rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues

LncRNA-induced lysosomal localization of NHE1 promotes increased lysosomal pH in macrophages leading to atherosclerosis

ANRIL, also referred to as CDKN2B-AS1, is a lncRNA gene implicated in the pathogenesis of multiple human diseases including atherosclerotic coronary artery disease, however, definitive in vivo evidence is lacking and the underlying molecular mechanism is largely unknown. In this study, we show that ANRIL overexpression causes atherosclerosis in vivo as transgenic mouse overexpression of full-length ANRIL (NR_003529) increases inflammation and aggravates atherosclerosis under ApoE-/- background (ApoE-/-ANRIL mice). Mechanistically, ANRIL reduces the expression of miR-181b-5p, which leads to increased TMEM106B expression. TMEM106B is significantly up-regulated in atherosclerotic lesions of both human CAD patients and ApoE-/-ANRIL mice. TMEM106B interacts and co-localizes with Na+-H+ exchanger NHE1, which results in mis-localization of NHE1 from cell membranes to lysosomal membranes, leading to increased lysosomal pH in macrophages. Large truncation and point mutation analyses define the critical amino acids for TMEM106B-NHE1 interaction and lysosomal pH regulation as F115 and F117 on TMEM106B and I537, C538, and G539 on NHE1. Topological analysis suggests that both N-terminus and C-terminus of NHE1 are located inside lysosomal lumen, and NHE1 is an important new proton efflux channel involved in raising lysosomal pH. A short TMEM106B peptide (YGRKKRRQRRR-L111A112V113F114F115L116F117) disrupting the TMEM106B-NHE1 interaction normalized lysosomal pH in macrophages with ANRIL overexpression. Our data demonstrate that ANRIL promotes atherosclerosis in vivo and identify the ANRIL/miR-181b-5p/TMEM106B-NHE1/lysosomal pH axis as the underlying molecular pathogenic mechanism for the chromosome 9p21.3 genetic locus for coronary artery disease.

Increased TMEM106B levels lead to lysosomal dysfunction which affects synaptic signaling and neuronal health

BACKGROUND

Genetic variation in Transmembrane protein 106B (TMEM106B) is known to influence the risk and presentation in several neurodegenerative diseases and modifies healthy aging. While evidence from human studies suggests that the risk allele is associated with higher levels of TMEM106B, the contribution of elevated levels of TMEM106B to neurodegeneration and aging has not been assessed and it remains unclear how TMEM106B modulates disease risk.

METHODS

To study the effect of increased TMEM106B levels, we generated Cre-inducible transgenic mice expressing human wild-type TMEM106B. We evaluated lysosomal and neuronal health using in vitro and in vivo assays including transmission electron microscopy, immunostainings, behavioral testing, electrophysiology, and bulk RNA sequencing.

RESULTS

We created the first transgenic mouse model that successfully overexpresses TMEM106B, with a 4- to 8-fold increase in TMEM106B protein levels in heterozygous (hTMEM106B(+)) and homozygous (hTMEM106B(++)) animals, respectively. We showed that the increase in TMEM106B protein levels induced lysosomal dysfunction and age-related downregulation of genes associated with neuronal plasticity, learning, and memory. Increased TMEM106B levels led to altered synaptic signaling in 12-month-old animals which further exhibited an anxiety-like phenotype. Finally, we observed mild neuronal loss in the hippocampus of 21-month-old animals.

CONCLUSION

Characterization of the first transgenic mouse model that overexpresses TMEM106B suggests that higher levels of TMEM106B negatively impacts brain health by modifying brain aging and impairing the resilience of the brain to the pathomechanisms of neurodegenerative disorders. This novel model will be a valuable tool to study the involvement and contribution of increased TMEM106B levels to aging and will be essential to study the many age-related diseases in which TMEM106B was genetically shown to be a disease- and risk-modifier.

The roles and functions of TMEM protein family members in cancers, cardiovascular and kidney diseases (Review)

Transmembrane protein (TMEM) is a type of membrane proteins, encoded by TMEM gene, also known as integral membrane protein. TMEM gene family contains various members and its encoded proteins have various functions and expressed in numerous organs. It has been proved to be widely involved in the formation of a lot of organelle membranes, enzymes, receptors and channels, mediating numerous normal physiological functions and regulating various disease processes. At present, accumulating evidences at home and abroad have shown that TMEM is involved in regulating the occurrence and development of different tumors, cardiovascular and kidney diseases. The improved understanding of molecular mechanisms of TMEM genes and proteins may provide new directions and ideas for the prevention, diagnosis and treatment of diseases. In the present review, the roles of TMEM and biological functions in various cancers, cardiovascular and kidney diseases were discussed.

Interaction of genetic variants and methylation in transcript-level expression regulation in Alzheimer's disease by multi-omics data analysis

BACKGROUND

Alzheimer's disease (AD) presents a significant public health problem and major cause of dementia. Not only genetic but epigenetic factors contribute to complex and heterogeneous molecular mechanisms underlying AD risk; in particular, single nucleotide polymorphisms (SNPs) and DNA methylation can lead to dysregulation of gene expression in the AD brain. Each of these regulators has been independently studied well in AD progression, however, their interactive roles, particularly when they are located differently, still remains unclear. Here, we aimed to explore the interplay between SNPs and DNA methylation in regulating transcript expression levels in the AD brain through an integrative analysis of whole-genome sequencing, RNA-seq, and methylation data measured from the dorsolateral prefrontal cortex.

RESULTS

We identified 179 SNP-methylation combination pairs that showed statistically significant interactions associated with the expression of 67 transcripts (63 unique genes), enriched in functional pathways, including immune-related and post-synaptic assembly pathways. Particularly, a number of HLA family genes (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DRB5, HLA-DPA1, HLA-K, HLA-DQB1, and HLA-DMA) were observed as having expression changes associated with the interplay.

CONCLUSIONS

Our findings especially implicate immune-related pathways as targets of these regulatory interactions. SNP-methylation interactions may thus contribute to the molecular complexity underlying immune-related pathogenies in AD patients. Our study provides a new molecular knowledge in the context of the interplay between genetic and epigenetic regulations, in that it concerns transcript expression status in AD.

Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease

Background

Late-onset Alzheimer's disease (LOAD) has been associated with alterations in the morphology of multiple brain structures, and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.

Objective

To determine causal pathways from genetic risk variants of LOAD via brain morphology to LOAD.

Methods

Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer's Disease Neuroimaging Initiative.

Results

Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala and inferior lateral ventricle mediated the effect of APOE ε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the inferior lateral ventricles, increased the risk of LOAD as well as vice versa.

Conclusions

Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.

Divergent and Convergent TMEM106B Pathology in Murine Models of Neurodegeneration and Human Disease

TMEM106B is a lysosomal/late endosome protein that is a potent genetic modifier of multiple neurodegenerative diseases as well as general aging. Recently, TMEM106B was shown to form insoluble aggregates in postmortem human brain tissue, drawing attention to TMEM106B pathology and the potential role of TMEM106B aggregation in disease. In the context of neurodegenerative diseases, TMEM106B has been studied in vivo using animal models of neurodegeneration, but these studies rely on overexpression or knockdown approaches. To date, endogenous TMEM106B pathology and its relationship to known canonical pathology in animal models has not been reported. Here, we analyze histological patterns of TMEM106B in murine models of C9ORF72-related amyotrophic lateral sclerosis and frontotemporal dementia (C9-ALS/FTD), SOD1-related ALS, and tauopathy and compare these to postmortem human tissue from patients with C9-ALS/FTD, Alzheimer's disease (AD), and AD with limbic-predominant age-related TDP-43 encephalopathy (AD/LATE). We show that there are significant differences between TMEM106B pathology in mouse models and human patient tissue. Importantly, we also identified convergent evidence from both murine models and human patients that links TMEM106B pathology to TDP-43 nuclear clearance specifically in C9-ALS. Similarly, we find a relationship at the cellular level between TMEM106B pathology and phosphorylated Tau burden in Alzheimer's disease. By characterizing endogenous TMEM106B pathology in both mice and human postmortem tissue, our work reveals considerations that must be taken into account when analyzing data from in vivo mouse studies and elucidates new insights supporting the involvement of TMEM106B in the pathogenesis and progression of multiple neurodegenerative diseases.

Divergent and Convergent TMEM106B Pathology in Murine Models of Neurodegeneration and Human Disease

TMEM106B is a lysosomal/late endosome protein that is a potent genetic modifier of multiple neurodegenerative diseases as well as general aging. Recently, TMEM106B was shown to form insoluble aggregates in postmortem human brain tissue, drawing attention to TMEM106B pathology and the potential role of TMEM106B aggregation in disease. In the context of neurodegenerative diseases, TMEM106B has been studied in vivo using animal models of neurodegeneration, but these studies rely on overexpression or knockdown approaches. To date, endogenous TMEM106B pathology and its relationship to known canonical pathology in animal models has not been reported. Here, we analyze histological patterns of TMEM106B in murine models of C9ORF72-related amyotrophic lateral sclerosis and frontotemporal dementia (C9-ALS/FTD), SOD1-related ALS, and tauopathy and compare these to postmortem human tissue from patients with C9-ALS/FTD, Alzheimer's disease (AD), and AD with limbic-predominant age-related TDP-43 encephalopathy (AD/LATE). We show that there are significant differences between TMEM106B pathology in mouse models and human patient tissue. Importantly, we also identified convergent evidence from both murine models and human patients that links TMEM106B pathology to TDP-43 nuclear clearance specifically in C9-ALS. Similarly, we find a relationship at the cellular level between TMEM106B pathology and phosphorylated Tau burden in Alzheimer's disease. By characterizing endogenous TMEM106B pathology in both mice and human postmortem tissue, our work reveals considerations that must be taken into account when analyzing data from in vivo mouse studies and elucidates new insights supporting the involvement of TMEM106B in the pathogenesis and progression of multiple neurodegenerative diseases.

Genome-wide association study meta-analysis of neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration

Neurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro-axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels could provide new insights into molecular mechanisms underlying neurodegenerative disorders. In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 (SLC39A11). We observe association of three loci at 1q43 (FMN2), 12q14, and 12q21 with blood NfL levels in the meta-analysis of African-American ancestry. In the trans-ethnic meta-analysis, we identify three additional genome-wide significant loci at 1p32 (FGGY), 6q14 (TBX18), and 4q21. In the post-GWAS analyses, we observe the association of higher NfL polygenic risk score with increased plasma levels of total-tau, Aβ-40, Aβ-42, and higher incidence of Alzheimer's disease in the Rotterdam Study. Furthermore, Mendelian randomization analysis results suggest that a lower kidney function could cause higher blood NfL levels. This study uncovers multiple genetic loci of blood NfL levels, highlighting the genes related to molecular mechanism of neurodegeneration.

