Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource

Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

BACKGROUND

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals.

RESULTS

Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization.

CONCLUSIONS

We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver-derived transcriptomics datasets (ntotal = 137) which enabled ML-methods to classify spaceflown vs. ground control animals (AUC ≥ 0.87) while mitigating the variability from mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses

RNA-Sequencing is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has not been demonstrated. In fact, many common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. However, adding spike-ins appropriately is challenging with complex plant tissue, and carefully considering how they are added is essential to their successful use. We demonstrate that adding external RNA spike-ins as a normalization control produces differences in RNA-Seq analysis compared to traditional normalization methods, even between two times of day in untreated plants. We illustrate the use of RNA spike-ins with 3' RNA-Seq and present a normalization pipeline that accounts for differences in total transcriptional levels. We evaluate the effect of normalization methods on identifying differentially expressed genes in the context of identifying the effect of the time of day on gene expression and response to chilling stress in sorghum.

Correlation-based network integration of lung RNA sequencing and DNA methylation data in chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis.

Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT

ABSTRACT

For monogenic diseases caused by pathogenic loss-of-function DNA variants, attention focuses on dysregulated gene-specific pathways, usually considering molecular subtypes together within causal genes. To better understand phenotypic variability in hereditary hemorrhagic telangiectasia (HHT), we subcategorized pathogenic DNA variants in ENG/endoglin, ACVRL1/ALK1, and SMAD4 if they generated premature termination codons (PTCs) subject to nonsense-mediated decay. In 3 patient cohorts, a PTC-based classification system explained some previously puzzling hemorrhage variability. In blood outgrowth endothelial cells (BOECs) derived from patients with ACVRL1+/PTC, ENG+/PTC, and SMAD4+/PTC genotypes, PTC-containing RNA transcripts persisted at low levels (8%-23% expected, varying between replicate cultures); genes differentially expressed to Bonferroni P < .05 in HHT+/PTC BOECs clustered significantly only to generic protein terms (isopeptide-bond/ubiquitin-like conjugation) and pulse-chase experiments detected subtle protein maturation differences but no evidence for PTC-truncated protein. BOECs displaying highest PTC persistence were discriminated in unsupervised hierarchical clustering of near-invariant housekeeper genes, with patterns compatible with higher cellular stress in BOECs with >11% PTC persistence. To test directionality, we used a HeLa reporter system to detect induction of activating transcription factor 4 (ATF4), which controls expression of stress-adaptive genes, and showed that ENG Q436X but not ENG R93X directly induced ATF4. AlphaFold accurately modeled relevant ENG domains, with AlphaMissense suggesting that readthrough substitutions would be benign for ENG R93X and other less rare ENG nonsense variants but more damaging for Q436X. We conclude that PTCs should be distinguished from other loss-of-function variants, PTC transcript levels increase in stressed cells, and readthrough proteins and mechanisms provide promising research avenues.

hybridexpress: an R/Bioconductor package for comparative transcriptomic analyses of hybrids and their progenitors

Hybridization, the process of crossing individuals from diverse genetic backgrounds, plays a pivotal role in evolution, biological invasiveness, and crop breeding. At the transcriptional level, hybridization often leads to complex nonadditive effects, presenting challenges for understanding its consequences. Although standard transcriptomic analyses exist to compare hybrids to their progenitors, such analyses have not been implemented in a software package, hindering reproducibility. We introduce hybridexpress, an R/Bioconductor package designed to facilitate the analysis, visualization, and comparison of gene expression patterns in hybrid triplets (hybrids and their progenitors). hybridexpress provides users with a user-friendly and comprehensive workflow that includes all standard comparative analyses steps, including data normalization, calculation of midparent expression values, sample clustering, expression-based gene classification into categories and classes, and overrepresentation analysis for functional terms. We illustrate the utility of hybridexpress through comparative transcriptomic analyses of cotton allopolyploidization and rice root trait heterosis. hybridexpress is designed to streamline comparative transcriptomic studies of hybrid triplets, advancing our understanding of evolutionary dynamics in allopolyploids, and enhancing plant breeding strategies. hybridexpress is freely accessible from Bioconductor (https://bioconductor.org/packages/HybridExpress) and its source code is available on GitHub (https://github.com/almeidasilvaf/HybridExpress).

Comparative transcriptome analysis of canola carrying a single vs stacked resistance genes against clubroot

Pyramiding resistance genes may expand the efficacy and scope of a canola variety against clubroot (Plasmodiophora brassicae), a serious threat to canola production in western Canada. However, the mechanism(s) of multigenic resistance, especially the potential interaction among clubroot resistance (CR) genes, are not well understood. In this study, transcriptome was compared over three canola (Brassica napus L.) inbred/hybrid lines carrying a single CR gene in chromosome A03 (CRaM, Line 16) or A08 (Crr1rutb, Line 20), and both genes (CRaM+Crr1rutb, Line 15) inoculated with a field population (L-G2) of P. brassicae pathotype X, a new variant found in western Canada recently. The line16 was susceptible, while lines 15 and 20 were partially resistant. Functional annotation identified differential expression of genes (DEGs) involved in biosynthetic processes responsive to stress and regulation of cellular process; The Venn diagram showed that the partially resistant lines 15 and 20 shared 1,896 differentially expressed genes relative to the susceptible line 16, and many of these DEGs are involved in defense responses, activation of innate immunity, hormone biosynthesis and programmed cell death. The transcription of genes involved in Pathogen-Associated Molecular Pattern (PAMP)-Triggered and Effector-Triggered Immunity (PTI and ETI) was particularly up-regulated, and the transcription level was higher in line 15 (CRaM + Crr1rutb) than in line 20 (Crr1rutb only) for most of the DEGs. These results indicated that the partial resistance to the pathotype X was likely conferred by the CR gene Crr1rutb for both lines 15 and 20 that functioned via the activation of both PTI and ETI signaling pathways. Additionally, these two CR genes might have synergistic effects against the pathotype X, based on the higher transcription levels of defense-related DEGs expressed by inoculated line 15, highlighting the benefit of gene stacking for improved canola resistance as opposed to a single CR gene alone.

