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bioRxiv2026

Alternative polyadenylation and the sex-specific gene expression program in hemp

Hemp (Cannabis sativa) produces a wide array of medicinally significant compounds, including cannabidiol (CBD). These compounds are predominantly synthesized in female hemp inflorescences. The proposed research utilizes next-generation sequencing-based transcriptome analysis using a 3{square}-end-directed approach to identify differentially expressed genes between male and female hemp plants at the early vegetative stage. 886 differentially expressed genes (DEGs) were identified, a majority of which were upregulated in males compared to females. We hypothesized that alternative RNA processing contributes to sex-specific gene expression. To this end, 932 genes were identified that exhibited significant changes in poly(A) site usage when comparing males and females. These genes were much more likely to be differentially expressed, supportive of this hypothesis. Males tend to have longer 3 UTRs with canonical motifs found in the Near-Upstream Elements (NUE), compared to the shorter 3 UTRs in females, which have A-rich motifs near the cleavage site. This suggests that polyadenylation remodels hemp mRNAs with distal poly(A) sites being preferred in males. To further investigate when this sex-specific gene expression program is established, RNA was isolated from plants at various developmental stages, such as developing seeds, four-day-old seedlings, and different developmental stages up to four weeks after sowing. Diagnostic male-specific genes were analyzed using RT/PCR. The results indicate that sex-specific gene expression is not evident in seeds but rather is set during or after germination. SignificanceO_LIHemp males tend to have longer 3 UTRs with canonical motifs found in the Near-Upstream Elements (NUE), compared to the shorter 3 UTRs in females, which have A-rich motifs near the cleavage site. C_LIO_LIThe sex-specific gene expression program is not yet established in mature seed but is set in the time between germination and 4 days of growth. C_LI

plant biology↗

bioRxiv2025

A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics

Spatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for understanding complex tissue architectures, such as those found in cancers. Seurat, by far the most popular tool for analyzing ST data, uses the Wilcoxon rank-sum test by default for differential expression analysis. However, as a nonparametric method that disregards spatial correlations, the Wilcoxon test can lead to inflated false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we propose a Generalized Estimating Equations (GEE) framework as a robust solution for differential gene expression analysis in ST. We conducted a comprehensive comparison of the GEE-based tests with existing methods, including the Wilcoxon rank-sum test and z-test. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE test with robust standard error, referred to as the Independent GEE, demonstrated superior Type I error control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed poor calibration of the p-values and potential false positive findings from the Wilcoxon rank-sum test. Our comparative study based on simulations and real data applications suggests that the Independent GEE test is well-suited for ST data, offering more accurate identification of biologically relevant gene expression changes and complementing the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub. Author SummarySpatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for studying complex tissue architectures and disease etiology. Seurat, a widely used software tool for analyzing ST data, relies on the Wilcoxon rank-sum test for differential expression analysis. However, this test ignores spatial correlations, leading to inaccurate control of false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we have proposed a Generalized Estimating Equation (GEE) framework as a robust solution for differential gene expression analysis in ST. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE-based test demonstrated superior false positive rate control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed potential false positive findings from the Wilcoxon rank-sum test. We recommend the GEE method to be a useful complement to the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub.

bioinformatics↗

bioRxiv2021

Integration of differential gene expression with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Gene expression alterations occur in all mouse tissues during aging, but recent works highlight minor rather than major dysregulation amplitude for most genes, questioning whether differentially expressed genes on their own provide deep insight into aging biology. To clarify this issue, we have combined differential gene expression with weighted gene correlation network analysis (WGCNA) to identify expression signatures accounting for the pairwise relations between gene expression profiles and the cumulative effect of genes with small fold- changes during aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice. Functional enrichment analysis of the overlap of genes identified in both approaches showed that immunity-related responses, mitochondrial energy metabolism, tissue regeneration and detoxification are prominently altered in the brain, heart, muscle, and liver, respectively, reflecting an age-related global loss of tissue function. While data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, particularly proteostasis-related pathways, which we highlight as important features of murine tissue physiological aging.

genetics↗

bioRxiv2018

Novel significant stage-specific differentially expressed genes in liver hepatocellular carcinoma

Liver cancer is among the top deadly cancers worldwide with a very poor prognosis, and the liver is a particularly vulnerable site for metastasis of other cancers. In this study, we developed a novel computational framework for the stage-specific analysis of hepatocellular carcinoma initiation and progression. Using publicly available clinical and RNA-Seq data of cancer samples and controls, we annotated the gene expression matrix with sample stages. We performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in gene expression in cancer samples relative to control. Using a contrast against the control, we were able to identify differentially expressed genes (log fold change >2) that were significant at an adjusted p-value < 10E-3. In order to identify genes that were specific to each stage without confounding differential expression in other stages, we developed a full set of pairwise stage contrasts and enforced a p-value threshold (<0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI and GNMT, and ten stage-IV specific genes including GABRD, PGAM2 and PECAM1. Of these, only APOBEC3B is an established cancer driver gene. DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD, well studied in literature on other cancers, emerged as a stage-IV specific gene. Our findings could be validated using multiple sources of omics data as well as experimentally. The biomarkers identified herein could potentially underpin diagnosis as well as pinpoint drug targets.

cancer biology↗

bioRxiv2023

An abundance of brain-expressed genes show ectopic activation in lung adenocarcinoma

Tumoral transformation processes sometimes include activation of unscheduled gene expression programs in the cancer cells. This is best exemplified by the so-called cancer-testis (CT) genes, a group of genes expressed in testicular germ cells that become activated in tumors of various somatic origins, through a process of DNA demethylation. Here, we explored the possibility that other tissue-specific gene clusters may become ectopically activated in tumors. Lung adenocarcinoma (LUAD) was used as a model, as all necessary transcriptomic datasets were available, including that of AT2 cells, the cell-of- origin of LUAD. We found that besides CT genes, an abundant group of genes expressed in the brain (CB genes, n=63) or in both brain and testis (CBT genes, n=28) become aberrantly activated in LUAD cell lines and tissues. Interestingly, activation of CB and CBTgene clusters was also detected in various other tumor types. Most CB/CBT genes appeared to exert neuronal functions. Moreover, a significant number of them encode antigens involved in neurological paraneoplastic syndromes. Neither neuroendocrine transdifferentiation, which occurs in 10-20% LUAD, nor DNA demethylation appeared to be responsible of the ectopic activation of CB and CBTgene clusters. Instead, prediction tools and depletion experiments identified the REST repressor as a regulator of a number of CB/CBT genes. Conclusion: Together, our data indicate that tumor development is associated with aberrant activation of a brain gene expression program, supporting the assumption that acquisition of neuronal functions might contribute to malignancy.

cancer biology↗

bioRxiv2026

Prenatal cannabinoid exposure induces sex-specific alterations in placental growth and lipid metabolism gene expression

BackgroundPrenatal cannabis use is becoming increasingly more commonplace. However, cannabis exposure is linked to adverse pregnancy outcomes, including gestational hypertension, preeclampsia, and preterm birth. The aim of this study was to determine the morphological and molecular effects of prenatal cannabinoid exposure on the placenta. MethodsPregnant Sprague-Dawley rats were exposed daily to vaporized THC (100 mg/mL) starting at gestational day (GD)5 until GD19 when dams were sacrificed and fetuses and placentas collected. Fetuses were genotyped for genetic sex and transcriptomic analysis was performed on male and female THC-exposed and control placentas. ResultsOn GD19, both the fetuses and placentas from the THC group were significantly larger than the control. When separated by sex, both male and female THC fetuses were significantly larger; however, only male THC placentas were significantly larger than male control placentas with no significant difference in placental weight between female control and THC placentas. RNA-sequencing revealed enriched biological processes related to nutrient transport and lipid catabolism, protein-lipid complex formation, and lipoprotein particle remodeling and organization. Further transcriptomic analysis determined that the differentially expressed genes and enriched biological processes related to lipid metabolism were preferentially enriched in the female THC placentas compared to the male, suggesting a sex-specific effect. DiscussionCollectively, these data present sex-specific effects of prenatal cannabinoid exposure on placental growth and global gene expression. These data also suggest that sex influences gene expression of genes related to lipid metabolism in the THC-exposed placentas.

