What are housekeeping genes?

Murine glial protrusion transcripts predict localized Drosophila glial mRNAs involved in plasticity

The polarization of cells often involves the transport of specific mRNAs and their localized translation in distal projections. Neurons and glia are both known to contain long cytoplasmic processes, while localized transcripts have only been studied extensively in neurons, not glia, especially in intact nervous systems. Here, we predict 1,740 localized Drosophila glial transcripts by extrapolating from our meta-analysis of seven existing studies characterizing the localized transcriptomes and translatomes of synaptically associated mammalian glia. We demonstrate that the localization of mRNAs in mammalian glial projections strongly predicts the localization of their high-confidence Drosophila homologs in larval motor neuron-associated glial projections and are highly statistically enriched for genes associated with neurological diseases. We further show that some of these localized glial transcripts are specifically required in glia for structural plasticity at the nearby neuromuscular junction synapses. We conclude that peripheral glial mRNA localization is a common and conserved phenomenon and propose that it is likely to be functionally important in disease.

FEAtl: a comprehensive web-based expression atlas for functional genomics in tropical and subtropical fruit crops

BACKGROUND

Fruit crops, including tropical and subtropical fruits like Avocado (Persea americana), Fig (Ficus carica), Date Palm (Phoenix dactylifera), Mango (Mangifera indica), Guava (Psidium guajava), Papaya (Carica papaya), Pineapple (Ananas comosus), and Banana (Musa acuminata) are economically vital, contributing significantly to global agricultural output, as classified by the FAO's World Programme for the Census of Agriculture. Advancements in next-generation sequencing, have transformed fruit crop breeding by providing in-depth genomic and transcriptomic data. RNA sequencing enables high-throughput analysis of gene expression, and functional genomics, crucial for addressing horticultural challenges and enhancing fruit production. The genomic and expression data for key tropical and sub-tropical fruit crops is currently lacking a comprehensive expression atlas, revealing a significant gap in resources for horticulturists who require a unified platform with diverse datasets across various conditions and cultivars.

RESULTS

The Fruit Expression Atlas (FEAtl), available at http://backlin.cabgrid.res.in/FEAtl/ , is a first-ever extensive and unified expression atlas for tropical and subtropical fruit crops developed using 3-tier architecture. The expressivity of coding and non-coding genes, encompassing 2,060 RNA-Seq samples across 91 tissue types and 177 BioProjects, it provides a comprehensive view of gene expression patterns for different tissues under various conditions. FEAtl features multiple tabs that cater to different aspects of the dataset, namely, Home, About, Analyze, Statistics, and Team and contains seven central functional modules: Transcript Information,Sample Information, Expression Profiles in FPKM and TPM, Functional Analysis, Genes Based on Tau Score, and Search for Specific Gene. The expression of a transcript of interest can be easily queried by searching by tissue ID and transcript type. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.

CONCLUSIONS

This atlas represents a groundbreaking compilation of a wide array of information pertaining to eight distinct fruit crops and serves as a fundamental resource for comparative analysis among different fruit species and is a catalyst for functional genomic studies. Database availability: http://backlin.cabgrid.res.in/FEAtl/ .

A systematic review on the selection of reference genes for gene expression studies in rodents: are the classics the best choice?

Rodents are commonly used as animal models in studies investigating various experimental conditions, often requiring gene expression analysis. Quantitative real-time reverse transcription PCR (RT-qPCR) is the most widely used tool to quantify target gene expression levels under different experimental conditions in various biological samples. Relative normalization with reference genes is a crucial step in RT-qPCR to obtain reliable quantification results. In this work, the main reference genes used in gene expression studies among the three rodents commonly employed in scientific research-hamster, rat, and mouse-are analyzed and described. An individual literature search for each rodent was conducted using specific search terms in three databases: PubMed, Scopus, and Web of Science. A total of 157 articles were selected (rats = 73, mice = 79, and hamsters = 5), identifying various reference genes. The most commonly used reference genes were analyzed according to each rodent, sample type, and experimental condition evaluated, revealing a great variability in the stability of each gene across different samples and conditions. Classic genes, which are expected to be stably expressed in both samples and conditions analyzed, demonstrated greater variability, corroborating existing concerns about the use of these genes. Therefore, this review provides important insights for researchers seeking to identify suitable reference genes for their validation studies in rodents.

