Incidence and Clinical Significance of De Novo

Kidney transplantation has evolved over more than half a century and remarkable progress has been made in patient and graft outcomes. Despite these advances, chronic allograft dysfunction remains a major problem. Among other reasons, de novo formation of antibodies against donor human leukocyte antigens has been recognized as one of the major risk factors for reduced allograft survival. The type of treatment in the presence of donor specific antibodies (DSA) posttransplantation is largely related to the clinical syndrome the patient presents with at the time of detection. There is no consensus regarding the treatment of stable renal transplant recipients with circulating de novo DSA. On the contrast, in acute or chronic allograft dysfunction transplant centers use various protocols in order to reduce the amount of circulating DSA and achieve long-term graft survival. These protocols include removal of the antibodies by plasmapheresis, intravenous administration of immunoglobulin, or depletion of B cells with anti-CD20 monoclonal antibodies along with tacrolimus and mycophenolate mofetil. This review aims at the comprehension of the clinical correlations of de novo DSA in kidney transplant recipients, assessment of their prognostic value, and providing insights into the management of these patients

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody–antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to “shared” antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intralocus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new crossreactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second featurebased analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific crossreactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient’s workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that “public” anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests

Rituximab and mycophenolate mofetil for relapsing proliferative lupus nephritis: a long-term prospective study

Methods. This prospective, observational study included 10 women with biopsy-proven relapse of proliferative lupus nephritis occurring during maintenance with mycophenolate mofetil (MMF) or azathioprine. The long-term outcome after a single course of the B-cell depleting anti-CD20 antibody rituximab (4 weekly infusions of 375 mg/m(2)), combined with daily MMF (2 g) and prednisolone (0.5 mg/ kg/day for 4 weeks, tapered thereafter) is presented. Results. While renal function was not severely impaired at baseline, partial remission (> 50% improvement in all abnormal renal parameters) was achieved in eight patients at a median of 3.5 months. In seven patients, with 24-h urinary protein of 2.5 +/- 1.1 g (mean +/- SD), complete remission, associated with increases in serum complement levels and decreases in anti-dsDNA titres, was subsequently established (normal serum creatinine/albumin levels, inactive urine sediment and 24-h urinary protein < 0.5 g). Complete nephritis remission was sustained at the follow-up end (median of 38 months) in six patients. Combination treatment was well tolerated. Conclusions. The efficacy of this low-toxicity combination was particularly evident in patients with subnephrotic proteinuria due to proliferative lupus nephritis relapse. Controlled trials to define the role of rituximab/MMF in this condition are warranted

Depletion of B Lymphocytes in Idiopathic Membranous Glomerulopathy: Results from Patients with Extended Follow-Up

Aims: To assess the long-term therapeutic benefit of temporary depletion of B lymphocytes in patients with idiopathic membranous glomerulopathy (MGN) and search for potential predictors of response. Patients and Methods: The patients included had been diagnosed with biopsy-proven MGN in the absence of secondary causes. Estimated glomerular filtration rate should be above 30 ml/min/1.73 m2 and 24-hour proteinuria 3 g/day or more. Patients who had been treated with cyclosporine or cytotoxic agents the year prior to study entry were excluded. Depletion of B cells was achieved with rituximab, which was administered intravenously for 4 consecutive weeks. Partial remission was defined as a >50% decrease in proteinuria with absolute proteinuria 50% decrease in proteinuria and an absolute protein excretion Results: Twelve patients were studied (4 females/8 males) with a mean age of 51.3 years. No major adverse effects were observed. During a median follow-up time of 48 months, 11/12 (91.6%) patients achieved remission [7/12 (58.3%) complete remission and 4/12 (33.3%) partial remission], while 1 patient did not respond to therapy. Twelve months after therapy, 68.8% (p = 0.003) of cases had achieved partial and 28.4% complete remission. Measurements of lymphocyte subpopulations did not reveal any changes except for the B cell depletion. B cell infiltrates captured per mm3 of renal tissue in the diagnostic biopsy did not correlate with subsequent response. Conclusion: Depletion of B cells in idiopathic MGN was well tolerated and resulted in significant and long-lasting response rates in a series of 12 patients

The Impressive Healing Power of Autologous Fibroblasts Isolated from Early Cultures of Skin Biopsies for the Treatment of Diabetic Foot Ulcers: Preliminary Results Regarding 2 Cases

Aims: The purpose of this study was to investigate whether we can quickly, effectively, and with relatively low cost, heal long-standing (>8 months) diabetic foot ulcers using autologous skin fibroblasts. Place and Duration of Study: Immunology & National Histocompatibility Department and 2nd Department of Surgery, ‘G. Gennimatas’ General Hospital, ‘Demetrios Voyatzoglou’ Diabetic Foot Clinic, ‘A. Fleming’ General Hospital, Department of Biology, National and Kapodistrian University of Athens, Athens, Greece, between June 2011 and May 2012. Study Design and Methodology: Early, autologous skin fibroblasts arisen in large numbers from small split-thickness skin biopsies, cultured in high concentration of fetal bovine serum, and dispersed in patients΄ own serum, were injected subcutaneously into the surrounding healthy tissue of uninfected diabetic foot ulcers of two type 2 diabetic patients without peripheral angiopathy. Results: There was complete healing in 11 and 27 weeks in patients 1 & 2, respectively. The early cultured fibroblasts showed impressive healing power for diabetic foot ulcers. On the contrary, the power of the prolonged cultured fibroblast diminished steadily, while the fibroblasts undergone the freezing-thawing procedure were not effective. Conclusion: The healing was complete, quick, safe, permanent, without scars or hyperkeratosis, and relatively inexpensive

An Approach to Identify HLA Class II Immunogenic Epitopes in the Greek Population through Machine Learning Algorithms

Current pre-transplantation routine matching involves serum anti-HLA antibodies quantification but cannot always preclude unfavorable graft outcomes. Epitope-based matching is proposed as a more precise approach, but to date no epitope-matching algorithm provides a satisfactory predictive tool for transplantation outcomes. In this study, anti-HLA-II loci responses from 1748 patients were analyzed with unsupervised machine learning algorithms, namely principal component analysis (PCA) and antigenic distances, projected as dendrograms. PCA for anti-HLA-DR anti-bodies revealed three main clusters of responses: anti-HLA-DR51 combined with anti-HLA-DRB1*01, anti-HLA-DR52 combined with anti-HLA-DRB1*08 and anti-HLA-DR53 combined with anti-HLA-DRB1*10. The dendrogram for anti-HLA-DR confirmed the pattern and showed further bisection of each cluster. Common epitopes present exclusively in all HLA molecules of each cluster were determined following the HLA epitope registry. Thus, we propose that 19 out of 123 HLA-DR epitopes are those that mainly lead anti-HLA-DR responses in the studied population. Likewise, we identified 22 out of 83 epitopes responsible for anti-HLA-DQ and 13 out of 62 responsible for anti-HLA-DP responses. Interpretation of these results may elucidate mechanisms of interlocus cross-reactivity, providing an alternative way of estimating the significance of each epitope in a population and thus suggesting a novel strategy towards optimal donor selection

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody–antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to “shared” antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intralocus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new crossreactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second featurebased analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific crossreactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody–antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to “shared” antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intralocus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new crossreactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second featurebased analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific crossreactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry