Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization

Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq

We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.

Identifying vital nodes for yeast network by dynamic network entropy

BACKGROUND

The 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.

RESULTS

Considering 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.

CONCLUSIONS

It 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.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Single-cell heterogeneity in ribosome content and the consequences for the growth laws

Previous work has suggested that the ribosome content of a cell is optimized to maximize growth given the nutrient availability. The resulting correlation between ribosome number and growth rate appears to be independent of the rate limiting nutrient and has been reported in many organisms. The robustness and universality of this observation has given it the classification of a "growth law." These laws have had powerful impacts on many biological disciplines. They have fueled predictions about how organisms evolve to maximize reproduction, and informed models about how cells regulate growth. Due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear if individual cells tightly regulate their ribosome content to match their environment. Here, we use recent ground-breaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we find enormous variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells showing transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to the levels of non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive of divergent growth strategies. These results suggest that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability, at least not in a way that can be captured by a unifying law that applies to all cell types. Overall, this work encourages the expansion of these "laws" and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity.

A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition

Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

Evidence for novel mechanisms that control cell-cycle entry and cell size

Entry into the cell cycle in late G1 phase occurs only when sufficient growth has occurred. In budding yeast, a cyclin called Cln3 is thought to link cell-cycle entry to cell growth. Cln3 accumulates during growth in early G1 phase and eventually helps trigger expression of late G1 phase cyclins that drive cell-cycle entry. All current models for cell-cycle entry assume that expression of late G1 phase cyclins is initiated at the transcriptional level. Current models also assume that the sole function of Cln3 in cell-cycle entry is to promote transcription of late G1 phase cyclins, and that Cln3 works solely in G1 phase. Here, we show that cell cycle-dependent expression of the late G1 phase cyclin Cln2 does not require any functions of the CLN2 promoter. Moreover, Cln3 can influence accumulation of Cln2 protein via posttranscriptional mechanisms. Finally, we show that Cln3 has functions in mitosis that strongly influence cell size. Together, these discoveries reveal the existence of surprising new mechanisms that challenge current models for control of cell-cycle entry and cell size.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Gene loss and cis-regulatory novelty shaped core histone gene evolution in the apiculate yeast Hanseniaspora uvarum

Core histone genes display a remarkable diversity of cis-regulatory mechanisms despite their protein sequence conservation. However, the dynamics and significance of this regulatory turnover are not well understood. Here, we describe the evolutionary history of core histone gene regulation across 400 million years in budding yeasts. We find that canonical mode of core histone regulation-mediated by the trans-regulator Spt10-is ancient, likely emerging between 320 and 380 million years ago and is fixed in the majority of extant species. Unexpectedly, we uncovered the emergence of a novel core histone regulatory mode in the Hanseniaspora genus, from its fast-evolving lineage, which coincided with the loss of 1 copy of its paralogous core histone genes. We show that the ancestral Spt10 histone regulatory mode was replaced, via cis-regulatory changes in the histone control regions, by a derived Mcm1 histone regulatory mode and that this rewiring event occurred with no changes to the trans-regulator, Mcm1, itself. Finally, we studied the growth dynamics of the cell cycle and histone synthesis in genetically modified Hanseniaspora uvarum. We find that H. uvarum divides rapidly, with most cells completing a cell cycle within 60 minutes. Interestingly, we observed that the regulatory coupling between histone and DNA synthesis was lost in H. uvarum. Our results demonstrate that core histone gene regulation was fixed anciently in budding yeasts, however it has greatly diverged in the Hanseniaspora fast-evolving lineage.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a functional homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Protein production dynamics and physiological adaptation of recombinant Komagataella phaffii at near-zero growth rates

BACKGROUND

Specific productivity (qP) in yeast correlates with growth, typically peaking at intermediate or maximum specific growth rates (μ). Understanding the factors limiting productivity at extremely low μ might reveal decoupling strategies, but knowledge of production dynamics and physiology in such conditions is scarce. Retentostats, a type of continuous cultivation, enable the well-controlled transition to near-zero µ through the combined retention of biomass and limited substrate supply. Recombinant Komagataella phaffii (syn Pichia pastoris) secreting a bivalent single domain antibody (VHH) was cultivated in aerobic, glucose-limited retentostats to investigate recombinant protein production dynamics and broaden our understanding of relevant physiological adaptations at near-zero growth conditions.

