High-throughput Datasets Research Articles

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization.

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different subcellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well-studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.

Obtaining genetics insights from deep learning via explainable artificial intelligence.

Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.

Newfound Coding Potential of Transcripts Unveils Missing Members of Human Protein Communities

Recent proteogenomic approaches have led to the discovery that regions of the transcriptome previously annotated as non-coding regions [i.e., untranslated regions (UTRs), open reading frames overlapping annotated coding sequences in a different reading frame, and non-coding RNAs] frequently encode proteins, termed alternative proteins (altProts). This suggests that previously identified protein–protein interaction (PPI) networks are partially incomplete because altProts are not present in conventional protein databases. Here, we used the proteogenomic resource OpenProt and a combined spectrum- and peptide-centric analysis for the re-analysis of a high-throughput human network proteomics dataset, thereby revealing the presence of 261 altProts in the network. We found 19 genes encoding both an annotated (reference) and an alternative protein interacting with each other. Of the 117 altProts encoded by pseudogenes, 38 are direct interactors of reference proteins encoded by their respective parental genes. Finally, we experimentally validate several interactions involving altProts. These data improve the blueprints of the human PPI network and suggest functional roles for hundreds of altProts.

Gene expression based inference of cancer drug sensitivity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

Machine learning integration of multimodal data identifies key features of blood pressure regulation.

SummaryBackgroundAssociation studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP).MethodsWe included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB).FindingsOur model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts.InterpretationWe show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies.FundingChronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

Keystone Microorganisms Regulate the Methanogenic Potential in Coals with Different Coal Ranks.

Microorganisms are the core drivers of coal biogeochemistry and are closely related to the formation of coalbed methane. However, it remains poorly understood about the network relationship and stability of microbial communities in coals with different ranks. In this study, a high-throughput sequencing data set was analyzed to understand the microbial co-occurrence network in coals with different ranks including anthracite, medium-volatile bituminous, and high-volatile bituminous. The results showed similar topological properties for the microbial networks among coals with different ranks, but a great difference was found in the microbial composition in different large modules among coals with different ranks, and these three networks had three, four, and four large modules with seven, nine, and nine phyla, respectively. Among these networks, a total of 46 keystone taxa were identified in large modules, and these keystone taxa were different in coals with different ranks. Bacteria dominated the keystone taxa in the microbial network, and these bacterial keystone taxa mainly belonged to phyla Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. Besides, the removal of the key microbial data could reduce the community stability of microbial communities in bituminous coals. A partial least-squares path model further showed that these bacterial keystone taxa indirectly affected methanogenic potential by maintaining the microbial community stability and bacterial diversity. In summary, these results showed that keystone taxa played an important role in determining the community diversity, maintaining the microbial community stability, and controlling the methanogenic potential, which is of great significance for understanding the microbial ecology and the geochemical cycle of coal seams.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

Signaling factors potentially associated to the pathogenesis of Adult T-cell leukemia /lymphoma: A network-analysis and novel findings assessment

Adult T-cell leukemia/lymphoma (ATLL) is a human T-cell leukemia virus (HTLV) type 1-associated disease of TCD4+ cell transformation. Despite extensive studies on ATLL development and progression, the fundamental processes of HTLV-1 oncogenicity are yet to be understood. This study aimed to integrate high-throughput microarray datasets to find novel genes involved in the mechanism of ATLL progression. For this purpose, five microarray datasets were downloaded from the Gene Expression Omnibus database and then profoundly analyzed. Differentially expressed genes and miRNAs were determined using the MetaDE package in the R software and the GEO2R web tool. The STRING database was utilized to construct the protein-protein interaction network and explore hub genes. Gene ontology and pathway enrichment analysis were carried out by employing the EnrichR web tool. Furthermore, flow cytometry was employed to assess the CD4/CD8 ratio, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the high-throughput data analysis results. Four miRNAs, including hsa-mir-146, hsa-mir-451, hsa-mir-31, and hsa-mir-125, were among the statistically significant differentially expressed miRNAs between healthy individuals and ATLL patients. Moreover, 924 differentially expressed genes were identified between normal and ATLL samples. Further network analysis highlighted 59 hub genes mainly regulating pathways implicated in viral interferences, immunological processes, cancer, and apoptosis pathways. Among the identified hub genes, RhoA and PRKACB were most considerable in the high-throughput analysis and were further validated by qRT-PCR. The RhoA and PRKACB expression were significantly down-regulated in ATLL patients compared to asymptomatic carriers (p<0.0001 and p=0.004) and healthy subjects (p=0.043 and p=0.002). Therefore, these corresponding miRNAs and proteins could be targeted for diagnosis purposes and designing effective treatments.