Lysosomal TMEM106B interacts with galactosylceramidase to regulate myelin lipid metabolism

TMEM106B is an endolysosomal transmembrane protein not only associated with multiple neurological disorders including frontotemporal dementia, Alzheimer's disease, and hypomyelinating leukodystrophy but also potentially involved in COVID-19. Additionally, recent studies have identified amyloid fibrils of C-terminal TMEM106B in both aged healthy and neurodegenerative brains. However, so far little is known about physiological functions of TMEM106B in the endolysosome and how TMEM106B is involved in a wide range of human conditions at molecular levels. Here, we performed lipidomic analysis of the brain of TMEM106B-deficient mice. We found that TMEM106B deficiency significantly decreases levels of two major classes of myelin lipids, galactosylceramide and its sulfated derivative sulfatide. Subsequent co-immunoprecipitation assay showed that TMEM106B physically interacts with galactosylceramidase. We also found that galactosylceramidase activity was significantly increased in TMEM106B-deficient brains. Thus, our results suggest that TMEM106B interacts with galactosylceramidase to regulate myelin lipid metabolism and have implications for TMEM106B-associated diseases.

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction

Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

TMEM106B C-terminal fragments aggregate and drive neurodegenerative proteinopathy

Genetic variation in the lysosomal and transmembrane protein 106B (TMEM106B) modifies risk for a diverse range of neurodegenerative disorders, especially frontotemporal lobar degeneration (FTLD) with progranulin (PGRN) haplo-insufficiency, although the molecular mechanisms involved are not yet understood. Through advances in cryo-electron microscopy (cryo-EM), homotypic aggregates of the C-Terminal domain of TMEM106B (TMEM CT) were discovered as a previously unidentified cytosolic proteinopathy in the brains of FTLD, Alzheimer's disease, progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB) patients. While it remains unknown what role TMEM CT aggregation plays in neuronal loss, its presence across a range of aging related dementia disorders indicates involvement in multi-proteinopathy driven neurodegeneration. To determine the TMEM CT aggregation propensity and neurodegenerative potential, we characterized a novel transgenic C. elegans model expressing the human TMEM CT fragment constituting the fibrillar core seen in FTLD cases. We found that pan-neuronal expression of human TMEM CT in C. elegans causes neuronal dysfunction as evidenced by behavioral analysis. Cytosolic aggregation of TMEM CT proteins accompanied the behavioral dysfunction driving neurodegeneration, as illustrated by loss of GABAergic neurons. To investigate the molecular mechanisms driving TMEM106B proteinopathy, we explored the impact of PGRN loss on the neurodegenerative effect of TMEM CT expression. To this end, we generated TMEM CT expressing C. elegans with loss of pgrn-1, the C. elegans ortholog of human PGRN. Neither full nor partial loss of pgrn-1 altered the motor phenotype of our TMEM CT model suggesting TMEM CT aggregation occurs downstream of PGRN loss of function. We also tested the ability of genetic suppressors of tauopathy to rescue TMEM CT pathology. We found that genetic knockout of spop-1, sut-2, and sut-6 resulted in weak to no rescue of proteinopathy phenotypes, indicating that the mechanistic drivers of TMEM106B proteinopathy may be distinct from tauopathy. Taken together, our data demonstrate that TMEM CT aggregation can kill neurons. Further, expression of TMEM CT in C. elegans neurons provides a useful model for the functional characterization of TMEM106B proteinopathy in neurodegenerative disease.

Physiological and pathological functions of TMEM106B in neurodegenerative diseases

As an integral lysosomal transmembrane protein, transmembrane protein 106B (TMEM106B) regulates several aspects of lysosomal function and is associated with neurodegenerative diseases. The TMEM106B gene mutations lead to lysosomal dysfunction and accelerate the pathological progression of Neurodegenerative diseases. Yet, the precise mechanism of TMEM106B in Neurodegenerative diseases remains unclear. Recently, different research teams discovered that TMEM106B is an amyloid protein and the C-terminal domain of TMEM106B forms amyloid fibrils in various Neurodegenerative diseases and normally elderly individuals. In this review, we discussed the physiological functions of TMEM106B. We also included TMEM106B gene mutations that cause neurodegenerative diseases. Finally, we summarized the identification and cryo-electronic microscopic structure of TMEM106B fibrils, and discussed the promising therapeutic strategies aimed at TMEM106B fibrils and the future directions for TMEM106B research in neurodegenerative diseases.

CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only

Anticancer peptides (ACPs) recently have been receiving increasing attention in cancer therapy due to their low consumption, few adverse side effects, and easy accessibility. However, it remains a great challenge to identify anticancer peptides via experimental approaches, requiring expensive and time-consuming experimental studies. In addition, traditional machine-learning-based methods are proposed for ACP prediction mainly depending on hand-crafted feature engineering, which normally achieves low prediction performance. In this study, we propose CACPP (Contrastive ACP Predictor), a deep learning framework based on the convolutional neural network (CNN) and contrastive learning for accurately predicting anticancer peptides. In particular, we introduce the TextCNN model to extract the high-latent features based on the peptide sequences only and exploit the contrastive learning module to learn more distinguishable feature representations to make better predictions. Comparative results on the benchmark data sets indicate that CACPP outperforms all the state-of-the-art methods in the prediction of anticancer peptides. Moreover, to intuitively show that our model has good classification ability, we visualize the dimension reduction of the features from our model and explore the relationship between ACP sequences and anticancer functions. Furthermore, we also discuss the influence of data set construction on model prediction and explore our model performance on the data sets with verified negative samples.

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review

Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.

Three-dimensional morphology scoring of hepatocellular carcinoma stratifies prognosis and immune infiltration

BACKGROUND

The morphological attributes could serve as pivotal indicators precipitating early recurrence and dismal overall survival in hepatocellular carcinoma (HCC), and quantifying morphological features may better stratify the prognosis of HCC.

OBJECTIVE

To develop a radiomics approach based on 3D tumor morphology features for predicting the prognosis of HCC and identifying differentially expressed genes related to morphology to guide HCC treatment.

