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Paenibacillus larvae, the causative agent of the devastating honey-bee disease American Foulbrood, produces the cationic polyketide-peptide hybrid paenilamicin that displays high antibacterial and antifungal activity. Its biosynthetic gene cluster contains a gene coding for theN-acetyltransferase PamZ. We show that PamZ acts as self-resistance factor inP. larvaeby deactivation of paenilamicin. Using tandem MS, NMR spectroscopy and synthetic diastereomers, we identified the N-terminal amino group of the agmatinamic acid as theN-acetylation site. These findings highlight the pharmacophore region of paenilamicin, which we very recently identified as a new ribosome inhibitor. Here, we further elucidated the crystal structure of PamZ:acetyl-CoA complex at 1.34 Å resolution. An unusual tandem-domain architecture provides a well-defined substrate-binding groove decorated with negatively-charged residues to specifically attract the cationic paenilamicin. Our results will help to understand the mode of action of paenilamicin and its role in pathogenicity ofP. larvaeto fight American Foulbrood.

We present an agent-based epidemiological model that is based on an agent-based model for traffic and mobility. The model consists of individual agents that follow individual daily activity plans, which include, for each activity, locations, start times, and end times. Evidently, one can place a virus spreading dynamic on top of this, by infecting one or more agents, and then track the resulting virus dynamics through the model. Normally, the model is used to investigate non-pharmaceutical interventions. In the present paper, we undertake steps to better understand the infection graph. It becomes clear that the typical infection graph representation that connects individual people is an even more expensive representation than our original, already expensive data-driven mobility model. We then undertake first steps towards analyzing the model with respect to a possible percolation transition.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19.This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. The model is validated against the infection dynamics in Berlin (Germany).The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reinfection rate R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%.Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.Author summaryEvidently, there is an interest in models that are able to predict the effect of interventions in the face of pandemic diseases. The so-called compartmental models have difficulties to include effects that stem from spatial, demographic or temporal inhomongeneities. Person-centric models, often using social contact matrices, are difficult and time-consuming to build up. In the present paper, we describe how we built a largely data-driven person-centric infection model within less than a month when COVID-19 took hold in Germany. The model is based on our extensive experience with mobility modelling, and a synthetic data pipeline that starts with mobile phone data, while taking the infection dynamics and the disease progression from the literature. The approach makes the model portable to all places that have similar so-called activity-based models of travel in place, which are many places world-wide, and the number is continuously increasing. The model has been used since its inception to regularly advise the German government on expected consequences of interventions.

Advancing technology and higher availability of Virtual Reality (VR) devices sparked its application in various research fields. For instance, health-related research showed that simulated nature environments in VR could reduce arousal and increase valence levels. This study investigates how the amount of possible interactivity influences the presence in nature environments and consequences on arousal and valence. After inducing fear (high arousal and low valence) through a VR-horror game, it was tested how participants recovered if they played a VR-nature game with either no, limited, or extensive interaction. The horror game proved to be a valid stimulus for inducing high arousal and low valence with a successful manipulation check. Igroup presence questionnaire (IPQ) scores showed that more interaction with the virtual environment increases spatial presence. A beneficial effect of experiencing nature can also be concluded. Results from the Self-Assessment Manikin questionnaire (SAM) scores for valence indicate a significant increase in the conditions with extensive and limited interaction compared to the control group. The VR Nature experience did significantly decrease arousal and increase valence compared to the post-horror game ratings. The physiological responses support this finding. These results can increase the effectiveness of health-related VR-applications to elevate mood levels by either implementing plenty of interactions and consequently increasing spatial presence or doing the opposite and leaving out any interactions at all.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. Results show that in Berlin (Germany), behavioral changes of the population mostly happened before the government-initiated so-called contact ban came into effect. Also, the model is used to determine differentiated changes to the reinfection rate for different interventions such as reductions in activity participation, the wearing of masks, or contact tracing followed by quarantine-at-home. One important result is that successful contact tracing reduces the reinfection rate by about 30 to 40%, and that if contact tracing becomes overwhelmed then infection rates immediately jump up accordingly, making rather strong lockdown measures necessary to bring the reinfection rate back to below one.

