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Background: Epicardial adipose tissue (EAT) surrounds the epicardium and can mediate harmful effects related to coronary artery disease (CAD). Objective: We explored the regional differences between adipose stores surrounding diseased and non-diseased segments of coronary arteries in patients with advanced CAD. Methods: We enrolled 32 patients with known CAD who underwent coronary artery bypass graft (CABG) surgery. Inflammatory mediators were measured in EAT biopsies collected from a region of the left anterior descending artery (LAD) with severe stenosis (diseased segment) and without stenosis (non-diseased segment). Results : Mean age was 64.3±11.1 years, and mean EAT thickness was 7.4±1.9 mm. Dyslipidemia was the most prevalent comorbidity (81% of the patients). Out of a total of 11 cytokines, resistin (p=0.039), matrix metallopeptidase 9 (MMP-9) (p=0.020), C-C motif chemokine ligand 5 (CCL-5) (p=0.021), and follistatin (p=0.038) were significantly increased in the diseased compared with the non-diseased EAT segments. Indexed tumor necrosis factor-alpha (TNF-α), defined as the diseased to non-diseased cytokine levels ratio, was significantly correlated with increased EAT thickness both in the whole cohort (p=0.043) and in a subpopulation of patients with dyslipidemia (p=0.009). Treatment with lipid-lowering agents significantly decreased indexed TNF-α levels (p=0.015). No significant alterations were observed in the circulating levels of these cytokines with respect to CAD-associated comorbidities. Conclusion: Perivascular EAT is a source of cytokine secretion in distinct areas surrounding the coronary arteries in patients with advanced CAD. Adipocyte-derived TNF-α is a prominent mediator of local inflammation.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

Background: Atrial fibrillation (AF) affects quality of life and prognosis of patients with cardiovascular disease. Resistin plays an important role in inflammatory response to internal and external factors. The aim of this study is to evaluate the association between resistin and permanent AF (PAF) in patients with cardiovascular disease. Methods: In our study, we included 146 patients with cardiovascular disease. Plasma resistin concentrations and demographic characteristics of patients were recorded. The patients were divided in two groups: 118 patients without a history of PAF (NonAF group), and 28 patients with a history of PAF (AF group). Association of resistin with PAF and other variables was examined by parametric and non-parametric tests, and multivariable linear and univariable logistic regression analysis. Results: No differences of demographic characteristics (gender, age and body mass index (BMI)) between two groups were observed (P > 0.05). Higher median levels of resistin were observed in group AF than in group NonAF (6.90 ng/mL vs. 5.83 ng/mL, P = 0.03). Multivariate linear regression analysis (adjusted to gender, age, BMI, hypertension, diabetes mellitus, coronary artery disease, and mitral valve disease) showed that resistin was associated with PAF (beta= 0.79, 95% confidence interval (CI): 0.08 to 1.51, P = 0.03). Conclusions: Our analysis showed that plasma resistin was associated with PAF, and resistin concentration was higher in patients with AF compared to those without AF. Cardiol Res. 2020;11(5):286-293 doi: https://doi.org/10.14740/cr1121

Osteoarthritis (OA) is characterized by irreversible cartilage degradation with very limited therapeutic interventions. Drug candidates targeted at prototypic players had limited success until now and systems based approaches might be necessary. Consequently, drug evaluation platforms should consider the biological complexity looking beyond well-known contributors of OA. In this study an ex vivo model of cartilage degradation, combined with measuring releases of 27 proteins, was utilized to study 9 drug candidates. After an initial single drug evaluation step the 3 most promising compounds were selected and employed in an exhaustive combinatorial experiment. The resulting most and least promising treatment candidates were selected and validated in an independent study. This included estimation of mechanical properties via finite element modelling (FEM) and quantification of cartilage degradation as glycosaminoglycan (GAG) release. The most promising candidate showed increase of Young's modulus, decrease of hydraulic permeability and decrease of GAG release. The least promising candidate exhibited the opposite behaviour. The study shows the potential of a novel drug evaluation platform in identifying treatments that might reduce cartilage degradation. It also demonstrates the promise of exhaustive combination experiments and a connection between chondrocyte responses at the molecular level with changes of biomechanical properties at the tissue level.

