Deciphering the genetic architecture of prolificacy related traits in an experimental Iberian x Meishan F2 intercross

Els caràcters reproductius són de gran interès en la indústria porcina per tal de millorar l'eficiència productiva. En estudis previs, utilitzant un creuament experimental F2 entre les races Ibèric (Ib) i Meishan (Me) es van identificar varis QTL afectant varis caràcters reproductius (Noguera et al., 2009, Fernandez-Rodriguez et al., 2010, Rodriguez et al., 2005). En particular, es van identificar 2 QTL amb efecte al nombre de garrins nascuts vius (NV) i al nombre total de garrins nascuts (NT) localitzats en els cromosomes porcins 13 (SSC13) i SSC17 (Noguera et al., 2009). Per tal de poder identificar els gens responsables dels QTL en el SSC13, es van analitzar quatre gens candidats (ITIH1, ITIH3, ITIH4 and MUC4). La caracterització del clúster de gens ITIH va permetre identificar que aquests tenen un efecte sobre el NV però que és independent dels QTL associats a la mida de la ventrada. Els anàlisis del gen MUC4, que es troba localitzat dins l'interval de confiança del QTL en el SSC13, van determinar una associació significativa entre un SNP dins d'aquest gen i els caràcters NV i NT, tot i que l'efecte era superior pel NV. A més, l'expressió del gen MUC4 en l'úter és dues vegades superior en truges d'alta prolificitat. Per tal de millorar el nostre coneixement envers l'arquitectura genètica dels caràcters relacionats amb la prolificitat, es va analitzar el transcriptoma a nivell d'expressió gènica en úter així com també els nivells d'expressió de miRNAs tant en úter com en ovari. Aquests estudis es van realitzar utilitzant truges F2 IbxMe que presentaven fenotips extrems pels nivells de prolificitat definits com el nombre d'embrions (NE) units a l'úter a dia 30-32 de la gestació. En l'úter de les truges d'alta prolificitat es van identificar 101 gens (upregulated) implicats en la resposta inflamatòria enfront als estímuls i en el desenvolupament del teixit muscular. Per altra banda, 196 gens (downregulated) es van relacionar amb el desenvolupament del teixit muscular, l'organització d'unió cel·lular, en processos d'adhesió, en la regulació biològica, en processos del sistema muscular i circulatori i en el transport. L'estudi del microRNAoma va identificar els miR-125b-5p, miR-200C-3p, miR-23b-3p, miR-23-3p i miR-99-5p com els més abundants en l'úter de les truges gestants, mentre que l'expressió de miR-139- 5p, miR-150-5p, miR-27-3p i miR-20-5P es van associar amb els nivells de prolificitat. Entre tots els possibles gens diana per als miRNAs relacionats amb la prolificitat, es troben 32 gens localitzats dins l'interval de confiança per als QTL de prolificitat i, per tant, es van proposar com a bons gens candidats per a ser estudiats. Entre aquests, es troba el gen MUC4 que és de gran interès per a ser el responsable del QTL en el SSC13 ja que reuneix varis criteris; es localitza dins l'interval de confiança del QTL en el SSC13, té un efecte sobre la mida de la ventrada, el seu nivell d'expressió es relaciona amb els nivells de prolificitat i pot ser regulat pel miR-150-5p, que també es va trobar diferencialment expressat en relació amb els nivells de prolificitat. D'altra banda, en ovari, els miR-146a-5p i miR-142-3p, involucrats en processos del sistema immunològic i en la homeòstasis cel·lular, estan diferencialment expressats en relació amb els nivells de prolificitat. Quatre gens diana per aquests miRNAs (LRRK1, CCL8, CPEB2 and BAT1) es troben dins l'interval de confiança per als QTL amb efecte per la prolificitat, fet que fa que siguin bons candidats a ser estudiats. Finalment, es va dissenyar un nou mètode RT-qPCR molt específic, sensible i precís per tal de mesurar els nivell d'expressió dels miRNAs mitjançant l'ús d'encebadors d'ADN.Reproductive traits are of great interest in the swine industry to improve the pig efficiency production. In a previous study, several QTL affecting reproductive traits were identified in an Iberian (Ib) x Meishan (Me) F2 population (Noguera et al., 2009, Fernandez-Rodriguez et al., 2010, Rodriguez et al., 2005). In particular, two QTL for the number of piglets born alive (NBA) and the total number of piglets born (TNB) were located on porcine chromosomes 13 (SSC13) and SSC17 (Noguera et al., 2009). To identify genes responsible for the prolificacy QTL on SSC13, four candidate genes (ITIH1, ITIH3, ITIH4 and MUC4) were analysed. Analyses for ITIH gene cluster showed that these genes had an effect on NBA independent of litter size QTL. Results for MUC4 gene, located within the confidence interval of the QTL on SSC13, determined that a SNP within the MUC4 gene was associated with NBA and TNB although the effect was stronger for NBA. In addition, uterine MUC4 expression was two-fold higher in high prolificacy sows. To better understand the genetic basis of prolificacy related traits, transcriptome analyses, which included the study of uterine gene expression as well as the miRNA expression profile in the uterus and in the ovary, was performed. For this, IbxMe F2 sows displaying extreme phenotypes regarding the prolificacy levels defined as the number of embryos (NE) attached to the uterus at day 30-32 of the gestation were used. In uterus of high prolificacy sows, 101 genes involved in the inflammatory response to stimulus and muscle tissue development were upregulated whereas 196 genes that participated in muscle tissue development, cell junction organization and adhesion, biological regulation, muscle and circulatory system processes, and transport were downregulated. The microRNAome identified the miR-125b-5p, miR-200c-3p, miR-23b-3p, miR-23a-3p and miR-99a-5p as the most abundant miRNAs in uterus of pregnant sows while expression of miR-139-5p, miR-150-5p, miR-27a-3p and miR-20-5p was associated with prolificacy levels. Among predicted gene target for uterine prolificacy-related miRNAs, 32 were located within the confidence interval of the QTL for NBA and TNB and therefore, they are proposed to be good candidate genes to be further investigated. Importantly, among these candidate genes, it is found MUC4 gene which is of great interest to be the responsible for the prolificacy QTL on SSC13 because it fulfilled several criteria: it is located within the QTL confidence interval with an effect on NBA and TNB, its expression level varies regarding the prolificacy level of sows, and it is targeted by miR-150-5p, a miRNA that was also found diferentially expressed regarding the prolificacy levels. On the other hand, ovarian miR-146a-5p and miR-142-3p, involved in immune system processes and cellular homeostasis, were differentially expressed regarding prolificacy levels. Four predicted target genes (LRRK1, CCL8, CPEB2 and BAT1), located within confidence intervals for prolificacy QTL, are good candidate genes to be studied for QTL on litter size. Alternatively, we have designed a new RT-qPCR methodology, by using DNA primers to measure miRNA expression, which is highly specific, sensitive and accurate

miRNA Expression Profile Analysis in Kidney of Different Porcine Breeds

MicroRNAs (miRNAs) are important post-transcriptional regulators in eukaryotes that target mRNAs repressing their expression. The uncertain process of pig domestication, with different origin focuses, and the selection process that commercial breeds suffered, have generated a wide spectrum of breeds with clear genetic and phenotypic variability. The aim of this work was to define the miRNAs expression profile in kidney of several porcine breeds. Small RNA libraries from kidney were elaborated and high-throughput sequenced with the 454 Genome Sequencer FLX (Roche). Pigs used were classified into three groups: the European origin group (Iberian breed and European Wild Boar ancestor), European commercial breeds (Landrace, Large White and Piétrain breeds) and breeds with Asian origin (Meishan and Vietnamese breeds). A total of 229 miRNAs were described in the pig kidney miRNA profile, including 110 miRNAs out of the 257 previously described pig miRNAs and 119 orthologous miRNAs. The most expressed miRNAs in pig kidney microRNAome were Hsa-miR-200b-3p, Ssc-miR-125b and Ssc-miR-23b. Moreover, 5 novel porcine miRNAs and 3 orthologous miRNAs could be validated through RT-qPCR. miRNA sequence variation was determined in 116 miRNAs, evidencing the presence of isomiRs. 125 miRNAs were differentially expressed between breed groups. The identification of breed-specific miRNAs, which could be potentially associated to certain phenotypes, is becoming a new tool for the study of the genetic variability underlying complex traits and furthermore, it adds a new layer of complexity to the interesting process of pig evolution

