
==== Front
Curr Genomics
Curr Genomics
CG
Current Genomics
1389-2029
1875-5488
Bentham Science Publishers

39086996
CG-25-226
10.2174/0113892029302230240319042208
Life Sciences, Genetics & Genomics, Genetics & Heredity
Genomic and Metagenomic Insights into the Distribution of Nicotine-degrading Enzymes in Human Microbiota
Nicotine-degrading Enzymes in Human Microbiota
Guan Ying 1
Zhu Zhouhai 1
Peng Qiyuan 1
Li Meng 1
Li Xuan 2
Yang Jia-Wei 2
Lu Yan-Hong 2
Wang Meng 2
Xie Bin-Bin 2*
1 Joint Institute of Tobacco and Health, Kunming, 650106, Yunnan, China;
2 State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
* Address correspondence to this author at the State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China; E-mail: xbb@sdu.edu.cn
20 3 2024
2024
25 3 226235
03 1 2024
03 3 2024
10 3 2024
© 2024 The Author(s). Published by Bentham Science Publishers
2024
The Author(s)
https://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode.
Introduction

Nicotine degradation is a new strategy to block nicotine-induced pathology. The potential of human microbiota to degrade nicotine has not been explored.

Aims

This study aimed to uncover the genomic potentials of human microbiota to degrade nicotine.

Methods

To address this issue, we performed a systematic annotation of Nicotine-Degrading Enzymes (NDEs) from genomes and metagenomes of human microbiota. A total of 26,295 genomes and 1,596 metagenomes for human microbiota were downloaded from public databases and five types of NDEs were annotated with a custom pipeline. We found 959 NdhB, 785 NdhL, 987 NicX, three NicA1, and three NicA2 homologs.

Results

Genomic classification revealed that six phylum-level taxa, including Proteobacteria, Firmicutes, Firmicutes_A, Bacteroidota, Actinobacteriota, and Chloroflexota, can produce NDEs, with Proteobacteria encoding all five types of NDEs studied. Analysis of NicX prevalence revealed differences among body sites. NicX homologs were found in gut and oral samples with a high prevalence but not found in lung samples. NicX was found in samples from both smokers and non-smokers, though the prevalence might be different.

Conclusion

This study represents the first systematic investigation of NDEs from the human microbiota, providing new insights into the physiology and ecological functions of human microbiota and shedding new light on the development of nicotine-degrading probiotics for the treatment of smoking-related diseases.

Keywords

Nicotine-degrading enzymes
human microbiota
metagenomes
NicX
nicotine degradation
genome annotation
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pmc1 INTRODUCTION

Tobacco smoke contains thousands of chemicals, including the well-known nicotine [1-4], and tobacco smoking is the leading cause of preventable death [5]. Smoking cessation is effective in improving the quality of life, but it is difficult to achieve for smokers. Nicotine degradation is considered a new strategy to block nicotine-induced pathology [6, 7]. Consistently, a recent study reported that smoking-related non-alcoholic steatohepatitis (NASH) can be alleviated by the degradation of gut nicotine with a gut bacterium Bacteroides xylanisolvens J1101, whose genome encodes a nicotine-degrading enzyme (NDE), NicX [8]. B. xylanisolvens is the first reported endogenous nicotine-degrading microbe from human microbiota, and its NicX is the first reported nicotine-degrading enzyme from human microbiota. Sequence and structural analyses have revealed that NicX from B. xylanisolvens is homologous to NicA1, an NDE from an environmental bacterium, P. putida S16 [8]. This observation implies that human microbiota may have NDEs that are also present in environmental microorganisms. It remains unclear whether other bacteria from human microbiota can also degrade nicotine. It is also unclear what kinds of NDEs human microbiota can produce.

Compared to the human microbiota, nicotine degradation by environmental bacteria has been studied more extensively. So far, three pathways for nicotine degradation have been identified from environmental bacteria: the pyridine pathway, the pyrrolidine pathway, and the variant of the pyridine and pyrrolidine pathway [9, 10]. The pyridine pathway has been systematically studied in Paenarthrobacter nicotinovorans pAO1, in which the first step is the hydroxylation of nicotine by nicotine dehydrogenase (Ndh), which contains three subunits, NdhM (medium subunit), NdhS (small subunit), and NdhL (large subunit), generating 6-hydroxynicotine [11, 12]. The pyrrolidine pathway has been systematically studied in P. putida S16, in which the nicotine is firstly converted to N-methylmyosmine by a nicotine oxidoreductase NicA1 or NicA2 [13, 14]. The variant of pyridine and pyrrolidine pathway was systematically studied in Agrobacterium sp. S33 [15]. Similar to the pyridine pathway, the first step of this pathway is also the hydroxylation of nicotine catalyzed by a nicotine dehydrogenase, which has a small subunit (NdhA) and a large subunit (NdhB) [15]. However, it is unclear whether the pathways existing in environmental bacteria also exist in human microbiota.

While nicotine degradation by bacteria seems a promising strategy to alleviate smoking-related diseases, only an endogenous nicotine-degrading bacterium has been identified from human microbiota, limiting the development of nicotine-degrading probiotics for alleviating smoking-induced diseases. To address this issue, we performed systematic annotations of nicotine-degrading enzymes in the human microbiota. By analyzing 26,295 genomes and 1,596 metagenomes from human microbiota, we found homologs of the current known NDEs, revealed bacterial groups producing these enzymes, and uncovered the distribution preference of NDE-producing microbiota in different body sites. This study provides insights into the nicotine-degrading potential of human microbiota, shedding new light on the development of nicotine-degrading probiotics for the treatment of smoking-related diseases.

