A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence.

Aging and senescence are characterized by pervasive transcriptional dysfunction, including increased expression of transposons and introns. Our aim was to elucidate mechanisms behind this increased expression. Most transposons are found within genes and introns, with a large minority being close to genes. This raises the possibility that transcriptional readthrough and intron retention are responsible for age-related changes in transposon expression rather than expression of autonomous transposons. To test this, we compiled public RNA-seq datasets from aged human fibroblasts, replicative and drug-induced senescence in human cells, and RNA-seq from aging mice and senescent mouse cells. Indeed, our reanalysis revealed a correlation between transposons expression, intron retention, and transcriptional readthrough across samples and within samples. Both intron retention and readthrough increased with aging or cellular senescence and these transcriptional defects were more pronounced in human samples as compared to those of mice. In support of a causal connection between readthrough and transposon expression, analysis of models showing induced transcriptional readthrough confirmed that they also show elevated transposon expression. Taken together, our data suggest that elevated transposon reads during aging seen in various RNA-seq dataset are concomitant with multiple transcriptional defects. Intron retention and transcriptional readthrough are the most likely explanation for the expression of transposable elements that lack a functional promoter.

Molecular mapping of the broad bean wilt virus 2 resistance locus bwvr in Capsicum annuum using BSR-seq.

KEY MESSAGE: Bulked segregant RNA seq of pools of pepper accessions that are susceptible or resistant to Broad bean wilt virus 2 identifies a gene that might confer resistance to this devastating pathogen. The single-stranded positive-sense RNA virus Broad bean wilt virus 2 (BBWV2) causes substantial damage to pepper (Capsicum annuum) cultivation. Here, we describe mapping the BBWV2 resistance locus bwvr using a F7:8 recombinant inbred line (RIL) population constructed by crossing the BBWV2-resistant pepper accession 'SNU-C' with the susceptible pepper accession 'ECW30R.' All F1 plants infected with the BBWV2 strain PAP1 were susceptible to the virus, and the RIL population showed a 1:1 ratio of resistance to susceptibility, indicating that this trait is controlled by a single recessive gene. To map bwvr, we performed bulked segregant RNA-seq (BSR-seq). We sequenced pools of resistant and susceptible lines from the RILs and aligned the reads to the high-quality 'Dempsey' reference genome to identify variants between the pools. This analysis identified 519,887 variants and selected the region from 245.9-250.8 Mb of the Dempsey reference genome as the quantitative trait locus region for bwvr. To finely map bwvr, we used newly designed high-resolution melting (HRM) and Kompetitive allele specific PCR (KASP) markers based on variants obtained from the BSR-seq reads and the PepperSNP16K array. Comparative analysis identified 11 SNU-C-specific SNPs within the bwvr locus. Using markers derived from these variants, we mapped the candidate bwvr locus to the region from 246.833-246.949 kb. SNU-C-specific variants clustered near DEM.v1.00035533 within the bwvr locus. DEM.v1.00035533 encodes the nitrate transporter NPF1.2 and contains a SNP within its 5' untranslated region. The bwvr locus, which contains four genes including DEM.v1.00035533, could represent a valuable resource for global pepper breeding programs.

Deciphering antifungal and antibiofilm mechanisms of isobavachalcone against Cryptococcus neoformans through RNA-seq and functional analyses.

