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Paulson, J. N.

Publications and source records attributed to Paulson, J. N..

18 recordsLinked to original sources

Removing an S-layer enables endolysin-derived probes to detect Paenibacillus thiaminolyticus in mixed bacterial populations

The emerging neonatal bacterial pathogen Paenibacillus thiaminolyticus causes sepsis and postinfectious hydrocephalus, yet few tools are available to study or specifically detect it. We developed fluorescent probes based on cell wall-binding domains (CBDs) from bacteriophage-derived cell-wall hydrolases. CBDs can target structurally conserved cell-wall glycans, making them attractive affinity reagents for bacterial detection. None of the 11 probes we designed labeled intact cells under standard conditions, and we identified a surface layer (S-layer) that prevented access to their cell-wall targets. Brief acidic treatment (pH 2.0) released abundant high-molecular-mass proteins from the cell surface and enabled labeling by five distinct CBD probes, each containing a single C-terminal S-layer homology domain. Mass spectrometry identified the main released protein as an ortholog of the P. alvei SpaA S-layer protein, which we designate SlsA. Across 26 surveyed P. thiaminolyticus genomes, we identified four distinct SlsA variants with 58-79% pairwise amino acid identity, encoded within a conserved cluster homologous to the P. alvei S-layer-associated locus. After either the acidic treatment or a quick flame fixation, the lead probe mGL-BDPt9 labeled six P. thiaminolyticus strains, including three Ugandan clinical isolates, as well as a P. dendritiformis clinical isolate from a US infant, and three additional Paenibacillus species. mGL-BDPt9 also distinguished P. thiaminolyticus from five gram-positive sepsis pathogens in pairwise mixtures and detected it in a five-species suspension. Together, these findings establish the presence of an S-layer in P. thiaminolyticus and provide a rapid fluorescent labeling strategy for multiple Paenibacillus pathogens associated with neonatal paenibacilliosis.

microbiology↗

Transcriptional immunogenomic analysis reveals distinct immunological clusters in pediatric nervous system tumours

To inform immunotherapy approaches in children, we performed an immunogenomic analysis of RNA-seq data from 925 treatment-naive pediatric nervous system tumours (pedNST) spanning 12 cancer types from three public data sets. Within pedNST, we uncovered four broad immune clusters: Pediatric Inflamed (10%), Myeloid Predominant (30%), Immune Neutral (43%) and Immune Excluded (17%). We validated these clusters using immunohistochemistry, methylation immune inference, and segmentation analysis of tissue images. We report shared biology of these immune clusters within and across cancer types, and characterization of specific immune-cell frequencies as well as T- and B-cell repertoires. We found no associations between immune infiltration levels and tumour mutational burden, although molecular cancer entities were enriched within specific immune clusters. Given the heterogeneity within pedNST, our findings suggest personalized immunogenomic profiling is needed to guide selection of immunotherapeutic strategies.

genomics↗

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗

Type IV pili is a critical virulence factor in clinical isolates of Paenibacillus thiaminolyticus

