Molecular genetics of chicken egg quality

Faultless quality in eggs is important in all production steps, from chicken to packaging, transportation, storage, and finally to the consumer. The egg industry (specifically transportation and packing) is interested in robustness, the consumer in safety and taste, and the chicken itself in the reproductive performance of the egg. High quality is commercially profitable, and egg quality is currently one of the key traits in breeding goals. In conventional breeding schemes, the more traits that are included in a selection index, the slower the rate of genetic progress for all the traits will be. The unveiling of the genes underlying the traits, and subsequent utilization of this genomic information in practical breeding, would enhance the selection progress, especially with traits of low inheritance, genderconfined traits, or traits which are difficult to assess. In this study, two experimental mapping populations were used to identify quantitative trait loci (QTL) of egg quality traits. A whole genome scan was conducted in both populations with different sets of microsatellite markers. Phenotypic observations of albumen quality, internal inclusions, egg taint, egg shell quality traits, and production traits during the entire production period were collected. To study the presence of QTL, a multiple marker linear regression was used. Polymorphisms found in candidate genes were used as SNP (single nucleotide polymorphism) markers to refine the map position of QTL by linkage and association. Furthermore, independent commercial egg layer lines were utilized to confirm some of the associations. Albumen quality, the incidence of internal inclusions, and egg taint were first mapped with the whole genome scan and fine-mapped with subsequent analyses. In albumen quality, two distinct QTL areas were found on chromosome 2. Vimentin, a gene maintaining the mechanical integrity of the cells, was studied as a candidate gene. Neither sequencing nor subsequent analysis using SNP within the gene in the QTL analysis suggested that variation in this gene could explain the effect on albumen thinning. The same mapping approach was used to study the incidence of internal inclusions, specifically, blood and meat spots. Linkage analysis revealed one genome-wide significant region on chromosome Z. Fine-mapping exposed that the QTL overlapped with a tight junction protein gene ZO-2, and a microsatellite marker inside the gene. Sequencing of a fragment of the gene revealed several SNPs. Two novel SNPs were found to be located in a miRNA (gga-mir-1556) within the ZO-2. MicroRNA-SNP and an exonic synonymous SNP were genotyped in the populations and showed significant association to blood and meat spots. A good congruence between the experimental population and commercial breeds was achieved both in QTL locations and in association results. As a conclusion, ZO-2 and gga-mir-1556 remained candidates for having a role in susceptibility to blood and meat spot defects across populations. This is the first report of QTL affecting blood and meat spot frequency in chicken eggs, albeit the effect explained only 2 % of the phenotypic variance. Fishy taint is a disorder, which is a characteristic of brown layer lines. Marker-trait association analyses of pooled samples indicated that egg-taint and the FMO3 gene map to chicken chromosome 8 and that the variation found by sequencing in the chicken FMO3 gene was associated with the TMA content of the egg. The missense mutation in the FMO3 changes an evolutionary, highly conserved amino acid within the FMO-characteristic motif (FATGY). In conclusion, several QTL regions affecting egg quality traits were successfully detected. Some of the QTL findings, such as albumen quality, remained at the level of wide chromosomal regions. For some QTL, a putative causative gene was indicated: miRNA gga-mir-1556 and/or its host gene ZO-2 might have a role in susceptibility to blood and meat spot defects across populations. Nonetheless, fishy taint in chicken eggs was found to be caused with a substitution within a conserved motif of the FMO3 gene. This variation has been used in a breeding program to eliminate fishy-taint defects from commercial egg layer lines. Objective The objective of this thesis was to map loci affecting economically important egg quality traits in chickens and to increase knowledge of the molecular genetics of these complex traits. The aim was to find markers linked to the egg quality traits, and finally unravel the variation in the genes underlying the phenotypic variation of internal egg quality. QTL mapping methodology was used to identify chromosomal regions affecting various production and egg quality traits (I, III, IV). Three internal egg quality traits were selected for fine-mapping (II, III, IV). Some of the results were verified in independent mapping populations and present-day commercial lines (III, IV). The ultimate objective was to find markers to be applied in commercial selection programs

Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships

© 2015 Song et al.; licensee BioMed Central Ltd. Background: Intensive research based on the inverse expression relationship has been undertaken to discover the miRNA-mRNA regulatory modules involved in the infection of Hepatitis C virus (HCV), the leading cause of chronic liver diseases. However, biological studies in other fields have found that inverse expression relationship is not the only regulatory relationship between miRNAs and their targets, and some miRNAs can positively regulate a mRNA by binding at the 5' UTR of the mRNA.Results: This work focuses on the detection of both inverse and positive regulatory relationships from a paired miRNA and mRNA expression data set of HCV patients through a 'change-to-change' method which can derive connected discriminatory rules. Our study uncovered many novel miRNA-mRNA regulatory modules. In particular, it was revealed that GFRA2 is positively regulated by miR-557, miR-765 and miR-17-3p that probably bind at different locations of the 5' UTR of this mRNA. The expression relationship between GFRA2 and any of these three miRNAs has not been studied before, although separate research for this gene and these miRNAs have all drawn conclusions linked to hepatocellular carcinoma. This suggests that the binding of mRNA GFRA2 with miR-557, miR-765, or miR-17-3p, or their combinations, is worthy of further investigation by experimentation. We also report another mRNA QKI which has a strong inverse expression relationship with miR-129 and miR-493-3p which may bind at the 3' UTR of QKI with a perfect sequence match. Furthermore, the interaction between hsa-miR-129-5p (previous ID: hsa-miR-129) and QKI is supported with CLIP-Seq data from starBase. Our method can be easily extended for the expression data analysis of other diseases.Conclusion: Our rule discovery method is useful for integrating binding information and expression profile for identifying HCV miRNA-mRNA regulatory modules and can be applied to the study of the expression profiles of other complex human diseases

Involvement of genes and non-coding RNAs in cancer: profiling using microarrays

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs, RNAs that do not code for proteins) that regulate the expression of target genes. MiRNAs can act as tumor suppressor genes or oncogenes in human cancers. Moreover, a large fraction of genomic ultraconserved regions (UCRs) encode a particular set of ncRNAs whose expression is altered in human cancers. Bioinformatics studies are emerging as important tools to identify associations between miRNAs/ncRNAs and CAGRs (Cancer Associated Genomic Regions). ncRNA profiling, the use of highly parallel devices like microarrays for expression, public resources like mapping, expression, functional databases, and prediction algorithms have allowed the identification of specific signatures associated with diagnosis, prognosis and response to treatment of human tumors

Applications of Graphene Quantum Dots in Biomedical Sensors

Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in biological matrices. Considering these pathophysiologies, various research works have become necessary to fabricate biosensors for their early diagnosis and treatment, using nanomaterials like quantum dots (QDs). These nanomaterials effectively ameliorate the sensor performance with respect to their reproducibility, selectivity as well as sensitivity. In particular, graphene quantum dots (GQDs), which are ideally graphene fragments of nanometer size, constitute discrete features such as acting as attractive fluorophores and excellent electro-catalysts owing to their photo-stability, water-solubility, biocompatibility, non-toxicity and lucrativeness that make them favorable candidates for a wide range of novel biomedical applications. Herein, we reviewed about 300 biomedical studies reported over the last five years which entail the state of art as well as some pioneering ideas with respect to the prominent role of GQDs, especially in the development of optical, electrochemical and photoelectrochemical biosensors. Additionally, we outline the ideal properties of GQDs, their eclectic methods of synthesis, and the general principle behind several biosensing techniques.DFG, 428780268, Biomimetische Rezeptoren auf NanoMIP-Basis zur Virenerkennung und -entfernung mittels integrierter Ansätz

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Recent advances in biosensing approaches for point-of-care breast cancer diagnostics: challenges and future prospects

