BMC Genomics最新文献

The establishment and maintenance of spermatogenesis is a complex process involving a vast of regulatory pathways. There is growing evidence revealing that long noncoding RNAs (lncRNA) play important roles in regulating testicular development and spermatogenesis in a stage-specific way. However, our understanding of how lncRNA regulates testicular development and spermatogenesis in black goats is quite limited. In the current study, we screened the transcriptomes (lncRNA and mRNA) of testicular from Guangxi black goats before puberty (3 days old, D3; 30 days old, D30), puberty (90 days old, D90) and postpuberty (180 days old, D180), in order to identify the lncRNA interaction with mRNAs contributes to goat spermatogenesis. The RNA-sequencing (RNA-seq) analysis showed that there were 1211, 12,180, 834 differential lncRNAs and 1196, 8838,269 differential mRNAs at the ages of D30 vs. D3, D90 vs. D30, and D180 vs. D90. The lncRNAs showed the most significantly changes from D30 to D90, which indicated that D90 was a key node of lncRNAs participated in the regulation of testicular development and spermatogenesis in black goat. According to functional enrichment analysis of GO and KEGG, we found that differentially expressed lncRNAs (DE lncRNAs) and their target genes regulated spermatogenesis through signal pathways including MAPK, Ras, and PI3K-Akt. Using cis- and trans-acting, 39 DE lncRNAs-targeted genes were found to be enriched for male reproduction. Of these, LOC108635509, which specific expressed in testis and upregulated the expression levels at D90, was found participated in the regulation of testicular development through promoting the proliferation of Sertoli cells (SCs). Overall, this study provides new insight into the regulatory mechanisms that support spermatogenesis and testicular development in black goats.

Background: Assessing farm animals' welfare is crucial, yet practical physiological tools are still lacking. In this study, we tested whether the peripheral blood mononuclear cell (PBMC) transcriptome shows variations in association with sows' welfare. To do this, we compared animals whose welfare states were assumed to differ due to their lives in more or less enriched environments and to their different dominance statuses. Sows were housed in a conventional (C, n = 36) or enriched (E, n = 35) environments from gestation day 0 (G0) until three weeks before farrowing (G105), after which they were transferred to individual farrowing crates. From G99 to G103, behavioral analyses were conducted, and sows' dominance status was evaluated. A subset of 28 multiparous sows (C, n = 14 and E, n = 14) was selected for the collection of saliva on G35 and G98 and hair on G98 for cortisol measurement, and of blood samples for PBMC transcriptome analysis on G98 and on lactation day 12 (L12).

Results: Both environmental enrichment (EE) and dominance status influenced cortisol and variables related to social and exploratory behavior, indicating an influence on sows' welfare. In the transcriptomic analysis, among the 12,260 genes submitted to differential analysis on G98, EE impacted 31 genes, while dominance status impacted 449 genes. Compared with subordinate sows (SUB), dominant (DOM) sows exhibited an upregulation of genes related to inflammatory process and plasma cell function, and downregulation of genes related to B-cell activation. In groups of sows, dominance status is partly related to sows' parity; therefore, we compared the effect of dominance with that of parity. Some common genes emerged when comparing high-parity (HP) vs. low-parity (LP) sows (542 differentially expressed genes (DEGs), including 180 in common with dominance-related genes), indicating that some effects of dominance on the transcriptome during gestation were in fact more due to age or reproductive cycles than to dominance itself. EE and dominance effects appeared relatively short-term, as DEG numbers decreased on L12 (four DEGs for E vs. C, 25 for DOM vs. SUB).

Conclusions: Dominance status exerted a more pronounced influence on sows' PBMC transcriptome than did environmental enrichment. In particular, dominance status modulated genes associated with B cells and plasma cell functions. Some of the genes identified in this study could be tested in the future as potential molecular markers of well-being.

