

In this review, we illuminate the critical link between RRM-E2F, RRM-Bcl2, RRM-HDAC for the therapy of cancer. Altogether, this review presents an overview of all types of SiRNA targeted for cancer therapy with special emphasis on RRM2 for controlling the tumor progression.

The bZIP transcription factor FpAda1 is essential for fungal growth and conidiation in Fusarium pseudograminearum

Fusarium pseudograminearum is an important pathogen of Fusarium crown rot and Fusarium head blight, which is able to infect wheat and barley worldwide, causing great economic losses. Transcription factors (TFs) of the basic leucine zipper (bZIP) protein family control important processes in all eukaryotes. In this study, we identified a gene, designated FpAda1, encoding a bZIP TF in F. pseudograminearum. The homolog of FpAda1 is also known to affect hyphal growth in Neurospora crassa. Deletion of FpAda1 in F. pseudograminearum resulted in defects in hyphal growth, mycelial branching and conidia formation. Pathogenicity assays showed that virulence of the Δfpada1 mutant was dramatically decreased on wheat coleoptiles and barley leaves. However, wheat coleoptile inoculation assay showed that Δfpada1 could penetrate and proliferate in wheat cells. Moreover, the FpAda1 was required for abnormal nuclear morphology in conidia and transcription of FpCdc2 and FpCdc42. Taken together, these results indicate that FpAda1 is an important transcription factor involved in growth and development in F. pseudograminearum.

Transcriptional responses to DNA damage

In response to the threat of DNA damage, cells exhibit a dramatic and multi-factorial response spanning from transcriptional changes to protein modifications, collectively known as the DNA damage response (DDR). Here, we review the literature surrounding the transcriptional response to DNA damage. We review differences in observed transcriptional responses as a function of cell cycle stage and emphasize the importance of experimental design in these transcriptional response studies. We additionally consider topics including structural challenges in the transcriptional response to DNA damage as well as the connection between transcription and protein abundance.

Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin K1

The K1 A/B toxin secreted by virus-infected Saccharomyces cerevisiae strains kills sensitive cells via disturbance of cytoplasmic membrane functions. Despite decades of research, the mechanisms underlying K1 toxicity and immunity have not been elucidated yet. In a novel approach, this study aimed to characterize transcriptome changes in K1-treated sensitive yeast cells in a time-dependent manner. Global transcriptional profiling revealed substantial cellular adaptations in target cells resulting in 1,189 differentially expressed genes in total. Killer toxin K1 induced oxidative, cell wall and hyperosmotic stress responses as well as rapid down-regulation of transcription and translation. Essential pathways regulating energy metabolism were also significantly affected by the toxin. Remarkably, a futile cycle of the osmolytes trehalose and glycogen was identified probably representing a critical feature of K1 intoxication. In silico analysis suggested several transcription factors involved in toxin-triggered signal transduction. The identified transcriptome changes provide valuable hints to illuminate the still unknown molecular events leading to K1 toxicity and immunity implicating an evolutionarily conserved response at least initially counteracting ionophoric toxin action.

DNA damage response activates respiration and thereby enlarges dNTP pools to promote cell survival in budding yeast

The DNA damage response (DDR) is an evolutionarily conserved process essential for cell survival. Previously, we found that decreased histone expression induces mitochondrial respiration, raising the question whether the DDR also stimulates respiration. Here, using oxygen consumption and ATP assays, RT-qPCR and ChIP-qPCR methods, and dNTP analyses, we show that DDR activation in the budding yeast Saccharomyces cerevisiae, either by genetic manipulation or by growth in the presence of genotoxic chemicals, induces respiration. We observed that this induction is conferred by reduced transcription of histone genes and globally decreased DNA nucleosome occupancy. This globally altered chromatin structure increased the expression of genes encoding enzymes of tricarboxylic acid cycle, electron transport chain, oxidative phosphorylation, elevated oxygen consumption, and ATP synthesis. The elevated ATP levels resulting from DDR-stimulated respiration drove enlargement of dNTP pools; cells with a defect in respiration failed to increase dNTP synthesis and exhibited reduced fitness in the presence of DNA damage. Together, our results reveal an unexpected connection between respiration and the DDR and indicate that the benefit of increased dNTP synthesis in the face of DNA damage outweighs possible cellular damage due to increased oxygen metabolism.

