Transcriptomics and Functional Analysis of Copper Stress Response in the Sulfate-Reducing Bacterium Desulfovibrio alaskensis G20.

Copper (Cu) is an essential micronutrient required as a co-factor in the catalytic center of many enzymes. However, excess Cu can generate pleiotropic effects in the microbial cell. In addition, leaching of Cu from pipelines results in elevated Cu concentration in the environment, which is of public health concern. Sulfate-reducing bacteria (SRB) have been demonstrated to grow in toxic levels of Cu. However, reports on Cu toxicity towards SRB have primarily focused on the degree of toxicity and subsequent elimination. Here, Cu(II) stress-related effects on a model SRB, Desulfovibrio alaskensis G20, is reported. Cu(II) stress effects were assessed as alterations in the transcriptome through RNA-Seq at varying Cu(II) concentrations (5 µM and 15 µM). In the pairwise comparison of control vs. 5 µM Cu(II), 61.43% of genes were downregulated, and 38.57% were upregulated. In control vs. 15 µM Cu(II), 49.51% of genes were downregulated, and 50.5% were upregulated. The results indicated that the expression of inorganic ion transporters and translation machinery was massively modulated. Moreover, changes in the expression of critical biological processes such as DNA transcription and signal transduction were observed at high Cu(II) concentrations. These results will help us better understand the Cu(II) stress-response mechanism and provide avenues for future research.

Identification and function prediction of novel microRNAs in adenosine monophosphate activated protein kinase-activated Sertoli cells of immature boar.

This study was carried out with the objective to identify function prediction of novel microRNAs (miRNAs) in immature boar Sertoli cells (SCs) treated with 5-aminoimidazole-4-carboxamide-1-ß-D-ribofuranoside (AICAR), which is an agonist of adenosine monophosphate-activated protein kinase (AMPK) for regulating cellular energy homeostasis. Two small RNA libraries (control and AICAR treatment) prepared from immature boar SCs were constructed and sequenced by the Illumina small RNA deep sequencing. We identified 77 novel miRNAs and predicted 177 potential target genes for 26 differential novel miRNAs (four miRNAs up-regulation and 22 miRNAs down-regulation) in AICAR-treated SCs. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway suggested that target genes of differential novel miRNAs were implicated in many biological processes and metabolic pathways. Our findings provided useful information for the functional regulation of novel miRNAs and target mRNAs on AMPK-activated immature boar SCs.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes.

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Biological Trace Information Extracted from Bioaerosols Using NGS Analysis

Bioaerosols are atmospheric particles with a biological trace, such as viruses, bacteria, fungi, and plant material such as pollen and plant debris. In this study, we analyzed the biological information in bioaerosols using next generation sequencing of the trace DNA. The samples were collected using an Andersen air sampler and separated into two groups according to particulate matter (PM) size: small (PM2.5) and large (PM10). Amplification and sequencing of the bacterial 16S rDNA gene, prokaryotic internal transcribed spacer 1 (ITS1) region and DNA sequence of a plant chloroplast gene (rbcL) were carried out using several sets of specific primers targeting animal and plant sequences. Lots of bacterial information was detected from the bioaerosols. The most abundant bacteria in several samples were of the Actinobacteria (class), Alphaproteobacteria, Bacilli, and Clostridia. For the animal detection using internal transcribed spacer 1, only uncultured fungi were detected in more than half of the hits, with a high number of Cladosporium sp. in the samples. For the plant identification, the ITS1 information only matched fungal species. However, targeting of the rbcL region revealed diverse plant information, such as Medicago papillosa. In conclusion, traces of bacteria, fungi, and plants could be detected in the bioaerosols, but not of animals using our primers.

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11.

Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

Two paralogous EcfG σ factors hierarchically orchestrate the activation of the General Stress Response in Sphingopyxis granuli TFA.

Under ever-changing environmental conditions, the General Stress Response (GSR) represents a lifesaver for bacteria in order to withstand hostile situations. In α-proteobacteria, the EcfG-type extracytoplasmic function (ECF) σ factors are the key activators of this response at the transcriptional level. In this work, we address the hierarchical function of the ECF σ factor paralogs EcfG1 and EcfG2 in triggering the GSR in Sphingopyxis granuli TFA and describe the role of EcfG2 as global switch of this response. In addition, we define a GSR regulon for TFA and use in vitro transcription analysis to study the relative contribution of each EcfG paralog to the expression of selected genes. We show that the features of each promoter ultimately dictate this contribution, though EcfG2 always produced more transcripts than EcfG1 regardless of the promoter. These first steps in the characterisation of the GSR in TFA suggest a tight regulation to orchestrate an adequate protective response in order to survive in conditions otherwise lethal.

On Information Extraction and Decoding Mechanisms Improved by Noisy Amplification in Signaling Pathways.

The cells need to process information about extracellular stimuli. They encode, transmit and decode the information to elicit an appropriate response. Studies aimed at understanding how such information is decoded in the signaling pathways to generate a specific cellular response have become essential. Eukaryotic cells decode information through two different mechanisms: the feed-forward loop and the promoter affinity. Here, we investigate how these two mechanisms improve information transmission. A detailed comparison is made between the stochastic model of the MAPK/ERK pathway and a stochastic minimal decoding model. The maximal amount of transmittable information was computed. The results suggest that the decoding mechanism of the MAPK/ERK pathway improve the channel capacity because it behaves as a noisy amplifier. We show a positive dependence between the noisy amplification and the amount of information extracted. Additionally, we show that the extrinsic noise can be tuned to improve information transmission. This investigation has revealed that the feed-forward loop and the promoter affinity motifs extract information thanks to processes of amplification and noise addition. Moreover, the channel capacity is enhanced when both decoding mechanisms are coupled. Altogether, these findings suggest novel characteristics in how decoding mechanisms improve information transmission.

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

Global variation of soil microbial carbon-use efficiency in relation to growth temperature and substrate supply.

Soil microbial carbon-use efficiency (CUE), which is defined as the ratio of growth over C uptake, is commonly assumed as a constant or estimated by a temperature-dependent function in current microbial-explicit soil carbon (C) models. The temperature-dependent function (i.e., CUE = CUE0 + m × (T - 20)) simulates the dynamic CUE based on the specific CUE at a given reference temperature (i.e., CUE0) and a temperature response coefficient (i.e., m). Here, based on 780 observations from 98 sites, we showed a divergent spatial distribution of the soil microbial CUE (0.5 ± 0.25; mean ± SD) at the global scale. Then, the key parameters CUE0 and m in the above equation were estimated as 0.475 and -0.016, respectively, based on the observations with the Markov chain Monte Carlo technique. We also found a strong dependence of microbial CUE on the type of C substrate. The multiple regression analysis showed that glucose influences the variation of measured CUE associated with the environmental factors. Overall, this study confirms the global divergence of soil microbial CUE and calls for the incorporation of C substrate beside temperature in estimating the microbial CUE in different biomes.

Symptom Science: Advocating for Inclusion of Functional Genetic Polymorphisms.

Incorporating biologically based data into symptom science research can contribute substantially to understanding commonly experienced symptoms across chronic conditions. The purpose of this literature review was to identify functional polymorphisms associated with common symptoms (i.e., pain, sleep disturbance, fatigue, affective and cognitive symptoms) with the goal of identifying a parsimonious list of functional genetic polymorphisms with evidence to advocate for their inclusion in symptom science research. PubMed was searched to identify genes and functional polymorphisms associated with symptoms across chronic conditions, revealing eight functional genetic polymorphisms in seven different genes that showed evidence of association with at least three or more symptoms and/or symptom clusters: BDNF rs6265, COMT rs4680, FKBP5 rs3800373, IL-6 rs1800795, NFKB2 rs1056890, SLC6A4 5-HTTLPR+rs25531, and TNFA rs1799964 and rs1800629. Of these genes, three represent protein biomarkers previously identified as common data elements for symptom science research (BDNF, IL-6, and TNFA), and the polymorphisms in these genes identified through the search are known to impact secretion or level of transcription of these protein biomarkers. Inclusion of genotype data for polymorphisms offers great potential to further advance scientific knowledge of the biological basis of individual symptoms and symptom clusters across studies. Additionally, these polymorphisms have the potential to be used as targets to optimize precision health through the identification of individuals at risk for poor symptom experiences as well as the development of symptom management interventions.

Transcriptome Profiles of Human Visceral Adipocytes in Obesity and Colorectal Cancer Unravel the Effects of Body Mass Index and Polyunsaturated Fatty Acids on Genes and Biological Processes Related to Tumorigenesis.

Obesity, a low-grade inflammatory condition, represents a major risk factor for the development of several pathologies including colorectal cancer (CRC). Although the adipose tissue inflammatory state is now recognized as a key player in obesity-associated morbidities, the underlying biological processes are complex and not yet precisely defined. To this end, we analyzed transcriptome profiles of human visceral adipocytes from lean and obese subjects affected or not by CRC by RNA sequencing (n = 6 subjects/category), and validated selected modulated genes by real-time qPCR. We report that obesity and CRC, conditions characterized by the common denominator of inflammation, promote changes in the transcriptional program of adipocytes mostly involving pathways and biological processes linked to extracellular matrix remodeling, and metabolism of pyruvate, lipids and glucose. Interestingly, although the transcriptome of adipocytes shows several alterations that are common to both disorders, some modifications are unique under obesity (e.g., pathways associated with inflammation) and CRC (e.g., TGFß signaling and extracellular matrix remodeling) and are influenced by the body mass index (e.g., processes related to cell adhesion, angiogenesis, as well as metabolism). Indeed, cancer-induced transcriptional program is deeply affected by obesity, with adipocytes from obese individuals exhibiting a more complex response to the tumor. We also report that in vitro exposure of adipocytes to ω3 and ω6 polyunsaturated fatty acids (PUFA) endowed with either anti- or pro-inflammatory properties, respectively, modulates the expression of genes involved in processes potentially relevant to carcinogenesis, as assessed by real-time qPCR. All together our results suggest that genes involved in pyruvate, glucose and lipid metabolism, fibrosis and inflammation are central in the transcriptional reprogramming of adipocytes occurring in obese and CRC-affected individuals, as well as in their response to PUFA exposure. Moreover, our results indicate that the transcriptional program of adipocytes is strongly influenced by the BMI status in CRC subjects. The dysregulation of these interrelated processes relevant for adipocyte functions may contribute to create more favorable conditions to tumor establishment or favor tumor progression, thus linking obesity and colorectal cancer.

MicroRNA-22 Suppresses Breast Cancer Cell Growth and Increases Paclitaxel Sensitivity by Targeting NRAS.

In recent study, microRNAs have various important functions in diverse biological processes and progression of cancer. In human breast cancer, microRNA-22 has been reported to be downregulated. However, molecular mechanism of microRNA-22 in breast cancer progression and chemosensitivity has not been well studied. In our study, these results demonstrated that microRNA-22 expression levels were significantly reduced in 40 pairs of human breast cancer tissues when compared to normal tissues. Enforced expression of microRNA-22 inhibited activity of cell proliferation and cell migration in breast cancer cells. Furthermore, microRNA-22 targeted NRAS proto-oncogene, GTPase (NRAS) in breast cancer cells. The expression levels of NRAS in human clinical specimens were higher in breast cancer tissues when compared to normal tissues. Moreover, microRNA-22 sensitized breast cancer cells to paclitaxel by regulation of NRAS. Our results then demonstrated that microRNA-22 functioned as a tumor suppressor microRNA and indicated potential application for the diagnosis and treatment of cancer in the future.

The Cellular Senescence-Inhibited Gene Is Essential for PPM1A Myristoylation To Modulate Transforming Growth Factor ß Signaling.

The cellular senescence-inhibited gene (CSIG) is implicated in important biological processes, including cellular senescence and apoptosis. Our work showed that CSIG is involved in the myristoylation of the serine/threonine protein phosphatase PPM1A. Previous research has shown that myristoylation is necessary for PPM1A to dephosphorylate Smad2 and Smad3. However, the control and the biological significance of the myristoylation remain poorly understood. In this study, we found that CSIG knockdown disturbs PPM1A myristoylation and reduces the dephosphorylation by PPM1A of its substrate Smad2. By regulating PPM1A myristoylation, CSIG is involved in modulating the signaling of transforming growth factor ß (TGF-ß). Further study of the mechanism indicated that CSIG facilitates the interaction between N-myristoyltransferase 1 (NMT1) and PPM1A. Taking the data together, we found that CSIG is a regulator of PPM1A myristoylation and TGF-ß signaling. By promoting the myristoylation of PPM1A, CSIG enhanced the phosphatase activity of PPM1A and further inhibited TGF-ß signaling. This work not only extends the biological significance of CSIG but also provides new ideas and a reference for the study of the regulatory mechanism of myristoylation.

Deciphering cellular biological processes to clinical application: a new perspective for Tα1 treatment targeting multiple diseases.

BACKGROUND: Thymosin alpha 1 (Tα1) is a well-recognized immune response modulator in a wide range of disorders, particularly infections and cancer. The bioinformatic analysis of public databases allows drug repositioning, predicting a new potential area of clinical intervention. We aimed to decipher the cellular network induced by Tα1 treatment to confirm present use and identify new potential clinical applications. RESEARCH DESIGN AND METHODS: We used the transcriptional profile of human peripheral blood mononuclear cells treated in vitro with Tα1 to perform the enrichment network analysis by the Metascape online tools and the disease enrichment analysis by the DAVID online tool. RESULTS: Networked cellular responses reflected Tα1 regulated biological processes including immune and metabolic responses, response to compounds and oxidative stress, ion homeostasis, peroxisome biogenesis and drug metabolic process. Beyond cancer and infections, the analysis evidenced the association with disorders such as kidney chronic failure, diabetes, cardiovascular, chronic respiratory, neuropsychiatric, neurodegenerative and autoimmune diseases. CONCLUSIONS: In addition to the known ability to promote immune response pathways, the network enrichment analysis demonstrated that Tα1 regulates cellular metabolic processes and oxidative stress response. Notable, the analysis highlighted the association with several diseases, suggesting new translational implication of Tα1 treatment in pathological conditions unexpected until now.

A new approach of gene co-expression network inference reveals significant biological processes involved in porcine muscle development in late gestation.

The integration of genetic information in the cellular and nuclear environments is crucial for deciphering the way in which the genome functions under different physiological conditions. Experimental techniques of 3D nuclear mapping, a high-flow approach such as transcriptomic data analyses, and statistical methods for the development of co-expressed gene networks, can be combined to develop an integrated approach for depicting the regulation of gene expression. Our work focused more specifically on the mechanisms involved in the transcriptional regulation of genes expressed in muscle during late foetal development in pig. The data generated by a transcriptomic analysis carried out on muscle of foetuses from two extreme genetic lines for birth mortality are used to construct networks of differentially expressed and co-regulated genes. We developed an innovative co-expression networking approach coupling, by means of an iterative process, a new statistical method for graph inference with data of gene spatial co-localization (3D DNA FISH) to construct a robust network grouping co-expressed genes. This enabled us to highlight relevant biological processes related to foetal muscle maturity and to discover unexpected gene associations between IGF2, MYH3 and DLK1/MEG3 in the nuclear space, genes that are up-regulated at this stage of muscle development.

Screening key candidate genes and pathways involved in insulinoma by microarray analysis.

Insulinoma is a rare type tumor and its genetic features remain largely unknown. This study aimed to search for potential key genes and relevant enriched pathways of insulinoma.The gene expression data from GSE73338 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified between insulinoma tissues and normal pancreas tissues, followed by pathway enrichment analysis, protein-protein interaction (PPI) network construction, and module analysis. The expressions of candidate key genes were validated by quantitative real-time polymerase chain reaction (RT-PCR) in insulinoma tissues.A total of 1632 DEGs were obtained, including 1117 upregulated genes and 514 downregulated genes. Pathway enrichment results showed that upregulated DEGs were significantly implicated in insulin secretion, and downregulated DEGs were mainly enriched in pancreatic secretion. PPI network analysis revealed 7 hub genes with degrees more than 10, including GCG (glucagon), GCGR (glucagon receptor), PLCB1 (phospholipase C, beta 1), CASR (calcium sensing receptor), F2R (coagulation factor II thrombin receptor), GRM1 (glutamate metabotropic receptor 1), and GRM5 (glutamate metabotropic receptor 5). DEGs involved in the significant modules were enriched in calcium signaling pathway, protein ubiquitination, and platelet degranulation. Quantitative RT-PCR data confirmed that the expression trends of these hub genes were similar to the results of bioinformatic analysis.The present study demonstrated that candidate DEGs and enriched pathways were the potential critical molecule events involved in the development of insulinoma, and these findings were useful for better understanding of insulinoma genesis.

A data mining paradigm for identifying key factors in biological processes using gene expression data.

A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

Symptom Science: Omics Supports Common Biological Underpinnings Across Symptoms.

For precision health care to be successful, an in-depth understanding of the biological mechanisms for symptom development and severity is essential. Omics-based research approaches facilitate identification of the biological underpinnings of symptoms. We reviewed literature for omics-based approaches and exemplar symptoms (sleep disruption, cognitive impairment, fatigue, gastrointestinal [GI] distress, and pain) to identify genes associated with the symptom or symptoms across disease processes. The review yielded 27 genes associated with more than one symptom. ABCB1 (MDR1), APOE, BDNF, CNR1, COMT, DAT1 (SLC6A3), DRD4, ESR1, HLA-DRB1, IL10, IL1B, IL6, LTA, PTGS2 (COX-2), SLC6A4, and TNF were associated with cognitive impairment and pain, which had the most genes in common. COMT and TNF were related to all symptoms except sleep disruption. IL1B was associated with all symptoms except cognitive impairment. IL10, IL1A, IL1B, IL1RN, IL6, and IL8 (CXCL8) were linked with all the exemplar symptoms in various combinations. ABCB1 (MDR1) and SLC6A4 were associated with cognitive impairment, GI distress, and pain. IL10 and IL6 were linked to cognitive impairment, fatigue, and pain. APOE and BDNF were associated with sleep disruption, cognitive impairment, and pain. The 27 genes were associated with canonical pathways including immune, inflammatory, and cell signaling. The pathway analysis generated a 15-gene model from the 27 as well as 3 networks, which incorporated new candidate genes. The findings support the hypothesis of overlapping biological underpinnings across the exemplar symptoms. Candidate genes may be targeted in future omics research to identify mechanisms of co-occurring symptoms for potential precision treatments.

CRISPR/Cas9-based knockouts reveal that CpRLP1 is a negative regulator of the sex pheromone PR-IP in the Closterium peracerosum-strigosum-littorale complex.

Heterothallic strains of the Closterium peracerosum-strigosum-littorale (C. psl.) complex have two sexes, mating-type plus (mt+) and mating-type minus (mt-). Conjugation between these two sexes is regulated by two sex pheromones, protoplast-release-inducing protein (PR-IP) and PR-IP Inducer, which are produced by mt+ and mt- cells, respectively. PR-IP mediates the release of protoplasts from mt- cells during mating. In this study, we examined the mechanism of action of CpRLP1 (receptor-like protein 1), which was previously identified in a cDNA microarray analysis as one of the PR-IP-inducible genes. Using CRISPR/Cas9 technology, we generated CpRLP1 knockout mutants in mt- cells of the C. psl. complex. When the knockout mt- cells were mixed with wild-type mt+ cells, conjugation was severely reduced. Many cells released protoplasts without pairing, suggesting a loss of synchronization between the two mating partners. Furthermore, the knockout mutants were hypersensitive to PR-IP. We conclude that CpRLP1 is a negative regulator of PR-IP that regulates the timing of protoplast release in conjugating C. psl. cells. As the first report of successful gene knockout in the class Charophyceae, this study provides a basis for research aimed at understanding the ancestral roles of genes that are indispensable for the development of land plants.