Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development

Identifying and analyzing novel epilepsy-related genes using random walk with restart algorithm.

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies

Loss of COPZ1 induces NCOA4 mediated autophagy and ferroptosis in glioblastoma cell lines

Dysregulated iron metabolism is a hallmark of many cancers, including glioblastoma (GBM). However, its role in tumor progression remains unclear. Herein, we identified coatomer protein complex subunit zeta 1 (COPZ1) as a therapeutic target candidate which significantly dysregulated iron metabolism in GBM cells. Overexpression of COPZ1 was associated with increasing tumor grade and poor prognosis in glioma patients based on analysis of expression data from the publicly available database The Cancer Genome Atlas (P < 0.001). Protein levels of COPZ1 were significantly increased in GBM compared to non-neoplastic brain tissue samples in immunohistochemistry and western blot analysis. SiRNA knockdown of COPZ1 suppressed proliferation of U87MG, U251 and P3#GBM in vitro. Stable expression of a COPZ1 shRNA construct in U87MG inhibited tumor growth in vivo by ~60% relative to controls at day 21 after implantation (P < 0.001). Kaplan–Meier analysis of the survival data demonstrated that the overall survival of tumor bearing animals increased from 20.8 days (control) to 27.8 days (knockdown, P < 0.05). COPZ1 knockdown also led to the increase in nuclear receptor coactivator 4 (NCOA4), resulting in the degradation of ferritin, and a subsequent increase in the intracellular levels of ferrous iron and ultimately ferroptosis. These data demonstrate that COPZ1 is a critical mediator in iron metabolism. The COPZ1/NCOA4/FTH1 axis is therefore a novel therapeutic target for the treatment of human GBM.publishedVersio

Primary tumor site specificity is preserved in patient-derived tumor xenograft models

Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients

Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles

Association between H-RAS T81C genetic polymorphism and gastrointestinal cancer risk: A population based case-control study in China

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal cancer, such as gastric, colon and rectal cancer, is a major medical and economic burden worldwide. However, the exact mechanism of gastrointestinal cancer development still remains unclear. <it>RAS </it>genes have been elucidated as major participants in the development and progression of a series of human tumours and the single nucleotide polymorphism at <it>H-RAS </it>cDNA position 81 was demonstrated to contribute to the risks of bladder, oral and thyroid carcinoma. Therefore, we hypothesized that this polymorphisms in <it>H-RAS </it>could influence susceptibility to gastrointestinal cancer as well, and we conducted this study to test the hypothesis in Chinese population.</p> <p>Methods</p> <p>A population based case-control study, including 296 cases with gastrointestinal cancer and 448 healthy controls selected from a Chinese population was conducted. <it>H-RAS </it>T81C polymorphism was genotyped by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) assay.</p> <p>Results</p> <p>In the healthy controls, the TT, TC and CC genotypes frequencies of <it>H-RAS </it>T81C polymorphism, were 79.24%, 19.87% and 0.89%, respectively, and the C allele frequency was 10.83%. Compared with TT genotype, the TC genotype was significantly associated with an increased risk of gastric cancer (adjusted OR = 3.67, 95%CI = 2.21–6.08), while the CC genotype showed an increased risk as well (adjusted OR = 3.29, 95%CI = 0.54–19.86), but it was not statistically significant. In contrast, the frequency of TC genotype was not significantly increased in colon cancer and rectal cancer patients. Further analysis was performed by combining TC and CC genotypes compared against TT genotype. As a result, a statistically significant risk with adjusted OR of 3.65 (95%CI, 2.22–6.00) was found in gastric cancer, while no significant association of <it>H-RAS </it>T81C polymorphism with colon cancer and rectal cancer was observed.</p> <p>Conclusion</p> <p>These findings indicate, for the first time, that there is an <it>H-RAS </it>T81C polymorphism existing in Chinese population, and this SNP might be a low penetrance gene predisposition factor for gastric cancer.</p

Opportunities for linking research to policy: lessons learned from implementation research in sexual and reproductive health within the ANSER network

Background: The uptake of findings from sexual and reproductive health and rights research into policy-making remains a complex and non-linear process. Different models of research utilisation and guidelines to maximise this in policy-making exist, however, challenges still remain for researchers to improve uptake of their research findings and for policy-makers to use research evidence in their work. Methods: A participatory workshop with researchers was organised in November 2017 by the Academic Network for Sexual and Reproductive Health and Rights Policy (ANSER) to address this gap. ANSER is a consortium of experienced researchers, some of whom have policy-making experience, working on sexual and reproductive health and rights issues across 16 countries and 5 continents. The experiential learning cycle was used to guide the workshop discussions based on case studies and to encourage participants to focus on key lessons learned. Workshop findings were thematically analysed using specific stages from Hanney et al.’s (Health Res Policy Syst 1:2, 2003) framework on the place of policy-making in the stages of assessment of research utilisation and outcomes. Results: The workshop identified key strategies for translating research into policy, including joint agenda-setting between researchers and policy-makers, as well as building trust and partnerships with different stakeholders. These were linked to stages within Hanney et al.’s framework as opportunities for engaging with policy-makers to ensure uptake of research findings. Conclusion: The engagement of stakeholders during the research development and implementation phases, especially at strategic moments, has a positive impact on uptake of research findings. The strategies and stages described in this paper can be applied to improve utilisation of research findings into policy development and implementation globally

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations