Person: Paulson, Joseph
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Paulson
First Name
Joseph
Name
Paulson, Joseph
9 results
Search Results
Now showing 1 - 9 of 9
Publication Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection(BioMed Central, 2016) Morris, Alison; Paulson, Joseph; Talukder, Hisham; Tipton, Laura; Kling, Heather; Cui, Lijia; Fitch, Adam; Pop, Mihai; Norris, Karen A.; Ghedin, ElodieBackground: Longitudinal studies of the lung microbiome are challenging due to the invasive nature of sample collection. In addition, studies of the lung microbiome in human disease are usually performed after disease onset, limiting the ability to determine early events in the lung. We used a non-human primate model to assess lung microbiome alterations over time in response to an HIV-like immunosuppression and determined impact of the lung microbiome on development of obstructive lung disease. Cynomolgous macaques were infected with the SIV-HIV chimeric virus SHIV89.6P. Bronchoalveolar lavage fluid samples were collected pre-infection and every 4 weeks for 53 weeks post-infection. The microbiota was characterized at each time point by 16S ribosomal RNA (rRNA) sequencing. Results: We observed individual variation in the composition of the lung microbiota with a proportion of the macaques having Tropheryma whipplei as the dominant organism in their lungs. Bacterial communities varied over time both within and between animals, but there did not appear to be a systematic alteration due to SHIV infection. Development of obstructive lung disease in the SHIV-infected animals was characterized by a relative increase in abundance of oral anaerobes. Network analysis further identified a difference in community composition that accompanied the development of obstructive disease with negative correlations between members of the obstructed and non-obstructed groups. This emphasizes how species shifts can impact multiple other species, potentially resulting in disease. Conclusions: This study is the first to investigate the dynamics of the lung microbiota over time and in response to immunosuppression in a non-human primate model. The persistence of oral bacteria in the lung and their association with obstruction suggest a potential role in pathogenesis. The lung microbiome in the non-human primate is a valuable tool for examining the impact of the lung microbiome in human health and disease. Electronic supplementary material The online version of this article (doi:10.1186/s40168-016-0183-0) contains supplementary material, which is available to authorized users.Publication Regulatory network changes between cell lines and their tissues of origin(BioMed Central, 2017) Lopes-Ramos, Camila; Paulson, Joseph; Chen, Cho-Yi; Kuijjer, Marieke; Fagny, Maud; Platig, John; Sonawane, Abhijeet; Demeo, Dawn; Quackenbush, John; Glass, KimberlyBackground: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues. Electronic supplementary material The online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.Publication Estimating gene regulatory networks with pandaR(Oxford University Press, 2017) Schlauch, Daniel; Paulson, Joseph; Young, Albert; Glass, Kimberly; Quackenbush, JohnAbstract PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Contact: johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.Publication Metaviz: interactive statistical and visual analysis of metagenomic data(Oxford University Press, 2018) Wagner, Justin; Chelaru, Florin; Kancherla, Jayaram; Paulson, Joseph; Zhang, Alexander; Felix, Victor; Mahurkar, Anup; Elmqvist, Niklas; Corrada Bravo, HéctorAbstract Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.Publication Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment(BioMed Central, 2016) Pop, Mihai; Paulson, Joseph; Chakraborty, Subhra; Astrovskaya, Irina; Lindsay, Brianna R.; Li, Shan; Bravo, Héctor Corrada; Harro, Clayton; Parkhill, Julian; Walker, Alan W.; Walker, Richard I.; Sack, David A.; Stine, O. ColinBackground: Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in inhabitants from low-income countries and in visitors to these countries. The impact of the human intestinal microbiota on the initiation and progression of ETEC diarrhea is not