Cytocompatibility Evaluation of PEG-Methylsulfone Hydrogels.

Methylsulfone derivatized poly(ethylene) glycol (PEG) macromers can be biofunctionalized with thiolated ligands and cross-linked with thiol-based cross-linkers to obtain bioactive PEG hydrogels for in situ cell encapsulation. Methylsulfonyl-thiol (MS-SH) reactions present several advantages for this purpose when compared to other thiol-based cross-linking systems. They proceed with adequate and tunable kinetics for encapsulation, they reach a high conversion degree with good selectivity, and they generate stable reaction products. Our previous work demonstrated the cytocompatibility of cross-linked PEG-MS/thiol hydrogels in contact with fibroblasts. However, the cytocompatibility of the in situ MS-SH cross-linking reaction itself, which generates methylsulfinic acid as byproduct at the cross-linked site, remains to be evaluated. These studies are necessary to evaluate the potential of these systems for in vivo applications. Here we perform an extensive cytocompatibility study of PEG hydrogels during in situ cross-linking by the methylsulfonyl-thiol reaction. We compare these results with maleimide-thiol cross-linked PEGs which are well established for cell culture and in vivo experiments and do not involve the release of a byproduct. We show that fibroblasts and endothelial cells remain viable after in situ polymerization of methylsulfonyl-thiol gels on the top of the cell layers. Cell viability seems better than after in situ cross-linking hydrogels with maleimide-thiol chemistry. The endothelial cell proinflammatory phenotype is low and similar to the one obtained by the maleimide-thiol reaction. Finally, no activation of monocytes is observed. All in all, these results demonstrate that the methylsulfonyl-thiol chemistry is cytocompatible and does not trigger high pro-inflammatory responses in endothelial cells and monocytes. These results make methylsulfonyl-thiol chemistries eligible for in vivo testing and eventually clinical application in the future.

Associations of plasma proteomics with type 2 diabetes and related traits: results from the longitudinal KORA S4/F4/FF4 Study.

AIMS/HYPOTHESIS: This study aimed to elucidate the aetiological role of plasma proteins in glucose metabolism and type 2 diabetes development. METHODS: We measured 233 proteins at baseline in 1653 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort study (median follow-up time: 13.5 years). We used logistic regression in the cross-sectional analysis (n=1300), and Cox regression accounting for interval-censored data in the longitudinal analysis (n=1143). We further applied two-level growth models to investigate associations with repeatedly measured traits (fasting glucose, 2 h glucose, fasting insulin, HOMA-B, HOMA-IR, HbA1c), and two-sample Mendelian randomisation analysis to investigate causal associations. Moreover, we built prediction models using priority-Lasso on top of Framingham-Offspring Risk Score components and evaluated the prediction accuracy through AUC. RESULTS: We identified 14, 24 and four proteins associated with prevalent prediabetes (i.e. impaired glucose tolerance and/or impaired fasting glucose), prevalent newly diagnosed type 2 diabetes and incident type 2 diabetes, respectively (28 overlapping proteins). Of these, IL-17D, IL-18 receptor 1, carbonic anhydrase-5A, IL-1 receptor type 2 (IL-1RT2) and matrix extracellular phosphoglycoprotein were novel candidates. IGF binding protein 2 (IGFBP2), lipoprotein lipase (LPL) and paraoxonase 3 (PON3) were inversely associated while fibroblast growth factor 21 was positively associated with incident type 2 diabetes. LPL was longitudinally linked with change in glucose-related traits, while IGFBP2 and PON3 were linked with changes in both insulin- and glucose-related traits. Mendelian randomisation analysis suggested causal effects of LPL on type 2 diabetes and fasting insulin. The simultaneous addition of 12 priority-Lasso-selected biomarkers (IGFBP2, IL-18, IL-17D, complement component C1q receptor, V-set and immunoglobulin domain-containing protein 2, IL-1RT2, LPL, CUB domain-containing protein 1, vascular endothelial growth factor D, PON3, C-C motif chemokine 4 and tartrate-resistant acid phosphatase type 5) significantly improved the predictive performance (ΔAUC 0.0219; 95% CI 0.0052, 0.0624). CONCLUSIONS/INTERPRETATION: We identified new candidates involved in the development of derangements in glucose metabolism and type 2 diabetes and confirmed previously reported proteins. Our findings underscore the importance of proteins in the pathogenesis of type 2 diabetes and the identified putative proteins can function as potential pharmacological targets for diabetes treatment and prevention.

Deciphering Stromal Changes between Metastatic and Non-metastatic Canine Mammary Carcinomas.

Cancer-associated stroma (CAS) is widely recognized to influence development and progression of epithelial tumours including breast cancer. Canine mammary tumours (CMTs) such as simple canine mammary carcinomas represent valuable models for human breast cancer also with respect to stromal reprogramming. However, it remains unclear whether and how CAS changes in metastatic tumours compared to non-metastatic ones. To characterize stromal changes between metastatic and non-metastatic CMTs and identify potential drivers of tumour progression, we analysed CAS and matched normal stroma from 16 non-metastatic and 15 metastatic CMTs by RNA-sequencing of microdissected FFPE tissue. We identified 1438 differentially regulated genes between CAS and normal stroma, supporting previous results demonstrating stromal reprogramming in CMTs to be comparable with CAS in human breast cancer and validating deregulation of pathways and genes associated with CAS. Using primary human fibroblasts activated by treatment with TGFß, we demonstrate some of the strongest expression changes to be conserved in fibroblasts across species. Furthermore, we identify 132 differentially expressed genes between CAS from metastatic and non-metastatic tumours, with strong changes in pathways including chemotaxis, regulation of apoptosis, immune response and TGFß signalling and validate deregulation of several targets using RT-qPCR. Finally, we identify specific upregulation of COL6A5, F5, GALNT3, CIT and MMP11 in metastatic CAS, suggesting high stromal expression of these targets to be linked to malignancy and metastasis of CMTs. In summary, our data present a resource supporting further research into stromal changes of the mammary gland in relation to metastasis with implications for both canine and human mammary cancer.

