A Novel Quantitative Approach for Eliminating Sample-To-Sample Variation Using a Hue Saturation Value Analysis Program

Objectives: As computing technology and image analysis techniques have advanced, the practice of histology has grown from a purely qualitative method to one that is highly quantified. Current image analysis software is imprecise and prone to wide variation due to common artifacts and histological limitations. In order to minimize the impact of these artifacts, a more robust method for quantitative image analysis is required. Methods and Results: Here we present a novel image analysis software, based on the hue saturation value color space, to be applied to a wide variety of histological stains and tissue types. By using hue, saturation, and value variables instead of the more common red, green, and blue variables, our software offers some distinct advantages over other commercially available programs. We tested the program by analyzing several common histological stains, performed on tissue sections that ranged from 4 µm to 10 µm in thickness, using both a red green blue color space and a hue saturation value color space. Conclusion: We demonstrated that our new software is a simple method for quantitative analysis of histological sections, which is highly robust to variations in section thickness, sectioning artifacts, and stain quality, eliminating sample-to-sample variation

The evaluation, application, and expansion of 16s amplicon metagenomics

Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken 2, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data. My evaluation of 16S methods should improve 16S amplicon analyses by advocating for the modernization of computational tools; my analysis of infant nasopharyngeal microbiomes lays groundwork for future predictive models for childhood disease and longitudinal microbiomic studies; my analysis of gut microbes illuminates the mechanisms through which bacteria can mediate cardiovascular health. Taken together, the research I present here represents a significant contribution to 16S metagenomics and its application to epidemiology, clinical nutritional science

Interactive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)

I will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design. The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. We demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.NIH U01CA22041

Nasopharyngeal dysbiosis precedes the development of lower respiratory tract infections in young infants, a longitudinal infant cohort study

DATA AVAILABITY STATEMENT: GitHub. Infant_Nasopharyngeal_Dysbiosis. DOI: https://github.com/tfaits/Infant_Nasopharyngeal_Dysbiosis This project contains the following underlying data: - All code, processed data, and the sample information metadata - Taxon counts tables are called "species.RDS", "genus.RDS", and "phylum.RDS". For strain/subspecies-level counts, "PathoScopeTable.txt" has the unfiltered/unprocessed outputs from PathoScope. The raw and processed sequencing data from this study are available in the SRA repository, under NIH Sequence Read Archive, BioProject: PRJNA817266.BACKGROUND Infants suffering from lower respiratory tract infections (LRTIs) have distinct nasopharyngeal (NP) microbiome profiles that correlate with severity of disease. Whether these profiles precede the infection or are a consequence of it, is unknown. In order to answer this question, longitudinal studies are needed. METHODS We conducted a retrospective analysis of NP samples collected in a longitudinal birth cohort study of Zambian mother-infant pairs. Samples were collected every two weeks from 1-week through 14-weeks of age. Ten of the infants in the cohort who developed LRTI were matched 1:3 with healthy comparators. We completed 16S rRNA gene sequencing on the samples each of these infants contributed and compared the NP microbiome of the healthy infants to infants who developed LRTI. RESULTS The infant NP microbiome maturation was characterized by transitioning from Staphylococcus dominant to respiratory-genera dominant profiles during the first three months of life, similar to what is described in the literature. Interestingly, infants who developed LRTI had distinct NP microbiome characteristics before infection, in most cases as early as the first week of life. Their NP microbiome was characterized by the presence of Novosphingobium, Delftia, high relative abundance of Anaerobacillus, Bacillus, and low relative abundance of Dolosigranulum, compared to the healthy controls. Mothers of infants with LRTI also had low relative abundance of Dolosigranulum in their baseline samples compared to mothers of infants that did not develop an LRTI. CONCLUSIONS Our results suggest that specific characteristics of the NP microbiome precede LRTI in young infants and may be present in their mothers as well. Early dysbiosis may play a role in the causal pathway leading to LRTI or could be a marker of underlying immunological, environmental, or genetic characteristics that predispose to LRTI.https://gatesopenresearch.org/Veterinary Tropical DiseasesSDG-03:Good heatlh and well-bein

Genesis and growth of extracellular vesicle-derived microcalcification in atherosclerotic plaques

