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Dive into the research topics where Robert E. Settlage is active.

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Featured researches published by Robert E. Settlage.


Nature Communications | 2012

Draft genome sequence and genetic transformation of the oleaginous alga Nannochloropis gaditana

Randor Radakovits; Robert E. Jinkerson; Susan I. Fuerstenberg; Hongseok Tae; Robert E. Settlage; Jeffrey L. Boore; Matthew C. Posewitz

The potential use of algae in biofuels applications is receiving significant attention. However, none of the current algal model species are competitive production strains. Here we present a draft genome sequence and a genetic transformation method for the marine microalga Nannochloropsis gaditana CCMP526. We show that N. gaditana has highly favourable lipid yields, and is a promising production organism. The genome assembly includes nuclear (~29 Mb) and organellar genomes, and contains 9,052 gene models. We define the genes required for glycerolipid biogenesis and detail the differential regulation of genes during nitrogen-limited lipid biosynthesis. Phylogenomic analysis identifies genetic attributes of this organism, including unique stramenopile photosynthesis genes and gene expansions that may explain the distinguishing photoautotrophic phenotypes observed. The availability of a genome sequence and transformation methods will facilitate investigations into N. gaditana lipid biosynthesis and permit genetic engineering strategies to further improve this naturally productive alga.


The ISME Journal | 2015

Host adaptive immunity alters gut microbiota.

Husen Zhang; Joshua B. Sparks; Saikumar V Karyala; Robert E. Settlage; Xin M. Luo

It has long been recognized that the mammalian gut microbiota has a role in the development and activation of the host immune system. Much less is known on how host immunity regulates the gut microbiota. Here we investigated the role of adaptive immunity on the mouse distal gut microbial composition by sequencing 16 S rRNA genes from microbiota of immunodeficient Rag1−/− mice, versus wild-type mice, under the same housing environment. To detect possible interactions among immunological status, age and variability from anatomical sites, we analyzed samples from the cecum, colon, colonic mucus and feces before and after weaning. High-throughput sequencing showed that Firmicutes, Bacteroidetes and Verrucomicrobia dominated mouse gut bacterial communities. Rag1− mice had a distinct microbiota that was phylogenetically different from wild-type mice. In particular, the bacterium Akkermansia muciniphila was highly enriched in Rag1−/− mice compared with the wild type. This enrichment was suppressed when Rag1−/− mice received bone marrows from wild-type mice. The microbial community diversity increased with age, albeit the magnitude depended on Rag1 status. In addition, Rag1−/− mice had a higher gain in microbiota richness and evenness with increase in age compared with wild-type mice, possibly due to the lack of pressure from the adaptive immune system. Our results suggest that adaptive immunity has a pervasive role in regulating gut microbiota’s composition and diversity.


Genome Biology | 2014

Genome analysis of a major urban malaria vector mosquito, Anopheles stephensi

Xiaofang Jiang; Ashley Peery; A. Brantley Hall; Atashi Sharma; Xiao Guang Chen; Robert M. Waterhouse; Aleksey Komissarov; Michelle M. Riehle; Yogesh S. Shouche; Maria V. Sharakhova; Dan Lawson; Nazzy Pakpour; Peter Arensburger; Victoria L M Davidson; Karin Eiglmeier; Scott J. Emrich; Phillip George; Ryan C. Kennedy; Shrinivasrao P. Mane; Gareth Maslen; Chioma Oringanje; Yumin Qi; Robert E. Settlage; Marta Tojo; Jose M. C. Tubio; Maria F. Unger; Bo Wang; Kenneth D. Vernick; José M. C. Ribeiro; Anthony A. James

