The Microbiome and Genetics core (MGC) of the Cancer and Inflammation Program (CIP) runs its microbiome facility in Building 37 of Bethesda with a small team consisting of a research technician, three bioinformaticians and one scientist. The primary function is to meet the growing interest and challenges of characterizing the role of the microbiota in cancer and inflammatory processes. Having established reliable and reproducible methods to isolate and characterize nucleic acids of microbiota isolated from feces and other sources, the core has worked with a range of source materials and PIs to help effectively determine changes in microbial representation between experimental samples. Over the past year the core has expanded its process repertoire, adding whole genome sequencing of microbial isolates as well as shotgun metagenomics to its repertoire of 16S amplicon metagenomics. We have isolated DNA from a variety of mammalian sources. These additions 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 Miseq platform and shotgun metagenomes of the microbiota are run on the higher capacity Nexseq. 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 almost 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 was 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, the Qiime 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. For shotgun metagenomics, we are using two exploratory pipelines, the publically available HUMAnN2 and one developed in-house to explore the bioinformatics challenges in going from defined amplicon targets such as rRNA to whole genome or transcriptome sequencing. We are using software (Picrust, Pathoscope) that offers insights into the genomic data generated and also have explored metabolic pathways using MetaPhlAn. We have sequenced and fully assembled genomes of Corynebacterial isolates and fully annotated the genes and plasmids of the isolates using the prokka software. Our work has been recognized by coauthorships with collaborators or acknowledgements elsewhere. We continue to support analysis in genetics of HLA expression. 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. 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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