The venom of honeybees (Apis mellifera L.) : from proteomics to recombinant production of potential allergens

De recente ontwikkelingen in de diagnostiek en therapie van bijengifallergie brengen een nood met zich mee om de samenstelling van bijengif zo volledig mogelijk te kennen. Met dit onderzoek hebben we geprobeerd een bijdrage te leveren tot het ontrafelen van deze puzzel. In ons onderzoek zijn we er in geslaagd in het gif van de honingbij 3 nieuwe proteïnen te ontdekken: PVF1, icarapine en MRJP9. Icarapine is een proteïne waarvan de functie momenteel nog niet gekend is. Het heeft een kenmerkend domein dat eveneens bij andere insecten waargenomen wordt. Icarapine kent een alternatieve splitsing van mRNA bij de transcriptie. Het is een potentieel nieuw allergeen omwille van de IgE-bindende eigenschappen van het recombinant proteïne. De annotatie van PVF1 is gebaseerd op de volledige coderende sequentie van het overeenkomstig cDNA uit gifklierweefsel, welke een sterke homologie vertoont met het Drosophila melanogaster PVF1. Het vervult een aantal functies waaronder het verhogen van de capillaire permeabiliteit. Ook hier werd alternatieve splitsing waargenomen. Dit is trouwens typisch voor deze familie. svVEGF, een gifcomponent bij adders, kan ook bij deze familie worden ondergebracht. MRJP9 behoort samen met MRJP8 tot de MRJP/yellow familie. De ‘major royal jelly proteins’ zijn bijenspecifiek, terwijl de “yellow“ leden bij andere insecten en bij bacteriën worden waargenomen. De functionaliteit is niet goed gekend. Het lijkt erop dat deze twee proteïnen nauwst verwant zijn het yellow proteïne waaruit de MRJP familie ontstaan is. MRJP9 kan met bijna zekerheid als een allergeen bestempeld worden. Van een ander allergeen, de protease inhibitor Api m 6, werd alleische variatie waargenomen. Dit verklaart de C-terminale proteïne variatie van deze component. Deze allelische variatie gaat meestal gepaard met het voorkomen van indels die de correcte assemblage van het bijengenoom vertraagd en bemoeilijkt hebben. Het aangerijkt bijengifpreparaat vertoont, naast cellulaire componenten en de meeste bijengifcomponenten, een aantal proteïnen die in de toekomst onze verdere aandacht zullen krijgen. Deze laatste studie leidde tot de identificatie van opvallend veel antioxidantia in de meest dominante spots. Er konden ook een aantal proteïnen geïdentificeerd worden waarvan sommige homologen bij de mens allergie veroorzaken. Naast het vinden van een reeks nieuwe gifcomponenten waarvan sommigen mogelijks nieuwe allergenen vertegenwoordigen onthult deze studie ook een opvallende proteïne heterogeniteit die zijn oorsprong vindt in allelische variatie en alternatieve splitsing van het transcript. Dit laatste aspect werd eveneens waargenomen bij de antibacteriële component apidaecine. Mogelijk is dit fenomeen kenmerkend voor de componenten die onderhevig zijn aan een hoge selectiedruk

Analysis of the overall resource consumption of a Flemish dairy farm using Exergetic Life Cycle Assessment

To deal with environmental challenges such as pollution and resource depletion, the potential environmental impact of agricultural products is commonly evaluated using the Life Cycle Assessment (LCA) methodology. For livestock systems, emission-related impacts such as global warming have been frequently studied in this way. During the past decades, intensifi-cation of agricultural systems to improve yields coincided with an increased material and energy throughput. Therefore, we focus on resource consumption in this paper. We applied an exergy-based approach to quantify total resource use and to calculate resource efficien-cies, both at system level and at life cycle level. We have performed a case study of an in-tensive confinement-based dairy farm in Flanders to illustrate our approach

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle

Publication history: Accepted - 9 February 2022; Published online - 26 March 2022Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.This paper is the result of the concerted effort of all participants and support from the networks of COST Action FA1302 “METHAGENE: Large-scale methane measurements on individual ruminants for genetic evaluations.” The authors thank all individuals and groups who have directly or indirectly contributed to this work; special thanks are due to the technical and financial support from the COST Action FA1302 of the European Union. In addition, all financial and technical support from all participating countries and research centers involved in this work is greatly acknowledged

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes

Biofiltration of methane from ruminants gas effluent using Autoclaved Aerated Concrete as the carrier material

Abstract The performance of Methane-Oxidizing Bacteria (MOB) immobilized on Autoclaved Aerated Concrete (AAC) in a biofilter setup to remove methane from ruminants gas effluent was investigated. Two dairy cows were housed in respiration chambers for two days where the exhaust gas from the chambers was used as the biofilter feed. MOB consumed methane at an average Removal Efficiency (RE) of 17.5% and Elimination Capacity of 67.3 g m-3 d-1. Several factors that might cause this low RE are the: (a) low methane inlet concentration (average concentration = 61.9 ppmv), (b) presence of ammonia in the inlet gas (average concentration = 1.54 ppmv), (c) the high gas feed flow rate (1.2 m3 h-1), and (d) the lowering humidity level in the biofilter (average RE = 15.9%). By using AAC as the carrier material, carbon dioxide was removed in the biofilter by the likely carbonation reaction with AAC (average RE = 4.02%). Thus, complete carbon sequestration from the converted methane was obtained. Overall, our results showed that an environmentally friendly methane biofilter process could be achieved when using ACC as the carrier material