Glycation is a non-enzymatic posttranslational modification coming from the reaction between reducing sugars and free amino groups in proteins, where early glycation products (fructosyl-lysine, FL) and advanced glycation end products (AGEs) are formed. The occurrence of glycation and accumulation of AGEs have been closely associated with hepatocellular carcinoma (HCC). Here, we reported the characterization of differential glycation in HCC using tissue proteomics with stable isotopic labeling; early glycation-modified peptides were enriched with boronate affinity chromatography (BAC), and AGEs-modified peptides were fractionated with basic reversed-phase separation. By this integrated approach, 3717 and 1137 early and advanced glycated peptides corresponding to 4007 sites on 1484 proteins were identified with a false discovery rate (FDR) of no more than 1%. One hundred fifty-five sites were modified with both early and advanced end glycation products. Five early and 7 advanced glycated peptides were quantified to be differentially expressed in HCC tissues relative to paired adjacent tissues. Most (8 out of 10) of the proteins corresponding to the differential glycated peptides have previously been reported with dysregulation in HCC. The results together may deepen our knowledge of glycation as well as provide insights for therapeutics.

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.

RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences.Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis-the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods and tools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster\'s combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew\'s correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

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.

RNA sequencing (RNA-seq) has emerged as a powerful tool for assessing genome-wide gene expression, revolutionizing various fields of biology. However, analyzing large RNA-seq datasets can be challenging, especially for students or researchers lacking bioinformatics experience. To address these challenges, we present a comprehensive guide to provide step-by-step workflows for analyzing RNA-seq data, from raw reads to functional enrichment analysis, starting with considerations for experimental design. This is designed to aid students and researchers working with any organism, irrespective of whether an assembled genome is available. Within this guide, we employ various recognized bioinformatics tools to navigate the landscape of RNA-seq analysis and discuss the advantages and disadvantages of different tools for the same task. Our protocol focuses on clarity, reproducibility, and practicality to enable users to navigate the complexities of RNA-seq data analysis easily and gain valuable biological insights from the datasets. Additionally, all scripts and a sample dataset are available in a GitHub repository to facilitate the implementation of the analysis pipeline. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Analysis of data from a model plant with an available reference genome Basic Protocol 2: Gene ontology enrichment analysis Basic Protocol 3: De novo assembly of data from non-model plants.