Identification of mutations in porcine STAT5A that contributes to the transcription of CISH

Identification of causative genes or genetic variants associated with phenotype traits benefits the genetic improvement of animals. CISH plays a role in immunity and growth, however, the upstream transcriptional factors of porcine CISH and the genetic variations in these factors remain unclear. In this study, we firstly identified the minimal core promoter of porcine CISH and confirmed the existence of STATx binding sites. Overexpression and RT-qPCR demonstrated STAT5A increased CISH transcriptional activity (P < 0.01) and mRNA expression (P < 0.01), while GATA1 inhibited CISH transcriptional activity (P < 0.01) and the following mRNA expression (P < 0.05 or P < 0.01). Then, the putative functional genetic variations of porcine STAT5A were screened and a PCR-SSCP was established for genotype g.508A>C and g.566C>T. Population genetic analysis showed the A allele frequency of g.508A>C and C allele frequency of g.566C>T was 0.61 and 0.94 in Min pigs, respectively, while these two alleles were fixed in the Landrace population. Statistical analysis showed that Min piglets with CC genotype at g.566C>T or Hap1: AC had higher 28-day body weight, 35-day body weight, and ADG than TC or Hap3: CT animals (P < 0.05, P < 0.05). Further luciferase activity assay demonstrated that the activity of g.508A>C in the C allele was lower than the A allele (P < 0.05). Collectively, the present study demonstrated that STAT5A positively regulated porcine CISH transcription, and SNP g.566C>T in the STAT5A was associated with the Min piglet growth trait

Characterization of porcine cytokine inducible SH2-containing protein gene and its association with piglet diarrhea traits

Objective The cytokine inducible SH2-containing protein (CISH), which might play a role in porcine intestine immune responses, was one of the promising candidate genes for piglet anti-disease traits. An experiment was conducted to characterize the porcine CISH (pCISH) gene and to evaluate its genetic effects on pig anti-disease breeding. Methods Both reverse transcription polymerase chain reaction (RT-PCR) and PCR were performed to obtain the sequence of pCISH gene. A pEGFP-C1-CISH vector was constructed and transfected into PK-15 cells to analysis the distribution of pCISH. The sequences of individuals were compared with each other to find the polymorphisms in pCISH gene. The association analysis was performed in Min pigs and Landrace pigs to evaluate the genetic effects on piglet diarrhea traits. Results In the present research, the coding sequence and genomic sequence of pCISH gene was obtained. Porcine CISH was mainly localized in cytoplasm. TaqI and HaeIII PCR restriction fragment length polymorphism (RFLP) assays were established to detect single nucleotide polymorphisms (SNPs); A-1575G in promoter region and A2497C in Intron1, respectively. Association studies indicated that SNP A-1575G was significantly associated with diarrhea index of Min piglets (p<0.05) and SNP A2497C was significantly associated with the diarrhea trait of both Min pig and Landrace piglets (p<0.05). Conclusion This study suggested that the pCISH gene might be a novel candidate gene for pig anti-disease traits, and further studies are needed to confirm the results of this preliminary research

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset