The highly dynamic CRISPR1 system of Streptococcus agalactiae controls the diversity of its mobilome

Clustered regularly interspaced short palindromic repeats (CRISPR) confer immunity against mobile genetic elements (MGEs) in prokaryotes. Streptococcus agalactiae, a leading cause of neonatal infections contains in its genome two CRISPR/Cas systems. We show that type 1‐C CRISPR2 is present in few strains but type 2‐A CRISPR1 is ubiquitous. Comparative sequence analysis of the CRISPR1 spacer content of 351 S. agalactiae strains revealed that it is extremely diverse due to the acquisition of new spacers, spacer duplications and spacer deletions that witness the dynamics of this system. The spacer content profile mirrors the S. agalactiae population structure. Transfer of a conjugative transposon targeted by CRISPR1 selected for spacer rearrangements, suggesting that deletions and duplications pre‐exist in the population. The comparison of protospacers located within MGE or the core genome and protospacer‐associated motif‐shuffling demonstrated that the GG motif is sufficient to discriminate self and non‐self and for spacer selection and integration. Strikingly more than 40% of the 949 different CRISPR1 spacers identified target MGEs found in S. agalactiae genomes. We thus propose that the S. agalactiae type II‐A CRISPR1/Cas system modulates the cohabitation of the species with its mobilome, as such contributing to the diversity of MGEs in the population.     

Severe bacterial neonatal infections in Madagascar,Senegal, and Cambodia: A multicentric community-based cohort study

BackgroundSevere bacterial infections (SBIs) are a leading cause of neonatal deaths in low- and middle-income countries (LMICs). However, most data came from hospitals, which do not include neonates who did not seek care or were treated outside the hospital. Studies from the community are scarce, and few among those available were conducted with high-quality microbiological techniques. The burden of SBI at the community level is therefore largely unknown. We aimed here to describe the incidence, etiology, risk factors, and antibiotic resistance profiles of community-acquired neonatal SBI in 3 LMICs.Methods and findingsThe BIRDY study is a prospective multicentric community-based mother and child cohort study and was conducted in both urban and rural areas in Madagascar (2012 to 2018), Cambodia (2014 to 2018), and Senegal (2014 to 2018). All pregnant women within a geographically defined population were identified and enrolled. Their neonates were actively followed from birth to 28 days to document all episodes of SBI. A total of 3,858 pregnant women (2,273 (58.9%) in Madagascar, 814 (21.1%) in Cambodia, and 771 (20.0%) in Senegal) were enrolled in the study, and, of these, 31.2% were primigravidae. Women enrolled in the urban sites represented 39.6% (900/2,273), 45.5% (370/814), and 61.9% (477/771), and those enrolled in the rural sites represented 60.4% (1,373/2,273), 54.5% (444/814), and 38.1% (294/771) of the total in Madagascar, Cambodia, and Senegal, respectively. Among the 3,688 recruited newborns, 49.6% were male and 8.7% were low birth weight (LBW). The incidence of possible severe bacterial infection (pSBI; clinical diagnosis based on WHO guidelines of the Integrated Management of Childhood Illness) was 196.3 [95% confidence interval (CI) 176.5 to 218.2], 110.1 [88.3 to 137.3], and 78.3 [59.5 to 103] per 1,000 live births in Madagascar, Cambodia, and Senegal, respectively. The incidence of pSBI differed between urban and rural sites in all study countries. In Madagascar, we estimated an incidence of 161.0 pSBI per 1,000 live births [133.5 to 194] in the urban site and 219.0 [192.6 to 249.1] pSBI per 1,000 live births in the rural site (p = 0.008). In Cambodia, estimated incidences were 141.1 [105.4 to 189.0] and 85.3 [61.0 to 119.4] pSBI per 1,000 live births in urban and rural sites, respectively (p = 0.025), while in Senegal, we estimated 103.6 [76.0 to 141.2] pSBI and 41.5 [23.0 to 75.0] pSBI per 1,000 live births in urban and rural sites, respectively (p = 0.006). The incidences of culture-confirmed SBI were 15.2 [10.6 to 21.8], 6.5 [2.7 to 15.6], and 10.2 [4.8 to 21.3] per 1,000 live births in Madagascar, Cambodia, and Senegal, respectively, with no difference between urban and rural sites in each country. The great majority of early-onset infections occurred during the first 3 days of life (72.7%). The 3 main pathogens isolated were Klebsiella spp. (11/45, 24.4%), Escherichia coli (10/45, 22.2%), and Staphylococcus spp. (11/45, 24.4%). Among the 13 gram-positive isolates, 5 were resistant to gentamicin, and, among the 29 gram-negative isolates, 13 were resistant to gentamicin, with only 1 E. coli out of 10 sensitive to ampicillin. Almost one-third of the isolates were resistant to both first-line drugs recommended for the management of neonatal sepsis (ampicillin and gentamicin). Overall, 38 deaths occurred among neonates with SBI (possible and culture-confirmed SBI together). LBW and foul-smelling amniotic fluid at delivery were common risk factors for early pSBI in all 3 countries. A main limitation of the study was the lack of samples from a significant proportion of infants with pBSI including 35 neonatal deaths. Without these samples, bacterial infection and resistance profiles could not be confirmed.ConclusionsIn this study, we observed a high incidence of neonatal SBI, particularly in the first 3 days of life, in the community of 3 LMICs. The current treatment for the management of neonatal infection is hindered by antimicrobial resistance. Our findings suggest that microbiological diagnosis of SBI remains a challenge in these settings and support more research on causes of neonatal death and the implementation of early interventions (e.g., follow-up of at-risk newborns during the first days of life) to decrease the burden of neonatal SBI and associated mortality and help achieve Sustainable Development Goal 3.

