Multi-kinase control of environmental stress responsive transcription

Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales

All biological systems are subject to perturbations arising from thermal fluctuations, external environments, or mutations. Yet, while biological systems consist of thousands of interacting components, recent high-throughput experiments have shown that their response to perturbations is surprisingly low dimensional: confined to only a few stereotyped changes out of the many possible. In this review, we explore a unifying dynamical systems framework-soft modes-to explain and analyze low dimensionality in biology, from molecules to ecosystems. We argue that this soft mode framework makes nontrivial predictions that generalize classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.

Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales

All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out of the many possible. Here, we explore a unifying dynamical systems framework - soft modes - to explain and analyze low-dimensionality in biology, from molecules to eco-systems. We argue that this one framework of soft modes makes non-trivial predictions that generalize classic ideas from developmental biology to disparate systems, namely: phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.

Ksp1 is an autophagic receptor protein for the Snx4-assisted autophagy of Ssn2/Med13

Ksp1 is a casein II-like kinase whose activity prevents aberrant macroautophagy/autophagy induction in nutrient-rich conditions in yeast. Here, we describe a kinase-independent role of Ksp1 as a novel autophagic receptor protein for Ssn2/Med13, a known cargo of Snx4-assisted autophagy of transcription factors. In this pathway, a subset of conserved transcriptional regulators, Ssn2/Med13, Rim15, and Msn2, are selectively targeted for vacuolar proteolysis following nitrogen starvation, assisted by the sorting nexin heterodimer Snx4-Atg20. Here we show that phagophores also engulf Ksp1 alongside its cargo for vacuolar proteolysis. Ksp1 directly associates with Atg8 following nitrogen starvation at the interface of an Atg8-family interacting motif (AIM)/LC3-interacting region (LIR) in Ksp1 and the LIR/AIM docking site (LDS) in Atg8. Mutating the LDS site prevents the autophagic degradation of Ksp1. However, deletion of the C terminal canonical AIM still permitted Ssn2/Med13 proteolysis, suggesting that additional non-canonical AIMs may mediate the Ksp1-Atg8 interaction. Ksp1 is recruited to the perivacuolar phagophore assembly site by Atg29, a member of the trimeric scaffold complex. This interaction is independent of Atg8 and Snx4, suggesting that Ksp1 is recruited early to phagophores, with Snx4 delivering Ssn2/Med13 thereafter. Finally, normal cell survival following prolonged nitrogen starvation requires Ksp1. Together, these studies define a kinase-independent role for Ksp1 as an autophagic receptor protein mediating Ssn2/Med13 degradation. They also suggest that phagophores built by the trimeric scaffold complex are capable of receptor-mediated autophagy. These results demonstrate the dual functionality of Ksp1, whose kinase activity prevents autophagy while it plays a scaffolding role supporting autophagic degradation.Abbreviations: 3-AT: 3-aminotriazole; 17C: Atg17-Atg31-Atg29 trimeric scaffold complex; AIM: Atg8-family interacting motif; ATG: autophagy related; CKM: CDK8 kinase module; Cvt: cytoplasm-to-vacuole targeting; IDR: intrinsically disordered region; LIR: LC3-interacting region; LDS: LIR/AIM docking site; MoRF: molecular recognition feature; NPC: nuclear pore complex; PAS: phagophore assembly site; PKA: protein kinase A; RBP: RNA-binding protein; UPS: ubiquitin-proteasome system. SAA-TF: Snx4-assisted autophagy of transcription factors; Y2H: yeast two-hybrid.

Yeast Rim11 kinase responds to glutathione-induced stress by regulating the transcription of phospholipid biosynthetic genes

Glutathione (GSH), a tripeptide composed of glycine, cysteine, and glutamic acid, is an abundant thiol found in a wide variety of cells, ranging from bacterial to mammalian cells. Adequate levels of GSH are essential for maintaining iron homeostasis. The ratio of oxidized/reduced GSH is strictly regulated in each organelle to maintain the cellular redox potential. Cellular redox imbalances cause defects in physiological activities, which can lead to various diseases. Although there are many reports regarding the cellular response to GSH depletion, studies on stress response to high levels of GSH are limited. Here, we performed genome-scale screening in the yeast Saccharomyces cerevisiae and identified RIM11, BMH1, and WHI2 as multicopy suppressors of the growth defect caused by GSH stress. The deletion strains of each gene were sensitive to GSH. We found that Rim11, a kinase important in the regulation of meiosis, was activated via autophosphorylation upon GSH stress in a glucose-rich medium. Furthermore, RNA-seq revealed that transcription of phospholipid biosynthetic genes was downregulated under GSH stress, and introduction of multiple copies of RIM11 counteracted this effect. These results demonstrate that S. cerevisiae copes with GSH stress via multiple stress-responsive pathways, including a part of the adaptive pathway to glucose limitation.

