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Soleymani F, Paquet E, Viktor HL, Michalowski W. Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2779-2797. [PMID: 39050782 PMCID: PMC11268121 DOI: 10.1016/j.csbj.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/13/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.
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Affiliation(s)
- Farzan Soleymani
- Telfer School of Management, University of Ottawa, ON, K1N 6N5, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, K1N 6N5, Canada
| | - Herna Lydia Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, K1N 6N5, Canada
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Sun F, Xiao M, Ji D, Zheng F, Shi T. Deciphering potential causative factors for undiagnosed Waardenburg syndrome through multi-data integration. Orphanet J Rare Dis 2024; 19:226. [PMID: 38844942 PMCID: PMC11155130 DOI: 10.1186/s13023-024-03220-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 05/19/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND Waardenburg syndrome (WS) is a rare genetic disorder mainly characterized by hearing loss and pigmentary abnormalities. Currently, seven causative genes have been identified for WS, but clinical genetic testing results show that 38.9% of WS patients remain molecularly unexplained. In this study, we performed multi-data integration analysis through protein-protein interaction and phenotype-similarity to comprehensively decipher the potential causative factors of undiagnosed WS. In addition, we explored the association between genotypes and phenotypes in WS with the manually collected 443 cases from published literature. RESULTS We predicted two possible WS pathogenic genes (KIT, CHD7) through multi-data integration analysis, which were further supported by gene expression profiles in single cells and phenotypes in gene knockout mouse. We also predicted twenty, seven, and five potential WS pathogenic variations in gene PAX3, MITF, and SOX10, respectively. Genotype-phenotype association analysis showed that white forelock and telecanthus were dominantly present in patients with PAX3 variants; skin freckles and premature graying of hair were more frequently observed in cases with MITF variants; while aganglionic megacolon and constipation occurred more often in those with SOX10 variants. Patients with variations of PAX3 and MITF were more likely to have synophrys and broad nasal root. Iris pigmentary abnormality was more common in patients with variations of PAX3 and SOX10. Moreover, we found that patients with variants of SOX10 had a higher risk of suffering from auditory system diseases and nervous system diseases, which were closely associated with the high expression abundance of SOX10 in ear tissues and brain tissues. CONCLUSIONS Our study provides new insights into the potential causative factors of WS and an alternative way to explore clinically undiagnosed cases, which will promote clinical diagnosis and genetic counseling. However, the two potential disease-causing genes (KIT, CHD7) and 32 potential pathogenic variants (PAX3: 20, MITF: 7, SOX10: 5) predicted by multi-data integration in this study are all computational predictions and need to be further verified through experiments in follow-up research.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Dong Ji
- Department of Otolaryngology, Head and Neck Surgery, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Feng Zheng
- Wuhu Hospital and Health Science Center, East China Normal University, Shanghai, 200241, China
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, the Institute of Biomedical Sciences and the School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Beijing Advanced Innovation Center, for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing, 100083, China.
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3
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Yao D, Mei S, Tang W, Xu X, Lu Q, Shi Z. AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA). PLoS One 2023; 18:e0289966. [PMID: 38100461 PMCID: PMC10723669 DOI: 10.1371/journal.pone.0289966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/31/2023] [Indexed: 12/17/2023] Open
Abstract
Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.
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Affiliation(s)
- Di Yao
- Institute of Industrial Internet and Internet of Things, China Academy of Information and Communications Technology (CAICT), China
| | - Shuyuan Mei
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Wangyang Tang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Xingyu Xu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Qiulun Lu
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
| | - Zhiguang Shi
- Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China
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Gupta MK, Gouda G, Sultana S, Punekar SM, Vadde R, Ravikiran T. Structure-related relationship: Plant-derived antidiabetic compounds. STUDIES IN NATURAL PRODUCTS CHEMISTRY 2023:241-295. [DOI: 10.1016/b978-0-323-91294-5.00008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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5
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Rout M, Kour B, Vuree S, Lulu SS, Medicherla KM, Suravajhala P. Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks. World J Clin Cases 2022; 10:5957-5964. [PMID: 35949812 PMCID: PMC9254192 DOI: 10.12998/wjcc.v10.i18.5957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/02/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023] Open
Abstract
An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, University of Oklahoma Health Sciences Centre, Oklahoma City, OK 73104, United States
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Bhumandeep Kour
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sajitha S Lulu
- Department of Biotechnology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Prashanth Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Vallikavu PO, Amritapuri, Clappana, Kollam 690525, Kerala, India
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6
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Zainal-Abidin RA, Afiqah-Aleng N, Abdullah-Zawawi MR, Harun S, Mohamed-Hussein ZA. Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow. Life (Basel) 2022; 12:life12050650. [PMID: 35629318 PMCID: PMC9143887 DOI: 10.3390/life12050650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/04/2022] Open
Abstract
Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.