Endometrial gene expression profile of pregnant sows with extreme phenotypes for reproductive efficiency

Prolificacy can directly impact porcine profitability, but large genetic variation and low heritability have been found regarding litter size among porcine breeds. To identify key differences in gene expression associated to swine reproductive efficiency, we performed a transcriptome analysis of sows' endometrium from an Iberian x Meishan F2 population at day 30-32 of gestation, classified according to their estimated breeding value (EBV) as high (H, EBV > 0) and low (L, EBV < 0) prolificacy phenotypes. For each sample, mRNA and small RNA libraries were RNA-sequenced, identifying 141 genes and 10 miRNAs differentially expressed between H and L groups. We selected four miRNAs based on their role in reproduction, and five genes displaying the highest differences and a positive mapping into known reproductive QTLs for RT-qPCR validation on the whole extreme population. Significant differences were validated for genes: PTGS2 (p = 0.03; H/L ratio = 3.50), PTHLH (p = 0.03; H/L ratio = 3.69), MMP8 (p = 0.01; H/L ratio = 4.41) and SCNN1G (p = 0.04; H/L ratio = 3.42). Although selected miRNAs showed similar expression levels between H and L groups, significant correlation was found between the expression level of ssc-miR-133a (p < 0.01) and ssc-miR-92a (p < 0.01) and validated genes. These results provide a better understanding of the genetic architecture of prolificacy-related traits and embryo implantation failure in pigs

Functional Implications of Human-Specific Changes in Great Ape microRNAs

microRNAs are crucial post-transcriptional regulators of gene expression involved in a wide range of biological processes. Although microRNAs are highly conserved among species, the functional implications of existing lineage-specific changes and their role in determining differences between humans and other great apes have not been specifically addressed. We analyzed the recent evolutionary history of 1,595 human microRNAs by looking at their intra-and inter-species variation in great apes using high-coverage sequenced genomes of 82 individuals including gorillas, orangutans, bonobos, chimpanzees and humans. We explored the strength of purifying selection among microRNA regions and found that the seed and mature regions are under similar and stronger constraint than the precursor region. We further constructed a comprehensive catalogue of microRNA species-specific nucleotide substitutions among great apes and, for the first time, investigated the biological relevance that human-specific changes in microRNAs may have had in great ape evolution. Expression and functional analyses of four microRNAs (miR-299-3p, miR-503-3p, miR508-3p and miR-541-3p) revealed that lineage-specific nucleotide substitutions and changes in the length of these microRNAs alter their expression as well as the repertoires of target genes and regulatory networks. We suggest that the studied molecular changes could have modified crucial microRNA functions shaping phenotypes that, ultimately, became human-specific. Our work provides a frame to study the impact that regulatory changes may have in the recent evolution of our species.Peer reviewe

The role of viral and host microRNAs in the Aujeszky's disease virus during the infection process

