Exploring Aquaporin Diversity in Elateroidea: Insights From RNA-Seq Data Sets.

Osmoregulation, the physiological regulation of water and ion balance, is vital for the survival of both aquatic and terrestrial insects. In freshwater aquatic insects, such as those within the Lampyridae family, this function is important due to the natural variation of aquatic habitats. Aquaporins play a key role in this process by facilitating the rapid transport of water molecules across cell membranes, maintaining cellular water balance, and adapting to changes in external salinity. In this study, I investigate the genetic diversity and expression levels of aquaporins in Elateroidea, particularly focusing on the Lampyridae family, using transcriptomic data and in silico analyses. The results reveal the diversity of aquaporins and compare gene expression patterns between freshwater aquatic Lampyridae and terrestrial Elateroidea species, such as Lycidae, Phengodidae, and Elateridae. Phylogenetic analyses identify seven distinct clades of aquaporins and uncovered gene duplication events related to the diversification of Elateridae and Lampyridae. A comparative abundance analysis indicated higher aquaporin expression in aquatic fireflies, aligning with the need for efficient osmoregulation in aquatic environments. Additionally, stage-specific expression patterns in Aspisoma lineatum (Neotropical firefly) and Aquatica lateralis (Paleartic firefly) suggest species-specific strategies for coping with osmotic challenges during development. This study provides insights into the evolutionary adaptations of aquaporins in Elateroidea, highlighting their importance in both aquatic and terrestrial insect physiology.

RNA-seq validation: software for selection of reference and variable candidate genes for RT-qPCR.

BACKGROUND: Real-time quantitative PCR (RT-qPCR) is one of the most widely used gene expression analyses for validating RNA-seq data. This technique requires reference genes that are stable and highly expressed, at least across the different biological conditions present in the transcriptome. Reference and variable candidate gene selection is often neglected, leading to misinterpretation of the results. RESULTS: We developed a software named "Gene Selector for Validation" (GSV), which identifies the best reference and variable candidate genes for validation within a quantitative transcriptome. This tool also filters the candidate genes concerning the RT-qPCR assay detection limit. GSV was compared with other software using synthetic datasets and performed better, removing stable low-expression genes from the reference candidate list and creating the variable-expression validation list. GSV software was used on a real case, an Aedes aegypti transcriptome. The top GSV reference candidate genes were selected for RT-qPCR analysis, confirming that eiF1A and eiF3j were the most stable genes tested. The tool also confirmed that traditional mosquito reference genes were less stable in the analyzed samples, highlighting the possibility of inappropriate choices. A meta-transcriptome dataset with more than ninety thousand genes was also processed successfully. CONCLUSION: The GSV tool is a time and cost-effective tool that can be used to select reference and validation candidate genes from the biological conditions present in transcriptomic data.

Application of coincidence index in the discovery of co-expressed metabolic pathways.

Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented-with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeonHalobacterium salinarumand the bacteriumEscherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq.

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (fully glycosylated); c. Equine FSH (eqFSH) (hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10-15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent distinctly different regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.

BACKGROUND: Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data. MAIN BODY: The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis. CONCLUSIONS: According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

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.

Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity.

During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome.

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing.

Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.

Discovery of circulating miRNAs as biomarkers of chronic Chagas heart disease via a small RNA-Seq approach.

Chagas disease affects approximately 7 million people worldwide in Latin America and is a neglected tropical disease. Twenty to thirty percent of chronically infected patients develop chronic Chagas cardiomyopathy decades after acute infection. Identifying biomarkers of Chagas disease progression is necessary to develop better therapeutic and preventive strategies. Circulating microRNAs are increasingly reliable biomarkers of disease and therapeutic targets. To identify new circulating microRNAs for Chagas disease, we performed exploratory small RNA sequencing from the plasma of patients and performed de novo miRNA prediction, identifying potential new microRNAs. The levels of the new microRNAs temporarily named miR-Contig-1519 and miR-Contig-3244 and microRNAs that are biomarkers for nonchagasic cardiomyopathies, such as miR-148a-3p and miR-224-5p, were validated by quantitative reverse transcription. We found a specific circulating microRNA signature defined by low miR-Contig-3244, miR-Contig-1519, and miR-148a-3 levels but high miR-224-5p levels for patients with chronic Chagas disease. Finally, we predicted in silico that these altered circulating microRNAs could affect the expression of target genes involved in different cellular pathways and biological processes, which we will explore in the future.

Integration of ATAC-seq and RNA-seq identifies MX1-mediated AP-1 transcriptional regulation as a therapeutic target for Down syndrome.

BACKGROUND: Growing evidence has suggested that Type I Interferon (I-IFN) plays a potential role in the pathogenesis of Down Syndrome (DS). This work investigates the underlying function of MX1, an effector gene of I-IFN, in DS-associated transcriptional regulation and phenotypic modulation. METHODS: We performed assay for transposase-accessible chromatin with high-throughout sequencing (ATAC-seq) to explore the difference of chromatin accessibility between DS derived amniocytes (DSACs) and controls. We then combined the annotated differentially expressed genes (DEGs) and enriched transcriptional factors (TFs) targeting the promoter region from ATAC-seq results with the DEGs in RNA-seq, to identify key genes and pathways involved in alterations of biological processes and pathways in DS. RESULTS: Binding motif analysis showed a significant increase in chromatin accessibility of genes related to neural cell function, among others, in DSACs, which is primarily regulated by members of the activator protein-1 (AP-1) transcriptional factor family. Further studies indicated that MX Dynamin Like GTPase 1 (MX1), defined as one of the key effector genes of I-IFN, is a critical upstream regulator. Its overexpression induced expression of AP-1 TFs and mediated inflammatory response, thus leading to decreased cellular viability of DS cells. Moreover, treatment with specific AP-1 inhibitor T-5224 improved DS-associated phenotypes in DSACs. CONCLUSIONS: This study demonstrates that MX1-mediated AP-1 activation is partially responsible for cellular dysfunction of DS. T-5224 effectively ameliorated DS-associated phenotypes in DSACs, suggesting it as a potential treatment option for DS patients.

The Genetic Dissection of Nitrogen Use-Related Traits in Flax (Linum usitatissimum L.) at the Seedling Stage through the Integration of Multi-Locus GWAS, RNA-seq and Genomic Selection.

Nitrogen (N), the most important macro-nutrient for plant growth and development, is a key factor that determines crop yield. Yet its excessive applications pollute the environment and are expensive. Hence, studying nitrogen use efficiency (NUE) in crops is fundamental for sustainable agriculture. Here, an association panel consisting of 123 flax accessions was evaluated for 21 NUE-related traits at the seedling stage under optimum N (N+) and N deficiency (N-) treatments to dissect the genetic architecture of NUE-related traits using a multi-omics approach integrating genome-wide association studies (GWAS), transcriptome analysis and genomic selection (GS). Root traits exhibited significant and positive correlations with NUE under N- conditions (r = 0.33 to 0.43, p < 0.05). A total of 359 QTLs were identified, accounting for 0.11% to 23.1% of the phenotypic variation in NUE-related traits. Transcriptomic analysis identified 1034 differentially expressed genes (DEGs) under contrasting N conditions. DEGs involved in N metabolism, root development, amino acid transport and catabolism and others, were found near the QTLs. GS models to predict NUE stress tolerance index (NUE_STI) trait were tested using a random genome-wide SNP dataset and a GWAS-derived QTLs dataset. The latter produced superior prediction accuracy (r = 0.62 to 0.79) compared to the genome-wide SNP marker dataset (r = 0.11) for NUE_STI. Our results provide insights into the QTL architecture of NUE-related traits, identify candidate genes for further studies, and propose genomic breeding tools to achieve superior NUE in flax under low N input.

Transcriptome Profiling after Early Spinal Cord Injury in the Axolotl and Its Comparison with Rodent Animal Models through RNA-Seq Data Analysis.

BACKGROUND: Traumatic spinal cord injury (SCI) is a disabling condition that affects millions of people around the world. Currently, no clinical treatment can restore spinal cord function. Comparison of molecular responses in regenerating to non-regenerating vertebrates can shed light on neural restoration. The axolotl (Ambystoma mexicanum) is an amphibian that regenerates regions of the brain or spinal cord after damage. METHODS: In this study, we compared the transcriptomes after SCI at acute (1-2 days after SCI) and sub-acute (6-7 days post-SCI) periods through the analysis of RNA-seq public datasets from axolotl and non-regenerating rodents. RESULTS: Genes related to wound healing and immune responses were upregulated in axolotls, rats, and mice after SCI; however, the immune-related processes were more prevalent in rodents. In the acute phase of SCI in the axolotl, the molecular pathways and genes associated with early development were upregulated, while processes related to neuronal function were downregulated. Importantly, the downregulation of processes related to sensorial and motor functions was observed only in rodents. This analysis also revealed that genes related to pluripotency, cytoskeleton rearrangement, and transposable elements (e.g., Sox2, Krt5, and LOC100130764) were among the most upregulated in the axolotl. Finally, gene regulatory networks in axolotls revealed the early activation of genes related to neurogenesis, including Atf3/4 and Foxa2. CONCLUSIONS: Immune-related processes are upregulated shortly after SCI in axolotls and rodents; however, a strong immune response is more noticeable in rodents. Genes related to early development and neurogenesis are upregulated beginning in the acute stage of SCI in axolotls, while the loss of motor and sensory functions is detected only in rodents during the sub-acute period of SCI. The approach employed in this study might be useful for designing and establishing regenerative therapies after SCI in mammals, including humans.

