Name: high-throughput transcriptome analysis for investigating host-pathogen interactions
Uploaded: 2022-03-05T12:30:00
Description: Watch this Scientific Journal Video about High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions at JoVE.com

HN is funded by FAPESP (grant numbers: #2017/50137-3, 2012/19278-6, 2018/14933-2, 2018/21934-5, and 2013/08216-2) and CNPq (313662/2017-7).
We are particularly thankful to the following grants for fellows: ANAG (FAPESP Process 2019/13880-5), VEM (FAPESP Process 2019/16418-0), IMSC (FAPESP Process 2020/05284-0), APV (FAPESP Process 2019/27146-1) and, RLTO (CNPq Process 134204/2019-0).

The samples used in this protocol were approved by the ethics committees from both the Department of Microbiology of the Institute of Biomedical Sciences at the University of S&#227;o Paulo and the Federal University of Sergipe (Protocols: 54937216.5.0000.5467 and 54835916.2.0000.5546, respectively).
1. Docker desktop installation
NOTE: Steps to prepare the Docker environment are different among the operating systems (OSs). Therefore, Mac users must f...

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C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq+analysis+of+chikungunya+virus+infection+and+identification+of+granzyme+A+as+a+major+promoter+of+arthritic+inflammation.">RNA-Seq analysis of chikungunya virus infection and identification of granzyme A as a major promoter of arthritic inflammation.</a> PLOS Pathogens. 13 (2), 1006155 (2017).</li><li>Gon&#231;alves, A. N. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+impact+of+sample+heterogeneity+on+transcriptome+analysis+of+human+diseases+using+MDP+webtool.">Assessing the impact of sample heterogeneity on transcriptome analysis of human diseases using MDP webtool.</a> Frontiers in Genetics. 10, 971 (2019).</li><li>Russo, P. S. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CEMiTool:+a+Bioconductor+package+for+performing+comprehensive+modular+co-expression+analyses.">CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.</a> BMC Bioinformatics. 19 (1), 56 (2018).</li><li>Costa-Silva, J., Domingues, D., Lopes, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq+differential+expression+analysis:+An+extended+review+and+a+software+tool.">RNA-Seq differential expression analysis: An extended review and a software tool.</a> PloS One. 12 (12), 0190152 (2017).</li><li>Seyednasrollah, F., Laiho, A., Elo, L. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+software+packages+for+detecting+differential+expression+in+RNA-seq+studies.">Comparison of software packages for detecting differential expression in RNA-seq studies.</a> Briefings in Bioinformatics. 16 (1), 59-70 (2015).</li><li>Zhang, B., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+general+framework+for+weighted+gene+co-expression+network+analysis.">A general framework for weighted gene co-expression network analysis.</a> Statistical Applications in Genetics and Molecular Biology. 4, (2005).</li><li>Cheng, C. W., Beech, D. J., Wheatcroft, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advantages+of+CEMiTool+for+gene+co-expression+analysis+of+RNA-seq+data.">Advantages of CEMiTool for gene co-expression analysis of RNA-seq data.</a> Computers in Biology and Medicine. 125, 103975 (2020).</li><li>Cardozo, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=webCEMiTool:+Co-expression+modular+analysis+made+easy.">webCEMiTool: Co-expression modular analysis made easy.</a> Frontiers in Genetics. 10, 146 (2019).</li><li>de Lima, D. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long+noncoding+RNAs+are+involved+in+multiple+immunological+pathways+in+response+to+vaccination.">Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination.</a> Proceedings of the National Academy of Sciences of the United States of America. 116 (34), 17121-17126 (2019).</li><li>Prada-Medina, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systems+immunology+of+diabetes-tuberculosis+comorbidity+reveals+signatures+of+disease+complications.">Systems immunology of diabetes-tuberculosis comorbidity reveals signatures of disease complications.</a> Scientific Reports. 7 (1), 1999 (2017).</li><li>Chen, E. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enrichr:+interactive+and+collaborative+HTML5+gene+list+enrichment+analysis+tool.">Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.</a> BMC Bioinformatics. 14, 128 (2013).</li><li>Kuleshov, M. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enrichr:+a+comprehensive+gene+set+enrichment+analysis+web+server+2016+update.">Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.</a> Nucleic Acids Research. 44, 90-97 (2016).</li><li>Raudvere, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=g:Profiler:+a+web+server+for+functional+enrichment+analysis+and+conversions+of+gene+lists+(2019+update).">g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).</a> Nucleic Acids Research. 47, 191-198 (2019).</li><li>Young, M. D., Wakefield, M. J., Smyth, G. 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The preparation of the sequencing libraries is a crucial step toward answering biological questions in the best possible way. The type of transcripts of interest of the study will guide which type of sequencing library will be chosen and drive bioinformatic analyses. For example, from the sequencing of a pathogen and host interaction, according to the type of sequencing, it is possible to identify sequences from both or just from the host transcripts.
Next-generation sequencing equipment, e.g....

