Transcriptomic Perturbations Research Articles

A promising drug repurposing approach for Alzheimer's treatment: Givinostat improves cognitive behavior and pathological features in APP/PS1 mice

Alzheimer's disease (AD) is the most common neurodegenerative disease, characterized by memory loss, speech and motor defects, personality changes, and psychological disorders. The exact cause of AD remains unclear. Current treatments focus on maintaining neurotransmitter levels or targeting β-amyloid (Aβ) protein, but these only alleviate symptoms and do not reverse the disease. Developing new drugs is time-consuming, costly, and has a high failure rate. Utilizing multi-omics for drug repositioning has emerged as a new strategy. Based on transcriptomic perturbation data of over 40,000 drugs in human cells from the LINCS-L1000 database, our study employed the Jaccard index and hypergeometric distribution test for reverse transcriptional feature matching analysis, identifying Givinostat as a potential treatment for AD. Our research found that Givinostat improved cognitive behavior and brain pathology in models and enhanced hippocampal synaptic plasticity. Transcriptome sequencing revealed increased expression of mitochondrial respiratory chain complex proteins in the brains of APP/PS1 mice after Givinostat treatment. Functionally, Givinostat restored mitochondrial membrane potential, reduced reactive oxygen species, and increased ATP content in Aβ-induced HT22 cells. Additionally, it improved mitochondrial morphology and quantity in the hippocampus of APP/PS1 mice and enhanced brain glucose metabolic activity. These effects are linked to Givinostat promoting mitochondrial biogenesis and improving mitochondrial function. In summary, Givinostat offers a promising new strategy for AD treatment by targeting mitochondrial dysfunction.

Programmable editing of primary MicroRNA switches stem cell differentiation and improves tissue regeneration

Programmable RNA editing is harnessed for modifying mRNA. Besides mRNA, miRNA also regulates numerous biological activities, but current RNA editors have yet to be exploited for miRNA manipulation. To engineer primary miRNA (pri-miRNA), the miRNA precursor, we present a customizable editor REPRESS (RNA Editing of Pri-miRNA for Efficient Suppression of miRNA) and characterize critical parameters. The optimized REPRESS is distinct from other mRNA editing tools in design rationale, hence enabling editing of pri-miRNAs that are not editable by other RNA editing systems. We edit various pri-miRNAs in different cells including adipose-derived stem cells (ASCs), hence attenuating mature miRNA levels without disturbing host gene expression. We further develop an improved REPRESS (iREPRESS) that enhances and prolongs pri-miR-21 editing for at least 10 days, with minimal perturbation of transcriptome and miRNAome. iREPRESS reprograms ASCs differentiation, promotes in vitro cartilage formation and augments calvarial bone regeneration in rats, thus implicating its potentials for engineering miRNA and applications such as stem cell reprogramming and tissue regeneration.

Structural lesions and transcriptomic specializations shape gradient perturbations in Wilson disease.

Functional dysregulations in multiple regions are caused by excessive copper deposition in the brain in Wilson disease (WD) patients. The genetic mechanism of WD is thought to involve the abnormal expression of ATP7B in the liver, whereas the biological and molecular processes involved in functional dysregulation within the brain remain unexplored. The objective of this study was to unravel the underpinnings of functional gradient perturbations underlying structural lesions and transcriptomic specializations in WD. In this study, we included 105 WD patients and 93 healthy controls who underwent structural and functional MRI assessments. We used the diffusion mapping embedding model to derive the functional connectome gradient and further employed gray matter volume to uncover structure-function decoupling for WD. Then, we used Neurosynth, clinical data, and whole-brain gene expression data to examine the meta-analytic cognitive function, clinical phenotypes, and transcriptomic specializations related to WD gradient alterations. Compared with controls, WD patients exhibited global topographic changes in the principal pramary-to-transmodal gradient. Meta-analytic terms and clinical characteristics were correlated with these gradient alterations in motor-related processing, higher-order cognition, neurological symptoms, and age. Spatial correlations revealed structure-function decoupling in multiple networks, especially in subcortical and visual networks. Within the cortex, the spatial association between gradient alterations and gene expression profiles has revealed transcriptomic specilizations in WD that display properties indicative of ion homeostasis, neural development, and motor control. Furthermore, for the first time, we characterized the role of the ATP7B gene in impacting subcortical function. The transcriptomic specializations of WD were also associated with other neurological and psychiatric disorders. Finally, we revealed that structural lesions and gradient perturbations may share similar transcriptomic specializations in WD. In conclusion, these findings bridged functional gradient perturbations to structural lesions and gene expression profiles in WD patients, possibly promoting our understanding of the neurobiological mechanisms underlying the emergence of complex neurological and psychiatric phenotypes.

