Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Serum RNA Profile Reflects Fluid Status and Atrophic Retinal Changes in Neovascular Age-Related Macular Degeneration

The increasing prevalence of age-related macular degeneration (AMD), a disease that can result in the loss of central vision, is an emerging problem worldwide due to aging societies. Growing patient numbers create a challenge for the healthcare system. Understanding the mechanisms of AMD pathogenesis will aid in early, personalized, and efficient intervention, helping to mitigate this issue. Current diagnostic methods rely on optical coherence tomography and angiography imaging, which identify existing damages, but do not provide information on the mechanisms behind them. In the present work, we demonstrate a difference in the serum RNA profile between neovascular AMD (nAMD) patients and controls. Moreover, the RNA profile of nAMD patients corresponded with anatomical changes in the retinal fluid compartments as well as atrophic changes of the retina. We followed two independent ways to control false positive leads, and when these approaches were combined, thioredoxin-related transmembrane protein 4 (TMX4) was observed to be differentially expressed by both approaches. This finding opens a new pathway in AMD studies, which are limited due to restricted access to live human target material and the limited value of animal models of human AMD.

Phylogenomic Inference Suggests Differential Deep Time Phylogenetic Signals from Nuclear and Organellar Genomes in Gymnosperms

The living gymnosperms include about 1200 species in five major groups: cycads, ginkgo, gnetophytes, Pinaceae (conifers I), and cupressophytes (conifers II). Molecular phylogenetic studies have yet to reach a unanimously agreed-upon relationship among them. Moreover, cytonuclear phylogenetic incongruence has been repeatedly observed in gymnosperms. We collated a comprehensive dataset from available genomes of 17 gymnosperms across the five major groups and added our own high-quality assembly of a species from Podocarpaceae (the second largest conifer family) to increase sampling width. We used these data to infer reconciled nuclear species phylogenies using two separate methods to ensure the robustness of our conclusions. We also reconstructed organelle phylogenomic trees from 42 mitochondrial and 82 plastid genes from 38 and 289 gymnosperm species across the five major groups, respectively. Our nuclear phylogeny consistently recovers the Ginkgo-cycads clade as the first lineage split from other gymnosperm clades and the Pinaceae as sister to gnetophytes (the Gnepines hypothesis). In contrast, the mitochondrial tree places cycads as the earliest lineage in gymnosperms and gnetophytes as sister to cupressophytes (the Gnecup hypothesis) while the plastomic tree supports the Ginkgo-cycads clade and gnetophytes as the sister to cupressophytes. We also examined the effect of mitochondrial RNA editing sites on the gymnosperm phylogeny by manipulating the nucleotide and amino acid sequences at these sites. Only complete removal of editing sites has an effect on phylogenetic inference, leading to a closer congruence between mitogenomic and nuclear phylogenies. This suggests that RNA editing sites carry a phylogenetic signal with distinct evolutionary traits.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

A single workflow for multi-species blood transcriptomics

BACKGROUND

Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons.

RESULTS

Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated.

CONCLUSIONS

Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis

BACKGROUND

This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS).

METHODS

We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments.

RESULTS

We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments.

CONCLUSIONS

This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.

Reverse transcription-quantitative PCR (RT-qPCR) without the need for prior removal of DNA

The procedure illustrated in this paper represents a new method for transcriptome analysis by PCR (Polymerase Chain Reaction), which circumvents the need for elimination of potential DNA contamination. Compared to the existing methodologies, our method is more precise, simpler and more reproducible because it preserves the RNA's integrity, does not require materials and/or reagents that are used for elimination of DNA and it also reduces the number of samples that should be set up as negative controls. This novel procedure involves the use of a specifically modified primer during reverse transcription step, which contains mismatched bases, thus producing cDNA molecules that differ from genomic DNA. By using the same modified primer in PCR amplification, only cDNA template is amplified since genomic DNA template is partially heterologous to the primer. In this way, amplification by PCR is unaffected by any potential DNA contamination since it is specific only for the cDNA template. Furthermore, it accurately reflects the initial RNA concentration of the sample, which is prone to changes due to various physical or enzymatic treatments commonly used by the current methodologies for DNA elimination. The method is particularly suitable for quantification of highly repetitive DNA transcripts, such as satellite DNA.