BACKGROUND:The ability of animals and their microbiomes to adapt to starvation and then restore homeostasis after refeeding is fundamental to their continued survival and symbiosis. The intestine is the primary site of nutrient absorption and microbiome interaction, however our understanding of intestinal adaptations to starvation and refeeding remains limited. Here we used RNA sequencing and 16S rRNA gene sequencing to uncover changes in the intestinal transcriptome and microbiome of zebrafish subjected to long-term starvation and refeeding compared to continuously fed controls. RESULTS:Starvation over 21 days led to increased diversity and altered composition in the intestinal microbiome compared to fed controls, including relative increases in Vibrio and reductions in Plesiomonas bacteria. Starvation also led to significant alterations in host gene expression in the intestine, with distinct pathways affected at early and late stages of starvation. This included increases in the expression of ribosome biogenesis genes early in starvation, followed by decreased expression of genes involved in antiviral immunity and lipid transport at later stages. These effects of starvation on the host transcriptome and microbiome were almost completely restored within 3 days after refeeding. Comparison with published datasets identified host genes responsive to starvation as well as high-fat feeding or microbiome colonization, and predicted host transcription factors that may be involved in starvation response. CONCLUSIONS:Long-term starvation induces progressive changes in microbiome composition and host gene expression in the zebrafish intestine, and these changes are rapidly reversed after refeeding. Our identification of bacterial taxa, host genes and host pathways involved in this response provides a framework for future investigation of the physiological and ecological mechanisms underlying intestinal adaptations to food restriction.

Compositional oscillations of the gut microbiome are essential for normal peripheral circadian rhythms, both of which are disrupted in diet-induced obesity (DIO). Although time-restricted feeding (TRF) maintains circadian synchrony and protects against DIO, its impact on the dynamics of the cecal gut microbiome is modest. Thus, other regions of the gut, particularly the ileum, the nexus for incretin and bile acid signaling, may play an important role in entraining peripheral circadian rhythms. We demonstrate the effect of diet and feeding rhythms on the ileal microbiome composition and transcriptome in mice. The dynamic rhythms of ileal microbiome composition and transcriptome are dampened in DIO. TRF partially restores diurnal rhythms of the ileal microbiome and transcriptome, increases GLP-1 release, and alters the ileal bile acid pool and farnesoid X receptor (FXR) signaling, which could explain how TRF exerts its metabolic benefits. Finally, we provide a web resource for exploration of ileal microbiome and transcriptome circadian data.

The human genome sequence has profoundly altered our understanding of biology, human diversity, and disease. The path from the first draft sequence to our nascent era of personal genomes and genomic medicine has been made possible only because of the extraordinary advancements in DNA sequencing technologies over the past 10 years. Here, we discuss commonly used high-throughput sequencing platforms, the growing array of sequencing assays developed around them, as well as the challenges facing current sequencing platforms and their clinical application.

Atherosclerotic cardiovascular disease (ASCVD) proceeds through a series of stages: initiation, progression (or regression), and complications. By integrating known biology regarding molecular signatures of each stage with recent advances in high-dimensional molecular data acquisition platforms (to assay the genome, epigenome, transcriptome, proteome, metabolome, and gut microbiome), snapshots of each phase of atherosclerotic cardiovascular disease development can be captured. In this review, we will summarize emerging approaches for assessment of atherosclerotic cardiovascular disease risk in humans using peripheral blood molecular signatures and molecular imaging approaches. We will then discuss the potential (and challenges) for these snapshots to be integrated into a personalized movie providing dynamic readouts of an individual's atherosclerotic cardiovascular disease risk status throughout the life course.

INTRODUCTION:Pancreatic cancer is a public health problem because its mortality rate is close to its incidence rate. If it were possible to detect this cancer before the onset of symptoms, 5-year survival could reach 75%. Numerous studies have attempted to accelerate the diagnosis to improve survival. Saliva presents interesting characteristics as a fluid for screening and diagnosis. Its many components provide a promising source of constitutive biomarkers with a specific signature of the disease. The aim of this work was to determine the interest of studying the metabolome, the transcriptome and the microbiome of saliva in screening for pancreatic cancer. MATERIALS AND METHODS:A review of the literature was conducted using the PubMed search engine. The last search was conducted in July 2017. RESULTS:Nine references, all original studies, published between 2010 and 2017 were included. DISCUSSION:Different combinations of metabolites, RNA and bacteria were found. Analysis of the saliva transcriptome and metabolome seems to be the most promising avenue. CONCLUSION:The identification of an early salivary signature of pancreatic cancer is still in its infancy and the results obtained here must be confirmed in larger prospective multicentre studies.

