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.
This study of kidney transplantation describes a three‐way classifier based on global gene expression profiling of peripheral blood and the blood signatures of patients with excellent functioning grafts that can be used in the setting of acute kidney transplant dysfunction to accurately distinguish between biopsy‐proven acute rejection and acute dysfunction with no rejection.
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.
Interstitial fibrosis and tubular atrophy (IFTA) is found in approximately 25% of 1‐year biopsies posttransplant. It is known that IFTA correlates with decreased graft survival when histological ...evidence of inflammation is present. Identifying the mechanistic etiology of IFTA is important to understanding why long‐term graft survival has not changed as expected despite improved immunosuppression and dramatically reduced rates of clinical acute rejection (AR) (Services UDoHaH. http://www.ustransplant.org/annual_reports/current/509a_ki.htm). Gene expression profiles of 234 graft biopsy samples were obtained with matching clinical and outcome data. Eighty‐one IFTA biopsies were divided into subphenotypes by degree of histological inflammation: IFTA with AR, IFTA with inflammation, and IFTA without inflammation. Samples with AR (n = 54) and normally functioning transplants (TX; n = 99) were used in comparisons. A novel analysis using gene coexpression networks revealed that all IFTA phenotypes were strongly enriched for dysregulated gene pathways and these were shared with the biopsy profiles of AR, including IFTA samples without histological evidence of inflammation. Thus, by molecular profiling we demonstrate that most IFTA samples have ongoing immune‐mediated injury or chronic rejection that is more sensitively detected by gene expression profiling. These molecular biopsy profiles correlated with future graft loss in IFTA samples without inflammation.
In this multicenter study featuring gene expression profiles with matching demographic and long‐term follow‐ up data, the authors demonstrate that biopsies with interstitial fibrosis and tubular atrophy without alternative explanations for pathogenesis and regardless of histological reads with or without inflammation, have significantly worse graft survival than those with biopsy‐proven normal transplant histology and differential gene expression changes that indicate ongoing cellular immune‐mediated injury that correlate with worse long‐term outcomes. See Maluf et al's editorial on page 1947.
Purpose To assess the repeatability of a new swept-source optical coherence tomography (SS-OCT) biometer (IOLMaster 700) and evaluate the agreement with an optical low-coherence reflectometry (OLCR) ...biometer (Lenstar 900) in cataractous eyes. Setting Tertiary eyecare facility. Design Prospective evaluation of a diagnostic test. Methods Eyes of patients had 3 consecutive scans acquired by the same operator. The repeatability of measurements was assessed using the within-subject standard deviation and coefficient of variation (CoV). Agreement between the mean measurements of each machine was evaluated. Results The study comprised 100 eyes of 100 patients. The median lens thickness and axial length (AL) measurements were significantly greater ( P < .001) with SS-OCT (4.29 mm and 23.5 mm, respectively, versus 4.25 mm and 23.4 mm, respectively); anterior chamber depth (ACD) was greater with OLCR (2.72 mm versus 2.70 mm, respectively) ( P < .001). The within-subject standard deviation and CoV of the AL (0.01 and 0.05, respectively) and the ACD (0.04 and 1.22, respectively) measurements showed lower variability with SS-OCT (0.05 and 0.21, respectively, for AL; 0.06 and 1.99, respectively, for ACD) than with OLCR. Good agreement was seen for anterior segment measurements between the 2 devices. The 95% limits of agreement of most parameters fell within the test–retest variability of the parameters. The SS-OCT biometer also showed better penetration in dense posterior subcapsular cataracts, measuring AL successfully in 96% of cases. Conclusions The new SS-OCT biometer showed good repeatability and agreement with the OLCR biometer. The SS-OCT biometer measured the AL with fewer dropouts compared with the OLCR even in dense cataracts. Financial Disclosure None of the authors has a financial or proprietary interest in any material or method mentioned.
Anxiety disorders are increasingly prevalent, affect people's ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, ...resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed.
Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track ...core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.
Our aim was to determine outcomes with transplanting kidneys from deceased donors with acute kidney injury, defined as a donor with terminal serum creatinine ≥2.0 mg/dL, or a donor requiring acute ...renal replacement therapy. We included all patients who received deceased donor kidney transplant from June 2004 to October 2013. There were 162 AKI donor transplant recipients (21% of deceased donor transplants): 139 in the standard criteria donor (SCD) and 23 in the expanded criteria donor (ECD) cohort. 71% of the AKI donors had stage 3 (severe AKI), based on acute kidney injury network (AKIN) staging. Protocol biopsies were done at 1, 4, and 12 months posttransplant. One and four month formalin‐fixed paraffin embedded (FFPE) biopsies from 48 patients (24 AKI donors, 24 non‐AKI) underwent global gene expression profiling using DNA microarrays (96 arrays). DGF was more common in the AKI group but eGFR, graft survival at 1 year and proportion with IF/TA>2 at 1 year were similar for the two groups. At 1 month, there were 898 differentially expressed genes in the AKI group (p‐value <0.005; FDR <10%), but by 4 months there were no differences. Transplanting selected kidneys from deceased donors with AKI is safe and has excellent outcomes.
