modEnrichr: a suite of gene set enrichment analysis tools for model organisms

Dr.Emb Appyter: A web platform for drug discovery using embedding vectors

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user-friendly web-based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response-based methods, and embeds numerous compounds using them. The Faiss-based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug-set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr.

Prediction of Interactomic HUB Genes in Periodontitis With Acute Myocardial Infarction

BACKGROUND

Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at risk for AMI, has been one important research topic. According to recent research, AMI and PD interact via the systemic production of certain proinflammatory and anti-inflammatory cytokines, small signal molecules, and enzymes that control the onset and development of both disorders' chronic inflammatory reactions. This study uses machine learning to identify the interactome hub biomarker genes in acute myocardial infarction and periodontitis.

METHODS

GSE208194 and GSE222883 were chosen for our research after a thorough search using keywords related to the study's goal from the gene expression omnibus (GEO) datasets. DEGs were identified from the GEOR tool, and the hub gene was identified using Cytoscape-cytohubba. Using expression values, Random Forest, Adaptive Boosting, and Naive Bayes, widgets-generated transcriptomics data, were labelled, and divided into 80/20 training and testing data with cross-validation. ROC curve, confusion matrix, and AUC were determined. In addition, Functional Enrichment Analysis of Differentially Expressed Gene analysis was performed.

RESULTS

Random Forest, AdaBoost, and Naive Bayes models with 99%, 100%, and 75% AUC, respectively. Compared to RF, AdaBoost, and NB classification models, AdaBoost had the highest AUC. Categorization algorithms may be better predictors than important biomarkers.

CONCLUSIONS

Machine learning model predicts hub and non-hub genes from genomic datasets with periodontitis and acute myocardial infarction.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will only be maintained if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74-94% of the variance in aneuploid strains' growth rates is explained by the additive cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of snoRNAs and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis

BACKGROUND AND OBJECTIVE

In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms.

MATERIALS AND METHODS

Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes.

RESULT

The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes.

CONCLUSION

The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Organogenetic transcriptomes of the Drosophila embryo at single cell resolution

To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs - the salivary gland and trachea - and clarify the embryonic functions of another - the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.

The ubiquitin thioesterase YOD1 ameliorates mutant Huntingtin induced pathology in Drosophila

Huntington's disease (HD) is a neurodegenerative disorder caused by a dominant gain-of-function mutation in the huntingtin gene, resulting in an elongated polyglutamine repeat in the mutant Huntingtin (mHtt) that mediates aberrant protein interactions. Previous studies implicated the ubiquitin-proteasome system in HD, suggesting that restoring cellular proteostasis might be a key element in suppressing pathology. We applied genetic interaction tests in a Drosophila model to ask whether modulating the levels of deubiquitinase enzymes affect HD pathology. By testing 32 deubiquitinase genes we found that overexpression of Yod1 ameliorated all analyzed phenotypes, including neurodegeneration, motor activity, viability, and longevity. Yod1 did not have a similar effect in amyloid beta overexpressing flies, suggesting that the observed effects might be specific to mHtt. Yod1 overexpression did not alter the number of mHtt aggregates but moderately increased the ratio of larger aggregates. Transcriptome analysis showed that Yod1 suppressed the transcriptional effects of mHtt and restored the expression of genes involved in neuronal plasticity, vesicular transport, antimicrobial defense, and protein synthesis, modifications, and clearance. Furthermore, Yod1 overexpression in HD flies leads to the upregulation of genes involved in transcriptional regulation and synaptic transmission, which might be part of a response mechanism to mHtt-induced stress.

Transcriptome profiling brings new insights into the ethanol stress responses of Spathaspora passalidarum

Spathaspora passalidarum is a xylose-fermenting microorganism promising for the fermentation of lignocellulosic hydrolysates. This yeast is more sensitive to ethanol than Saccharomyces cerevisiae for unclear reasons. An RNA-seq experiment was performed to identify transcriptional changes in S. passalidarum in response to ethanol and gain insights into this phenotype. The results showed the upregulation of genes associated with translation and the downregulation of genes encoding proteins involved in lipid metabolism, transporters, and enzymes from glycolysis and fermentation pathways. Our results also revealed that genes encoding heat-shock proteins and involved in antioxidant response were upregulated, whereas the osmotic stress response of S. passalidarum appears impaired under ethanol stress. A pseudohyphal morphology of S. passalidarum colonies was observed in response to ethanol stress, which suggests that ethanol induces a misperception of nitrogen availability in the environment. Changes in the yeast fatty acid profile were observed only after 12 h of ethanol exposure, coinciding with the recovery of the yeast xylose consumption ability. These findings suggest that the lack of fast membrane lipid adjustments, the halt in nutrient absorption and cellular metabolism, and the failure to induce the expression of osmotic stress-responsive genes are the main aspects underlying the low ethanol tolerance of S. passalidarum. KEY POINTS: • Ethanol stress halts Spathaspora passalidarum metabolism and fermentation • Genes encoding nutrient transporters showed downregulation under ethanol stress • Ethanol induces a pseudohyphal cell shape, suggesting a misperception of nutrients.

Dissecting morphogenetic apoptosis through a genetic screen in Drosophila

Apoptosis is an essential cellular process both in normal development and pathological contexts. Screens performed to date have focused on the cell autonomous aspect of the process, deciphering the apoptotic cascade leading to cell destruction through the activation of caspases. However, the nonautonomous aspect of the apoptotic pathway, including signals regulating the apoptotic pattern or those sent by the apoptotic cell to its surroundings, is still poorly understood. Here, we describe an unbiased RNAi-based genetic screen whose goal is to identify elements of the "morphogenetic apoptosis pathway" in an integrated model system, the Drosophila leg. We screened about 1,400 candidates, using adult joint morphology, morphogenetic fold formation, and apoptotic pattern as readouts for the identification of potential apoptosis-related genes. We identified 41 genes potentially involved in specific aspects of morphogenetic apoptosis: (1) regulation of the apoptotic process; (2) formation, extrusion, and elimination of apoptotic bodies; and (3) contribution to morphogenesis downstream of apoptosis.