MATERIALS AND METHODS

Retrospective study of 357 HCC patients. Radiomic features were extracted from MRI tumor regions; 14 morphology-related features predicted early HCC recurrence and patient stratification via LASSO-Cox modeling. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. RNA sequencing from the Cancer Imaging Archive (TCIA) examined drug sensitivity and stratified HCC using morphological immunity genes, validating recurrence and prognosis.

RESULTS

Patients were split into training (n = 225), test (n = 132), and 50 TCIA dataset cohorts. Two features (Maximum2DdiameterColumn, Sphericity) in Cox regression stratified patients into high/low-risk Morphological Radiological Score (Morph-RS) groups. Significant OS and RFS were seen across all sets. Differentially expressed genes focused on T cell receptor signaling; low-risk group had higher T cells (P = 0.039), B cells (P = 0.041), NK cells (P = 0.018). SN-38, GSK2126458 might treat high-risk morphology. Morphology-immune genes stratified HCC, showing significant RFS/OS differences.

CONCLUSION

Tumor Morph-RS effectively stratifies HCC patients' recurrence and prognosis. Limited immune infiltration seen in Morph-RS high-risk groups signifies the potential of employing tumor morphology as a potent visual biomarker for diagnosing and managing HCC.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Association of TMEM106B with Cortical APOE Gene Expression in Neurodegenerative Conditions

The e4 allele of the apolipoprotein E gene is the strongest genetic risk factor for sporadic Alzheimer's disease. Nevertheless, how APOE is regulated is still elusive. In a trans-eQTL analysis, we found a genome-wide significant association between transmembrane protein 106B (TMEM106B) genetic variants and cortical APOE mRNA levels in human brains. The goal of this study is to determine whether TMEM106B is mis-regulated in Alzheimer's disease or in other neurodegenerative conditions. Available genomic, transcriptomic and proteomic data from human brains were downloaded from the Mayo Clinic Brain Bank and the Religious Orders Study and Memory and Aging Project. An in-house mouse model of the hippocampal deafferentation/reinnervation was achieved via a stereotaxic lesioning surgery to the entorhinal cortex, and mRNA levels were measured using RNAseq technology. In human temporal cortices, the mean TMEM106B expression was significantly higher in Alzheimer's disease compared to cognitively unimpaired individuals. In the mouse model, hippocampal Tmem106b reached maximum levels during the early phase of reinnervation. These results suggest an active response to tissue damage that is consistent with compensatory synaptic and terminal remodeling.

Integrating multi-omics data to reveal the effect of genetic variant rs6430538 on Alzheimer's disease risk

Introduction

Growing evidence highlights a potential genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD); however, the role of the PD risk variant rs6430538 in AD remains unclear.

Methods

In Stage 1, we investigated the risk associated with the rs6430538 C allele in seven large-scale AD genome-wide association study (GWAS) cohorts. In Stage 2, we performed expression quantitative trait loci (eQTL) analysis to calculate the cis-regulated effect of rs6430538 on TMEM163 in both AD and neuropathologically normal samples. Stage 3 involved evaluating the differential expression of TMEM163 in 4 brain tissues from AD cases and controls. Finally, in Stage 4, we conducted a transcriptome-wide association study (TWAS) to identify any association between TMEM163 expression and AD.

Results

The results showed that genetic variant rs6430538 C allele might increase the risk of AD. eQTL analysis revealed that rs6430538 up-regulated TMEM163 expression in AD brain tissue, but down-regulated its expression in normal samples. Interestingly, TMEM163 showed differential expression in entorhinal cortex (EC) and temporal cortex (TCX). Furthermore, the TWAS analysis indicated strong associations between TMEM163 and AD in various tissues.

Discussion

In summary, our findings suggest that rs6430538 may influence AD by regulating TMEM163 expression. These discoveries may open up new opportunities for therapeutic strategies targeting AD.

Tracing TMEM106B fibril deposition in aging and Parkinson's disease with dementia brains

Transmembrane protein 106B (TMEM106B), previously identified as a risk factor in frontotemporal lobar degeneration, has recently been detected to form fibrillar aggregates in the brains of patients with various neurodegenerative diseases (NDs) and normal elders. While the specifics of when and where TMEM106B fibrils accumulate in human brains, as well as their connection to aging and disease progression, remain poorly understood. Here, we identified an antibody (NBP1-91311) that directly binds to TMEM106B fibrils extracted from the brain in vitro and to Thioflavin S-positive TMEM106B fibrillar aggregates in brain sections. We discovered that TMEM106B fibrils deposit in the human brain in an age-dependent manner. Notably, the TMEM106B fibril load in the brains of Parkinson's disease with dementia patients was significantly higher than in age-matched elders. Additionally, we found that TMEM106B fibrils predominantly accumulate in astrocytes and neurons and do not co-localize with the pathological deposition formed by other amyloid proteins such as α-synuclein, Aβ, and Tau. Our work provides a comprehensive analysis of the burden and cellular distribution of TMEM106B fibrils in human brains, underscoring the impact of both aging and disease conditions on TMEM106B fibril deposition. This highlights the potential significance of TMEM106B fibrils in various age-related NDs.

TMEM106B reduction does not rescue GRN deficiency in iPSC-derived human microglia and mouse models

Heterozygous mutations in the granulin (GRN) gene are a leading cause of frontotemporal lobar degeneration with TDP-43 aggregates (FTLD-TDP). Polymorphisms in TMEM106B have been associated with disease risk in GRN mutation carriers and protective TMEM106B variants associated with reduced levels of TMEM106B, suggesting that lowering TMEM106B might be therapeutic in the context of FTLD. Here, we tested the impact of full deletion and partial reduction of TMEM106B in mouse and iPSC-derived human cell models of GRN deficiency. TMEM106B deletion did not reverse transcriptomic or proteomic profiles in GRN-deficient microglia, with a few exceptions in immune signaling markers. Neither homozygous nor heterozygous Tmem106b deletion normalized disease-associated phenotypes in Grn -/-mice. Furthermore, Tmem106b reduction by antisense oligonucleotide (ASO) was poorly tolerated in Grn -/-mice. These data provide novel insight into TMEM106B and GRN function in microglia cells but do not support lowering TMEM106B levels as a viable therapeutic strategy for treating FTD-GRN.

Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

Lysosomal TMEM106B interacts with galactosylceramidase to regulate myelin lipid metabolism

TMEM106B is an endolysosomal transmembrane protein not only associated with multiple neurological disorders including frontotemporal dementia, Alzheimer's disease, and hypomyelinating leukodystrophy but also potentially involved in COVID-19. Additionally, recent studies have identified amyloid fibrils of C-terminal TMEM106B in both aged healthy and neurodegenerative brains. However, so far little is known about physiological functions of TMEM106B in the endolysosome and how TMEM106B is involved in a wide range of human conditions at molecular levels. Here, we performed lipidomic analysis of the brain of TMEM106B-deficient mice. We found that TMEM106B deficiency significantly decreases levels of two major classes of myelin lipids, galactosylceramide and its sulfated derivative sulfatide. Subsequent co-immunoprecipitation assay showed that TMEM106B physically interacts with galactosylceramidase. We also found that galactosyceramidase activity was significantly increased in TMEM106B-deficient brains. Thus, our results reveal a novel function of TMEM106B interacting with galactosyceramidase to regulate myelin lipid metabolism and have implications for TMEM106B-associated diseases.

Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning

DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.

TMEM106B aggregation in neurodegenerative diseases: linking genetics to function

BACKGROUND

Mutations of the gene TMEM106B are risk factors for diverse neurodegenerative diseases. Previous understanding of the underlying mechanism focused on the impairment of lysosome biogenesis caused by TMEM106B loss-of-function. However, mutations in TMEM106B increase its expression level, thus the molecular process linking these mutations to the apparent disruption in TMEM106B function remains mysterious.

MAIN BODY

Recent new studies reported that TMEM106B proteins form intracellular amyloid filaments which universally exist in various neurodegenerative diseases, sometimes being the dominant form of protein aggregation. In light of these new findings, in this review we systematically examined previous efforts in understanding the function of TMEM106B in physiological and pathological conditions. We propose that TMEM106B aggregations could recruit normal TMEM106B proteins and interfere with their function.

CONCLUSIONS

TMEM106B mutations could lead to lysosome dysfunction by promoting the aggregation of TMEM106B and reducing these aggregations may restore lysosomal function, providing a potential therapeutic target for various neurodegenerative diseases.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Shared genetic risk loci between Alzheimer's disease and related dementias, Parkinson's disease, and amyotrophic lateral sclerosis

BACKGROUND

Genome-wide association studies (GWAS) have indicated moderate genetic overlap between Alzheimer's disease (AD) and related dementias (ADRD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), neurodegenerative disorders traditionally considered etiologically distinct. However, the specific genetic variants and loci underlying this overlap remain almost entirely unknown.

METHODS

We leveraged state-of-the-art GWAS for ADRD, PD, and ALS. For each pair of disorders, we examined each of the GWAS hits for one disorder and tested whether they were also significant for the other disorder, applying Bonferroni correction for the number of variants tested. This approach rigorously controls the family-wise error rate for both disorders, analogously to genome-wide significance.

RESULTS

Eleven loci with GWAS hits for one disorder were also associated with one or both of the other disorders: one with all three disorders (the MAPT/KANSL1 locus), five with ADRD and PD (near LCORL, CLU, SETD1A/KAT8, WWOX, and GRN), three with ADRD and ALS (near GPX3, HS3ST5/HDAC2/MARCKS, and TSPOAP1), and two with PD and ALS (near GAK/TMEM175 and NEK1). Two of these loci (LCORL and NEK1) were associated with an increased risk of one disorder but decreased risk of another. Colocalization analysis supported a shared causal variant between ADRD and PD at the CLU, WWOX, and LCORL loci, between ADRD and ALS at the TSPOAP1 locus, and between PD and ALS at the NEK1 and GAK/TMEM175 loci. To address the concern that ADRD is an imperfect proxy for AD and that the ADRD and PD GWAS have overlapping participants (nearly all of which are from the UK Biobank), we confirmed that all our ADRD associations had nearly identical odds ratios in an AD GWAS that excluded the UK Biobank, and all but one remained nominally significant (p < 0.05) for AD.

CONCLUSIONS

In one of the most comprehensive investigations to date of pleiotropy between neurodegenerative disorders, we identify eleven genetic risk loci shared among ADRD, PD, and ALS. These loci support lysosomal/autophagic dysfunction (GAK/TMEM175, GRN, KANSL1), neuroinflammation/immunity (TSPOAP1), oxidative stress (GPX3, KANSL1), and the DNA damage response (NEK1) as transdiagnostic processes underlying multiple neurodegenerative disorders.

Deep ensemble learning for accurate retinal vessel segmentation

Significant progress has been made in deep learning-based retinal vessel segmentation in recent years. However, the current methods suffer from low performance and the robust of the models is not that good. Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. The results of benchmarking comparisons indicate that our model outperforms the existing ones on multiple datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field.

Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods

Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.

