Conventional RNA-seq Research Articles

An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions

BackgroundDual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively.ResultsIn all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities.ConclusionThe enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Immunoactive signatures of circulating tRNA- and rRNA-derived RNAs in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is the most prevalent lung disease, and macrophages play a central role in the inflammatory response in COPD. We here report a comprehensive characterization of circulating short non-coding RNAs (sncRNAs) in plasma from patients with COPD. While circulating sncRNAs are increasingly recognized for their regulatory roles and biomarker potential in various diseases, the conventional RNA-seq method cannot fully capture these circulating sncRNAs due to their heterogeneous terminal structures. By pre-treating the plasma RNAs with T4 polynucleotide kinase, which converts all RNAs to those with RNA-seq susceptible ends (5'-phosphate and 3'-hydroxyl), we comprehensively sequenced a wide variety of non-microRNA sncRNAs, such as 5'-tRNA halves containing a 2',3'-cyclic phosphate. We discovered a remarkable accumulation of the 5'-half derived from tRNA ValCAC in plasma from COPD patients, whereas the 5'-tRNA GlyGCC half is predominant in healthy donors. Further, the 5'-tRNA ValCAC half activates human macrophages via Toll-like receptor 7 and induces cytokine production. Additionally, we identified circulating rRNA-derived fragments that were upregulated in COPD patients and demonstrated their ability to induce cytokine production in macrophages. Our findings provide evidence of circulating, immune-active sncRNAs in patients with COPD, suggesting that they serve as inflammatory mediators in the pathogenesis of COPD.

Abstract PO4-23-11: Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing

Abstract Background Understanding the mechanisms of resistance to CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is pivotal in exploring new therapeutic strategies for hormone receptor-positive (HR+), HER2-negative metastatic breast cancer. To decipher these resistance mechanisms, we analyzed the alterations in comprehensive RNA-seq (coding and non-coding RNAs) of HR+ HER2-negative BC cell lines via thermostable group II intron reverse transcriptase sequencing (TGIRT-seq). Methods We established Tamoxifen-resistant (TMR), Abemaciclib-resistant (ACR), Palbociclib-resistant (PCR), Tamoxifen/Abemaciclib double-resistant (TMR-ACR), and Tamoxifen/Palbociclib double-resistant (TMR-PCR) BC cell lines from MCF7 and T47D HR+/HER2- BC cell lines through stepwise dose-escalation continuous drug exposure. We performed a TGIRT-seq transcriptomic analysis using a protocol that allows the sequencing of both long and short non-coding and protein-coding RNAs in a single library. All libraries were sequenced using paired-end 150 bp on the Novaseq platform, resulting in an average of 50 million reads per library. For analysis, raw reads underwent adapter trimming, small RNA mapping, whole genome mapping, and the generation of annotated genes' read count. We used raw counts to detect differentially expressed genes (DEGs) with DESeq2 in R using the cut-off (log2 [fold change] > 1, FDR < 1e-3). We then used the selected DEGs for Gene Set Enrichment Analysis (GSEA) and Kaplan-Meier survival analysis from the TCGA database. Results The TGIRT-seq analysis identified 1171 to 3472 DEGs in different drug resistance and cell line combinations. Most DEGs in resistant cells were consistently downregulated compared to parent cells. Seventy-five percent of DEGs were protein-coding genes, with the rest being non-coding RNAs, such as small nucleolar RNAs, microRNAs, long non-coding RNAs, and transposable element RNAs, which conventional RNA-seq poorly detects. In principal component analysis (PCA) plots, replicates per drug resistance clustered clearly in both cell line backgrounds. PCA also indicated distinctive pathways for acquiring drug resistance in MCF7 and T47D cells. The estrogen receptor (ER) expression decreased, while HER2 increased in resistant cell lines. DEGs identified were enriched in ESR1-related pathways in all MCF7 and T47D cell lines resistant models. Several ER-regulating genes like IL-1R1 and RET were upregulated, while ADCY1 was consistently downregulated across different resistance types. A significant overlap with single-resistance DEGs was observed among the DEGs in double-resistance cell lines, but 20-50% of up/down-regulated DEGs in double-resistance cell lines were uniquely altered. Several previously identified DEGs (e.g., SALL4, TOP2A) and 21 novel candidate genes correlated with poor survival outcomes. Conclusion The analysis identified unique DEGs in double resistance cell lines, suggesting that double resistance mechanisms may not merely be a cumulative effect of single resistance mechanisms, necessitating further investigation and validation. CDK4/6i and/or ET-resistant BC cell lines displayed significant transcriptome reprogramming during the development of drug resistance. In previously published research, ESR1-related pathway alterations were proposed in tamoxifen resistance cell lines. Here we find that CDK4/6i-resistant cells also modify these ESR1-related pathways. Additionally, we identified several targetable genes, such as IL-1R1 and RET, involved in ESR1-related pathways that could pave the way for developing new treatment strategies. Citation Format: Toshiaki Iwase, Hengyi Xu, Nakyung Oh, Jangsoon Lee, Alan M Lambowitz, Naoto Ueno. Resistance Mechanisms to CDK4/6 Inhibitors and/or Tamoxifen Using Comprehensive Thermostable Group II Intron Reverse Transcriptase Sequencing [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-23-11.

An expanded transcriptome atlas for Bacteroides thetaiotaomicron reveals a small RNA that modulates tetracycline sensitivity.

Plasticity in gene expression allows bacteria to adapt to diverse environments. This is particularly relevant in the dynamic niche of the human intestinal tract; however, transcriptional networks remain largely unknown for gut-resident bacteria. Here we apply differential RNA sequencing (RNA-seq) and conventional RNA-seq to the model gut bacterium Bacteroides thetaiotaomicron to map transcriptional units and profile their expression levels across 15 in vivo-relevant growth conditions. We infer stress- and carbon source-specific transcriptional regulons and expand the annotation of small RNAs (sRNAs). Integrating this expression atlas with published transposon mutant fitness data, we predict conditionally important sRNAs. These include MasB, which downregulates tetracycline tolerance. Using MS2 affinity purification and RNA-seq, we identify a putative MasB target and assess its role in the context of the MasB-associated phenotype. These data-publicly available through the Theta-Base web browser ( http://micromix.helmholtz-hiri.de/bacteroides/ )-constitute a valuable resource for the microbiome community.

Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments.

A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.

A low-coverage 3' RNA-seq to detect homeolog expression in polyploid wheat.

Although allopolyploid species are common among natural and crop species, it is not easy to distinguish duplicated genes, known as homeologs, during their genomic analysis. Yet, cost-efficient RNA sequencing (RNA-seq) is to be developed for large-scale transcriptomic studies such as time-series analysis and genome-wide association studies in allopolyploids. In this study, we employed a 3' RNA-seq utilizing 3' untranslated regions (UTRs) containing frequent mutations among homeologous genes, compared to coding sequence. Among the 3' RNA-seq protocols, we examined a low-cost method Lasy-Seq using an allohexaploid bread wheat, Triticum aestivum. HISAT2 showed the best performance for 3' RNA-seq with the least mapping errors and quick computational time. The number of detected homeologs was further improved by extending 1 kb of the 3' UTR annotation. Differentially expressed genes in response to mild cold treatment detected by the 3' RNA-seq were verified with high-coverage conventional RNA-seq, although the latter detected more differentially expressed genes. Finally, downsampling showed that even a 2 million sequencing depth can still detect more than half of expressed homeologs identifiable by the conventional 32 million reads. These data demonstrate that this low-cost 3' RNA-seq facilitates large-scale transcriptomic studies of allohexaploid wheat and indicate the potential application to other allopolyploid species.

HdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

Transcriptomic analysis of human cytomegalovirus to survey the indirect effects on renal transplant recipients

Post-transplant human cytomegalovirus (HCMV) viremia has been linked to adverse “indirect effects” among transplant patients. HCMV-created immunomodulatory mechanisms could be associated with the indirect effects. ObjectiveIn the present study, the RNA-Seq whole transcriptome of renal transplant (RT) patients was analyzed to seek the underlying pathobiologic pathways associated with the long-term indirect effects of HCMV. MethodsTo investigate the activated biological pathways in HCMV infection, total RNA was extracted from PBMCs of 2 RT patients with active HCMV and 2 RT patients without infection and then were sequenced using RNA-Seq. The resulted raw data were analyzed by conventional RNA-Seq software to determine the Differentially Expressed Genes (DEGs). Afterward, Gene Ontology (GO) and pathway enrichment analyses were conducted to determine the enriched pathways and biological processes by DEGs. Eventually, the relative expressions of some significant genes were validated in the twenty external RT patients. ResultThe analysis of RNA-Seq data related to RT patients with HCMV active viremia led to the identification of 140 up-regulated and 100 down-regulated DEGs. KEGG pathway analysis revealed the enrichment of DEGs in IL18 signaling, AGE-RAGE signaling pathway in diabetic complications, signaling by GPCR, Platelet activation, signaling and aggregation, Estrogen signaling pathway and signaling by Wnt due to HCMV infection. The expression levels of six genes involved in enriched pathways including F3, PTX3, ADRA2B, GNG11, GP9, HBEGF were then verified using RT-qPCR. The results were in consistent with RNA-Seq resultsoutcomes. ConclusionThis study specifies some pathobiological pathways which are activated in HCMV active infection and could be linked to the adverse indirect effects caused by HCMV infection in transplant patients.

Dynamic activity in cis-regulatory elements of leukocytes identifies transcription factor activation and stratifies COVID-19 severity in ICU patients

Transcription factor programs mediating the immune response to coronavirus disease 2019 (COVID-19) are not fully understood. Capturing active transcription initiation from cis-regulatory elements such as enhancers and promoters by capped small RNA sequencing (csRNA-seq), in contrast to capturing steady-state transcripts by conventional RNA-seq, allows unbiased identification of the underlying transcription factor activity and regulatory pathways. Here, we profile transcription initiation in critically ill COVID-19 patients, identifying transcription factor motifs that correlate with clinical lung injury and disease severity. Unbiased clustering reveals distinct subsets of cis-regulatory elements that delineate the cell type, pathway-specific, and combinatorial transcription factor activity. We find evidence of critical roles of regulatory networks, showing that STAT/BCL6 and E2F/MYB regulatory programs from myeloid cell populations are activated in patients with poor disease outcomes and associated with COVID-19 susceptibility genetic variants. More broadly, we demonstrate how capturing acute, disease-mediated changes in transcription initiation can provide insight into the underlying molecular mechanisms and stratify patient disease severity.

Selective ablation of 3' RNA ends and processive RTs facilitate direct cDNA sequencing of full-length host cell and viral transcripts.

Alternative splicing (AS) is necessary for viral proliferation in host cells and a critical regulatory component of viral gene expression. Conventional RNA-seq approaches provide incomplete coverage of AS due to their short read lengths and are susceptible to biases and artifacts introduced in prevailing library preparation methodologies. Moreover, viral splicing studies are often conducted separately from host cell transcriptome analysis, precluding an assessment of the viral manipulation of host splicing machinery. To address current limitations, we developed a quantitative full-length direct cDNA sequencing strategy to simultaneously profile viral and host cell transcripts. This nanopore-based approach couples processive reverse transcriptases with a novel one-step chemical ablation of 3′ RNA ends (termed CASPR), which decreases ribosomal RNA reads and enriches polyadenylated coding sequences. We extensively validate our approach using synthetic reference transcripts and show that CASPR doubles the breadth of coverage per transcript and increases detection of long transcripts (>4 kb), while being functionally equivalent to PolyA+ selection for transcript quantification. We used our approach to interrogate host cell and HIV-1 transcript dynamics during viral reactivation and identified novel putative HIV-1 host factors containing exon skipping or novel intron retentions and delineated the HIV-1 transcriptional state associated with these differentially regulated host factors.

Comparative Toxicotranscriptomics of Single Cell RNA-Seq and Conventional RNA-Seq in TCDD-Exposed Testicular Tissue.

In this report, we compare the outcomes and limitations of two methods of transcriptomic inquiry on adult zebrafish testes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) during sexual differentiation: conventional or bulk RNA-seq (bulk-seq) and single cell RNA sequencing (scRNA-seq) data. scRNA-seq has emerged as a valuable tool for uncovering cell type-specific transcriptome dynamics which exist in heterogeneous tissue. Our lab previously showed the toxicological value of the scRNA-seq pipeline to characterize the sequelae of TCDD exposure in testes, demonstrating that loss of spermatids and spermatozoa, but not other cell types, contributed to the pathology of infertility in adult male zebrafish exposed during sexual differentiation. To investigate the potential for technical artifacts in scRNA-seq such as cell dissociation effects and reduced transcriptome coverage, we compared bulk-sequenced and scRNA-seq-paired samples from control and TCDD-exposed samples to understand what is gained and lost in scRNA-seq vs bulk-seq, both transcriptomically and toxicologically. We hypothesized that the testes may be sensitive to tissue disruption as they contain multiple cell types under constant division and/or maturation, and that TCDD exposure may mediate the extent of sensitivity. Thus, we sought to understand the extent to which this dissociation impacts the toxicological value of data returned from scRNA-seq. We confirm that the required dissociation of individual cells from intact tissue has a significant impact on gene expression, affecting gene pathways with the potential to confound toxicogenomics studies on exposures if findings are not well-controlled and well-situated in context. Additionally, a common scRNA-seq method using cDNA amplified from the 3’ end of mRNA under-detects low-expressing transcripts including transcription factors. We confirm this, and show TCDD-related genes may be overlooked by scRNA-seq, however, this under-detection effect is not mediated by TCDD exposure. Even so, scRNA-seq generally extracted toxicologically relevant information better than the bulk-seq method in the present study. This report aims to inform future experimental design for transcriptomic investigation in the growing field of toxicogenomics by demonstrating the differential information extracted from sequencing cells—despite being from the same tissue and exposure scheme—is influenced by the specific protocol used, with implications for the interpretation of exposure-induced risk.

The naturally occurring flavonoid nobiletin reverses methotrexate resistance via inhibition of P-glycoprotein synthesis

Methotrexate (MTX) is the first-line treatment for rheumatoid arthritis (RA). However, after long-term treatment, some patients develop resistance. P-glycoprotein (P-gp), as an indispensable drug transporter, is essential for mediating this MTX resistance. In addition, nobiletin (NOB), a naturally occurring polymethoxylated flavonoid, has also been shown to reverse P-gp–mediated MTX resistance in RA groups; however, the precise role of NOB in this process is still unclear. Here, we administered MTX and NOB alone or in combination to collagen II-induced arthritic (CIA) mice and evaluated disease severity using the arthritis index, synovial histopathological changes, immunohistochemistry, and P-gp expression. In addition, we used conventional RNA-seq to identify targets and possible pathways through which NOB reverses MTX-induced drug resistance. We found that NOB in combination with MTX could enhance its performance in synovial tissue and decrease P-gp expression in CIA mice compared to MTX treatment alone. In vitro, in MTX-resistant fibroblast-like synoviocytes from CIA cells (CIA-FLS/MTX), we show that NOB treatment downregulated the PI3K/AKT/HIF-1α pathway, thereby reducing the synthesis of the P-gp protein. In addition, NOB significantly inhibited glycolysis and metabolic activity of CIA-FLS/MTX cells, which could reduce the production of ATP and block P-gp, ultimately decreasing the efflux of MTX and maintaining its anti-RA effects. In conclusion, this study shows that NOB overcomes MTX resistance in CIA-FLS/MTX cells through the PI3K/AKT/HIF-1α pathway, simultaneously influencing metabolic processes and inhibiting P-gp–induced drug efflux.

Articles of Significant Interest In This Issue.

