Cell growth and division are tightly coordinated to cell size. In budding yeast, increasing cell size promotes the G1/S transition, called Start, by activating the transcription factor SBF, which drives a large fraction of cell-cycle–dependent gene expression. Part of this regulation arises because the concentration of the SBF inhibitor Whi5 decreases as cells grow. However, cells lacking Whi5 can still maintain a relatively accurate size when the SBF activator Cln3 is also removed, indicating that there are additional size control mechanisms. To understand how cell size is mechanistically translated into the activity of SBF-regulated promoters, we quantified the binding kinetics of Whi5 and SBF in live cells using single-molecule fluorescence microscopy. We found that increasing cell size is associated with both a decreased chromatin affinity of Whi5 and an increased chromatin affinity of SBF, accompanied by a higher SBF:Whi5 cell copy-number ratio. Chromatin-binding trends under basal and Whi5 overexpression conditions indicate that Whi5 restricts SBF association with chromatin. The transition point at which SBF binding overtakes Whi5 binding coincides with the onset of the expression of the G1 cyclins CLN1 and CLN2, two SBF targets that are important for committing cells to division. Reduced Whi5 binding reflects changes in its chromatin-association rate, as Whi5 and SBF dwell times on chromatin remain ~10 s and are largely independent of cell size. Together, these results show how changes in SBF and Whi5 abundance and chromatin association transmit cell size information to the genome to regulate the size-dependent Start transition in budding yeast.

Cyclocybe chaxingu is a well-known edible fungus in China, in which pileus size and color are key traits determining its commercial value. However, the molecular genetic mechanisms underlying the morphological development of its pileus remains limited at present. To address this, our study first completed the high-quality genome assembly of the monokaryotic strain Ag.c0002-1 of albino C. chaxingu, anchoring it to 13 chromosomes via Hi-C technology. The final genome size was 51.7 Mb with a GC content of 51.06%, and 11,332 protein-coding genes were annotated. Phenotypic observations and comparative transcriptome analyses were then conducted on the pilei of the brown cultivar Ag.c0067 and the white cultivar Ag.c0002 at the primordium, elongation, and mature stages. Phenotypic analysis revealed continuous pileus expansion accompanied by progressive color lightening in both cultivars during development. Comparative transcriptomic analyses revealed significant differences in gene expression patterns between the two cultivars across developmental stages. KEGG enrichment analysis indicated that pileus expansion is closely associated with pathways related to DNA replication, cell cycle of yeast, carbon metabolism, and carbohydrate digestion and absorption. Among these, differentially expressed genes involved in cell division tended to be downregulated, whereas genes associated with energy metabolism and substance transport were upregulated, providing the necessary energy and material support for pileus growth. Changes in pileus pigmentation were primarily associated with tyrosine metabolism, betalain biosynthesis, tryptophan metabolism, and melanogenesis pathways. Notably, the downregulation of tyrosinase genes and the upregulation of glutathione S-transferase genes during development may represent major molecular mechanisms underlying pileus color lightening. Overall, this study provides important insights into the molecular mechanisms regulating pileus development and pigmentation in C. chaxingu, while also offering valuable theoretical support for genetic analysis of basidiomycete morphogenesis and the molecular breeding of edible mushrooms.

Yeast is a widely used model organism in biological and proteomics research. Conventional bottom-up proteomic analysis of yeast cells requires disruption of the rigid cell wall to extract proteins, which is often associated with lengthy procedures, significant technical variations, and noticeable sample loss. Here, we present an "in-cell proteomics" approach that eliminates cell lysis and digests proteins directly in the yeast cells after a rapid methanol fixation. The approach integrates all the sample processing into a single filter device, offering a simple yet highly effective and sensitive approach for yeast proteomics analysis. We applied this approach to characterize proteome dynamics in the budding yeast Saccharomyces cerevisiae during cell cycle progression and following DNA damage. With single-shot LC-MS, we were able to detect and quantify around 3500 yeast proteins from the in-cell digests. Our study introduces a novel in-cell approach for yeast proteomics analysis and presents a quantitative proteome map of yeast cell-cycle progression with high temporal resolution for cell division cycle (Cdc) proteins. It also provides a comprehensive, time-resolved view of proteome-wide dynamics and remodeling throughout the yeast cell cycle in response to methyl methanesulfonate (MMS)-induced DNA damage. SUMMARY: Yeast proteomics studies often require detergent-based and/or mechanical disruption procedures for cell lysis and protein digestion. We reported an "in-cell proteomics" approach that eliminates cell lysis and digests proteins directly in the yeast cells after a simple methanol fixation. The approach integrates all the sample processing into a single filter device, offering a rapid yet highly effective and sensitive approach for yeast proteomics analysis. Using this method, we were able to characterize proteome dynamics in the budding yeast Saccharomyces cerevisiae during cell cycle progression and following DNA damage.

Microarray gene expression data are high-dimensional and complex, with patterns that may appear only under specific conditions. Traditional clustering often misses these local patterns, whereas biclustering can reveal groups of genes with coordinated expression across particular conditions. In this paper, we propose a biclustering approach using average Kendall correlation, which captures nonlinear and monotonic relationships often overlooked by standard measures like Euclidean distance or Pearson correlation. To efficiently search for optimal biclusters, we implement a modified stellar mass black-hole optimization (MSBO) approach that integrates the Nelder–Mead simplex method with Lévy flight to enhance both local and global search capabilities. The proposed technique is validated on couple of widely used benchmark gene expression datasets, namely the yeast cell cycle and lymphoma. The biological importance of the identified biclusters is evaluated with the help of the gene ontology (GO) database. Experimental results demonstrate that our method outperforms traditional approaches in identifying statistically significant and biologically relevant biclusters, achieving a p-value of 3.73 × 10−16. These findings address the pressing need for more effective biclustering techniques in the study of microarray data.

Sarcomyxa edulis is a characteristic low-temperature, edible mushroom in Northeast China. It has a delicious taste and rich nutritional and medicinal value. S. edulis can undergo explosive fruiting, neat fruiting, and unified harvesting, making it suitable for factory production. The molecular mechanisms underlying fruiting body development in S. edulis remain poorly understood. This study employed transcriptome analysis to compare the post-ripening mycelium (NPM) and primordial fruiting bodies (PRMs) of the thermostable S. edulis strain PQ650759, which uniquely forms primordia under constant temperature. A total of 4862 differentially expressed genes (DEGs) (|log2(fold change)| ≥ 1) were identified and found to be predominantly enriched in biological processes such as cell wall organization, DNA replication, and carbohydrate metabolism. KEGG pathway analysis revealed significant enrichment in 20 metabolic pathways, including mismatch repair, yeast cell cycle, and starch/sucrose metabolism. Ten candidate genes (e.g., SKP1, MRE11, GPI) linked to cell cycle regulation, DNA repair, and energy metabolism were randomly selected and prioritized for functional analysis. Quantitative PCR validation confirmed the reliability of transcriptome data, with expression trends consistent across both methods. Our findings provide critical insights into the molecular regulation of fruiting body development in S. edulis and establish a foundation for future mechanistic studies and strain optimization in industrial cultivation.

A series of sequential events orchestrates cell growth and division, set in motion by cyclin-dependent kinases (Cdks). In the "qualitative model" for Cdk control, order is achieved by cell cycle stage-specific cyclins. However, single-cyclin cells retain cell cycle order. In an alternative "quantitative model," increasing Cdk activity triggers substrate phosphorylation at sequential thresholds. Here, we test a key prediction from the quantitative model: the best Cdk substrates should be the first to be phosphorylated. Phosphoproteome analysis of synchronous budding yeast cultures, against expectations, reveals little correlation between known invitro Cdk phosphorylation rates and observed invivo phosphorylation timing. Incorporating Cdk-counteracting phosphatases that impose phosphorylation thresholds does not improve the correlation. Instead of kinase-phosphatase control (i.e., "regulator control"), our phosphoproteome patterns reveal signatures of "substrate control," including substrate-defined phosphorylation waves. The changing behavior of the substrates themselves therefore contributes to ordering their Cdk phosphorylation during the budding yeast cell cycle.