Background: Animal venom systems are considered as valuable model for investigating the molecular mechanisms underlying phenotypic evolution. Stonefish are the most venomous and dangerous fish because of severe human envenomation and occasionally fatalities, whereas the genomic background of their venom has not been fully explored compared with that in other venomous animals.

Results: In this study, we followed modern venomic pipelines to decode the Synanceia verrucosa venom components. A catalog of 478 toxin genes was annotated based on our assembled chromosome-level genome. Integrative analysis of the high-quality genome, the transcriptome of the venom gland, and the proteome of crude venom revealed mechanisms underlying the venom complexity in S. verrucosa. Six tandem-duplicated neoVTX subunit genes were identified as the major source for the neoVTX protein production. Further isoform sequencing revealed massive alternative splicing events with a total of 411 isoforms demonstrated by the six genes, which further contributed to the venom diversity. We then characterized 12 dominantly expressed toxin genes in the venom gland, and 11 of which were evidenced to produce the venom protein components, with the neoVTX proteins as the most abundant. Other major venom proteins included a presumed CRVP, Kuntiz-type serine protease inhibitor, calglandulin protein, and hyaluronidase. Besides, a few of highly abundant non-toxin proteins were also characterized and they were hypothesized to function in housekeeping or hemostasis maintaining roles in the venom gland. Notably, gastrotropin like non-toxin proteins were the second highest abundant proteins in the venom, which have not been reported in other venomous animals and contribute to the unique venom properties of S. verrucosa.

Conclusions: The results identified the major venom composition of S. verrucosa, and highlighted the contribution of neoVTX genes to the diversity of venom composition through tandem-duplication and alternative splicing. The diverse neoVTX proteins in the venom as lethal particles are important for understanding the adaptive evolution of S. verrucosa. Further functional studies are encouraged to exploit the venom components of S. verrucosa for pharmaceutical innovation.

Background: Fishes are susceptible to hypoxia stress, while the common carp is known for its high tolerance to hypoxia. The hypoxia-inducible factor (HIF) pathway directly regulates the cell's response to hypoxia. Still, it is currently unknown which members of the hif-α genes are present in common carp and their specific functions.

Results: In this study, we found that the hif-1α, hif-2α, and hif-3α genes of common carp all contained twice the number of copies of their orthologs in zebrafish. Common carp has four copies of the hif-1α gene, of which the two hif-1αa genes were expressed at low levels in the vast majority of tissues, while the two hif-1αb genes were expressed at high levels in multiple tissues. We silenced the two hif-1αb genes using chitosan nanoparticles (CSNPs) carrying siRNA and subjected two groups to hypoxic stress. Transcriptome sequencing results show that whether under normoxia or hypoxia, the number of differentially expressed genes (DEGs) caused by silencing the hif-1αb genes in the heart exceeds 1,000, far more than the number of DEGs in the gills or brain. GO enrichment and KEGG enrichment showed that DEGs in the heart were mainly related to immune function and myocardial contraction. DEGs in the gills and brain also enriched many immune-related terms, and some DEGs in the gills were related to iron metabolism and erythropoiesis. Among the paralogs, the two hif-1αa genes were most obviously up-regulated under normoxia, while the hif-3α genes were most obviously up-regulated under hypoxia. We did not find any downstream genes of the HIF pathway that were specifically regulated by the hif-1αb genes.

Conclusions: The main effect site of the common carp hif-1αb genes is the heart, and their main functions are to regulate immune response and myocardial contraction. Their functions are partially redundant with the hif-1αa genes and hif-3α genes. When their expressions are inhibited, the expression of hif-1αa genes or hif-3α genes would be up-regulated in specific contexts, thereby compensating for their loss of function. The downstream genes of the HIF pathway in common carp may be generally regulated by multiple hif-α genes.

Background: Pearl millet (Pennisetum glaucum (L.) R. Br.) is a vital cereal crop, predominantly cultivated in arid and semi-arid regions of Asia and Africa. It serves as a staple food for millions, while also being utilized as forage and an energy crop. The crop's resistance to heat and drought, coupled with its high biomass, positions it as a promising candidate for climate-resilient agriculture. A detailed understanding of its gene expression patterns across various tissues and developmental stages is essential for enhancing its yield and quality. This study aims to fill this knowledge gap by employing RNA-seq to identify housekeeping genes (HKGs) and tissue-specific genes (TSGs) in pearl millet.

Results: Our analysis of RNA-seq data from nine tissues (seed, germ, radicle, leaf, root, tillering tissue, stem, spike, and grain) across eight developmental stages in pearl millet accession Tifleaf3 revealed a comprehensive gene expression profile. We identified 461 HKGs that exhibited stable expression across all tissues and stages, providing robust internal references for RT-qPCR. Additionally, 8091 TSGs were discovered, many of which showed distinctive expression patterns in tissues such as spike, stem, and leaf. Functional enrichment analysis of these genes using GO and KEGG pathways highlighted their roles in key biological processes and pathways, indicating their potential in crop trait enhancement. Protein-protein interaction networks constructed for stem and leaf tissues further illuminated the regulatory mechanisms underlying the transition from vegetative to reproductive growth stages.

Conclusion: This study presents a detailed transcriptomic landscape of pearl millet, identifying a set of HKGs and TSGs that are crucial for understanding the molecular basis of its growth and development. We provided valuable options for transcript normalization and crucial targets for exploring gene function for the plant growth and development in pearl millet. The insights gained from this work are instrumental for breeding programs aimed at enhancing the productivity of pearl millet, thereby contributing to food and energy security.

Background: O-Methyltransferase (OMTs) is a class of conserved multifunctional enzymes that play important roles in plant developmental regulation, hormone signaling, secondary metabolite synthesis and abiotic stress response. The GiOMT gene family has been identified and analyzed in species such as citrus, alfalfa, Populus and grape, but has not been reported in Glycyrrhiza inflata Bat.

Results: In this study, we systematically identified and analyzed the GiOMT gene family of G. inflata by bioinformatics, and analyzed their physicochemical properties, conserved motifs, conserved structural domains, gene structures, phylogenetic relationships, chromosomal localization and fragment duplications, and the expression patterns of GiOMT genes in combination with transcriptomic data and qRT-PCR. The results showed that a total of 41 GiOMTs were identified in G. inflata, which were named GiOMT1 ~ GiOMT41 based on their chromosomal locations. Protein characterization showed that 29 GiOMT proteins were hydrophilic and 12 GiOMT proteins were hydrophobic. Subcellular predicted localization revealed that most GiOMT proteins localized in the cytoplasm and chloroplasts. Phylogenetic relationships showed that the OMT genes of three species, G. inflata, Arabidopsis and alfalfa, were distributed in three taxa, while the GiOMT genes were distributed in taxa I and II. Promoters of GiOMT genes contained light responsive element and many hormone responsive elements. The expression levels of GiOMT genes under UV-B stress were varied, indicating that GiOMT gene was in response to abiotic stresses in G. inflata.

Conclusion: In this study, we investigated the genome-wide identification, structure, evolution and expression analysis of the GiOMT gene in G. inflata. The basal sequence of GiOMT genes was highly conserved throughout the evolutionary history of G. inflata. Most of the GiOMT genes were highly expressed in roots and were involved in the response to UV-B stress. The GiOMT genes may lead to the accumulation of flavonoids and enhancement of G. inflata quality and drug activity in G. inflata under UV-B radiation.

