Browsing by Subject "Network analysis"
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Item Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease(Bentham Science, 2019) Li, Jin; Chen, Feng; Zhang, Qiushi; Meng, Xianglian; Yao, Xiaohui; Risacher, Shannon L.; Yan, Jingwen; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of MedicineBackground: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. Objective: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. Methods: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. Results: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. Conclusion: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.Item Genome-wide network-based pathway analysis of CSF t-tau/Aβ1-42 ratio in the ADNI cohort(Springer Nature, 2017-05-30) Cong, Wang; Meng, Xianglian; Li, Jin; Zhang, Qiushi; Chen, Feng; Liu, Wenjie; Wang, Ying; Cheng, Sipu; Yao, Xiaohui; Yan, Jingwen; Kim, Sungeun; Saykin, Andrew J.; Liang, Hong; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBACKGROUND: The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS: The CSF and genotyping data of 843 study subjects (199 CN, 85 SMC, 239 EMCI, 207 LMCI, 113 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. PLINK was used to perform GWAS on the t-tau/Aβ1-42 ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS: This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.Item Loss of Inpp5d has disease‐relevant and sex‐specific effects on glial transcriptomes(Wiley, 2024) Dabin, Luke C.; Kersey, Holly; Kim, Byungwook; Acri, Dominic J.; Sharify, Daniel; Lee-Gosselin, Audrey; Lasagna-Reeves, Cristian A.; Oblak, Adrian L.; Lamb, Bruce T.; Kim, Jungsu; Medical and Molecular Genetics, School of MedicineIntroduction: Inpp5d is genetically associated with Alzheimer's disease risk. Loss of Inpp5d alters amyloid pathology in models of amyloidosis. Inpp5d is expressed predominantly in microglia but its function in brain is poorly understood. Methods: We performed single-cell RNA sequencing to study the effect of Inpp5d loss on wild-type mouse brain transcriptomes. Results: Loss of Inpp5d has sex-specific effects on the brain transcriptome. Affected genes are enriched for multiple neurodegeneration terms. Network analyses reveal a gene co-expression module centered around Inpp5d in female mice. Inpp5d loss alters Pleotrophin (PTN), Prosaposin (PSAP), and Vascular Endothelial Growth Factor A (VEGFA) signaling probability between cell types. Discussion: Our data suggest that the normal function of Inpp5d is entangled with mechanisms involved in neurodegeneration. We report the effect of Inpp5d loss without pathology and show that this has dramatic effects on gene expression. Our study provides a critical reference for researchers of neurodegeneration, allowing separation of disease-specific changes mediated by Inpp5d in disease from baseline effects of Inpp5d loss. Highlights: Loss of Inpp5d has different effects in male and female mice. Genes dysregulated by Inpp5d loss relate to neurodegeneration. Total loss of Inpp5d in female mice collapses a conserved gene co-expression module. Loss of microglial Inpp5d affects the transcriptome of other cell types.Item Mapping Diversity, Equity, and Inclusion research in medical education journals: An exploratory bibliometric analysis, 2018-2022(2023-05-16) Ramirez, Mirian; Dolan, LeviObjectives: More medical schools are incorporating Diversity, Equity, and Inclusion (DEI) competencies into their institutional practices as the academic medicine community support programs and projects designed to build cultural humility in medical education, clinical care, and research. This study aims to identify and analyze items covering DEI-related topics published in medical education journals in the last five years (2018-2022). We performed a bibliometric analysis investigating the overall patterns of published articles, including the annual publication distribution, distribution by journal, and analysis of keywords. This approach aims to contribute to a better understanding of the characteristics of DEI-related research in medical education. Methods: We conducted a bibliometric analysis on articles published in a set of 56 core medical education journals indexed in Pubmed from 2018 to 2022. The searches were conducted in January 2023. To retrieve and gather citations for published articles, we used the Pubmed database. First, we used the NLM catalog to search and identify the subset of journals that are referenced in NCBI database records classified with “Education,Medical”[Mesh] OR "education"[MeSH Subheading] in the English language. Next, we developed and executed a comprehensive search to find articles that contained terms related to DEI; we used the Association of American Medical Colleges (AAMC) "Diversity, Equity, and Inclusion Competencies Across the Learning Continuum" ([LINK]https://store.aamc.org/downloadable/download/sample/sample_id/512/[/LINK]) as