2 MATERIALS AND METHODS

2.1 Genome and Metagenome Collection

Genome and metagenome sequences were downloaded from nine large projects, including (1) 2,820 assembled genomes from the Human Microbiome Project (HMP) [16] (NCBI BioProject accession PRJNA43021), (2) 8,622 genomes from the Expanded Human Oral Microbiome Database (eHOMD) [17] (https://www.homd.org/ftp//genomes/PROKKA/V10.1/), (3) 6,185 metagenome-assembled genomes from human reference gut microbiome catalog [18] (5,414 from https://www.mbiomenet.org/HRGM/listdir.php?directory=data, 280 from NCBI BioProject accession PRJNA678426, and 491 from NCBI BioProject accession PRJNA730993), (4) 3,324 genomes from the reference genomes of cultivated human gut bacteria project [19] (1,520 from NCBI BioProject PRJNA482748 and 1,804 from NCBI BioProject PRJNA903559), (5) 97 genomes from a cultured biobank of human gut microbiome [20] (NCBI BioProject PRJNA656402), (6) 216 genomes from a culture study by Browne et al. [21] (European Nucleotide Archive, ENA project accession PRJEB10915), (7) 452 metagenomes from Human Oral v1.0 [22] (https://www.ebi.ac.uk/metagenomics/genome-catalogues/human-oral-v1-0), (8) 4,579 metagenome-assembled genomes from the Unified Human Gastrointestinal Genome (UHGG) collection [23] (ENA project accession PRJEB33885), and (9) short reads for 558 metagenomes from Integrative Human Microbiome Project (iHMP) [24] (https://www.hmpdacc.org/).

In addition, the reads files for other eight metagenomic studies were downloaded, including 107 gut samples from Chen et al. [8], 162 gut samples from Wang et al. [25], 9 gut samples from Mas-Lloret et al. [26], 234 gut samples and 295 oral samples from Zhang et al. [27], 38 lung samples from Ren et al. [28], 47 lung samples from Zheng et al. [29], 18 lung samples from Feigelman et al. [30], and 128 gut samples from Ma et al. [31]. For these samples, the associated information (smoking status and the sampling body site) was obtained, if available.

2.2 Metagenome Assembly

The downloaded reads were firstly trimmed with trimmomatic v.0.39 [32] and then assembled using MEGAHIT v.1.2.9 [33]. Protein coding genes were predicted with MetaGeneMark v.3.38 [34].

2.3 Reference NDE Sequence Collection

Reference protein sequences for NDEs were downloaded from the NCBI nr database, including NdhL from strain pAO1 (CAD47952.1), NdhL from strain Y22 (AHK24899.1), NdhB from strain S33 (AMD56949.1), VppAL from strain SJY1 (AIH15806.1), NicA1 from strain S16 (AEJ10966.1), NicA2 from strain S16 (AEJ14620.1), and Nox from strain HZN6 (AGH68979.1). NicX sequence from B. xylanisolvens J1101 was obtained from the literature [8].

2.4 Outgroup Sequence Collection

To achieve high prediction accuracy, especially to reduce false positives, outgroup sequences of NDEs were obtained and used as negative controls. These outgroup sequences were not NDEs but showed detectable similarities to NDEs. The xanthine dehydrogenase large subunit sequences (XdhB, Uniprot accessions O54051, Q8RLC0, and Q9I3J0; the NCBI nr database accession ALT22391.1) were used as the outgroup for NdhB and NdhL. The group II intron reverse transcriptase sequences were used as the outgroup for NicA1 (PDB ID 5G2X) and NicX (PDB IDs 5HHJ and 6AR3). No outgroup sequences were found for NicA2.

2.5 NDE Annotation Pipeline

For each type of NDEs, the references were searched against the protein sequences of each downloaded (meta) genome with DIAMOND v.2.0.15.153 (parameters “--very-sensitive -e 0.001 -k 100000 --max-hsps 0 -f 6 qseqid sseqid pident qcovhsp qlen slen length mismatch gapopen qstart qend sstart send evalue bitscore”) [35]. Hits with the reference coverage < 40% or identity < 30% were excluded. Hits with sequence length < 50% of the query reference sequence length or > 150% of the query reference sequence length were also excluded. The remaining hits were mentioned as the candidate NDEs. The candidate NDEs were then filtered with the help of outgroup sequences based on the sequence search score and phylogenetic analyses as follows. Each candidate NDE sequence was searched against the NDE reference sequences and the corresponding outgroup sequence(s) with DIAMOND v.2.0.15.153 (parameters “--very-sensitive -e 0.001 -k 100000 --max-hsps 0 -f 6 qseqid sseqid pident qcovhsp qlen slen length mismatch gapopen qstart qend sstart send evalue bitscore”). The highest score with the reference sequences (ScoreRef) was compared with the highest score with the outgroup sequences (ScoreOut). The candidate sequence with ScoreRef < 1.2*ScoreOut was excluded. Next, the obtained sequences were aligned by using MAFFT v.7.520 (parameters “--maxiterate 1000 --globalpair --reorder”) [36] and a phylogenetic tree was reconstructed for each type of NDE with FastTree v.2.1.11 with default parameters [37]. A candidate sequence was excluded if its phylogenetic distance to the most closely related reference sequences was close to or bigger than that to the most closely related outgroup sequences. The resultant sequences were thought of as the annotated NDEs.