Exon-junction complex association with stalled ribosomes and slow translation-independent disassembly

Exon junction complexes are deposited at exon-exon junctions during splicing. They are primarily known to activate non-sense mediated degradation of transcripts harbouring premature stop codons before the last intron. According to a popular model, exon-junction complexes accompany mRNAs to the cytoplasm where the first translating ribosome pushes them out. However, they are also removed by uncharacterized, translation-independent mechanisms. Little is known about kinetic and transcript specificity of these processes. Here we tag core subunits of exon-junction complexes with complementary split nanoluciferase fragments to obtain sensitive and quantitative assays for complex formation. Unexpectedly, exon-junction complexes form large stable mRNPs containing stalled ribosomes. Complex assembly and disassembly rates are determined after an arrest in transcription and/or translation. 85% of newly deposited exon-junction complexes are disassembled by a translation-dependent mechanism. However as this process is much faster than the translation-independent one, only 30% of the exon-junction complexes present in cells at steady state require translation for disassembly. Deep RNA sequencing shows a bias of exon-junction complex bound transcripts towards microtubule and centrosome coding ones and demonstrate that the lifetimes of exon-junction complexes are transcript-specific. This study provides a dynamic vision of exon-junction complexes and uncovers their unexpected stable association with ribosomes.

Evaluation of Toll-like Receptor 4 (TLR4) Involvement in Human Atrial Fibrillation: A Computational Study

In the present study, we have explored the involvement of Toll-like Receptor 4 (TLR4) in atrial fibrillation (AF), by using a meta-analysis of publicly available human transcriptomic data. The meta-analysis revealed 565 upregulated and 267 downregulated differentially expressed genes associated with AF. Pathway enrichment analysis highlighted a significant overrepresentation in immune-related pathways for the upregulated genes. A significant overlap between AF differentially expressed genes and TLR4-modulated genes was also identified, suggesting the potential role of TLR4 in AF-related transcriptional changes. Additionally, the analysis of other Toll-like receptors (TLRs) revealed a significant association with TLR2 and TLR3 in AF-related gene expression patterns. The examination of MYD88 and TICAM1, genes associated with TLR4 signalling pathways, indicated a significant yet nonspecific enrichment of AF differentially expressed genes. In summary, this study offers novel insights into the molecular aspects of AF, suggesting a pathophysiological role of TLR4 and other TLRs. By targeting these specific receptors, new treatments might be designed to better manage AF, offering hope for improved outcomes in affected patients.

A novel tumor mutation-related long non-coding RNA signature for predicting overall survival and immunotherapy response in lung adenocarcinoma

Background

Immunotherapy has changed the treatment landscape for lung cancer. This study aims to construct a tumor mutation-related model that combines long non-coding RNA (lncRNA) expression levels and tumor mutation levels in tumor genomes to detect the possibilities of the lncRNA signature as an indicator for predicting the prognosis and response to immunotherapy in lung adenocarcinoma (LUAD).

Methods

We downloaded the tumor mutation profiles and RNA-seq expression database of LUAD from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were extracted based on the cumulative number of mutations. Cox regression analyses were used to identify the prognostic lncRNA signature, and the prognostic value of the five selected lncRNAs was validated by using survival analysis and the receiver operating characteristic (ROC) curve. We used qPCR to validate the expression of five selected lncRNAs between human lung epithelial and human lung adenocarcinoma cell lines. The ImmuCellAI, immunophenoscore (IPS) scores and Tumor Immune Dysfunction and Exclusion (TIDE) analyses were used to predict the response to immunotherapy for this mutation related lncRNA signature.

Results

A total of 162 lncRNAs were detected among the differentially expressed lncRNAs between the Tumor mutational burden (TMB)-high group and the TMB-low group. Then, five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as tumor mutation-related candidates for constructing the prognostic prediction model. Kaplan‒Meier curves showed that the overall survival of the low-risk group was significantly better than that of the high-risk group, and the results of the GSE50081 set were consistent. The expression levels of PD1, PD-L1 and CTLA4 in the low-risk group were higher than those in the high-risk group. The IPS scores and TIDE scores of patients in the low-risk group were significantly higher than those in the high-risk group.

Conclusion

Our findings demonstrated that the five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as candidates for constructing the tumor mutation-related model which may serve as an indicator of tumor mutation levels and have important implications for predicting the response to immunotherapy in LUAD.

Effect of RNA-Seq data normalization on protein interactome mapping for Alzheimer's disease

High throughput RNA sequencing brings new perspective to the elucidation of molecular mechanisms of diseases. Normalization is the first and most important step for RNA-Seq data, and it can differ based on the purpose of the analysis. Within-sample normalization methods (eg. TPM) are preferred when genes in a sample are compared with each other, and between-sample normalization methods (eg. deseq2, TMM, Voom) are used when the samples in a dataset are compared. Normalization approaches rescale the data, and, therefore, they affect the results of the analysis. Here, we selected two most commonly used Alzheimer's disease RNA-Seq datasets from ROSMAP and Mayo Clinic cohorts and mapped the differentially expressed genes on human protein interactome to discover disease-specific subnetworks. To this end, the raw count data were first processed with four different, commonly used RNA-Seq normalization methods (deseq2, TMM, Voom and TPM). Then, covariate adjustment was applied to the normalized data for gender, age of death and post-mortem interval. Each normalized dataset was separately mapped on the human protein-protein interaction network either in covariate-adjusted or non-adjusted form. Capturing known Alzheimer's disease genes and genes associated with the disease-related functional terms in the discovered subnetworks were the criteria to compare different normalization methods. Based on our results, applying covariate adjustment has a positive effect on normalization by removing the confounder effects. Covariate-adjusted TMM and covariate-adjusted deseq2 methods performed better in both transcriptome datasets.

