Characterizing and annotating the genome using RNA-seq data

Multi-omics insights into growth and fruiting body development in the entomopathogenic fungus Cordycepsblackwelliae

Cordycepsblackwelliae is an entomopathogenic fungus with significant potential for research and development due to its ease of cultivation. However, the lack of omics-based studies has limited our understanding of the molecular mechanisms governing its growth and fruiting body development. This study employed a multi-omics approach, integrating genomic, transcriptomic and metabolomic analyses. Utilising both Illumina and Nanopore sequencing technologies, we assembled a 31.06 Mb nuclear genome comprising 11 scaffolds, with telomere presence at one or both ends in eight scaffolds and annotated 8,138 identified genes (8,136 from genome prediction and two from local BLAST searches). Transcriptomic analysis identified 2,078 differentially expressed genes across three developmental stages: liquid culture mycelia, wheat culture mycelia and fruiting bodies. Amongst these, 745 genes were up-regulated in fruiting bodies, primarily associated with biosynthetic and catabolic pathways. Metabolomic analysis identified 1,161 metabolites, with 1,014 showing significant variations across developmental stages. Integrated transcriptomic and metabolomic analyses uncovered 17 genes positively correlated with 34 metabolites, which are likely crucial regulators of fruiting body development. These findings provide new insights into the molecular networks underlying C.blackwelliae growth and fruiting body formation.

The Aldehyde Dehydrogenase Superfamily in Brassica napus L.: Genome-Wide Identification and Expression Analysis Under Low-Temperature Conditions

The Aldehyde Dehydrogenase (ALDH) superfamily comprises a group of NAD+ or NADP+-dependent enzymes that play essential roles in responding to abiotic stresses in plants. In Brassica napus L., however, the increasing frequency of extremely low temperatures during winter in recent years has significantly affected both yield and quality. This study conducted a genome-wide screening of ALDH superfamily genes, analyzing their gene structures, evolutionary relationships, protein physicochemical properties, and expression patterns under low-temperature stress to explore the function of the ALDH superfamily gene in cold tolerance in Brassica napus L. A total of six BnALDH genes with significant differences in expression levels were verified utilizing quantitative real-time polymerase chain reaction (qRT-PCR), revealing that BnALDH11A2, BnALDH7B2, BnALDH3F5, BnALDH12A3, BnALDH2B6, and BnALDH7B3 all exhibited higher expression in cold-tolerant material 24W233 compared with cold-sensitive material 24W259. Additionally, a single nucleotide polymorphism (SNP) in the BnALDH11A2 promoter region shows differences between the cold-tolerant (24W233) and the cold-sensitive (24W259) Brassica napus varieties, and it may be associated with the cold tolerance of these two varieties. This comprehensive analysis offers valuable insights into the role of ALDH family genes in low-temperature stress adaptation in Brassica napus and offers genetic resources for the development of novel cold-tolerant cultivars.

Integrative analyses of long and short-read RNA sequencing reveal the spliced isoform regulatory network of seedling growth dynamics in upland cotton

The polyploid genome of cotton has significantly increased the transcript complexity. Recent advances in full-length transcript sequencing are now widely used to characterize the complete landscape of transcriptional events. Such studies in cotton can help us to explore the genetic mechanisms of the cotton seedling growth. Through long-read single-molecule RNA sequencing, this study compared the transcriptomes of three yield contrasting genotypes of upland cotton. Our analysis identified different numbers of spliced isoforms from 31,166, 28,716, and 28,713 genes in SJ48, Z98, and DT8 cotton genotypes, respectively, most of which were novel compared to previous cotton reference transcriptomes, and showed significant differences in the number of exon structures and coding sequence length due to intron retention. Quantification of isoform expression revealed significant differences in expression in the root and leaf of each genotype. An array of key isoform target genes showed protein kinase or phosphorylation functions, and their protein interaction network contained most of the circadian oscillator proteins. Spliced isoforms from the GIGANTEA (GI) protien were differentially regulated in each genotype and might be expected to regulate translational activities, including the sequence and function of target proteins. In addition, these spliced isoforms generate diurnal expression profiles in cotton leaves, which may alter the transcriptional regulatory network of seedling growth. Silencing of the novel spliced GI isoform Gh_A02G0645_N17 significantly affected biomass traits, contributed to variable growth, and increased transcription of the early flowering pathway gene ELF in cotton. Our high-throughput hybrid sequencing results will be useful to dissect functional differences among spliced isoforms in the polyploid cotton genome.

