Transcriptional atlas analysis from multiple tissues reveals the expression specificity patterns in beef cattle.

BACKGROUND: A comprehensive analysis of gene expression profiling across tissues can provide necessary information for an in-depth understanding of their biological functions. We performed a large-scale gene expression analysis and generated a high-resolution atlas of the transcriptome in beef cattle. RESULTS: Our transcriptome atlas was generated from 135 bovine tissues in adult beef cattle, covering 51 tissue types of major organ systems (e.g., muscular system, digestive system, immune system, reproductive system). Approximately 94.76% of sequencing reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We detected a total of 60,488 transcripts, and 32% of them were not reported before. We identified 2654 housekeeping genes (HKGs) and 477 tissue-specific genes (TSGs) across tissues. Using weighted gene co-expression network analysis, we obtained 24 modules with 237 hub genes (HUBGs). Functional enrichment analysis showed that HKGs mainly maintain the basic biological activities of cells, while TSGs were involved in tissue differentiation and specific physiological processes. HKGs in bovine tissues were more conserved in terms of expression pattern as compared to TSGs and HUBGs among multiple species. Finally, we obtained a subset of tissue-specific differentially expressed genes (DEGs) between beef and dairy cattle and several functional pathways, which may be involved in production and health traits. CONCLUSIONS: We generated a large-scale gene expression atlas across the major tissues in beef cattle, providing valuable information for enhancing genome assembly and annotation. HKGs, TSGs, and HUBGs further contribute to better understanding the biology and evolution of multiple tissues in cattle. DEGs between beef and dairy cattle also fill in the knowledge gaps about differential transcriptome regulation of bovine tissues underlying economically important traits.

Ectopic expression of a combination of 5 genes detects high risk forms of T-cell acute lymphoblastic leukemia.

BACKGROUND: T cell acute lymphoblastic leukemia (T-ALL) defines a group of hematological malignancies with heterogeneous aggressiveness and highly variable outcome, making therapeutic decisions a challenging task. We tried to discover new predictive model for T-ALL before treatment by using a specific pipeline designed to discover aberrantly active gene. RESULTS: The expression of 18 genes was significantly associated with shorter survival, including ACTRT2, GOT1L1, SPATA45, TOPAZ1 and ZPBP (5-GEC), which were used as a basis to design a prognostic classifier for T-ALL patients. The molecular characterization of the 5-GEC positive T-ALL unveiled specific characteristics inherent to the most aggressive T leukemic cells, including a drastic shut-down of genes located on the mitochondrial genome and an upregulation of histone genes, the latter characterizing high risk forms in adult patients. These cases fail to respond to the induction treatment, since 5-GEC either predicted positive minimal residual disease (MRD) or a short-term relapse in MRD negative patients. CONCLUSION: Overall, our investigations led to the discovery of a homogenous group of leukemic cells with profound alterations of their biology. It also resulted in an accurate predictive tool that could significantly improve the management of T-ALL patients.

Systematic analysis of 1298 RNA-Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas.

Soybean (Glycine max [L.] Merr.) is a major crop in animal feed and human nutrition, mainly for its rich protein and oil contents. The remarkable rise in soybean transcriptome studies over the past 5 years generated an enormous amount of RNA-seq data, encompassing various tissues, developmental conditions and genotypes. In this study, we have collected data from 1298 publicly available soybean transcriptome samples, processed the raw sequencing reads and mapped them to the soybean reference genome in a systematic fashion. We found that 94% of the annotated genes (52 737/56 044) had detectable expression in at least one sample. Unsupervised clustering revealed three major groups, comprising samples from aerial, underground and seed/seed-related parts. We found 452 genes with uniform and constant expression levels, supporting their roles as housekeeping genes. On the other hand, 1349 genes showed heavily biased expression patterns towards particular tissues. A transcript-level analysis revealed that 95% (70 963 of 74 490) of the assembled transcripts have intron chains exactly matching those from known transcripts, whereas 3256 assembled transcripts represent potentially novel splicing isoforms. The dataset compiled here constitute a new resource for the community, which can be downloaded or accessed through a user-friendly web interface at http://venanciogroup.uenf.br/resources/. This comprehensive transcriptome atlas will likely accelerate research on soybean genetics and genomics.

