Biology subjects

Nightingale, A.

Publications and source records attributed to Nightingale, A..

3 recordsLinked to original sources

bioRxiv2025

Forest biogeochemical monitoring indicates altered microbial communities, macronutrient availability, CO2 emissions and litter chemistry in root zone soil of oak trees with Acute Decline symptoms

Acute and Chronic Oak Decline (AOD & COD) widely impact oak woodlands across Europe. AOD is known to cause rapid decline in tree health in a short span of 3-5 years. However, the interactions between such oak decline and soil biogeochemical dynamics are not fully understood. We selected three oak trees for each of the three treatments for biogeochemical monitoring: 1. No symptoms (Healthy), 2. AOD & 3. COD symptoms, in Writtle woodlands, Essex, UK. The selected trees were used for quarterly root zone soil sampling (cores to depth of 40 cm), and monthly surface soil (0-10 cm), leaf litter and GHG emissions sampling over a year (2022-23). Soil samples were characterised for their physico-chemical properties, total nutrient content & availability. Leaf litter samples were sorted into branches, leaves, seeds and flowers, weighed and their chemistry characterised. Soil microbial communities were characterised using phospholipid fatty acid analyses. Almost all the measured properties showed significant changes with season and depth, indicating a strong temporal and depth effect on forest soil biogeochemical dynamics. Notably macro-nutrient contents of leaf litter were greater in the summer period, which resulted in increased soil nutrient availability in autumn. This pattern was particularly exaggerated in AOD trees, which had greater nutrient content in litterfall in summer and greater nutrient availability in root zone soil in autumn than those of COD and Healthy trees. We found that AOD root zone soil have altered microbial communities, decreased CO2 emissions and increased macronutrient availability. Taken together, our study shows poor physiological nutrient management by diseased AOD trees revealed by greater nutrient concentration in AOD leaf litter indicating the lack of nutrient resorption and re-allocation before leaf senescence, and less active root zone soil under AOD trees, shown by reduced soil CO2 emissions and increased nutrient availability in oak root influenced zones. Hence, we conclude that the biogeochemical dynamics of AOD root zone soils are significantly different to that of healthy and COD trees, however controlled experiments are needed alongside these field observations to disentangle the causes from effects in understanding the relationship between soil health and oak decline. HighlightsO_LIWe monitored forest soil biogeochemical dynamics under oak trees of different health status C_LIO_LIAcute Oak Decline (AOD) trees had greater macronutrient availability in root zone soils C_LIO_LIAOD trees had greater nutrient content in litter added to the root zone soil C_LIO_LIAOD root zone soils had altered microbial community structure & reduced CO2 emissions C_LIO_LIWe conclude that AOD trees have poor physiological nutrient management and less active rhizosphere compared to healthy trees. C_LI

ecology↗

bioRxiv2020

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

bioinformatics↗

bioRxiv2017

UniProt Genomic Mapping for Deciphering Functional Effects of Missense Variants

Understanding the association of genetic variation with its functional consequences in proteins is essential for the interpretation of genomic data and identifying causal variants in diseases. Integration of protein function knowledge with genome annotation can assist in rapidly comprehending genetic variation within complex biological processes. Here, we describe mapping UniProtKB human sequences and positional annotations such as active sites, binding sites, and variants to the human genome (GRCh38) and the release of a public genome track hub for genome browsers. To demonstrate the power of combining protein annotations with genome annotations for functional interpretation of variants, we present specific biological examples in disease-related genes and proteins. Computational comparisons of UniProtKB annotations and protein variants with ClinVar clinically annotated SNP data show that 32% of UniProtKB variants co-locate with 8% of ClinVar SNPs. The majority of co-located UniProtKB disease-associated variants (86%) map to pathogenic ClinVar SNPs. UniProt and ClinVar are collaborating to provide a unified clinical variant annotation for genomic, protein and clinical researchers. The genome track hubs, and related UniProtKB files, are downloadable from the UniProt FTP site and discoverable as public track hubs at the UCSC and Ensembl genome browsers.

genomics↗