RESULTS

By the end of the retentostat cultivation, doubling times of approx. two months were reached, corresponding to µ = 0.00047 h-1. Despite these extremely slow growth rates, the proportion of viable cells remained high, and de novo synthesis and secretion of the VHH were observed. The average qP at the end of the retentostat was estimated at 0.019 mg g-1 h-1. Transcriptomics indicated that genes involved in protein biosynthesis were only moderately downregulated towards zero growth, while secretory pathway genes were mostly regulated in a manner seemingly detrimental to protein secretion. Adaptation to near-zero growth conditions of recombinant K. phaffii resulted in significant changes in the total protein, RNA, DNA and lipid content, and lipidomics revealed a complex adaptation pattern regarding the lipid class composition. The higher abundance of storage lipids as well as storage carbohydrates indicates that the cells are preparing for long-term survival.

CONCLUSIONS

In conclusion, retentostat cultivation proved to be a valuable tool to identify potential engineering targets to decouple growth and protein production and gain important insights into the physiological adaptation of K. phaffii to near-zero growth conditions.

Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data

The origin and evolution of cell types has emerged as a key topic in evolutionary biology. Driven by rapidly accumulating single-cell datasets, recent attempts to infer cell type evolution have largely been limited to pairwise comparisons because we lack approaches to build cell phylogenies using model-based approaches. Here we approach the challenges of applying explicit phylogenetic methods to single-cell data by using principal components as phylogenetic characters. We infer a cell phylogeny from a large, comparative single-cell dataset of eye cells from five distantly related mammals. Robust cell type clades enable us to provide a phylogenetic, rather than phenetic, definition of cell type, allowing us to forgo marker genes and phylogenetically classify cells by topology. We further observe evolutionary relationships between diverse vessel endothelia and identify the myelinating and non-myelinating Schwann cells as sister cell types. Finally, we examine principal component loadings and describe the gene expression dynamics underlying the function and identity of cell type clades that have been conserved across the five species. A cell phylogeny provides a rigorous framework towards investigating the evolutionary history of cells and will be critical to interpret comparative single-cell datasets that aim to ask fundamental evolutionary questions.

Maintaining transcriptional homeostasis during cell cycle

The preservation of gene expression patterns that define cellular identity throughout the cell division cycle is essential to perpetuate cellular lineages. However, the progression of cells through different phases of the cell cycle severely disrupts chromatin accessibility, epigenetic marks, and the recruitment of transcriptional regulators. Notably, chromatin is transiently disassembled during S-phase and undergoes drastic condensation during mitosis, which is a significant challenge to the preservation of gene expression patterns between cell generations. This article delves into the specific gene expression and chromatin regulatory mechanisms that facilitate the preservation of transcriptional identity during replication and mitosis. Furthermore, we emphasize our recent findings revealing the unconventional role of yeast centromeres and mitotic chromosomes in maintaining transcriptional fidelity beyond mitosis.

Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

BACKGROUND

Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.

RESULTS

We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).

CONCLUSION

Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

IMPORTANCE

We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

Targeting APEX2 to the mRNA encoding fatty acid synthase β in yeast identifies interacting proteins that control its abundance in the cell cycle

Profiling the repertoire of proteins associated with a given mRNA during the cell cycle is unstudied. Furthermore, it is easier to ask and answer what mRNAs a specific protein might bind to than the other way around. Here, we implemented an RNA-centric proximity labeling technology at different points in the cell cycle in highly synchronous yeast cultures. To understand how the abundance of FAS1, encoding fatty acid synthase, peaks late in the cell cycle, we identified proteins that interact with the FAS1 transcript in a cell cycle-dependent manner. We used dCas13d-APEX2 fusions to target FAS1 and label nearby proteins, which were then identified by mass spectrometry. The glycolytic enzyme Tdh3p, a known RNA-binding protein, interacted with the FAS1 mRNA, and it was necessary for the periodic abundance of Fas1p in the cell cycle. These results point to unexpected connections between major metabolic pathways. They also underscore the role of mRNA-protein interactions for gene expression during cell division.

Sis2 regulates yeast replicative lifespan in a dose-dependent manner

Application of microfluidic platforms facilitated high-precision measurements of yeast replicative lifespan (RLS); however, comparative quantification of lifespan across strain libraries has been missing. Here we microfluidically measure the RLS of 307 yeast strains, each deleted for a single gene. Despite previous reports of extended lifespan in these strains, we found that 56% of them did not actually live longer than the wild-type; while the remaining 44% showed extended lifespans, the degree of extension was often different from what was previously reported. Deletion of SIS2 gene led to the largest RLS increase observed. Sis2 regulated yeast lifespan in a dose-dependent manner, implying a role for the coenzyme A biosynthesis pathway in lifespan regulation. Introduction of the human PPCDC gene in the sis2Δ background neutralized the lifespan extension. RNA-seq experiments revealed transcriptional increases in cell-cycle machinery components in sis2Δ background. High-precision lifespan measurement will be essential to elucidate the gene network governing lifespan.

Genome dilution by cell growth drives starvation-like proteome remodeling in mammalian and yeast cells

Cell size is tightly controlled in healthy tissues and single-celled organisms, but it remains unclear how size influences cell physiology. Increasing cell size was recently shown to remodel the proteomes of cultured human cells, demonstrating that large and small cells of the same type can be biochemically different. Here, we corroborate these results in mouse hepatocytes and extend our analysis using yeast. We find that size-dependent proteome changes are highly conserved and mostly independent of metabolic state. As eukaryotic cells grow larger, the dilution of the genome elicits a starvation-like proteome phenotype, suggesting that growth in large cells is limited by the genome in a manner analogous to a limiting nutrient. We also demonstrate that the proteomes of replicatively-aged yeast are primarily determined by their large size. Overall, our data suggest that genome concentration is a universal determinant of proteome content in growing cells.

Glucose to lactate shift reprograms CDK-dependent mitotic decisions and its communication with MAPK Sty1 in Schizosaccharomyces pombe

Cell cycle regulation in response to biochemical cues is a fundamental event associated with many diseases. The regulation of such responses in complex metabolic environments is poorly understood. This study reveals unknown aspects of the metabolic regulation of cell division in Schizosaccharomyces pombe. We show that changing the carbon source from glucose to lactic acid alters the functions of the cyclin-dependent kinase (CDK) Cdc2 and mitogen-activated protein kinase (MAPK) Sty1, leading to unanticipated outcomes in the behavior and fate of such cells. Functional communication of Cdc2 with Sty1 is known to be an integral part of the cellular response to aberrant Cdc2 activity in S. pombe. Our results show that cross-talk between Cdc2 and Sty1, and the consequent Sty1-dependent regulation of Cdc2 activity, appears to be compromised and the relationship between Cdc2 activity and mitotic timing is also reversed in the presence of lactate. We also show that the biochemical status of cells under these conditions is an important determinant of the altered molecular functions mentioned above as well as the altered behavior of these cells.

Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression

Cells respond to environmental and developmental stimuli by remodeling their transcriptomes through regulation of both mRNA transcription and mRNA decay. A central goal of biology is identifying the global set of regulatory relationships between factors that control mRNA production and degradation and their target transcripts and construct a predictive model of gene expression. Regulatory relationships are typically identified using transcriptome measurements and causal inference algorithms. RNA kinetic parameters are determined experimentally by employing run-on or metabolic labeling (e.g. 4-thiouracil) methods that allow transcription and decay rates to be separately measured. Here, we develop a deep learning model, trained with single-cell RNA-seq data, that both infers causal regulatory relationships and estimates RNA kinetic parameters. The resulting in silico model predicts future gene expression states and can be perturbed to simulate the effect of transcription factor changes. We acquired model training data by sequencing the transcriptomes of 175,000 individual Saccharomyces cerevisiae cells that were subject to an external perturbation and continuously sampled over a one hour period. The rate of change for each transcript was calculated on a per-cell basis to estimate RNA velocity. We then trained a deep learning model with transcriptome and RNA velocity data to calculate time-dependent estimates of mRNA production and decay rates. By separating RNA velocity into transcription and decay rates, we show that rapamycin treatment causes existing ribosomal protein transcripts to be rapidly destabilized, while production of new transcripts gradually slows over the course of an hour. The neural network framework we present is designed to explicitly model causal regulatory relationships between transcription factors and their genes, and shows superior performance to existing models on the basis of recovery of known regulatory relationships. We validated the predictive power of the model by perturbing transcription factors in silico and comparing transcriptome-wide effects with experimental data. Our study represents the first step in constructing a complete, predictive, biophysical model of gene expression regulation.

