A Computational Workflow for Analysis of 3' Tag-Seq Data.

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow.

Regulatory transcription factors control many important biological processes, including cellular differentiation, responses to environmental perturbations and stresses, and host-pathogen interactions. Determining the genome-wide binding of regulatory transcription factors to DNA is essential to understanding the function of transcription factors in these often complex biological processes. Cleavage under targets and release using nuclease (CUT&RUN) is a modern method for genome-wide mapping of in vivo protein-DNA binding interactions that is an attractive alternative to the traditional and widely used chromatin immunoprecipitation followed by sequencing (ChIP-seq) method. CUT&RUN is amenable to a higher-throughput experimental setup and has a substantially higher dynamic range with lower per-sample sequencing costs than ChIP-seq. Here, a comprehensive CUT&RUN protocol and accompanying data analysis workflow tailored for genome-wide analysis of transcription factor-DNA binding interactions in the human fungal pathogen Candida albicans are described. This detailed protocol includes all necessary experimental procedures, from epitope tagging of transcription factor-coding genes to library preparation for sequencing; additionally, it includes a customized computational workflow for CUT&RUN data analysis.

Dysbiotic Subgingival Microbial Communities in Periodontally Healthy Patients With Rheumatoid Arthritis.

OBJECTIVE: Studies that demonstrate an association between rheumatoid arthritis (RA) and dysbiotic oral microbiomes are often confounded by the presence of extensive periodontitis in these individuals. This study was undertaken to investigate the role of RA in modulating the periodontal microbiome by comparing periodontally healthy individuals with RA to those without RA. METHODS: Subgingival plaque was collected from periodontally healthy individuals (22 with RA and 19 without RA), and the 16S gene was sequenced on an Illumina MiSeq platform. Bacterial biodiversity and co-occurrence patterns were examined using the QIIME and PhyloToAST pipelines. RESULTS: The subgingival microbiomes differed significantly between patients with RA and controls based on both community membership and the abundance of lineages, with 41.9% of the community differing in abundance and 19% in membership. In contrast to the sparse and predominantly congeneric co-occurrence networks seen in controls, RA patients revealed a highly connected grid containing a large intergeneric hub anchored by known periodontal pathogens. Predictive metagenomic analysis (PICRUSt) demonstrated that arachidonic acid and ester lipid metabolism pathways might partly explain the robustness of this clustering. As expected from a periodontally healthy cohort, Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans were not significantly different between groups; however, Cryptobacterium curtum, another organism capable of producing large amounts of citrulline, emerged as a robust discriminant of the microbiome in individuals with RA. CONCLUSION: Our data demonstrate that the oral microbiome in RA is enriched for inflammophilic and citrulline-producing organisms, which may play a role in the production of autoantigenic citrullinated peptides in RA.

Smoking, pregnancy and the subgingival microbiome.

The periodontal microbiome is known to be altered during pregnancy as well as by smoking. However, despite the fact that 2.1 million women in the United States smoke during their pregnancy, the potentially synergistic effects of smoking and pregnancy on the subgingival microbiome have never been studied. Subgingival plaque was collected from 44 systemically and periodontally healthy non-pregnant nonsmokers (control), non-pregnant smokers, pregnant nonsmokers and pregnant smokers and sequenced using 16S-pyrotag sequencing. 331601 classifiable sequences were compared against HOMD. Community ordination methods and co-occurrence networks were used along with non-parametric tests to identify differences between groups. Linear Discriminant Analysis revealed significant clustering based on pregnancy and smoking status. Alpha diversity was similar between groups, however, pregnant women (smokers and nonsmokers) demonstrated higher levels of gram-positive and gram-negative facultatives, and lower levels of gram-negative anaerobes when compared to smokers. Each environmental perturbation induced distinctive co-occurrence patterns between species, with unique network anchors in each group. Our study thus suggests that the impact of each environmental perturbation on the periodontal microbiome is unique, and that when they are superimposed, the sum is greater than its parts. The persistence of these effects following cessation of the environmental disruption warrants further investigation.

PhyloToAST: Bioinformatics tools for species-level analysis and visualization of complex microbial datasets.

The 16S rRNA gene is widely used for taxonomic profiling of microbial ecosystems; and recent advances in sequencing chemistry have allowed extremely large numbers of sequences to be generated from minimal amounts of biological samples. Analysis speed and resolution of data to species-level taxa are two important factors in large-scale explorations of complex microbiomes using 16S sequencing. We present here new software, Phylogenetic Tools for Analysis of Species-level Taxa (PhyloToAST), that completely integrates with the QIIME pipeline to improve analysis speed, reduce primer bias (requiring two sequencing primers), enhance species-level analysis, and add new visualization tools. The code is free and open source, and can be accessed at http://phylotoast.org.