stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

The Oryza sativa transcriptome responds spatiotemporally to polystyrene nanoplastic stress

Nanoplastic represents an emerging abiotic stress facing modern agriculture, impacting global crop production. However, the molecular response of crop plants to this stress remains poorly understood at a spatiotemporal resolution. We therefore used RNA sequencing to profile the transcriptome expressed in rice (Oryza sativa) root and leaf organs at 1, 2, 4, and 8 d post exposure with nanoplastic. We revealed a striking similarity between the rice biomass dynamics in aboveground parts to that in belowground parts during nanoplastic stress, but transcriptome did not. At the global transcriptomic level, a total of 2332 differentially expressed genes were identified, with the majority being spatiotemporal specific, reflecting that nanoplastics predominantly regulate three processes in rice seedlings: (1) down-regulation of chlorophyll biosynthesis, photosynthesis, and starch, sucrose and nitrogen metabolism, (2) activation of defense responses such as brassinosteroid biosynthesis and phenylpropanoid biosynthesis, and (3) modulation of jasmonic acid and cytokinin signaling pathways by transcription factors. Notably, the genes involved in plant-pathogen interaction were shown to be successively modulated by both root and leaf organs, particularly plant disease defense genes (OsWRKY24, OsWRKY53, Os4CL3, OsPAL4, and MPK5), possibly indicating that nanoplastics affect rice growth indirectly through other biota. Finally, we associated biomass phenotypes with the temporal reprogramming of rice transcriptome by weighted gene co-expression network analysis, noting a significantly correlation with photosynthesis, carbon metabolism, and phenylpropanoid biosynthesis that may reflect the mechanisms of biomass reduction. Functional analysis further identified PsbY, MYB, cytochrome P450, and AP2/ERF as hub genes governing these pathways. Overall, our work provides the understanding of molecular mechanisms of rice in response to nanoplastics, which in turn suggests how rice might behave in a nanoplastic pollution scenario.

Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling

Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.

Sex differences in brain cell-type specific chromatin accessibility in schizophrenia

Our understanding of the sex-specific role of the non-coding genome in serious mental illness remains largely incomplete. To address this gap, we explored sex differences in 1,393 chromatin accessibility profiles, derived from neuronal and non-neuronal nuclei of two distinct cortical regions from 234 cases with serious mental illness and 235 controls. We identified sex-specific enhancer-promoter interactions and showed that they regulate genes involved in X-chromosome inactivation (XCI). Examining chromosomal conformation allowed us to identify sex-specific cis - and trans -regulatory domains (CRDs and TRDs). Co-localization of sex-specific TRDs with schizophrenia common risk variants pinpointed male-specific regulatory regions controlling a number of metabolic pathways. Additionally, enhancers from female-specific TRDs were found to regulate two genes known to escape XCI, ( XIST and JPX ), underlying the importance of TRDs in deciphering sex differences in schizophrenia. Overall, these findings provide extensive characterization of sex differences in the brain epigenome and disease-associated regulomes.

Cytokine profiling and transcriptomics in mononuclear cells define immune variants in Meniere Disease

Meniere Disease (MD) is a chronic inner ear disorder characterized by vertigo attacks, sensorineural hearing loss, tinnitus, and aural fullness. Extensive evidence supporting the inflammatory etiology of MD has been found, therefore, by using transcriptome analysis, we aim to describe the inflammatory variants of MD. We performed Bulk RNAseq on 45 patients with definite MD and 15 healthy controls. MD patients were classified according to their basal levels of IL-1β into 2 groups: high and low. Differentially expression analysis was performed using the ExpHunter Suite, and cell type proportion was evaluated using the estimation algorithms xCell, ABIS, and CIBERSORTx. MD patients showed 15 differentially expressed genes (DEG) compared to controls. The top DEGs include IGHG1 (p = 1.64  ×  10-6) and IGLV3-21 (p = 6.28  ×  10-3), supporting a role in the adaptative immune response. Cytokine profiling defines a subgroup of patients with high levels of IL-1β with up-regulation of IL6 (p = 7.65  ×  10-8) and INHBA (p = 3.39  ×  10-7) genes. Transcriptomic data from peripheral blood mononuclear cells support a proinflammatory subgroup of MD patients with high levels of IL6 and an increase in naïve B-cells, and memory CD8+ T cells.

Transcriptional chronology reveals conserved genes involved in pennate diatom sexual reproduction

Sexual reproduction is a major driver of adaptation and speciation in eukaryotes. In diatoms, siliceous microalgae with a unique cell size reduction-restitution life cycle and among the world's most prolific primary producers, sex also acts as the main mechanism for cell size restoration through the formation of an expanding auxospore. However, the molecular regulators of the different stages of sexual reproduction and size restoration are poorly explored. Here, we combined RNA sequencing with the assembly of a 55 Mbp reference genome for Cylindrotheca closterium to identify patterns of gene expression during different stages of sexual reproduction. These were compared with a corresponding transcriptomic time series of Seminavis robusta to assess the degree of expression conservation. Integrative orthology analysis revealed 138 one-to-one orthologues that are upregulated during sex in both species, among which 56 genes consistently upregulated during cell pairing and gametogenesis, and 11 genes induced when auxospores are present. Several early, sex-specific transcription factors and B-type cyclins were also upregulated during sex in other pennate and centric diatoms, pointing towards a conserved core regulatory machinery for meiosis and gametogenesis across diatoms. Furthermore, we find molecular evidence that the pheromone-induced cell cycle arrest is short-lived in benthic diatoms, which may be linked to their active mode of mate finding through gliding. Finally, we exploit the temporal resolution of our comparative analysis to report the first marker genes for auxospore identity called AAE1-3 ("Auxospore-Associated Expression"). Altogether, we introduce a multi-species model of the transcriptional dynamics during size restoration in diatoms and highlight conserved gene expression dynamics during different stages of sexual reproduction.