Cryptococcus neoformans has been designated as critical fungal pathogens by the World Health Organization, mainly due to limited treatment options and the prevalence of antifungal resistance. Consequently, the utilization of novel antifungal agents is crucial for the effective treatment of C. neoformans infections. This study exposed that the minimum inhibitory concentration (MIC) of isobavachalcone (IBC) against C. neoformans H99 was 8 µg/mL, and IBC dispersed 48-h mature biofilms by affecting cell viability at 16 µg/mL. The antifungal efficacy of IBC was further validated through microscopic observations using specific dyes and in vitro assays, which confirmed the disruption of cell wall/membrane integrity. RNA-Seq analysis was employed to decipher the effect of IBC on the C. neoformans H99 transcriptomic profiles. Real-time quantitative reverse transcription PCR (RT-qPCR) analysis was performed to validate the transcriptomic data and identify the differentially expressed genes. The results showed that IBC exhibited various mechanisms to impede the growth, biofilm formation, and virulence of C. neoformans H99 by modulating multiple dysregulated pathways related to cell wall/membrane, drug resistance, apoptosis, and mitochondrial homeostasis. The transcriptomic findings were corroborated by the antioxidant analyses, antifungal drug sensitivity, molecular docking, capsule, and melanin assays. In vivo antifungal activity analysis demonstrated that IBC extended the lifespan of C. neoformans-infected Caenorhabditis elegans. Overall, the current study unveiled that IBC targeted multiple pathways simultaneously to inhibit growth significantly, biofilm formation, and virulence, as well as to disperse mature biofilms of C. neoformans H99 and induce cell death.

The landscape of abiotic and biotic stress-responsive splice variants with deep RNA-seq datasets in hot pepper.

Alternative splicing (AS) is a widely observed phenomenon in eukaryotes that plays a critical role in development and stress responses. In plants, the large number of RNA-seq datasets in response to different environmental stressors can provide clues for identification of condition-specific and/or common AS variants for preferred agronomic traits. We report RNA-seq datasets (350.7 Gb) from Capsicum annuum inoculated with one of three bacteria, one virus, or one oomycete and obtained additional existing transcriptome datasets. In this study, we investigated the landscape of AS in response to environmental stressors, signaling molecules, and tissues from 425 total samples comprising 841.49 Gb. In addition, we identified genes that undergo AS under specific and shared stress conditions to obtain potential genes that may be involved in enhancing tolerance to stressors. We uncovered 1,642,007 AS events and identified 4,354 differential alternative splicing genes related to environmental stressors, tissues, and signaling molecules. This information and approach provide useful data for basic-research focused on enhancing tolerance to environmental stressors in hot pepper or establishing breeding programs.

scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

The integration of single-cell and bulk RNA-seq atlas reveals ERS-mediated acinar cell damage in acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a clinically common acute abdominal disease, whose pathogenesis remains unclear. The severe patients usually have multiple complications and lack specific drugs, leading to a high mortality and poor outcome. Acinar cells are recognized as the initial site of AP. However, there are no precise single-cell transcriptomic profiles to decipher the landscape of acinar cells during AP, which are the missing pieces of jigsaw we aimed to complete in this study. METHODS: A single-cell sequencing dataset was used to identify the cell types in pancreas of AP mice and to depict the transcriptomic maps in acinar cells. The pathways' activities were evaluated by gene sets enrichment analysis (GSEA) and single-cell gene sets variation analysis (GSVA). Pseudotime analysis was performed to describe the development trajectories of acinar cells. We also constructed the protein-protein interaction (PPI) network and identified the hub genes. Another independent single-cell sequencing dataset of pancreas samples from AP mice and a bulk RNA sequencing dataset of peripheral blood samples from AP patients were also analyzed. RESULTS: In this study, we identified genetic markers of each cell type in the pancreas of AP mice based on single-cell sequencing datasets and analyzed the transcription changes in acinar cells. We found that acinar cells featured acinar-ductal metaplasia (ADM), as well as increased endocytosis and vesicle transport activity during AP. Notably, the endoplasmic reticulum stress (ERS) and ER-associated degradation (ERAD) pathways activated by accumulation of unfolded/misfolded proteins in acinar cells could be pivotal for the development of AP. CONCLUSION: We deciphered the distinct roadmap of acinar cells in the early stage of AP at single-cell level. ERS and ERAD pathways are crucially important for acinar homeostasis and the pathogenesis of AP.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling.

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Genome-wide characterization of post-transcriptional processes related to wood formation in Dalbergia odorifera.

BACKGROUND: Alternative polyadenylation (APA), alternative splicing (AS), and long non-coding RNAs (lncRNAs) play regulatory roles in post-transcriptional processes in plants. However, little is known about their involvement in xylem development in Dalbergia odorifera, a valuable rosewood species with medicinal and commercial significance. We addressed this by conducting Isoform Sequencing (Iso-Seq) using PacBio's SMRT technology and combined it with RNA-seq analysis (RNA sequencing on Illumina platform) after collecting xylem samples from the transition zone and the sapwood of D. odorifera. RESULTS: We identified 14,938 full-length transcripts, including 9,830 novel isoforms, which has updated the D. odorifera genome annotation. Our analysis has revealed that 4,164 genes undergo APA, whereas 3,084 genes encounter AS. We have also annotated 118 lncRNAs. Furthermore, RNA-seq analysis identified 170 differential alternative splicing (DAS) events, 344 genes with differential APA site usage (DE-APA), and 6 differentially expressed lncRNAs in the transition zone when compared to the sapwood. AS, APA, and lncRNAs are differentially regulated during xylem development. Differentially expressed APA genes were enriched for terpenoid and flavonoid metabolism, indicating their role in the heartwood formation. Additionally, DE-APA genes were associated with cell wall biosynthesis and terpenoid metabolism, implying an APA's role in wood formation. A DAS gene (involved in chalcone accumulation) with a significantly greater inclusion of the last exon in the transition zone than in the sapwood was identified. We also found that differentially expressed lncRNAs targeted the genes related to terpene synthesis. CONCLUSIONS: This study enhances our understanding of the molecular regulatory mechanisms underlying wood formation in D. odorifera, and provides valuable genetic resources and insights for its molecular-assisted breeding.

Integration of single-cell RNA-seq and bulk RNA-seq data to construct and validate a cancer-associated fibroblast-related prognostic signature for patients with ovarian cancer.

BACKGROUND: To establish a prognostic risk profile for ovarian cancer (OC) patients based on cancer-associated fibroblasts (CAFs) and gain a comprehensive understanding of their role in OC progression, prognosis, and therapeutic efficacy. METHODS: Data on OC single-cell RNA sequencing (scRNA-seq) and total RNA-seq were collected from the GEO and TCGA databases. Seurat R program was used to analyze scRNA-seq data and identify CAFs clusters corresponding to CAFs markers. Differential expression analysis was performed on the TCGA dataset to identify prognostic genes. A CAF-associated risk signature was designed using Lasso regression and combined with clinicopathological variables to develop a nomogram. Functional enrichment and the immune landscape were also analyzed. RESULTS: Five CAFs clusters were identified in OC using scRNA-seq data, and 2 were significantly associated with OC prognosis. Seven genes were selected to develop a CAF-based risk signature, primarily associated with 28 pathways. The signature was a key independent predictor of OC prognosis and relevant in predicting the results of immunotherapy interventions. A novel nomogram combining CAF-based risk and disease stage was developed to predict OC prognosis. CONCLUSION: The study highlights the importance of CAFs in OC progression and suggests potential for innovative treatment strategies. A CAF-based risk signature provides a highly accurate prediction of the prognosis of OC patients, and the developed nomogram shows promising results in predicting the OC prognosis.

Immune microniches shape intestinal Treg function.

The intestinal immune system is highly adapted to maintaining tolerance to the commensal microbiota and self-antigens while defending against invading pathogens1,2. Recognizing how the diverse network of local cells establish homeostasis and maintains it in the complex immune environment of the gut is critical to understanding how tolerance can be re-established following dysfunction, such as in inflammatory disorders. Although cell and molecular interactions that control T regulatory (Treg) cell development and function have been identified3,4, less is known about the cellular neighbourhoods and spatial compartmentalization that shapes microorganism-reactive Treg cell function. Here we used in vivo live imaging, photo-activation-guided single-cell RNA sequencing5-7 and spatial transcriptomics to follow the natural history of T cells that are reactive towards Helicobacter hepaticus through space and time in the settings of tolerance and inflammation. Although antigen stimulation can occur anywhere in the tissue, the lamina propria-but not embedded lymphoid aggregates-is the key microniche that supports effector Treg (eTreg) cell function. eTreg cells are stable once their niche is established; however, unleashing inflammation breaks down compartmentalization, leading to dominance of CD103+SIRPα+ dendritic cells in the lamina propria. We identify and validate the putative tolerogenic interaction between CD206+ macrophages and eTreg cells in the lamina propria and identify receptor-ligand pairs that are likely to govern the interaction. Our results reveal a spatial mechanism of tolerance in the lamina propria and demonstrate how knowledge of local interactions may contribute to the next generation of tolerance-inducing therapies.

Single-Cell RNA Sequencing Reveals an Immune Landscape of CD4+ T Cells in Coronary Culprit Plaques With Acute Coronary Syndrome in Humans-Brief Report.

BACKGROUND: Acute coronary syndrome (ACS) involves plaque-related thrombosis, causing primary ischemic cardiomyopathy or lethal arrhythmia. We previously demonstrated a unique immune landscape of myeloid cells in the culprit plaques causing ACS by using single-cell RNA sequencing. Here, we aimed to characterize T cells in a single-cell level, assess clonal expansion of T cells, and find a therapeutic target to prevent ACS. METHODS: We obtained the culprit lesion plaques from 4 patients with chronic coronary syndrome (chronic coronary syndrome plaques) and the culprit lesion plaques from 3 patients with ACS (ACS plaques) who were candidates for percutaneous coronary intervention with directional coronary atherectomy. Live CD45+ immune cells were sorted from each pooled plaque samples and applied to the 10× platform for single-cell RNA sequencing analysis. We also extracted RNA from other 3 ACS plaque samples and conducted unbiased TCR (T-cell receptor) repertoire analysis. RESULTS: CD4+ T cells were divided into 5 distinct clusters: effector, naive, cytotoxic, CCR7+ (C-C chemokine receptor type 7) central memory, and FOXP3 (forkhead box P3)+ regulatory CD4+ T cells. The proportion of central memory CD4+ T cells was higher in the ACS plaques. Correspondingly, dendritic cells also tended to express more HLAs (human leukocyte antigens) and costimulatory molecules in the ACS plaques. The velocity analysis suggested the differentiation flow from central memory CD4+ T cells into effector CD4+ T cells and that from naive CD4+ T cells into central memory CD4+ T cells in the ACS plaques, which were not observed in the chronic coronary syndrome plaques. The bulk repertoire analysis revealed clonal expansion of TCRs in each patient with ACS and suggested that several peptides in the ACS plaques work as antigens and induced clonal expansion of CD4+ T cells. CONCLUSIONS: For the first time, we revealed single cell-level characteristics of CD4+ T cells in patients with ACS. CD4+ T cells could be therapeutic targets of ACS. REGISTRATION: URL: https://upload.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000046521; Unique identifier: UMIN000040747.

High-quality faba bean reference transcripts generated using PacBio and Illumina RNA-seq data.

The genome of faba bean was first published in 2023. To promote future molecular breeding studies, we improved the quality of the faba genome based on high-density genetic maps and the Illumina and Pacbio RNA-seq datasets. Two high-density genetic maps were used to conduct the scaffold ordering and orientation of faba bean, culminating in an increased length (i.e., 14.28 Mbp) of chromosomes and a decrease in the number of scaffolds by 45. In gene model mining and optimisation, the PacBio and Illumina RNA-seq datasets from 37 samples allowed for the identification and correction 121,606 transcripts, and the data facilitated a prediction of 15,640 alternative splicing events, 2,148 lncRNAs, and 1,752 fusion transcripts, thus allowing for a clearer understanding of the gene structures underlying the faba genome. Moreover, a total of 38,850 new genes including 56,188 transcripts were identified compared with the reference genome. Finally, the genetic data of the reference genome was integrated and a comprehensive and complete faba bean transcriptome sequence of 103,267 transcripts derived from 54,753 uni-genes was formed.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

RNA-Seq analysis reveals the mechanism in response to cold stress of peach cv 'Dingjiaba'.

BACKGROUND: 'Dingjiaba' is an important Prunus persica cultivar (cv) mainly grown in the Hexi corridor in northwest China, which has an inherited strong cold tolerance. OBJECTIVE: To compare the transcriptome and physiology data of leaves of cvs 'Dingjiaba' (D) and 'Kanoiwa' (K) following cold treatment at different time periods, in order to gain new insights into the mechanisms of cold adaptation in 'Dingjiaba'. MATERIALS AND METHODS: We analyzed the transcriptomic and physiological data of leaves of D and K cvs exposed to 0 h (D0/K0), 2 h (D2/K2), 6 h (D6/K6) and 12 h (D12/K12) cold stress. RESULTS: Low temperature stress caused membrane damage and led to increased rate of electrolyte leakage and increased MDA content. Cold stress induced the accumulation of soluble sugars, soluble proteins and proline in leaves of both cvs, with a lower increase in K compared to D. Transcriptome analysis identified 4,631, 5,069, 5,662 and 3,886 differentially expressed genes (DEGs) between D0 and K0, D2 and K2, D6 and K6 and D12 and K12, respectively. The differentially expressed genes significantly enriched in metabolic pathways and biosynthesis of secondary metabolites. We further validated the reliability of sequencing data of the RNA-Seq with Real-Time Quantitative PCR, which suggested that the expression trend of the RNA-Seq were same as RT-PCR. CONCLUSIONS: These results provide novel insights into a series of molecular mechanisms underlying physiological metabolism and defense. https://doi.org/10.54680/fr24210110312.

Preparation of Healthy Single Neuron or Astrocyte Suspension from Adult Mouse Brain for RNA-seq.

Single-cell RNA sequencing (scRNA-seq) has been widely applied in neuroscience research, enabling the investigation of cellular heterogeneity at the transcriptional level, the characterization of rare cell types, and the detailed analysis of the stochastic nature of gene expression. Isolation of single nerve cells in good health, especially from the adult rodent brain, is the most difficult and critical process for scRNA-seq. Here, we describe methods to optimize protease digestion of brain slices, which enable yield of millions of cells in good health from the adult brain.

Integration of single-cell RNA-seq and bulk RNA-seq to construct liver hepatocellular carcinoma stem cell signatures to explore their impact on patient prognosis and treatment.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC. METHODS: The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs. RESULTS: A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs. CONCLUSION: The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.

Transcriptomic Data Analysis Using the Galaxy Platform: Coffee (Coffea arabica L.) Flowers as Example.

Coffee, an important agricultural product for tropical producing countries, is facing challenges due to climate change, including periods of drought, irregular rain distribution, and high temperatures. These changes result in plant water stress, leading to significant losses in coffee productivity and quality. Understanding the processes that affect coffee flowering is crucial for improving productivity and quality. In this chapter, we describe a protocol for transcriptome analysis using available Internet software, mainly in the Galaxy Platform, using RNA-Seq data from flowers collected from different parts of the coffee tree. The methods presented in this chapter provide a comprehensive protocol for transcriptome analysis of differentially expressed genes from flowers of coffee plant. This knowledge can be utilized in coffee genetic improvement programs, particularly in the selection of cultivars that are tolerant to water deficit.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data.

BACKGROUND: In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis. RESULTS: We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise. CONCLUSION: Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .

RNA-seq analysis-based study on the effects of gestational diabetes mellitus on macrosomia.

Background: Both the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group. Results: The NN group's placental weight differed significantly from that of the other groups. The structure of NN group's placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn't be discounted. Conclusion: In diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn't be ignored because of their tight relationships.

Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset.

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.