Stability and expression patterns of housekeeping genes in Mediterranean mussels (Mytilus galloprovincialis) under field investigations

The use of marine mussels as biological models encompasses a broad range of research fields, in which the application of RNA analyses disclosed novel biomarkers of environmental stress and investigated biochemical mechanisms of action. Quantitative real-time PCR (qPCR) is the gold standard for these studies, and despite its wide use and available protocols, it may be affected by technical flaws requiring reference gene data normalization. In this study, stability of housekeeping genes commonly employed as reference genes in qPCR analyses with Mytilus galloprovincialis was explored under field conditions. Mussels were collected from farms in the Northwestern Adriatic Sea. The sampling strategy considered latitudinal gradients of environmental parameters (proxied by location), gender, and their interactions with seasonality. Analyses of gene stability were performed using different algorithms. BestKeeper and geNorm agreed that combination of the ribosomal genes 18S ribosomal RNA (18S) and 28S ribosomal RNA (28S) was the best normalization strategy in the conditions tested, which agrees with available evidence. NormFinder provided different normalization strategies, involving combinations of tubulin (TUB)/28S (Gender/Season effect) or TUB/helicase (HEL) (Location/Season effect). Since NormFinder considers data grouping and computes both intra- and inter-group stability variations, it should work better with complex experimental designs and dataset structuring. Under the selected normalization strategies, expressions of the variable housekeeping genes actin (ACT) and elongation factor-1α (EF1) correlated with seasonal and latitudinal changes of abiotic environmental factors and mussel physiological status. Results point to consider ACT and EF1 expressions as molecular biomarkers of mussel general physiological status in field studies.

Expression patterns of housekeeping genes and tissue-specific genes in black goats across multiple tissues

Black goats are a significant meat breed in southern China. To investigate the expression patterns and biological functions of genes in various tissues of black goats, we analyzed housekeeping genes (HKGs), tissue-specific genes (TSGs), and hub genes (HUBGs) across 23 tissues. Additionally, we analyzed HKGs in 13 tissues under different feeding conditions. We identified 2968 HKGs, including six important ones. Interestingly, HKGs in grazing black goats demonstrated higher and more stable expression levels. We discovered a total of 9912 TSGs, including 134 newly identified ones. The number of TSGs for mRNA and lncRNA were nearly equal, with 127 mRNA TSGs expressed solely in one tissue. Additionally, the predicted functions of tissue-specific long non-coding RNAs (lncRNAs) targeting mRNAs corresponded with the physiological functions of the tissues.Weighted gene co-expression network analysis (WGCNA) constructed 30 modules, where the dark grey module consists almost entirely of HKGs, and TSGs are located in modules most correlated with their respective tissues. Additionally, we identified 289 HUBGs, which are involved in regulating the physiological functions of their respective tissues. Overall, these identified HKGs, TSGs, and HUBGs provide a foundation for studying the molecular mechanisms affecting the genetic and biological processes of complex traits in black goats.

Embryonic Leucine Promotes Early Postnatal Growth via mTOR Signalling in Japanese Quails

Nutritional cues during embryonic development can alter developmental trajectories and affect postnatal growth. However, the specific mechanisms by which nutrients influence avian growth remain largely unknown. Amino acids can directly interact with the nutrient-sensing pathways, such as the insulin-like growth factor 1 (IGF-1)/mechanistic target of rapamycin (mTOR) pathways, which are known to regulate growth. We examined the effects of embryonic leucine on gene expression and phenotypic growth in Japanese quails by injecting 2.5 mg leucine or saline (control) into Japanese quail eggs on the tenth day of incubation and incubating them under standard conditions. The treatment groups had similar hatching success and size at hatching. However, between 3 and 7 days post-hatching, quails treated with embryonic leucine showed increased growth in body mass and wing, tarsus, head, and intestinal lengths, lasting up to 21 days. The hepatic expression of IGF1, IGF1R, mTOR, and RPS6K1 was upregulated in leucine-treated quails, while the expression of FOXO1 remained unaffected. In conclusion, a subtle increase in embryonic leucine may induce developmental programming effects in Japanese quail by interacting with the IGF-1/mTOR nutrient-sensing pathway to promote growth. This study highlights the role of embryonic amino acids as crucial nutrients for enhancing growth. It provides valuable insight into nutrient intervention strategies during embryonic development to potentially improve poultry growth performance.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets.

The effect of GnRH-a on the angiogenesis of endometriosis

PURPOSE

Neoangiogenesis is necessary for adhesion and invasiveness of endometriotic lesions in women affected by endometriosis. Vascular endothelial growth factor (VEGF) is one of the main components of angiogenesis and is part of the major pathway tissue factor (TF)-protease activated receptor-2 (PAR-2)-VEGF that leads to neoangiogenesis. Specificity protein 1 (SP1) is a transcriptional factor that has recently been studied for its crucial role in angiogenesis via a specific pathway. We hypothesize that by blocking angiogenetic pathways we can suppress endometriotic lesions. Gonadotrophin-releasing hormone-agonists (GnRH-a) are routinely used, especially preoperatively, in endometriosis. It would be of great interest to clarify which angiogenetic pathways are affected and, thereby, pave the way for further research into antiangiogenetic effects on endometriosis.

METHODS

We used quantitative real-time polymerase chain reaction (qRT-PCR) to study mRNA expression levels of TF, PAR-2, VEGF, and SP1 in endometriotic tissues of women who underwent surgery for endometriosis and received GnRH-a (leuprolide acetate) preoperatively.

RESULTS

VEGF, TF, and PAR-2 expression is significantly lower in patients who received treatment (p < 0,001) compared to those who did not, whereas SP1 expression is not altered (p = 0.779).

CONCLUSIONS

GnRH-a administration does affect some pathways of angiogenesis in endometriotic lesions, but not all of them. Therefore, supplementary treatments that affect the SP1 pathway of angiogenesis should be developed to enhance the antiangiogenetic effect of GnRH-a in patients with endometriosis.

TRIAL REGISTRATION

Clinicaltrial.gov ID: NCT06106932.

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

Transcriptional repression and enhancer decommissioning silence cell cycle genes in postmitotic tissues

The mechanisms that maintain a non-cycling status in postmitotic tissues are not well understood. Many cell cycle genes have promoters and enhancers that remain accessible even when cells are terminally differentiated and in a non-cycling state, suggesting their repression must be maintained long term. In contrast, enhancer decommissioning has been observed for rate-limiting cell cycle genes in the Drosophila wing, a tissue where the cells die soon after eclosion, but it has been unclear if this also occurs in other contexts of terminal differentiation. In this study, we show that enhancer decommissioning also occurs at specific, rate-limiting cell cycle genes in the long-lived tissues of the Drosophila eye and brain, and we propose this loss of chromatin accessibility may help maintain a robust postmitotic state. We examined the decommissioned enhancers at specific rate-limiting cell cycle genes and show that they encode for dynamic temporal and spatial expression patterns that include shared, as well as tissue-specific elements, resulting in broad gene expression with developmentally controlled temporal regulation. We extend our analysis to cell cycle gene expression and chromatin accessibility in the mammalian retina using a published dataset and find that the principles of cell cycle gene regulation identified in terminally differentiating Drosophila tissues are conserved in the differentiating mammalian retina. We propose a robust, non-cycling status is maintained in long-lived postmitotic tissues through a combination of stable repression at most cell cycle genes, alongside enhancer decommissioning at specific rate-limiting cell cycle genes.

Identification and selection of reference genes for analysis of gene expression by quantitative real-time PCR in the euhalophyte Suaeda altissima (L.) Pall

Сoding sequences of seven housekeeping genes: actin SaACT7, ubiquitin-conjugating protein SaUBC10, glyceraldehyde-3-phosphate dehydrogenase SaGAPDH, protein of the large subunit of ribosomes SaL2, α-tubulin SaTUA, translation elongation factor SaeEF1α, and protein phosphatase SaPP2A were identified as candidate reference genes for expression analysis of target genes in the extremely salt tolerant plant Suaeda altissima (L.) Pall. The expression profiles of the genes differed. SaACT7 and SaeEF1α demonstrated the highest expression levels, while the lowest expression levels were found for SaPP2A and SaTUA. SaPP2A and SaeEF1α genes were the most stably expressed at different steady-state salinity levels and different nitrate concentrations in nutrient solutions (NSs). SaL2, SaPP2A, and SaeEF1α genes showed the greatest stability of expression when nitrate was added to nutrient solution of plants grown under conditions of nitrate deficiency. Less constant expression was demonstrated in this experiment by SaACT7 and SaTUA. SaL2, SaACT7, SaeEF1α, and SaUBC10 genes showed the smallest expression changes under salt shock. To validate the use of the most stably expressed genes for normalization of gene expression, we checked them as reference genes to study the expression of the nitrate transporter gene SaNPF6.3 in S. altissima roots under conditions of different salinity and different nitrate supply.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Surveying the landscape of RNA isoform diversity and expression across 9 GTEx tissues using long-read sequencing data

