A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

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.

High-risk neuroblastoma with NF1 loss of function is targetable using SHP2 inhibition

Reoccurring/high-risk neuroblastoma (NB) tumors have the enrichment of non-RAS/RAF mutations along the mitogen-activated protein kinase (MAPK) signaling pathway, suggesting that activation of MEK/ERK is critical for their survival. However, based on preclinical data, MEK inhibitors are unlikely to be active in NB and have demonstrated dose-limiting toxicities that limit their use. Here, we explore an alternative way to target the MAPK pathway in high-risk NB. We find that NB models are among the most sensitive among over 900 tumor-derived cell lines to the allosteric SHP2 inhibitor SHP099. Sensitivity to SHP099 in NB is greater in models with loss or low expression of the RAS GTPase activation protein (GAP) neurofibromin 1 (NF1). Furthermore, NF1 is lower in advanced and relapsed NB and NF1 loss is enriched in high-risk NB tumors regardless of MYCN status. SHP2 inhibition consistently blocks tumor growth in high-risk NB mouse models, revealing a new drug target in relapsed NB.

Expression and activation of nuclear hormone receptors result in neuronal differentiation and favorable prognosis in neuroblastoma

BACKGROUND

Neuroblastoma (NB), a childhood tumor derived from the sympathetic nervous system, presents with heterogeneous clinical behavior. While some tumors regress spontaneously without medical intervention, others are resistant to therapy, associated with an aggressive phenotype. MYCN-amplification, frequently occurring in high-risk NB, is correlated with an undifferentiated phenotype and poor prognosis. Differentiation induction has been proposed as a therapeutic approach for high-risk NB. We have previously shown that MYCN maintains an undifferentiated state via regulation of the miR-17 ~ 92 microRNA cluster, repressing the nuclear hormone receptors (NHRs) estrogen receptor alpha (ERα) and the glucocorticoid receptor (GR).

METHODS

Cell viability was determined by WST-1. Expression of differentiation markers was analyzed by Western blot, RT-qPCR, and immunofluorescence analysis. Metabolic phenotypes were studied using Agilent Extracellular Flux Analyzer, and accumulation of lipid droplets by Nile Red staining. Expression of angiogenesis, proliferation, and neuronal differentiation markers, and tumor sections were assessed by immunohistochemistry. Gene expression from NB patient as well as adrenal gland cohorts were analyzed using GraphPad Prism software (v.8) and GSEA (v4.0.3), while pseudo-time progression on post-natal adrenal gland cells from single-nuclei transcriptome data was computed using scVelo.

RESULTS

Here, we show that simultaneous activation of GR and ERα potentiated induction of neuronal differentiation, reduced NB cell viability in vitro, and decreased tumor burden in vivo. This was accompanied by a metabolic reprogramming manifested by changes in the glycolytic and mitochondrial functions and in lipid droplet accumulation. Activation of the retinoic acid receptor alpha (RARα) with all-trans retinoic acid (ATRA) further enhanced the differentiated phenotype as well as the metabolic switch. Single-cell nuclei transcriptome analysis of human adrenal glands indicated a sequential expression of ERα, GR, and RARα during development from progenitor to differentiated chromaffin cells. Further, in silico analysis revealed that patients with higher combined expression of GR, ERα, and RARα mRNA levels had elevated expression of neuronal differentiation markers and a favorable outcome.

CONCLUSION

Together, our findings suggest that combination therapy involving activation of several NHRs could be a promising pharmacological approach for differentiation treatment of NB patients.

AC-PCoA: Adjustment for confounding factors using principal coordinate analysis

Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data.

In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.

With today’s unprecedented amount of data, researchers are challenged by the need to enhance meaningful signals without the interference of unwanted confounders hidden inside the data. Data visualization is an important step toward exploring and explaining data in order to intuitively identify the dominant patterns. Principal coordinate analysis (PCoA), as a visualization tool, allows flexible ways to define pairwise distances and project the samples into lower dimensions without changing the distances. However, when visualizing large-scale biological datasets, the true patterns are often hindered by unwanted confounding variations, either biologically or technically in origin. To eliminate these confounding factors and recover underlying signals, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, and showed that it significantly outperforms existing methods in visualization through three simulation studies and five real datasets. We further showed that the low-dimensional representations given by AC-PCoA provide promising results in statistical testing, clustering, and classification as well.

Needle: a fast and space-efficient prefilter for estimating the quantification of very large collections of expression experiments

MOTIVATION

The ever-growing size of sequencing data is a major bottleneck in bioinformatics as the advances of hardware development cannot keep up with the data growth. Therefore, an enormous amount of data is collected but rarely ever reused, because it is nearly impossible to find meaningful experiments in the stream of raw data.

RESULTS

As a solution, we propose Needle, a fast and space-efficient index which can be built for thousands of experiments in <2 h and can estimate the quantification of a transcript in these experiments in seconds, thereby outperforming its competitors. The basic idea of the Needle index is to create multiple interleaved Bloom filters that each store a set of representative k-mers depending on their multiplicity in the raw data. This is then used to quantify the query.

AVAILABILITY AND IMPLEMENTATION

https://github.com/seqan/needle.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of RNA degradation on next-generation sequencing transcriptome data

RNA sequencing is an innovative technology to study transcriptomes in both biological and clinical research. However, clinical specimens from patients undergoing surgical operations have a major challenge due to sample degradation. This study replicated the process of RNA degradation by maintaining cells at room temperature to achieve none, slight, middle, and high levels of RNA degradation with decreasing RNA integrity numbers (RIN) of approximately 9.8, 6.7, 4.4, and 2.5, respectively. Next, the differential expression of mRNA and long non-coding RNA (lncRNA) was analyzed in the four degradation groups along with pathway enrichment analysis. The results showed that the similarity of lncRNAs exhibited significant differences even for a slight level of RNA degradation compared with the non-degraded RNA sample. Also, the RNA degradation process was found to be universal, global, and random; the differentially expressed genes increased with an increase in degradation but the pathway enrichment phenomenon was not significantly observed.

Growing Maize Root: Lectins Involved in Consecutive Stages of Cell Development

Proteins that carry specific carbohydrate-binding lectin domains have a great variety and are ubiquitous across the plant kingdom. In turn, the plant cell wall has a complex carbohydrate composition, which is subjected to constant changes in the course of plant development. In this regard, proteins with lectin domains are of great interest in the context of studying their contribution to the tuning and monitoring of the cell wall during its modifications in the course of plant organ development. We performed a genome-wide screening of lectin motifs in the Zea mays genome and analyzed the transcriptomic data from five zones of primary maize root with cells at different development stages. This allowed us to obtain 306 gene sequences encoding putative lectins and to relate their expressions to the stages of root cell development and peculiarities of cell wall metabolism. Among the lectins whose expression was high and differentially regulated in growing maize root were the members of the EUL, dirigent–jacalin, malectin, malectin-like, GNA and Nictaba families, many of which are predicted as cell wall proteins or lectin receptor-like kinases that have direct access to the cell wall. Thus, a set of molecular players was identified with high potential to play important roles in the early stages of root morphogenesis.

Bookend: precise transcript reconstruction with end-guided assembly

We developed Bookend, a package for transcript assembly that incorporates data from different RNA-seq techniques, with a focus on identifying and utilizing RNA 5' and 3' ends. We demonstrate that correct identification of transcript start and end sites is essential for precise full-length transcript assembly. Utilization of end-labeled reads present in full-length single-cell RNA-seq datasets dramatically improves the precision of transcript assembly in single cells. Finally, we show that hybrid assembly across short-read, long-read, and end-capture RNA-seq datasets from Arabidopsis thaliana, as well as meta-assembly of RNA-seq from single mouse embryonic stem cells, can produce reference-quality end-to-end transcript annotations.

A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies

There is growing recognition across broad sectors of the scientific community that use of genomic biomarkers has the potential to reduce the need for conventional rodent carcinogenicity studies of industrial chemicals, agrochemicals, and pharmaceuticals through a weight-of-evidence approach. These biomarkers fall into 2 major categories: (1) sets of gene transcripts that can identify distinct tumorigenic mechanisms of action; and (2) cancer driver gene mutations indicative of rapidly expanding growth-advantaged clonal cell populations. This call-to-action article describes a collaborative approach launched to develop and qualify biomarker gene expression panels that measure widely accepted molecular pathways linked to tumorigenesis and their activation levels to predict tumorigenic doses of chemicals from short-term exposures. Growing evidence suggests that application of such biomarker panels in short-term exposure rodent studies can identify both tumorigenic hazard and tumorigenic activation levels for chemical-induced carcinogenicity. In the future, this approach will be expanded to include methodologies examining mutations in key cancer driver gene mutation hotspots as biomarkers of both genotoxic and nongenotoxic chemical tumor risk. Analytical, technical, and biological validation studies of these complementary genomic tools are being undertaken by multisector and multidisciplinary collaborative teams within the Health and Environmental Sciences Institute. Success from these efforts will facilitate the transition from current heavy reliance on conventional 2-year rodent carcinogenicity studies to more rapid animal- and resource-sparing approaches for mechanism-based carcinogenicity evaluation supporting internal and regulatory decision-making.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Development and validation of an RNA-seq-based transcriptomic risk score for asthma

Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. The objective of this study was to develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information. We analyzed RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples. Logistic least absolute shrinkage and selection operator (Lasso) regression analysis in the training set identified 73 differentially expressed genes (DEG) to form a weighted RSRS that discriminated asthmatics from healthy subjects with area under the curve (AUC) of 0.80 in the testing set after adjustment for age and gender. The 73-gene RSRS was validated in three independent RNA-seq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively. To explore their biological and molecular functions in asthma phenotype, we examined the 73 genes by enrichment pathway analysis and found that these genes were significantly (p < 0.0001) enriched for DNA replication, recombination, and repair, cell-to-cell signaling and interaction, and eumelanin biosynthesis and developmental disorder. Further in-silico analyses of the 73 genes using Connectivity map shows that drugs (mepacrine, dactolisib) and genetic perturbagens (PAK1, GSR, RBM15 and TNFRSF12A) were identified and could potentially be repurposed for treating asthma. These findings show the promise for RNA-seq risk scores to stratify and predict disease risk.

Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats

Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.

CAR T-cell manufacturing from naive/stem memory T-lymphocytes enhances antitumor responses while curtailing cytokine release syndrome

Chimeric antigen receptor (CAR) T cell expansion and persistence represent key factors to achieve complete responses and prevent relapses. These features are typical of early memory T cells, which can be highly enriched through optimized manufacturing protocols. Here, we investigated the efficacy and safety profiles of CAR T cell products generated from preselected naive/stem memory T cells (T_N/SCM), as compared with unselected T cells (T_BULK). Notwithstanding their reduced effector signature in vitro, limiting CAR T_N/SCM doses showed superior antitumor activity and the unique ability to counteract leukemia rechallenge in hematopoietic stem/precursor cell–humanized mice, featuring increased expansion rates and persistence together with an ameliorated exhaustion and memory phenotype. Most relevantly, CAR T_N/SCM proved to be intrinsically less prone to inducing severe cytokine release syndrome, independently of the costimulatory endodomain employed. This safer profile was associated with milder T cell activation, which translated into reduced monocyte activation and cytokine release. These data suggest that CAR T_N/SCM are endowed with a wider therapeutic index compared with CAR T_BULK.

Research perspectives-Pipelines to human tendon transcriptomics

Tendon transcriptomics is a rapidly growing field in musculoskeletal biology. The ultimate aim of many current tendon transcriptomic studies is characterization of in vitro, ex vivo, or in vivo, healthy, and diseased tendon microenvironments to identify the underlying pathways driving human tendon pathology. The transcriptome interfaces between genomic, proteomic, and metabolomic signatures of the tendon cellular niche and the response of this niche to stimuli. Some of the greatest bottlenecks in tendon transcriptomics relate to the availability and quality of human tendon tissue, hence animal tissues are frequently used even though human tissue is most translationally relevant. Here, we review the variability associated with human donor and procurement factors, such as whether the tendon is cadaveric or a clinical remnant, and how these variables affect the quality and relevance of the transcriptomes obtained. Moreover, age, sex, and health demographic variables impact the human tendon transcriptome. Tendons present tissue-specific challenges for cell, nuclei, and RNA extraction that include a dense extracellular matrix, low cellularity, and therefore low RNA yield of variable quality. Consideration of these factors is particularly important for single-cell and single-nuclei resolution transcriptomics due to the necessity for unbiased and representative cell or nuclei populations. Different cell, nuclei, and RNA extraction methods, library preparation, and quality control methods are used by the tendon research community and attention should be paid to these when designing and reporting studies. We discuss the different components and challenges of human tendon transcriptomics, and propose pipelines, quality control, and reporting guidelines for future work in the field.

Noncoding RNA as Diagnostic and Prognostic Biomarkers in Cerebrovascular Disease

Noncoding RNAs (ncRNAs), such as microRNAs, long noncoding RNAs, and circular RNAs, play an important role in the pathophysiology of cerebrovascular diseases (CVDs). They are effectively detectable in body fluids, potentially suggesting new biomarkers for the early detection and prognosis of CVDs. In this review, the physiological functions of circulating ncRNAs and their potential role as diagnostic and prognostic markers in patients with cerebrovascular diseases are discussed, especially in acute ischemic stroke, subarachnoid hemorrhage, and moyamoya disease.

Prenatal opioid-exposed infant extracellular miRNA signature obtained at birth predicts severity of neonatal opioid withdrawal syndrome

Prenatal opioid exposure (POE) is commonly associated with neonatal opioid withdrawal syndrome (NOWS), which is characterized by a broad variability in symptoms and severity. Currently there are no diagnostic tools to reliably predict which infants will develop severe NOWS, while risk stratification would allow for proactive decisions about appropriate clinical monitoring and interventions. The aim of this prospective cohort study was to assess if extracellular microRNAs (miRNAs) in umbilical cord plasma of infants with POE could predict NOWS severity. Participants (n = 58) consisted of pregnant women receiving medications for opioid use disorder and their infants. NOWS severity was operationalized as the need for pharmacologic treatment and prolonged hospitalization (≥ 14 days). Cord blood miRNAs were assessed using semi-quantitative qRT-PCR arrays. Receiver operating characteristic curves and area under the curve (AUC) were estimated. The expression of three miRNAs (miR-128-3p, miR-30c-5p, miR-421) predicted need for pharmacologic treatment (AUC: 0.85) and prolonged hospitalization (AUC: 0.90). Predictive validity improved after two miRNAs (let-7d-5p, miR-584-5p) were added to the need for pharmacologic treatment model (AUC: 0.94) and another two miRNAs (let-7b-5p, miR-10-5p) to the prolonged hospitalization model (AUC: 0.99). Infant cord blood extracellular miRNAs can proactively identify opioid-exposed neonates at high-risk for developing severe NOWS.

EIF4EBP1 is transcriptionally upregulated by MYCN and associates with poor prognosis in neuroblastoma

Neuroblastoma (NB) accounts for 15% of cancer-related deaths in childhood despite considerable therapeutic improvements. While several risk factors, including MYCN amplification and alterations in RAS and p53 pathway genes, have been defined in NB, the clinical outcome is very variable and difficult to predict. Since genes of the mechanistic target of rapamycin (mTOR) pathway are upregulated in MYCN-amplified NB, we aimed to define the predictive value of the mTOR substrate-encoding gene eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1) expression in NB patients. Using publicly available data sets, we found that EIF4EBP1 mRNA expression is positively correlated with MYCN expression and elevated in stage 4 and high-risk NB patients. In addition, high EIF4EBP1 mRNA expression is associated with reduced overall and event-free survival in the entire group of NB patients in three cohorts, as well as in stage 4 and high-risk patients. This was confirmed by monitoring the clinical value of 4EBP1 protein expression, which revealed that high levels of 4EBP1 are significantly associated with prognostically unfavorable NB histology. Finally, functional analyses revealed that EIF4EBP1 expression is transcriptionally controlled by MYCN binding to the EIF4EBP1 promoter in NB cells. Our data highlight that EIF4EBP1 is a direct transcriptional target of MYCN whose high expression is associated with poor prognosis in NB patients. Therefore, EIF4EBP1 may serve to better stratify patients with NB.

Ensemble of nucleic acid absolute quantitation modules for copy number variation detection and RNA profiling

Current gold standard for absolute quantitation of a specific DNA sequence is droplet digital PCR (ddPCR), which has been applied to copy number variation (CNV) detection. However, the number of quantitation modules in ddPCR is limited by fluorescence channels, which thus limits the CNV sensitivity due to sampling error following Poisson distribution. Here we develop a PCR-based molecular barcoding NGS approach, quantitative amplicon sequencing (QASeq), for accurate absolute quantitation scalable to over 200 quantitation modules. By attaching barcodes to individual target molecules with high efficiency, 2-plex QASeq exhibits higher and more consistent conversion yield than ddPCR in absolute molecule count quantitation. Multiplexed QASeq improves CNV sensitivity allowing confident distinguishment of 2.05 ploidy from normal 2.00 ploidy. We apply multiplexed QASeq to serial longitudinal plasma cfDNA samples from patients with metastatic ERBB2+ (HER2+ ) breast cancer seeking association with tumor progression. We further show an RNA QASeq panel for targeted expression profiling.

Impact of gene annotation choice on the quantification of RNA-seq data

Background

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.

Results

In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

Conclusion

In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04644-8.

Perplexity: evaluating transcript abundance estimation in the absence of ground truth

Background

There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.

Results

We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.

Conclusions

Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.

Transcriptomics Reveals How Minocycline-Colistin Synergy Overcomes Antibiotic Resistance in Multidrug-Resistant Klebsiella pneumoniae

Multidrug-resistant Gram-negative bacteria are a rapidly growing public health threat, and the development of novel antimicrobials has failed to keep pace with their emergence. Synergistic combinations of individually ineffective drugs present a potential solution, yet little is understood about the mechanisms of most such combinations. Here, we show that the combination of colistin (polymyxin E) and minocycline has a high rate of synergy against colistin-resistant and minocycline-intermediate or -resistant strains of Klebsiella pneumoniae. Furthermore, using transcriptome sequencing (RNA-Seq), we characterized the transcriptional profiles of these strains when treated with the drugs individually and in combination. We found a striking similarity between the transcriptional profiles of bacteria treated with the combination of colistin and minocycline at individually subinhibitory concentrations and those of the same isolates treated with minocycline alone. We observed a similar pattern with the combination of polymyxin B nonapeptide (a polymyxin B analogue that lacks intrinsic antimicrobial activity) and minocycline. We also found that genes involved in polymyxin resistance and peptidoglycan biosynthesis showed significant differential gene expression in the different treatment conditions, suggesting possible mechanisms for the antibacterial activity observed in the combination. These findings suggest that the synergistic activity of this combination against bacteria resistant to each drug alone involves sublethal outer membrane disruption by colistin, which permits increased intracellular accumulation of minocycline.

The Power of Single-Cell RNA Sequencing in eQTL Discovery

Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.

Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

Background

A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries.

Results

Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ^′ end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription.

Conclusions

Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08352-8).

Allele-specific expression reveals genes with recurrent cis-regulatory alterations in high-risk neuroblastoma

BACKGROUND

Neuroblastoma is a pediatric malignancy with a high frequency of metastatic disease at initial diagnosis. Neuroblastoma tumors have few recurrent protein-coding mutations but contain extensive somatic copy number alterations (SCNAs) suggesting that mutations that alter gene dosage are important drivers of tumorigenesis. Here, we analyze allele-specific expression in 96 high-risk neuroblastoma tumors to discover genes impacted by cis-acting mutations that alter dosage.

RESULTS

We identify 1043 genes with recurrent, neuroblastoma-specific allele-specific expression. While most of these genes lie within common SCNA regions, many of them exhibit allele-specific expression in copy neutral samples and these samples are enriched for mutations that are predicted to cause nonsense-mediated decay. Thus, both SCNA and non-SCNA mutations frequently alter gene expression in neuroblastoma. We focus on genes with neuroblastoma-specific allele-specific expression in the absence of SCNAs and find 26 such genes that have reduced expression in stage 4 disease. At least two of these genes have evidence for tumor suppressor activity including the transcription factor TFAP2B and the protein tyrosine phosphatase PTPRH.

CONCLUSIONS

In summary, our allele-specific expression analysis discovers genes that are recurrently dysregulated by both large SCNAs and other cis-acting mutations in high-risk neuroblastoma.

R-ODAF: Omics data analysis framework for regulatory application

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.

RNA sequencing and its applications in cancer and rare diseases

With the invention of RNA sequencing over a decade ago, diagnosis and identification of the gene-related diseases entered a new phase that enabled more accurate analysis of the diseases that are difficult to approach and analyze. RNA sequencing has availed in-depth study of transcriptomes in different species and provided better understanding of rare diseases and taxonomical classifications of various eukaryotic organisms. Development of single-cell, short-read, long-read and direct RNA sequencing using both blood and biopsy specimens of the organism together with recent advancement in computational analysis programs has made the medical professional's ability in identifying the origin and cause of genetic disorders indispensable. Altogether, such advantages have evolved the treatment design since RNA sequencing can detect the resistant genes against the existing therapies and help medical professions to take a further step in improving methods of treatments towards higher effectiveness and less side effects. Therefore, it is of essence to all researchers and scientists to have deeper insight in all available methods of RNA sequencing while taking a step-in therapy design.

Comparison of Two Illumina Whole Transcriptome RNA Sequencing Library Preparation Methods Using Human Cancer FFPE Specimens

Objective: RNA extraction and library preparation from formalin-fixed, paraffin-embedded (FFPE) samples are crucial pre-analytical steps towards achieving optimal downstream RNA sequencing (RNASeq) results. In this study, we assessed 2 Illumina library preparation methods for RNA-Seq analysis using archived FFPE samples from human cancer indications at 2 independent vendors. Methods: Twenty-five FFPE samples from 5 indications (non-small cell lung cancer, colorectal cancer, renal carcinoma, breast cancer, and hepatocellular carcinoma) were included, covering a wide range of sample storage durations (3-25 years-old), sample qualities, and specimen types (resection vs core needle biopsy). Each sample was processed independently by both vendors. Total RNA was isolated using the Qiagen miRNeasy FFPE kit followed by library construction using either TruSeq Stranded Total RNA library preparation kit with Ribo-Zero Gold, or TruSeq RNA Access library preparation kit. Libraries were normalized to 20 pM and sequenced on an Illumina HiSeq 2500 using V3 chemistry in paired-end mode with a read length of 2 × 50 bp. The data were processed through a standard RNASeq pipeline to produce counts and transcripts per millions for each gene in each sample to compare 2 library kits at 2 different vendors. Results: Our data showed that TruSeq RNA Access libraries yield over 80% exonic reads across different quality samples, indicating higher selectivity of the exome pull down by the capture approach compared to the random priming of the TruSeq Stranded Total kit. The overall QC data for FFPE RNA extraction, library preparation, and sequencing generated by the 2 vendors are comparable, and downstream gene expression quantification results show high concordance as well. With the TruSeq Stranded Total kit, the mean Spearman correlation between vendors was 0.87 and the mean Pearson correlation was 0.76. With the TruSeq RNA Access kit, the mean Spearman correlation between vendors was 0.89 and the mean Pearson correlation was 0.73. Interestingly, examination of the cross-vendor correlations compared to various common QC statistics suggested that library concentration is better correlated with consistency between vendors than is the RNA quantity. Conclusions: Our analyses provide evidence to guide selection of sequencing methods for FFPE samples in which the sample quality may be severely compromised.

Forgotten Actors: Glycoside Hydrolases During Elongation Growth of Maize Primary Root

Plant cell enlargement is coupled to dynamic changes in cell wall composition and properties. Such rearrangements are provided, besides the differential synthesis of individual cell wall components, by enzymes that modify polysaccharides in muro. To reveal enzymes that may contribute to these modifications and relate them to stages of elongation growth in grasses, we carried out a transcriptomic study of five zones of the primary maize root. In the initiation of elongation, significant changes occur with xyloglucan: once synthesized in the meristem, it can be linked to other polysaccharides through the action of hetero-specific xyloglucan endotransglycosidases, whose expression boosts at this stage. Later, genes for xyloglucan hydrolases are upregulated. Two different sets of enzymes capable of modifying glucuronoarabinoxylans, mainly bifunctional α-arabinofuranosidases/β-xylosidases and β-xylanases, are expressed in the maize root to treat the xylans of primary and secondary cell walls, respectively. The first set is highly pronounced in the stage of active elongation, while the second is at elongation termination. Genes encoding several glycoside hydrolases that are able to degrade mixed-linkage glucan are downregulated specifically at the active elongation. It indicates the significance of mixed-linkage glucans for the cell elongation process. The possibility that many glycoside hydrolases act as transglycosylases in muro is discussed.

Systematic comparative analysis of strand-specific RNA-seq library preparation methods for low input samples

Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies—Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) —alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount.

MYCN upregulates the transsulfuration pathway to suppress the ferroptotic vulnerability in MYCN-amplified neuroblastoma

Ferroptosis is an iron-dependent, oxidative form of cell death that is countered mainly by glutathione peroxidase 4 (GPX4) and the production of glutathione (GSH), which is formed from cysteine. The identification of the cancers that may benefit from pharmacological ferroptotic induction is just emerging. We recently demonstrated that inducing ferroptosis genetically or pharmacologically in MYCN-amplified neuroblastoma (NB) is a novel and effective way to kill these cells. MYCN increases iron metabolism and subsequent hydroxyl radicals through increased expression of the transferrin receptor 1 (TfR1) and low levels of the ferroportin receptor. To counter increased hydroxyl radicals, MYCN binds to the promoter of SLC3A2 (solute carrier family 3 member 2). SLC3A2 is a subunit of system Xc-, which is the cysteine-glutamate antiporter that exports glutamate and imports cystine. Cystine is converted to cysteine intracellularly. Here, we investigated other ways MYCN may increase cysteine levels. By performing metabolomics in a syngeneic NB cell line either expressing MYCN or GFP, we demonstrate that the transsulfuration pathway is activated by MYCN. Furthermore, we demonstrate that MYCN-amplified NB cell lines and tumors have higher levels of cystathionine beta-synthase (CBS), the rate-limiting enzyme in transsulfuration, which leads to higher levels of the thioether cystathionine (R-S-(2-amino-2-carboxyethyl)-l-homocysteine). In addition, MYCN-amplified NB tumors have high levels of methylthioadenosine phosphorylase (MTAP), an enzyme that helps salvage methionine following polyamine metabolism. MYCN directly binds to the promoter of MTAP. We propose that MYCN orchestrates both enhanced cystine uptake and enhanced activity of the transsulfuration pathway to counteract increased reactive oxygen species (ROS) from iron-induced Fenton reactions, ultimately contributing to a ferroptosis vulnerability in MYCN-amplified neuroblastoma.

Identifying and optimizing human endometrial gene expression signatures for endometrial dating

STUDY QUESTION

What are the key considerations for developing an enhanced transcriptomic method for secretory endometrial tissue dating?

SUMMARY ANSWER

Multiple gene expression signature combinations can serve as biomarkers for endometrial dating, but their predictive performance is variable and depends on the number and identity of the genes included in the prediction model, the dataset characteristics and the technology employed for measuring gene expression.

WHAT IS KNOWN ALREADY

Among the new generation of transcriptomic endometrial dating (TED) tools developed in the last decade, there exists variation in the technology used for measuring gene expression, the gene makeup and the prediction model design. A detailed study, comparing prediction performance across signatures for understanding signature behaviour and discrepancies in gene content between them, is lacking.

STUDY DESIGN, SIZE, DURATION

A multicentre prospective study was performed between July 2018 and October 2020 at five different centres from the same group of clinics (Spain). This study recruited 281 patients and finally included in the gene expression analysis 225 Caucasian patients who underwent IVF treatment. After preprocessing and batch effect filtering, gene expression measurements from 217 patients were combined with artificial intelligence algorithms (support vector machine, random forest and k-nearest neighbours) allowing evaluation of different prediction models. In addition, secretory-phase endometrial transcriptomes from gene expression omnibus (GEO) datasets were analysed for 137 women, to study the endometrial dating capacity of genes independently and grouped by signatures. This provided data on the consistency of prediction across different gene expression technologies and datasets.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Endometrial biopsies were analysed using a targeted TruSeq (Illumina) custom RNA expression panel called the endometrial dating panel (ED panel). This panel included 301 genes previously considered relevant for endometrial dating as well as new genes selected for their anticipated value in detecting the secretory phase. Final samples (n = 217) were divided into a training set for signature discovery and an independent testing set for evaluation of predictive performance of the new signature. In addition, secretory-phase endometrial transcriptomes from GEO were analysed for 137 women to study endometrial dating capacity of genes independently and grouped by signatures. Predictive performance among these signatures was compared according to signature gene set size.

MAIN RESULTS AND THE ROLE OF CHANCE

Testing of the ED panel allowed development of a model based on a new signature of 73 genes, which we termed 'TED' and delivers an enhanced tool for the consistent dating of the secretory phase progression, especially during the mid-secretory endometrium (3-8 days after progesterone (P) administration (P + 3-P + 8) in a hormone replacement therapy cycle). This new model showed the best predictive capacity in an independent test set for staging the endometrial tissue in the secretory phase, especially in the expected window of implantation (average of 114.5 ± 7.2 h of progesterone administered; range in our patient population of 82-172 h). Published sets of genes, in current use for endometrial dating and the new TED genes, were evaluated in parallel in whole-transcriptome datasets and in the ED panel dataset. TED signature performance was consistently excellent for all datasets assessed, frequently outperforming previously published sets of genes with a smaller number of genes for dating the endometrium in the secretory phase. Thus, this optimized set exhibited prediction consistency across datasets.

LARGE SCALE DATA

The data used in this study is partially available at GEO database. GEO identifiers GSE4888, GSE29981, GSE58144, GSE98386.

LIMITATIONS, REASONS FOR CAUTION

Although dating the endometrial biopsy is crucial for investigating endometrial progression and the receptivity process, further studies are needed to confirm whether or not endometrial dating methods in general are clinically useful and to guide the specific use of TED in the clinical setting.

WIDER IMPLICATIONS OF THE FINDINGS

Multiple gene signature combinations provide adequate endometrial dating, but their predictive performance depends on the identity of the genes included, the gene expression platform, the algorithms used and dataset characteristics. TED is a next-generation endometrial assessment tool based on gene expression for accurate endometrial progression dating especially during the mid-secretory.

STUDY FUNDING/COMPETING INTEREST(S)

Research funded by IVI Foundation (1810-FIVI-066-PD). P.D.-G. visiting scientist fellowship at Oxford University (BEFPI/2010/032) and Josefa Maria Sanchez-Reyes' predoctoral fellowship (ACIF/2018/072) were supported by a program from the Generalitat Valenciana funded by the Spanish government. A.D.-P. is supported by the FPU/15/01398 predoctoral fellowship from the Ministry of Science, Innovation and Universities (Spanish Government). D.W. received support from the NIHR Oxford Biomedical Research Centre. The authors do not have any competing interests to declare.

Using FIBexDB for In-Depth Analysis of Flax Lectin Gene Expression in Response to Fusarium oxysporum Infection

Plant proteins with lectin domains play an essential role in plant immunity modulation, but among a plurality of lectins recruited by plants, only a few members have been functionally characterized. For the analysis of flax lectin gene expression, we used FIBexDB, which includes an efficient algorithm for flax gene expression analysis combining gene clustering and coexpression network analysis. We analyzed the lectin gene expression in various flax tissues, including root tips infected with Fusarium oxysporum. Two pools of lectin genes were revealed: downregulated and upregulated during the infection. Lectins with suppressed gene expression are associated with protein biosynthesis (Calreticulin family), cell wall biosynthesis (galactose-binding lectin family) and cytoskeleton functioning (Malectin family). Among the upregulated lectin genes were those encoding lectins from the Hevein, Nictaba, and GNA families. The main participants from each group are discussed. A list of lectin genes, the expression of which can determine the resistance of flax, is proposed, for example, the genes encoding amaranthins. We demonstrate that FIBexDB is an efficient tool both for the visualization of data, and for searching for the general patterns of lectin genes that may play an essential role in normal plant development and defense.

The life cycle-dependent transcriptional profile of the obligate intracellular amoeba symbiont Amoebophilus asiaticus

Free-living amoebae often harbor obligate intracellular bacterial symbionts. Amoebophilus (A.) asiaticus is a representative of a lineage of amoeba symbionts in the phylum Bacteroidota. Here, we analyse the transcriptome of A. asiaticus strain 5a2 at four time points during its infection cycle and replication within the Acanthamoeba host using RNA sequencing. Our results reveal a dynamic transcriptional landscape throughout different A. asiaticus life cycle stages. Many intracellular bacteria and pathogens utilize eukaryotic-like proteins (ELPs) for host cell interaction and the A. asiaticus 5a2 genome shows a particularly high abundance of ELPs. We show the expression of all genes encoding ELPs and found many ELPs to be differentially expressed. At the replicative stage of A. asiaticus, ankyrin repeat proteins and tetratricopeptide/Sel1-like repeat proteins were upregulated. At the later time points, high expression levels of a type 6 secretion system that likely prepares for a new infection cycle after lysing its host, were found. This study reveals comprehensive insights into the intracellular lifestyle of A. asiaticus and highlights candidate genes for host cell interaction. The results from this study have implications for other intracellular bacteria such as other amoeba-associated bacteria and the arthropod symbionts Cardinium forming the sister lineage of A. asiaticus.

W. Emerging technologies and their impact on regulatory science

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

OUP accepted manuscript

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed ‘DANA’—an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.

The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies

Introduction

Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial.

Methods

We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths.

Results and Discussion

Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.

Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Single Cell Scale Neuronal and Glial Gene Expression and Putative Cell Phenotypes and Networks in the Nucleus Tractus Solitarius in an Alcohol Withdrawal Time Series

Alcohol withdrawal syndrome (AWS) is characterized by neuronal hyperexcitability, autonomic dysregulation, and severe negative emotion. The nucleus tractus solitarius (NTS) likely plays a prominent role in the neurological processes underlying these symptoms as it is the main viscerosensory nucleus in the brain. The NTS receives visceral interoceptive inputs, influences autonomic outputs, and has strong connections to the limbic system and hypothalamic-pituitary-adrenal axis to maintain homeostasis. Our prior analysis of single neuronal gene expression data from the NTS shows that neurons exist in heterogeneous transcriptional states that form distinct functional subphenotypes. Our working model conjectures that the allostasis secondary to alcohol dependence causes peripheral and central biological network decompensation in acute abstinence resulting in neurovisceral feedback to the NTS that substantially contributes to the observed AWS. We collected single noradrenergic and glucagon-like peptide-1 (GLP-1) neurons and microglia from rat NTS and measured a subset of their transcriptome as pooled samples in an alcohol withdrawal time series. Inflammatory subphenotypes predominate at certain time points, and GLP-1 subphenotypes demonstrated hyperexcitability post-withdrawal. We hypothesize such inflammatory and anxiogenic signaling contributes to alcohol dependence via negative reinforcement. Targets to mitigate such dysregulation and treat dependence can be identified from this dataset.

Systematic Genome-Wide Profiles Reveal Alternative Splicing Landscape and Implications of Splicing Regulator DExD-Box Helicase 21 in Aggressive Progression of Adrenocortical Carcinoma

Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients (p < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including BAG2, CXorf56, DExD-Box Helicase 21 (DDX21), HSPB1, MBNL3, MSI1, RBMXL2, and SEC31B, were identified in regulatory networks of ACC. DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of DDX21 for predicting prognosis for patients with ACC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-021-00026-x.

Multicenter assessment of shotgun metagenomics for pathogen detection

BACKGROUND

Shotgun metagenomics has been used clinically for diagnosing infectious diseases. However, most technical assessments have been limited to individual sets of reference standards, experimental workflows, and laboratories.

METHODS

A reference panel and performance metrics were designed and used to examine the performance of shotgun metagenomics at 17 laboratories in a coordinated collaborative study. We comprehensively assessed the reliability, key performance determinants, reproducibility, and quantitative potential.