Detecting intragenic trans-splicing events from non-co-linearly spliced junctions by hybrid sequencing

Trans-spliced RNAs (ts-RNAs) are a type of non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting ts-RNAs is often interfered by experimental artifacts, circular RNAs (circRNAs) and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same gene, are often mistaken for circRNAs through analyses of RNA-seq data. Here we developed a bioinformatics pipeline (NCLscan-hybrid), which integrated short and long RNA-seq reads to minimize false positives and proposed out-of-circle and rolling-circle long reads to distinguish between intragenic ts-RNAs and circRNAs. Combining NCLscan-hybrid screening and multiple experimental validation steps successfully confirmed that four NCL events, which were previously regarded as circRNAs in databases, originated from trans-splicing. CRISPR-based endogenous genome modification experiments further showed that flanking intronic complementary sequences can significantly contribute to ts-RNA formation, providing an efficient/specific method to deplete ts-RNAs. We also experimentally validated that one ts-RNA (ts-ARFGEF1) played an important role for p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of ts-RNAs, expanding future studies for identification, biogenesis, and function of these important but understudied transcripts.

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms.

AVAILABILITY AND IMPLEMENTATION

Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.

Transcriptomic effects of the foraging gene shed light on pathways of pleiotropy and plasticity

Genes are often pleiotropic and plastic in their expression, features which increase and diversify the functionality of the genome. The foraging (for) gene in Drosophila melanogaster is highly pleiotropic and a long-standing model for studying individual differences in behavior and plasticity from ethological, evolutionary, and genetic perspectives. Its pleiotropy is known to be linked to its complex molecular structure; however, the downstream pathways and interactors remain mostly elusive. To uncover these pathways and interactors and gain a better understanding of how pleiotropy and plasticity are achieved at the molecular level, we explore the effects of different for alleles on gene expression at baseline and in response to 4 h of food deprivation, using RNA sequencing analysis in different Drosophila larval tissues. The results show tissue-specific transcriptomic dynamics influenced by for allelic variation and food deprivation, as well as genotype by treatment interactions. Differentially expressed genes yielded pathways linked to previously described for phenotypes and several potentially novel phenotypes. Together, these findings provide putative genes and pathways through which for might regulate its varied phenotypes in a pleiotropic, plastic, and gene-structure-dependent manner.

Dysregulated miRNA and mRNA Expression Affect Overlapping Pathways in a Huntington's Disease Model

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in the Huntingtin gene. Transcriptional dysregulation is one of the main cellular processes affected by mutant Huntingtin (mHtt). In this study, we investigate the alterations in miRNA and mRNA expression levels in a Drosophila model of HD by RNA sequencing and assess the functional effects of misregulated miRNAs in vivo. We found that in head samples of HD flies, the level of 32 miRNAs changed significantly; half of these were upregulated, while the other half were downregulated. After comparing miRNA and mRNA expression data, we discovered similarities in the impacted molecular pathways. Additionally, we observed that the putative targets of almost all dysregulated miRNAs were overrepresented among the upregulated mRNAs. We tested the effects of overexpression of five misregulated miRNAs in the HD model and found that while mir-10 and mir-219 enhanced, mir-137, mir-305, and mir-1010 ameliorated mHtt-induced phenotypes. Based on our results, we propose that while altered expression of mir-10, mir-137, and mir-1010 might be part of HD pathology, the upregulation of mir-305 might serve as a compensatory mechanism as a response to mHtt-induced transcriptional dysregulation.

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

BACKGROUND

There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates.

RESULTS

First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors.

CONCLUSIONS

DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

Lipid droplets and peroxisomes are co-regulated to drive lifespan extension in response to mono-unsaturated fatty acids

Dietary mono-unsaturated fatty acids (MUFAs) are linked to longevity in several species. But the mechanisms by which MUFAs extend lifespan remain unclear. Here we show that an organelle network involving lipid droplets and peroxisomes is critical for MUFA-induced longevity in Caenorhabditis elegans. MUFAs upregulate the number of lipid droplets in fat storage tissues. Increased lipid droplet number is necessary for MUFA-induced longevity and predicts remaining lifespan. Lipidomics datasets reveal that MUFAs also modify the ratio of membrane lipids and ether lipids-a signature associated with decreased lipid oxidation. In agreement with this, MUFAs decrease lipid oxidation in middle-aged individuals. Intriguingly, MUFAs upregulate not only lipid droplet number but also peroxisome number. A targeted screen identifies genes involved in the co-regulation of lipid droplets and peroxisomes, and reveals that induction of both organelles is optimal for longevity. Our study uncovers an organelle network involved in lipid homeostasis and lifespan regulation, opening new avenues for interventions to delay aging.

Mimicked Mixing-Induced Heterogeneities of Industrial Bioreactors Stimulate Long-Lasting Adaption Programs in Ethanol-Producing Yeasts

Commercial-scale bioreactors create an unnatural environment for microbes from an evolutionary point of view. Mixing insufficiencies expose individual cells to fluctuating nutrient concentrations on a second-to-minute scale while transcriptional and translational capacities limit the microbial adaptation time from minutes to hours. This mismatch carries the risk of inadequate adaptation effects, especially considering that nutrients are available at optimal concentrations on average. Consequently, industrial bioprocesses that strive to maintain microbes in a phenotypic sweet spot, during lab-scale development, might suffer performance losses when said adaptive misconfigurations arise during scale-up. Here, we investigated the influence of fluctuating glucose availability on the gene-expression profile in the industrial yeast Ethanol Red™. The stimulus-response experiment introduced 2 min glucose depletion phases to cells growing under glucose limitation in a chemostat. Even though Ethanol Red™ displayed robust growth and productivity, a single 2 min depletion of glucose transiently triggered the environmental stress response. Furthermore, a new growth phenotype with an increased ribosome portfolio emerged after complete adaptation to recurring glucose shortages. The results of this study serve a twofold purpose. First, it highlights the necessity to consider the large-scale environment already at the experimental development stage, even when process-related stressors are moderate. Second, it allowed the deduction of strain engineering guidelines to optimize the genetic background of large-scale production hosts.

NDEx IQuery: a multi-method network gene set analysis leveraging the Network Data Exchange

MOTIVATION

The investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes that are active or modulated in a specific experimental context.

RESULTS

The Network Data Exchange Integrated Query (NDEx IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The NDEx IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. NDEx IQuery's integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources.

AVAILABILITY AND IMPLEMENTATION

NDEx IQuery is available at https://www.ndexbio.org/iquery and is implemented in Javascript and Java.

Human Endogenous Retrovirus (HERV) Transcriptome Is Dynamically Modulated during SARS-CoV-2 Infection and Allows Discrimination of COVID-19 Clinical Stages

SARS-CoV-2 infection is known to trigger an important inflammatory response, which has a major role in COVID-19 pathogenesis. In infectious and inflammatory contexts, the modulation of human endogenous retroviruses (HERV) has been broadly reported, being able to further sustain innate immune responses due to the expression of immunogenic viral transcripts, including double-stranded DNA (dsRNA), and eventually, immunogenic proteins. To gain insights on this poorly characterized interplay, we performed a high-throughput expression analysis of ~3,300 specific HERV loci in the peripheral blood mononuclear cells (PBMCs) of 10 healthy controls and 16 individuals being either convalescent after the infection (6) or retesting positive after convalescence (10) (Gene Expression Onmibus [GEO] data set GSE166253). Results showed that the exposure to SARS-CoV-2 infection modulates HERV expression according to the disease stage and reflecting COVID-19 immune signatures. The differential expression analysis between healthy control (HC) and COVID-19 patients allowed us to identify a total of 282 differentially expressed HERV loci (deHERV) in the individuals exposed to SARS-CoV-2 infection, independently from the clinical form. In addition, 278 and 60 deHERV loci that were specifically modulated in individuals convalescent after COVID19 infection (C) and patients that retested positive to SARS-CoV-2 after convalescence (RTP) as individually compared to HC, respectively, as well as 164 deHERV loci between C and RTP patients were identified. The identified HERV loci belonged to 36 different HERV groups, including members of all three classes. The present study provides an exhaustive picture of the HERV transcriptome in PBMCs and its dynamic variation in the presence of COVID-19, revealing specific modulation patterns according to the infection stage that can be relevant to the disease clinical manifestation and outcome. IMPORTANCE We report here the first high-throughput analysis of HERV loci expression along SARS-CoV-2 infection, as performed with peripheral blood mononuclear cells (PBMCs). Such cells are not directly infected by the virus but have a crucial role in the plethora of inflammatory and immune events that constitute a major hallmark of COVID-19 pathogenesis. Results provide a novel and exhaustive picture of HERV expression in PBMCs, revealing specific modulation patterns according to the disease condition and the concomitant immune activation. To our knowledge, this is the first set of deHERVs whose expression is dynamically modulated across COVID-19 stages, confirming a tight interplay between HERV and cellular immunity and revealing specific transcriptional signatures that can have an impact on the disease clinical manifestation and outcome.

Performing in spite of starvation: How Saccharomyces cerevisiae maintains robust growth when facing famine zones in industrial bioreactors

In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

Divergence and introgression among the virilis group of Drosophila

Speciation with gene flow is now widely regarded as common. However, the frequency of introgression between recently diverged species and the evolutionary consequences of gene flow are still poorly understood. The virilis group of Drosophila contains 12 species that are geographically widespread and show varying levels of prezygotic and postzygotic isolation. Here, we use de novo genome assemblies and whole-genome sequencing data to resolve phylogenetic relationships and describe patterns of introgression and divergence across the group. We suggest that the virilis group consists of three, rather than the traditional two, subgroups. Some genes undergoing rapid sequence divergence across the group were involved in chemical communication and desiccation tolerance, and may be related to the evolution of sexual isolation and adaptation. We found evidence of pervasive phylogenetic discordance caused by ancient introgression events between distant lineages within the group, and more recent gene flow between closely related species. When assessing patterns of genome-wide divergence in species pairs across the group, we found no consistent genomic evidence of a disproportionate role for the X chromosome as has been found in other systems. Our results show how ancient and recent introgressions confuse phylogenetic reconstruction, but may play an important role during early radiation of a group.

High-throughput proteomics: a methodological mini-review

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

The continuum of Drosophila embryonic development at single-cell resolution

Drosophila melanogaster is a powerful, long-standing model for metazoan development and gene regulation. We profiled chromatin accessibility in almost 1 million and gene expression in half a million nuclei from overlapping windows spanning the entirety of embryogenesis. Leveraging developmental asynchronicity within embryo collections, we applied deep neural networks to infer the age of each nucleus, resulting in continuous, multimodal views of molecular and cellular transitions in absolute time. We identify cell lineages; infer their developmental relationships; and link dynamic changes in enhancer usage, transcription factor (TF) expression, and the accessibility of TFs' cognate motifs. With these data, the dynamics of enhancer usage and gene expression can be explored within and across lineages at the scale of minutes, including for precise transitions like zygotic genome activation.

