Alzheimer's Disease rs11767557 Variant Regulates EPHA1 Gene Expression Specifically in Human Whole Blood

ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning

Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.

iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module

Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques because conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV built on a modified densely connected convolutional network (DenseNet) and an improved convolutional block attention module (CBAM). Coding was performed using one-hot and nucleotide chemical property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules were used to evaluate the significance of the advanced features and then input into a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to obtain the desired results without using the complex mathematical formulas involved.

The relationship of early- and late-onset Alzheimer’s disease genes with COVID-19

Individuals with Alzheimer’s disease and other neurodegenerative diseases have been exposed to excess risk by the COVID-19 pandemic. COVID-19’s main manifestations include high body temperature, dry cough, and exhaustion. Nevertheless, some affected individuals may have an atypical presentation at diagnosis but suffer neurological signs and symptoms as the first disease manifestation. These findings collectively show the neurotropic nature of SARS-CoV-2 virus and its ability to involve the central nervous system. In addition, Alzheimer’s disease and COVID-19 has a number of common risk factors and comorbid conditions including age, sex, hypertension, diabetes, and the expression of APOE ε4. Until now, a plethora of studies have examined the COVID-19 disease but only a few studies has yet examined the relationship of COVID-19 and Alzheimer’s disease as risk factors of each other. This review emphasizes the recently published evidence on the role of the genes of early- or late-onset Alzheimer’s disease in the susceptibility of individuals currently suffering or recovered from COVID-19 to Alzheimer’s disease or in the susceptibility of individuals at risk of or with Alzheimer’s disease to COVID-19 or increased COVID-19 severity and mortality. Furthermore, the present review also draws attention to other uninvestigated early- and late-onset Alzheimer’s disease genes to elucidate the relationship between this multifactorial disease and COVID-19.

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

Recent development of machine learning methods in sumoylation sites prediction

Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.

Melatonin and Melatonergic Influence on Neuronal Transcription Factors: Implications for the Development of Novel Therapies for Neurodegenerative Disorders

Melatonin is a multifunctional signalling molecule that is secreted by the mammalian pineal gland, and also found in a number of organisms including plants and bacteria. Research has continued to uncover an ever-increasing number of processes in which melatonin is known to play crucial roles in mammals. Amongst these functions is its contribution to cell multiplication, differentiation and survival in the brain. Experimental studies show that melatonin can achieve these functions by influencing transcription factors which control neuronal and glial gene expression. Since neuronal survival and differentiation are processes that are important determinants of the pathogenesis, course and outcome of neurodegenerative disorders; the known and potential influences of melatonin on neuronal and glial transcription factors are worthy of constant examination. In this review, relevant scientific literature on the role of melatonin in preventing or altering the course and outcome of neurodegenerative disorders, by focusing on melatonin's influence on transcription factors is examined. A number of transcription factors whose functions can be influenced by melatonin in neurodegenerative disease models have also been highlighted. Finally, the therapeutic implications of melatonin's influences have also been discussed and the potential limitations to its applications have been highlighted.

A comprehensive review of the imbalance classification of protein post-translational modifications

Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.

iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory

Enhancers are regulatory DNA sequences that could be bound by specific proteins named transcription factors (TFs). The interactions between enhancers and TFs regulate specific genes by increasing the target gene expression. Therefore, enhancer identification and classification have been a critical issue in the enhancer field. Unfortunately, so far there has been a lack of suitable methods to identify enhancers. Previous research has mainly focused on the features of the enhancer's function and interactions, which ignores the sequence information. As we know, the recurrent neural network (RNN) and long short-term memory (LSTM) models are currently the most common methods for processing time series data. LSTM is more suitable than RNN to address the DNA sequence. In this paper, we take the advantages of LSTM to build a method named iEnhancer-EBLSTM to identify enhancers. iEnhancer-ensembles of bidirectional LSTM (EBLSTM) consists of two steps. In the first step, we extract subsequences by sliding a 3-mer window along the DNA sequence as features. Second, EBLSTM model is used to identify enhancers from the candidate input sequences. We use the dataset from the study of Quang H et al. as the benchmarks. The experimental results from the datasets demonstrate the efficiency of our proposed model.

Genetics of neuropsychiatric symptoms in patients with Alzheimer's disease: A 1-year follow-up study

AIM

The aim of this study was to investigate the associations between candidate gene variants and domains of neuropsychiatric symptoms (NPS) and the changes in these associations over a 1-year period.

METHODS

Seven hundred and ninety-three Taiwanese participants (47.8% female) with Alzheimer's disease (AD) were enrolled. Genes associated with a risk of developing AD were selected as candidate genes. NPS were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q), and the NPI-Q total score and sub-scores for the Psychosis, Mood, and Frontal Syndrome domains were calculated.

RESULTS

Patients with AD and the APOE ε4 allele exhibited more obvious symptoms of psychosis. Mood symptoms were associated with CD33 rs3865444 and EPHA1 rs11767557, and frontal symptoms were associated with SORL1 rs3824968. A 1-year Time × Alleles interaction effect of CD33 rs3865444 on mood symptoms was discerned.

CONCLUSION

Risk genes of AD, which are also associated with NPS, are APOE ε4 for psychosis, CD33 and EPHA1 for mood symptoms, and SORL1 for frontal symptoms. The association between CD33 and mood symptoms is dynamic and could change over 1 year; however, the results should be interpreted with caution because corrections for multiple comparisons were not performed.

A Mendelian Randomization Study on Infant Length and Type 2 Diabetes Mellitus Risk

OBJECTIVE

Infant length (IL) is a positively associated phenotype of type 2 diabetes mellitus (T2DM), but the causal relationship of which is still unclear. Here, we applied a Mendelian randomization (MR) study to explore the causal relationship between IL and T2DM, which has the potential to provide guidance for assessing T2DM activity and T2DM- prevention in young at-risk populations.

MATERIALS AND METHODS

To classify the study, a two-sample MR, using genetic instrumental variables (IVs) to explore the causal effect was applied to test the influence of IL on the risk of T2DM. In this study, MR was carried out on GWAS data using 8 independent IL SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated by the inverse-variance weighted method for the assessment of the risk the shorter IL brings to T2DM. Sensitivity validation was conducted to identify the effect of individual SNPs. MR-Egger regression was used to detect pleiotropic bias of IVs.

RESULTS

The pooled odds ratio from the IVW method was 1.03 (95% CI 0.89-1.18, P = 0.0785), low intercept was -0.477, P = 0.252, and small fluctuation of ORs ranged from -0.062 ((0.966 - 1.03) / 1.03) to 0.05 ((1.081 - 1.03) / 1.03) in leave-one-out validation.

CONCLUSION

We validated that the shorter IL causes no additional risk to T2DM. The sensitivity analysis and the MR-Egger regression analysis also provided adequate evidence that the above result was not due to any heterogeneity or pleiotropic effect of IVs.

SERPINA1 gene expression in whole blood links the rs6647 variant G allele to an increased risk of large artery atherosclerotic stroke

The rs6647 variant G allele in SERPINA1 gene was reported to be associated with the risk of large artery atherosclerotic stroke (LAS), however, the mechanism remains unclear. Here, we performed a functional annotation of the rs6647 variant by using the software HaploReg version 4.1 (HaploReg v4.1). Next, the expression quantitative trait loci (eQTLs) analysis of multiple datasets was conducted for determining the association between the rs6647 and SERPINA1 expression in various tissues. Then, a case-control gene expression analysis was done using two independent ischemic stroke (IS) gene expression datasets. Finally, SERPINA1 expression in whole blood samples from 8 LAS patients and 14 healthy persons were compared. The functional annotation suggested that the rs6647 regulates gene expression in multiple human tissues especially in brain and blood. The eQTLs analysis revealed a significant association of the rs6647 G allele with increased expression of SERPINA1 gene only in whole blood. Compared with the controls, there was an increased expression of SERPINA1 gene in whole blood in both IS patients and LAS patients. SERPINA1 gene expression in whole blood bridges the rs6647 variant G allele with increased LAS risk, providing new insights into the mechanisms underlying role of the rs6647 in determining LAS risk.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

Impact of Vitamin D Binding Protein Levels on Alzheimer's Disease: A Mendelian Randomization Study

Until now, observational studies, randomized controlled trials (RCTs), and Mendelian randomization (MR) studies have explored the impact of vitamin D on Alzheimer's disease (AD), and reported inconsistent findings. In MR studies, the sensitivity analysis by removing GC rs2282679 variant highlighted no association of 25OHD levels with AD risk, which indicates that vitamin D-binding protein (DBP) encoded by GC may have distinct effects on AD risk. Here, we aim to clarify this assumption. We selected the GC rs2282679 variant associated with DBP levels (p = 3.30E-76) as the instrumental variable, and extracted the summary statistics of rs2282679 variant in multiple AD GWAS datasets from IGAP, Complex Trait Genetics (CTG) lab, and UK Biobank. We then performed a MR study to investigate the causal association between DBP levels and AD. In IGAP, MR analysis showed that the genetically DBP levels (per 1 standard deviation (SD) increase 50 mg/L) were significantly associated with reduced AD risk (OR = 0.63, 95% CI: 0.45-0.89, p = 0.009). Importantly, the estimates from two sensitivity analyses were consistent with the main estimate in terms of direction and magnitude. Meanwhile, we found no causal association between DBP levels and other four AD phenotypes in CTG lab and UK Biobank. In summary, we highlight the role of DBP levels in AD risk, and provide strong support evidence that DBP may be the therapeutic agent for the treatment of AD. Meanwhile, our findings clarify the assumption that DBP may drive the observed relationship between 25OHD levels and AD.

Circulating Vitamin D Levels and Alzheimer's Disease: A Mendelian Randomization Study in the IGAP and UK Biobank

Observational studies strongly supported the association of low levels of circulating 25-hydroxyvitamin D (25OHD) and cognitive impairment or dementia in aging populations. However, randomized controlled trials have not shown clear evidence that vitamin D supplementation could improve cognitive outcomes. In fact, some studies reported the association between vitamin D and cognitive impairment based on individuals aged 60 years and over. However, it is still unclear that whether vitamin D levels are causally associated with Alzheimer's disease (AD) risk in individuals aged 60 years and over. Here, we performed a Mendelian randomization (MR) study to investigate the causal association between vitamin D levels and AD using a large-scale vitamin D genome-wide association study (GWAS) dataset and two large-scale AD GWAS datasets from the IGAP and UK Biobank with individuals aged 60 years and over. Our results showed that genetically increased 25OHD levels were significantly associated with reduced AD risk in individuals aged 60 years and over. Hence, our findings in combination with previous literature indicate that maintaining adequate vitamin D status in older people especially aged 60 years and over, may contribute to slow down cognitive decline and forestall AD. Long-term randomized controlled trials are required to test whether vitamin D supplementation may prevent AD in older people especially those aged 60 years and may be recommended as preventive agents.

Impact of Serum Calcium Levels on Alzheimer's Disease: A Mendelian Randomization Study

BACKGROUND

Altered calcium homeostasis is hypothesized to underlie Alzheimer's disease (AD). However, it remains unclear whether serum calcium levels are genetically associated with AD risk.

OBJECTIVE

To develop effective therapies, we should establish the causal link between serum calcium levels and AD.

METHODS

Here, we performed a Mendelian randomization study to investigate the causal association of increased serum calcium levels with AD risk using the genetic variants from a large-scale serum calcium genome-wide association study (GWAS) dataset (61,079 individuals of European descent) and a large-scale AD GWAS dataset (54,162 individuals including 17,008 AD cases and 37,154 controls of European descent). Here, we selected the inverse-variance weighted (IVW) as the main analysis method. Meanwhile, we selected other three sensitivity analysis methods to examine the robustness of the IVW estimate.

RESULTS

IVW analysis showed that the increased serum calcium level (per 1 standard deviation (SD) increase 0.5 mg/dL) was significantly associated with a reduced AD risk (OR = 0.57, 95% CI 0.35-0.95, p = 0.031). Meanwhile, all the estimates from other sensitivity analysis methods were consistent with the IVW estimate in terms of direction and magnitude.