Water stress modulates terpene biosynthesis and morphophysiology at different ploidal levels in Lippia alba (Mill.) N. E. Brown (Verbenaceae)

Monoterpenes are the main component in essential oils of Lippia alba. In this species, the chemical composition of essential oils varies with genome size: citral (geraniol and neral) is dominant in diploids and tetraploids, and linalool in triploids. Because environmental stress impacts various metabolic pathways, we hypothesized that stress responses in L. alba could alter the relationship between genome size and essential oil composition. Water stress affects the flowering, production, and reproduction of plants. Here, we evaluated the effect of water stress on morphophysiology, essential oil production, and the expression of genes related to monoterpene synthesis in diploid, triploid, and tetraploid accessions of L. alba cultivated in vitro for 40 days. First, using transcriptome data, we performed de novo gene assembly and identified orthologous genes using phylogenetic and clustering-based approaches. The expression of candidate genes related to terpene biosynthesis was estimated by real-time quantitative PCR. Next, we assessed the expression of these genes under water stress conditions, whereby 1% PEG-4000 was added to MS medium. Water stress modulated L. alba morphophysiology at all ploidal levels. Gene expression and essential oil production were affected in triploid accessions. Polyploid accessions showed greater growth and metabolic tolerance under stress compared to diploids. These results confirm the complex regulation of metabolic pathways such as the production of essential oils in polyploid genomes. In addition, they highlight aspects of genotype and environment interactions, which may be important for the conservation of tropical biodiversity.

Transcriptomic analysis reveals the molecular mechanisms of Boehmeria nivea L. in response to antimonite and antimonate stresses

Soil antimony (Sb) pollution is a global concern that threatens food security and human health. Boehmeria nivea L. (ramie) is a promising phytoremediation plant exhibiting high tolerance and enrichment capacity for Sb. To reveal the molecular mechanisms and thus enhance the ramie uptake, transport, and detoxification of Sb with practical strategies, a hydroponic experiment was conducted to compare the physiological and transcriptomic responses of ramie towards antimonite (Sb(Ⅲ)) and antimonate (Sb(Ⅴ)). Phenotypic results showed that Sb(Ⅲ) had a stronger inhibitory effect on the growth of ramie. Root Sb content under Sb(Ⅲ) was 2.43 times higher than that in Sb(Ⅴ) treatment. Based on the ribonucleic acid sequencing (RNA-Seq) technique, 3915 and 999 significant differentially expressed genes (DEGs) were identified under Sb(Ⅲ) and Sb(Ⅴ), respectively. Transcriptomic analysis revealed that ramie showed different adaptation strategies to Sb(Ⅲ) and Sb(V). Key DEGs and their involved pathways such as catalytic activity, carbohydrate metabolisms, phenylpropanoid biosynthesis, and cell wall modification were identified to perform crucial roles in Sb tolerance and detoxification. Two heavy metal-associated domain-type genes, six heavy metal-associated isoprenylated plant proteins, and nine ABC transporters showed possible roles in the transport and detoxification of Sb. The significant upregulation of NRAMP5 and three NIPs suggested their roles in the transport of Sb(V). This study is the basis for future research to identify the exact genes and biological processes that can effectively enhance Sb accumulation or improve plant tolerance to Sb, thereby promoting the phytoremediation of Sb-polluted soils.