2.6 Taxonomic Classification

For each genome that contained genes for NDEs, the taxonomic classification was inferred using Genome Taxonomy Database Toolkit (GTDB-Tk) v.2.1.1 [38] with the workflow ‘classify_wf’ against the GTDB release R06-RS202 [39].

2.7 Phylogenetic Tree Reconstruction

The protein sequences for each type of NDEs were aligned using MAFFT v.7.520 (parameters “--maxiterate 1000 --globalpair --reorder”) [36]. The resultant alignment was inspected with the help of BioEdit v.7.0.9.0 [40] to remove sequences with too long insertions or deletions. The obtained alignment was used to reconstruct the phylogenetic tree with FastTree v.2.1.11 with default parameters [37]. The tree was visualized with the help of MEGA v.11.0.13 [41].

2.8 Three-dimensional Structure Prediction

The three-dimensional structures were predicted by using AlphaFold2 [42].

3 RESULTS

3.1 Collecting Genomes, Metagenomes, and Reference Sequences for NDEs

We collected a total of 26,295 assembled genomes for the human microbiome from public databases, including 3,637 genomes for pure cultures and 22,658 metagenome-assembled genomes. Moreover, 558 assembled metagenomes were downloaded. Short reads for 1,038 microbiota samples were also downloaded. For the genome and metagenome sequences without annotation, we predicted the protein-encoding genes. For the short reads from metagenome sequencing, we first assembled the metagenomes and then performed annotation. We also performed taxonomic classification for downloaded genomes (for both pure cultures and metagenome-assembled genomes) based on the genome sequence. Taken together, our final data sets contained protein sequences for a total of 26,295 genomes and 1,596 metagenomes.

To improve the annotation accuracy, we only included the protein sequences of the well-characterized NDEs as the reference. These sequences included (1) NicX from the gut bacterium B. xylanisolvens J1101, (2) Ndh large subunit L (NdhL) from P. nicotinovorans pAO1 and NdhL from Rhodococcus sp. Y22 (the pyridine pathway), (3) NicA1 and NicA2 from P. putida S16 (the pyrrolidine pathway), and (4) Ndh large subunit B (NdhB) from Agrobacterium sp. S33 and NdhB (designated as VppAL) from Ochrobactrum sp. SJY1 (the variant of pyridine and pyrrolidine pathway). In addition, we also collected the sequences for enzymes distantly related to NdhL, NdhB, NicX, or NicA1. These outgroup sequences were used to refine our prediction results, especially for excluding ambiguous predictions. No suitable outgroup sequence was found for NicA2.

3.2 Annotation of NDE Homologs

We developed an annotation pipeline to predict NDEs from genomes and metagenomes. To achieve high accuracy, ‘outgroup’ sequences were included in the pipeline as negative controls. These outgroup sequences were not from NDEs but showed detectable similarities to NDEs. In our pipeline, each obtained candidate NDE sequence was compared to the reference and outgroup, and those showing higher or close similarities to the outgroup than to the reference were excluded (Materials and Methods for details).

With the above pipeline, we predicted 959 NdhB, 785 NdhL, 987 NicX, three NicA1, and three NicA2 homologs (Fig. 1a and Supplementary materials 1-5), clearly indicating that NDEs are widely present in human microbiota and that NicX is not the only type of NDEs present in human microbiota. The distribution of different types of NDEs was clearly different. NdhB, NdhL, and NicX were rich in human microbiota. A few NicA1 and NicA2 homologs were also found. Interestingly, it was noted that nearly all NdhB and NdhL homologs were found in genomes, and few were found in metagenomes (Fig. 1a). In comparison, only a minor portion of NicX homologs were found in the genomes. Analyses of the (meta)genome number revealed the same distribution (Fig. 1b). However, the reason for this observation is unclear. It might be related to the abundance of NDE-producing bacteria in the microbiota.

Since the annotation pipeline has excluded the candidate NDE homologs showing high similarities to the outgroup sequences, for all types of NDEs except for NicA2, the predicted NDEs had shorter distances to the reference NDEs than to the outgroup sequences on the phylogenetic trees (Fig. 2a-e, green, reference, orange, outgroup). It was noted that while most NDE homologs were distantly related to the reference sequences, there were cases that predicted that homologs were highly similar to the references. For example, 67 NicX homologs were identical to the reference. Among them, four were from genomes, and taxonomic classification revealed that all four sequences (genome CABIXU02, gene_4266; genome HRGM_ Genome_1421, CDS_04332; genome AM23-12_scaffold, gene_567; genome GCA_001405055.1, gene_1552) were from the genus Bacteroides, including one (genome GCA_001405055.1, gene_1552) from B. xylanisolvens, the species in which the reference NicX was found. For NdhB, three homologs with identical sequences to the reference were found (genome SEQF8741.1, gene_00298; genome SEQF8748.1, gene_03516; and genome SEQF8770.1, gene_03351). Taxonomic classification revealed that these identical sequences were also from the genus Agrobacterium, to which the source organism of the reference NdhB also belongs. For the three homologs found for NicA1, one (genome SEQF8252.1, gene_03737) showed high identity (73.2%) to the reference sequence (Fig. 2d). This NicA1 homolog was found in the genus Pseudomonas, to which the source organism of the reference NicA1 also belongs.

