Functional Genomics Experiments Research Articles

EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.

Deep neural networks (DNNs) have been widely applied to predict the molecular functions of the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typically generate limited amounts of data, constrained by the activity levels of the molecular function under study inside the cell. Recently, EvoAug was introduced to train a genomic DNN with evolution-inspired augmentations. EvoAug-trained DNNs have demonstrated improved generalization and interpretability with attribution analysis. However, EvoAug only supports PyTorch-based models, which limits its applications to a broad class of genomic DNNs based in TensorFlow. Here, we extend EvoAug's functionality to TensorFlow in a new package, we call EvoAug-TF. Through a systematic benchmark, we find that EvoAug-TF yields comparable performance with the original EvoAug package. EvoAug-TF is freely available for users and is distributed under an open-source MIT license. Researchers can access the open-source code on GitHub (https://github.com/p-koo/evoaug-tf). The pre-compiled package is provided via PyPI (https://pypi.org/project/evoaug-tf) with in-depth documentation on ReadTheDocs (https://evoaug-tf.readthedocs.io). The scripts for reproducing the results are available at (https://github.com/p-koo/evoaug-tf_analysis).

Regulation of otic neurosensory specification by Notch and Wnt signalling: insights from RNA-seq screenings in the embryonic chicken inner ear.

The Notch and Wnt signalling pathways play key roles in the formation of inner ear sensory organs, but little is known about their transcriptional effectors and targets in this context. Here, we perturbed Notch and Wnt activities in the embryonic chicken otic vesicle using pharmacological treatment or in ovo electroporation of plasmid DNA, and used RNA-Seq to analyse the resulting changes in gene expression. Compared to pharmacological treatments, in ovo electroporation changed the expression of fewer genes, a likely consequence of the variability and mosaicism of transfection. The pharmacological inhibition of Notch activity induced a rapid change in the expression of known effectors of this pathway and genes associated with neurogenesis, consistent with a switch towards an otic neurosensory fate. The Wnt datasets contained many genes associated with a neurosensory biological function, confirming the importance of this pathway for neurosensory specification in the otocyst. Finally, the results of a preliminary gain-of-function screening of selected transcription factors and Wnt signalling components suggest that the endogenous programs of otic neurosensory specification are very robust, and in general unaffected by the overexpression of a single factor. Altogether this work provides new insights into the effectors and candidate targets of the Notch and Wnt pathways in the early developing inner ear and could serve as a useful reference for future functional genomics experiments in the embryonic avian inner ear.

STARGATE-X: a Python package for statistical analysis on the REACTOME network.

Many important aspects of biological knowledge at the molecular level can be represented by pathways. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through biochemical networks linking the biochemical reactions and the compounds (e.g., proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in systems biology. Here we focus on Reactome, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool StARGate-X (STatistical Analysis of the Reactome multi-GrAph Through nEtworkX) to carry out an automated analysis of the connectivity properties of Reactome biochemical reaction network and of its biological hierarchy (i.e., cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at https://github.com/marinoandrea/stargate-x; (2) show the effectiveness of our tool by providing an analysis of the Reactome network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of StARGate-X, we provide a detailed automated analysis of the Reactome network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within Reactome through easy-to-use APIs (Application Programming Interfaces).

Glucocorticoid mediated inhibition of LKB1 mutant non-small cell lung cancers.

The glucocorticoid receptor (GR) is an important anti-cancer target in lymphoid cancers but has been understudied in solid tumors like lung cancer, although glucocorticoids are often given with chemotherapy regimens to mitigate side effects. Here, we identify a dexamethasone-GR mediated anti-cancer response in a subset of aggressive non-small cell lung cancers (NSCLCs) that harbor Serine/Threonine Kinase 11 (STK11/LKB1) mutations. High tumor expression of carbamoyl phosphate synthase 1 (CPS1) was strongly linked to the presence of LKB1 mutations, was the best predictor of NSCLC dexamethasone (DEX) sensitivity (p < 10-16) but was not mechanistically involved in DEX sensitivity. Subcutaneous, orthotopic and metastatic NSCLC xenografts, biomarker-selected, STK11/LKB1 mutant patient derived xenografts, and genetically engineered mouse models with KRAS/LKB1 mutant lung adenocarcinomas all showed marked in vivo anti-tumor responses with the glucocorticoid dexamethasone as a single agent or in combination with cisplatin. Mechanistically, GR activation triggers G1/S cell cycle arrest in LKB1 mutant NSCLCs by inducing the expression of the cyclin-dependent kinase inhibitor, CDKN1C/p57(Kip2). All findings were confirmed with functional genomic experiments including CRISPR knockouts and exogenous expression. Importantly, DEX-GR mediated cell cycle arrest did not interfere with NSCLC radiotherapy, or platinum response in vitro or with platinum response in vivo. While DEX induced LKB1 mutant NSCLCs in vitro exhibit markers of cellular senescence and demonstrate impaired migration, in vivo DEX treatment of a patient derived xenograft (PDX) STK11/LKB1 mutant model resulted in expression of apoptosis markers. These findings identify a previously unknown GR mediated therapeutic vulnerability in STK11/LKB1 mutant NSCLCs caused by induction of p57(Kip2) expression with both STK11 mutation and high expression of CPS1 as precision medicine biomarkers of this vulnerability.

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 588,452 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women.

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45years-old) had four-fold more basal tumors and worst progression-free survival (median of 101months), compared with the 45-65years group (median of 168months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

Glutathione peroxidase 2 is a metabolic driver of the tumor immune microenvironment and immune checkpoint inhibitor response

BackgroundThe existence of immunologically ‘cold tumors’ frequently found across a wide spectrum of tumor types represents a significant challenge for cancer immunotherapy. Cold tumors have poor baseline pan-leukocyte infiltration, including...

GeMI: interactive interface for transformer-based Genomic Metadata Integration.

The Gene Expression Omnibus (GEO) is a public archive containing >4 million digital samples from functional genomics experiments collected over almost two decades. The accompanying metadata describing the experiments suffer from redundancy, inconsistency and incompleteness due to the prevalence of free text and the lack of well-defined data formats and their validation. To remedy this situation, we created Genomic Metadata Integration (GeMI; http://gmql.eu/gemi/), a web application that learns to automatically extract structured metadata (in the form of key-value pairs) from the plain text descriptions of GEO experiments. The extracted information can then be indexed for structured search and used for various downstream data mining activities. GeMI works in continuous interaction with its users. The natural language processing transformer-based model at the core of our system is a fine-tuned version of the Generative Pre-trained Transformer 2 (GPT2) model that is able to learn continuously from the feedback of the users thanks to an active learning framework designed for the purpose. As a part of such a framework, a machine learning interpretation mechanism (that exploits saliency maps) allows the users to understand easily and quickly whether the predictions of the model are correct and improves the overall usability. GeMI’s ability to extract attributes not explicitly mentioned (such as sex, tissue type, cell type, ethnicity and disease) allows researchers to perform specific queries and classification of experiments, which was previously possible only after spending time and resources with tedious manual annotation. The usefulness of GeMI is demonstrated on practical research use cases. Database URL http://gmql.eu/gemi/

Gene Selection in Binary Classification Problems Within Functional Genomics Experiments via Robust Fisher Score

This study proposes a supervised feature selection technique for classification in high dimensional binary class problems by adding robustness in the conventional Fisher Score. The proposed method utilizes the more robust measure of location, i.e. the median and measure of dispersion known as Rousseeuw and Croux statistic (Qn). Initially, a minimum subset of genes is identified by the greedy search approach, which is then combined with the top ranked genes obtained via the proposed Robust Fisher Score (RFish). To remove redundancy in the selected genes, Least Absolute Shrinkage and Selection Operator (LASSO) is then applied. The proposed method is validated on five publicly available datasets and is further assessed in a detailed simulation study. The results of the proposed method are compared with six well known feature selection methods based on prediction performance via Random Forest (RF), Support Vector Machine (SVM) and k Nearest Neighbour (k-NN) classifiers. The findings are presented in boxplots and barplots, which show that the proposed method (RFish) outperforms all the other methods in the majority of cases.

Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome.