Hydrocephalus, the leading indication for childhood neurosurgery worldwide, is particularly prevalent in low-and-middle-income countries (LMICs). Hydrocephalus preceded by an infection, or postinfectious hydrocephalus (PIH), accounts for up to 60% of hydrocephalus in LMICs. Since many children with hydrocephalus suffer poor long-term outcomes despite surgical intervention, prevention of hydrocephalus remains paramount. Our previous studies implicated a novel bacterial pathogen, Paenibacillus thiaminolyticus, as a contributor to PIH in Uganda. Here we report the isolation of three P. thiaminolyticus strains, Mbale, Mbale2, and Mbale3, from patients with PIH and the demonstration that the three clinical isolates exhibit virulence in mice while P. thiaminolyticus type strain, B-4156, does not. We constructed complete genome assemblies of the clinical isolates as well as the reference strain and performed comparative genomics and proteomics analyses to identify potential virulence factors. One candidate virulence factor is a cluster of genes carried on a mobile genetic element that encodes a type IV pilus and is present in all three PIH patient strains but absent in the type strain. Proteomic and transcriptomic data confirmed the expression of this cluster of genes in the Mbale strain, while CRISPR-mediated deletion of the gene cluster substantially reduced the virulence of this strain. Our comparative proteogenomic analysis also identified various antibiotic resistance loci in the virulent strains. These results provide insight into the mechanism of virulence of Paenibacillus thiaminolyticus and suggest avenues for the diagnosis and treatment of this novel bacterial pathogen. Author SummaryPostinfectious hydrocephalus (PIH), a devastating sequela of neonatal infection, is associated with increased childhood mortality and morbidity. Paenibacillus thiaminolyticus was recently identified as the dominant organism highly associated with PIH in an African cohort. Our whole-genome sequencing, RNA sequencing and proteomics of three clinical isolates and a type strain in combination with CRISPR editing has revealed the type IV pili (T4P), encoded in a mobile genetic element, as a critical virulence factor for P. thiaminolyticus infection. Given the widespread presence of T4P in pathogens, the presence of T4P operon could serve as an important diagnostic and therapeutic target in P. thiaminolyticus and related bacteria.

microbiology↗

Differential richness inference for 16S rRNA marker gene surveys

Individual and environmental health outcomes are frequently linked to changes in the diversity of associated microbial communities. This makes deriving health indicators based on microbiome diversity measures essential. While microbiome data generated using high throughput 16S rRNA marker gene surveys are appealing for this purpose, 16S surveys also generate a plethora of spurious microbial taxa. When this artificial inflation in the observed number of taxa (i.e., richness, a diversity measure) is ignored, we find that changes in the abundance of detected taxa confound current methods for inferring differences in richness. Here we argue that the evidence of our own experiments, theory guided exploratory data analyses and existing literature, support the conclusion that most sub-genus discoveries are spurious artifacts of clustering 16S sequencing reads. We proceed based on this finding to model a 16S surveys systematic patterns of sub-genus taxa generation as a function of genus abundance to derive a robust control for false taxa accumulation. Such controls unlock classical regression approaches for highly flexible differential richness inference at various levels of the surveyed microbial assemblage: from sample groups to specific taxa collections. The proposed methodology for differential richness inference is available through an R package, Prokounter. Package availability: https://github.com/mskb01/prokounter

bioinformatics↗

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗

Vaginal Microbiome Topic Modelling of Laboring Ugandan Women With and Without Fever

The composition of the maternal vaginal microbiome may influence the duration of pregnancy, onset of labor and even neonatal outcomes. Maternal microbiome research in sub Saharan-Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. We examined the vaginal microbiome composition of 99 laboring Ugandan women using routine microbiology and 16S ribosomal DNA sequencing from two hypervariable regions (V1-V2 and V3-V4), using standard hierarchical methods. We then introduce Grades of Membership (GoM) modeling for the vaginal microbiome, a method often used in the text mining machine learning literature. Leveraging GoM models, we create a basis composed of a small number of microbial topics whose linear combination optimally represents each patient yielding more accurate associations. We identified relationships between defined communities and the presentation or absence of intrapartum fever. Using a random forest model we showed that by including novel microbial topic models we improved upon clinical variables to predict maternal fever. We also show by integrating clinical variables with a microbial topic model into this model found young maternal age, fever report earlier in the current pregnancy, and longer labors, as well as a more diverse, less Lactobacillus dominated microbiome were features of labor associated with intrapartum fever. These results better define relationships between presentation or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial communities, and improve our understanding of the impact of the microbiome on maternal and neonatal infection risk.

genomics↗

Bacterial communities of herbivores and pollinators that have co-evolved Cucurbita spp.

