IU Indianapolis ScholarWorks :: Browsing by Subject "Systems biology"

Browsing by Subject "Systems biology"

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An induced pluripotent stem cell model of Schwann cell differentiation reveals NF2- related gene regulatory networks
(Research Square, 2025-06-16) Lazaro, Olivia; Li, Sihong; Carter, William; Smiley, Jake; Awosika, Oluwamayowa; Robertson, Sylvia; Haskell, Angela; Hinkel, Raven; Hickey, Brooke E.; Angus, Steven P.; House, Austin; Clapp, D. Wade; Syed, Abdul Q.; Johnson, Travis S.; Rhodes, Steven D.; Pediatrics, School of Medicine
Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2-related schwannomatosis (NF2-SWN). We have developed a novel human induced pluripotent stem cell (hiPSC) model for the study of Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To further validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we have identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. Through integrated analysis of multiple datasets and genetic disruption of NF2 via CRISPR-Cas9 gene editing in hiPSC derived SCPs, we have identified a series of novel ECM associated genes regulated by Merlin. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of rare diseases such as NF2-SWN which lack effective medical therapies.
Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer
(American Society for Biochemistry and Molecular Biology, 2019-08-09) Zhan, Xiaohui; Cheng, Jun; Huang, Zhi; Han, Zhi; Helm, Bryan; Liu, Xiaowen; Zhang, Jie; Wang, Tian-Fu; Ni, Dong; Huang, Kun; Medicine, School of Medicine
Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.
d-Cystine di(m)ethyl ester reverses the deleterious effects of morphine on ventilation and arterial blood gas chemistry while promoting antinociception
(Springer Nature, 2021-05-11) Gaston, Benjamin; Baby, Santhosh M.; May, Walter J.; Young, Alex P.; Grossfield, Alan; Bates, James N.; Seckler, James M.; Wilson, Christopher G.; Lewis, Stephen J.; Pediatrics, School of Medicine
We have identified thiolesters that reverse the negative effects of opioids on breathing without compromising antinociception. Here we report the effects of d-cystine diethyl ester (d-cystine diEE) or d-cystine dimethyl ester (d-cystine diME) on morphine-induced changes in ventilation, arterial-blood gas chemistry, A-a gradient (index of gas-exchange in the lungs) and antinociception in freely moving rats. Injection of morphine (10 mg/kg, IV) elicited negative effects on breathing (e.g., depression of tidal volume, minute ventilation, peak inspiratory flow, and inspiratory drive). Subsequent injection of d-cystine diEE (500 μmol/kg, IV) elicited an immediate and sustained reversal of these effects of morphine. Injection of morphine (10 mg/kg, IV) also elicited pronounced decreases in arterial blood pH, pO2 and sO2 accompanied by pronounced increases in pCO2 (all indicative of a decrease in ventilatory drive) and A-a gradient (mismatch in ventilation-perfusion in the lungs). These effects of morphine were reversed in an immediate and sustained fashion by d-cystine diME (500 μmol/kg, IV). Finally, the duration of morphine (5 and 10 mg/kg, IV) antinociception was augmented by d-cystine diEE. d-cystine diEE and d-cystine diME may be clinically useful agents that can effectively reverse the negative effects of morphine on breathing and gas-exchange in the lungs while promoting antinociception. Our study suggests that the d-cystine thiolesters are able to differentially modulate the intracellular signaling cascades that mediate morphine-induced ventilatory depression as opposed to those that mediate morphine-induced antinociception and sedation.
Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer’s Disease
(Sage, 2024) Xie, Linhui; Raj, Yash; Varathan, Pradeep; He, Bing; Yu, Meichen; Nho, Kwangsik; Salama, Paul; Saykin, Andrew J.; Yan, Jingwen; Electrical and Computer Engineering, Purdue School of Engineering and Technology
Background: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.
Editorial: Transplant Rejection and Tolerance—Advancing the Field through Integration of Computational and Experimental Investigation
(Frontiers Media S.A., 2017-05-30) Raimondi, Giorgio; Wood, Kathryn J.; Perelson, Alan S.; Arciero, Julia C.; Mathematical Sciences, School of Science
Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer
(Frontiers Media, 2023-06-12) Yang, Grace; Huang, Shaoyang; Hu, Kevin; Lu, Alex; Yang, Jonathan; Meroueh, Noah; Dang, Pengtao; Wang, Yijie; Zhu, Haiqi; Cao, Sha; Zhang, Chi; Electrical and Computer Engineering, School of Engineering and Technology
Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.
From genes to networks: in systematic points of view
(Springer Nature, 2011) Zhang, Ke; Liu, Yunlong; Yang, Jack Y.; Arabnia, Hamid R.; Niemierko, Andrzej; Ghafoor, Arif; Li, Weizhong; Deng, Youping; Medical and Molecular Genetics, School of Medicine
We present a report of the BIOCOMP'10 - The 2010 International Conference on Bioinformatics & Computational Biology and other related work in the area of systems biology.
INTEGRATIVE SYSTEM BIOLOGY STUDIES ON HIGH THROUGHPUT GENOMICS AND PROTEOMICS DATASET
(2012-03-20) Sonachalam, Madhankumar; Chen, Jake Yue; Shen, Li; Zhou, Yaoqi
The post genomic era has propelled us to the view that the biological systems are complex network of interacting genes, proteins and small molecules that give rise to biological form and function. The past decade has seen the advent of number of new technologies designed to study the biological systems on a genome wide scale. These new technologies offers an insight in to the activity of thousands of genes and proteins in cell thereby changed the conventional reductionist view of the systems. However the deluge of data surpasses the analytical and critical abilities of the researches and thereby demands the development of new computational methods. The challenge no longer lies in the acquisition of expression profiles, but rather in the interpretation for the results to gain insights into biological mechanisms. In three different case studies, we applied various system biology techniques on publicly available and in-house genomics and proteomics data set to identify sub-network signatures. In First study, we integrated prior knowledge from gene signatures, GSEA and gene/protein network modeling to identify pathways involved in colorectal cancer, while in second, we identified plasma based network signatures for Alzheimer's disease by combining various feature selection and classification approach. In final study, we did an integrated miRNA-mRNA analysis to identify the role of Myeloid Derived Stem Cells (MDSCs) in T-Cell suppression.
Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease
(Oxford University Press, 2021) Xie, Linhui; He, Bing; Varathan, Pradeep; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Salama, Paul; Yan, Jingwen; BioHealth Informatics, School of Informatics and Computing
A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.
The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases
(BMC, 2019-12-20) Zhao, Zhongming; Dai, Yulin; Zhang, Chi; Mathé, Ewy; Wei, Lai; Wang, Kai; Medical and Molecular Genetics, School of Medicine
Between June 9–11, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics.

Browsing by Subject "Systems biology"

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