Liquorilactobacillus nageliiL. nagelii
Its ID gene (16S) · each bar is one letter A C G T NCBI 16S
What's this?
Zoomed in: letters 115–138
  1. A
  2. G
  3. A
  4. C
  5. T
  6. T
  7. G
  8. A
  9. G
  10. T
  11. G
  12. C
  13. A
  14. G
  15. A
  16. A
  17. G
  18. A
  19. G
  20. G
  21. A
  22. G
  23. A
  24. G

Its ID gene: the stretch scientists read to tell microbes apart. Each bar is one letter of its DNA. Every species has its own order of letters, so the pattern works like a fingerprint. Close relatives share most of it.

16S rRNA gene, V4 region: the 253 letters between the standard primers (515F/806R), which are the same in every species. One reference sequence; strains can differ by a letter or two. NCBI NR_041007.1

NCBI Taxonomy 82688 Plate · Bacillota

Liquorilactobacillus nagelii

Formerly Lactobacillus nagelii.

See it on the map →
Also known as Lactobacillus nagelii
Lab traits Gram +Rod-shapedNon-motileAnaerobe Lab-tested · BacDive
The whole of its DNA, to scale

Its genome

Genome · 2.6 million letters · 37% G+C
Shortest we have Mycoplasmoides genitalium 0.6M
⋮ 723 shorter
Liquorilactobacillus nagelii 2.6M
Liquorilactobacillus average 2.6M
⋮ 1,628 longer
Longest we have Bradyrhizobium altum 9.8M

Small for a gut microbe: longer than 31% of the 2,354 we have genomes for.

37% G+C, higher than 16% of gut microbes.

To scale: if its ID gene were 1 cm long, its genome would stretch 17 meters. Yours would stretch 21 kilometers. One reference genome; strains vary. NCBI GCF_019444005.1

Capabilities, not measurements of anyone's gut

What it can do

Not seen in its lab tests: ferment fiber, eat inulin, use lactose.

All 36 capabilities and their evidence
CapabilityVerdict Genome analysis Lab tests Published research
Can ferment fiberNot seen—0 of 1 strain · strip test No papers
Can eat inulinNot seen—0 of 1 strain · strip test No papers
Can use lactoseNot seen—0 of 1 strain No papers
Can make butyrateNot checked——No papers
Can make propionateNot checked——No papers
Can make lactic acidNot checked——No papers
Can make acetateNot checked——No papers
Can make succinateNot checked——No papers
Can make formateNot checked——No papers
Can eat resistant starchNot checked——No papers
Can eat starchNot checked——No papers
Can eat pectinNot checked——No papers
Can feed on gut mucusNot checked——No papers
Can change bile acidsNot checked——No papers
Can free bile acidsNot checked——No papers
Can make secondary bile acidsNot checked——No papers
Can reshape bile acidsNot checked——No papers
Can make B vitaminsNot checked——No papers
Can make thiaminNot checked——No papers
Can make riboflavinNot checked——No papers
Can make niacinNot checked——No papers
Can make pyridoxineNot checked——No papers
Can make biotinNot checked——No papers
Can make folateNot checked——No papers
Can make cobalaminNot checked——No papers
Can make vitamin K2Not checked——No papers
Can make hydrogenNot checked——No papers
Can make hydrogen sulfideNot checked——No papers
Can make methaneNot checked——No papers
Can ferment proteinNot checked——No papers
Can make indoleNot checked——No papers
Can make GABANot checked——No papers
Can make TMANot checked——No papers
Can make histamineNot checked——No papers
Can make tyramineNot checked——No papers
Can make ethanolNot checked——No papers

AGORA2 (Heinken et al. 2023) via VMH, CC BY 4.0 · BacDive (DSMZ), CC BY 4.0 · NJC19 (Lim et al., Scientific Data 2020), CC0

What’s typical · a description, not a comparison

In healthy guts

Everyone · 12,213 people · 129 datasets
How common

Found in under 1% of healthy people.