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Affiliation(s)
| | - Nor Afiqah-Aleng
- Institute of Marine Biotechnology, Universiti Malaysia Terengganu, Kuala Nerus 21030, Malaysia
- Correspondence: (N.A.-A.); (Z.-A.M.-H.)
| | | | - Sarahani Harun
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia;
| | - Zeti-Azura Mohamed-Hussein
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia;
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Correspondence: (N.A.-A.); (Z.-A.M.-H.)
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Song J, Peng W, Wang F. An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:758-768. [PMID: 30763245 DOI: 10.1109/tcbb.2019.2897931] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.
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8
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Gupta MK, Vadde R. Applications of Computational Biology in Gastrointestinal Malignancies. IMMUNOTHERAPY FOR GASTROINTESTINAL MALIGNANCIES 2020:231-251. [DOI: 10.1007/978-981-15-6487-1_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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9
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Song J, Peng W, Wang F. A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph. BMC Bioinformatics 2019; 20:238. [PMID: 31088372 PMCID: PMC6518800 DOI: 10.1186/s12859-019-2847-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 04/24/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. RESULTS Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. CONCLUSIONS The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. AVAILABILITY The source code can be obtained at https://github.com/weiba/Subdyquency .
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Affiliation(s)
- Junrong Song
- Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, People's Republic of China
| | - Wei Peng
- Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, People's Republic of China.
| | - Feng Wang
- Faculty of Management and Economics/Computer center/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Lianhua Road, 650050, Kunming, People's Republic of China
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10
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Al-Suhaimi E, Ravinayagam V, Jermy BR, Mohamad T, Elaissari A. Protein/ Hormone Based Nanoparticles as Carriers for Drugs Targeting Protein-Protein Interactions. Curr Top Med Chem 2019; 19:444-456. [PMID: 30836918 DOI: 10.2174/1568026619666190304152320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 01/02/2019] [Accepted: 01/24/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND In this review, protein-protein interactions (PPIs) were defined, and their behaviors in normal in disease conditions are discussed. Their status at nuclear, molecular and cellular level was underscored, as for their interference in many diseases. Finally, the use of protein nanoscale structures as possible carriers for drugs targeting PPIs was highlighted. OBJECTIVE The objective of this review is to suggest a novel approach for targeting PPIs. By using protein nanospheres and nanocapsules, a promising field of study can be emerged. METHODS To solidify this argument, PPIs and their biological significance was discussed, same as their role in hormone signaling. RESULTS We shed the light on the drugs that targets PPI and we suggested the use of nanovectors to encapsulate these drugs to possibly achieve better results. CONCLUSION Protein based nanoparticles, due to their advantages, can be suitable carriers for drugs targeting PPIs. This can open a new opportunity in the emerging field of multifunctional therapeutics.
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Affiliation(s)
- Ebtesam Al-Suhaimi
- Biology Department, College of Science, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
| | - Vijaya Ravinayagam
- Deanship of Scientific Research & Nanomedicine Research Department, Institute of Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
| | - B. Rabindran Jermy
- Nanomedicine Research Department, Institute of Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
| | - Tarhini Mohamad
- University Lyon, University Claude Bernard Lyon-1, CNRS, LAGEP-UMR 5007, F- 69622 Lyon, France
| | - Abdelhamid Elaissari
- University Lyon, University Claude Bernard Lyon-1, CNRS, LAGEP-UMR 5007, F- 69622 Lyon, France
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11
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Gupta MK, Vadde R. Identification and characterization of differentially expressed genes in Type 2 Diabetes using in silico approach. Comput Biol Chem 2019; 79:24-35. [PMID: 30708140 DOI: 10.1016/j.compbiolchem.2019.01.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 12/26/2018] [Accepted: 01/23/2019] [Indexed: 12/14/2022]
Abstract
Diabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.
