Microbial Metallomics
Researched by:
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Karen Pendergrass
ID
Karen Pendergrass
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.
Microbial Metallomics is the study of how microorganisms interact with metal ions in biological systems, particularly within the human microbiome.
Research feed -
Karen Pendergrass
Researched by:
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Karen Pendergrass
ID
Karen Pendergrass
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass
Overview 
Research Feed Overview
Microbial Metallomics is the study of how microorganisms interact with metal ions in biological systems, particularly within the human microbiome. This field examines the role of metals as cofactors in microbial metabolism, virulence, and competitive survival strategies, as well as how microbial communities influence metal bioavailability, sequestration, and toxicity. By integrating microbiology, biochemistry, and bioinorganic chemistry, Microbial Metallomics provides insights into microbial metal homeostasis, host-pathogen interactions, and the impact of metal disturbances on health and disease.
Research Feed
Urine metallomics signature as an indicator of pancreatic cancer†
May 20, 2020
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Metals •
Metals
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This study identifies a distinct urinary metallomic signature in pancreatic cancer patients, marked by altered calcium, magnesium, copper, and zinc levels, along with lighter zinc isotopic composition. These findings suggest that non-invasive urine tests could enable early PDAC detection by leveraging trace metal imbalances and stable isotope shifts.
What was studied?
This study explored the utility of urinary metallomic profiling—specifically concentrations and isotopic composition of essential metals—as a non-invasive diagnostic tool for pancreatic ductal adenocarcinoma (PDAC). The researchers examined urine samples from PDAC patients and healthy controls to identify specific metal dyshomeostasis and isotopic shifts that could serve as biomarkers for PDAC detection.
Who was studied?
Urine samples from 21 patients diagnosed with PDAC and 46 healthy control subjects were analyzed. All samples were collected under ethical approval through the Barts Pancreas Tissue Bank.
Most important findings:
A distinct urinary metallomic signature was identified in pancreatic ductal adenocarcinoma (PDAC) patients, characterized by decreased calcium and magnesium and increased zinc and copper levels. The multivariate model integrating these four elements exhibited outstanding diagnostic accuracy, achieving an area under the curve (AUC) of 0.995 with 99.5% sensitivity. Moreover, stable zinc isotope analysis revealed a shift toward isotopically lighter zinc in PDAC patients (median δ⁶⁶Zn = −0.15‰) compared to healthy controls (median δ⁶⁶Zn = +0.02‰), likely due to oxidative stress-induced oxidation of cysteine-rich metallothioneins, which preferentially bind lighter isotopes. From a microbiome-metallomic perspective, such trace metal imbalances—particularly involving zinc and copper—may influence microbial community structure by selectively enriching pathogenic taxa and diminishing beneficial ones. Although the microbiome was not directly assessed in this study, the metallomic disturbances observed may serve as indirect indicators of host-microbe dysregulation, especially relevant in gastrointestinal malignancies such as PDAC.
| Element | Change in PDAC vs. Control |
|---|---|
| Calcium (Ca) | Decreased (***p <0.0001) |
| Magnesium (Mg) | Decreased (**p = 0.0002) |
| Zinc (Zn) | Increased (*p = 0.015) |
| Copper (Cu) | Increased (*p = 0.02) |
Greatest implications of the study:
This work provides strong preliminary evidence that urinary metallomic profiles—specifically Ca, Mg, Cu, Zn concentrations and zinc isotopic signatures—can serve as non-invasive biomarkers for PDAC detection. It is the first study to report isotopic zinc alterations in urine associated with PDAC and proposes a compelling mechanistic link to oxidative stress and metalloprotein dysregulation. If validated in larger cohorts, this approach could represent a breakthrough in early detection of pancreatic cancer, a malignancy notorious for its asymptomatic progression and poor prognosis. The authors propose that isotopic measurements, which offer significantly greater resolution than standard clinical assays, could even function as prognostic tools if longitudinally correlated with disease progression.
Metallomic analysis of brain tissues distinguishes between cases of dementia with Lewy bodies, Alzheimer’s disease, and Parkinson’s disease dementia
June 26, 2024
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Metallomic Signatures •
Metallomic Signatures
Did you know?