Detecting differential transcript usage in complex diseases with SPIT

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and developmental stages, contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function and underpin disease pathogenesis. Analyzing DTU via RNA sequencing (RNA-seq) data is vital, but the genetic heterogeneity in populations with complex diseases presents an intricate challenge due to diverse causal events and undetermined subtypes. Although the majority of common diseases in humans are categorized as complex, state-of-the-art DTU analysis methods often overlook this heterogeneity in their models. We therefore developed SPIT, a statistical tool that identifies predominant subgroups in transcript usage within a population along with their distinctive sets of DTU events. This study provides comprehensive assessments of SPIT's methodology and applies it to analyze brain samples from individuals with schizophrenia, revealing previously unreported DTU events in six candidate genes.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Accounting for isoform expression increases power to identify genetic regulation of gene expression

A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.

FSHD muscle shows perturbation in fibroadipogenic progenitor cells, mitochondrial function and alternative splicing independently of inflammation

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable myopathy. FSHD is highly heterogeneous, with patients following a variety of clinical trajectories, complicating clinical trials. Skeletal muscle in FSHD undergoes fibrosis and fatty replacement that can be accelerated by inflammation, adding to heterogeneity. Well controlled molecular studies are thus essential to both categorize FSHD patients into distinct subtypes and understand pathomechanisms. Here, we further analyzed RNA-sequencing data from 24 FSHD patients, each of whom donated a biopsy from both a non-inflamed (TIRM-) and inflamed (TIRM+) muscle, and 15 FSHD patients who donated peripheral blood mononucleated cells (PBMCs), alongside non-affected control individuals. Differential gene expression analysis identified suppression of mitochondrial biogenesis and up-regulation of fibroadipogenic progenitor (FAP) gene expression in FSHD muscle, which was particularly marked on inflamed samples. PBMCs demonstrated suppression of antigen presentation in FSHD. Gene expression deconvolution revealed FAP expansion as a consistent feature of FSHD muscle, via meta-analysis of 7 independent transcriptomic datasets. Clustering of muscle biopsies separated patients in an unbiased manner into clinically mild and severe subtypes, independently of known disease modifiers (age, sex, D4Z4 repeat length). Lastly, the first genome-wide analysis of alternative splicing in FSHD muscle revealed perturbation of autophagy, BMP2 and HMGB1 signalling. Overall, our findings reveal molecular subtypes of FSHD with clinical relevance and identify novel pathomechanisms for this highly heterogeneous condition.

Differential detection workflows for multi-sample single-cell RNA-seq data

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain

Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.

Detecting host responses to microbial stimulation using primary epithelial organoids

The intestinal epithelium is constantly exposed to microbes residing in the lumen. Traditionally, the response to microbial interactions has been studied in cell lines derived from cancerous tissues, e.g. Caco-2. It is, however, unclear how the responses in these cancer cell lines reflect the responses of a normal epithelium and whether there might be microbial strain-specific effects. To address these questions, we derived organoids from the small intestine from a cohort of healthy individuals. Culturing intestinal epithelium on a flat laminin matrix induced their differentiation, facilitating analysis of microbial responses via the apical membrane normally exposed to the luminal content. Here, it was evident that the healthy epithelium across multiple individuals (n = 9) demonstrates robust acute both common and strain-specific responses to a range of probiotic bacterial strains (BB-12Ⓡ, LGGⓇ, DSM33361, and Bif195). Importantly, parallel experiments using the Caco-2 cell line provide no acute response. Collectively, we demonstrate that primary epithelial cells maintained as organoids represent a valuable resource for assessing interactions between the epithelium and luminal microbes across individuals, and that these models are likely to contribute to a better understanding of host microbe interactions.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

Alteration of microglial metabolism and inflammatory profile contributes to neurotoxicity in a hiPSC-derived microglia model of frontotemporal dementia 3

Frontotemporal dementia (FTD) is a common cause of early-onset dementia, with no current treatment options. FTD linked to chromosome 3 (FTD3) is a rare sub-form of the disease, caused by a point mutation in the Charged Multivesicular Body Protein 2B (CHMP2B). This mutation causes neuronal phenotypes, such as mitochondrial deficiencies, accompanied by metabolic changes and interrupted endosomal-lysosomal fusion. However, the contribution of glial cells to FTD3 pathogenesis has, until recently, been largely unexplored. Glial cells play an important role in most neurodegenerative disorders as drivers and facilitators of neuroinflammation. Microglia are at the center of current investigations as potential pro-inflammatory drivers. While gliosis has been observed in FTD3 patient brains, it has not yet been systematically analyzed. In the light of this, we investigated the role of microglia in FTD3 by implementing human induced pluripotent stem cells (hiPSC) with either a heterozygous or homozygous CHMP2B mutation, introduced into a healthy control hiPSC line via CRISPR-Cas9 precision gene editing. These hiPSC were differentiated into microglia to evaluate the pro-inflammatory profile and metabolic state. Moreover, hiPSC-derived neurons were cultured with conditioned microglia media to investigate disease specific interactions between the two cell populations. Interestingly, we identified two divergent inflammatory microglial phenotypes resulting from the underlying mutations: a severe pro-inflammatory profile in CHMP2B homozygous FTD3 microglia, and an "unresponsive" CHMP2B heterozygous FTD3 microglial state. These findings correlate with our observations of increased phagocytic activity in CHMP2B homozygous, and impaired protein degradation in CHMP2B heterozygous FTD3 microglia. Metabolic mapping confirmed these differences, revealing a metabolic reprogramming of the CHMP2B FTD3 microglia, displayed as a compensatory up-regulation of glutamine metabolism in the CHMP2B homozygous FTD3 microglia. Intriguingly, conditioned CHMP2B homozygous FTD3 microglia media caused neurotoxic effects, which was not evident for the heterozygous microglia. Strikingly, IFN-γ treatment initiated an immune boost of the CHMP2B heterozygous FTD3 microglia, and conditioned microglia media exposure promoted neural outgrowth. Our findings indicate that the microglial profile, activity, and behavior is highly dependent on the status of the CHMP2B mutation. Our results suggest that the heterozygous state of the mutation in FTD3 patients could potentially be exploited in form of immune-boosting intervention strategies to counteract neurodegeneration.