Robust identification of perturbed cell types in single-cell RNA-seq data

Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.

An atlas of the human liver diurnal transcriptome and its perturbation by hepatitis C virus infection

Chronic liver disease and cancer are global health challenges. The role of the circadian clock as a regulator of liver physiology and disease is well established in rodents, however, the identity and epigenetic regulation of rhythmically expressed genes in human disease is less well studied. Here we unravel the rhythmic transcriptome and epigenome of human hepatocytes using male human liver chimeric mice. We identify a large number of rhythmically expressed protein coding genes in human hepatocytes of male chimeric mice, which includes key transcription factors, chromatin modifiers, and critical enzymes. We show that hepatitis C virus (HCV) infection, a major cause of liver disease and cancer, perturbs the transcriptome by altering the rhythmicity of the expression of more than 1000 genes, and affects the epigenome, leading to an activation of critical pathways mediating metabolic alterations, fibrosis, and cancer. HCV-perturbed rhythmic pathways remain dysregulated in patients with advanced liver disease. Collectively, these data support a role for virus-induced perturbation of the hepatic rhythmic transcriptome and pathways in cancer development and may provide opportunities for cancer prevention and biomarkers to predict HCC risk.

Synthetic genomes unveil the effects of synonymous recoding.

Engineering the genetic code of an organism provides the basis for (i) making any organism safely resistant to natural viruses and (ii) preventing genetic information flow into and out of genetically modified organisms while (iii) allowing the biosynthesis of genetically encoded unnatural polymers1-4. Achieving these three goals requires the reassignment of multiple of the 64 codons nature uses to encode proteins. However, synonymous codon replacement-recoding-is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using whole-genome synthesis, multiplexed directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple recoded genomes. Using this information, we assemble a synthetic Escherichia coli genome in seven sections using only 57 codons to encode proteins. By discovering the rules responsible for the lethality of synonymous recoding and developing a data-driven multi-omics-based genome construction workflow that troubleshoots synthetic genomes, we overcome the lethal effects of 62,007 synonymous codon swaps and 11,108 additional genomic edits. We show that synonymous recoding induces transcriptional noise including new antisense RNAs, leading to drastic transcriptome and proteome perturbation. As the elimination of select codons from an organism's genetic code results in the widespread appearance of cryptic promoters, we show that synonymous codon choice may naturally evolve to minimize transcriptional noise. Our work provides the first genome-scale description of how synonymous codon changes influence organismal fitness and paves the way for the construction of functional genomes that provide genetic firewalls from natural ecosystems and safely produce biopolymers, drugs, and enzymes with an expanded chemistry.

Arsenic exposure at environmentally relevant levels induced metabolic toxicity in development mice: Mechanistic insights from integrated transcriptome and metabolome

Emerging evidence has linked arsenic exposure and metabolic homeostasis, but the mechanism is incompletely understood, especially at relatively low concentrations. In this study, we used a mouse model to evaluate the health impacts and metabolic toxicity of arsenic exposure in drinking water at environmentally relevant levels (0.25 and 1.0 ppm). Our results indicated that arsenic damaged intestinal barrier and induced arsenic accumulation, oxidative stress, and pathological changes in the liver and illum. Interestingly, arsenic increased the hepatic triglyceride (TG) and total cholesterol (TC), while reduced serum TG and TC levels. The liver transcriptome found that arsenic exposure caused transcriptome perturbation and promoted hepatic lipid accumulation by regulating the exogenous fatty acids degradation and apolipoproteins related genes. The serum metabolomics identified 74 and 88 differential metabolites in 0.25 and 1.0 ppm, respectively. The KEGG disease and subcellular location analysis indicated that arsenic induced liver and intestinal diseases, and the mitochondrion might be the target organelle for arsenic-induced toxicity. Co-enrichment of transcriptome and metabolome identified 24 metabolites and 9 genes as metabolic toxicity biomarkers. Moreover, 40 male (20 nonalcoholic fatty liver disease (NAFLD) cases and 20 healthy controls) was further selected to validate our findings. Importantly, the significantly changed L-palmitoylcarnitine, 3-hydroxybutyric acid, 2-hydroxycaproic acid and 6 genes of Hadha, Acadl, Aldh3a2, Cpt1a, Cpt2, and Acox1 were found in the NAFLD cases. The results from integrated multi-omics and chemical-protein network analysis indicated that L-palmitoylcarnitine played a critical role in metabolic toxicity by regulating mitochondrial fatty acids β-oxidation genes (Cpt1a, Cpt2). In conclusion, these findings provided new clues for the metabolic toxicity of arsenic exposure at environmentally relevant levels, which involved in the late-life NAFLD development. Our results also contribute to understanding the human responses and phenotypic changes to this hazardous material exposure in the environment.