Porcine production is a primary market in the world economy. Controlling swine diseases in the farm is essential in order to achieve the sector necessities. Aujeszky's disease is a viral condition affecting pigs and is endemic in many countries of the world, causing important economic losses in the swine industry. microRNAs (miRNAs) are non-coding RNAs which modulates gene expression in animals, plants and viruses. With the aim of understanding miRNA roles during the Aujeszky's disease virus [ADV] (also known as suid herpesvirus type 1 [SuHV-1]) infection, the expression profiles of host and viral miRNAs were determined through deep sequencing in SuHV-1 infected porcine cell line (PK-15) and in an animal experimental SuHV-1 infection with virulent (NIA-3) and attenuated (Begonia) strains. In the in vivo approach miR-206, miR-133a, miR-133b and miR-378 presented differential expression between virus strains infection. In the in vitro approach, most miRNAs were down-regulated in infected groups. miR-92a and miR-92b-3p were up-regulated in Begonia infected samples. Functional analysis of all this over expressed miRNAs during the infection revealed their association in pathways related to viral infection processes and immune response. Furthermore, 8 viral miRNAs were detected by stem loop RT-qPCR in both in vitro and in vivo approaches, presenting a gene regulatory network affecting 59 viral genes. Most described viral miRNAs were related to Large Latency Transcript (LLT) and to viral transcription activators EP0 and IE180, and also to regulatory genes regarding their important roles in the host-pathogen interaction during viral infection

Deciphering the genetic architecture of prolificacy related traits in an experimental Iberian x Meishan F2 intercross