Evaluating the utility of an immune checkpoint-related lncRNA signature for identifying the prognosis and immunotherapy response of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer globally. However, the survival rate of lung adenocarcinoma patients remains low. Immune checkpoints and long noncoding RNAs are emerging as vital tools for predicting the immunotherapeutic response and outcomes of patients with lung adenocarcinoma. It is critical to identify lncRNAs associated with immune checkpoints in lung adenocarcinoma patients. In this study, immune checkpoint-related lncRNAs (IClncRNAs) were analysed and identified by coexpression. Based on the immune checkpoint-related lncRNAs, we divided patients with lung adenocarcinoma into two clusters and constructed a risk model. Kaplan–Meier analysis, Gene Set Enrichment Analysis, and nomogram analysis of the 2 clusters and the risk model were performed. Finally, the potential immunotherapeutic prediction value of this model was discussed. The risk model consisting of 6 immune checkpoint-related lncRNAs was an independent predictor of survival. Through regrouping the patients with this model, we can distinguish between them more effectively in terms of their immunotherapeutic response, tumour microenvironment, and chemotherapy response. This risk model based on immune checkpoint-based lncRNAs may have an excellent clinical value for predicting the immunotherapeutic response and outcomes of patients with LUAD.

Influenza A Virus Infection Reactivates Human Endogenous Retroviruses Associated with Modulation of Antiviral Immunity

Human endogenous retrovirus (HERVs), normally silenced by methylation or mutations, can be reactivated by multiple environmental factors, including infections with exogenous viruses. In this work, we investigated the transcriptional activity of HERVs in human A549 cells infected by two wild-type (PR8M, SC35M) and one mutated (SC35MΔNS1) strains of Influenza A virus (IAVs). We found that the majority of differentially expressed HERVs (DEHERVS) and genes (DEGs) were up-regulated in the infected cells, with the most significantly enriched biological processes associated with the genes differentially expressed exclusively in SC35MΔNS1 being linked to the immune system. Most DEHERVs in PR8M and SC35M are mammalian apparent LTR retrotransposons, while in SC35MΔNS1, more HERV loci from the HERVW9 group were differentially expressed. Furthermore, up-regulated pairs of HERVs and genes in close chromosomal proximity to each other tended to be associated with immune responses, which implies that specific HERV groups might have the potential to trigger specific gene networks and influence host immunological pathways.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N_BrCa = 228,951, N_PrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N_BrCa = 621, N_PrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P_cond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)—a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Methylome-wide association studies identify genetically-influenced CpGs associated with breast and prostate cancer risk and (epi)genome-transcriptome mechanistic relationships, with lipid metabolism genes implicated as potential therapeutic targets.

FUNAGE-Pro: comprehensive web server for gene set enrichment analysis of prokaryotes

Recent advances in the field of high throughput (meta-)transcriptomics and proteomics call for easy and rapid methods enabling to explore not only single genes or proteins but also extended biological systems. Gene set enrichment analysis is commonly used to find relations in a set of genes and helps to uncover the biological meaning in results derived from high-throughput data. The basis for gene set enrichment analysis is a solid functional classification of genes. Here, we describe a comprehensive database containing multiple functional classifications of genes of all (>55 000) publicly available complete bacterial genomes. In addition to the most common functional classes such as COG and GO, also KEGG, InterPro, PFAM, eggnog and operon classes are supported. As classification data for features is often not available, we offer fast annotation and classification of proteins in any newly sequenced bacterial genome. The web server FUNAGE-Pro enables fast functional analysis on single gene sets, multiple experiments, time series data, clusters, and gene network modules for any prokaryote species or strain. FUNAGE-Pro is freely available at http://funagepro.molgenrug.nl.

The Nab2 RNA-binding protein patterns dendritic and axonal projections through a planar cell polarity-sensitive mechanism

RNA-binding proteins support neurodevelopment by modulating numerous steps in post-transcriptional regulation, including splicing, export, translation, and turnover of mRNAs that can traffic into axons and dendrites. One such RNA-binding protein is ZC3H14, which is lost in an inherited intellectual disability. The Drosophila melanogaster ZC3H14 ortholog, Nab2, localizes to neuronal nuclei and cytoplasmic ribonucleoprotein granules and is required for olfactory memory and proper axon projection into brain mushroom bodies. Nab2 can act as a translational repressor in conjunction with the Fragile-X mental retardation protein homolog Fmr1 and shares target RNAs with the Fmr1-interacting RNA-binding protein Ataxin-2. However, neuronal signaling pathways regulated by Nab2 and their potential roles outside of mushroom body axons remain undefined. Here, we present an analysis of a brain proteomic dataset that indicates that multiple planar cell polarity proteins are affected by Nab2 loss, and couple this with genetic data that demonstrate that Nab2 has a previously unappreciated role in restricting the growth and branching of dendrites that elaborate from larval body-wall sensory neurons. Further analysis confirms that Nab2 loss sensitizes sensory dendrites to the genetic dose of planar cell polarity components and that Nab2-planar cell polarity genetic interactions are also observed during Nab2-dependent control of axon projection in the central nervous system mushroom bodies. Collectively, these data identify the conserved Nab2 RNA-binding protein as a likely component of post-transcriptional mechanisms that limit dendrite growth and branching in Drosophila sensory neurons and genetically link this role to the planar cell polarity pathway. Given that mammalian ZC3H14 localizes to dendritic spines and controls spine density in hippocampal neurons, these Nab2-planar cell polarity genetic data may highlight a conserved path through which Nab2/ZC3H14 loss affects morphogenesis of both axons and dendrites in diverse species.