Clinical evidence links arterial calcification and cardiovascular risk. Finite-element modelling of the stress distribution within atherosclerotic plaques has suggested that subcellular microcalcifications in the fibrous cap may promote material failure of the plaque, but that large calcifications can stabilize it. Yet the physicochemical mechanisms underlying such mineral formation and growth in atheromata remain unknown. Here, by using three-dimensional collagen hydrogels that mimic structural features of the atherosclerotic fibrous cap, and high-resolution microscopic and spectroscopic analyses of both the hydrogels and of calcified human plaques, we demonstrate that calcific mineral formation and maturation results from a series of events involving the aggregation of calcifying extracellular vesicles, and the formation of microcalcifications and ultimately large calcification zones. We also show that calcification morphology and the plaque’s collagen content – two determinants of atherosclerotic plaque stability - are interlinked

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Abstract Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools

<i>animalcules</i>: Interactive Microbiome Analytics and Visualization in R

AbstractBackgroundMicrobial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data.ResultsTo address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis are enhanced in the toolkit, while new methods such as biomarker identification are introduced. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface.ConclusionsWe present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html.</jats:sec

Animalcules: Interactive Microbiome Analytics and Visualization in R

Abstract Background: Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data.Results: To address some of these challenges, we have developed Animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis are enhanced in the toolkit, while new methods such as biomarker identification are introduced. Powerful interactive and dynamic figures generated by Animalcules enable users to understand their data and discover new insights. Animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface.Conclusions: We present Animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. Animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/animalcules.html).</jats:p

animalcules: interactive microbiome analytics and visualization in R

Abstract Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. Results To address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, the toolkit features traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis, combined with new methods for biomarker identification are. In addition, animalcules provides interactive and dynamic figures that enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. Conclusions We present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html. </jats:sec

Abstract 17335: Dietary Carbohydrate Quality Affects Plasma Lipid Profile and the Microbiome

Considerable data is available on the effect of carbohydrate (carb) quantity on CVD risk factors but data for carb quality is limited. Our objective was to determine the relative comparability for an isocaloric exchange of unrefined-carb (endosperm+germ+bran, e.g., whole wheat flour), refined-carb (endosperm only, e.g., white flour) and simple-carb (sucrose, e.g., high fructose corn syrup), on cardiometabolic risk factors and the gut microbiome. All foods/beverages (60%E total carb, 15%E protein, 27%E fat [7%E SFA, 10%E MUFA, 10%E PUFA], 80mg cholesterol/1000 kcal) were provided to study subjects (n=11 men and women, 65 years, BMI 27.5 kg/m 2 , LDL-C ≥100 mg/dL) for 5 weeks (randomized, single-blind, cross-over design). Body weight was maintained constant. At the end of each diet phase plasma lipid profile, inflammatory factors and glucose homeostasis were determined using standard methods. Fecal microbiota taxa was characterized by 16S rRNA sequencing and data analyzed using QIIME and PathoScope. Plasma LDL-C levels differed among diets (125 ± 29 mg/dL b , 129 ± 29 mg/dL a,b and 136 ± 24 mg/dL a , unrefined-, simple- and refined-carb , respectively). Carb quality had no significant effect on HDL-C, TG, FFA, glucose, insulin, CRP or IL-6 levels. There were 21 genera that had a mean relative abundance (RA) of ≥1% and 4 genera (Roseburia, Oscillospira, Ruminococcus and Coprococcus) varied significantly across diets but after multiple testing adjustment, only Roseburia RA variations remained significant ( 2.58% a , 0.99% b , and 0.81% b , unrefined-, simple- and refined-carb , respectively). Interesting trends were observed between the RA of the following genera and plasma lipids: Roseburia was negatively associated with LDL-C (r=0.28, p=0.097) and HDL-C (r=-0.39, p=0.022); Oscillospira was negatively associated with TG (r=-0.53, p=0.097), and Anaerostipes was positively associated with TG (r=0.40, p=0.0585) levels. These data suggest that carb quality alters the RA of butyrate-producing bacteria, presumably leading to higher short chain fatty acid production, with subsequent alterations in plasma lipids. This study provides novel information about the impact of carb quality on the phylogenetic structure and functional capacity of the fecal microbiome. </jats:p