Assessing the complementary information from an increased number of biologically relevant features in liquid biopsy-derived RNA-Seq data

Liquid biopsy-derived RNA sequencing (lbRNA-seq) exhibits significant promise for clinic-oriented cancer diagnostics due to its non-invasiveness and ease of repeatability. Despite substantial advancements, obstacles like technical artefacts and process standardisation impede seamless clinical integration. Alongside addressing technical aspects such as normalising fluctuating low-input material and establishing a standardised clinical workflow, the lack of result validation using independent datasets remains a critical factor contributing to the often low reproducibility of liquid biopsy-detected biomarkers. Considering the outlined drawbacks, our objective was to establish a workflow/methodology characterised by: 1. Harness the rich diversity of biological features accessible through lbRNA-seq data, encompassing a holistic range of molecular and functional attributes. These components are seamlessly integrated via a Machine Learning-based Ensemble Classification framework, enabling a unified and comprehensive analysis of the intricate information encoded within the data. 2. Implementing and rigorously benchmarking intra-sample normalisation methods to heighten their relevance within clinical settings. 3. Thoroughly assessing its efficacy across independent test sets to ascertain its robustness and potential utility. Using ten datasets from several studies comprising three different sources of biological material, we first show that while the best-performing normalisation methods depend strongly on the dataset and coupled Machine Learning method, the rather simple Counts Per Million method is generally very robust, showing comparable performance to cross-sample methods. Subsequently, we demonstrate that the innovative biofeature types introduced in this study, such as the Fraction of Canonical Transcript, harbour complementary information. Consequently, their inclusion consistently enhances prediction power compared to models relying solely on gene expression-based biofeatures. Finally, we demonstrate that the workflow is robust on completely independent datasets, generally from different labs and/or different protocols. Taken together, the workflow presented here outperforms generally employed methods in prediction accuracy and may hold potential for clinical diagnostics application due to its specific design.

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues

Motivation

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods.

Results

In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.

Availability and implementation

https://github.com/Papatheodorou-Group/CATD_snakemake.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Metatranscriptomics reveals diversity of symbiotic interaction and mechanisms of carbon exchange in the marine cyanolichen Lichina pygmaea

Lichens are exemplar symbioses based upon carbon exchange between photobionts and their mycobiont hosts. Historically considered a two-way relationship, some lichen symbioses have been shown to contain multiple photobiont partners; however, the way in which these photobiont communities react to environmental change is poorly understood. Lichina pygmaea is a marine cyanolichen that inhabits rocky seashores where it is submerged in seawater during every tidal cycle. Recent work has indicated that L. pygmaea has a complex photobiont community including the cyanobionts Rivularia and Pleurocapsa. We performed rRNA-based metabarcoding and mRNA metatranscriptomics of the L. pygmaea holobiont at high and low tide to investigate community response to immersion in seawater. Carbon exchange in L. pygmaea is a dynamic process, influenced by both tidal cycle and the biology of the individual symbiotic components. The mycobiont and two cyanobiont partners exhibit distinct transcriptional responses to seawater hydration. Sugar-based compatible solutes produced by Rivularia and Pleurocapsa in response to seawater are a potential source of carbon to the mycobiont. We propose that extracellular processing of photobiont-derived polysaccharides is a fundamental step in carbon acquisition by L. pygmaea and is analogous to uptake of plant-derived carbon in ectomycorrhizal symbioses.

Post-transcriptional regulation shapes the transcriptome of quiescent budding yeast

To facilitate long-term survival, cells must exit the cell cycle and enter quiescence, a reversible non-replicative state. Budding yeast cells reprogram their gene expression during quiescence entry to silence transcription, but how the nascent transcriptome changes in quiescence is unknown. By investigating the nascent transcriptome, we identified over a thousand noncoding RNAs in quiescent and G1 yeast cells, and found noncoding transcription represented a larger portion of the quiescent transcriptome than in G1. Additionally, both mRNA and ncRNA are subject to increased post-transcriptional regulation in quiescence compared to G1. We found that, in quiescence, the nuclear exosome-NNS pathway suppresses over one thousand mRNAs, in addition to canonical noncoding RNAs. RNA sequencing through quiescent entry revealed two distinct time points at which the nuclear exosome controls the abundance of mRNAs involved in protein production, cellular organization, and metabolism, thereby facilitating efficient quiescence entry. Our work identified a previously unknown key biological role for the nuclear exosome-NNS pathway in mRNA regulation and uncovered a novel layer of gene-expression control in quiescence.

A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.

Urbanization reduces gut bacterial microbiome diversity in a specialist ground beetle, Carabus convexus

Urbanization is rapidly shaping and transforming natural environments, creating networks of modified land types. These urbanization-driven modifications lead to local extinctions of several species, but the surviving ones also face numerous novel selection pressures, including exposure to pollutants, habitat alteration, and shifts in food availability and diversity. Based on the assumption that the environmental pool of microorganisms is reduced in urban habitats due to habitat alteration, biodiversity loss, and pollution, we hypothesized that the diversity of bacterial microbiome in digestive tracts of arthropods would be lower in urban than rural habitats. Investigating the gut bacterial communities of a specialist ground beetle, Carabus convexus, in forested rural versus urban habitats by next generation high-throughput sequencing of the bacterial 16S rRNA gene, we identified 3839 bacterial amplicon sequence variants. The composition of gut bacterial samples did not significantly differ by habitat (rural vs. urban), sex (female vs. male), sampling date (early vs. late spring), or their interaction. The microbiome diversity (evaluated by the Rényi diversity function), however, was higher in rural than urban adults. Our findings demonstrate that urbanization significantly reduced the diversity of the gut bacterial microbiome in C. convexus.

