Genetic manipulation of the human gut bacterium Eggerthella lenta reveals a widespread family of transcriptional regulators

Dong, Xu; Guthrie, Ben G H; Alexander, Margaret; Noecker, Cecilia; Ramírez, Lorenzo Álvarez; Glasser, Nathaniel R.; Balskus, Emily P.

doi:10.1038/s41467-022-33576-3

Cited by 23 publications

(20 citation statements)

References 48 publications

(74 reference statements)

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“…E. lenta abundance via gavage led to alterations in immune cell profile, decrease in T regulatory cells with a significant increase in CD4 + T cells and B cells that produce IL-17 and IFN-γ, which are in confirmation with a recent study showing E. lenta activates T H 17 cells via cardiac glycoside reductase operon, increasing colitis severity ( 50 , 51 ). The observations suggest that E. lenta reduces myeloid suppressors that regulate immune homeostasis, contributing to production of proinflammatory cytokines augmenting disease severity.…”

Section: Discussionsupporting

confidence: 85%

Eggerthella lenta augments preclinical autoantibody production and metabolic shift mimicking senescence in arthritis

Balakrishnan,

Luckey,

Wright

et al. 2023

Sci. Adv.

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Although the etiology of rheumatoid arthritis (RA) is unknown, a strong genetic predisposition and the presence of preclinical antibodies before the onset of symptoms is documented. An expansion of Eggerthella lenta is associated with severe disease in RA. Here, using a humanized mouse model of collagen-induced arthritis, we determined the impact of E. lenta abundance on RA severity. Naïve mice gavaged with E. lenta produce preclinical rheumatoid factor and, when induced for arthritis, develop severe disease. The augmented antibody response was much higher in female mice, and among patients with RA, women had higher average load of E. lenta . Expansion of E. lenta increased CXCL5 and CD4 T cells, and both interleukin-17– and interferon-γ–producing B cells. Further, E. lenta gavage caused gut dysbiosis and decline in amino acids and nicotinamide adenine dinucleotide with an increase in microbe-dependent bile acids and succinyl carnitine causing systemic senescent-like inflammation.

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Section: Discussionsupporting

confidence: 85%

Eggerthella lenta augments preclinical autoantibody production and metabolic shift mimicking senescence in arthritis

Balakrishnan,

Luckey,

Wright

et al. 2023

Sci. Adv.

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“…E. lenta is an important microbe exerting diverse metabolic activities toward bile acids, medications, and short-chain fatty acids, particularly acetate for energy metabolism . Taken together, these results suggest that E.…”

Section: Discussionmentioning

confidence: 73%

“…Of note, these functions were unaffected by other medications. E. lenta is an important microbe exerting diverse metabolic activities toward bile acids, medications, 42 and short-chain fatty acids, particularly acetate for energy metabolism. 43 Taken together, these results suggest that E. lenta survival and function may be compromised by β-blocker use.…”

Section: ■ Discussionmentioning

confidence: 99%

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management

Shearer,

Shah,

Shen-Tu

et al. 2024

ACS Pharmacol. Transl. Sci.

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Human gut microbiota are recognized as critical players in both metabolic disease and drug metabolism. However, medication−microbiota interactions in cardiometabolic diseases are not well understood. To gain a comprehensive understanding of how medication intake impacts the gut microbiota, we investigated the association of microbial structure with the use of single or multiple medications in a cohort of 134 middle-aged adults diagnosed with cardiometabolic disease, recruited from Alberta's Tomorrow Project. Predominant cardiometabolic prescription medication classes (12 total) were included in our analysis. Multivariate Association with Linear Model (MaAsLin2) was employed and results were corrected for age, BMI, sex, and diet to evaluate the relationship between microbial features and single-or multimedication use. Highly individualized microbiota profiles were observed across participants, and increasing medication use was negatively correlated with α-diversity. A total of 46 associations were identified between microbial composition and single medications, exemplified by the depletion of Akkermansia muciniphila by β-blockers and statins, and the enrichment of Escherichia/Shigella and depletion of Bacteroides xylanisolvens by metformin. Metagenomics prediction further indicated alterations in microbial functions associated with single medications such as the depletion of enzymes involved in energy metabolism encoded by Eggerthella lenta due to β-blocker use. Specific dual medication combinations also had profound impacts, including the depletion of Romboutsia and Butyriciocccus by statin plus metformin. Together, these results show reductions in bacterial diversity as well as species and microbial functional potential associated with both single-and multimedication use in cardiometabolic disease.

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“…We performed a taxonomic split on the original CAZy database sequences and selected 3 bacterial genomes with pre-annotated CAZymes in each genome: Bacteroides thetaiotaomicron, Eggerthella lenta, and Ruminococcus gnavus . We selected these bacteria based on the varying proportions of CAZymes per number of total proteins ( B. thetaiotaomicron : 7.6%, E. lenta : 1.1%, and R. gnavus : 3.0%) as well as biological relevance: E. lenta is very prevalent and found in the gut microbiomes of 80% of humans 44 , R. gnavus is linked to patients with CD and produces a proinflammatory carbohydrate 45 , and B. thetaiotaomicron is one of the most prevalent members of the gut microbiota and dedicates a large portion of its genome to the processing and utilization of carbohydrates 46 . We obtained these exact protein sequences from the CAZy sequence database to use as the reference set for dbCAN2 DIAMOND+CAZy.…”

Section: Resultsmentioning

confidence: 99%

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

Thurimella,

Mohamed,

Graham

et al. 2023

Preprint

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In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn's disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

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Genetic manipulation of the human gut bacterium Eggerthella lenta reveals a widespread family of transcriptional regulators

Cited by 23 publications

References 48 publications

Eggerthella lenta augments preclinical autoantibody production and metabolic shift mimicking senescence in arthritis

Eggerthella lenta augments preclinical autoantibody production and metabolic shift mimicking senescence in arthritis

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

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