Kidney tertiary lymphoid structures in Lupus Nephritis develop into large interconnected networks and resemble lymph nodes in gene signature

Immune aggregates organized as tertiary lymphoid structures (TLS) are observed within the kidneys of patients with systemic lupus erythematosus and lupus nephritis (LN). Renal TLS was characterized in lupus-prone New Zealand black × New Zealand white F1 mice analyzing cell composition and vessel formation. RNA sequencing was performed on transcriptomes isolated from lymph nodes, macrodissected TLS from kidneys, and total kidneys of mice at different disease stages by using a personal genome machine and RNA sequencing. Formation of TLS was found in anti–double-stranded DNA antibody–positive mice, and the structures were organized as interconnected large networks with distinct T/B cell zones with adjacent dendritic cells, macrophages, plasma cells, high endothelial venules, supporting follicular dendritic cells network, and functional germinal centers. Comparison of gene profiles of whole kidney, renal TLS, and lymph nodes revealed a similar gene signature of TLS and lymph nodes. The up-regulated genes within the kidneys of lupus-prone mice during LN development reflected TLS formation, whereas the down-regulated genes were involved in metabolic processes of the kidney cells. A comparison with human LN gene expression revealed similar up-regulated genes as observed during the development of murine LN and TLS. In conclusion, kidney TLS have a similar cell composition, structure, and gene signature as lymph nodes and therefore may function as a kidney-specific type of lymph node

Mesenchymal stem cells and T cells in the formation of Tertiary Lymphoid Structures in Lupus Nephritis

Tertiary lymphoid structures (TLS) develop in the kidneys of lupus-prone mice and systemic lupus erythematosus (SLE) patients with lupus nephritis (LN). Here we investigated the presence of mesenchymal stem cells (MSCs) in the development of TLS in murine LN, as well as the role of human MSCs as lymphoid tissue organizer (LTo) cells on the activation of CD4+ T cells from three groups of donors including Healthy, SLE and LN patients. Mesenchymal stem like cells were detected within the pelvic wall and TLS in kidneys of lupus-prone mice. An increase in LTβ, CXCL13, CCL19, VCAM1 and ICAM1 gene expressions were detected during the development of murine LN. Human MSCs stimulated with the pro-inflammatory cytokines TNF-α and IL-1β significantly increased the expression of CCL19, VCAM1, ICAM1, TNF-α, and IL-1β. Stimulated MSCs induced proliferation of CD4+ T cells, but an inhibitory effect was observed when in co-culture with non-stimulated MSCs. A contact dependent increase in Th2 and Th17 subsets were observed for T cells from the Healthy group after co-culture with stimulated MSCs. Our data suggest that tissue-specific or/and migratory MSCs could have pivotal roles as LTo cells in accelerating early inflammatory processes and initiating the formation of kidney specific TLS in chronic inflammatory conditions

Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform

The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays

Positron emission tomography and single photon emission computed tomography imaging of tertiary lymphoid structures during the development of lupus nephritis

Lymphoid neogenesis occurs in tissues targeted by chronic inflammatory processes, such as infection and autoimmunity. In systemic lupus erythematosus (SLE), such structures develop within the kidneys of lupus-prone mice ((NZBXNZW)F1) and are observed in kidney biopsies taken from SLE patients with lupus nephritis (LN). The purpose of this prospective longitudinal animal study was to detect early kidney changes and tertiary lymphoid structures (TLS) using in vivo imaging. Positron emission tomography (PET) by tail vein injection of 18-F-fluoro-2-deoxy-D-glucose (18F-FDG)(PET/FDG) combined with computed tomography (CT) for anatomical localization and single photon emission computed tomography (SPECT) by intraperitoneal injection of 99mTC labeled Albumin Nanocoll (99mTC-Nanocoll) were performed on different disease stages of NZB/W mice (n = 40) and on aged matched control mice (BALB/c) (n = 20). By using one-way ANOVA analyses, we compared two different compartmental models for the quantitative measure of 18F-FDG uptake within the kidneys. Using a new five-compartment model, we observed that glomerular filtration of 18FFDG in lupus-prone mice decreased significantly by disease progression measured by anti-dsDNA Ab production and before onset of proteinuria. We could not visualize TLS within the kidneys, but we were able to visualize pancreatic TLS using 99mTC Nanocoll SPECT. Based on our findings, we conclude that the five-compartment model can be used to measure changes of FDG uptake within the kidney. However, new optimal PET/SPECT tracer administration sites together with more specific tracers in combination with magnetic resonance imaging (MRI) may make it possible to detect formation of TLS and LN before clinical manifestations

Machine learning derived input-function in a dynamic 18F-FDG PET study of mice

Tracer kinetic modelling, based on dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue regions were delineated in images from 68 FDG PET/computed tomography mouse scans. Two machine learning-based models were trained for AIF prediction, based on Gaussian processes (GP) and a long short-term memory (LSTM) recurrent neural network, respectively. Because blood data were unavailable, a reference AIF was formed by fitting an established AIF model to vena cava and left ventricle image data. The predicted and reference AIFs were compared by the area under curve (AUC) and root mean square error (RMSE). Net-influx rate constants, Ki, were calculated with a two-tissue compartment model, using both predicted and reference AIFs for three tissue regions in each mouse scan, and compared by means of error, ratio, correlation coefficient, P value and Bland-Altman analysis. The impact of different tissue regions on AIF prediction was evaluated by training a GP and an LSTM model on subsets of tissue regions, and calculating the RMSE between the reference and the predicted AIF curve. Both models generated AIFs with AUCs similar to reference. The LSTM models resulted in lower AIF RMSE, compared to GP. Ki from both models agreed well with reference values, with no significant differences. Myocardium was highlighted as important for AIF prediction, but AIFs with similar RMSE were obtained also without myocardium in the input data. Machine learning can be used for accurate and non-invasive prediction of an image-derived reference AIF in FDG studies of mice. We recommend the LSTM approach, as this model predicts AIFs with lower errors, compared to GP