Understanding the functional consequences of genetic variation in the non-coding regions of the human genome remains a challenge. We introduce h ere a computational tool, TURF, to prioritize regulatory variants with tissue-specific function by leveraging evidence from functional genomics experiments, including over 3000 functional genomics datasets from the ENCODE project provided in the RegulomeDB database. TURF is able to generate prediction scores at both organism and tissue/organ-specific levels for any non-coding variant on the genome. We present that TURF has an overall top performance in prediction by using validated variants from MPRA experiments. We also demonstrate how TURF can pick out the regulatory variants with tissue-specific function over a candidate list from associate studies. Furthermore, we found that various GWAS traits showed the enrichment of regulatory variants predicted by TURF scores in the trait-relevant organs, which indicates that these variants can be a valuable source for future studies.

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

GeneWalk identifies relevant gene functions for a biological context using network representation learning

A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk (github.com/churchmanlab/genewalk) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.

Data Sanitization to Reduce Private Information Leakage from Functional Genomics

The generation of functional genomics datasets is surging, because they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intent behind functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to broadly share raw reads for better statistical power and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, enabling principled privacy-utility trade-offs. Our protocol works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA sequencing. It involves quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques.

The aim of this study is to filter out the most informative genes that mainly regulate the target tissue class, increase classification accuracy, reduce the curse of dimensionality, and discard redundant and irrelevant genes. This paper presented the idea of gene selection using bagging sub-forest (BSF). The proposed method provided genes importance grounded on the idea specified in the standard random forest algorithm. The new method is compared with three state-of-the art methods, i.e., Wilcoxon, masked painter and proportional overlapped score (POS). These methods were applied on 5 data sets, i.e. Colon, Lymph node breast cancer, Leukaemia, Serrated colorectal carcinomas, and Breast Cancer. Comparison was done by selecting top 20 genes by applying the gene selection methods and applying random forest (RF) and support vector machine (SVM) classifiers to assess their predictive performance on the datasets with selected genes. Classification accuracy, Brier score, and sensitivity have been used as performance measures. The proposed method gave better results than the other methods using both random forest and SVM classifiers on all the datasets among all the feature selection methods. The proposed method showed improved performance in terms of classification accuracy, Brier score and sensitivity, and hence, could be used as a novel method for gene selection to classify tissue samples into their correct classes.

Abstract B51: MAPK regulation of an innate immune response in KRAS-mutant lung adenocarcinoma

Abstract Mutations in the KRAS oncogene are frequent in lung adenocarcinomas (LUADs). Unfortunately, KRAS lacks a pharmacologically available inhibitor for precision oncology. Immunotherapies have recently become available and are now FDA approved for LUAD, although only a subset of patients show clinical benefit. Recent work has unveiled biomarkers of immunotherapy response and include interferon γ (IFNγ) and T-cell inflamed gene signatures, suggesting that active IFN signaling within the tumor microenvironment (TME) and infiltration of antitumor T-cells are necessary for response. Thus, strategies that increase T-cell inflammation within LUAD are predicted to broaden immunotherapy response. Using human and murine KRAS-mutant LUAD cell lines, we demonstrated variable transcriptional induction of an innate immune response after 1-2-day treatment with the MEK inhibitor, trametinib. In particular, murine lung cancer cell lines 832B and LKR10 demonstrated induction of CXCL10 transcript, an innate immune chemotactic factor for effector T cells and natural killer (NK) cells. This variable induction was also seen at the protein level with CXCL10 protein increasing 8- and 4-fold, in 832B and LKR10 respectively, after a 3-day treatment. Human KRAS-mutant LUAD cell line, H358, exhibited a 17-fold induction in CXCL10 protein secretion following a 3-day treatment with trametinib. The mechanism(s) by which MAPK inhibition leads to induction of innate immune signaling remains ill defined. IKK-16, an inhibitor of IKKα/β and downstream NF-kB signaling, but not the JAK inhibitor, ruxolitinib, blocked trametinib-induced CXCL10 secretion in H358 cells. While the data support a central role for NF-kB signaling in this response, the complexity of innate immune signaling and IKK-NF-kB pathways prevents their full dissection with traditional candidate-based approaches. Therefore, we have initiated a functional genomics approach using CRISPR/Cas9-mediated tagging of the CXCL10gene in human lung cancer cell lines. While H358 cells are in process, we successfully generated EGFR mutant PC9 cells that bear GFP-tagged CXC10 and exhibit strong induction of CXCL10-GFP when treated with the 3rd-generation EGFR inhibitor, AZD9291. These CXCL10-GFP tagged cell lines will be deployed in functional genomics experiments to define in an unbiased manner signal pathway and transcription factor components that mediate oncogene-targeted drug-induced CXCL10 induction. A full elucidation of the mechanism by which oncogene pathways suppress innate immune pathways and CXCL10 expression may highlight novel ways in which to enhance antitumor inflammation and increase the depth of the therapeutic response to oncogene-targeted drugs. Citation Format: Daniel Sisler, Sean Korpela, Natalia Gurule, Trista Hinz, Lindsay Marek, Bonnie Bullock, Howard Li, Molishree Joshi, Colin Sempeck, Raphael Nemenoff, Lynn Heasley. MAPK regulation of an innate immune response in KRAS-mutant lung adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr B51.

