The midterm meeting of the Climgen project took place at the Universidad Complutense de Madrid: UCM (The Complutense University of Madrid) on the 14th and 15th of June 2016. The meeting was held over two days with progress/update presentations for each work package on the first day and data sharing discussions with other groups/collaborators on the second day.
The following paragraphs are a summary of the progress and updates by partners for their corresponding Work Packages:
WP1 has mainly been involved in the day to day administration of the Climgen project. As coordinator (Cardiff University) of the Climgen project, Michael Bruford has been in discussions with FACCE-JPI on the format of the midterm report (due by October/November 2016) but no decision has been made yet. WP1 is in the process to deliver on a public report that will be uploaded to the Climgen website by Pablo Orozco terWengel by the end of June. This work package has been involved in the coordination of two meetings: 1) the kick-off meeting at Cardiff University in January 2015 and 2) the midterm meeting at the Universidad Complutense de Madrid in June 2016. WP1 has also been involved in the coordination of all progress and financial reports. Lastly, we have been involved in the first sampling trip in Romania during May 2016 (see progress under partner 5).
Work Package 2, Partner 2 – UNICATT is coordinating the collection of case studies and collaborating with other partners to build the project database. To this end UNICATT has contributed to the identification of the variables describing the metadata information that ClimGen is going to collect for the selected case studies in order to perform association analyses between genotypes and phenotypes/environmental traits. A different number of variables has been selected for each target species (71 for cattle, 74 for sheep, 75 for goats and 49 for pigs), but all species share a common subset of 33 variables considered as “mandatory” for the inclusion of a dataset within ClimGen case studies. Overall, the selected variables allow to collect information at the individual level on the breed, sex, age, geographical provenance, availability of biological material (e.g. tissue samples, extracted DNA etc.) and molecular information (SNP genotypes, whole-genome sequence data etc.), morphological traits (coat color, horns presence/absence), known diseases or disease resistance, veterinary treatments, breed purpose and typical products, herd management, presence of breeders’ associations etc. The aforementioned metadata variables correspond to the fields included in the excel template files prepared in collaboration between WP2 and WP4. UNICATT has also contributed to the standardization of the file formats for the collection of molecular information: PLINK software . ped and .map files for SNP genotype data, and .gvcf files for sequence data. Moreover, UNICATT has also drafted the first part of the “ClimGen User guide to data collection”, i.e. a manual for data providers describing the meaning of each metadata variable and explaining how to fill in the excel template files with metadata information. UNICATT has also contributed 18 case studies to the project.
Within WP3, the effects of heat stress in Large White pigs are investigated. Under experimental conditions, pigs are exposed to high environmental temperature and humidity, as measured by a combined temperature humidity index (THI) in the range 76-90 (temperature between 25 – 33 Celsius, average humidity 90%). A group of pigs kept in standard commercial conditions (lower temperature and humidity) is used as control. Blood is collected at different time points, and the clinical conditions of the animals is monitored. From blood leukocytes, RNA and DNA are extracted. RNA is used to study the differential expression of genes in pigs housed at high THI vs the control group. DNA is used to detect and quantify methylation along the genome: methylation of DNA is one mechanism by which gene expression is regulated in mammals. The experiment is still ongoing. Results will help understand the physiological response to heat stress in commercial Large White pigs.
By influencing patterns of gene expression, DNA methylation contributes to the variation of important ecological traits, but its role in adaptive mechanisms remains unclear: selection might act on the molecular mechanisms responsible for targeting the genomic regions that are methylated, and/or specific non-heritable methylation patterns might be environmentally induced leading to acclimation. However, adaptive mechanisms cannot be understood without considering their interactions with epigenetic factors that have been often overlooked until now. Our objective is to initiate addressing this question on small ruminants.
In the frame of the Climgen project, we aim at characterizing the epigenetic landscape of sheep and goat in Morocco. For each species, we constituted 2 sets of 12 individuals representative of contrasted environments with regards to the temperature (extreme temperature annual ranges). We characterised the whole genome coverage methylation profile of each individual from skin samples by a MeDIP-seq approach. We are currently analysing the data for identifying the genomic regions (and genes) that are differentially methylated according to the variation of this environmental parameter.
The red-legged partridge (Alectoris rufa) has a great socio-economic importance as a game species, reared by millions in farms in several European countries. The ability to respond to a wide spectrum of pathogens and environmental changes is key for a farm-reared animal that is facing higher pathogen exposure and specially for those submitted to restocking programs. In this study, RNA-sequencing and de novo assembly of genes expressed in different immune tissues were performed with the aim of detecting the transcriptomic signatures of red-legged partridge immune response (IR). More than 94 million reads were obtained and assembled producing a final annotated partridge transcriptome which comprises almost 7,000 unigenes, representing the genes expressed after non-infectious challenges aimed at eliciting both innate and acquired IRs. Also, a differential expression analysis identified 1488 up- and 107 down-regulated loci in individuals with high IR versus low IR. Partridges displaying higher innate IR show an enhanced activation of host defence gene pathways complemented with a tightly controlled desensitization that facilitates the return to cellular homeostasis. These findings indicate that the immune system’s ability to respond to environmental aggressions extensively involved transcriptional and post-transcriptional regulations, and expand our understanding on the molecular mechanisms of the avian immunity system, opening the possibility of improving disease resistance or robustness. This work also allowed to identify a total of 12,828 microsatellites and 33,857 Single Nucleotide Polymorphisms (SNPs) and the latter included in the genes identified have still to be listed for use in Gene Assisted Selection (GAS) as a tool to help uncover individual variability in the resistance to infections and other external aggressions in red-legged partridges. Also, the candidate gene sequences and the large number of potential genetic markers from the red-legged partridge transcriptome provide new tools for future studies in this and related species, thus contributing to the on-going development of genomic resources in avian species.