In a community-based, prospective cohort study, Bich-Tram Huynh and colleagues investigate the incidence and factors associated with several bacterial infections among neonates in rural and urban areas of three low-middle income countries.     

Trait positions for elevated invasiveness in adaptive ecological networks

Our ability to predict the outcome of invasion declines rapidly as non-native species progress through intertwined ecological barriers to establish and spread in recipient ecosystems. This is largely due to the lack of systemic knowledge on key processes at play as species establish self-sustaining populations within the invaded range. To address this knowledge gap, we present a mathematical model that captures the eco-evolutionary dynamics of native and non-native species interacting within an ecological network. The model is derived from continuous-trait evolutionary game theory (i.e., Adaptive Dynamics) and its associated concept of invasion fitness which depicts dynamic demographic performance that is both trait mediated and density dependent. Our approach allows us to explore how multiple resident and non-native species coevolve to reshape invasion performance, or more precisely invasiveness, over trait space. The model clarifies the role of specific traits in enabling non-native species to occupy realised opportunistic niches. It also elucidates the direction and speed of both ecological and evolutionary dynamics of residing species (natives or non-natives) in the recipient network under different levels of propagule pressure. The versatility of the model is demonstrated using four examples that correspond to the invasion of (i) a horizontal competitive community; (ii) a bipartite mutualistic network; (iii) a bipartite antagonistic network; and (iv) a multi-trophic food web. We identified a cohesive trait strategy that enables the success and establishment of non-native species to possess high invasiveness. Specifically, we find that a non-native species can achieve high levels of invasiveness by possessing traits that overlap with those of its facilitators (and mutualists), which enhances the benefits accrued from positive interactions, and by possessing traits outside the range of those of antagonists, which mitigates the costs accrued from negative interactions. This ‘central-to-reap, edge-to-elude’ trait strategy therefore describes the strategic trait positions of non-native species to invade an ecological network. This model provides a theoretical platform for exploring invasion strategies in complex adaptive ecological networks.

     

Viral and Atypical Bacterial Etiology of Acute Respiratory Infections in Children under 5 Years Old Living in a Rural Tropical Area of Madagascar

Background

In Madagascar, very little is known about the etiology and prevalence of acute respiratory infections (ARIs) in a rural tropical area. Recent data are needed to determine the viral and atypical bacterial etiologies in children with defined clinical manifestations of ARIs.

Methods

During one year, we conducted a prospective study on ARIs in children between 2 to 59 months in the community hospital of Ampasimanjeva, located in the south-east of Madagascar. Respiratory samples were analyzed by multiplex real-time RT-PCR, including 18 viruses and 2 atypical bacteria. The various episodes of ARI were grouped into four clinical manifestations with well-documented diagnosis: “Community Acquired Pneumonia”(CAP, group I), “Other acute lower respiratory infections (Other ALRIs, group II)”, “Upper respiratory tract infections with cough (URTIs with cough, group III)”and “Upper respiratory tract infections without cough (URTIs without cough, group IV)”.

Results

295 children were included in the study between February 2010 and February 2011. Viruses and/or atypical bacteria respiratory pathogens were detected in 74.6% of samples, the rate of co-infection was 27.3%. Human rhinovirus (HRV; 20.5%), metapneumovirus (HMPV A/B, 13.8%), coronaviruses (HCoV, 12.5%), parainfluenza virus (HPIV, 11.8%) and respiratory syncytial virus A and B (RSV A/B, 11.8%) were the most detected. HRV was predominantly single detected (23.8%) in all the clinical groups while HMPV A/B (23.9%) was mainly related to CAP (group I), HPIV (17.3%) to the “Other ALRIs” (group II), RSV A/B (19.5%) predominated in the group “URTIs with cough” (group III) and Adenovirus (HAdV, 17.8%) was mainly detected in the “without cough” (group IV).

