Karyopherins: potential biological elements involved in the delayed graft function in renal transplant recipients

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Acute Kidney Injury in Kidney Transplant Patients in Intensive Care Unit: From Pathogenesis to Clinical Management

Kidney transplantation is the first-choice treatment for end-stage renal disease (ESRD). Kidney transplant recipients (KTRs) are at higher risk of experiencing a life-threatening event requiring intensive care unit (ICU) admission, mainly in the late post-transplant period (more than 6 months after transplantation). Urosepsis and bloodstream infections account for almost half of ICU admissions in this population; in addition, potential side effects related to immunosuppressive treatment should be accounted for cytotoxic and ischemic changes induced by calcineurin inhibitor (CNI), sirolimus/CNI-induced thrombotic microangiopathy and posterior reversible encephalopathy syndrome. Throughout the ICU stay, Acute Kidney Injury (AKI) incidence is common and ranges from 10% to 80%, and up to 40% will require renal replacement therapy. In-hospital mortality can reach 30% and correlates with acute illness severity and admission diagnosis. Graft survival is subordinated to baseline estimated glomerular filtration rate (eGFR), clinical presentation, disease severity and potential drug nephrotoxicity. The present review aims to define the impact of AKI events on short- and long-term outcomes in KTRs, focusing on the epidemiologic data regarding AKI incidence in this subpopulation; the pathophysiological mechanisms underlying AKI development and potential AKI biomarkers in kidney transplantation, graft and patients' outcomes; the current diagnostic work up and management of AKI; and the modulation of immunosuppression in ICU-admitted KTRs.

Biomarkers as diagnostic tests for delayed graft function in kidney transplantation

Delayed graft function (DGF) after kidney transplantation is associated with inferior outcomes and higher healthcare costs. DGF is currently defined as the requirement for dialysis within seven days post-transplant; however, this definition is subjective and nonspecific. Novel biomarkers have potential to improve objectivity and enable earlier diagnosis of DGF. We reviewed the literature to describe the range of novel biomarkers previously studied to predict DGF. We identified marked heterogeneity and low reporting quality of published studies. Among the novel biomarkers, serum NGAL had the greatest potential as a biomarker to predict DGF, but requires further assessment and validation through larger scale studies of diagnostic test performance. Given inadequacies in the dialysis-based definition, coupled with the high incidence and impact of DGF, such studies should be pursued.

Emerging biomarkers of delayed graft function in kidney transplantation

Delayed Graft Function (DGF) is one of the most common early complications in kidney transplantation, associated with poor graft outcomes, prolonged post-operative hospitalization and higher rejection rates. Given the severe shortage of high-quality organs for transplantation, DGF incidence is expected to raise in the next years because of the use of nonstandard kidneys from Extended Criteria Donors (ECD) and from Donors after Circulatory Death (DCD). Alongside conventional methods for the evaluation of renal allograft [e.g. serum creatinine Glomerular Filtration Rate (GFR), needle biopsy], recent advancements in omics technologies, including proteomics, metabolomics and transcriptomics, may allow to discover novel biomarkers associated with DGF occurrence, in order to identify early preclinical signs of renal dysfunction and to improve the quality of graft management. Here, we gather contributions from basic scientists and clinical researchers to describe new omics studies in renal transplantation, reporting the emerging biomarkers of DGF that may implement and improve conventional approaches.

Predictive model for delayed graft function based on easily available pre-renal transplant variables

Identification of pre-transplant factors influencing delayed graft function (DGF) could have an important clinical impact. This could allow clinicians to early identify dialyzed chronic kidney disease (CKD) patients eligible for special transplant programs, preventive therapeutic strategies and specific post-transplant immunosuppressive treatments. To achieve these objectives, we retrospectively analyzed main demographic and clinical features, follow-up events and outcomes registered in a large dedicated dataset including 2,755 patients compiled collaboratively by four Italian renal/transplant units. The years of transplant ranged from 1984 to 2012. Statistical analysis clearly demonstrated that some recipients' characteristics at the time of transplantation (age and body weight) and dialysis-related variables (modality and duration) were significantly associated with DGF development (p ≤ 0.001). The area under the receiver-operating characteristic (ROC) curve of the final model based on the four identified variables predicting DGF was 0.63 (95 % CI 0.61, 0.65). Additionally, deciles of the score were significantly associated with the incidence of DGF (p value for trend <0.001). Therefore, in conclusion, in our study we identified a pre-operative predictive model for DGF, based on inexpensive and easily available variables, potentially useful in routine clinical practice in most of the Italian and European dialysis units.

Personalization of the immunosuppressive treatment in renal transplant recipients: the great challenge in "omics" medicine

Renal transplantation represents the most favorable treatment for patients with advanced renal failure and it is followed, in most cases, by a significant enhancement in patients’ quality of life. Significant improvements in one-year renal allograft and patients’ survival rates have been achieved over the last 10 years primarily as a result of newer immunosuppressive regimens. Despite these notable achievements in the short-term outcome, long-term graft function and survival rates remain less than optimal. Death with a functioning graft and chronic allograft dysfunction result in an annual rate of 3%–5%. In this context, drug toxicity and long-term chronic adverse effects of immunosuppressive medications have a pivotal role. Unfortunately, at the moment, except for the evaluation of trough drug levels, no clinically useful tools are available to correctly manage immunosuppressive therapy. The proper use of these drugs could potentiate therapeutic effects minimizing adverse drug reactions. For this purpose, in the future, “omics” techniques could represent powerful tools that may be employed in clinical practice to routinely aid the personalization of drug treatment according to each patient’s genetic makeup. However, it is unquestionable that additional studies and technological advances are needed to standardize and simplify these methodologies.