[Liver Tissue-specific Genes IGFALS,CYP3A4,SLC22A1 and CYP2E1 May be Associated with Poor Prognosis of Liver Cancer].

Objective To explore the function and mechanism of related genes in the occurrence and development of liver cancer, and the possibility of key genes as potential biomarkers and prognostic indicators for the treatment of liver cancer.Methods We selected 4 datasets(GSE57957, GSE121248, GSE36376 and GSE14520)from the GEO database.With P<0.05 and |log2FC|>1 as the thresholds, we used GEO2R and Venn Diagram Software to filter out the common significant differentially expressed genes(DEGs).Cytoscape 3.6.1 plug-ins CytoHubba and molecular complex detection(MCODE)were used to screen out the hub genes and modules of DEGs.In addition, survival analysis of DEGs was performed by gene expression profiling(GEPIA), and Human Protein Atlas(HPA)were used to examine the protein expression levels of key genes in normal liver tissue and liver cancer tissue.Results There were 45 obviously up-regulated genes and 132 down-regulated genes, and MCODE identified 13 clusters.The cluster 1 and cluster 2 with higher scores included 16 genes and 13 genes, respectively.Among the 32 significant DEGs, IGFALS, HGFAC, CYP3A4, SLC22A1, TAT and CYP2E1 demonstrated significantly higher expression levels in liver tissue than in other organs.The HPA immunohistochemistry(IHC)data showed that the expression levels of IGFALS, CYP3A4, SLC22A1 and CYP2E1 in liver cancer tissue were significantly down-regulated and related to the low overall survival rate of patients.Conclusion The liver tissue-specific genes IGFALS, CYP3A4, SLC22A1 and CYP2E1 are under-expressed in liver cancer and associated with poor prognosis, which may be potential biomarkers and prognostic indicators for liver cancer.

Non-Coding Transcriptome Maps across Twenty Tissues of the Korean Black Chicken, Yeonsan Ogye.

Yeonsan Ogye is a rare Korean domestic chicken breed whose entire body, including feathers and skin, has a unique black coloring. Although some protein-coding genes related to this unique feature have been examined, non-coding elements have not been widely investigated. Thus, we evaluated coding and non-coding transcriptome expression and identified long non-coding RNAs functionally linked to protein-coding genes in Ogye. High-throughput RNA sequencing and DNA methylation sequencing were performed to profile the expression of 14,264 Ogye protein-coding and 6900 long non-coding RNA (lncRNA) genes and detect DNA methylation in 20 different tissues of an individual Ogye. Approximately 75% of Ogye lncRNAs and 45% of protein-coding genes showed tissue-specific expression. For some genes, tissue-specific expression levels were inversely correlated with DNA methylation levels in their promoters. Approximately 39% of tissue-specific lncRNAs displayed functional associations with proximal or distal protein-coding genes. Heat shock transcription factor 2-associated lncRNAs appeared to be functionally linked to protein-coding genes specifically expressed in black skin tissues, more syntenically conserved in mammals, and differentially expressed in black relative to in white tissues. Pending experimental validation, our findings increase the understanding of how the non-coding genome regulates unique phenotypes and can be used for future genomic breeding of chickens.

Transposable elements contribute to tissue-specific gene regulation in humans.