Mounting evidence indicates that immunogenic therapies engaging the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress favor proficient cancer cell-immune interactions, by stimulating the release of immunomodulatory/proinflammatory factors by stressed or dying cancer cells. UPR-driven transcription of proinflammatory cytokines/chemokines exert beneficial or detrimental effects on tumor growth and antitumor immunity, but the cell-autonomous machinery governing the cancer cell inflammatory output in response to immunogenic therapies remains poorly defined. Here, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic treatments. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated a NF-κB/AP-1-inflammatory stress response, which dissociated from both cell death and UPR. This stress-induced inflammation was specifically abolished by the IRE1α-kinase inhibitor KIRA6. Supernatants from immunogenic chemotherapy and KIRA6 co-treated cancer cells were deprived of proinflammatory/chemoattractant factors and failed to mobilize neutrophils and induce dendritic cell maturation. Furthermore, KIRA6 significantly reduced the in vivo vaccination potential of dying cancer cells responding to immunogenic chemotherapy. Mechanistically, we found that the anti-inflammatory effect of KIRA6 was still effective in IRE1α-deficient cells, indicating a hitherto unknown off-target effector of this IRE1α-kinase inhibitor. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry, and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the IKK-driven NF-κB pathway. In sum, our study unravels that HSP60 is a KIRA6-inhibitable upstream regulator of the NF-κB/AP-1-inflammatory stress responses evoked by immunogenic treatments. It also urges caution when interpreting the anti-inflammatory action of IRE1α chemical inhibitors. ispartof: CELL DEATH AND DIFFERENTIATION vol:29 issue:1 pages:230-245 ispartof: location:England status: published

Summary: A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies. : Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response. Keywords: drug response prediction, precision medicine, machine learning, deep neural networks, DNN

MotivationThe analysis and comparison of compounds’ transcriptomic signatures can help elucidate a compound’s Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound’s effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs.ResultsWe created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds’ gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.Availability and ImplementationThe source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM.ContactEmail for correspondence: leo@mail.ntua.gr.Supplementary informationAccompanying supplementary material are available online.

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals. Funding was provided by Generalitat de Catalunya (Grant No. 2020 FI_B 00680), European Commission (Grant No. MSCA-2020-ITN-ETN GA: 955735) and Ministerio de Ciencia, Innovación y Universidades (Grant No. HOLOA-DPI2016- 80283-C2-1/2-R).

Non-Alcoholic Fatty Liver Disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines in vitro steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced in silico approach screened 20'000 compounds, whilst complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified 6 compounds, including the known anti-steatogenic drugs Resveratrol and Sirolimus. In short, Gallamine triethiotide, Diflorasone, Fenoterol and Pralidoxime ameliorate steatosis similarly to Resveratrol/Sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing.

Multiple myeloma (MM) is a haematological malignancy being characterized by clonal plasma cell proliferation in the bone marrow. Targeting the proteasome with specific inhibitors (PIs) has been proven a promising therapeutic strategy and PIs have been approved for the treatment of MM and mantle‐cell lymphoma; yet, while outcome has improved, most patients inevitably relapse. As relapse refers to MM cells that survive therapy, we sought to identify the molecular responses induced in MM cells after non‐lethal proteasome inhibition. By using bortezomib (BTZ), epoxomicin (EPOX; a carfilzomib‐like PI) and three PIs, namely Rub999, PR671A and Rub1024 that target each of the three proteasome peptidases, we found that only BTZ and EPOX are toxic in MM cells at low concentrations. Phosphoproteomic profiling after treatment of MM cells with non‐lethal (IC10) doses of the PIs revealed inhibitor‐ and cell type‐specific readouts, being marked by the activation of tumorigenic STAT3 and STAT6. Consistently, cytokine/chemokine profiling revealed the increased secretion of immunosuppressive pro‐tumorigenic cytokines (IL6 and IL8), along with the inhibition of potent T cell chemoattractant chemokines (CXCL10). These findings indicate that MM cells that survive treatment with therapeutic PIs shape a pro‐tumorigenic immunosuppressive cellular and secretory bone marrow microenvironment that enables malignancy to relapse.