Developmental Biology↗

bioRxiv2019

Gene Expression of Functionally-Related Genes Coevolves Across Fungal Species: Detecting Coevolution of Gene Expression Using Phylogenetic Comparative Methods

BackgroundResearchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. ResultsWe compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. ConclusionsOur work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

evolutionary biology↗

bioRxiv2022

SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

MotivationSpatially resolved transcriptomics (SRT) technologies have been developed to simultaneously profile gene expression while retaining physical information. To explore differentially expressed genes using SRT in the context of various conditions, statistical methods are needed to perform spatial differential expression analysis. ResultsWe propose that a new probabilistic framework, spatialDEG, can perform differential expression analysis by leveraging spatial information on gene expression with spatial information. SpatialDEG utilizes the average information algorithm and can be scalable to tens of thousands of genes. Comprehensive simulations demonstrated that spatialDEG can identify genes differentially expressed in tissues across different conditions with a controlled type-I error rate. We further applied spatialDEG to analyze datasets for human dorsolateral prefrontal cortex and mouse whole liver. AvailabilityThe R package spatialDEG can be downloaded from https://github.com/Shufeyangyi2015310117/spatialDEG.

genetics↗

bioRxiv2025

Transcriptomic analysis of chronic chikungunya in the Reunionese CHIKGene cohort uncovers a shift in gene expression more than 10 years after infection

AimIn 2005-2006, a chikungunya epidemic of unprecedented magnitude hit Reunion Island, which raised a public health concern through the substantial proportions of long-lasting manifestations. To understand the pathophysiology underlying chronic chikungunya (CC), we designed the CHIKGene cohort study and collected blood samples from 133 subjects diagnosed with CC and from 86 control individuals that had recovered within 3 months, 12-to-15 years after exposure. MethodsWe conducted bulk RNAseq analysis on peripheral blood mononuclear cells to find differentially expressed genes (DEGs), gene set enrichment analysis (GSEA) and gene ontologies to uncover top-level enriched terms associated with DEGs, and weighted gene correlation network analysis (WGCNA) to elucidate underlying cellular processes. ResultsAmong 1549 DEGs, gene expression analysis identified 10 top genes including NR4A2 and TRIM58 (upregulated in CC), IGHG3 and IGHV3-49 (downregulated in CC) linked to immune regulation, OSBP2 (upregulated in CC) and SEMA6B (downregulated in CC) linked to neuronal homeostasis and axon guidance, respectively. GSEA and WGCNA unveiled cellular processes such as "Metabolism of RNA" and "Cell Cycle". ConclusionsThis study uncovers a shift in gene expression of CC subjects. IGHG3 and IGHV3-49 gene shut-offs spotlight the importance of neutralizing antibodies against chikungunya virus in the progression to chronic disease. Human diseases associations highlight connections to rheumatoid arthritis, nervous and cardiac systems. GSEA and WGCNA bounce the hypotheses of a persistent viral reservoir or an increased susceptibility to RNA viral pathogens with new onset infections. Together, our findings might offer potential targets for therapeutic options aimed at alleviating chronic chikungunya.

bioinformatics↗

bioRxiv2026

In-silico cell sorting revealed granulocyte-specific single-cell-type gene expression from peripheral blood bulk expression data and its application as host response biomarkers to discriminate bacterial and viral infections

Peripheral Blood transcriptome analysis evaluated the bulk transcript abundance (TA) covering all leukocyte cell populations. However, there are 2 main problems in using bulk expression as biomarkers: (1) A long list of differential expression genes (DEGs) was found, and (2) DEGs cannot be attributed to a host response of any specific cell-type. TA assays after conventional cell sorting, as the gold-standard method, is too tedious for routine use. Recently, we showed that by using a ratio-based biomarker, RBB (ratio of two stringently selected genes), it is feasible to interrogate the gene expression of a single cell-type (monocyte and B lymphocyte) in peripheral whole blood (WB) directly. Here, we apply this in-silico cell sorting algorithm (DIRECT LS-TA, Direct Leukocyte Single cell-type Transcript Abundance) to granulocytes in WB samples to reveal RBBs specific to granulocytes. This DIRECT LS-TA approach without the need for cell-sorting was applied to public datasets to differentiate the 2 types of infection (bacterial vs viral infection). The following RBBs measured in WB correlate with the expression of target (numerator) genes in purified granulocytes, thus cell-sorting can be avoided by using these RBBs: ARG1/SRGN, ANXA3/SRGN, RSAD2/SRGN. Together with monocyte DIRECT LS-TA biomarkers, IFI27/PSAP, direct quantification of 4 genes provided optimal differentiation of viral from bacterial infection. Meta-analysis and unsupervised machine learning classification confirmed the superior performance of DIRECT LS-TA biomarkers. These RBBs found by prior In-silico cell-sorting identified pairs of genes that are used to formulate as ratio-based biomarkers (RBBs) to represent gene expression of granulocytes inside whole blood cell-mixture samples which was useful to triage febrile patients into two major categories of febrile diseases between viral and bacterial infection with high degree of sensitivity and specificity.