Cell cycle progression of the yeast Saccharomyces cerevisiae is largely driven by the expression of cyclins, which in turn bind the cyclin-dependent kinase CDK1 providing specificity. Due to the duplication of the yeast genome during evolution, most of the cyclins are present as a pair of paralogues, which are considered to have similar functions and periods of expression. Here, we use single molecule inexpensive fluorescence in situ hybridization (smiFISH) to measure the expression of five pairs of paralogous genes relevant for cell cycle progression (CLN1/CLN2, CLB5/CLB6, CLB3/CLB4, CLB1/CLB2 and ACE2/SWI5) in a large number of unsynchronized single cells representing all cell cycle phases. We systematically compare their expression patterns and strengths. In addition, we also analyze the effect of the knockout of one part of each pair on the expression of the other gene. In order to classify cells into specific cell cycle phases, we developed a convolutional neural network (CNN). We find that the expression levels of some cell-cycle related paralogues differ in their correlation, with CLN1 and CLN2 showing strong correlation and CLB3 and CLB4 showing weakest correlation. The temporal profiles of some pairs also differ. Upon deletion of their paralogue, CLB1 and CLB2 seem to compensate for the expression of the other gene, while this was not observed for ACE2/SWI5. Interestingly, CLB1 and CLB2 also seem to share work between mother and bud in the G2 phase, where CLB2 is primarily expressed in the bud and CLB1 in the mother. Taken together, our results suggest that paralogues related to yeast cell cycle progression should not be considered as the same but differ both in their expression strength and timing as well in their precise role in cell cycle regulation.

Bimolecular fluorescence complementation (BiFC) is a technique that enables real-time observation within living cells of the interaction between two proteins forming a complex, determining the location where such interaction occurs within the cell, and even the association and dissociation cycles in response to physiological cues. Here, we describe in detail the use of bimolecular fluorescence complementation to visualize the assembly and disassembly of cohesin over the fission yeast cell cycle.

The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 40 mutant yeast strains (83% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits ‘Cdc14 endocycles’ (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.

Proteins are the principal macromolecular constituent of proliferating cells, and protein synthesis is viewed as a primary metric of cell growth. While there are celebrated examples of proteins whose levels are periodic in the cell cycle (e.g., cyclins), the concentration of most proteins was not thought to change in the cell cycle, but some recent results challenge this notion. The 'bulk' protein is the focus of this article, specifically the rate of its synthesis, in the budding yeast Saccharomyces cerevisiae.

Budding yeast, Saccharomyces cerevisiae, is widely used as a model organism to study the genetics underlying eukaryotic cellular processes and growth critical to cancer development, such as cell division and cell cycle progression. The budding yeast cell cycle is also one of the best-studied dynamical systems owing to its thoroughly resolved genetics. However, the dynamics underlying the crucial cell cycle decision point called the START transition, at which the cell commits to a new round of DNA replication and cell division, are under-studied. The START machinery involves a central cyclin-dependent kinase; cyclins responsible for starting the transition, bud formation, and initiating DNA synthesis; and their transcriptional regulators. However, evidence has shown that the mechanism is more complicated than a simple irreversible transition switch. Activating a key transcription regulator SBF requires the phosphorylation of its inhibitor, Whi5, or an SBF/MBF monomeric component, Swi6, but not necessarily both. Also, the timing and mechanism of the inhibitor Whi5's nuclear export, while important, are not critical for the timing and execution of START. Therefore, there is a need for a consolidated model for the budding yeast START transition, reconciling regulatory and spatial dynamics. We built a detailed mathematical model (START-BYCC) for the START transition in the budding yeast cell cycle based on established molecular interactions and experimental phenotypes. START-BYCC recapitulates the underlying dynamics and correctly emulates key phenotypic traits of ~150 known START mutants, including regulation of size control, localization of inhibitor/transcription factor complexes, and the nutritional effects on size control. Such a detailed mechanistic understanding of the underlying dynamics gets us closer towards deconvoluting the aberrant cellular development in cancer.

DNA−protein crosslinks (DPCs) challenge faithful DNA replication and smooth passage of genomic information. Our study unveils the cullin E3 ubiquitin ligase Rtt101 as a DPC repair factor. Genetic analyses demonstrate that Rtt101 is essential for resistance to a wide range of DPC types including topoisomerase 1 crosslinks, in the same pathway as the ubiquitin-dependent aspartic protease Ddi1. Using an in vivo inducible Top1-mimicking DPC system, we reveal the significant impact of Rtt101 ubiquitination on DPC removal across different cell cycle phases. High-throughput methods coupled with next-generation sequencing specifically highlight the association of Rtt101 with replisomes as well as colocalization with DPCs. Our findings establish Rtt101 as a main contributor to DPC repair throughout the yeast cell cycle.