We predicted the three-dimensional structures of the NDE homologs distantly related to the reference. Structural superposition revealed that these distant NDE homologs had highly similar overall structures to the reference NDEs (Fig. 2, right panels), except that the local structures of the short N-terminal stretches were not predicted for several sequences.

3.3 Taxonomic Classification of Bacteria Producing NDEs

To reveal the potentials of different taxa to encode NDEs and the phylogenetic distribution of the NDEs, genomes harboring NDE genes were taxonomically classified based on the genome sequences (Figs. 3a-f). It was clearly shown that different NDEs had different distributions. At the phylum level, NDEs were found in six taxa. NicX had the widest distribution, and it was found in five phyla, including Proteobacteria (43.5%), Firmicutes_A (26.3%), Bacteroidota (19.9%), Firmicutes (9.1%), and Actinobacteriota (1.1%). NdhL was found in four phyla, including Proteobacteria (77.4%), Actinobacteriota (19.4%), Firmicutes (3.0%), and Chloroflexota (0.2%). NdhB was found only in two phyla, including Proteobacteria (99.7%) and Bacteroidota (0.3%). For NicA1, only three homologs were found, and they were all from the same phylum, Proteobacteria. For NicA2, all three homologs were also from the same phylum, Proteobacteria. Generally, the phylum Proteobacteria showed the strongest potential to encode various types of NDEs, and it encoded all five types of NDEs studied here. In comparison, only two types of NDEs (NicX and NdhB) were found in the phylum Bacteroidota.

Comparison at different taxonomic levels revealed that NicX, NdhL, and NdhB shared a number of lower-rank taxa, though the ratios may vary. Generally, NdhL and NdhB had similar distributions. For example, at the genus level, all NDEs were found in a total of 77 genera. There were 26 genera containing both NdhL (found in 42 genera) and NdhB (found in 37 genera). In comparison, there were only six genera (Bradyrhizobium, Burkholderia, Pseudomonas, Pseudomonas_A, Pseudomonas_E, and Variovorax) containing both NicX (found in 31 genera) and NdhB, and only six genera (Bradyrhizobium, Burkholderia, Pseudomonas_A, Pseudomonas_E, Rhizobium, and Variovorax) containing both NicX and NdhL. Moreover, a remarkable difference in the distribution between NdhL and NdhB was also observed. The genus Mycobacterium was the most abundant genus possessing NdhL (16.4%). No NdhB was found in this genus.

3.4 Different Distributions Among Body Sites

The above analyses revealed that NicX homologs were found in a number of microbiota samples. To investigate whether NicX is associated with specific body sites, the proportions of microbiota samples encoding NicX (prevalence) were calculated. The collected samples could be classified into three categories, i.e., gut samples (n = 640), lung samples (n = 103), and oral samples (n = 295). It was clearly shown that NicX homologs were found in a high proportion of gut samples (41.7%) and oral samples (36.6%) (Fig. 4a). Differently, no NicX homologs were found in lung samples (0%). The oral samples could be further classified into three sub- categories: tooth (n = 72), plaque (n = 134), and saliva (n = 89) [27]. Comparison of these sub-categories also revealed remarkable differences. NicX was found in up to 76.4% of tooth samples, which was 2.05 times the proportion of plaque samples (37.3%) (Fig. 4b). In comparison, the proportion of saliva samples was very low (3.4%). These results demonstrated that NicX had different distributions among body sites.

3.5 Comparison of Distributions between Smokers and Non-smokers

To investigate whether the NDE distribution in microbiota is related to smoking status, the ratios of gut microbiota samples encoding NicX were compared between smokers and non-smokers. Datasets from two studies were included in our analyses [8, 25]. Results indicated that NDEs were present not only in the microbiota of smokers but also in that of non-smokers (Fig. 5). Furthermore, it was noted that the NDE prevalence levels were different between the two studies (Fig. 5). In the study of Chen et al. [8], smokers and non-smokers showed a high and similar NDE prevalence (64.1% vs. 67.6%). In the study of Wang et al. [25], both smokers and non-smokers showed NDE prevalence values lower than that in the study of Chen et al. [8]. Interestingly, the NDE prevalence in smokers was higher than that in non-smokers (44.6% vs. 24.3%).

4 DISCUSSION

Though NDEs from environmental bacteria have been extensively studied [9-15], NicX is the only NDE found in human microbiota so far [8]. The degradation of gut nicotine has been suggested as a strategy to reduce the harmful effects of smoking [6, 7]. A gut bacterium strain, B. xylanisolvens J1101, is able to alleviate smoking-related NASH by degrading gut nicotine microbiota with its NDE, NicX [8]. This study represented the first report on the systematic investigation of NDE-producing abilities of the human microbiota. The study uncovered a large number of bacterial taxa that could produce NDEs, providing new insights into the ecological functions of these taxa in human microbiota. This study also revealed thousands of NDE homologs from the human microbiota, representing a valuable resource of NDEs that merit further studies.