BACKGROUND:Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD. METHODS:OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS). RESULTS:A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization. CONCLUSIONS:This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

Since the completion of the human genome project in 2003, extraordinary progress has been made in genome sequencing technologies, which has led to a decreased cost per megabase and an increase in the number and diversity of sequenced genomes. An astonishing complexity of genome architecture has been revealed, bringing these sequencing technologies to even greater advancements. Some approaches maximize the number of bases sequenced in the least amount of time, generating a wealth of data that can be used to understand increasingly complex phenotypes. Alternatively, other approaches now aim to sequence longer contiguous pieces of DNA, which are essential for resolving structurally complex regions. These and other strategies are providing researchers and clinicians a variety of tools to probe genomes in greater depth, leading to an enhanced understanding of how genome sequence variants underlie phenotype and disease.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.

With the development of next-generation sequencing (NGS) technologies it became possible to simultaneously analyze millions of variants. Despite the quality improvement, it is generally still required to confirm the variants before reporting. However, in recent years the dominant idea is that one could define the quality thresholds for "high quality" variants which do not require orthogonal validation. Despite that, no works to date report the concordance between variants from whole genome sequencing and their gold-standard Sanger validation. In this study we analyzed the concordance for 1756 WGS variants in order to establish the appropriate thresholds for high-quality variants filtering. Resulting thresholds allowed us to drastically reduce the number of variants which require validation, to 4.8% and 1.2% of the initial set for caller-agnostic (DP, AF) and caller-dependent (QUAL) thresholds, respectively.

N6-methyladenosine (mA), the most abundant internal mRNA modification in eukaryotes, plays a vital role in regulating adipogenesis. However, its underlying mechanism remains largely unknown. Our previous study found that gene has mA modification in both muscle and fat tissue. In this study, we interfered with and genes After we cultured rabbit preadipocytes respectively. Oil red O staining and triglyceride assay were used to detect adipocyte differentiation. RT-qPCR was used to detect gene expression level and MeRIP-qPCR was used to detect the mA modification level of gene. The results showed that promoted the differentiation of adipocytes. At the same time, up regulated the expression of gene and down regulated the mA modification level of gene. Finally, we found that inhibited adipocyte differentiation. Together, we showed that promoted adipocyte differentiation by regulating gene through mA modification.

The goal of this study was to analyze the reproductive performance of does, growth of their kits, and chemical composition of their milk over nine consecutive parities in order to indicate the boundary of female reproductive profitability. The novelty of this study results from the combinations of three factors: extensive reproductive rhythm, commercial farming conditions, and a period of nine consecutive parities, showing the actual lifespan of a rabbit doe on commercial farms. The data was collected on 60 Hycole females kept at a commercial rabbit farm. Throughout the study, 32 does were excluded due to different reasons (e.g., excluded by means of selection-43.8% and mortalities-25.0%). The does were first inseminated at 28 weeks of age. Following artificial inseminations were conducted 14-15 days after each parturition. All kits were weaned at the age of 35 days. The following characteristics were analysed: body weight of rabbit does at artificial insemination, milk production per lactation, litter size, litter weight, average kit weight, and milk chemical composition. Rabbit does had a significant decrease in kindling rate between the eighth and the ninth parity (by 10.0 percentage points; = 0.039). The litter size at weaning in the ninth parity was significantly lower to litters weaned at other analysed parities. The amount of milk produced per lactation was affected by the parity order (6.31-6.76 kg; = 0.042). The litter weights on day 21 and 35 were the lowest at ninth parity. The content of total solids (TS), solids-not-fat, and fat was affected by the parity order on both analysed lactation days. The content of TS and fat in rabbit milk was characterized with a decreasing trend over the analysed period, on both lactation days. The results clearly indicate that rabbit does under extensive reproductive cycles characterize with a very good reproductive performance and can be successfully used for reproduction even up to the eighth parity. However, further research is needed if keeping them longer will not be profitable.

Fur is an important economic trait in rabbits. The identification of genes that influence fur development and knowledge regarding the actions of these genes provides useful tools for improving fur quality. However, the mechanism of fur development is unclear. To obtain candidate genes related to fur development, the transcriptomes of tissues from backs and bellies of Chinchilla rex rabbits were compared. Of the genes analyzed, 336 showed altered expression in the two groups (285 upregulated and 51 downregulated, P ≤ 0.05, fold-change ≥2 or ≤0.5). Using GO and KEGG to obtain gene classes that were differentially enriched, we found several genes to be involved in many important biological processes. In addition, we identified several signaling pathways involved in fur development, including the Wnt and MAPK signaling pathways, revealing mechanisms of skin and hair follicle development, and epidermal cell and keratinocytes differentiation. The obtained rabbit transcriptome and differentially expressed gene profiling data provided comprehensive gene expression information for SFRP2, FRZB, CACNG1, SLC25A4, and SLC16A3. To validate the RNA-seq data, the expression levels of eight differentially expressed genes involved in fur development were confirmed by qRT-PCR. The results of rabbit transcriptomic profiling provide a basis for understanding the molecular mechanisms of fur development.