1Executive summaryWe use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions:Complete lockdown works. About 10 days after lockdown, the infection dynamics dies down. This assumes that lockdown is complete, which can be guaranteed in the simulation, but not in reality. Still, it gives strong support to the argument that it is never too late for complete lockdown.As a rule of thumb, we would suggest complete lockdown no later than once 10% of hospital capacities available for COVID-19 are in use, and possibly much earlier. This is based on the following insights:Even after lockdown, the infection dynamics continues at home, leading to another tripling of the cases before the dynamics is slowed.There will be many critical cases coming from people who were infected before lockdown. Because of the exponential growth dynamics, their number will be large.Researchers with more detailed disease progression models should improve upon these statements.Our simulations say that complete removal of infections at child care, primary schools, workplaces and during leisure activities will not be enough to sufficiently slow down the infection dynamics. It would have been better, but still not sufficient, if initiated earlier.Infections in public transport play an important role. In the simulations shown later, removing infections in the public transport system reduces the infection speed and the height of the peak by approximately 20%. Evidently, this depends on the infection parameters, which are not well known. – This does not point to reducing public transport capacities as a reaction to the reduced demand, but rather use it for lower densities of passengers and thus reduced infection rates.In our simulations, removal of infections at child care, primary schools, workplaces, leisure activities, and in public transport may barely have been sufficient to control the infection dynamics if implemented early on. Now according to our simulations it is too late for this, and (even) harsher measures will have to be initiated until possibly a return to such a restrictive, but still somewhat functional regime will again be possible.Evidently, all of these results have to be taken with care. They are based on preliminary infection parameters taken from the literature, used inside a model that has more transport/movement details than all others that we are aware of but still not enough to describe all aspects of reality, and suffer from having to write computer code under time pressure. Optimally, they should be confirmed independently. Short of that, given current knowledge we believe that they provide justification for “complete lockdown” at the latest when about 10% of available hospital capacities for COVID-19 are in use (and possibly earlier; we are no experts of hospital capabilities).1What was not investigated in detail in our simulations was contact tracing, i.e. tracking down the infection chains and moving all people along infection chains into quarantine. The case of Singapore has so far shown that this may be successful. Preliminary simulation of that tactic shows that it is difficult to implement for COVID-19, since the incubation time is rather long, people are contagious before they feel sick, or maybe never feel sufficiently sick at all. We will investigate in future work if and how contact tracing can be used together with a restrictive, but not totally locked down regime.When opening up after lockdown, it would be important to know the true fraction of people who are already immune, since that would slow down the infection dynamics by itself. For Wuhan, the currently available numbers report that only about 0.1% of the population was infected, which would be very far away from “herd immunity”. However, there have been and still may be many unknown infections (Frankfurter Allgemeine Zeitung GmbH 2020).

Covering: 2011 to end of 2014 The Gram-positive, spore-forming bacterium Paenibacillus larvae (P. larvae) is the causative agent of the epizootic American Foulbrood (AFB), a fatal brood disease of the western honey bee (Apis mellifera). AFB is one of the most destructive honey bee diseases since it is not only lethal for infected larvae but also for the diseased colonies. Due to the high impact of honey bees on ecology and economy this epizootic is a severe and pressing problem. Knowledge about virulence mechanisms and the underlying molecular mechanisms remain largely elusive. Recent genome sequencing of P. larvae revealed its potential to produce unknown secondary metabolites, like nonribosomal peptides and peptide-polyketide hybrids. This article highlights recent findings on secondary metabolites synthesized by P. larvae and discusses their role in virulence and pathogenicity towards the bee larvae.

Das sporenbildende Bakterium Paenibacillus larvae ist der Erreger der Amerikanischen Faulbrut, einer weltweit auftretenden, hochpathogenen Bienenkrankheit. Wir konnten ein komplexes Hybrid-Gencluster aus nichtribosomalen Peptidsynthetasen (NRPS) und Polyketidsynthasen (PKS) im Genom von P. larvae aufklaren. Hier prasentieren wir die Isolierung und die Strukturaufklarung der antibakteriellen und antifungalen Produkte dieses Genclusters, die Paenilamicine genannt wurden. Die einzigartigen Strukturen der Paenilamicine geben tiefe Einblicke in die zugrunde liegende, komplexe Biosynthese-Maschinerie. Bienenlarven-Koinfektionstests zeigten, dass die Paenilamicine von P. larvae fur den Kampf um seine okologische Nische benotigt werden und nicht direkt fur das Sterben der Bienenlarven verantwortlich sind. Ihre antibakterielle und antimykotische Aktivitat machen die Paenilamicine zu vielversprechenden Kandidaten fur die Wirkstoffentwicklung.