BACKGROUND: Transposable elements (TEs) contribute to approximately half of the human genome, and along with many other functions, they have been known to play a role in gene regulation in the genome. With TEs' active/repressed states varying across tissue and cell types, they have the potential to regulate gene expression in a tissue-specific manner. OBJECTIVE AND METHODS: To provide a systematic analysis of TEs' contribution in tissue-specific gene regulation, we examined the regulatory elements and genes in association with TE-derived regulatory sequences in 14 human cell lines belonging to 10 different tissue types using the functional genomics data from the ENCODE project. Specifically, we separately analyzed regulatory regions identified by three different approaches (DNase hypersensitive sites (DHS), histone active sites (HA), and histone repressive sites (HR)). RESULTS: These regulatory regions showed to be distinct from each other by sharing less than 2.5% among all three types and more than 95% showed to be cell line-specific. Despite a lower total TE content overall than the genome average, each regulatory sequence type showed enrichment for one or two specific TE type(s): DHS for long terminal repeats (LTRs) and DNA transposons, HA for short interspersed nucleotide elements (SINEs), and HR for LTRs. In contrast, SINE was shown to be overrepresented in all three types of regulatory sequences located in gene-neighboring regions. TE-regulated genes were mostly shown to have cell line specific pattern, and tissue-specific genes (TSGs) showed higher usage of TE regulatory sequences in the tissue of their expression. While TEs in the regulatory sequences showed to be older than their genome-wide counterparts, younger TEs were shown to be more likely used in cell line specific regulatory sequences. CONCLUSIONS: Collectively, our study provided further evidence enforcing an important contribution of TEs to tissue-specific gene regulation in humans.

Computational recognition of regulator genes and signature for ferroptosis with implications on immunological properties and clinical management of atopic dermatitis.

Background: Atopic dermatitis (AD) is a common chronic dermatitis of autoimmune origin that considerably affects the quality of life of patients. Ferroptosis, a newly regulated form of cell death, is essential for inflammation-related damage-associated molecular patterns (DAMPs). In this study, we aimed to identify ferroptosis regulators relevant to AD pathogenesis and reveal the mechanisms by which ferroptosis regulates the pathogenesis of AD. Methods: We analyzed the GEO AD cohorts (GSE16161, GSE32924, GSE107361, and GSE120721), identifying AD-related differentially expressed genes (DEGs) using edgeR. Co-expression and STRING database analyses were used to elucidate the interactions between DEGs and ferroptosis markers. Through functional enrichment analysis, we defined potential biological functions within the protein-protein interaction (PPI) network and developed FerrSig using LASSO regression. The utility of FerrSig in guiding the clinical management of AD was evaluated using the GSE32473 cohort. Subsequently, our in silico findings were confirmed, and mechanistic insights were expanded through both in vitro and in vivo studies, validating the relevance of FerrSig. Results: In the GEO AD cohort, 278 DEGs were identified, including seven ferroptosis signature genes. Co-expression analysis and STRING database review revealed a 63-node PPI network linked to cell cycle and pro-inflammatory pathways. Four ferroptosis genes (ALOXE3, FABP4, MAP3K14, and EGR1) were selected to create FerrSig, which was significantly downregulated in samples collected from patients with AD. In addition, immune-related signaling pathways were significantly differentially enriched between the stratifications of samples collected from patients with AD with high and low ferritin levels, whereas in the GSE32473 cohort, FerrSig was significantly increased in cohorts effectively treated with pimecrolimus or betamethasone. Finally, in vitro and in vivo models showed a notable FerrSig decrease in patients with AD versus healthy control. Treatment with betamethasone and tacrolimus restored FerrSig, and the magnitude of the increase in FerrSig was higher in samples collected from patients with AD with better efficacy assessments. In addition, FerrSig was significantly positively correlated with the ferroptosis inhibitors GPX4 and SLC7A11 and negatively correlated with reactive oxygen species (ROS) levels and p-STAT3/STAT3. This implies that the FerrSig signature genes may regulate ferroptosis through the JAK/STAT3 signaling pathway. Conclusion: Our study further explored the pathogenesis of AD, and FerrSig could serve as a potential biomarker for identifying AD morbidity risks and determining treatment efficacy.

Comprehensive analyses of 435 goat transcriptomes provides insight into male reproduction.