Direct comparison of canine and human immune responses using transcriptomic and functional analyses

The canine spontaneous cancer model is increasingly utilized to evaluate new combined cancer immunotherapy approaches. While the major leukocyte subsets and phenotypes are closely related in dogs and humans, the functionality of T cells and antigen presenting cells in the two species has not been previously compared in detail. Such information would be important in interpreting immune response data and evaluating the potential toxicities of new cancer immunotherapies in dogs. To address this question, we used in vitro assays to compare the transcriptomic, cytokine, and proliferative responses of activated canine and human T cells, and also compared responses in activated macrophages. Transcriptomic analysis following T cell activation revealed shared expression of 515 significantly upregulated genes and 360 significantly downregulated immune genes. Pathway analysis identified 33 immune pathways shared between canine and human activated T cells, along with 34 immune pathways that were unique to each species. Activated human T cells exhibited a marked Th1 bias, whereas canine T cells were transcriptionally less active overall. Despite similar proliferative responses to activation, canine T cells produced significantly less IFN-γ than human T cells. Moreover, canine macrophages were significantly more responsive to activation by IFN-γ than human macrophages, as reflected by co-stimulatory molecule expression and TNF-α production. Thus, these studies revealed overall broad similarity in responses to immune activation between dogs and humans, but also uncovered important key quantitative and qualitative differences, particularly with respect to T cell responses, that should be considered in designing and evaluating cancer immunotherapy studies in dogs.

Evidence of steady-state fibroblast subtypes in the normal human breast as cells-of-origin for perturbed-state fibroblasts in breast cancer

BACKGROUND

Human breast cancer most frequently originates within a well-defined anatomical structure referred to as the terminal duct lobular unit (TDLU). This structure is endowed with its very own lobular fibroblasts representing one out of two steady-state fibroblast subtypes-the other being interlobular fibroblasts. While cancer-associated fibroblasts (CAFs) are increasingly appreciated as covering a spectrum of perturbed states, we lack a coherent understanding of their relationship-if any-with the steady-state fibroblast subtypes. To address this, we here established two autologous CAF lines representing inflammatory CAFs (iCAFs) and myofibroblast CAFs (myCAFs) and compared them with already established interlobular- and lobular fibroblasts with respect to their origin and impact on tumor formation.

METHODS

Primary breast tumor-derived CAFs were transduced to express human telomerase reverse transcriptase (hTERT) and sorted into CD105low and CD105high populations using fluorescence-activated cell sorting (FACS). The two populations were tested for differentiation similarities to iCAF and myCAF states through transcriptome-wide RNA-Sequencing (RNA-Seq) including comparison to an available iCAF-myCAF cell state atlas. Inference of origin in interlobular and lobular fibroblasts relied on RNA-Seq profiles, immunocytochemistry and growth characteristics. Osteogenic differentiation and bone formation assays in culture and in vivo were employed to gauge for origin in bone marrow-derived mesenchymal stem cells (bMSCs). Functional characteristics were assessed with respect to contractility in culture and interaction with tumor cells in mouse xenografts. The cells' gene expression signatures were tested for association with clinical outcome of breast cancer patients using survival data from The Cancer Genome Atlas database.

RESULTS

We demonstrate that iCAFs have properties in common with interlobular fibroblasts while myCAFs and lobular fibroblasts are related. None of the CAFs qualify as bMSCs as revealed by lack of critical performance in bone formation assays. Functionally, myCAFs and lobular fibroblasts are almost equally tumor promoting as opposed to iCAFs and interlobular fibroblasts. A myCAF gene signature is found to associate with poor breast cancer-specific survival.

CONCLUSIONS

We propose that iCAFs and myCAFs originate in interlobular and lobular fibroblasts, respectively, and more importantly, that the tumor-promoting properties of lobular fibroblasts render the TDLU an epicenter for breast cancer evolution.

Internal and external normalization of nascent RNA sequencing run-on experiments

In experiments with significant perturbations to transcription, nascent RNA sequencing protocols are dependent on external spike-ins for reliable normalization. Unlike in RNA-seq, these spike-ins are not standardized and, in many cases, depend on a run-on reaction that is assumed to have constant efficiency across samples. To assess the validity of this assumption, we analyze a large number of published nascent RNA spike-ins to quantify their variability across existing normalization methods. Furthermore, we develop a new biologically-informed Bayesian model to estimate the error in spike-in based normalization estimates, which we term Virtual Spike-In (VSI). We apply this method both to published external spike-ins as well as using reads at the [Formula: see text] end of long genes, building on prior work from Mahat (Mol Cell 62(1):63-78, 2016. https://doi.org/10.1016/j.molcel.2016.02.025 ) and Vihervaara (Nat Commun 8(1):255, 2017. https://doi.org/10.1038/s41467-017-00151-0 ). We find that spike-ins in existing nascent RNA experiments are typically under sequenced, with high variability between samples. Furthermore, we show that these high variability estimates can have significant downstream effects on analysis, complicating biological interpretations of results.