Research progress of anti-environmental factor stress mechanism and anti-stress tolerance way of Saccharomyces cerevisiae during the brewing process

Saccharomyces cerevisiae plays a decisive role in the brewing of alcohol products, and the ideal growth and fermentation characteristics can give the pure flavor of alcohol products. However, S. cerevisiae can be affected profoundly by environmental factors during the brewing process, which have negative effects on the growth and fermentation characteristics of S. cerevisiae, and seriously hindered the development of brewing industry. Therefore, we summarized the environmental stress factors (ethanol, organic acids, temperature and osmotic pressure) that affect S. cerevisiae during the brewing process. Their impact mechanisms and the metabolic adaption of S. cerevisiae in response to these stress factors. Of note, S. cerevisiae can increase the ability to resist stress factors by changing the cell membrane components, expressing transcriptional regulatory factors, activating the anti-stress metabolic pathway and enhancing ROS scavenging ability. Meantime, the strategies and methods to improve the stress- tolerant ability of S. cerevisiae during the brewing process were also introduced. Compared with the addition of exogenous anti-stress substances, mutation breeding and protoplast fusion, it appears that adaptive evolution and genetic engineering are able to generate ideal environmental stress tolerance strains of S. cerevisiae and are more in line with the needs of the current brewing industry.

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

The Phantom Mark: Enigmatic roles of phospho-Threonine 4 modification of the C-terminal domain of RNA polymerase II

The largest subunit of RNA polymerase II (Pol II) has an unusual carboxyl-terminal domain (CTD). This domain is composed of a tandemly repeating heptapeptide, Y1 S2 P3 T4 S5 P6 S7 , that has multiple roles in regulating Pol II function and processing newly synthesized RNA. Transient phosphorylation of Ser2 and Ser5 of the YS2 PTS5 PS repeat have well-defined roles in recruiting different protein complexes and coordinating sequential steps in gene transcription. As such, these phospho-marks encipher a molecular recognition code, colloquially termed the CTD code. In contrast, the contribution of phospho-Threonine 4 (pThr4/pT4) to the CTD code remains opaque and contentious. Fuelling the debate on the relevance of this mark to gene expression are the findings that replacing Thr4 with a valine or alanine has varied impact on cellular function in different species and independent proteomic analyses disagree on the relative abundance of pThr4 marks. Yet, substitution with negatively charged residues is lethal and even benign mutations selectively disrupt synthesis and 3' processing of distinct sets of coding and non-coding transcripts. Suggestive of non-canonical roles, pThr4 marked Pol II regulates distinct gene classes in a species- and signal-responsive manner. Hinting at undiscovered roles of this elusive mark, multiple signal-responsive kinases phosphorylate Thr4 at target genes. Here, we focus on this under-explored residue and postulate that the pThr4 mark is superimposed on the canonical CTD code to selectively regulate expression of targeted genes without perturbing genome-wide transcriptional processes. This article is categorized under: RNA Processing > 3' End Processing RNA Processing > Processing of Small RNAs RNA Processing > Splicing Regulation/Alternative Splicing.

Performing in spite of starvation: How Saccharomyces cerevisiae maintains robust growth when facing famine zones in industrial bioreactors

In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

The high osmolarity glycerol (HOG) pathway in fungi†

While fungi are widely occupying nature, many species are responsible for devastating mycosis in humans. Such niche diversity explains how quick fungal adaptation is necessary to endow the capacity of withstanding fluctuating environments and to cope with host-imposed conditions. Among all the molecular mechanisms evolved by fungi, the most studied one is the activation of the phosphorelay signalling pathways, of which the high osmolarity glycerol (HOG) pathway constitutes one of the key molecular apparatus underpinning fungal adaptation and virulence. In this review, we summarize the seminal knowledge of the HOG pathway with its more recent developments. We specifically described the HOG-mediated stress adaptation, with a particular focus on osmotic and oxidative stress, and point out some lags in our understanding of its involvement in the virulence of pathogenic species including, the medically important fungi Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus, compared to the model yeast Saccharomyces cerevisiae. Finally, we also highlighted some possible applications of the HOG pathway modifications to improve the fungal-based production of natural products in the industry.

Paralogs in the PKA Regulon Traveled Different Evolutionary Routes to Divergent Expression in Budding Yeast

Functional divergence of duplicate genes, or paralogs, is an important driver of novelty in evolution. In the model yeast Saccharomyces cerevisiae, there are 547 paralog gene pairs that survive from an interspecies Whole Genome Hybridization (WGH) that occurred ~100MYA. In this work, we report that ~1/6th (110) of these WGH paralogs pairs (or ohnologs) are differentially expressed with a striking pattern upon Protein Kinase A (PKA) inhibition. One member of each pair in this group has low basal expression that increases upon PKA inhibition, while the other has moderate and unchanging expression. For these genes, expression of orthologs upon PKA inhibition in the non-WGH species Kluyveromyces lactis and for PKA-related stresses in other budding yeasts shows unchanging expression, suggesting that lack of responsiveness to PKA was likely the typical ancestral phenotype prior to duplication. Promoter sequence analysis across related budding yeast species further revealed that the subsequent emergence of PKA-dependence took different evolutionary routes. In some examples, regulation by PKA and differential expression appears to have arisen following the WGH, while in others, regulation by PKA appears to have arisen in one of the two parental lineages prior to the WGH. More broadly, our results illustrate the unique opportunities presented by a WGH event for generating functional divergence by bringing together two parental lineages with separately evolved regulation into one species. We propose that functional divergence of two ohnologs can be facilitated through such regulatory divergence.

Protein kinase A controls yeast growth in visible light

Background

A wide variety of photosynthetic and non-photosynthetic species sense and respond to light, having developed protective mechanisms to adapt to damaging effects on DNA and proteins. While the biology of UV light-induced damage has been well studied, cellular responses to stress from visible light (400–700 nm) remain poorly understood despite being a regular part of the life cycle of many organisms. Here, we developed a high-throughput method for measuring growth under visible light stress and used it to screen for light sensitivity in the yeast gene deletion collection.

Results

We found genes involved in HOG pathway signaling, RNA polymerase II transcription, translation, diphthamide modifications of the translational elongation factor eEF2, and the oxidative stress response to be required for light resistance. Reduced nuclear localization of the transcription factor Msn2 and lower glycogen accumulation indicated higher protein kinase A (cAMP-dependent protein kinase, PKA) activity in many light-sensitive gene deletion strains. We therefore used an ectopic fluorescent PKA reporter and mutants with constitutively altered PKA activity to show that repression of PKA is essential for resistance to visible light.

Conclusion

We conclude that yeast photobiology is multifaceted and that protein kinase A plays a key role in the ability of cells to grow upon visible light exposure. We propose that visible light impacts on the biology and evolution of many non-photosynthetic organisms and have practical implications for how organisms are studied in the laboratory, with or without illumination.

Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics

Gene expression is thought to be affected not only by the concentration of transcription factors (TFs) but also the dynamics of their nuclear translocation. Testing this hypothesis requires direct control of TF dynamics. Here, we engineer CLASP, an optogenetic tool for rapid and tunable translocation of a TF of interest. Using CLASP fused to Crz1, we observe that, for the same integrated concentration of nuclear TF over time, changing input dynamics changes target gene expression: pulsatile inputs yield higher expression than continuous inputs, or vice versa, depending on the target gene. Computational modeling reveals that a dose-response saturating at low TF input can yield higher gene expression for pulsatile versus continuous input, and that multi-state promoter activation can yield the opposite behavior. Our integrated tool development and modeling approach characterize promoter responses to Crz1 nuclear translocation dynamics, extracting quantitative features that may help explain the differential expression of target genes.

CLASP is a modular optogenetic strategy to control the nuclear localization of transcription factors (TFs) and elicit gene expression from their cognate promoters. CLASP control of Crz1 nuclear localization, coupled with computational modeling, revealed how promoters can differentially decode dynamic transcription factor signals. The integrated strategy of CLASP development and modeling presents a generalized approach to causally investigate the transcriptional consequences of dynamic TF nuclear shuttling.