Els caràcters reproductius són de gran interès en la indústria porcina per tal de millorar l’eficiència productiva. En estudis previs, utilitzant un creuament experimental F2 entre les races Ibèric (Ib) i Meishan (Me) es van identificar varis QTL afectant varis caràcters reproductius (Noguera et al., 2009, Fernandez-Rodriguez et al., 2010, Rodriguez et al., 2005). En particular, es van identificar 2 QTL amb efecte al nombre de garrins nascuts vius (NV) i al nombre total de garrins nascuts (NT) localitzats en els cromosomes porcins 13 (SSC13) i SSC17 (Noguera et al., 2009). Per tal de poder identificar els gens responsables dels QTL en el SSC13, es van analitzar quatre gens candidats (ITIH1, ITIH3, ITIH4 and MUC4). La caracterització del clúster de gens ITIH va permetre identificar que aquests tenen un efecte sobre el NV però que és independent dels QTL associats a la mida de la ventrada. Els anàlisis del gen MUC4, que es troba localitzat dins l’interval de confiança del QTL en el SSC13, van determinar una associació significativa entre un SNP dins d’aquest gen i els caràcters NV i NT, tot i que l’efecte era superior pel NV. A més, l’expressió del gen MUC4 en l’úter és dues vegades superior en truges d’alta prolificitat. Per tal de millorar el nostre coneixement envers l’arquitectura genètica dels caràcters relacionats amb la prolificitat, es va analitzar el transcriptoma a nivell d’expressió gènica en úter així com també els nivells d’expressió de miRNAs tant en úter com en ovari. Aquests estudis es van realitzar utilitzant truges F2 IbxMe que presentaven fenotips extrems pels nivells de prolificitat definits com el nombre d’embrions (NE) units a l’úter a dia 30-32 de la gestació. En l’úter de les truges d’alta prolificitat es van identificar 101 gens (upregulated) implicats en la resposta inflamatòria enfront als estímuls i en el desenvolupament del teixit muscular. Per altra banda, 196 gens (downregulated) es van relacionar amb el desenvolupament del teixit muscular, l'organització d’unió cel·lular, en processos d’adhesió, en la regulació biològica, en processos del sistema muscular i circulatori i en el transport. L’estudi del microRNAoma va identificar els miR-125b-5p, miR-200C-3p, miR-23b-3p, miR-23-3p i miR-99-5p com els més abundants en l'úter de les truges gestants, mentre que l'expressió de miR-139- 5p, miR-150-5p, miR-27-3p i miR-20-5P es van associar amb els nivells de prolificitat. Entre tots els possibles gens diana per als miRNAs relacionats amb la prolificitat, es troben 32 gens localitzats dins l’interval de confiança per als QTL de prolificitat i, per tant, es van proposar com a bons gens candidats per a ser estudiats. Entre aquests, es troba el gen MUC4 que és de gran interès per a ser el responsable del QTL en el SSC13 ja que reuneix varis criteris; es localitza dins l’interval de confiança del QTL en el SSC13, té un efecte sobre la mida de la ventrada, el seu nivell d’expressió es relaciona amb els nivells de prolificitat i pot ser regulat pel miR-150-5p, que també es va trobar diferencialment expressat en relació amb els nivells de prolificitat. D’altra banda, en ovari, els miR-146a-5p i miR-142-3p, involucrats en processos del sistema immunològic i en la homeòstasis cel·lular, estan diferencialment expressats en relació amb els nivells de prolificitat. Quatre gens diana per aquests miRNAs (LRRK1, CCL8, CPEB2 and BAT1) es troben dins l’interval de confiança per als QTL amb efecte per la prolificitat, fet que fa que siguin bons candidats a ser estudiats. Finalment, es va dissenyar un nou mètode RT-qPCR molt específic, sensible i precís per tal de mesurar els nivell d’expressió dels miRNAs mitjançant l’ús d’encebadors d’ADN.Reproductive traits are of great interest in the swine industry to improve the pig efficiency production. In a previous study, several QTL affecting reproductive traits were identified in an Iberian (Ib) x Meishan (Me) F2 population (Noguera et al., 2009, Fernandez-Rodriguez et al., 2010, Rodriguez et al., 2005). In particular, two QTL for the number of piglets born alive (NBA) and the total number of piglets born (TNB) were located on porcine chromosomes 13 (SSC13) and SSC17 (Noguera et al., 2009). To identify genes responsible for the prolificacy QTL on SSC13, four candidate genes (ITIH1, ITIH3, ITIH4 and MUC4) were analysed. Analyses for ITIH gene cluster showed that these genes had an effect on NBA independent of litter size QTL. Results for MUC4 gene, located within the confidence interval of the QTL on SSC13, determined that a SNP within the MUC4 gene was associated with NBA and TNB although the effect was stronger for NBA. In addition, uterine MUC4 expression was two-fold higher in high prolificacy sows. To better understand the genetic basis of prolificacy related traits, transcriptome analyses, which included the study of uterine gene expression as well as the miRNA expression profile in the uterus and in the ovary, was performed. For this, IbxMe F2 sows displaying extreme phenotypes regarding the prolificacy levels defined as the number of embryos (NE) attached to the uterus at day 30-32 of the gestation were used. In uterus of high prolificacy sows, 101 genes involved in the inflammatory response to stimulus and muscle tissue development were upregulated whereas 196 genes that participated in muscle tissue development, cell junction organization and adhesion, biological regulation, muscle and circulatory system processes, and transport were downregulated. The microRNAome identified the miR-125b-5p, miR-200c-3p, miR-23b-3p, miR-23a-3p and miR-99a-5p as the most abundant miRNAs in uterus of pregnant sows while expression of miR-139-5p, miR-150-5p, miR-27a-3p and miR-20-5p was associated with prolificacy levels. Among predicted gene target for uterine prolificacy-related miRNAs, 32 were located within the confidence interval of the QTL for NBA and TNB and therefore, they are proposed to be good candidate genes to be further investigated. Importantly, among these candidate genes, it is found MUC4 gene which is of great interest to be the responsible for the prolificacy QTL on SSC13 because it fulfilled several criteria: it is located within the QTL confidence interval with an effect on NBA and TNB, its expression level varies regarding the prolificacy level of sows, and it is targeted by miR-150-5p, a miRNA that was also found diferentially expressed regarding the prolificacy levels. On the other hand, ovarian miR-146a-5p and miR-142-3p, involved in immune system processes and cellular homeostasis, were differentially expressed regarding prolificacy levels. Four predicted target genes (LRRK1, CCL8, CPEB2 and BAT1), located within confidence intervals for prolificacy QTL, are good candidate genes to be studied for QTL on litter size. Alternatively, we have designed a new RT-qPCR methodology, by using DNA primers to measure miRNA expression, which is highly specific, sensitive and accurate

Determination of Reference microRNAs for Relative Quantification in Porcine Tissues

Relative quantification is the strategy of choice for processing RT-qPCR data in microRNAs (miRNAs) expression studies. Normalisation of relative quantification data is performed by using reference genes. In livestock species, such as pigs, the determination of reference miRNAs and the optimal number of them has not been widely studied. In this study, the stability of ten miRNAs (Ssc-let-7a, Ssc-miR-103, Ssc-miR-17-3p, Hsa-miR-25, Hsa-miR-93, Ssc-miR-106a, Ssc-miR-191, Ssc-miR-16, Ssc-miR-26a and Ssc-miR-17-5p) was investigated by RT-qPCR in different tissues (skeletal muscle, kidney, liver, ovary and uterus) and in different pig breeds (Iberian, Landrace, Large White, Meishan and Vietnamese) as variation factors. Stability values were calculated with geNorm and NormFinder algorithms obtaining high correlation between them (r2 = 0.99). The analyses showed that tissue is an important variability factor in miRNAs expression stability whereas breed is not a determinant factor. All ten miRNAs analysed had good stability values and, therefore, can be used as reference miRNAs. When all tissues were considered, miR-93 was the most stable miRNA. Dividing data set by tissues, let-7a was the most stable in skeletal muscle and ovary, miR-17-5p in kidney, miR-26a in liver and miR-103 in uterus. Moreover, the optimal number of reference miRNAs to be used for proper normalisation data was determined. It is suggested the use of five reference miRNAs (miR-93, miR-25, miR-106a, miR-17-5p and miR-26a) in multi-tissue experimental designs and the use of three reference miRNAs as the optimal number in single tissues studies (let-7a, miR-17-5p and miR-25 in skeletal muscle; miR-17-5p, miR-93 and miR-26a in kidney, miR-26a, miR-103 and let-7a in liver, let-7a, miR-25 and miR-106a in ovary and miR-103, let-7a and miR-93 in uterus). Overall, this study provides valuable information about the porcine reference miRNAs that can be used in order to perform a proper normalisation when relative quantification by RT-qPCR studies is undertaken

miRNA Expression Profile Analysis in Kidney of Different Porcine Breeds

MicroRNAs (miRNAs) are important post-transcriptional regulators in eukaryotes that target mRNAs repressing their expression. The uncertain process of pig domestication, with different origin focuses, and the selection process that commercial breeds suffered, have generated a wide spectrum of breeds with clear genetic and phenotypic variability. The aim of this work was to define the miRNAs expression profile in kidney of several porcine breeds. Small RNA libraries from kidney were elaborated and high-throughput sequenced with the 454 Genome Sequencer FLX (Roche). Pigs used were classified into three groups: the European origin group (Iberian breed and European Wild Boar ancestor), European commercial breeds (Landrace, Large White and Piétrain breeds) and breeds with Asian origin (Meishan and Vietnamese breeds). A total of 229 miRNAs were described in the pig kidney miRNA profile, including 110 miRNAs out of the 257 previously described pig miRNAs and 119 orthologous miRNAs. The most expressed miRNAs in pig kidney microRNAome were Hsa-miR-200b-3p, Ssc-miR-125b and Ssc-miR-23b. Moreover, 5 novel porcine miRNAs and 3 orthologous miRNAs could be validated through RT-qPCR. miRNA sequence variation was determined in 116 miRNAs, evidencing the presence of isomiRs. 125 miRNAs were differentially expressed between breed groups. The identification of breed-specific miRNAs, which could be potentially associated to certain phenotypes, is becoming a new tool for the study of the genetic variability underlying complex traits and furthermore, it adds a new layer of complexity to the interesting process of pig evolution