Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

Elucidating the regulatory role of long non-coding RNAs in drought stress response during seed germination in leaf mustard

Leaf mustard (Brassica juncea L. Czern & Coss), an important vegetable crop, experiences pronounced adversity due to seasonal drought stress, particularly at the seed germination stage. Although there is partial comprehension of drought-responsive genes, the role of long non-coding RNAs (lncRNAs) in adjusting mustard's drought stress response is largely unexplored. In this study, we showed that the drought-tolerant cultivar 'Weiliang' manifested a markedly lower base water potential (-1.073 MPa vs -0.437 MPa) and higher germination percentage (41.2% vs 0%) than the drought-susceptible cultivar 'Shuidong' under drought conditions. High throughput RNA sequencing techniques revealed a significant repertoire of lncRNAs from both cultivars during germination under drought stress, resulting in the identification of 2,087 differentially expressed lncRNAs (DELs) and their correspondingly linked 12,433 target genes. It was noted that 84 genes targeted by DEL exhibited enrichment in the photosynthesis pathway. Gene network construction showed that MSTRG.150397, a regulatory lncRNA, was inferred to potentially modulate key photosynthetic genes (Psb27, PetC, PetH, and PsbW), whilst MSTRG.107159 was indicated as an inhibitory regulator of six drought-responsive PIP genes. Further, weighted gene co-expression network analysis (WGCNA) corroborated the involvement of light intensity and stress response genes targeted by the identified DELs. The precision and regulatory impact of lncRNA were verified through qPCR. This study extends our knowledge of the regulatory mechanisms governing drought stress responses in mustard, which will help strategies to augment drought tolerance in this crop.

Transcriptomic data reveals the dynamics of terpenoids biosynthetic pathway of fenugreek

Medicinal plants are rich sources for treating various diseases due their bioactive secondary metabolites. Fenugreek (Trigonella foenum-graecum) is one of the medicinal plants traditionally used in human nutrition and medicine which contains an active substance, called diosgenin, with anticancer properties. Biosynthesis of this important anticancer compound in fenugreek can be enhanced using eliciting agents which involves in manipulation of metabolite and biochemical pathways stimulating defense responses. Methyl jasmonate elicitor was used to increase diosgenin biosynthesis in fenugreek plants. However, the molecular mechanism and gene expression profiles underlying diosgening accumulation remain unexplored. In the current study we performed an extensive analysis of publicly available RNA-sequencing datasets to elucidate the biosynthesis and expression profile of fenugreek plants treated with methyl jasmonate. For this purpose, seven read datasets of methyl jasmonate treated plants were obtained that were covering several post-treatment time points (6-120 h). Transcriptomics analysis revealed upregulation of several key genes involved in diosgenein biosynthetic pathway including Squalene synthase (SQS) as the first committed step in diosgenin biosynthesis as well as Squalene Epoxidase (SEP) and Cycloartenol Synthase (CAS) upon methyl jasmonate application. Bioinformatics analysis, including gene ontology enrichment and pathway analysis, further supported the involvement of these genes in diosgenin biosynthesis. The bioinformatics analysis led to a comprehensive validation, with expression profiling across three different fenugreek populations treated with the same methyl jasmonate application. Initially, key genes like SQS, SEP, and CAS showed upregulation, followed by later upregulation of Δ24, suggesting dynamic pathway regulation. Real-time PCR confirmed consistent upregulation of SQS and SEP, peaking at 72 h. Additionally, candidate genes Δ24 and SMT1 highlighted roles in directing metabolic flux towards diosgenin biosynthesis. This integrated approach validates the bioinformatics findings and elucidates fenugreek's molecular response to methyl jasmonate elicitation, offering insights for enhancing diosgenin yield. The assembled transcripts and gene expression profiles are deposited in the Zenodo open repository at https://doi.org/10.5281/zenodo.8155183 .

Genomic and Transcriptomic Analysis of Pea (Pisum sativum L.) Breeding Line 'Triumph' with High Symbiotic Responsivity

Pea (Pisum sativum L.), like most legumes, forms mutualistic symbioses with nodule bacteria and arbuscular mycorrhizal (AM) fungi. The positive effect of inoculation is partially determined by the plant genotype; thus, pea varieties with high and low symbiotic responsivity have been described, but the molecular genetic basis of this trait remains unknown. Here, we compare the symbiotically responsive breeding line 'Triumph' of grain pea with its parental cultivars 'Vendevil' (a donor of high symbiotic responsivity) and 'Classic' (a donor of agriculturally valuable traits) using genome and transcriptome sequencing. We show that 'Triumph' inherited one-fourth of its genome from 'Vendevil', including the genes related to AM and nodule formation, and reveal that under combined inoculation with nodule bacteria and AM fungi, 'Triumph' and 'Vendevil', in contrast to 'Classic', demonstrate similar up-regulation of the genes related to solute transport, hormonal regulation and flavonoid biosynthesis in their roots. We also identify the gene PsGLP2, whose expression pattern distinguishing 'Triumph' and 'Vendevil' from 'Classic' correlates with difference within the promoter region sequence, making it a promising marker for the symbiotic responsivity trait. The results of this study may be helpful for future molecular breeding programs aimed at creation of symbiotically responsive cultivars of pea.

CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response

Background/aim

Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms.

Materials and methods

We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona.

Results

One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots.

Conclusion

Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

Prediction of alternative pre-mRNA splicing outcomes

To understand the biological impact of alternative pre-mRNA splicing, it is vital to know which exons are involved, what protein domains they encode, and how the translated isoforms differ. Therefore, we developed a computational pipeline (RiboSplitter) focused on functional effect prediction. It builds on event-based alternative splicing detection with additional filtering steps leading to more efficient statistical testing, and with detection of isoform-specific protein changes. A key methodological advance is reading frame prediction by translating exonic DNA in all possible frames, then finding a single open reading frame, or a single frame with matches to known proteins of the gene. This allowed unambiguous translation in 93.9% of alternative splicing events when tested on RNA-sequencing data of B cells from Sjögren's syndrome patients. RiboSplitter does not depend on reference annotations and translates events even when one or both isoform(s) are novel (unannotated). RiboSplitter's visualizations illustrate each event with translation outcomes, show event location within the gene, and align exons to protein domains.

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Genome annotation: From human genetics to biodiversity genomics

Within the next decade, the genomes of 1.8 million eukaryotic species will be sequenced. Identifying genes in these sequences is essential to understand the biology of the species. This is challenging due to the transcriptional complexity of eukaryotic genomes, which encode hundreds of thousands of transcripts of multiple types. Among these, a small set of protein-coding mRNAs play a disproportionately large role in defining phenotypes. Due to their sequence conservation, orthology can be established, making it possible to define the universal catalog of eukaryotic protein-coding genes. This catalog should substantially contribute to uncovering the genomic events underlying the emergence of eukaryotic phenotypes. This piece briefly reviews the basics of protein-coding gene prediction, discusses challenges in finalizing annotation of the human genome, and proposes strategies for producing annotations across the eukaryotic Tree of Life. This lays the groundwork for obtaining the catalog of all genes-the Earth's code of life.

Transcriptomic and physiological analysis of the effect of octanoic acid on Meloidogyne incognita

Root knot nematodes are the most devastating root pathogens, causing severe damage and serious economic losses to agriculture worldwide. Octanoic acid has been reported as one of the nematicides, and its mode of action is not fully understood. The main objective of this study was to elucidate the effect of octanoic acid on Meloidogyne incognita by transcriptomic analysis combined with physiological and biochemical assays. In the toxicity assays with octanoic acid, the threshold concentration with nematicidal activity and the maximum concentration to which nematodes could respond were 0.03 μL/mL and 0.08 μL/mL respectively. Microscopic observation combined with protein and carbohydrates assays confirmed that the structure of the second-stage juveniles (J2s) was severely disrupted after 72 h of immersion in octanoic acid. Transcriptome analysis has shown that octanoic acid can interfere with the nematode energy metabolism, lifespan and signaling. Although the effects are multifaceted, the findings strongly point to the cuticle, lysosomes, and extracellular regions and spaces as the primary targets for octanoic acid. In addition, nematodes can withstand the negative effects of low concentration of octanoic acid to some extent by up-regulating the defense enzyme system and heterologous metabolic pathways. These findings will help us to explore the nematicidal mechanism of octanoic acid and provide important target genes for the development of new nematicides in the future.

Transcriptome profile analysis of Indian mustard (Brassica juncea L.) during seed germination reveals the drought stress-induced genes associated with energy, hormone, and phenylpropanoid pathways

Indian mustard (Brassica juncea L. Czern and Coss) is an important oil and vegetable crop frequently affected by seasonal drought stress during seed germination, which retards plant growth and causes yield loss considerably. However, the gene networks regulating responses to drought stress in leafy Indian mustard remain elusive. Here, we elucidated the underlying gene networks and pathways of drought response in leafy Indian mustard using next-generation transcriptomic techniques. Phenotypic analysis showed that the drought-tolerant leafy Indian mustard cv. 'WeiLiang' (WL) had a higher germination rate, antioxidant capacity, and better growth performance than the drought-sensitive cv. 'ShuiDong' (SD). Transcriptome analysis identified differentially expressed genes (DEGs) in both cultivars under drought stress during four germination time points (i.e., 0, 12, 24, and 36 h); most of which were classified as drought-responsive, seed germination, and dormancy-related genes. In the Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, three main pathways (i.e., starch and sucrose metabolism, phenylpropanoid biosynthesis, and plant hormone signal transduction) were unveiled involved in response to drought stress during seed germination. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) identified several hub genes (novel.12726, novel.1856, BjuB027900, BjuA003402, BjuA021578, BjuA005565, BjuB006596, novel.12977, and BjuA033308) associated with seed germination and drought stress in leafy Indian mustard. Taken together, these findings deepen our understanding of the gene networks for drought responses during seed germination in leafy Indian mustard and provide potential target genes for the genetic improvement of drought tolerance in this crop.

Intra-Specific Variation in Desiccation Tolerance of Citrus sinensis 'bingtangcheng' (L.) Seeds under Different Environmental Conditions in China

Intra-specific variation in seed storage behaviour observed in several species has been related to different maternal environments. However, the particular environmental conditions and molecular processes involved in intra-specific variation of desiccation tolerance remain unclear. We chose Citrus sinensis 'bingtangcheng' for the present study due to its known variability in desiccation tolerance amongst seed lots. Six seed lots of mature fruits were harvested across China and systematically compared for drying sensitivity. Annual sunshine hours and average temperature from December to May showed positive correlations with the level of seed survival of dehydration. Transcriptional analysis indicated significant variation in gene expression between relatively desiccation-tolerant (DT) and -sensitive (DS) seed lots after harvest. The major genes involved in late seed maturation, such as heat shock proteins, showed higher expression in the DT seed lot. Following the imposition of drying, 80% of stress-responsive genes in the DS seed lot changed to the stable levels seen in the DT seed lot prior to and post-desiccation. However, the changes in expression of stress-responsive genes in DS seeds did not improve their tolerance to desiccation. Thus, higher desiccation tolerance of Citrus sinensis 'bingtangcheng' seeds is modulated by the maternal environment (e.g., higher annual sunshine hours and seasonal temperature) during seed development and involves stable expression levels of stress-responsive genes.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

RNA-Seq Profiling between Commercial and Indigenous Iranian Chickens Highlights Differences in Innate Immune Gene Expression

The purpose of the current study was to examine transcriptomic-based profiling of differentially expressed innate immune genes between indigenous and commercial chickens. In order to compare the transcriptome profiles of the different chicken breeds, we extracted RNA from blood samples of the Isfahan indigenous chicken (as indigenous) and Ross broiler chicken (as commercial) breeds. RNA-Seq yielded totals of 36,763,939 and 31,545,002 reads for the indigenous and commercial breeds, respectively, with clean reads then aligned to the chicken reference genome (Galgal5). Overall, 1327 genes were significantly differentially expressed, of which 1013 genes were upregulated in the commercial versus the indigenous breed, while 314 were more highly expressed in the indigenous birds. Furthermore, our results demonstrated that the SPARC, ATP6V0D2, IL4I1, SMPDL3A, ADAM7, TMCC3, ULK2, MYO6, THG1L and IRG1 genes were the most significantly expressed genes in the commercial birds and the PAPPA, DUSP1, PSMD12, LHX8, IL8, TRPM2, GDAP1L1, FAM161A, ABCC2 and ASAH2 genes were the most significant in the indigenous chickens. Of notable finding in this study was that the high-level gene expressions of heat-shock proteins (HSPs) in the indigenous breeds could serve as a guideline for future genetic improvement. This study identified genes with breed-specific expression, and comparative transcriptome analysis helped understanding of the differences in underlying genetic mechanisms between commercial and local breeds. Therefore, the current results can be used to identify candidate genes for further breed improvement.