Metallomic signatures can reveal hidden drivers of disease by mapping how trace metals like nickel, iron, and cadmium shape microbial behavior and immune responses. These signatures not only help identify toxic exposures but also spotlight metal-dependent pathogens, offering new targets for precision-guided therapies.
Dementia with Lewy bodies (DLB) brains show widespread copper depletion and region-specific sodium, manganese, iron, and selenium alterations. While copper loss is common to AD and PDD, DLB presents a distinct metallomic fingerprint, enabling disease differentiation via PCA. Metallomic profiling may aid in diagnosing overlapping dementias and reveals unique pathophysiological signatures.
What was studied?
This original research study investigated whether the metallomic profile of dementia with Lewy bodies (DLB) differs from that of Alzheimer’s disease (AD) and Parkinson’s disease dementia (PDD). The study sought to determine if post-mortem changes in elemental concentrations—particularly in essential metals—could help differentiate these often-overlapping neurodegenerative conditions. Using ICP-MS (Inductively Coupled Plasma–Mass Spectrometry), the authors quantified concentrations of nine elements (Na, Mg, K, Ca, Mn, Fe, Cu, Zn, and Se) across 10 brain regions from DLB patients and age-/sex-matched controls. These findings were directly compared to previously published metallomic profiles for AD and PDD, produced using identical methodologies. Multivariate analyses (PCA and PLS-DA) were employed to assess the potential for disease discrimination based on metal signatures.
Who was studied?
The study analyzed post-mortem brain tissue from 23 DLB patients and 20 controls, collected across ten distinct brain regions. Comparative analyses included prior datasets from similarly matched AD and PDD patient cohorts.
What were the most important findings?
n this study, region-specific metallomic profiling revealed distinct trace element alterations in Dementia with Lewy Bodies (DLB). Copper (Cu) levels were consistently decreased in five of ten DLB brain regions, including the cingulate gyrus (CG), middle temporal gyrus (MTG), primary visual cortex (PVC), substantia nigra (SN), and putamen (PUT), suggesting a widespread Cu deficiency. Sodium (Na) was elevated in four regions—medulla (MED), cerebellum (CB), MTG, and CG—while more localized changes were observed for other metals. Iron (Fe) levels were increased in the motor cortex (MCX) and CG, whereas manganese (Mn) was decreased in both the PVC and MED. Calcium (Ca) was specifically reduced in the hippocampus, and selenium (Se) was also decreased in the PVC. No significant differences in magnesium, potassium, or zinc levels were observed between DLB and control brains. Multivariate analyses, including Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA), demonstrated that DLB could be distinctly separated from Alzheimer’s disease (AD) and Parkinson’s disease dementia (PDD) based on metallomic signatures. Specifically, CG, MTG, and PVC profiles enabled discrimination between DLB and AD, while the PVC alone differentiated DLB from PDD. Notably, copper depletion emerged as the only common alteration across DLB, AD, and PDD, underscoring its potential central role in the pathogenesis of neurodegenerative diseases. The authors propose that these metallomic fingerprints may reflect disease-specific mechanisms, including variations in oxidative stress, protein aggregation, and mitochondrial dysfunction.
What are the greatest implications of this study?
This study provides compelling evidence that distinct metallomic signatures exist across DLB, AD, and PDD, despite shared pathology such as copper depletion. It strengthens the emerging concept that trace metal dysregulation is disease-specific, rather than a general byproduct of neurodegeneration. The findings support the idea that metallomic profiling—potentially via cerebrospinal fluid or advanced imaging in living patients—could improve differential diagnosis of dementias with overlapping clinical features. Furthermore, the study reinforces the hypothesis that metal dyshomeostasis, particularly copper depletion, may be a contributing pathogenic mechanism, impairing antioxidant defenses and mitochondrial function. These findings could inform new diagnostic tools and therapeutic targets.
Metallomic Signatures
A metallomic signature is the condition-specific profile of trace metals and metal-binding molecules that reflects disrupted metal homeostasis.