Identification of new reference genes with stable expression patterns for gene expression studies using human cancer and normal cell lines

Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3−3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.

Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3-3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.

Exploring the modulation of MLH1 and MSH2 gene expression in hesperetin-treated breast cancer cells (BT-474)

The major mortality factor for women globally is breast cancer, and current treatments have several adverse effects. Hesperetin (HSP) is a flavone that occurs naturally with anti-tumor capabilities and has been investigated as a potential treatment for cancer. This study aimed to investigate the cytotoxic and anti-malignant potential of HSP on breast cancer cells (BT-474) and normal cells (MCF-10a). The results indicated that HSP has dose-dependent cytotoxicity in BT-474 and MCF-10a cells. The elevated concentration of HSP lowered cell viability and proliferation. The half-maximal inhibitory concentration (IC50) of HSP in BT-474 cancer cells after a 48-h exposure was 279.2 μM/ml, while the IC50 in normal cells was 855.4 μM/ml. The cytotoxicity of HSP was more significant in cancer cell lines than in normal cell lines and this aspect presents a favorable factor in utilizing the drug for the treatment of breast cancer. The apoptotic effect of HSP in BT-474 cells was investigated, and it was found that the higher the concentration of HSP more the cells underwent apoptosis. Furthermore, the highest concentration of HSP led to overexpression of the MLH1 and MSH2 genes in both breast cancer and normal cell lines. Overall, our study suggests that HSP has an anticancer effect on breast cancer cell lines, and the effect is concentration dependent.

Shiftless Is a Novel Member of the Ribosome Stress Surveillance Machinery That Has Evolved to Play a Role in Innate Immunity and Cancer Surveillance

A longstanding paradox in molecular biology has centered on the question of how very long proteins are synthesized, despite numerous measurements indicating that ribosomes spontaneously shift reading frame at rates that should preclude their ability completely translate their mRNAs. Shiftless (SFL; C19orf66) was originally identified as an interferon responsive gene encoding an antiviral protein, indicating that it is part of the innate immune response. This activity is due to its ability to bind ribosomes that have been programmed by viral sequence elements to shift reading frame. Curiously, Shiftless is constitutively expressed at low levels in mammalian cells. This study examines the effects of altering Shiftless homeostasis, revealing how it may be used by higher eukaryotes to identify and remove spontaneously frameshifted ribosomes, resolving the apparent limitation on protein length. Data also indicate that Shiftless plays a novel role in the ribosome-associated quality control program. A model is proposed wherein SFL recognizes and arrests frameshifted ribosomes, and depending on SFL protein concentrations, either leads to removal of frameshifted ribosomes while leaving mRNAs intact, or to mRNA degradation. We propose that SFL be added to the growing pantheon of proteins involved in surveilling translational fidelity and controlling gene expression in higher eukaryotes.

Optimal reference genes for RNA tissue analysis in small animal models of hemorrhagic fever viruses

Reverse-transcription quantitative polymerase chain reaction assays are frequently used to evaluate gene expression in animal model studies. Data analyses depend on normalization using a suitable reference gene (RG) to minimize effects of variation due to sample collection, sample processing, or experimental set-up. Here, we investigated the suitability of nine potential RGs in laboratory animals commonly used to study viral hemorrhagic fever infection. Using tissues (liver, spleen, gonad [ovary or testis], kidney, heart, lung, eye, brain, and blood) collected from naïve animals and those infected with Crimean-Congo hemorrhagic fever (mice), Nipah (hamsters), or Lassa (guinea pigs) viruses, optimal species-specific RGs were identified based on five web-based algorithms to assess RG stability. Notably, the Ppia RG demonstrated stability across all rodent tissues tested. Optimal RG pairs that include Ppia were determined for each rodent species (Ppia and Gusb for mice; Ppia and Hrpt for hamsters; and Ppia and Gapdh for guinea pigs). These RG pair assays were multiplexed with viral targets to improve assay turnaround time and economize sample usage. Finally, a pan-rodent Ppia assay capable of detecting Ppia across multiple rodent species was developed and successfully used in ecological investigations of field-caught rodents, further supporting its pan-species utility.

Viral load: We need a new look at an old problem?

The concept of viral load was introduced in the 1980s to measure the amount of viral genetic material in a person's blood, primarily for human immunodeficiency virus (HIV). It has since become crucial for monitoring HIV infection progression and assessing the efficacy of antiretroviral therapy. However, during the coronavirus disease 2019 pandemic, the term "viral load" became widely popularized, not only for the scientific community but for the general population. Viral load plays a critical role in both clinical patient management and research, providing valuable insights for antiviral treatment strategies, vaccination efforts, and epidemiological control measures. As measuring viral load is so important, why don't researchers discuss the best way to do it? Is it simply acceptable to use raw Ct values? Relying solely on Ct values for viral load estimation can be problematic due to several reasons. First, Ct values can vary between different quantitative polymerase chain reaction assays, platforms, and laboratories, making it difficult to compare data across studies. Second, Ct values do not directly measure the quantity of viral particles in a sample and they can be influenced by various factors such as initial viral load, sample quality, and assay sensitivity. Moreover, variations in viral RNA extraction and reverse-transcription steps can further impact the accuracy of viral load estimation, emphasizing the need for careful interpretation of Ct values in viral load assessment. Interestingly, we did not observe scientific articles addressing different strategies to quantify viral load. The absence of standardized and validated methods impedes the implementation of viral load monitoring in clinical management. The variability in cell quantities within samples and the variation in viral particle numbers within infected cells further challenge accurate viral load measurement and interpretation. To advance the field and improve patient outcomes, there is an urgent need for the development and validation of tailored, standardized methods for precise viral load quantification.