Even though alternative RNA splicing was discovered nearly 50 years ago (1977), we still understand very little about most isoforms arising from a single gene, including in which tissues they are expressed and if their functions differ. Human gene annotations suggest remarkable transcriptional complexity, with approximately 252,798 distinct RNA isoform annotations from 62,710 gene bodies (Ensembl v109; 2023), emphasizing the need to understand their biological effects. For example, 256 gene bodies have ≥50 annotated isoforms and 30 have ≥100, where one protein-coding gene (MAPK10) even has 192 distinct RNA isoform annotations. Whether such isoform diversity results from biological redundancy or spurious alternative splicing (i.e., noise), or whether individual isoforms have specialized functions (even if subtle) remains a mystery for most genes. Recent studies by Aguzzoli-Heberle et al., Leung et al., and Glinos et al. demonstrated long-read RNAseq enables improved RNA isoform quantification for essentially any tissue, cell type, or biological condition (e.g., disease, development, aging, etc.), making it possible to better assess individual isoform expression and function. While each study provided important discoveries related to RNA isoform diversity, deeper exploration is needed. We sought to quantify and characterize real isoform usage across tissues (compared to annotations). We used long-read RNAseq data from 58 GTEx samples across nine tissues (three brain, two heart, muscle, lung, liver, and cultured fibroblasts) generated by Glinos et al. and found considerable isoform diversity within and across tissues. Cerebellar hemisphere was the most transcriptionally complex tissue (22,522 distinct isoforms; 3,726 unique); liver was least diverse (12,435 distinct isoforms; 1,039 unique). We highlight gene clusters exhibiting high tissue-specific isoform diversity per tissue (e.g., TPM1 expresses 19 in heart's atrial appendage). We also validated 447 of the 700 new isoforms discovered by Aguzzoli-Heberle et al. and found that 88 were expressed in all nine tissues, while 58 were specific to a single tissue. This study represents a broad survey of the RNA isoform landscape, demonstrating isoform diversity across nine tissues and emphasizes the need to better understand how individual isoforms from a single gene body contribute to human health and disease.

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

Analysing the genetic code degeneracy: a consequence towards bacterial staining

As 20 naturally occurring amino acids are coded by 61 mRNA codons out of 64, it is obvious that 61→20 cannot have one-to-one mapping which generates the problem of codon degeneracy. Despite several efforts there is no specific outcome which can describe this well-known enigmatic degeneracy of the codon table. Since, every biological behaviour is regulated by protein which in turn consists of amino acids bearing the inherent characteristics of degeneracy among mRNA codons (Crick F.H.C. The Origin of the Genetic Code. J. Mol. Biol.1968; 38: 367-379), it is worthy to analyse the impact of such degeneracy on biological behaviours. Here, based on mathematical models using the concept of b-type of the nucleotide bases and hamming distances, an effort has been initiated to understand the impact of biasness of genetic code degeneracy on biological behaviours. The proposed models have been utilized to understand the characteristic features of bacterial genes of gram-positive and gram-negative bacteria. To the best of our knowledge, this is the first mathematical model to capture the effect of genetic code degeneracy, showing a paradigm towards understanding the behavioural difference between gram-positive and gram-negative bacteria, and thereby opening a new avenue for revealing differential biological properties.Communicated by Ramaswamy H. Sarma.

Housekeeping protein-coding genes interrogated with tissue and individual variations

Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw .

Contact lenses as novel tear fluid sampling vehicles for total RNA isolation, precipitation, and amplification

The tear fluid is a readily accessible, potential source for biomarkers of disease and could be used to monitor the ocular response to contact lens (CL) wear or ophthalmic pathologies treated by therapeutic CLs. However, the tear fluid remains largely unexplored as a biomarker source for RNA-based molecular analyses. Using a rabbit model, this study sought to determine whether RNA could be collected from commercial CLs and whether the duration of CL wear would impact RNA recovery. The results were referenced to standardized strips of filtered paper (e.g., Shirmer Strips) placed in the inferior fornix. By performing total RNA isolation, precipitation, and amplification with commercial kits and RT-PCR methods, CLs were found to have no significant differences in RNA concentration and purity compared to Schirmer Strips. The study also identified genes that could be used to normalize RNA levels between tear samples. Of the potential control genes or housekeeping genes, GAPDH was the most stable. This study, which to our knowledge has never been done before, provides a methodology for the detection of RNA and gene expression changes from tear fluid that could be used to monitor or study eye diseases.

Transcriptional repression and enhancer decommissioning silence cell cycle genes in postmitotic tissues

The mechanisms that maintain a non-cycling status in postmitotic tissues are not well understood. Many cell cycle genes have promoters and enhancers that remain accessible even when cells are terminally differentiated and in a non-cycling state, suggesting their repression must be maintained long term. In contrast, enhancer decommissioning has been observed for rate-limiting cell cycle genes in the Drosophila wing, a tissue where the cells die soon after eclosion, but it has been unclear if this also occurs in other contexts of terminal differentiation. In this study, we show that enhancer decommissioning also occurs at specific, rate-limiting cell cycle genes in the long-lived tissues of the Drosophila eye and brain, and we propose this loss of chromatin accessibility may help maintain a robust postmitotic state. We examined the decommissioned enhancers at specific rate-limiting cell cycle genes and show that they encode dynamic temporal and spatial expression patterns that include shared, as well as tissue-specific elements, resulting in broad gene expression with developmentally controlled temporal regulation. We extend our analysis to cell cycle gene expression and chromatin accessibility in the mammalian retina using a published dataset, and find that the principles of cell cycle gene regulation identified in terminally differentiating Drosophila tissues are conserved in the differentiating mammalian retina. We propose a robust, non-cycling status is maintained in long-lived postmitotic tissues through a combination of stable repression at most cell cycle gens, alongside enhancer decommissioning at specific rate-limiting cell cycle genes.

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

BACKGROUND

Salsola laricifolia is a typical C3-C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies.

RESULTS

Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L-1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts.

CONCLUSION

The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

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.

Embryonic methionine triggers post-natal developmental programming in Japanese quail

Embryonic development is one of the most sensitive and critical stages when maternal effects may influence the offspring's phenotype. In birds and other oviparous species, embryonic development is confined to the eggs, therefore females must deposit resources into the eggs to prepare the offspring for the prevailing post-natal conditions. However, the mechanisms of such phenotypic adjustments remain poorly understood. We simulated a maternal nutritional transfer by injecting 1 mg of L-methionine solution into Japanese quail eggs before the onset of incubation. The increase in early methionine concentration in eggs activated the insulin/insulin-like signalling and mechanistic target of rapamycin (IIS/mTOR) signalling pathways and affected post-natal developmental trajectories. Chicks from methionine-supplemented eggs had higher expression of liver IGF1 and mTOR genes at hatching but were similar in size, and the phenotypic effects of increased growth became apparent only a week later and remained up to three weeks. Circulating levels of insulin-like growth factor-1 (IGF-1) and expression of ribosomal protein serine 6 kinase 1 (RPS6K1), the mTOR downstream effector, were elevated only three weeks after hatching. These results show that specific nutritional cues may have phenotypic programming effects by sequentially activating specific nutrient-sensing pathways and achieving transgenerational phenotypic plasticity.

Metazoa-level USCOs as markers in species delimitation and classification

Metazoa-level universal single-copy orthologs (mzl-USCOs) are universally applicable markers for DNA taxonomy in animals that can replace or supplement single-gene barcodes. Previously, mzl-USCOs from target enrichment data were shown to reliably distinguish species. Here, we tested whether USCOs are an evenly distributed, representative sample of a given metazoan genome and therefore able to cope with past hybridization events and incomplete lineage sorting. This is relevant for coalescent-based species delimitation approaches, which critically depend on the assumption that the investigated loci do not exhibit autocorrelation due to physical linkage. Based on 239 chromosome-level assembled genomes, we confirmed that mzl-USCOs are genetically unlinked for practical purposes and a representative sample of a genome in terms of reciprocal distances between USCOs on a chromosome and of distribution across chromosomes. We tested the suitability of mzl-USCOs extracted from genomes for species delimitation and phylogeny in four case studies: Anopheles mosquitos, Drosophila fruit flies, Heliconius butterflies and Darwin's finches. In almost all instances, USCOs allowed delineating species and yielded phylogenies that corresponded to those generated from whole genome data. Our phylogenetic analyses demonstrate that USCOs may complement single-gene DNA barcodes and provide more accurate taxonomic inferences. Combining USCOs from sources that used different versions of ortholog reference libraries to infer marker orthology may be challenging and, at times, impact taxonomic conclusions. However, we expect this problem to become less severe as the rapidly growing number of reference genomes provides a better representation of the number and diversity of organismal lineages.

Assessment of Reference Genes Stability in Cortical Bone of Obese and Diabetic Mice

Introduction

Bone, a pivotal structural organ, is susceptible to disorders with profound health implications. The investigation of gene expression in bone tissue is imperative, particularly within the context of metabolic diseases such as obesity and diabetes that augment the susceptibility to bone fractures. The objective of this study is to identify a set of internal control genes for the analysis of gene expression.

Methods

This study employs reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to assess gene expression in bone tissue. We selected fourteen housekeeping genes and assessed their stability in the cortical bone of mouse models for obesity and diabetes using four well-established algorithms (GeNorm, BestKeeper, NormFinder, and the comparative Delta Ct method).

Results and Conclusion

We identified Rpl13a as the mostly stably expressed reference gene in cortical bone tissue from mouse models of obesity and diabetes (db/db), while Gapdh was found to be the most stable reference gene in another diabetes model, KKAy mice. Additionally, Ef1a, Ppia, Rplp0, and Rpl22 were identified as alternative genes suitable for normalizing gene expression in cortical bone from obesity and diabetes mouse models. These findings enhance RT-qPCR accuracy and reliability, offering a strategic guide to select reference gene for studying bone tissue gene expression in metabolic disorders.

Optimal control of ribosome population for gene expression under periodic nutrient intake

Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.

Advanced breast cancer diagnosis: Multiplex RT-qPCR for precise typing and angiogenesis profiling

Breast cancer (BC) remains the foremost cause of cancer-related mortality, with an estimated 2.3 million new cases anticipated globally. The timely diagnosis of BC is pivotal for effective treatment. Currently, BC diagnosis predominantly relies on Immunohistochemistry (IHC), a method known for its sluggishness, expense, and dependence on proficient pathologists for confident cancer typing. In this study, we introduce a novel approach to enhance the accuracy, speed, and cost-effectiveness of BC diagnosis. We employ multiplex Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) with touch-down methods, which consistently yield significantly lower Cycle Threshold (CT) values. The study evaluates gene expression profiles of HER2, PGR, ESR, and Ki67 genes across 61 samples representing four BC subtypes, using RPL13A as the endogenous control gene. The results demonstrate that our method offers remarkable precision, nearly equivalent to IHC, in detecting gene expressions vital for BC diagnosis and subtyping. Moreover, we explore the gene expression of Hif1A, ANG, and VEGFR genes involved in angiogenesis, shedding light on the metastatic potential of the tested BC tumours. Notably, numerous samples exhibit elevated levels of Hif1A and VEGFR, indicating their potential as valuable biomarkers for assessing metastatic status. Collectively, our RT-qPCR methodology emerges as a powerful diagnostic tool for swiftly identifying BC subtypes and can be complemented with other essential tumorigenic biomarker assessments, such as angiogenesis, to further refine cancer characterisation and inform personalised therapeutic strategies for BC patients. This innovation holds the promise of revolutionising BC diagnosis and treatment, offering expedited and reliable insights for improved patient care.