ToxMVA: An end-to-end multi-view deep autoencoder method for protein toxicity prediction

Effectively predicting protein toxicity plays an essential step in the early stage of protein-based drug discovery, which is of great help to speed up novel drug screening and reduce costs. Recently, several relevant datasets have been designed, and then machine learning-based methods have been proposed to predict the toxicity of the protein and have shown satisfactory performance. However, previous studies generally directly concatenate different protein features, which may introduce irrelevant information and decrease model performance. In this study, we present a novel end-to-end deep learning-based method called ToxMVA, to predict protein toxicity. To be specific, we first build comprehensive feature profiles of proteins based on primary sequences, including sequential, physicochemical, and contextual semantic information. Next, an autoencoder network is introduced to integrate the multi-view information for obtaining a more concise and accurate feature representation. Extensive experimental results on three datasets demonstrate that ToxMVA has superior performance for protein toxicity prediction and shows better robustness among three different datasets.

KRAS is a prognostic biomarker associated with diagnosis and treatment in multiple cancers

KRAS encodes K-Ras proteins, which take part in the MAPK pathway. The expression level of KRAS is high in tumor patients. Our study compared KRAS expression levels between 33 kinds of tumor tissues. Additionally, we studied the association of KRAS expression levels with diagnostic and prognostic values, clinicopathological features, and tumor immunity. We established 22 immune-infiltrating cell expression datasets to calculate immune and stromal scores to evaluate the tumor microenvironment. KRAS genes, immune check-point genes and interacting genes were selected to construct the PPI network. We selected 79 immune checkpoint genes and interacting related genes to calculate the correlation. Based on the 33 tumor expression datasets, we conducted GSEA (genome set enrichment analysis) to show the KRAS and other co-expressed genes associated with cancers. KRAS may be a reliable prognostic biomarker in the diagnosis of cancer patients and has the potential to be included in cancer-targeted drugs.

Cognitive performance protects against Alzheimer's disease independently of educational attainment and intelligence

Mendelian-randomization (MR) studies using large-scale genome-wide association studies (GWAS) have identified causal association between educational attainment and Alzheimer's disease (AD). However, the underlying mechanisms are still required to be explored. Here, we conduct univariable and multivariable MR analyses using large-scale educational attainment, cognitive performance, intelligence and AD GWAS datasets. In stage 1, we found significant causal effects of educational attainment on cognitive performance (beta = 0.907, 95% confidence interval (CI): 0.884-0.930, P < 1.145E-299), and vice versa (beta = 0.571, 95% CI: 0.557-0.585, P < 1.145E-299). In stage 2, we found that both increase in educational attainment (odds ratio (OR) = 0.72, 95% CI: 0.66-0.78, P = 1.39E-14) and cognitive performance (OR = 0.69, 95% CI: 0.64-0.75, P = 1.78E-20) could reduce the risk of AD. In stage 3, we found that educational attainment may protect against AD dependently of cognitive performance (OR = 1.07, 95% CI: 0.90-1.28, P = 4.48E-01), and cognitive performance may protect against AD independently of educational attainment (OR = 0.69, 95% CI: 0.53-0.89, P = 5.00E-03). In stage 4, we found significant causal effects of cognitive performance on intelligence (beta = 0.907, 95% CI: 0.877-0.938, P < 1.145E-299), and vice versa (beta = 0.957, 95% CI: 0.937-0.978, P < 1.145E-299). In stage 5, we identified that cognitive performance may protect against AD independently of intelligence (OR = 0.74, 95% CI: 0.61-0.90, P = 2.00E-03), and intelligence may protect against AD dependently of cognitive performance (OR = 1.17, 95% CI: 0.40-3.43, P = 4.48E-01). Collectively, our univariable and multivariable MR analyses highlight the protective role of cognitive performance in AD independently of educational attainment and intelligence. In addition to the intelligence, we extend the mechanisms underlying the associations of educational attainment with AD.

MultiscaleDTA: a multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction

The task of predicting drug-target affinity (DTA) plays an increasingly important role at the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and achieved outstanding performance, which is beneficial for speeding up the development of new drugs. However, most convolutional neural networks (CNNs) based methods ignore the significance of information from CNN layers with different scales to DTA prediction. In addition, each feature provides different contributions to the final task. Therefore, in this study, we propose a novel end-to-end deep learning-based framework, called MultiscaleDTA, to predict drug-target binding affinity. MultiscaleDTA incorporates multi-scale CNNs and a self-attention mechanism to capture multi-scale and comprehensive features for characterizing the intrinsic properties of drugs and targets. Extensive experimental results on both regression and binary classification tasks demonstrate that MultiscaleDTA has achieved competitive performance compared to state-of-the-art methods.

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction

In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can facilitate its finding and speed up its application in treating cancer. However, many recent approaches are based on machine learning, which not only restricts the representation ability of the models but also requires a complex hand-crafted feature extraction process. Here, we propose AntiMF, a deep learning model that utilizes multi-view mechanism based on different feature extraction models. Comparative results show that our model has a better performance than the state-of-the-art methods in the prediction of anticancer peptides. By using an ensemble learning framework to extract representation, AntiMF can capture the different dimensional information, which can make model representation more complete. Moreover, we visualize what AntiMF learns on one of its ensemble models to intuitively show the effectivity of our model, indicating that AntiMF has the great potential ability to be an effective and useful model to identify anticancer peptides accurately.

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion

DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is imperative to design an efficient and accurate identification tool. Considering the time-consuming and expensive traditional experimental technology and the insufficient number of samples in the biological computing method based on structural information, we proposed a machine learning algorithm based on sequence information to identify DNA binding proteins, named multi-view Least Squares Support Vector Machine via Hilbert-Schmidt Independence Criterion (multi-view LSSVM via HSIC). This method took 6 feature sets as multi-view input and trains a single view through the LSSVM algorithm. Then, we integrated HSIC into LSSVM as a regular term to reduce the dependence between views and explored the complementary information of multiple views. Subsequently, we trained and coordinated the submodels and finally combined the submodels in the form of weights to obtain the final prediction model. On training set PDB1075, the prediction results of our model were better than those of most existing methods. Independent tests are conducted on the datasets PDB186 and PDB2272. The accuracy of the prediction results was 85.5% and 79.36%, respectively. This result exceeded the current state-of-the-art methods, which showed that the multi-view LSSVM via HSIC can be used as an efficient predictor.