CONCLUSION

In summary, we provided evidence that increased serum calcium levels could reduce the risk of AD. Meanwhile, randomized controlled study should be conducted to clarify whether diet calcium intake or calcium supplement, or both could reduce the risk of AD.

Selecting Essential MicroRNAs Using a Novel Voting Method

Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs.

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs

Background:

Polycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic sub-unit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are character-ized by high tissue-specificity; however, little is known about the tissue profile of the EZH2-interacting lncRNAs.

Objective:

Here we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno-precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In ad-dition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, ma-jority of the lncRNAs were firstly reported to be associated with EZH2.

Conclusion:

Our findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Genetic and Expression Analysis of COPI Genes and Alzheimer's Disease Susceptibility

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly and the leading cause of dementia in humans. Evidence shows that cellular trafficking and recycling machineries are associated with AD risk. A recent study found that the coat protein complex I (COPI)-dependent trafficking in vivo could significantly reduce amyloid plaques in the cortex and hippocampus of neurological in the AD mouse models and identified 12 single-nucleotide polymorphisms in COPI genes to be significantly associated with increased AD risk using 6,795 samples. Here, we used a large-scale GWAS dataset to investigate the potential association between the COPI genes and AD susceptibility by both SNP and gene-based tests. The results showed that only rs9898218 was associated with AD risk with P = 0.017. We further conducted an expression quantitative trait loci (eQTLs) analysis and found that rs9898218 G allele was associated with increased COPZ2 expression in cerebellar cortex with P = 0.0184. Importantly, the eQTLs analysis in whole blood further indicated that 11 of these 12 genetic variants could significantly regulate the expression of COPI genes. Hence, these findings may contribute to understand the association between COPI genes and AD susceptibility.

Polygenic Risk Score for Alzheimer's Disease Is Associated With Ch4 Volume in Normal Subjects

Alzheimer's disease (AD) is a common neurodegenerative disease. APOE is the strong genetic risk factor of AD. The existing genome-wide association studies have identified many single nucleotide polymorphisms (SNPs) with minor effects on AD risk and the polygenic risk score (PRS) is presented to combine the effect of these SNPs. On the other hand, the volumes of various brain regions in AD patients have significant changes compared to that in normal individuals. Ch4 brain region containing at least 90% cholinergic neurons is the most extensive and conspicuous in the basal forebrain. Here, we investigated the relationship between the combined effect of AD-associated SNPs and Ch4 volume using the PRS approach. Our results showed that Ch4 volume in AD patients is significantly different from that in normal control subjects (p-value < 2.2 × 10-16). AD PRS, is not associated with the Ch4 volume in AD patients, excluding the APOE region (p-value = 0.264) and including the APOE region (p-value = 0.213). However, AD best-fit PRS, excluding the APOE region, is associated with Ch4 volume in normal control subjects (p-value = 0.015). AD PRS based on 8070 SNPs could explain 3.35% variance of Ch4 volume. In addition, the p-value of AD PRS model in normal control subjects, including the APOE region, is 0.006. AD PRS based on 8079 SNPs could explain 4.23% variance of Ch4 volume. In conclusion, PRS based on AD-associated SNPs is significantly related to Ch4 volume in normal subjects but not in patients.

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review

Background:

Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.

Methods:

Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.

Results:

We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.

Conclusion:

In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

Protective Variants in Alzheimer's Disease

PURPOSE OF REVIEW

Over the last decade over 40 loci have been associated with risk of Alzheimer's disease (AD). However, most studies have either focused on identifying risk loci or performing unbiased screens without a focus on protective variation in AD. Here, we provide a review of known protective variants in AD and their putative mechanisms of action. Additionally, we recommend strategies for finding new protective variants.

RECENT FINDINGS

Recent Genome-Wide Association Studies have identified both common and rare protective variants associated with AD. These include variants in or near APP, APOE, PLCG2, MS4A, MAPT-KANSL1, RAB10, ABCA1, CCL11, SORL1, NOCT, SCL24A4-RIN3, CASS4, EPHA1, SPPL2A, and NFIC.

