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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Key biological processes and essential genes for Proteus mirabilis biofilm development inhibition by protocatechuic acid.

Proteus mirabilis is an opportunistic pathogen linked to human urinary tract infections, and is potentially present as a foodborne pathogen within poultry products, including broiler chickens. This report outlines the inhibitory impacts of protocatechuic acid (PCA) on P. mirabilis isolated from a broiler slaughterhouse in China as well as its biofilm. This investigation encompasses assays related to motility and adhesion, bacterial metabolic activity, extracellular polymer (EPS) production, and scavenging capacity. The findings demonstrated that PCA reduced biofilm formation by 61 %. Transcriptomics findings identified that PCA limited the expression of genes like PstS that promote adhesin formation, rbsA and RcsB that alter bacterial chemotaxis, lipopolysaccharide synthesis genes LpxA and EptB, and cell wall synthesis genes MurF and MrdA, and affects the Regulator of Capsule Synthesis (RCS) two-component modulation system. Weighted gene co-expression network analysis (WGCNA) was conducted to identify the core genes. Furthermore, the binding sites of PCA to cytochrome oxidases cydA and cydB, two subunits of ATP synthase atpI and atpH, and ftsZ, which regulate bacterial division, were predicted via molecular docking. Metabolome analysis determined that PCA critically influenced coenzyme A biosynthesis, nucleotide metabolism, alanine, aspartic acid, and glutamate metabolic pathways of P. mirabilis. Therefore, PCA impacts metabolism within bacteria via various pathways, limiting the levels of extracellular polymer and bacterial viability to hinder biofilm formation. Additionally, we prepared an antibacterial plastic film containing protocatechuic acid using PVA as the monomer and CNC as the reinforcing agent. We examined the mechanical and antibacterial properties of this film. When used to wrap chicken, it reduced the total number of colonies, slowed the deterioration of chicken, and maintained the freshness of chicken. In conclusion, the information outlined in this study complements our comprehension of P. mirabilis inhibition by PCA and provides clues for the reduction of foodborne infections associated with P. mirabilis.

Housekeeping Gene Stability in Adipose Mesenchymal Stromal Cells Cultivated in Serum/Xeno-Free Media for Osteoarthritis.

Among the available therapeutics for the conservative treatment of osteoarthritis (OA), mesenchymal stromal cells (MSCs)-based products appear to be the most promising. Alongside minimally manipulated cell-based orthobiologics, where MSCs are the engine of the bioactive properties, cell expansion under good manufacturing practice (GMP) settings is actively studied to obtain clinical-grade pure populations able to concentrate the biological activity. One of the main characteristics of GMP protocols is the use of clinical-grade reagents, including the recently released serum-free/xeno-free (SFM/XFM) synthetic media, which differ significantly from the traditional reagents like those based on fetal bovine serum (FBS). As SFM/XFM are still poorly characterized, a main lack is the notion of reliable housekeeping genes (HKGs) for molecular studies, either standalone or in combination with standard conditions. Indeed, the aim of this work was to test the stability of five commonly used HKGs (ACTB, EF1A, GAPDH, RPLP0, and TBP) in adipose-derived MSCs (ASCs) cultivated in two commercially available SFM/XFM and to compare outcomes with those obtained in FBS. Four different applets widely recognized by the scientific community (NormFinder, geNorm, comparative ΔCt method, and BestKeeper) were used and data were merged to obtain a final stability order. The analysis showed that cells cultured in both synthetic media had a similar ranking for HKGs stability (GAPDH being best), albeit divergent from FBS expanded products (EF1A at top). Moreover, it was possible to identify specific HKGs for side by side studies, with EF1A/TBP being the most reliable normalizers for single SFM/XFM vs. FBS cultured cells and TBP the best one for a comprehensive analysis of all samples. In addition, stability of HKGs was donor-dependent. The normalization effect on selected genes coding for factors known to be involved in OA pathology, and whose amount should be carefully considered for the selection of the most appropriate MSC-based treatment, showed how HKGs choice might affect the perceived amount for the different media or donor. Overall, this work confirms the impact of SFM/XFM conditions on HKGs stability performance, which resulted similarly for both synthetic media analyzed in the study.

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.

Construction of an arrayed CRISPRi library as a resource for essential gene function studies in Streptococcus mutans.

IMPORTANCE: The construction of arrayed mutant libraries has advanced the field of bacterial genetics by allowing researchers to more efficiently study the exact function and importance of encoded genes. In this study, we constructed an arrayed clustered regularly interspaced short palindromic repeats interference (CRISPRi) library, known as S treptococcus mutans arrayed CRISPRi (SNAP), as a resource to study >250 essential and growth-supporting genes in Streptococcus mutans. SNAP will be made available to the research community, and we anticipate that its distribution will lead to high-quality, high-throughput, and reproducible studies of essential genes.

Screening the optimal housekeeping genes (HKGs) of placenta tissues by RNA-sequence and qRT-PCR throughout gestation in goat (Capra Hircus).

Selection of stable housekeeping genes (HKGs) is very important for accurate calculation of relative expression levels of target genes by quantitative real-time polymerase chain reaction (qRT-PCR). At present, the appropriate HKGs have not been identified in placental tissues throughout the pregnancy of the goat. In our study, 20 HKGs were tentatively selected from RNA-seq data and previous reports. The cycle threshold (Ct) of HKGs was determined by qRT-PCR in trophoblast membrane and cotyledon villus collected from 38 Dazu Black goats on gestation days of 20, 25, 30, 45, 60, 90, 120, and 150 (birth). The expression stability of the HKGs was analyzed by geNorm, Normfinder, Bestkeeper and Delta Ct algorithms, and comprehensively evaluated by ReFinder and ComprFinder. In addition, the optimal HKGs were further verified by placenta-specific genes (SPP1, VEGFA and PAG6). The 16 candidate HKGs (except POP4, TBP, RNF10, UBC) showed a qualified Ct value, less than 28. Among them, YWHAZ, EIF3K and PPIB showed the most stable expression in placental tissues during early, mid-late pregnancy and postpartum, but the least stable expression was B2M at early and mid-late stage, and PPIB at postpartum. After comprehensive analysis, RPLP0, EIF3K and YWHAZ were found to be the most stable placental HKGs throughout pregnancy. The classical HKGs, ACTB, GAPDH and 18S RNA have unstable expressions and even ranked at the bottom of the list from comprehensive index, suggesting an inappropriate for target gene normalization. Taken together, our study confirmed that YWHAZ, EIF3K, HMBS and RPLP0 may be the optimal HKGs in goat placenta at different stage of pregnancy, which provided a valuable reference of HKGs on functional gene expression detection for further research on placenta development and growth in ruminants.