Protocol for Analyzing Epigenetic Regulation Mechanisms in Breast Cancer.

DNA methylation and gene expression are two critical aspects of the epigenetic landscape that contribute significantly to cancer pathogenesis. Analysis of aberrant genome-wide methylation patterns can provide insights into how these affect the cancer transcriptome and possible clinical implications for cancer diagnosis and treatment. The role of tumor suppressors and oncogenes is well known in tumorigenesis. Epigenetic alterations can significantly impact the expression and function of these critical genes, contributing to the initiation and progression of cancer. This protocol chapter presents a unified workflow to explore the role of DNA methylation in gene expression regulation in breast cancer by identifying differentially expressed genes whose promoter or gene body regions are differentially methylated using various Bioconductor packages in R environment. Functional enrichment analysis of these genes can help in understanding the mechanisms leading to tumorigenesis due to epigenetic alterations.

Antennal transcriptome analysis reveals sensory receptors potentially associated with host detection in the livestock pest Lucilia cuprina.

BACKGROUND: Lucilia cuprina (Wiedemann, 1830) (Diptera: Calliphoridae) is the main causative agent of flystrike of sheep in Australia and New Zealand. Female flies lay eggs in an open wound or natural orifice, and the developing larvae eat the host's tissues, a condition called myiasis. To improve our understanding of host-seeking behavior, we quantified gene expression in male and female antennae based on their behavior. METHODS: A spatial olfactometer was used to evaluate the olfactory response of L. cuprina mated males and gravid females to fresh or rotting beef. Antennal RNA-Seq analysis was used to identify sensory receptors differentially expressed between groups. RESULTS: Lucilia cuprina females were more attracted to rotten compared to fresh beef (> fivefold increase). However, males and some females did not respond to either type of beef. RNA-Seq analysis was performed on antennae dissected from attracted females, non-attracted females and males. Transcripts encoding sensory receptors from 11 gene families were identified above a threshold (≥ 5 transcript per million) including 49 ATP-binding cassette transporters (ABCs), two ammonium transporters (AMTs), 37 odorant receptors (ORs), 16 ionotropic receptors (IRs), 5 gustatory receptors (GRs), 22 odorant-binding proteins (OBPs), 9 CD36-sensory neuron membrane proteins (CD36/SNMPs), 4 chemosensory proteins (CSPs), 4 myeloid lipid-recognition (ML) and Niemann-Pick C2 disease proteins (ML/NPC2), 2 pickpocket receptors (PPKs) and 3 transient receptor potential channels (TRPs). Differential expression analyses identified sex-biased sensory receptors. CONCLUSIONS: We identified sensory receptors that were differentially expressed between the antennae of both sexes and hence may be associated with host detection by female flies. The most promising for future investigations were as follows: an odorant receptor (LcupOR46) which is female-biased in L. cuprina and Cochliomyia hominivorax Coquerel, 1858; an ABC transporter (ABC G23.1) that was the sole sensory receptor upregulated in the antennae of females attracted to rotting beef compared to non-attracted females; a female-biased ammonia transporter (AMT_Rh50), which was previously associated with ammonium detection in Drosophila melanogaster Meigen, 1830. This is the first report suggesting a possible role for ABC transporters in L. cuprina olfaction and potentially in other insects.

Diverse genetic alteration dysregulates neuropeptide and intracellular signalling in the suprachiasmatic nuclei.

In mammals, intrinsic 24 h or circadian rhythms are primarily generated by the suprachiasmatic nuclei (SCN). Rhythmic daily changes in the transcriptome and proteome of SCN cells are controlled by interlocking transcription-translation feedback loops (TTFLs) of core clock genes and their proteins. SCN cells function as autonomous circadian oscillators, which synchronize through intercellular neuropeptide signalling. Physiological and behavioural rhythms can be severely disrupted by genetic modification of a diverse range of genes and proteins in the SCN. With the advent of next generation sequencing, there is unprecedented information on the molecular profile of the SCN and how it is affected by genetically targeted alteration. However, whether the expression of some genes is more readily affected by genetic alteration of the SCN is unclear. Here, using publicly available datasets from recent RNA-seq assessments of the SCN from genetically altered and control mice, we evaluated whether there are commonalities in transcriptome dysregulation. This was completed for four different phases across the 24 h cycle and was augmented by Gene Ontology Molecular Function (GO:MF) and promoter analysis. Common differentially expressed genes (DEGs) and/or enriched GO:MF terms included signalling molecules, their receptors, and core clock components. Finally, examination of the JASPAR database indicated that E-box and CRE elements in the promoter regions of several commonly dysregulated genes. From this analysis, we identify differential expression of genes coding for molecules involved in SCN intra- and intercellular signalling as a potential cause of abnormal circadian rhythms.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

OBJECTIVES: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. METHODS: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. RESULTS: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. CONCLUSION: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Exosomal miRNA Changes Associated with Restoration to Sinus Rhythm in Atrial Fibrillation Patients.

