Seasonal Dynamics of the Airborne Bacterial Community and Selected Viruses in a Children’s Daycare Center

Children’s daycare centers appear to be hubs of respiratory infectious disease transmission, yet there is only limited information about the airborne microbial communities that are present in daycare centers. We have investigated the microbial community of the air in a daycare center, including seasonal dynamics in the bacterial community and the presence of specific viral pathogens. We collected filters from the heating, ventilation, and air conditioning (HVAC) system of a daycare center every two weeks over the course of a year. Amplifying and sequencing the 16S rRNA gene revealed that the air was dominated by Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes that are commonly associated with the human skin flora. Clear seasonal differences in the microbial community were not evident; however, the community structure differed when the daycare center was closed and unoccupied for a 13-day period. These results suggest that human occupancy, rather than the environment, is the major driver in shaping the microbial community structure in the air of the daycare center. Using PCR for targeted viruses, we detected a seasonal pattern in the presence of respiratory syncytial virus that included the period of typical occurrence of the disease related to the virus; however, we did not detect the presence of adenovirus or rotavirus at any time.     

Maize rhizosphere priming: field estimates using <Superscript>13</Superscript>C natural abundance

Introduction

Root-mediated changes in soil organic matter (SOM) decomposition, termed rhizosphere priming effects (RPE), play crucial roles in the global carbon (C) cycle, but their mechanisms and field relevance remain ambiguous. We hypothesize that nitrogen (N) shortages may intensify SOM decomposition in the rhizosphere because of increase of fine roots and rhizodeposition.

Methods

RPE and their dependence on N-fertilization were studied using a C₃-to-C₄ vegetation change. N-fertilized and unfertilized soil cores, with and without maize, were incubated in the field for 50 days. Soil CO₂ efflux was measured, partitioned for SOM- and root-derived CO₂, and RPE was calculated. Plant biomass, microbial biomass C (MBC) and N (MBN), and enzyme activities (β-1,4-glucosidase; N-acetylglucosaminidase; L-leucine aminopeptidase) were analyzed.

Results

Roots enhanced SOM mineralization by 35 % and 126 % with and without N, respectively. This was accompanied by higher specific root-derived CO₂ in unfertilized soils. MBC, MBN and enzyme activities increased in planted soils, indicating microbial activation, causing positive RPE. N-fertilization had minor effects on MBC and MBN, but it reduced β-1,4-glucosidase and L-leucine aminopeptidase activities under maize through lower root-exudation. In contrast, N-acetylglucosaminidase activity increased with N-fertilization in planted and unplanted soils.

Conclusions

This study showed the field relevance of RPE and confirmed that, despite higher root biomass, N availability reduces RPE by lowering root and microbial activity.

     

Cache Domains That are Homologous to,but Different from PAS Domains Comprise the Largest Superfamily of Extracellular Sensors in Prokaryotes

Cellular receptors usually contain a designated sensory domain that recognizes the signal. Per/Arnt/Sim (PAS) domains are ubiquitous sensors in thousands of species ranging from bacteria to humans. Although PAS domains were described as intracellular sensors, recent structural studies revealed PAS-like domains in extracytoplasmic regions in several transmembrane receptors. However, these structurally defined extracellular PAS-like domains do not match sequence-derived PAS domain models, and thus their distribution across the genomic landscape remains largely unknown. Here we show that structurally defined extracellular PAS-like domains belong to the Cache superfamily, which is homologous to, but distinct from the PAS superfamily. Our newly built computational models enabled identification of Cache domains in tens of thousands of signal transduction proteins including those from important pathogens and model organisms. Furthermore, we show that Cache domains comprise the dominant mode of extracellular sensing in prokaryotes.     

High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation

Metabolomics - Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require...     

QTL sequencing strategy to map genomic regions associated with resistance to ascochyta blight in chickpea

Whole‐genome sequencing‐based bulked segregant analysis (BSA) for mapping quantitative trait loci (QTL) provides an efficient alternative approach to conventional QTL analysis as it significantly reduces the scale and cost of analysis with comparable power to QTL detection using full mapping population. We tested the application of next‐generation sequencing (NGS)‐based BSA approach for mapping QTLs for ascochyta blight resistance in chickpea using two recombinant inbred line populations CPR‐01 and CPR‐02. Eleven QTLs in CPR‐01 and six QTLs in CPR‐02 populations were mapped on chromosomes Ca1, Ca2, Ca4, Ca6 and Ca7. The QTLs identified in CPR‐01 using conventional biparental mapping approach were used to compare the efficiency of NGS‐based BSA in detecting QTLs for ascochyta blight resistance. The QTLs on chromosomes Ca1, Ca4, Ca6 and Ca7 overlapped with the QTLs previously detected in CPR‐01 using conventional QTL mapping method. The QTLs on chromosome Ca4 were detected in both populations and overlapped with the previously reported QTLs indicating conserved region for ascochyta blight resistance across different chickpea genotypes. Six candidate genes in the QTL regions identified using NGS‐based BSA on chromosomes Ca2 and Ca4 were validated for their association with ascochyta blight resistance in the CPR‐02 population. This study demonstrated the efficiency of NGS‐based BSA as a rapid and cost‐effective method to identify QTLs associated with ascochyta blight in chickpea.