A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets

A comparative assessment of reference genes in mouse brown adipocyte differentiation and thermogenesis in vitro

Adipogenic differentiation and thermogenesis in brown adipose tissue (BAT) undergo dynamic processes, altering phenotypes and gene expressions. Proper reference genes in gene expression analysis are crucial to mitigate experimental variances and ensure PCR efficacy. Unreliable reference genes can lead to erroneous gene expression quantification, resulting in data misinterpretation. This study focused on identifying suitable reference genes for mouse brown adipocyte research, utilizing brown adipocytes from the Ucp1-luciferase ThermoMouse model. Comparative analysis of gene expression data under adipogenesis and thermogenesis conditions was conducted, validating 13 housekeeping genes through various algorithms, including DeltaCq, BestKeeper, geNorm, Normfinder, and RefFinder. Tbp and Rer1 emerged as optimal references for Ucp1 and Pparg expression in brown adipogenesis, while Tbp and Ubc were ideal for the expression analysis of these target genes in thermogenesis. Conversely, certain conventional references, including Actb, Tubb5, and Gapdh, proved unstable as reference genes under both conditions. These findings stress the critical consideration of reference gene selection in gene expression analysis within specific biological systems to ensure accurate conclusions.

Increased expression of miR-320b in blood plasma of patients in response to SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Recent research has demonstrated how epigenetic mechanisms regulate the host-virus interactions in COVID-19. It has also shown that microRNAs (miRNAs) are one of the three fundamental mechanisms of the epigenetic regulation of gene expression and play an important role in viral infections. A pilot study published by our research group identified, through next-generation sequencing (NGS), that miR-4433b-5p, miR-320b, and miR-16-2-3p are differentially expressed between patients with COVID-19 and controls. Thus, the objectives of this study were to validate the expression of these miRNAs using quantitative real-time polymerase chain reaction (qRT-PCR) and to perform in silico analyses. Patients with COVID-19 (n = 90) and healthy volunteers (n = 40) were recruited. MiRNAs were extracted from plasma samples and validated using qRT-PCR. In addition, in silico analyses were performed using mirPath v.3 software. MiR-320b was the only miRNA upregulated in the case group com-pared to the control group. The in silico analyses indicated the role of miR-320b in the regulation of the KITLG gene and consequently in the inflammatory process. This study confirmed that miR-320b can distinguish patients with COVID-19 from control participants; however, further research is needed to determine whether this miRNA can be used as a target or a biomarker.

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.