A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

Ningappa, Mylarappa; Rahman, Syed Ashiqur; Higgs, Brandon W.; Ashokkumar, Chethan; Sahni, Nidhi; Sindhi, Rakesh; Das, Jishnu

doi:10.1016/j.xcrm.2022.100605

Cited by 8 publications

(7 citation statements)

References 44 publications

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“… 68 Network approaches have also been used in conjunction with gene expression data-identified molecular networks underlying rejection in pediatric liver transplant. 69 Similar modules have been discovered in different autoimmune diseases using a trait-based network propagation approach where six gene modules were found to be enriched with at least two groups of traits associated with inflammatory bowel disease (IBD), multiple sclerosis, and systemic lupus. 70 A concerted effort has been made to generate PPI networks between host and different viruses (Influenza, HIV, SARS-CoV-2, etc.…”

Section: Machine Learning Approaches That Leverage Biological Priorsmentioning

confidence: 78%

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

Xiao,

Rosen,

Chhibbar

et al. 2023

Human Vaccines & Immunotherapeutics

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A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

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Section: Machine Learning Approaches That Leverage Biological Priorsmentioning

confidence: 78%

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

Xiao,

Rosen,

Chhibbar

et al. 2023

Human Vaccines & Immunotherapeutics

Self Cite

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“…We also developed another complementary approach that combines transcriptomic data with the modularity of the underlying protein interactome network to identify expression modules that underlie a clinical outcome of interest (rejection in the context of pediatric liver transplantation). 117 The same framework can be extrapolated to asthma multi-omic data. Overall, these inference frameworks without or with prior knowledge are complementary; while the former does not require any priors, the latter leverages higher-order structures in pathways or prior-knowledge biological networks.…”

Section: Machine Learning Tools To Improve Clinical Care Of Asthmamentioning

confidence: 98%

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

Ray¹,

Das²,

Wenzel³

2022

Cell Reports Medicine

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“…Similar findings have been noted in pediatric liver transplant. Integrating RNA and protein networks from the peripheral immune system before transplant identified multi-omic networks that predict rejection [53 ▪▪ ]. Importantly, molecular profiles of peripheral immune cells have previously been tied to postoperative infection risk and multi-organ dysfunction, suggesting they can be used predict and monitor patients throughout the perioperative period [54,55,56 ▪ ,57].…”

Section: Applying Network Biology In Liver Transplant Patientsmentioning

confidence: 99%

Improving liver transplant outcomes with transplant-omics and network biology

Scarpa

2023

Current Opinion in Organ Transplantation

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Purpose of review Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the ‘transplant-ome’ – the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients. Recent findings In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period. Summary The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

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A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation

Cited by 8 publications

References 44 publications

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

Improving liver transplant outcomes with transplant-omics and network biology

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