A comprehensive rat transcriptome built from large scale RNA-seq-based annotation

Integrated analysis reveals NLRC4 as a potential biomarker in sepsis pathogenesis

Sepsis remains a significant global health burden and contributor to mortality, yet the precise molecular mechanisms underlying the immune response are not fully elucidated. To gain insight into this issue, we performed a comprehensive analysis using a variety of techniques including bulk RNA sequencing, single-cell RNA sequencing, and enzyme-linked immunosorbent assay (ELISA). We performed enrichment analysis of differentially expressed genes in sepsis and healthy individuals by utilizing Gene Ontology (GO) analysis and indicated significant enrichment of immune-related response. Following Weighted Gene Co-Expression Network Analysis (WGCNA) and protein-protein interaction analysis (PPI) were used to identify key immune-related hub genes and validated by ELISA to show that NLRC4 is highly expressed in sepsis. Additionally, an analysis of scRNA-seq data from newly diagnosed sepsis, sepsis diagnosis at 6 hours, and healthy samples demonstrates a significant increase in both the expression levels and proportions of NLRC4 in sepsis monocytes and neutrophils. In addition, using pySCENIC we identified upstream transcription factors that regulate NLRC4. Our study provides valuable insights into the identification of NLRC4 in peripheral blood as a potential candidate gene for the diagnosis and treatment of sepsis.

Rat Models of Hormone Receptor-Positive Breast Cancer

Hormone receptor-positive (HR+) breast cancer (BC) is the most common type of breast cancer among women worldwide, accounting for 70-80% of all invasive cases. Patients with HR+ BC are commonly treated with endocrine therapy, but intrinsic or acquired resistance is a frequent problem, making HR+ BC a focal point of intense research. Despite this, the malignancy still lacks adequate in vitro and in vivo models for the study of its initiation and progression as well as response and resistance to endocrine therapy. No mouse models that fully mimic the human disease are available, however rat mammary tumor models pose a promising alternative to overcome this limitation. Compared to mice, rats are more similar to humans in terms of mammary gland architecture, ductal origin of neoplastic lesions and hormone dependency status. Moreover, rats can develop spontaneous or induced mammary tumors that resemble human HR+ BC. To date, six different types of rat models of HR+ BC have been established. These include the spontaneous, carcinogen-induced, transplantation, hormone-induced, radiation-induced and genetically engineered rat mammary tumor models. Each model has distinct advantages, disadvantages and utility for studying HR+ BC. This review provides a comprehensive overview of all published models to date.

Transcriptomic profiling of early synucleinopathy in rats induced with preformed fibrils

Examination of early phases of synucleinopathy when inclusions are present, but long before neurodegeneration occurs, is critical to both understanding disease progression and the development of disease modifying therapies. The rat alpha-synuclein (α-syn) preformed fibril (PFF) model induces synchronized synucleinopathy that recapitulates the pathological features of Parkinson's disease (PD) and can be used to study synucleinopathy progression. In this model, phosphorylated α-syn (pSyn) inclusion-containing neurons and reactive microglia (major histocompatibility complex-II immunoreactive) peak in the substantia nigra pars compacta (SNpc) months before appreciable neurodegeneration. However, it remains unclear which specific genes are driving these phenotypic changes. To identify transcriptional changes associated with early synucleinopathy, we used laser capture microdissection of the SNpc paired with RNA sequencing (RNASeq). Precision collection of the SNpc allowed for the assessment of differential transcript expression in the nigral dopamine neurons and proximal glia. Transcripts upregulated in early synucleinopathy were mainly associated with an immune response, whereas transcripts downregulated were associated with neurotransmission and the dopamine pathway. A subset of 29 transcripts associated with neurotransmission/vesicular release and the dopamine pathway were verified in a separate cohort of males and females to confirm reproducibility. Within this subset, fluorescent in situ hybridization (FISH) was used to localize decreases in the Syt1 and Slc6a3 transcripts to pSyn inclusion-containing neurons. Identification of transcriptional changes in early synucleinopathy provides insight into the molecular mechanisms driving neurodegeneration.

Gene expression variability across cells and species shapes the relationship between renal resident macrophages and infiltrated macrophages

BACKGROUND

Two main subclasses of macrophages are found in almost all solid tissues: embryo-derived resident tissue macrophages and bone marrow-derived infiltrated macrophages. These macrophage subtypes show transcriptional and functional divergence, and the programs that have shaped the evolution of renal macrophages and related signaling pathways remain poorly understood. To clarify these processes, we performed data analysis based on single-cell transcriptional profiling of renal tissue-resident and infiltrated macrophages in human, mouse and rat.

RESULTS

In this study, we (i) characterized the transcriptional divergence among species and (ii) illustrated variability in expression among cells of each subtype and (iii) compared the gene regulation network and (iv) ligand-receptor pairs in human and mouse. Using single-cell transcriptomics, we mapped the promoter architecture during homeostasis.

