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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Voluntary Running Sustains the Correction of Inflammation-Related Gene Expression Conferred by AAV Gene Therapy in Mdx Mice

Duchenne Muscular Dystrophy is a fatal disease characterized by persistent skeletal muscle degeneration, inflammation, and fibrosis. Gene therapy using an adeno-associated virus derived vector and a microdystrophin transgene is currently under investigation in patients but the impact of physical activity on long-term therapeutic outcome remains poorly understood. Recently, we reported 21-weeks of voluntary wheel running complemented the positive endurance and muscle function outcomes of gene therapy in mdx mice. In the present study, we performed a transcriptomic analysis of the gene expression changes associated with functional recovery in the diaphragm. RNA-Sequencing and bioinformatic analysis revealed 2881 dysregulated genes in untreated and unexercised mdx mice including inflammatory and fibrotic signaling pathways frequently affected in Duchenne Muscular Dystrophy patients. Among the dysregulated genes, 774 were rescued towards WT after adeno-associated virus microdystrophin injection. Importantly, 93% of the rescued genes were maintained by voluntary running, which indicates that physical exercise has no significant impact on the outcome of gene therapy in the mdx diaphragm. Our study provides vital information that could help guide DMD patient follow-up protocols after treatment with gene therapy.

pathology↗

bioRxiv2018

Programmed Delayed Splicing: A Mechanism for Timed Inflammatory Gene Expression

Inflammation involves timed gene expression, suggesting that the fine-tuned onset, amplitude, and termination of expression of hundreds of genes is of critical importance to organismal homeostasis. Recent study of post-transcriptional regulation of inflammatory gene expression led to the suggestion of a regulatory role for pre-mRNA splicing. Here, using a hybrid capture approach to purify incompletely spliced, chromatin-associated pre-mRNAs, we use deep sequencing to study pre-mRNA splicing of the NF-B transcriptome. By freezing transcription and examining subsequent splicing of complete transcripts, we find many introns splice tens to hundreds of times slower than average. Investigating the basis of these delays, we focused on evolutionarily conserved introns with suboptimal splice donor sequences and found that strengthening these donor sites by as few as two nucleotides in minigene reporter assays markedly increased gene expression for several targets. This suggests that such sites can act as timing elements that both delay mRNA production and limit expression amplitude. To broaden this mechanistic view, we applied deep learning sequence-to-function models with feature attribution to identify additional regulatory sequences--both intronic and exonic--that may contribute to delayed splicing through mechanisms independent of donor site strength. This integrated approach revealed non-canonical motifs enriched in slow-splicing introns, pointing to a broader repertoire of cis-elements that can fine-tune transcript maturation during inflammation. Together, these findings support a model in which the temporal regulation of pre-mRNA splicing serves as a layer of control in inflammatory gene expression, and raise the possibility that similar timing mechanisms operate in other rapid-response transcriptional programs.

immunology↗

bioRxiv2020

scDesign2: an interpretable simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

In the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, a transparent simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.

bioinformatics↗

bioRxiv2019

Towards establishing internal validity for correlated gene expression measures in imaging genomics of functional networks: why distance corrections and external face validity alone fall short. Reply to "Distance is not everything in imaging genomics of functional networks: reply to a commentary on Correlated gene expression supports synchronous activity in brain networks"

The primary claim of the Richiardi et. al. 2015 Science article1 is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF2,3. Here, thirteen distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6-15mm radius) to test its influence on SSF false positive rate (SSF-FPR) among the simulated noise networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulations using: 1) all cortical samples 2) all samples with non-rsfMRI cluster centers and 3) only non-rsfMRI samples. Results show that SSF-FPR is influenced only by cluster size (r>0.9, p<0.001), not by rsfMRI samples. Simulations using 14mm radius clusters most resembled rsfMRI networks. When thresholding at p<10-4, the SSF-FPR was 0.47. Genes that maximize SF have high global spatial autocorrelation. In conclusion, SSF is unrelated to rsfMRI networks. The main conclusion of Richiardi et. al. 2015 is based on a finding that is [~]50% likely to be a false positive, not less than 0.01% as originally reported in the article1. We discuss why distance corrections alone and external face validity are insufficient to establish a trustworthy relationship between correlated gene expression measures and rsfMRI networks, and propose more rigorous approaches to preclude common pitfalls in related studies.

neuroscience↗

bioRxiv2024

Miro1 expression alters global gene expression, ERK1/2 phosphorylation, oxidation, and cell cycle progression