immunology↗

bioRxiv2018

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

bioinformatics↗

bioRxiv2021

Robust regulatory architecture of pan-neuronal gene expression

Pan-neuronally expressed genes, such as genes involved in the synaptic vesicle release cycle or in neuropeptide maturation, are critical for proper function of all neurons, but the transcriptional control mechanisms that direct such genes to all neurons of a nervous system remain poorly understood. We show here that six members of the CUT family of homeobox genes control pan-neuronal identity specification in C. elegans. Single CUT mutants show barely any effects on pan-neuronal gene expression or global nervous system function, but such effects become apparent and progressively worsen upon removal of additional CUT family members, indicating a critical role of gene dosage. Overexpression of each individual CUT gene rescued the phenotype of compound mutants, corroborating that gene dosage, rather than the activity of specific members of the gene family, is critical for CUT gene function. Genome-wide binding profiles as well as mutation of CUT binding sites by CRISPR/Cas9 genome engineering show that CUT genes directly control the expression of pan-neuronal features. Moreover, CUT genes act in conjunction with neuron-type specific transcription factors to control pan-neuronal gene expression. Our study, therefore, provides a previously missing key insight into how neuronal gene expression programs are specified and reveals a highly buffered and robust mechanism that controls the most critical functional features of all neuronal cell types.

neuroscience↗

bioRxiv2022

Testicular Dysgenesis Syndrome-like morphology and gene expression, and activation of Hypoxia Inducible Factor 1 Alpha in juvenile lamb testes following developmental exposure to low-level environmental chemical mixture.

Current declines in male reproductive health may, in part, be driven by anthropogenic environmental chemical exposure. Using the biosolid treated pasture (BTP) sheep model, this study examined the effects of gestational exposure to a translationally relevant mixture of environmental chemicals. At 8 weeks of age, ram lambs gestationally exposed to BTP were lighter than control, and their testes contained fewer germ cells and had a greater proportion of Sertoli-cell-only seminiferous tubules. Effects of biosolid exposure on the testicular transcriptome correlated with human testicular dysgenesis syndrome (TDS) patient data. Common differentially expressed genes indicated changes in apoptotic and mTOR signalling, which concurs with previous gene expression data from neonatal BTP lambs. Gene expression data and immunohistochemistry indicates increased HIF1 activation and nuclear localisation in BTP exposed animals, known to disrupt testosterone synthesis. Together, these results provide a potential mechanism for the pathogenesis of this phenotype, and TDS in humans.

pharmacology and toxicology↗

bioRxiv2019

Microarray Gene Expression DatasetRe-Analysis Reveals Variability in InfluenzaInfection and Vaccination.

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination. We also investigate genetic variation due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

immunology↗

bioRxiv2019

Highly Genotype- and Tissue-specific Single-Parent Expression Drives Dynamic Gene Expression Complementation in Maize Hybrids

