RESUMEN
Quorum quenching (QQ) is the enzymatic degradation of molecules used by bacteria for synchronizing their behavior within communities. QQ has attracted wide attention due to its potential to inhibit biofilm formation and suppress the production of virulence factors. Through its capacity to limit biofouling and infections, QQ has applications in water treatment, aquaculture, and healthcare. Several different QQ enzymes have been described; however, they often lack the high stability and catalytic efficiency required for industrial applications. Previously, we identified genes from genome sequences of Red Sea sediment bacteria encoding potential QQ enzymes. In this study, we report that one of them, named LrsL, is a metallo-ß-lactamase superfamily QQ enzyme with outstanding catalytic features. X-ray crystallography shows that LrsL is a zinc-binding dimer. LrsL has an unusually hydrophobic substrate binding pocket that can accommodate a broad range of acyl-homoserine lactones (AHLs) with exceptionally high affinity. In vitro, LrsL achieves the highest catalytic efficiency reported thus far for any QQ enzyme with a Kcat /KM of 3 × 107. LrsL effectively inhibited Pseudomonas aeruginosa biofilm formation without affecting bacterial growth. Furthermore, LrsL suppressed the production of exopolysaccharides required for biofilm production. These features, and its capacity to regain its function after prolonged heat denaturation, identify LrsL as a robust and unusually efficient QQ enzyme for clinical and industrial applications.
RESUMEN
To study and understand the importance of Internet of Things-driven citizen science (IoT-CS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged.
