Sensing Host Health: Insights from Sensory Protein Signature of the Metagenome

16S rRNA sequencing analysis of the oral and fecal microbiota in colorectal cancer positives versus colorectal cancer negatives in Iranian population

BACKGROUND

Colorectal cancer (CRC) poses a significant healthcare challenge, accounting for nearly 6.1% of global cancer cases. Early detection, facilitated by population screening utilizing innovative biomarkers, is pivotal for mitigating CRC incidence. This study aims to scrutinize the fecal and salivary microbiomes of CRC-positive individuals (CPs) in comparison to CRC-negative counterparts (CNs) to enhance early CRC diagnosis through microbial biomarkers.

MATERIAL AND METHODS

A total of 80 oral and stool samples were collected from Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran, encompassing both CPs and CNs undergoing screening. Microbial profiling was conducted using 16S rRNA sequencing assays, employing the Nextera XT Index Kit on an Illumina NovaSeq platform.

RESULTS

Distinct microbial profiles were observed in saliva and stool samples of CPs, diverging significantly from those of CNs at various taxonomic levels, including phylum, family, and species. Saliva samples from CPs exhibited abundance of Calothrix parietina, Granulicatella adiacens, Rothia dentocariosa, and Rothia mucilaginosa, absent in CNs. Additionally, Lachnospiraceae and Prevotellaceae were markedly higher in CPs' feces, while the Fusobacteria phylum was significantly elevated in CPs' saliva. Conversely, the non-pathogenic bacterium Akkermansia muciniphila exhibited a significant decrease in CPs' fecal samples compared to CNs.

CONCLUSION

Through meticulous selection of saliva and stool microbes based on Mean Decrease GINI values and employing logistic regression for saliva and support vector machine models for stool, we successfully developed a microbiota test with heightened sensitivity and specificity for early CRC detection.

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface

Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity (r2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon (r2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean).IMPORTANCEThe ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.