Papaya ringspot virus (PRSV) is one of the most devastating viruses of papaya that has significantly hampered papaya production across the globe. Although PRSV resistance is known in some of its wild relatives, such as Vasconcellea cauliflora and in some of the improved papaya genotypes, the molecular basis of this resistance mechanism has not been studied and understood. Plant microRNAs are an important class of small RNAs that regulate the gene expression in several plant species against the invading plant pathogens. These miRNAs are known to manifest the expression of genes involved in resistance against plant pathogens, through modulation of the plant\'s biochemistry and physiology. In this study we made an attempt to study the overall expression pattern of small RNAs and more specifically the miRNAs in different papaya genotypes from India, that exhibit varying levels of tolerance or resistance to PRSV. Our study found that the expression of some of the miRNAs was differentially regulated in these papaya genotypes and they had entirely different miRNA expression profile in healthy and PRSV infected symptomatic plants. This data may help in improvement of papaya cultivars for resistance against PRSV through new breeding initiatives or biotechnological approaches such as genome editing.

Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is one of the most devastating agricultural pests worldwide due to its high reproductive and invasive abilities. The elucidation of its gonadal developmental characteristics and the identification of sex-related genes will provide a useful genetic basis for reproductive-based pest control. Here, the gonadal transcriptome of B. dorsalis was sequenced, and novel gonad-specific expressed genes were analyzed. A total of 1338, 336, 35, and 479 differentially expressed genes (DEGs) were found in the testis (TE), ovary (OV), female accessory gland (FAG), and male accessory gland (MAG), respectively. Furthermore, 463 highly expressed gonad-specific genes were identified, with the TE having the highest number of specific highly expressed genes, at 402, followed by 51 in the OV, 9 in the MAG, and only 1 in the FAG. Strikingly, approximately half of highly expressed gonad-specific genes were uncharacterized. Then, it was found that 35, 17, 3, 2, and 1 of 202 uncharacterized highly expressed TE-specific genes encoded proteins that contained transmembrane domains, signal peptides, high-mobility group boxes, the zinc finger domain, and the BTB/POZ domain, respectively. Interestingly, approximately 40% of uncharacterized highly expressed gonad-specific genes encoding proteins were not predicted to possess functional motifs or domains. Finally, the spatiotemporal expression and sequence characterization of six novel highly expressed gonad-specific genes were analyzed. Altogether, our findings provide a valuable dataset for future functional analyses of sex-related genes and potential target sites for pest control.

As ancient organisms, tree ferns play a crucial role as an evolutionary bridge between lower and higher plant species, providing various utilitarian benefits. However, they face challenges such as overexploitation, climate change, adverse environmental conditions, and insect pests, resulting in conservation concerns. In this study, we provide an overview of metabolic and transcriptomic resources of leaves in two typical tree ferns, A. spinulosa and A. metteniana, and explore the resistance genes for the first time. The landscape of metabolome showed that the compound skimmin may hold medicinal significance. A total of 111 differentially accumulated metabolites (DAMs) were detected, with pathway enrichment analysis highlighting 14 significantly enriched pathways, including 2-oxocarboxylic acid metabolism possibly associated with environmental adaptations. A total of 14,639 differentially expressed genes (DEGs) were found, among which 606 were resistance (R) genes. We identified BAM1 as a significantly differentially expressed R gene, which is one of the core genes within the R gene interaction network. Both the maximum-likelihood phylogenetic tree and the PPI network revealed a close relationship between BAM1, FLS2, and TMK. Moreover, BAM1 showed a significant positive correlation with neochlorogenic acid and kaempferol-7-O-glucoside. These metabolites, known for their antioxidant and anti-inflammatory properties, likely play a crucial role in the defense response of tree ferns. This research provides valuable insights into the metabolic and transcriptomic differences between A. spinulosa and A. metteniana, enhancing our understanding of resistance genes in tree ferns.

With the development of gene testing technology, we have found many different genes, and lncRNA is one of them. LncRNAs refer to a non-protein coding RNA molecule with a length of more than 200bp, which is one of the focuses of research on human malignant diseases such as LUAD. LncRNAs act as an oncogene or inhibitor to regulate the occurrence and progression of tumors. The differential expression of LncRNAs promotes or inhibits the progression of lung adenocarcinoma by affecting cell proliferation, metastasis, invasion, and apoptosis, thus affecting the prognosis and survival rate of patients. Therefore, LncRNAs can be used as a potential target for diagnosis and treatment of cancer. The early diagnosis of the disease was made through the detection of tumor markers. Because lung adenocarcinoma is not easy to diagnose in the early stage and tumor markers are easy to ignore, LncRNAs play an important role in the diagnosis and treatment of lung adenocarcinoma. The main purpose of this article is to summarize the known effects of LncRNAs on lung adenocarcinoma, the effect of differential expression of LncRNAs on the progression of lung adenocarcinoma, and related signal transduction pathways. And to provide a new idea for the future research of lung adenocarcinoma-related LncRNAs.