BackgroundThe progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast.ResultsConsidering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor.ConclusionsIt is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

The NuA3 complex is a major regulator of gene transcription and the cell cycle in yeast. Five core subunits are required for complex assembly and function, but it remains unclear how these subunits interact to form the complex. Here, we report that the Taf14 subunit of the NuA3 complex binds to two other subunits of the complex, Yng1 and Sas3, and describe the molecular mechanism by which the extra-terminal domain of Taf14 recognizes the conserved motif present in Yng1 and Sas3. Structural, biochemical, and mutational analyses show that two motifs are sandwiched between the two extra-terminal domains of Taf14. The head-to-toe dimeric complex enhances the DNA binding activity of Taf14, and the formation of the hetero-dimer involving the motifs of Yng1 and Sas3 is driven by sequence complementarity. In vivo assays in yeast demonstrate that the interactions of Taf14 with both Sas3 and Yng1 are required for proper function of the NuA3 complex in gene transcription and DNA repair. Our findings suggest a potential basis for the assembly of three core subunits of the NuA3 complex, Taf14, Yng1 and Sas3.

The cell cycle and the transcriptome dynamics of yeast exposed to extracellular self-DNA during an aerobic batch culture on glucose have been investigated using cytofluorimetric and RNA-seq analyses. In parallel, the same study was conducted on yeast cells growing in the presence of (heterologous) nonself-DNA. The self-DNA treatment determined a reduction in the growth rate and a major elongation of the diauxic lag phase, as well as a significant delay in the achievement of the stationary phase. This was associated with significant changes in the cell cycle dynamics, with slower exit from the G0 phase, followed by an increased level of cell percentage in the S phase, during the cultivation. Comparatively, the exposure to heterologous DNA did not affect the growth curve and the cell cycle dynamics. The transcriptomic analysis showed that self-DNA exposure produced a generalized downregulation of transmembrane transport and an upregulation of genes associated with sulfur compounds and the pentose phosphate pathway. Instead, in the case of the nonself treatment, a clear response to nutrient deprivation was detected. Overall, the presented findings represent further insights into the complex functional mechanisms of self-DNA inhibition.

Microarray technology has generated vast amounts of gene expression data with distinct patterns. Based on the premise that genes of correlated functions tend to exhibit similar expression patterns, various machine learning methods have been applied to capture these specific patterns in microarray data. However, the discrepancy between the rich expression profiles and the limited knowledge of gene functions has been a major hurdle to the understanding of cellular networks. To bridge this gap so as to properly comprehend and interpret expression data, we introduce Relevance Feedback to microarray analysis and propose an interactive learning framework to incorporate the expert knowledge into the decision module. In order to find a good learning method and solve two intrinsic problems in microarray data: high dimensionality and small sample size, we also propose a semi-supervised learning algorithm: Kernel Discriminant-EM (KDEM). This algorithm efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in Discriminant-EM (DEM) to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Relevance Feedback technique and KDEM together construct an efficient and effective interactive semi-supervised learning framework for microarray analysis. Extensive experiments on the yeast cell cycle regulation data set and Plasmodium falciparum red blood cell cycle data set show the promise of this approach.

Identifying target genes in Gene Regulation Network (GRN) models has always been challenging in Systems Biology. In this regard, indirect gene regulatory hierarchical architectures may be promising enough, considering varied topological structures and unknown gene regulation factors. Such causal regulations can be investigated, keeping in force all perturbation experiments of a dataset. Contemporary research primarily highlights direct interaction networks, which mostly forego the inevitable presence of a third entity, if any, towards varied forms of causal regulations. In this article, we have developed a hierarchical algorithm that unveils the genetic wiring through the Fused Least Absolute Shrinkage and Selection Operator (Fused-LASSO) technique with a Topological Overlap (TO) measure as the interaction structure. The potential power of this proposed model is studied over YEAST Cell Cycle, and Human cancer cell line data (HeLa). In this connection, the different statistically significant hierarchical regulation outcomes maintaining parity with the direct interaction structures, if any, to the target genes may throw new light on gene regulation statistics.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets

The highly conserved TOR signaling pathway is crucial for coordinating cellular growth with the cell cycle machinery in eukaryotes. One of the two TOR complexes in budding yeast, TORC1, integrates environmental cues and promotes cell growth. While cells grow, they need to copy their chromosomes, segregate them in mitosis, divide all their components during cytokinesis, and finally physically separate mother and daughter cells to start a new cell cycle apart from each other. To maintain cell size homeostasis and chromosome stability, it is crucial that mechanisms that control growth are connected and coordinated with the cell cycle. Successive periods of high and low TORC1 activity would participate in the adequate cell cycle progression. Here, we review the known molecular mechanisms through which TORC1 regulates the cell cycle in the budding yeast Saccharomyces cerevisiae that have been extensively used as a model organism to understand the role of its mammalian ortholog, mTORC1.

Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.