American foulbrood (AFB) caused by the bee pathogenic bacterium Paenibacillus larvae is the most devastating bacterial disease of honey bees worldwide. From AFB-dead larvae, pure cultures of P. larvae can normally be cultivated indicating that P. larvae is able to defend its niche against all other bacteria present. Recently, comparative genome analysis within the species P. larvae suggested the presence of gene clusters coding for multi-enzyme complexes, such as non-ribosomal peptide synthetases (NRPSs). The products of these enzyme complexes are known to have a wide range of biological activities including antibacterial activities. We here present our results on antibacterial activity exhibited by vegetative P. larvae and the identification and analysis of a novel antibacterially active P. larvae tripeptide (called sevadicin; Sev) produced by a NRPS encoded by a gene cluster found in the genome of P. larvae. Identification of Sev was ultimately achieved by comparing the secretome of wild-type P. larvae with knockout mutants of P. larvae lacking production of Sev. Subsequent mass spectrometric studies, enantiomer analytics and chemical synthesis revealed the sequence and configuration of the tripeptide, D-Phe-D-ALa-Trp, which was shown to have antibacterial activity. The relevance of our findings is discussed in respect to host-pathogen interactions.

The Gram-positive bacterium Paenibacillus larvae is the etiological agent of American Foulbrood. This bacterial infection of honey bee brood is a notifiable epizootic posing a serious threat to global honey bee health because not only individual larvae but also entire colonies succumb to the disease. In the recent past considerable progress has been made in elucidating molecular aspects of host pathogen interactions during pathogenesis of P. larvae infections. Especially the sequencing and annotation of the complete genome of P. larvae was a major step forward and revealed the existence of several giant gene clusters coding for non-ribosomal peptide synthetases which might act as putative virulence factors. We here present the detailed analysis of one of these clusters which we demonstrated to be responsible for the biosynthesis of bacillibactin, a P. larvae siderophore. We first established culture conditions allowing the growth of P. larvae under iron-limited conditions and triggering siderophore production by P. larvae. Using a gene disruption strategy we linked siderophore production to the expression of an uninterrupted bacillibactin gene cluster. In silico analysis predicted the structure of a trimeric trithreonyl lactone (DHB-Gly-Thr)3 similar to the structure of bacillibactin produced by several Bacillus species. Mass spectrometric analysis unambiguously confirmed that the siderophore produced by P. larvae is identical to bacillibactin. Exposure bioassays demonstrated that P. larvae bacillibactin is not required for full virulence of P. larvae in laboratory exposure bioassays. This observation is consistent with results obtained for bacillibactin in other pathogenic bacteria.

The spore-forming bacterium Paenibacillus larvae is the causative agent of American Foulbrood (AFB), a fatal disease of honey bees that occurs worldwide. Previously, we identified a complex hybrid nonribosomal peptide/polyketide synthesis (NRPS/PKS) gene cluster in the genome of P. larvae. Herein, we present the isolation and structure elucidation of the antibacterial and antifungal products of this gene cluster, termed paenilamicins. The unique structures of the paenilamicins give deep insight into the underlying complex hybrid NRPS/PKS biosynthetic machinery. Bee larval co-infection assays reveal that the paenilamicins are employed by P. larvae in fighting ecological niche competitors and are not directly involved in killing the bee larvae. Their antibacterial and antifungal activities qualify the paenilamicins as attractive candidates for drug development.

Paenibacillus larvae is the etiological agent of American Foulbrood (AFB) a world-wide distributed devastating disease of the honey bee brood. Previous comparative genome analysis and more recently, the elucidation of the bacterial genome, provided evidence that this bacterium harbors putative functional nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) and therefore, might produce nonribosomal peptides (NRPs) and polyketides (PKs). Such biosynthesis products have been shown to display a wide-range of biological activities such as antibacterial, antifungal or cytotoxic activity. Herein we present an in silico analysis of the first NRPS/PKS hybrid of P. larvae and we show the involvement of this cluster in the production of a compound named paenilamicin (Pam). For the characterization of its in vitro and in vivo bioactivity, a knock-out mutant strain lacking the production of Pam was constructed and subsequently compared to wild-type species. This led to the identification of Pam by mass spectrometry. Purified Pam-fractions showed not only antibacterial but also antifungal and cytotoxic activities. The latter suggested a direct effect of Pam on honey bee larval death which could, however, not be corroborated in laboratory infection assays. Bee larvae infected with the non-producing Pam strain showed no decrease in larval mortality, but a delay in the onset of larval death. We propose that Pam, although not essential for larval mortality, is a virulence factor of P. larvae influencing the time course of disease. These findings are not only of significance in elucidating and understanding host-pathogen interactions but also within the context of the quest for new compounds with antibiotic activity for drug development.