Proteomics reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients.

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Previous studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. We addressed the gap of existing knowledge with regard to a differentiated understanding of disease dynamics in SARS-CoV-2 infection while specifically considering disease stage and severity. We biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of the individualized disease phase assignment, proteome analysis revealed a severity dependent general type-2-centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response characterizing severe disease was successfully validated in a second cohort. Together with the regulation of proteins related to SARS-CoV-2-specific symptoms identified by proteome screening, we not only confirmed results from previous studies but provide novel information for biomarker and therapy development.

Influence of the autotaxin-lysophosphatidic acid axis on cellular function and cytokine expression in different breast cancer cell lines.

Previous studies provide high evidence that autotaxin (ATX)-lysophosphatidic acid (LPA) signaling through LPA receptors (LPAR) plays an important role in breast cancer initiation, progression, and invasion. However, its specific role in different breast cancer cell lines remains to be fully elucidated to offer improvements in targeted therapies. Within this study, we analyzed in vitro the effect of LPA 18:1 and the LPAR1, LPAR3 (and LPAR2) inhibitor Ki16425 on cellular functions of different human breast cancer cell lines (MDA-MB-231, MDA-MB-468, MCF-7, BT-474, SKBR-3) and the human breast epithelial cell line MCF-10A, as well as Interleukin 8 (IL-8), Interleukin 6 (IL-6) and tumor necrosis factor (TNF)-alpha cytokine secretion after LPA-incubation. ATX-LPA signaling showed a dose-dependent stimulatory effect especially on cellular functions of triple-negative and luminal A breast cancer cell lines. Ki16425 inhibited the LPA-induced stimulation of triple-negative breast cancer and luminal A cell lines in variable intensity depending on the functional assay, indicating the interplay of different LPAR in those assays. IL-8, IL-6 and TNF-alpha secretion was induced by LPA in MDA-MB-468 cells. This study provides further evidence about the role of the ATX-LPA axis in different breast cancer cell lines and might contribute to identify subtypes suitable for a future targeted therapy of the ATX-LPA axis.

Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study.

BACKGROUND: The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers. METHODS: Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow-up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives-controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross-validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. RESULTS: In the cross-sectional analysis, we identified kallikrein-6, C-C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1-SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37-1.95), 1.31 (1.14-1.51), 1.24 (1.06-1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61-0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08-1.61), 1.28 (1.03-1.59), respectively]. Including protein biomarkers selected in ≥90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13-0.20), 0.22 (0.18-0.25), 0.12 (0.08-0.17), respectively]. In the longitudinal analysis, N-terminal prohormone brain natriuretic peptide (NT-proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10-1.77), 1.60 (1.15-2.24), respectively]. CONCLUSIONS: Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT-proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population.

CONTEXT: Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE: This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c). METHODS: We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS: Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION: The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.

Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease-a comparison in the MONICA/KORA study.

BACKGROUND: Biomarkers may contribute to our understanding of the pathophysiology of various diseases. Type 2 diabetes (T2D) and coronary heart disease (CHD) share many clinical and lifestyle risk factors and several biomarkers are associated with both diseases. The current analysis aims to assess the relevance of biomarkers combined to pathway groups for the development of T2D and CHD in the same cohort. METHODS: Forty-seven serum biomarkers were measured in the MONICA/KORA case-cohort study using clinical chemistry assays and ultrasensitive molecular counting technology. The T2D (CHD) analyses included 689 (568) incident cases and 1850 (2004) non-cases from three population-based surveys. At baseline, the study participants were 35-74 years old. The median follow-up was 14 years. We computed Cox regression models for each biomarker, adjusted for age, sex, and survey. Additionally, we assigned the biomarkers to 19 etiological pathways based on information from literature. One age-, sex-, and survey-controlled average variable was built for each pathway. We used the R2PM coefficient of determination to assess the explained disease risk. RESULTS: The associations of many biomarkers, such as several cytokines or the iron marker soluble transferrin receptor (sTfR), were similar in strength for T2D and CHD, but we also observed important differences. Lipoprotein (a) (Lp(a)) and N-terminal pro B-type natriuretic peptide (NT-proBNP) even demonstrated opposite effect directions. All pathway variables together explained 49% of the T2D risk and 21% of the CHD risk. The insulin-like growth factor binding protein 2 (IGFBP-2, IGF/IGFBP system pathway) best explained the T2D risk (about 9% explained risk, independent of all other pathway variables). For CHD, the myocardial-injury- and lipid-related-pathways were most important and both explained about 4% of the CHD risk. CONCLUSIONS: The biomarker-derived pathway variables explained a higher proportion of the T2D risk compared to CHD. The ranking of the pathways differed between the two diseases, with the IGF/IGFBP-system-pathway being most strongly associated with T2D and the myocardial-injury- and lipid-related-pathways with CHD. Our results help to better understand the pathophysiology of the two diseases, with the ultimate goal of pointing out targets for lifestyle intervention and drug development to ideally prevent both T2D and CHD development.