How Plants Sense Wounds: Damaged-Self Recognition Is Based on Plant-Derived Elicitors and Induces Octadecanoid Signaling

Background: Animal-derived elicitors can be used by plants to detect herbivory but they function only in specific insect– plant interactions. How can plants generally perceive damage caused by herbivores? Damaged-self recognition occurs when plants perceive molecular signals of damage: degraded plant molecules or molecules localized outside their original compartment. Methodology/Principal Findings: Flame wounding or applying leaf extract or solutions of sucrose or ATP to slightly wounded lima bean (Phaseolus lunatus) leaves induced the secretion of extrafloral nectar, an indirect defense mechanism. Chemically related molecules that would not be released in high concentrations from damaged plant cells (glucose, fructose, salt, and sorbitol) did not elicit a detectable response, excluding osmotic shock as an alternative explanation. Treatments inducing extrafloral nectar secretion also enhanced endogenous concentrations of the defense hormone jasmonic acid (JA). Endogenous JA was also induced by mechanically damaging leaves of lima bean, Arabidopsis, maize, strawberry, sesame and tomato. In lima bean, tomato and sesame, the application of leaf extract further increased endogenous JA content, indicating that damaged-self recognition is taxonomically widely distributed. Transcriptomic patterns obtained with untargeted 454 pyrosequencing of lima bean in response to flame wounding or the application of leaf extract or JA were highly similar to each other, but differed from the response to mere mechanical damage. W

Global dataset of soil organic carbon in tidal marshes.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

Global dataset of soil organic carbon in tidal marshes

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1 in the top 30 cm and 231 ± 134 Mg SOC ha−1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

Global dataset of soil organic carbon in tidal marshes

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−¹ in the top 30 cm and 231 ± 134 Mg SOC ha−¹ in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.ISSN:2052-446

Genetic diversity of HLA system in two populations from Nuevo León, Mexico: Monterrey and rural Nuevo León

We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) alleles by PCR-SSP based typing in 665 Mexicans from the state of Nuevo León living in the city of Monterrey (N = 226) and rural communities (N = 439), to obtain information regarding allelic and haplotypic frequencies. We find that the most frequent haplotypes in the state of Nuevo León include 12 Native American and three European haplotypes. Admixture estimates revealed that the main genetic components in the state of Nuevo León are Native American (54.53 ± 0.87 by ML; 48.88 of Native American haplotypes) and European (38.67 ± 4.06 by ML; 32.59 of European haplotypes), and a less prominent African genetic component (6.80 ± 4.30 by ML; 8.26 of African haplotypes)

Genetic diversity of HLA system in a population sample from Aguascalientes, Mexico

We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) alleles by PCR-SSP based typing in 95 Mexicans from the state of Aguascalientes to obtain information regarding allelic and haplotypic frequencies and their linkage disequilibrium. We find that the most frequent haplotypes in the state of Aguascalientes include four Native American, three European and one Asian haplotypes. Admixture estimates revealed that the main genetic components in the state of Aguascalientes are Native American (54.53 ± 3.22 by ML; 44.21 of Native American haplotypes) and European (44.34 ± 0.45 by ML; 40.53 of European haplotypes), and a relatively low African genetic component (1.13 ± 2.33 by ML; 5.26 of African haplotypes)

The immunogenetic diversity of the HLA system in Mexico correlates with underlying population genetic structure

We studied HLA class I (HLA-A, -B) and class II (HLA-DRB1, -DQB1) allele groups and alleles by PCR-SSP based typing in a total of 15,318 mixed ancestry Mexicans from all the states of the country divided into 78 sample sets, providing information regarding allelic and haplotypic frequencies and their linkage disequilibrium, as well as admixture estimates and genetic substructure. We identified the presence of 4268 unique HLA extended haplotypes across Mexico and find that the ten most frequent (HF > 1%) HLA haplotypes with significant linkage disequilibrium (Δ’≥0.1) in Mexico (accounting for 20% of the haplotypic diversity of the country) are of primarily Native American ancestry (A*02~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*08~DQB1*04, A*68~B*39~DRB1*04~DQB1*03:02, A*02~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*14~DQB1*03:01, A*24~B*35~DRB1*04~DQB1*03:02, A*24~B*39~DRB1*04~DQB1*03:02, A*02~B*40:02~DRB1*04~DQB1*03:02, A*68~B*35~DRB1*04~DQB1*03:02, A*02~B*15:01~DRB1*04~DQB1*03:02). Admixture estimates obtained by a maximum likelihood method using HLA-A/-B/-DRB1 as genetic estimators revealed that the main genetic components in Mexico as a whole are Native American (ranging from 37.8% in the northern part of the country to 81.5% in the southeastern region) and European (ranging from 11.5% in the southeast to 62.6% in northern Mexico). African admixture ranged from 0.0 to 12.7% not following any specific pattern. We were able to detect three major immunogenetic clusters correlating with genetic diversity and differential admixture within Mexico: North, Central and Southeast, which is in accordance with previous reports using genome-wide data. Our findings provide insights into the population immunogenetic substructure of the whole country and add to the knowledge of mixed ancestry Latin American population genetics, important for disease association studies, detection of demographic signatures on population variation and improved allocation of public health resources.1 Introduction 2 Subjects, materials and methods 2.1 Subjects 2.2 HLA typing 2.3 Statistical analysis 2.3.1 HLA allelic and haplotypic diversity 2.3.2 Admixture proportions calculations 2.3.3 Genetic diversity and genetic substructure assessment 3 Results 3.1 HLA allele groups 3.2 Haplotypic diversity 3.3 Admixture estimates 3.4 Genetic diversity and genetic substructure assessment 4 Discussion 4.1 Admixture estimates in Mexican populations and immunogenetic diversity 4.2 The Native American immunogenetic component in Mexican populations 4.3 Implications of the study of alleles and haplotypes of the HLA system in Mexican populations and final considerations 5 Conclusio

Research priorities for harnessing plant microbiomes in sustainable agriculture

Feeding a growing world population amidst climate change requires optimizing the reliability, resource use, and environmental impacts of food production. One way to assist in achieving these goals is to integrate beneficial plant microbiomes-i.e., those enhancing plant growth, nutrient use efficiency, abiotic stress tolerance, and disease resistance-into agricultural production. This integration will require a large-scale effort among academic researchers, industry researchers, and farmers to understand and manage plant-microbiome interactions in the context of modern agricultural systems. Here, we identify priorities for research in this area: (1) develop model host-microbiome systems for crop plants and non-crop plants with associated microbial culture collections and reference genomes, (2) define core microbiomes and metagenomes in these model systems, (3) elucidate the rules of synthetic, functionally programmable microbiome assembly, (4) determine functional mechanisms of plant-microbiome interactions, and (5) characterize and refine plant genotype-by-environment-by-microbiome-by-management interactions. Meeting these goals should accelerate our ability to design and implement effective agricultural microbiome manipulations and management strategies, which, in turn, will pay dividends for both the consumers and producers of the world food supply