The Microbiome and Genetics core (MGC) of the Cancer and Inflammation Program (CIP) has established its microbiome facility in Building 37 of Bethesda over the past year, entailing considerable turnover in personnel and equipment to meet the growing interest and challenges of characterizing the role of the microbiota in cancer and inflammatory processes. The team now assembled consists of a research technician, two bioinformaticians and one scientist, with a second scientist joining at end of July 2106. The scientific challenges that were met over the past year, range from experimental (establishing reliable and reproducible methods to isolate and characterize nucleic acids of microbiota isolated from feces and other sources) to bioinformatic (construction of bioinformatics pipelines to effectively determine changes in microbial representation between experimental samples). Robotic sample preparation platforms were harnessed to maximize throughput and reproducibility, both for nucleic acid isolation and for barcoded library preparation. Quantification is accomplished using qPCR or spectroscopy. Following purification, barcoding and quantification, an Illumina MiSeq system is used to sequence amplicons of 16S rRNA genes. Among more important challenges of the core is to ensure high reproducibility of data and analyses and to offer and considerable comprehension of the bioinformatics output while being able to maintain a very low cost to users. To date samples from more than 20 projects have been processed from inside CIP and NCI as well as with outside collaborators and approximately 200 Giga base pairs of sequence data generated and analyzed from the MiSeq platform since it has been up and running. Across the projects, different challenges ranging from how to isolate DNA from high or from lower bacterial biomass sources, how to partition analyses from different sources and which treatments maximize the signal to noise ratio of experiments have been met successfully. The bioinformatic challenges began with storage, delivery and backup of large amounts of information which has been successfully accomplished. Two analytical approaches to determining microbial abundances were utilized, the Qiime and mothur platforms have been tested extensively. Over the past year we have compared both and maximized their suitability for our purposes through modifying our analysis pipelines to take advantage of components of each. The analyses are also limited by the quality of databases of ribomsomal RNA. We developed a database of fully vetted, high quality rRNA sequences for use in identifying components of the microbiome in samples. To further explore microbiome and move to metagenomics, we recently acquired the higher throughput Nexseq platform from Illumina. We have already generated almost 100Gbp of data on this machine and and exploring the bioinformatics challenges in going from defined amplicon targets such as rRNA to whole genome or transcriptome sequencing. We are exploring software (Picrust, Pathoscope) that offers insights into the genomic data generated. We continue to support analysis in genetics of HLA expression. Over the past year we have been involved in the production of papers determining the characteristics of promoter regions of HLA-A, -B and -C in relation to expression of these genes. We have been instrumental in helping to show that expression affects outcomes infectious and autoimmune disease such as HIV and infection and well as transplantation, Crohn disease. The genetic elements that control expression are of considerable interest and we continue to support groups working on their characterization.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Scientific Cores Intramural Research (ZIC)
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Basic Sciences
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Ramsuran, Veron; Hernández-Sanchez, Pedro G; O'hUigin, Colm et al. (2017) Sequence and Phylogenetic Analysis of the Untranslated Promoter Regions for HLA Class I Genes. J Immunol 198:2320-2329
Lou, Hong; Villagran, Guillermo; Boland, Joseph F et al. (2015) Genome Analysis of Latin American Cervical Cancer: Frequent Activation of the PIK3CA Pathway. Clin Cancer Res 21:5360-70
Ramsuran, Veron; Kulkarni, Smita; O'huigin, Colm et al. (2015) Epigenetic regulation of differential HLA-A allelic expression levels. Hum Mol Genet 24:4268-75
Rizvi, Syed Monem; Salam, Nasir; Geng, Jie et al. (2014) Distinct assembly profiles of HLA-B molecules. J Immunol 192:4967-76
Dean, Michael; Bendfeldt, Giovana; Lou, Hong et al. (2014) Increased incidence and disparity of diagnosis of retinoblastoma patients in Guatemala. Cancer Lett 351:59-63
Bashirova, Arman A; Martin-Gayo, Enrique; Jones, Des C et al. (2014) LILRB2 interaction with HLA class I correlates with control of HIV-1 infection. PLoS Genet 10:e1004196
Petersdorf, Effie W; Gooley, Theodore A; Malkki, Mari et al. (2014) HLA-C expression levels define permissible mismatches in hematopoietic cell transplantation. Blood 124:3996-4003
Dean, Michael; Lou, Hong (2013) Genetics and genomics of prostate cancer. Asian J Androl 15:309-13
Ranasinghe, Srinika; Cutler, Sam; Davis, Isaiah et al. (2013) Association of HLA-DRB1-restricted CD4? T cell responses with HIV immune control. Nat Med 19:930-3
Jiménez-Morales, Silvia; Martínez-Aguilar, Nora; Gamboa-Becerra, Roberto et al. (2013) Polymorphisms in metalloproteinase-9 are associated with the risk for asthma in Mexican pediatric patients. Hum Immunol 74:998-1002

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