The authors show that transplanting select kidneys from deceased donors with severe acute injury results in excellent outcomes, including graft survival, and resolution of early differences in allograft gene expression for tissue damage, cell death, and inflammation.
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.
Worldwide, one person dies every 40 seconds by suicide, a potentially preventable tragedy. A limiting step in our ability to intervene is the lack of objective, reliable predictors. We have ...previously provided proof of principle for the use of blood gene expression biomarkers to predict future hospitalizations due to suicidality, in male bipolar disorder participants. We now generalize the discovery, prioritization, validation, and testing of such markers across major psychiatric disorders (bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) in male participants, to understand commonalities and differences. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation and high suicidal ideation states (n=37 participants out of a cohort of 217 psychiatric participants followed longitudinally). We then used a convergent functional genomics (CFG) approach with existing prior evidence in the field to prioritize the candidate biomarkers identified in the discovery step. Next, we validated the top biomarkers from the prioritization step for relevance to suicidal behavior, in a demographically matched cohort of suicide completers from the coroner's office (n=26). The biomarkers for suicidal ideation only are enriched for genes involved in neuronal connectivity and schizophrenia, the biomarkers also validated for suicidal behavior are enriched for genes involved in neuronal activity and mood. The 76 biomarkers that survived Bonferroni correction after validation for suicidal behavior map to biological pathways involved in immune and inflammatory response, mTOR signaling and growth factor regulation. mTOR signaling is necessary for the effects of the rapid-acting antidepressant agent ketamine, providing a novel biological rationale for its possible use in treating acute suicidality. Similarly, MAOB, a target of antidepressant inhibitors, was one of the increased biomarkers for suicidality. We also identified other potential therapeutic targets or biomarkers for drugs known to mitigate suicidality, such as omega-3 fatty acids, lithium and clozapine. Overall, 14% of the top candidate biomarkers also had evidence for involvement in psychological stress response, and 19% for involvement in programmed cell death/cellular suicide (apoptosis). It may be that in the face of adversity (stress), death mechanisms are turned on at a cellular (apoptosis) and organismal level. Finally, we tested the top increased and decreased biomarkers from the discovery for suicidal ideation (CADM1, CLIP4, DTNA, KIF2C), prioritization with CFG for prior evidence (SAT1, SKA2, SLC4A4), and validation for behavior in suicide completers (IL6, MBP, JUN, KLHDC3) steps in a completely independent test cohort of psychiatric participants for prediction of suicidal ideation (n=108), and in a future follow-up cohort of psychiatric participants (n=157) for prediction of psychiatric hospitalizations due to suicidality. The best individual biomarker across psychiatric diagnoses for predicting suicidal ideation was SLC4A4, with a receiver operating characteristic (ROC) area under the curve (AUC) of 72%. For bipolar disorder in particular, SLC4A4 predicted suicidal ideation with an AUC of 93%, and future hospitalizations with an AUC of 70%. SLC4A4 is involved in brain extracellular space pH regulation. Brain pH has been implicated in the pathophysiology of acute panic attacks. We also describe two new clinical information apps, one for affective state (simplified affective state scale, SASS) and one for suicide risk factors (Convergent Functional Information for Suicide, CFI-S), and how well they predict suicidal ideation across psychiatric diagnoses (AUC of 85% for SASS, AUC of 89% for CFI-S). We hypothesized a priori, based on our previous work, that the integration of the top biomarkers and the clinical information into a universal predictive measure (UP-Suicide) would show broad-spectrum predictive ability across psychiatric diagnoses. Indeed, the UP-Suicide was able to predict suicidal ideation across psychiatric diagnoses with an AUC of 92%. For bipolar disorder, it predicted suicidal ideation with an AUC of 98%, and future hospitalizations with an AUC of 94%. Of note, both types of tests we developed (blood biomarkers and clinical information apps) do not require asking the individual assessed if they have thoughts of suicide, as individuals who are truly suicidal often do not share that information with clinicians. We propose that the widespread use of such risk prediction tests as part of routine or targeted healthcare assessments will lead to early disease interception followed by preventive lifestyle modifications and proactive treatment.
Blood biomarkers may provide a scientifically useful and clinically usable peripheral signal in psychiatry, as they have been doing for other fields of medicine. Jumping to premature conclusions, ...negative or positive, can create confusion in this field. Reproducibility is a hallmark of good science. We discuss some recent examples from this dynamic field, and show some new data in support of previously published biomarkers for suicidality (SAT1, MARCKS and SKA2). Methodological clarity and rigor in terms of biomarker discovery, validation and testing is needed. We propose a set of principles for what constitutes a good biomarker, similar in spirit to the Koch postulates used at the birth of the field of infectious diseases.
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