Identification and Characterization of Full-Length cDNAs in Channel Catfish (Ictalurus punctatus) and Blue Catfish (Ictalurus furcatus)

Background: Genome annotation projects, gene functional studies, and phylogenetic analyses for a given organism all greatly benefit from access to a validated full-length cDNA resource. While increasingly common in model species, fulllength cDNA resources in aquaculture species are scarce. Methodology and Principal Findings: Through in silico analysis of catfish (Ictalurus spp.) ESTs, a total of 10,037 channel catfish and 7,382 blue catfish cDNA clones were identified as potentially encoding full-length cDNAs. Of this set, a total of 1,169 channel catfish and 933 blue catfish full-length cDNA clones were selected for re-sequencing to provide additional coverage and ensure sequence accuracy. A total of 1,745 unique gene transcripts were identified from the full-length cDNA set, including 1,064 gene transcripts from channel catfish and 681gene transcripts from blue catfish, with 416 transcripts shared between the two closely related species. Full-length sequence characteristics (ortholog conservation, UTR length, Kozak sequence, and conserved motifs) of the channel and blue catfish were examined in detail. Comparison of gene ontology composition between full-length cDNAs and all catfish ESTs revealed that the full-length cDNA set is representative of the gene diversity encoded in the catfish transcriptome. Conclusions: This study describes the first catfish full-length cDNA set constructed from several cDNA libraries. The catfish full-length cDNA sequences, and data gleaned from sequence characteristics analysis, will be a valuable resource fo

Clinically adjudicated deceased donor acute kidney injury and graft outcomes

Background: Acute kidney injury (AKI) in deceased donors is not associated with graft failure (GF). We hypothesize that hemodynamic AKI (hAKI) comprises the majority of donor AKI and may explain this lack of association. Methods: In this ancillary analysis of the Deceased Donor Study, 428 donors with available charts were selected to identify those with and without AKI. AKI cases were classified as hAKI, intrinsic (iAKI), or mixed (mAKI) based on majority adjudication by three nephrologists. We evaluated the associations between AKI phenotypes and delayed graft function (DGF), 1-year eGFR and GF. We also evaluated differences in urine biomarkers among AKI phenotypes. Results: Of the 291 (68%) donors with AKI, 106 (36%) were adjudicated as hAKI, 84 (29%) as iAKI and 101 (35%) as mAKI. Of the 856 potential kidneys, 669 were transplanted with 32% developing DGF and 5% experiencing GF. Median 1-year eGFR was 53 (IQR: 41-70) ml/min/1.73m2. Compared to non-AKI, donors with iAKI had higher odds DGF [aOR (95%CI); 4.83 (2.29, 10.22)] and had lower 1-year eGFR [adjusted B coefficient (95% CI): -11 (-19, -3) mL/min/1.73 m2]. hAKI and mAKI were not associated with DGF or 1-year eGFR. Rates of GF were not different among AKI phenotypes and non-AKI. Urine biomarkers such as NGAL, LFABP, MCP-1, YKL-40, cystatin-C and albumin were higher in iAKI. Conclusion: iAKI was associated with higher DGF and lower 1-year eGFR but not with GF. Clinically phenotyped donor AKI is biologically different based on biomarkers and may help inform decisions regarding organ utilization

Measures of Global Health Status on Dialysis Signal Early Rehospitalization Risk after Kidney Transplantation

<div><p>Background</p><p>Early rehospitalization (<30 days) after discharge from kidney transplantation (KT) is associated with poor outcomes. We explored summary metrics of pre-transplant health status that may improve the identification of KT recipients at risk for early rehospitalization and mortality after transplant.</p><p>Materials and Methods</p><p>We performed a retrospective cohort study of 8,870 adult (≥ 18 years) patients on hemodialysis who received KT between 2000 and 2010 at United States transplant centers. We linked Medicare data to United Network for Organ Sharing data and data from a national dialysis provider to examine pre-KT (1) Elixhauser Comorbidity Index, (2) physical function (PF) measured by the Short Form 36 Health Survey, and (3) the number of hospitalizations during the 12 months before KT as potential predictors of early rehospitalization after KT. We also explored whether these metrics are confounders of the known association between early rehospitalization and post-transplant mortality.</p><p>Results</p><p>The median age was 52 years (interquartile range [IQR] 41, 60) and 63% were male. 29% were rehospitalized in <30 days, and 20% died during a median follow-up time of five years (IQR 3.6–6.5). In a multivariable logistic model, kidney recipients with more pre-KT Elixhauser comorbidities (adjusted odds ratio [aOR] 1.09 per comorbidity, 95% Confidence Interval [CI] 1.07–1.11), the poorest pre-KT PF (aOR 1.24, 95% CI 1.08–1.43), or >1 pre-KT hospitalizations (aOR 1.32, 95% CI 1.17–1.49) were more likely to be rehospitalized. All three health status metrics and early rehospitalization were independently associated with post-KT mortality in a multivariable Cox model (adjusted hazard ratio for rehospitalization: 1.41, 95% CI 1.28–1.56)</p><p>Conclusions</p><p>Pre-transplant metrics of health status, measured by dialysis providers or administrative data, are independently associated with early rehospitalization and mortality risk after KT. Transplant providers may consider utilizing metrics of pre-KT global health status as early signals of vulnerability when transitioning care after KT.</p></div

Distribution of Health Metrics, Stratified by Rehospitalization Status.

<p>Distribution of Health Metrics, Stratified by Rehospitalization Status.</p

Comparison of Logistic Regression Models for Early Rehospitalization.

<p>Comparison of Logistic Regression Models for Early Rehospitalization.</p

Association of Early Rehospitalization and Post-Transplant Mortality in Cox Proportional Hazards Models, with Adjustment for Pre-Transplant Health Status.

<p>Association of Early Rehospitalization and Post-Transplant Mortality in Cox Proportional Hazards Models, with Adjustment for Pre-Transplant Health Status.</p

Participant Inclusion Flow Diagram.

<p>Participant Inclusion Flow Diagram.</p

Prediction of the outcome of early rehospitalization after kidney transplantation using global health metrics.

<p>Baseline and subsequent logistic models adjusted for (1) <i>recipient</i> age category at transplant, sex, race, hepatitis C serostatus, obesity by body mass index (≥30 kg/m²), dialysis vintage (years), time on the waitlist (years), history of diabetes, history of previous solid organ transplant, education status, (2) <i>donor</i> type (live vs. deceased donor, expanded criteria deceased [ECD] donor); (3) <i>allograft</i> variables of delayed graft function, and (4) <i>process-of-care</i> variables of length of initial transplant hospitalization (days), weekend discharge (defined as discharge on Saturday or Sunday), and low transplant center volume (defined as <150 kidney transplants performed, on average, per year).</p

Adjusted Probability (with 95% Confidence Intervals) of Rehospitalization Based on Pre-Transplant Health Metrics.

<p>Adjusted for (1) <i>recipient</i> age category at transplant, sex, race, hepatitis C serostatus, obesity by body mass index (≥30 kg/m²), dialysis vintage (years), time on the waitlist (years), history of diabetes, history of previous solid organ transplant, education status, (2) <i>donor</i> type (live vs. deceased donor, expanded criteria deceased [ECD] donor); (3) <i>allograft</i> variables of delayed graft function, and (4) <i>process-of-care</i> variables of length of initial transplant hospitalization (days), weekend discharge (defined as discharge on Saturday or Sunday), and low transplant center volume (defined as <150 kidney transplants performed, on average, per year).</p

Poor Global Health Status and Early Rehospitalization Both Augment Mortality Risk after Kidney Transplantation.

<p>Cox models also adjusted for (1) <i>recipient</i> age category at transplant, sex, race, hepatitis C serostatus, obesity by body mass index (≥30 kg/m²), dialysis vintage (years), time on the waitlist (years), history of diabetes, history of previous solid organ transplant, education status, (2) <i>donor</i> type (live vs. deceased donor, expanded criteria deceased [ECD] donor); (3) <i>allograft</i> variables of delayed graft function, and (4) <i>process-of-care</i> variables of length of initial transplant hospitalization (days), weekend discharge (defined as discharge on Saturday or Sunday), and low transplant center volume (defined as <150 kidney transplants performed, on average, per year).</p