yet well understood. Results: We used 16S rRNA (ribosomal RNA) gene sequencing to study changes in the fecal microbiota of 12 volunteers during a human challenge study with ETEC (H10407) and subsequent treatment with ciprofloxacin. Five subjects developed severe diarrhea and seven experienced few or no symptoms. Diarrheal symptoms were associated with high concentrations of fecal E. coli as measured by quantitative culture, quantitative PCR, and normalized number of 16S rRNA gene sequences. Large changes in other members of the microbiota varied greatly from individual to individual, whether or not diarrhea occurred. Nonetheless the variation within an individual was small compared to variation between individuals. Ciprofloxacin treatment reorganized microbiota populations; however, the original structure was largely restored at one and three month follow-up visits. Conclusion: Symptomatic ETEC infections, but not asymptomatic infections, were associated with high fecal concentrations of E. coli. Both infection and ciprofloxacin treatment caused variable changes in other bacteria that generally reverted to baseline levels after three months. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2777-0) contains supplementary material, which is available to authorized users.Publication Temporal Variations in Cigarette Tobacco Bacterial Community Composition and Tobacco-Specific Nitrosamine Content Are Influenced by Brand and Storage Conditions(Frontiers Media S.A., 2017) Chopyk, Jessica; Chattopadhyay, Suhana; Kulkarni, Prachi; Smyth, Eoghan M.; Hittle, Lauren E.; Paulson, Joseph; Pop, Mihai; Buehler, Stephanie S.; Clark, Pamela I.; Mongodin, Emmanuel F.; Sapkota, Amy R.Tobacco products, specifically cigarettes, are home to microbial ecosystems that may play an important role in the generation of carcinogenic tobacco-specific nitrosamines (TSNAs), as well as the onset of multiple adverse human health effects associated with the use of these products. Therefore, we conducted time-series experiments with five commercially available brands of cigarettes that were either commercially mentholated, custom-mentholated, user-mentholated, or non-mentholated. To mimic user storage conditions, the cigarettes were incubated for 14 days under three different temperatures and relative humidities (i.e., pocket, refrigerator, and room). Overall, 360 samples were collected over the course of 2 weeks and total DNA was extracted, PCR amplified for the V3V4 hypervariable region of the 16S rRNA gene and sequenced using Illumina MiSeq. A subset of samples (n = 32) was also analyzed via liquid chromatography with tandem mass spectrometry for two TSNAs: N’-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Comparative analyses of the five tobacco brands revealed bacterial communities dominated by Pseudomonas, Pantoea, and Bacillus, with Pseudomonas relatively stable in abundance regardless of storage condition. In addition, core bacterial operational taxonomic units (OTUs) were identified in all samples and included Bacillus pumilus, Rhizobium sp., Sphingomonas sp., unknown Enterobacteriaceae, Pantoea sp., Pseudomonas sp., Pseudomonas oryzihabitans, and P. putida. Additional OTUs were identified that significantly changed in relative abundance between day 0 and day 14, influenced by brand and storage condition. In addition, small but statistically significant increases in NNN levels were observed in user- and commercially mentholated brands between day 0 and day 14 at pocket conditions. These data suggest that manufacturing and user manipulations, such as mentholation and storage conditions, may directly impact the microbiome of cigarette tobacco as well as the levels of carcinogens.Publication A Reference Map of the Human Binary Protein Interactome(Nature Research, 2020-04-08) Luck, Katja; Kim, Dae-Kyum; Lambourne, Luke; Spirohn, Kerstin; Begg, Bridget E; Bian, Wenting; Brignall, Ruth; Cafarelli, Tiziana; Campos-Laborie, Francisco J.; Charloteaux, Benoit; Choi, Dongsic; Coté, Atina; Daley, Meaghan; Deimling, Steven; Desbuleux, Alice; Dricot, Amélie; Gebbia, Marinella; Hardy, Madeleine; Kishore, Nishka; Knapp, Jennifer; Kovács, István A.; Lemmens, Irma; Mee, Miles W.; Mellor, Joseph