Upregulation of CCNB2 and Its Perspective Mechanisms in Cerebral Ischemic Stroke and All Subtypes of Lung Cancer: A Comprehensive Study

Cyclin B2 (CCNB2) belongs to type B cell cycle family protein, which is located on chromosome 15q22, and it binds to cyclin-dependent kinases (CDKs) to regulate their activities. In this study, 103 high-throughput datasets related to all subtypes of lung cancer (LC) and cerebral ischemic stroke (CIS) with the data of CCNB2 expression were collected. The analysis of standard mean deviation (SMD) and summary receiver operating characteristic (SROC) reflecting expression status demonstrated significant up-regulation of CCNB2 in LC and CIS (Lung adenocarcinoma: SMD = 1.40, 95%CI [0.98–1.83], SROC = 0.92, 95%CI [0.89–0.94]. Lung squamous cell carcinoma: SMD = 2.56, 95%CI [1.64–3.48]. SROC = 0.97, 95%CI [0.95–0.98]. Lung small cell carcinoma: SMD = 3.01, 95%CI [2.01–4.01]. SROC = 0.98, 95%CI [0.97–0.99]. CIS: SMD = 0.29, 95%CI [0.05–0.53], SROC = 0.68, 95%CI [0.63–0.71]). Simultaneously, protein-protein interaction (PPI) analysis indicated that CCNB2 is the hub molecule of crossed high-expressed genes in CIS and LC. Through Multiscale embedded gene co-expression network analysis (MEGENA), a gene module of CIS including 76 genes was obtained and function enrichment analysis of the CCNB2 module genes implied that CCNB2 may participate in the processes in the formation of CIS and tissue damage caused by CIS, such as “cell cycle,” “protein kinase activity,” and “glycosphingolipid biosynthesis.” Afterward, via single-cell RNA-seq analysis, CCNB2 was found up-regulated on GABAergic neurons in brain organoids as well as T cells expressing proliferative molecules in LUAD. Concurrently, the expression of CCNB2 distributed similarly to TOP2A as a module marker of cell proliferation in cell cluster. These findings can help in the field of the pathogenesis of LC-related CIS and neuron repair after CIS damage.

Clinical assessment and molecular mechanism of the upregulation of Toll-like receptor 2 (TLR2) in myocardial infarction

ObjectiveThe prevalence and mortality of cardiovascular diseases remain ranked first worldwide. Myocardial infarction (MI) is the central cause of death from cardiovascular diseases, seriously endangering human health. The clinical implication of toll-like receptor 2 (TLR2) remains contradictory, and its mechanism is still unknown. Hence, the objective of this study was to elucidate the clinical value and molecular mechanism of TLR2 in MI.MethodsAll high-throughput datasets and eligible literature were screened, and the expression levels of TLR2 were collected from the MI. The integrated expression level of TLR2 was displayed by calculating the standardized mean difference (SMD) and the area under the curve (AUC) of the summary receiver operating characteristic curve (sROC). The related TLR2 genes were sent for pathway analyses by gene ontology (GO), Kyoto encyclopedia of genes and genome (KEGG), and disease ontology (DO). Single-cell RNA-seq was applied to ascertain the molecular mechanism of TLR2 in MI.ResultsNine microarrays and four reported data were available to calculate the comprehensive expression level of TLR2 in MI, including 325 cases of MI and 306 cases of controls. The SMD was 2.55 (95% CI = 1.35–3.75), and the AUC was 0.76 (95% CI = 0.72–0.79), indicating the upregulation of TLR2 in MI. The related TLR2 genes were primarily enriched in the pathways of atherosclerosis, arteriosclerotic cardiovascular disease, and arteriosclerosis, suggesting the clinical role of TLR2 in the progression of MI. Afterward, TLR2 was upregulated in myeloid cells in MI.ConclusionsTLR2 may have a crucial role in progressing from coronary atherosclerosis to MI. The upregulation of TLR2 may have a favorable screening value for MI.

Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction.

Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.

Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.

ExoPLOT: Representation of alternative splicing in human tissues and developmental stages with transposed element (TE) involvement

Advances in RNA high-throughput sequencing and large-scale functional assays yield new insights into the multifaceted activities of transposed elements (TE) and many other previously undiscovered sequence elements. Currently, no tool for easy access, analysis, quantification, and visualization of alternatively spliced exons across multiple tissues or developmental stages is available. Also, analysis pipelines demand computational skills or hardware requirements, which often are hard to meet by wet-lab scientists. We developed ExoPLOT to enable simplified access to massive RNA high throughput sequencing datasets to facilitate the analysis of alternative splicing across many biological samples. To demonstrate the functonality of ExoPLOT, we analyzed the contributon of exonized TEs to human coding sequences (CDS). mRNA splice variants containing the TE-derived exon were quantified and compared to expression levels of TE-free splice variants. For analysis, we utilized 313 human cerebrum, cerebellum, heart, kidney, liver, ovary, and testis transcriptomes, representing various pre- and postnatal developmental stages. ExoPLOT visualizes the relative expression levels of alternative transcripts, e.g., caused by the insertion of new TE-derived exons, across different developmental stages of and among multiple tissues. This tool also provides a unique link between evolution and function during exonization (gain of a new exon) and exaptation (recruitment/co-optation) of a new exon. As input for analysis, we derived a database of 1151 repeat-masked, exonized TEs, representing all prominent families of transposons in the human genome and the collection of human consensus coding sequences (CCDS). ExoPLOT screened preprocessed RNA high-throughput sequencing datasets from seven human tissues to quantify and visualize the dynamics in RNA splicing for these 1151 TE-derived exons during the entire human organ development. In addition, we successfully mapped and analyzed 993 recently described exonized sequences from the human frontal cortex onto these 313 transcriptome libraries. ExoPLOT's approach to preprocessing RNA deep sequencing datasets facilitates alternative splicing analysis and significantly reduces processing times. In addition, ExoPLOT's design allows studying alternative RNA isoforms other than TE-derived in a customized – coordinate-based manner and is available at http://retrogenomics3.uni-muenster.de:3838/exz-plot-d/.

A compendium of validated pain genes.

The development of novel pain therapeutics hinges on the identification and rigorous validation of potential targets. Model organisms provide a means to test the involvement of specific genes and regulatory elements in pain. Here we provide a list of genes linked to pain-associated behaviors. We capitalize on results spanning over three decades to identify a set of 242 genes. They support a remarkable diversity of functions spanning action potential propagation, immune response, GPCR signaling, enzymatic catalysis, nucleic acid regulation, and intercellular signaling. Making use of existing tissue and single-cell high-throughput RNA sequencing datasets, we examine their patterns of expression. For each gene class, we discuss archetypal members, with an emphasis on opportunities for additional experimentation. Finally, we discuss how powerful and increasingly ubiquitous forward genetic screening approaches could be used to improve our ability to identify pain genes. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Molecular and Cellular Physiology.

DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency.

MotivationCRISPR/Cas9 technology has been revolutionizing the field of gene editing in recent years. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs. Thus, computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have low correlation with functional and endogenous editing. Another difficulty arises from the fact that functional and endogenous editing efficiency is more difficult to measure, and as a result, functional and endogenous datasets are too small to train accurate machine-learning models on.ResultsWe developed DeepCRISTL, a deep-learning model to predict the on-target efficiency given a gRNA sequence. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA on-target editing efficiency, and then uses transfer learning (TL) to fine-tune the model and fit it to the functional and endogenous prediction task. We pre-trained the DeepCRISTL model on more than 150 000 gRNAs, produced through the DeepHF study as a high-throughput dataset of three Cas9 enzymes. We improved the DeepHF model by multi-task and ensemble techniques and achieved state-of-the-art results over each of the three enzymes: up to 0.89 in Spearman correlation between predicted and measured on-target efficiencies. To fine-tune model weights to predict on-target efficiency of functional or endogenous datasets, we tested several TL approaches, with gradual learning being the overall best performer, both when pre-trained on DeepHF and when pre-trained on CRISPROn, another high-throughput dataset. DeepCRISTL outperformed state-of-the-art methods on all functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by the model in each dataset. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging TL to utilize both high-throughput datasets, and smaller and more biologically relevant datasets, such as functional and endogenous datasets.Availability and implementationDeepCRISTL is available via github.com/OrensteinLab/DeepCRISTL.Supplementary information Supplementary data are available at Bioinformatics online.