Panx1 channels promote both anti- and pro-seizure-like activities in the zebrafish via p2rx7 receptors and ATP signaling

The molecular mechanisms of excitation/inhibition imbalances promoting seizure generation in epilepsy patients are not fully understood. Evidence suggests that Pannexin1 (Panx1), an ATP release channel, modulates the excitability of the brain. In this report, we performed electrophysiological, behavioral, and molecular phenotyping experiments on zebrafish larvae bearing genetic or pharmacological knockouts of Panx1a and Panx1b channels, each homologous to human PANX1. When Panx1a function is lost, or both channels are under pharmacological blockade, seizures with ictal-like events and seizure-like locomotion are reduced in the presence of pentylenetetrazol. Transcriptome profiling by RNA-seq demonstrates a spectrum of distinct metabolic and cell signaling states which correlate with the loss of Panx1a. Furthermore, the pro- and anticonvulsant activities of both Panx1 channels affect ATP release and involve the purinergic receptor P2rx7. Our findings suggest a subfunctionalization of Panx1 enabling dual roles in seizures, providing a unique and comprehensive perspective to understanding seizure mechanisms in the context of this channel.

Anti-inflammatory mechanism of rhein in treating asthma based on network pharmacology

OBJECTIVE

To predict the anti-inflammatory targets and related pathways of rhein in the treatment of asthma by using network pharmacology, and to further explore its potential mechanism in asthma.

METHODS

The corresponding targets of rhein were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the rhein-target network was constructed with Cytoscape 3.7.1 software. The Genbank and Drugbank databases were used to collect and screen asthma targets, and the rhein-target-disease interaction network was constructed. A target protein-protein interaction (PPI) network was constructed using the STRING database to screen key targets. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to identify biological processes and signaling pathways. The anti-asthmatic effects of rhein were tested in vitro, and the expression levels of proteins in the mitogen-activated protein kinase/nuclear factor kappa-B (MAPK/ NF-κB) signaling pathway were assessed by western blot analysis.

RESULTS

Altogether, 83 targets of rhein were screened in the relevant databases, 989 targets of asthma were obtained in the National Center for Biotechnology Information (NCBI) GENE Database. PPI network analysis and KEGG pathway enrichment analysis predicted that rhein could regulate the epidermal active growth factor receptor (EGFR), mitogen-activated protein kinase 14 (MAPK14), tumour necrosis factor receptor superfamily member 1A (TNFRSF1A), receptor tyrosine-protein kinase erbB-2 (ERBB2), and other signaling pathways. Furthermore, we selected the MAPK signaling pathway to determine the anti-inflammatory effects of rhein. Consistently, further experiments demonstrated that rhein was shown to inhibit HBE cells inflammation.

CONCLUSION

The anti-inflammatory mechanism of rhein in the treatment of asthma may be related to EGFR, MAPK14, TNFRSF1A and ERBB2 as well as their signaling pathways. To prevent the exacerbation of asthma, instead of targeting a single pathway or a single target, all these targets and their signaling pathways should be controlled holistically. Rhein may alleviate the inflammation of asthma by inhibiting the MAPK/NF-κB pathway.

Immune checkpoints related-LncRNAs can identify different subtypes of lung cancer and predict immunotherapy and prognosis

BACKGROUND

Non-small cell lung cancer is the most common subtype of lung cancer in the world. However, the survival rate of non-small cell lung cancer patients remains low currently. Immune checkpoint and long non-coding RNAs are emerging as critical roles in prognostic significance and the immunotherapeutic response of non-small cell lung cancer. It is critical to discern LncRNAs related with immune checkpoints in patients with Non-small cell lung cancer.

METHODS

In this study, immune checkpoint-linked LncRNAs were determined and achieved by the co-expression analysis. Immune checkpoint-linked LncRNAs with noteworthy prognostic value (P < 0.05) gained were next utilized to separate into two cluster by non-negative matrix factorization (NMF). Univariate and a least absolute shrinkage and selection operator were applied to construct an immune checkpoint-linked LncRNAs model. Kaplan-Meier analysis, Gene Set Enrichment Analysis, and the nomogram were utilized to investigate the LncRNAs model. Lastly, the capability immunotherapy and chemotherapy prediction value of this risk model were also estimated.

RESULTS

The model consisting of ten immune checkpoint-related LncRNAs was acknowledged to be a self-determining predictor of prognosis. Through regrouping the NSCLC patients by this model, difference between them more efficiently on immunotherapeutic response, tumor microenvironment and chemotherapy response could be discovered. This risk model related to the immune checkpoint-based LncRNAs may have an excellent clinical prediction for prognosis and the immunotherapeutic response in patients with NSCLC.

CONCLUSIONS

We performed an integrative analysis of LncRNAs linked with immune checkpoints and emphasized the significance of NSCLC subtypes classification, immune checkpoints related LncRNAs in estimating the tumor microenvironment score, immune cell infiltration of the tumor, immunotherapy, and chemotherapy response.

Checkpoint activation drives global gene expression changes in Drosophila nuclear lamina mutants

The nuclear lamina (NL) lines the inner nuclear membrane. This extensive protein network organizes chromatin and contributes to the regulation of transcription, DNA replication, and repair. Lap2-emerin-MAN1 domain (LEM-D) proteins are key members of the NL, representing proteins that connect the NL to the genome through shared interactions with the chromatin-binding protein Barrier-to-Autointegration Factor (BAF). Functions of the LEM-D protein emerin and BAF are essential during Drosophila melanogaster oogenesis. Indeed, loss of either emerin or BAF blocks germ cell development and causes loss of germline stem cells, defects linked to the deformation of NL structure, and non-canonical activation of Checkpoint kinase 2 (Chk2). Here, we investigate the contributions of emerin and BAF to gene expression in the ovary. Profiling RNAs from emerin and baf mutant ovaries revealed that nearly all baf misregulated genes were shared with emerin mutants, defining a set of NL-regulated genes. Strikingly, loss of Chk2 restored the expression of most NL-regulated genes, identifying a large class of Chk2-dependent genes (CDGs). Nonetheless, some genes remained misexpressed upon Chk2 loss, identifying a smaller class of emerin-dependent genes (EDGs). Properties of EDGs suggest a shared role for emerin and BAF in the repression of developmental genes. Properties of CDGs demonstrate that Chk2 activation drives global misexpression of genes in the emerin and baf mutant backgrounds. Notably, CDGs were found upregulated in lamin-B mutant backgrounds. These observations predict that Chk2 activation might have a general role in gene expression changes found in NL-associated diseases, such as laminopathies.