Arboviruses, such as dengue, yellow fever, chikungunya, and zika, have been widely associated with several endemic outbreaks and have emerged as one of the main pathogens responsible for infecting humans in the last decades1,2. Individuals infected with the chikungunya virus (CHIKV) often have fever, headache, rash, polyarthralgia, and arthritis3,4,5. Viruses can subvert the gene expression of the cell and influence various host signaling pathways. Recently, blood transcriptome studies utilized RNA-seq to identify the differentially expressed genes (DEGs) associated with acute CHIKV infection in comparison with convalescence6 or healthy controls7. CHIKV-infected children had up-regulated genes that are involved in innate immunity, such as the ones related to cellular sensors for viral RNA, JAK/STAT signaling, and toll-like receptor signaling pathways6. Adults acutely infected with CHIKV also showed induction of genes related to innate immunity, such as the ones related to monocytes and dendritic cell activation, and to antiviral responses7. The signaling pathways enriched with down-regulated genes included the ones related to adaptive immunity, such as T cell activation and differentiation and enrichment in T and B cells7.
Several methods can be used to analyze transcriptome data of host and pathogen genes. Often, RNA-seq library preparation starts with the enrichment of mature poly-A transcripts. This step removes most of the ribosomal RNA (rRNA) and in some of the cases viral/bacterial RNAs. However, when the biological question involves the pathogen transcript detection and RNA are sequenced independent of the previous selection, many other different transcripts could be detected by sequencing. For example, subgenomic mRNAs have been shown to be an important factor to verify the severity of the diseases8. In addition, for certain viruses such as CHIKV and SARS-CoV-2, even poly-A enriched libraries generate viral reads that can be utilized in downstream analyses9,10. When focused on the analysis of the host transcriptome, researchers can investigate the biological perturbation across samples, identify differentially expressed genes and enriched pathways, and generate co-expression modules7,11,12. This protocol highlights transcriptome analyses of CHIKV-infected patients and healthy individuals using different bioinformatic approaches (Figure 1A). Data from a previously published study7 consisting of 20 healthy and 39 CHIKV acutely infected individuals were used to generate the representative results.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CEMiTool</td>
			<td>Computational Systems Biology Laboratory</td>
			<td>1.12.2</td>
			<td>Discovery and the analysis of co-expression gene modules in a fully automatic manner, while providing a user-friendly HTML report with high-quality graphs.</td>
		</tr>
		<tr>
			<td>EdgeR</td>
			<td>Bioconductor (Maintainer: Yunshun Chen [yuchen at wehi.edu.au])</td>
			<td>3.30.3</td>
			<td>Differential expression analysis of RNA-seq expression profiles with biological replication</td>
		</tr>
		<tr>
			<td>EnhancedVolcano</td>
			<td>Bioconductor (Maintainer: Kevin Blighe [kevin at clinicalbioinformatics.co.uk])</td>
			<td>1.6.0</td>
			<td>Publication-ready volcano plots with enhanced colouring and labeling</td>
		</tr>
		<tr>
			<td>FastQC</td>
			<td>Babraham Bioinformatics</td>
			<td>0.11.9</td>
			<td>Aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing</td>
		</tr>
		<tr>
			<td>FeatureCounts</td>
			<td>Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research</td>
			<td>2.0.0</td>
			<td>Assign mapped sequencing reads to specified genomic features</td>
		</tr>
		<tr>
			<td>MDP</td>
			<td>Computational Systems Biology Laboratory</td>
			<td>1.8.0</td>
			<td>Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls</td>
		</tr>
		<tr>
			<td>R</td>
			<td>R Core Group</td>
			<td>4.0.3</td>
			<td>Programming language and free software environment for statistical computing and graphics</td>
		</tr>
		<tr>
			<td>STAR</td>
			<td>Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research</td>
			<td>2.7.6a</td>
			<td>Aligner designed to specifically address many of the challenges of RNA-seq data mapping using a strategy to account for spliced alignments</td>
		</tr>
		<tr>
			<td>Bowtie2</td>
			<td>Johns Hopkins University</td>
			<td>2.4.2</td>
			<td>Ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences</td>
		</tr>
		<tr>
			<td>Trimmomatic</td>
			<td>THE USADEL LAB</td>
			<td>0.39</td>
			<td>Trimming adapter sequence tasks for Illumina paired-end and single-ended data</td>
		</tr>
		<tr>
			<td>Get Docker</td>
			<td>Docker</td>
			<td>20.10.2</td>
			<td>Create a bioinformatic environment reproducible and predictable (https://docs.docker.com/get-docker/)</td>
		</tr>
		<tr>
			<td>WSL2-Kernel</td>
			<td>Windows</td>
			<td>NA</td>
			<td>https://docs.microsoft.com/en-us/windows/wsl/wsl2-kernel</td>
		</tr>
		<tr>
			<td>Get Docker Linux</td>
			<td>Docker</td>
			<td>NA</td>
			<td>https://docs.docker.com/engine/install/ubuntu/</td>
		</tr>
		<tr>
			<td>Docker Linux Repository</td>
			<td>Docker</td>
			<td>NA</td>
			<td>https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository</td>
		</tr>
		<tr>
			<td>MDP Website</td>
			<td>Computational Systems Biology Laboratory</td>
			<td>NA</td>
			<td>https://mdp.sysbio.tools</td>
		</tr>
		<tr>
			<td>Enrichr Website</td>
			<td>MaayanLab</td>
			<td>NA</td>
			<td>https://maayanlab.cloud/Enrichr/</td>
		</tr>
		<tr>
			<td>webCEMiTool</td>
			<td>Computational Systems Biology Laboratory</td>
			<td>NA</td>
			<td>https://cemitool.sysbio.tools/</td>
		</tr>
		<tr>
			<td>gProfiler</td>
			<td>Bioinformatics, Algorithmics and Data Mining Group</td>
			<td>NA</td>
			<td>https://biit.cs.ut.ee/gprofiler/gost</td>
		</tr>
		<tr>
			<td>goseq</td>
			<td>Bioconductor (Maintainer: Matthew Young [my4 at sanger.ac.uk])</td>
			<td>NA</td>
			<td>http://bioconductor.org/packages/release/bioc/html/goseq.html</td>
		</tr>
		<tr>
			<td>SRA NCBI study</td>
			<td>NCBI</td>
			<td>NA</td>
			<td>https://www.ncbi.nlm.nih.gov/bioproject/PRJNA507472/</td>
		</tr>
	</tbody>
</table>

high-throughput transcriptome analysis for investigating host-pathogen interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The computing environment for transcriptome analyses was created and configured on the Docker platform. This approach allows beginner Linux users to use Linux terminal systems without a priori management knowledge. The Docker platform uses the resources of the host OS to create a service container that includes specific users&#39; tools (Figure 1B). A container based on the Linux OS Ubuntu 20.04 distribution was created and it was fully configured for transcriptomic analyses, which is access...

Watch this Scientific Journal Video about High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions at JoVE.com

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Welcome to the protocol of high-throughput transcriptome analysis for investigating host-pathogen interactions. This protocol is divided in the following steps. Quality control to filter low-quality reads and also to remove adapter sequences Sequencing and annotations, where are you have to map the reads into a reference genomes and annotate the reads into the genes.Statistical and co-expression analysis, which defines the differentially expressed genes and also finds the co-expression modules. Molecular degree of perturbation analysis to find potential outlier samples. And finally, the functional analysis to determine the biological functions of differentially expressed genes.All the tools utilizing these pipelines were pre-installed in a Linux system and encapsulated into a Docker container. The samples utilizing these protocols derived from a paper published by our group in PLOS Pathogen. The samples comprise 20 healthy people and 39 patients infected with Chikungunya virus.The blood samples were collected, and RNA sequencing was performed. To install Docker in Windows system, you have to follow these steps. Go to the official webpage of Docker, and click in Get Started.Find the installer for Docker Desktop for windows. Download the file. Install locally in your machine.Make sure that these two options are marked. After installing the program, downloads the Docker image for this protocol. Go to the Windows terminal.Execute the commands to downloads the image. After downloading the image, you can see the file in the Docker desktop, and from this image, we can initiate the container. After you click in the round button, you have to expand the original parameters and options to define the name of the container and to associate a folder in your local computer with the folder inside Docker.After this, you click in Run to initiate the container. You can then access the terminal, which is in the Linux system inside the Docker. Type the bash commands, and then you can execute all the commands of this protocol.First, we have to execute the source to make all the tools of this protocol available. You should access the directory scripts. To perform a transcriptomic analysis, you have to download first the reference genome.For this, you have to execute the following commands. After the genome is download, you have to download the annotation of the genes. To do this, you have to type the following commands.Next, you have to configure the fastq-dump. This is allow you to downloads the sequencing files of the examples. After typing the following commands, you have to use the Tab button to go to the Tools option and to mark the options currents directory.Use the Tab buttons to save, and then ok. And then exit the tool fastq-dump. Now we can initiate the downloads of the reads by typing the following commands.The quality control consists and evaluates graphically the probability of errors in the sequencing reads. In this step, you have also to remove the technical sequences such as adapters. To generate the quality control graphs, you have to run the FastQC program.To remove the adapter sequences and the low-quality sequences, you have to type the following commands. With the good-quality reads, we have now to map the reads into the reference genome. After the mapping, we are gonna have to annotate the genes according to the human genes and then count the number of reads that match each human gene.The first step is to index the reference genome by typing the following command. And then we type this commands to map the reads into the human genome. Next, you should run the scripts that annotate the reads.After mapping and annotation, you can perform the differential expression analysis which it consists in finding the genes whose expression is higher or lower in one group compared to another. To identify the differentially expressed genes, or DEGs, you have to run following commands. After this, you can transfer the data results from the Docker to your local computer.For this, go to the terminal and type the following commands to save all the results to a local folder. To perform the remaining analysis, you also have to copy all the files of the directory data to a directory in your local computer. In your local computer, you will be able to see the directories where you saved the data from Docker.As you can see, you can access all the libraries. You can also open the HTML file containing the quality