Development of a telomere vector-based approach to overcome limitations caused by lethal phenotypes in the study of essential genes in Magnaporthe oryzae.

Reverse genetic approaches are common tools in genomics for elucidating gene functions, involving techniques such as gene deletion followed by screening for aberrant phenotypes. If the generation of gene deletion mutants fails, the question arises whether the failure stems from technical issues or because the gene of interest (GOI) is essential, meaning that the deletion causes lethality. In this report, we introduce a novel method for assessing gene essentiality using the phytopathogenic ascomycete Magnaporthe oryzae. The method is based on the observation that telomere vectors are lost in transformants during cultivation without selection pressure. We tested the hypothesis that essential genes can be identified in deletion mutants co-transformed with a telomere vector. The M. oryzae gene MoPKC, described in literature as essential, was chosen as GOI. Using CRISPR/Cas9 technology transformants with deleted GOI were generated and backed up by a telomere vector carrying a copy of the GOI and conferring fenhexamid resistance. Transformants in which the GOI deletion in the genome was not successful lost the telomere vector on media without fenhexamid. In contrast, transformants with confirmed GOI deletion retained the telomere vector even in absence of fenhexamid selection. In the latter case, the maintenance of the telomere indicates that the GOI is essential for the surveillance of the fungi, as it would have been lost otherwise. The method presented here allows to test for essentiality of genes when no mutants can be obtained from gene deletion approaches, thereby expanding the toolbox for studying gene function in ascomycetes.

Developmental and Housekeeping Genes: Two Types of Genetic Organization in the Drosophila Genome.

We developed a procedure for locating genes on Drosophila melanogaster polytene chromosomes and described three types of chromosome structures (gray bands, black bands, and interbands), which differed markedly in morphological and genetic properties. This was reached through the use of our original methods of molecular and genetic analysis, electron microscopy, and bioinformatics data processing. Analysis of the genome-wide distribution of these properties led us to a bioinformatics model of the Drosophila genome organization, in which the genome was divided into two groups of genes. One was constituted by 65, in which the genome was divided into two groups, 62 genes that are expressed in most cell types during life cycle and perform basic cellular functions (the so-called "housekeeping genes"). The other one was made up of 3162 genes that are expressed only at particular stages of development ("developmental genes"). These two groups of genes are so different that we may state that the genome has two types of genetic organization. Different are the timings of their expression, chromatin packaging levels, the composition of activating and deactivating proteins, the sizes of these genes, the lengths of their introns, the organization of the promoter regions of the genes, the locations of origin recognition complexes (ORCs), and DNA replication timings.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.

BACKGROUND: Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging. RESULTS: Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters. CONCLUSION: Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Online application for the diagnosis of atherosclerosis by six genes.

BACKGROUND: Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS. METHODS: We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction. RESULTS: Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples. CONCLUSIONS: A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform.

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

CRISPR/Cas9 targeting of passenger single nucleotide variants in haploinsufficient or essential genes expands cancer therapy prospects.

CRISPR/Cas9 technology has effectively targeted cancer-specific oncogenic hotspot mutations or insertion-deletions. However, their limited prevalence in tumors restricts their application. We propose a novel approach targeting passenger single nucleotide variants (SNVs) in haploinsufficient or essential genes to broaden therapeutic options. By disrupting haploinsufficient or essential genes through the cleavage of DNA in the SNV region using CRISPR/Cas9, we achieved the selective elimination of cancer cells without affecting normal cells. We found that, on average, 44.8% of solid cancer patients are eligible for our approach, a substantial increase compared to the 14.4% of patients with CRISPR/Cas9-applicable oncogenic hotspot mutations. Through in vitro and in vivo experiments, we validated our strategy by targeting a passenger mutation in the essential ribosomal gene RRP9 and haploinsufficient gene SMG6. This demonstrates the potential of our strategy to selectively eliminate cancer cells and expand therapeutic opportunities.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel essential gene in prostate cancer metastasis.

Due to the limited effectiveness of current treatments, the survival rate of patients with metastatic castration-resistant prostate cancer (mCRPC) is significantly reduced. Consequently, it is imperative to identify novel therapeutic targets for managing these patients. Since the invasive ability of cells is crucial for establishing and maintaining metastasis, the aim of this study was to identify the essential regulators of invasive abilities of mCRPC cells by conducting two independent high-throughput CRISPR/Cas9 screenings. Furthermore, some of the top hits were validated using siRNA technology, with protein arginine methyltransferase 7 (PRMT7) emerging as the most promising candidate. We demonstrated that its inhibition or depletion via genetic or pharmacological approaches significantly reduces invasive, migratory and proliferative abilities of mCRPC cells in vitro. Moreover, we confirmed that PRMT7 ablation reduces cell dissemination in chicken chorioallantoic membrane and mouse xenograft assays. Molecularly, PRMT7 reprograms the expression of several adhesion molecules by methylating various transcription factors, such as FoxK1, resulting in the loss of adhesion from the primary tumor and increased motility of mCRPC cells. Furthermore, PRMT7 higher expression correlates with tumor aggressivity and poor overall survival in prostate cancer patients. Thus, this study demonstrates that PRMT7 is a potential therapeutic target and potential biomarker for mPCa.

PUM1 and PGK1 are Favorable Housekeeping Genes over Established Biodosimetry-related Housekeeping Genes such as HPRT1, ITFG1, DPM1, MRPS5, 18S rRNA and Others after Radiation Exposure.

In gene expression (GE) studies, housekeeping genes (HKGs) are required for normalization purposes. In large-scale inter-laboratory comparison studies, significant differences in dose estimates are reported and divergent HKGs are employed by the teams. Among them, the 18S rRNA HKG is known for its robustness. However, the high abundance of 18S rRNA copy numbers requires dilution, which is time-consuming and a possible source of errors. This study was conducted to identify the most promising HKGs showing the least radiation-induced GE variance after radiation exposure. In the screening stage of this study, 35 HKGs were analyzed. This included selected HKGs (ITFG1, MRPS5, and DPM1) used in large-scale biodosimetry studies which were not covered on an additionally employed pre-designed 96-well platform comprising another 32 HKGs used for different exposures. Altogether 41 samples were examined, including 27 ex vivo X-ray irradiated blood samples (0, 0.5, 4 Gy), six X-irradiated samples (0, 0.5, 5 Gy) from two cell lines (U118, A549), as well as eight non-irradiated tissue samples to encompass multiple biological entities. In the independent validation stage, the most suitable candidate genes were examined from another 257 blood samples, taking advantage of already stored material originating from three studies. These comprise 100 blood samples from ex vivo X-ray irradiated (0-4 Gy) healthy donors, 68 blood samples from 5.8 Gy irradiated (cobalt-60) Rhesus macaques (RM) (LD29/60) collected 0-60 days postirradiation, and 89 blood samples from chemotherapy-(CTx) treated breast tumor patients. CTx and radiation-induced GE changes in previous studies appeared comparable. RNA was isolated, converted into cDNA, and GE was quantified employing TaqMan assays and quantitative RT-PCR. We calculated the standard deviation (SD) and the interquartile range (IQR) as measures of GE variance using raw cycle threshold (Ct) values and ranked the HKGs accordingly. Dose, time, age, and sex-dependent GE changes were examined employing the parametrical t-test and non-parametrical Kruskal Wallis test, as well as linear regression analysis. Generally, similar ranking results evolved using either SD or IQR GE measures of variance, indicating a tight distribution of GE values. PUM1 and PGK1 showed the lowest variance among the first ten most suitable genes in the screening phase. MRPL19 revealed low variance among the first ten most suitable genes in the screening phase only for blood and cells, but certain comparisons indicated a weak association of MRPL19 with dose (P = 0.02-0.09). In the validation phase, these results could be confirmed. Here, IQR Ct values from, e.g., X-irradiated blood samples were 0.6 raw Ct values for PUM1 and PGK1, which is considered to represent GE differences as expected due to methodological variance. Overall, when compared, the GE variance of both genes was either comparable or lower compared to 18S rRNA. Compared with the IQR GE values of PUM1 and PGKI, twofold-fivefold increased values were calculated for the biodosimetry HKG HPRT1, and comparable values were calculated for biodosimetry HKGs ITFG1, MRPS5, and DPM1. Significant dose-dependent associations were found for ITFG1 and MRPS5 (P = 0.001-0.07) and widely absent or weak (P = 0.02-0.07) for HPRT1 and DPM1. In summary, PUM1 and PGK1 appeared most promising for radiation exposure studies among the 35 HKGs examined, considering GE variance and adverse associations of GE with dose.