Metabolically healthy obesity is characterized as a comorbidity-free obesity status, however the exact pathogenetic mechanisms implicated in its transition to unhealthy obesity have not yet been unveiled. Our aim was to investigate the effect of metabolic health on the proteomic profile both in serum and visceral fat of morbidly obese subjects. 28 patients undergoing bariatric surgery were prospectively enrolled. They were divided into two groups: metabolically healthy (MHO, n = 18) and unhealthy (MUO, n = 10) obese patients. 30 biomarkers were measured in serum and visceral adipose tissue with the use of targeted proteomic analysis (Luminex assays). TNF weak inducer of apoptosis (TWEAK) (p = 0.043), TNF related apoptosis inducing ligand (TRAIL) (p = 0.037), Growth differentiation factor-15 (GDF-15) (p = 0.04), Resistin (RETN) (p = 0.047), Matrix metalloproteinase-9 (MMP-9) (p = 0.011) and C-terminal telopeptide (ICTP) (p = 0.022) were up-regulated in the MUO group in the visceral white adipose tissue. Moreover, C-C motif ligand-3 (CCL-3) (p = 0.056), Interleukin-20 (IL-20) (p = 0.04), Prokineticin-1 (PROK-1) (p = 0.028) and TWEAK (p = 0.016) were found to be suppressed in the serum of MHO group. Significant correlations between serum and adipose tissue levels of certain cytokines were also observed, while 16 biomarkers were associated with BMI. Our results indicate metabolic health substantially attenuates the expression of TWEAK, TRAIL, GDF-15, RETN, MMP-9 and ICTP expression locally, in the visceral white adipose tissue, and the expression of CCL-3, IL-20, PROK-1 and TWEAK in the peripheral blood. Intriguingly, different cytokines –except for TWEAK- are up-regulated in each site, suggesting that obesity is not a homogenous but a multi-dimensional disease.

There is a plethora of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) serological tests based either on nucleocapsid phosphoprotein (N), S1-subunit of spike glycoprotein (S1) or receptor binding domain (RBD). Although these single-antigen based tests demonstrate high clinical performance, there is growing evidence regarding their limitations in epidemiological serosurveys. To address this, we developed a Luminex-based multiplex immunoassay that detects total antibodies (IgG/IgM/IgA) against the N, S1 and RBD antigens and used it to compare antibody responses in 1225 blood donors across Greece. Seroprevalence based on single-antigen readouts was strongly influenced by both the antigen type and cut-off value and ranged widely [0.8% (95% CI 0.4–1.5%)–7.5% (95% CI 6.0–8.9%)]. A multi-antigen approach requiring partial agreement between RBD and N or S1 readouts (RBD&N|S1 rule) was less affected by cut-off selection, resulting in robust seroprevalence estimation [0.6% (95% CI 0.3–1.1%)–1.2% (95% CI 0.7–2.0%)] and accurate identification of seroconverted individuals.

This work was supported by the European Union 7FP-Programme (CombiMS, grant No 305397) and the European Sys4MS project (Horizon2020: Eracosysmed: ID-43). RM and the Section for Neuroimmunology and MS Research, University Zurich (UZH), have been supported by a European Research Council Advanced Grant (ERC 340733-HLA-DR15 in MS), by a Clinical Research Priority Project - disease heterogeneity of MS (CRPPMS; UZH), by the Swiss National Science Foundation and the Swiss MS Society. Open Access funding enabled and organized by Projekt DEAL.

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations’ processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies M.N. acknowledges financial support from the German Research Foundation (DFG) via the scholarship “Forschungsstipendium” (PN: 387071423). L.G.A. acknowledges funding through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE co-financed by the European Union and Greek national funds (Project Code: T1EDK-02829). K.W. acknowledges funding from the Swiss National Science Foundation (SNF PP00P2_163678/1). L.B., C.R.W., M.A.G.B., and J.N. acknowledge financial support from the Spanish Ministry of Science and Innovation (HOLOA-DPI2016-80283-C2-1-R, RYC-2015-18888). L.B., C.L.M., C.R.W., L.G.A., M.A.G.B., and J.N. further acknowledge financial support from the European Commission (Disc4All-H2020-MSCA-ITN-ETN-2020 GA: 955735).

Cellular stress evoked by immunogenic anticancer treatments engaging the unfolded protein response (UPR) can elicit inflammation with conflicting therapeutic outcomes. To define cell-autonomous mechanisms coupling the UPR to molecular mediators of inflammation, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic-treatments. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated NF-κB/AP-1-inflammatory pathways, which were abolished by the IRE1α-kinase inhibitor KIRA6. Cell-free fractions of chemotherapy and KIRA6 co-treated cancer cells were deprived of pro-inflammatory/chemoattractant factors and failed to mobilize innate immune cells. Strikingly, these potent KIRA6 anti-inflammatory effects were found to be independent of IRE1α. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the NF-κB pathway. In sum, our study unravels that inflammation evoked by immunogenic treatments is curtailed by KIRA6 independently of IRE1α and further suggests great caution in interpreting the anti-inflammatory action of IRE1α chemical inhibitors.

Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. To make this complex problem computationally tractable, compound differences were mapped to biological effect alterations using Siamese Graph Convolutional Neural Networks. The proposed model was able to encode molecular graph pairs and identify structurally dissimilar compounds that affect similar biological processes with high precision. Additionally, by utilizing deep ensembles to estimate uncertainty, we were able to provide reliable and accurate predictions for chemical structures that are very different from the ones used during training. Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathway signature of a compound perturbation, using only its chemical structure as input, and subsequently identify which substructures influenced the predicted pathways. As a use case, this approach was used to infer important substructures and affected signaling pathways of FDA-approved anticancer drugs.

We evaluated the antibody responses in 259 potential convalescent plasma donors for Covid-19 patients. Different assays were used: a commercial ELISA detecting antibodies against the recombinant spike protein (S1); a multiplex assay detecting total and specific antibody isotypes against three SARS-CoV-2 antigens (S1, basic nucleocapsid (N) protein and receptor-binding domain (RBD)); and an in-house ELISA detecting antibodies to complete spike, RBD and N in 60 of these donors. Neutralizing antibodies (NAb) were also evaluated in these 60 donors. Analyzed samples were collected at a median time of 62 (14–104) days from the day of first symptoms or positive PCR (for asymptomatic patients). Anti-SARS-CoV-2 antibodies were detected in 88% and 87.8% of donors using the ELISA and the multiplex assay, respectively. The multivariate analysis showed that age ≥50 years (p < 0.001) and need for hospitalization (p < 0.001) correlated with higher antibody titers, while asymptomatic status (p < 0.001) and testing >60 days after symptom onset (p = 0.001) correlated with lower titers. Interestingly, pseudotype virus-neutralizing antibodies (PsNAbs) significantly correlated with spike and with RBD antibodies by ELISA. Sera with high PsNAb also showed a strong ability to neutralize active SARS-CoV-2 virus, with hospitalized patients showing higher titers. Therefore, convalescent plasma donors can be selected based on the presence of high RBD antibody titers.

More than 300 SARS-COV-2 serological tests have recently been developed using either the nucleocapsid phosphoprotein (N), the spike glycoprotein subunit (S1), and more recently the receptor binding domain (RBD). Most of the assays report very good clinical performance characteristics in well-controlled clinical settings. However, there is a growing belief that good performance characteristics that are obtained during clinical performance trials might not be sufficient to deliver good diagnostic results in population-wide screens that are usually characterized with low seroprevalence. In this paper, we developed a serological assay against N, S1 and RBD using a bead-based multiplex platform and a rules-based computational approach to assess the performance of single and multi-antigen readouts in well-defined clinical samples and in a population-wide serosurvey from blood donors. Even though assays based on single antigen readouts performed similarly well in the clinical samples, there was a striking difference between the antigens on the population-wide screen. Asymptomatic individuals with low antibody titers and sub-optimal assay specificity might contribute to the large discrepancies in population studies with low seroprevalence. A multi-antigen assay requiring partial agreement between RBD, N and S1 readouts exhibited enhanced specificity, less dependency on assay cut-off values and an overall more robust performance in both sample settings. Our data suggest that assays based on multiple antigen readouts combined with a rules-based computational consensus can provide a more robust platform for routine antibody screening.One Sentence SummaryClinical and Population-level performance of single and multiplex SARS-CoV-2 serological assays.

In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically in “brisk”, “non-brisk” and “absent”. The meaning of “brisk” is “active, energetic”, implying a functional connotation which has never been proved. Recently, it has been shown that not all brisk infiltrates have a good prognosis, that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns of tumor infiltrating lymphocytes (TILs) in primary melanoma are heterogeneous functional categories that can be further sub-classified according to the functional status of their lymphocytes as active, transitional or exhausted. We found that only activation of TILs correlates with spontaneous regression, and that the good prognosis of melanomas with a brisk pattern could be due to the fact that in these case the Tcy that are in contact with the melanoma cells are still active, and consequently the melanoma is still under immune control at the moment of surgical excision. The main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis. Finally, we show a bioinformatic pipeline that, starting from common immunofluorescence stainings, can transform the tissue into a digitalized image, which represents the starting point for multiple deeper levels of analysis.

Introduction To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community. Methods We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure. Results We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration–approved drug nilotinib. Conclusion These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data., Graphical abstract