OUP accepted manuscript

Mitochondria are subcellular organelles present in almost all eukaryotic cells, which play a central role in cellular metabolism. Different tissues, health and age conditions are characterized by a difference in mitochondrial structure and composition. The visual data mining platform mitoXplorer 1.0 was developed to explore the expression dynamics of genes associated with mitochondrial functions that could help explain these differences. It, however, lacked functions aimed at integrating mitochondria in the cellular context and thus identifying regulators that help mitochondria adapt to cellular needs. To fill this gap, we upgraded the mitoXplorer platform to version 2.0 (mitoXplorer 2.0). In this upgrade, we implemented two novel integrative functions, network analysis and transcription factor enrichment, to specifically help identify signalling or transcriptional regulators of mitochondrial processes. In addition, we implemented several other novel functions to allow the platform to go beyond simple data visualization, such as an enrichment function for mitochondrial processes, a function to explore time-series data, the possibility to compare datasets across species and an IDconverter to help facilitate data upload. We demonstrate the usefulness of these functions in three specific use cases. mitoXplorer 2.0 is freely available without login at http://mitoxplorer2.ibdm.univ-mrs.fr.

LncRNA SFTA1P mediates positive feedback regulation of the Hippo-YAP/TAZ signaling pathway in non-small cell lung cancer

Long non-coding RNAs (lncRNAs) regulate numerous biological processes involved in both development and carcinogenesis. Hippo-YAP/TAZ signaling, a critical pathway responsible for organ size control, is often dysregulated in a variety of cancers. However, the nature and function of YAP/TAZ-regulated lncRNAs during tumorigenesis remain largely unexplored. By profiling YAP/TAZ-regulated lncRNAs, we identified SFTA1P as a novel transcriptional target and a positive feedback regulator of YAP/TAZ signaling. Using non-small cell lung cancer (NSCLC) cell lines, we show that SFTA1P is transcriptionally activated by YAP/TAZ in a TEAD-dependent manner. Functionally, knockdown of SFTA1P in NSCLC cell lines inhibited proliferation, induced programmed cell death, and compromised their tumorigenic potential. Mechanistically, SFTA1P knockdown decreased TAZ protein abundance and consequently, the expression of YAP/TAZ transcriptional targets. We provide evidence that this phenomenon could potentially be mediated via its interaction with TAZ mRNA to regulate TAZ translation. Our results reveal SFTA1P as a positive feedback regulator of Hippo-YAP/TAZ signaling, which may serve as the molecular basis for lncRNA-based therapies against YAP/TAZ-driven cancers.

Nucleolin Aptamer N6L Reprograms the Translational Machinery and Acts Synergistically with mTORi to Inhibit Pancreatic Cancer Proliferation

Simple Summary

Pancreatic cancer is an aggressive disease characterized by its invasiveness, rapid progression, and resistance to conventional therapy. There is a need to identify new molecules to improve current therapies. The aim of this study was to analyze how the pancreatic cancer cells react to the treatment with an inhibitor of nucleolin, N6L. To this end, we analyzed how the translation was affected in the cells during the treatment. We discovered that in response to N6L, a signaling pathway called the mTOR pathway was activated and was involved in the activation of translation of a subset of mRNA that could be involved in the resistance of the cells to the treatment. Indeed, we showed that the combined action of inhibitors of the mTOR pathway with N6L synergistically inhibited the cancer cells’ proliferation. We propose that this new combination of molecules could be a novel therapeutic option for pancreatic cancer.

Abstract

We previously showed that N6L, a pseudopeptide that targets nucleolin, impairs pancreatic ductal adenocarcinoma (PDAC) growth and normalizes tumor vessels in animal models. In this study, we analyzed the translatome of PDAC cells treated with N6L to identify the pathways that were either repressed or activated. We observed a strong decrease in global protein synthesis. However, about 6% of the mRNAs were enriched in the polysomes. We identified a 5′TOP motif in many of these mRNAs and demonstrated that a chimeric RNA bearing a 5‘TOP motif was up-regulated by N6L. We demonstrated that N6L activates the mTOR pathway, which is required for the translation of these mRNAs. An inhibitory synergistic effect in PDAC cell lines, including patient-derived xenografts and tumor-derived organoids, was observed when N6L was combined with mTOR inhibitors. In conclusion, N6L reduces pancreatic cells proliferation, which then undergoes translational reprogramming through activation of the mTOR pathway. N6L and mTOR inhibitors act synergistically to inhibit the proliferation of PDAC and human PDX cell lines. This combotherapy of N6L and mTOR inhibitors could constitute a promising alternative to treat pancreatic cancer.

A budding yeast model for human disease mutations in the EXOSC2 cap subunit of the RNA exosome complex

RNA exosomopathies, a growing family of diseases, are linked to missense mutations in genes encoding structural subunits of the evolutionarily conserved, 10-subunit exoribonuclease complex, the RNA exosome. This complex consists of a three-subunit cap, a six-subunit, barrel-shaped core, and a catalytic base subunit. While a number of mutations in RNA exosome genes cause pontocerebellar hypoplasia, mutations in the cap subunit gene EXOSC2 cause an apparently distinct clinical presentation that has been defined as a novel syndrome SHRF (short stature, hearing loss, retinitis pigmentosa, and distinctive facies). We generated the first in vivo model of the SHRF pathogenic amino acid substitutions using budding yeast by modeling pathogenic EXOSC2 missense mutations (p.Gly30Val and p.Gly198Asp) in the orthologous S. cerevisiae gene RRP4 The resulting rrp4 mutant cells show defects in cell growth and RNA exosome function. Consistent with altered RNA exosome function, we detect significant transcriptomic changes in both coding and noncoding RNAs in rrp4-G226D cells that model EXOSC2 p.Gly198Asp, suggesting defects in nuclear surveillance. Biochemical and genetic analyses suggest that the Rrp4 G226D variant subunit shows impaired interactions with key RNA exosome cofactors that modulate the function of the complex. These results provide the first in vivo evidence that pathogenic missense mutations present in EXOSC2 impair the function of the RNA exosome. This study also sets the stage to compare exosomopathy models to understand how defects in RNA exosome function underlie distinct pathologies.