A Workflow Guide to RNA-Seq Analysis of Chaperone Function and Beyond

RNA sequencing (RNA-seq) is a powerful method of transcriptional analysis that allows for the sequence identification and quantification of cellular transcripts. RNA-seq can be used for differential gene expression (DGE) analysis, gene fusion detection, allele-specific expression, isoform and splice variant quantification, and identification of novel genes. These applications can be used for downstream systems biology analyses such as gene ontology or pathway analysis to provide insight into processes altered between biological conditions. Given the wide range of signaling pathways subject to chaperone activity as well as numerous chaperone functions in RNA metabolism, RNA-seq may provide a valuable tool for the study of chaperone proteins in biology and disease. This chapter outlines an example RNA-seq workflow to determine differentially expressed (DE) genes between two or more sample conditions and provides some considerations for RNA-seq experimental design.

Tsukushi proteoglycan maintains RNA splicing and developmental signaling network in GFAP-expressing subventricular zone neural stem/progenitor cells

Tsukushi (TSK) proteoglycan dysfunction leads to hydrocephalus, a condition defined by excessive fluid collection in the ventricles and lateral ventricular enlargement. TSK injections into the LV at birth are effective at rescuing the lateral ventricle (LV). TSK regulates the activation of the Wnt signaling to facilitate the proper expansion of the LV and maintain the fate of the neural stem cell lineage. However, the molecular mechanism by which TSK acts on neural stem/progenitor cells (NSCs) during LV development is unknown. We demonstrated that TSK is crucial for the splicing and development-associated gene regulation of GFAP-expressing subventricular zone (SVZ) NSCs. We isolated GFAP-expressing NSCs from the SVZ of wild-type (GFAPGFP/+/TSK+/+) and TSK knock-out (GFAPGFP/+/TSK−/−) mice on postnatal day 3 and compared their transcriptome and splicing profiles. TSK deficiency in NSCs resulted in genome-wide missplicing (alteration in exon usage) and transcriptional dysregulation affecting the post-transcriptional regulatory processes (including splicing, cell cycle, and circadian rhythm) and developmental signaling networks specific to the cell (including Wnt, Sonic Hedgehog, and mTOR signaling). Furthermore, TSK deficiency prominently affected the splicing of genes encoding RNA and DNA binding proteins in the nervous SVZ and non-nervous muscle tissues. These results suggested that TSK is involved in the maintenance of correct splicing and gene regulation in GFAP-expressing NSCs, thereby protecting cell fate and LV development. Hence, our study provides a critical insight on hydrocephalus development.

Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Background

RNA preparations contaminated with genomic DNA (gDNA) are frequently disregarded by RNA-seq studies. Such contamination may generate false results; however, their effect on the outcomes of RNA-seq analyses is unknown. To address this gap in our knowledge, here we added different concentrations of gDNA to total RNA preparations and subjected them to RNA-seq analysis.

Results

We found that the contaminating gDNA altered the quantification of transcripts at relatively high concentrations. Differentially expressed genes (DEGs) resulting from gDNA contamination may therefore contribute to higher rates of false enrichment of pathways compared with analogous samples lacking numerous DEGs. A strategy was developed to correct gene expression levels in gDNA-contaminated RNA samples, which assessed the magnitude of contamination to improve the reliability of the results.

Conclusions

Our study indicates that caution must be exercised when interpreting results associated with low-abundance transcripts. The data provided here will likely serve as a valuable resource to evaluate the influence of gDNA contamination on RNA-seq analysis, particularly related to the detection of putative novel gene elements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08785-1.

The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets

Background

Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally.

Methods

Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information.

Results

We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity.

Conclusions

We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses.

Cutaneous leishmaniasis: multiomics approaches to unravel the role of immune cells checkpoints

INTRODUCTION

Cutaneous leishmaniasis (CL) is the most frequent form of leishmaniases, associated with skin inflammation and ulceration. Understanding the interaction of different phagocytic cells in the recognition and uptake of different Leishmania species is critical for controlling the infection. Phagocytic cells have a pivotal role as professional antigen-presenting cells that bridge the innate and adaptive immunity and shape the outcome of the disease.

AREAS COVERED

Here we reviewed new technologies with high-throughput data collection capabilities along with systems biology approaches which are recently being used to decode the paradox of CL immunology.

EXPERT OPINION

We emphasized on the crosstalk between DC and T-cells while focusing on the immune checkpoints interactions between the human immune system and the Leishmania species. Further, we discussed omics technologies including bulk RNA sequencing, reverse transcriptase-multiplex ligation dependent probe amplification (RT-MLPA), and proximity extension assay (PEA) in studies on human blood or tissue-driven samples from CL patients in which we have so far been involved.

Dynamic CD4+ T cell heterogeneity defines subset-specific suppression and PD-L1-blockade-driven functional restoration in chronic infection

Inhibiting PD-1:PD-L1 signaling has transformed therapeutic immune restoration. CD4+ T cells sustain immunity in chronic infections and cancer, yet little is known about how PD-1 signaling modulates CD4+ helper T (TH) cell responses or the ability to restore CD4+ TH-mediated immunity by checkpoint blockade. We demonstrate that PD-1:PD-L1 specifically suppressed CD4+ TH1 cell amplification, prevents CD4+ TH1 cytokine production and abolishes CD4+ cytotoxic killing capacity during chronic infection in mice. Inhibiting PD-L1 rapidly restored these functions, while simultaneously amplifying and activating TH1-like T regulatory cells, demonstrating a system-wide CD4-TH1 recalibration. This effect coincided with decreased T cell antigen receptor signaling, and re-directed type I interferon (IFN) signaling networks towards dominant IFN-γ-mediated responses. Mechanistically, PD-L1 blockade specifically targeted defined populations with pre-established, but actively suppressed proliferative potential, with limited impact on minimally cycling TCF-1+ follicular helper T cells, despite high PD-1 expression. Thus, CD4+ T cells require unique differentiation and functional states to be targets of PD-L1-directed suppression and therapeutic restoration.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris)

BACKGROUND

Species interactions can promote mating behavior divergence, particularly when these interactions are costly due to maladaptive hybridization. Selection against hybridization can indirectly cause evolution of reproductive isolation within species, a process termed cascade reinforcement. This process can drive incipient speciation by generating divergent selection pressures among populations that interact with different species assemblages. Theoretical and empirical studies indicate that divergent selection on gene expression networks has the potential to increase reproductive isolation among populations. After identifying candidate synaptic transmission genes derived from neurophysiological studies in anurans, we test for divergence of gene expression in a system undergoing cascade reinforcement, the Upland Chorus Frog (Pseudacris feriarum).