Most NDE homologs found in this study are distantly related to the reference NDE sequences from the environmental bacteria, suggesting that these NDEs encoded by human microbiota diverged from their possible common ancestors a long time ago. Similar to these observations, a great difference was also noted between the two NdhL reference sequences from the environmental bacteria (Fig. 2b, green). The reference NicX from the gut bacterium B. xylanisolvens J1101 showed similarity to the NicA1 from the environmental bacterium P. putida S16 [8]. Furthermore, it was noted that several NdhB and NicA1 homologs showed high similarities to their respective references from environmental bacteria. Taxonomic annotation revealed that these NDE homologs were from the same genus or species as the reference sequences. These results indicated that, while maintaining a highly conserved three-dimensional structure, NDEs from distant taxa have less conserved sequences.

It was noted that the main types of NDE sequences annotated in genomes are different from those in metagenomes (NdhB + NdhL vs. NicX) (Fig. 1). The reason for this observation remains unclear. One possible reason is that the number of genomes included in this dataset is small compared to the high diversity of the microorganisms in the human microbiota. As a result, those found in genomes may not be found in metagenomes due to the low abundances of the species harboring the NDE genes in microbiota samples. Similarly, those found in metagenomes may not be found in genomes since the microorganisms may not be included in the genome collection. It was also noted that NicX is the only NDE type that has a large number of sequences from both genomes and metagenomes. One possible reason is that the reference sequence of NicX is from a human gut bacterium [8] compared to other NDEs whose reference sequences are all from environmental bacteria. The relatively high similarities between the NicX reference sequences and homologs allow for the detection of more sequences from various microorganisms and microbiota.

The annotation pipeline used here was designed to achieve high prediction accuracy by including ‘outgroup’ sequences as negative controls. For each type of NDE, the reference sequence was searched against the NCBI nr database. The sequence names of the resultant top hits were checked. If the top hits have been consistently assigned a name different from the correct name of NDE, it means that the top hits have been assigned the wrong name in the database. For example, the top hits of NicX had the name ‘group II intron reverse transcriptase/group II intron reverse transcriptase/nicotine-degrading enzyme’ or ’group II intron reverse transcriptase/maturase’. These results suggested that the group II intron reverse transcriptase has a sequence similar to NicX, and NicX sequences were assigned the wrong name, ‘group II intron reverse transcriptase’, in the database. This readily happened when only ‘group II intron reverse transcriptase’ sequences were available in the reference database. Therefore, experimentally verified ‘group II intron reverse transcriptase’ sequences were used as outgroup sequences for NicX. By comparing the similarities to the reference and the outgroup, only sequences with remarkably higher similarities to NicX than to the outgroup ‘group II intron reverse transcriptase’ were kept in our results.

This study represents the first systematic investigation of NDEs from the human microbiota. Our results revealed that NDEs from the three major nicotine degradation pathways identified in environmental bacteria also exist in human microbiota. This study revealed a large number of taxa from human microbiota that have the ability to produce NDEs and uncovered different distributions of NDEs among body sites and between smokers and non-smokers, providing insights into the physiology and ecological functions of these taxa in human microbiota. The sequences of thousands of NDE homologs found in this study represent a valuable resource for the study of NDE enzymology.

Tobacco smoke contains various detrimental components, such as nicotine. Nicotine degradation is thought of as a new strategy to block nicotine-induced pathology [6, 7]. Chen et al. (2022) demonstrated the feasibility of this strategy [8]. In their study, a bacterium harboring an NDE gene nicX was used to degrade gut nicotine in mice to alleviate smoking-related NASH [8], suggesting that probiotics harboring NDEs may be used in the treatment of smoking-related diseases. So far, no other studies on screening nicotine-degrading gut bacteria have been reported and it remains unclear whether such bacteria are widely present in the human gut microbiota. This study showed that the NDE genes are widely present in human gut microbiota and thus demonstrated the feasibility of screening nicotine-degrading gut bacteria for the treatment of smoking-related diseases. There are two possible ways to screen the nicotine-degrading bacteria from the gut. One is the cultivation and screening of nicotine-degrading bacteria from gut samples. An increasing number of nicotine-degrading bacteria have been isolated from environmental samples [43]. Moreover, the media methods used in these studies may be helpful for the design of nicotine enrichment/selection media for gut bacteria. An alternative way is screening nicotine-degrading strains from species whose members have been found to harbor NDE genes. One such candidate species is B. xylanisolvens, which has been verified to be safe for in vivo use [44], and the strain B. xylanisolvens J1101 has the nicX gene [8]. Another candidate is Blautia wexlerae. It has been shown that Blautia is a new functional genus with potential probiotic properties [45], and our data showed that the nicX gene was widely present in B. wexlerae (Figs. 3e and f).

CONCLUSION

This study provides new insights into the association between human microbiota and health and sheds new light on the development of nicotine-degrading probiotics for the treatment of smoking-related diseases.

ACKNOWLEDGEMENTS

Declared none.

LIST OF ABBREVIATIONS

eHOMD Expanded Human Oral Microbiome Database

HMP Human Microbiome Project

iHMP Integrative Human Microbiome Project

NASH Non-alcoholic Steatohepatitis

NDEs Nicotine-degrading Enzymes

Ndh Nicotine Dehydrogenase

UHGG Unified Human Gastrointestinal Genome

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

HUMAN AND ANIMAL RIGHTS

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The authors confirm that the data supporting the findings of this research are available within the article.