In WP4, a database to store phenotypes, genotypes and environmental metadata (e.g. climate) relevant for the projects is realized. A relational database with user-friendly interfaces for data input/output is planned. Environmental metadata from the project are integrated with available environmental data from public databases (http://www.worldclim.org/current: ~100Gb; 5 environmental variables, each with 13 parameters for each world “sector”). A post-GIS database is implemented to directly link GIS (geographical information system) information and environmental data. All variables to be included in the database have been standardized, and this information collected in a “codebook” with title “Climgen Collection Manual”, which has been used to prepare a template file for data collection to be distributed to all project partners. All data introduced in the database are automatically checked for consistency and correctness, to ensure the quality of the project database.
The work done by PTP during this first part of the project for WP4 and WP2 was to develop the necessary infrastructure for the collection of genomic and phenotypic data and meta-data for the entire project. In the first phase, in collaboration with other partners, a standard excel template file have been established, one for each species studied in the project, to be later used for the collection of information. Template files implement some features for the protection of specific fields (fields that should not be modified by the user, such as those containing the variable names). In many cases the acceptable values are shown in the drop-down lists of values to be selected, and a specific value must therefore be chosen rather than typed.
In the second phase, PTP has created a dedicated virtual server in order to have a single point of collection and distribution of genomic data and meta-data. This server is sFTP accessible on the internet and is equipped with access control and data backup functionalities. The database is managed with a PostgreSQL RDBM (relational database management) protocol. A web interface was developed and is available to partners for the consultation of the metadata database. This interface was implemented in Django. Different categories of users can have different levels of access to different sections of the database. Since data modification is only possible through subsidiary procedures, based on the compilation of template files, the access to the data via the Django web-interface is by default allowed only in “read mode” and data cannot be edited.
Also a dedicated software to check the correctness and consistency of the information entered in the database was developed. The software has been deposited in the following GitHub public repository: https://github.com/nicolazzie/ClimgenChecker.git. To improve the usability of the software, this was also endowed with a graphic interface (GUI) for Windows and Mac users. The management of data standardization was done using the software Zanardi. Zanardi is, essentially, a modular software that performs a wide range of genomic analysis. Its main purpose is to be able to streamline and perform genomic analysis from a set of input files that contain the data. Zanardi runs under any Linux/Unix-like and Mac environment. It was written in Python 2 (v.2.7.6), bash and R (for graphical output). As a result, Zanardi’s only requirements are Python 2.6+, 1.7+ Java and R 3+ with the add-on package “ggplot2” installed.
Activities under this Work Package are expected to start in July/August 2016.
In WP6, methods are compared for analysing the genetic adaptation in modern animal populations that is required to address possible environmental changes, and new conservation strategies for genetic resources are applied where the priority settings among the breeds are based on both neutral and adaptive genetic variation. In WP6 so far, parameters for simulation analyses have been set. There are four main parameter sets to be considered in the coming simulations: so called ‘global parameters’ (e.g. heritability of traits considered in the simulations), historical population parameters (e.g. demographic factors), parameters associated with a modern population (e.g. ratio of males and females taken part in breeding, mating design, replacement ratio) and genome parameters (e.g. number of chromosomes). The ClimGen partners are welcome to give suggestions for parameter settings. In the WP6, the simulations will be conducted using QMSim software and the target animal species will be domestic cattle (dairy breeds). One part of simulations is introgression analysis where investigations will focus on introgression of new genetic variation from conserved (native) animal populations in modern production breeds in order to improve resilience of cattle industry. Suggestions for genes (alleles) that could be introduced to modern production breeds have been searched from recent cattle genomics studies.
Partner 5 has circulated two newsletters thus far under WP7 and the next newsletter will be due at the end of August. In this newsletter we will report on the Climgen midterm meeting that was held in Spain, Madrid from the 14th to the 15th of June 2016.
We have also organized the sampling of sheep and goats in Romania under WP4. The first sampling trip in Romania from sheep and goat included blood samples and morphological measurements of each of the individuals. So far we have sampled 43 goats and 39 sheep, from the North and South of Romania from the following counties: Suceava, Iasi, Neamt, Botosani (cold and rainy climate), Braila, Tulcea, Constanta (warm, and dry climate). We aim to sample a total of 144 goats and 144 sheep. The first sampling trip was carried out with one of the other partners in the Climgen project (Cardiff University). We also collected geographical data such as latitude, longitude and elevation for each sampling locality.
The slides of the presentations held during this meeting are in the internal site.