How big a slice of the gut
  1. largest Segatella copri 6.2%
  2. 2nd largest Phocaeicola vulgatus 5.0%
  3. 3rd largest Faecalibacterium prausnitzii 4.7%
  4. 4th largest Bacteroides uniformis 3.2%
  5. 5th largest Agathobacter rectalis 2.5%
  6. 6th largest Alistipes putredinis 2.3%
  7. 7th largest Bifidobacterium longum 2.1%
  8. 8th largest Ruminococcus bromii 1.8%
  9. Liquorilactobacillus nagelii under 0.01%
  10. 2,169 other microbes, each a sliver 56.7%
  11. 15.6%

On average, a sliver, under 0.01% of the microbes in a healthy gut.

By age and sex

Every group, drawn the same way. Only some samples record age or sex, so each group says how many people it comes from. Tap one to see it above.

By age
367 people · 0% carry it 0.00% of the microbes
198 people · 5 datasets 0% carry it 0.00% of the microbes
57 people · 0% carry it 0.00% of the microbes
1,283 people · 27 datasets 0% carry it 0.00% of the microbes
142 people · 15 datasets 0% carry it 0.00% of the microbes
By sex
2,063 people · 0% carry it 0.00% of the microbes
1,530 people · 29 datasets 0% carry it 0.00% of the microbes

Where these numbers come from

Who
12,213 healthy people from 129 datasets in GMrepo, by its own “Health” label. Whole-genome sequencing only; one sample is one person.
Dataset
A dataset is the samples one study published. We analyse them ourselves, so a count of datasets isn’t a count of what those studies concluded.
How common
The share of them whose sample has any of it.
Slice
Its average share of the microbes in a healthy gut, across everyone, carriers or not. The slices add up to 84.4%: the rest is reads GMrepo couldn’t place at this rank, shown as “Not identified to a species”.
Age and sex
From the samples that record them (about 17% record an age and 29% a sex). A group with fewer than 50 people isn’t shown.
Ranks
A slice’s rank is a place among the 2,178 microbes we have numbers for, within the group shown: exact in the top 10, then rounded bands.
Analysis
Ours, of GMrepo’s per-sample data. GMrepo on Data sources
The 129 datasets behind these numbers

Largest first by people. Each is a GMrepo project.

DatasetPeopleWith ageWith sex
A dataset in the United States PRJNA695570 ↗ 1,767 0 1,759
A dataset in Japan PRJNA1077400 ↗ 1,268 0 0
A dataset in Spain PRJEB61680 ↗ 839 0 0
Zhong et al. 2019 · the Netherlands PRJEB26795 ↗ 572 0 0
A dataset in the Netherlands PRJNA942468 ↗ 517 0 0
Shang et al. 2021 · Australia PRJNA492716 ↗ 370 0 84
A dataset in the United States PRJNA398089 ↗ 363 237 363
A dataset in the United States PRJNA795985 ↗ 290 0 0
Zhang et al. 2022 · China PRJNA801051 ↗ 239 0 0
A dataset in Ireland PRJEB46398 ↗ 237 0 0
Valles-Colomer et al. 2023 · Italy PRJEB63351 ↗ 225 0 0
A dataset in the United States PRJNA633576 ↗ 225 223 0
Lee et al. 2024 · the United Kingdom PRJEB52380 ↗ 177 0 0
A dataset in the United States PRJEB37924 ↗ 164 0 0
A dataset in China PRJNA542578 ↗ 151 0 0
A dataset in the United States PRJNA993675 ↗ 142 142 142
Lee et al. 2022 · South Korea PRJEB45443 ↗ 112 0 0
A dataset in China PRJEB65297 ↗ 111 0 0
A dataset in India PRJNA397112 ↗ 110 110 0
A dataset in Japan PRJDB13214 ↗ 105 0 0
A dataset in China PRJNA686835 ↗ 104 0 0
A dataset in China PRJNA763023 ↗ 100 0 0
A dataset in China PRJNA994318 ↗ 100 0 0
A dataset in the United States PRJEB28543 ↗ 96 96 96
A dataset in Italy PRJNA716780 ↗ 94 94 94
A dataset in China PRJNA509999 ↗ 93 0 0
A dataset in the United States PRJNA878603 ↗ 92 92 92
Guo et al. 2023 · the United States PRJNA751448 ↗ 91 91 91
A dataset in China PRJNA793776 ↗ 89 89 89
A dataset in China PRJNA888860 ↗ 88 0 0
A dataset in Ireland PRJEB15388 ↗ 85 0 0
A dataset in China PRJNA731589 ↗ 85 85 0
A dataset in Israel PRJNA994433 ↗ 80 80 0
A dataset in Canada PRJNA521587 ↗ 79 78 79
La Reau et al. 2023 · the United States PRJNA917645 ↗ 76 0 0
Pomyen et al. 2023 · Thailand PRJNA932948 ↗ 76 0 76
A dataset in the United States PRJEB53403 ↗ 75 45 45
Eriksen et al. 2023 · Sweden PRJNA633381 ↗ 74 0 0
A dataset in China PRJNA356102 ↗ 72 0 0
A dataset in China PRJNA689961 ↗ 70 0 0
A dataset in China PRJNA449784 ↗ 69 0 0
Duru et al. 2024 · Finland PRJEB59350 ↗ 66 0 0
Roth-Schulze et al. 2021 · Australia PRJNA604850 ↗ 66 66 48
Wan et al. 2024 · China PRJNA1037036 ↗ 64 64 64
A dataset in China PRJNA686821 ↗ 64 0 0
Fulcher et al. 2022 · the United States PRJNA836336 ↗ 64 0 0
Hu et al. 2023 · China PRJNA893406 ↗ 64 0 0
A dataset in Italy PRJNA553191 ↗ 62 0 0
Wilson et al. 2024 · the United States PRJEB59709 ↗ 59 0 0
A dataset in the United States PRJNA822660 ↗ 58 58 58
A dataset in the United States PRJNA314903 ↗ 50 0 0
A dataset in the United States PRJNA379741 ↗ 50 0 50
A dataset in China PRJNA674716 ↗ 50 0 0
A dataset in China PRJNA762484 ↗ 49 20 21
Yan et al. 2023 · China PRJNA808806 ↗ 48 0 0
Stockdale et al. 2022 · Ireland PRJNA813736 ↗ 48 40 41
Tao et al. 2025 · China PRJNA1135717 ↗ 47 0 0
A dataset in the United States PRJNA660443 ↗ 44 0 0
A dataset in China PRJNA946183 ↗ 44 0 0
A dataset in China PRJNA530971 ↗ 43 0 0
Ye et al. 2023 · China PRJNA853814 ↗ 42 42 0
A dataset in China PRJNA943232 ↗ 42 42 42
