Stop-Signaling Reduces Split Decisions without Impairing Accuracy in the Honeybee Nest-Site Selection Process

In the honeybee swarm nest-site selection process, individual bees gather information about available candidate sites and communicate the information to other bees. The swarm makes an agreement for a candidate site when the number of bees that supports the site reaches a threshold. This threshold is usually referred to as the quorum threshold and it is shown by many studies as a key parameter that is a compromise between the accuracy and speed of decisions. In the present work, we use a model of the honeybee Apis mellifera nest-site selection process to study how the quorum threshold and discovery time of candidate sites have major impact on two unfavorable situations in selecting a nest site: decision deadlock and decision split. We show that cross-inhibitory stop-signaling, delivered among bees supporting different sites, enables swarms to avoid the decision split problem in addition to avoiding the decision deadlock problem that has been previously proposed. We also show that stop-signaling improves decision speed, but compromises decision accuracy in swarms using high quorum thresholds by causing the swarms to be trapped in local optima (e.g., choosing a sub-optimal option that is encountered first). On the other hand, we demonstrate that stop-signaling can reduce split decisions without compromising decision accuracy in swarms using low quorum thresholds when it is compared to the accuracy of swarms using the same threshold values but not exhibiting stop-signaling. Based on our simulations, we suggest that swarms using low quorum thresholds (as well as swarms with large population sizes) would benefit more from exhibiting the stop-signaling activity than not exhibiting it.     

Surface proteomics and label-free quantification of Leptospira interrogans serovar Pomona

Leptospirosis is a re-emerging zoonosis with a global distribution. Surface-exposed outer membrane proteins (SE-OMPs) are crucial for bacterial–host interactions. SE-OMPs locate and expose their epitope on cell surface where is easily accessed by host molecules. This study aimed to screen for surface-exposed proteins and their abundance profile of pathogenic Leptospira interrogans serovar Pomona. Two complementary approaches, surface biotinylation and surface proteolytic shaving, followed by liquid chromatography tandem-mass spectrometry (LC-MS/MS) were employed to identify SE-OMPs of intact leptospires. For quantitative comparison, in-depth label-free analysis of SE-OMPs obtained from each method was performed using MaxQuant. The total number of proteins identified was 1,001 and 238 for surface biotinylation and proteinase K shaving, respectively. Among these, 39 were previously known SE-OMPs and 68 were predicted to be localized on the leptospiral surface. Based on MaxQuant analysis for relative quantification, six known SE-OMPs including EF- Tu, LipL21, LipL41, LipL46, Loa22, and OmpL36, and one predicted SE-OMPs, LipL71 were found in the 20 most abundant proteins, in which LipL41 was the highest abundant SE-OMP. Moreover, uncharacterized LIC14011 protein (LIP3228 ortholog in serovar Pomona) was identified as a novel predicted surface βb-OMP. High-abundance leptospiral SE-OMPs identified in this study may play roles in virulence and infection and are potential targets for development of vaccine or diagnostic tests for leptospirosis.