Transcriptomic analysis has been widely used in comparative experiments to uncover biological mechanisms in various species. However, a simple tool is still lacking to optimize and integrate the features from multiple R packages. In this study, we developed TOmicsVis (Transcriptomics Visualization) (CRAN: https://cran.r-project.org/package=TOmicsVis, v2.0.0), an R package that provides a comprehensive solution for transcriptomics analysis and visualization. It utilizes 46 R packages to design 40 suitable functions for the streamlined analysis of multigroup transcriptomic projects, which covers six main categories: Sample Statistics, Traits Analysis, Differential Expression, Advanced Analysis, GO and KEGG Enrichment, and Table Operation. TOmicsVis can be performed either locally or online (https://shiny.hiplot.cn/tomicsvis-shiny/), which provides significant convenience for researchers without coding training. These user-friendly visualization functions and built-in analysis capabilities enable researchers to monitor experimental data dynamics promptly and explore transcriptomics data quickly.

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals.
Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue\'s component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization.
We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

BACKGROUND: Maedi-visna virus (MVV) is a lentivirus that infects monocyte/macrophage lineage cells in sheep, goats, and wild ruminants and causes pneumonia, mastitis, arthritis, and encephalitis. The immune response to MVV infection is complex, and a complete understanding of its infection and pathogenesis is lacking. This study investigated the in vivo transcriptomic patterns of lung tissues in sheep exposed to MVV using the RNA sequencing technology.
RESULTS: The results indicated that 2,739 genes were significantly differentially expressed, with 1,643 downregulated genes and 1,096 upregulated genes. Many variables that could be unique to MVV infections were discovered. Gene Ontology analysis revealed that a significant proportion of genes was enriched in terms directly related to the immune system and biological responses to viral infections. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the most enriched pathways were related to virus-host cell interactions and inflammatory responses. Numerous immune-related genes, including those encoding several cytokines and interferon regulatory factors, were identified in the protein-protein interaction network of differentially expressed genes (DEGs). The expression of DEGs was evaluated using real-time polymerase chain reaction and western blot analysis. CXCL13, CXCL6, CXCL11, CCR1, CXCL8, CXCL9, CXCL10, TNFSF8, TNFRSF8, IL7R, IFN-γ, CCL2, and MMP9 were upregulated. Immunohistochemical analysis was performed to identify the types of immune cells that infiltrated MVV-infected tissues. B cells, CD4+ and CD8+ T cells, and macrophages were the most prevalent immune cells correlated with MVV infection in the lungs.
CONCLUSIONS: Overall, the findings of this study provide a comprehensive understanding of the in vivo host response to MVV infection and offer new perspectives on the gene regulatory networks that underlie pathogenesis in natural hosts.

Pathogen perception generates the activation of signal transduction cascades to host defense. White pine blister rust (WPBR) is caused by Cronartium ribicola J.C. Fisch and affects a number of species of Pinus. One of the most severely affected species is Pinus albicaulis Engelm (whitebark pine). WPBR resistance in the species is a polygenic and complex trait that requires an optimized immune response. We identified early responses in 2-year-old seedlings after four days of fungal inoculation and compared the underlying transcriptomic response with that of healthy non-inoculated individuals. A de novo transcriptome assembly was constructed with 56,796 high quality-annotations derived from the needles of susceptible and resistant individuals in a resistant half-sib family. Differential expression analysis identified 599 differentially expressed transcripts, from which 375 were upregulated and 224 were downregulated in the inoculated seedlings. These included components of the initial phase of active responses to abiotic factors and stress regulators, such as those involved in the first steps of flavonoid biosynthesis. Four days after the inoculation, infected individuals showed an overexpression of chitinases, reactive oxygen species (ROS) regulation signaling, and flavonoid intermediates. Our research sheds light on the first stage of infection and emergence of disease symptoms among whitebark pine seedlings. RNA sequencing (RNA-seq) data encoding hypersensitive response, cell wall modification, oxidative regulation signaling, programmed cell death, and plant innate immunity were differentially expressed during the defense response against C. ribicola.

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel\'s Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

NanoString nCounter is a medium-throughput technology used in mRNA and miRNA differential expression studies. It offers several advantages, including the absence of an amplification step and the ability to analyze low-grade samples. Despite its considerable strengths, the popularity of the nCounter platform in experimental research stabilized in 2022 and 2023, and this trend may continue in the upcoming years. Such stagnation could potentially be attributed to the absence of a standardized analytical pipeline or the indication of optimal processing methods for nCounter data analysis. To standardize the description of the nCounter data analysis workflow, we divided it into five distinct steps: data pre-processing, quality control, background correction, normalization and differential expression analysis. Next, we evaluated eleven R packages dedicated to nCounter data processing to point out functionalities belonging to these steps and provide comments on their applications in studies of mRNA and miRNA samples.