Subcellular mitochondrial positioning in cells is necessary for localized energy and signaling requirements. Mitochondria are strategically trafficked throughout the cytoplasm via the actin cytoskeleton, microtubule motor proteins, and adaptor proteins. Miro1, an outer mitochondrial membrane adaptor protein, is necessary for attachment of mitochondria to microtubule motor proteins for trafficking. Previous work showed when Miro1 is deleted (Miro1-/-) from mouse embryonic fibroblasts (MEFs), the mitochondria become sequestered to the perinuclear space, disrupting subcellular energy and reactive oxygen species gradients. Here, we show that Miro1-/- MEFs grow slower compared to Miro1+/+ and Miro1-/- MEFs stably re-expressing the Myc-Miro1 plasmid. Miro1-/- MEFs have a have a cell cycle defect with decreased percentage of cells in G1 and increased cells in the S phase of the cell cycle. We conducted the first ever RNA sequencing experiment dependent upon Miro1 expression and found differential expression in cell proliferation and migration genes upon deletion of Miro1, including the MAP Kinase signaling pathway. We find that ERK1/2 phosphorylation is elevated both spatially (cytoplasm and nucleus) and temporally following serum stimulation in Miro1-/- MEFs. We investigated the expression levels and oxidation of the Dual Specificity Phosphatases (DUSP1-6), ERK1/2 target phosphatases. We found no differences in DUSP1-6 expression and oxidation under asynchronous and synchronized cells. Lastly, we evaluated the oxidation status of ERK1/2 and found an increase in ERK1/2 oxidation in the Miro1-/- MEFs compared to Miro1+/+ and Myc-Miro1. These data highlight transcriptional control based off Miro1 expression and demonstrate the highly dynamic regulation of ERK1/2 upon deletion of Miro1 that may support the observed cell cycle and proliferation defects.

cell biology↗

bioRxiv2020

Investigating an in-silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

In clinical practice, antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in-silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We determined the transcriptomic profile of citalopram remitters by performing a transcriptomic-wide association study on STAR*D data (N =1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity map database. Spearman correlation, Pearson correlation, and the Kolmogorov Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p-value for each rank by using a permutation procedure. The drugs with the top ranks were those having high positive correlation with the expression profiles of remitters and they may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p=0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second highest ranked antidepressants, respectively (p=0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements it can be applicable at the individual level to tailor treatment prescription.

bioinformatics↗

bioRxiv2022

Degraded RNA from human anterior cruciate ligaments yields valid gene expression profiles

BackgroundCorrelating gene expression patterns with biomechanical properties of connective tissues provides insight into molecular processes underlying development and maintenance of the tissues physical characteristics. Cadaveric tissues such as human knees are widely considered suitable for biomechanical studies, but their usefulness for gene expression experiments is potentially limited by unavoidable, nuclease-mediated degradation of RNA. MethodsWe quantified mRNAs encoding 84 extracellular matrix components and five housekeeping proteins in intact and partially degraded RNA from human anterior cruciate ligaments (ACLs), and compared relative amounts of the individual mRNAs by regression analysis. ResultsHuman ACL RNA degraded in vitro by limited ribonuclease digestion (N=6) resembled degraded RNA isolated from cadaveric tissue. PCR threshold cycle (Ct) values from degraded RNAs ranged variably higher than values obtained from their corresponding non-degraded RNAs, reflecting both the expected loss of target templates in the degraded preparations as well as differences in the extent of degradation. Relative Ct values obtained for mRNA targets in degraded preparations correlated strongly with the corresponding target levels in non-degraded RNA, both for each ACL as well as for pooled results from all six ACLs. Nuclease-mediated degradation produced similar, strongly correlated losses of housekeeping and non-housekeeping gene mRNAs. Expression profiling of RNA degraded in situ yielded comparable results, confirming that in vitro digestion suitably mimicked degradation by endogenous ribonucleases in frozen and thawed ACL. ConclusionPCR-based expression analyses can yield valid mRNA profiles from partially degraded RNA preparations such as those obtained from cadaveric knees and other skeletal motion segments used in biomechanical studies. Legitimate comparisons between variably degraded tissues can be made by normalizing quantitative data to an appropriate housekeeping transcript.

molecular biology↗

bioRxiv2024

DNA glycosylases Ogg1 and Mutyh mediate gene expression of PRC2 target genes important for neuronal processes underlying memory formation

