Regional variation limits applications of healthy gut microbiome reference ranges and disease models

He, Yan; Wu, Wei; Zheng, Huimin; Li, Pan; McDonald, Daniel; Sheng, Haoyue; Chen, Mu-Xuan; Chen, Zihui; Ji, Guiyuan; Zheng, Zuoping; Mujagond, Prabhakar; Chen, Xiao-Jiao; Rong, Zuhua; Chen, Peng; Lyu, Li-Yi; Wang, Xian; Wu, Chong-Bin; Yu, Nan; Xu, Yanjun; Yin, Jia; Raes, Jeroen; Knight, Rob; Ma, Wenjun; Zhou, Hongwei

doi:10.1038/s41591-018-0164-x

Cited by 685 publications

(620 citation statements)

References 43 publications

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“…Moreover, we detected significantly lower abundances of Akkermansia muciniphila (MLG-2159) and Clostridium bartlettii (MLG-7540) and higher abundances of Bacteroides caccae (MLG-10234 and MLG-10325), Bacteroides finegoldii (MLG-10154 and MLG-10159), and Collinsella intestinalis (MLG-10084) in TN-T2D patients compared with NGT and Pre-DM individuals ( Figure 2A , P < 0.05). Finally, the abundance of Megasphaera elsdenii (MLG-1568) was significantly higher in both TN-T2D and Pre-DM individuals than in NGT individuals ( Figure 2A , P < 0.05), in line with the positive correlation between the relative abundance of the genus Megasphaera and T2D recently reported in a large cohort with about 7000 individuals from South China [35]. Several key findings were further validated in faecal samples of 94 treatment naïve T2D patients in Shanghai (Gu et al, 2017a), such as a lower abundance of A. muciniphila and C. bartlettii compared to NGT and Pre-DM individuals, and a lower abundance of E.coli compared to Pre-DM individuals in this study ( Figure 2A, Table S5 , Wilcoxon rank test, P < 0.05).…”

Section: Resultssupporting

confidence: 84%

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

Huang

Ren

et al. 2019

Preprint

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BackgroundThe gut microbiota plays important roles in modulating host metabolism. Previous studies have demonstrated differences in the gut microbiome of T2D and prediabetic individuals compared to healthy individuals, with distinct disease-related microbial profiles being reported in groups of different age and ethnicity. However, confounding factors such as anti-diabetic medication hamper identification of the gut microbial changes in disease development.MethodWe used a combination of in-depth metagenomics and metaproteomics analyses of faecal samples from treatment-naïve type 2 diabetic (TN-T2D, n=77), pre-diabetic (Pre-DM, n=80), and normal glucose tolerant (NGT, n=97) individuals to investigate compositional and functional changes of the gut microbiota and the faecal content of microbial and host proteins in Pre-DM and treatment-naïve T2D individuals to elucidate possible host-microbial interplays characterising different disease stages.FindingsWe observed distinct differences characterizing the gut microbiota of these three groups and validated several key features in an independent TN-T2D cohort. We also demonstrated that the content of several human antimicrobial peptides and pancreatic enzymes differed in faecal samples between three groups, such as reduced faecal level of antimicrobial peptides and pancreatic enzymes in TN-T2D.InterpretationOur findings suggest a complex, disease stage-dependent interplay between the gut microbiota and the host and emphasize the value of metaproteomics to gain further insight into interplays between the gut microbiota and the host.FundingNational Key Research and Development Program of China, No. 2017YFC0909703, Shenzhen Municipal Government of China, No. JCYJ20170817145809215, and National Natural Science Foundation of China, No. 31601073.

