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Abstract: Hemoglobin (Hb) H disease is a moderate form of α-thalassemia resulting from various genetic defects. A novel frameshift mutation cd 43/44(−C) at the α2-globin gene was identified in a Chinese boy with hemoglobin H disease by sequencing. The proband''s mother carries a common α-thalassemia deletion while his father was normal both in the hematological phenotype and α-globin genotype, which suggested that it occurred as a de novo mutation. Molecular studies revealed a compound heterozygote for the Southeast Asian α-thalassemia deletion and this novel spontaneous mutation (−/αTα) and the patient exhibited the clinical manifestation of classic hemoglobin H disease. Based on the results of excluding the possibility of a somatic mosaicism of a point mutation in the α2-globin gene, we progress that this de novo single-base deletion should have arisen during the spermatogenic process or earlier embryonic stage. The present study provides information in determining a supplementary model of inheritance for α-thalassemia, which should be useful in genetic counseling. [Copyright &y& Elsevier]

Breast cancer is the most prevalent cancer in female patients worldwide. Tissue factor pathway inhibitor 2 (TFPI-2) is identified as an important tumor suppressor in various cancers. Recent studies have shown that TFPI-2 translocates into the nucleus, where it modulates the transcription of the matrix metalloproteinase-2 (MMP-2) gene. However, its biological role and molecular mechanisms in the progression of breast cancer remain unclear. In this study, we identified 5125 differentially expressed genes (DEGs) from RNA sequencing (RNA-seq) in TFPI-2-overexpressing MDA231 cells compared with control cells. Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) analysis shown that cell cycle, cell differentiation, proteoglycans in cancer, and pathways associated with cancer were highly enriched in downregulated DEGs. Integration of the RNA-seq and ChIP-sequencing (ChIP-seq) data identified 73 genes directly controlled by TFPI-2 in MDA231 cells. Among them, melanocyte inducing transcription factor (MITF) gene expression was repressed by TFPI-2, which was further verified by a luciferase reporter assay and ChIP-quantitative PCR. Our study provides evidence of a novel role of TFPI-2 in human breast cancer involving targeting of the MITF. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a disease characterized by metabolic disorders. This study aimed to examine the effects of resveratrol treatment on ovulation in the PCOS rat model. Quantitative real-time PCR and immunohistochemistry were used to determine the mRNA and protein expression levels. TNUEL assay was used to evaluate cell apoptosis in ovary. The metabolites were evaluated by liquid chromatography with tandem mass spectrometry. Resveratrol alleviated disrupted estrous cycle and improved granular cell layers, and reversed the decreased proliferation and increased cell apoptosis of granulosa cells in the ovarian tissues of PCOS rats. Resveratrol restored the changes in the mRNA expression levels in the rate-limiting genes of glycolysis in the PCOS ovary. The expression of lactate dehydrogenase A (LDH-A), pyruvate kinase isozyme M2 (PKM2), and sirtuin 1 (SIRT1) was significantly downregulated in ovarian tissues of the PCOS rats; while the resveratrol treatment significantly increased the expression of LDH-A, PKM2, and SIRT1 in the ovarian tissues of PCOS rats. Collectively, the protective effects of resveratrol in the PCOS rats may be associated with the regulation of glycolysis-related mediators including PKM2, LDH-A, and SIRT1. Resveratrol may represent a good candidate in alleviating the development of PCOS. [ABSTRACT FROM AUTHOR]

Voxel-based 3D convolutional neural networks (CNNs) have been applied to predict protein-ligand binding affinity. However, the memory usage and computation cost of these voxel-based approaches increase cubically with respect to spatial resolution and sometimes make volumetric CNNs intractable at higher resolutions. Therefore, it is necessary to develop memory-efficient alternatives that can accelerate the convolutional operation on 3D volumetric representations of the protein-ligand interaction. In this study, we implement a novel volumetric representation, OctSurf, to characterize the 3D molecular surface of protein binding pockets and bound ligands. The OctSurf surface representation is built based on the octree data structure, which has been widely used in computer graphics to efficiently represent and store 3D object data. Vanilla 3D-CNN approaches often divide the 3D space of objects into equal-sized voxels. In contrast, OctSurf recursively partitions the 3D space containing the protein-ligand pocket into eight subspaces called octants. Only those octants containing van der Waals surface points of protein or ligand atoms undergo the recursive subdivision process until they reach the predefined octree depth, whereas unoccupied octants are kept intact to reduce the memory cost. Resulting non-empty leaf octants approximate molecular surfaces of the protein pocket and bound ligands. These surface octants, along with their chemical and geometric features, are used as the input to 3D-CNNs. Two kinds of CNN architectures, VGG and ResNet, are applied to the OctSurf representation to predict binding affinity. The OctSurf representation consumes much less memory than the conventional voxel representation at the same resolution. By restricting the convolution operation to only octants of the smallest size, our method also alleviates the overall computational overhead of CNN. A series of experiments are performed to demonstrate the disk storage and computational efficiency of the proposed learning method. Our code is available at the following GitHub repository: https://github.uconn.edu/mldrugdiscovery/OctSurf. [Display omitted] • A novel 3D Deep learning approach for the protein-ligand binding prediction. • Octree-based volumetric representation of the molecular surface. • Reduce the computational cost by restricting the CNN operation over surface octants. [ABSTRACT FROM AUTHOR]

