Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias

Alcohol Exposure Induces Nucleolar Stress and Apoptosis in Mouse Neural Stem Cells and Late-Term Fetal Brain

Prenatal alcohol exposure (PAE) is a leading cause of neurodevelopmental disability through its induction of neuronal growth dysfunction through incompletely understood mechanisms. Ribosome biogenesis regulates cell cycle progression through p53 and the nucleolar cell stress response. Whether those processes are targeted by alcohol is unknown. Pregnant C57BL/6J mice received 3 g alcohol/kg daily at E8.5-E17.5. Transcriptome sequencing was performed on the E17.5 fetal cortex. Additionally, primary neural stem cells (NSCs) were isolated from the E14.5 cerebral cortex and exposed to alcohol to evaluate nucleolar stress and p53/MDM2 signaling. Alcohol suppressed KEGG pathways involving ribosome biogenesis (rRNA synthesis/processing and ribosomal proteins) and genes that are mechanistic in ribosomopathies (Polr1d, Rpl11; Rpl35; Nhp2); this was accompanied by nucleolar dissolution and p53 stabilization. In primary NSCs, alcohol reduced rRNA synthesis, caused nucleolar loss, suppressed proliferation, stabilized nuclear p53, and caused apoptosis that was prevented by dominant-negative p53 and MDM2 overexpression. Alcohol's actions were dose-dependent and rapid, and rRNA synthesis was suppressed between 30 and 60 min following alcohol exposure. The alcohol-mediated deficits in ribosomal protein expression were correlated with fetal brain weight reductions. This is the first report describing that pharmacologically relevant alcohol levels suppress ribosome biogenesis, induce nucleolar stress in neuronal populations, and involve the ribosomal/MDM2/p53 pathway to cause growth arrest and apoptosis. This represents a novel mechanism of alcohol-mediated neuronal damage.

Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis

In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when the effects of homozygosity for recessive mutations are studied in non-isogenic backgrounds, genes located proximal to the mutation on the same chromosome often appear over-represented among those genes identified as differentially expressed (DE). One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect functional responses to the mutation but, instead, result from an unequal distribution of expression quantitative trait loci (eQTLs) between sample groups of mutant or wild-type genotypes. This is problematic because eQTL expression differences are difficult to distinguish from genes that are DE due to functional responses to a mutation. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) are also observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between those sample groups subjected to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting-based approaches. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. Our method for DAR analysis requires only RNA-sequencing data, facilitating its application across new and existing datasets.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Whole blood RNA extraction efficiency contributes to variability in RNA sequencing data sets

Numerous methodologies are used for blood RNA extraction, and large quantitative differences in recovered RNA content are reported. We evaluated three archived data sets to determine how extraction methodologies might influence mRNA and lncRNA sequencing results. The total quantity of RNA recovered /ml of blood affects RNA sequencing by impacting the recovery of weakly expressed mRNA, and lncRNA transcripts. Transcript expression (TPM counts) plotted in relation to transcript size (base pairs, bp) revealed a 30% loss of short to midsized transcripts in some data sets. Quantitative recovery of RNA is of considerable importance, and it should be viewed more judiciously. Transcripts common to the three data sets were subsequently normalized and transcript mean TPM counts and TPM count coefficient of variation (CV) were plotted in relation to increasing transcript size. Regression analysis of mean TPM counts versus transcript size revealed negative slopes in two of the three data sets suggesting a reduction of TPM transcript counts with increasing transcript size. In the third data set, the regression slope line of mRNA transcript TPM counts approximates zero and TPM counts increased in proportion to transcript size over a range of 200 to 30,000 bp. Similarly, transcript TPM count CV values also were uniformly distributed over the range of transcript sizes. In the other data sets, the regression CV slopes increased in relation to transcript size. The recovery of weakly expressed and /or short to midsized mRNA and lncRNA transcripts varies with different RNA extraction methodologies thereby altering the fundamental sequencing relationship between transcript size and TPM counts. Our analysis identifies differences in RNA sequencing results that are dependent upon the quantity of total RNA recovery from whole blood. We propose that incomplete RNA extraction directly impacts the recovery of mRNA and lncRNA transcripts from human blood and speculate these differences contribute to the "batch" effects commonly identified between sequencing results from different archived data sets.

Deep annotation of long noncoding RNAs by assembling RNA-seq and small RNA-seq data

Long noncoding RNAs (lncRNAs) are increasingly being recognized as modulators in various biological processes. However, due to their low expression, their systematic characterization is difficult to determine. Here, we performed transcript annotation by a newly developed computational pipeline, termed RNA-seq and small RNA-seq combined strategy (RSCS), in a wide variety of cellular contexts. Thousands of high-confidence potential novel transcripts were identified by the RSCS, and the reliability of the transcriptome was verified by analysis of transcript structure, base composition, and sequence complexity. Evidenced by the length comparison, the frequency of the core promoter and the polyadenylation signal motifs, and the locations of transcription start and end sites, the transcripts appear to be full length. Furthermore, taking advantage of our strategy, we identified a large number of endogenous retrovirus-associated lncRNAs, and a novel endogenous retrovirus-lncRNA that was functionally involved in control of Yap1 expression and essential for early embryogenesis was identified. In summary, the RSCS can generate a more complete and precise transcriptome, and our findings greatly expanded the transcriptome annotation for the mammalian community.

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.

Effective extraction of polyribosomes exposes gene expression strategies in primary astrocytes

Regulation of mRNA translation in astrocytes gains a growing interest. However, until now, successful ribosome profiling of primary astrocytes has not been reported. Here, we optimized the standard 'polysome profiling' method and generated an effective protocol for polyribosome extraction, which enabled genome-wide assessment of mRNA translation dynamics along the process of astrocyte activation. Transcriptome (RNAseq) and translatome (Riboseq) data generated at 0, 24 and 48 h after cytokines treatment, revealed dynamic genome-wide changes in the expression level of ∼12 000 genes. The data clarify whether a change in protein synthesis rate results from a change in mRNA level or translation efficiency per se. It exhibit different expression strategies, based on changes in mRNA abundance and/or translation efficiency, which are specifically assigned to gene subsets depending on their function. Moreover, the study raises an important take-home message related to the possible presence of 'difficult to extract' polyribosome sub-groups, in all cell types, thus illuminating the impact of ribosomes extraction methodology on experiments addressing translation regulation.

Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis

In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when homozygous mutations are studied in non-isogenic backgrounds, genes from the same chromosome as a mutation often appear over-represented among differentially expressed (DE) genes. One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect true biological responses to the mutation but, instead, result from differences in representation of expression quantitative trait loci (eQTLs) between sample groups selected on the basis of mutant or wild-type genotype. This is problematic when inclusion of spurious DE genes in a functional enrichment study results in incorrect inferences of mutation effect. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) can also be observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between groups of samples subject to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting of gene-level rankings. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. The DAR metric provides a solid foundation for addressing the eQTL issue in new and existing datasets because it relies solely on RNA-sequencing data.

A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

Influence of RNA-Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation

RNA sequencing (RNA-Seq) is popular for measuring gene expression in non-model organisms, including wild populations. While RNA-Seq can detect gene expression variation among wild-caught individuals and yield important insights into biological function, sampling methods can also affect gene expression estimates. We examined the influence of multiple technical variables on estimated gene expression in a non-model fish, the westslope cutthroat trout (Oncorhynchus clarkii lewisi), using two RNA-Seq library types: 3' RNA-Seq (QuantSeq) and whole mRNA-Seq (NEB). We evaluated effects of dip netting versus electrofishing, and of harvesting tissue immediately versus 5 min after euthanasia on estimated gene expression in blood, gill, and muscle. We found no significant differences in gene expression between sampling methods or tissue collection times with either library type. When library types were compared using the same blood samples, 58% of genes detected by both NEB and QuantSeq showed significantly different expression between library types, and NEB detected 31% more genes than QuantSeq. Although the two library types recovered different numbers of genes and expression levels, results with NEB and QuantSeq were consistent in that neither library type showed differences in gene expression between sampling methods and tissue harvesting times. Our study suggests that researchers can safely rely on different fish sampling strategies in the field. In addition, while QuantSeq is more cost effective, NEB detects more expressed genes. Therefore, when it is crucial to detect as many genes as possible (especially low expressed genes), when alternative splicing is of interest, or when working with an organism lacking good genomic resources, whole mRNA-Seq is more powerful.

RNA-seq coupling two different methods of castration reveals new insights into androgen deficiency-caused degeneration of submaxillary gland in male Sprague Dawley rats

Background

Salivary gland (SMG) degeneration and dysfunction are common symptoms that occur after sex hormone deprivation, but the underlying mechanisms remain largely unknown. Additionally, immunocastration, which causes drop of sex hormones, has been developed as an alternative to surgical castration, however whether it exerts similar effects as surgical castration on the salivary glands is unknown. Through histological and RNA-seq analysis, we assessed changes in morphology and transcriptome of SMG in response to immunocastration (IM) versus surgical castration (bilateral orchiectomy, ORC).

Results

Compared to entire males (EM), ORC caused severe degeneration of SMG in rats, as evidenced by both decreased (P < 0.01) SMG weight and organ index, and by decreased (P < 0.01) quantity of SMG acini and ducts. IM had minimal effects (P > 0.05) on SMG weight and organ index, but it still caused degeneration (P < 0.05) of the acini and ducts. Even though, the quantity of both SMG acini and ducts was much higher (P < 0.001) in IM than in ORC. Functional enrichment analysis of the common regulated genes by ORC/IM revealed disrupted epithelial cell development, angiogenesis, anatomical structure morphogenesis and enhanced cell death are associated with SMG degeneration in deprivation of androgens. Integrated data analysis shown that there existed a selective hyperfunction of SMG ribosome and mitochondrion in ORC but not in IM, which might be associated with more severe degeneration of SMG in ORC than in IM.

Conclusions

Our findings suggested that both surgical castration and immunocastration caused SMG degeneration by disrupting epithelial cell development, angiogenesis, anatomical structure morphogenesis and enhancing cell death. But, surgical castration selectively induced hyperfunction of SMG ribosome and mitochondrion, thus causing more severe degeneration of SMG than immunocastration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08521-9.

RNA-Seq is not required to determine stable reference genes for qPCR normalization

Assessment of differential gene expression by qPCR is heavily influenced by the choice of reference genes. Although numerous statistical approaches have been proposed to determine the best reference genes, they can give rise to conflicting results depending on experimental conditions. Hence, recent studies propose the use of RNA-Seq to identify stable genes followed by the application of different statistical approaches to determine the best set of reference genes for qPCR data normalization. In this study, however, we demonstrate that the statistical approach to determine the best reference genes from commonly used conventional candidates is more important than the preselection of ‘stable’ candidates from RNA-Seq data. Using a qPCR data normalization workflow that we have previously established; we show that qPCR data normalization using conventional reference genes render the same results as stable reference genes selected from RNA-Seq data. We validated these observations in two distinct cross-sectional experimental conditions involving human iPSC derived microglial cells and mouse sciatic nerves. These results taken together show that given a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data do not offer any significant advantage over commonly used reference genes for normalizing qPCR assays.

RTqPCR is a powerful technique that is widely used to quantify gene expression in research and diagnostics of different diseases. The technique involves making multiple copies (amplification) of a specific target DNA. The amplified target DNA binds to a molecule that emits fluorescence upon binding. The extent of fluorescence correlates to the amount of DNA present. To precisely quantify this fluorescence (and thus the quantities of target DNA), internal control genes also called as reference genes need to be determined. Such genes, in principle, do not have varied expression across samples and would exhibit the same fluorescence in all samples. They can thus be used to normalize the expression of the Target DNA. Unfortunately, choosing the right reference gene is very tricky and poor choice of reference genes results in unreliable data both in research and in diagnostics. In this study, we validate a statistical approach to find stably expressed reference genes for any experimental setting using a given set of candidates. We compare our approach to RNA sequencing which quantifies the expression of thousands of genes at the same time. We highlight the advantages of our approach which is cost effective and saves a lot of time when compared to sequencing.

Polysomal mRNA Association and Gene Expression in Trypanosoma brucei

Background: The contrasting physiological environments of  Trypanosoma brucei procyclic (insect vector) and bloodstream (mammalian host) forms necessitates deployment of different molecular processes and, therefore, changes in protein expression. Transcriptional regulation is unusual in  T. brucei because the arrangement of genes is polycistronic; however, genes which are transcribed together are subsequently cleaved into separate mRNAs by  trans-splicing. Following pre-mRNA processing, the regulation of mature mRNA stability is a tightly controlled cellular process. While many stage-specific transcripts have been identified, previous studies using RNA-seq suggest that changes in overall transcript level do not necessarily reflect the abundance of the corresponding protein.

Methods: To better understand the regulation of gene expression in  T. brucei, we performed a bioinformatic analysis of RNA-seq on total, sub-polysomal, and polysomal mRNA samples. We further cross-referenced our dataset with a previously published proteomics dataset to identify new protein coding sequences.

Results: Our analyses showed that several long non-coding RNAs are more abundant in the sub-polysome samples, which possibly implicates them in regulating cellular differentiation in  T. brucei. We also improved the annotation of the  T.brucei genome by identifying new putative protein coding transcripts that were confirmed by mass spectrometry data.

Conclusions: Several long non-coding RNAs are more abundant in the sub-polysome cellular fractions and might pay a role in the regulation of gene expression. We hope that these data will be of wide general interest, as well as being of specific value to researchers studying gene regulation expression and life stage transitions in  T. brucei.

Comparative transcriptomics reveal tissue level specialization towards diet in prickleback fishes

Beyond a few obvious examples (e.g., gut length, amylase activity), digestive and metabolic specializations towards diet remain elusive in fishes. Thus, we compared gut length, δ¹³C and δ¹⁵N signatures of the liver, and expressed genes in the intestine and liver of wild-caught individuals of four closely-related, sympatric prickleback species (family Stichaeidae) with different diets: Xiphister mucosus (herbivore), its sister taxon X. atropurpureus (omnivore), Phytichthys chirus (omnivore) and the carnivorous Anoplarchus purpurescens. We also measured the same parameters after feeding them carnivore or omnivore diets in the laboratory for 4 weeks. Growth and isotopic signatures showed assimilation of the laboratory diets, and gut length was significantly longer in X. mucosus in comparison to the other fishes, whether in the wild, or in the lab consuming the different diets. Dozens of genes relating to digestion and metabolism were observed to be under selection in the various species, but P. chirus stood out with some genes in the liver showing strong positive selection, and these genes correlating with differing isotopic incorporation of the laboratory carnivore diet in this species. Although the intestine showed variation in the expression of hundreds of genes in response to the laboratory diets, the liver exhibited species-specific gene expression patterns that changed very little (generally <40 genes changing expression, with P. chirus providing an exception). Overall, our results suggest that the intestine is plastic in function, but the liver may be where specialization manifests since this tissue shows species-specific gene expression patterns that match with natural diet.