We aimed to identify serum exosomal microRNAs (miRNAs) associated with the transition from atrial fibrillation (AF) to sinus rhythm (SR) and investigate their potential as biomarkers for the early recurrence of AF within three months post-treatment. We collected blood samples from eight AF patients at Chang Gung Memorial Hospital in Taiwan both immediately before and within 14 days following rhythm control treatment. Exosomes were isolated from these samples, and small RNA sequencing was performed. Using DESeq2 analysis, we identified nine miRNAs (16-2-3p, 22-3p, 23a-3p, 23b-3p, 125a-5p, 328-3p, 423-5p, 504-5p, and 582-3p) associated with restoration to SR. Further analysis using the DIABLO model revealed a correlation between the decreased expression of miR-125a-5p and miR-328-3p and the early recurrence of AF. Furthermore, early recurrence is associated with a longer duration of AF, presumably indicating a more extensive state of underlying cardiac remodeling. In addition, the reads were mapped to mRNA sequences, leading to the identification of 14 mRNAs (AC005041.1, ARHGEF12, AMT, ANO8, BCL11A, DIO3OS, EIF4ENIF1, G2E3-AS1, HERC3, LARS, NT5E, PITX1, SLC16A12, and ZBTB21) associated with restoration to SR. Monitoring these serum exosomal miRNA and mRNA expression patterns may be beneficial for optimizing treatment outcomes in AF patients.

Unraveling the molecular landscape of Ataxia Telangiectasia: Insights into Neuroinflammation, immune dysfunction, and potential therapeutic target.

BACKGROUND: Ataxia Telangiectasia (AT) is a genetic disorder characterized by compromised DNA repair, cerebellar degeneration, and immune dysfunction. Understanding the molecular mechanisms driving AT pathology is crucial for developing targeted therapies. METHODS: In this study, we conducted a comprehensive analysis to elucidate the molecular mechanisms underlying AT pathology. Using publicly available RNA-seq datasets comparing control and AT samples, we employed in silico transcriptomics to identify potential genes and pathways. We performed differential gene expression analysis with DESeq2 to reveal dysregulated genes associated with AT. Additionally, we constructed a Protein-Protein Interaction (PPI) network to explore the interactions between proteins implicated in AT. RESULTS: The network analysis identified hub genes, including TYROBP and PCP2, crucial in immune regulation and cerebellar function, respectively. Furthermore, pathway enrichment analysis unveiled dysregulated pathways linked to AT pathology, providing insights into disease progression. CONCLUSION: Our integrated approach offers a holistic understanding of the complex molecular landscape of AT and identifies potential targets for therapeutic intervention. By combining transcriptomic analysis with network-based methods, we provide valuable insights into the underlying mechanisms of AT pathogenesis.

Identification of Differentially Expressed Genes in Human Colorectal Cancer Using RNASeq Data Validated on the Molecular Level with Real-Time PCR.

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study's analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival.

N-of-one differential gene expression without control samples using a deep generative model.

Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Response of the nitrogen processing bacterial community to water level fluctuation and nitrate availability in an intact marsh soil column.

Wetlands are known to experience fluctuations in water levels and receive exogenous nitrogen inputs that affect various organisms, including soil microorganisms. To study the impact of these factors on microbial diversity, we collected intact soil columns from a Phragmites australis-dominated site in the Qixing River National Nature Reserve in Northeast China. In a laboratory experiment, we simulated water level fluctuations and exogenous nitrogen inputs to the soil columns and examined the associated changes in the relative abundance of 51 bacterial genera involved in nitrogen cycling processes. Our findings revealed that different bacterial genera exhibited varying relative abundances across treatments. Specifically, Massilia showed the highest total relative abundance at the genus level, while Planctomyces had the second highest, and Campylobacter had the lowest abundance. The DESeq2 model, based on negative binomial distribution, revealed that the tags of bacterial genera were significantly correlated with soil depth, but not with water levels or nitrogen concentrations. However, the addition of a 30 mg/L nitrate solution caused a decrease in the relative abundances of bacterial genera with decreasing water levels, while a 60 mg/L concentration of nitrogen resulted in a decrease and then an increase in the relative abundances of bacterial genera with decreasing water levels. Our study provides valuable insights into the response of nitrogen-cycling bacteria to changes in different environmental conditions.