BackgroundAnopheles stephensi is the key vector of malaria throughout the Indian subcontinent and Middle East and an emerging model for molecular and genetic studies of mosquito-parasite interactions. The type form of the species is responsible for the majority of urban malaria transmission across its range.ResultsHere, we report the genome sequence and annotation of the Indian strain of the type form of An. stephensi. The 221 Mb genome assembly represents more than 92% of the entire genome and was produced using a combination of 454, Illumina, and PacBio sequencing. Physical mapping assigned 62% of the genome onto chromosomes, enabling chromosome-based analysis. Comparisons between An. stephensi and An. gambiae reveal that the rate of gene order reshuffling on the X chromosome was three times higher than that on the autosomes. An. stephensi has more heterochromatin in pericentric regions but less repetitive DNA in chromosome arms than An. gambiae. We also identify a number of Y-chromosome contigs and BACs. Interspersed repeats constitute 7.1% of the assembled genome while LTR retrotransposons alone comprise more than 49% of the Y contigs. RNA-seq analyses provide new insights into mosquito innate immunity, development, and sexual dimorphism.ConclusionsThe genome analysis described in this manuscript provides a resource and platform for fundamental and translational research into a major urban malaria vector. Chromosome-based investigations provide unique perspectives on Anopheles chromosome evolution. RNA-seq analysis and studies of immunity genes offer new insights into mosquito biology and mosquito-parasite interactions.


Journal of Proteome Research | 2010

Application of an End-to-End Biomarker Discovery Platform to Identify Target Engagement Markers in Cerebrospinal Fluid by High Resolution Differential Mass Spectrometry

Cloud P. Paweletz; Matthew C. Wiener; Andrey Bondarenko; Nathan A. Yates; Qinghua Song; Andy Liaw; Anita Y. H. Lee; Brandon Hunt; Ernst S. Henle; Fanyu Meng; Holly Sleph; Marie A. Holahan; Sethu Sankaranarayanan; Adam J. Simon; Robert E. Settlage; Jeffrey R. Sachs; Mark S. Shearman; Alan B. Sachs; Jacquelynn J. Cook; Ronald C. Hendrickson

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF A beta 42 peptide, a known pharmacodynamic marker for gamma-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF.


Diabetes | 2015

Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease

Jingzhong Ding; Lindsay M. Reynolds; Tanja Zeller; Christian P. Müller; Kurt Lohman; Barbara J. Nicklas; Stephen B. Kritchevsky; Zhiqing Huang; Alberto de la Fuente; Nicola Soranzo; Robert E. Settlage; Chia Chi Chuang; Timothy D. Howard; Ning Xu; Mark O. Goodarzi; Y. D Ida Chen; Jerome I. Rotter; David Siscovick; John S. Parks; Susan K. Murphy; David R. Jacobs; Wendy S. Post; Russell P. Tracy; Philipp S. Wild; Stefan Blankenberg; Ina Hoeschele; David M. Herrington; Charles E. McCall; Yongmei Liu

Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC.


Gut Pathogens | 2014

Probiotics and virulent human rotavirus modulate the transplanted human gut microbiota in gnotobiotic pigs

Husen Zhang; Haifeng Wang; Megan L. Shepherd; Ke Wen; Guohua Li; Xingdong Yang; Jacob Kocher; Ernawati Giri-Rachman; Allan W. Dickerman; Robert E. Settlage; Lijuan Yuan

We generated a neonatal pig model with human infant gut microbiota (HGM) to study the effect of a probiotic on the composition of the transplanted microbiota following rotavirus vaccination and challenge. All the HGM-transplanted pigs received two doses of an oral attenuated rotavirus vaccine. The gut microbiota of vaccinated pigs were investigated for effects of Lactobacillus rhamnosus GG (LGG) supplement and homotypic virulent human rotavirus (HRV) challenge. High-throughput sequencing of V4 region of 16S rRNA genes demonstrated that HGM-transplanted pigs carried microbiota similar to that of the C-section delivered baby. Firmicutes and Proteobacteria represented over 98% of total bacteria in the human donor and the recipient pigs. HRV challenge caused a phylum-level shift from Firmicutes to Proteobacteria. LGG supplement prevented the changes in microbial communities caused by HRV challenge. In particular, members of Enterococcus in LGG-supplemented pigs were kept at the baseline level, while they were enriched in HRV challenged pigs. Taken together, our results suggested that HGM pigs are valuable for testing the microbiota’s response to probiotic interventions for treating infantile HRV infection.


PLOS ONE | 2015

High Resolution Discovery Proteomics Reveals Candidate Disease Progression Markers of Alzheimer's Disease in Human Cerebrospinal Fluid.

