Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/573402

Biomarkers for capturing disease pathology as molecular process hyperstructure

Lukas, A.·Heinzel, A.·Mayer, B.
View original source ↗Export metadata ↓

Abstract

BackgroundPrecision of drugs in clinical development but also of approved treatment sees limits, documented as attrition in clinical stage drug testing and suboptimal number needed to treat in clinical practice. Precision medicine aims at approaching a causal relation of disease pathology, treatment mechanism of action and clinical outcome. The instance linking pathology, clinical phenotype and drug response is disease characteristics amenable for quantitation, including established clinical phenotyping parameters and upcoming molecular profiling and biomarkers. Molecular biomarkers situated at the interface of pathology-specific molecular process architecture and drug mechanism of action promise capturing aspects allowing assessment of treatment response.\n\nResultsApproximating a set of 1,008 disease terms as pathology molecular networks provides 3,860 molecular processes involving 4,602 protein coding genes. Assembling this process set in a hierarchical cluster using mean shortest paths among processes as distance measure allows representation of molecular processes in cumulative aggregation. This procedure transforms human disease pathology into a static instance of a molecular process hyperstructure involving 1,340 aggregate levels in a molecular architecture. The hyperstructure allows evaluating molecular biomarker candidates at different levels of molecular process aggregation in terms of biomarker-specific entropies. Interpretation as information content reflects the capacity of a biomarker for sensing molecular process configuration.\n\nDeriving entropies across aggregation levels for a reference set of 1,502 biomarker candidates identifies significant spread in information content of individual biomarkers. Exemplified on biomarker panels holding evidence for prognostic capacity and factors serving as drug targets from selected chronic diseases, biomarker entropies allow interpretation in terms of sensitivity for capturing process context and specificity for informing on the status of individual processes afflicted with a given pathology.\n\nConclusionsHigh entropy biomarkers provide candidate molecular proxies for clinical phenotyping parameters, and low entropy biomarkers add information on specifics of disease pathology. Combining high and low entropy biomarkers in panels may offer relevant resolution of molecular process configurations for improving patient stratification with respect to minimizing variance in drug response.

Explore related subjects

pathologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lukas, A., Heinzel, A., Mayer, B.. 2019-03-11. Biomarkers for capturing disease pathology as molecular process hyperstructure. https://doi.org/10.1101/573402

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

NAE1-Dependent Protein Neddylation Preserves Endothelial Identity and Vascular Integrity

Background: Endothelial dysfunction is a central driver of cardiovascular and inflammatory diseases, yet the post-translational mechanisms that preserve endothelial homeostasis remain incompletely understood. Protein neddylation, the covalent conjugation of a ubiquitin-like modifier, regulates diverse cellular processes, yet its physiological role in the vascular endothelium remains unknown. This study investigated whether protein neddylation is required to preserve endothelial identity and vascular homeostasis. Methods: We generated tamoxifen-inducible endothelial-specific Nae1 knockout mice to inhibit neddylation and combined bulk RNA sequencing, single-cell and single-nucleus transcriptomics, quantitative proteomics, biochemical analyses, and gain- and loss-of-function approaches to define the role of endothelial neddylation in vascular homeostasis and inflammatory injury. Results: Endothelial-specific Nae1 deletion caused rapid mortality associated with vascular leakage, platelet accumulation, inflammation, and multi-organ injury. Multi-omics analyses demonstrated profound loss of endothelial identity, characterized by suppression of core endothelial programs and activation of inflammatory, procoagulant, and pyroptotic pathways. Single-cell analyses revealed progressive endothelial dysfunction culminating in depletion of the endothelial population and remodeling of the vascular niche. Mechanistically, endothelial neddylation deficiency activated gasdermin D (GSDMD)- and gasdermin E (GSDME)-dependent pyroptosis, whereas dual inhibition of GSDMD and GSDME markedly attenuated inflammatory transcriptomic remodeling, vascular injury, hepatocyte death, immune cell infiltration, and platelet accumulation. Translational analyses demonstrated reduced endothelial neddylation in experimental endotoxemia and decreased expression of neddylation pathway components in human atherosclerosis and COVID-19 datasets. Conversely, restoration of endothelial neddylation partially reversed inflammatory endothelial transcriptomic reprogramming in vivo. Conclusions: NAE1-dependent protein neddylation is an essential regulator of endothelial identity and vascular integrity. Loss of endothelial neddylation promotes gasdermin-dependent pyroptosis and thrombo-inflammatory vascular injury, whereas restoration of the neddylation pathway mitigates inflammatory endothelial dysfunction. These findings identify endothelial neddylation as a fundamental mechanism maintaining vascular homeostasis and a potential therapeutic target for cardiovascular and inflammatory diseases.

pathology↗
bioRxiv2019

Alzheimer’s genetic risk factor FERMT2 (Kindlin-2) is regulated by risk allele-binding of miR-4504 and leads to synaptic dysfunction in an APP-dependent manner

Although APP metabolism is being intensively investigated, a large fraction of its modulators are yet to be characterized. In this context, we combined two genome-wide high-content screenings to assess the functional impact of miRNAs and genes on APP metabolism and the signaling pathways involved. This approach highlighted the involvement of FERMT2 (or Kindlin-2), a genetic risk factor of Alzheimers disease (AD), as a potential key modulator of axon guidance; a neuronal process that depends on the regulation of APP metabolism. We found that FERMT2 directly interacts with APP to modulate its metabolism and that FERMT2 under-expression impacts axonal growth, synaptic connectivity and long-term potentiation in an APP-dependent manner. Lastly, the rs7143400-T allele, which is associated with an increased AD risk and localized within the 3UTR of FERMT2, induced a down-regulation of FERMT2 expression through binding of miR-4504 among others. This miRNA is mainly expressed in neurons and significantly overexpressed in AD brains compared to controls. Altogether, our data provide strong evidence for a detrimental effect of FERMT2 under-expression in neurons and insight on how this may influence AD pathogenesis.

pathology↗
bioRxiv2019

Improving the diagnosis and classification of Ph-negative myeloproliferative neoplasms through deep phenotyping

Myeloproliferative neoplasms (MPNs) are clonal disorders characterized by excessive proliferation of myeloid lineages. Accurate classification and appropriate management of MPNs requires integration of clinical, morphological and genetic findings. Despite major advances in understanding the molecular and genetic basis, morphological assessment of the bone marrow trephine (BMT) remains paramount in differentiating between MPN subtypes and reactive conditions. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative and poorly reproducible criteria. To address this, we have developed a machine-learning strategy for the automated identification and quantitative analysis of megakaryocyte morphology using clinical BMT samples. Using a sample cohort of recently diagnosed or established ET (n = 48) and reactive control cases (n = 42) we demonstrated a high predictive accuracy (AUC = 0.95) of automated tissue ET diagnosis based upon these specific megakaryocyte phenotypes. These separate morphological phenotypes showed evidence of specific genotype associations, which offers promise that an automated cell phenotyping approach may be of clinical diagnostic utility as an adjunct to standard genetic and molecular tests. This has great potential to assist in the routine assessment of newly diagnosed or suspected MPN patients and those undergoing treatment / clinical follow-up. The extraction of quantitative morphological data from BMT sections will also have value in the assessment of new therapeutic strategies directed towards the bone marrow microenvironment and can provide clinicians and researchers with objective, quantitative data without significant demands upon current routine specimen workflows.

pathology↗