A systematic analysis of genes related to reproduction is crucial for obtaining a comprehensive understanding of the molecular mechanisms that underlie male reproductive traits in mammals. Here, we utilized 435 goat transcriptome datasets to unveil the testicular tissue-specific genes (TSGs), allele-specific expression (ASE) genes and their uncharacterized transcriptional features related to male goat reproduction. Results showed a total of 1790 TSGs were identified in goat testis, which was the most among all tissues. GO enrichment analyses suggested that testicular TSGs were mainly involved in spermatogenesis, multicellular organism development, spermatid development, and flagellated sperm motility. Subsequently, a total of 95 highly conserved TSGs (HCTSGs), 508 middle conserved TSGs (MCTSGs) and 42 no conserved TSGs (NCTSGs) were identified in goat testis. GO enrichment analyses suggested that the HCTSGs and MCTSGs has a more important association with male reproduction than NCTSGs. Additionally, we identified 644 ASE genes, including 88 tissue-specific ASE (TS-ASE) genes (e.g., FSIP2, TDRD9). GO enrichment analyses indicated that both ASE genes and TS-ASE genes were associated with goat male reproduction. Overall, this study revealed an extensive gene set involved in the regulation of male goat reproduction and their dynamic transcription patterns. Data reported here provide valuable insights for a further improvement of the economic benefits of goats as well as future treatments for male infertility.

Tissue-Targeted Transcriptomics Reveals SEMA3D Control of Hypoglossal Nerve Projection to Mouse Tongue Primordia.

The mouse hypoglossal nerve originates in the occipital motor nuclei at embryonic day (E)10.5 and projects a long distance, reaching the vicinity of the tongue primordia, the lateral lingual swellings, at E11.5. However, the details of how the hypoglossal nerve correctly projects to the primordia are poorly understood. To investigate the molecular basis of hypoglossal nerve elongation, we used a novel transcriptomic approach using the ROKU method. The ROKU algorithm identified 3825 genes specific for lateral lingual swellings at E11.5, of which 34 genes were predicted to be involved in axon guidance. Ingenuity Pathway Analysis-assisted enrichment revealed activation of the semaphorin signaling pathway during tongue development, and quantitative PCR showed that the expressions of Sema3d and Nrp1 in this pathway peaked at E11.5. Immunohistochemistry detected NRP1 in the hypoglossal nerve and SEMA3D as tiny granules in the extracellular space beneath the epithelium of the tongue primordia and in lateral and anterior regions of the mandibular arch. Fewer SEMA3D granules were localized around hypoglossal nerve axons and in the space where they elongated. In developing tongue primordia, tissue-specific regulation of SEMA3D might control the route of hypoglossal nerve projection via its repulsive effect on NRP1.

Expression patterns of housekeeping genes and tissue-specific genes in black goats across multiple tissues.

Black goats are a significant meat breed in southern China. To investigate the expression patterns and biological functions of genes in various tissues of black goats, we analyzed housekeeping genes (HKGs), tissue-specific genes (TSGs), and hub genes (HUBGs) across 23 tissues. Additionally, we analyzed HKGs in 13 tissues under different feeding conditions. We identified 2968 HKGs, including six important ones. Interestingly, HKGs in grazing black goats demonstrated higher and more stable expression levels. We discovered a total of 9912 TSGs, including 134 newly identified ones. The number of TSGs for mRNA and lncRNA were nearly equal, with 127 mRNA TSGs expressed solely in one tissue. Additionally, the predicted functions of tissue-specific long non-coding RNAs (lncRNAs) targeting mRNAs corresponded with the physiological functions of the tissues.Weighted gene co-expression network analysis (WGCNA) constructed 30 modules, where the dark grey module consists almost entirely of HKGs, and TSGs are located in modules most correlated with their respective tissues. Additionally, we identified 289 HUBGs, which are involved in regulating the physiological functions of their respective tissues. Overall, these identified HKGs, TSGs, and HUBGs provide a foundation for studying the molecular mechanisms affecting the genetic and biological processes of complex traits in black goats.