Maize exhibits tremendous gene expression variation between different lines. Complementation of diverse gene expression patterns in hybrids could play an important role in the manifestation of heterosis. In this study, we used transcriptome data of five different tissues from 33 maize inbreds and 89 hybrids (430 samples in total) to survey the global gene expression landscape of F1-hybrids relative to their inbred parents. Analysis of this data set revealed that single parent expression (SPE), which is defined as gene expression in only one of the two parents, while commonly observed, is highly genotype- and tissue-specific. Genes that have SPE in at least one pair of inbreds also tend to be tissue-specific. Genes with SPE caused by genomic presence/absence variation (PAV SPE) are much more frequently expressed in hybrids than genes that are present in the genome of both inbreds, but expressed in only a single-parent (non-PAV SPE) (74.7% vs. 59.7%). For non-PAV SPE genes, allele specific expression was used to investigate whether parental alleles not expressed in the inbred line (\"silent allele\") can be actively transcribed in the hybrid. We found that expression of the silent allele in the hybrid is relatively rare ([~]6.3% of non-PAV SPE genes), but is observed in almost all hybrids and tissues. Non-PAV SPE genes with expression of the silent allele in the hybrid are more likely to exhibit above high-parent expression level in the hybrid than those that do not express the silent allele. Finally, both PAV SPE and non-PAV SPE genes are highly enriched for being classified as non-syntenic, but depleted for curated genes with experimentally determined functions. This study provides a more comprehensive understanding of the potential role of non-PAV SPE and PAV SPE genes in heterosis.

genomics↗

bioRxiv2024

Trans2express - de novo transcriptome assembly pipeline optimized for gene expression analysis

BackgroundAs genomes of many eukaryotic species, especially plants, are large and complex, their de novo sequencing and assembly is still a difficult task despite progress in sequencing technologies. An alternative to genome assembly is the assembly of transcriptome, the set of RNA products of the expressed genes. While a bunch of de novo transcriptome assemblers exists, the challenges of transcriptomes (the existence of isoforms, the uneven expression levels across genes) complicates the generation of high-quality assemblies suitable for downstream analyses. ResultsWe developed Trans2express - a web-based tool and a pipeline of de novo hybrid transcriptome assembly and postprocessing based on rnaSPAdes with a set of subsequent filtrations. The pipeline was tested on Arabidopsis thaliana cDNA sequencing data obtained using Illumina and Oxford Nanopore Technologies platforms. The comparison of structural characteristics of the transcriptome assembly with reference Arabidopsis genome revealed the high quality of assembled transcriptome with 86.1% of Arabidopsis expressed genes assembled as a single contig. We tested the applicability of the transcriptome assembly for gene expression analysis and showed high congruence of gene expression levels and sets of differentially expressed genes between analyses based on genome and based on the transcriptome assembly. ConclusionsWe present Trans2express - a protocol for de novo hybrid transcriptome assembly aimed at recovering of a single transcript per gene. We expect this protocol to promote the characterization of transcriptomes and gene expression analysis in non-model plants and web-based tool to be of use to a wide range of plant biologists.

bioinformatics↗

bioRxiv2023

Multi-Omics Insights into Grapevine Dormancy-Budburst Transition: Gene Expression, miRNA Regulation, and DNA Methylation

In grapevine, the transition from a dormant bud to budburst is a critical developmental process related to vegetative and reproductive growth. We generated a time series analysis (five sampling time points) and used transcriptome, small RNA, and whole-genome bisulfite sequencing to characterize this transition. Ecodormant buds took an average of 17 days to budburst. Transcriptome analysis identified a total of 7002 differentially expressed genes across all sampling times and revealed that the brassinosteroid metabolism and the linoleic acid metabolism pathways are upregulated and downregulated respectively across all time points. Gene expression cluster analysis identified the activation of the photosynthesis pathway and photosynthesis related genes during this transition. miRNA expression analysis identified a steady increase in expression of two miRNAs, miR159a and miR159b during the transition from dormancy to budburst. Our analysis suggests that these two miRNAs promote budburst by repressing the expression of auxin responsive genes. Finally, a continued increase in CG methylation levels was observed during the dormancy to budburst transition. Differential methylation analysis, considering dormant buds as the control stage, yielded 6354 differentially methylated regions across the genome. Two glucosidase genes exhibited increases in promoter methylation and a corresponding decrease in gene expression in our analysis. This study provides a multi-omics view of grapevine bud transition from dormancy to bud burst and unveils the interacting genetic and epigenetic networks regulating this process.

plant biology↗

bioRxiv2018

Stably expressed genes in single-cell RNA-sequencing

MotivationIn single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g., synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery.\n\nResultsWe compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final product are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well.\n\nSupplementary informationThe Supplement is available on bioRxiv, and the gene set database is available through GitHub.\n\nContactjohanngb@umich.edu

bioinformatics↗