Microbiome Biomarkers
Microbiome Biomarkers - Related Videos
04:57
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data
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Cited by 81 •
2022
LEfSe (LDA Effect Size) is a tool for high-dimensional biomarker mining to identify genomic features (such as genes, pathways, and taxonomies) that significantly characterize two or more groups in microbiome data.
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
01:22
Microbiome of the Eye
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2026
The human eye has a specialized microbiota that reflects its unique anatomical and immunological environment. This low-biomass microbial community predominantly colonizes the conjunctiva and eyelid margins, playing a vital role in ocular surface homeostasis and defense. Despite its proximity to the richly colonized facial skin, the ocular surface maintains a distinct microbial profile due to continuous mechanical and biochemical defense mechanisms.The conjunctival surface hosts fewer microbial...
10:51
Co-culture of Living Microbiome with Microengineered Human Intestinal Villi in a Gut-on-a-Chip Microfluidic Device
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Cited by 68 •
2016
We describe an in vitro protocol to co-culture gut microbiome and intestinal villi for an extended period using a human gut-on-a-chip microphysiological system.
08:28
An Overview of bGDGT Biomarker Analysis for Paleoclimatology
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types...
FAQs
Microbiome biomarkers can be derived from three analytical layers: which microbes are present, what genes or functions they encode, and which molecules they produce. These layers capture different biological signals, so a compositional pattern may not match a functional or metabolite pattern. Comparing them with host physiology or clinical outcomes helps identify the most informative signal for a biological state.
An informative signal depends on a reproducible relationship, not simply on detecting a difference in one sample. Researchers therefore examine whether a microbial feature or microbiome-derived molecule consistently corresponds with disease processes, host physiology, or treatment response. Establishing this consistency strengthens interpretation and supports later use in risk assessment, patient stratification, or monitoring.
Validation across populations, sampling methods, and analytical platforms tests whether a reported pattern remains detectable under different study conditions. A feature that appears informative in one setting may not show the same relationship elsewhere. This cross-context evaluation is essential before applying the result to disease characterization, treatment-response prediction, or microbiome-based diagnostic development.
The workflow begins with a sample such as stool, saliva, or tissue, followed by analysis of microbial composition, gene functions, or microbiome-derived metabolites. Researchers then associate the measured patterns with host physiology or clinical outcomes. Consistent associations can nominate candidate biomarkers, while comparison with relevant biological states helps determine their potential use.
Stool, saliva, and tissue are examples of samples used to examine microbiome-related signals. Each provides material for assessing microbial composition, gene functions, or derived molecules, but findings require careful validation in relation to the sampling method. Considering the sample source helps researchers interpret whether the measured pattern is relevant to the biological or clinical question.
Validated signals can contribute to disease risk assessment, distinguish healthy and diseased states, and help stratify patients according to biologically relevant patterns. They may also support prediction of treatment response and the development of microbiome-based diagnostics. These applications depend on linking measured microbial features or molecules with consistent clinical outcomes rather than treating every difference as informative.
By relating microbial composition, gene functions, or metabolites to host physiology, researchers can characterize how microbial features correspond with biological states. Tracking these signals during therapy may reveal changes associated with treatment response. This makes microbiome biomarkers useful in biology for connecting microbial measurements with host condition and for evaluating whether patterns change alongside clinical outcomes.