Network Analysis of Publicly Available RNA-seq Provides Insights into the Molecular Mechanisms of Plant Defense against Multiple Fungal Pathogens in Arabidopsis thaliana.

Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

Transcriptomic landscape of the interaction between the entomopathogenic fungus Beauveria bassiana and its tolerant host Tribolium castaneum revealed by dual RNA-seq.

Entomopathogenic fungi such as Beauveria bassiana are the only insect pathogens able to start the infection process by penetrating through the host cuticle. However, some insects try to avoid fungal infection by embedding their cuticle with antifungal compounds. This is the case of the red flour beetle Tribolium castaneum, which generates economical loss of great significance in stored product environments worldwide. In this study, T. castaneum adults were fed during different time periods (from 3 to 72 h) on B. bassiana conidia-covered corn kernels. The progression of fungal infection was monitored using the dual RNA-seq technique to reconstruct the temporal transcriptomic profile and to perform gene enrichment analyses in both interacting organisms. After mapping the total reads with the B. bassiana genome, 904 genes were identified during this process. The more expressed fungal genes were related to carbon catabolite repression, cation binding, peptidase inhibition, redox processes, and stress response. Several immune-related genes from Toll, IMD, and JNK pathways, as well as genes related to chitin modification, were found to be differentially expressed in fungus-exposed T. castaneum. This study represents the first dual transcriptomic approach to help understand the interaction between the entomopathogenic fungus B. bassiana and its tolerant host T. castaneum.

Downregulation of Microcephaly-Causing Genes as a Mechanism for ZIKV Teratogenesis: A Meta-analysis of RNA-Seq Studies.

Zika virus (ZIKV) is a neurotropic teratogen that causes congenital Zika syndrome (CZS), characterized by brain and eye anomalies. Impaired gene expression in neural cells after ZIKV infection has been demonstrated; however, there is a gap in the literature of studies comparing whether the differentially expressed genes in such cells are similar and how it can cause CZS. Therefore, the aim of this study was to compare the differential gene expression (DGE) after ZIKV infection in neural cells through a meta-analysis approach. Through the GEO database, studies that evaluated DGE in cells exposed to the Asian lineage of ZIKV versus cells, of the same type, not exposed were searched. From the 119 studies found, five meet our inclusion criteria. Raw data of them were retrieved, pre-processed, and evaluated. The meta-analysis was carried out by comparing seven datasets, from these five studies. We found 125 upregulated genes in neural cells, mainly interferon-stimulated genes, such as IFI6, ISG15, and OAS2, involved in the antiviral response. Furthermore, 167 downregulated, involved with cellular division. Among these downregulated genes, classic microcephaly-causing genes stood out, such as CENPJ, ASPM, CENPE, and CEP152, demonstrating a possible mechanism by which ZIKV impairs brain development and causes CZS.

Novel evaluation approach for molecular signature-based deconvolution methods.

The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by diverse molecular signatures (MS), have been developed to uncover such TIME interplay from RNA-seq tumor biopsies. MS-DM pairs were benchmarked against each other by means of different metrics, such as Pearson's correlation, R2 and RMSE, but these only evaluate the linear association of the estimated proportion related to the expected one, missing the analysis of prediction-dependent bias trends and cell identification accuracy. We present a novel protocol composed of four tests allowing appropriate evaluation of the cell type identification performance and proportion prediction accuracy of molecular signature-deconvolution method pair by means of certainty and confidence cell-type identification scores (F1-score, distance to the optimal point and error rates) as well the Bland-Altman method for error-trend analysis. Our protocol was used to benchmark six state-of-the-art DMs (CIBERSORTx, DCQ, DeconRNASeq, EPIC, MIXTURE and quanTIseq) paired to five murine tissue-specific MSs, revealing a systematic overestimation of the number of different cell types across almost all methods.

Dynamics of annatto pigment synthesis and accumulation in seeds of Bixa orellana L. revealed by integrated chemical, anatomical, and RNA-Seq analyses.