Identifying local associations in biological time series: algorithms, statistical significance, and applications

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Streamlining N-terminally anchored yeast surface display via structural insights into S. cerevisiae Pir proteins

Surface display co-opts yeast's innate ability to embellish its cell wall with mannoproteins, thus converting the yeast's outer surface into a growing and self-sustaining catalyst. However, the efficient toolbox for converting the enzyme of interest into its surface-displayed isoform is currently lacking, especially if the isoform needs to be anchored to the cell wall near the isoform's N-terminus, e.g., through a short GPI-independent protein anchor. Aiming to advance such N-terminally anchored surface display, we employed in silico and machine-learning strategies to study the 3D structure, function, genomic organisation, and evolution of the Pir protein family, whose members evolved to covalently attach themselves near their N-terminus to the β-1,3-glucan of the cell wall. Through the newly-gained insights, we rationally engineered 14 S. cerevisiae Hsp150 (Pir2)-based fusion proteins. We quantified their performance, uncovering guidelines for efficient yeast surface display while developing a construct that promoted a 2.5-fold more efficient display of a reporter protein than the full-length Hsp150. Moreover, we developed a Pir-tag, i.e., a peptide spanning only 4.5 kDa but promoting as efficient surface display of a reporter protein as the full-length Hsp150. These constructs fortify the existing surface display toolbox, allowing for a prompt and routine refitting of intracellular proteins into their N-terminally anchored isoforms.

Branched-chain amino acid synthesis is coupled to TOR activation early in the cell cycle in yeast

How cells coordinate their metabolism with division determines the rate of cell proliferation. Dynamic patterns of metabolite synthesis during the cell cycle are unexplored. We report the first isotope tracing analysis in synchronous, growing budding yeast cells. Synthesis of leucine, a branched-chain amino acid (BCAA), increases through the G1 phase of the cell cycle, peaking later during DNA replication. Cells lacking Bat1, a mitochondrial aminotransferase that synthesizes BCAAs, grow slower, are smaller, and are delayed in the G1 phase, phenocopying cells in which the growth-promoting kinase complex TORC1 is moderately inhibited. Loss of Bat1 lowers the levels of BCAAs and reduces TORC1 activity. Exogenous provision of valine and, to a lesser extent, leucine to cells lacking Bat1 promotes cell division. Valine addition also increases TORC1 activity. In wild-type cells, TORC1 activity is dynamic in the cell cycle, starting low in early G1 but increasing later in the cell cycle. These results suggest a link between BCAA synthesis from glucose to TORC1 activation in the G1 phase of the cell cycle.

A novel piecewise-linear method for detecting associations between variables

Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients' default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future.

Mitochondria and the cell cycle in budding yeast

As centers for energy production and essential biosynthetic activities, mitochondria are vital for cell growth and proliferation. Accumulating evidence suggests an integrated regulation of these organelles and the nuclear cell cycle in distinct organisms. In budding yeast, a well-established example of this coregulation is the coordinated movement and positional control of mitochondria during the different phases of the cell cycle. The molecular determinants involved in the inheritance of the fittest mitochondria by the bud also seem to be cell cycle-regulated. In turn, loss of mtDNA or defects in mitochondrial structure or inheritance often lead to a cell cycle delay or arrest, indicating that mitochondrial function can also regulate cell cycle progression, possibly through the activation of cell cycle checkpoints. The up-regulation of mitochondrial respiration at G2/M, presumably to fulfil energetic requirements for progression at this phase, also supports a mitochondria-cell cycle interplay. Cell cycle-linked mitochondrial regulation is accomplished at the transcription level and through post-translational modifications, predominantly protein phosphorylation. Here, we address mitochondria-cell cycle interactions in the yeast Saccharomyces cerevisiae and discuss future challenges in the field.

PKA regulatory subunit Bcy1 couples growth, lipid metabolism, and fermentation during anaerobic xylose growth in Saccharomyces cerevisiae

Organisms have evolved elaborate physiological pathways that regulate growth, proliferation, metabolism, and stress response. These pathways must be properly coordinated to elicit the appropriate response to an ever-changing environment. While individual pathways have been well studied in a variety of model systems, there remains much to uncover about how pathways are integrated to produce systemic changes in a cell, especially in dynamic conditions. We previously showed that deletion of Protein Kinase A (PKA) regulatory subunit BCY1 can decouple growth and metabolism in Saccharomyces cerevisiae engineered for anaerobic xylose fermentation, allowing for robust fermentation in the absence of division. This provides an opportunity to understand how PKA signaling normally coordinates these processes. Here, we integrated transcriptomic, lipidomic, and phospho-proteomic responses upon a glucose to xylose shift across a series of strains with different genetic mutations promoting either coupled or decoupled xylose-dependent growth and metabolism. Together, results suggested that defects in lipid homeostasis limit growth in the bcy1Δ strain despite robust metabolism. To further understand this mechanism, we performed adaptive laboratory evolutions to re-evolve coupled growth and metabolism in the bcy1Δ parental strain. The evolved strain harbored mutations in PKA subunit TPK1 and lipid regulator OPI1, among other genes, and evolved changes in lipid profiles and gene expression. Deletion of the evolved opi1 gene partially reverted the strain's phenotype to the bcy1Δ parent, with reduced growth and robust xylose fermentation. We suggest several models for how cells coordinate growth, metabolism, and other responses in budding yeast and how restructuring these processes enables anaerobic xylose utilization.

Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor

The ability to independently control the expression of different genes is important for quantitative biology. Using budding yeast, we characterize GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z₃EV, blue-light inducible optogenetic systems El222-LIP, El222-GLIP, and red-light inducible PhyB-PIF3. We report kinetic parameters, noise scaling, impact on growth, and the fundamental leakiness of each system using an intuitive unit, maxGAL1. We uncover disadvantages of widely used tools, e.g., nonmonotonic activity of MET3pr and GALL, slow off kinetics of the doxycycline- and estradiol-inducible systems tetOpr and Z₃EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two previously uncharacterized systems: strongLOV, a more light-sensitive El222 mutant, and ARG3pr, which is induced in the absence of arginine or presence of methionine. To demonstrate fine control over gene circuits, we experimentally tune the time between cell cycle Start and mitosis, artificially simulating near-wild-type timing. All strains, constructs, code, and data ( https://promoter-benchmark.epfl.ch/ ) are made available.

Contributions of mutation and selection to regulatory variation: lessons from the Saccharomyces cerevisiae TDH3 gene

Heritable variation in gene expression is common within and among species and contributes to phenotypic diversity. Mutations affecting either cis- or trans-regulatory sequences controlling gene expression give rise to variation in gene expression, and natural selection acting on this variation causes some regulatory variants to persist in a population for longer than others. To understand how mutation and selection interact to produce the patterns of regulatory variation we see within and among species, my colleagues and I have been systematically determining the effects of new mutations on expression of the TDH3 gene in Saccharomyces cerevisiae and comparing them to the effects of polymorphisms segregating within this species. We have also investigated the molecular mechanisms by which regulatory variants act. Over the past decade, this work has revealed properties of cis- and trans-regulatory mutations including their relative frequency, effects, dominance, pleiotropy and fitness consequences. Comparing these mutational effects to the effects of polymorphisms in natural populations, we have inferred selection acting on expression level, expression noise and phenotypic plasticity. Here, I summarize this body of work and synthesize its findings to make inferences not readily discernible from the individual studies alone. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

The N-terminal disordered region of ChsB regulates its efficient transport to the hyphal apical surface in Aspergillus nidulans

In fungi, the cell wall plays a crucial role in morphogenesis and response to stress from the external environment. Chitin is one of the main cell wall components in many filamentous fungi. In Aspergillus nidulans, a class III chitin synthase ChsB plays a pivotal role in hyphal extension and morphogenesis. However, little is known about post-translational modifications of ChsB and their functional impacts. In this study, we showed that ChsB is phosphorylated in vivo. We characterized strains that produce ChsB using stepwise truncations of its N-terminal disordered region or deletions of some residues in that region and demonstrated its involvement in ChsB abundance on the hyphal apical surface and in hyphal tip localization. Furthermore, we showed that some deletions in this region affected the phosphorylation states of ChsB, raising the possibility that these states are important for the localization of ChsB to the hyphal surface and the growth of A. nidulans. Our findings indicate that ChsB transport is regulated by its N-terminal disordered region.

One-carbon metabolic enzymes are regulated during cell division and make distinct contributions to the metabolome and cell cycle progression in Saccharomyces cerevisiae

Enzymes of one-carbon (1C) metabolism play pivotal roles in proliferating cells. They are involved in the metabolism of amino acids, nucleotides, and lipids and the supply of all cellular methylations. However, there is limited information about how these enzymes are regulated during cell division and how cell cycle kinetics are affected in several loss-of-function mutants of 1C metabolism. Here, we report that the levels of the S. cerevisiae enzymes Ade17p and Cho2p, involved in the de novo synthesis of purines and phosphatidylcholine (PC), respectively, are cell cycle-regulated. Cells lacking Ade17p, Cho2p, or Shm2p (an enzyme that supplies 1C units from serine) have distinct alterations in size homeostasis and cell cycle kinetics. Loss of Ade17p leads to a specific delay at START, when cells commit to a new round of cell division, while loss of Shm2p has broader effects, reducing growth rate. Furthermore, the inability to synthesize PC de novo in cho2Δ cells delays START and reduces the coherence of nuclear elongation late in the cell cycle. Loss of Cho2p also leads to profound metabolite changes. Besides the expected changes in the lipidome, cho2Δ cells have reduced levels of amino acids, resembling cells shifted to poorer media. These results reveal the different ways that 1C metabolism allocates resources to affect cell proliferation at multiple cell cycle transitions.

RNA localization to the mitotic spindle is essential for early development and is regulated by kinesin-1 and dynein

Mitosis is a fundamental and highly regulated process that acts to faithfully segregate chromosomes into two identical daughter cells. Localization of gene transcripts involved in mitosis to the mitotic spindle might be an evolutionarily conserved mechanism to ensure that mitosis occurs in a timely manner. We identified many RNA transcripts that encode proteins involved in mitosis localized at the mitotic spindles in dividing sea urchin embryos and mammalian cells. Disruption of microtubule polymerization, kinesin-1 or dynein results in lack of spindle localization of these transcripts in the sea urchin embryo. Furthermore, results indicate that the cytoplasmic polyadenylation element (CPE) within the 3'UTR of the Aurora B transcript, a recognition sequence for CPEB, is essential for RNA localization to the mitotic spindle in the sea urchin embryo. Blocking this sequence results in arrested development during early cleavage stages, suggesting that RNA localization to the mitotic spindle might be a regulatory mechanism of cell division that is important for early development.

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

Transcriptome analysis of the binucleate ciliate Tetrahymena thermophila with asynchronous nuclear cell cycles

Tetrahymena thermophila harbors two functionally and physically distinct nuclei within a shared cytoplasm. During vegetative growth, the "cell cycles" of the diploid micronucleus and polyploid macronucleus are offset. Micronuclear S phase initiates just before cytokinesis and is completed in daughter cells before onset of macronuclear DNA replication. Mitotic micronuclear division occurs mid-cell cycle, while macronuclear amitosis is coupled to cell division. Here we report the first RNA-seq cell cycle analysis of a binucleated ciliated protozoan. RNA was isolated across 1.5 vegetative cell cycles, starting with a macronuclear G1 population synchronized by centrifugal elutriation. Using MetaCycle, 3244 of the 26,000+ predicted genes were shown to be cell cycle regulated. Proteins present in both nuclei exhibit a single mRNA peak that always precedes their macronuclear function. Nucleus-limited genes, including nucleoporins and importins, are expressed before their respective nucleus-specific role. Cyclin D and A/B gene family members exhibit different expression patterns that suggest nucleus-restricted roles. Periodically expressed genes cluster into seven cyclic patterns. Four clusters have known PANTHER gene ontology terms associated with G1/S and G2/M phase. We propose that these clusters encode known and novel factors that coordinate micro- and macronuclear-specific events such as mitosis, amitosis, DNA replication, and cell division.

Mitochondrial respiration promotes Cdc37-dependent stability of the Cdk1 homolog Cdc28

Cdc28, the homolog of mammalian Cdk1, is a conserved key regulatory kinase for all major cell cycle transitions in yeast. We have found that defects in mitochondrial respiration (including deletion of ATP2, an ATP synthase subunit) inhibit growth of cells carrying a degron allele of Cdc28 (cdc28td) or Cdc28 temperature-sensitive mutations (cdc28-1 and cdc28-1N) at semi-permissive temperatures. Loss of cell proliferation in the atp2Δcdc28td double mutant is associated with aggravated cell cycle arrest and mitochondrial dysfunction, including mitochondrial hyperpolarization and fragmentation. Unexpectedly, in mutants defective in mitochondrial respiration, steady-state protein levels of mutant cdc28 are strongly reduced, accounting for the aggravated growth defects. Stability of Cdc28 is promoted by the Hsp90-Cdc37 chaperone complex. Our results show that atp2Δcdc28td double-mutant cells, but not single mutants, are sensitive to chemical inhibition of the Hsp90-Cdc37 complex, and exhibit reduced levels of additional Hsp90-Cdc37 client kinases, suggesting an inhibition of this complex. In agreement, overexpression of CDC37 improved atp2Δcdc28td cell growth and Cdc28 levels. Overall, our study shows that simultaneous disturbance of mitochondrial respiration and Cdc28 activity reduces the capacity of Cdc37 to chaperone client kinases, leading to growth arrest.

Unsupervised Grouped Axial Data Modeling via Hierarchical Bayesian Nonparametric Models With Watson Distributions

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis.

RNA Interference Analysis of the Functions of Cyclin B in Male Reproductive Development of the Oriental River Prawn (Macrobrachium nipponense)

Cyclin B (CycB) plays essential roles in cell proliferation and promotes gonad development in many crustaceans. The goal of this study was to investigate the regulatory roles of this gene in the reproductive development of male oriental river prawns (Macrobrachium nipponense). A phylo-genetic tree analysis revealed that the protein sequence of Mn-CycB was most closely related to those of freshwater prawns, whereas the evolutionary distance from crabs was much longer. A quantitative PCR analysis showed that the expression of Mn-CycB was highest in the gonad of both male and female prawns compared to that in other tissues (p < 0.05), indicating that this gene may play essential roles in the regulation of both testis and ovary development in M. nipponense. In males, Mn-CycB expression in the testis and androgenic gland was higher during the reproductive season than during the non-reproductive season (p < 0.05), implying that CycB plays essential roles in the reproductive development of male M. nipponense. An RNA interference analysis revealed that the Mn-insulin-like androgenic gland hormone expression decreased as the Mn-CycB expression decreased, and that few sperm were detected 14 days after the dsCycB treatment, indicating that CycB positively affects testis development in M. nipponense. The results of this study highlight the functions of CycB in M. nipponense, and they can be applied to studies of male reproductive development in other crustacean species.

The Polo kinase Cdc5 is regulated at multiple levels in the adaptation response to telomere dysfunction

Telomere dysfunction activates the DNA damage checkpoint to induce a cell cycle arrest. After an extended period of time, however, cells can bypass the arrest and undergo cell division despite the persistence of the initial damage, a process called adaptation to DNA damage. The Polo kinase Cdc5 in Saccharomyces cerevisiae is essential for adaptation and for many other cell cycle processes. How the regulation of Cdc5 in response to telomere dysfunction relates to adaptation is not clear. Here, we report that Cdc5 protein level decreases after telomere dysfunction in a Mec1-, Rad53- and Ndd1-dependent manner. This regulation of Cdc5 is important to maintain long-term cell cycle arrest but not for the initial checkpoint arrest. We find that both Cdc5 and the adaptation-deficient mutant protein Cdc5-ad are heavily phosphorylated and several phosphorylation sites modulate adaptation efficiency. The PP2A phosphatases are involved in Cdc5-ad phosphorylation status and contribute to adaptation mechanisms. We finally propose that Cdc5 orchestrates multiple cell cycle pathways to promote adaptation.

Engineered cell differentiation and sexual reproduction in probiotic and mating yeasts

G protein-coupled receptors (GPCRs) enable cells to sense environmental cues and are indispensable for coordinating vital processes including quorum sensing, proliferation, and sexual reproduction. GPCRs comprise the largest class of cell surface receptors in eukaryotes, and for more than three decades the pheromone-induced mating pathway in baker's yeast Saccharomyces cerevisiae has served as a model for studying heterologous GPCRs (hGPCRs). Here we report transcriptome profiles following mating pathway activation in native and hGPCR-signaling yeast and use a model-guided approach to correlate gene expression to morphological changes. From this we demonstrate mating between haploid cells armed with hGPCRs and endogenous biosynthesis of their cognate ligands. Furthermore, we devise a ligand-free screening strategy for hGPCR compatibility with the yeast mating pathway and enable hGPCR-signaling in the probiotic yeast Saccharomyces boulardii. Combined, our findings enable new means to study mating, hGPCR-signaling, and cell-cell communication in a model eukaryote and yeast probiotics.

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

Background

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

Results

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

Conclusions

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

Supplementary Information

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

Identification of the Novel Gene Markers Based on the Gene Profile among Different Severity of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is caused by repeated blockage of the upper respiratory airways during sleep. The traditional evaluation methods for OSA severity are yet limited. This study aimed to screen gene signatures to effectively evaluate OSA severity. Expression profiles of peripheral blood mononuclear cells in the different severities of OSA patients were accessed from Gene Expression Omnibus (GEO) database. A total of 446 differentially expressed genes (DEGs) were screened among the varying severities of OSA samples by analysis of variance (ANOVA) test. A total of 1,152 DEGs were screened between the pre- and post-treatment OSA samples by using t test. Overlap of the two groups of DEGs was selected (88 DEGs) for Metascape enrichment analysis. Afterwards, Mfuzz package was used to perform soft clustering analysis on these 88 genes, by which 6 clusters were obtained. It was observed that the gene expression condition of the cluster 3 was positively associated with OSA severity degree; also, the gene expression condition in cluster 4 was negatively correlated with OSA severity. A total of 10 gene markers related to OSA progression were selected from cluster 3 and cluster 4. Their expression levels and correlation were analyzed. The marker genes in cluster 3 and cluster 4 were examined, finding that most genes were significantly correlated with apnea hypopnea index (AHI). An accurate and objective assessment of the severity of OSA is of great significance for formulating follow-up treatment strategies for patients with OSA. In this paper, a set of marker genes that can detect the severity of OSA were screened by bioinformatics methods, which could be jointly used with the traditional OSA diagnostic index to achieve a more reliable OSA severity evaluation.

The CfMcm1 Regulates Pathogenicity, Conidium Germination, and Sexual Development in Colletotrichum fructicola

Glomerella leaf spot (GLS), caused by Colletotrichum fructicola, is a severe disease worldwide on apple, causing defoliation, leaf and fruit spot, and substantial yield loss. However, little is known about its molecular mechanisms of pathogenesis. Previous transcriptome analysis revealed that a transcription factor, CfMcm1, was induced during leaf infection. In the present work, expression pattern analysis verified that the CfMcm1 gene was strongly expressed in conidia and early infection. Phenotypic analysis revealed that the gene deletion mutant ΔCfMcm1 lost pathogenicity to apple leaves by inhibiting conidial germination and appressorium formation. In addition to appressorium-mediated pathogenicity, ΔCfMcm1 colonization and hyphal extension in wounded apple fruit was also reduced, and conidial germination mode and conidial color were altered. ΔCfMcm1 displayed impairment of cell wall integrity and response to stress caused by oxidation, osmosis, and an acid environment. Furthermore, the deletion mutant produced fewer and smaller perithecia and no ascospores. In contrast, melanin deposition in mycelia of ΔCfMcm1 was strengthened. Further comparative transcriptome and quantitative PCR analysis revealed that CfMcm1 modulated expression of genes related to conidial development (CfERG5A, CfERG5B, CfHik5, and CfAbaA), appressorium formation (CfCBP1 and CfCHS7), pectin degradation (CfPelA and CfPelB), sexual development (CfMYB, CfFork, CfHMG, and CfMAT1-2-1), and melanin biosynthesis (CfCmr1, CfPKS1, CfT4HR1, CfTHR1, and CfSCD1). Our results demonstrated that CfMcm1 is a pivotal regulator possessing multiple functions in pathogenicity, asexual and sexual reproduction, and melanin biosynthesis.