Background: Thalassemia is one of the most common monogenic hemolytic disorders in the world. Hong Kongαα (HKαα) thalassemia was initially found among the people of southern China. Because of the complexity of genetic changes in HKαα thalassemia, we lack a precise sequence analysis of the HKαα allele. Here we aim to detect the specific genotype and trace the law of inheritance of this rare genotype. Methods: We recruited an unprecedented huge pedigree containing 11 individuals carrying the HKαα thalassemia gene and 4 nongenetic‐related patients suffering from HKαα from south China. Regular hematological analysis and routine genetic screening were performed on the pedigree and two‐round nested PCR (polymerase chain reaction) for HKαα thalassemia were performed on each individual. The first‐generation gene sequencing was performed on six individuals, including four nongenetic‐related patients. Result: We found that five family members were positive for the HKαα allele. Patients Ⅱ‐2, Ⅲ‐1, and Ⅱ‐3 with only HKαα/‐‐SEA or HKαα/‐α4.2 presented with α‐thalassemia minor trait. Ⅰ‐1, the carrier of both HKαα/‐α3.7 and β41‐42/βN, showed a typical β‐thalassemia trait. Fetus with genotype HKαα/‐α4.2 alone was not likely to suffer from any deleterious effects after birth. The whole sequence of HKαα allele revealed that HKαα alleles in the six patients shared a high similarity, implying that all HKαα alleles are likely from the same ancestor. Moreover, pedigree and sequencing analyses demonstrated that the HKαα allele contained αααanti4.2 mutation, ‐α3.7 mutation, and a fragment from α‐hemoglobin gene; thus, the composition and formation of HKαα allele was revealed. Finally, the high similarity and composition of HKαα alleles implies that once HKαα formed, αααanti4.2 and ‐α3.7 mutations tended to be a fusion gene and quite impossible to be inherited separately. Conclusion: The two‐round nested PCR is an effective method to detect HKαα allele. Besides, our study for the first time revealed the sequence of the HKαα allele, the evidence of the same ancestor with HKαα thalassemia and enriched the composition as well as the formation mechanism of HKαα allele, and immediately opened up novel potential diagnosis and prenatal counseling for HKαα thalassemia. [ABSTRACT FROM AUTHOR]

Aims: Thalassemia is a dangerous hematolytic genetic disease. In south China, ∼24% Chinese carry alpha-thalassemia or beta-thalassemia gene mutations. Given the fact that the invasive sampling procedures can only be performed by professionals in experienced centers, it may increase the risk of miscarriage or infection. Thus, most people are worried about the invasive operation. As such, a noninvasive and accurate prenatal diagnosis is needed for appropriate genetic counseling for families with high risks. Here we sought to develop capture probes and their companion analysis methods for the noninvasive prenatal detection of deletional and nondeletional thalassemia. Materials and Methods: Two families diagnosed as carriers of either beta-thalassemia gene or Southeast Asian deletional alpha-thalassemia gene mutation were recruited. The maternal plasma and amniotic fluid were collected for prenatal diagnosis. Probes targeting exons of the genes of interest and the highly heterozygous SNPs within the 1Mb flanking region were designed. The target capture sequencing was performed with plasma DNA from the pregnant woman and genomic DNA from the couples and their children. Then the parental haplotype was constructed by the trios-based strategy. The fetal haplotype was deduced from the parental haplotype with a hidden Markov model-based algorithm. Results: The fetal genotypes were successfully deduced in both families noninvasively. The noninvasively constructed haplotypes of both fetuses were identical to the invasive prenatal diagnosis results with an accuracy rate of 100% in the target region. Conclusion: Our study demonstrates that the effective noninvasive prenatal diagnosis of alpha-thalassemia and beta-thalassemia can be achieved with the targeted capture sequencing and the haplotype-assisted analysis method. [ABSTRACT FROM AUTHOR]

Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems. [ABSTRACT FROM AUTHOR]