a guide to identifying the terminology to use in the search. We used Excel to aggregate, clean up and analyze the data,and VOSviewer software was utilized to create the topic analysis and visualization map. Results: For the 2018-2022 time frame, 3,158 (out of 33,395, 9.5%) publications covering DEI topics were published in the selected journals. We will undertake term co-occurrence analysis on the keywords in the abstract as well as publishing trend analysis, source analysis, and overall dataset analysis as part of our study. Conclusions: This poster will show an examination of articles about DEI subjects written in medical education journals over the previous five years and indexed in Pubmed. We will outline the characteristics of the top journals that published the research and topic trends of articles. The findings may support researchers and faculty in the health sciences disciplines when making decisions for developing a publication and research strategy. Researchers who seek DEI pathways to meet their promotion and tenure criteria will also benefit from understanding the results of this analysis.Item Mapping longitudinal scientific progress, collaboration and impact of the Alzheimer’s disease neuroimaging initiative(PLOS, 2017-11-02) Yao, Xiaohui; Yan, Jingwen; Ginda, Michael; Börner, Katy; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of MedicineBackground Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD.Item Network analysis of pseudogene-gene relationships: from pseudogene evolution to their functional potentials(Pacific Symposium on Biocomputing, 2018) Johnson, Travis S.; Li, Sihong; Kho, Jonathan R.; Huang, Kun; Zhang, Yan; Medicine, School of MedicinePseudogenes are fossil relatives of genes. Pseudogenes have long been thought of as "junk DNAs", since they do not code proteins in normal tissues. Although most of the human pseudogenes do not have noticeable functions, ∼20% of them exhibit transcriptional activity. There has been evidence showing that some pseudogenes adopted functions as lncRNAs and work as regulators of gene expression. Furthermore, pseudogenes can even be "reactivated" in some conditions, such as cancer initiation. Some pseudogenes are transcribed in specific cancer types, and some are even translated into proteins as observed in several cancer cell lines. All the above have shown that pseudogenes could have functional roles or potentials in the genome. Evaluating the relationships between pseudogenes and their gene counterparts could help us reveal the evolutionary path of pseudogenes and associate pseudogenes with functional potentials. It also provides an insight into the regulatory networks involving pseudogenes with transcriptional and even translational activities.In this study, we develop a novel approach integrating graph analysis, sequence alignment and functional analysis to evaluate pseudogene-gene relationships, and apply it to human gene homologs and pseudogenes. We generated a comprehensive set of 445 pseudogene-gene (PGG) families from the original 3,281 gene families (13.56%). Of these 438 (98.4% PGG, 13.3% total) were non-trivial (containing more than one pseudogene). Each PGG family contains multiple genes and pseudogenes with high sequence similarity. For each family, we generate a sequence alignment network and phylogenetic trees recapitulating the evolutionary paths. We find evidence supporting the evolution history of olfactory family (both genes and pseudogenes) in human, which also supports the validity of our analysis method. Next, we evaluate these networks in respect to the gene ontology from which we identify functions enriched in these pseudogene-gene families and infer functional impact of pseudogenes involved in the networks. This demonstrates the application of our PGG network database in the study of pseudogene function in disease context.Item PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers(Oxford University Press, 2019-05) Johnson, Travis S.; Li, Sihong; Franz, Eric; Huang, Zhi; Dan Li, Shuyu; Campbell, Moray J.; Huang, Kun; Zhang, Yan; Medicine, School of MedicineBACKGROUND: Long thought "relics" of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene-parent gene relationships without leveraging other homologous genes/pseudogenes. RESULTS: We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four "flavors" of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a "one stop shop" for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. CONCLUSIONS: Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike.Item Pseudogene-gene functional networks are prognostic of patient survival in breast cancer(BMC, 2020) Smerekanych, Sasha; Johnson, Travis S.; Huang, Kun; Zhang, Yan; Medicine, School of MedicineBackground: Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. Results: In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. Conclusions: We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.