Base excision repair (BER) initiated by DNA glycosylases is known to preserve genomic integrity by removing damaged bases. Recently, several DNA glycosylases were identified as potential readers of epigenetic modifications and proteins involved in BER have been associated with active DNA demethylation. DNA glycosylases Ogg1 and Mutyh were shown to alter the hippocampal transcriptome associated with cognitive function and independent of global DNA damage accumulation. However, the mechanism of DNA glycosylases in regulating cognition and their role in epigenetic remodeling in the brain remains elusive. Here we report that the combined deficiency of Ogg1 and Mutyh impairs spatial but not associative long-term memory. We demonstrate that Ogg1 or Mutyh modulate DNA methylation at gene regulatory regions of polycomb repressive complex 2 (PRC2) target genes in the adult hippocampus. Moreover, we find that the distribution of the PRC2 complex and histone modifications associated with PRC2 activity changes in both hippocampal neurons and glia depend on Ogg1 and Mutyh. Epigenetic alterations correlated with cell-type specific gene expression changes which were associated with pathways important for neuronal function and cognition. Our results provide a novel role for Ogg1 and Mutyh beyond DNA repair in modulating the epigenome to control transcriptional responses in the brain important for memory formation.

neuroscience↗

bioRxiv2016

Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.\n\nHere we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50 measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation. Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.

bioinformatics↗

bioRxiv2023

Rapid TetOn-mediated gene expression in neuronsacross the lifespan using uTTOP

Conditional expression of genes of interest is essential for interrogation of cellular development and function. Although tools exist for conditional gene expression, techniques for rapid-onset, temporally precise expression are lacking. The doxycyclineinducible TetOn expression system allows for this in numerous organ systems, however, transgenic TetOn expression cassettes become silenced in the nervous system during postnatal development. Here, we circumvent this silencing with uTTOP: in utero electroporation of Transposable TetOn Plasmids. When electroporated as transposable elements that integrate into the genome, the TetOn system allowed for robust DOX-dependent induction of expression across the postnatal lifespan of the mouse. We demonstrated induction in neurons of sensorimotor and retrospleninal cortex, hippocampus and the olfactory bulb. Latency to peak induction was [≤]12 hours, a several fold increase in induction kinetics over existing methodology for in vivo conditional expression. To demonstrate the utility of uTTOP, we induced ectopic expression of Sonic hedgehog in adult mouse layer 2/3 cortical neurons, demonstrating that its expression can diversify expression of Kir4.1 in surrounding astrocytes. The rapid induction kinetics of uTTOP allowed us to show that Kir4.1 upregulation significantly lags onset of Shh expression by [~]2 days, a difference in expression time course that is likely not resolvable with current methods. Together, these data demonstrate that uTTOP is a powerful and flexible system for conditional gene expression in multiple brain areas across the mouse lifespan.

neuroscience↗

bioRxiv2024

M2WISH: an easy and efficient protocol for whole-mount mRNA in situ hybridization that allows 3D cell resolution of gene expression in Arabidopsis thaliana.

Gene expression analysis is essential for understanding the mechanisms involved in plant development. Here, we developed M2WISH, a protocol based on MicroWave treatment for Wholemount mRNA In Situ Hybridization in Arabidopsis. By permeabilizing tissues without damaging cellular organisation this protocol results in high and homogeneous hybridization yields that enables systematic analysis of gene expression dynamics. Moreover, when combined with cellular histochemical staining, M2WISH provides a cellular resolution of gene expression on roots, aerial meristems, leaves and embryos in the seed. We applied M2WISH to study the spatial dynamics of WUSCHEL (WUS) and CLAVATA3 (CLV3) expression during in vitro meristematic conversion of roots into shoot apical meristems. Thus, we showed that shoot apical meristems could arise from two different types of root structures that differed by their CLV3 gene expression patterns. We constructed 3D cellular representations of WUS and CLV3 gene co-expression pattern, and stressed the variability inherent to meristem conversion. Thus, this protocol generates a large amount of data on the localization of gene expression, which can be used to model complex systems.

plant biology↗

bioRxiv2025

Mineralized Tissue-Targeting Expression System for Local Control of Gene Expression