Mammalian spermatogenesis is a highly complicated and intricately organized process involving spermatogonia propagation (mitosis) and meiotic differentiation into mature sperm cells (spermiogenesis). In pigs, spermatogonia development and the role of somatic cells in spermatogenesis were previously investigated in detail. However, the characterization of key molecules fundamental to pig spermiogenesis remains less explored. Here we compared spermatogenesis between humans and pigs, focusing on spermiogenesis, by integrative testicular single-cell RNA sequencing (scRNA-seq) analysis. Human and pig testicular cells were clustered into 26 different groups, with cell-type-specific markers and signalling pathways. For spermiogenesis, pseudo-time analysis classified the lineage differentiation routes for round, elongated spermatids and spermatozoa. Moreover, markers and molecular pathways specific to each type of spermatids were examined for humans and pigs, respectively. Furthermore, high-dimensional weighted gene co-expression network analysis (hdWGCNA) identified gene modules specific for each type of human and pig spermatids. Hub genes (pig: SNRPD2.1 related to alternative splicing; human: CATSPERZ, Ca[2+] ion channel) potentially involved in spermiogenesis were also revealed. Taken together, our integrative analysis found that human and pig spermiogeneses involve specific genes and molecular pathways and provided resources and insights for further functional investigation on spermatid maturation and male reproductive ability.

Meishan pigs are a well-known indigenous pig breed in China characterized by a high fertility. Notably, the number of endometrial grands is significantly higher in Meishan pigs than Duroc pigs. The characteristics of the endometrial tissue are related to litter size. Therefore, we used the assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA-sequencing (RNA-seq) to analyze the mechanisms underlying the differences in fecundity between the breeds. We detected the key transcription factors, including Double homeobox (Dux), Ladybird-like homeobox gene 2 (LBX2), and LIM homeobox 8 (Lhx8), with potentially pivotal roles in the regulation of the genes related to endometrial development. We identified the differentially expressed genes between the breeds, including , , , , , , , , , , and , with roles in epithelial cell differentiation, fertility, and ovulation. Interestingly, , , and , which were upregulated in the Meishan pigs in the RNA-seq analysis, were identified again by the integration of the ATAC-seq and RNA-seq data. Moreover, we identified genes in the cancer or immune pathways, FoxO signaling, Wnt signaling, and phospholipase D signaling pathways. These ATAC-seq and RNA-seq analyses revealed the accessible chromatin and potential mechanisms underlying the differences in the endometrial tissues between the two types of pigs.

The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the next-generation sequencing of RNA (RNA-seq) has several advantages over other techniques, such as the capability of detecting all the transcripts in one assay over RT-qPCR, such as its higher accuracy and broader dynamic range over microarrays or the ability to detect novel transcripts, including non-coding RNA molecules, at nucleotide-level resolution over both techniques. Despite these advantages, many microbiology laboratories have not yet applied RNA-seq analyses to their investigations. The high cost of the equipment for next-generation sequencing is no longer an issue since this intermediate part of the analysis can be provided by commercial or central services. Here, we detail a protocol for the first part of the analysis, the RNA extraction and an introductory protocol to the bioinformatics analysis of the sequencing data to generate the differential expression results.

RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences.Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis-the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods and tools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.

The assembly of primordial follicles in mammals represents one of the most critical processes in ovarian biology. It directly affects the number of oocytes available to a female throughout her reproductive life. Premature depletion of primordial follicles contributes to the ovarian pathology primary ovarian insufficiency (POI). To delineate the developmental trajectory and regulatory mechanisms of oocytes during the process, we performed RNA-seq on single germ cells from newborn (P0.5) ovaries. Three cell clusters were classified which corresponded to three cell states (germ cell cyst, cyst breakdown, and follicle) in the newborn ovary. By Monocle analysis, a uniform trajectory of oocyte development was built with a series of genes showed dynamic changes along the pseudo-timeline. Gene Ontology term enrichment revealed a significant decrease in meiosis-related genes and a dramatic increase in oocyte-specific genes which marked the transition from a germ cell to a functional oocyte. We then established a network of regulons by using single-cell regulatory network inference and clustering (SCENIC) algorithm and identified possible candidate transcription factors that may maintain transcription programs during follicle formation. Following functional studies further revealed the differential regulation of the identified regulon Id2 and its family member Id1, on the establishment of primordial follicle pool by using siRNA knockdown and genetic modified mouse models. In summary, our study systematically reconstructed molecular cascades in oocytes and identified a series of genes and molecular pathways in follicle formation and development.