control reports. You can also access a directory containing the differentially expressed genes.And inside this directory, you will find the volcano plots where you can see the genes that are up-or downregulated in the one group versus another, in this case, patients infected with Chikungunya virus versus healthy controls. All the remaining steps of this protocol are gonna be executed in web tools using your browser. Let's first start with CEMiTool.Go to the browser and type the following address. CEMiTool identifies co-expression modules from expression data sets provided by the users. In the main page, you can go to the menu and click in the button Run.This will open a new page where you can upload the expression file. This file is in the directory data of your local computer. You will see that there three expression files, and the one that we are gonna use for the CEMiTool is a normalization call tmm.Then you have to select the phenodata file, the same thing for the file containing the protein-protein interactions, and finally, upload the file containing the gene sets or pathways. The gene sets file enables CEMiTool to perform enrichment analysis for each one of the co-expression module. Next, you should to expand the parameter section and click in Apply VST.After that, you can just click Run CEMiTool. After you run CEMiTool, you will see that 12 co-expression modules were identified. By clicking here, you can download all the results of these analysis.Another tool that we are gonna utilize in this protocol is MDP, or Molecular Degree of Perturbation. Just type in your browser mdp.sysbio.tools. MDP calculates the molecular distance of each sample compared to a reference group of samples, in this case, the healthy controls, in order to find not only potential outliers but also how perturbed are each samples compared to this group.In the Run page, you can just upload the expression file by clicking the button and selecting the file. Then you have to upload the phenodata file. Then you have to define which column contain the information about the group or the class and then which class or group correspond to the control group.After this, you can just run MDP. The bar graph shows for each one of the samples as a bar the score of molecular degree of perturbation, and the colors represent the different groups. And the box plot is another way of visualizing the same results where you see on each dots the is a different samples separate by two groups.To perform the functional analysis, we are gonna use the Enrichr tool. For this, you have to select the list of genes that were differentially expressed, either up-or downregulated, and use it as a input gene list in Enrichr tool. You will see that there are different tabs.All the results can also be downloaded to your local computer. The computer environment for transcriptome analysis has been placed on the Docker platform. This approach allows users with no prior experience with Linux system to utilize a terminal.In this container, there is a predefined folder structure for dataset and scripts which are necessary for all the analysis. In the pipeline, users will utilize blood transcriptome data from 20 healthy individuals and 39 patients acutely infected with Chikungunya virus. The sequencing platform returns a set of FASTQ files containing the DNA sequence, i.e.the reads, and the associated quality for each nucleotide base. The Phred quality scale indicates the probability of an incorrect reading for each base. Tools identify and remove low-quality reads from samples and to increase the probability of mapping reads.In this step, the mapping module, the high-quality reads recovered are used as inputs to align them against the human reference genome. CEMiTool identifies and analyze co-expression modules. Genes within the same module are co-expressed, which means that they exhibit similar patterns of expression across the samples of the data sets.The network analysis provides information about the most connected genes, i.e. the hubs. The names of those genes are shown in the network.The size of the nodes is proportional to its degree of connectivity. The results obtained from the DEG analysis were summarized in the volcano plots. The analysis of the molecular degree of perturbation permits the identification of perturbed samples from healthy and infected individuals.MDP suggests which samples are potential biological outliers. Removing those samples will impact the downstream results. A functional enrichment analysis using AURA can be performed with Enrichr tool.These steps helps to interpret the results by revealing common functional roles of several genes that were differentially expressed. The biological process shown in the bar graphs are the top 10 enriched gene sets based on their p-value ranking. In conclusion, these protocols covers all steps of RNA-Seq analysis.The pipeline was developed and encapsulated into the non-commercial system named Docker. On an image and made available for the scientific community. Due to the container system, all scripts and tools are under the same specific version to guarantee reproducibility.Furthermore, parts of the bioinformatics analysis was performed via free user-friendly web tools.

