Sunil M. Kurian

De novo kidney transplantation without use of calcineurin inhibitors preserves renal structure and function at two years.

We performed a randomized prospective trial comparing calcineurin inhibitor (CNI)‐free to CNI‐based immunosuppression to determine the impact on renal function, structure and gene expression. Sixty‐one kidney recipients treated with basiliximab mycophenolate mofetil (MMF) and prednisone (P) were randomly assigned to concentration‐controlled sirolimus or cyclosporine. Two years post‐transplant 55 patients underwent renal function studies, 48 (87%) underwent transplant biopsies; all classified by Banff scoring and 41 by DNA microarrays. Comparing sirolimus/MMF/P to cyclosporine/MMF/P there was a significantly lower serum creatinine (1.35 vs. 1.81 mg/dL; p = 0.008), higher Cockroft‐Gault glomerular filtration rate (GFR) (80.4 vs. 63.4 mL/min; p = 0.008), iothalamate GFR (60.6 vs. 49.2 mL/min; p = 0.018) and Banff 0 (normal) biopsies (66.6 vs. 20.8%; p = 0.013). Regression analysis of calculated GFRs from 1 to 36 months yielded a positive slope for sirolimus of 3.36 mL/min/year, and a negative slope for cyclosporine of −1.58 mL/min/year (p = 0.008). Gene expression profiles from kidneys with higher Banff chronic allograft nephropathy (CAN) scores confirmed significant up‐regulation of genes responsible for immune/inflammation and fibrosis/tissue remodeling. At 2 years the sirolimus‐treated recipients have better renal function, a diminished prevalence of CAN and down‐regulated expression of genes responsible for progression of CAN. All may provide for an alternative natural history with improved graft survival.

Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes.

A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug‐induced toxicities.

Identifying blood biomarkers for mood disorders using convergent functional genomics.

There are to date no objective clinical laboratory blood tests for mood disorders. The current reliance on patient self-report of symptom severity and on the clinicians’ impression is a rate-limiting step in effective treatment and new drug development. We propose, and provide proof of principle for, an approach to help identify blood biomarkers for mood state. We measured whole-genome gene expression differences in blood samples from subjects with bipolar disorder that had low mood vs those that had high mood at the time of the blood draw, and separately, changes in gene expression in brain and blood of a mouse pharmacogenomic model. We then integrated our human blood gene expression data with animal model gene expression data, human genetic linkage/association data and human postmortem brain data, an approach called convergent functional genomics, as a Bayesian strategy for cross-validating and prioritizing findings. Topping our list of candidate blood biomarker genes we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6 and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of 10 top candidate biomarkers (five for high mood and five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.

Kidney transplantation with sirolimus and mycophenolate mofetil-based immunosuppression: 5-year results of a randomized prospective trial compared to calcineurin inhibitor drugs.

Background. We report the 5-year outcomes from a randomized prospective trial in primary adult renal allograft recipients, designed to evaluate calcineurin inhibitor (CNI)-free immunosuppression on kidney transplant function. Methods. Sixty-one patients were randomized to either sirolimus (n=31) or cyclosporine (n=30) after basiliximab induction and mycophenolate mofetil (MMF) with steroids. Sirolimus was concentration controlled at 10–12 ng/mL for at least 6 months. Results. After 5 years, sirolimus-MMF-steroids compared to cyclosporine-MMF-steroids provides similar patient survival (87.1 vs. 90%, P=0.681), acute rejection rates (12.9 vs. 23.3%, P=0.22), total cholesterol (209.1 vs. 204.3 mg/dL, P=0.973), urine protein/creatinine ratios (0.398 vs. 0.478 mg/dL, P=0.72), and overall medical and surgical morbidity (P=NS). Although unadjusted patient survival was similar, sirolimus based CNI-free patients had longer death censored graft survival (96.4 vs. 76.7%, P=0.0265), higher glomerular filtration rate (GFR) by the abbreviated Modified Diet in Renal Disease (66.7 vs. 50.7 cc/min, P=0.0075), and fewer graft losses from chronic allograft nephropathy. The Banff chronic scores at two years were strong predictors of 5-year GFR. At 5 years, there were six de novo (three solid organ, three skin) cancers in the CNI group and only two de novo (one skin, one leukemia, no solid organ) cancers in the sirolimus group (P=NS). Conclusions. This study of low to moderate risk patients demonstrates that excellent 5-year kidney transplant outcomes can be achieved without CNI drugs, when therapeutic drug monitoring of sirolimus is employed. The application of CNI drug avoidance protocols to high-risk recipients (retransplants, highly sensitized, etc.), extrarenal allograft recipients, or alternative drug regimens such as steroid or MMF elimination should be subjected to controlled trials.