Timely and accurate diagnosis of breast cancer is essential for efficient treatment and the best possible survival rates. Biosensors have emerged as a smart diagnostic platform for the detection of biomarkers specific to the onset, recurrence, and therapeutic drug monitoring of breast cancer. There have been exciting recent developments, including significant improvements in the validation, sensitivity, specificity, and integration of sample processing steps to develop point-of-care (POC) integrated micro-total analysis systems for clinical settings. The present review highlights various biosensing modalities (electrical, optical, piezoelectric, mass, and acoustic sensing). It provides deep insights into their design principles, signal amplification strategies, and comparative performance analysis. Finally, this review emphasizes the status of existing integrated micro-total analysis systems (μ-TAS) for personalized breast cancer therapeutics and associated challenges and outlines the approach required to realize their successful translation into clinical settings

Design of bioinformatic tools for integrative analysis of microRNA-mRNA interactome applied to digestive cancers

[spa] En esta tesis se han desarrollado e implementado distintas herramientas bioinformáticas que permiten el estudio de las interacciones miRNA-mRNA en contextos celulares específicos. oncretamente se ha creado un paquete de R (miRComb) que calcula las interacciones miRNA-mRNA partiendo de expresión de miRNAs y mRNAs, y predicciones bloinformáticas de bases de datos preexistentes. Las interacciones miRNA-mRNA finales son aquellas que muestran una correlación negativa y han estado predichas por al meno una base de datos. Como valor añadido, el paquete miRComb realiza un resumen en pdf con los resultados básicos del análisis (número de interacciones, número de mRNAs target por miRNA, análisis funcional, etc.), que permite comparar los datos de distintos estudios. Hemos aplicado esta metodología en el contexto de cánceres digestivos. En un primer estudio hemos utilizado datos públicos de 5 cánceres digestivos (colon, recto, esófago, stómago e hígado) y hemos determinado las interacciones miRNA-mRNA comunes entre ellos y específicas de cada uno. En un segundo estudio, hemos utilizado la misma metodología para analizar datos de IRNA-mRNA en biopsias de pacientes del Hospital Clínic de Barcelona con cáncer de páncreas. En este estudio hemos descrito interacciones miRNA-mRNA en el contexto de cáncer pancreático y hemos podido validar dos de ellas a nivel experimental. En resumen, podemos concluir que el paquete miRComb es una herramienta útil para el estudio del interactoma de miRNA-mRNA, y que ha servido para establecer hipótesis biológicas que luego se han podido comprobar en el laboratori

Integrative methods for reconstruction of dynamic networks in chondrogenesis

Application of human mesenchymal stem cells represents a promising approach in the field of regenerative medicine. Specific stimulation can give rise to chondrocytes, osteocytes or adipocytes. Investigation of the underlying biological processes which induce the observed cellular differentiation is essential to efficiently generate specific tissues for therapeutic purposes. Upon treatment with diverse stimuli, gene expression levels of cultivated human mesenchymal stem cells were monitored using time series microarray experiments for the three lineages. Application of gene network inference is a common approach to identify the regulatory dependencies among a set of investigated genes. This thesis applies the NetGenerator V2.0 tool, which is capable to deal with multiple time series data, which investigates the effect of multiple external stimuli. The applied model is based on a system of linear ordinary differential equations, whose parameters are optimised to reproduce the given time series datasets. Several procedures in the inference process were adapted in this new version in order to allow for the integration of multiple datasets. Network inference was applied on in silico network examples as well as on multi-experiment microarray data of mesenchymal stem cells. The resulting chondrogenesis model was evaluated on the basis of several features including the model adaptation to the data, total number of connections, proportion of connections associated with prior knowledge and the model stability in a resampling procedure. Altogether, NetGenerator V2.0 has provided an automatic and efficient way to integrate experimental datasets and to enhance the interpretability and reliability of the resulting network. In a second chondrogenesis model, the miRNA and mRNA time series data were integrated for the purpose of network inference. One hypothesis of the model was verified by experiments, which demonstrated the negative effect of miR-524-5p on downstream genes