Logical design of synthetic cis-regulatory DNA for genetic tracing of cell identities and state changes

Descriptive data are rapidly expanding in biomedical research. Instead, functional validation methods with sufficient complexity remain underdeveloped. Transcriptional reporters allow experimental characterization and manipulation of developmental and disease cell states, but their design lacks flexibility. Here, we report logical design of synthetic cis-regulatory DNA (LSD), a computational framework leveraging phenotypic biomarkers and trans-regulatory networks as input to design reporters marking the activity of selected cellular states and pathways. LSD uses bulk or single-cell biomarkers and a reference genome or custom cis-regulatory DNA datasets with user-defined boundary regions. By benchmarking validated reporters, we integrate LSD with a computational ranking of phenotypic specificity of putative cis-regulatory DNA. Experimentally, LSD-designed reporters targeting a wide range of cell states are functional without minimal promoters. Applied to broadly expressed genes from human and mouse tissues, LSD generates functional housekeeper-like sLCRs compatible with size constraints of AAV vectors for gene therapy applications. A mesenchymal glioblastoma reporter designed by LSD outperforms previously validated ones and canonical cell surface markers. In genome-scale CRISPRa screens, LSD facilitates the discovery of known and novel bona fide cell-state drivers. Thus, LSD captures core principles of cis-regulation and is broadly applicable to studying complex cell states and mechanisms of transcriptional regulation.

Loop-Mediated Isothermal Amplification of DNA (LAMP) as an Alternative Method for Determining Bacteria in Wound Infections

The increasing number of patients with chronic wounds requires the development of quick and accurate diagnostics methods. One of the key and challenging aspects of treating ulcers is to control wound infection. Early detection of infection is essential for the application of suitable treatment methods, such as systemic antibiotics or other antimicrobial agents. Clinically, the most frequently used method for detecting microorganisms in wounds is through a swab and culture on appropriate media. This test has major limitations, such as the long bacterial growth time and the selectivity of bacterial growth. This article presents an overview of molecular methods for detecting bacteria in wounds, including real-time polymerase chain reaction (rtPCR), quantitative polymerase chain reaction (qPCR), genotyping, next-generation sequencing (NGS), and loop-mediated isothermal amplification (LAMP). We focus on the LAMP method, which has not yet been widely used to detect bacteria in wounds, but it is an interesting alternative to conventional detection methods. LAMP does not require additional complicated equipment and provides the fastest detection time for microorganisms (approx. 30 min reaction). It also allows the use of many pairs of primers in one reaction and determination of up to 15 organisms in one sample. Isothermal amplification of DNA is currently the easiest and most economical method for microbial detection in wound infection. Direct visualization of the reaction with dyes, along with omitting DNA isolation, has increased the potential use of this method.

A study of gene expression in the living human brain

A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").

Whole-body gene expression atlas of an adult metazoan

Gene activity defines cell identity, drives intercellular communication, and underlies the functioning of multicellular organisms. We present the single-cell resolution atlas of gene activity of a fertile adult metazoan: Caenorhabditis elegans. This compendium comprises 180 distinct cell types and 19,657 expressed genes. We predict 7541 transcription factor expression profile associations likely responsible for defining cellular identity. We predict thousands of intercellular interactions across the C. elegans body and the ligand-receptor pairs that mediate them, some of which we experimentally validate. We identify 172 genes that show consistent expression across cell types, are involved in basic and essential functions, and are conserved across phyla; therefore, we present them as experimentally validated housekeeping genes. We developed the WormSeq application to explore these data. In addition to the integrated gene-to-systems biology, we present genome-scale single-cell resolution testable hypotheses that we anticipate will advance our understanding of the molecular mechanisms, underlying the functioning of a multicellular organism and the perturbations that lead to its malfunction.

APOE Locus-Associated Mitochondrial Function and Its Implication in Alzheimer's Disease and Aging

