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

Browsing by Subject "Systems biology"

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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.
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.
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.
L-BAIBA Synergizes with Sub-Optimal Mechanical Loading to Promote New Bone Formation
(Wiley, 2023-04-24) Prideaux, Matt; Smargiassi, Alberto; Peng, Gang; Brotto, Marco; Robling, Alexander G.; Bonewald, Lynda F.; Anatomy, Cell Biology and Physiology, School of Medicine
The L-enantiomer of β-aminoisobutyric acid (BAIBA) is secreted by contracted muscle in mice, and exercise increases serum levels in humans. In mice, L-BAIBA reduces bone loss with unloading, but whether it can have a positive effect with loading is unknown. Since synergism can be more easily observed with sub-optimal amounts of factors/stimulation, we sought to determine whether L-BAIBA could potentiate the effects of sub-optimal loading to enhance bone formation. L-BAIBA was provided in drinking water to C57Bl/6 male mice subjected to either 7 N or 8.25 N of sub-optimal unilateral tibial loading for 2 weeks. The combination of 8.25 N and L-BAIBA significantly increased the periosteal mineral apposition rate and bone formation rate compared to loading alone or BAIBA alone. Though L-BAIBA alone had no effect on bone formation, grip strength was increased, suggesting a positive effect on muscle function. Gene expression analysis of the osteocyte-enriched bone showed that the combination of L-BAIBA and 8.25 N induced the expression of loading-responsive genes such as Wnt1, Wnt10b, and the TGFb and BMP signaling pathways. One dramatic change was the downregulation of histone genes in response to sub-optimal loading and/or L-BAIBA. To determine early gene expression, the osteocyte fraction was harvested within 24 hours of loading. A dramatic effect was observed with L-BAIBA and 8.25 N loading as genes were enriched for pathways regulating the extracellular matrix (Chad, Acan, Col9a2), ion channel activity (Scn4b, Scn7a, Cacna1i), and lipid metabolism (Plin1, Plin4, Cidec). Few changes in gene expression were observed with sub-optimal loading or L-BAIBA alone after 24 hours. These results suggest that these signaling pathways are responsible for the synergistic effects between L-BAIBA and sub-optimal loading. Showing that a small muscle factor can enhance the effects of sub-optimal loading of bone may be of relevance for individuals unable to benefit from optimal exercise.
Mathematical modeling of the effects of Wnt-10b on bone metabolism
(Wiley, 2022) Cook, Carley V.; Islam, Mohammad Aminul; Smith, Brenda J.; Ford Versypt, Ashlee N.; Obstetrics and Gynecology, School of Medicine
Bone health is determined by factors including bone metabolism or remodeling. Wnt-10b alters osteoblastogenesis through pre-osteoblast proliferation and differentiation and osteoblast apoptosis rate, which collectively lead to the increase of bone density. To model this, we adapted a previously published model of bone remodeling. The resulting model for the bone compartment includes differential equations for active osteoclasts, pre-osteoblasts, osteoblasts, osteocytes, and the amount of bone present at the remodeling site. Our alterations to the original model consist of extending it past a single remodeling cycle and implementing a direct relationship to Wnt-10b. Four new parameters were estimated and validated using normalized data from mice. The model connects Wnt-10b to bone metabolism and predicts the change in trabecular bone volume caused by a change in Wnt-10b input. We find that this model predicts the expected increase in pre-osteoblasts and osteoblasts while also pointing to a decrease in osteoclasts when Wnt-10b is increased.
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
(Springer Nature, 2021-06-08) Wang, Tongxin; Shao, Wei; Huang, Zhi; Tang, Haixu; Zhang, Jie; Ding, Zhengming; Huang, Kun; Medicine, School of Medicine
To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Browsing by Subject "Systems biology"

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