SUMMARY

There are very few protective variants with functional evidence and a derived allele with a frequency below 20%. Additional fine mapping and multi-omic studies are needed to further validate and characterize known variants as well as specialized genome-wide scans to identify novel variants.

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.

Genetic Variants Associated With Neurodegenerative Diseases Regulate Gene Expression in Immune Cell CD14+ Monocytes

Until now, large-scale genome-wide association studies have identified 94 genes associated with Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Expression quantitative trait locus (eQTL) analysis showed that six genetic variants around six of these 94 genes could drive both disease susceptibility and altered expression of six nearby genes including CD33 (rs3865444), PILRB (rs1476679), NUP160 (rs10838725), LRRK2 (rs76904798), RGS1 (rs1323292), and METTL21B (rs701006). However, two of these six genetic variants rs1476679 and rs76904798 variants could regulate the expression of PILRB and LRRK2 only in the human monocyte-derived microglia-like (MDMi) cells, but not in human peripheral blood monocytes. Here, we aim to verify these findings using another two eQTL datasets in human peripheral blood immune cell CD14+ monocytes. The results that showed that rs1476679 and rs76904798 variants or their proxy variants could significantly regulate the expression of PILRB and LRRK2 in immune cell CD14+ monocytes and human peripheral blood. We believe that these findings provide important supplementary information about the regulatory mechanisms by which both variants influence PILRB and LRRK2 gene expression and neurodegenerative disease risk.

Functional Prediction of Chronic Kidney Disease Susceptibility Gene PRKAG2 by Comprehensively Bioinformatics Analysis

The genetic predisposition to chronic kidney disease (CKD) has been widely evaluated especially using the genome-wide association studies, which highlighted some novel genetic susceptibility variants in many genes, and estimated glomerular filtration rate to diagnose and stage CKD. Of these variants, rs7805747 in PRKAG2 was identified to be significantly associated with both serum creatinine and CKD with genome wide significance level. Until now, the potential mechanism by which rs7805747 affects CKD risk is still unclear. Here, we performed a functional analysis of rs7805747 variant using multiple bioinformatics software and databases. Using RegulomeDB and HaploReg (version 4.1), rs7805747 was predicated to locate in enhancer histone marks (Liver, Duodenum Mucosa, Fetal Intestine Large, Fetal Intestine Small, and Right Ventricle tissues). Using GWAS analysis in PhenoScanner, we showed that rs7805747 is not only associated with CKD, but also is significantly associated with other diseases or phenotypes. Using metabolite analysis in PhenoScanner, rs7805747 is identified to be significantly associated with not only the serum creatinine, but also with other 16 metabolites. Using eQTL analysis in PhenoScanner, rs7805747 is identified to be significantly associated with gene expression in multiple human tissues and multiple genes including PRKAG2. The gene expression analysis of PRKAG2 using 53 tissues from GTEx RNA-Seq of 8555 samples (570 donors) in GTEx showed that PRKAG2 had the highest median expression in Heart-Atrial Appendage. Using the gene expression profiles in human CKD, we further identified different expression of PRKAG2 gene in CKD cases compared with control samples. In summary, our findings provide new insight into the underlying susceptibility of PRKAG2 gene to CKD.

Influence of ADAM10 Polymorphisms on Plasma Level of Soluble Receptor for Advanced Glycation End Products and The Association With Alzheimer's Disease Risk