RESULTS

Our analyses identified seven candidate synaptic transmission genes that have diverged between ancestral and reinforced populations of P. feriarum, including five that encode synaptic vesicle proteins. Our gene correlation network analyses revealed four genetic modules that have diverged between these populations, two possessing a significant concentration of neurotransmission enrichment terms: one for synaptic membrane components and the other for metabolism of the neurotransmitter nitric oxide. We also ascertained that a greater number of genes have diverged in expression by geography than by sex. Moreover, we found that more genes have diverged within females as compared to males between populations. Conversely, we observed no difference in the number of differentially-expressed genes within the ancestral compared to the reinforced population between the sexes.

CONCLUSIONS

This work is consistent with the idea that divergent selection on mating behaviors via cascade reinforcement contributed to evolution of gene expression in P. feriarum. Although our study design does not allow us to fully rule out the influence of environment and demography, the fact that more genes diverged in females than males points to a role for cascade reinforcement. Our discoveries of divergent candidate genes and gene networks related to neurotransmission support the idea that neural mechanisms of acoustic mating behaviors have diverged between populations, and agree with previous neurophysiological studies in frogs. Increasing support for this hypothesis, however, will require additional experiments under common garden conditions. Our work points to the importance of future replicated and tissue-specific studies to elucidate the relative contribution of gene expression divergence to the evolution of reproductive isolation during incipient speciation.

Transcriptomic analysis of rat kidney reveals a potential mechanism of sex differences in susceptibility to cisplatin-induced nephrotoxicity

Although cisplatin is an effective platinum-based anticancer drug against solid cancer, its availability is limited owing to its adverse side effects. Our study aimed to identify the potential relationship within cisplatin-induced multi-organ physiological changes and genetic factors associated with sex differences in nephrotoxicity susceptibility. To investigate this, mice received a single intraperitoneal injection of cisplatin. Cisplatin administration resulted in renal dysfunction, as evidenced by the elevation in serum biomarkers of renal damage (blood urea nitrogen and creatinine) and the degree of histopathological alterations. In particular, along with testicular damage and low testosterone levels, we also observed a decrease in male-specific (CYP3A2) or male-dominant (CYP2B1 and CYP3A1) CYP isoforms in the livers of rats with hepatotoxicity following cisplatin treatment, which may be associated with an imbalance in male hormone regulation caused by renal and testicular injury. Notably, we found that male rats were more susceptible to cisplatin-induced nephrotoxicity, as characterized by histopathological and biochemical analyses. Therefore, RNA sequencing was performed at baseline (pre-treatment) and at 48 h following cisplatin administration (post-treatment) to identify the genes associated with sex differences in nephrotoxicity susceptibility. Gap junctions, which play a role in replenishing damaged cells to maintain tissue homeostasis, and mismatch repair associated with a pathological apoptotic mechanism against cisplatin nephrotoxicity were significantly enriched only in males following cisplatin treatment. Moreover, among the 322 DEGs showing different basal expression patterns between males and females before cisplatin treatment, the male expressed high levels of genes, which are responsible for transmembrane transport and regulation of apoptotic process, pre-cisplatin treatment; additionally, genes involved in the PI3K-Akt signaling pathway and the oxidation-reduction process were significantly lower in males before cisplatin treatment. Collectively, our comprehensive findings provided valuable insight into the potential mechanisms of sex differences in cisplatin-induced nephrotoxicity susceptibility.

Urinary extracellular vesicles: Assessment of pre-analytical variables and development of a quality control with focus on transcriptomic biomarker research

Urinary extracellular vesicles (uEV) are a topical source of non-invasive biomarkers for health and diseases of the urogenital system. However, several challenges have become evident in the standardization of uEV pipelines from collection of urine to biomarker analysis. Here, we studied the effect of pre-analytical variables and developed means of quality control for uEV isolates to be used in transcriptomic biomarker research. We included urine samples from healthy controls and individuals with type 1 or type 2 diabetes and normo-, micro- or macroalbuminuria and isolated uEV by ultracentrifugation. We studied the effect of storage temperature (-20°C vs. -80°C), time (up to 4 years) and storage format (urine or isolated uEV) on quality of uEV by nanoparticle tracking analysis, electron microscopy, Western blotting and qPCR. Urinary EV RNA was compared in terms of quantity, quality, and by mRNA or miRNA sequencing. To study the stability of miRNA levels in samples isolated by different methods, we created and tested a list of miRNAs commonly enriched in uEV isolates. uEV and their transcriptome were preserved in urine or as isolated uEV even after long-term storage at -80°C. However, storage at -20°C degraded particularly the GC-rich part of the transcriptome and EV protein markers. Transcriptome was preserved in RNA samples extracted with and without DNAse, but read distributions still showed some differences in e.g. intergenic and intronic reads. MiRNAs commonly enriched in uEV isolates were stable and concordant between different EV isolation methods. Analysis of never frozen uEV helped to identify surface characteristics of particles by EM. In addition to uEV, qPCR assays demonstrated that uEV isolates commonly contained polyoma viruses. Based on our results, we present recommendations how to store and handle uEV isolates for transcriptomics studies that may help to expedite standardization of the EV biomarker field.

Transcription Factor GmWRKY46 Enhanced Phosphate Starvation Tolerance and Root Development in Transgenic Plants

Phosphorus (P) is one of the essential macronutrients, whose deficiency limits the growth and development of plants. In this study, we investigated the possible role of GmWRKY46 in the phosphate (Pi) starvation stress tolerance of soybean. GmWRKY46 belonged to the group III subfamily of the WRKY transcription factor family, which was localized in the nucleus and had transcriptional activator activity. GmWRKY46 could be strongly induced by Pi starvation, especially in soybean roots. Overexpression of GmWRKY46 significantly enhanced tolerance to Pi starvation and lateral root development in transgenic Arabidopsis. RNA-seq analysis showed that overexpression of GmWRKY46 led to change in many genes related to energy metabolisms, stress responses, and plant hormone signal transduction in transgenic Arabidopsis. Among these differential expression genes, we found that overexpression of AtAED1 alone could enhance the tolerance of transgenic Arabidopsis to Pi starvation. Y1H and ChIP-qPCR analyses showed that GmWRKY46 could directly bind to the W-box motif of the AtAED1 promoter in vitro and in vivo. Furthermore, results from intact soybean composite plants with GmWRKY46 overexpression showed that GmWRKY46 was involved in hairy roots development and subsequently affected plant growth and Pi uptake. These results provide a basis for the molecular genetic breeding of soybean tolerant to Pi starvation.