Identification of blood biomarkers for psychosis using convergent functional genomics.

There are to date no objective clinical laboratory blood tests for psychotic disease states. We provide proof of principle for a convergent functional genomics (CFG) approach to help identify and prioritize blood biomarkers for two key psychotic symptoms, one sensory (hallucinations) and one cognitive (delusions). We used gene expression profiling in whole blood samples from patients with schizophrenia and related disorders, with phenotypic information collected at the time of blood draw, then cross-matched the data with other human and animal model lines of evidence. Topping our list of candidate blood biomarkers for hallucinations, we have four genes decreased in expression in high hallucinations states (Fn1, Rhobtb3, Aldh1l1, Mpp3), and three genes increased in high hallucinations states (Arhgef9, Phlda1, S100a6). All of these genes have prior evidence of differential expression in schizophrenia patients. At the top of our list of candidate blood biomarkers for delusions, we have 15 genes decreased in expression in high delusions states (such as Drd2, Apoe, Scamp1, Fn1, Idh1, Aldh1l1), and 16 genes increased in high delusions states (such as Nrg1, Egr1, Pvalb, Dctn1, Nmt1, Tob2). Twenty-five of these genes have prior evidence of differential expression in schizophrenia patients. Predictive scores, based on panels of top candidate biomarkers, show good sensitivity and negative predictive value for detecting high psychosis states in the original cohort as well as in three additional cohorts. These results have implications for the development of objective laboratory tests to measure illness severity and response to treatment in devastating disorders such as schizophrenia.

Discovery and validation of blood biomarkers for suicidality.

Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case–case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence—genetic and functional genomic data—as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner’s office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1–acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813). Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis.

Biomarkers for Early and Late Stage Chronic Allograft Nephropathy by Proteogenomic Profiling of Peripheral Blood

Background Despite significant improvements in life expectancy of kidney transplant patients due to advances in surgery and immunosuppression, Chronic Allograft Nephropathy (CAN) remains a daunting problem. A complex network of cellular mechanisms in both graft and peripheral immune compartments complicates the non-invasive diagnosis of CAN, which still requires biopsy histology. This is compounded by non-immunological factors contributing to graft injury. There is a pressing need to identify and validate minimally invasive biomarkers for CAN to serve as early predictors of graft loss and as metrics for managing long-term immunosuppression. Methods We used DNA microarrays, tandem mass spectroscopy proteomics and bioinformatics to identify genomic and proteomic markers of mild and moderate/severe CAN in peripheral blood of two distinct cohorts (n = 77 total) of kidney transplant patients with biopsy-documented histology. Findings Gene expression profiles reveal over 2400 genes for mild CAN, and over 700 for moderate/severe CAN. A consensus analysis reveals 393 (mild) and 63 (moderate/severe) final candidates as CAN markers with predictive accuracy of 80% (mild) and 92% (moderate/severe). Proteomic profiles show over 500 candidates each, for both stages of CAN including 302 proteins unique to mild and 509 unique to moderate/severe CAN. Conclusions This study identifies several unique signatures of transcript and protein biomarkers with high predictive accuracies for mild and moderate/severe CAN, the most common cause of late allograft failure. These biomarkers are the necessary first step to a proteogenomic classification of CAN based on peripheral blood profiling and will be the targets of a prospective clinical validation study.

microRNA regulation of molecular networks mapped by global microRNA, mRNA, and protein expression in activated T-lymphocytes

