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).

<ol>
	<li>Weaver, S. C., Charlier, C., Vasilakis, N., Lecuit, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Zika,+Chikungunya,+and+Other+Emerging+Vector-Borne+Viral+Diseases.">Zika, Chikungunya, and Other Emerging Vector-Borne Viral Diseases.</a> Annual Review of Medicine. 69, 395-408 (2018).</li><li>Burt, F. J., 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=Chikungunya+virus:+an+update+on+the+biology+and+pathogenesis+of+this+emerging+pathogen.">Chikungunya virus: an update on the biology and pathogenesis of this emerging pathogen.</a> The Lancet. Infectious Diseases. 17 (4), 107-117 (2017).</li><li>Hua, C., Combe, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chikungunya+virus-associated+disease.">Chikungunya virus-associated disease.</a> Current Rheumatology Reports. 19 (11), 69 (2017).</li><li>Suhrbier, A., Jaffar-Bandjee, M. -. C., Gasque, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Arthritogenic+alphaviruses-an+overview.">Arthritogenic alphaviruses-an overview.</a> Nature Reviews Rheumatology. 8 (7), 420-429 (2012).</li><li>Nakaya, H. I., 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=Gene+profiling+of+chikungunya+virus+arthritis+in+a+mouse+model+reveals+significant+overlap+with+rheumatoid+arthritis.">Gene profiling of chikungunya virus arthritis in a mouse model reveals significant overlap with rheumatoid arthritis.</a> Arthritis and Rheumatism. 64 (11), 3553-3563 (2012).</li><li>Michlmayr, D., 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=Comprehensive+innate+immune+profiling+of+chikungunya+virus+infection+in+pediatric+cases.">Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases.</a> Molecular Systems Biology. 14 (8), 7862 (2018).</li><li>Soares-Schanoski, 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+analysis+of+subjects+acutely+infected+with+the+Chikungunya+virus.">Systems analysis of subjects acutely infected with the Chikungunya virus.</a> PLOS Pathogens. 15 (6), 1007880 (2019).</li><li>Alexandersen, S., Chamings, A., Bhatta, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SARS-CoV-2+genomic+and+subgenomic+RNAs+in+diagnostic+samples+are+not+an+indicator+of+active+replication.">SARS-CoV-2 genomic and subgenomic RNAs in diagnostic samples are not an indicator of active replication.</a> Nature Communications. 11 (1), 6059 (2020).</li><li>Wang, D., 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=The+SARS-CoV-2+subgenome+landscape+and+its+novel+regulatory+features.">The SARS-CoV-2 subgenome landscape and its novel regulatory features.</a> Molecular Cell. 81 (10), 2135-2147 (2021).</li><li>Wilson, J. A. 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. K., Oshlack, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+ontology+analysis+for+RNA-seq:+accounting+for+selection+bias.">Gene ontology analysis for RNA-seq: accounting for selection bias.</a> Genome Biology. 11 (2), 14 (2010).</li></ol>

The authors have nothing to disclose.

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><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 ...

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4.1K Views.  University of São Paulo. Welkom bij het protocol van high-throughput transcriptoomanalyse voor het onderzoeken van gastheer-pathogeen interacties. Dit protocol is onderverdeeld in de volgende stappen. Kwaliteitscontrole om leesbewerkingen van lage kwaliteit te filteren en ook om adaptersequenties sequencing en annotaties te verwijderen, waar moet je de reads in kaart brengen in een referentiegenomen en de reads in de genen annoteren.Statistische en co-expressie analyse, die de differentieel tot expressie gebra...

Video: High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Genetics

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 pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

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

Generation and Multi-phenotypic High-content Screening of Coxiella burnetii Transposon Mutants

Invasion and colonization of host cells by bacterial pathogens depend on the activity of a large number of prokaryotic proteins, defined as virulence factors, which can subvert and manipulate key host functions. The study of host/pathogen interactions is therefore extremely important to understand bacterial infections and develop alternative strategies to counter infectious diseases. This approach however, requires the development of new high-throughput assays for the unbiased, automated identification and characterization of bacterial virulence determinants. Here, we describe a method for the generation of a GFP-tagged mutant library by transposon mutagenesis and the development of high-content screening approaches for the simultaneous identification of multiple transposon-associated phenotypes. Our working model is the intracellular bacterial pathogen Coxiellaburnetii, the etiological agent of the zoonosis Q fever, which is associated with severe outbreaks with a consequent health and economic burden. The obligate intracellular nature of this pathogen has, until recently, severely hampered the identification of bacterial factors involved in host pathogen interactions, making of Coxiella the ideal model for the implementation of high-throughput/high-content approaches.