Insects, like all animals, are exposed to diverse environmental microbes throughout their life cycle. Yet, we know little about variation in the microbial communities associated with the majority of wild, unmanaged insect species. Here, we use a 16S rRNA gene metabarcoding approach to characterize temporal and geographic variation in the gut bacterial communities of herbivores (Acalymma vittatum and A. trivittatum) and pollinators (Eucera (Peponapis) pruinosa) that have co-evolved with the plant genus Cucurbita (pumpkin, squash, zucchini and gourds). Overall, we find high variability in the composition of bacterial communities in squash bees and beetles collected from different geographic locations and different time points throughout a growing season. Still, many of the most common OTUs are shared in E. (P.) pruinosa, A. vittatum and A. trivittatum. This suggests these insects may be exposed to similar environmental microbial sources while foraging on the same genus of host plants, and that similar microbial taxa may aid in digestion of Cucurbita plant material. The striped cucumber beetle A. vittatum can also transmit Erwinia tracheiphila, the causal agent of bacterial wilt of cucurbits. We find that few field-collected A. vittatum individuals have detectable E. tracheiphila, and when this plant pathogen is detected, it comprises less than 1% of the gut bacterial community. Together, these results are consistent with previous studies showing that plant feeding insects have highly variable gut bacterial communities, and provides a first step towards understanding the spatiotemporal variation in the microbial communities associated with herbivores and pollinators that depend on Cucurbita host plants.

ecology↗

An introduced crop plant is driving diversification of the virulent bacterial pathogen Erwinia tracheiphila

Erwinia tracheiphila is the causal agent of bacterial wilt of cucurbits, an economically important phytopathogen affecting few cultivated Cucurbitaceae host plant species in temperate Eastern North America. However, essentially nothing is known about E. tracheiphila population structure or genetic diversity. To address this shortcoming, a representative collection of 88 E. tracheiphila isolates was gathered from throughout its geographic range, and their genomes were sequenced. Phylogenomic analysis revealed three genetic clusters with distinct hrpT3SS virulence gene repertoires, host plant association patterns, and geographic distributions. The low genetic variation within each cluster suggests a recent population bottleneck followed by population expansion. We showed that in the field and greenhouse, cucumber (Cucumis sativus), which was introduced to North America by early Spanish conquistadors, is the most susceptible host plant species, and the only species susceptible to isolates from all three lineages. The establishment of large agricultural populations of highly susceptible C. sativus in temperate Eastern North America may have facilitated the original emergence of E. tracheiphila into cucurbit agro-ecosystems, and this introduced plant species may now be acting as a highly susceptible reservoir host. Our findings have broad implications for agricultural sustainability by drawing attention to how worldwide crop plant movement, agricultural intensification and locally unique environments may affect the emergence, evolution, and epidemic persistence of virulent microbial pathogens.\n\nImportanceErwinia tracheiphila is a virulent phytopathogen that infects two genera of cucurbit crop plants, Cucurbita spp. (pumpkin and squash) and Cucumis spp. (muskmelon and cucumber). One of the unusual ecological traits of this pathogen is that it is limited to temperate Eastern North America. Here, we complete the first large-scale sequencing of an E. tracheiphila isolate collection. From phylogenomic, comparative genomic, and empirical analyses, we find that introduced Cucumis spp. crop plants are driving the diversification of E. tracheiphila into multiple, closely related lineages. Together, the results from this study show that locally unique biotic (plant population) and abiotic (climate) conditions can drive the evolutionary trajectories of locally endemic pathogens in unexpected ways.

evolutionary biology↗

Cancer subtype identification using somatic mutation data

BACKGROUNDWith the onset of next generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.\n\nMETHODSHere, we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5, 805 primary tumors.\n\nRESULTSWe show that, for most cancer types, de-sparsified mutation data associates with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.\n\nCONCLUSIONSThis study is an important step towards understanding mutational patterns in cancer.

genomics↗

Histopathological image QTL discovery of thyroid autoimmune disease variants

Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image Quantitative Trait Loci (iQTL) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype and Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling, and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data, and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.\n\nOne Sentence SummaryWe use a histopathological image QTL analysis to identify genomic variants associated with immune cell infiltration.