Lineage-tracing and translatomic analysis of damage-inducible mitotic cochlear progenitors identifies candidate genes regulating regeneration

Cochlear supporting cells (SCs) are glia-like cells critical for hearing function. In the neonatal cochlea, the greater epithelial ridge (GER) is a mitotically quiescent and transient organ, which has been shown to nonmitotically regenerate SCs. Here, we ablated Lgr5+ SCs using Lgr5-DTR mice and found mitotic regeneration of SCs by GER cells in vivo. With lineage tracing, we show that the GER houses progenitor cells that robustly divide and migrate into the organ of Corti to replenish ablated SCs. Regenerated SCs display coordinated calcium transients, markers of the SC subtype inner phalangeal cells, and survive in the mature cochlea. Via RiboTag, RNA-sequencing, and gene clustering algorithms, we reveal 11 distinct gene clusters comprising markers of the quiescent and damaged GER, and damage-responsive genes driving cell migration and mitotic regeneration. Together, our study characterizes GER cells as mitotic progenitors with regenerative potential and unveils their quiescent and damaged translatomes.

Application of the 3' mRNA-Seq using unique molecular identifiers in highly degraded RNA derived from formalin-fixed, paraffin-embedded tissue

Background

Archival formalin-fixed, paraffin-embedded (FFPE) tissue samples with clinical and histological data are a singularly valuable resource for developing new molecular biomarkers. However, transcriptome analysis remains challenging with standard mRNA-seq methods as FFPE derived-RNA samples are often highly modified and fragmented. The recently developed 3′ mRNA-seq method sequences the 3′ region of mRNA using unique molecular identifiers (UMI), thus generating gene expression data with minimal PCR bias. In this study, we evaluated the performance of 3′ mRNA-Seq using Lexogen QuantSeq 3′ mRNA-Seq Library Prep Kit FWD with UMI, comparing with TruSeq Stranded mRNA-Seq and RNA Exome Capture kit. The fresh-frozen (FF) and FFPE tissues yielded nucleotide sizes range from 13 to > 70% of DV200 values; input amounts ranged from 1 ng to 100 ng for validation.

Results

The total mapped reads of QuantSeq 3′ mRNA-Seq to the reference genome ranged from 99 to 74% across all samples. After PCR bias correction, 3 to 56% of total sequenced reads were retained. QuantSeq 3′ mRNA-Seq data showed highly reproducible data across replicates in Universal Human Reference RNA (UHR, R > 0.94) at input amounts from 1 ng to 100 ng, and FF and FFPE paired samples (R = 0.92) at 10 ng. Severely degraded FFPE RNA with ≤30% of DV200 value showed good concordance (R > 0.87) with 100 ng input. A moderate correlation was observed when directly comparing QuantSeq 3′ mRNA-Seq data with TruSeq Stranded mRNA-Seq (R = 0.78) and RNA Exome Capture data (R > 0.67).

Conclusion

In this study, QuantSeq 3′ mRNA-Seq with PCR bias correction using UMI is shown to be a suitable method for gene quantification in both FF and FFPE RNAs. 3′ mRNA-Seq with UMI may be applied to severely degraded RNA from FFPE tissues generating high-quality sequencing data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08068-1.

A cell-type-specific atlas of the inner ear transcriptional response to acoustic trauma

Noise-induced hearing loss (NIHL) results from a complex interplay of damage to the sensory cells of the inner ear, dysfunction of its lateral wall, axonal retraction of type 1C spiral ganglion neurons, and activation of the immune response. We use RiboTag and single-cell RNA sequencing to survey the cell-type-specific molecular landscape of the mouse inner ear before and after noise trauma. We identify induction of the transcription factors STAT3 and IRF7 and immune-related genes across all cell-types. Yet, cell-type-specific transcriptomic changes dominate the response. The ATF3/ATF4 stress-response pathway is robustly induced in the type 1A noise-resilient neurons, potassium transport genes are downregulated in the lateral wall, mRNA metabolism genes are downregulated in outer hair cells, and deafness-associated genes are downregulated in most cell types. This transcriptomic resource is available via the Gene Expression Analysis Resource (gEAR; https://umgear.org/NIHL) and provides a blueprint for the rational development of drugs to prevent and treat NIHL.

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers.

SARS-CoV-2 Subgenomic RNAs: Characterization, Utility, and Perspectives

SARS-CoV-2, the etiologic agent at the root of the ongoing COVID-19 pandemic, harbors a large RNA genome from which a tiered ensemble of subgenomic RNAs (sgRNAs) is generated. Comprehensive definition and investigation of these RNA products are important for understanding SARS-CoV-2 pathogenesis. This review summarizes the recent progress on SARS-CoV-2 sgRNA identification, characterization, and application as a viral replication marker. The significance of these findings and potential future research areas of interest are discussed.

Polysomal mRNA Association and Gene Expression in Trypanosoma brucei

Background: The contrasting physiological environments of  Trypanosoma brucei procyclic (insect vector) and bloodstream (mammalian host) forms necessitates deployment of different molecular processes and, therefore, changes in protein expression. Transcriptional regulation is unusual in  T. brucei because the arrangement of genes is polycistronic; however, genes which are transcribed together are subsequently cleaved into separate mRNAs by  trans-splicing. Following pre-mRNA processing, the regulation of mature mRNA stability is a tightly controlled cellular process. While many stage-specific transcripts have been identified, previous studies using RNA-seq suggest that changes in overall transcript level do not necessarily reflect the abundance of the corresponding protein. Methods: To better understand the regulation of gene expression in  T. brucei, we performed a bioinformatic analysis of RNA-seq on total, sub-polysomal, and polysomal mRNA samples. We further cross-referenced our dataset with a previously published proteomics dataset to identify new protein coding sequences. Results: Our analyses showed that several long non-coding RNAs are more abundant in the sub-polysome samples, which possibly implicates them in regulating cellular differentiation in  T. brucei. We also improved the annotation of the  T.brucei genome by identifying new putative protein coding transcripts that were confirmed by mass spectrometry data. Conclusions: Several long non-coding RNAs are more abundant in the sub-polysome cellular fractions and might pay a role in the regulation of gene expression. We hope that these data will be of wide general interest, as well as being of specific value to researchers studying gene regulation expression and life stage transitions in  T. brucei.

Polysomal mRNA Association and Gene Expression in Trypanosoma brucei

Background: The contrasting physiological environments of  Trypanosoma brucei procyclic (insect vector) and bloodstream (mammalian host) forms necessitates deployment of different molecular processes and, therefore, changes in protein expression. Transcriptional regulation is unusual in  T. brucei because the arrangement of genes is polycistronic; however, genes which are transcribed together are subsequently cleaved into separate mRNAs by  trans-splicing. Following pre-mRNA processing, the regulation of mature mRNA stability is a tightly controlled cellular process. While many stage-specific transcripts have been identified, previous studies using RNA-seq suggest that changes in overall transcript level do not necessarily reflect the abundance of the corresponding protein. Methods: To better understand the regulation of gene expression in  T. brucei, we performed a bioinformatic analysis of RNA-seq on total, sub-polysomal, and polysomal mRNA samples. We further cross-referenced our dataset with a previously published proteomics dataset to identify new protein coding sequences. Results: Our analyses showed that several long non-coding RNAs are more abundant in the sub-polysome samples, which possibly implicates them in regulating cellular differentiation in  T. brucei. We also improved the annotation of the  T.brucei genome by identifying new putative protein coding transcripts that were confirmed by mass spectrometry data. Conclusions: Several long non-coding RNAs are more abundant in the sub-polysome cellular fractions and might pay a role in the regulation of gene expression. We hope that these data will be of wide general interest, as well as being of specific value to researchers studying gene regulation expression and life stage transitions in  T. brucei.

A trans locus causes a ribosomopathy in hypertrophic hearts that affects mRNA translation in a protein length-dependent fashion

BACKGROUND

Little is known about the impact of trans-acting genetic variation on the rates with which proteins are synthesized by ribosomes. Here, we investigate the influence of such distant genetic loci on the efficiency of mRNA translation and define their contribution to the development of complex disease phenotypes within a panel of rat recombinant inbred lines.

RESULTS

We identify several tissue-specific master regulatory hotspots that each control the translation rates of multiple proteins. One of these loci is restricted to hypertrophic hearts, where it drives a translatome-wide and protein length-dependent change in translational efficiency, altering the stoichiometric translation rates of sarcomere proteins. Mechanistic dissection of this locus across multiple congenic lines points to a translation machinery defect, characterized by marked differences in polysome profiles and misregulation of the small nucleolar RNA SNORA48. Strikingly, from yeast to humans, we observe reproducible protein length-dependent shifts in translational efficiency as a conserved hallmark of translation machinery mutants, including those that cause ribosomopathies. Depending on the factor mutated, a pre-existing negative correlation between protein length and translation rates could either be enhanced or reduced, which we propose to result from mRNA-specific imbalances in canonical translation initiation and reinitiation rates.

CONCLUSIONS

We show that distant genetic control of mRNA translation is abundant in mammalian tissues, exemplified by a single genomic locus that triggers a translation-driven molecular mechanism. Our work illustrates the complexity through which genetic variation can drive phenotypic variability between individuals and thereby contribute to complex disease.

Macrophages with reduced expressions of classical M1 and M2 surface markers in human bronchoalveolar lavage fluid exhibit pro-inflammatory gene signatures

The classical M1/M2 polarity of macrophages may not be applicable to inflammatory lung diseases including chronic obstructive pulmonary disease (COPD) due to the complex microenvironment in lungs and the plasticity of macrophages. We examined macrophage sub-phenotypes in bronchoalveolar lavage (BAL) fluid in 25 participants with CD40 (a M1 marker) and CD163 (a M2 marker). Of these, we performed RNA-sequencing on each subtype in 10 patients using the Illumina NextSeq 500. Approximately 25% of the macrophages did not harbor classical M1 or M2 surface markers (double negative, DN), and these cells were significantly enriched in COPD patients compared with non-COPD patients (46.7% vs. 14.5%, p < 0.001). 1886 genes were differentially expressed in the DN subtype compared with  all other subtypes at a 10% false discovery rate. The 602 up-regulated genes included 15 mitochondrial genes and were enriched in 86 gene ontology (GO) biological processes including inflammatory responses. Modules associated with cellular functions including oxidative phosphorylation were significantly down-regulated in the DN subtype. Macrophages in the human BAL fluid, which were negative for both M1/M2 surface markers, harbored a gene signature that was pro-inflammatory and suggested dysfunction in cellular homeostasis. These macrophages may contribute to the pathogenesis and manifestations of inflammatory lung diseases such as COPD.