Detecting differential transcript usage in complex diseases with SPIT

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and different developmental stages, thereby contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function, potentially leading to pathogenesis of diseases. Detecting such events for single-gene genetic traits is relatively uncomplicated; however, the heterogeneity of populations with complex diseases presents an intricate challenge due to the presence of diverse causal events and undetermined subtypes. SPIT is the first statistical tool that quantifies the heterogeneity in transcript usage within a population and identifies predominant subgroups along with their distinctive sets of DTU events. We provide comprehensive assessments of SPIT's methodology in both single-gene and complex traits and report the results of applying SPIT to analyze brain samples from individuals with schizophrenia. Our analysis reveals previously unreported DTU events in six candidate genes.

Mutation of Arabidopsis SME1 and Sm core assembly improves oxidative stress resilience

Alternative splicing is a key posttranscriptional gene regulatory process, acting in diverse adaptive and basal plant processes. Splicing of precursor-messenger RNA (pre-mRNA) is catalyzed by a dynamic ribonucleoprotein complex, designated the spliceosome. In a suppressor screen, we identified a nonsense mutation in the Smith (Sm) antigen protein SME1 to alleviate photorespiratory H₂O₂-dependent cell death in catalase deficient plants. Similar attenuation of cell death was observed upon chemical inhibition of the spliceosome, suggesting pre-mRNA splicing inhibition to be responsible for the observed cell death alleviation. Furthermore, the sme1-2 mutants showed increased tolerance to the reactive oxygen species inducing herbicide methyl viologen. Both an mRNA-seq and shotgun proteomic analysis in sme1-2 mutants displayed a constitutive molecular stress response, together with extensive alterations in pre-mRNA splicing of transcripts encoding metabolic enzymes and RNA binding proteins, even under unstressed conditions. Using SME1 as a bait to identify protein interactors, we provide experimental evidence for almost 50 homologs of the mammalian spliceosome-associated protein to reside in the Arabidopsis thaliana spliceosome complexes and propose roles in pre-mRNA splicing for four uncharacterized plant proteins. Furthermore, as for sme1-2, a mutant in the Sm core assembly protein ICLN resulted in a decreased sensitivity to methyl viologen. Taken together, these data show that both a perturbed Sm core composition and assembly results in the activation of a defense response and in enhanced resilience to oxidative stress.

CRISPRi screen for enhancing heterologous α-amylase yield in Bacillus subtilis

Yield improvements in cell factories can potentially be obtained by fine-tuning the regulatory mechanisms for gene candidates. In pursuit of such candidates, we performed RNA-sequencing of two α-amylase producing Bacillus strains and predict hundreds of putative novel non-coding transcribed regions. Surprisingly, we found among hundreds of non-coding and structured RNA candidates that non-coding genomic regions are proportionally undergoing the highest changes in expression during fermentation. Since these classes of RNA are also understudied, we targeted the corresponding genomic regions with CRIPSRi knockdown to test for any potential impact on the yield. From differentially expression analysis, we selected 53 non-coding candidates. Although CRISPRi knockdowns target both the sense and the antisense strand, the CRISPRi experiment cannot link causes for yield changes to the sense or antisense disruption. Nevertheless, we observed on several instances with strong changes in enzyme yield. The knockdown targeting the genomic region for a putative antisense RNA of the 3' UTR of the skfA-skfH operon led to a 21% increase in yield. In contrast, the knockdown targeting the genomic regions of putative antisense RNAs of the cytochrome c oxidase subunit 1 (ctaD), the sigma factor sigH, and the uncharacterized gene yhfT decreased yields by 31 to 43%.

Integrative Meta-Analysis of Huntington's Disease Transcriptome Landscape

Huntington's disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

Isoform-resolved mRNA profiling of ribosome load defines interplay of HIF and mTOR dysregulation in kidney cancer

Hypoxia inducible factor (HIF) and mammalian target of rapamycin (mTOR) pathways orchestrate responses to oxygen and nutrient availability. These pathways are frequently dysregulated in cancer, but their interplay is poorly understood, in part because of difficulties in simultaneous measurement of global and mRNA-specific translation. Here, we describe a workflow for measurement of ribosome load of mRNAs resolved by their transcription start sites (TSSs). Its application to kidney cancer cells reveals extensive translational reprogramming by mTOR, strongly affecting many metabolic enzymes and pathways. By contrast, global effects of HIF on translation are limited, and we do not observe reported translational activation by HIF2A. In contrast, HIF-dependent alterations in TSS usage are associated with robust changes in translational efficiency in a subset of genes. Analyses of the interplay of HIF and mTOR reveal that specific classes of HIF1A and HIF2A transcriptional target gene manifest different sensitivity to mTOR, in a manner that supports combined use of HIF2A and mTOR inhibitors in treatment of kidney cancer.

A new method for mRNA profiling of ribosome load defines the pan-genomic interplay of transcriptional and translational regulation mediated by environment-sensing HIF and mTOR pathways in kidney cancer cells, offering insights into rational therapy.

The impact of PrsA over-expression on the Bacillus subtilis transcriptome during fed-batch fermentation of alpha-amylase production

The production of the alpha-amylase (AMY) enzyme in Bacillus subtilis at a high rate leads to the accumulation of unfolded AMY, which causes secretion stress. The over-expression of the PrsA chaperone aids enzyme folding and reduces stress. To identify affected pathways and potential mechanisms involved in the reduced growth, we analyzed the transcriptomic differences during fed-batch fermentation between a PrsA over-expressing strain and control in a time-series RNA-seq experiment. We observe transcription in 542 unannotated regions, of which 234 had significant changes in expression levels between the samples. Moreover, 1,791 protein-coding sequences, 80 non-coding genes, and 20 riboswitches overlapping UTR regions of coding genes had significant changes in expression. We identified putatively regulated biological processes via gene-set over-representation analysis of the differentially expressed genes; overall, the analysis suggests that the PrsA over-expression affects ATP biosynthesis activity, amino acid metabolism, and cell wall stability. The investigation of the protein interaction network points to a potential impact on cell motility signaling. We discuss the impact of these highlighted mechanisms for reducing secretion stress or detrimental aspects of PrsA over-expression during AMY production.

