Cell cycle population effects in perturbation studies

Size fractionated NET-Seq reveals a conserved architecture of transcription units around yeast genes

Genomes from yeast to humans are subject to pervasive transcription. A single round of pervasive transcription is sufficient to alter local chromatin conformation, nucleosome dynamics and gene expression, but is hard to distinguish from background signals. Size fractionated native elongating transcript sequencing (sfNET-Seq) was developed to precisely map nascent transcripts independent of expression levels. RNAPII-associated nascent transcripts are fractionation into different size ranges before library construction. When anchored to the transcription start sites (TSS) of annotated genes, the combined pattern of the output metagenes gives the expected reference pattern. Bioinformatic pattern matching to the reference pattern identified 9542 transcription units in Saccharomyces cerevisiae, of which 47% are coding and 53% are noncoding. In total, 3113 (33%) are unannotated noncoding transcription units. Anchoring all transcription units to the TSS or polyadenylation site (PAS) of annotated genes reveals distinctive architectures of linked pairs of divergent transcripts approximately 200nt apart. The Reb1 transcription factor is enriched 30nt downstream of the PAS only when an upstream (TSS -60nt with respect to PAS) noncoding transcription unit co-occurs with a downstream (TSS +150nt) coding transcription unit and acts to limit levels of upstream antisense transcripts. The potential for extensive transcriptional interference is evident from low abundance unannotated transcription units with variable TSS (median -240nt) initiating within a 500nt window upstream of, and transcribing over, the promoters of protein-coding genes. This study confirms a highly interleaved yeast genome with different types of transcription units altering the chromatin landscape in distinctive ways, with the potential to exert extensive regulatory control.

Influence of cell volume on the gene transcription rate

Cells vary in volume throughout their life cycle and in many other circumstances, while their genome remains identical. Hence, the RNA production factory must adapt to changing needs, while maintaining the same production lines. This paradox is resolved by different mechanisms in distinct cells and circumstances. RNA polymerases have evolved to cope with the particular circumstances of each case and the different characteristics of the several RNA molecule types, especially their stabilities. Here we review current knowledge on these issues. We focus on the yeast Saccharomyces cerevisiae, where many of the studies have been performed, although we compare and discuss the results obtained in other eukaryotes and propose several ideas and questions to be tested and solved in the future. TAKE AWAY.

The effect of developmental variation on expression QTLs in a multi parental Caenorhabditis elegans population

Regulation of gene expression plays a crucial role in developmental processes and adaptation to changing environments. expression quantitative trait locus (eQTL) mapping is a technique used to study the genetic regulation of gene expression using the transcriptomes of recombinant inbred lines (RILs). Typically, the age of the inbred lines at the time of RNA sampling is carefully controlled. This is necessary because the developmental process causes changes in gene expression, complicating the interpretation of eQTL mapping experiments. However, due to genetics and variation in ambient micro-environments, organisms can differ in their "developmental age," even if they are of the same chronological age. As a result, eQTL patterns are affected by developmental variation in gene expression. The model organism Caenorhabditis elegans is particularly suited for studying the effect of developmental variation on eQTL mapping patterns. In a span of days, C. elegans transitions from embryo through 4 larval stages to adult while undergoing massive changes to its transcriptome. Here, we use C. elegans to investigate the effect of developmental age variation on eQTL patterns and present a normalization procedure. We used dynamical eQTL mapping, which includes the developmental age as a cofactor, to separate the variation in development from genotypic variation and explain variation in gene expression levels. We compare classical single marker eQTL mapping and dynamical eQTL mapping using RNA-seq data of ∼200 multi-parental RILs of C. elegans. The results show that (1) many eQTLs are caused by developmental variation, (2) most trans-bands are developmental QTLs, and (3) dynamical eQTL mapping detects additional eQTLs not found with classical eQTL mapping. We recommend that correction for variation in developmental age should be strongly considered in eQTL mapping studies given the large impact of processes like development on the transcriptome.

Candida albicans exhibits heterogeneous and adaptive cytoprotective responses to antifungal compounds

Candida albicans, an opportunistic human pathogen, poses a significant threat to human health and is associated with significant socio-economic burden. Current antifungal treatments fail, at least in part, because C. albicans can initiate a strong drug tolerance response that allows some cells to grow at drug concentrations above their minimal inhibitory concentration. To better characterize this cytoprotective tolerance program at the molecular single-cell level, we used a nanoliter droplet-based transcriptomics platform to profile thousands of individual fungal cells and establish their subpopulation characteristics in the absence and presence of antifungal drugs. Profiles of untreated cells exhibit heterogeneous expression that correlates with cell cycle stage with distinct metabolic and stress responses. At 2 days post-fluconazole exposure (a time when tolerance is measurable), surviving cells bifurcate into two major subpopulations: one characterized by the upregulation of genes encoding ribosomal proteins, rRNA processing machinery, and mitochondrial cellular respiration capacity, termed the Ribo-dominant (Rd) state; and the other enriched for genes encoding stress responses and related processes, termed the Stress-dominant (Sd) state. This bifurcation persists at 3 and 6 days post-treatment. We provide evidence that the ribosome assembly stress response (RASTR) is activated in these subpopulations and may facilitate cell survival.

mRNA decapping activators Pat1 and Dhh1 regulate transcript abundance and translation to tune cellular responses to nutrient availability

