Inflammation of the kidneys is one of the most severe complications of lupus. It affects about half of lupus patients, and often proceeds even when the patients are treated with strong drugs. Dr. Clark found that the immune reaction causing this inflammation does not happen in mouse models of lupus, so he had to find another way to study it. There are many types of cells in the kidney, and these cells can affect each other. Dr. Clark’s lab developed computer programs that can analyze images taken of kidney cells, finding the difference between when cells of different types are just resting next to each other peacefully versus when they work together to cause inflammation. Each person has a different mix of these cells, and the makeup of the mix can affect the course of disease.

With LRA’s Lupus Mechanisms and Targets Award, Dr. Clark hopes that by examining the precise mix of cells from the kidneys of lupus patients, and how they interact with each other, this technology can predict how each patient will fare. This also points to the possibility of developing personalized drugs that would help different patient groups.

What this study means for people with lupus

Customized maps of the cells causing inflammation in the kidneys of lupus patients could help doctors foresee how each person will progress and help researchers develop more personalized treatments.

Lupus nephritis is the most common severe manifestation of systemic lupus erythematosus (SLE) and contributes significantly to overall mortality. Up to 50% of SLE patients develop lupus nephritis and these patients are usually treated with cyclophosphamide or mycophenolate mofetil. Despite such aggressive treatments, up to 50% of patients progress to renal failure within 5 years of diagnosis. Renal biopsies in lupus nephritis are used to assess the extent of renal involvement, assign prognosis and to aid in making therapeutic decisions. Current classification criteria focus on glomerular injury. However, commonly used measures of glomerular disease activity do not predict subsequent clinical course. Rather, tubulointerstitial inflammation (TII) and scarring, and not glomerular injury, predict progression to renal failure. In most patients with moderate or severe TII, the inflammatory infiltrate is organized into either well-circumscribed T:B cell aggregates or germinal centers (GCs) containing follicular dendritic cells. Our work has demonstrated that these histological manifestations of tertiary lymphoid neogenesis are associated with in situ selection of B cells expressing antibodies specific for inflammatory epitopes, functionally competent T follicular helper (TFH) cells and both conventional dendritic and plasmacytoid dendritic cells presenting antigen to CD4+ T cells. However, extensive work has also demonstrated that Lupus TII is mechanistically heterogeneous. Furthermore, as demonstrated in preliminary results, TII heterogeneity is prognostically important. Therefore, we propose an overall model of lupus TII in which individual immune cell populations, and functional relationships between cells, assemble into different and prognostically meaningful adaptive cell networks. This model will be tested in the following Specific Aims: Aim 1. Determine the relationships between immune cell frequencies, antigen presentation and prognosis. Hypothesis: In lupus TII, relative immune cell frequencies and antigen presentation relationships will define different mechanistic and prognostic subsets. Aim 2: Determine the role of complex in situ immune cell networks in lupus TII. Hypothesis: Different adaptive and innate cell networks will define different prognostic patient groups. This grant proposal is focused on applying high-dimensional multicolor confocal microscopy and novel machine learning-based computational approaches to identify and quantify immune cell architectures in human lupus TII. Understanding these in situ mechanisms is critical for obtaining a deeper understanding of lupus nephritis pathobiology, defining mechanistically meaningful disease heterogeneity and identifying new therapeutic targets.