Insights into the functions and RNA binding of Trypanosoma brucei ZC3H22, RBP9 and DRBD7

Trypanosoma brucei is unusually reliant on mRNA-binding proteins to control mRNA fate, because its protein-coding genes lack individual promoters. We here focus on three trypanosome RNA-binding proteins. ZC3H22 is specific to Tsetse fly forms, RBP9 is preferentially expressed in bloodstream forms; and DRBD7 is constitutively expressed. Depletion of RBP9 or DRBD7 did not affect bloodstream-form trypanosome growth. ZC3H22 depletion from procyclic forms caused cell clumping, decreased expression of genes required for cell growth and proliferation, and increased expression of some epimastigote markers. Apart from decreases in mRNAs encoding enzymes of glucose metabolism, levels of most ZC3H22-bound transcripts were unaffected by ZC3H22 depletion. We compared ZC3H22, RBP9 and DRBD7 RNA binding with that of 16 other RNA-binding proteins. ZC3H22, PUF3 and ERBP1 show a preference for ribosomal protein mRNAs. RBP9 preferentially binds mRNAs that are more abundant in bloodstream forms than in procyclic forms. RBP9, ZC3H5, ZC3H30 and DRBD7 prefer mRNAs with long coding regions; UBP1-associated mRNAs have long 3′-untranslated regions; and RRM1 prefers mRNAs with long 3′or 5′-untranslated regions. We suggest that proteins that prefer long mRNAs may have relatively short or degenerate binding sites, and that preferences for A or U increase binding in untranslated regions.

Technical bias and the reproducibility crisis: The problem of systemic errors resulting from artefacts of equipment, methods or dataset has been underappreciated

Technical bias can cause lack of reproducibility. While harder to identify than other bias, it can cause consistent systemic errors in experimental data and analysis.

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms

Background

This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).

Methods

A total of 112 PDAC patients from TCGA and 48 patients from GEO were included in the analysis. The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) was carried out to identify potential mechanisms of these genes affecting prognosis.

Results

In TCGA database, high expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total scores calculated. Genome-wide co-expression and GSEA analysis suggested that the GPC2 may affect prognosis through sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway.

Conclusion

GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and combination of these genes showed a higher efficiency for prognosis prediction.

A combined transcriptomic approach to identify candidates for an anti-tick vaccine blocking B. afzelii transmission

Ixodes ricinus is the vector for Borrelia afzelii, the predominant cause of Lyme borreliosis in Europe, whereas Ixodes scapularis is the vector for Borrelia burgdorferi in the USA. Transcription of several I. scapularis genes changes in the presence of B. burgdorferi and contributes to successful infection. To what extend B. afzelii influences gene expression in I. ricinus salivary glands is largely unknown. Therefore, we measured expression of uninfected vs. infected tick salivary gland genes during tick feeding using Massive Analysis of cDNA Ends (MACE) and RNAseq, quantifying 26.179 unique transcripts. While tick feeding was the main differentiator, B. afzelii infection significantly affected expression of hundreds of transcripts, including 465 transcripts after 24 h of tick feeding. Validation of the top-20 B. afzelii-upregulated transcripts at 24 h of tick feeding in ten biological genetic distinct replicates showed that expression varied extensively. Three transcripts could be validated, a basic tail protein, a lipocalin and an ixodegrin, and might be involved in B. afzelii transmission. However, vaccination with recombinant forms of these proteins only marginally altered B. afzelii infection in I. ricinus-challenged mice for one of the proteins. Collectively, our data show that identification of tick salivary genes upregulated in the presence of pathogens could serve to identify potential pathogen-blocking vaccine candidates.

A multi-omic analysis of the dorsal striatum in an animal model of divergent genetic risk for alcohol use disorder

The development of selectively bred high and low alcohol-preferring mice (HAP and LAP, respectively) has allowed for an assessment of the polygenetic risk for pathological alcohol consumption and phenotypes associated with alcohol use disorder (AUD). Accumulating evidence indicates that the dorsal striatum (DS) is a central node in the neurocircuitry underlying addictive processes. Therefore, knowledge of differential gene, protein, and phosphorylated protein expression in the DS of HAP and LAP mice may foster new insights into how aberrant DS functioning may contribute to AUD-related phenotypes. To begin to elucidate these basal differences, a complementary and integrated analysis of DS tissue from alcohol-naïve male and female HAP and LAP mice was performed using RNA sequencing, quantitative proteomics, and phosphoproteomics. These datasets were subjected to a thorough analysis of gene ontology, pathway enrichment, and hub gene assessment. Analyses identified 2,108, 390, and 521 significant differentially expressed genes, proteins, and phosphopeptides, respectively between the two lines. Network analyses revealed an enrichment in the differential expression of genes, proteins, and phosphorylated proteins connected to cellular organization, cytoskeletal protein binding, and pathways involved in synaptic transmission and functioning. These findings suggest that the selective breeding to generate HAP and LAP mice may lead to a rearrangement of synaptic architecture which could alter DS neurotransmission and plasticity differentially between mouse lines. These rich datasets will serve as an excellent resource to inform future studies on how inherited differences in gene, protein, and phosphorylated protein expression contribute to AUD-related phenotypes.