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Section: Resultssupporting

confidence: 84%

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

Huang

Ren

et al. 2019

Preprint

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“…Companies which manufacture high-throughput DNA extraction will need to invest in research and development to reduce well-to-well contamination. Lastly, measuring and accounting for well-to-well contamination identification and reduction will be critical for diagnostic research going forward [35–40].…”

Section: Discussionmentioning

confidence: 99%

Quantifying and understanding well-to-well contamination in microbiome research

Minich

Sanders

Almasi‐Hashiani

et al. 2019

Preprint

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Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria each assigned a particular well in a plate, we assess the frequency at which sequences from each source appears in other wells. We evaluate the effects of different DNA extraction methods performed in two labs using a consistent plate layout including blanks, low biomass, and high biomass samples. Well-to-well contamination occurred primarily during DNA extraction, and to a lesser extent in library preparation, while barcode leakage was negligible. Labs differed in the levels of contamination. DNA extraction methods differed in their occurrences and levels of well-to-well contamination, with robotic methods having more well-to-well contamination while manual methods having higher background contaminants. Well-to-well contamination was observed to occur primarily in neighboring samples, with rare events up to 10 wells apart. The effect of well-to-well was greatest in samples with lower biomass, and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of crosstalk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, and samples of similar biomass processed together. Researchers should evaluate well-to-well contamination in study design and avoid removal of taxa or OTUs appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants.ImportanceMicrobiome research has uncovered magnificent biological and chemical stories across nearly all areas of life science, at times creating controversy when findings reveal fantastic descriptions of microbes living and even thriving in once thought to be sterile environments. Scientists have refuted many of these claims because of contamination, which has led to robust requirements including use of controls for validating accurate portrayals of microbial communities. In this study, we describe a previously undocumented form of contamination, well-to-well contamination and show that contamination primarily occurs during DNA extraction rather than PCR, is highest in plate-based methods as compared to single tube extraction, and occurs in higher frequency in low biomass samples. This finding has profound importance on the field as many current techniques to ‘decontaminate’ a dataset simply relies on an assumption that microbial reads found in blanks are contaminants from ‘outside’ namely the reagents or consumables.

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“…We started by testing the associations between the taxonomic composition of the microbiome and the selected socioeconomic risk and caregiver behavior covariates. To maximize scientific rigor, we constructed a CCA model, which is based on a Euclidian distance, that first accounted for the selected demography (child ethnicity) and the selected gut-linked covariates (previously shown to influence gut [25–28]; geographic location) by determining the amount of variance explained. These two demography and gut history covariates accounted for 19.9% of the total variance in taxonomic composition.…”

Section: Resultsmentioning

confidence: 99%

Gut feelings begin in childhood: how the gut metagenome links to early environment, caregiving, and behavior

Flannery

Stagaman

Burns

et al. 2019

Preprint

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32Psychosocial environments impact normative behavioral development in children, increasing the 33 risk of problem behaviors and psychiatric disorders across the lifespan. Converging evidence 34 demonstrates early normative development is affected by the gut microbiome, which itself can be 35 altered by early psychosocial environments. Nevertheless, these relationships are poorly 36 understood in childhood, particularly beyond peri-and postnatal microbial colonization. To 37 determine the gut microbiome's role in the associations between childhood adversity and 38 behavioral development, we conducted a metagenomic investigation among cross-sectional 39 sample of early school-aged children with a range of adverse experiences and caregiver stressors 40 and relationships. Our results indicate that the taxonomic and functional composition of the gut 41 microbiome links to behavioral dysregulation during a critical period of child development. 42Furthermore, our analysis reveals that both socioeconomic risk exposure and child behaviors 43 associate with the relative abundances of specific taxa (e.g., Bacteroides and Bifidobacterium 44 species) as well as functional modules encoded in their genomes (e.g., monoamine metabolism) 45that have been linked to cognition and health. We also identified heretofore novel linkages 46 between gut microbiota, their functions, and behavior. These findings hold important 47 translational implications for developmental psychology and microbiome sciences alike, as they 48suggest that caregiver behavior might mitigate the impact of socioeconomic risk on the 49 microbiome and modify the relationship between subclinical symptoms of behavioral 50 dysregulation and the gut microbiome in early school-aged children. 51 52

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Regional variation limits applications of healthy gut microbiome reference ranges and disease models

Cited by 685 publications

References 43 publications

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics

Quantifying and understanding well-to-well contamination in microbiome research

Gut feelings begin in childhood: how the gut metagenome links to early environment, caregiving, and behavior

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