The Microbiome and Genetics core (MGC) of the Cancer and Inflammation Program (CIP) runs its microbiome facility in Building 37 of Bethesda with a team currently consisting of two research technicians, three bioinformaticians and one scientist. The primary function is to meet the growing interest and challenges of characterizing the role of the microbiota both in cancer and inflammatory processes as well as in general health. Having established reliable and reproducible methods to isolate and characterize nucleic acids of microbiota isolated from fecal sources, the core has worked with a range of source materials and PIs to help effectively determine changes in microbial representation between experimental samples. The core has expanded its process repertoire so that four distinct experimental characterizations are offered, comprising 16S amplicon sequencing, whole genome sequencing of microbial isolates, shotgun metagenomics and shotgun transcriptomics. DNA has been extracted from source organisms such as human, mouse and macaque and source materials as varied as fecal pellets, anal and vaginal swabs, and saliva. The expansion of services offered beyond amplicon sequencing enable the core to look at potential metabolic pathway changes induced by changes in gene content and composition of the microbiota. Robotic sample preparation platforms (Eppendorf 5073 and 5075) are used 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 is used to sequence amplicons of 16S rRNA genes. For genomic approaches, the same DNA isolation process is used and as little as 1ng of DNA is subjected to breakage and library preparation by transposon driven 'tagmentation'. Whole genome sequencing from isolates is done on the Illumina Miseq platform and shotgun metagenomes of the microbiota are run on the higher capacity Nexseq in the core or HiSeq and NovaSeq platforms elsewhere. In the past year samples from more than 40 projects have been processed from inside CIP and NCI as well as for collaborators from other NIH institutes and about 1Tb of sequenced base pairs of data generated and analyzed from these platforms. 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. We are handling samples associated with both clinical and with basic scientific research. The bioinformatic challenges began with storage, delivery and backup of large amounts of information. This is achieved using both Illumina's cloud server as well as a backup system at the computer center of FNLCR. We continue to make available two analytical approaches to determining microbial abundances for 16S amplicons, the Qiime2 and mothur platforms and have tested them extensively. Our favored pipeline to take advantage of components of each. The analyses are also limited by the quality of databases of ribomsomal RNA. We continue to develop a database of fully vetted, high quality rRNA sequences for use in identifying components of the microbiome in samples. Standard outputs generated by MGC bioinformatics show taxonomic representations for all samples in case-control studies (alpha and beta diversities), unifrac distances estimated between samples and sample differences illustrated using principal component analyses (PCA) as well as statistical evaluation of differences. Additional analyses and inferences (eg rarefaction curves, PICRUSt) can be made available if called for in the research project. Assembly, analysis and annotation for shotgun metagenomics is far more complex than amplicon based characterizations. For these more challenging procedures, specialized bioinformaticpipelines (e.g.YAMS, MetaPhlAn2, MetAMOS) designed to interrogate the complex data are used. YAMS works from assembled contigs to build up genomes de novo, whereas MetaPhlAn2 uses homology mapping of reads against a database of known genomes. Standard output for YAMS involves taxonomic representations (starting with the proportion of host DNA), rarefaction analyses, breakdowns of metadata into all defined categories and analysis by PCA genomic annotation of assembled contigs, gene and gene family (using Prokka) and biochemical pathway analyses. Our microbiome work has been recognized by coauthorships with collaborators or acknowledgements elsewhere. We also continue to support analysis in genetics of HLA expression and the role this plays in cancer, autoimmunity and infectious disease outcomes. 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; in epigenetic regulation of HLA-A expression; in showing a role for HLA-DP expression in graft-versus-host disease; in determining risk of Kaposi Sarcoma for particular HLA and KIR combinations and also in characterizing the role of APOL1 variants in kidney disease. These studies build upon our work in helping to show that expression affects outcomes infectious and autoimmune disease such as HIV and infection and well as autoimmune related conditions such as Crohn disease or even transplant rejection. Current investigations concern the role of neoantigen recognition by HLA alleles in immunotherapy outcomes. The genetic elements that control immune gene expression are of considerable interest and we continue to support groups working on their characterization.
| 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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