Loss of Gene Information: Discrepancies between RNA Sequencing, cDNA Microarray, and qRT-PCR

Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, we compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, we found differences in the gene expression profiles of the aforementioned techniques. Here, we present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, we suggest that these findings need to be considered when dealing with data from transcriptome analyses.

Advances in understanding the molecular pathology of gynecological malignancies: the role and potential of RNA sequencing

For many years technological limitations restricted the progress of identifying the underlying genetic causes of gynecologicalcancers. However, during the past decade, high-throughput next-generation sequencing technologies have revolutionized cancer research. RNA sequencing has arisen as a very useful technique in expanding our understanding of genome changes in cancer. Cancer is characterized by the accumulation of genetic alterations affecting genes, including substitutions, insertions, deletions, translocations, gene fusions, and alternative splicing. If these aberrant genes become transcribed, aberrations can be detected by RNA sequencing, which will also provide information on the transcript abundance revealing the expression levels of the aberrant genes. RNA sequencing is considered the technique of choice when studying gene expression and identifying new RNA species. This is due to the quantitative and qualitative improvement that it has brought to transcriptome analysis, offering a resolution that allows research into different layers of transcriptome complexity. It has also been successful in identifying biomarkers, fusion genes, tumor suppressors, and uncovering new targets responsible for drug resistance in gynecological cancers. To illustrate that we here review the role of RNA sequencing in studies that enhanced our understanding of the molecular pathology of gynecological cancers.

Next-generation sequencing technologies: An overview

Since the days of Sanger sequencing, next-generation sequencing technologies have significantly evolved to provide increased data output, efficiencies, and applications. These next generations of technologies can be categorized based on read length. This review provides an overview of these technologies as two paradigms: short-read, or "second-generation," technologies, and long-read, or "third-generation," technologies. Herein, short-read sequencing approaches are represented by the most prevalent technologies, Illumina and Ion Torrent, and long-read sequencing approaches are represented by Pacific Biosciences and Oxford Nanopore technologies. All technologies are reviewed along with reported advantages and disadvantages. Until recently, short-read sequencing was thought to provide high accuracy limited by read-length, while long-read technologies afforded much longer read-lengths at the expense of accuracy. Emerging developments for third-generation technologies hold promise for the next wave of sequencing evolution, with the co-existence of longer read lengths and high accuracy.

Multi-omic approach provides insights into osmoregulation and osmoconformation of the crab Scylla paramamosain

Osmoregulation and osmoconformation are two mechanisms through which aquatic animals adapt to salinity fluctuations. The euryhaline crab Scylla paramamosain, being both an osmoconformer and osmoregulator, is an excellent model organism to investigate salinity adaptation mechanisms in brachyurans. In the present study, we used transcriptomic and proteomic approaches to investigate the response of S. paramamosain to salinity stress. Crabs were transferred from a salinity of 25 ppt to salinities of 5 ppt or 33 ppt for 6 h and 10 days. Data from both approaches revealed that exposure to 5 ppt resulted in upregulation of ion transport and energy metabolism associated genes. Notably, acclimation to low salinity was associated with early changes in gene expression for signal transduction and stress response. In contrast, exposure to 33 ppt resulted in upregulation of genes related to amino acid metabolism, and amino acid transport genes were upregulated only at the early stage of acclimation to this salinity. Our study reveals contrasting mechanisms underlying osmoregulation and osmoconformation within the salinity range of 5–33 ppt in the mud crab, and provides novel candidate genes for osmotic signal transduction, thereby providing insights on understanding the salinity adaptation mechanisms of brachyuran crabs.

OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.

Genome-wide transcriptional response of papain-like cysteine protease-mediated resistance against Xanthomonas oryzae pv. oryzae in rice

Transgenic rice overexpressing PLCP attenuated the virulence of Xanthomonas oryzae pv. oryzae through extensive activation of transduction signal and transcription activities that orchestrate downstream responses including the biosynthesis of secondary metabolites and up-regulation of several pathogenesis-related proteins. High-throughput transcriptome investigations of plant immunity highlight the complexity of gene networks leading to incompatible interaction with the pathogen. Accumulating findings implicate papain-like cysteine proteases (PLCPs) as a central hub in plant defense. While diverse roles of PLCPs in different pathosystems have become more evident, information on gene networks and signaling pathways necessary to orchestrate downstream responses are lacking. To understand the biological significance of cysteine protease against Xanthomonas oryzae pv. oryzae, PLCP overexpression and knockout rice lines were generated. The pathogenicity test revealed the attenuation of Xanthomonas oryzae pv. oryzae race K3a virulence in transgenic lines which is ascribed to high hydrogen peroxide and free salicylic acid accumulation. Next-generation sequencing of RNA from transgenic and wild-type plants identified 1597 combined differentially expressed genes, 1269 of which were exclusively regulated in the transgenic libraries. It was found that PLCP aids rice to circumvent infection through the extensive activation of transduction signal and transcription factors that orchestrate downstream responses, including up-regulation of multiple pathogenesis-related proteins and biosynthesis of secondary metabolites.

Ccrn4l as a pre-dose marker for prediction of cisplatin-induced hepatotoxicity susceptibility

Clinical cisplatin use is often limited by its drug-induced liver injury (DILI). Particularly, individual differences in susceptibility to DILI can cause life-threatening medical conditions. This study aimed to uncover the inherent genetic factors determining individual variations in hepatotoxicity susceptibility. Rats were subjected to liver biopsy and a 3-week postoperative recovery period before cisplatin administration. At 2 days post-treatment with cisplatin, the rats exhibited histopathological and serum biochemical alterations in the liver, and changes in hydrogen peroxide and cytochrome P450-2E1 levels. Based on these results of liver-related biochemical markers, 32 rats were grouped into the susceptible (top five) and resistant (bottom five) groups. Using RNA-sequencing, we compared gene expressions in the liver pre-biopsied from these two groups before cisplatin treatment and found 161 differently expressed genes between the Susceptible and Resistant groups. Among them, the clock-controlled Ccrn4l responsible for 'rhythmic process' was identified as a common gene downregulated inherently prior to drug exposure in both cisplatin- and acetaminophen-sensitive animals. Additionally, low Ccrn4l levels before cisplatin treatment in the Susceptible group were maintained even after treatment, with decreased antioxidants, increased nitration, and apoptosis. The relationship of Ccrn4l with catalase and mitochondrial RNAs in the liver was confirmed by correlation of their hepatic levels among individuals and similar patterns of circadian variation in their mRNA expression. Remarkably, Ccrn4l knockdown promoted cisplatin-induced mitochondrial dysfunction in WB-F344 cells with antioxidant catalase and apoptosis-related Bax changes. Inherent individual hepatic Ccrn4l level might be a novel factor affecting cisplatin-induced hepatotoxicity susceptibility, possibly through regulation of mitochondrial and antioxidant functions.