Bioinformatics Analysis of miRNA Sequencing Data.

The bioinformatics analysis of miRNA is a complicated task with multiple operations and steps involved from processing of raw sequence data to finally identifying accurate microRNAs associated with the phenotypes of interest. A complete analysis process demands a high level of technical expertise in programming, statistics, and data management. The goal of this chapter is to reduce the burden of technical expertise and provide readers the opportunity to understand crucial steps involved in the analysis of miRNA sequencing data.In this chapter, we describe methods and tools employed in processing of miRNA reads, quality control, alignment, quantification, and differential expression analysis.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data.

Background: Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it. Methods: We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project. Results: The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

Dental caries as a risk factor for bacterial blood stream infection (BSI) in children undergoing hematopoietic cell transplantation (HCT).

Background: Hematopoietic cell transplantation (HCT) is a potentially curative therapy for a wide range of pediatric malignant and nonmalignant diseases. However, complications, including blood stream infection (BSI) remain a major cause of morbidity and mortality. While certain bacteria that are abundant in the oral microbiome, such as S. mitis, can cause BSI, the role of the oral microbial community in the etiology of BSI is not well understood. The finding that the use of xylitol wipes, which specifically targets the cariogenic bacteria S. mutans is associated with reduced BSI in pediatric patients, lead us to investigate dental caries as a risk factor for BSI. Methods: A total of 41 pediatric patients admitted for allogenic or autologous HCT, age 8 months to 25 years, were enrolled. Subjects with high dental caries risk were identified as those who had dental restorations completed within 2 months of admission for transplant, or who had untreated decay. Fisher's exact test was used to determine if there was a significant association between caries risk and BSI. Dental plaque and saliva were collected on a cotton swab from a subset of four high caries risk (HCR) and four low caries risk (LCR) children following pretransplant conditioning. 16SrRNA sequencing was used to compare the microbiome of HCR and LCR subjects and to identify microbes that were significantly different between the two groups. Results: There was a statistically significant association between caries risk and BSI (p < 0.035) (Fisher's exact test). Multivariate logistic regression analysis showed children in the high dental caries risk group were 21 times more likely to have BSI, with no significant effect of age or mucositis severity. HCR subjects showed significantly reduced microbial alpha diversity as compared to LCR subjects. LEfse metagenomic analyses, showed the oral microbiome in HCR children enriched in order Lactobacillales. This order includes Streptococcus and Lactobacillus, both which contain bacteria primarily associated with dental caries. Discussion: These findings support the possibility that the cariogenic microbiome can enhance the risk of BSI in pediatric populations. Future metagenomic analyses to measure microbial differences at, before, and after conditioning related to caries risk, may further unravel the complex relationship between the oral microbiome, and whether it affects health outcomes such as BSI.

Energetics, but not development, is impacted in coral embryos exposed to ocean acidification.

In light of the chronic stress and mass mortality reef-building corals face under climate change, it is critical to understand the processes driving reef persistence and replenishment, including coral reproduction and development. Here, we quantified gene expression and sensitivity to ocean acidification across a set of developmental stages in the rice coral, Montipora capitata. Embryos and swimming larvae were exposed to pH treatments of 7.8 (ambient), 7.6 (low) and 7.3 (extreme low) from fertilization to 9 days post-fertilization. Embryo and larval volume, and stage-specific gene expression were compared between treatments to determine the effects of acidified seawater on early development. Although there was no measurable size differentiation between pH treatments at the fertilized egg and prawn chip (9 h post-fertilization) stages, early gastrulae and larvae raised in reduced pH treatments were significantly smaller than those raised in ambient seawater, suggesting an energetic cost to developing under low pH. However, no differentially expressed genes were found until the swimming larval stage. Notably, gene expression patterns of larvae developing at pH 7.8 and pH 7.3 were more similar than those of larvae developing at pH 7.6. Larvae from pH 7.6 showed upregulation of genes involved in cell division, regulation of transcription, lipid metabolism and response to oxidative stress in comparison to the other two treatments. Although low pH appears to increase energetic demands and trigger oxidative stress in larvae, the developmental process is robust to this at a molecular level, with the swimming larval stage reached in all pH treatments.