Discovery of a proliferation essential gene signature and actin-like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple-positive breast cancer.

BACKGROUND: Patients with triple-positive breast cancer (TPBC) have a higher risk of recurrence and lower survival rates than patients with other luminal breast cancers. However, there are few studies on the predictive biomarkers of prognosis and treatment responses in TPBC. METHODS: Proliferation essential genes (PEGs) were acquired from clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) technology, and cohorts of patients with TPBC were obtained from public databases and our cohort. To develop a TPBC-PEG signature, Cox regression and least absolute shrinkage and selection operator regression analyses were applied. Functional analyses were performed with gene set enrichment analysis. The relationship between candidate genes and neoadjuvant chemotherapy (NACT) sensitivity was explored via real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) on the basis of clinical samples. RESULTS: Among 900 TPBC-PEGs, 437 showed significant differential expression between TPBC and normal tissues. Three prognostic PEGs (actin-like 6A [ACTL6A], chaperonin containing TCP1 subunit 2 [CCT2], and threonyl-TRNA synthetase [TARS]) were identified and used to construct the PEG signature. Patients with high PEG signature scores exhibited a worse overall survival and lower sensitivity to NACT than patients with low PEG signature scores. RT-qPCR results indicated that ACTL6A and CCT2 expression were significantly upregulated in patients who lacked sensitivity to NACT. IHC results showed that the ACTL6A protein was highly expressed in patients with NACT resistance and nonpathological complete responses. CONCLUSIONS: This efficient PEG signature prognostic model can predict the outcomes of TPBC. Furthermore, ACTL6A expression level was associated with the response to NACT, and could serve as an important factor in predicting prognosis and drug sensitivity of patients with TPBC.

Identification of the Reference Genes for Relative qRT-PCR Assay in Two Experimental Models of Rabbit and Horse Subcutaneous ASCs.

Obtaining accurate and reliable gene expression results in real-time RT-PCR (qRT-PCR) data analysis requires appropriate normalization by carefully selected reference genes, either a single or a combination of multiple housekeeping genes (HKGs). The optimal reference gene/s for normalization should demonstrate stable expression across varying conditions to diminish potential influences on the results. Despite the extensive database available, research data are lacking regarding the most appropriate HKGs for qRT-PCR data analysis in rabbit and horse adipose-derived stem cells (ASCs). Therefore, in our study, we comprehensively assessed and compared the suitability of some widely used HKGs, employing RefFinder and NormFinder, two extensively acknowledged algorithms for robust data interpretation. The rabbit and horse ASCs were obtained from subcutaneous stromal vascular fraction. ASCs were induced into tri-lineage differentiation, followed by the eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) treatment of the adipose-differentiated rabbit ASCs, while horse experimental groups were formed based on adipogenic, osteogenic, and chondrogenic differentiation. At the end of the experiment, the total mRNA was obtained and used for the gene expression evaluation of the observed factors. According to our findings, glyceraldehyde 3-phosphate dehydrogenase was identified as the most appropriate endogenous control gene for rabbit ASCs, while hypoxanthine phosphoribosyltransferase was deemed most suitable for horse ASCs. The obtained results underscore that these housekeeping genes exhibit robust stability across diverse experimental conditions, remaining unaltered by the treatments. In conclusion, the current research can serve as a valuable baseline reference for experiments evaluating gene expression in rabbit and horse ASCs. It highlights the critical consideration of housekeeping gene abundance and stability in qPCR experiments, emphasizing the need for an individualized approach tailored to the specific requirements of the study.

Validation of Endogenous Control Genes by Real-Time Quantitative Reverse Transcriptase Polymerase Chain Reaction for Acute Leukemia Gene Expression Studies.