FUNDING

This work was supported by the Joint Institute of Tobacco and Health Open Project Fund (Grant No. 2021539200340050), Science and Technology Project of China Tobacco Yunnan Industrial Co., Ltd (Grant No. 2021JC07), State Key Laboratory of Microbial Technology Open Projects Fund (Grant No. M2022-04), and the Youth Interdisciplinary Science and Innovative Research Groups of Shandong University (Grant No. 2020QNQT006).

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

SUPPLEMENTARY MATERIAL

Supplementary material is available on the publisher’s website along with the published article.

Fig. (1) NDEs annotated from genomes and metagenomes. (a). Sequence number for each type of NDEs found from genomes (black) and metagenomes (red). (b). The number of genomes (black) and metagenomes (red) that contained NDEs.

Fig. (2) Phylogenetic trees for NDEs and structure superposition. (a). NdhB. Green, the reference sequences, including NdhB from strain S33 and VppAL from strain SJY1. Orange, the outgroup sequences. Right panel, structures of NdhB from S33 (magenta) and NdhB from genome HRGM_Genome_4330 (CDS_00127) (cyan). (b). NdhL. Green, references sequences, including NdhL from strain pAO1 and that from strain Y22. Orange, outgroup sequences. Right panel, structures of NdhL from strain pAO1 (magenta) and NdhB from genome SEQF5863.1 (gene_03686) (cyan). (c). NicX. Green, reference sequence, i.e., NicX from strain J1101. Orange, outgroup sequence. Right panel, structures of NicX from strain J1101 (magenta) and NicX from genome HRGM_Genome_1350 (CDS_00200) (cyan). (d). NicA1. Green, reference sequence, i.e., NicA1 from strain S16. Orange, outgroup sequence. Right panel, structures of NicA1 from S16 (magenta) and NicA1 from genome HRGM_Genome_1933 (CDS_08276) (cyan). (e). NicA2. Green, reference sequence, i.e., NicA2 from strain S16. Right panel, structures of NicA2 from S16 (magenta) and NicA2 from genome SEQF8741.1 (gene_00296) (cyan). The bar indicated 0.50 substitutions per site. RT, the group II intron reverse transcriptase. XDH, the xanthine dehydrogenase large subunit.

Fig. (3) Taxonomic classification of genomes that encode NicX, NdhB, and NdhL. (a). The phylum level. (b). The class level. (c). The order level. (d). The family level. (e). The genus level. (f). The species level.

Fig. (4) Prevalence of NicX in human microbiota samples from different body sites. A total of 640 gut samples, 103 lung samples, and 295 oral samples were analyzed (a). The oral samples included 72 tooth samples, 134 plaque samples, and 89 saliva samples (b).

Fig. (5) Prevalence of NicX in gut microbiota from smokers and non-smokers. (a). Prevalence based on samples from the study [8]. A total of 39 smoker samples and 68 non-smoker samples were analyzed. (b). Prevalence based on samples from the study [25]. A total of 92 smoker samples and 70 non-smoker samples were analyzed. S, smokers; NS, non-smokers.
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REFERENCES