MicroRNAs (miRNAs) regulate specific immune mechanisms, but their genome-wide regulation of T lymphocyte activation is largely unknown. We performed a multidimensional functional genomics analysis to integrate genome-wide differential mRNA, miRNA, and protein expression as a function of human T lymphocyte activation and time. We surveyed expression of 420 human miRNAs in parallel with genome-wide mRNA expression. We identified a unique signature of 71 differentially expressed miRNAs, 57 of which were previously not known as regulators of immune activation. The majority of miRNAs are upregulated, mRNA expression of these target genes is downregulated, and this is a function of binding multiple miRNAs (combinatorial targeting). Our data reveal that consideration of this complex signature, rather than single miRNAs, is necessary to construct a full picture of miRNA-mediated regulation. Molecular network mapping of miRNA targets revealed the regulation of activation-induced immune signaling. In contrast, pathways populated by genes that are not miRNA targets are enriched for metabolism and biosynthesis. Finally, we specifically validated miR-155 (known) and miR-221 (novel in T lymphocytes) using locked nucleic acid inhibitors. Inhibition of these two highly upregulated miRNAs in CD4+ T cells was shown to increase proliferation by removing suppression of four target genes linked to proliferation and survival. Thus, multiple lines of evidence link top functional networks directly to T lymphocyte immunity, underlining the value of mapping global gene, protein, and miRNA expression.

Molecular Mechanisms of Chronic Kidney Transplant Rejection via Large-Scale Proteogenomic Analysis of Tissue Biopsies

The most common cause of kidney transplant failure is the poorly characterized histopathologic entity interstitial fibrosis and tubular atrophy (IFTA). There are no known unifying mechanisms, no effective therapy, and no proven preventive strategies. Possible mechanisms include chronic immune rejection, inflammation, drug toxicity, and chronic kidney injury from secondary factors. To gain further mechanistic insight, we conducted a large-scale proteogenomic study of kidney transplant biopsies with IFTA of varying severity. We acquired proteomic data using tandem mass spectrometry with subsequent quantification, analysis of differential protein expression, validation, and functional annotations to known molecular networks. We performed genome-wide expression profiling in parallel. More than 1400 proteins with unique expression profiles traced the progression from normal transplant biopsies to biopsies with mild to moderate and severe disease. Multiple sets of proteins were mapped to different functional pathways, many increasing with histologic severity, including immune responses, inflammatory cell activation, and apoptosis consistent with the chronic rejection hypothesis. Two examples include the extensive population of the alternative rather than the classical complement pathway, previously not appreciated for IFTA, and a comprehensive control network for the actin cytoskeleton and cell signaling of the acute-phase response. In summary, this proteomic effort using kidney tissue contributes mechanistic insight into several biologic processes associated with IFTA.

Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling.

There are no minimally invasive diagnostic metrics for acute kidney transplant rejection (AR), especially in the setting of the common confounding diagnosis, acute dysfunction with no rejection (ADNR). Thus, though kidney transplant biopsies remain the gold standard, they are invasive, have substantial risks, sampling error issues and significant costs and are not suitable for serial monitoring. Global gene expression profiles of 148 peripheral blood samples from transplant patients with excellent function and normal histology (TX; n = 46), AR (n = 63) and ADNR (n = 39), from two independent cohorts were analyzed with DNA microarrays. We applied a new normalization tool, frozen robust multi‐array analysis, particularly suitable for clinical diagnostics, multiple prediction tools to discover, refine and validate robust molecular classifiers and we tested a novel one‐by‐one analysis strategy to model the real clinical application of this test. Multiple three‐way classifier tools identified 200 highest value probesets with sensitivity, specificity, positive predictive value, negative predictive value and area under the curve for the validation cohort ranging from 82% to 100%, 76% to 95%, 76% to 95%, 79% to 100%, 84% to 100% and 0.817 to 0.968, respectively. We conclude that peripheral blood gene expression profiling can be used as a minimally invasive tool to accurately reveal TX, AR and ADNR in the setting of acute kidney transplant dysfunction.