Multiplexed CRISPR-Cas9 mutagenesis of rice PSBS1 noncoding sequences for transgene-free overexpression.

Understanding CRISPR-Cas9's capacity to produce native overexpression (OX) alleles would accelerate agronomic gains achievable by gene editing. To generate OX alleles with increased RNA and protein abundance, we leveraged multiplexed CRISPR-Cas9 mutagenesis of noncoding sequences upstream of the rice PSBS1 gene. We isolated 120 gene-edited alleles with varying non-photochemical quenching (NPQ) capacity in vivo-from knockout to overexpression-using a high-throughput screening pipeline. Overexpression increased OsPsbS1 protein abundance two- to threefold, matching fold changes obtained by transgenesis. Increased PsbS protein abundance enhanced NPQ capacity and water-use efficiency. Across our resolved genetic variation, we identify the role of 5'UTR indels and inversions in driving knockout/knockdown and overexpression phenotypes, respectively. Complex structural variants, such as the 252-kb duplication/inversion generated here, evidence the potential of CRISPR-Cas9 to facilitate significant genomic changes with negligible off-target transcriptomic perturbations. Our results may inform future gene-editing strategies for hypermorphic alleles and have advanced the pursuit of gene-edited, non-transgenic rice plants with accelerated relaxation of photoprotection.

The molecular landscape of sepsis severity in infants: enhanced coagulation, innate immunity, and T cell repression.

Sepsis remains a major cause of mortality and morbidity in infants. In recent years, several gene marker strategies for the early identification of sepsis have been proposed but only a few have been independently validated for adult cohorts and applicability to infant sepsis remains unclear. Biomarkers to assess disease severity and risks of shock also represent an important unmet need. To elucidate characteristics driving sepsis in infants, we assembled a multi-transcriptomic dataset from public microarray datasets originating from five independent studies pertaining to bacterial sepsis in infant < 6-months of age (total n=335). We utilized a COmbat co-normalization strategy to enable comparative evaluation across multiple studies while preserving the relationship between cases and controls. We found good concordance with only two out of seven of the published adult sepsis gene signatures (accuracy > 80%), highlighting the narrow utility of adult-derived signatures for infant diagnosis. Pseudotime analysis of individual subjects' gene expression profiles showed a continuum of molecular changes forming tight clusters concurrent with disease progression between healthy controls and septic shock cases. In depth gene expression analyses between bacteremia, septic shock, and healthy controls characterized lymphocyte activity, hemostatic processes, and heightened innate immunity during the molecular transition toward a state of shock. Our analysis revealed the presence of multiple significant transcriptomic perturbations that occur during the progression to septic shock in infants that are characterized by late-stage induction of clotting factors, in parallel with a heightened innate immune response and a suppression of adaptive cell functionality.

Rhamnogalacturonan lyase 1 (RGL1), as a suppressor of E3 ubiquitin ligase Arabidopsis thaliana ring zinc finger 1 (AtRZF1), is involved in dehydration response to mediate proline synthesis and pectin rhamnogalacturonan-I composition.