To determine the role of A disintegrin and metalloproteinase 10 (ADAM10) in genetic susceptibility to Alzheimer's disease (AD) in a representative Chinese sample, we genotyped 362 AD patients and 370 healthy controls for the rs514049A/C and rs653765C/T polymorphisms in the ADAM10 promoter using the SNaPshot technique. We also examined the potential impact of these polymorphisms on the plasma level of soluble receptor for advanced glycation end products (sRAGE), a decoy receptor whose reduction has been associated with a higher risk of AD. Additionally, a meta-analysis was performed using the present study and the largest GWAS from the International Genomics of Alzheimer's Project (IGAP). No significant differences were found in the distributions of genotypes or alleles between AD patients and control subjects. However, age-at-onset stratification analysis revealed that there were significant differences in the genotypes (P = 0.015) and alleles (P = 0.006) of the rs653765 SNP. Furthermore, patients with the rs653765 CC genotype showed a lower ADAM10 level and a faster cognitive deterioration than those in patients with the CT/TT genotype in late-onset AD (LOAD), and the rs653765 CC polymorphism was able to regulate the production of the ADAM10 substrate sRAGE. In contrast, the rs514049 polymorphism was not statistically associated with AD. In the meta-analysis, we observed that both rs514049 (A allele vs. C allele, P = 0.002) and rs653765 (C allele vs. T allele, P = 0.004) were associated with AD risk. The present study indicated that the rs653765 polymorphism might be associated with the risk and development of LOAD; in particular, the risk genotype, CC, may decrease the expression of ADAM10, influencing the plasma levels of sRAGE, and thus may be correlated with the clinical progression of AD.

Alzheimer's Disease and Rheumatoid Arthritis: A Mendelian Randomization Study

Alzheimer’s disease (AD) is the most common neurodegenerative disease. In recent years, multiple pathway analyses of AD genome-wide association studies (GWAS) have been conducted, and provided strong support for immune pathways in AD. Rheumatoid arthritis (RA) is a chronic autoimmune disease. It is reported that antirheumatic drugs had protective effect on dementia in RA patients. However, observational studies have reported a controversial inverse relationship between AD and RA. In addition, Mendelian randomization studies have also been performed to evaluate the association of RA with AD. However, these studies reported inconsistent association of RA with AD. Until now, it is still unclear that AD is a causally associated with RA. Here, we performed a Mendelian randomization study to investigate the causal association of AD with RA. We analyzed the large-scale AD GWAS dataset (74,046 individuals) and RA GWAS dataset (58,284 individuals) from the European descent. However, we did not identify any significant association of AD with RA using inverse-variance weighted meta-analysis (IVW), weighted median regression and MR-Egger regression.

Circulating vitamin E levels and Alzheimer's disease: a Mendelian randomization study

Alzheimer's disease (AD) is the leading cause of dementia in older adults. It is more than 50 years since vitamin E was recognized as critical for optimal neurological health. Clinical studies have yielded inconsistent findings on the effect of vitamin E on AD risk. Thus, it remains unclear whether vitamin E levels are genetically associated with AD risk. We performed a Mendelian randomization study to investigate association of circulating vitamin E levels with AD using large-scale vitamin E genome-wide association study data set (N = 7781 individuals of European descent) and AD genome-wide association study data set (N = 54,162 individuals [including 17,008 AD cases and 37,154 controls of European descent]). Mendelian randomization-Egger intercept test showed no significant pleiotropy (β = -0.113; p = 0.296). Inverse-variance weighted (odds ratio = 0.96, 95% confidence interval: 0.47-1.94, p = 0.936) and weighted median analyses (odds ratio = 1.13, 95% confidence interval: 0.35-3.69, p = 0.836) showed no significant association between vitamin E and AD. Together with previous literature, this suggests that vitamin E supplementation may not forestall AD in the general population.

Genetic Variant rs755622 Regulates Expression of the Multiple Sclerosis Severity Modifier D-Dopachrome Tautomerase in a Sex-Specific Way

Multiple sclerosis (MS) is a sex-specific autoimmune disease involving central nervous system. Previous studies determined that macrophage migration inhibitory factor (MIF) and its homologue D-dopachrome tautomerase (DDT) sex-specifically affect MS progression. Moreover, other studies reported that rs755622 polymorphism in promoter region of MIF gene is associated with risk of MS and affects the promoter activity to regulate MIF expression in a sex-specific way. Given that MIF and DDT share a part of promoter sequence, we surmise that rs755622 can also regulate DDT expression in a sex-specific way. However, this has not yet been studied. Here, we used five large-scale expression quantitative trait loci (eQTLs) and two RNA-seq datasets from brain and blood to assess the potential influence of rs755622 variant on expression of DDT in different genders by the linear regression and differential expression analysis. The results show that the minor allele frequency of rs755622 and expression of DDT are significantly increased in males for MS subjects and this minor allele variant can significantly upregulate DDT expression for males but not females, which suggests that the regulation of DDT expression level by rs755622 can affect MS progression in males. These findings further support and expand conclusions of previous studies and may help to better understand the mechanisms of MS.

