Quantitative neurogenetics: applications in understanding disease

Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.

Evaluation of Inflammatory and Calcification after Implantation of Bioabsorbable Poly-L-Lactic Acid/Amorphous Calcium Phosphate Scaffolds in Porcine Coronary Arteries

Purpose. Our previous research has confirmed that the addition of nano-amorphous calcium phosphate (ACP) materials can improve the support of poly-L-lactic acid (PLLA) vascular scaffolds. Based on this, we continued to explore the effect of novel bioresorbable scaffold composed of PLLA and ACP nanoparticles on inflammation and calcification of surrounding tissues after scaffold implantation in porcine coronary artery. Methods. PLLA/ACP scaffolds in the experimental group and PLLA scaffolds in the control group were implanted into the coronary arteries of small pigs. Serum levels of C-reactive protein (CRP), calcium (Ca), and alkaline phosphatase (ALP) were measured before implantation and at 1, 6, 12, and 24 months after operation. Intravascular ultrasonography (IVUS) was performed to evaluate the vascular calcification score. The scaffold and surrounding tissues were hematoxylin-eosin staining for inflammation score. The scaffold and surrounding tissues were stained with NF-κB and ALP, and the positive expression index was calculated. Western blot was used to detect the expression of IL-6 and BMP-2 in the tissues around the scaffold. Results. There was no statistically significant difference between the two groups in CRP, calcium, and ALP at preimplant, 1 month, 6 months, 12 months, and 24 months ( P > 0.05 ). The inflammation score, NF-κB positive expression index, and calcification score in the PLLA/ACP group were lower than that in the PLLA group at 12 months and 24 months ( P < 0 05 ). The ALP positive expression index in the PLLA/ACP group was lower than that in the PLLA group at 6 months, 12 months, and 24 months ( P < 0 05 ). Western blot results showed that the IL-6 expression level in the PLLA/ACP group was significantly lower than that in the control group at 6 months, 12 months, and 24 months ( P < 0.05 ). The expression of BMP-2 in the PLLA/ACP group was significantly lower than that in the control group at 12 months and 24 months ( P < 0.05 ). Conclusion. The PLLA/ACP composite scaffold has good biocompatibility. The incorporation of nanoscale ACP can reduce the inflammatory response caused by the acid metabolites of PLLA scaffolds, reduce the expression of procalcification factors in the body, and inhibit tissue calcification. The PLLA/ACP composite scaffold provides reliable guidance for the application and development of degradable vascular scaffold.

Description of the expression index index for patients with gnatic from of mesial occlusion

An Index has been developed to assess the severity of dentofacial anomalies. The index values reflect the severity of maxillofacial abnormalities and is the rationale for choosing a method for treating patients with a gnatic form of mesial occlusion or for compensatory orthodontic treatment. The proposed technique is a convenient express method for the diagnosis and treatment planning of dentoalveolar anomalies.

Comparative evaluation of IL-17 and TGF-β expression in tissues of patients with chronic periodontitis and healthy individuals using real-time PCR

Background. The present study aimed to determine the association between periodontal disease and the Th17/Treg balance by examining the genetic expression of IL-17 and TGF-β, which influence incidence and suppression of inflammation. Methods. In this case-control study, samples were collected in a randomized and task-oriented order. Thirty-seven patients referred to professional periodontology clinics in Mashhad and the Periodontology Department of the Mashhad Dentistry Faculty for periodontal (case) or crown-length (control) surgery was enrolled. IL-17 and TGF-β gene expression indices were measured in tissue samples by real-time polymerase chain reaction. Results. The IL-17 gene expression index was higher in the case group (2.68±0.91) than in the control group (1.68±0.41), but this difference was not significant. The TGF-β gene expression index was significantly higher in the case group (54.42±7.88) than in the control group (24.12±3.38). Conclusion. L-17 and TGF-β expression is increased in chronic periodontitis patients, but TGF-β plays a more important role in periodontal inflammation in patients with chronic periodontitis. Further studies of the roles of Th17 and Treg cells are warranted.

Bioinformatics Analysis of Transcriptomic Data Reveals Refined Functional Networks for the Self-Renewal of Mouse Spermatogonial Stem Cells

Spermatogonial stem cells (SSCs) are exquisitely regulated to reach a balance between proliferation and differentiation in the niche of seminiferous epithelium. Several extrinsic factors such as GDNF are reported to switch the transition, activating various intrinsic signaling pathways. Transcriptomics analysis could provide a comprehensive landscape of gene expression and regulation. Here, we reanalyzed a previously published transcriptome of two cell types (standing for self-renewing and differentiating SSCs correspondingly). First, we proposed a new parameter, the expression index, to sort the genes considering both absolute and relative expression levels. Using a dynamic statistical model, we identified a list of 1119 candidate genes for SSC self-renewal with the best enrichment of canonical markers. Finally, based on interaction relations, we further optimized the list and constructed a refined network containing integrated information of interactions, expression alternations, biological functions, and disease associations. Further annotation of the 521 refined genes involved in the network revealed an enrichment of well-studied signaling pathways. We believe that the refined network could help us better understand the regulation of SSCs’ fates, as well as find novel regulators or targets for SSC self-renewal or preservation of male fertility.

Robust background normalization method for one-channel microarrays

Background:Microarray technology, aims to measure the amount of changes in transcripted messages for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different experimental conditions, these measurements can include both systematic and random erroneous signals. For this reason, we present a novel gene expression index, called multi-RGX (Multiple-probe Robust Gene Expression Index) for one-channel microarrays.Methods:Multi-RGX, different from other gene expression indices, considers the long-tailed symmetric (LTS) density, covering a wider range of distributions for modelling gene expressions on the log-scale, resulting in robust inference and it takes into account both probe and gene specific intensities. Furthermore, we derive the covariance-variance matrix of model parameters from the observed Fisher information matrix and test the performance of the multi-RGX method in three different datasets.Results:Our method is found to be a promising method regarding its alternatives in terms of accuracy and computational time.Conclusion:Multi-RGX gives accurate results with respect to its alternatives, with a reduction in computational cost.