Can Pathogenic and Nonpathogenic Bacteria Be Distinguished by Sensory Protein Abundance?

Navigating commensal dysbiosis: Gastrointestinal host-pathogen interplay orchestrating opportunistic infections

The gut commensals, which are usually symbiotic or non-harmful bacteria that live in the gastrointestinal tract, have a positive impact on the health of the host. This review, however, specifically discuss distinct conditions where commensals aid in the development of pathogenic opportunistic infections. We discuss that the categorization of gut bacteria as either pathogens or non-pathogens depends on certain circumstances, which are significantly affected by the tissue microenvironment and the dynamic host-microbe interaction. Under favorable circumstances, commensals have the ability to transform into opportunistic pathobionts by undergoing overgrowth. These conditions include changes in the host's physiology, simultaneous infection with other pathogens, effective utilization of nutrients, interactions between different species of bacteria, the formation of protective biofilms, genetic mutations that enhance pathogenicity, acquisition of genes associated with virulence, and the ability to avoid the host's immune response. These processes allow commensals to both initiate infections themselves and aid other pathogens in populating the host. This review highlights the need of having a detailed and sophisticated knowledge of the two-sided nature of gut commensals. Although commensals mostly promote health, they may also become harmful in certain changes in the environment or the body's functioning. This highlights the need of acknowledging the intricate equilibrium in interactions between hosts and microbes, which is crucial for preserving intestinal homeostasis and averting diseases. Finally, we also emphasize the further need of research to better understand and anticipate the behavior of gut commensals in different situations, since they play a crucial and varied role in human health and disease.

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.

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

The opportunistic nature of gut commensal microbiota

The abundance of gut commensals has historically been associated with health-promoting effects despite the fact that the definition of good or bad microbiota remains condition-specific. The beneficial or pathogenic nature of microbiota is generally dictated by the dimensions of host-microbiota and microbe-microbe interactions. With the increasing popularity of gut microbiota in human health and disease, emerging evidence suggests opportunistic infections promoted by those gut bacteria that are generally considered beneficial. Therefore, the current review deals with the opportunistic nature of the gut commensals and aims to summarise the concepts behind the occasional commensal-to-pathogenic transformation of the gut microbes. Specifically, relevant clinical and experimental studies have been discussed on the overgrowth and bacteraemia caused by commensals. Three key processes and their underlying mechanisms have been summarised to be responsible for the opportunistic nature of commensals, viz. improved colonisation fitness that is dictated by commensal-pathogen interactions and availability of preferred nutrients; pathoadaptive mutations that can trigger the commensal-to-pathogen transformation; and evasion of host immune response as a survival and proliferation strategy of the microbes. Collectively, this review provides an updated concept summary on the underlying mechanisms of disease causative events driven by gut commensal bacteria.

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

The human microbiota, which comprises an ensemble of taxonomically and functionally diverse but often mutually cooperating microorganisms, benefits its host by shaping the host immunity, energy harvesting, and digestion of complex carbohydrates as well as production of essential nutrients. Dysbiosis in the human microbiota, especially the gut microbiota, has been reported to be linked to several diseases and metabolic disorders. Recent studies have further indicated that tracking these dysbiotic variations could potentially be exploited as biomarkers of disease states. However, the human microbiota is not geography agnostic, and hence a taxonomy-based (microbiome) biomarker for disease diagnostics has certain limitations. In comparison, (microbiome) function-based biomarkers are expected to have a wider applicability. Given that (i) the host physiology undergoes certain changes in the course of a disease and (ii) host-associated microbial communities need to adapt to this changing microenvironment of their host, we hypothesized that signatures emanating from the abundance of bacterial proteins associated with the signal transduction system (herein referred to as sensory proteins [SPs]) might be able to distinguish between healthy and diseased states. To test this hypothesis, publicly available metagenomic data sets corresponding to three diverse health conditions, namely, colorectal cancer, type 2 diabetes mellitus, and schizophrenia, were analyzed. Results demonstrated that SP signatures (derived from host-associated metagenomic samples) indeed differentiated among healthy individual and patients suffering from diseases of various severities. Our finding was suggestive of the prospect of using SP signatures as early biomarkers for diagnosing the onset and progression of multiple diseases and metabolic disorders. IMPORTANCE The composition of the human microbiota, a collection of host-associated microbes, has been shown to differ among healthy and diseased individuals. Recent studies have investigated whether tracking these variations could be exploited for disease diagnostics. It has been noted that compared to microbial taxonomies, the ensemble of functional proteins encoded by microbial genes are less likely to be affected by changes in ethnicity and dietary preferences. These functions are expected to help the microbe adapt to changing environmental conditions. Thus, healthy individuals might harbor a different set of genes than diseased individuals. To test this hypothesis, we analyzed metagenomes from healthy and diseased individuals for signatures of a particular group of proteins called sensory proteins (SP), which enable the bacteria to sense and react to changes in their microenvironment. Results demonstrated that SP signatures indeed differentiate among healthy individuals and those suffering from diseases of various severities.