Fatty acids are a requirement for normal development, however, since humans are unable to de novo produce essential fatty acids, they must be obtained from diet. Atlantic salmon is a major dietary source of nutritious and digestible fatty acids. Here, we set out to uncover the genomic basis of individual fatty acids and indices (saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, n-3, and n-6) in 208 North American Atlantic salmon, to understand selection potential toward increasing relative quantities of essential fatty acids and to identify candidate genes for future research. Total n-6 (pro-inflammatory) was higher than total n-3 (anti-inflammatory) fatty acids with a ratio of 1 : 1.31 (n-3 : n-6). Heritability of fatty acids ranged from 0 to 0.99, however, most fatty acids and indices had moderate to high heritabilities (ranged from 0.20 to 0.88), implying that selection for improvement of traits could be possible. We found the same significant markers on chromosome 23 (based on false discovery rate thresholds of 2.0e-6 and suggestive significant thresholds of 2.0e-5 in Manhattan plots) in four fatty acids (γ-linoleic acid, stearidonic acid, dihimo-γ-linolenic acid, and eicosatrienoic acid), where three genes (sin3b, acbd6, and fads2) are known to be involved in lipid metabolism. These genes, fads2 in particular, would all make ideal candidates for future functional studies. In addition, there were four fatty acids with loci over the suggestive significant threshold with a variety of markers on different chromosomes (lauric acid, stearic acid, eicosatetraenoic acid (ETA), and docosadienoic acid), with associated genes that had relevant functions to fatty acids or adipose cells in general.

Background: Traditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment.

Results: This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility.

Conclusion: CGRclust is a novel, scalable, alignment-free DNA sequence clustering method that uses CGR images of DNA sequences and CNNs for twin contrastive clustering of unlabelled primary DNA sequences, achieving superior or comparable accuracy and performance over current approaches. CGRclust demonstrated enhanced reliability, by consistently achieving over 80% accuracy in more than 90% of the datasets analyzed. In particular, CGRclust performed especially well in clustering viral DNA datasets, where it consistently outperformed all competing methods.

Background: Cyclocarya paliurus is a high-value tree, and it contains a variety of bioactive secondary metabolites which have broad application prospects in medicine, food and health care. Triterpenoids can improve the bioactive function of C. paliurus health tea and also improve the efficacy of health care tea.

Results: The results of this study showed that there were 69 kinds were terpenoids, and triterpenoids accounted for more than 80%. We excavated 5 kinds of triterpenoid metabolites with high content and significant difference dynamics, namely, corosolic acid, asiatic acid, maslinic acid, ursolic acid and oleanolic acid. The co-expression analysis identified CYP71D8 and CYP716A15 co-expressed with β-AS may generate oleanane type triterpenoids by modifying β-amyrin, while CYP71AN24 and CYP98A2 co-expressed with LUS may play a key role in lupine type triterpenoids biosynthesis. MYB,Whirly,WRKY and bHLH families, which showed strong correlation with function genes, may play an important role in the regulation of P450 and OSC expression. A total of 20 modules were identified by WGCNA analysis, and CYP71AU50 and CYP716A15 in tan and orange modules may play a major role in the synthesis of oleanolic acid, ursolic acid and asiatic acid, while CYP82D47 in lightcyan 1 module may be the hub gene for the biosynthesis of corosolic acid and maslinic acid.

Conclusions: Our findings mined candidate genes closely related to triterpenoid synthesis in C. paliurus. The results of this paper can provide scientific reference for breeding high-content triterpenoid varieties of C. paliurus.

Background: Achieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions. These intractable problems lead to challenges in exploring intact representations from heterogeneous data, which often result in inaccuracies in multi-omics information analysis.

Results: To address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). Leveraging autoencoder modules, DeepMoIC extracts compact representations from omics data and incorporates a patient similarity network through the similarity network fusion algorithm. To handle non-Euclidean data and explore high-order omics information effectively, we design a Deep GCN module with two strategies: residual connection and identity mapping. With extracted higher-order representations, our approach consistently outperforms state-of-the-art models on a pan-cancer dataset and 3 cancer subtype datasets.

Conclusion: The introduction of Deep GCN shows encouraging performance in terms of supervised multi-omics feature learning, offering promising insights for precision medicine in cancer research. DeepMoIC can potentially be an important tool in the field of cancer subtype classification because of its capacity to handle complex multi-omics data and produce reliable classification findings.