The tissue-specific expression of silkworm cuticle protein gene ASSCP2 is mediated by the Sox-2 transcription factor.

The silk gland of silkworm is a unique organ in which silk proteins are synthesized, secreted, and transformed into fibers. The anterior silk gland (ASG) is located at the end of the silk gland, and is thought to be involved in silk fibrosis. In our previous study, a cuticle protein, ASSCP2, was identified. This protein is specifically and highly expressed in the ASG. In this work, the transcriptional regulation mechanism of ASSCP2 gene was studied by a transgenic route. The ASSCP2 promoter was analyzed, truncated sequentially, and used to initiate the expression of EGFP gene in silkworm larvae. After egg injection, seven transgenic silkworm lines were isolated. Molecular analysis revealed that the green fluorescent signal could not be detected when the promoter was truncated to -257 bp, suggesting that the -357 to -257 sequence is the key region responsible for the transcriptional regulation of the ASSCP2 gene. Furthermore, an ASG specific transcription factor Sox-2 was identified. EMSA assays showed that Sox-2 binds with the -357 to -257 sequence, and thus regulates the tissue-specific expression of ASSCP2. This study on the transcriptional regulation of ASSCP2 gene provides theoretical and experimental basis for further studies of the regulatory mechanism of tissue-specific genes.

Atypical Modes of CTCF Binding Facilitate Tissue-Specific and Neuronal Activity-Dependent Gene Expression States.

Multivalent binding of CTCF to variable DNA sequences is thought to underlie its ability to mediate diverse cellular functions. CTCF typically binds a 20 base-pair consensus DNA sequence, but the full diversity of CTCF binding sites (CBS) within the genome has not been interrogated. We assessed CTCF occupancy in cultured cortical neurons and observed surprisingly that ~ 22% of CBS lack the consensus CTCF motif. We report here that sequence diversity at most of these atypical CBS involves degeneracy at specific nucleotide positions within the consensus CTCF motif, which likely affect the binding of CTCF zinc fingers 6 and 7. This mode of atypical CTCF binding defines most CBS at gene promoters, as well as CBS that are dynamically altered during neural differentiation and following neuronal stimulation, revealing how atypical CTCF binding could influence gene activity. Dynamic CBS are distributed both within and outside loop anchors and TAD boundaries, suggesting both looping-dependent and independent roles for CTCF. Finally, we describe a second mode of atypical CTCF binding to DNA sequences that are completely unrelated to the consensus CTCF motif, which are enriched within the bodies of tissue-specific genes. These tissue-specific atypical CBS are also enriched in H3K27ac, which marks cis-regulatory elements within chromatin, including enhancers. Overall, these results indicate how atypical CBS could dynamically regulate gene activity patterns during differentiation, development, and in response to environmental cues.

Validation and Functional Analysis of Reference and Tissue-Specific Genes in Adipose Tissue of Freshwater Drum, Aplodinotus grunniens, under Starvation and Hypothermia Stress.

Adipose tissue is critical to the growth, development, and physiological health of animals. Reference genes play an essential role in normalizing the expression of mRNAs. Tissue-specific genes are preferred for their function and expression in specific tissues or cell types. Identification of these genes contributes to understanding the tissue-gene relationship and the etiology and discovery of new tissue-specific targets. Therefore, reference genes and tissue-specific genes in the adipose tissue of Aplodinotus grunniens were identified to explore their function under exogenous starvation (1 d, 2 w, 6 w) and hypothermic stress (18 °C and 10 °C for 2 d and 8 d) in this study. Results suggest that 60SRP was the most stable reference gene in adipose tissue. Meanwhile, eight genes were validated as tissue-specific candidates from the high-throughput sequencing database, while seven of them (ADM2, ß2GP1, CAMK1G, CIDE3, FAM213A, HSL, KRT222, and NCEH1) were confirmed in adipose tissue. Additionally, these seven tissue-specific genes were active in response to starvation and hypothermic stress in a time- or temperature-dependent manner. These results demonstrate that adipose-specific genes can be identified using stable internal reference genes, thereby identifying specific important functions under starvation and hypothermic stress, which provides tissue-specific targets for adipose regulation in A. grunniens.