Targeted control of gene expression in mineralized tissue would enable the use of nucleic acids to modulate the local microenvironment at diseased sites, ultimately promoting bone regeneration. Piperazine-linked bisphosphonate ionizable lipids provide a facile approach to targeting the transfection of mineralized tissue with lipid nanoparticles (LNPs). Here, we develop a Mineralized Tissue-Targeting Expression System (MiTEX) using bisphosphonate LNPs to locally target mineralized tissues by adsorption to mineral surfaces and bone graft materials. MiTEX demonstrated a significant increase in the adsorption of RNA onto hydroxyapatite substrates, which retained the ability to transfect bone mesenchymal cells via the adsorbed layer of mRNA LNPs. Bone graft scaffolds functionalized by adsorbed Cre mRNA-LNP were implanted to genetically label newly formed bone tissues in vivo. The surface affinity and adsorption of bisphosphonate lipids provided a local reservoir in mineralized tissues, sustaining the in vivo delivery of MiTEX. Furthermore, the targeted delivery of RNA therapeutics was demonstrated using STAT3 siRNA to modulate gene expression and proinflammatory cytokine release in ex vivo periodontal tissues. The design of this new RNA-functionalized delivery platform will promote the development of precision nucleic acid therapeutics for local anti-inflammatory treatments and bone regeneration at mineralized tissue interfaces.

bioengineering↗

bioRxiv2022

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the users gene lists, aiding in hypothesis generation and data-driven literature review. HighlightsBy curating metadata from uniformly processed human RNA-seq studies, we created a database of 3162 differential expression analyses. These analyses include TCGA, GTEx, and 142 unique studies in SRA, involving 985 distinct experimental conditions. The Differential Expression Enrichment Tool (DEET) allows users to systematically compare their gene lists to this database.

bioinformatics↗

bioRxiv2023

Declining old pole physiology gradually enhances gene expression asymmetry in bacteria

Gene expression is a heterogeneous process at the single-cell level. This heterogeneity is often coupled to individual growth rates, which are also highly stochastic, leading to the emergence of multiple physiological states within bacterial populations. Although recent advances have shown that cellular aging acts as a deterministic driver of growth asymmetry, the relationship between aging and gene expression heterogeneity remains elusive. Here we show that old poles undergo a progressive decline in gene expression as mother cells age, contributing to enhance phenotypic heterogeneity in bacterial populations. We quantified the activity of promoters with distinct activity profiles: a constitutive promoter, whose expression positively correlates with growth, and the promoter of RpoS, the general stress response sigma factor, for which growth and expression are mutually inhibitory. We demonstrate that mother cells have lower gene expression for both promoters. This asymmetry could not be explained by metabolic rate differences, but rather by the increasing intracellular heterogeneity of mother cells. As a mother ages, the declining activity of its old pole produces intracellular gradients in gene expression. This intracellular asymmetry manifests in the next generation as mother-daughter asymmetry, thus representing a source of phenotypic heterogeneity for the population. Our results show that bacterial asymmetry is built into the declining physiology of mother cells across generations, illustrating the deterministic nature of aging in bacterial systems. These findings provide further evidence for cellular aging as a mechanism to enhance the variance of metabolic states found in bacterial populations, with possible consequences for stress response and survival.

evolutionary biology↗

bioRxiv2021

Gene Expression Analysis of Glioma Neural Stem Cells Shows Disturbed Amino Acids Metabolism and Axonal Growth Cone Dynamics in Glioblastoma Multiforme.

Glioblastoma multiforme(GBM) is a group of fatal and aggressive tumors of the central nervous system. Despite advancements in the treatment of GBM, patients diagnosed with these tumors typically have a poor prognosis and poor quality of life as the disease develops. The single-cell RNA high-throughput sequencing processed data for Glioma cancer stem cells were taken from GEO and analyzed to find out the underlying expression differences at the gene level between glioma neural stem cells(GSCs) and Normal neural stem cells(NSCs). In the current study, we have performed an RNA-sequencing analysis between GSCs and NSCs to better understand the origin of GBM. We have performed bioinformatics analysis on the transcriptional profile of 134 samples which consisted of 75 GSCs and 59 NSCs obtained from the NCBI bio project(PRJNA546254). First, an exploratory analysis was performed which showed significant variation patterns between GSCs and NSCs. Subsequently, Deseq2 differential gene expression analysis identified 1436 differentially expressed genes between GSCs and NSCs[(padj. value <0.05, log2 fold change (>=+/-1.5)]. This study reveals genes like MAOA, MAOB, GATM, GLDC, AMT, and SHMT1 as the key features contributing to the disturbed processes of Glycine, threonine, and serine amino acid metabolism, axonal cone growth curve, and cell migration in Glioma. Conclusively, our study also depicts gene expression changes in amyloid beta-binding protein in between GSCs and NSCs which plays an important role in tumor microenvironment formation. Besides, the results presented here reveal new insight into the progression of GBM and the identification of novel genes involved in gliomagenesis.

cancer biology↗

bioRxiv2026

GEO-BERT: A Retrieval augmented LLM Framework for Intelligent Dataset Retrieval from the Gene Expression Omnibus

The Gene Expression Omnibus (GEO) is a commonly used data repository of high throughput gene expression and other functional genomics data. This platform is globally used by researchers for submitting experimental datasets, accessing these datasets for hypothesis validation, reusing them in data-driven research, generating ideas for new research directions. Despite global usage, searching and identifying relevant and usable datasets in GEO remains challenging with an underlying keyword based search system needing manual intervention for the retrieval of desired datasets. GEO's inconsistent metadata and varying data annotation practices across submissions, variable data quality are contributing to its challenge for building structured query tools. With the recent advancement of Large Language Models (LLMs), it is timely to revisit the GEO datasets and its query system. Here, we present an AI chat-bot, GEO-BERT, leveraging an underlying retrieval augmented generator framework on the GEO metadata with a novel hierarchical hybrid field aware retrieval system to mine the datasets. GEO-BERT is locally installable, available for programmers and project developers (https://github.com/mrumi/GEOBERTtest) and readily usable by regular users on subset of test data. We demonstrated that GEO-BERT enhances the semantic search performance not currently available by any of the leading existing AI-powered chatbots, while improving the performance of GEO's traditional search performance for project development. Given the global usage of GEO and the latest advancement of the large language models, GEO-BERT fills the gap for context-aware extraction of GEO datasets.

bioinformatics↗

bioRxiv2019

Temperature dependent sex-biased gene expression in the gametophytes of the kelp Saccharina latissima

Saccharina latissima is an economically and ecologically relevant kelp species in Europe and North America. In kelps, the sexuality is expressed during the haploid life stage and the microscopic gametophytes exhibit significant sexual dimorphism. To understand the sex-dependent impact of temperature on the gametophyte stage, we analyzed for the first time, gene expression profiles of male and female gametophytes at three different temperatures (4{degrees}C, 12{degrees}C and 20{degrees}C) characteristic for the species distribution range by using RNA-sequencing. We identified several differentially expressed genes between sexes; while female biased genes were enriched in general metabolism and energy production, male biased genes function within cell cycle and signaling. In our study, temperature modulated sex-biased gene expression, with only a small percentage of differentially expressed genes consistently male (7%) or female-biased (12%) at the three temperatures. Female gametophytes responded stronger to higher temperatures than males, suggesting that males are more heat tolerant. Differences between S. latissima and other brown algal gender-dependent gene expression might mirror the different evolutionary and ecological contexts. Genomic information on kelp gametophyte is still scarce and thus this study adds to our knowledge on sex differences in abiotic stress responses in macroalgae at the transcriptomic level.\n\nHighlightThe transcriptomic basis for sexual dimorphism and associated metabolic needs are described for the kelp Saccharina latissima. Temperature modulates sex-biased gene expression resulting in a stronger stress response in females.

plant biology↗

bioRxiv2022

Commensal Microbiota Regulate Renal Gene Expression

The gut microbiome impacts host gene expression not only in the colon, but also at distal sites including liver, white adipose tissue, and spleen. The gut microbiome also influences the kidney and is associated with renal diseases and pathologies; however, a role for the gut microbiome to modulate renal gene expression has not been examined. To determine if microbes modulate renal gene expression, we used whole-organ RNA sequencing (RNA-Seq) to compare gene expression in C57Bl/6 mice that are germ-free (lacking gut microbiota) versus conventionalized (with gut microbiota). 16S sequencing showed that males and females were similarly conventionalized, although Verrucomicrobia was higher in male mice. We find that renal gene expression is differentially regulated in the presence versus absence of microbiota, and that these changes are largely sex-specific. Although microbes also influence gene expression in the liver and large intestine, most differentially expressed genes (DEGs) in the kidney are not similarly regulated in the liver or large intestine. This demonstrates that the influence of the gut microbiota on gene expression is tissue specific. However, a minority of genes (n=4 in males, n=6 in females) were similarly regulated in all three tissues examined, including genes associated with circadian rhythm (Per1 in males and Per2 in females) and metal binding (Mt1 and Mt2 in both males and females). Finally, using a previously published single cell RNA-Seq (scRNA-Seq) dataset, we assigned a subset of DEGs to specific kidney cell types, revealing clustering of DEGs by cell type and/or sex. NEW & NOTEWORTHYIt is unknown whether the microbiome influences host gene expression in the kidney. Here, we utilize an unbiased, bulk RNA-Seq approach to compare gene expression in the kidneys of male and female mice with or without gut microbiota, as well as in liver and large intestine. This report demonstrates that renal gene expression is modulated by the microbiome in a sex- and tissue-specific manner.

physiology↗