Strain-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline

The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of acclimated S. marinoi grown in a common garden. Our experiment included eight strains from source populations spanning the Baltic Sea salinity cline. Gene expression analysis revealed that low salinities induced changes in the cellular metabolism of S. marinoi, including upregulation of photosynthesis and storage compound biosynthesis, increased nutrient demand, and a complex response to oxidative stress. However, the strain effect overshadowed the salinity effect, as strains differed significantly in their response, both regarding the strength and the strategy (direction of gene expression) of their response. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond to environmental change.

A comparison of methods for multiple degree of freedom testing in repeated measures RNA-sequencing experiments

Background

As the cost of RNA-sequencing decreases, complex study designs, including paired, longitudinal, and other correlated designs, become increasingly feasible. These studies often include multiple hypotheses and thus multiple degree of freedom tests, or tests that evaluate multiple hypotheses jointly, are often useful for filtering the gene list to a set of interesting features for further exploration while controlling the false discovery rate. Though there are several methods which have been proposed for analyzing correlated RNA-sequencing data, there has been little research evaluating and comparing the performance of multiple degree of freedom tests across methods.

Methods

We evaluated 11 different methods for modelling correlated RNA-sequencing data by performing a simulation study to compare the false discovery rate, power, and model convergence rate across several hypothesis tests and sample size scenarios. We also applied each method to a real longitudinal RNA-sequencing dataset.

Results

Linear mixed modelling using transformed data had the best false discovery rate control while maintaining relatively high power. However, this method had high model non-convergence, particularly at small sample sizes. No method had high power at the lowest sample size. We found a mix of conservative and anti-conservative behavior across the other methods, which was influenced by the sample size and the hypothesis being evaluated. The patterns observed in the simulation study were largely replicated in the analysis of a longitudinal study including data from intensive care unit patients experiencing cardiogenic or septic shock.

Conclusions

Multiple degree of freedom testing is a valuable tool in longitudinal and other correlated RNA-sequencing experiments. Of the methods that we investigated, linear mixed modelling had the best overall combination of power and false discovery rate control. Other methods may also be appropriate in some scenarios.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-022-01615-8).

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection

Better methods to interrogate host-pathogen interactions during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are imperative to help understand and prevent this disease. Here we implemented RNA-sequencing (RNA-seq) using Oxford Nanopore Technologies (ONT) long-reads to measure differential host gene expression, transcript polyadenylation and isoform usage within various epithelial cell lines permissive and non-permissive for SARS-CoV-2 infection. SARS-CoV-2-infected and mock-infected Vero (African green monkey kidney epithelial cells), Calu-3 (human lung adenocarcinoma epithelial cells), Caco-2 (human colorectal adenocarcinoma epithelial cells) and A549 (human lung carcinoma epithelial cells) were analyzed over time (0, 2, 24, 48 hours). Differential polyadenylation was found to occur in both infected Calu-3 and Vero cells during a late time point (48 hpi), with Gene Ontology (GO) terms such as viral transcription and translation shown to be significantly enriched in Calu-3 data. Poly(A) tails showed increased lengths in the majority of the differentially polyadenylated transcripts in Calu-3 and Vero cell lines (up to ~101 nt in mean poly(A) length, padj = 0.029). Of these genes, ribosomal protein genes such as RPS4X and RPS6 also showed downregulation in expression levels, suggesting the importance of ribosomal protein genes during infection. Furthermore, differential transcript usage was identified in Caco-2, Calu-3 and Vero cells, including transcripts of genes such as GSDMB and KPNA2, which have previously been implicated in SARS-CoV-2 infections. Overall, these results highlight the potential role of differential polyadenylation and transcript usage in host immune response or viral manipulation of host mechanisms during infection, and therefore, showcase the value of long-read sequencing in identifying less-explored host responses to disease.

Placental Transcription Profiling in 6-23 Weeks' Gestation Reveals Differential Transcript Usage in Early Development

The human placenta is a rapidly developing transient organ that is key to pregnancy success. Early development of the conceptus occurs in a low oxygen environment before oxygenated maternal blood begins to flow into the placenta at ~10-12 weeks' gestation. This process is likely to substantially affect overall placental gene expression. Transcript variability underlying gene expression has yet to be profiled. In this study, accurate transcript expression profiles were identified for 84 human placental chorionic villus tissue samples collected across 6-23 weeks' gestation. Differential gene expression (DGE), differential transcript expression (DTE) and differential transcript usage (DTU) between 6-10 weeks' and 11-23 weeks' gestation groups were assessed. In total, 229 genes had significant DTE yet no significant DGE. Integration of DGE and DTE analyses found that differential expression patterns of individual transcripts were commonly masked upon aggregation to the gene-level. Of the 611 genes that exhibited DTU, 534 had no significant DGE or DTE. The four most significant DTU genes ADAM10, VMP1, GPR126, and ASAH1, were associated with hypoxia-responsive pathways. Transcript usage is a likely regulatory mechanism in early placentation. Identification of functional roles will facilitate new insight in understanding the origins of pregnancy complications.

HNF4A modulates glucocorticoid action in the liver

The glucocorticoid receptor (GR) is a nuclear receptor critical to the regulation of energy metabolism and inflammation. The actions of GR are dependent on cell type and context. Here, we demonstrate the role of liver lineage-determining factor hepatocyte nuclear factor 4A (HNF4A) in defining liver specificity of GR action. In mouse liver, the HNF4A motif lies adjacent to the glucocorticoid response element (GRE) at GR binding sites within regions of open chromatin. In the absence of HNF4A, the liver GR cistrome is remodeled, with loss and gain of GR recruitment evident. Loss of chromatin accessibility at HNF4A-marked sites associates with loss of GR binding at weak GRE motifs. GR binding and chromatin accessibility are gained at sites characterized by strong GRE motifs, which show GR recruitment in non-liver tissues. The functional importance of these HNF4A-regulated GR sites is indicated by an altered transcriptional response to glucocorticoid treatment in the Hnf4a-null liver.