Alteration of circRNA and lncRNA expression profile in exosomes derived from periodontal ligament stem cells undergoing osteogenic differentiation

OBJECTIVE

This study investigated circRNA and lncRNA expression profile in exosomes derived from periodontal ligament stem cell (PDLSC) before and after its osteogenic differentiation.

DESIGN

Exosomes derived from PDLSCs before (EX0) and after osteogenic induction for 5 (EX5) and 7 (EX7) days were harvested and exosomal circRNAs and lncRNAs were analyzed by RNA sequencing. Certain RNAs showing significantly altered expression were selected for qRT-PCR verification. The circRNA-miRNA-mRNA network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed.

RESULTS

All groups of exosomes showed typical characteristics under nanoparticle tracking analysis, flow cytometry assay and transmission electron microscopy. 69-557 circRNAs and 2907-11581 lncRNAs were found in EX0, EX5 and EX7, which were broadly distributed across the 24 pairs of human chromosomes. Compared with EX0, 3 circRNAs and 2 lncRNAs were up-regulated and 39 circRNAs and 5 lncRNAs down-regulated consistently through out of EX5 and EX7, p < 0.05. qRT-PCR confirmed certain those consistently expressed RNAs, such as circ lysophosphatidic acid receptor 1 (LPAR1). KEGG analysis showed that those consistent expressed RNAs closely related to TGF-beta pathway, MAPK pathway, mTOR pathway and FoxO signaling pathways regulating pluripotency of stem cells.

CONCLUSIONS

Exosomal circRNAs and lncRNAs had significant expression changes during the early phase of osteogenic differentiation of PDLSCs. Further study would be taken for understanding the roles of exosomal circRNAs and lncRNAs playing in osteogenic differentiation of PDLSCs.

Phenotypic and gene expression features associated with variation in chronic ethanol consumption in heterogeneous stock collaborative cross mice

Of the more than 100 studies that have examined relationships between excessive ethanol consumption and the brain transcriptome, few rodent studies have examined chronic consumption. Heterogeneous stock collaborative cross mice freely consumed ethanol vs. water for 3 months. Transcriptional differences were examined for the central nucleus of the amygdala, a brain region known to impact ethanol preference. Early preference was modestly predictive of final preference and there was significant escalation of preference in females only. Genes significantly correlated with female preference were enriched in annotations for the primary cilium and extracellular matrix. A single module in the gene co-expression network was enriched in genes with an astrocyte annotation. The key hub node was the master regulator, orthodenticle homeobox 2 (Otx2). These data support an important role for the extracellular matrix, primary cilium and astrocytes in ethanol preference and consumption differences among individual female mice of a genetically diverse population.

Detection of disease-associated microRNAs - application for autism spectrum disorders

Autism spectrum disorders (ASD) diagnostic procedure still lacks a uniform biological marker. This review gathers the information on microRNAs (miRNAs) specifically as a possible source of biomarkers of ASD. Extracellular vesicles, and their subset of exosomes, are believed to be a tool of cell-to-cell communication, and they are increasingly considered to be carriers of such a marker. The interest in studying miRNAs in extracellular vesicles grows in all fields of study and therefore should not be omitted in the field of neurodevelopmental disorders. The summary of miRNAs associated with brain cells and ASD either studied directly in the tissue or biofluids are gathered in this review. The heterogeneity in findings from different studies points out the fact that unified methods should be established, beginning with the determination of the accurate patient and control groups, through to sample collection, processing, and storage conditions. This review, based on the available literature, proposes the standardized approach to obtain the results that would not be affected by technical factors. Nowadays, the method of high-throughput sequencing seems to be the most optimal to analyze miRNAs. This should be followed by the uniformed bioinformatics procedure to avoid misvalidation. At the end, the proper validation of the obtained results is needed. With such an approach as is described in this review, it would be possible to obtain a reliable biomarker that would characterize the presence of ASD.

3' MACE RNA-sequencing allows for transcriptome profiling in human tissue samples after long-term storage

This study aims to compare the potential of standard RNA-sequencing (RNA-Seq) and 3' massive analysis of c-DNA ends (MACE) RNA-sequencing for the analysis of fresh tissue and describes transcriptome profiling of formalin-fixed paraffin-embedded (FFPE) archival human samples by MACE. To compare MACE to standard RNA-Seq on fresh tissue, four healthy conjunctiva from four subjects were collected during vitreoretinal surgery, halved and immediately transferred to RNA lysis buffer without prior fixation and then processed for either standard RNA-Seq or MACE RNA-Seq analysis. To assess the impact of FFPE preparation on MACE, a third part was fixed in formalin and processed for paraffin embedding, and its transcriptional profile was compared with the unfixed specimens analyzed by MACE. To investigate the impact of FFPE storage time on MACE results, 24 FFPE-treated conjunctival samples from 24 patients were analyzed as well. Nineteen thousand six hundred fifty-nine transcribed genes were detected by both MACE and standard RNA-Seq on fresh tissue, while 3251 and 2213 transcripts were identified explicitly by MACE or RNA-Seq, respectively. Standard RNA-Seq tended to yield longer detected transcripts more often than MACE technology despite normalization, indicating that the MACE technology is less susceptible to a length bias. FFPE processing revealed negligible effects on MACE sequencing results. Several quality-control measurements showed that long-term storage in paraffin did not decrease the diversity of MACE libraries. We noted a nonlinear relation between storage time and the number of raw reads with an accelerated decrease within the first 1000 days in paraffin, while the numbers remained relatively stable in older samples. Interestingly, the number of transcribed genes detected was independent on FFPE storage time. RNA of sufficient quality and quantity can be extracted from FFPE samples to obtain comprehensive transcriptome profiling using MACE technology. We thus present MACE as a novel opportunity for utilizing FFPE samples stored in histological archives.