CONCLUSIONS

Transcriptionally divergent genes, such as the differentially TF-encoding genes expressed in resident and infiltrated macrophages across the three species, vary among cells and include distinct promoter structures. The gene regulatory network in infiltrated macrophages shows comparatively better species-wide consistency than resident macrophages. The conserved transcriptional gene regulatory network in infiltrated macrophages among species is uniquely enriched in pathways related to kinases, and TFs associated with largely conserved regulons among species are uniquely enriched in kinase-related pathways.

Integrative analysis of Iso-Seq and RNA-seq reveals dynamic changes of alternative promoter, alternative splicing and alternative polyadenylation during Angiotensin II-induced senescence in rat primary aortic endothelial cells

In eukaryotes, alternative promoter (AP), alternative splicing (AS), and alternative polyadenylation (APA) are three crucial regulatory mechanisms that modulate message RNA (mRNA) diversity. Although AP, AS and APA are involved in diverse biological processess, whether they have dynamic changes in Angiotensin II (Ang II) induced senescence in rat primary aortic endothelial cells (RAECs), an important cellular model for studying cardiovascular disease, remains unclear. Here we integrated both PacBio single-molecule long-read isoform sequencing (Iso-Seq) and Illumina short-read RNA sequencing (RNA-seq) to analyze the changes of AP, AS and APA in Ang II-induced senescent RAECs. Iso-Seq generated 36,278 isoforms from 10,145 gene loci and 65.81% of these isoforms are novel, which were further cross-validated by public data obtained by other techonologies such as CAGE, PolyA-Seq and 3'READS. APA contributed most to novel isoforms, followed by AS and AP. Further investigation showed that AP, AS and APA could all contribute to the regulation of isoform, but AS has more dynamic changes compared to AP and APA upon Ang II stimulation. Genes undergoing AP, AS and APA in Ang II-treated cells are enriched in various pathways related to aging or senescence, suggesting that these molecular changes are involved in functional alterations during Ang II-induced senescence. Together, the present study largely improved the annotation of rat genome and revealed gene expression changes at isoform level, extending the understanding of the complexity of gene regulation in Ang II-treated RAECs, and also provided novel clues for discovering the regulatory mechanism undelying Ang II caused vascular senescence and diseases.

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

A bioinformatics framework for targeted gene expression assay design: Application to in vitro developmental neurotoxicity screening in a rat model

Brain development involves a series of intricately choreographed neuronal differentiation and maturation steps that are acutely vulnerable to interferences from chemical exposures. Many genes involved in neurodevelopmental processes show evolutionarily conserved expression patterns in mammals and may constitute useful indicators/biomarkers for the evaluation of potential developmental neurotoxicity. Based on these premises, this study developed a bioinformatics framework to guide the design of a gene expression-based in vitro developmental neurotoxicity assay targeting evolutionary conserved genes associated with neuronal differentiation and maturation in rat cerebellar granule cells (CGCs). Rat, mouse and human genes involved in neurodevelopment and presenting one-to-one orthology were selected and orthologous exons within these genes were identified. PCR primer sets were designed within these orthologous exons and their specificity was evaluated in silico. The performance and specificity of rat, mouse and human PCR primer sets were then confirmed experimentally. Finally, RT-qPCR analyses in CGCs exposed in vitro to well-known neurotoxicants (Chlorpyrifos and Chlorpyrifos oxon) uncovered perturbations of expression levels for most of the selected genes. This bioinformatics framework for gene and target sequence selection may facilitate the identification of transcriptional biomarkers for developmental neurotoxicity assays and the comparison of gene expression data across experimental models from different mammalian species.

Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats

Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.

Beyond Genes: Inclusion of Alternative Splicing and Alternative Polyadenylation to Assess the Genetic Architecture of Predisposition to Voluntary Alcohol Consumption in Brain of the HXB/BXH Recombinant Inbred Rat Panel

Post transcriptional modifications of RNA are powerful mechanisms by which eukaryotes expand their genetic diversity. For instance, researchers estimate that most transcripts in humans undergo alternative splicing and alternative polyadenylation. These splicing events produce distinct RNA molecules, which in turn yield distinct protein isoforms and/or influence RNA stability, translation, nuclear export, and RNA/protein cellular localization. Due to their pervasiveness and impact, we hypothesized that alternative splicing and alternative polyadenylation in brain can contribute to a predisposition for voluntary alcohol consumption. Using the HXB/BXH recombinant inbred rat panel (a subset of the Hybrid Rat Diversity Panel), we generated over one terabyte of brain RNA sequencing data (total RNA) and identified novel splice variants (via StringTie) and alternative polyadenylation sites (via aptardi) to determine the transcriptional landscape in the brains of these animals. After establishing an analysis pipeline to ascertain high quality transcripts, we quantitated transcripts and integrated genotype data to identify candidate transcript coexpression networks and individual candidate transcripts associated with predisposition to voluntary alcohol consumption in the two-bottle choice paradigm. For genes that were previously associated with this trait (e.g., Lrap, Ift81, and P2rx4) (Saba et al., Febs. J., 282, 3556–3578, Saba et al., Genes. Brain. Behav., 20, e12698), we were able to distinguish between transcript variants to provide further information about the specific isoforms related to the trait. We also identified additional candidate transcripts associated with the trait of voluntary alcohol consumption (i.e., isoforms of Mapkapk5, Aldh1a7, and Map3k7). Consistent with our previous work, our results indicate that transcripts and networks related to inflammation and the immune system in brain can be linked to voluntary alcohol consumption. Overall, we have established a pipeline for including the quantitation of alternative splicing and alternative polyadenylation variants in the transcriptome in the analysis of the relationship between the transcriptome and complex traits.