Chromatin-enriched RNAs mark active and repressive cis-regulation: An analysis of nuclear RNA-seq

Long noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. Chromatin-enriched RNAs (cheRNAs) are a unique class of lncRNAs that are tightly bound to chromatin and putatively function to locally cis-activate gene transcription. CheRNAs can be identified by biochemical fractionation of nuclear RNA followed by RNA sequencing, but until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and develop a new pipeline, Tuxedo-ch, which outperforms other approaches. Tuxedo-ch assembles a more complete transcriptome and identifies cheRNA with higher accuracy than other approaches. We used Tuxedo-ch to analyze benchmark datasets of K562 cells and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to enhancer RNAs (eRNAs). We quantify the transcriptional correlation of icheRNA and adjacent genes and show that icheRNA is more positively associated with neighboring gene expression than eRNA or cap analysis of gene expression (CAGE) signals. We also explore two novel genomic associations of cheRNA, which indicate that cheRNAs may function to promote or repress gene expression in a context-dependent manner. IcheRNA loci with significant levels of H3K9me3 modifications are associated with active enhancers, consistent with the hypothesis that enhancers are derived from ancient mobile elements. In contrast, antisense cheRNA (as-cheRNA) may play a role in local gene repression, possibly through local RNA:DNA:DNA triple-helix formation.

Nuclear RNA-seq provides a powerful way to profile the transcriptional landscape, especially the noncoding transcriptome. Through analyzing nuclear RNA-seq, the chromatin-enriched RNA (cheRNA) class of gene regulatory non-coding RNAs was identified. The computational framework presented here provides a reliable approach to identifying cheRNAs from nuclear RNA-seq, and for studying cell-type specific gene regulation. We find that intergenic cheRNA, including transcripts mapped to regions with high levels of classically repressive H3K9me3-marks, may act as a transcriptional activator. In contrast, antisense cheRNA, which originates from the DNA strand complementary to the candidate target protein-coding gene may interact with diverse chromatin modulators to repress local transcription. Our new pipeline allows the identification of a more complete set of cheRNAs than other approaches. A future challenge will be to refine the functional mechanisms of cheRNAs by exploring their regulatory roles, which are involved in diverse molecular and cellular processes in humans and other organisms.

A Four-Chemokine Signature Is Associated with a T-cell-Inflamed Phenotype in Primary and Metastatic Pancreatic Cancer

PURPOSE

The molecular drivers of antitumor immunity in pancreatic ductal adenocarcinoma (PDAC) are poorly understood, posing a major obstacle for the identification of patients potentially amenable for immune-checkpoint blockade or other novel strategies. Here, we explore the association of chemokine expression with effector T-cell infiltration in PDAC.

EXPERIMENTAL DESIGN

Discovery cohorts comprised 113 primary resected PDAC and 107 PDAC liver metastases. Validation cohorts comprised 182 PDAC from The Cancer Genome Atlas and 92 PDACs from the Australian International Cancer Genome Consortium. We explored associations between immune cell counts by immunohistochemistry, chemokine expression, and transcriptional hallmarks of antitumor immunity by RNA sequencing (RNA-seq), and mutational burden by whole-genome sequencing.

RESULTS

Among all known human chemokines, a coregulated set of four (CCL4, CCL5, CXCL9, and CXCL10) was strongly associated with CD8⁺ T-cell infiltration (P < 0.001). Expression of this "4-chemokine signature" positively correlated with transcriptional metrics of T-cell activation (ZAP70, ITK, and IL2RB), cytolytic activity (GZMA and PRF1), and immunosuppression (PDL1, PD1, CTLA4, TIM3, TIGIT, LAG3, FASLG, and IDO1). Furthermore, the 4-chemokine signature marked tumors with increased T-cell activation scores (MHC I presentation, T-cell/APC costimulation) and elevated expression of innate immune sensing pathways involved in T-cell priming (STING and NLRP3 inflammasome pathways, BATF3-driven dendritic cells). Importantly, expression of this 4-chemokine signature was consistently indicative of a T-cell-inflamed phenotype across primary PDAC and PDAC liver metastases.

CONCLUSIONS

A conserved 4-chemokine signature marks resectable and metastatic PDAC tumors with an active antitumor phenotype. This could have implications for the appropriate selection of PDAC patients in immunotherapy trials.

Molecular Basis of Soybean Resistance to Soybean Aphids and Soybean Cyst Nematodes

Soybean aphid (SBA; Aphis glycines Matsumura) and soybean cyst nematode (SCN; Heterodera glycines Ichninohe) are major pests of the soybean (Glycine max [L.] Merr.). Substantial progress has been made in identifying the genetic basis of limiting these pests in both model and non-model plant systems. Classical linkage mapping and genome-wide association studies (GWAS) have identified major and minor quantitative trait loci (QTLs) in soybean. Studies on interactions of SBA and SCN effectors with host proteins have identified molecular cues in various signaling pathways, including those involved in plant disease resistance and phytohormone regulations. In this paper, we review the molecular basis of soybean resistance to SBA and SCN, and we provide a synthesis of recent studies of soybean QTLs/genes that could mitigate the effects of virulent SBA and SCN populations. We also review relevant studies of aphid–nematode interactions, particularly in the soybean–SBA–SCN system.

Understanding sequencing data as compositions: an outlook and review

Motivation

Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models.

Results

The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

Supplementary information

Supplementary data are available at Bioinformatics online.

Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer

Next-generation sequencing has produced a large quantity of DNA or RNA sequences related to the processes occurring within tumors and their microenvironment in a reasonable time and cost. These data have been used to guide the identification of neoantigens and to determine their specific T-cell receptors. Furthermore, adoptive T-cell therapy targeting neoantigens is under development for cancer treatment. In this review, we first provide an overview of sequencing technologies and the updated findings concerning neoantigens related to adoptive T-cell therapy and then summarize the methods and principles underlying the development of next-generation sequencing-based neoantigen-reactive T-cell therapy for cancer.