Experimental assessment of robust reference genes for qRT-PCR in lung cancer studies

Stable internal reference genes are crucial for quantitative real-time PCR (qRT-PCR) analyses in lung cancer studies. Widely used reference genes are mostly chosen by intuition or from pan-cancer transcriptome data and lack experimental validation by qRT-PCR in the context of lung cancer. This study evaluated the stability of candidate reference genes in lung cancer cell lines under normal homeostasis, hypoxia, and serum deprivation to screen for robust reference genes for qRT-PCR in lung cancer studies. The stability of reference gene combinations was also assessed. We found that most of the stably expressed genes from pan-cancer transcriptome analyses were not sufficiently stable under some of the tested conditions. CIAO1, CNOT4, and SNW1 were found to be the most stable reference genes under various conditions. Greater stability was achieved by combining more reference genes. We further used the hypoxia biomarker hypoxia-inducible factor (HIF)-2α to demonstrate that choosing inappropriate reference genes can lead to incorrect qRT-PCR results. We also found that the stable reference genes were irrelevant to malignancy, which may explain their stability under various conditions that cancer cells often encounter. This study provides a list of validated and stable qRT-PCR reference genes and reference gene combinations for lung cancer that may standardize qRT-PCR experiments in future lung cancer studies.

Discovery of two new isoforms of the human DUT gene

In human cells two dUTPase isoforms have been described: one nuclear (DUT-N) and one mitochondrial (DUT-M), with cognate localization signals. In contrast, here we identified two additional isoforms; DUT-3 without any localization signal and DUT-4 with the same nuclear localization signal as DUT-N. Based on an RT-qPCR method for simultaneous isoform-specific quantification we analysed the relative expression patterns in 20 human cell lines of highly different origins. We found that the DUT-N isoform is expressed by far at the highest level, followed by the DUT-M and the DUT-3 isoform. A strong correlation between expression levels of DUT-M and DUT-3 suggests that these two isoforms may share the same promoter. We analysed the effect of serum starvation on the expression of dUTPase isoforms compared to non-treated cells and found that the mRNA levels of DUT-N decreased in A-549 and MDA-MB-231 cells, but not in HeLa cells. Surprisingly, upon serum starvation DUT-M and DUT-3 showed a significant increase in the expression, while the expression level of the DUT-4 isoform did not show any changes. Taken together our results indicate that the cellular dUTPase supply may also be provided in the cytoplasm and starvation stress induced expression changes are cell line dependent.

Inhibition and eradication of Pseudomonas aeruginosa biofilms by secondary metabolites of Nocardiopsis lucentensis EMB25

Millions of people worldwide have been impacted by biofilm-associated disorders, which are impregnable owing to frequent changes in surface antigens and gene expression. Globally, about 11% of nosocomial infections, including cystic fibrosis, chronic wound infections, and post-surgical infections, are caused by Pseudomonas aeruginosa, the most prevalent Gram-negative bacterial species. Moreover, biofilms are highly resistant to the host's immune system, and exhibit increased tolerance to stress factors such as starvation, dehydration, and antimicrobials. Here, we have isolated a rare halophilic actinobacteria, Nocardiopsis lucentensis EMB25, and utilized the secondary metabolites for inhibition and eradication of P. aeruginosa biofilm. For the first time, N. lucentensis EMB25 bacteria was explored to study the anti-effect of secondary metabolites on pre-established biofilm. The secondary metabolites targeted the quorum sensing pathway and were found to bind to LasR and RhlR, as confirmed via molecular docking. Also, the reduction in virulence factors, rhamnolipids and pyocyanin further supported the study as these two are regulated by LasR and RhlR. In addition, the downregulation of various QS system genes lasA, lasB, rhlA, rhlB, and pqsA confirmed that the secondary metabolites act on two main regulators of the quorum sensing pathway, LasR, and RhlR. The findings of this study support the bioprospecting of previously unknown and extreme-condition actinobacteria as a rich source of novel bioactives against infections caused by bacterial biofilms.

Identification of suitable house-keeping genes during chikungunya virus infection

PURPOSE

Quantitative PCR (qPCR) is a reliable and robust technique for gene expression analysis, but its efficacy is dependent on the normalization of qPCR data with the stably expressed reference gene. Selection of a suitable reference gene is mandatory for accurate gene expression analysis, till data the most appropriate reference gene during chikungunya virus infection has not been elucidated.

METHOD

In this study the expression of reference genes(GAPDH, GUSB, HPRT, Beta-actin, 18S rRNA) was analysed during chikungunya virus infection by quantitative PCR. The stability of the house-keeping genes was evaluated with three bioinformatics softwares: BestKeeper, NormFinder and GeNorm.

RESULT

The significant variation in the expression of house-keeping genes (GusB, Beta-actin, HPRT) was observed during chikungunya virus infection; whereas GAPDH and 18S rRNA was most stable. The stability of reference genes analysed by the bioinformatics software further corroborate the results of qPCR.

CONCLUSION

This is first study that identifies and validates the most suitable reference gene for normalization of qPCR data during chikungunya based gene expression analysis. This could serve as a reference study for the researchers working on different aspects of chikungunya virus infections.