Quantitative catalogue of mammalian mitotic chromosome-associated RNAs

The faithful transmission of a cell's identity and functionality to its daughters during mitosis requires the proper assembly of mitotic chromosomes from interphase chromatin in a process that involves significant changes in the genome-bound material, including the RNA. However, our understanding of the RNA that is associated with the mitotic chromosome is presently limited. Here, we present complete and quantitative characterizations of the full-length mitotic chromosome-associated RNAs (mCARs) for 3 human cell lines, a monkey cell line, and a mouse cell line derived from high-depth RNA sequencing (3 replicates, 47 M mapped read pairs for each replicate). Overall, we identify, on average, more than 20,400 mCAR species per cell-type (including isoforms), more than 5,200 of which are enriched on the chromosome. Notably, overall, more than 2,700 of these mCARs were previously unknown, which thus also expands the annotated genome of these species. We anticipate that these datasets will provide an essential resource for future studies to better understand the functioning of mCARs on the mitotic chromosome and in the cell.

Engraftment of essential functions through multiple fecal microbiota transplants in chronic antibiotic-resistant pouchitis-a case study using metatranscriptomics

BACKGROUND

Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). Around 50% of patients will experience pouchitis, an idiopathic inflammatory condition. Antibiotics are the backbone of treatment of pouchitis; however, antibiotic-resistant pouchitis develops in 5-10% of those patients. It has been shown that fecal microbiota transplantation (FMT) is an effective treatment for UC, but results for FMT antibiotic-resistant pouchitis are inconsistent.

METHODS

To uncover which metabolic activities were transferred to the recipients during FMT and helped the remission, we performed a longitudinal case study of the gut metatranscriptomes from three patients and their donors. The patients were treated by two to three FMTs, and stool samples were analyzed for up to 140 days.

RESULTS

Reduced expression in pouchitis patients compared to healthy donors was observed for genes involved in biosynthesis of amino acids, cofactors, and B vitamins. An independent metatranscriptome dataset of UC patients showed a similar result. Other functions including biosynthesis of butyrate, metabolism of bile acids, and tryptophan were also much lower expressed in pouchitis. After FMT, these activities transiently increased, and the overall metatranscriptome profiles closely mirrored those of the respective donors with notable fluctuations during the subsequent weeks. The levels of the clinical marker fecal calprotectin were concordant with the metatranscriptome data. Faecalibacterium prausnitzii represented the most active species contributing to butyrate synthesis via the acetyl-CoA pathway. Remission occurred after the last FMT in all patients and was characterized by a microbiota activity profile distinct from donors in two of the patients.

CONCLUSIONS

Our study demonstrates the clear but short-lived activity engraftment of donor microbiota, particularly the butyrate biosynthesis after each FMT. The data suggest that FMT triggers shifts in the activity of patient microbiota towards health which need to be repeated to reach critical thresholds. As a case study, these insights warrant cautious interpretation, and validation in larger cohorts is necessary for generalized applications. In the long run, probiotics with high taxonomic diversity consisting of well characterized strains could replace FMT to avoid the costly screening of donors and the risk of transferring unwanted genetic material. Video Abstract.

Transcriptome-wide profiling of acute stress induced changes in ribosome occupancy level using external standards

Acute cellular stress is known to induce a global reduction in mRNA translation through suppression of cap dependent translation. Selective translation in response to acute stress has been shown to play important roles in regulating the stress response. However, accurately profiling translational changes transcriptome-wide in response to acute cellular stress has been challenging. Commonly used data normalization methods operate on the assumption that any systematic shifts are experimental artifacts. Consequently, if applied to profiling acute cellular stress-induced mRNA translation changes, these methods are expected to produce biased estimates. To address this issue, we designed, produced, and evaluated a panel of 16 oligomers to serve as external standards for ribosome profiling studies. Using Sodium Arsenite treatment-induced oxidative stress in lymphoblastoid cell lines as a model system, we applied spike-in oligomers as external standards. We found our spike-in oligomers to display a strong linear correlation between the observed and the expected quantification, with small ratio compression at the lower concentration range. Using the expected fold changes constructed from spike-in controls, we found in our dataset that TMM normalization, a popular global scaling normalization approach, produced 87.5% false positives at a significant cutoff that is expected to produce only 10% false positive discoveries. In addition, TMM normalization produced a systematic shift of fold change by 3.25 fold. These results highlight the consequences of applying global scaling approaches to conditions that clearly violate their key assumptions. In contrast, we found RUVg normalization using spike-in oligomers as control genes recapitulated the expected stress induced global reduction of translation and resulted in little, if any, systematic shifts in the expected fold change. Our results clearly demonstrated the utility of our spike-in oligomers, both for constructing expected results as controls and for data normalization.

Functional filter for whole-genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5 kb from coding DNA

Despite whole-genome sequencing (WGS), many cases of single-gene disorders remain unsolved, impeding diagnosis and preventative care for people whose disease-causing variants escape detection. Since early WGS data analytic steps prioritize protein-coding sequences, to simultaneously prioritize variants in non-coding regions rich in transcribed and critical regulatory sequences, we developed GROFFFY, an analytic tool that integrates coordinates for regions with experimental evidence of functionality. Applied to WGS data from solved and unsolved hereditary hemorrhagic telangiectasia (HHT) recruits to the 100,000 Genomes Project, GROFFFY-based filtration reduced the mean number of variants/DNA from 4,867,167 to 21,486, without deleting disease-causal variants. In three unsolved cases (two related), GROFFFY identified ultra-rare deletions within the 3' untranslated region (UTR) of the tumor suppressor SMAD4, where germline loss-of-function alleles cause combined HHT and colonic polyposis (MIM: 175050). Sited >5.4 kb distal to coding DNA, the deletions did not modify or generate microRNA binding sites, but instead disrupted the sequence context of the final cleavage and polyadenylation site necessary for protein production: By iFoldRNA, an AAUAAA-adjacent 16-nucleotide deletion brought the cleavage site into inaccessible neighboring secondary structures, while a 4-nucleotide deletion unfolded the downstream RNA polymerase II roadblock. SMAD4 RNA expression differed to control-derived RNA from resting and cycloheximide-stressed peripheral blood mononuclear cells. Patterns predicted the mutational site for an unrelated HHT/polyposis-affected individual, where a complex insertion was subsequently identified. In conclusion, we describe a functional rare variant type that impacts regulatory systems based on RNA polyadenylation. Extension of coding sequence-focused gene panels is required to capture these variants.

Donor white blood cell differential is the single largest determinant of whole blood gene expression patterns

It has become widely accepted that sample cellular composition is a significant determinant of the gene expression patterns observed in any transcriptomic experiment performed with bulk tissue. Despite this, many investigations currently performed with whole blood do not experimentally account for possible inter-specimen differences in cellularity, and often assume that any observed gene expression differences are a result of true differences in nuclear transcription. In order to determine how confounding of an assumption this may be, in this study, we recruited a large cohort of human donors (n = 138) and used a combination of next generation sequencing and flow cytometry to quantify and compare the underlying contributions of variance in leukocyte counts versus variance in other biological factors to overall variance in whole blood transcript levels. Our results suggest that the combination of donor neutrophil and lymphocyte counts alone are the primary determinants of whole blood transcript levels for up to 75% of the protein-coding genes expressed in peripheral circulation, whereas the other factors such as age, sex, race, ethnicity, and common disease states have comparatively minimal influence. Broadly, this infers that a majority of gene expression differences observed in experiments performed with whole blood are driven by latent differences in leukocyte counts, and that cell count heterogeneity must be accounted for to meaningfully biologically interpret the results.

Addressing erroneous scale assumptions in microbe and gene set enrichment analysis

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Characterization of pathogenic factors for premenstrual dysphoric disorder using machine learning algorithms in rats

We established a methodology using machine learning algorithms for determining the pathogenic factors for premenstrual dysphoric disorder (PMDD). PMDD is a disease characterized by emotional and physical symptoms that occurs before menstruation in women of childbearing age. Owing to the diverse manifestations and various pathogenic factors associated with this disease, the diagnosis of PMDD is time-consuming and challenging. In the present study, we aimed to establish a methodology for diagnosing PMDD. Using an unsupervised machine-learning algorithm, we divided pseudopregnant rats into three clusters (C1 to C3), depending on the level of anxiety- and depression-like behaviors. From the results of RNA-seq and subsequent qPCR of the hippocampus in each cluster, we identified 17 key genes for building a PMDD diagnostic model using our original two-step feature selection with supervised machine learning. By inputting the expression levels of these 17 genes into the machine learning classifier, the PMDD symptoms of another group of rats were successfully classified as C1-C3 with an accuracy of 96%, corresponding to the classification by behavior. The present methodology would be applicable for the clinical diagnosis of PMDD using blood samples instead of samples from the hippocampus in the future.

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution

Background

Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

Methods

Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

Results

Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood

Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.

Deciphering the molecular basis of abiotic stress response in cucumber (Cucumis sativus L.) using RNA-Seq meta-analysis, systems biology, and machine learning approaches

Abiotic stress in cucumber (Cucumis sativus L.) may trigger distinct transcriptome responses, resulting in significant yield loss. More insight into the molecular underpinnings of the stress response can be gained by combining RNA-Seq meta-analysis with systems biology and machine learning. This can help pinpoint possible targets for engineering abiotic tolerance by revealing functional modules and key genes essential for the stress response. Therefore, to investigate the regulatory mechanism and key genes, a combination of these approaches was utilized in cucumber subjected to various abiotic stresses. Three significant abiotic stress-related modules were identified by gene co-expression network analysis (WGCNA). Three hub genes (RPL18, δ-COP, and EXLA2), ten transcription factors (TFs), one transcription regulator, and 12 protein kinases (PKs) were introduced as key genes. The results suggest that the identified PKs probably govern the coordination of cellular responses to abiotic stress in cucumber. Moreover, the C2H2 TF family may play a significant role in cucumber response to abiotic stress. Several C2H2 TF target stress-related genes were identified through co-expression and promoter analyses. Evaluation of the key identified genes using Random Forest, with an area under the curve of ROC (AUC) of 0.974 and an accuracy rate of 88.5%, demonstrates their prominent contributions in the cucumber response to abiotic stresses. These findings provide novel insights into the regulatory mechanism underlying abiotic stress response in cucumber and pave the way for cucumber genetic engineering toward improving tolerance ability under abiotic stress.

mRNA stability and m6A are major determinants of subcellular mRNA localization in neurons

For cells to perform their biological functions, they need to adopt specific shapes and form functionally distinct subcellular compartments. This is achieved in part via an asymmetric distribution of mRNAs within cells. Currently, the main model of mRNA localization involves specific sequences called "zipcodes" that direct mRNAs to their proper locations. However, while thousands of mRNAs localize within cells, only a few zipcodes have been identified, suggesting that additional mechanisms contribute to localization. Here, we assess the role of mRNA stability in localization by combining the isolation of the soma and neurites of mouse primary cortical and mESC-derived neurons, SLAM-seq, m6A-RIP-seq, the perturbation of mRNA destabilization mechanisms, and the analysis of multiple mRNA localization datasets. We show that depletion of mRNA destabilization elements, such as m6A, AU-rich elements, and suboptimal codons, functions as a mechanism that mediates the localization of mRNAs associated with housekeeping functions to neurites in several types of neurons.

Myocardial RNA Sequencing Reveals New Potential Therapeutic Targets in Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) represents a global health challenge, with limited therapies proven to enhance patient outcomes. This makes the elucidation of disease mechanisms and the identification of novel potential therapeutic targets a priority. Here, we performed RNA sequencing on ventricular myocardial biopsies from patients with HFpEF, prospecting to discover distinctive transcriptomic signatures. A total of 306 differentially expressed mRNAs (DEG) and 152 differentially expressed microRNAs (DEM) were identified and enriched in several biological processes involved in HF. Moreover, by integrating mRNA and microRNA expression data, we identified five potentially novel miRNA-mRNA relationships in HFpEF: the upregulated hsa-miR-25-3p, hsa-miR-26a-5p, and has-miR4429, targeting HAPLN1; and NPPB mRNA, targeted by hsa-miR-26a-5p and miR-140-3p. Exploring the predicted miRNA-mRNA interactions experimentally, we demonstrated that overexpression of the distinct miRNAs leads to the downregulation of their target genes. Interestingly, we also observed that microRNA signatures display a higher discriminative power to distinguish HFpEF sub-groups over mRNA signatures. Our results offer new mechanistic clues, which can potentially translate into new HFpEF therapies.

Responsiveness to endurance training can be partly explained by the number of favorable single nucleotide polymorphisms an individual possesses

Cardiorespiratory fitness is a key component of health-related fitness. It is a necessary focus of improvement, especially for those that have poor fitness and are classed as untrained. However, much research has shown individuals respond differentially to identical training programs, suggesting the involvement of a genetic component in individual exercise responses. Previous research has focused predominantly on a relatively low number of candidate genes and their overall influence on exercise responsiveness. However, examination of gene-specific alleles may provide a greater level of understanding. Accordingly, this study aimed to investigate the associations between cardiorespiratory fitness and an individual's genotype following a field-based endurance program within a previously untrained population. Participants (age: 29 ± 7 years, height: 175 ± 9 cm, mass: 79 ± 21 kg, body mass index: 26 ± 7 kg/m2) were randomly assigned to either a training (n = 21) or control group (n = 24). The training group completed a periodized running program for 8-weeks (duration: 20-30-minutes per session, intensity: 6-7 Borg Category-Ratio-10 scale rating, frequency: 3 sessions per week). Both groups completed a Cooper 12-minute run test to estimate cardiorespiratory fitness at baseline, mid-study, and post-study. One thousand single nucleotide polymorphisms (SNPs) were assessed via saliva sample collections. Cooper run distance showed a significant improvement (0.23 ± 0.17 km [11.51 ± 9.09%], p < 0.001, ES = 0.48 [95%CI: 0.16-0.32]), following the 8-week program, whilst controls displayed no significant changes (0.03 ± 0.15 km [1.55 ± 6.98%], p = 0.346, ES = 0.08, [95%CI: -0.35-0.95]). A significant portion of the inter-individual variation in Cooper scores could be explained by the number of positive alleles a participant possessed (r = 0.92, R2 = 0.85, p < 0.001). These findings demonstrate the relative influence of key allele variants on an individual's responsiveness to endurance training.

Maternal and fetal mitochondrial gene dysregulation in hypertensive disorders of pregnancy

Mitochondrial dysfunction has been implicated in pregnancy-induced hypertension (PIH). The role of mitochondrial gene dysregulation in PIH, and consequences for maternal-fetal interactions, remain elusive. Here, we investigated mitochondrial gene expression and dysregulation in maternal and placental tissues from pregnancies with and without PIH; further, we measured circulating mitochondrial DNA (mtDNA) mutational load, an index of mtDNA integrity. Differential gene expression analysis followed by Time Course Gene Set Analysis (TcGSA) was conducted on publicly available high throughput sequencing transcriptomic data sets. Mutational load analysis was carried out on peripheral mononuclear blood cells from healthy pregnant individuals and individuals with preeclampsia. Thirty mitochondrial differentially expressed genes (mtDEGs) were detected in the maternal cell-free circulating transcriptome, whereas nine were detected in placental transcriptome from pregnancies with PIH. In PIH pregnancies, maternal mitochondrial dysregulation was associated with pathways involved in inflammation, cell death/survival, and placental development, whereas fetal mitochondrial dysregulation was associated with increased production of extracellular vesicles (EVs) at term. Mothers with preeclampsia did not exhibit a significantly different degree of mtDNA mutational load. Our findings support the involvement of maternal mitochondrial dysregulation in the pathophysiology of PIH and suggest that mitochondria may mediate maternal-fetal interactions during healthy pregnancy.NEW & NOTEWORTHY This study identifies aberrant maternal and fetal expression of mitochondrial genes in pregnancies with gestational hypertension and preeclampsia. Mitochondrial gene dysregulation may be a common etiological factor contributing to the development of de novo hypertension in pregnancy-associated hypertensive disorders.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

NORMSEQ: a tool for evaluation, selection and visualization of RNA-Seq normalization methods

RNA-sequencing has become one of the most used high-throughput approaches to gain knowledge about the expression of all different RNA subpopulations. However, technical artifacts, either introduced during library preparation and/or data analysis, can influence the detected RNA expression levels. A critical step, especially in large and low input datasets or studies, is data normalization, which aims at eliminating the variability in data that is not related to biology. Many normalization methods have been developed, each of them relying on different assumptions, making the selection of the appropriate normalization strategy key to preserve biological information. To address this, we developed NormSeq, a free web-server tool to systematically assess the performance of normalization methods in a given dataset. A key feature of NormSeq is the implementation of information gain to guide the selection of the best normalization method, which is crucial to eliminate or at least reduce non-biological variability. Altogether, NormSeq provides an easy-to-use platform to explore different aspects of gene expression data with a special focus on data normalization to help researchers, even without bioinformatics expertise, to obtain reliable biological inference from their data. NormSeq is freely available at: https://arn.ugr.es/normSeq.

SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation

The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these technologies poses a significant challenge for a comprehensive delineation of cell type-specific spatial patterns. Here, we propose SPADE (SPAtial DEconvolution), an in-silico method designed to address this challenge by incorporating spatial patterns during cell type decomposition. SPADE utilizes a combination of single-cell RNA sequencing data, spatial location information, and histological information to computationally estimate the proportion of cell types present at each spatial location. In our study, we showcased the effectiveness of SPADE by conducting analyses on synthetic data. Our results indicated that SPADE was able to successfully identify cell type-specific spatial patterns that were not previously identified by existing deconvolution methods. Furthermore, we applied SPADE to a real-world dataset analyzing the developmental chicken heart, where we observed that SPADE was able to accurately capture the intricate processes of cellular differentiation and morphogenesis within the heart. Specifically, we were able to reliably estimate changes in cell type compositions over time, which is a critical aspect of understanding the underlying mechanisms of complex biological systems. These findings underscore the potential of SPADE as a valuable tool for analyzing complex biological systems and shedding light on their underlying mechanisms. Taken together, our results suggest that SPADE represents a significant advancement in the field of spatial transcriptomics, providing a powerful tool for characterizing complex spatial gene expression patterns in heterogeneous tissues.

Identification and characterization of aldehyde dehydrogenase (ALDH) gene superfamily in garlic and expression profiling in response to drought, salinity, and ABA

In response to biotic and abiotic stressors, aldehydes are detoxified and converted to carboxylic acids by aldehyde dehydrogenases (ALDHs), which are enzymes that use NAD+/NADP+ as cofactors. Garlic (Allium sativum L.) has not yet undergone a systematic examination of the ALDH superfamily, despite the genome sequence having been made public. In this investigation, we identified, characterized, and profiled the expression of the garlic ALDH gene family over the entire genome. The ALDH Gene Nomenclature Committee (AGNC) classification was used to classify and name the 34 ALDH genes that were discovered. Except for chromosome 8, all AsALDH genes were dispersed across the chromosomes. AsALDH genes have various localizations, according to predictions about subcellular localization. The AsALDH proteins are more varied and closely related to rice than to Arabidopsis, according to a study of conserved motifs and phylogenetic relationships. The presence of stress modulation pathways is indicated by the abundance of stress-related cis-elements in the AsALDH genes' promoter regions. Analysis of the RNA-seq data showed that AsALDHs expressed differently in various tissues and at various developmental stages. Nine AsALDHs were chosen for study using RT-qPCR, and the results revealed that the majority of the genes were upregulated in response to ABA and downregulated in response to salinity and drought. The results of this study improved our knowledge of the traits, evolutionary background, and biological functions of AsALDHs genes in growth and development.

Navigating bottlenecks and trade-offs in genomic data analysis

Genome sequencing and analysis allow researchers to decode the functional information hidden in DNA sequences as well as to study cell to cell variation within a cell population. Traditionally, the primary bottleneck in genomic analysis pipelines has been the sequencing itself, which has been much more expensive than the computational analyses that follow. However, an important consequence of the continued drive to expand the throughput of sequencing platforms at lower cost is that often the analytical pipelines are struggling to keep up with the sheer amount of raw data produced. Computational cost and efficiency have thus become of ever increasing importance. Recent methodological advances, such as data sketching, accelerators and domain-specific libraries/languages, promise to address these modern computational challenges. However, despite being more efficient, these innovations come with a new set of trade-offs, both expected, such as accuracy versus memory and expense versus time, and more subtle, including the human expertise needed to use non-standard programming interfaces and set up complex infrastructure. In this Review, we discuss how to navigate these new methodological advances and their trade-offs.

Atlantic sturgeon and shortnose sturgeon exhibit highly divergent transcriptomic responses to acute heat stress

In comparison to most modern teleost fishes, sturgeons generally display muted stress responses. While a muted stress response appears to be ubiquitous across sturgeon species, the mechanisms unpinning this muted response have not been fully described. The objective of this study was to determine the patterns of hematological and transcriptomic change in muscle tissue following an acute high temperature stress (critical thermal maxima; CT_max) in two locally co-occurring but evolutionarily distant sturgeon species (Atlantic and shortnose sturgeon). The most striking pattern found was that Atlantic sturgeon launched a vigorous transcriptomic response at CT_max, whereas shortnose sturgeon did not. In contrast, shortnose sturgeon have significantly higher cortisol than Atlantics at CT_max, reconfirming that shortnose have a less muted cortisol stress response. Atlantic sturgeon downregulated a number of processes, included RNA creation/processing, methylation and immune processes. Furthermore, a number of genes related to heat shock proteins were differentially expressed at CT_max in Atlantic sturgeon but none of these genes were significantly changed in shortnose sturgeon. We also note that the majority of differentially expressed genes of both species are undescribed and have no known orthologues. These results suggest that, while sturgeons as a whole may show muted stress responses, individual sturgeon species likely use different inducible strategies to cope with acute high temperature stress.

Beyond Transcript Concentrations: Quantifying Polyploid Expression Responses per Biomass, per Genome, and per Cell with RNA-Seq

RNA-seq has been used extensively to study expression responses to polyploidy. Most current methods for normalizing RNA-seq data yield estimates of transcript concentrations (transcripts per transcriptome). The implicit assumption of these normalization methods is that transcriptome size is equivalent between the samples being compared such that transcript concentrations are equivalent to transcripts per cell. In recent years, however, evidence has mounted that transcriptome size can vary dramatically in response to a range of factors including polyploidy and that such variation is ubiquitous. Where such variation exists, transcript concentration is often a poor or even misleading proxy for expression responses at other biologically relevant scales (e.g., expression per cell). Thus, it is important that transcriptomic studies of polyploids move beyond simply comparing transcript concentrations if we are to gain a complete understanding of how genome multiplication affects gene expression. I discuss this issue in more detail and summarize a suite of approaches that can leverage RNA-seq to quantify expression responses per genome, per cell, and per unit of biomass.