We have examined the roles of yeast mRNA decapping-activators Pat1 and Dhh1 in repressing the translation and abundance of specific mRNAs in nutrient-replete cells using ribosome profiling, RNA-Seq, CAGE analysis of capped mRNAs, RNA Polymerase II ChIP-Seq, and TMT-mass spectrometry of mutants lacking one or both factors. Although the Environmental Stress Response (ESR) is activated in dhh1Δ and pat1Δ mutants, hundreds of non-ESR transcripts are elevated in a manner indicating cumulative repression by Pat1 and Dhh1 in wild-type cells. These mRNAs show both reduced decapping and diminished transcription in the mutants, indicating that impaired mRNA turnover drives transcript derepression in cells lacking Dhh1 or Pat1. mRNA degradation stimulated by Dhh1/Pat1 is not dictated by poor translation nor enrichment for suboptimal codons. Pat1 and Dhh1 also collaborate to reduce translation and protein production from many mRNAs. Transcripts showing concerted translational repression by Pat1/Dhh1 include mRNAs involved in cell adhesion or utilization of the poor nitrogen source allantoin. Pat1/Dhh1 also repress numerous transcripts involved in respiration, catabolism of non-preferred carbon or nitrogen sources, or autophagy; and we obtained evidence for elevated respiration and autophagy in the mutants. Thus, Pat1 and Dhh1 function as post-transcriptional repressors of multiple pathways normally activated only during nutrient limitation.

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

The Dynamic Fungal Genome: Polyploidy, Aneuploidy and Copy Number Variation in Response to Stress

Fungal species have dynamic genomes and often exhibit genomic plasticity in response to stress. This genome plasticity often comes with phenotypic consequences that affect fitness and resistance to stress. Fungal pathogens exhibit genome plasticity in both clinical and agricultural settings and often during adaptation to antifungal drugs, posing significant challenges to human health. Therefore, it is important to understand the rates, mechanisms, and impact of large genomic changes. This review addresses the prevalence of polyploidy, aneuploidy, and copy number variation across diverse fungal species, with special attention to prominent fungal pathogens and model species. We also explore the relationship between environmental stress and rates of genomic changes and highlight the mechanisms underlying genotypic and phenotypic changes. A comprehensive understanding of these dynamic fungal genomes is needed to identify novel solutions for the increase in antifungal drug resistance.

Impact of variability in cell cycle periodicity on cell population dynamics

The cell cycle consists of a series of orchestrated events controlled by molecular sensing and feedback networks that ultimately drive the duplication of total DNA and the subsequent division of a single parent cell into two daughter cells. The ability to block the cell cycle and synchronize cells within the same phase has helped understand factors that control cell cycle progression and the properties of each individual phase. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. The rate and factors that control cellular desynchronization remain largely unknown. In this study, using a combination of experiments and simulations, we investigate the desynchronization properties in cervical cancer cells (HeLa) starting from the G1/S boundary following double-thymidine block. Propidium iodide (PI) DNA staining was used to perform flow cytometry cell cycle analysis at regular 8 hour intervals, and a custom auto-similarity function to assess the desynchronization and quantify the convergence to an asynchronous state. In parallel, we developed a single-cell phenomenological model the returns the DNA amount across the cell cycle stages and fitted the parameters using experimental data. Simulations of population of cells reveal that the cell cycle desynchronization rate is primarily sensitive to the variability of cell cycle duration within a population. To validate the model prediction, we introduced lipopolysaccharide (LPS) to increase cell cycle noise. Indeed, we observed an increase in cell cycle variability under LPS stimulation in HeLa cells, accompanied with an enhanced rate of cell cycle desynchronization. Our results show that the desynchronization rate of artificially synchronized in-phase cell populations can be used a proxy of the degree of variance in cell cycle periodicity, an underexplored axis in cell cycle research.

Decapping factor Dcp2 controls mRNA abundance and translation to adjust metabolism and filamentation to nutrient availability

Degradation of most yeast mRNAs involves decapping by Dcp1/Dcp2. DEAD-box protein Dhh1 has been implicated as an activator of decapping, in coupling codon non-optimality to enhanced degradation, and as a translational repressor, but its functions in cells are incompletely understood. RNA-Seq analyses coupled with CAGE sequencing of all capped mRNAs revealed increased abundance of hundreds of mRNAs in dcp2 Δ cells that appears to result directly from impaired decapping rather than elevated transcription, which was confirmed by ChIP-Seq analysis of RNA Polymerase II occupancies genome-wide. Interestingly, only a subset of mRNAs requires Dhh1 for targeting by Dcp2, and also generally requires the other decapping activators Pat1, Lsm2, Edc3 or Scd6; whereas most of the remaining transcripts utilize NMD factors for Dcp2-mediated turnover. Neither inefficient translation initiation nor stalled elongation appears to be a major driver of Dhh1-enhanced mRNA degradation. Surprisingly, ribosome profiling revealed that dcp2 Δ confers widespread changes in relative TEs that generally favor well-translated mRNAs. Because ribosome biogenesis is reduced while capped mRNA abundance is increased by dcp2 Δ, we propose that an increased ratio of mRNA to ribosomes increases competition among mRNAs for limiting ribosomes to favor efficiently translated mRNAs in dcp2 Δ cells. Interestingly, genes involved in respiration or utilization of alternative carbon or nitrogen sources are derepressed, and both mitochondrial function and cell filamentation (a strategy for nutrient foraging) are elevated by dcp2 Δ, suggesting that mRNA decapping sculpts gene expression post-transcriptionally to fine-tune metabolic pathways and morphological transitions according to nutrient availability.

The environmental stress response regulates ribosome content in cell cycle-arrested S. cerevisiae

Prolonged cell cycle arrests occur naturally in differentiated cells and in response to various stresses such as nutrient deprivation or treatment with chemotherapeutic agents. Whether and how cells survive prolonged cell cycle arrests is not clear. Here, we used S. cerevisiae to compare physiological cell cycle arrests and genetically induced arrests in G1-, meta- and anaphase. Prolonged cell cycle arrest led to growth attenuation in all studied conditions, coincided with activation of the Environmental Stress Response (ESR) and with a reduced ribosome content as determined by whole ribosome purification and TMT mass spectrometry. Suppression of the ESR through hyperactivation of the Ras/PKA pathway reduced cell viability during prolonged arrests, demonstrating a cytoprotective role of the ESR. Attenuation of cell growth and activation of stress induced signaling pathways also occur in arrested human cell lines, raising the possibility that the response to prolonged cell cycle arrest is conserved.

Network Topology Can Explain Differences in Pleiotropy Between Cis- and Trans-regulatory Mutations

A mutation's degree of pleiotropy (i.e., the number of traits it alters) is predicted to impact the probability of the mutation being detrimental to fitness. For mutations that impact gene expression, mutations acting in cis have been hypothesized to generally be less pleiotropic than mutations affecting the same gene's expression in trans, suggesting that cis-regulatory mutations should be less deleterious and more likely to fix over evolutionary time. Here, we use expression and fitness data from Saccharomyces cerevisiae gene deletion strains to test these hypotheses. By treating deletion of each gene as a cis-regulatory mutation affecting its own expression and deletions of other genes affecting expression of this focal gene as trans-regulatory mutations, we find that cis-acting mutations do indeed tend to be less pleiotropic than trans-acting mutations affecting expression of the same gene. This pattern was observed for the vast majority of genes in the data set and could be explained by the topology of the regulatory network controlling gene expression. Comparing the fitness of cis- and trans-acting mutations affecting expression of the same gene also confirmed that trans-acting deletions tend to be more deleterious. These findings provide strong support for pleiotropy playing a role in the preferential fixation of cis-regulatory alleles over evolutionary time.

BRANEnet: embedding multilayer networks for omics data integration

Background

Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.

Results

We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04955-w.

Yeast osmoregulation - glycerol still in pole position

In response to osmotic dehydration cells sense, signal, alter gene expression, and metabolically counterbalance osmotic differences. The main compatible solute/osmolyte that accumulates in yeast cells is glycerol, which is produced from the glycolytic intermediate dihydroxyacetone phosphate. This review covers recent advancements in understanding mechanisms involved in sensing, signaling, cell-cycle delays, transcriptional responses as well as post-translational modifications on key proteins in osmoregulation. The protein kinase Hog1 is a key-player in many of these events, however, there is also a growing body of evidence for important Hog1-independent mechanisms playing vital roles. Several missing links in our understanding of osmoregulation will be discussed and future avenues for research proposed. The review highlights that this rather simple experimental system—salt/sorbitol and yeast—has developed into an enormously potent model system unravelling important fundamental aspects in biology.

Real age prediction from the transcriptome with RAPToR

Transcriptomic data is often affected by uncontrolled variation among samples that can obscure and confound the effects of interest. This variation is frequently due to unintended differences in developmental stages between samples. The transcriptome itself can be used to estimate developmental progression, but existing methods require many samples and do not estimate a specimen's real age. Here we present real-age prediction from transcriptome staging on reference (RAPToR), a computational method that precisely estimates the real age of a sample from its transcriptome, exploiting existing time-series data as reference. RAPToR works with whole animal, dissected tissue and single-cell data for the most common animal models, humans and even for non-model organisms lacking reference data. We show that RAPToR can be used to remove age as a confounding factor and allow recovery of a signal of interest in differential expression analysis. RAPToR will be especially useful in large-scale single-organism profiling because it eliminates the need for accurate staging or synchronisation before profiling.

Reliance of Host-Encoded Regulators of Retromobility on Ty1 Promoter Activity or Architecture

The Ty1 retrotransposon family is maintained in a functional but dormant state by its host, Saccharomyces cerevisiae. Several hundred RHF and RTT genes encoding co-factors and restrictors of Ty1 retromobility, respectively, have been identified. Well-characterized examples include MED3 and MED15, encoding subunits of the Mediator transcriptional co-activator complex; control of retromobility by Med3 and Med15 requires the Ty1 promoter in the U3 region of the long terminal repeat. To characterize the U3-dependence of other Ty1 regulators, we screened a library of 188 known rhf and rtt mutants for altered retromobility of Ty1his3AI expressed from the strong, TATA-less TEF1 promoter or the weak, TATA-containing U3 promoter. Two classes of genes, each including both RHFs and RTTs, were identified. The first class comprising 82 genes that regulated Ty1his3AI retromobility independently of U3 is enriched for RHF genes that restrict the G1 phase of the cell cycle and those involved in transcriptional elongation and mRNA catabolism. The second class of 51 genes regulated retromobility of Ty1his3AI driven only from the U3 promoter. Nineteen U3-dependent regulators (U3DRs) also controlled retromobility of Ty1his3AI driven by the weak, TATA-less PSP2 promoter, suggesting reliance on the low activity of U3. Thirty-one U3DRs failed to modulate P_PSP2-Ty1his3AI retromobility, suggesting dependence on the architecture of U3. To further investigate the U3-dependency of Ty1 regulators, we developed a novel fluorescence-based assay to monitor expression of p22-Gag, a restriction factor expressed from the internal Ty1i promoter. Many U3DRs had minimal effects on levels of Ty1 RNA, Ty1i RNA or p22-Gag. These findings uncover a role for the Ty1 promoter in integrating signals from diverse host factors to modulate Ty1 RNA biogenesis or fate.

Effects of aneuploidy on cell behaviour and function

Aneuploidy, a genomic alternation characterized by deviations in the copy number of chromosomes, affects organisms from early development through to aging. Although it is a main cause of human pregnancy loss and a hallmark of cancer, how aneuploidy affects cellular function has been elusive. The last two decades have seen rapid advances in the understanding of the causes and consequences of aneuploidy at the molecular and cellular levels. These studies have uncovered effects of aneuploidy that can be beneficial or detrimental to cells and organisms in an environmental context-dependent and karyotype-dependent manner. Aneuploidy also imposes general stress on cells that stems from an imbalanced genome and, consequently, also an imbalanced proteome. These insights provide the fundamental framework for understanding the impact of aneuploidy in genome evolution, human pathogenesis and drug resistance.

The interaction between the Spt6-tSH2 domain and Rpb1 affects multiple functions of RNA Polymerase II

The conserved transcription elongation factor Spt6 makes several contacts with the RNA Polymerase II (RNAPII) complex, including a high-affinity interaction between the Spt6 tandem SH2 domain (Spt6-tSH2) and phosphorylated residues of the Rpb1 subunit in the linker between the catalytic core and the C-terminal domain (CTD) heptad repeats. This interaction contributes to generic localization of Spt6, but we show here that it also has gene-specific roles. Disrupting the interface affected transcription start site selection at a subset of genes whose expression is regulated by this choice, and this was accompanied by changes in a distinct pattern of Spt6 accumulation at these sites. Splicing efficiency was also diminished, as was apparent progression through introns that encode snoRNAs. Chromatin-mediated repression was impaired, and a distinct role in maintaining +1 nucleosomes was identified, especially at ribosomal protein genes. The Spt6-tSH2:Rpb1 interface therefore has both genome-wide functions and local roles at subsets of genes where dynamic decisions regarding initiation, transcript processing, or termination are made. We propose that the interaction modulates the availability or activity of the core elongation and histone chaperone functions of Spt6, contributing to coordination between RNAPII and its accessory factors as varying local conditions call for dynamic responses.

Transcriptional and chromatin-based partitioning mechanisms uncouple protein scaling from cell size

Biosynthesis scales with cell size such that protein concentrations generally remain constant as cells grow. As an exception, synthesis of the cell-cycle inhibitor Whi5 "sub-scales" with cell size so that its concentration is lower in larger cells to promote cell-cycle entry. Here, we find that transcriptional control uncouples Whi5 synthesis from cell size, and we identify histones as the major class of sub-scaling transcripts besides WHI5 by screening for similar genes. Histone synthesis is thereby matched to genome content rather than cell size. Such sub-scaling proteins are challenged by asymmetric cell division because proteins are typically partitioned in proportion to newborn cell volume. To avoid this fate, Whi5 uses chromatin-binding to partition similar protein amounts to each newborn cell regardless of cell size. Disrupting both Whi5 synthesis and chromatin-based partitioning weakens G1 size control. Thus, specific transcriptional and partitioning mechanisms determine protein sub-scaling to control cell size.

Shortening of membrane lipid acyl chains compensates for phosphatidylcholine deficiency in choline-auxotroph yeast

Phosphatidylcholine (PC) is an abundant membrane lipid component in most eukaryotes, including yeast, and has been assigned multiple functions in addition to acting as building block of the lipid bilayer. Here, by isolating S. cerevisiae suppressor mutants that exhibit robust growth in the absence of PC, we show that PC essentiality is subject to cellular evolvability in yeast. The requirement for PC is suppressed by monosomy of chromosome XV or by a point mutation in the ACC1 gene encoding acetyl-CoA carboxylase. Although these two genetic adaptations rewire lipid biosynthesis in different ways, both decrease Acc1 activity, thereby reducing average acyl chain length. Consistently, soraphen A, a specific inhibitor of Acc1, rescues a yeast mutant with deficient PC synthesis. In the aneuploid suppressor, feedback inhibition of Acc1 through acyl-CoA produced by fatty acid synthase (FAS) results from upregulation of lipid synthesis. The results show that budding yeast regulates acyl chain length by fine-tuning the activities of Acc1 and FAS and indicate that PC evolved by benefitting the maintenance of membrane fluidity.

A flow cytometric journey into cell cycle analysis

Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.

Integrated Multi-Omics Analysis of Mechanisms Underlying Yeast Ethanol Tolerance

For yeast cells, tolerance to high levels of ethanol is vital both in their natural environment and in industrially relevant conditions. We recently genotyped experimentally evolved yeast strains adapted to high levels of ethanol and identified mutations linked to ethanol tolerance. In this study, by integrating genomic sequencing data with quantitative proteomics profiles from six evolved strains (data set identifier PXD006631) and construction of protein interaction networks, we elucidate exactly how the genotype and phenotype are related at the molecular level. Our multi-omics approach points to the rewiring of numerous metabolic pathways affected by genomic and proteomic level changes, from energy-producing and lipid pathways to differential regulation of transposons and proteins involved in cell cycle progression. One of the key differences is found in the energy-producing metabolism, where the ancestral yeast strain responds to ethanol by switching to respiration and employing the mitochondrial electron transport chain. In contrast, the ethanol-adapted strains appear to have returned back to energy production mainly via glycolysis and ethanol fermentation, as supported by genomic and proteomic level changes. This work is relevant for synthetic biology where systems need to function under stressful conditions, as well as for industry and in cancer biology, where it is important to understand how the genotype relates to the phenotype.

Multimodal regularised linear models with flux balance analysis for mechanistic integration of omics data

MOTIVATION

High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modelling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multi-source and multi-omic nature of these data types while preserving mechanistic interpretation.

RESULTS

Here we investigate different regularisation techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularisation frameworks including group, view-specific and principal component regularisation, and experimentally compare them using data from 1,143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularisation employed. In multi-omic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularised linear models compared to data-hungry methods based on neural networks.

AVAILABILITY

All data, models, and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Suboptimal Global Transcriptional Response Increases the Harmful Effects of Loss-of-Function Mutations

The fitness impact of loss-of-function mutations is generally assumed to reflect the loss of specific molecular functions associated with the perturbed gene. Here, we propose that rewiring of the transcriptome upon deleterious gene inactivation is frequently nonspecific and mimics stereotypic responses to external environmental change. Consequently, transcriptional response to gene deletion could be suboptimal and incur an extra fitness cost. Analysis of the transcriptomes of ∼1,500 single-gene deletion Saccharomyces cerevisiae strains supported this scenario. First, most transcriptomic changes are not specific to the deleted gene but are rather triggered by perturbations in functionally diverse genes. Second, gene deletions that alter the expression of dosage-sensitive genes are especially harmful. Third, by elevating the expression level of downregulated genes, we could experimentally mitigate the fitness defect of gene deletions. Our work shows that rewiring of genomic expression upon gene inactivation shapes the harmful effects of mutations.

The conserved elongation factor Spn1 is required for normal transcription, histone modifications, and splicing in Saccharomyces cerevisiae

Spn1/Iws1 is a conserved protein involved in transcription and chromatin dynamics, yet its general in vivo requirement for these functions is unknown. Using a Spn1 depletion system in Saccharomyces cerevisiae, we demonstrate that Spn1 broadly influences several aspects of gene expression on a genome-wide scale. We show that Spn1 is globally required for normal mRNA levels and for normal splicing of ribosomal protein transcripts. Furthermore, Spn1 maintains the localization of H3K36 and H3K4 methylation across the genome and is required for normal histone levels at highly expressed genes. Finally, we show that the association of Spn1 with the transcription machinery is strongly dependent on its binding partner, Spt6, while the association of Spt6 and Set2 with transcribed regions is partially dependent on Spn1. Taken together, our results show that Spn1 affects multiple aspects of gene expression and provide additional evidence that it functions as a histone chaperone in vivo.

Gene Transcription as a Limiting Factor in Protein Production and Cell Growth

Cell growth is driven by the synthesis of proteins, genes, and other cellular components. Defining processes that limit biosynthesis rates is fundamental for understanding the determinants of cell physiology. Here, we analyze the consequences of engineering cells to express extremely high levels of mCherry proteins, as a tool to define limiting processes that fail to adapt upon increasing biosynthetic demands. Protein-burdened cells were transcriptionally and phenotypically similar to mutants of the Mediator, a transcription coactivator complex. However, our binding data suggest that the Mediator was not depleted from endogenous promoters. Burdened cells showed an overall increase in the abundance of the majority of endogenous transcripts, except for highly expressed genes. Our results, supported by mathematical modeling, suggest that wild-type cells transcribe highly expressed genes at the maximal possible rate, as defined by the transcription machinery’s physical properties. We discuss the possible cellular benefit of maximal transcription rates to allow a coordinated optimization of cell size and cell growth.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Stress-induced expression is enriched for evolutionarily young genes in diverse budding yeasts

The Saccharomycotina subphylum (budding yeasts) spans 400 million years of evolution and includes species that thrive in diverse environments. To study niche-adaptation, we identify changes in gene expression in three divergent yeasts grown in the presence of various stressors. Duplicated and non-conserved genes are significantly more likely to respond to stress than genes that are conserved as single-copy orthologs. Next, we develop a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis reveals that genes that emerged in recent evolutionary time are enriched amongst stress-responsive genes for each species. This gene expression pattern suggests that budding yeasts share a stress adaptation mechanism, whereby selective pressure leads to functionalization of young genes to improve growth in adverse conditions. Further characterization of young genes from species that thrive in harsh environments can inform the design of more robust strains for biotechnology.

Fermentation parameters of industrial processes are often not the ideal growth conditions for industrial microbes. Here, the authors reveal that young genes are more responsive to environmental stress than ancient genes using a new gene age assignment method and provide targeted genes for metabolic engineering.

Inhibition of transcription leads to rewiring of locus-specific chromatin proteomes

Transcription of a chromatin template involves the concerted interaction of many different proteins and protein complexes. Analyses of specific factors showed that these interactions change during stress and upon developmental switches. However, how the binding of multiple factors at any given locus is coordinated has been technically challenging to investigate. Here we used Epi-Decoder in yeast to systematically decode, at one transcribed locus, the chromatin binding changes of hundreds of proteins in parallel upon perturbation of transcription. By taking advantage of improved Epi-Decoder libraries, we observed broad rewiring of local chromatin proteomes following chemical inhibition of RNA polymerase. Rapid reduction of RNA polymerase II binding was accompanied by reduced binding of many other core transcription proteins and gain of chromatin remodelers. In quiescent cells, where strong transcriptional repression is induced by physiological signals, eviction of the core transcriptional machinery was accompanied by the appearance of quiescent cell–specific repressors and rewiring of the interactions of protein-folding factors and metabolic enzymes. These results show that Epi-Decoder provides a powerful strategy for capturing the temporal binding dynamics of multiple chromatin proteins under varying conditions and cell states. The systematic and comprehensive delineation of dynamic local chromatin proteomes will greatly aid in uncovering protein–protein relationships and protein functions at the chromatin template.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

BACKGROUND

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task.

RESULTS

We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4).

CONCLUSION

Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

Two roles for the yeast transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA

Deletions within genes coding for subunits of the transcription coactivator SAGA caused strong genome-wide defects in transcription and SAGA-mediated chromatin modifications. In contrast, rapid SAGA depletion produced only modest transcription defects at 13% of protein-coding genes - genes that are generally more sensitive to rapid TFIID depletion. However, transcription of these 'coactivator-redundant' genes is strongly affected by rapid depletion of both factors, showing the overlapping functions of TFIID and SAGA at this gene set. We suggest that this overlapping function is linked to TBP-DNA recruitment. The remaining 87% of expressed genes that we term 'TFIID-dependent' are highly sensitive to rapid TFIID depletion and insensitive to rapid SAGA depletion. Genome-wide mapping of SAGA and TFIID found binding of both factors at many genes independent of gene class. Promoter analysis suggests that the distinction between the gene classes is due to multiple components rather than any single regulatory factor or promoter sequence motif.

Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions

Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.

Global Transcriptional Programs in Archaea Share Features with the Eukaryotic Environmental Stress Response

The environmental stress response (ESR), a global transcriptional program originally identified in yeast, is characterized by a rapid and transient transcriptional response composed of large, oppositely regulated gene clusters. Genes induced during the ESR encode core components of stress tolerance, macromolecular repair, and maintenance of homeostasis. In this review, we investigate the possibility for conservation of the ESR across the eukaryotic and archaeal domains of life. We first re-analyze existing transcriptomics data sets to illustrate that a similar transcriptional response is identifiable in Halobacterium salinarum, an archaeal model organism. To substantiate the archaeal ESR, we calculated gene-by-gene correlations, gene function enrichment, and comparison of temporal dynamics. We note reported examples of variation in the ESR across fungi, then synthesize high-level trends present in expression data of other archaeal species. In particular, we emphasize the need for additional high-throughput time series expression data to further characterize stress-responsive transcriptional programs in the Archaea. Together, this review explores an open question regarding features of global transcriptional stress response programs shared across domains of life.

Complex genetic and epigenetic regulation deviates gene expression from a unifying global transcriptional program

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

How can we understand expression changes observed as a result of environmental or genetic perturbations? This issue has been conventionally answered by examining small groups of genes whose expression becomes qualitatively altered after these perturbations. But this approach is too simplistic, as we now know that extensive variation is typically observed. To explain this variation, recent works proposed a model in which genome-wide changes are explained by the action of a general program of transcription. Our manuscript reasons that given the complexity of eukaryotic transcriptional control, a unifying program of regulation cannot be achievable. Instead, we propose within an integrated framework of resource allocation that a rich structure of deviations from it exists and that by characterizing these deviations we can fully appreciate large-scale expression change.

The Kinetochore Protein Spc105, a Novel Interaction Partner of LaeA, Regulates Development and Secondary Metabolism in Aspergillus flavus

Nuclear protein LaeA is known as the global regulator of secondary metabolism in Aspergillus. LaeA connects with VeA and VelB to form a heterotrimeric complex, which coordinates fungal development and secondary metabolism. Here, we describe a new interaction partner of LaeA, the kinetochore protein Spc105, from the aflatoxin-producing fungus Aspergillus flavus. We showed that in addition to involvement in nuclear division, Spc105 is required for normal conidiophore development and sclerotia production of A. flavus. Moreover, Spc105 positively regulates the production of secondary metabolites such as aflatoxin and kojic acid, and negatively regulates the production of cyclopiazonic acid. Transcriptome analysis of the Δspc105 strain revealed that 23 backbone genes were differentially expressed, corresponding to 19 of the predicted 56 secondary metabolite gene clusters, suggesting a broad regulatory role of Spc105 in secondary metabolism. Notably, the reduced expression of laeA in our transcriptome data led to the discovery of the correlation between Spc105 and LaeA, and double mutant analysis indicated a functional interdependence between Spc105 and LaeA. Further, in vitro and in vivo protein interaction assays revealed that Spc105 interacts directly with the S-adenosylmethionine (SAM)-binding domain of LaeA, and that the leucine zipper motif in Spc105 is required for this interaction. The Spc105-LaeA interaction identified in our study indicates a cooperative interplay of distinct regulators in A. flavus, providing new insights into fungal secondary metabolism regulation networks.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Establishment and Maintenance of Chromatin Architecture Are Promoted Independently of Transcription by the Histone Chaperone FACT and H3-K56 Acetylation in Saccharomyces cerevisiae

FACT (FAcilitates Chromatin Transcription/Transactions) is a histone chaperone that can destabilize or assemble nucleosomes. Acetylation of histone H3-K56 weakens a histone-DNA contact that is central to FACT activity, suggesting that this modification could affect FACT functions. We tested this by asking how mutations of H3-K56 and FACT affect nucleosome reorganization activity in vitro, and chromatin integrity and transcript output in vivo Mimics of unacetylated or permanently acetylated H3-K56 had different effects on FACT activity as expected, but the same mutations had surprisingly similar effects on global transcript levels. The results are consistent with emerging models that emphasize FACT's importance in establishing global chromatin architecture prior to transcription, promoting transitions among different states as transcription profiles change, and restoring chromatin integrity after it is disturbed. Optimal FACT activity required the availability of both modified and unmodified states of H3-K56. Perturbing this balance was especially detrimental for maintaining repression of genes with high nucleosome occupancy over their promoters and for blocking antisense transcription at the +1 nucleosome. The results reveal a complex collaboration between H3-K56 modification status and multiple FACT functions, and support roles for nucleosome reorganization by FACT before, during, and after transcription.

The diverse consequences of aneuploidy

Aneuploidy, or imbalanced chromosome number, has profound effects on eukaryotic cells. In humans, aneuploidy is associated with various pathologies, including cancer, which suggests that it mediates a proliferative advantage under these conditions. Here, we discuss physiological changes triggered by aneuploidy, such as altered cell growth, transcriptional changes, proteotoxic stress, genomic instability and response to interferons, and how cancer cells adapt to the changing aneuploid genome.

A constitutive active allele of the transcription factor Msn2 mimicking low PKA activity dictates metabolic remodeling in yeast

In yeast, protein kinase A (PKA) adjusts transcriptional profiles, metabolic rates, and cell growth in accord with carbon source availability. PKA affects gene expression mostly via the transcription factors Msn2 and Msn4, two key regulators of the environmental stress response. Here we analyze the role of the PKA-Msn2 signaling module using an Msn2 allele that harbors serine-to-alanine substitutions at six functionally important PKA motifs (Msn2A6) . Expression of Msn2A6 mimics low PKA activity, entails a transcription profile similar to that of respiring cells, and prevents formation of colonies on glucose-containing medium. Furthermore, Msn2A6 leads to high oxygen consumption and hence high respiratory activity. Substantially increased intracellular concentrations of several carbon metabolites, such as trehalose, point to a metabolic adjustment similar to diauxic shift. This partial metabolic switch is the likely cause for the slow-growth phenotype in the presence of glucose. Consistently, Msn2A6 expression does not interfere with growth on ethanol and tolerated is to a limited degree in deletion mutant strains with a gene expression signature corresponding to nonfermentative growth. We propose that the lethality observed in mutants with hampered PKA activity resides in metabolic reprogramming that is initiated by Msn2 hyperactivity.

A role for Mog1 in H2Bub1 and H3K4me3 regulation affecting RNAPII transcription and mRNA export

Monoubiquitination of histone H2B (to H2Bub1) is required for downstream events including histone H3 methylation, transcription, and mRNA export. The mechanisms and players regulating these events have not yet been completely delineated. Here, we show that the conserved Ran-binding protein Mog1 is required to sustain normal levels of H2Bub1 and H3K4me3 in Saccharomyces cerevisiae Mog1 is needed for gene body recruitment of Rad6, Bre1, and Rtf1 that are involved in H2B ubiquitination and genetically interacts with these factors. We provide evidence that the absence of MOG1 impacts on cellular processes such as transcription, DNA replication, and mRNA export, which are linked to H2Bub1. Importantly, the mRNA export defect in mog1Δ strains is exacerbated by the absence of factors that decrease H2Bub1 levels. Consistent with a role in sustaining H2Bub and H3K4me3 levels, Mog1 co-precipitates with components that participate in these modifications such as Bre1, Rtf1, and the COMPASS-associated factors Shg1 and Sdc1. These results reveal a novel role for Mog1 in H2B ubiquitination, transcription, and mRNA biogenesis.

Decoupling Yeast Cell Division and Stress Defense Implicates mRNA Repression in Translational Reallocation during Stress

Stress tolerance and rapid growth are often competing interests in cells. Upon severe environmental stress, many organisms activate defense systems concurrent with growth arrest. There has been debate as to whether aspects of the stress-activated transcriptome are regulated by stress or an indirect byproduct of reduced proliferation. For example, stressed Saccharomyces cerevisiae cells mount a common gene expression program called the environmental stress response (ESR) [1] comprised of ∼300 induced (iESR) transcripts involved in stress defense and ∼600 reduced (rESR) mRNAs encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RiBi) important for division. Because ESR activation also correlates with reduced growth rate in nutrient-restricted chemostats and prolonged G1 in slow-growing mutants, an alternate proposal is that the ESR is simply a consequence of reduced division [2-5]. A major challenge is that past studies did not separate effects of division arrest and stress defense; thus, the true responsiveness of the ESR-and the purpose of stress-dependent rESR repression in particular-remains unclear. Here, we decoupled cell division from the stress response by following transcriptome, proteome, and polysome changes in arrested cells responding to acute stress. We show that the ESR cannot be explained by changes in growth rate or cell-cycle phase during stress acclimation. Instead, failure to repress rESR transcripts reduces polysome association of induced transcripts, delaying production of their proteins. Our results suggest that stressed cells alleviate competition for translation factors by removing mRNAs and ribosomes from the translating pool, directing translational capacity toward induced transcripts to accelerate protein production.

Application of Continuous Culture Methods to Recombinant Protein Production in Microorganisms

Depending on the environmental conditions, cells adapt their metabolism and specific growth rate. Rearrangements occur on many different levels such as macromolecular composition, gene and protein expression, morphology and metabolic flux patterns. As the interplay of these processes also determines the output of a recombinant protein producing system, having control over specific growth rate of the culture is advantageous. Continuous culture methods were developed to grow cells in a constant environment and have been used for decades to study basic microbial physiology in a controlled and reproducible manner. Our review summarizes the uses of continuous cultures in cell physiology studies and process development, with a focus on recombinant protein-producing microorganisms.

Cellular Stress Associated with Aneuploidy

Aneuploidy, chromosome stoichiometry that deviates from exact multiples of the haploid compliment of an organism, exists in eukaryotic microbes, several normal human tissues, and the majority of solid tumors. Here, we review the current understanding about the cellular stress states that may result from aneuploidy. The topics of aneuploidy-induced proteotoxic, metabolic, replication, and mitotic stress are assessed in the context of the gene dosage imbalance observed in aneuploid cells. We also highlight emerging findings related to the downstream effects of aneuploidy-induced cellular stress on the immune surveillance against aneuploid cells.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

The Transcriptome of the Zebrafish Embryo After Chemical Exposure: A Meta-Analysis

Numerous studies have been published in the past years investigating the transcriptome of the zebrafish embryo (ZFE) upon being subjected to chemical stress. Aiming at a more mechanistic understanding of the results of such studies, knowledge about commonalities of transcript regulation in response to chemical stress is needed. Thus, our goal in this study was to identify and interpret genes and gene sets constituting a general response to chemical exposure. Therefore, we aggregated and reanalyzed published toxicogenomics data obtained with the ZFE. We found that overlap of differentially transcribed genes in response to chemical stress across independent studies is generally low and the most commonly differentially transcribed genes appear in less than 50% of all treatments across studies. However, effect size analysis revealed several genes showing a common trend of differential expression, among which genes related to calcium homeostasis emerged as key, especially in exposure settings up to 24 h post-fertilization. Additionally, we found that these and other downregulated genes are often linked to anatomical regions developing during the respective exposure period. Genes showing a trend of increased expression were, among others, linked to signaling pathways (e.g., Wnt, Fgf) as well as lysosomal structures and apoptosis. The findings of this study help to increase the understanding of chemical stress responses in the developing zebrafish embryo and provide a starting point to improve experimental designs for this model system. In future, improved time- and concentration-resolved experiments should offer better understanding of stress response patterns and access to mechanistic information.