Ronald C. Hendrickson; Anita Y. H. Lee; Qinghua Song; Andy Liaw; Matt Wiener; Cloud P. Paweletz; Jeffrey L. Seeburger; Jenny Li; Fanyu Meng; Ekaterina G. Deyanova; Matthew T. Mazur; Robert E. Settlage; Xuemei Zhao; Katie Southwick; Yi Du; Dan Holder; Jeffrey R. Sachs; Omar Laterza; Aimee Dallob; Derek L Chappell; Karen Snyder; Vijay Modur; Elizabeth King; Catharine Joachim; Andrey Bondarenko; Mark S. Shearman; Keith A. Soper; A. David Smith; William Z. Potter; Ken S. Koblan

Disease modifying treatments for Alzheimer’s disease (AD) constitute a major goal in medicine. Current trends suggest that biomarkers reflective of AD neuropathology and modifiable by treatment would provide supportive evidence for disease modification. Nevertheless, a lack of quantitative tools to assess disease modifying treatment effects remains a major hurdle. Cerebrospinal fluid (CSF) biochemical markers such as total tau, p-tau and Ab42 are well established markers of AD; however, global quantitative biochemical changes in CSF in AD disease progression remain largely uncharacterized. Here we applied a high resolution open discovery platform, dMS, to profile a cross-sectional cohort of lumbar CSF from post-mortem diagnosed AD patients versus those from non-AD/non-demented (control) patients. Multiple markers were identified to be statistically significant in the cohort tested. We selected two markers SME-1 (p<0.0001) and SME-2 (p = 0.0004) for evaluation in a second independent longitudinal cohort of human CSF from post-mortem diagnosed AD patients and age-matched and case-matched control patients. In cohort-2, SME-1, identified as neuronal secretory protein VGF, and SME-2, identified as neuronal pentraxin receptor-1 (NPTXR), in AD were 21% (p = 0.039) and 17% (p = 0.026) lower, at baseline, respectively, than in controls. Linear mixed model analysis in the longitudinal cohort estimate a decrease in the levels of VGF and NPTXR at the rate of 10.9% and 6.9% per year in the AD patients, whereas both markers increased in controls. Because these markers are detected by mass spectrometry without the need for antibody reagents, targeted MS based assays provide a clear translation path for evaluating selected AD disease-progression markers with high analytical precision in the clinic.


PLOS ONE | 2013

Characterizing the Genetic Basis for Nicotine Induced Cancer Development: A Transcriptome Sequencing Study.

Jasmin H. Bavarva; Hongseok Tae; Robert E. Settlage; Harold R. Garner

Nicotine is a known risk factor for cancer development and has been shown to alter gene expression in cells and tissue upon exposure. We used Illumina® Next Generation Sequencing (NGS) technology to gain unbiased biological insight into the transcriptome of normal epithelial cells (MCF-10A) to nicotine exposure. We generated expression data from 54,699 transcripts using triplicates of control and nicotine stressed cells. As a result, we identified 138 differentially expressed transcripts, including 39 uncharacterized genes. Additionally, 173 transcripts that are primarily associated with DNA replication, recombination, and repair showed evidence for alternative splicing. We discovered the greatest nicotine stress response by HPCAL4 (up-regulated by 4.71 fold) and NPAS3 (down-regulated by -2.73 fold); both are genes that have not been previously implicated in nicotine exposure but are linked to cancer. We also discovered significant down-regulation (-2.3 fold) and alternative splicing of NEAT1 (lncRNA) that may have an important, yet undiscovered regulatory role. Gene ontology analysis revealed nicotine exposure influenced genes involved in cellular and metabolic processes. This study reveals previously unknown consequences of nicotine stress on the transcriptome of normal breast epithelial cells and provides insight into the underlying biological influence of nicotine on normal cells, marking the foundation for future studies.


Proteomics | 2011

The ABRF Proteomics Research Group studies: educational exercises for qualitative and quantitative proteomic analyses.