ECALIBRATOR: A Comparative Tool to Identify Key Genes and Pathways for Eucalyptus Defense Against Biotic Stressors.

Many pests and pathogens threaten Eucalyptus plantations. The study of defense responses in this economically important wood and fiber crop enables the discovery of novel pathways and genes, which may be adopted to improve resistance. Various functional genomics experiments have been conducted in Eucalyptus-biotic stress interactions following the availability of the Eucalyptus grandis genome, however, comparisons between these studies were limited largely due to a lack of comparative tools. To this end, we developed eCALIBRATOR http://ecalibrator.bi.up.ac.za, a tool for the comparison of Eucalyptus biotic stress interaction. The tool, which is not limited to Eucalyptus, allows the comparison of various datasets, provides a visual output in the form of Venn diagrams and clustering and extraction of lists for gene ontology enrichment analyses. We also demonstrate the usefulness of the tool in revealing pathways and key gene targets to further functionally characterize. We identified 708 differentially expressed E. grandis genes in common among responses to the insect pest Leptocybe invasa, oomycete pathogen Phytophthora cinnamomi and fungus Chrysoporthe austroafricana. Within this set of genes, one of the Gene Ontology terms enriched was “response to organonitrogen compound,” with NITRATE TRANSPORTER 2.5 (NRT2.5) being a key gene, up-regulated under susceptible interactions and down-regulated under resistant interactions. Although previous functional genetics studies in Arabidopsis thaliana support a role in nitrate acquisition and remobilization under long-term nitrate starvation, the importance of NRT2.5 in plant defense is unclear. The T-DNA mutants of AtNRT2.5 were more resistant to Pseudomonas syringae pv. tomato pv tomato DC3000 inoculation than the wild-type counterpart, supporting a direct role for NRT2.5 in plant defense. Future studies will focus on characterizing the Eucalyptus ortholog of NRT2.5.

Regulation of PLPP3 gene expression by NF-κB family transcription factors

Lipid phosphate phosphatase 3 (LPP3), encoded by the PLPP3 gene, is an integral membrane enzyme that dephosphorylates phosphate esters of glycero- and sphingophospholipids. Cell surface LPP3 can terminate the signaling actions of bioactive lysophosphatidic acid (LPA) and sphingosine 1 phosphate, which likely explains its role in developmental angiogenesis, vascular injury responses, and cell migration. Heritable variants in the final intron PLPP3 associate with interindividual variability in coronary artery disease risk that may result from disruption of enhancer sequences that normally act in cis to increase expression of the gene. However, the mechanisms regulating PLPP3 expression are not well understood. We show that the human PLPP3 promoter contains three functional NF-κB response elements. All of these are required for maximal induction of PLPP3 promoter activity in reporter assays. The identified sequences recruit RelA and RelB components of the NF-κB transcription complex to chromatin, and these transcription factors bind to the identified target sequences in two different cell types. LPA promotes binding of Rel family transcription factors to the PLPP3 promoter and increases PLPP3 gene expression through mechanisms that are attenuated by an NF-κB inhibitor, LPA receptor antagonists, and inhibitors of phosphoinositide 3 kinase. These findings indicate that up-regulation of PLPP3 during inflammation and atherosclerosis results from canonical activation of the NF-κB signaling cascade to increase PLPP3 expression through nuclear import and binding of RelA and RelB transcription factors to the PLPP3 promoter and suggest a mechanism by which the LPP3 substrate, LPA, can regulate PLPP3 expression.