H3K27me3-H3K4me1 transition at bivalent promoters instructs lineage specification in development.

BACKGROUND: Bivalent genes, of which promoters are marked by both H3K4me3 (trimethylation of histone H3 on lysine 4) and H3K27me3 (trimethylation of histone H3 on lysine 27), play critical roles in development and tumorigenesis. Monomethylation on lysine 4 of histone H3 (H3K4me1) is commonly associated with enhancers, but H3K4me1 is also present at promoter regions as an active bimodal or a repressed unimodal pattern. Whether the co-occurrence of H3K4me1 and bivalent marks at promoters plays regulatory role in development is largely unknown. RESULTS: We report that in the process of lineage differentiation, bivalent promoters undergo H3K27me3-H3K4me1 transition, the loss of H3K27me3 accompanies by bimodal pattern loss or unimodal pattern enrichment of H3K4me1. More importantly, this transition regulates tissue-specific gene expression to orchestrate the development. Furthermore, knockout of Eed (Embryonic Ectoderm Development) or Suz12 (Suppressor of Zeste 12) in mESCs (mouse embryonic stem cells), the core components of Polycomb repressive complex 2 (PRC2) which catalyzes H3K27 trimethylation, generates an artificial H3K27me3-H3K4me1 transition at partial bivalent promoters, which leads to up-regulation of meso-endoderm related genes and down-regulation of ectoderm related genes, thus could explain the observed neural ectoderm differentiation failure upon retinoic acid (RA) induction. Finally, we find that lysine-specific demethylase 1 (LSD1) interacts with PRC2 and contributes to the H3K27me3-H3K4me1 transition in mESCs. CONCLUSIONS: These findings suggest that H3K27me3-H3K4me1 transition plays a key role in lineage differentiation by regulating the expression of tissue specific genes, and H3K4me1 pattern in bivalent promoters could be modulated by LSD1 via interacting with PRC2.

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.

Expression Profile of Housekeeping Genes and Tissue-Specific Genes in Multiple Tissues of Pigs.

Pigs have become an ideal model system for human disease research and development and an important farm animal that provides a valuable source of nutrition. To profile the all-sided gene expression and their biological functions across multiple tissues, we conducted a comprehensive analysis of gene expression on a large scale around the side of housekeeping genes (HKGs), tissue specific genes (TSGs), and the co-expressed genes in 14 various tissues. In this study, we identified 2351 HKGs and 3018 TSGs across tissues, among which 4 HKGs (COX1, UBB, OAZ1/NPFF) exhibited low variation and high expression levels, and 31 particular TSGs (e.g., PDC, FKBP6, STAT2, and COL1A1) were exclusively expressed in several tissues, including endocrine brain, ovaries, livers, backfat, jejunum, kidneys, lungs, and longissimus dorsi muscles. We also obtained 17 modules with 230 hub genes (HUBGs) by weighted gene co-expression network analysis. On the other hand, HKGs functions were enriched in the signaling pathways of the ribosome, spliceosome, thermogenesis, oxidative phosphorylation, and nucleocytoplasmic transport, which have been highly suggested to involve in the basic biological tissue activities. While TSGs were highly enriched in the signaling pathways that were involved in specific physiological processes, such as the ovarian steroidogenesis pathway in ovaries and the renin-angiotensin system pathway in kidneys. Collectively, these stable, specifical, and co-expressed genes provided useful information for the investigation of the molecular mechanism for an understanding of the genetic and biological processes of complex traits in pigs.

Robust and rigorous identification of tissue-specific genes by statistically extending tau score.

OBJECTIVES: In this study, we aimed to identify tissue-specific genes for various human tissues/organs more robustly and rigorously by extending the tau score algorithm. INTRODUCTION: Tissue-specific genes are a class of genes whose functions and expressions are preferred in one or several tissues restrictedly. Identification of tissue-specific genes is essential for discovering multi-cellular biological processes such as tissue-specific molecular regulations, tissue development, physiology, and the pathogenesis of tissue-associated diseases. MATERIALS AND METHODS: Gene expression data derived from five large RNA sequencing (RNA-seq) projects, spanning 96 different human tissues, were retrieved from ArrayExpress and ExpressionAtlas. The first step is categorizing genes using significant filters and tau score as a specificity index. After calculating tau for each gene in all datasets separately, statistical distance from the maximum expression level was estimated using a new meaningful procedure. Specific expression of a gene in one or several tissues was calculated after the integration of tau and statistical distance estimation, which is called as extended tau approach. Obtained tissue-specific genes for 96 different human tissues were functionally annotated, and some comparisons were carried out to show the effectiveness of the extended tau method. RESULTS AND DISCUSSION: Categorization of genes based on expression level and identification of tissue-specific genes for a large number of tissues/organs were executed. Genes were successfully assigned to multiple tissues by generating the extended tau approach as opposed to the original tau score, which can assign tissue specificity to single tissue only.

Screening of Bovine Tissue-Specific Expressed Genes and Identification of Genetic Variation Within an Adipose Tissue-Specific lncRNA Gene.

Global classification of bovine genes is important for studies of biology and tissue-specific gene editing. Herein, we classified the tissue-specific expressed genes and uncovered an important variation in the promoter region of an adipose tissue-specific lncRNA gene. Statistical analysis demonstrated that the number of genes specifically expressed in the brain was the highest, while it was lowest in the adipose tissues. A total of 1,575 genes were found to be significantly higher expressed in adipose tissues. Bioinformatic analysis and qRT-PCR were used to uncover the expression profiles of the 23 adipose tissue-specific and highly expressed genes in 8 tissues. The results showed that most of the 23 genes have higher expression level in adipose tissue. Besides, we detected a 12 bp insertion/deletion (indel) variation (rs720343880) in the promoter region of an adipose tissue-specific lncRNA gene (LOC100847835). The different genotypes of this variation were associated with carcass traits of cattle. Therefore, the outcomes of the present study can be used as a starting point to explore the development of cattle organs and tissues, as well as to improve the quality of cattle products.

Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples.

RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

A Tissue-Specific Landscape of Alternative Polyadenylation, lncRNAs, TFs, and Gene Co-expression Networks in Liriodendron chinense.

Liriodendron chinense is an economically and ecologically important deciduous tree species. Although the reference genome has been revealed, alternative polyadenylation (APA), transcription factors (TFs), long non-coding RNAs (lncRNAs), and co-expression networks of tissue-specific genes remain incompletely annotated. In this study, we used the bracts, petals, sepals, stamens, pistils, leaves, and shoot apex of L. chinense as materials for hybrid sequencing. On the one hand, we improved the annotation of the genome. We detected 13,139 novel genes, 7,527 lncRNAs, 1,791 TFs, and 6,721 genes with APA sites. On the other hand, we found that tissue-specific genes play a significant role in maintaining tissue characteristics. In total, 2,040 tissue-specific genes were identified, among which 9.2% of tissue-specific genes were affected by APA, and 1,809 tissue-specific genes were represented in seven specific co-expression modules. We also found that bract-specific hub genes were associated plant defense, leaf-specific hub genes were involved in energy metabolism. Moreover, we also found that a stamen-specific hub TF Lchi25777 may be involved in the determination of stamen identity, and a shoot-apex-specific hub TF Lchi05072 may participate in maintaining meristem characteristic. Our study provides a landscape of APA, lncRNAs, TFs, and tissue-specific gene co-expression networks in L. chinense that will improve genome annotation, strengthen our understanding of transcriptome complexity, and drive further research into the regulatory mechanisms of tissue-specific genes.