Generation and network analysis of an RNA-seq transcriptional atlas for the rat

The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and downloaded 7700 rat RNA-seq datasets from public repositories, downsampled them to a common depth and quantified expression. Data from 585 rat tissues and cells, averaged from each BioProject, can be visualized and queried at http://biogps.org/ratatlas. Gene co-expression network (GCN) analysis revealed clusters of transcripts that were tissue or cell type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species, while some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN analysis on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell type-specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single-cell RNA-seq data.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

The Chinese pine genome and methylome unveil key features of conifer evolution

Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development.

CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

With the exponential growth of sequence information stored over the last decade, including that of de novo assembled contigs from RNA-Seq experiments, quantification of chimeric sequences has become essential when assembling read data. In transcriptomics, de novo assembled chimeras can closely resemble underlying transcripts, but patterns such as those seen between co-evolving sites, or mapped read counts, become obscured. We have created a de Bruijn based de novo assembler for RNA-Seq data that utilizes a classification system to describe the complexity of underlying graphs from which contigs are created. Each contig is labelled with one of three levels, indicating whether or not ambiguous paths exist. A by-product of this is information on the range of complexity of the underlying gene families present. As a demonstration of CStones ability to assemble high-quality contigs, and to label them in this manner, both simulated and real data were used. For simulated data, ten million read pairs were generated from cDNA libraries representing four species, Drosophila melanogaster, Panthera pardus, Rattus norvegicus and Serinus canaria. These were assembled using CStone, Trinity and rnaSPAdes; the latter two being high-quality, well established, de novo assembers. For real data, two RNA-Seq datasets, each consisting of ≈30 million read pairs, representing two adult D. melanogaster whole-body samples were used. The contigs that CStone produced were comparable in quality to those of Trinity and rnaSPAdes in terms of length, sequence identity of aligned regions and the range of cDNA transcripts represented, whilst providing additional information on chimerism. Here we describe the details of CStones assembly and classification process, and propose that similar classification systems can be incorporated into other de novo assembly tools. Within a related side study, we explore the effects that chimera’s within reference sets have on the identification of differentially expression genes. CStone is available at: https://sourceforge.net/projects/cstone/.

Within transcriptome reference sets, non-chimeric sequences are representations of transcribed genes, while artificially generated chimeric ones are mosaics of two or more pieces of DNA incorrectly pieced together. One area where such sets are utilized is in the quantification of gene expression patterns; where RNA-Seq reads are mapped to the sequences within, and subsequent count values reflect expression levels. Artificial chimeras can have a negative impact on count values by erroneously increasing variation in relation to the reads being mapped. Reference sets can be created from de novo assembled contigs, but chimeras can be introduced during the assembly process via the required traversal of graphs, representing gene families, constructed from the RNA-Seq data. Graph complexity determines how likely chimeras will arise. We have created CStone, a de novo assembler that utilizes a classification system to describe such complexity. Contigs created by CStone are labelled in a manner that indicates whether or not they are non-chimeric. This encourages contig dependent results to be presented with increased objectivity by maintaining the context of ambiguity associated with the assembly process. CStone has been tested extensively. Additionally, we have quantified the relationship between chimeras within reference sets and the identification of differentially expressed genes.

Alternative Animal Models of Aging Research

Most research on mechanisms of aging is being conducted in a very limited number of classical model species, i.e., laboratory mouse (Mus musculus), rat (Rattus norvegicus domestica), the common fruit fly (Drosophila melanogaster) and roundworm (Caenorhabditis elegans). The obvious advantages of using these models are access to resources such as strains with known genetic properties, high-quality genomic and transcriptomic sequencing data, versatile experimental manipulation capabilities including well-established genome editing tools, as well as extensive experience in husbandry. However, this approach may introduce interpretation biases due to the specific characteristics of the investigated species, which may lead to inappropriate, or even false, generalization. For example, it is still unclear to what extent knowledge of aging mechanisms gained in short-lived model organisms is transferable to long-lived species such as humans. In addition, other specific adaptations favoring a long and healthy life from the immense evolutionary toolbox may be entirely missed. In this review, we summarize the specific characteristics of emerging animal models that have attracted the attention of gerontologists, we provide an overview of the available data and resources related to these models, and we summarize important insights gained from them in recent years. The models presented include short-lived ones such as killifish (Nothobranchius furzeri), long-lived ones such as primates (Callithrix jacchus, Cebus imitator, Macaca mulatta), bathyergid mole-rats (Heterocephalus glaber, Fukomys spp.), bats (Myotis spp.), birds, olms (Proteus anguinus), turtles, greenland sharks, bivalves (Arctica islandica), and potentially non-aging ones such as Hydra and Planaria.