genomics↗

Understanding Tissue-specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

genomics↗

Metaviz: interactive statistical and visual analysis of metagenomic data

Along with the survey techniques of 16S rRNA amplicon and whole-metagenome shotgun sequencing, an array of tools exists for clustering, taxonomic annotation, normalization, and statistical analysis of microbiome sequencing results. Integrative and interactive visualization that enables researchers to perform exploratory analysis in this feature rich hierarchical data is an area of need. In this work, we present Metaviz, a web browser-based tool for interactive exploratory metagenomic data analysis. Metaviz can visualize abundance data served from an R session or a Python web service that queries a graph database. As metagenomic sequencing features have a hierarchy, we designed a novel navigation mechanism to explore this feature space. We visualize abundance counts with heatmaps and stacked bar plots that are dynamically updated as a user selects taxonomic features to inspect. Metaviz also supports common data exploration techniques, including PCA scatter plots to interpret variability in the dataset and alpha diversity boxplots for examining ecological community composition. The Metaviz application and documentation is hosted at http://www.metaviz.org.

bioinformatics↗

Longitudinal differential abundance analysis of microbial marker-gene surveys using smoothing splines

BackgroundHigh-throughput targeted sequencing of the 16S ribosomal RNA marker gene is often used to profile and characterize the taxonomic composition of microbial communities. This type of big high-through sequencing data is rapidly being applied to various infectious diseases like diarrhea. While many studies are limited to single \"snapshots\" of these communities, there is increasing recognition that longitudinal profiling of these communities are required to understand community dynamics and the complex relationships between dynamics and phenotypes of interest. Statistical methods that determine microbial features that are differentially expressed are required as an initial step to characterizing phenotypic associations with community dynamics in big data and infectious diseases.\n\nResultsWe present a novel method for longitudinal marker-gene surveys based on smoothing splines that allows discovery and inference of time periods where specific microbial features are differentially abundant. We applied our method to three 16S marker-gene surveys, including, groups of gnotobiotic mice on two diets, patients challenged with ETEC (H10407), and a vaginal microbiome of healthy women. Employing our methodology we recover known bacterial differences and highlight a few extra species providing insight into when specific changes occurred. Additionally, in the cohort challenged with ETEC we recover proposed probiotic bacteria Bacteroides xylanisolvens, Collinsella aerofaciens, and Faecalibacterium prausnitzii associatons with healthy individuals.\n\nConclusionsThe method presented is, to our knowledge, the first flexible method of its kind implemented as a software capable of detecting time periods of differential abundance for microbial features species between two or more sample groups of interest. Our method is available within the metagenomeSeq open-source software for analysis of metagenomic package available through the Bioconductor project and is termed metaSplines.

genomics↗

A network-based approach to eQTL interpretation and SNP functional characterization

Expression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only include cis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified both cis- and trans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.

genomics↗

Smooth Quantile Normalization

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

genomics↗

Transcriptional landscape of cell lines and their tissues of origin

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell lines transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models.\n\nHighlightsO_LICell lines differ from their source tissues in gene expression and regulation\nC_LIO_LIDistinct cell lines share altered patterns of cell cycle regulation\nC_LIO_LICell cycle genes are less strongly targeted by repressive TFs in cell lines\nC_LIO_LICell lines share expression with their source tissue, but at reduced levels\nC_LI

genomics↗

Sexual dimorphism in gene expression and regulatory networks across human tissues

Sexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks.\n\nHighlightsO_LISexual dimorphism manifests in both gene expression and gene regulatory networks\nC_LIO_LISubstantial sexual dimorphism in regulatory networks was found in several tissues\nC_LIO_LIMany differentially regulated genes are not differentially expressed\nC_LIO_LISex hormone receptors do not exhibit sexually dimorphic expression in most tissues\nC_LI

genomics↗