Generation and Multi-phenotypic High-content Screening of Coxiella burnetii Transposon Mutants

Coxiella burnetii is an obligate intracellular Gram-negative bacterium responsible for the zoonotic disease Q fever. Here we describe methods for the generation of Coxiella fluorescent transposon mutants as well as the automated identification and analysis of the resulting internalization, replication and cytotoxic phenotypes.

Invasion and colonization of host cells by bacterial pathogens depend on the activity of a large number of prokaryotic proteins, defined as virulence ...

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene transcripts from infected tissues. Pathogen RNA constitutes a tiny portion of the total RNA isolated from infected tissue samples. Both microarray and RNAseq technologies have difficulties in generating reliable reads for weakly expressed pathogen genes. Mutant pathogen strains with reduced in vivo proliferation pose an even bigger challenge. Here we describe an in vivo gene expression profiling protocol that is very fast, extremely sensitive and highly reproducible. We developed this protocol during our investigation of the fungal pathogen Candida albicans in a murine model of hematogenously disseminated candidiasis. Using this protocol, we have documented time courses of dynamically regulated C. albicans gene expression during kidney infection, and discovered unexpected features of gene expression responses to antifungal drug treatment in vivo.

We developed a protocol that is fast, sensitive and reproducible for pathogen gene expression profiling during an infection.

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene ...

Using a Bacterial Pathogen to Probe for Cellular and Organismic-level Host Responses

There are a variety of strategies bacterial pathogens employ to survive and proliferate once inside the eukaryotic cell. The so-called 'cytosolic' pathogens (Listeria monocytogenes, Shigella flexneri, Burkholderia pseudomallei, Francisella tularensis, and Rickettsia spp.) gain access to the infected cell cytosol by physically and enzymatically degrading the primary vacuolar membrane. Once in the cytosol, these pathogens both proliferate as well as generate sufficient mechanical forces to penetrate the plasma membrane of the host cell in order to infect new cells. Here, we show how this terminal step of the cellular infection cycle of L. monocytogenes (Lm) can be quantified by both colony-forming unit assays and flow cytometry and give examples of how both pathogen- and host-encoded factors impact this process. We also show a close correspondence of Lm infection dynamics of cultured cells infected in vitro and those of hepatic cells derived from mice infected in vivo. These function-based assays are relatively simple and can be readily scaled up for discovery-based high-throughput screens for modulators of eukaryotic cell function.

We describe both in vitro and in vivo infection assays that can be used to analyze the activities of host-encoding factors.

There are a variety of strategies bacterial pathogens employ to survive and proliferate once inside the eukaryotic cell. The so-called 'cytosolic' ...

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s global proteome under varying conditions. This powerful strategy applied to the interactions of microbial pathogens with the desired host comprehensively characterizes both perspectives towards infection. Herein, the workflow describes label-free quantification (LFQ) of the infectome of Cryptococcus neoformans, a fungal facultative intracellular pathogen that is the causative agent of the deadly disease cryptococcosis, in the presence of immortalized macrophage cells. The protocol details the proper protein preparation techniques for both pathogen and mammalian cells within a single experiment, resulting in appropriate peptide submission for liquid-chromatography (LC)-MS/MS analysis. The high throughput generic nature of LFQ allows a wide dynamic range of protein identification and quantification, as well as transferability to any host-pathogen infection setting, maintaining extreme sensitivity. The method is optimized to catalogue extensive, unbiased protein abundance profiles of a pathogen within infection-mimicking conditions. Specifically, the method demonstrated here provides essential information on C. neoformans pathogenesis, such as protein production necessary for virulence and identifies critical host proteins responding to microbial invasion.

Here, we present a protocol to profile the interplay between host and pathogen during infection by mass spectrometry-based proteomics. This protocol uses label-free quantification to measure changes in protein abundance of both host (e.g., macrophages) and pathogen (e.g., Cryptococcus neoformans) in a single experiment.