Antisense transcription-dependent chromatin signature modulates sense transcript dynamics

Antisense transcription is widespread in genomes. Despite large differences in gene size and architecture, we find that yeast and human genes share a unique, antisense transcription-associated chromatin signature. We asked whether this signature is related to a biological function for antisense transcription. Using quantitative RNA-FISH, we observed changes in sense transcript distributions in nuclei and cytoplasm as antisense transcript levels were altered. To determine the mechanistic differences underlying these distributions, we developed a mathematical framework describing transcription from initiation to transcript degradation. At GAL1, high levels of antisense transcription alter sense transcription dynamics, reducing rates of transcript production and processing, while increasing transcript stability. This relationship with transcript stability is also observed as a genome-wide association. Establishing the antisense transcription-associated chromatin signature through disruption of the Set3C histone deacetylase activity is sufficient to similarly change these rates even in the absence of antisense transcription. Thus, antisense transcription alters sense transcription dynamics in a chromatin-dependent manner.

Multiple signaling kinases target Mrc1 to prevent genomic instability triggered by transcription-replication conflicts

Conflicts between replication and transcription machineries represent a major source of genomic instability and cells have evolved strategies to prevent such conflicts. However, little is known regarding how cells cope with sudden increases of transcription while replicating. Here, we report the existence of a general mechanism for the protection of genomic integrity upon transcriptional outbursts in S phase that is mediated by Mrc1. The N-terminal phosphorylation of Mrc1 blocked replication and prevented transcription-associated recombination (TAR) and genomic instability during stress-induced gene expression in S phase. An unbiased kinome screening identified several kinases that phosphorylate Mrc1 at the N terminus upon different environmental stresses. Mrc1 function was not restricted to environmental cues but was also required when unscheduled transcription was triggered by low fitness states such as genomic instability or slow growth. Our data indicate that Mrc1 integrates multiple signals, thereby defining a general safeguard mechanism to protect genomic integrity upon transcriptional outbursts.

During S phase of the cell cycle, transcription and replication need to be coordinated in order to avoid conflicts leading to potential genomic instability. Here, the authors find that Mrc1 integrates signals from different kinases to regulate replication during unscheduled transcription events.

How yeast coordinates metabolism, growth and division

All cells, especially single cell organisms, need to adapt their metabolism, growth and division coordinately to the available nutrients. This coordination is mediated by extensive cross-talk between nutrient signaling, metabolism, growth, and the cell division cycle, which is only gradually being uncovered: Nutrient signaling not only controls entry into the cell cycle at the G1/S transition, but all phases of the cell cycle. Metabolites are even sensed directly by cell cycle regulators to prevent cell cycle progression in absence of sufficient metabolic fluxes. In turn, cell cycle regulators such as the cyclin-dependent kinase directly control metabolic fluxes during cell cycle progression. In this review, I highlight some recent advances in our understanding of how metabolism and the cell division cycle are coordinated in the model organism Saccharomyces cerevisiae.

MPK1/SLT2 Links Multiple Stress Responses with Gene Expression in Budding Yeast by Phosphorylating Tyr1 of the RNAP II CTD

The RNA polymerase II largest subunit C-terminal domain consists of repeated YSPTSPS heptapeptides. The role of tyrosine-1 (Tyr1) remains incompletely understood, as, for example, mutating all Tyr1 residues to Phe (Y1F) is lethal in vertebrates but a related mutant has only a mild phenotype in S. pombe. Here we show that Y1F substitution in budding yeast resulted in a strong slow-growth phenotype. The Y1F strain was also hypersensitive to several different cellular stresses that involve MAP kinase signaling. These phenotypes were all linked to transcriptional changes, and we also identified genetic and biochemical interactions between Tyr1 and both transcription initiation and termination factors. Further studies uncovered defects related to MAP kinase I (Slt2) pathways, and we provide evidence that Slt2 phosphorylates Tyr1 in vitro and in vivo. Our study has thus identified Slt2 as a Tyr1 kinase, and in doing so provided links between stress response activation and Tyr1 phosphorylation.

Interaction Dynamics Determine Signaling and Output Pathway Responses

The understanding of interaction dynamics in signaling pathways can shed light on pathway architecture and provide insights into targets for intervention. Here, we explored the relevance of kinetic rate constants of a key upstream osmosensor in the yeast high-osmolarity glycerol-mitogen-activated protein kinase (HOG-MAPK) pathway to signaling output responses. We created mutant pairs of the Sln1-Ypd1 complex interface that caused major compensating changes in the association (k_on) and dissociation (k_off) rate constants (kinetic perturbations) but only moderate changes in the overall complex affinity (K_d). Yeast cells carrying a Sln1-Ypd1 mutant pair with moderate increases in k_on and k_off displayed a lower threshold of HOG pathway activation than wild-type cells. Mutants with higher k_on and k_off rates gave rise to higher basal signaling and gene expression but impaired osmoadaptation. Thus, the k_on and k_off rates of the components in the Sln1 osmosensor determine proper signaling dynamics and osmoadaptation.