CircEXOC6B interacting with RRAGB, an mTORC1 activator, inhibits the progression of colorectal cancer by antagonizing the HIF1A-RRAGB-mTORC1 positive feedback loop

BackgroundIn recent years, an increasing number of studies have indicated that circular RNA plays crucial roles in regulating tumor development and chemoresistance. Using two high-throughput RNA sequence datasets, we previously found that circEXOC6B was downregulated in colon cancer. However, its role and mechanism in colorectal cancer (CRC) remained unknown.MethodsReal-time quantitative PCR was used to examine the expression of circEXOC6B in CRC tissues. In vivo and in vitro functional experiments were performed to determine the suppressor role of circEXOC6B in CRC progression. RNA pull-down, mass spectrometry, RNA-binding protein immunoprecipitation, co-immunoprecipitation, fluorescence in situ hybridization, and immunofluorescence were applied to investigate the possible mechanisms connecting circEXOC6B to CRC growth and 5-fluorouracil-induced apoptosis. Chromatin immunoprecipitation, dual-luciferase assay, western blot, and immunohistochemistry were used to explore the mechanisms underlying the HIF1A regulation of RRAGB transcription.ResultscircEXOC6B was downregulated in CRC tissues, and its lower expression was associated with poor prognosis of patients. Functional experiments showed that circEXOC6B inhibited growth and increased the 5-fluorouracil-induced apoptosis of CRC cells in vitro and in vivo. Mechanistically, circEXOC6B inhibited the heterodimer formation of RRAGB by binding to it, thereby suppressing the mTORC1 pathway and HIF1A level. In addition, HIF1A upregulated the transcription of RRAGB by binding to its promoter region. Altogether, the results demonstrated that a HIF1A-RRAGB-mTORC1 positive feedback loop drives tumor progression in CRC, which could be interrupted by circEXOC6B.ConclusionscircEXOC6B inhibits the progression of CRC and enhances the chemosensitivity of CRC cells to 5-fluorouracil by antagonizing the HIF1A-RRAGB-mTORC1 positive feedback loop. circEXOC6B is a possible therapeutic target for CRC treatment.

Comprehensive Bioinformatics Analysis to Reveal Key RNA Targets and Hub Competitive Endogenous RNA Network of Keratoconus.

Keratoconus (KC) is the most common corneal ectatic disease, with its pathological mechanisms unclear. We mainly performed bioinformatics approaches to reveal core RNA targets and hub competitive endogenous RNA (ceRNA) network and explored the potential regulatory mechanisms of ceRNA in KC. The high-throughput sequencing datasets GSE77938 and GSE151631 were downloaded from the Gene Expression Omnibus (GEO) database. The differential expression of mRNAs and lncRNAs was identified using the DESeq2 package. Functional enrichment analyses and protein–protein interaction (PPI) were executed. Then, the hub genes were filtered and molecular docking analysis was performed. Moreover, we predicted miRNAs through a website database and validated them using quantitative PCR (qPCR). Eventually, the lncRNA–miRNA–mRNA regulatory network was constructed by Cytoscape. We revealed that 428 intersected differentially expressed mRNA (DEGs) and 68 intersected differentially expressed lncRNA (DELs) were shared between the two datasets. Functional enrichment results innovatively showed that the ubiquitin-dependent protein catabolic process was upregulated in KC. The pathway enrichment showed that DEGs were mainly involved in NF-kB signaling and neurodegenerative diseases. In addition, we uncovered the top 20 hub genes in which FBXW11, FBXO9, RCHY1, and CD36 were validated by qPCR. Particularly, a small-molecule drug triptolide was predicted by molecular docking to be a candidate drug for treating KC. Moreover, we innovatively predicted and validated four core miRNAs (miR-4257, miR-4494, miR-4263, and miR-4298) and constructed a ceRNA network that contained 165 mRNA, eight lncRNAs, and four core miRNAs. Finally, we proposed a potential regulatory mechanism for KC. Overall, we uncovered a hub ceRNA network that might underlie a critical posttranslational regulatory mechanism in KC, in which miR-4257, miR-4494, miR-4263, and miR-4298 could be valuable biomarkers and provided core RNAs therapeutic targets for KC.