Paleogenomics Using the 454 Sequencing Platform

2008 ◽  
pp. 183-199
Author(s):  
M. Thomas P. Gilbert
2021 ◽  
Author(s):  
Yilin Deng ◽  
Xuewei Ding ◽  
Qingyuan Song ◽  
Gang Zhao ◽  
Lei Han ◽  
...  

Abstract Purpose The purpose of this study was to characterize alterations in mucosa-associated microbiota in different anatomical locations of the stomach during gastric cancer progression and to identify associations between Helicobacter pylori infection and gastric microbial changes in patients with gastric cancer. Methods Twenty-five H. pylori negative subjects with chronic gastritis and thirty-four subjects with gastric cancer were recruited, including H. pylori negative and positive patients with tumors in the antrum and the corpus. Gastric mucosa-associated microbiota were determined by 16S ribosomal RNA gene sequencing using a 454 sequencing platform. Results We found that individuals with chronic gastritis from three different anatomical sites exhibited different microbiota compositions, although the microbial alpha diversity, richness and beta diversity were similar. Compared to patients with chronic gastritis, the gastric microbiota compositions were significantly different at the order level in the antrum and the corpus of patients with gastric cancer, which was dependent on the H. pylori infection status. Microbial alpha diversity and species richness, however, were similar between chronic gastritis and gastric cancer cases and independent of H. pylori status. The microbial community structure in patients with gastric cancer was distinct from that in patients with chronic gastritis. In addition, we found that the presence of H. pylori markedly altered the structure in gastric corpus cancer, but only mildly affected the antrum. Conclusion Our data revealed distinct niche-specific microbiota alterations during the progression from gastritis to gastric cancer. These alterations may reflect adaptions of the microbiota to the diverse specific environmental habitats in the stomach, and may play an important, as yet undetermined, role in gastric carcinogenesis.


2014 ◽  
Vol 15 (1) ◽  
pp. 33 ◽  
Author(s):  
Ram Shrestha ◽  
Baruch Lubinsky ◽  
Vijay B Bansode ◽  
Mónica BJ Moinz ◽  
Grace P McCormack ◽  
...  

Nutrients ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 337 ◽  
Author(s):  
Julio Plaza-Díaz ◽  
Antonio Gómez-Fernández ◽  
Natalia Chueca ◽  
María Torre-Aguilar ◽  
Ángel Gil ◽  
...  

New microbiome sequencing technologies provide novel information about the potential interactions among intestinal microorganisms and the host in some neuropathologies as autism spectrum disorders (ASD). The microbiota–gut–brain axis is an emerging aspect in the generation of autistic behaviors; evidence from animal models suggests that intestinal microbial shifts may produce changes fitting the clinical picture of autism. The aim of the present study was to evaluate the fecal metagenomic profiles in children with ASD and compare them with healthy participants. This comparison allows us to ascertain how mental regression (an important variable in ASD) could influence the intestinal microbiota profile. For this reason, a subclassification in children with ASD by mental regression (AMR) and no mental regression (ANMR) phenotype was performed. The present report was a descriptive observational study. Forty-eight children aged 2–6 years with ASD were included: 30 with ANMR and 18 with AMR. In addition, a control group of 57 normally developing children was selected and matched to the ASD group by sex and age. Fecal samples were analyzed with a metagenomic approach using a next-generation sequencing platform. Several differences between children with ASD, compared with the healthy group, were detected. Namely, Actinobacteria and Proteobacteria at phylum level, as well as, Actinobacteria, Bacilli, Erysipelotrichi, and Gammaproteobacteria at class level were found at higher proportions in children with ASD. Additionally, Proteobacteria levels showed to be augmented exclusively in AMR children. Preliminary results, using a principal component analysis, showed differential patterns in children with ASD, ANMR and AMR, compared to healthy group, both for intestinal microbiota and food patterns. In this study, we report, higher levels of Actinobacteria, Proteobacteria and Bacilli, aside from Erysipelotrichi, and Gammaproteobacteria in children with ASD compared to healthy group. Furthermore, AMR children exhibited higher levels of Proteobacteria. Further analysis using these preliminary results and mixing metagenomic and other “omic” technologies are needed in larger cohorts of children with ASD to confirm these intestinal microbiota changes.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Savannah Mwesigwa ◽  
◽  
Lesedi Williams ◽  
Gaone Retshabile ◽  
Eric Katagirya ◽  
...  

AbstractHuman immunodeficiency virus (HIV) infection remains a significant public health burden globally. The role of viral co-infection in the rate of progression of HIV infection has been suggested but not empirically tested, particularly among children. We extracted and classified 42 viral species from whole-exome sequencing (WES) data of 813 HIV-infected children in Botswana and Uganda categorised as either long-term non-progressors (LTNPs) or rapid progressors (RPs). The Ugandan participants had a higher viral community diversity index compared to Batswana (p = 4.6 × 10−13), and viral sequences were more frequently detected among LTNPs than RPs (24% vs 16%; p = 0.008; OR, 1.9; 95% CI, 1.6–2.3), with Anelloviridae showing strong association with LTNP status (p = 3 × 10−4; q = 0.004, OR, 3.99; 95% CI, 1.74–10.25). This trend was still evident when stratified by country, sex, and sequencing platform, and after a logistic regression analysis adjusting for age, sex, country, and the sequencing platform (p = 0.02; q = 0.03; OR, 7.3; 95% CI, 1.6–40.5). Torque teno virus (TTV), which made up 95% of the Anelloviridae reads, has been associated with reduced immune activation. We identify an association between viral co-infection and prolonged AIDs-free survival status that may have utility as a biomarker of LTNP and could provide mechanistic insights to HIV progression in children, demonstrating the added value of interrogating off-target WES reads in cohort studies.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.


2021 ◽  
Vol 38 (3) ◽  
pp. 727-734
Author(s):  
Jiexia Yang ◽  
Yaping Hou ◽  
Fangfang Guo ◽  
Haishan Peng ◽  
Dongmei Wang ◽  
...  

Abstract Background Noninvasive prenatal testing (NIPT) has been widely used to screen for fetal aneuploidies, including fetal sex chromosome aneuploidies (SCAs). However, there is less information on the performance of NIPT in detecting SCAs. Methods A cohort of 47,800 pregnancies was recruited to review the high-risk NIPT results for SCAs. Cell-free fetal DNA (cffDNA) was extracted and sequenced. All NIPT high-risk cases were recommended to undergo invasive prenatal diagnosis for karyotyping analysis and chromosome microarray analysis (CMA). Results A total of 238 high-risk cases were detected by NIPT, including 137 cases of 45,X, 27 cases of 47,XXX, and 74 cases of 47,XYY/47,XXY. Prenatal diagnosis, including karyotyping analysis and CMA, was available in 170 cases. The positive predictive value (PPV) was 30.00% for 45,X, 70.58% for 47,XXX, and 81.13% for 47,XYY/47,XXY. In addition, 13 cases of sex chromosome mosaicism and 9 cases of sex chromosome CNVs were incidentally found in this study. Conclusion Our study showed that NIPT was reliable for screening SCAs based on a large sample, and it performed better in predicting sex chromosome trisomies than monosomy X. Our study will provide an important reference for clinical genetic counseling and further processing of the results.


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