Search

D-amino acid oxidase (DAO) has been reported to be associated with schizophrenia. This study aimed to search for genetic variants associated with this gene. The genomic regions of all exons, highly conserved regions of introns, and promoters of this gene were sequenced. Potentially meaningful single-nucleotide polymorphisms (SNPs) obtained from direct sequencing were selected for genotyping in 600 controls and 912 patients with schizophrenia and in a replicated sample consisting of 388 patients with schizophrenia. Genetic associations were examined using single-locus and haplotype association analyses. In single-locus analyses, the frequency of the C allele of a novel SNP rs55944529 located at intron 8 was found to be significantly higher in the original large patient sample (p = 0.016). This allele was associated with a higher level of DAO mRNA expression in the Epstein-Barr virus-transformed lymphocytes. The haplotype distribution of a haplotype block composed of rs11114083-rs2070586-rs2070587-rs55944529 across intron 1 and intron 8 was significantly different between the patients and controls and the haplotype frequencies of AAGC were significantly higher in patients, in both the original (corrected p < 0.0001) and replicated samples (corrected p = 0.0003). The CGTC haplotype was specifically associated with the subgroup with deficits in sustained attention and executive function and the AAGC haplotype was associated with the subgroup without such deficits. The DAO gene was a susceptibility gene for schizophrenia and the genomic region between intron 1 and intron 8 may harbor functional genetic variants, which may influence the mRNA expression of DAO and neurocognitive functions in schizophrenia.

Background: Polyglutamine (polyQ) diseases are dominant neurodegenerative diseases caused by an expansion of the polyQ‐encoding CAG repeats in the disease‐causing gene. The length of the CAG repeats is the major determiner of the age at onset (AO) of polyQ diseases, including Huntington's disease (HD) and spinocerebellar ataxia type 3 (SCA3). Objective: We set out to identify common genetic variant(s) that may affect the AO of polyQ diseases. Methods: Three hundred thirty‐seven patients with HD or SCA3 were enrolled for targeted sequencing of 583 genes implicated in proteinopathies. In total, 16 genes were identified as containing variants that are associated with late AO of polyQ diseases. For validation, we further investigate the variants of PIAS1 because PIAS1 is an E3 SUMO (small ubiquitin‐like modifier) ligase for huntingtin (HTT), the protein linked to HD. Results: Biochemical analyses revealed that the ability of PIAS1S510G to interact with mutant huntingtin (mHTT) was less than that of PIAS1WT, resulting in lower SUMOylation of mHTT and lower accumulation of insoluble mHTT. Genetic knock‐in of PIAS1S510G in a HD mouse model (R6/2) ameliorated several HD‐like deficits (including shortened life spans, poor grip strength and motor coordination) and reduced neuronal accumulation of mHTT. Conclusions: Our findings suggest that PIAS1 is a genetic modifier of polyQ diseases. The naturally occurring variant, PIAS1S510G, is associated with late AO in polyQ disease patients and milder disease severity in HD mice. Our study highlights the possibility of targeting PIAS1 or pathways governing protein homeostasis as a disease‐modifying approach for treating patients with HD. © 2021 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Abstract Background In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations. Results This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis. Conclusions This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.

Abstract Background Genome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried. Results We developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone. Conclusions ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.

Abstract Background Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production. Conclusion In this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.

Abstract Background Schizophrenia is a complex disease with multiple factors contributing to its pathogenesis. In addition to environmental factors, genetic factors may also increase susceptibility. In other words, schizophrenia is a highly heritable disease. Some candidate genes have been deduced on the basis of their known function with others found on the basis of chromosomal location. Individuals with multiple candidate genes may have increased risk. However it is not clear what kind of gene combinations may produce the disease phenotype. Their collective effect remains to be studied. Results Most pathways except metabolic pathways are rich in protein-protein interactions (PPIs). Thus, the PPI network contains pathway information, even though the upstream-downstream relation of PPI is yet to be explored. Here we have constructed a PPI sub-network by extracting the nearest neighbour of the 36 reported candidate genes described in the literature. Although these candidate genes were discovered by different approaches, most of the proteins formed a cluster. Two major protein interaction modules were identified on the basis of the pairwise distance among the proteins in this sub-network. The large and small clusters might play roles in synaptic transmission and signal transduction, respectively, based on gene ontology annotation. The protein interactions in the synaptic transmission cluster were used to explain the interaction between the NRG1 and CACNG2 genes, which was found by both linkage and association studies. This working hypothesis is supported by the co-expression analysis based on public microarray gene expression. Conclusion On the basis of the protein interaction network, it appears that the NRG1-triggered NMDAR protein internalization and the CACNG2 mediated AMPA receptor recruiting may act together in the glutamatergic signalling process. Since both the NMDA and AMPA receptors are calcium channels, this process may regulate the influx of Ca2+. Reducing the cation influx might be one of the disease mechanisms for schizophrenia. This PPI network analysis approach combined with the support from co-expression analysis may provide an efficient way to propose pathogenetic mechanisms for various highly heritable diseases.

Although the function of the BRCA1 gene has been extensively studied, the relationship between BRCA1 gene expression and tumor aggressiveness remains controversial in sporadic breast cancers. Because the BRCA1 protein is known to regulate estrogen signaling, we selected microarray data of ER+ breast cancers from the GEO public repository to resolve previous conflicting findings. The BRCA1 gene expression level in highly proliferative luminal B tumors was shown to be higher than that in luminal A tumors. Survival analysis using a cure model indicated that patients of early ER+ breast cancers with high BRCA1 expression developed rapid distant metastasis. In addition, the proliferation marker genes MKI67 and PCNA, which are characteristic of aggressive tumors, were also highly expressed in patients with high BRCA1 expression. The associations among high BRCA1 expression, high proliferation marker expression, and high risk of distant metastasis emerged in independent datasets, regardless of tamoxifen treatment. Tamoxifen therapy could improve the metastasis-free fraction of high BRCA1 expression patients. Our findings link BRCA1 expression with proliferation and possibly distant metastasis via the ER signaling pathway. We propose a testable hypothesis based on these consistent results and offer an interpretation for our reported associations. [ABSTRACT FROM AUTHOR]

Abstract Background The mosquito, Armigeres subalbatus, mounts a distinctively robust innate immune response when infected with the nematode Brugia malayi, a causative agent of lymphatic filariasis. In order to mine the transcriptome for new insight into the cascade of events that takes place in response to infection in this mosquito, 6 cDNA libraries were generated from tissues of adult female mosquitoes subjected to immune-response activation treatments that lead to well-characterized responses, and from aging, naïve mosquitoes. Expressed sequence tags (ESTs) from each library were produced, annotated, and subjected to comparative analyses. Results Six libraries were constructed and used to generate 44,940 expressed sequence tags, of which 38,079 passed quality filters to be included in the annotation project and subsequent analyses. All of these sequences were collapsed into clusters resulting in 8,020 unique sequence clusters or singletons. EST clusters were annotated and curated manually within ASAP (A Systematic Annotation Package for Community Analysis of Genomes) web portal according to BLAST results from comparisons to Genbank, and the Anopheles gambiae and Drosophila melanogaster genome projects. Conclusion The resulting dataset is the first of its kind for this mosquito vector and provides a basis for future studies of mosquito vectors regarding the cascade of events that occurs in response to infection, and thereby providing insight into vector competence and innate immunity.