The Apolipoprotein E (APOE) locus has garnered significant clinical interest because of its association with Alzheimer's disease (AD) and longevity. This genetic association appears across multiple genes in the APOE locus. Despite the apparent differences between AD and longevity, both conditions share a commonality of aging-related changes in mitochondrial function. This commonality is likely due to accumulative biological effects partly exerted by the APOE locus. In this study, we investigated changes in mitochondrial structure/function-related markers using oxidative stress-induced human cellular models and postmortem brains (PMBs) from individuals with AD and normal controls. Our results reveal a range of expressional alterations, either upregulated or downregulated, in these genes in response to oxidative stress. In contrast, we consistently observed an upregulation of multiple APOE locus genes in all cellular models and AD PMBs. Additionally, the effects of AD status on mitochondrial DNA copy number (mtDNA CN) varied depending on APOE genotype. Our findings imply a potential coregulation of APOE locus genes possibly occurring within the same topologically associating domain (TAD) of the 3D chromosome conformation. The coordinated expression of APOE locus genes could impact mitochondrial function, contributing to the development of AD or longevity. Our study underscores the significant role of the APOE locus in modulating mitochondrial function and provides valuable insights into the underlying mechanisms of AD and aging, emphasizing the importance of this locus in clinical research.

Cyclospora cayetanensis comprises at least 3 species that cause human cyclosporiasis

The apicomplexan parasite Cyclospora cayetanensis causes seasonal foodborne outbreaks of the gastrointestinal illness cyclosporiasis. Prior to the coronavirus disease-2019 pandemic, annually reported cases were increasing in the USA, leading the US Centers for Disease Control and Prevention to develop a genotyping tool to complement cyclosporiasis outbreak investigations. Thousands of US isolates and 1 from China (strain CHN_HEN01) were genotyped by Illumina amplicon sequencing, revealing 2 lineages (A and B). The allelic composition of isolates was examined at each locus. Two nuclear loci (CDS3 and 360i2) distinguished lineages A and B. CDS3 had 2 major alleles: 1 almost exclusive to lineage A and the other to lineage B. Six 360i2 alleles were observed – 2 exclusive to lineage A (alleles A1 and A2), 2 to lineage B (B1 and B2) and 1 (B4) was exclusive to CHN_HEN01 which shared allele B3 with lineage B. Examination of heterozygous genotypes revealed that mixtures of A- and B-type 360i2 alleles occurred rarely, suggesting a lack of gene flow between lineages. Phylogenetic analysis of loci from whole-genome shotgun sequences, mitochondrial and apicoplast genomes, revealed that CHN_HEN01 represents a distinct lineage (C). Retrospective examination of epidemiologic data revealed associations between lineage and the geographical distribution of US infections plus strong temporal associations. Given the multiple lines of evidence for speciation within human-infecting Cyclospora, we provide an updated taxonomic description of C. cayetanensis, and describe 2 novel species as aetiological agents of human cyclosporiasis: Cyclospora ashfordi sp. nov. and Cyclospora henanensis sp. nov. (Apicomplexa: Eimeriidae).

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Expression Profile of Housekeeping Genes and Tissue-Specific Genes in Multiple Tissues of Pigs

Pigs have become an ideal model system for human disease research and development and an important farm animal that provides a valuable source of nutrition. To profile the all-sided gene expression and their biological functions across multiple tissues, we conducted a comprehensive analysis of gene expression on a large scale around the side of housekeeping genes (HKGs), tissue specific genes (TSGs), and the co-expressed genes in 14 various tissues. In this study, we identified 2351 HKGs and 3018 TSGs across tissues, among which 4 HKGs (COX1, UBB, OAZ1/NPFF) exhibited low variation and high expression levels, and 31 particular TSGs (e.g., PDC, FKBP6, STAT2, and COL1A1) were exclusively expressed in several tissues, including endocrine brain, ovaries, livers, backfat, jejunum, kidneys, lungs, and longissimus dorsi muscles. We also obtained 17 modules with 230 hub genes (HUBGs) by weighted gene co-expression network analysis. On the other hand, HKGs functions were enriched in the signaling pathways of the ribosome, spliceosome, thermogenesis, oxidative phosphorylation, and nucleocytoplasmic transport, which have been highly suggested to involve in the basic biological tissue activities. While TSGs were highly enriched in the signaling pathways that were involved in specific physiological processes, such as the ovarian steroidogenesis pathway in ovaries and the renin-angiotensin system pathway in kidneys. Collectively, these stable, specifical, and co-expressed genes provided useful information for the investigation of the molecular mechanism for an understanding of the genetic and biological processes of complex traits in pigs.

Functional orderly topography of brain networks associated with gene expression heterogeneity

The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Comparative analysis of human, macaque and mouse function and genetic heterogeneity in the brain reveals links between gene expression and orderly topography of functional networks.