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Affiliation(s)
- Manoj Kumar Gupta
- Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa 516003, Andhra Pradesh, India.
| | - Ramakrishna Vadde
- Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa 516003, Andhra Pradesh, India.
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12
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Cui H, Zhao N, Korkin D. Multilayer View of Pathogenic SNVs in Human Interactome through In Silico Edgetic Profiling. J Mol Biol 2018; 430:2974-2992. [DOI: 10.1016/j.jmb.2018.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/28/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022]
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Ali Z, Chandrasekera PC, Pippin JJ. Animal research for type 2 diabetes mellitus, its limited translation for clinical benefit, and the way forward. Altern Lab Anim 2018; 46:13-22. [PMID: 29553794 DOI: 10.1177/026119291804600101] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Obesity and type 2 diabetes mellitus (T2DM) have reached pandemic proportions worldwide, and considerable research efforts have been dedicated to investigating disease pathology and therapeutic options. The two hallmark features of T2DM, insulin resistance and pancreatic dysfunction, have been studied extensively by using various animal models. Despite the knowledge acquired from such models, particularly mechanistic discoveries that sometimes mimic human T2DM mechanisms or pathways, many details of human T2DM pathogenesis remain unknown, therapeutic options remain limited, and a cure has eluded research. Emerging human data have raised concern regarding inter-species differences at many levels (e.g. in gene regulation, pancreatic cytoarchitecture, glucose transport, and insulin secretion regulation), and the subsequent impact of these differences on the clinical translation of animal research findings. Therefore, it is important to recognise and address the translational gap between basic animal-based research and the clinical advances needed to prevent and treat T2DM. The purpose of this report is to identify some limitations of T2DM animal research, and to propose how greater human relevance and applicability of hypothesis-driven basic T2DM research could be achieved through the use of human-based data acquisition at various biological levels. This report addresses how in vitro, in vivo and in silico technologies could be used to investigate particular aspects of human glucose regulation. We do not propose that T2DM animal research has been without value in the identification of mechanisms, pathways, or potential targets for therapies, nor do we claim that human-based methods can provide all the answers. We recognise that the ultimate goal of T2DM animal research is to identify ways to advance the prevention, recognition and treatment of T2DM in humans, but postulate that this is where the use of animal models falls short, despite decades of effort. The best way to achieve this goal is by prioritising human-centred research.
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Affiliation(s)
- Zeeshan Ali
- Physicians Committee for Responsible Medicine, Washington, DC, USA
| | | | - John J Pippin
- Physicians Committee for Responsible Medicine, Washington, DC, USA
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14
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Nday CM, Eleftheriadou D, Jackson G. Shared pathological pathways of Alzheimer's disease with specific comorbidities: current perspectives and interventions. J Neurochem 2018; 144:360-389. [PMID: 29164610 DOI: 10.1111/jnc.14256] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 11/10/2017] [Accepted: 11/10/2017] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease (AD) belongs to one of the most multifactorial, complex and heterogeneous morbidity-leading disorders. Despite the extensive research in the field, AD pathogenesis is still at some extend obscure. Mechanisms linking AD with certain comorbidities, namely diabetes mellitus, obesity and dyslipidemia, are increasingly gaining importance, mainly because of their potential role in promoting AD development and exacerbation. Their exact cognitive impairment trajectories, however, remain to be fully elucidated. The current review aims to offer a clear and comprehensive description of the state-of-the-art approaches focused on generating in-depth knowledge regarding the overlapping pathology of AD and its concomitant ailments. Thorough understanding of associated alterations on a number of molecular, metabolic and hormonal pathways, will contribute to the further development of novel and integrated theranostics, as well as targeted interventions that may be beneficial for individuals with age-related cognitive decline.
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Affiliation(s)
- Christiane M Nday
- Department of Chemical Engineering, Laboratory of Inorganic Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Despoina Eleftheriadou
- Department of Chemical Engineering, Laboratory of Inorganic Chemistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Graham Jackson
- Department of Chemistry, University of Cape Town, Rondebosch, Cape Town, South Africa
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Huang X, Liu H, Li X, Guan L, Li J, Tellier LCAM, Yang H, Wang J, Zhang J. Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning. BMC Neurol 2018; 18:5. [PMID: 29320986 PMCID: PMC5763548 DOI: 10.1186/s12883-017-1010-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 12/21/2017] [Indexed: 11/23/2022] Open
Abstract
Background Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions. Methods We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome. Results We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale. Conclusions In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers. Electronic supplementary material The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiaoyan Huang
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China.,BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Hankui Liu
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Xinming Li
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Liping Guan
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Jiankang Li
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China
| | - Laurent Christian Asker M Tellier
- BGI-Shenzhen, Shenzhen, 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China.,Department of Biology, Bioinformatics, University of Copenhagen, Copenhagen, Denmark
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou, 310058, China
| | - Jianguo Zhang
- BGI-Shenzhen, Shenzhen, 518083, China. .,China National GeneBank, BGI-Shenzhen, Shenzhen, 518120, China. .,Shenzhen Key Lab of Neurogenomics, BGI-Shenzhen, Shenzhen, 518120, China.
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16
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Disease gene classification with metagraph representations. Methods 2017; 131:83-92. [DOI: 10.1016/j.ymeth.2017.06.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/23/2017] [Accepted: 06/30/2017] [Indexed: 12/28/2022] Open
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17
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Xu Y, Xia J, Liu S, Stein S, Ramon C, Xi H, Wang L, Xiong X, Zhang L, He D, Yang W, Zhao X, Cheng X, Yang X, Wang H. Endocytosis and membrane receptor internalization: implication of F-BAR protein Carom. Front Biosci (Landmark Ed) 2017; 22:1439-1457. [PMID: 28199211 DOI: 10.2741/4552] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Endocytosis is a cellular process mostly responsible for membrane receptor internalization. Cell membrane receptors bind to their ligands and form a complex which can be internalized. We previously proposed that F-BAR protein initiates membrane curvature and mediates endocytosis via its binding partners. However, F-BAR protein partners involved in membrane receptor endocytosis and the regulatory mechanism remain unknown. In this study, we established database mining strategies to explore mechanisms underlying receptor-related endocytosis. We identified 34 endocytic membrane receptors and 10 regulating proteins in clathrin-dependent endocytosis (CDE), a major process of membrane receptor internalization. We found that F-BAR protein FCHSD2 (Carom) may facilitate endocytosis via 9 endocytic partners. Carom is highly expressed, along with highly expressed endocytic membrane receptors and partners, in endothelial cells and macrophages. We established 3 models of Carom-receptor complexes and their intracellular trafficking based on protein interaction and subcellular localization. We conclude that Carom may mediate receptor endocytosis and transport endocytic receptors to the cytoplasm for receptor signaling and lysosome/proteasome degradation, or to the nucleus for RNA processing, gene transcription and DNA repair.
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Affiliation(s)
- Yanjie Xu
- Center Department of Cardiology, Second Affiliated Hospital of Nanchang University, Nan Chang, Jiang Xi, 330006, China, and Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Jixiang Xia
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Suxuan Liu
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140,and Department of Cardiology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Sam Stein
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Cueto Ramon
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Hang Xi
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Luqiao Wang
- Center Department of Cardiology, Second Affiliated Hospital of Nanchang University, Nan Chang, Jiang Xi, 330006, China, and Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Xinyu Xiong
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Lixiao Zhang
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Dingwen He
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nan Chang, Jiang Xi, 330006, China
| | - William Yang
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140
| | - Xianxian Zhao
- Department of Cardiology, Changhai Hospital, Second Military Medical University, Shanghai, 200433, China
| | - Xiaoshu Cheng
- Center Department of Cardiology, Second Affiliated Hospital of Nanchang University, Nan Chang, Jiang Xi, 330006, China
| | - Xiaofeng Yang
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140, and Cardiovascular Research, Temple University School of Medicine, Philadelphia, PA, 19140, and Thrombosis Research, Temple University School of Medicine
| | - Hong Wang
- Center for Metabolic Disease Research, Temple University School of Medicine, Philadelphia, PA, 19140, and Cardiovascular Research, Temple University School of Medicine, Philadelphia, PA, 19140, and Thrombosis Research, Temple University School of Medicine,
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