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s ...

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells

Identification of emerging bacterial pathogens is critical for human health and security. Bacterial adherence to host cells is an essential step in bacterial infections and constitutes a hallmark of potential threat. Therefore, examining the adherence of bacteria to host cells can be used as a component of bacterial threat assessment. A standard method for enumerating bacterial adherence to host cells is to co-incubate bacteria with host cells, harvest the adherent bacteria, plate the harvested cells on solid media, and then count the resultant colony forming units (CFU). Alternatively, bacterial adherence to host cells can be evaluated using immunofluorescence microscopy-based approaches. However, conventional strategies for implementing these approaches are time-consuming and inefficient. Here, a recently developed automated fluorescence microscopy-based imaging method is described. When combined with high-throughput image processing and statistical analysis, the method enables rapid quantification of bacteria that adhere to host cells. Two bacterial species, Gram-negative Pseudomonas aeruginosa and Gram-positive Listeria monocytogenes and corresponding negative controls, were tested to demonstrate the protocol. The results show that this approach rapidly and accurately enumerates adherent bacteria and significantly reduces experimental workloads and timelines.

Detection of host-bacterial pathogen interactions based on phenotypic adherence using high-throughput fluorescence labeling imaging along with automated statistical analysis methods enables rapid evaluation of potential bacterial interactions with host cells.

Identification of emerging bacterial pathogens is critical for human health and security. Bacterial adherence to host cells is an essential step in ...

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and microbial communities, a comprehensive workflow for DNA and RNA extraction was developed. The workflow concurrently generates viral and microbial metagenomes, as well as metatranscriptomes, from a single sample for next-generation sequencing. The coupling of these approaches provides an overview of both the taxonomical characteristics and the community encoded functions. The presented methods use Cystic Fibrosis (CF) sputum, a problematic sample type, because it is exceptionally viscous and contains high amount of mucins, free neutrophil DNA, and other unknown contaminants. The protocols described here target these problems and successfully recover viral and microbial DNA with minimal human DNA contamination. To complement the metagenomics studies, a metatranscriptomics protocol was optimized to recover both microbial and host mRNA that contains relatively few ribosomal RNA (rRNA) sequences. An overview of the data characteristics is presented to serve as a reference for assessing the success of the methods. Additional CF sputum samples were also collected to (i) evaluate the consistency of the microbiome profiles across seven consecutive days within a single patient, and (ii) compare the consistency of metagenomic approach to a 16S ribosomal RNA gene-based sequencing. The results showed that daily fluctuation of microbial profiles without antibiotic perturbation was minimal and the taxonomy profiles of the common CF-associated bacteria were highly similar between the 16S rDNA libraries and metagenomes generated from the hypotonic lysis (HL)-derived DNA. However, the differences between 16S rDNA taxonomical profiles generated from total DNA and HL-derived DNA suggest that hypotonic lysis and the washing steps benefit in not only removing the human-derived DNA, but also microbial-derived extracellular DNA that may misrepresent the actual microbial profiles.

Using the cystic fibrosis airway as an example, the manuscript presents a comprehensive workflow comprising a combination of metagenomic and metatranscriptomic approaches to characterize the microbial and viral communities in animal-associated samples.

The accessibility of high-throughput sequencing has revolutionized many fields of biology. In order to better understand host-associated viral and ...

Biology

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts whose expression is associated with insecticide resistance phenotypes in Anopheles gambiae mosquitoes. The application can be used online or downloaded and used locally by anyone. The local application can be modified to add new insecticide resistance datasets generated from multiple -omics platforms. This guide demonstrates how to add new datasets and handle missing data. Furthermore, IR-TEx can be completely and easily recoded to use-omics datasets from any experimental data, making it a valuable resource to many researchers. The protocol illustrates the utility of IR-TEx in identifying new insecticide resistance candidates using the the microsomal glutathione transferase, GSTMS1, as an example. This transcript is upregulated in multiple pyrethroid resistant populations from C&#244;te D&#39;Ivoire and Burkina Faso. The identification of co-correlated transcripts provides further insight into the putative roles of this gene.

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts ...

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with&#160;pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

The protocol submitted here explains the complete in silico pipeline needed to predict and functionally characterize circRNAs from RNA-sequencing transcriptome data studying host-pathogen interactions.

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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