Mapping and differential expression analysis from short-read RNA-Seq data in model organisms

Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing （RNA-Seq） to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.

Identification and analysis of differentially expressed genes involved in dark-induced photoperiod response and senescence of soybean leaves by suppression subtractive hybridization

A cDNA subtractive library enriched for dark-induced up-regulated ESTs was constructed by suppression subtractive hybridization(SSH) from leaf tissues of soybean cultivar DongNong L13 treated with short-day(8-h light/16-h dark) and long-day(16-h light/8-h dark) conditions.A total of 148 clones were sequenced,representing 76 unique ESTs which corresponded to about 20% of 738 clones from the cDNA library and showed a significant up-regulation of at least three fold verified by dot blot hybridization.The putative functions of ESTs were predicted by Blastn and Blastx.The 43 differentially expressed genes identified by subtractions were classified according to their putative functions generated by Blast analysis.Genetic functional analysis indicated that putative proteins encoded by these genes were related to diverse functions during organism development,which include biological regulation pathways such as transcription,signal transduction and programmed cell death,protein,nucleic acid and carbohydrate macromolecule degradation,the cell wall modification,primary and secondary metabolism and stress response.Two soybean transcription factors enhanced in SD conditions,GAMYB-binding protein and DNA binding protein RAV cDNAs that may be involved in SD soybean photoperiod response,had been isolated using 5'-and 3'-rapid amplification of cDNA ends(RACE)(Genbank Accession numbers DQ112540 and DQ147914).

Transcriptome analysis of adherens junction pathway-related genes after peripheral nerve injury

The neural regeneration process is driven by a wide range of molecules and pathways. Adherens junctions are critical cellular junctions for the integrity of peripheral nerves. However, few studies have systematically characterized the transcript changes in the adherens junction pathway following injury. In this study, a rat model of sciatic nerve crush injury was established by forceps. Deep sequencing data were analyzed using comprehensive transcriptome analysis at 0, 1, 4, 7, and 14 days after injury. Results showed that most individual molecules in the adherens junctions were either upregulated or downregulated after nerve injury. The m RNA expression of ARPC1 B, ARPC3, TUBA8, TUBA1 C, CTNNA2, ACTN3, MET, HGF, NME1 and ARF6, which are involved in the adherens junction pathway and in remodeling of adherens junctions, was analyzed using quantitative real-time polymerase chain reaction. Most of these genes were upregulated in the sciatic nerve stump following peripheral nerve injury, except for CTNNA2, which was downregulated. Our findings reveal the dynamic changes of key molecules in adherens junctions and in remodeling of adherens junctions. These key genes provide a reference for the selection of clinical therapeutic targets for peripheral nerve injury.

Neuroprotective effects of long noncoding RNAs involved in ischemic postconditioning after ischemic stroke

During acute reperfusion,the expression profiles of long noncoding RNAs in adult rats with focal cerebral ischemia undergo broad changes.However,whether long noncoding RNAs are involved in neuroprotective effects following focal ischemic stroke in rats remains unclear.In this study,RNA isolation and library preparation was performed for long noncoding RNA sequencing,followed by determining the coding potential of identified long noncoding RNAs and target gene prediction.Differential expression analysis,long noncoding RNA functional enrichment analysis,and co-expression network analysis were performed comparing ischemic rats with and without ischemic postconditioning rats.Rats were subjected to ischemic postconditioning via the brief and repeated occlusion of the middle cerebral artery or femoral artery.Quantitative real-time reverse transcription-polymerase chain reaction was used to detect the expression levels of differentially expressed long noncoding RNAs after ischemic postconditioning in a rat model of ischemic stroke.The results showed that ischemic postconditioning greatly affected the expression profile of long noncoding RNAs and mRNAs in the brains of rats that underwent ischemic stroke.The predicted target genes of some of the identified long noncoding RNAs(cis targets)were related to the cellular response to ischemia and stress,cytokine signal transduction,inflammation,and apoptosis signal transduction pathways.In addition,15 significantly differentially expressed long noncoding RNAs were identified in the brains of rats subjected to ischemic postconditioning.Nine candidate long noncoding RNAs that may be related to ischemic postconditioning were identified by a long noncoding RNA expression profile and long noncoding RNA-mRNA co-expression network analysis.Expression levels were verified by quantitative real-time reverse transcription-polymerase chain reaction.These results suggested that the identified long noncoding RNAs may be involved in the neuroprotective effects associated with ischemic postconditioning following ischemic stroke.The experimental animal procedures were approved by the Animal Experiment Ethics Committee of Kunming Medical University(approval No.KMMU2018018)in January 2018.

Transcriptomics of Chinese Sapium Sebiferum(L.)Roxb seed to reveal key enzymes involved in oil accumulation

Chinese Sapium sebiferum(L.)Roxb has great potential to emerge as a promising biodiesel resource plant due to abundant oil content in seed.However,the unavailability of transcriptomic and genomic data impedes the progress of application in biodiesel production.Transcriptome analysis will help to further exploit tallow and kernel oil utilization in industry and facilitate molecular-breeding-based genetic improvement of S.sebiferum.Illumina hiseq 2000 sequencing platform was used to perform de novo transcriptome assembly and gene expression analyses of seeds from three strains in three developmental stages.A total of 188,254 unigenes over 200 bp were assembled with average length of 699 bp.These transcriptic unigenes were functionally assigned by GO and KEGG pathways.RPKM was used to identify differentially expressed unigenes of five cDNA libraries.Transcriptic genes endcoding enzymes related to fatty acid metabolism were evaluated.qRT-PCR was employed to confirm five key enzymes involved in fatty acid and triacylglycerol biosynthesis.This study provided comprehensive gene expression information of Chinese S.sebiferum seed at transcriptional level.These transcriptome data will facilitate high-oil-content genetic cultivation of S.sebiferum,which will further exploit tallow and kernel oil utilization in biodiesel industry.

PKC-SWI6 signaling regulates asexual development,cell wall integrity,stress response,and lifestyle transition in the nematode-trapping fungus Arthrobotrys oligospora

Predatory fungi possess intricate signal transduction systems that regulate their development and support successful infection of the host.Herein,we characterized three components of the cell wall integrity-controlling pathway,namely protein kinase C(Ao PKC),SLT2-MAPK(Ao SLT2),and SWI6(Ao SWI6),in a representative nematode-trapping fungus Arthrobotrys oligospora,using gene disruption and multi-omics approaches.The phenotypic traits(such as mycelia development,conidiation,stress response,and trap morphogenesis) and metabolic profiles of ΔAopkc and ΔAoswi6 mutants were similar but differed from those of the ΔAoslt2 mutants.Transcriptomic analysis indicated that the genes differentially expressed in the absence of Aoswi6 were involved in DNA replication,repair,and recombination during trap formation.Moreover,the yeast two-hybrid assay showed that Ao PKC interacted with Ao SWI6,suggesting that in A.oligospora,PKC can directly regulate SWI6,bypassing the SLT2signaling cascade.Conclusively,our findings deepen our understanding of the regulatory mechanism of asexual development and lifestyle switching in nematode-trapping fungi.

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

Genome-wide transcriptome profiling identifies genes that are prone to differential expression(DE)across contexts,as well as genes with changes specific to the experimental manipulation.Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes(DEGs)allows more efficient prediction of which genes are specific to a given biological process under scrutiny.Currently,common DEGs or pathways can only be identified through the laborious manual curation of experiments,an inordinately time-consuming endeavor.Here we pioneer an approach,Specific cOntext Pattern Highlighting In Expression data(SOPHIE),for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated.We apply SOPHIE to diverse datasets including those from human,human cancer,and bacterial pathogen Pseudomonas aeruginosa.SOPHIE identifies common DEGs in concordance with previously described,manually and systematically determined common DEGs.Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes.SOPHIE’s measure of specificity can complement log2 fold change values generated from traditional DE analyses.For example,by filtering the set of DEGs,one can identify genes that are specifically relevant to the experimental condition of interest.Consequently,these results can inform future research directions.All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns.Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.