FINDINGS

Assay performance varied significantly across sites and microbial classes, with a read depth of 20 millions as a generally cost-efficient assay setting. Results of mapped reads by shotgun metagenomics could indicate relative and intra-site (but not absolute or inter-site) microbial abundance.

INTERPRETATION

Assay performance was significantly impacted by the microbial type, the host context, and read depth, which emphasizes the importance of these factors when designing reference reagents and benchmarking studies. Across sites, workflows and platforms, false positive reporting and considerable site/library effects were common challenges to the assay's accuracy and quantifiability. Our study also suggested that laboratory-developed shotgun metagenomics tests for pathogen detection should aim to detect microbes at 500 CFU/mL (or copies/mL) in a clinically relevant host context (10^5 human cells/mL) within a 24h turn-around time, and with an efficient read depth of 20M.

FUNDING

This work was supported by National Science and Technology Major Project of China (2018ZX10102001).

How many markers are needed to robustly determine a cell's type?

Our understanding of cell types has advanced considerably with the publication of single-cell atlases. Marker genes play an essential role for experimental validation and computational analyses such as physiological characterization, annotation, and deconvolution. However, a framework for quantifying marker replicability and selecting replicable markers is currently lacking. Here, using high-quality data from the Brain Initiative Cell Census Network (BICCN), we systematically investigate marker replicability for 85 neuronal cell types. We show that, due to dataset-specific noise, we need to combine 5 datasets to obtain robust differentially expressed (DE) genes, particularly for rare populations and lowly expressed genes. We estimate that 10 to 200 meta-analytic markers provide optimal downstream performance and make available replicable marker lists for the 85 BICCN cell types. Replicable marker lists condense interpretable and generalizable information about cell types, opening avenues for downstream applications, including cell type annotation, selection of gene panels, and bulk data deconvolution.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

Performance evaluation of transcriptomics data normalization for survival risk prediction

One pivotal feature of transcriptomics data is the unwanted variations caused by disparate experimental handling, known as handling effects. Various data normalization methods were developed to alleviate the adverse impact of handling effects in the setting of differential expression analysis. However, little research has been done to evaluate their performance in the setting of survival outcome prediction, an important analysis goal for transcriptomics data in biomedical research. Leveraging a unique pair of datasets for the same set of tumor samples-one with handling effects and the other without, we developed a benchmarking tool for conducting such an evaluation in microRNA microarrays. We applied this tool to evaluate the performance of three popular normalization methods-quantile normalization, median normalization and variance stabilizing normalization-in survival prediction using various approaches for model building and designs for sample assignment. We showed that handling effects can have a strong impact on survival prediction and that quantile normalization, a most popular method in current practice, tends to underperform median normalization and variance stabilizing normalization. We demonstrated with a small example the reason for quantile normalization's poor performance in this setting. Our finding highlights the importance of putting normalization evaluation in the context of the downstream analysis setting and the potential of improving the development of survival predictors by applying median normalization. We make available our benchmarking tool for performing such evaluation on additional normalization methods in connection with prediction modeling approaches.

Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed-ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.

cdev: a ground-truth based measure to evaluate RNA-seq normalization performance

Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

Impact of human gene annotations on RNA-seq differential expression analysis

BACKGROUND

Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated-a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.

RESULTS

Using "mappability", a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.

CONCLUSIONS

We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

Revealing protein-protein interactions at the transcriptome scale by sequencing

We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.

Establishment and Validation of a Prognostic Immune Signature in Neuroblastoma

BACKGROUND

Neuroblastoma (NBL) is the most common extracranial solid tumor in childhood, and patients with high-risk neuroblastoma had a relatively poor prognosis despite multimodal treatment. To improve immunotherapy efficacy in neuroblastoma, systematic profiling of the immune landscape in neuroblastoma is an urgent need.

METHODS

RNA-seq and according clinical information of neuroblastoma were downloaded from the TARGET database and GEO database (GSE62564). With an immune-related-gene set obtained from the ImmPort database, Immune-related Prognostic Gene Pairs for Neuroblastoma (IPGPN) for overall survival (OS) were established with the TARGET-NBL cohort and then verified with the GEO-NBL cohort. Immune cell infiltration analysis was subsequently performed. The integrated model was established with IPGPN and clinicopathological parameters. Immune cell infiltration was analyzed with the XCELL algorithm. Functional enrichment analysis was performed with clusterProfiler package in R.

RESULTS

Immune-related Prognostic Gene Pairs for Neuroblastoma was successfully established with seven immune-related gene pairs (IGPs) involving 13 unique genes in the training cohort. In the training cohort, IPGPN successfully stratified neuroblastoma patients into a high and low immune-risk groups with different OS (HR=3.92, P = 2 × 10^-8) and event-free survival (HR=3.66, P=2 × 10^-8). ROC curve analysis confirmed its predictive power. Consistently, high IPGPN also predicted worse OS (HR=1.84, P = .002) and EFS in validation cohort (HR=1.38, P = .06) Moreover, higher activated dendritic cells, M1 macrophage, Th1 CD4⁺, and Th2 CD4⁺ T cell enrichment were evident in low immune-risk group. Further integrating IPGPN with age and stage demonstrated improved predictive performance than IPGPN alone.

CONCLUSION

Herein, we presented an immune landscape with IPGPN for prognosis prediction in neuroblastoma, which complements the present understanding of the immune signature in neuroblastoma.