Natural variation of macrophage activation as disease-relevant phenotype predictive of inflammation and cancer survival

Buscher, Konrad; Ehinger, Erik; Gupta, Pritha; Pramod, Akula Bala; Wolf, Dennis; Tweet, George; Pan, Calvin; Mills, Charles D.; Lusis, Aldons J.; Ley, Klaus

doi:10.1038/ncomms16041

Cited by 100 publications

(66 citation statements)

References 58 publications

(101 reference statements)

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“…When aggregated together, the mouse model predictions of human sepsis pathways increased in total coverage, but tended to be less precise. This runs contrary to observations that including multiple mouse strains in an experiment and using the increased heterogeneity of the mouse cohort as a surrogate for human disease heterogeneity improved translation of insights from mouse models to human context (29). In the context of the ssANN, increasing the heterogeneity of the mouse cohort in the training set by inclusion of multiple strains resulted in better predictions than when the ssANN was trained on individual mouse sepsis strains, particularly when the human cohort being predicted was itself heterogeneous (Figure 3b).…”

Section: Discussioncontrasting

confidence: 58%

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

Brubaker

Proctor

Haigis

et al. 2018

Preprint

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ABSTRACT28 The high failure rate of therapeutics showing promise in mouse disease models to translate to 29 patients is a pressing challenge in biomedical science. However, mouse models are a useful 30 tool for evaluating mechanisms of disease and prioritizing novel therapeutic agents for clinical 31 trials. Though retrospective studies have examined the fidelity of mouse models of inflammatory 32 disease to their respective human in vivo conditions, approaches for prospective translation of 33 insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-34 supervised learning approach for prospective inference of disease-associated human in vivo 35 differentially expressed genes and pathways from mouse model experiments. We examined 36 36 transcriptomic case studies where comparable phenotypes were available for mouse and 37 human inflammatory diseases and assessed multiple computational approaches for inferring 38 human in vivo biology from mouse model datasets. We found that a semi-supervised artificial 39 neural network identified significantly more true human in vivo associations than interpreting (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/346122 doi: bioRxiv preprint first posted online Jun. 13, 2018; 97The utility of mouse models for studying inflammatory pathologies in particular was recently 98 assessed by a pair of studies examining the correspondence between gene expression in 99 murine models of inflammatory pathologies and human contexts (1, 2). The human and mouse 100 microarray cohorts assembled by the two studies had the rare property that mouse molecular 101 and phenotype data were well matched to human in vivo molecular and phenotype data. This 102property enabled systematic examination of the similarities and discrepancies between mice 103 and humans. These studies analyzed the same cohorts of mouse and human studies and came 104to conflicting conclusions about the relevance of mouse models for inflammatory disease . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/346122 doi: bioRxiv preprint first posted online Jun. 13, 2018; 105 research, with Seok et al. concluding that mouse models poorly mimic human pathologies, 106whereas Takao et al. concluded that mouse models usefully mimic human pathologies (1, 2). 107The key methodological difference between the two studies was that while Seok et al. examined 117The aim of our study here is to address the challenge of prospective inference of human . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 142Developing a framework fo...

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Section: Discussioncontrasting

confidence: 58%

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

Brubaker

Proctor

Haigis

et al. 2018

Preprint

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“…Indeed, when we examined the FBR of monocytes derived from three different donors, it was found that, interestingly, different individuals exhibited varying degrees of immune responses to the same Ti microbeads (Figure d). While two donors (Donors B and C) did not exhibit noticeable difference in M1/M2 differentiation of their PBMC‐derived monocytes, one donor (Donar A) had strong pro‐inflammatory reaction to the Ti microbeads (Figure e,f), clearly indicating that there is likely a “population spectrum” of responses to the same implant possibly due to differences in receptor expressions and cytokine profiles of the immune cells . It should be noted that, this dataset achieved with human primary monocytes from healthy donors was different from that observed when human THP‐1 monocyte cell line was used (which always showed pro‐inflammatory phenotype).…”

Section: Parameters and Constants Used For Modelingmentioning

confidence: 78%

“…None of the existing approaches allow for personalized screening of the FBR to implants. Indeed, literature suggests a significant level of inter‐individual variation that is driven by individual's immunological profile. Therefore, there is a strong need for personalized assessment of FBR at low cost and in a higher‐throughput/rapid manner to select the most suitable implant material with optimal parameters, for a given patient.…”

Section: Parameters and Constants Used For Modelingmentioning

confidence: 99%

A Foreign Body Response‐on‐a‐Chip Platform

Sharifi

Htwe

Righi

et al. 2019

Adv Healthcare Materials

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Understanding the foreign body response (FBR) and desiging strategies to modulate such a response represent a grand challenge for implant devices and biomaterials. Here, the development of a microfluidic platform is reported, i.e., the FBR-on-a-chip (FBROC) for modeling the cascade of events during immune cell response to implants. The platform models the native implant microenvironment where the implants are interfaced directly with surrounding tissues, as well as vasculature with circulating immune cells. The study demonstrates that the release of cytokines such as monocyte chemoattractant protein 1 (MCP-1) from the extracellular matrix (ECM)-like hydrogels in the bottom tissue chamber induces trans-endothelial migration of circulating monocytes in the vascular channel toward the hydrogels, thus mimicking implant-induced inflammation. Data using patient-derived peripheral blood mononuclear cells further reveal interpatient differences in FBR, highlighting the potential of this platform for monitoring FBR in a personalized manner. The prototype FBROC platform provides an enabling strategy to interrogate FBR on various implants, including biomaterials and engineered tissue constructs, in a physiologically relevant and individual-specific manner.

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“…However, these often fail to capture the underlying biological structure 13,14 because most correlations are non-linear 15,16 . Recently, we described heterogeneity in macrophage polarization by correlating population variability with the expression of known relevant genes 17 . This approach still requires user input (the relevant genes).…”

Section: Introductionmentioning

confidence: 99%

“…It uses pre-processing filters, t-SNEbased dimensionality reduction, and intuitive visualizations (including movies) for analysis of co-expressed networks of genes. Unlike previous approaches 17 , PRESTO is hypothesis-free and processes all data while blind to sample designation. We demonstrate the usefulness of the tool for exploring co-expressed biological pathways in published pre-clinical data as well as valuable diagnostic signatures from clinical data.…”

Section: Introductionmentioning

confidence: 99%

PRESTO, a new tool for integrating large-scale -omics data and discovering disease-specific signatures

McArdle

Buscher

Ehinger

et al. 2018

Preprint

Self Cite

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Background: Cohesive visualization and interpretation of hyperdimensional, large-scale -omics data is an ongoing challenge, particularly for biologists and clinicians involved in current highly complex sequencing studies. Multivariate studies are often better suited towards non-linear network analysis than differential expression testing. Here, we present PRESTO, a 'PREdictive Stochastic neighbor embedding Tool for Omics', which allows unsupervised dimensionality reduction of multivariate data matrices with thousands of subjects or conditions. PRESTO is intuitively integrated into an interactive user interface that helps to visualize the multidimensional patterns in genome-wide transcriptomic data from basic science and clinical studies.Results: PRESTO was tested with multiple input omics' platforms, including microarray and proteomics from both mouse and human clinical datasets. PRESTO can analyze up to tens of thousands of genes and shows no increase in processing time with a large number of samples or patients. In complex datasets, such as those with multiple time points, several patient groups, or diverse mouse strains, PRESTO outperformed conventional methods. Core co-expressed gene networks were intuitively grouped in clusters, or gates, after dimensionality reduction and remained consistent across users. Networks were identified and assigned to physiological and pathological functions that cannot be gleaned from conventional bioinformatics analyses. PRESTO detected gene networks from the natural variations among mouse macrophages and human blood leukocytes. We applied PRESTO to clinical transcriptomic and proteomic data from large patient cohorts and detected disease-defining signatures in antibody-mediated kidney transplant rejection, renal cell carcinoma, and relapsing acute myeloid leukemia (AML). In AML, PRESTO confirmed a previously described gene signature and found a new signature of 10 genes that is highly predictive of patient outcome. Conclusions: PRESTO offers an important integration of powerful bioinformatics tools with an interactive userinterface that increases data analysis accessibility beyond bioinformaticians and 'coders'. Here, we show that PRESTO out performs conventional methods, such as DE analysis, in multi-dimensional datasets and can identify biologically relevant co-expression gene networks. In paired samples or time points, co-expression networks could be compared for insight into longitudinal regulatory mechanisms. Additionally, PRESTO identified disease-specific signatures in clinical datasets with highly significant diagnostic and prognostic potential.

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Natural variation of macrophage activation as disease-relevant phenotype predictive of inflammation and cancer survival

Cited by 100 publications

References 58 publications

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

A framework for translation of genomic responses from mouse models to human inflammatory disease contexts

A Foreign Body Response‐on‐a‐Chip Platform

PRESTO, a new tool for integrating large-scale -omics data and discovering disease-specific signatures

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