David B. Friedman; Tracy M. Andacht; Maureen K. Bunger; Allis S. Chien; David H. Hawke; Jeroen Krijgsveld; William S. Lane; Kathryn S. Lilley; Michael J. MacCoss; Robert L. Moritz; Robert E. Settlage; Nicholas E. Sherman; Susan T. Weintraub; H. Ewa Witkowska; Nathan A. Yates; Christoph W. Turck

Resource (core) facilities have played an ever‐increasing role in furnishing the scientific community with specialized instrumentation and expertise for proteomics experiments in a cost‐effective manner. The Proteomics Research Group (PRG) of the Association of Biomolecular Resource Facilities (ABRF) has sponsored a number of research studies designed to enable participants to try new techniques and assess their capabilities relative to other laboratories analyzing the same samples. Presented here are results from three PRG studies representing different samples that are typically analyzed in a core facility, ranging from simple protein identification to targeted analyses, and include intentional challenges to reflect realistic studies. The PRG2008 study compares different strategies for the qualitative characterization of proteins, particularly the utility of complementary methods for characterizing truncated protein forms. The use of different approaches for determining quantitative differences for several target proteins in human plasma was the focus of the PRG2009 study. The PRG2010 study explored different methods for determining specific constituents while identifying unforeseen problems that could account for unanticipated results associated with the different samples, and included 15N‐labeled proteins as an additional challenge. These studies provide a valuable educational resource to research laboratories and core facilities, as well as a mechanism for establishing good laboratory practices.


PLOS ONE | 2015

Gene Expression Profiling of Human Vaginal Cells In Vitro Discriminates Compounds with Pro-Inflammatory and Mucosa-Altering Properties: Novel Biomarkers for Preclinical Testing of HIV Microbicide Candidates

Irina A. Zalenskaya; Theresa Joseph; Jasmin H. Bavarva; Nazita Yousefieh; Suzanne S. Jackson; Titilayo Fashemi; Hidemi S. Yamamoto; Robert E. Settlage; Raina N. Fichorova; Gustavo F. Doncel

Background Inflammation and immune activation of the cervicovaginal mucosa are considered factors that increase susceptibility to HIV infection. Therefore, it is essential to screen candidate anti-HIV microbicides for potential mucosal immunomodulatory/inflammatory effects prior to further clinical development. The goal of this study was to develop an in vitro method for preclinical evaluation of the inflammatory potential of new candidate microbicides using a microarray gene expression profiling strategy. Methods To this end, we compared transcriptomes of human vaginal cells (Vk2/E6E7) treated with well-characterized pro-inflammatory (PIC) and non-inflammatory (NIC) compounds. PICs included compounds with different mechanisms of action. Gene expression was analyzed using Affymetrix U133 Plus 2 arrays. Data processing was performed using GeneSpring 11.5 (Agilent Technologies, Santa Clara, CA). Results Microarraray comparative analysis allowed us to generate a panel of 20 genes that were consistently deregulated by PICs compared to NICs, thus distinguishing between these two groups. Functional analysis mapped 14 of these genes to immune and inflammatory responses. This was confirmed by the fact that PICs induced NFkB pathway activation in Vk2 cells. By testing microbicide candidates previously characterized in clinical trials we demonstrated that the selected PIC-associated genes properly identified compounds with mucosa-altering effects. The discriminatory power of these genes was further demonstrated after culturing vaginal cells with vaginal bacteria. Prevotella bivia, prevalent bacteria in the disturbed microbiota of bacterial vaginosis, induced strong upregulation of seven selected PIC-associated genes, while a commensal Lactobacillus gasseri associated to vaginal health did not cause any changes. Conclusions In vitro evaluation of the immunoinflammatory potential of microbicides using the PIC-associated genes defined in this study could help in the initial screening of candidates prior to entering clinical trials. Additional characterization of these genes can provide further insight into the cervicovaginal immunoinflammatory and mucosal-altering processes that facilitate or limit HIV transmission with implications for the design of prevention strategies.

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Hongseok Tae

Virginia Bioinformatics Institute

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Harold R. Garner

National Institutes of Health

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Ronald C. Hendrickson

Memorial Sloan Kettering Cancer Center

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Jasmin H. Bavarva

Virginia Bioinformatics Institute

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Kent M. Reed

University of Minnesota

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