Did you know?
Helicobacter pylori survives your stomach acid using a nickel-powered enzyme (urease) that makes ammonia to neutralize the acid around it. Take away the nickel and it cannot colonize.
Helicobacter pylori is a spiral gastric pathogen that infects about half the world and is the leading cause of gastric cancer. It is built around the metal nickel: its urease and [NiFe] hydrogenase are nickel enzymes essential for surviving stomach acid, which makes nickel handling both its strength and a drug target.
Microbiome-targeted interventions (MBTIs) are validated using a dual-evidence logical framework. First, the intervention must realign the condition’s microbiome signature by increasing beneficial taxa that are consistently depleted and reducing pathogenic taxa that are consistently enriched. Second, the intervention must demonstrate measurable clinical benefit. Concordance of these effects in the same context validates the intervention as an MBTI and supports the clinical relevance of the microbiome signature.
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreHelicobacter pylori is a Gram-negative, spiral (helical) bacterium that colonizes the stomach lining of roughly half the world's population, usually acquired in childhood and persisting for life.[1] It is the leading cause of chronic gastritis and peptic ulcer disease and is classified as a Group 1 (definite) carcinogen: chronic infection is the strongest known risk factor for gastric cancer.[1] On this database it appears as a differential taxon across microbiome studies, most meaningfully in the gastric niche it is built to occupy.
What makes H. pylori distinctive on this site is its dependence on the metal nickel. To survive stomach acid it uses the nickel-dependent enzyme urease to generate ammonia and neutralize its microenvironment, and it uses a nickel-dependent [NiFe] hydrogenase to draw energy from molecular hydrogen for colonization.[2][3] The host counters by using calprotectin to withhold nickel and zinc, so this is a nutritional immunity battle fought over an unusual metal, exactly the lens this database reads pathogens through.[3]
H. pylori is a Gram-negative, microaerophilic, helically shaped rod with multiple sheathed flagella that let it burrow through the gastric mucus layer to reach the epithelium.[1] Its spiral shape and motility, together with acid-neutralizing urease, are what let it colonize the otherwise hostile acidic stomach.[2]
H. pylori is a true gastric pathogen, not a commensal. Persistent infection drives chronic gastritis and peptic ulcers, and in a subset of people it progresses to gastric adenocarcinoma and MALT lymphoma; it is a Group 1 carcinogen and the strongest known risk factor for gastric cancer.[1] Its virulence factors activate host cell-signaling pathways (PI3K/Akt, JAK/STAT, Ras/Raf/ERK) that drive uncontrolled cell proliferation.[1]
H. pylori pairs acid survival with a toxin-and-injection toolkit that reprograms gastric cells.
| Virulence factor | Description and role |
|---|---|
| CagA (cytotoxin-associated gene A) | Injected into gastric cells by a type IV secretion system, CagA reprograms host signaling and is the virulence factor most strongly linked to gastric cancer.[1] |
| VacA (vacuolating cytotoxin A) | Forms pores and vacuoles in host cells, damages the epithelium, and modulates the immune response.[1] |
| Urease | A nickel-dependent enzyme that neutralizes stomach acid and is essential for initial colonization (detailed under Metallomics).[2] |
| [NiFe] hydrogenase | A nickel enzyme that uses molecular hydrogen as an energy source to power colonization; CagA translocation is energetically linked to this hydrogen metabolism.[3] |
| Flagella and helical shape | Sheathed flagella and a corkscrew shape drive motility through gastric mucus to the epithelial surface.[1] |
H. pylori is the textbook example of a pathogen built around nickel: two of its key virulence enzymes are nickel metalloenzymes, and the fight over nickel at the infection site is a nutritional-immunity battle.
| Metal / ion | Key features in H. pylori |
|---|---|
| Nickel (Ni) | The urease that neutralizes stomach acid is a nickel-dependent metalloenzyme; acidic pH induces UreI-gated urea entry and nickel insertion into the urease apoenzyme, boosting acid neutralization and survival.[2][4] A second nickel enzyme, the [NiFe] hydrogenase, powers colonization.[3] Because these are load-bearing, urease and nickel handling are actively pursued drug targets.[4] |
| Host nickel and zinc withholding | The neutrophil protein calprotectin sequesters nickel and zinc to starve the pathogen; dietary nickel loading that overwhelms this defense is a candidate route by which metal exposure perturbs H. pylori virulence.[3] |
Read through the nutritional-immunity lens, the nickel systems that let H. pylori survive the stomach are also its openings.
| Weak point | Why it is exploitable |
|---|---|
| Nickel and urease dependence | Because urease and the [NiFe] hydrogenase are nickel-dependent and essential for gastric survival, urease inhibitors and nickel restriction attack a load-bearing system, and urease inhibition is an active drug-discovery target.[4][2] |
| Eradication therapy | Standard treatment is combination antibiotic therapy with acid suppression, though rising antibiotic resistance is reducing cure rates.[1] |
| A narrow gastric niche | It is exquisitely adapted to the acidic stomach; disrupting its acid-neutralization or hydrogen metabolism removes the very adaptations that let it live there.[2][3] |
Clinical eradication is managed by clinicians; the entries below are classified by our validation method and are not medical advice. The microbiome through-line is nickel: starve the metal its acid-survival enzymes depend on.
| Intervention | Class | Status |
|---|---|---|
| Eradication therapy (antibiotics + acid suppression) | Drug | Validated |
| Urease inhibition | Concept | Validation In Progress |
| Nickel restriction / calprotectin support | Concept | Validation In Progress |
| Intervention | Mechanism |
| Eradication therapy | Combination antibiotics plus a proton-pump inhibitor clear the organism; resistance is rising.[1] |
| Urease inhibition | Blocking the nickel-dependent urease removes acid neutralization the organism needs to colonize, an active drug-discovery target.[4] |
| Nickel restriction / calprotectin | Withholding nickel starves urease and the [NiFe] hydrogenase; calprotectin is the host's nickel-sequestering defense.[3] |
Untargeted dietary nickel loading. Because H. pylori's acid-survival enzymes are nickel-dependent, excess nickel that overwhelms host calprotectin sequestration is a candidate route by which metal exposure could support its virulence.[3]
Where H. pylori (NCBI:txid210) appears as a differentially abundant taxon across the Microbiome Medicine corpus. Each row aggregates every experiment in which the organism moved in a given condition; direction is its change in the case/exposure group, and grade is the strongest single study's methodology weight (A·D·S·C·R), the same engine that grades every signature on this site.
Across 6 conditions and 7 studies, the signal is genuinely mixed: enriched in 5, depleted in 1, and direction-conflicting in 0 (directional agreement 0.67). Because H. pylori is a gastric pathogen rather than a stable gut resident, its appearances in gut-focused studies are best read as ecological signals or carriage, not measures of gastric infection, so the aggregate evidence tier is Low.
How to read these. H. pylori lives in the stomach, not the gut, so a low-abundance differential signal in a gut or fecal study is unusual and usually reflects carriage, sampling of the upper tract, or an ecological shift rather than active gastric infection. Species-level assignment can be uncertain at low abundance. This is why direction can conflict between cohorts and the aggregate tier stays Low.
Internal summaries of the 7 studies we reviewed in which H. pylori was a differential taxon across this corpus.
This study investigated the duodenal mucosa-associated microbiota and its surrounding microenvironment in relation to hyperglycemia, an area far less studied than stool microbiota in metabolic disease. The researchers compared paired stool and duodenal microbial samples between hyperglycemic and normoglycemic individuals. They also assessed the duodenal microenvironment directly by measuring tissue oxygen saturation, serum inflammatory markers, and zonulin as a marker of gut permeability. The goal was to determine whether duodenal, rather than stool, microbial changes track more closely with glycemic status.
The study population consisted of 33 subjects with hyperglycemia, defined as HbA1c of 5.7% or higher and fasting plasma glucose above 100 mg/dl, compared against 21 normoglycemic subjects. Both groups contributed paired stool and duodenal samples, allowing direct comparison of microbiota across two body sites within the same individuals. No further demographic details are given in the abstract.
Hyperglycemic subjects had a significantly higher duodenal bacterial count than normoglycemic subjects, along with increased pathobionts and reduced beneficial flora. This bacterial overload correlated with elevated serum zonulin and higher TNF-alpha, suggesting a link to increased gut permeability and inflammation. The hyperglycemic group also showed reduced duodenal oxygen saturation, higher total leukocyte count, and lower IL-10, indicating a systemic proinflammatory state. Notably, unlike stool flora, duodenal bacterial profile variability was specifically associated with glycemic status.
These findings suggest the duodenal microbiome and its local microenvironment, rather than stool alone, may play a distinct role in the pathogenesis of hyperglycemia and prediabetes. The association between bacterial overload, reduced oxygen saturation, and systemic inflammatory markers points to a possible mechanistic pathway linking small intestinal dysbiosis to metabolic dysfunction. This work highlights the duodenum as an underexplored but potentially important site for understanding and possibly intervening in early glycemic disturbances.
Accumulating evidence has indicated the role of gut microbiota in remodeling host immune signatures, but various interplays underlying colorectal cancers (CRC) with deficient DNA mismatch repair (dMMR) and proficient DNA mismatch repair (pMMR) remain poorly understood. This study aims to decipher the gut microbiome-host immune interactions between dMMR and pMMR CRC.
We performed metagenomic sequencing and metabolomic analysis of fecal samples from a cohort encompassing 455 participants, including 21 dMMR CRC, 207 pMMR CRC, and 227 healthy controls. Among them, 50 tumor samples collected from 5 dMMR CRC and 45 pMMR CRC were conducted bulk RNA sequencing.
Pronounced microbiota and metabolic heterogeneity were identified with 211 dMMR-enriched species, such as Fusobacterium nucleatum and Akkermansia muciniphila, 2 dMMR-depleted species, such as Flavonifractor plautii, 13 dMMR-enriched metabolites, such as retinoic acid, and 77 dMMR-depleted metabolites, such as lactic acid, succinic acid, and 2,3-dihydroxyvaleric acid. F. plautii was enriched in pMMR CRC and it was positively associated with fatty acid degradation, which might account for the accumulation of dMMR-depleted metabolites classified as short chain organic acid (lactic acid, succinic acid, and 2,3-dihydroxyvaleric acid) in pMMR CRC. The microbial-metabolic association analysis revealed the characterization of pMMR CRC as the accumulation of lactate induced by the depletion of specific gut microbiota which was negatively associated with antitumor immune, whereas the nucleotide metabolism and peptide degradation mediated by dMMR-enriched species characterized dMMR CRC. MMR-specific metabolic landscapes were related to distinctive immune features, such as CD8+ T cells, dendritic cells and M2-like macrophages.
Our mutiomics results delineate a heterogeneous landscape of microbiome-host immune interactions within dMMR and pMMR CRC from aspects of bacterial communities, metabolic features, and correlation with immunocyte compartment, which infers the underlying mechanism of heterogeneous immune responses.
Preterm birth prediction is essential for improving neonatal outcomes. While many machine learning techniques have been applied to predict preterm birth using health records, inflammatory markers, and vaginal microbiome data, the role of prenatal oral microbiome remains unclear. This study aimed to compare oral microbiome compositions between a preterm and a full-term birth group, identify oral microbiome associated with preterm birth, and develop a preterm birth prediction model using machine learning of oral microbiome compositions. Participants included singleton pregnant women admitted to Jeonbuk National University Hospital between 2019 and 2021. Subjects were divided into a preterm and a full-term birth group based on pregnancy outcomes. Oral microbiome samples were collected using mouthwash within 24 h before delivery and 16S ribosomal RNA sequencing was performed to analyze taxonomy. Differentially abundant taxa were identified using DESeq2. A random forest classifier was applied to predict preterm birth based on the oral microbiome. A total of 59 women participated in this study, with 30 in the preterm birth group and 29 in the full-term birth group. There was no significant difference in maternal clinical characteristics between the preterm and the full-birth group. Twenty-five differentially abundant taxa were identified, including 22 full-term birth-enriched taxa and 3 preterm birth-enriched taxa. The random forest classifier achieved high balanced accuracies (0.765 ± 0.071) using the 9 most important taxa. Our study identified 25 differentially abundant taxa that could differentiate preterm and full-term birth groups. A preterm birth prediction model was developed using machine learning of oral microbiome compositions in mouthwash samples. Findings of this study suggest the potential of using oral microbiome for predicting preterm birth. Further multi-center and larger studies are required to validate our results before clinical applications.
Recent research studies are showing breast tissues as a place where various species of microorganisms can thrive and cannot be considered sterile, as previously thought. We analysed the microbial composition of primary tumour tissue and normal breast tissue and found differences between them and between multiple breast cancer phenotypes. We sequenced the transcriptome of breast tumours and normal tissues (from cancer-free women) of 23 individuals from Slovakia and used bioinformatics tools to uncover differences in the microbial composition of tissues. To analyse our RNA-seq data (rRNA depleted), we used and tested Kraken2 and Metaphlan3 tools. Kraken2 has shown higher reliability for our data. Additionally, we analysed 91 samples obtained from SRA database, originated in China and submitted by Sichuan University. In breast tissue, the most enriched group were Proteobacteria, then Firmicutes and Actinobacteria for both datasets, in Slovak samples also Bacteroides, while in Chinese samples Cyanobacteria were more frequent. We have observed changes in the microbiome between cancerous and healthy tissues and also different phenotypes of diseases, based on the presence of circulating tumour cells and few other markers.
This study examined how the gastric mucosal microbiota differs across distinct microhabitats within the stomach in the context of gastric cancer (GC). Researchers compared microbial diversity, composition, bacterial co-occurrence networks, and predicted functional profiles across normal, peritumoral, and tumoral tissue. The goal was to determine whether GC-associated stomach microhabitats, rather than cancer stage or type, shape the gastric microbiota.
The cohort consisted of 276 patients with gastric cancer who had not received preoperative chemotherapy and were enrolled retrospectively. Tissue samples were collected from three microhabitats: 230 normal, 247 peritumoral, and 229 tumoral samples. Microbial community composition was assessed using 16S rRNA gene sequencing on the MiSeq platform.
The composition and diversity of the gastric microbiota were determined by the specific stomach microhabitat rather than by GC stage or type. Bacterial richness was decreased in both peritumoral and tumoral microhabitats compared to normal tissue, and the co-occurrence network of abundant bacteria was simplified in the tumoral microhabitat. Helicobacter pylori, Prevotella copri, and Bacteroides uniformis were significantly decreased in tumoral tissue, while Prevotella melaninogenica, Streptococcus anginosus, and Propionibacterium acnes were increased there.
These findings indicate that the tumor microenvironment reshapes the local microbiota in a site-specific manner, with reduced diversity and simplified microbial networks marking the transition from normal to tumoral tissue. The consistent shifts in specific taxa across microhabitats suggest the gastric microbiota could serve as a biomarker of tissue state within the same stomach. This microhabitat-based framework highlights the importance of sampling location when characterizing microbiota changes associated with gastric cancer.
This study compared the mucosa-associated bacterial community of gastric cancer tissue with each patient's own adjacent non-cancer tissue, using paired samples to control for host genetics and environment, and profiled composition, co-occurrence networks, and predicted functions by 16S rRNA (V4 to V5) sequencing.
124 gastric mucosa samples (cancer plus paired adjacent non-cancer) from 62 gastric adenocarcinoma patients who underwent subtotal gastrectomy at the First Hospital of China Medical University (2012 to 2014), median age 60, excluding anyone recently treated with antibiotics, proton-pump inhibitors, probiotics, chemotherapy, or radiotherapy.
Tumor tissue showed higher microbial richness and diversity than non-cancer tissue, with a denser co-occurrence network. Proteobacteria dominated both groups but were relatively lower in cancer, while Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria rose. LEfSe flagged 49 differentially abundant taxa (LDA above 3): 33 enriched in cancer, largely oral bacteria such as Peptostreptococcus, Streptococcus, and Fusobacterium, and 16 enriched in non-cancer tissue, largely lactic-acid bacteria. Predicted purine-metabolism and denitrification functions were enriched in the cancer community.
The results point to translocated oral bacteria, rather than Helicobacter pylori alone, as a feature of the gastric-cancer microenvironment and a possible contributor to or marker of carcinogenesis. As a cross-sectional tissue study, it establishes association rather than causation.
The esophageal microbiome has been proposed to be involved in a range of diseases including the esophageal adenocarcinoma cascade; however, little is currently known about its function and relationship to the host. Here, the esophageal microbiomes of 106 prospectively recruited patients were assessed using 16S rRNA and 18S rRNA amplicon sequencing as well as shotgun sequencing, and associations with age, gender, proton pump inhibitor use, host genetics, and disease were tested.
The esophageal microbiome was found to cluster into functionally distinct community types (esotypes) defined by the relative abundances of Streptococcus and Prevotella. While age was found to be a significant factor driving microbiome composition, bacterial signatures and functions such as enrichment with Gram-negative oral-associated bacteria and microbial lactic acid production were associated with the early stages of the esophageal adenocarcinoma cascade. Non-bacterial microbes such as archaea, Candida spp., and bacteriophages were also identified in low abundance in the esophageal microbiome. Specific host SNPs in NOTCH2, STEAP2-AS1, and NREP were associated with the composition of the esophageal microbiome in our cohort.
This study provides the most comprehensive assessment of the esophageal microbiome to date and identifies novel signatures and host markers that can be investigated further in the context of esophageal adenocarcinoma development.
2026-07-04
Helicobacter pylori majorTaxon page created: biology (morphology, pathogenicity, CagA/VacA virulence), the nickel-dependent urease and hydrogenase metallome and vulnerabilities, interventions, the data-derived Conditions table across 6 conditions, and the full research feed.
Alipour M.
Molecular mechanism of Helicobacter pylori-induced gastric cancer.J Gastrointest Cancer. 2020
Scott DR, Marcus EA, Weeks DL, Sachs G.
Mechanisms of acid resistance due to the urease system of Helicobacter pylori.Gastroenterology. 2002
Fiori-Duarte AT, Rodrigues RP, Kitagawa RR, Kawano DF.
Insights into the design of inhibitors of the urease enzyme - a major target for the treatment of Helicobacter pylori infections.Curr Med Chem. 2020
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