Herpesviruses employ extensively overlapping transcript structures whose expression is difficult to quantify using conventional methods, such as RT-qPCR or RNA-seq. Casco et al. (e01635-21) examine this using Epstein-Barr virus as a model system. They demonstrate that conventional RNA-seq analysis is accurate for non-overlapping genes, but inaccurate for overlapping transcripts, compared to a gold standard (CAGE-seq). They further show that read assignment methods that take this overlap into account significantly improve the accuracy of transcript quantification from RNA-seq.

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.

Omnipresence of Partitiviruses in Rice Aggregate Sheath Spot Symptom-Associated Fungal Isolates from Paddies in Thailand.

Partitiviruses are one of the most prevalent double-stranded RNA viruses that have been identified mostly in filamentous fungi and plants. Partitiviruses generally infect host fungi asymptomatically but infrequently exert significant effect(s) on morphology and virulence, thus being considered a potential source of biological control agents against pathogenic fungi. In this study, we performed a screening for mycoviruses of a collection of Thai isolates of rice fungal pathogen Rhizoctonia oryzae-sativae, a causal agent of rice aggregated sheath spot disease. As a result, 36% of tested isolates carried potentially viral double-stranded RNAs with sizes ranging from 2 to 3 kbp. By conventional cDNA library construction and RNA-seq, we determined six new alphapartitiviruses that infected three isolates: tentatively named Rhizoctonia oryzae-sativae partitivirus 1 to 6 (RosPV1-6). Furthermore, RT-PCR detection of each virus revealed their omnipresent nature in different R. oryzae-sativae isolates. Although virus-curing of basidiomycetous fungi is generally difficult, our repeated attempts successfully obtained virus-free (for RosPV1, RosPV2, and uncharacterized partitiviruses), isogenic strain of R. oryzae-sativae TSS190442. The virus-cured strain showed slightly faster colony growth on the synthetic media and severe symptom development on the rice sheath compared to its virus-infected counterpart. Overall, this study shed light on the distribution of partitiviruses in R. oryzae-sativae in a paddy environment and exemplified a virus-curing protocol that may be applicable for other basidiomycetous fungi.

Accurate Quantification of Overlapping Herpesvirus Transcripts from RNA Sequencing Data.

Herpesviruses employ extensive bidirectional transcription of overlapping genes to overcome length constraints on their gene product repertoire. As a consequence, many lytic transcripts cannot be measured individually by reverse transcription-quantitative PCR (RT-qPCR) or conventional RNA sequencing (RNA-seq) analysis. A. G. Bruce, S. Barcy, T. DiMaio, E. Gan, et al. (Pathogens 6:11, 2017, https://doi.org/10.3390/pathogens6010011) have proposed an approximation method using unique coding sequences (UCDS) to estimate lytic gene abundance from Kaposi's sarcoma-associated herpesvirus (KSHV) RNA-seq data. Although UCDS has been widely employed, its accuracy, to our knowledge, has never been rigorously validated for any herpesvirus. In this study, we use cap analysis of gene expression sequencing (CAGE-seq) as a gold-standard to determine the accuracy of UCDS for estimating Epstein-Barr virus (EBV) lytic gene expression levels from RNA-seq data. We also introduce the Unique TranScript (UTS) method, which, like UCDS, estimates transcript abundance from changes in mean RNA-seq read depth. UTS is distinguished by its use of empirically determined 5' and 3' transcript ends rather than coding sequence annotations. Compared to conventional read assignment, both UCDS and UTS improved the accuracy of quantitation of overlapping genes, with UTS giving the most-accurate results. The UTS method discards fewer reads and may be advantageous for experiments with less sequencing depth. UTS is compatible with any aligner and, unlike isoform-aware alignment methods, can be implemented on a laptop computer. Our findings demonstrate that the accuracy achieved by complex and expensive techniques such as CAGE-seq can be approximated using conventional short-read RNA-seq data when read assignment methods address transcript overlap. Although our study focuses on EBV transcription, the UTS method should be applicable across all herpesviruses as well as to other genomes with extensively overlapping transcriptomes. IMPORTANCE Many viruses employ extensively overlapping transcript structures. This complexity makes it difficult to quantify gene expression by using conventional methods, including RNA-seq. Although high-throughput techniques that overcome these limitations exist, they are complex, expensive, and scarce in the herpesvirus literature relative to short-read RNA-seq. Here, using Epstein-Barr virus (EBV) as a model, we demonstrate that conventional RNA-seq analysis methods fail to accurately quantify the abundances of many overlapping transcripts. We further show that the previously described Unique CoDing Sequence (UCDS) method and our Unique TranScript (UTS) method greatly improve the accuracy of EBV lytic gene measurements obtained from RNA-seq data. The UTS method has the advantages of discarding fewer reads and being implementable on a laptop computer. Although this study focuses on EBV, the UCDS and UTS methods should be applicable across herpesviruses and for other viruses that make extensive use of overlapping transcription.

Highly Sensitive Viral RNA-Seq Is a Powerful Tool to Analyze the Viral Transcripts in HTLV-1-Infected Individuals

IntroductionHuman T-cell leukemia virus type 1 (HTLV-1) is a retrovirus which causes two major pathologies in infected individuals, Adult T-cell leukemia-lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). There are tens of thousands of different infected clones in asymptomatic HTLV-1 carriers (AC) and each clone can be identified by the viral integration site. Monoclonal integration of HTLV-1 provirus is observed in the host genomic DNA in ATL cells, suggesting that these originate from a single virus-infected clone. It is well known that Tax and HBZ, encoded in the sense and antisense strands of HTLV-1 provirus, respectively, have the ability to induce oncogenesis. The question is how a certain infected clone, among various different ones, becomes a malignant clone. One hypothesis is that a malignant clone may express viral gene transcripts, associated with oncogenesis, more abundantly than a non-malignant clone. Another hypothesis is that HTLV-1 integration may induce an abnormal transcript in the host cell, such as the aberrant expression of the host gene nearby the integration site or the generation of a chimeric transcript between the virus and the host. To analyze HTLV-1 transcript or explore the presence of virus-host chimeric transcripts efficiently, we aimed to establish a method with higher resolution than the conventional RNA-seq by using DNA probe targeting for the entire HTLV-1 sequence. And then, we applied the protocol to characterize the pattern of transcription from HTLV-1 provirus and analyzed virus-host chimeric transcripts in patients' samples.MethodsTo check the efficiency and possible bias of the highly sensitive viral RNA-seq analysis, we analyzed viral transcript of an HTLV-1-infected clone, TBX-4B. Subsequently, mRNA-seq was performed with PBMCs from 6 AC, 4 HAM/TSP, and 5 ATL patients. After cDNA libraries synthesis, HTLV-1 sequences were enriched by the probe-based capture method as we reported previously for DNA-seq (Miyazato et al, Sci Rep 2016). The enriched libraries were sequenced in an Illumina NextSeq device.ResultsWe analyzed HTLV-1 transcript of a virus-infected T-cell clone both before and after the enrichment by viral-specific DNA probes. The proportion of viral sequences within the total number of reads was significantly increased from 0.05% to 80.62% (1600-fold) by the enrichment method. We found that most of the viral regions were significantly and similarly enriched by the additional enrichment step, suggesting the DNA probe-based enrichment of HTLV-1 sequences is applicable to RNA-seq. Next, we applied the protocol to analyze PBMC samples from HTLV-1-infected individuals. The results showed that the level of anti-sense HTLV-1 transcription was significantly higher than that of the sense transcription, not only in ATL patients, which is consistent with previous reports by other groups, but also in AC and HAM/TSP patients. Average proportion of sense and antisense signals were 3.42% and 96.46%, respectively. Even after the enrichment step, tax expression, a sense viral transcript, was undetectable in most of the samples except for one AC and one ATL patient, while an anti-sense transcript HBZ was detectable in all infected individuals. There was no significant difference in the expression level of HBZ between different clinical states. We identified more abundant virus-host chimeric transcripts in ATL samples than in HAM/TSP patients or AC. In ATL cases, HTLV-1 generated chimeric transcripts with the following host genes, HSD17B11, COLEC12, AHSA2, EEF2, TAOK1, KIAA0100, and KIF1B . The presence of abundant chimeric transcripts was validated by RT-PCR.DiscussionWe have established a highly sensitive viral RNA-seq for HTLV-1 transcript analysis. That significantly increased the sensitivity of detection of proviral and chimeric transcripts between virus and the host genome. The result was not consistent with the hypothesis that a malignant clone may express viral gene transcripts more abundantly than a non-malignant clone. Thus, the presence of chimeric transcripts could play a role in cell proliferation and help us reveal the mechanism of clonal selection for HTLV-1-infected cells. We are now going to analyze virus-host chimeric transcripts detected by the high sensitive RNA-seq in more patients' samples to clarify their association with oncogenesis. [Display omitted] DisclosuresYamano:Daiichi Sankyo: Research Funding; Ono Pharma: Research Funding. Utsunomiya:Celgene: Honoraria; Novartis Pharma: Honoraria; Roche Diagnostics: Honoraria; Japan Blood Products Organization: Honoraria; Daiichi Sankyo: Honoraria; Kyowa Hakko Kirin: Honoraria, Research Funding; Astellas Pharma: Honoraria; Chugai Pharma: Honoraria; Bristol-Myers Squibb: Honoraria; Siemens: Honoraria; Nippon Shinyaku: Honoraria; HUYA Bioscience International: Honoraria; Pfizer: Honoraria.

Applications of single-cell and bulk RNA sequencing in onco-immunology

The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.

Single Cell Label-Free Probing of Chromatin Dynamics During B Lymphocyte Maturation.

Large-scale intracellular signaling during developmental growth or in response to environmental alterations are largely orchestrated by chromatin within the cell nuclei. Chemical and conformational modifications of the chromatin architecture are critical steps in the regulation of differential gene expression and ultimately cell fate determination. Therefore, establishing chemical properties of the nucleus could provide key markers for phenotypic characterization of cellular processes on a scale of individual cells. Raman microscopy is a sensitive technique that is capable of probing single cell chemical composition—and sub-cellular regions—in a label-free optical manner. As such, it has great potential in both clinical and basic research. However, perceived limitations of Raman spectroscopy such as low signal intensity and the difficulty in linking alterations in vibrational signals directly with ensuing biological effects have hampered advances in the field. Here we use immune B lymphocyte development as a model to assess chromatin and transcriptional changes using confocal Raman microscopy in combination with microfluidic devices and correlative transcriptomics, thereby linking changes in chemical and structural properties to biological outcomes. Live B lymphocytes were assessed before and after maturation. Multivariate analysis was applied to distinguish cellular components within each cell. The spectral differences between non-activated and activated B lymphocytes were then identified, and their correlation with known intracellular biological changes were assessed in comparison to conventional RNA-seq analysis. Our data shows that spectral analysis provides a powerful tool to study gene activation that can complement conventional molecular biology techniques and opens the way for mapping the dynamics in the biochemical makeup of individual cells.

A two-stream convolutional neural network for microRNA transcription start site feature integration and identification

MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is the lack of condition-specific annotation of miRNA transcription start sites (TSSs). Unlike protein-coding genes, miRNA TSSs can be tens of thousands of nucleotides away from the precursor miRNAs and they are hard to be detected by conventional RNA-Seq experiments. A number of studies have been attempted to computationally predict miRNA TSSs. However, high-resolution condition-specific miRNA TSS prediction remains a challenging problem. Recently, deep learning models have been successfully applied to various bioinformatics problems but have not been effectively created for condition-specific miRNA TSS prediction. Here we created a two-stream deep learning model called D-miRT for computational prediction of condition-specific miRNA TSSs (http://hulab.ucf.edu/research/projects/DmiRT/). D-miRT is a natural fit for the integration of low-resolution epigenetic features (DNase-Seq and histone modification data) and high-resolution sequence features. Compared with alternative computational models on different sets of training data, D-miRT outperformed all baseline models and demonstrated high accuracy for condition-specific miRNA TSS prediction tasks. Comparing with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance.