Evolutionary history expands the range of signaling interactions in hybrid multikinase networks

Crosstalk involving two-component systems in Staphylococcus aureus signaling networks

Staphylococcus aureus poses a serious global threat to human health due to its pathogenic nature, adaptation to environmental stress, high virulence, and the prevalence of antimicrobial resistance. The signaling network in S. aureus coordinates and integrates various internal and external inputs and stimuli to adapt and formulate a response to the environment. Two-component systems (TCSs) of S. aureus play a central role in this network where surface-expressed histidine kinases (HKs) receive and relay external signals to their cognate response regulators (RRs). Despite the purported high fidelity of signaling, crosstalk within TCSs, between HK and non-cognate RR, and between TCSs and other systems has been detected widely in bacteria. The examples of crosstalk in S. aureus are very limited, and there needs to be more understanding of its molecular recognition mechanisms, although some crosstalk can be inferred from similar bacterial systems that share structural similarities. Understanding the cellular processes mediated by this crosstalk and how it alters signaling, especially under stress conditions, may help decipher the emergence of antibiotic resistance. This review highlights examples of signaling crosstalk in bacteria in general and S. aureus in particular, as well as the effect of TCS mutations on signaling and crosstalk.

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.

Two-component system GacS/GacA, a global response regulator of bacterial physiological behaviors

The signal transduction system of microorganisms helps them adapt to changes in their complex living environment. Two-component system (TCS) is a representative signal transduction system that plays a crucial role in regulating cellular communication and secondary metabolism. In Gram-negative bacteria, an unorthodox TCS consisting of histidine kinase protein GacS (initially called LemA) and response regulatory protein GacA is widespread. It mainly regulates various physiological activities and behaviors of bacteria, such as quorum sensing, secondary metabolism, biofilm formation and motility, through the Gac/Rsm (Regulator of secondary metabolism) signaling cascade pathway. The global regulatory ability of GacS/GacA in cell physiological activities makes it a potential research entry point for developing natural products and addressing antibiotic resistance. In this review, we summarize the progress of research on GacS/GacA from various perspectives, including the reaction mechanism, related regulatory pathways, main functions and GacS/GacA-mediated applications. Hopefully, this review will facilitate further research on GacS/GacA and promote its application in regulating secondary metabolism and as a therapeutic target.