Proline metabolism plays a crucial role in both environmental stress responses and plant growth. However, the specific mechanism by which proline contributes to abiotic stress processes remains to be elucidated. In this study, we utilized atrzf1 (Arabidopsis thaliana ring zinc finger 1) as a parental line for T-DNA tagging mutagenesis and identified a suppressor mutant of atrzf1, designated proline content alterative 31 (pca31). The pca31 mutant suppressed the insensitivity of atrzf1 to dehydration stress during early seedling growth. Using Thermal Asymmetric Interlaced-PCR, we found that the T-DNA of pca31 was inserted into the promoter region of the At2g22620 gene, which encodes the cell wall enzyme rhamnogalacturonan lyase 1 (RGL1). Enzymatic assays indicated that RGL1 exhibited rhamnogalacturonan lyase activity, influencing cell wall pectin composition. The decrease in RGL1 gene expression suppressed the transcriptomic perturbation of the atrzf1 mutant. Silencing of the RGL1 gene in atrzf1 resulted in a sensitive phenotype similar to pca31 under osmotic stress conditions. Treatment with mannitol, salt, hydrogen peroxide, and abscisic acid induced RGL1 expression. Furthermore, we uncovered that RGL1 plays a role in modulating root growth and vascular tissue development. Molecular, physiological, and genetic experiments revealed that the positive modulation of RGL1 during abiotic stress was linked to the AtRZF1 pathway. Taken together, these findings establish that pca31 acts as a suppressor of atrzf1 in abiotic stress responses through proline and cell wall metabolisms.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

A Noncanonical CD56dimCD16dim/- NK Cell Subset Indicative of Prior Cytotoxic Activity Is Elevated in Patients with Autoantibody-Mediated Neurologic Diseases.

Neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein Ab disease, and autoimmune myasthenia gravis (MG) are autoantibody-mediated neurologic conditions where autoantibodies can induce Ab-dependent cellular cytotoxicity (ADCC), a NK cell-mediated effector function. However, whether ADCC is a pathogenic mechanism in patients with these conditions has not been confirmed. We sought to characterize circulatory NK cells using functional assays, phenotyping, and transcriptomics to elucidate their role in pathology. NK cells from NMOSD patients and MG patients with elevated disease burden exhibited reduced ADCC and CD56dimCD16hi NK cells, along with an elevated frequency of CD56dimCD16dim/- NK cells. We determined that ADCC induces a similar phenotypic shift invitro. Bulk RNA sequencing distinguished the CD56dimCD16dim/- population from the canonical CD56dimCD16hi cytotoxic and CD56hiCD16- immunomodulatory subsets, as well as CD56hiCD16+ NK cells. Multiparameter immunophenotyping of NK cell markers, functional proteins, and receptors similarly showed that the CD56dimCD16dim/- subset exhibits a unique profile while still maintaining expression of characteristic NK markers CD56, CD94, and NKp44. Notably, expression of perforin and granzyme is reduced in comparison with CD56dimCD16hi NK cells. Moreover, they exhibit elevated trogocytosis capability, HLA-DR expression, and many chemokine receptors, including CCR7. In contrast with NMOSD and MG, myelin oligodendrocyte glycoprotein Ab disease NK cells did not exhibit functional, phenotypic, or transcriptomic perturbations. In summary, CD56dimCD16dim/- NK cells are a distinct peripheral blood immune cell population in humans elevated upon prior cytotoxic activity by the CD56dimCD16hi NK cell subset. The elevation of this subset in NMOSD and MG patients suggests prior ADCC activity.

Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network.

Single-cell CRISPR-based transcriptome screens are potent genetic tools for concomitantly assessing the expression profiles of cells targeted by a set of guides RNA (gRNA), and inferring target gene functions from the observed perturbations. However, due to various limitations, this approach lacks sensitivity in detecting weak perturbations and is essentially reliable when studying master regulators such as transcription factors. To overcome the challenge of detecting subtle gRNA induced transcriptomic perturbations and classifying the most responsive cells, we developed a new supervised autoencoder neural network method. Our Sparse supervised autoencoder (SSAE) neural network provides selection of both relevant features (genes) and actual perturbed cells. We applied this method on an in-house single-cell CRISPR-interference-based (CRISPRi) transcriptome screening (CROP-Seq) focusing on a subset of long non-coding RNAs (lncRNAs) regulated by hypoxia, a condition that promote tumor aggressiveness and drug resistance, in the context of lung adenocarcinoma (LUAD). The CROP-seq library of validated gRNA against a subset of lncRNAs and, as positive controls, HIF1A and HIF2A, the 2 main transcription factors of the hypoxic response, was transduced in A549 LUAD cells cultured in normoxia or exposed to hypoxic conditions during 3, 6 or 24h. We first validated the SSAE approach on HIF1A and HIF2 by confirming the specific effect of their knock-down during the temporal switch of the hypoxic response. Next, the SSAE method was able to detect stable short hypoxia-dependent transcriptomic signatures induced by the knock-down of some lncRNAs candidates, outperforming previously published machine learning approaches. This proof of concept demonstrates the relevance of the SSAE approach for deciphering weak perturbations in single-cell transcriptomic data readout as part of CRISPR-based screening.

MPN Transformation Is Characterized By Heterogeneous Shifts in Lineage Character

Introduction: The Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are characterized by the clonal proliferation of differentiated hematologic cells. However, there is a limited understanding of how perturbations at the transcriptional level may contribute to accelerated or blast-phase disease, together referred to here as transformation. Here we characterize transcriptomic characteristics and cell type composition of MPNs along the transition to transformed disease. Methods: RNAseq and clinical data were collected on 261 patients who were part of BeatAML, including those with MPN, de-novo AML, and transformed MPN. This was combined with data from 56 transformed MPN patients seen at MSK, 11 of whom also had paired chronic-phase data available. Differential expression analysis was performed with DESeq2; both GSEA and GSVA were used to evaluate gene-set enrichment and pathways of biological relevance. A custom RNA-seq lineage deconvolution algorithm was used to estimate hematopoietic stage proportions and was used for unsupervised hierarchical clustering of cases. Kaplan-Meier methods were used to estimate survival among transformed MPN lineage subtypes. Results: The initial analysis included 207 de novo AML cases and 75 MPN-BP cases from BeatAML and MSK on which RNAseq and molecular data were integrated across cohorts. Unsupervised analysis was performed using hierarchical clustering applied to batch corrected, variance stabilized RNA-seq count data. Transformed MPN cases were enriched in 2 of 6 clusters (N = 10, 33.3%; N = 36, 54.5%). Notably, the molecular characteristics in these clusters are varied, suggesting that multiple genomic perturbations may lead to similar pathways of transformation. Gene set variation analysis (GSVA) demonstrated a coalescence of transcriptomic perturbation into specific pathways for transformed MPNs, such as hematopoietic stem cell gene sets (q-value = 0.00018). On the contrary, AML cases typically demonstrated upregulation in later stages of hematopoiesis, such as monocytes (q-value =0.00016). In evaluating specific genes differentially expressed between transformed MPNs compared to de novo AML and tAML patients, microRNA MIR29B1 was particularly upregulated in MPN-BP (logFC = 9.52, q &amp;lt; 0.01), while the proto-oncogene CT45A1 had decreased expression in transformed MPN compared to other AMLs (logFC = -7.47, q &amp;lt; 0.01). Paired GSEA of the 11 patients with myelofibrosis MF and transformed disease showed that transformed disease was enriched for CD34-positive HSCs (NES = 2.64, q-value &amp;lt; 0.0001) and E2F transcription factors (NES 2.66, q-value &amp;lt; 0.0001), as well as doxorubicin resistance (NES 2.40, q-value &amp;lt; 0.0001). Given enrichment of the CD34 positive HSC geneset, we performed our RNA-seq based lineage deconvolution on each of the paired samples ( Figure 1). MPNs appeared to transform in a heterogenous fashion reminiscent of the biological diversity in AML. In aggregate, transformation was associated with decreased monocytic (p = 0.002), natural killer (NK) (p = 0.042) character and expansion of common lymphoid progenitor character (p = 0.022). Lineage composition changes varied, with certain samples showing HSC expansion (ID: 54910, ID: 46567) and others exhibiting LMPP expansion (ID: 29485, 2456). Figure 2 defines seven distinct clusters of transformed MPN patients, driven by their lineage components. While these clusters are transcriptionally distinct, we again see that they are not defined by their genomic or cytogenetic alterations. We evaluated for a difference in overall survival (OS) between each cluster and intermediate-risk de novo AML. We found significantly worse OS among patients in three MPN-BP clusters - MPP (p =0.008), LMPP (p = 0.038), and GMP (p &amp;lt; 0.001) - represented as clusters 2, 5, and 7 in Figure 2. Compared to intermediate risk AML, transformed MPNs had significantly worse OS (HR: 1.79 95% CI: 1.18, 2.71, p = 0.0057). Discussion: To our knowledge, this is the first ever description of the transcriptional characteristics of MPN-BP, its comparison to de novo AML, and the first paired comparison of transcriptional and lineage characteristics for patients with chronic-phase MPNs that subsequently transformed. Our study provides valuable insights into the transcriptomic landscape of transformed MPN including both accelerated and blast phase disease, and draws novel contrasts to de novo AML.

