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.

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.

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.

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.

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.

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.

Transcriptome-wide analysis reveals core sets of transcriptional regulators of sensome and inflammation genes in retinal microglia.

BACKGROUND: Retinal microglial cells (RMCs) play crucial roles in maintaining normal visual functions in a healthy eye. However, the underlying mechanisms of RMCs over-activation manifesting the alterations of sensome profile and inflammation state, which contribute to various retinal neurodegenerative diseases, remain elusive. Here, we aimed to identify the core set of sensome and pro-inflammatory genes and their regulators using transcriptome and data mining approaches. METHODS: We performed paired-end RNA-sequencing in primary microglial cell cultures treated with TNFα/IFNϒ (10 ng/ml for 12 h) and PBS as a control. Gene enrichment analysis and hierarchical clustering for the differentially expressed transcripts highlight functional pathways and network perturbations. We examined overlaps of the mouse microglial gene expression profiles with the data-mined human sensome and pro-inflammatory marker genes. The core sets of sensome and pro-inflammatory genes were selected and predicted for transcription factors (TFs). The identified TFs in RNA-Seq are validated by the quantitative PCR method. RESULTS: TNFα/IFNϒ induced 668 differentially expressed transcripts in retinal microglial cells relative to the control. Furthermore, gene enrichment analysis and the gene expression network revealed activated microglial genes, biological, molecular and inflammatory pathways. The overlapping analysis of the TNFα/IFNϒ-activated microglia genes and the data-mined human gene sets revealed 22 sensome and 61 pro-inflammatory genes. Based on network analysis, we determined 10 genes as the core sets of sensome and pro-inflammatory genes and predicted the top ten TFs that regulate them. The SP110, IRF1, FLI1, SP140 (sensome) and RELB, BATF2, NFKB2, TRAFD1, SP100, NFKB1 (inflammation) are differentially expressed between the TNFα/IFNϒ activated and the non-activated microglia which were validated by quantitative PCR. The outcomes indicate that these transcriptional regulators are highly expressed and may regulate the sensome and inflammatory genes of RMCs and switch them to over-activation. CONCLUSION: Our results comprise a powerful, cross-species functional genomics resource for sensome and inflammation of RMCs, which may provide novel therapeutic approaches to prevent retinal neurodegenerative diseases.

TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository.

BACKGROUND: In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. METHODS: In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. RESULTS: Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. CONCLUSION: We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Age and Sex-Related Changes in Human First-Trimester Placenta Transcriptome and Insights into Adaptative Responses to Increased Oxygen.

Physiological oxygen tension rises dramatically in the placenta between 8 and 14 weeks of gestation. Abnormalities in this period can lead to gestational diseases, whose underlying mechanisms remain unclear. We explored the changes at mRNA level by comparing the transcriptomes of human placentas at 8-10 gestational weeks and 12-14 gestational weeks. A total of 20 samples were collected and divided equally into four groups based on sex and age. Cytotrophoblasts were isolated and sequenced using RNAseq. Key genes were identified using two different methods: DESeq2 and weighted gene co-expression network analysis (WGCNA). We also constructed a local database of known targets of hypoxia-inducible factor (HIF) subunits, alpha and beta, to investigate expression patterns likely linked with changes in oxygen. Patterns of gene enrichment in and among the four groups were analyzed based on annotations of gene ontology (GO) and KEGG pathways. We characterized the similarities and differences between the enrichment patterns revealed by the two methods and the two conditions (age and sex), as well as those associated with HIF targets. Our results provide a broad perspective of the processes that are active in cytotrophoblasts during the rise in physiological oxygen, which should benefit efforts to discover possible drug-targeted genes or pathways in the human placenta.

aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.

BACKGROUND: Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices. RESULTS: We here introduce a new tool for estimating differential expression in noisy real-life data. It employs a novel normalization procedure (qtotal), which takes account of the overall distribution of read counts for data standardization enhancing reliable identification of differential gene expression, especially in case of asymmetrical distributions of up- and downregulated genes. The tool then introduces a polynomial algorithm (aFold) to model the uncertainty of read counts across treatments and genes. We extensively benchmark aFold on a variety of simulated and validated real-life data sets (e.g. ABRF, SEQC and MAQC-II) and show a higher ability to correctly identify differentially expressed genes under most tested conditions. aFold infers fold change values that are comparable across experiments, thereby facilitating data clustering, visualization, and other downstream applications. CONCLUSIONS: We here present a new transcriptomics analysis tool that includes both a data normalization method and a differential expression analysis approach. The new tool is shown to enhance reliable identification of significant differential expression across distinct data distributions. It outcompetes alternative procedures in case of asymmetrical distributions of up- versus down-regulated genes and also the presence of outliers, all common to real data sets.

Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.

BACKGROUND: Current normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data. RESULTS: We observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality. CONCLUSIONS: We show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain.

Transcriptomic dynamics in soybean near-isogenic lines differing in alleles for an aphid resistance gene, following infestation by soybean aphid biotype 2.

BACKGROUND: Genetic resistance of soybean [Glycine max (L.) Merr] against Aphis glycines provides effective management of this invasive pest, though the underlying molecular mechanisms are largely unknown. This study aimed to investigate genome-wide changes in gene expressions of soybean near-isogenic lines (NILs) either with the Rag5 allele for resistance or the rag5 allele for susceptibility to the aphid following infestation with soybean aphid biotype 2. RESULTS: The resistant (R)-NIL responded more rapidly to aphid infestation than the susceptible (S)-NIL, with differential expressions of 2496 genes during first 12 h of infestation (hai), compared to the aphid-free control. Although the majority of the differentially expressed genes (DEGs) in the R-NIL also responded to aphid infestation in S-NIL, overall the response time was longer and/or the magnitude of change was smaller in the S-NIL. In addition, 915 DEGs in R-NIL continued to be regulated at all time points (0, 6, 12, and 48 hai), while only 20 DEGs did so in S-NIL. Enriched gene ontology of the 2496 DEGs involved in plant defense responses including primary metabolite catalysis, oxidative stress reduction, and phytohormone-related signaling. By comparing R- vs. S-NIL, a total of 556 DEGs were identified. Of the 13 genes annotated in a 120-kb window of the Rag5 locus, two genes (Glyma.13 g190200 and Glyma.13 g190600) were differentially expressed (upregulated in S- or R-NIL), and another gene (Glyma.13 g190500) was induced up to 4-fold in the R-NIL at 6 and 12 h following aphid infestation. CONCLUSIONS: This study strengthens our understanding of the defense dynamics in compatible and incompatible interactions of soybean and soybean aphid biotype 2. Several DEGs (e.g., Glyma.13 g190200, Glyma.13 g190500, and Glyma.13 g190600) near the Rag5 locus are strong candidate genes for further investigations.

A robust (re-)annotation approach to generate unbiased mapping references for RNA-seq-based analyses of differential expression across closely related species.

BACKGROUND: RNA-seq based on short reads generated by next generation sequencing technologies has become the main approach to study differential gene expression. Until now, the main applications of this technique have been to study the variation of gene expression in a whole organism, tissue or cell type under different conditions or at different developmental stages. However, RNA-seq also has a great potential to be used in evolutionary studies to investigate gene expression divergence in closely related species. RESULTS: We show that the published genomes and annotations of the three closely related Drosophila species D. melanogaster, D. simulans and D. mauritiana have limitations for inter-specific gene expression studies. This is due to missing gene models in at least one of the genome annotations, unclear orthology assignments and significant gene length differences in the different species. A comprehensive evaluation of four statistical frameworks (DESeq2, DESeq2 with length correction, RPKM-limma and RPKM-voom-limma) shows that none of these methods sufficiently accounts for inter-specific gene length differences, which inevitably results in false positive candidate genes. We propose that published reference genomes should be re-annotated before using them as references for RNA-seq experiments to include as many genes as possible and to account for a potential length bias. We present a straight-forward reciprocal re-annotation pipeline that allows to reliably compare the expression for nearly all genes annotated in D. melanogaster. CONCLUSIONS: We conclude that our reciprocal re-annotation of previously published genomes facilitates the analysis of significantly more genes in an inter-specific differential gene expression study. We propose that the established pipeline can easily be applied to re-annotate other genomes of closely related animals and plants to improve comparative expression analyses.

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.

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.

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.

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.

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.

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.