C.; Pollis, Carl; Pons, Carles; Richardson, Aaron; Schlabach, Sadie; Teeking, Bridget; Yadav, Anupama; Babor, Mariana; Balcha, Dawit; Basha, Omer; Bowman-Colin, Christian; Chin, Suet-Feung; Choi, Soon Gang; Colabella, Claudia; Coppin, Georges; D'Amata, Cassandra; De Ridder, David; De Rouck, Steffi; Duran-Frigola, Miquel; Ennajdaoui, Hanane; Goebels, Florian; Goehring, Liana; Gopal, Anjali; Haddad, Ghazal; Hatchi, Elodie; Helmy, Mohamed; Jacob, Yves; Kassa, Yoseph; Landini, Serena; Li, Roujia; van Lieshout, Natascha; MacWilliams, Andrew; Markey, Dylan; Paulson, Joseph; Rangarajan, Sudharshan; Rasla, John; Rayhan, Ashyad; Rolland, Thomas; San Miguel Delgadillo, Adriana; Shen, Yun; Sheykhkarimli, Dayag; Sheynkman, Gloria; Simonovsky, Eyal; Taşan, Murat; Tejeda, Alexander; Tropepe, Vincent; Twizere, Jean-Claude; Wang, Yang; Weatheritt, Robert; Weile, Jochen; Xia, Yu; Yang, Xinping; Yeger-Lotem, Esti; Zhong, Quan; Aloy, Patrick; Bader, Gary D.; De Las Rivas, Javier; Gaudet, Suzanne; Hao, Tong; Rak, Janusz; Tavernier, Jan; Hill, David; Vidal, Marc; Roth, Frederick P.; Calderwood, MichaelGlobal insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome, transcriptome, and proteome data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.Publication Multisystem Analysis of Mycobacterium tuberculosis Reveals Kinase-Dependent Remodeling of the Pathogen-Environment Interface(American Society for Microbiology, 2018) Carette, Xavier; Platig, John; Young, David C.; Helmel, Michaela; Young, Albert T.; Wang, Zhe; Potluri, Lakshmi-Prasad; Moody, Cameron Stuver; Zeng, Jumei; Prisic, Sladjana; Paulson, Joseph; Muntel, Jan; Madduri, Ashoka V. R.; Velarde, Jorge; Mayfield, Jacob A.; Locher, Christopher; Wang, Tiansheng; Quackenbush, John; Rhee, Kyu Y.; Moody, David; Steen, Hanno; Husson, RobertABSTRACT Tuberculosis is the leading killer among infectious diseases worldwide. Increasing multidrug resistance has prompted new approaches for tuberculosis drug development, including targeted inhibition of virulence determinants and of signaling cascades that control many downstream pathways. We used a multisystem approach to determine the effects of a potent small-molecule inhibitor of the essential Mycobacterium tuberculosis Ser/Thr protein kinases PknA and PknB. We observed differential levels of phosphorylation of many proteins and extensive changes in levels of gene expression, protein abundance, cell wall lipids, and intracellular metabolites. The patterns of these changes indicate regulation by PknA and PknB of several pathways required for cell growth, including ATP synthesis, DNA synthesis, and translation. These data also highlight effects on pathways for remodeling of the mycobacterial cell envelope via control of peptidoglycan turnover, lipid content, a SigE-mediated envelope stress response, transmembrane transport systems, and protein secretion systems. Integrated analysis of phosphoproteins, transcripts, proteins, and lipids identified an unexpected pathway whereby threonine phosphorylation of the essential response regulator MtrA decreases its DNA binding activity. Inhibition of this phosphorylation is linked to decreased expression of genes for peptidoglycan turnover, and of genes for mycolyl transferases, with concomitant changes in mycolates and glycolipids in the cell envelope. These findings reveal novel roles for PknA and PknB in regulating multiple essential cell functions and confirm that these kinases are potentially valuable targets for new antituberculosis drugs. In addition, the data from these linked multisystems provide a valuable resource for future targeted investigations into the pathways regulated by these kinases in the M. tuberculosis cell.Publication Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data(BioMed Central, 2017) Paulson, Joseph; Chen, Cho-Yi; Lopes-Ramos, Camila; Kuijjer, Marieke; Platig, John; Sonawane, Abhijeet; Fagny, Maud; Glass, Kimberly; Quackenbush, JohnBackground: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. Results: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn. Electronic supplementary material The online version of this article (10.1186/s12859-017-1847-x) contains supplementary material, which is available to authorized users.