AnnoMiner is a new web-tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators

Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into different gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell-type specific gene expression. We have developed the AnnoMiner web-server, an innovative and flexible tool to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user-provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two different transcriptional regulators together with gene features. Third, AnnoMiner offers to explore the transcriptional deregulation of genes nearby, or within a specified genomic region surrounding a user-provided peak. AnnoMiner’s fourth function performs transcription factor or histone modification enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http://chimborazo.ibdm.univ-mrs.fr/AnnoMiner/.

KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis

Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas.

The RNA-binding protein Nab2 regulates the proteome of the developing Drosophila brain

The human ZC3H14 gene, which encodes a ubiquitously expressed polyadenosine zinc finger RNA-binding protein, is mutated in an inherited form of autosomal recessive, nonsyndromic intellectual disability. To gain insight into neurological functions of ZC3H14, we previously developed a Drosophila melanogaster model of ZC3H14 loss by deleting the fly ortholog, Nab2. Studies in this invertebrate model revealed that Nab2 controls final patterns of neuron projection within fully developed adult brains, but the role of Nab2 during development of the Drosophila brain is not known. Here, we identify roles for Nab2 in controlling the dynamic growth of axons in the developing brain mushroom bodies, which support olfactory learning and memory, and regulating abundance of a small fraction of the total brain proteome. The group of Nab2-regulated brain proteins, identified by quantitative proteomic analysis, includes the microtubule-binding protein Futsch, the neuronal Ig-family transmembrane protein turtle, the glial:neuron adhesion protein contactin, the Rac GTPase-activating protein tumbleweed, and the planar cell polarity factor Van Gogh, which collectively link Nab2 to the processes of brain morphogenesis, neuroblast proliferation, circadian sleep/wake cycles, and synaptic development. Overall, these data indicate that Nab2 controls the abundance of a subset of brain proteins during the active process of wiring the pupal brain mushroom body and thus provide a window into potentially conserved functions of the Nab2/ZC3H14 RNA-binding proteins in neurodevelopment.

Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus .

Gene Set Knowledge Discovery with Enrichr

Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2: Searching Enrichr by a single gene or key search term Basic Protocol 3: Preparing raw or processed RNA-seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4: Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5: Using Enrichr in Geneshot Basic Protocol 6: Using Enrichr in ARCHS4 Basic Protocol 7: Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8: Using the Enrichr API Basic Protocol 9: Adding an Enrichr button to a website.

The TP53-Related Signature Predicts Immune Cell Infiltration, Therapeutic Response, and Prognosis in Patients With Esophageal Carcinoma

TP53 mutation (TP53^MUT) is one of the most common gene mutations and frequently occurs in many cancers, especially esophageal carcinoma (ESCA), and it correlates with clinical prognostic outcomes. Nevertheless, the mechanisms by which TP53^MUT regulates the correlation between ESCA and prognosis have not been sufficiently studied. Here, in the current research, we constructed a TP53^MUT-related signature to predict the prognosis of patients with esophageal cancer and successfully verified this model in patients in the TP53 mutant group, esophageal squamous cell carcinoma group, and adenocarcinoma group. The risk scores proved to be better independent prognostic factors than clinical features, and prognostic features were combined with other clinical features to establish a convincing nomogram to predict overall survival from 1 to 3 years. In addition, we further predicted the tumor immune cell infiltration, chemical drugs, and immunotherapy responses between the high-risk group and low risk group. Finally, the gene expression of the seven-gene signature (AP002478.1, BHLHA15, FFAR2, IGFBP1, KCTD8, PHYHD1, and SLC26A9) can provide personalized prognosis prediction and insights into new treatments.

Scoring functions for drug-effect similarity

MOTIVATION

The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known.

RESULTS

We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap's successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing.

AVAILABILITY

https://bitbucket.org/ibima/moldrugeffectsdb.

CONTACT

steffen.moeller@uni-rostock.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Activation of HERV-K(HML-2) disrupts cortical patterning and neuronal differentiation by increasing NTRK3

The biological function and disease association of human endogenous retroviruses (HERVs) are largely elusive. HERV-K(HML-2) has been associated with neurotoxicity, but there is no clear understanding of its role or mechanistic basis. We addressed the physiological functions of HERV-K(HML-2) in neuronal differentiation using CRISPR engineering to activate or repress its expression levels in a human-pluripotent-stem-cell-based system. We found that elevated HERV-K(HML-2) transcription is detrimental for the development and function of cortical neurons. These effects are cell-type-specific, as dopaminergic neurons are unaffected. Moreover, high HERV-K(HML-2) transcription alters cortical layer formation in forebrain organoids. HERV-K(HML-2) transcriptional activation leads to hyperactivation of NTRK3 expression and other neurodegeneration-related genes. Direct activation of NTRK3 phenotypically resembles HERV-K(HML-2) induction, and reducing NTRK3 levels in context of HERV-K(HML-2) induction restores cortical neuron differentiation. Hence, these findings unravel a cell-type-specific role for HERV-K(HML-2) in cortical neuron development.