Reference genes are used as internal reaction controls for gene expression analysis, and for this reason, they are considered reliable and must meet several important criteria. In view of the absence of studies regarding the best reference gene for the analysis of acute leukemia patients, a panel of genes commonly used as endogenous controls was selected from the literature for stability analysis: Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Abelson murine leukemia viral oncogene human homolog 1 (ABL), Hypoxanthine phosphoribosyl-transferase 1 (HPRT1), Ribosomal protein lateral stalk subunit P0 (RPLP0), ß-actin (ACTB) and TATA box binding protein (TBP). The stability of candidate reference genes was analyzed according to three statistical methods of assessment, namely, NormFinder, GeNorm and R software (version 4.0.3). From this study's analysis, it was possible to identify that the endogenous set composed of ACTB, ABL, TBP and RPLP0 demonstrated good performances and stable expressions between the analyzed groups. In addition to that, the GAPDH and HPRT genes could not be classified as good reference genes, considering that they presented a high standard deviation and great variability between groups, indicating low stability. Given these findings, this study suggests the main endogenous gene set for use as a control/reference for the gene expression in peripheral blood and bone marrow samples from patients with acute leukemias is composed of the ACTB, ABL, TBP and RPLP0 genes. Researchers may choose two to three of these housekeeping genes to perform data normalization.

Stable Housekeeping Genes in Bone Marrow, Adipose Tissue, and Amniotic Membrane-Derived Mesenchymal Stromal Cells for Orthopedic Regenerative Medicine Approaches.

The therapeutic effect of mesenchymal stromal cells (MSCs) has been described for a variety of disorders, including those affecting musculoskeletal tissues. In this context, the literature reports several data about the regenerative effectiveness of MSCs derived from bone marrow, adipose tissue, and an amniotic membrane (BMSCs, ASCs, and hAMSCs, respectively), either when expanded or when acting as clinical-grade biologic pillars of products used at the point of care. To date, there is no evidence about the superiority of one source over the others from a clinical perspective. Therefore, a reliable characterization of the tissue-specific MSC types is mandatory to identify the most effective treatment, especially when tailored to the target disease. Because molecular characterization is a crucial parameter for cell definition, the need for reliable normalizers as housekeeping genes (HKGs) is essential. In this report, the stability levels of five commonly used HKGs (ACTB, EF1A, GAPDH, RPLP0, and TBP) were sifted into BMSCs, ASCs, and hAMSCs. Adult and fetal/neonatal MSCs showed opposite HKG stability rankings. Moreover, by analyzing MSC types side-by-side, comparison-specific HKGs emerged. The effect of less performant HKG normalization was also demonstrated in genes coding for factors potentially involved in and predicting MSC therapeutic activity for osteoarthritis as a model musculoskeletal disorder, where the choice of the most appropriate normalizer had a higher impact on the donors rather than cell populations when compared side-by-side. In conclusion, this work confirms HKG source-specificity for MSCs and suggests the need for cell-type specific normalizers for cell source or condition-tailored gene expression studies.

A critical threshold of MCM10 is required to maintain genome stability during differentiation of induced pluripotent stem cells into natural killer cells.

Natural killer (NK) cell deficiency (NKD) is a rare disease in which NK cell function is reduced, leaving affected individuals susceptible to repeated viral infections and cancer. Recently, a patient with NKD was identified carrying compound heterozygous variants of MCM10 (minichromosome maintenance protein 10), an essential gene required for DNA replication, that caused a significant decrease in the amount of functional MCM10. NKD in this patient presented as loss of functionally mature late-stage NK cells. To understand how MCM10 deficiency affects NK cell development, we generated MCM10 heterozygous (MCM10+/-) induced pluripotent stem cell (iPSC) lines. Analyses of these cell lines demonstrated that MCM10 was haploinsufficient, similar to results in other human cell lines. Reduced levels of MCM10 in mutant iPSCs was associated with impaired clonogenic survival and increased genomic instability, including micronuclei formation and telomere erosion. The severity of these phenotypes correlated with the extent of MCM10 depletion. Significantly, MCM10+/- iPSCs displayed defects in NK cell differentiation, exhibiting reduced yields of hematopoietic stem cells (HSCs). Although MCM10+/- HSCs were able to give rise to lymphoid progenitors, these did not generate mature NK cells. The lack of mature NK cells coincided with telomere erosion, suggesting that NKD caused by these MCM10 variants arose from the accumulation of genomic instability including degradation of chromosome ends.