Efficient proximal gradient algorithm for inference of differential gene networks

Background

Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes.

Results

In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1.

Conclusion

With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2749-x) contains supplementary material, which is available to authorized users.

The response to the DNA damaging agent methyl methanesulfonate in a fungal plant pathogen

DNA damage can cause mutations that in fungal plant pathogens lead to hypervirulence and resistance to pesticides. Almost nothing is known about the response of these fungi to DNA damage. We performed transcriptomic and phosphoproteomic analyses of Fusarium oxysporum exposed to methyl methanesulfonate (MMS). At the RNA level we observe massive induction of DNA repair pathways including the global genome nucleotide excision. Cul3, Cul4, several Ubiquitin-like ligases and components of the proteasome are significantly induced. In agreement, we observed drug synergism between a proteasome inhibitor and MMS. While our data suggest that Yap1 and Xbp1 networks are similarly activated in response to damage in yeast and F. oxysporum we were able to observe modules that were MMS-responsive in F. oxysporum and not in yeast. These include transcription/splicing modules that are upregulated and respiration that is down-regulated. In agreement, MMS treated cells are much more sensitive to a respiration inhibitor. At the phosphoproteomic level, Adenylate cyclase, which generates cAMP, is phosphorylated in response to MMS and forms a network of phosphorylated proteins that include cell cycle regulators and several MAPKs. Our analysis provides a starting point in understanding how genomic changes in response to DNA damage occur in Fusarium species.

Proteotoxicity from aberrant ribosome biogenesis compromises cell fitness

To achieve maximal growth, cells must manage a massive economy of ribosomal proteins (r-proteins) and RNAs (rRNAs) to produce thousands of ribosomes every minute. Although ribosomes are essential in all cells, natural disruptions to ribosome biogenesis lead to heterogeneous phenotypes. Here, we model these perturbations in Saccharomyces cerevisiae and show that challenges to ribosome biogenesis result in acute loss of proteostasis. Imbalances in the synthesis of r-proteins and rRNAs lead to the rapid aggregation of newly synthesized orphan r-proteins and compromise essential cellular processes, which cells alleviate by activating proteostasis genes. Exogenously bolstering the proteostasis network increases cellular fitness in the face of challenges to ribosome assembly, demonstrating the direct contribution of orphan r-proteins to cellular phenotypes. We propose that ribosome assembly is a key vulnerability of proteostasis maintenance in proliferating cells that may be compromised by diverse genetic, environmental, and xenobiotic perturbations that generate orphan r-proteins.

Cells are made up of thousands of different proteins that perform unique roles required for life. To create all of these proteins, cells use machines called ribosomes that are partly formed of elements known as r-proteins. When cells grow and divide, the ribosomes have to make copies of themselves through a process called ribosome biogenesis.

Although all cells need ribosomes, certain types of cells are especially sensitive to events that interfere with ribosome biogenesis. For example, patients that have mutations in genes needed for ribosome biogenesis produce fewer red blood cells, but their other cells and tissues are mostly healthy. It is not clear why some cells are more sensitive than others.

Ribosome biogenesis is very similar between different organisms, so researchers often use budding yeast as a model to study the process. Here, Tye et al. used genetic and chemical tools to interfere with ribosome biogenesis on short time scales, which made it possible to detect early on what was going wrong in the cells.

The experiments found that when ribosome biogenesis was disrupted, r-proteins that were waiting to be assembled into ribosomes quickly stuck to one another and formed clumps that reduced the ability of the yeast cells to grow. The cells responded by switching on a protein called Hsf1, which restored their ability to grow. Yeast cells that were growing quickly, and therefore making more ribosomes, were more sensitive to abnormal ribosome biogenesis than slow-growing cells.