A truncated HIV Tat demonstrates potent and specific latency reversal activity.

A major barrier to HIV-1 cure is caused by the pool of latently infected CD4 T-cells that persist under combination antiretroviral therapy (cART). This latent reservoir is capable of producing replication-competent infectious viruses once prolonged suppressive cART is withdrawn. Inducing the reactivation of HIV-1 gene expression in T-cells harboring a latent provirus in people living with HIV-1 under cART may result in depletion of this latent reservoir due to cytopathic effects or immune clearance. Studies have investigated molecules that reactivate HIV-1 gene expression, but to date, no latency reversal agent has been identified to eliminate latently infected cells harboring replication-competent HIV in cART-treated individuals. Stochastic fluctuations in HIV-1 tat gene expression have been described and hypothesized to allow the progression into proviral latency. We hypothesized that exposing latently infected CD4+ T-cells to Tat would result in effective latency reversal. Our results indicate the capacity of a truncated Tat protein and mRNA to reactivate HIV-1 in latently infected T-cells ex vivo to a similar degree as the protein kinase C agonist: phorbol 12-myristate 13-acetate, without T-cell activation or any significant transcriptome perturbation.

Neurodevelopmental deficits and cell-type-specific transcriptomic perturbations in a mouse model of HNRNPU haploinsufficiency.

Heterozygous de novo loss-of-function mutations in the gene expression regulator HNRNPU cause an early-onset developmental and epileptic encephalopathy. To gain insight into pathological mechanisms and lay the potential groundwork for developing targeted therapies, we characterized the neurophysiologic and cell-type-specific transcriptomic consequences of a mouse model of HNRNPU haploinsufficiency. Heterozygous mutants demonstrated global developmental delay, impaired ultrasonic vocalizations, cognitive dysfunction and increased seizure susceptibility, thus modeling aspects of the human disease. Single-cell RNA-sequencing of hippocampal and neocortical cells revealed widespread, yet modest, dysregulation of gene expression across mutant neuronal subtypes. We observed an increased burden of differentially-expressed genes in mutant excitatory neurons of the subiculum-a region of the hippocampus implicated in temporal lobe epilepsy. Evaluation of transcriptomic signature reversal as a therapeutic strategy highlights the potential importance of generating cell-type-specific signatures. Overall, this work provides insight into HNRNPU-mediated disease mechanisms and provides a framework for using single-cell RNA-sequencing to study transcriptional regulators implicated in disease.

Early transcriptional responses to human enteric fever challenge.

Enteric fever, caused by oral infection with typhoidal Salmonella serovars, presents as a non-specific febrile illness preceded by an incubation period of 5 days or more. The enteric fever human challenge model provides a unique opportunity to investigate the innate immune response during this incubation period, and how this response is altered by vaccination with the Vi polysaccharide or conjugate vaccine. We find that on the same day as ingestion of typhoidal Salmonella, there is already evidence of an immune response, with 199 genes upregulated in the peripheral blood transcriptome 12 hours post-challenge (false discovery rate <0.05). Gene sets relating to neutrophils, monocytes, and innate immunity were over-represented (false discovery rate <0.05). Estimating cell proportions from gene expression data suggested a possible increase in activated monocytes 12 hours post-challenge (P = 0.036, paired Wilcoxon signed-rank test). Furthermore, plasma TNF-α rose following exposure (P = 0.011, paired Wilcoxon signed-rank test). There were no significant differences in gene expression (false discovery rate <0.05) in the 12 hours response between those who did and did not subsequently develop clinical or blood culture confirmed enteric fever or between vaccination groups. Together, these results demonstrate early perturbation of the peripheral blood transcriptome after enteric fever challenge and provide initial insight into early mechanisms of protection.

MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny

BackgroundUnderstanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.ResultsTo address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.ConclusionsMAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.

IHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.