Gene Expression Profiling Studies Using Microarray in Osteoarthritis: Genes in Common and Different Conditions

Osteoarthritis (OA), which is characterized mainly by cartilage degradation, is the most prevalent joint disorder worldwide. Although OA is identified as a major cause of joint pain, disability, and socioeconomic burden, the etiology of OA is still not clearly known. Recently, gene microarray analysis has become an efficient method for the research of complex diseases and has been employed to determine what genes and pathways are involved in the pathological process of OA. In this review, OA study results over the last decade are summarized for gene expression profiling of various tissues, such as cartilage, subchondral bone, and synovium in human OA and mouse OA models. Many differentially expressed genes, which mainly involve matrix metabolism, bone turnover, and inflammation pathways, were identified in diseased compared with "normal" tissues. Nevertheless, rare common genes were reported from studies using different tissue sources, microarray chips, and research designs. Thus, future novel and carefully designed microarray studies are required to elucidate underlying genetic mechanisms in the pathogenesis of OA as well as new directions for potential OA-targeted pharmaceutical therapies.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value ~10-8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value ~10-6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value ~10-9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-value ~10-15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data

PURPOSE

Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype.

METHODS

In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival.

RESULTS

Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9-10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like "ELANE" (LUSC) and "AHSG" (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10^-5] and LUAD [HR = 2.70, p value = 3.31 × 10^-7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both.

CONCLUSION

This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients.

Transcriptome analysis indicates dominant effects on ribosome and mitochondrial function of a premature termination codon mutation in the zebrafish gene psen2

PRESENILIN 2 (PSEN2) is one of the genes mutated in early onset familial Alzheimer’s disease (EOfAD). PSEN2 shares significant amino acid sequence identity with another EOfAD-related gene PRESENILIN 1 (PSEN1), and partial functional redundancy is seen between these two genes. However, the complete range of functions of PSEN1 and PSEN2 is not yet understood. In this study, we performed targeted mutagenesis of the zebrafish psen2 gene to generate a premature termination codon close downstream of the translation start with the intention of creating a null mutation. Homozygotes for this mutation, psen2^S4Ter, are viable and fertile, and adults do not show any gross psen2-dependent pigmentation defects, arguing against significant loss of γ-secretase activity. Also, assessment of the numbers of Dorsal Longitudinal Ascending (DoLA) interneurons that are responsive to psen2 but not psen1 activity during embryogenesis did not reveal decreased psen2 function. Transcripts containing the S4Ter mutation show no evidence of destabilization by nonsense-mediated decay. Forced expression in zebrafish embryos of fusions of psen2^S4Ter 5’ mRNA sequences with sequence encoding enhanced green fluorescent protein (EGFP) indicated that the psen2^S4Ter mutation permits utilization of cryptic, novel downstream translation start codons. These likely initiate translation of N-terminally truncated Psen2 proteins lacking late endosomal/lysosomal localization sequences and that obey the “reading frame preservation rule” of PRESENILIN EOfAD mutations. Transcriptome analysis of entire brains from a 6-month-old family of wild type, heterozygous and homozygous psen2^S4Ter female siblings revealed profoundly dominant effects on gene expression likely indicating changes in ribosomal, mitochondrial, and anion transport functions.

Whole-Genome DNA Methylation Profiling of CD14+ Monocytes Reveals Disease Status and Activity Differences in Crohn's Disease Patients

Crohn's disease (CD) is a multifactorial incurable chronic disorder. Current medical treatment seeks to induce and maintain a state of remission. During episodes of inflammation, monocytes infiltrate the inflamed mucosa whereupon they differentiate into macrophages with a pro-inflammatory phenotype. Here, we sought to characterize the circulating monocytes by profiling their DNA methylome and relate it to the level of CD activity. We gathered an all-female age-matched cohort of 16 CD patients and 7 non-CD volunteers. CD patients were further subdivided into 8 CD patients with active disease (CD-active) and 8 CD patients in remission (CD-remissive) as determined by the physician global assessment. We identified 15 and 12 differentially methylated genes (DMGs) when comparing CD with non-CD and CD-active with CD-remissive, respectively. Differential methylation was predominantly found in the promoter regions of inflammatory genes. Comparing our observations with gene expression data on classical (CD14++CD16-), non-classical (CD14+CD16++) and intermediate (CD14++CD16+) monocytes indicated that while 7 DMGs were differentially expressed across the 3 subsets, the remaining DMGs could not immediately be associated with differences in known populations. We conclude that CD activity is associated with differences in DNA methylation at the promoter region of inflammation-associated genes.