These results indicate that how actively a cell is growing, and its ability to cope with r-proteins sticking together, may in part explain why certain cells are more vulnerable to events that interfere with ribosome biogenesis. Since human cells also have Hsf1, future experiments could investigate whether turning it on might also protect fast-growing human cells from such events.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

Chromosome territory relocation paradigm during DNA damage response: Some insights from molecular biology to physics

Among the many facets of DNA damage response (DDR), relocation of chromosome territories (CTs) is most intriguing. We have previously reported that cisplatin induced DDR in human dermal fibroblasts led to relocation of CTs 12, 15 from the nuclear periphery to its interior while CTs 19, 17 repositioned from the interior to its periphery. Studies of CT relocation remain nascent as we begin unraveling the role of key players in DDR to demonstrate its mechanistic basis. Consolidating our recent reports, we argue that γH2AX-signaling leads to enhanced recruitment of nuclear myosin 1 (NM1) to chromatin, which via its motor function, results in CT repositioning. Next, we invoke a novel systems-level theory that subsumed CTs as pairs, not solo entities, to present the physical basis for plasticity in interphase CT arrangement. Subsequently, we posited that our systems-level theory describes a unified physical basis for non-random positioning of CTs in interphase nuclei across disparate eukaryotes.

The Local Edge Machine: inference of dynamic models of gene regulation

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002

Alkylating Agent-Induced NRF2 Blocks Endoplasmic Reticulum Stress-Mediated Apoptosis via Control of Glutathione Pools and Protein Thiol Homeostasis

Alkylating agents are a commonly used cytotoxic class of anticancer drugs. Understanding the mechanisms whereby cells respond to these drugs is key to identify means to improve therapy while reducing toxicity. By integrating genome-wide gene expression profiling, protein analysis, and functional cell validation, we herein demonstrated a direct relationship between NRF2 and Endoplasmic Reticulum (ER) stress pathways in response to alkylating agents, which is coordinated by the availability of glutathione (GSH) pools. GSH is essential for both drug detoxification and protein thiol homeostasis within the ER, thus inhibiting ER stress induction and promoting survival, an effect independent of its antioxidant role. NRF2 accumulation induced by alkylating agents resulted in increased GSH synthesis via GCLC/GCLM enzyme, and interfering with this NRF2 response by either NRF2 knockdown or GCLC/GCLM inhibition with buthionine sulfoximine caused accumulation of damaged proteins within the ER, leading to PERK-dependent apoptosis. Conversely, upregulation of NRF2, through KEAP1 depletion or NRF2-myc overexpression, or increasing GSH levels with N-acetylcysteine or glutathione-ethyl-ester, decreased ER stress and abrogated alkylating agents-induced cell death. Based on these results, we identified a subset of lung and head-and-neck carcinomas with mutations in either KEAP1 or NRF2/NFE2L2 genes that correlate with NRF2 target overexpression and poor survival. In KEAP1-mutant cancer cells, NRF2 knockdown and GSH depletion increased cell sensitivity via ER stress induction in a mechanism specific to alkylating drugs. Overall, we show that the NRF2-GSH influence on ER homeostasis implicates defects in NRF2-GSH or ER stress machineries as affecting alkylating therapy toxicity. Mol Cancer Ther; 15(12); 3000-14. ©2016 AACR.

Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human

Alkylating agents are a key component of cancer chemotherapy. Several cellular mechanisms are known to be important for its survival, particularly DNA repair and xenobiotic detoxification, yet genomic screens indicate that additional cellular components may be involved. Elucidating these components has value in either identifying key processes that can be modulated to improve chemotherapeutic efficacy or may be altered in some cancers to confer chemoresistance. We therefore set out to reevaluate our prior Drosophila RNAi screening data by comparison to gene expression arrays in order to determine if we could identify any novel processes in alkylation damage survival. We noted a consistent conservation of alkylation survival pathways across platforms and species when the analysis was conducted on a pathway/process level rather than at an individual gene level. Better results were obtained when combining gene lists from two datasets (RNAi screen plus microarray) prior to analysis. In addition to previously identified DNA damage responses (p53 signaling and Nucleotide Excision Repair), DNA-mRNA-protein metabolism (transcription/translation) and proteasome machinery, we also noted a highly conserved cross-species requirement for NRF2, glutathione (GSH)-mediated drug detoxification and Endoplasmic Reticulum stress (ER stress)/Unfolded Protein Responses (UPR) in cells exposed to alkylation. The requirement for GSH, NRF2 and UPR in alkylation survival was validated by metabolomics, protein studies and functional cell assays. From this we conclude that RNAi/gene expression fusion is a valid strategy to rapidly identify key processes that may be extendable to other contexts beyond damage survival.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.

RNA Processing and Genome Stability: Cause and Consequence

It is emerging that the pathways that process newly transcribed RNA molecules also regulate the response to DNA damage at multiple levels. Here, we discuss recent insights into how RNA processing pathways participate in DNA damage recognition, signaling, and repair, selectively influence the expression of genome-stabilizing proteins, and resolve deleterious DNA/RNA hybrids (R-loops) formed during transcription and RNA processing. The importance of these pathways for the DNA damage response (DDR) is underscored by the growing appreciation that defects in these regulatory connections may be connected to the genome instability involved in several human diseases, including cancer.

Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide

Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

In vitro antiproliferative and apoptosis-inducing properties of a mononuclear copper(II) complex with dppz ligand, in two genotypically different breast cancer cell lines

In the background that there is concerted effort to discover newer metal-based cancer chemotherapeutic agents that could overcome the limitations in cisplatin and that copper, a biocompatible and redox-active metal, offers potential as alternative to cisplatin, the present study was undertaken to investigate the in vitro anti-proliferative properties of the mononuclear copper(II)complex [Cu(L)(diimine)] + where LH = 2-[(2-dimethylaminoethylimino)methyl]phenol and diimine = dipyrido[3,2-a:2',3'-c]phenazine (dppz) using breast cancer cell lines MCF-7 (ER(+ve) and p53(WT)) and MDA-MB-231(ER(-ve) and p53(mutant)) when cisplatin was used as positive control. The complex affected the viability of both the cell lines in dose-as well as duration-dependent manner as revealed in the MTT assay. The 24 and 48 h IC50 of the complex were several times lesser than those of cisplatin, and within this huge difference the efficacy of the complex was much superior with MCF-7 cell compared to MDA-MB-231 cell. The cell death was preferentially apoptosis, though necrosis also occurred to a certain extent. These inferences were substantiated by AO/EB fluorescent staining, Hoechst staining, assessment of mitochondrial transmembrane potential, comet assay for DNA damage, DCFH assay for reactive oxygen species (ROS) generation and Western blot of apoptosis-related proteins. Thus, the copper(II) dppz complex under investigation is much more efficient than cisplatin in affecting viability of the breast cancer cells. The underlying mechanism appears to be DNA damage-primed (in view of the known intercalation mode of binding of the complex with DNA) and ROS-associated mitochondria-mediated intrinsic apoptosis to a great extent but necrosis also has a role to a certain extent, which may also be a PARP-mediated cell death independent of apoptosis. Within the purview of this conclusion, the results indicate that the ER and/or p53 genotypes have a bearing on the efficacy of the complex as a cytotoxic agent since the response in the ER(-ve) and p53(mutant) MDA-MB-231 cell was not so prominent as in ER(+ve) and p53(WT) MCF-7 cell. Taken together, the complex has been shown to be a potential DNA damaging agent and, in the light of the superiority of the complex over cisplatin, we are further investigating the possibility of targeted nano-delivery of the complex to the tumor cells. When tested on a normal cell, 3T3, Cu(II)dppz was found to affect its viability but at concentrations very high compared to those for the breast cancer cells. Yet, this is a cause of concern and, therefore, we are working out a strategy for targeted delivery of this complex to the cancer cells only.

Tilting Plant Metabolism for Improved Metabolite Biosynthesis and Enhanced Human Benefit

The immense chemical diversity of plant-derived secondary metabolites coupled with their vast array of biological functions has seen this group of compounds attract considerable research interest across a range of research disciplines. Medicinal and aromatic plants, in particular, have been exploited for this biogenic pool of phytochemicals for products such as pharmaceuticals, fragrances, dyes, and insecticides, among others. With consumers showing increasing interests in these products, innovative biotechnological techniques are being developed and employed to alter plant secondary metabolism in efforts to improve on the quality and quantity of specific metabolites of interest. This review provides an overview of the biosynthesis for phytochemical compounds with medicinal and other related properties and their associated biological activities. It also provides an insight into how their biosynthesis/biosynthetic pathways have been modified/altered to enhance production.

Autoregulation of ZEB2 expression for zearalenone production in Fusarium graminearum

Several Fusarium species produce the polyketide mycotoxin zearalenone (ZEA), a causative agent of hyperestrogenic syndrome in animals that is often found in F. graminearum-infected cereals in temperate regions. The ZEA biosynthetic cluster genes PKS4, PKS13, ZEB1 and ZEB2 encode a reducing polyketide synthase, a non-reducing polyketide synthase, an isoamyl alcohol oxidase and a transcription factor respectively. In this study, the production of two isoforms (ZEB2L and ZEB2S) from the ZEB2 gene in F. graminearum via an alternative promoter was characterized. ZEB2L contains a basic leucine zipper (bZIP) DNA-binding domain at the N-terminus, whereas ZEB2S is an N-terminally truncated form of ZEB2L that lacks the bZIP domain. Interestingly, ZEA triggers the induction of both ZEB2L and ZEB2S transcription. ZEB2L and ZEB2S interact with each other to form a heterodimer that regulates ZEA production by reducing the binding affinity of ZEB2L for the ZEB2L gene promoter. Our study provides insight into the autoregulation of ZEB2 expression by alternative promoter usage and a feedback loop during ZEA production; this regulatory mechanism is similar to that observed in higher eukaryotes.

Personal exposure to PM2.5, genetic variants and DNA damage: a multi-center population-based study in Chinese

Exposure to particulate matter (e.g., PM2.5) may result in DNA damage, a major culprit in mutagenesis and environmental toxicity. DNA damage levels may vary among individuals simultaneously exposed to PM2.5, however, the genetic determinants are still unclear. To explore whether PM2.5 exposure and genetic variants contribute to the alteration in DNA damage, we recruited 328 subjects from three independent cohorts (119 from Zhuhai, 123 from Wuhan and 86 from Tianjin) in southern, central and northern China with different PM2.5 exposure levels. Personal 24-h PM2.5 exposure levels and DNA damage levels of peripheral blood lymphocytes were evaluated. Genotyping were performed using Illumina Human Exome BeadChip with 241,305 single nucleotide variants (SNVs). The DNA damage levels are consistent with the PM2.5 exposure levels of each cohort. A total of 35 SNVs were consistently associated with DNA damage levels among the three cohorts with pooled P values less than 1.00×10(-3) after adjustment for age, gender, smoking status and PM2.5 exposure levels, of which, 18 SNVs together with gender and PM2.5 exposure levels were independent factors contributing to DNA damage. Gene-based test revealed 3 genes significantly associated with DNA damage levels (P=5.11×10(-3) for POLH, P=2.88×10(-3) for RIT2 and P=2.29×10(-2) for CNTN4). Gene ontology (GO) analyses indicated that the identified variants were significantly enriched in DNA damage response pathway. Our findings highlight the importance of genetic variation as well as personal PM2.5 exposure in modulating individual DNA damage levels.

CRISPR/Cas9-mediated genome engineering of CHO cell factories: Application and perspectives

Chinese hamster ovary (CHO) cells are the most widely used production host for therapeutic proteins. With the recent emergence of CHO genome sequences, CHO cell line engineering has taken on a new aspect through targeted genome editing. The bacterial clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system enables rapid, easy and efficient engineering of mammalian genomes. It has a wide range of applications from modification of individual genes to genome-wide screening or regulation of genes. Facile genome editing using CRISPR/Cas9 empowers researchers in the CHO community to elucidate the mechanistic basis behind high level production of proteins and product quality attributes of interest. In this review, we describe the basis of CRISPR/Cas9-mediated genome editing and its application for development of next generation CHO cell factories while highlighting both future perspectives and challenges. As one of the main drivers for the CHO systems biology era, genome engineering with CRISPR/Cas9 will pave the way for rational design of CHO cell factories.

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

Stress-induced DNA damage: a case study in diffuse large B-cell lymphoma

DNA damage is one of the mechanisms of mutagenesis. Sequence integrity may be affected by the action of thermal changes, chemical agents, both endogenous and exogenous, and other environmental issues. Abnormally high mutation rates are referred to as genomic instability: a phenomenon closely related to the onset of cancer. Mutant genotypes may be able to confer some kind of selective advantage on subclonal cell populations, leading them to multiply until dominance in a localized tissue environment that later becomes the tumour. Cellular stress, especially that of oxidative and ionic nature, is a recognized trigger for DNA-damaging processes. A physico-chemical model has shown that high hysteresis rates in DNA denaturation curves may be indicative of dissipative processes inducing DNA damage, thus potentially leading to uncontrolled mutagenesis and genome instability. We here study selectively to what extent this phenomenon may occur by analysing the sequence length and composition effects on the thermodynamic behaviour and the presence of hysteresis in pressure-driven DNA denaturation; pronounced hysteresis in the denaturation/renaturation curves may indicate thermal susceptibility to DNA damage. In particular, we consider highly mutated regions of the genome characterized in diffuse large B-cell lymphoma on a recent whole exome next-generation sequencing effort.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Deconstructing the genetic basis of spent sulphite liquor tolerance using deep sequencing of genome-shuffled yeast

BACKGROUND

Identifying the genetic basis of complex microbial phenotypes is currently a major barrier to our understanding of multigenic traits and our ability to rationally design biocatalysts with highly specific attributes for the biotechnology industry. Here, we demonstrate that strain evolution by meiotic recombination-based genome shuffling coupled with deep sequencing can be used to deconstruct complex phenotypes and explore the nature of multigenic traits, while providing concrete targets for strain development.

RESULTS

We determined genomic variations found within Saccharomyces cerevisiae previously evolved in our laboratory by genome shuffling for tolerance to spent sulphite liquor. The representation of these variations was backtracked through parental mutant pools and cross-referenced with RNA-seq gene expression analysis to elucidate the importance of single mutations and key biological processes that play a role in our trait of interest. Our findings pinpoint novel genes and biological determinants of lignocellulosic hydrolysate inhibitor tolerance in yeast. These include the following: protein homeostasis constituents, including Ubp7p and Art5p, related to ubiquitin-mediated proteolysis; stress response transcriptional repressor, Nrg1p; and NADPH-dependent glutamate dehydrogenase, Gdh1p. Reverse engineering a prominent mutation in ubiquitin-specific protease gene UBP7 in a laboratory S. cerevisiae strain effectively increased spent sulphite liquor tolerance.

CONCLUSIONS

This study advances understanding of yeast tolerance mechanisms to inhibitory substrates and biocatalyst design for a biomass-to-biofuel/biochemical industry, while providing insights into the process of mutation accumulation that occurs during genome shuffling.

Mapping yeast transcriptional networks

The term “transcriptional network” refers to the mechanism(s) that underlies coordinated expression of genes, typically involving transcription factors (TFs) binding to the promoters of multiple genes, and individual genes controlled by multiple TFs. A multitude of studies in the last two decades have aimed to map and characterize transcriptional networks in the yeast Saccharomyces cerevisiae. We review the methodologies and accomplishments of these studies, as well as challenges we now face. For most yeast TFs, data have been collected on their sequence preferences, in vivo promoter occupancy, and gene expression profiles in deletion mutants. These systematic studies have led to the identification of new regulators of numerous cellular functions and shed light on the overall organization of yeast gene regulation. However, many yeast TFs appear to be inactive under standard laboratory growth conditions, and many of the available data were collected using techniques that have since been improved. Perhaps as a consequence, comprehensive and accurate mapping among TF sequence preferences, promoter binding, and gene expression remains an open challenge. We propose that the time is ripe for renewed systematic efforts toward a complete mapping of yeast transcriptional regulatory mechanisms.

Systems biology approaches for discovering biomarkers for traumatic brain injury

The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.

DNA-damaging agents in cancer chemotherapy: serendipity and chemical biology

DNA-damaging agents have a long history of use in cancer chemotherapy. The full extent of their cellular mechanisms, which is essential to balance efficacy and toxicity, is often unclear. In addition, the use of many anticancer drugs is limited by dose-limiting toxicities as well as the development of drug resistance. Novel anticancer compounds are continually being developed in the hopes of addressing these limitations; however, it is essential to be able to evaluate these compounds for their mechanisms of action. This review covers the current DNA-damaging agents used in the clinic, discusses their limitations, and describes the use of chemical genomics to uncover new information about the DNA damage response network and to evaluate novel DNA-damaging compounds.

Integrative approaches for finding modular structure in biological networks

A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.

Widespread misinterpretable ChIP-seq bias in yeast

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors appear to consistently bind to the gene bodies of highly transcribed genes in yeast. Strikingly, several types of negative control experiments, including a protein that is not expected to bind chromatin, also showed similar patterns of strong binding within gene bodies. These false positive signals were evident across sequencing platforms and immunoprecipitation protocols, as well as in previously published datasets from other labs. We show that these false positive signals derive from high rates of transcription, and are inherent to the ChIP procedure, although they are exacerbated by sequencing library construction procedures. This expression bias is strong enough that a known transcriptional repressor like Tup1 can erroneously appear to be an activator. Another type of background bias stems from the inherent nucleosomal structure of chromatin, and can potentially make it seem like certain factors bind nucleosomes even when they don't. Our analysis suggests that a mock ChIP sample offers a better normalization control for the expression bias, whereas the ChIP input is more appropriate for the nucleosomal periodicity bias. While these controls alleviate the effect of the biases to some extent, they are unable to eliminate it completely. Caution is therefore warranted regarding the interpretation of data that seemingly show the association of various transcription and chromatin factors with highly transcribed genes in yeast.

Drosophila UTX coordinates with p53 to regulate ku80 expression in response to DNA damage

UTX is known as a general factor that activates gene transcription during development. Here, we demonstrate an additional essential role of UTX in the DNA damage response, in which it upregulates the expression of ku80 in Drosophila, both in cultured cells and in third instar larvae. We further showed that UTX mediates the expression of ku80 by the demethylation of H3K27me3 at the ku80 promoter upon exposure to ionizing radiation (IR) in a p53-dependent manner. UTX interacts physically with p53, and both UTX and p53 are recruited to the ku80 promoter following IR exposure in an interdependent manner. In contrast, the loss of utx has little impact on the expression of ku70, mre11, hid and reaper, suggesting the specific regulation of ku80 expression by UTX. Thus, our findings further elucidate the molecular function of UTX.

Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae

Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

Genome-wide single-cell-level screen for protein abundance and localization changes in response to DNA damage in S. cerevisiae

An effective response to DNA damaging agents involves modulating numerous facets of cellular homeostasis in addition to DNA repair and cell-cycle checkpoint pathways. Fluorescence microscopy-based imaging offers the opportunity to simultaneously interrogate changes in both protein level and subcellular localization in response to DNA damaging agents at the single-cell level. We report here results from screening the yeast Green Fluorescent Protein (GFP)-fusion library to investigate global cellular protein reorganization on exposure to the alkylating agent methyl methanesulfonate (MMS). Broad groups of induced, repressed, nucleus- and cytoplasm-enriched proteins were identified. Gene Ontology and interactome analyses revealed the underlying cellular processes. Transcription factor (TF) analysis identified principal regulators of the response, and targets of all major stress-responsive TFs were enriched amongst the induced proteins. An unexpected partitioning of biological function according to the number of TFs targeting individual genes was revealed. Finally, differential modulation of ribosomal proteins depending on methyl methanesulfonate dose was shown to correlate with cell growth and with the translocation of the Sfp1 TF. We conclude that cellular responses can navigate different routes according to the extent of damage, relying on both expression and localization changes of specific proteins.

Checkpoint kinases regulate a global network of transcription factors in response to DNA damage

DNA damage activates checkpoint kinases that induce several downstream events, including widespread changes in transcription. However, the specific connections between the checkpoint kinases and downstream transcription factors (TFs) are not well understood. Here, we integrate kinase mutant expression profiles, transcriptional regulatory interactions, and phosphoproteomics to map kinases and downstream TFs to transcriptional regulatory networks. Specifically, we investigate the role of the Saccharomyces cerevisiae checkpoint kinases (Mec1, Tel1, Chk1, Rad53, and Dun1) in the transcriptional response to DNA damage caused by methyl methanesulfonate. The result is a global kinase-TF regulatory network in which Mec1 and Tel1 signal through Rad53 to synergistically regulate the expression of more than 600 genes. This network involves at least nine TFs, many of which have Rad53-dependent phosphorylation sites, as regulators of checkpoint-kinase-dependent genes. We also identify a major DNA damage-induced transcriptional network that regulates stress response genes independently of the checkpoint kinases.