A dataset in the United Kingdom PRJEB17978 ↗ 41 0 0
A dataset in China PRJNA588035 ↗ 39 0 0
A dataset in China PRJNA349463 ↗ 33 0 0
A dataset in India PRJNA531203 ↗ 31 0 0
A dataset in Switzerland PRJEB24557 ↗ 30 0 0
A dataset in India PRJNA792685 ↗ 30 0 0
A dataset in China PRJNA808649 ↗ 30 30 30
Pötgens et al. 2024 · Belgium PRJNA813705 ↗ 30 0 0
Grion et al. 2024 · Spain PRJNA961076 ↗ 30 0 0
A dataset in Canada PRJEB21933 ↗ 29 0 0
A dataset PRJEB32767 ↗ 29 0 0
A dataset in the United States PRJEB53401 ↗ 29 29 29
A dataset in Poland PRJNA1125836 ↗ 28 28 28
Chen et al. 2023 · China PRJEB55711 ↗ 27 0 0
A dataset in the United States PRJNA629755 ↗ 27 0 0
A dataset in China PRJNA818062 ↗ 27 0 0
Wang et al. 2023 · China PRJNA905444 ↗ 27 0 0
A dataset in Spain PRJEB62571 ↗ 26 0 0
A dataset in China PRJNA769284 ↗ 26 0 0
Lv et al. 2024 · China PRJEB52189 ↗ 25 0 0
Xiao et al. 2024 · China PRJNA938297 ↗ 25 25 25
A dataset in Germany PRJEB76677 ↗ 24 0 0
Elie et al. 2023 · France PRJNA648321 ↗ 24 0 0
A dataset in Mali PRJNA936589 ↗ 24 0 0
A dataset in the United States PRJNA1044118 ↗ 23 0 0
A dataset in Turkey PRJNA1167935 ↗ 23 0 0
A dataset in Russia PRJNA762199 ↗ 23 23 23
Ievina et al. 2024 · Latvia PRJEB80379 ↗ 22 0 0
A dataset in China PRJNA896336 ↗ 22 0 0
A dataset in China PRJNA688881 ↗ 21 0 0
Edwinson et al. 2022 · the United States PRJNA705217 ↗ 21 21 21
A dataset in France PRJEB55534 ↗ 20 0 0
A dataset in Canada PRJNA1022308 ↗ 20 0 0
A dataset in China PRJNA1138893 ↗ 20 0 0
Schaefer et al. 2022 · the United States PRJNA800662 ↗ 20 0 0
Ding et al. 2023 · China PRJNA999028 ↗ 20 0 0
A dataset in the United States PRJEB42155 ↗ 18 0 0
A dataset in Japan PRJNA665854 ↗ 18 0 0
A dataset in Canada PRJNA742740 ↗ 17 0 0
Stockdale et al. 2022 · the United States PRJNA626004 ↗ 16 0 0
A dataset in China PRJNA858062 ↗ 16 16 16
A dataset in China PRJNA429990 ↗ 15 0 0
Wan et al. 2019 · China PRJNA532538 ↗ 15 0 0
A dataset in China PRJNA624223 ↗ 15 15 15
A dataset in the United States PRJNA634145 ↗ 15 15 15
A dataset in China PRJNA734379 ↗ 15 0 15
Sadeghi et al. 2024 · France PRJEB70916 ↗ 14 14 14
A dataset in Singapore PRJNA272371 ↗ 14 14 14
A dataset in India PRJNA983946 ↗ 14 14 14
A dataset in Sweden PRJEB38997 ↗ 13 0 0
Seishima et al. 2019 · Japan PRJNA511372 ↗ 13 0 0
A dataset in China PRJEB54728 ↗ 12 0 0
A dataset in Switzerland PRJEB13835 ↗ 11 0 0
A dataset in Japan PRJNA846495 ↗ 11 0 0
Joensen et al. 2017 · Denmark PRJEB14038 ↗ 10 0 0
A dataset in the United Kingdom PRJEB53891 ↗ 10 0 0
A dataset in Iran PRJNA1050885 ↗ 10 0 0
A dataset in China PRJNA730640 ↗ 10 0 0
A dataset in Israel PRJNA939026 ↗ 10 9 0
Ye et al. 2023 · China PRJNA993632 ↗ 10 0 0
A dataset in China PRJNA1024678 ↗ 9 0 0
Wang et al. 2024 · Switzerland PRJNA363003 ↗ 9 0 0
A dataset in the United States PRJNA613834 ↗ 9 0 0
A dataset in the United States PRJNA896226 ↗ 8 0 0
A dataset in Spain PRJNA1029046 ↗ 7 0 0
A dataset in China PRJNA492189 ↗ 5 0 0
A dataset in France PRJNA287878 ↗ 2 0 0