Transcriptome profiling of interaction effects of soybean cyst nematodes and soybean aphids on soybean

Soybean aphid (Aphis glycines; SBA) and soybean cyst nematode (Heterodera glycines; SCN) are two major pests of soybean (Glycine max) in the United States of America. This study aims to characterize three-way interactions among soybean, SBA, and SCN using both demographic and genetic datasets. SCN-resistant and SCN-susceptible soybean cultivars with a combination of soybean aphids (biotype 1) and SCN (HG type 0) in a randomized complete block design (RCBD) with six blocks were used to evaluate the three-way interactions in a greenhouse setup. Treatments receiving SCN were infested at planting with 2000 nematode eggs, and the treatments with soybean aphids were infested at second trifoliate growth stage (V2) with 15 soybean aphids. The whole roots were sampled from plants at 5 and 30 days post SBA infestation for RNA sequencing using Illumina Hiseq. 3000. The data comprises of 47 libraries that are useful for further analyses of important genes, which are involved in interaction effects of SBA and SCN on soybean.

Bioinformatic analysis of bacteria and host cell dual RNA-sequencing experiments

Bacterial pathogens subvert host cells by manipulating cellular pathways for survival and replication; in turn, host cells respond to the invading pathogen through cascading changes in gene expression. Deciphering these complex temporal and spatial dynamics to identify novel bacterial virulence factors or host response pathways is crucial for improved diagnostics and therapeutics. Dual RNA sequencing (dRNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. This approach builds on the high sensitivity and resolution of RNA sequencing technology and is applicable to any bacteria that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. Several laboratory protocols have been presented that outline the collection, extraction and sequencing of total RNA for dRNA-Seq experiments, but there is relatively little guidance available for the detailed bioinformatic analyses required. This protocol outlines a typical dRNA-Seq experiment, based on a Chlamydia trachomatis-infected host cell, with a detailed description of the necessary bioinformatic analyses with currently available software tools.

DEWE: A novel tool for executing differential expression RNA-Seq workflows in biomedical research

BACKGROUND

Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills.

METHODS

Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results.

RESULTS

DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis.

CONCLUSIONS

The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe.

Clinical and prognostic value of chaperonin containing T-complex 1 subunit 3 in hepatocellular carcinoma: A Study based on microarray and RNA-sequencing with 4272 cases

Liver cancer is one of the few tumors with a steadily increasing morbidity and mortality; hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. We combined the expression profiles of Chaperonin Containing T-complex 1 Subunit 3 (CCT3) in HCC tissues based on microarray and RNA-sequencing data. The CCT3 expression levels were extracted and examined based on 421 samples from The Cancer Genome Atlas (TCGA) (HCC, n = 371; non-HCC, n = 50) and 3851 samples from 31 microarray or RNA-sequencing datasets (HCC, n = 1975; non-tumor = 1876). We used a variety of meta-analytic methods, including SMD forest maps, sensitivity analysis, subgroup analysis and sROC curves, to confirm the final results. Meanwhile, database-derived immunohistochemistry data was used for validation. We also further explained the potential mechanism of CCT3 in HCC through signal pathway analyses and PPI network construction with the CCT3 co-expressed genes. The mRNA and protein expression of CCT3 in HCC tissues were higher than in non-HCC tissues. The expression of CCT3 differed between groups when grouped according to clinicopathological parameters, such as race, family history, and histological grade. The results of standardised mean difference (SMD) forest map and summary receiver operating characteristic (sROC) curve revealed that CCT3 was highly expressed in HCC tissues and had a high ability to distinguish between cancer tissues and non-cancer tissues. The main form of CCT3 gene alteration in HCC was mRNA up-regulation and amplification (23%), and the most common mutation type was missense. The mRNA expression of CCT3 in HCC was negatively correlated with DNA methylation. According to the Kyoto Encyclopedia of Genes and Genomes pathway analysis, CCT3 can influence HCC occurrence and development through cell cycle and DNA replication pathways. In summary, this study carries out the staging and prognostic analysis of HCC. It suggests that CCT3 might play an important part in the tumorigenesis and progression of HCC and may have a certain prognostic value in HCC. Moreover, CCT3 might represent a promising biomarker for HCC.

Rhazyaminine from Rhazya stricta Inhibits Metastasis and Induces Apoptosis by Downregulating Bcl-2 Gene in MCF7 Cell Line

Background: The role of alkaloids isolated from Rhazya stricta Decne (Apocynaceae family) (RS) in targeting genes involved in cancer and metastasis remains to be elucidated. Objective: Identify and characterize new compounds from RS, which inhibit gene(s) involved in the survival, invasion, self-renewal, and metastatic processes of cancer cells. Methods: Bioinformatics study was performed using HISAT2, stringtie, and ballgown pipeline to understand expressional differences between a normal epithelial cell line-MCF10A and MCF7. NMR and ATR-FTIR were performed to elucidate the structure of rhazyaminine (R.A), isolated from R stricta. Cell viability assay was performed using 0, 25, and 50 μg/mL of total extract of R stricta (TERS) and R.A, respectively, for 0, 24, and 48 hours, followed by scratch assay. In addition, total RNA was isolated for RNA-seq analysis of MCF7 cell line treated with R.A followed by qRT-PCR analysis of Bcl-2 gene. Results: Deptor, which is upregulated in MCF7 compared with MCF10A as found in our bioinformatics study was downregulated by R.A. Furthermore, R.A effectively reduced cell viability to around 50% (P < .05) and restricted cell migration in scratch assay. Thirteen genes, related to metastasis and cancer stem cells, were downregulated by R.A according to RNA-seq analysis. Additionally, qRT-PCR validated the downregulation of Bcl-2 gene in R.A-treated cells by less than 0.5 folds (P < .05). Conclusion: R.A successfully downregulated key genes involved in apoptosis, cell survival, epithelial-mesenchymal transition, cancer stem cell proliferation, and Wnt signal transduction pathway making it an excellent “lead candidate” molecule for in vivo proof-of-concept studies.

MGL_3741 gene contributes to pathogenicity of Malassezia globosa in pityriasis versicolor

Dihydroxyacid dehydratase (DHAD) is a key enzyme in biosynthetic pathway of isoleucine and valine. This pathway is absent in human but exists in various organisms such as fungi. Using RNA-seq analysis in this study, we identified MGL_3741gene which encodes DHAD protein in Malassezia globosa (M. globosa). Furthermore, we found that mentioned gene is homologous to the Ustilago maydis, Saccharomyces cerevisiae, Aspergillus flavus, and Aspergillus fumigatus ILV3P. For understanding the probable role of this gene in pathogenicity of M. globosa, we applied Real-time PCR to investigate the differentially expressed of the MGL_3741 gene in healthy and pathogenic states. Our results indicate a significant difference between two mentioned stats. These results revealed that ILV3-like gene in M. globosa can be related to the pathogenicity of this yeast.

Transcriptome Profiling of Bigelowiella natans in Response to Light Stress

Bigelowiella natans is a marine chlorarachniophyte whose plastid was acquired secondarily via endosymbiosis with a green alga. During plastid evolution, the photosynthetic endosymbiont would have integrated with the host metabolic pathways. This would require the evolution and coordination of strategies to cope with changes in light intensity that includes changes in the expression of both endosymbiont and host-derived genes. To investigate the transcriptional response to light intensity in chlorarachniophytes, we conducted an RNA-seq experiment to identify differentially expressed genes following a 4-h shift to high or very-low light. A shift to high light altered the expression of over 2,000 genes, many involved with photosynthesis, PSII assembly, primary metabolism, and reactive-oxygen scavenging. These changes are an attempt to optimize photosynthesis and increase energy sinks for excess reductant, while minimizing photooxidative stress. A transfer to very-low light resulted in a lower photosynthetic performance and metabolic alteration, reflecting an energy-limited state. Genes located on the nucleomorph, the vestigial nucleus in the plastid, had few changes in expression in either light treatment, indicating this organelle has relinquished most transcriptional control to the nucleus. Overall, during plastid origin, both host and transferred endosymbiont genes evolved a harmonized transcriptional network to respond to a classic photosynthetic stress.

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods

BACKGROUND

Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2.

RESULTS

To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations.

CONCLUSIONS

Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.

A Workflow Guide to RNA-seq Analysis of Chaperone Function and Beyond

RNA sequencing (RNA-seq) is a powerful method of transcript analysis that allows for the sequence identification and quantification of cellular transcripts. RNA-seq has many applications including differential gene expression (DE) analysis, gene fusion detection, allele-specific expression, isoform and splice variant quantification, and identification of novel genes. These applications can be used for downstream systems biology analyses such as gene ontology analysis to provide insights into cellular processes altered between biological conditions. Given the wide range of signaling pathways subject to chaperone activity as well as numerous chaperone functions in RNA metabolism, RNA-seq may provide a valuable tool for the study of chaperone proteins in biology and disease. This chapter outlines an example RNA-seq workflow to determine differentially expressed (DE) genes between two or more sample conditions and provides some considerations for RNA-seq experimental design.

Functional Genomics

Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.

Comprehensive discovery of noncoding RNAs in acute myeloid leukemia cell transcriptomes

To detect diverse and novel RNA species comprehensively, we compared deep small RNA and RNA sequencing (RNA-seq) methods applied to a primary acute myeloid leukemia (AML) sample. We were able to discover previously unannotated small RNAs using deep sequencing of a library method using broader insert size selection. We analyzed the long noncoding RNA (lncRNA) landscape in AML by comparing deep sequencing from multiple RNA-seq library construction methods for the sample that we studied and then integrating RNA-seq data from 179 AML cases. This identified lncRNAs that are completely novel, differentially expressed, and associated with specific AML subtypes. Our study revealed the complexity of the noncoding RNA transcriptome through a combined strategy of strand-specific small RNA and total RNA-seq. This dataset will serve as an invaluable resource for future RNA-based analyses.

Interactions of Freshwater Cyanobacteria with Bacterial Antagonists

Cyanobacterial and algal mass development, or blooms, have severe effects on freshwater and marine systems around the world. Many of these phototrophs produce a variety of potent toxins, contribute to oxygen depletion, and affect water quality in several ways. Coexisting antagonists, such as cyanolytic bacteria, hold the potential to suppress, or even terminate, such blooms, yet the nature of this interaction is not well studied. We isolated 31 cyanolytic bacteria affiliated with the genera Pseudomonas, Stenotrophomonas, Acinetobacter, and Delftia from three eutrophic freshwater lakes in Sweden and selected four phylogenetically diverse bacterial strains with strong-to-moderate lytic activity. To characterize their functional responses to the presence of cyanobacteria, we performed RNA sequencing (RNA-Seq) experiments on coculture incubations, with an initial predator-prey ratio of 1:1. Genes involved in central cellular pathways, stress-related heat or cold shock proteins, and antitoxin genes were highly expressed in both heterotrophs and cyanobacteria. Heterotrophs in coculture expressed genes involved in cell motility, signal transduction, and putative lytic activity. l,d-Transpeptidase was the only significantly upregulated lytic gene in Stenotrophomonas rhizophila EK20. Heterotrophs also shifted their central metabolism from the tricarboxylic acid cycle to the glyoxylate shunt. Concurrently, cyanobacteria clearly show contrasting antagonistic interactions with the four tested heterotrophic strains, which is also reflected in the physical attachment to their cells. In conclusion, antagonistic interactions with cyanobacteria were initiated within 24 h, and expression profiles suggest varied responses for the different cyanobacteria and studied cyanolytes.IMPORTANCE Here, we present how gene expression profiles can be used to reveal interactions between bloom-forming freshwater cyanobacteria and antagonistic heterotrophic bacteria. Species-specific responses in both heterotrophs and cyanobacteria were identified. The study contributes to a better understanding of the interspecies cellular interactions underpinning the persistence and collapse of cyanobacterial blooms.

Exosomes and Exosomal MicroRNAs in Prostate Cancer Radiation Therapy

Despite current risk stratification systems using traditional clinicopathologic factors, many localized and locally advanced prostate cancers fail radical treatment (ie, radical prostatectomy, radiation therapy with or without androgen deprivation therapy). Therefore, a pressing need exists for enhanced methods of disease stratification through novel prognostic and predictive tools that can reliably be applied in clinical practice. Exosomes are 50- to 150-nm small vesicles released by cancer cells that reflect the genetic and nongenetic materials of parent cancer cells. Cancer cells can contain distinct sets of microRNA profiles, the expression of which can change owing to stress such as radiation therapy. These alterations or distinctions in contents allow exosomes to be used as prognostic and/or predictive biomarkers and to monitor the treatment response. Additionally, microRNAs have been shown to influence multiple processes in prostate tumorigenesis, including cell proliferation, induction of apoptosis, migration, oncogene inhibition, and radioresistance. Thus, comparative exosomal microRNA profiling at different levels could help portray tumor aggressiveness and response to radiation therapy. Although technical challenges persist in exosome isolation and characterization, recent improvements in microRNA profiling have evolved toward in-depth analyses of the exosomal cargo and its functions. We have reviewed the role of exosomes and exosomal microRNAs in biologic processes of prostate cancer progression and radiation therapy response, with a particular focus on the development of clinical assays for treatment personalization.