TMEM106B Acts as a Modifier of Cognitive and Motor Functions in Amyotrophic Lateral Sclerosis

The transmembrane protein 106B (TMEM106B) gene is a susceptibility factor and disease modifier of frontotemporal dementia, but few studies have investigated its role in amyotrophic lateral sclerosis. The aim of this work was to assess the impact of the TMEM106B rs1990622 (A–major risk allele; G–minor allele) on phenotypic variability of 865 patients with amyotrophic lateral sclerosis. Demographic and clinical features were compared according to genotypes by additive, dominant, and recessive genetic models. Bulbar onset was overrepresented among carriers of the AA risk genotype, together with enhanced upper motor neuron involvement and poorer functional status in patients harboring at least one major risk allele (A). In a subset of 195 patients, we found that the homozygotes for the minor allele (GG) showed lower scores at the Edinburgh Cognitive and Behavioral Amyotrophic Lateral Sclerosis Screen, indicating a more severe cognitive impairment, mainly involving the amyotrophic lateral sclerosis-specific cognitive functions and memory. Moreover, lower motor neuron burden predominated among patients with at least one minor allele (G). Overall, we found that TMEM106B is a disease modifier of amyotrophic lateral sclerosis, whose phenotypic effects encompass both sites of onset and functional status (major risk allele), motor functions (both major risk and minor alleles), and cognition (minor allele).

Genetic variant rs11136000 upregulates clusterin expression and reduces Alzheimer's disease risk

Clusterin (CLU) is an extracellular chaperone involved in reducing amyloid beta (Aβ) toxicity and aggregation. Although previous genome-wide association studies (GWAS) have reported a potential protective effect of CLU on Alzheimer's disease (AD) patients, how intron-located rs11136000 (CLU) affects AD risk by regulating CLU expression remains unknown. In this study, we integrated multiple omics data to construct the regulated pathway of rs11136000-CLU-AD. In step 1, we investigated the effects of variant rs11136000 on AD risk with different genders and diagnostic methods using GWAS summary statistics for AD from International Genomics of Alzheimer's Project (IGAP) and UK Biobank. In step 2, we assessed the regulation of rs11136000 on CLU expression in AD brain samples from Mayo clinic and controls from Genotype-Tissue Expression (GTEx). In step 3, we investigated the differential gene/protein expression of CLU in AD and controls from four large cohorts. The results showed that rs11136000 T allele reduced AD risk in either clinically diagnosed or proxy AD patients. By using expression quantitative trait loci (eQTL) analysis, rs11136000 variant downregulated CLU expression in 13 normal brain tissues, but upregulated CLU expression in cerebellum and temporal cortex of AD samples. Importantly, CLU was significantly differentially expressed in temporal cortex, dorsolateral prefrontal cortex and anterior prefrontal cortex of AD patients compared with normal controls. Together, rs11136000 may reduce AD risk by regulating CLU expression, which may provide important information about the biological mechanism of rs9848497 in AD progress.

Analysis of Genetic Association Between ABCA7 Polymorphism and Alzheimer’s Disease Risk in the Southern Chinese Population

Objective

The study aimed to clarify the association of the 21 single nucleotide polymorphisms (SNPs) with Alzheimer’s disease (AD) in the population of southern China.

Methods

A case-control study was conducted with a total sample size of 490 subjects (246 patients with AD and 244 age- and gender-matched healthy controls) enrolled in this study. Twenty-one selected SNPs were detected using SNaPshot assay and polymerase chain reaction (PCR) technique. Then, we assessed how these SNPs correlated with AD susceptibility.

Results

The results showed that rs3764650 of ABCA7 was closely correlated with risen AD morbidity in the allele [P = 0.010, odds ratio (OR) = 1.43, 95% confidence interval (CI) 1.09–1.89], dominant (P = 0.004, OR = 1.71, 95% CI 1.19–2.46), and additive (P = 0.012, OR = 1.42, 95% CI 1.08–1.86) models. However, rs4147929 of ABCA7 was related to higher AD risk in the allele (P = 0.006, OR = 1.45, 95% CI 1.11–1.89), dominant (P = 0.012, OR = 1.59, 95% CI 1.11–2.27), and additive (P = 0.010, OR = 1.40, 95% CI 1.08–1.81) models. In addition, the frequencies of the G-allele at rs3764650 (P = 0.030) and the A-allele at rs4147929 (P = 0.001) in AD were statistically higher in APOE ε4 carriers in comparison to non-carriers.

Conclusion

This study demonstrated that the G-allele at rs3764650 and the A-allele at rs4147929 appeared at higher risk for developing AD, particularly in APOE ε4 carriers. Moreover, it was observed that rs3764650 and rs4147929 of ABCA7 were linked to AD. More in-depth research with a relatively large sample is needed to make the results more convincing.

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Enhancers are a class of noncoding DNA elements located near structural genes. In recent years, their identification and classification have been the focus of research in the field of bioinformatics. However, due to their high free scattering and position variability, although the performance of the prediction model has been continuously improved, there is still a lot of room for progress. In this paper, density-based spatial clustering of applications with noise (DBSCAN) was used to screen the physicochemical properties of dinucleotides to extract dinucleotide-based auto-cross covariance (DACC) features; then, the features are reduced by feature selection Python toolkit MRMD 2.0. The reduced features are input into the random forest to identify enhancers. The enhancer classification model was built by word2vec and attention-based Bi-LSTM. Finally, the accuracies of our enhancer identification and classification models were 77.25% and 73.50%, respectively, and the Matthews' correlation coefficients (MCCs) were 0.5470 and 0.4881, respectively, which were better than the performance of most predictors.

Review of bioinformatics in Azheimer's Disease Research

Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods.

Bioinformatics analysis methods for cell-free DNA

As a kind of novel non-invasive marker for molecular detection, cell-free DNA (cfDNA) has potential value for the early diagnosis of diseases, prognosis assessment, and efficacy monitoring. The constant developments in molecular biology detection technologies have led to an increase in clinical studies on the use of cfDNA detection methods for patients, and many gratifying outcomes have been achieved. In this review, the contributions of bioinformatics tools to the study of cfDNA are well discussed. The focus of the review is on cfDNA identification signals, cfDNA identification methods, and the relationship of cfDNA with human diseases such as hepatic cancer, lung cancer, end-stage kidney disease, and ischemic stroke. The research significance and existing problems of using cfDNA as a biomarker for diseases are also discussed.

RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs

Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations.

Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX

BACKGROUND

Until now, Mendelian randomization (MR) studies have investigated the causal association of risk factors with Alzheimer's disease (AD) using large-scale AD genome-wide association studies (GWAS), GWAS by proxy (GWAX), and meta-analyses of GWAS and GWAX (GWAS+GWAX) datasets. However, it currently remains unclear about the consistency of MR estimates across these GWAS, GWAX, and GWAS+GWAX datasets.

METHODS

Here, we first selected 162 independent educational attainment genetic variants as the potential instrumental variables (N = 405,072). We then selected one AD GWAS dataset (N = 63,926), two AD GWAX datasets (N = 314,278 and 408,942), and three GWAS+GWAX datasets (N = 388,324, 455,258, and 472,868). Finally, we conducted a MR analysis to evaluate the impact of educational attainment on AD risk across these datasets. Meanwhile, we tested the genetic heterogeneity of educational attainment genetic variants across these datasets.

RESULTS

In AD GWAS dataset, MR analysis showed that each SD increase in years of schooling (about 3.6 years) was significantly associated with 29% reduced AD risk (OR=0.71, 95% CI: 0.60-0.84, and P=1.02E-04). In AD GWAX dataset, MR analysis highlighted that each SD increase in years of schooling significantly increased 84% AD risk (OR=1.84, 95% CI: 1.59-2.13, and P=4.66E-16). Meanwhile, MR analysis suggested the ambiguous findings in AD GWAS+GWAX datasets. Heterogeneity test indicated evidence of genetic heterogeneity in AD GWAS and GWAX datasets.

CONCLUSIONS

We highlighted significant difference and genetic heterogeneity in clinically diagnosed AD GWAS and self-report proxy phenotype GWAX. Our MR findings are consistent with recent findings in AD genetic variants. Hence, the GWAX and GWAS+GWAX findings and MR findings from GWAX and GWAS+GWAX should be carefully interpreted and warrant further investigation using the AD GWAS dataset.

A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning

Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Comprehensive Analysis Revealed that CDKN2A is a Biomarker for Immune Infiltrates in Multiple Cancers

The CDKN2A (cyclin dependent kinase inhibitor 2A/multiple tumor suppressor 1) gene, also known as the P16 gene, encodes multiple tumor suppressor 1 (MTS1), which belongs to the INK4 family. In tumor tissue, CDKN2A has a high expression level compared with normal tissue and reflects prognosis in tumor patients. Our research targeted the analysis of CDKN2A expression in 33 tumors and clinical parameters, patient prognosis and tumor immunity roles. The CDKN2A expression level was significantly correlated with the tumor mutation burden (TMB) in 10 tumors, and the expression of CDKN2A was also correlated with MSI (microsatellite instability) in 10 tumors. CDKN2A expression was associated with infiltrating lymphocyte (TIL) levels in 22 pancancers, thus suggesting that CDKN2A expression is associated with tumor immunity. Enrichment analysis indicated that CDKN2A expression was involved in natural killer cell-mediated cytotoxicity pathways, antigen processing and presentation, olfactory transduction pathways, and regulation of the autophagy pathway in multiple cancers. CDKN2A was significantly associated with several immune cell infiltrates in pantumors. CDKN2A may serve as a promising prognostic biomarker and is associated with immune infiltrates across cancers.

Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease 2019 (COVID-19) since December 2019 that has led to more than 160 million confirmed cases, including 3.3 million deaths. To understand the mechanism by which SARS-CoV-2 invades human cells and reveal organ-specific susceptible cell types for COVID-19, we conducted comprehensive bioinformatic analysis using public single-cell RNA sequencing datasets. Utilizing the expression information of six confirmed COVID-19 receptors (ACE2, TMPRSS2, NRP1, AXL, FURIN and CTSL), we demonstrated that macrophages are the most likely cells that may be associated with SARS-CoV-2 pathogenesis in lung. Besides the widely reported 'chemokine storm', we identified ribosome related pathways that may also be potential therapeutic target for COVID-19 lung infection patients. Moreover, cell-cell communication analysis and trajectory analysis revealed that M1-like macrophages showed the highest relation to severe COVID-19 patients. And we also demonstrated that up-regulation of chemokine pathways generally lead to severe symptoms, while down-regulation of ribosome and RNA activity related pathways are more likely to be mild. Other organ-specific susceptible cell type analyses could also provide potential targets for COVID-19 therapy. This work can provide clues for understanding the pathogenesis of COVID-19 and contribute to understanding the mechanism by which SARS-CoV-2 invades human cells.

DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.