A comparison of transcriptome analysis methods with reference genome.

BACKGROUND: The application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field. METHODS: In our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures. RESULTS: Cufflinks-Cuffdiff demands the highest computing resources and Kallisto-Sleuth demands the least. Gene expression values, fold change, p and q values of differential expression (DE) analysis are highly correlated among procedures using HTseq for quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels. HISAT2-StringTie-Ballgown is more sensitive to genes with low expression levels, while Kallisto-Sleuth may only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change and p value are chosen in DE analysis, StringTie-Ballgown produce the least number of DEGs, while HTseq-DESeq2, -edgeR or -limma generally produces more DEGs. The performance of Cufflinks-Cuffdiff and Kallisto-Sleuth varies in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures. CONCLUSION: Results are highly correlated among RNA-seq analysis procedures using HTseq for quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.

Screening and Identification of Human Endogenous Retrovirus-K mRNAs for Breast Cancer Through Integrative Analysis of Multiple Datasets.

Objective: Human endogenous retroviruses (HERVs) make up 8% of the human genome. HERVs are biologically active elements related to multiple diseases. HERV-K, a subfamily of HERVs, has been associated with certain types of cancer and suggested as an immunologic target in some tumors. The expression levels of HERV-K in breast cancer (BCa) have been studied as biomarkers and immunologic therapeutic targets. However, HERV-K has multiple copies in the human genome, and few studies determined the transcriptional profile of HERV-K copies across the human genome for BCa. Methods: Ninety-one HERV-K indexes with entire proviral sequences were used as the reference database. Nine raw sequencing datasets with 243 BCa and 137 control samples were mapped to this database by Salmon software. The differential proviral expression across several groups was analyzed by DESeq2 software. Results: First, the clustering of each dataset demonstrated that these 91 HERV-K proviruses could well cluster the BCa and control samples when the normal controls were normal cells or healthy donor tissues. Second, several common HERV-K proviruses that are closely related with BCa risk were significantly differentially expressed (p adj < 0.05 and absolute log2FC > 1.5) in the tissues and cell lines. Additionally, almost all the HERV-K proviruses had higher expression in BCa tissue than in healthy donor tissue. Notably, we first found the expression of 17p13.1 provirus that located with TP53 should regulate TP53 expression in ER+ and HER2+ BCa. Conclusion: The expression profiling of these 91 HERV-K proviruses can be used as biomarkers to distinguish individuals with BCa and healthy controls. Some proviruses, especially 17p13.1, were strongly associated with BCa risk. The results suggest that HERV-K expression profiles may be appropriate biomarkers and targets for BCa.

The expanded inhibitor of apoptosis gene family in oysters possesses novel domain architectures and may play diverse roles in apoptosis following immune challenge.

BACKGROUND: Apoptosis plays important roles in a variety of functions, including immunity and response to environmental stress. The Inhibitor of Apoptosis (IAP) gene family of apoptosis regulators is expanded in molluscs, including eastern, Crassostrea virginica, and Pacific, Crassostrea gigas, oysters. The functional importance of IAP expansion in apoptosis and immunity in oysters remains unknown. RESULTS: Phylogenetic analysis of IAP genes in 10 molluscs identified lineage specific gene expansion in bivalve species. Greater IAP gene family expansion was observed in C. virginica than C. gigas (69 vs. 40), resulting mainly from tandem duplications. Functional domain analysis of oyster IAP proteins revealed 3 novel Baculoviral IAP Repeat (BIR) domain types and 14 domain architecture types across gene clusters, 4 of which are not present in model organisms. Phylogenetic analysis of bivalve IAPs suggests a complex history of domain loss and gain. Most IAP genes in oysters (76% of C. virginica and 82% of C. gigas), representing all domain architecture types, were expressed in response to immune challenge (Ostreid Herpesvirus OsHV-1, bacterial probionts Phaeobacter inhibens and Bacillus pumilus, several Vibrio spp., pathogenic Aliiroseovarius crassostreae, and protozoan parasite Perkinsus marinus). Patterns of IAP and apoptosis-related differential gene expression differed between the two oyster species, where C. virginica, in general, differentially expressed a unique set of IAP genes in each challenge, while C. gigas differentially expressed an overlapping set of IAP genes across challenges. Apoptosis gene expression patterns clustered mainly by resistance/susceptibility of the oyster host to immune challenge. Weighted Gene Correlation Network Analysis (WGCNA) revealed unique combinations of transcripts for 1 to 12 IAP domain architecture types, including novel types, were significantly co-expressed in response to immune challenge with transcripts in apoptosis-related pathways. CONCLUSIONS: Unprecedented diversity characterized by novel BIR domains and protein domain architectures was observed in oyster IAPs. Complex patterns of gene expression of novel and conserved IAPs in response to a variety of ecologically-relevant immune challenges, combined with evidence of direct co-expression of IAP genes with apoptosis-related transcripts, suggests IAP expansion facilitates complex and nuanced regulation of apoptosis and other immune responses in oysters.

Endothelial Cell RNA-Seq Data: Differential Expression and Functional Enrichment Analyses to Study Phenotypic Switching.

RNA-seq is a common approach used to explore gene expression data between experimental conditions or cell types and ultimately leads to information that can shed light on the biological processes involved and inform further hypotheses. While the protocols required to generate samples for sequencing can be performed in most research facilities, the resulting computational analysis is often an area in which researchers have little experience. Here we present a user-friendly bioinformatics workflow which describes the methods required to take raw data produced by RNA sequencing to interpretable results. Widely used and well documented tools are applied. Data quality assessment and read trimming were performed by FastQC and Cutadapt, respectively. Following this, STAR was utilized to map the trimmed reads to a reference genome and the alignment was analyzed by Qualimap. The subsequent mapped reads were quantified by featureCounts. DESeq2 was used to normalize and perform differential expression analysis on the quantified reads, identifying differentially expressed genes and preparing the data for functional enrichment analysis. Gene set enrichment analysis identified enriched gene sets from the normalized count data and clusterProfiler was used to perform functional enrichment against the GO, KEGG, and Reactome databases. Example figures of the functional enrichment analysis results were also generated. The example data used in the workflow are derived from HUVECs, an in vitro model used in the study of endothelial cells, published and publicly available for download from the European Nucleotide Archive.

RNA-seq Data Analysis for Differential Expression.

Changes in the surrounding environment are mirrored by changes in the transcript profile of an organism. In the case of a plant pathogen, host colonization would be a challenge that triggers changes in transcript expression patterns. Determining the transcriptional profile could provide valuable clues on how an organism responds to defined stimuli, in this case, how a pathogen colonizes its host. Several robust data analysis methods and pipelines are available that can identify these differentially expressed transcripts. In this chapter we outline the steps and other caveats that are needed to run one such pipeline.

Brain-derived neurotrophic factor predominantly regulates the expression of synapse-related genes in the striatum: Insights from in vitro transcriptomics.

AIM: The striatum, a main component of the basal ganglia, is a critical part of the motor and reward systems of the brain. It consists of GABAergic and cholinergic neurons and receives projections of dopaminergic, glutamatergic, and serotonergic neurons from other brain regions. Brain-derived neurotrophic factor (BDNF) plays multiple roles in the central nervous system, and striatal BDNF has been suggested to be involved in psychiatric and neurodegenerative disorders. However, the transcriptomic impact of BDNF on the striatum remains largely unknown. In the present study, we performed transcriptomic profiling of striatal cells stimulated with BDNF to identify enriched gene sets (GSs) and their novel target genes in vitro. METHODS: We carried out RNA sequencing (RNA-Seq) of messenger RNA extracted from primary dissociated cultures of rat striatum stimulated with BDNF and conducted Generally Applicable Gene-set Enrichment (GAGE) analysis on 10599 genes. Significant differentially expressed genes (DEGs) were determined by differential expression analysis for sequence count data 2 (DESeq2). RESULTS: GAGE analysis identified significantly enriched GSs that included GSs related to regulation and dysregulation of synaptic functions, such as synaptic vesicle cycle and addiction to nicotine and morphine, respectively. It also detected GSs related to various types of synapses, including not only GABAergic and cholinergic synapses but also dopaminergic and glutamatergic synapses. DESeq2 revealed 72 significant DEGs, among which the highest significance was observed in the apolipoprotein L domain containing 1 (Apold1). CONCLUSIONS: The present study indicates that BDNF predominantly regulates the expression of synaptic-function-related genes and that BDNF promotes synaptogenesis in various subtypes of neurons in the developing striatum. Apold1 may represent a unique target gene of BDNF in the striatum.