Analysis of modular gene co-expression networks reveals molecular pathways underlying Alzheimer’s disease and progressive supranuclear palsy

A comprehensive understanding of the pathological mechanisms involved at different stages of neurodegenerative diseases is key for the advance of preventive and disease-modifying treatments. Gene expression alterations in the diseased brain is a potential source of information about biological processes affected by pathology. In this work, we performed a systematic comparison of gene expression alterations in the brains of human patients diagnosed with Alzheimer’s disease (AD) or Progressive Supranuclear Palsy (PSP) and animal models of amyloidopathy and tauopathy. Using a systems biology approach to uncover biological processes associated with gene expression alterations, we could pinpoint processes more strongly associated with tauopathy/PSP and amyloidopathy/AD. We show that gene expression alterations related to immune-inflammatory responses preponderate in younger, whereas those associated to synaptic transmission are mainly observed in older AD patients. In PSP, however, changes associated with immune-inflammatory responses and synaptic transmission overlap. These two different patterns observed in AD and PSP brains are fairly recapitulated in animal models of amyloidopathy and tauopathy, respectively. Moreover, in AD, but not PSP or animal models, gene expression alterations related to RNA splicing are highly prevalent, whereas those associated with myelination are enriched both in AD and PSP, but not in animal models. Finally, we identify 12 AD and 4 PSP genetic risk factors in cell-type specific co-expression modules, thus contributing to unveil the possible role of these genes to pathogenesis. Altogether, this work contributes to unravel the potential biological processes affected by amyloid versus tau pathology and how they could contribute to the pathogenesis of AD and PSP.

Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains

Chromosomal organization, scaling from the 147 base pair nucleosome to megabase-ranging domains encompassing multiple transcriptional units including heritability loci for psychiatric traits, remains largely unexplored in the human brain. Here, we construct promoter and enhancer enriched nucleosomal histone modification landscapes for adult prefrontal cortex (PFC) from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from (n=739) 388 controls and 351 subjects diagnosed with schizophrenia (SCZ) or bipolar disorder (BD). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 10⁴-10⁶ bp chromosomal organization, firmly integrated into Hi-C topologically associating domain (TAD) stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyperacetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilo- to megabase-scaling chromosomal domains.

A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis

Osteoarthritis is a prevalent joint disease and a major cause of disability worldwide with no curative therapy. Development of disease-modifying therapies requires a better understanding of the molecular mechanisms underpinning disease. A hallmark of osteoarthritis is cartilage degradation. To define molecular events characterizing osteoarthritis at the whole transcriptome level, we performed deep RNA sequencing in paired samples of low- and high-osteoarthritis grade knee cartilage derived from 124 patients undergoing total joint replacement. We detected differential expression between low- and high-osteoarthritis grade articular cartilage for 365 genes and identified a 38-gene signature in osteoarthritis cartilage by replicating our findings in an independent dataset. We also found differential expression for 25 novel long non-coding RNA genes (lncRNAs) and identified potential lncRNA interactions with RNA-binding proteins in osteoarthritis. We assessed alterations in the relative usage of individual gene transcripts and identified differential transcript usage for 82 genes, including ABI3BP, coding for an extracellular matrix protein, AKT1S1, a negative regulator of the mTOR pathway and TPRM4, coding for a transient receptor potential channel. We further assessed genome-wide differential splicing, for the first time in osteoarthritis, and detected differential splicing for 209 genes, which were enriched for extracellular matrix, proteoglycans and integrin surface interactions terms. In the largest study of its kind in osteoarthritis, we find that isoform and splicing changes, in addition to extensive differences in both coding and non-coding sequence expression, are associated with disease and demonstrate a novel layer of genomic complexity to osteoarthritis pathogenesis.

Patient-specific MDS-RS iPSCs define the mis-spliced transcript repertoire and chromatin landscape of SF3B1-mutant HSPCs

Genetically matched MDS-RS and normal patient-specific iPSC-HSPCs are used to derive a mutant SF3B1 splicing signature.

Integrated transcriptomics and chromatin accessibility nominate TEAD as a putative novel transcriptional regulator of SF3B1^K700E cells.

SF3B1^K700E is the most frequent mutation in myelodysplastic syndrome (MDS), but the mechanisms by which it drives MDS pathogenesis remain unclear. We derived a panel of 18 genetically matched SF3B1^K700E- and SF3B1^WT-induced pluripotent stem cell (iPSC) lines from patients with MDS with ring sideroblasts (MDS-RS) harboring isolated SF3B1^K700E mutations and performed RNA and ATAC sequencing in purified CD34⁺/CD45⁺ hematopoietic stem/progenitor cells (HSPCs) derived from them. We developed a novel computational framework integrating splicing with transcript usage and gene expression analyses and derived a SF3B1^K700E splicing signature consisting of 59 splicing events linked to 34 genes, which associates with the SF3B1 mutational status of primary MDS patient cells. The chromatin landscape of SF3B1^K700E HSPCs showed increased priming toward the megakaryocyte- erythroid lineage. Transcription factor motifs enriched in chromatin regions more accessible in SF3B1^K700E cells included, unexpectedly, motifs of the TEA domain (TEAD) transcription factor family. TEAD expression and transcriptional activity were upregulated in SF3B1-mutant iPSC-HSPCs, in support of a Hippo pathway-independent role of TEAD as a potential novel transcriptional regulator of SF3B1^K700E cells. This study provides a comprehensive characterization of the transcriptional and chromatin landscape of SF3B1^K700E HSPCs and nominates novel mis-spliced genes and transcriptional programs with putative roles in MDS-RS disease biology.

Transcript-targeted analysis reveals isoform alterations and double-hop fusions in breast cancer

Although transcriptome alteration is an essential driver of carcinogenesis, the effects of chromosomal structural alterations on the cancer transcriptome are not yet fully understood. Short-read transcript sequencing has prevented researchers from directly exploring full-length transcripts, forcing them to focus on individual splice sites. Here, we develop a pipeline for Multi-Sample long-read Transcriptome Assembly (MuSTA), which enables construction of a transcriptome from long-read sequence data. Using the constructed transcriptome as a reference, we analyze RNA extracted from 22 clinical breast cancer specimens. We identify a comprehensive set of subtype-specific and differentially used isoforms, which extended our knowledge of isoform regulation to unannotated isoforms including a short form TNS3. We also find that the exon-intron structure of fusion transcripts depends on their genomic context, and we identify double-hop fusion transcripts that are transcribed from complex structural rearrangements. For example, a double-hop fusion results in aberrant expression of an endogenous retroviral gene, ERVFRD-1, which is normally expressed exclusively in placenta and is thought to protect fetus from maternal rejection; expression is elevated in several TCGA samples with ERVFRD-1 fusions. Our analyses provide direct evidence that full-length transcript sequencing of clinical samples can add to our understanding of cancer biology and genomics in general.

Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states

Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.

Single-cell RNA-seq unravels alterations of the human spermatogonial stem cell compartment in patients with impaired spermatogenesis

Despite the high incidence of male infertility, only 30% of infertile men receive a causative diagnosis. To explore the regulatory mechanisms governing human germ cell function in normal and impaired spermatogenesis (crypto), we performed single-cell RNA sequencing (>30,000 cells). We find major alterations in the crypto spermatogonial compartment with increased numbers of the most undifferentiated spermatogonia (PIWIL4⁺). We also observe a transcriptional switch within the spermatogonial compartment driven by increased and prolonged expression of the transcription factor EGR4. Intriguingly, the EGR4-regulated chromatin-associated transcriptional repressor UTF1 is downregulated at transcriptional and protein levels. This is associated with changes in spermatogonial chromatin structure and fewer A_dark spermatogonia, characterized by tightly compacted chromatin and serving as reserve stem cells. These findings suggest that crypto patients are disadvantaged, as fewer cells safeguard their germline’s genetic integrity. These identified spermatogonial regulators will be highly interesting targets to uncover genetic causes of male infertility.

Crypto(zoospermic) men show increased number of PIWIL4⁺/EGR4⁺ spermatogonia

Crypto undifferentiated spermatogonia over-activate the EGR4 regulatory network

The predicted EGR4 target UTF1 is downregulated in crypto spermatogonia

Crypto testes show reduced numbers of UTF1⁺ A_dark reserve spermatogonia

Alveolar macrophages from persons living with HIV show impaired epigenetic response to Mycobacterium tuberculosis

Persons living with HIV (PLWH) are at increased risk of tuberculosis (TB). HIV-associated TB is often the result of recent infection with Mycobacterium tuberculosis (Mtb) followed by rapid progression to disease. Alveolar macrophages (AM) are the first cells of the innate immune system that engage Mtb, but how HIV and antiretroviral therapy (ART) impact on the anti-mycobacterial response of AM is not known. To investigate the impact of HIV and ART on the transcriptomic and epigenetic response of AM to Mtb, we obtained AM by bronchoalveolar lavage from 20 PLWH receiving ART, 16 control subjects who were HIV-free (HC), and 14 subjects who received ART as pre-exposure prophylaxis (PrEP) to prevent HIV infection. Following in-vitro challenge with Mtb, AM from each group displayed overlapping but distinct profiles of significantly up- and down-regulated genes in response to Mtb. Comparatively, AM isolated from both PLWH and PrEP subjects presented a substantially weaker transcriptional response. In addition, AM from HC subjects challenged with Mtb responded with pronounced chromatin accessibility changes while AM obtained from PLWH and PrEP subjects displayed no significant changes in their chromatin state. Collectively, these results revealed a stronger adverse effect of ART than HIV on the epigenetic landscape and transcriptional responsiveness of AM.

Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle

Motivation

Each year, the number of published bulk and single-cell RNA-seq datasets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell-type identification.

Results

We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq datasets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. In addition, we present novel potential DTU applications like the identification of cell-type specific transcript isoforms as biomarkers.

Availability and implementation

The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/.

Supplementary information

Supplementary data are available at Bioinformatics online.

The genome of the extremophile Artemia provides insight into strategies to cope with extreme environments

BACKGROUND

Brine shrimp Artemia have an unequalled ability to endure extreme salinity and complete anoxia. This study aims to elucidate its strategies to cope with these stressors.

RESULTS AND DISCUSSION

Here, we present the genome of an inbred A. franciscana Kellogg, 1906. We identified 21,828 genes of which, under high salinity, 674 genes and under anoxia, 900 genes were differentially expressed (42%, respectively 30% were annotated). Under high salinity, relevant stress genes and pathways included several Heat Shock Protein and Leaf Embryogenesis Abundant genes, as well as the trehalose metabolism. In addition, based on differential gene expression analysis, it can be hypothesized that a high oxidative stress response and endocytosis/exocytosis are potential salt management strategies, in addition to the expression of major facilitator superfamily genes responsible for transmembrane ion transport. Under anoxia, genes involved in mitochondrial function, mTOR signalling and autophagy were differentially expressed. Both high salt and anoxia enhanced degradation of erroneous proteins and protein chaperoning. Compared with other branchiopod genomes, Artemia had 0.03% contracted and 6% expanded orthogroups, in which 14% of the genes were differentially expressed under high salinity or anoxia. One phospholipase D gene family, shown to be important in plant stress response, was uniquely present in both extremophiles Artemia and the tardigrade Hypsibius dujardini, yet not differentially expressed under the described experimental conditions.

CONCLUSIONS

A relatively complete genome of Artemia was assembled, annotated and analysed, facilitating research on its extremophile features, and providing a reference sequence for crustacean research.

Whole-genome methylation analysis of testicular germ cells from cryptozoospermic men points to recurrent and functionally relevant DNA methylation changes

BACKGROUND

Several studies have reported an association between male infertility and aberrant sperm DNA methylation patterns, in particular in imprinted genes. In a recent investigation based on whole methylome and deep bisulfite sequencing, we have not found any evidence for such an association, but have demonstrated that somatic DNA contamination and genetic variation confound methylation studies in sperm of severely oligozoospermic men. To find out whether testicular germ cells (TGCs) of such patients might carry aberrant DNA methylation, we compared the TGC methylomes of four men with cryptozoospermia (CZ) and four men with obstructive azoospermia, who had normal spermatogenesis and served as controls (CTR).

RESULTS

There was no difference in DNA methylation at the whole genome level or at imprinted regions between CZ and CTR samples. However, using stringent filters to identify group-specific methylation differences, we detected 271 differentially methylated regions (DMRs), 238 of which were hypermethylated in CZ (binominal test, p < 2.2 × 10-16). The DMRs were enriched for distal regulatory elements (p = 1.0 × 10-6) and associated with 132 genes, 61 of which are differentially expressed at various stages of spermatogenesis. Almost all of the 67 DMRs associated with the 61 genes (94%) are hypermethylated in CZ (63/67, p = 1.107 × 10-14). As judged by single-cell RNA sequencing, 13 DMR-associated genes, which are mainly expressed during meiosis and spermiogenesis, show a significantly different pattern of expression in CZ patients. In four of these genes, the promoter is hypermethylated in CZ men, which correlates with a lower expression level in these patients. In the other nine genes, eight of which downregulated in CZ, germ cell-specific enhancers may be affected.

CONCLUSIONS

We found that impaired spermatogenesis is associated with DNA methylation changes in testicular germ cells at functionally relevant regions of the genome. We hypothesize that the described DNA methylation changes may reflect or contribute to premature abortion of spermatogenesis and therefore not appear in the mature, motile sperm.

The transcriptomic blueprint of molt in rooster using various tissues from Ginkkoridak (Korean long-tailed chicken)

BACKGROUND

Annual molt is a critical stage in the life cycle of birds. Although the most extensively documented aspects of molt are the renewing of plumage and the remodeling of the reproductive tract in laying hens, in chicken, molt deeply affects various tissues and physiological functions. However, with exception of the reproductive tract, the effect of molt on gene expression across the tissues known to be affected by molt has to date never been investigated. The present study aimed to decipher the transcriptomic effects of molt in Ginkkoridak, a Korean long-tailed chicken. Messenger RNA data available across 24 types of tissue samples (9 males) and a combination of mRNA and miRNA data on 10 males and 10 females blood were used.

RESULTS

The impact of molt on gene expression and gene transcript usage appeared to vary substantially across tissues types in terms of histological entities or physiological functions particularly related to nervous system. Blood was the tissue most affected by molt in terms of differentially expressed genes in both sexes, closely followed by meninges, bone marrow and heart. The effect of molt in blood appeared to differ between males and females, with a more than fivefold difference in the number of down-regulated genes between both sexes. The blueprint of molt in roosters appeared to be specific to tissues or group of tissues, with relatively few genes replicating extensively across tissues, excepted for the spliceosome genes (U1, U4) and the ribosomal proteins (RPL21, RPL23). By integrating miRNA and mRNA data, when chickens molt, potential roles of miRNA were discovered such as regulation of neurogenesis, regulation of immunity and development of various organs. Furthermore, reliable candidate biomarkers of molt were found, which are related to cell dynamics, nervous system or immunity, processes or functions that have been shown to be extensively modulated in response to molt.

CONCLUSIONS

Our results provide a comprehensive description at the scale of the whole organism deciphering the effects of molt on the transcriptome in chicken. Also, the conclusion of this study can be used as a valuable resource in transcriptome analyses of chicken in the future and provide new insights related to molt.

Adipocyte NR1D1 dictates adipose tissue expansion during obesity

The circadian clock component NR1D1 (REVERBα) is considered a dominant regulator of lipid metabolism, with global Nr1d1 deletion driving dysregulation of white adipose tissue (WAT) lipogenesis and obesity. However, a similar phenotype is not observed under adipocyte-selective deletion (Nr1d1Flox2-6:AdipoqCre), and transcriptional profiling demonstrates that, under basal conditions, direct targets of NR1D1 regulation are limited, and include the circadian clock and collagen dynamics. Under high-fat diet (HFD) feeding, Nr1d1Flox2-6:AdipoqCre mice do manifest profound obesity, yet without the accompanying WAT inflammation and fibrosis exhibited by controls. Integration of the WAT NR1D1 cistrome with differential gene expression reveals broad control of metabolic processes by NR1D1 which is unmasked in the obese state. Adipocyte NR1D1 does not drive an anticipatory daily rhythm in WAT lipogenesis, but rather modulates WAT activity in response to alterations in metabolic state. Importantly, NR1D1 action in adipocytes is critical to the development of obesity-related WAT pathology and insulin resistance.

Statistical Modeling of High Dimensional Counts

Statistical modeling of count data from RNA sequencing (RNA-seq) experiments is important for proper interpretation of results. Here I will describe how count data can be modeled using count distributions, or alternatively analyzed using nonparametric methods. I will focus on basic routines for performing data input, scaling/normalization, visualization, and statistical testing to determine sets of features where the counts reflect differences in gene expression across samples. Finally, I discuss limitations and possible extensions to the models presented here.

satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seq applications.

satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive scRNA-seq data. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs and scaling to scRNA-seq applications.

The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

Sex-specific DNA methylation differences in Alzheimer's disease pathology

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer's disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation differences in AD neuropathology, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort, we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with the AD Braak stage in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated genes in AD neuropathology, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation differences were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Our study implicated multiple new loci and biological processes that affected AD neuropathology in a sex-specific manner.