A Genetic Variant in the BCL2 Gene Associates with Adalimumab Response in Hidradenitis Suppurativa Clinical Trials and Regulates Expression of BCL2

Hidradenitis suppurativa is a chronic skin disease with a significant genetic component and prevalence from 0.5% to 4%. Adalimumab is the only treatment approved by either the European Medicines Agency or the U.S. Food and Drug Administration for the management of moderate to severe hidradenitis suppurativa. To identify genetic variants associated with adalimumab response, we performed a genome-wide association study (GWAS) from the most extensive two phase 3 hidradenitis suppurativa clinical trials (PIONEER I and II) to date. Through direct genotyping and imputation, we tested almost 7 million genetic variants with minor allele frequency > 5% and identified one single linkage disequilibrium block, located in the intron of the BCL2 gene, which reached genome-wide significance (lead single-nucleotide polymorphism, rs59532114; P= 2.35E-08). Bioinformatic analysis and functional genomics experiments suggested a correlation of the most strongly associated single-nucleotide polymorphism minor allele with increased BCL2 gene and protein expressions in hair follicle tissues. In reciprocal knockdown experiments, we found that BCL2 is down-regulated by TNF inhibition. These results highlight a pathway that involves BCL2 in response to adalimumab. Further work is required to determine how this pathway influences adalimumab effectiveness in patients with hidradenitis suppurativa.

Delivery of maize mosaic virus to planthopper vectors by microinjection increases infection efficiency and facilitates functional genomics experiments in the vector

The corn planthopper, Peregrinus maidis, not only causes direct damage to plants by feeding, but also transmits maize mosaic virus (MMV) to the plant hosts. The virus is transmitted in a propagative manner but the acquisition of MMV by the vector feeding on infected plants can result in low acquisition and inoculation efficiency. Here, we increased the acquisition efficiency by delivering the virus directly into the hemocoel through microinjection, which resulted in efficient virus infection of the insect and transmission to maize. We found that delivery of virus by injection of 10 ng MMV (50 nl, 200 μg/ml virions) into P. maidis resulted in 93% transmission efficiency. In dose-response experiments, MMV abundance in insects and transmission efficiency decreased as the amount of virus inoculum delivered into the hemocoel was reduced. Examination of virus distribution in the vector using immunolabeling and confocal microscopy revealed similar tissue distributions in the injected insects when compared to those of previous studies using feeding on plants for virus acquisition. The utility of virus inoculation by microinjection for functional analysis in virus-vector interaction was explored. Co-microinjection of MMV virions and the dsRNA of PI3Kδ (a transcript that is less abundant in MMV-infected insects), resulted in a reduction in PI3Kδ expression and higher virus titers in P. maidis. These findings demonstrated that virus microinjection is a robust method for obtaining large numbers of infected planthoppers that are competent in transmitting MMV and, in combination with RNAi, could significantly facilitate the functional analysis of P. maidis-MMV interactions.

NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data

BackgroundRecent advances in single-molecule sequencing techniques, such as Nanopore sequencing, improved read length, increased sequencing throughput, and enabled direct detection of DNA modifications through the analysis of raw signals. These DNA modifications include naturally occurring modifications such as DNA methylations, as well as modifications that are introduced by DNA damage or through synthetic modifications to one of the four standard nucleotides.MethodsTo improve the performance of detecting DNA modifications, especially synthetically introduced modifications, we developed a novel computational tool called NanoMod. NanoMod takes raw signal data on a pair of DNA samples with and without modified bases, extracts signal intensities, performs base error correction based on a reference sequence, and then identifies bases with modifications by comparing the distribution of raw signals between two samples, while taking into account of the effects of neighboring bases on modified bases (“neighborhood effects”).ResultsWe evaluated NanoMod on simulation data sets, based on different types of modifications and different magnitudes of neighborhood effects, and found that NanoMod outperformed other methods in identifying known modified bases. Additionally, we demonstrated superior performance of NanoMod on an E. coli data set with 5mC (5-methylcytosine) modifications.ConclusionsIn summary, NanoMod is a flexible tool to detect DNA modifications with single-base resolution from raw signals in Nanopore sequencing, and will facilitate large-scale functional genomics experiments that use modified nucleotides.