1 Hoffmann D. Hoffmann I. Letters to the Editor - Tobacco smoke components. Beitr. Tabforsch. Int. 1998 18 1 49 52 10.2478/cttr-2013-0668
2 Thielen A. Klus H. Müller L. Tobacco smoke: Unraveling a controversial subject. Exp. Toxicol. Pathol. 2008 60 2-3 141 156 10.1016/j.etp.2008.01.014 18485684
3 Borgerding M. Klus H. Analysis of complex mixtures – Cigarette smoke. Exp. Toxicol. Pathol. 2005 57 Suppl. 1 43 73 10.1016/j.etp.2005.05.010 16092717
4 Talhout R. Schulz T. Florek E. Van Benthem J. Wester P. Opperhuizen A. Hazardous compounds in tobacco smoke. Int. J. Environ. Res. Public Health 2011 8 2 613 628 10.3390/ijerph8020613 21556207
5 World Health Organization WHO global report on trends in prevalence of tobacco use 2000-2025. 4th ed Geneva World Health Organization 2021
6 Xue S. Schlosburg J.E. Janda K.D. A new strategy for smoking cessation: Characterization of a bacterial enzyme for the degradation of nicotine. J. Am. Chem. Soc. 2015 137 32 10136 10139 10.1021/jacs.5b06605 26237398
7 Dulchavsky M. Clark C.T. Bardwell J.C.A. Stull F. A cytochrome c is the natural electron acceptor for nicotine oxidoreductase. Nat. Chem. Biol. 2021 17 3 344 350 10.1038/s41589-020-00712-3 33432238
8 Chen B. Sun L. Zeng G. Shen Z. Wang K. Yin L. Xu F. Wang P. Ding Y. Nie Q. Wu Q. Zhang Z. Xia J. Lin J. Luo Y. Cai J. Krausz K.W. Zheng R. Xue Y. Zheng M.H. Li Y. Yu C. Gonzalez F.J. Jiang C. Gut bacteria alleviate smoking-related NASH by degrading gut nicotine. Nature 2022 610 7932 562 568 10.1038/s41586-022-05299-4 36261549
9 Zhang Z. Mei X. He Z. Xie X. Yang Y. Mei C. Xue D. Hu T. Shu M. Zhong W. Nicotine metabolism pathway in bacteria: Mechanism, modification, and application. Appl. Microbiol. Biotechnol. 2022 106 3 889 904 10.1007/s00253-022-11763-y 35072735
10 Mu Y. Chen Q. Parales R.E. Lu Z. Hong Q. He J. Qiu J. Jiang J. Bacterial catabolism of nicotine: Catabolic strains, pathways and modules. Environ. Res. 2020 183 109258 10.1016/j.envres.2020.109258 32311908
11 Grether-Beck S. Igloi G.L. Pust S. Schilz E. Decker K. Brandsch R. Structural analysis and molybdenum-dependent expression of the pAO1-encoded nicotine dehydrogenase genes of Arthrobacter nicotinovorans. Mol. Microbiol. 1994 13 5 929 936 10.1111/j.1365-2958.1994.tb00484.x 7815950
12 Baitsch D. Sandu C. Brandsch R. Igloi G.L. Gene cluster on pAO1 of Arthrobacter nicotinovorans involved in degradation of the plant alkaloid nicotine: Cloning, purification, and characterization of 2,6-dihydroxypyridine 3-hydroxylase. J. Bacteriol. 2001 183 18 5262 5267 10.1128/JB.183.18.5262-5267.2001 11514508
13 Tang H. Wang L. Meng X. Ma L. Wang S. He X. Wu G. Xu P. Novel nicotine oxidoreductase-encoding gene involved in nicotine degradation by Pseudomonas putida strain S16. Appl. Environ. Microbiol. 2009 75 3 772 778 10.1128/AEM.02300-08 19060159
14 Tang H. Wang L. Wang W. Yu H. Zhang K. Yao Y. Xu P. Systematic unraveling of the unsolved pathway of nicotine degradation in Pseudomonas. PLoS Genet. 2013 9 10 e1003923 10.1371/journal.pgen.1003923 24204321
15 Huang H. Yu W. Wang R. Li H. Xie H. Wang S. Genomic and transcriptomic analyses of Agrobacterium tumefaciens S33 reveal the molecular mechanism of a novel hybrid nicotine-degrading pathway. Sci. Rep. 2017 7 1 4813 10.1038/s41598-017-05320-1 28684751
16 Gevers D. Knight R. Petrosino J.F. Huang K. McGuire A.L. Birren B.W. Nelson K.E. White O. Methé B.A. Huttenhower C. The Human Microbiome Project: A community resource for the healthy human microbiome. PLoS Biol. 2012 10 8 e1001377 10.1371/journal.pbio.1001377 22904687
17 Escapa I.F. Chen T. Huang Y. Gajare P. Dewhirst F.E. Lemon K.P. New insights into human nostril microbiome from the expanded human oral microbiome database (eHOMD): A resource for the microbiome of the human aerodigestive tract. mSystems 2018 3 6 e00187-18 10.1128/mSystems.00187-18 30534599
18 Kim CY Lee M Yang S Kim K Yong D Kim HR Lee I Human reference gut microbiome catalog including newly assembled genomes from under-represented Asian metagenomes. Genome Med. 2021 13 1 134 10.1186/s13073-021-00950-7 34446072
19 Lin X. Hu T. Chen J. Liang H. Zhou J. Wu Z. Ye C. Jin X. Xu X. Zhang W. Jing X. Yang T. Wang J. Yang H. Kristiansen K. Xiao L. Zou Y. The genomic landscape of reference genomes of cultivated human gut bacteria. Nat. Commun. 2023 14 1 1663 10.1038/s41467-023-37396-x 36966151
20 Liu C. Du M.X. Abuduaini R. Yu H.Y. Li D.H. Wang Y.J. Zhou N. Jiang M.Z. Niu P.X. Han S.S. Chen H.H. Shi W.Y. Wu L. Xin Y.H. Ma J. Zhou Y. Jiang C.Y. Liu H.W. Liu S.J. Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank. Microbiome 2021 9 1 119 10.1186/s40168-021-01064-3 34020714
21 Browne H.P. Forster S.C. Anonye B.O. Kumar N. Neville B.A. Stares M.D. Goulding D. Lawley T.D. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 2016 533 7604 543 546 10.1038/nature17645 27144353
22 Richardson L. Allen B. Baldi G. Beracochea M. Bileschi M.L. Burdett T. Burgin J. Caballero-Pérez J. Cochrane G. Colwell L.J. Curtis T. Escobar-Zepeda A. Gurbich T.A. Kale V. Korobeynikov A. Raj S. Rogers A.B. Sakharova E. Sanchez S. Wilkinson D.J. Finn R.D. MGnify: The microbiome sequence data analysis resource in 2023. Nucleic Acids Res. 2023 51 D1 D753 D759 10.1093/nar/gkac1080 36477304
23 Almeida A. Nayfach S. Boland M. Strozzi F. Beracochea M. Shi Z.J. Pollard K.S. Sakharova E. Parks D.H. Hugenholtz P. Segata N. Kyrpides N.C. Finn R.D. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 2021 39 1 105 114 10.1038/s41587-020-0603-3 32690973
24 Integrative H.M.P. The integrative human microbiome project. Nature 2019 569 7758 641 648 10.1038/s41586-019-1238-8 31142853
25 Wang P. Dong Y. Jiao J. Zuo K. Han C. Zhao L. Ding S. Yang X. Chen M. Li J. Cigarette smoking status alters dysbiotic gut microbes in hypertensive patients. J. Clin. Hypertens. (Greenwich) 2021 23 7 1431 1446 10.1111/jch.14298 34029428
26 Mas-Lloret J. Obón-Santacana M. Ibáñez-Sanz G. Guinó E. Pato M.L. Rodriguez-Moranta F. Mata A. García-Rodríguez A. Moreno V. Pimenoff V.N. Gut microbiome diversity detected by high-coverage 16S and shotgun sequencing of paired stool and colon sample. Sci. Data 2020 7 1 92 10.1038/s41597-020-0427-5 32179734
27 Zhang X. Zhang D. Jia H. Feng Q. Wang D. Liang D. Wu X. Li J. Tang L. Li Y. Lan Z. Chen B. Li Y. Zhong H. Xie H. Jie Z. Chen W. Tang S. Xu X. Wang X. Cai X. Liu S. Xia Y. Li J. Qiao X. Al-Aama J.Y. Chen H. Wang L. Wu Q. Zhang F. Zheng W. Li Y. Zhang M. Luo G. Xue W. Xiao L. Li J. Chen W. Xu X. Yin Y. Yang H. Wang J. Kristiansen K. Liu L. Li T. Huang Q. Li Y. Wang J. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 2015 21 8 895 905 10.1038/nm.3914 26214836
28 Ren L. Zhang R. Rao J. Xiao Y. Zhang Z. Yang B. Cao D. Zhong H. Ning P. Shang Y. Li M. Gao Z. Wang J. Transcriptionally active lung microbiome and its association with bacterial biomass and host inflammatory status. mSystems 2018 3 5 e00199-18 10.1128/mSystems.00199-18 30417108
29 Zheng L. Sun R. Zhu Y. Li Z. She X. Jian X. Yu F. Deng X. Sai B. Wang L. Zhou W. Wu M. Li G. Tang J. Jia W. Xiang J. Lung microbiome alterations in NSCLC patients. Sci. Rep. 2021 11 1 11736 10.1038/s41598-021-91195-2 34083661
30 Feigelman R. Kahlert C.R. Baty F. Rassouli F. Kleiner R.L. Kohler P. Brutsche M.H. von Mering C. Sputum DNA sequencing in cystic fibrosis: Non-invasive access to the lung microbiome and to pathogen details. Microbiome 2017 5 1 20 10.1186/s40168-017-0234-1 28187782
31 Ma Y. Zhang Y. Jiang H. Xiang S. Zhao Y. Xiao M. Du F. Ji H. Kaboli P.J. Wu X. Li M. Wen Q. Shen J. Yang Z. Li J. Xiao Z. Metagenome analysis of intestinal bacteria in healthy people, patients with inflammatory bowel disease and colorectal cancer. Front. Cell. Infect. Microbiol. 2021 11 599734 10.3389/fcimb.2021.599734 33738265
32 Bolger A.M. Lohse M. Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014 30 15 2114 2120 10.1093/bioinformatics/btu170 24695404
33 Li D. Luo R. Liu C.M. Leung C.M. Ting H.F. Sadakane K. Yamashita H. Lam T.W. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 2016 102 3 11 10.1016/j.ymeth.2016.02.020 27012178
34 Zhu W. Lomsadze A. Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010 38 12 e132 10.1093/nar/gkq275 20403810
35 Buchfink B. Reuter K. Drost H.G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 2021 18 4 366 368 10.1038/s41592-021-01101-x 33828273
36 Nakamura T. Yamada K.D. Tomii K. Katoh K. Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics 2018 34 14 2490 2492 10.1093/bioinformatics/bty121 29506019
37 Price M.N. Dehal P.S. Arkin A.P. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One 2010 5 3 e9490 10.1371/journal.pone.0009490 20224823
38 Chaumeil P.A. Mussig A.J. Hugenholtz P. Parks D.H. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020 36 6 1925 1927 10.1093/bioinformatics/btz848 31730192
39 Parks D.H. Chuvochina M. Rinke C. Mussig A.J. Chaumeil P.A. Hugenholtz P. GTDB: An ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2022 50 D1 D785 D794 10.1093/nar/gkab776 34520557
40 Hall T.A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 1999 41 95 98
41 Tamura K. Stecher G. Kumar S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 2021 38 7 3022 3027 10.1093/molbev/msab120 33892491
42 Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Žídek A. Potapenko A. Bridgland A. Meyer C. Kohl S.A.A. Ballard A.J. Cowie A. Romera-Paredes B. Nikolov S. Jain R. Adler J. Back T. Petersen S. Reiman D. Clancy E. Zielinski M. Steinegger M. Pacholska M. Berghammer T. Bodenstein S. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596 7873 583 589 10.1038/s41586-021-03819-2 34265844
43 Liu J. Ma G. Chen T. Hou Y. Yang S. Zhang K.Q. Yang J. Nicotine-degrading microorganisms and their potential applications. Appl. Microbiol. Biotechnol. 2015 99 9 3775 3785 10.1007/s00253-015-6525-1 25805341
44 EFSA Panel on Dietetic Products Scientific Opinion on the safety of ‘heat-treated milk products fermented with Bacteroides xylanisolvens DSM 23964’ as a novel food. EFSA J. 2015 13 1 3956 10.2903/j.efsa.2015.3956
45 Liu X. Mao B. Gu J. Wu J. Cui S. Wang G. Zhao J. Zhang H. Chen W. Blautia —a new functional genus with potential probiotic properties? Gut Microbes 2021 13 1 1875796 10.1080/19490976.2021.1875796 33525961