Novel brain gene-expression patterns are associated with a novel predaceous behaviour in tadpoles

Novel behaviours can spur evolutionary change and sometimes even precede morphological innovation, but the evolutionary and developmental contexts for their origins can be elusive. One proposed mechanism to generate behavioural innovation is a shift in the developmental timing of gene-expression patterns underlying an ancestral behaviour, or molecular heterochrony. Alternatively, novel suites of gene expression, which could provide new contexts for signalling pathways with conserved behavioural functions, could promote novel behavioural variation. To determine the relative contributions of these alternatives to behavioural innovation, I used a species of spadefoot toad, Spea bombifrons. Based on environmental cues, Spea larvae develop as either of two morphs: 'omnivores' that, like their ancestors, feed on detritus, or 'carnivores' that are predaceous and cannibalistic. Because all anuran larvae undergo a natural transition to obligate carnivory during metamorphosis, it has been proposed that the novel, predaceous behaviour in Spea larvae represents the accelerated activation of gene networks influencing post-metamorphic behaviours. Based on comparisons of brain transcriptional profiles, my results reject widespread heterochrony as a mechanism promoting the expression of predaceous larval behaviour. They instead suggest that the evolution of this trait relied on novel patterns of gene expression that include components of pathways with conserved behavioural functions.

DNA Methylation Regulates Transcription Factor-Specific Neurodevelopmental but Not Sexually Dimorphic Gene Expression Dynamics in Zebra Finch Telencephalon

Song learning in zebra finches (Taeniopygia guttata) is a prototypical example of a complex learned behavior, yet knowledge of the underlying molecular processes is limited. Therefore, we characterized transcriptomic (RNA-sequencing) and epigenomic (RRBS, reduced representation bisulfite sequencing; immunofluorescence) dynamics in matched zebra finch telencephalon samples of both sexes from 1 day post hatching (1 dph) to adulthood, spanning the critical period for song learning (20 and 65 dph). We identified extensive transcriptional neurodevelopmental changes during postnatal telencephalon development. DNA methylation was very low, yet increased over time, particularly in song control nuclei. Only a small fraction of the massive differential expression in the developing zebra finch telencephalon could be explained by differential CpG and CpH DNA methylation. However, a strong association between DNA methylation and age-dependent gene expression was found for various transcription factors (i.e., OTX2, AR, and FOS) involved in neurodevelopment. Incomplete dosage compensation, independent of DNA methylation, was found to be largely responsible for sexually dimorphic gene expression, with dosage compensation increasing throughout life. In conclusion, our results indicate that DNA methylation regulates neurodevelopmental gene expression dynamics through steering transcription factor activity, but does not explain sexually dimorphic gene expression patterns in zebra finch telencephalon.

Differential transcript usage unravels gene expression alterations in Alzheimer's disease human brains

Alzheimer's disease (AD) is the leading cause of dementia in aging individuals. Yet, the pathophysiological processes involved in AD onset and progression are still poorly understood. Among numerous strategies, a comprehensive overview of gene expression alterations in the diseased brain could contribute for a better understanding of the AD pathology. In this work, we probed the differential expression of genes in different brain regions of healthy and AD adult subjects using data from three large transcriptomic studies: Mayo Clinic, Mount Sinai Brain Bank (MSBB), and ROSMAP. Using a combination of differential expression of gene and isoform switch analyses, we provide a detailed landscape of gene expression alterations in the temporal and frontal lobes, harboring brain areas affected at early and late stages of the AD pathology, respectively. Next, we took advantage of an indirect approach to assign the complex gene expression changes revealed in bulk RNAseq to individual cell types/subtypes of the adult brain. This strategy allowed us to identify previously overlooked gene expression changes in the brain of AD patients. Among these alterations, we show isoform switches in the AD causal gene amyloid-beta precursor protein (APP) and the risk gene bridging integrator 1 (BIN1), which could have important functional consequences in neuronal cells. Altogether, our work proposes a novel integrative strategy to analyze RNAseq data in AD and other neurodegenerative diseases based on both gene/transcript expression and regional/cell-type specificities.

Differential transcript usage in the Parkinson's disease brain

Studies of differential gene expression have identified several molecular signatures and pathways associated with Parkinson's disease (PD). The role of isoform switches and differential transcript usage (DTU) remains, however, unexplored. Here, we report the first genome-wide study of DTU in PD. We performed RNA sequencing following ribosomal RNA depletion in prefrontal cortex samples of 49 individuals from two independent case-control cohorts. DTU was assessed using two transcript-count based approaches, implemented in the DRIMSeq and DEXSeq tools. Multiple PD-associated DTU events were detected in each cohort, of which 23 DTU events in 19 genes replicated across both patient cohorts. For several of these, including THEM5, SLC16A1 and BCHE, DTU was predicted to have substantial functional consequences, such as altered subcellular localization or switching to non-protein coding isoforms. Furthermore, genes with PD-associated DTU were enriched in functional pathways previously linked to PD, including reactive oxygen species generation and protein homeostasis. Importantly, the vast majority of genes exhibiting DTU were not differentially expressed at the gene-level and were therefore not identified by conventional differential gene expression analysis. Our findings provide the first insight into the DTU landscape of PD and identify novel disease-associated genes. Moreover, we show that DTU may have important functional consequences in the PD brain, since it is predicted to alter the functional composition of the proteome. Based on these results, we propose that DTU analysis is an essential complement to differential gene expression studies in order to provide a more accurate and complete picture of disease-associated transcriptomic alterations.

Two-step mixed model approach to analyzing differential alternative RNA splicing

Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.

MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics

Missing values are a major issue in quantitative data-dependent mass spectrometry-based proteomics. We therefore present an innovative solution to this key issue by introducing a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based model component. It enables dramatically enhanced quantification of proteins with many missing values without having to resort to harmful assumptions for missingness. We demonstrate the superior performance of our method by comparing it with state-of-the-art methods in the field.