Interpretation

This study describes for the first time the etiology of respiratory infections in febrile children under 5 years in a malaria rural area of Madagascar and highlights the role of respiratory viruses in a well clinically defined population of ARIs.     

Invading a mutualistic network: to be or not to be similar

Biological invasion remains a major threat to biodiversity in general and a disruptor to mutualistic interactions in particular. While a number of empirical studies have directly explored the role of invasion in mutualistic pollination networks, a clear picture is yet to emerge and a theoretical model for comprehension still lacking. Here, using an eco‐evolutionary model of bipartite mutualistic networks with trait‐mediated interactions, we explore invader trait, propagule pressure, and network features of recipient community that contribute importantly to the success and impact of an invasion. High level of invasiveness is observed when invader trait differs from those of the community average, and level of interaction generalization equals to that of the community average. Moreover, multiple introductions of invaders with declining propagules enhance invasiveness. Surprisingly, the most successful invader is not always the one having the biggest impact on the recipient community. The network structure of recipient community, such as nestedness and modularity, is not a primary indicator of its invasibility; rather, the invasibility is best correlated with measurements of network stability such as robustness, resilience, and disruptiveness (a measure of evolutionary instability). Our model encompasses more general scenarios than previously studied in predicting invasion success and impact in mutualistic networks, and our results highlight the need for coupling eco‐evolutionary processes to resolve the invasion dilemma.     

Fine‐tuning the nested structure of pollination networks by adaptive interaction switching,biogeography and sampling effect in the Galápagos Islands

The structure of pollination networks, particularly its nestedness, contain important information on network assemblages. However, there is still limited understanding of the mechanisms underlying nested pollination network structures. Here, we investigate the role of adaptive interaction switching (AIS), island area, isolation, age and sampling effort in explaining the nestedness of pollination networks across ten Galápagos Islands. The AIS algorithm is inspired by Wallace's elimination of the unfit, where a species constantly replaces its least profitable mutualistic partner with a new partner selected at random. To explain network structures, we first use a dynamic model that includes functional response of pollination and AIS, with only species richness and binary connectance as input (hereafter the AIS model). Thereafter, other explanatory variables (isolation, area, age and sampling effort) were added to the model. In four out of ten islands, the pollination network was significantly nested, and predictions from the AIS model correlated with observed structures, explaining 69% variation in nestedness. Overall, in terms of independent contribution from hierarchical partitioning of variation in observed nestedness, the AIS model predictions contributed the most (37%), followed by sampling effort (28%) and island area (22%), with only trivial contributions from island isolation and age. Therefore, adaptive switching of biotic interactions seems to be key to ensure network function, with island biogeographic factors being only secondary. Although large islands could harbour more diverse assemblages and thus foster more nested structures, sufficient sampling proves to be essential for detecting non‐random network structures.     

Defining invasiveness and invasibility in ecological networks

The success of a biological invasion is context dependent, and yet two key concepts—the invasiveness of species and the invasibility of recipient ecosystems—are often defined and considered separately. We propose a framework that can elucidate the complex relationship between invasibility and invasiveness. It is based on trait-mediated interactions between species and depicts the response of an ecological network to the intrusion of an alien species, drawing on the concept of community saturation. Here, invasiveness of an introduced species with a particular trait is measured by its per capita population growth rate when the initial propagule pressure of the introduced species is very low. The invasibility of the recipient habitat or ecosystem is dependent on the structure of the resident ecological network and is defined as the total width of an opportunity niche in the trait space susceptible to invasion. Invasibility is thus a measure of network instability. We also correlate invasibility with the asymptotic stability of resident ecological network, measured by the leading eigenvalue of the interaction matrix that depicts trait-based interaction intensity multiplied by encounter rate (a pairwise product of propagule pressure of all members in a community). We further examine the relationship between invasibility and network architecture, including network connectance, nestedness and modularity. We exemplify this framework with a trait-based assembly model under perturbations in ways to emulate fluctuating resources and random trait composition in ecological networks. The maximum invasiveness of a potential invader (greatest intrinsic population growth rate) was found to be positively correlated with invasibility of the recipient ecological network. Additionally, ecosystems with high network modularity and high ecological stability tend to exhibit high invasibility. Where quantitative data are lacking we propose using a qualitative interaction matrix of the ecological network perceived by a potential invader so that the structural network stability and invasibility can be estimated from the literature or from expert opinion. This approach links network structure, invasiveness and invasibility in the context of trait-mediated interactions, such as the invasion of insects into mutualistic and antagonistic networks.