Identification of Reference Genes in Chordoma Cells Allows Cross-Comparison of Expression Studies Across Subtypes.

AIM: To present the best housekeeping genes including clival/sacral based chordoma, and the nucleus pulposus cells. MATERIAL AND METHODS: We investigated 13 candidate reference genes in public chordoma array transcriptome datasets, validated these genes by using RT-PCR, and evaluated their stability with NormFinder, geNorm, and Bestkeeper. RESULTS: YWHAZ, TBP and PGK1 genes were identified as the most stable reference genes as confirmed with three different approaches. Conversely, KRT8, KRT19 and GAPDH genes are less stable and not appropriate for use in chordoma research. CONCLUSION: For normalization of RT-PCR experiments in gene profiling of chordoma, we recommend the use of the stable genes YWHAZ, TBP and PGK1.

Essential genes identification model based on sequence feature map and graph convolutional neural network.

BACKGROUND: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. RESULTS: In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. CONCLUSIONS: Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

Computational analysis of genes with lethal knockout phenotype and prediction of essential genes in archaea.

The identification of microbial genes essential for survival as those with lethal knockout phenotype (LKP) is a common strategy for functional interrogation of genomes. However, interpretation of the LKP is complicated because a substantial fraction of the genes with this phenotype remains poorly functionally characterized. Furthermore, many genes can exhibit LKP not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes (conditionally essential genes). We analyzed the sets of LKP genes for two archaea, Methanococcus maripaludis and Sulfolobus islandicus, using a variety of computational approaches aiming to differentiate between essential and conditionally essential genes and to predict at least a general function for as many of the proteins encoded by these genes as possible. This analysis allowed us to predict the functions of several LKP genes including previously uncharacterized subunit of the GINS protein complex with an essential function in genome replication and of the KEOPS complex that is responsible for an essential tRNA modification as well as GRP protease implicated in protein quality control. Additionally, several novel antitoxins (conditionally essential genes) were predicted, and this prediction was experimentally validated by showing that the deletion of these genes together with the adjacent genes apparently encoding the cognate toxins caused no growth defect. We applied principal component analysis based on sequence and comparative genomic features showing that this approach can separate essential genes from conditionally essential ones and used it to predict essential genes in other archaeal genomes.IMPORTANCEOnly a relatively small fraction of the genes in any bacterium or archaeon is essential for survival as demonstrated by the lethal effect of their disruption. The identification of essential genes and their functions is crucial for understanding fundamental cell biology. However, many of the genes with a lethal knockout phenotype remain poorly functionally characterized, and furthermore, many genes can exhibit this phenotype not because their products perform essential cellular functions but because their knockout activates the toxicity of other genes. We applied state-of-the-art computational methods to predict the functions of a number of uncharacterized genes with the lethal knockout phenotype in two archaeal species and developed a computational approach to predict genes involved in essential functions. These findings advance the current understanding of key functionalities of archaeal cells.

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli.

Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152. This mobile CRISPRi library can be conjugated into other ordered genetic libraries to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two complementary crosses: (1) an Lpp deletion into every CRISPRi knockdown strain and (2) the lolA knockdown plasmid into the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed suppressing interactions for the loci in question.

PP2A complex disruptor SET prompts widespread hypertranscription of growth-essential genes in the pancreatic cancer cells.

Hyperactivation of the oncogenic transcription reflects the epigenetic plasticity of the cancer cells. Su(var)3-9, enhancer of zeste, Trithorax (SET) was described as a nuclear factor that stimulated transcription from the chromatin template. However, the mechanisms of SET-dependent transcription are unknown. Here, we found that overexpression of SET and CDK9 induced very similar transcriptome signatures in multiple cancer cell lines. SET localized in the transcription start site (TSS)-proximal regions and supported the RNA transcription. SET specifically bound the PP2A-C subunit and induced PP2A-A subunit repulsion from the C subunit, which indicated the role of SET as a PP2A-A/C complex disruptor in the TSS-proximal regions. Through blocking PP2A activity, SET assisted CDK9 to maintain Pol II CTD phosphorylation and activated mRNA transcription. Our findings position SET as a key factor that modulates chromatin PP2A activity, promoting the oncogenic transcription in the pancreatic cancer.