Research

JoVE Journal

Immunology and Infection

Use of an Optical Trap for Study of Host-Pathogen Interactions for Dynamic Live Cell Imaging

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and ...

Use of the EpiAirway Model for Characterizing Long-term Host-pathogen Interactions

Nontypeable Haemophilus influenzae  (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory ...

High-throughput Detection Method for Influenza Virus

Influenza virus is a respiratory pathogen that causes a high degree of morbidity and mortality every year in multiple parts of the world. Therefore, ...

Quantitative High-throughput Single-cell Cytotoxicity Assay For T Cells

Cancer immunotherapy can harness the specificity of  immune response to target and eliminate tumors. Adoptive cell therapy (ACT)  based on the adoptive ...

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate  large-scale comprehensive protein-protein interaction network maps. This is  instrumental to understand the ...

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

RNA viruses are responsible for major human diseases such as flu, bronchitis, dengue, Hepatitis C or measles. They also represent an emerging threat ...

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic ...

Evaluation of Host-Pathogen Responses and Vaccine Efficacy in Mice

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a ...

A High-throughput Shigella-specific Bactericidal Assay

A High-throughput Shigella-specific Bactericidal Assay

Serum bactericidal assays (SBAs) measure the functional activity of antibodies and have been used for many decades. SBAs directly measure antibody killing ...