An Extracellular Matrix-Based Signature Associated With Immune Microenvironment Predicts the Prognosis and Therapeutic Responses of Patients With Oesophageal Squamous Cell Carcinoma

Evidence has suggested that the cancer-associated extracellular matrix (ECM) could be recognised as immune-related biomarkers that modulate tumour progression and expansion. However, the ECM-associated immune effect on esophageal squamous cell carcinoma (ESCC) prognosis and therapy has not been well characterised. In our study, we first constructed an ECM-related signature including four genes CST1, NELL2, ADAMTSL4, and ANGPTL7 by multivariate Cox regression analyses. This signature could serve as a marker to evaluate the prognosis of patients with ESCC and was successfully validated in testing and combined (training plus testing) cohorts. We also found that there were significant different therapeutic responses to chemotherapy and targeted drugs between the high-risk and low-risk groups of patients defined by the signature. Furthermore, the expression of four genes and immune function analysis suggested that this ECM-related signature gene might play important roles in the changes of the tumour microenvironment. In conclusion, our findings demonstrated that the ECM-related signature might serve as an independent prognostic factor and provide a potential biomarker for chemotherapy responses for patients with ESCC.

CD155-Prognostic and Immunotherapeutic Implications Based on Multiple Analyses of Databases Across 33 Human Cancers

Growing evidence has suggested that CD155 participates in the regulation of many biological processes ranging cell growth, invasion, and migration from regulation of immune responses in most malignances. However, the impact of prognostic value and CD115-related immune response on the survival in multiple cancers remains incompletely clear. In our study, we assessed the prognostic significance and immune-associated mechanism of CD155 based on data from multiple databases and methods, including UCSC Xena, Oncomine, PrognoScan. We identified that CD155 was commonly upregulated in most human cancers, and High expression of CD155 was closely correlated with unfavorable clinical outcomes in 10/33 of human cancers, while CD155 at low level was responsible for better survival in KICH and PAAD. More intriguingly, CD155 expression had a significant interaction with immune function in several tumors by analyzing Tumor mutational burden and microsatellite in stability, immune score and stromal score. The correlation between immune infiltration and CD155 expression also indicated that CD155 expression positively correlated with CD4+ T cells in Head and Neck squamous cell carcinoma, Lung adenocarcinoma and Colon adenocarcinoma, while had inversely interaction with CD8+ T in Kidney renal clear cell carcinoma and Head and Neck squamous cell carcinoma as well as Tregs in Skin Cutaneous Melanoma, Head and Neck squamous cell carcinoma and Bladder Urothelial Carcinoma. These findings indicate CD155 correlates with cancer immunotherapy function. In conclusions, our observations revealed CD155 might function as immune-associated system in the development of human cancers, and acted as a promising prognostic and therapeutic target against human cancers.

The Hippo pathway controls myofibril assembly and muscle fiber growth by regulating sarcomeric gene expression

Skeletal muscles are composed of gigantic cells called muscle fibers, packed with force-producing myofibrils. During development, the size of individual muscle fibers must dramatically enlarge to match with skeletal growth. How muscle growth is coordinated with growth of the contractile apparatus is not understood. Here, we use the large Drosophila flight muscles to mechanistically decipher how muscle fiber growth is controlled. We find that regulated activity of core members of the Hippo pathway is required to support flight muscle growth. Interestingly, we identify Dlg5 and Slmap as regulators of the STRIPAK phosphatase, which negatively regulates Hippo to enable post-mitotic muscle growth. Mechanistically, we show that the Hippo pathway controls timing and levels of sarcomeric gene expression during development and thus regulates the key components that physically mediate muscle growth. Since Dlg5, STRIPAK and the Hippo pathway are conserved a similar mechanism may contribute to muscle or cardiomyocyte growth in humans.

Hyperacetylated chromatin domains mark cell type-specific genes and suggest distinct modes of enhancer function

Stratification of enhancers by signal strength in ChIP-seq assays has resulted in the establishment of super-enhancers as a widespread and useful tool for identifying cell type-specific, highly expressed genes and associated pathways. We examine a distinct method of stratification that focuses on peak breadth, termed hyperacetylated chromatin domains (HCDs), which classifies broad regions exhibiting histone modifications associated with gene activation. We find that this analysis serves to identify genes that are both more highly expressed and more closely aligned to cell identity than super-enhancer analysis does using multiple data sets. Moreover, genetic manipulations of selected gene loci suggest that some enhancers located within HCDs work at least in part via a distinct mechanism involving the modulation of histone modifications across domains and that this activity can be imported into a heterologous gene locus. In addition, such genetic dissection reveals that the super-enhancer concept can obscure important functions of constituent elements.

Super-enhancer are usually defined by high levels of chromatin modification and associate with cell-specific gene expression. Here, the authors define hyperacetylated chromatin domains (HCDs) by using histone hyperacetylation peak breadth information and show that HCDs associated more closely with cell identity than super-enhancers.

The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases

Model organisms are essential experimental platforms for discovering gene functions, defining protein and genetic networks, uncovering functional consequences of human genome variation, and for modeling human disease. For decades, researchers who use model organisms have relied on Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) for expertly curated annotations, and for access to integrated genomic and biological information obtained from the scientific literature and public data archives. Through the development and enforcement of data and semantic standards, these genome resources provide rapid access to the collected knowledge of model organisms in human readable and computation-ready formats that would otherwise require countless hours for individual researchers to assemble on their own. Since their inception, the MODs for the predominant biomedical model organisms [Mus sp (laboratory mouse), Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, and Rattus norvegicus] along with the GOC have operated as a network of independent, highly collaborative genome resources. In 2016, these six MODs and the GOC joined forces as the Alliance of Genome Resources (the Alliance). By implementing shared programmatic access methods and data-specific web pages with a unified "look and feel," the Alliance is tackling barriers that have limited the ability of researchers to easily compare common data types and annotations across model organisms. To adapt to the rapidly changing landscape for evaluating and funding core data resources, the Alliance is building a modern, extensible, and operationally efficient "knowledge commons" for model organisms using shared, modular infrastructure.