Analyzing 74,248 Samples Confirms the Association Between CLU rs11136000 Polymorphism and Alzheimer's Disease in Caucasian But Not Chinese population

Clusterin (CLU) is considered one of the most important roles for pathogenesis of Alzheimer's Disease (AD). The early genome-wide association studies (GWAS) identified the CLU rs11136000 polymorphism is significantly associated with AD in Caucasian. However, the subsequent studies are unable to replicate these findings in different populations. Although two independent meta-analyses show evidence to support significant association in Asian and Caucasian populations by integrating the data from 18 and 25 related GWAS studies, respectively, many of the following 18 studies also reported the inconsistent results. Moreover, there are six missed and a misclassified GWAS studies in the two meta-analyses. Therefore, we suspected that the small-scale and incompletion or heterogeneity of the samples maybe lead to different results of these studies. In this study, large-scale samples from 50 related GWAS studies (28,464 AD cases and 45,784 controls) were selected afresh from seven authoritative sources to reevaluate the effect of rs11136000 polymorphism to AD risk. Similarly, we identified that the minor allele variant of rs11136000 significantly decrease AD risk in Caucasian ethnicity using the allele, dominant and recessive model. Different from the results of the previous studies, however, the results showed a negligible or no association in Asian and Chinese populations. Collectively, our analysis suggests that, for Asian and Chinese populations, the variant of rs11136000 may be irrelevant to AD risk. We believe that these findings can help to improve the understanding of the AD's pathogenesis.

Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis

Integration of public genome-wide gene expression data together with Cox regression analysis is a powerful weapon to identify new prognostic gene signatures for cancer diagnosis and prognosis. Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC), however, it remains largely unknown about the specific gene prognostic signature of HBV-associated HCC. Using Robust Rank Aggreg (RRA) method to integrate seven whole genome expression datasets, we identified 82 up-regulated genes and 577 down-regulated genes in HBV-associated HCC patients. Combination of several enrichment analysis, univariate and multivariate Cox proportional hazards regression analysis, we revealed that a three-gene (SPP2, CDC37L1, and ECHDC2) prognostic signature could act as an independent prognostic indicator for HBV-associated HCC in both the discovery cohort and the internal testing cohort. Gene set enrichment analysis showed that the high-risk group with lower expression levels of the three genes was enriched in bladder cancer and cell cycle pathway, whereas the low-risk group with higher expression levels of the three genes was enriched in drug metabolism-cytochrome P450, PPAR signaling pathway, fatty acid and histidine metabolisms. This indicates that patients of HBV-associated HCC with higher expression of these three genes may preserve relatively good hepatic cellular metabolism and function, which may also protect HCC patients from persistent drug toxicity in response to various medication. Our findings suggest a three-gene prognostic model that serves as a specific prognostic signature for HBV-associated HCC.

Genetic variants regulate NR1H3 expression and contribute to multiple sclerosis risk

A recent study analyzed 2053 multiple sclerosis (MS) cases and 799 healthy controls to investigate whether five genetic variants (rs11039149, rs12221497, rs2279238, rs7120118 and rs7114704) in NR1H3 are associated with MS risk. However this study reported negative results. It is very important that the appropriate samples and approach should be used in replication studies, which may provide the correct interpretation of the results. Here, we evaluated the above findings using large-scale MS genome-wide association studies with a total of 27,148 samples including 9772 MS cases and 17,376 controls, and multiple expression quantitative trait loci datasets. The results suggest that rs7120118 and rs2279238 variants are significantly associated with MS risk, and could significantly regulate NR1H3 expression in kinds of human tissues and cells. In summary, these findings provide important supplementary information about the association between NR1H3 variants and MS risk.