Serum Long Noncoding RNA H19 and CKD Progression in IgA Nephropathy

BACKGROUND

IgA nephropathy (IgAN) is one of the most common primary glomerular diseases worldwide, especially in young Asian adults. Long RNA H19 is associated with renal pathologies, such as renal cell injury; however, a connection between serum H19 expression and kidney disease progression has not been demonstrated.

METHOD

Our cohort consisted of 204 patients with IgAN. Serum H19 levels were determined with reverse-transcription quantitative polymerase between 1 May, 2014 and 1 May, 2015. H19 levels were log-transformed and categorical variables were categorized according to cutoff points of a ROC curve. Restricted cubic spline and generalized estimating equation analyses were performed to determine the association between serum H19 and kidney disease progression.

RESULTS

H19 expression was significantly downregulated in patients with IgAN compared to healthy controls. Restricted cubic spline analyses showed that the relationship was negatively and linearly correlated (P for nonlinearly = 0.256). After adjusting for other potential clinical, pathologic, and treatment factors, H19 was found to be a protective factor for prognosis in IgAN (HR, 0.52; 95% CI 0.32-0.84; P = 0.008). ROC curve analysis showed that the clinical value of lncRNA H19 with CKD and area under the ROC curve was 0.746 (95% CI 0.663-0.829; P < 0.001) of the clinical prognostic value of H19. Serum restricted cubic spline analyses showed that the relationship was negatively and linearly correlated (P for non-linearly = 0.256). H19 > 0.097 in patients in IgAN was associated with a reduction of the risk of kidney progression by approximately 70% within 5 years compared to H19≤0.097 (HR, 0.30;95% CI 0.12-0.74; P = 0.009).

CONCLUSION

H19 is an independent protective factor, and a high level of H19 often indicates better renal outcome within 5 years.

Integration of transcriptomic and metabolomic reveals metabolic pathway alteration in earthworms (Eisenia fetida) under copper exposure

Copper is a trace element that necessary for plant growth in the soil. However, in recent years, due to human activities, the content of copper in soil exceeds the standard seriously, which is threatening the safety of soil animals, plants and even human beings. In this study, we investigated the effects and molecular mechanisms of 60 days long-term copper exposure on earthworms (Eisenia fetida) at 67.58 mg/kg, 168.96 mg/kg and 337.92 mg/kg concentration by using transcriptome and metabolomics. Transcriptome analysis showed that the expression of energy metabolism related genes (LDH, GYS, ATP6N, GAPDH, COX17), immune system related genes (E3.2.1.14) and detoxification related genes (UGT, CYP2U1, CYP1A1) were down-regulated, the expression of antioxidant system related genes (GCLC, HPGDS) were up-regulated in copper exposure experiment of earthworms. Similarly, metabolomics analysis revealed that the expression of energy metabolism related metabolites (Glucose-1-phosphate, Glucose-6-phosphate), TCA cycle related metabolites (fumaric acid, allantoic acid, malate, malic acid) were down-regulated, digestion and immune system related metabolites (Trehalose-6-phosphate) were up-regulated. Integrating transcriptome and metabolomics data, it was found that higher antioxidant capacity and accelerated TCA cycle metabolism may be an adaptive strategy for earthworms to adapt to long-term copper stress. Collectively, the results of this study will greatly contribute to incrementally understand the stress responses on copper exposure to earthworms and supply molecular level support for evaluating the environmental effects of copper on soil organisms.

Differentially expressed genes against Colletotrichum lindemuthiamum in a bean genotype carrying the Co-2 gene revealed by RNA-sequencing analysis

Anthracnose is responsible for large yield losses in common bean crops. RNA-sequencing was used to investigate the differentially expressed genes (DEGs) in response to race 38 of Colletotrichum lindemuthianum in two near-isogenic lines (A25 and A4804) that differ in the presence of a resistance gene located in the cluster Co-2. Their responses were analyzed at different hours after inoculation (0, 24, and 48) and within and between genotypes. In all, 2,850 DEGs were detected, with 2,373 assigned to at least one functional GO term. Enriched GO terms in the resistant genotype were mainly related to functions as a response to stimulus, hormone signaling, cellular component organization, phosphorylation activities, and transcriptional regulation. The region containing the Co-2 cluster was delimited at the end of chromosome Pv11 (46.65-48.65 Mb) through a comparison with the SNP genotypes, obtained using 'Genotyping by Sequencing,' among seven resistant lines harboring the Co-2 gene and the susceptible line A25. The delimited region contained 23 DEGs, including 8 typical R genes, that showed higher expression levels in the resistant genotype and non-changes in the susceptible genotype after inoculation. Six R genes encoding protein kinases and an LRR domain formed a cluster in a core region between 46.98 and 47.04 Mb. The alignment of the raw transcriptome reads in the core region revealed structural changes that were used to design four potential breeder-friendly DNA markers, and it revealed some alignments with the intergenic regions, suggesting the presence of genes in addition to those annotated in the reference genome.

Zooming in: PAGE-Northern Blot Helps to Analyze Anti-Sense Transcripts Originating from Human rIGS under Transcriptional Stress

Ribosomal intergenic spacer (rIGS), located between the 45S rRNA coding arrays in humans, is a deep, unexplored source of small and long non-coding RNA molecules transcribed in certain conditions to help a cell generate a stress response, pass through a differentiation state or fine tune the functioning of the nucleolus as a ribosome biogenesis center of the cell. Many of the non-coding transcripts originating from the rIGS are not characterized to date. Here, we confirm the transcriptional activity of the region laying a 2 kb upstream of the rRNA promoter, and demonstrate its altered expression under transcriptional stress, induced by a wide range of known transcription inhibitors. We managed to show an increased variability of anti-sense transcripts in alpha-amanitin treated cells by applying the low-molecular RNA fraction extracted from agarose gel to PAGE-northern. Also, the fractioning of RNA by size using agarose gel slices occurred, being applicable for determining the sizes of target transcripts via RT-PCR.

AGTAR: A novel approach for transcriptome assembly and abundance estimation using an adapted genetic algorithm from RNA-seq data

BACKGROUND

Recently, the rapid development of RNA-seq technologies has accelerated transcriptomics research. The accurate identification and quantification of transcripts based on RNA-seq data will facilitate the exploration of various potential biological mechanisms. However, due to the limitations of the current data analysis tools and RNA-seq technologies, full and accurate reconstruction of the transcriptome still faces many challenges.

RESULTS

We developed the adapted genetic algorithm (AGTAR) program, which can reliably assemble transcriptomes and estimate abundance based on RNA-seq data with or without genome annotation files. We defined a new concept, isoform junction abundance, to help enhance the accuracy of isoform identification and quantification. Isoform abundance and isoform junction abundance are estimated by an adapted genetic algorithm. The crossover and mutation probabilities of the algorithm can be adaptively adjusted to effectively prevent premature convergence. Both simulated and real data indicated that AGTAR's comprehensive ability to assemble transcripts is significantly superior to that achievable by the currently widely used tools with similar functions.

CONCLUSIONS

AGTAR is a tool for identifying and quantifying transcripts from RNA-seq data. It has the advantages of higher accuracy and ease of use. The AGTAR package is freely available at https://github.com/v4yuezi/AGTAR.git.

Thymic transcriptome analysis after Newcastle disease virus inoculation in chickens and the influence of host small RNAs on NDV replication

Newcastle disease (ND), caused by virulent Newcastle disease virus (NDV), is a contagious viral disease affecting various birds and poultry worldwide. In this project, differentially expressed (DE) circRNAs, miRNAs and mRNAs were identified by high-throughput RNA sequencing (RNA-Seq) in chicken thymus at 24, 48, 72 or 96 h post LaSota NDV vaccine injection versus pre-inoculation group. The vital terms or pathways enriched by vaccine-influenced genes were tested through KEGG and GO analysis. DE genes implicated in innate immunity were preliminarily screened out through GO, InnateDB and Reactome Pathway databases. The interaction networks of DE innate immune genes were established by STRING website. Considering the high expression of gga-miR-6631-5p across all the four time points, DE circRNAs or mRNAs with the possibility to bind to gga-miR-6631-5p were screened out. Among DE genes that had the probability to interact with gga-miR-6631-5p, 7 genes were found to be related to innate immunity. Furthermore, gga-miR-6631-5p promoted LaSota NDV replication by targeting insulin induced gene 1 (INSIG1) in DF-1 chicken fibroblast cells. Taken together, our data provided the comprehensive information about molecular responses to NDV LaSota vaccine in Chinese Partridge Shank Chickens and elucidated the vital roles of gga-miR-6631-5p/INSIG1 axis in LaSota NDV replication.

Identification of key regulators responsible for dysregulated networks in osteoarthritis by large-scale expression analysis

BACKGROUND

Osteoarthritis (OA) is a worldwide musculoskeletal disorder. However, disease-modifying therapies for OA are not available. Here, we aimed to characterize the molecular signatures of OA and to identify novel therapeutic targets and strategies to improve the treatment of OA.

METHODS

We collected genome-wide transcriptome data performed on 132 OA and 74 normal human cartilage or synovium tissues from 7 independent datasets. Differential gene expression analysis and functional enrichment were performed to identify genes and pathways that were dysregulated in OA. The computational drug repurposing method was used to uncover drugs that could be repurposed to treat OA.

RESULTS

We identified several pathways associated with the development of OA, such as extracellular matrix organization, inflammation, bone development, and ossification. By protein-protein interaction (PPI) network analysis, we prioritized several hub genes, such as JUN, CDKN1A, VEGFA, and FOXO3. Moreover, we repurposed several FDA-approved drugs, such as cardiac glycosides, that could be used in the treatment of OA.

CONCLUSIONS

We proposed that the hub genes we identified would play a role in cartilage homeostasis and could be important diagnostic and therapeutic targets. Drugs such as cardiac glycosides provided new possibilities for the treatment of OA.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

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

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

Identification of a hippocampal lncRNA-regulating network in cognitive dysfunction caused by chronic cerebral hypoperfusion

Cognitive dysfunction caused by chronic cerebral hypoperfusion is a common underlying cause of many cognition-related neurodegenerative diseases. The mechanisms of cognitive dysfunction caused by CCH are not clear. Long non-coding RNA is involved in synaptic plasticity and cognitive function, but whether lncRNA is involved in cognitive dysfunction caused by CCH has not yet been reported. In the present study, we identified the altered lncRNAs and mRNAs by deep RNA sequencing. A total of 128 mRNAs and 91 lncRNAs were up-regulated, and 108 mRNAs and 98 lncRNAs were down-regulated. Real-time reverse transcription-polymerase chain reaction verified the reliability of the lncRNA and mRNA sequencing. Gene Ontology and KEGG pathway analyses showed that differentially-expressed mRNAs were related to peptide antigen binding, the extracellular space, the monocarboxylic acid transport, and tryptophan metabolism. The co-expression analysis showed that 161 differentially expressed lncRNAs were correlated with DE mRNAs. By predicting the miRNA in which both DE lncRNAs and DE mRNAs bind together, we constructed a competitive endogenous RNA network. In this lncRNAs-miRNAs-mRNAs network, 559 lncRNA-miRNA-mRNA targeted pairs were identified, including 83 lncRNAs, 67 miRNAs, and 108 mRNAs. Through GO and KEGG pathway analysis, we further analyzed and predicted the regulatory function and potential mechanism of ceRNA network regulation. Our results are helpful for understanding the pathogenesis of cognitive dysfunction caused by CCH and provide direction for further research.

Transcriptome analysis of genes expressed in the earthworm Eisenia fetida in response to cadmium exposure

Eisenia fetida earthworm is an ecotoxicologically important test species to monitor various pollutants. However, there is a little knowledge about the effects of cadmium (Cd) on earthworms at the transcriptional level. Firstly, we exposed E. fetida to soils supplemented with different concentrations (10, 30, 60 mg/kg soil) of Cd. Moreover, we depicted the characterization of gene expressions with E. fetida using high-throughput profiling of gene expression. In addition, a comparison of the gene expression profiles between each Cd treatment group and the control group suggested that differential expressional genes (DEGs) mainly enriched in enzyme activity, metabolism, oxidative stress, regeneration and apoptosis pathways. 8 DEGs from these pathways had been selected randomly to confirm the data of RNA-seq. Among these DEGs, six genes (metallothionein-2, phytochelatin synthase 1a, CuZn superoxide dismutase, sex determining region Y-box 2, sex determining region Y-box 4b, TP53-regulated inhibitor of apoptosis 1-like) up-regulated and 2 genes (beta-1,4-endoglucanase, apoptosis-stimulating of p53 protein 2-like) down-regulated in response to Cd exposure. The alteration of them indicated that earthworms could reduce the toxicity and bioavailability of Cd in polluted soil ecosystems through different pathways. This work lays an important foundation for linking earthworm transcriptional level with the ecological risk of Cd in soil ecosystem.

Next generation sequencing data for use in risk assessment

Next generation sequencing (NGS) represents several powerful platforms that have revolutionized RNA and DNA analysis. The parallel sequencing of millions of DNA molecules can provide mechanistic insights into toxicology and provide new avenues for biomarker discovery with growing relevance for risk assessment. The evolution of NGS technologies has improved over the last decade with increased sensitivity and accuracy to foster new biomarker assays from tissue, blood and other biofluids. NGS sequencing technologies can identify transcriptional changes and genomic targets with base pair precision in response to chemical exposure. Further, there are several exciting movements within the toxicology community that incorporate NGS platforms into new strategies for more rapid toxicological characterizations. These include the Tox21 in vitro high throughput transcriptomic screening program, development of organotypic spheroids, alternative animal models, mining archival tissues, liquid biopsy and epigenomics. This review will describe NGS-based technologies, demonstrate how they can be used as tools for target discovery in tissue and blood, and suggest how they might be applied for risk assessment.

Biosensor for the Characterization of Gene Expression in Cells

We developed a new label-free biosensor technique for the detection of messenger ribonucleic acid (mRNA) that can be used in the prognosis and diagnosis of certain diseases. We observed a dependence of the negative dielectrophoresis (DEP) force applied to polystyrene microspheres (PMs) in conjugation with different types of mRNA and the frequency of the electric field produced by interdigitated microelectrodes. Since the frequency dependence of the negative DEP force is an effective transduction mechanism for the detection of mRNA, this sensing technology has the potential to be used in the diagnosis and identification of gene expression that is associated with various human disease.

Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine

High-throughput next generation sequencing (NGS) is a shotgun approach applied in a parallel fashion by which the genome is fragmented and sequenced through small pieces and then analyzed either by aligning to a known reference genome or by de novo assembly without reference genome. This technology has led researchers to conduct an explosion of sequencing related projects in multidisciplinary fields of science. However, due to the limitations of sequencing-based chemistry, length of sequencing reads and the complexity of genes, it is difficult to determine the sequences of some portions of the human genome, leaving gaps in genomic data that frustrate further analysis. Particularly, some complex genes are difficult to be accurately sequenced or mapped because they contain high GC-content and/or low complexity regions, and complicated pseudogenes, such as the genes encoding xenobiotic metabolizing enzymes and transporters (XMETs). The genetic variants in XMET genes are critical to predicate inter-individual variability in drug efficacy, drug safety and susceptibility to environmental toxicity. We summarized and discussed challenges, wet-lab methods, and bioinformatics algorithms in sequencing "complex" XMET genes, which may provide insightful information in the application of NGS technology for implementation in toxicogenomics and pharmacogenomics.

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Altered Wnt and NF-κB Signaling in Facet Joint Osteoarthritis: Insights from RNA Deep Sequencing

Facet joint osteoarthritis is common lumbar osteoarthritis characterized by facet joint cartilage degeneration. However, the molecular basis of facet joint osteoarthritis remains largely undetermined. In the current study, we collected facet joint tissue samples from 10 control patients and 48 patients with facet joint osteoarthritis (20 patients with moderate degeneration and 28 with severe degeneration). The control patients underwent internal fixation of the lumbar spine due to vertebral fracture. RNA deep sequencing was performed, and Bioinformatic tools were applied. Among top 30 enriched signaling pathways, we focused on two inflammation-related signaling pathways, Wnt and NF-κB signaling pathways. Subsequently, using the quantitative RT-PCR analysis, we confirmed that in Wnt signaling pathway, the mRNA levels of Dickkopf WNT Signaling Pathway Inhibitor 2 (DKK2), Sex-determining Region Y-box 17 (SOX17), MYC, Cyclin D1, Calcium/Calmodulin Dependent Protein Kinase II Alpha (CAMK2A), and Wnt Family Member 11 and 5 were increased in facet joint osteoarthritis, while the mRNA levels of WNT Inhibitory Factor 1, Casein Kinase 1 Alpha 1, Transcription Factor 7/Lymphoid Enhancer Binding Factor 1 (TCF7/LEF1), and VANGL Planar Cell Polarity Protein 2 were decreased. In NF-κB signaling pathway, the mRNA levels of C-C Motif Chemokine Ligand 4 (CCL4) and C-C Motif Chemokine Ligand 4 Like 2 (CCL4L2) were increased, while the mRNA levels of BCL2 Related Protein A1 were decreased. These results suggest that Wnt and NF-κB signaling may be altered in the process of facet joint cartilage degeneration. The present study will expand our understanding of the molecular bases underlying facet joint osteoarthritis.

Trends in herbgenomics

From Shen Nong's Herbal Classic (Shennong Bencao Jing) to the Compendium of Materia Medica (Bencao Gangmu) and the first scientific Nobel Prize for the mainland of China, each milestone in the historical process of the development of traditional Chinese medicine (TCM) involves screening, testing and integrating. After thousands of years of inheritance and development, herbgenomics (bencaogenomics) has bridged the gap between TCM and international advanced omics studies, promoting the application of frontier technologies in TCM. It is a discipline that uncovers the genetic information and regulatory networks of herbs to clarify their molecular mechanism in the prevention and treatment of human diseases. The main theoretical system includes genomics, functional genomics, proteomics, transcriptomics, metabolomics, epigenomics, metagenomics, synthetic biology, pharmacogenomics of TCM, and bioinformatics, among other fields. Herbgenomics is mainly applicable to the study of medicinal model plants, genomic-assisted breeding, herbal synthetic biology, protection and utilization of gene resources, TCM quality evaluation and control, and TCM drug development. Such studies will accelerate the application of cutting-edge technologies, revitalize herbal research, and strongly promote the development and modernization of TCM.

De novo assembly of a Chinese soybean genome

Soybean was domesticated in China and has become one of the most important oilseed crops. Due to bottlenecks in their introduction and dissemination, soybeans from different geographic areas exhibit extensive genetic diversity. Asia is the largest soybean market; therefore, a high-quality soybean reference genome from this area is critical for soybean research and breeding. Here, we report the de novo assembly and sequence analysis of a Chinese soybean genome for "Zhonghuang 13" by a combination of SMRT, Hi-C and optical mapping data. The assembled genome size is 1.025 Gb with a contig N50 of 3.46 Mb and a scaffold N50 of 51.87 Mb. Comparisons between this genome and the previously reported reference genome (cv. Williams 82) uncovered more than 250,000 structure variations. A total of 52,051 protein coding genes and 36,429 transposable elements were annotated for this genome, and a gene co-expression network including 39,967 genes was also established. This high quality Chinese soybean genome and its sequence analysis will provide valuable information for soybean improvement in the future.

Transcriptomic Studies of Malaria: a Paradigm for Investigation of Systemic Host-Pathogen Interactions

Transcriptomics, the analysis of genome-wide RNA expression, is a common approach to investigate host and pathogen processes in infectious diseases. Technical and bioinformatic advances have permitted increasingly thorough analyses of the association of RNA expression with fundamental biology, immunity, pathogenesis, diagnosis, and prognosis. Transcriptomic approaches can now be used to realize a previously unattainable goal, the simultaneous study of RNA expression in host and pathogen, in order to better understand their interactions. This exciting prospect is not without challenges, especially as focus moves from interactions in vitro under tightly controlled conditions to tissue- and systems-level interactions in animal models and natural and experimental infections in humans. Here we review the contribution of transcriptomic studies to the understanding of malaria, a parasitic disease which has exerted a major influence on human evolution and continues to cause a huge global burden of disease. We consider malaria a paradigm for the transcriptomic assessment of systemic host-pathogen interactions in humans, because much of the direct host-pathogen interaction occurs within the blood, a readily sampled compartment of the body. We illustrate lessons learned from transcriptomic studies of malaria and how these lessons may guide studies of host-pathogen interactions in other infectious diseases. We propose that the potential of transcriptomic studies to improve the understanding of malaria as a disease remains partly untapped because of limitations in study design rather than as a consequence of technological constraints. Further advances will require the integration of transcriptomic data with analytical approaches from other scientific disciplines, including epidemiology and mathematical modeling.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

Genome-wide analysis and functional prediction of long non-coding RNAs in mouse uterus during the implantation window

Establishment of the receptive uterus is a crucial step for embryo implantation. In this study, the expression profiles and characterization of long non-coding RNAs (lncRNAs) in pregnant mouse uteri on day 4, day 5 at implantation sites and inter-implantation sites were conducted using RNA-seq. A total of 7,764 putative lncRNA transcripts were identified, including 6,179 known lncRNA transcripts and 1,585 novel lncRNA transcripts. Bioinformatics analysis of the cis and trans lncRNA targets showed that the differentially expressed lncRNAs were mainly involved in tissue remodelling, immune response and metabolism-related processes, indicating that lncRNAs could be involved in the regulation of embryo implantation. We also discovered that differentially expressed lncRNAs might regulate multiple signalling pathways that play an important role in the regulation of embryo implantation. In addition, nine known lncRNAs and four novel lncRNAs were randomly selected and validated by qRT-PCR. The expression of Tug1, Neat1, Gas5, Malat1, H19 and Rmst were significantly regulated in the mouse uterus during the implantation window. Our results are the first to systematically identify lncRNAs in the mouse uterus and provide a catalogue of lncRNAs for further understanding their functions in pregnant mouse uteri during the implantation window.

Transcriptome analysis of three cotton pests reveals features of gene expressions in the mesophyll feeder Apolygus lucorum

The green mirid bug Apolygus lucorum is an agricultural pest that is known to cause damage to more than 150 plant species. Here, we report the transcriptomes of A. lucorum at three different developmental stages (the second and fifth instar nymphs and adults). A total of 98,236 unigenes with an average length of 1,335 nt was obtained, of which 50,640 were annotated, including those encoding digestive enzymes and cytochrome P450s. Comparisons with cotton bollworm and cotton aphid transcriptomes revealed distinct features of A. lucorum as a mesophyll feeder. The gene expression dynamics varied during development from young nymphs to adults. The high-quality transcriptome data and the gene expression dynamics reported here provide valuable data for a more comprehensive understanding of the physiology and development of mirid bugs, and for mining targets for their control.