Explore chapters and articles related to this topic
A Comprehensive HLA-DRB, -DQB, and -DPB Oligotyping Procedure by Hybridization with Sequence-Specific Oligonucleotide Probes
Published in M. Kam, Jeffrey L. Bidwell, Handbook of HLA TYPING TECHNIQUES, 2020
The authors have presented a comprehensive two-step oligotyping procedure allowing the resolution of 53 DRB1, 3 DRB3, 3 DRB5, 17 DQB1, and 22 DPB1 alleles. The procedure has been rationalized as a function of the information required, i.e., clinical context, HLA-disease associations, anthropological studies, and of the intention to handle a minimal number of probes (from a pool of 67 SSOs) for an optimal level of resolution (98 class II alleles). The discrimination of DRB1*0701-0702 (one substitution in exon 3) alleles is not included in this oligotyping protocol. In addition, 5 DRB1 alleles are deliberately not identified because they concern only silent substitutions (i.e., 11011 to 11012, 11041 to 11042, 08021 to 08022, 08031 to 08032, and 09011 to 09012).
Analysis of 16S rRNA genes reveals reduced Fusobacterial community diversity when translocating from saliva to GI sites
Published in Gut Microbes, 2020
Miles Richardson, Jihui Ren, Mara Roxana Rubinstein, Jamila A. Taylor, Richard A. Friedman, Bo Shen, Yiping W. Han
The 792 bp fragments were used for MED and tree generation. In order to identify low-level species or strains, the aligned sequences were clustered by MED,40 an automated version of oligotyping.41 This method searches for nucleotide positions in the input sequence that have high divergence, and iteratively decomposes them into groups of sequences, called nodes, which are representative sequences used for BLAST search. MED was run with parameters of minimum substantive abundance (-M) of 3 and relocation of outliers, with all other parameters set to their defaults. One sequence was removed due to too low a substantive abundance (1), and another was removed because of excess variations in nucleotide sites. A total of 381 sequences remained for analysis (Supplementary Table 1).
Gut microbiota in ALS: possible role in pathogenesis?
Published in Expert Review of Neurotherapeutics, 2019
Pamela A. McCombe, Robert D. Henderson, Aven Lee, John D. Lee, Trent M. Woodruff, Restuadi Restuadi, Allan McRae, Naomi R. Wray, Shyuan Ngo, Frederik J. Steyn
Post-sequencing quality control (QC) analyses of marker-gene sequencing data include a threshold for minimum counts of sequencing reads (usually >10,000 per sample) [47]. The reads that pass the QC process are clustered according to their similarity, and each cluster is compared to a database as implemented in the QIIME pipeline [52], now updated to QIIME2 (https://qiime2.org). There are several different methods for classification of reads. The first approach allocates reads to operation taxonomic units (OTUs), grouping by the similarity of sequence reads (usually 97% similarity) into single features, identifying the OTU by comparison to database reference sequences [52,53]. A closed-reference approach takes account of known and annotated species, losing information from sequence reads that cannot be aligned to the database reference. An open-reference approach assigns unannotated species to known species with closest genomic similarity. A de-novo clustering approach does not use reference sequence information but instead clusters the reads according to similarities within the data. However, this approach can fail to identify biologically meaningful sequences that are lost when merging reads into OTUs [47,54]. The current preferred approach is oligotyping (the default in QIIME2) in which reads are concatenated using only the highly informative sequence segments (i.e. those most variable between taxonomic classes). The resulting oligotypes provide taxonomic clustering by accounting for nucleotide variations in the context of a sequence that is otherwise similar [54–56]. This approach is implemented in the Deblur [56] and DADA2 [54] algorithms. The database used to identify cluster classes includes curated marker genes databases such as Ribosomal Database Project (RDP) [57], GreenGenes [58] and SILVA [59]. One key decision is whether to accept all DNA reads and normalize the data to account for differing numbers of reads between samples, or to perform rarefaction where reads are discarded from some samples to generate a similar number of reads across samples. This decision has been a subject of debate [60], but rarefaction is favored since analyses are based on proportions of taxonomic classes in the sample.
Blautia—a new functional genus with potential probiotic properties?
Published in Gut Microbes, 2021
Xuemei Liu, Bingyong Mao, Jiayu Gu, Jiaying Wu, Shumao Cui, Gang Wang, Jianxin Zhao, Hao Zhang, Wei Chen
A recent study analyzed the microbial community characteristics in the stool samples of 303 school-age children from urban or rural areas of five countries in temperate and tropical regions of Asia. The intestinal microbiota of the children was divided into two groups, Prevotella (P type) and Bifidobacterium/Bacteroides (BB type). The gut microbiota of children in China, Japan, Taiwan, and other temperate regions was mostly BB type, whereas that of children in Thailand, Indonesia, and other tropical places was mostly P type. Notably, Blautia was significantly enriched in the BB-type intestinal microbiota, accounting for 10% of the total BB-type bacterial composition but only 5% of the total P-type bacterial composition.6 Schnorr et al.62 found a difference in the intestinal microbial composition between Hadza and Italians, characterized by a lower Blautia abundance in Hadza. Differences in the human gut microbiota were also noted between different altitudes and geographies. Sequencing of the fecal microbiota of 208 Tibetans from six regions based on the analysis of the operational taxonomic units revealed Blautia to be the dominant genus in the human gut microbiota across all six regions. Further principal component analysis showed that the intestinal microbiota of Tibetans changed significantly with the increase in altitude, body mass index, and age; specifically, the abundance of facultative anaerobes increased. These findings suggest that the intestinal microbiota play an important role in regulating altitude and geographical adaptability.63 One study pointed out that the predominant intestinal genera in Japanese people were Bifidobacterium and Clostridium; that in the American, Chinese, French, and Spanish people was Bacteroides; and that in Australians was Blautia.64 Reportedly, differences in human gut microbial diversity between geographical locations are largely related to heredity, lifestyle, and diet.65 Interestingly, Blautia was reported of having strong taxonomic association in twin inheritance.66 To identify the differences in intestinal microbial communities between human and animal hosts, a study collected fecal samples from seven hosts, including human, pig, cattle, deer, dog, cat, and chicken, and sequenced the V6 region of the 16S rRNA genes. Two hundred high-resolution taxonomic units in Blautia were identified using oligotyping, and the Blautia oligotypes could accurately identify different host sources, suggesting that the genus has host specificity and host preference.67