Bixin is a commercially valuable apocarotenoid pigment found in the seed aril of Bixa orellana. The dynamics and regulation of its biosynthesis and accumulation during seed development remain largely unknown. Here, we combined chemical, anatomical, and transcriptomic data to provide stage-specific resolution of the cellular and molecular events occurring during B. orellana seed development. Seeds at five developmental stages (S1-S5) were used for analysis of bixin content and seed anatomy, and three of them (S1, S3, and S4) were selected for Illumina HiSeq sequencing. Bixin accumulated in large quantities in seeds compared with other tissues analyzed, particularly during the S2 stage, peaking at the S4 stage, and then decreasing slightly in the S5 stage. Anatomical analysis revealed that bixin accumulated in the large central vacuole of specialized cells, which were scattered throughout the developing mesotesta at the S2 stage, but enlarged progressively at later stages, until they occupied most of the parenchyma in the aril. A total of 13 million reads were generated and assembled into 73,381 protein-encoding contigs, from which 312 were identified as containing 1-deoxy-D-xylulose-5-phosphate/2-C-methyl-D-erythritol-4-phosphate (DOXP/MEP), carotenoid, and bixin pathways genes. Differential transcriptome expression analysis of these genes revealed that 50 of them were sequentially and differentially expressed through the seed developmental stages analyzed, including seven carotenoid cleavage dioxygenases, eight aldehyde dehydrogenases, and 22 methyltransferases. Taken together, these results show that bixin synthesis and accumulation in seeds of B. orellana are a developmentally regulated process involving the coordinated expression of DOXP/MEP, carotenoid, and bixin biosynthesis genes.

Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq.

PURPOSE: Cancer cells maintain cell growth, division, and survival through altered energy metabolism. However, research on metabolic reprogramming in lung adenocarcinoma (LUAD) is limited METHODS: We downloaded TCGA and GEO sequencing data. Consistent clustering with the ConsensusClusterPlus package was employed to detect the scores for four metabolism-related pathways. The LUAD samples in the TCGA dataset were clustered with ConsensusClusterPlus, and the optimal number of clusters was determined according to the cumulative distribution function (CDF). The cell score for each sample in the TCGA dataset was calculated using the MCPcounter estimate function of the MCPcounter package. RESULTS: We identified two subtypes by scoring the samples based on the 4 metabolism-related pathways and cluster dimensionality reduction. The prognosis of cluster B was obviously poorer than that of cluster A in patients with LUAD. The analysis of single-nucleotide variation (SNV) data showed that the top 15 genes in the four metabolic pathways with the most mutations were TKTL2, PGK2, HK3, EHHADH, GLUD2, PKLR, TKTL1, HADHB, CPT1C, HK1, HK2, PFKL, SLC2A3, PFKFB1, and CPT1A. The IFNγ score of cluster B was significantly higher than that of cluster A. The immune T-cell lytic activity score of cluster B was significantly higher than that of cluster A. We further identified 5 immune cell subsets from single-cell sequencing data. The top 5 marker genes of B cells were IGHM, JCHAIN, IGLC3, IGHA1, and IGKC. The C0 subgroup of monocytes had a higher pentose phosphate pathway (PPP) score than the C6 subgroup. CONCLUSIONS: Metabolism-related subtypes could be potential biomarkers in the prognosis prediction and treatment of LUAD.

RNA-seq reveals that anti-obesity irisin and triiodothyronine (T3) hormones differentially affect the purinergic signaling transcriptomics in differentiated human adipocytes.

The anti-obesity thyroid hormone, triiodothyronine (T3), and irisin, an exercise- and/or cold-induced myokine, stimulate thermogenesis and energy consumption while decreasing lipid accumulation. The involvement of ATP signaling in adipocyte cell function and obesity has attracted increasing attention, but the crosstalk between the purinergic signaling cascade and anti-obesity hormones lacks experimental evidence. In this study, we investigated the effects of T3 and irisin in the transcriptomics of membrane-bound purinoceptors, ectonucleotidase enzymes and nucleoside transporters participating in the purinergic signaling in cultured human adipocytes. The RNA-seq analysis revealed that differentiated adipocytes express high amounts of ADORA1, P2RY11, P2RY12, and P2RX6 gene transcripts, along with abundant levels of transcriptional products encoding to purine metabolizing enzymes (ENPP2, ENPP1, NT5E, ADA and ADK) and transporters (SLC29A1, SCL29A2). The transcriptomics of purinergic signaling markers changed in parallel to the upsurge of "browning" adipocyte markers, like UCP1 and P2RX5, after treatment with T3 and irisin. Upregulation of ADORA1, ADORA2A and P2RX4 gene transcription was obtained with irisin, whereas T3 preferentially upregulated NT5E, SLC29A2 and P2RY11 genes. Irisin was more powerful than T3 towards inhibition of the leptin gene transcription, the SCL29A1 gene encoding for the ENT1 transporter, the E-NPP2 (autotaxin) gene, and genes that encode for two ADP-sensitive P2Y receptors, P2RY1 and P2RY12. These findings indicate that anti-obesity irisin and T3 hormones differentially affect the purinergic signaling transcriptomics, which might point towards new directions for the treatment of obesity and related metabolic disorders that are worth to be pursued in future functional studies.