The Human Genome Organisation (HUGO) and a vision for Ecogenomics: the Ecological Genome Project.

BACKGROUND: The following outlines ethical reasons for widening the Human Genome Organisation's (HUGO) mandate to include ecological genomics. MAIN: The environment influences an organism's genome through ambient factors in the biosphere (e.g. climate and UV radiation), as well as the agents it comes into contact with, i.e. the epigenetic and mutagenic effects of inanimate chemicals and pollution, and pathogenic organisms. Emerging scientific consensus is that social determinants of health, environmental conditions and genetic factors work together to influence the risk of many complex illnesses. That paradigm can also explain the environmental and ecological determinants of health as factors that underlie the (un)healthy ecosystems on which communities rely. We suggest that The Ecological Genome Project is an aspirational opportunity to explore connections between the human genome and nature. We propose consolidating a view of Ecogenomics to provide a blueprint to respond to the environmental challenges that societies face. This can only be achieved by interdisciplinary engagement between genomics and the broad field of ecology and related practice of conservation. In this respect, the One Health approach is a model for environmental orientated work. The idea of Ecogenomics-a term that has been used to relate to a scientific field of ecological genomics-becomes the conceptual study of genomes within the social and natural environment. CONCLUSION: The HUGO Committee on Ethics, Law and Society (CELS) recommends that an interdisciplinary One Health approach should be adopted in genomic sciences to promote ethical environmentalism. This perspective has been reviewed and endorsed by the HUGO CELS and the HUGO Executive Board.

Steroid-tacrolimus drug-drug interaction and the effect of CYP3A genotypes.

AIMS: Tacrolimus, metabolized by CYP3A4 and CYP3A5 enzymes, is susceptible to drug-drug interactions (DDI). Steroids induce CYP3A genes to increase tacrolimus clearance, but the effect is variable. We hypothesized that the extent of the steroid-tacrolimus DDI differs by CYP3A4/5 genotypes. METHODS: Kidney transplant recipients (n = 2462) were classified by the number of loss of function alleles (LOF) (CYP3A5*3, *6 and *7 and CYP3A4*22) and steroid use at each tacrolimus trough in the first 6 months post-transplant. A population pharmacokinetic analysis was performed by nonlinear mixed-effect modelling (NONMEM) and stepwise covariate modelling to define significant covariates affecting tacrolimus clearance. A stochastic simulation was performed and translated into a Shiny application with the mrgsolve and Shiny packages in R. RESULTS: Steroids were associated with modestly higher (3%-11.8%) tacrolimus clearance. Patients with 0-LOF alleles receiving steroids showed the greatest increase (11.8%) in clearance compared to no steroids, whereas those with 2-LOFs had a negligible increase (2.6%) in the presence of steroids. Steroid use increased tacrolimus clearance by 5% and 10.3% in patients with 1-LOF and 3/4-LOFs, respectively. CONCLUSIONS: Steroids increase the clearance of tacrolimus but vary slightly by CYP3A genotype. This is important in individuals of African ancestry who are more likely to carry no LOF alleles, may more commonly receive steroid treatment, and will need higher tacrolimus doses.

Limited Sampling Strategies Fail to Accurately Predict Mycophenolic Acid Area Under the Curve in Kidney Transplant Recipients and the Impact of Enterohepatic Recirculation.

BACKGROUND: Therapeutic drug monitoring for mycophenolic acid (MPA) is challenging due to difficulties in measuring the area under the curve (AUC). Limited sampling strategies (LSSs) have been developed for MPA therapeutic drug monitoring but come with risk of unacceptable performance. The authors hypothesized that the poor predictive performance of LSSs were due to the variability in MPA enterohepatic recirculation (EHR). This study is the first to evaluate LSSs models performance in the context of EHR. METHODS: Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC0-12hr in our full cohort and separately in individuals with high and low EHR. RESULTS: None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC0-12hr (±15% of observed AUC0-12hr) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%). CONCLUSIONS: The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. Models and strategies that incorporate EHR during development are required to accurately ascertain MPA exposure.

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch.

BACKGROUND: Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications. METHODS: We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10-3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs. RESULTS: By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10-8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10-4. CONCLUSIONS: Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov.

Pharmacomicrobiomics: Immunosuppressive Drugs and Microbiome Interactions in Transplantation.

The human microbiome is associated with human health and disease. Exogenous compounds, including pharmaceutical products, are also known to be affected by the microbiome, and this discovery has led to the field of pharmacomicobiomics. The microbiome can also alter drug pharmacokinetics and pharmacodynamics, possibly resulting in side effects, toxicities, and unanticipated disease response. Microbiome-mediated effects are referred to as drug-microbiome interactions (DMI). Rapid advances in the field of pharmacomicrobiomics have been driven by the availability of efficient bacterial genome sequencing methods and new computational and bioinformatics tools. The success of fecal microbiota transplantation for recurrent Clostridioides difficile has fueled enthusiasm and research in the field. This review focuses on the pharmacomicrobiome in transplantation. Alterations in the microbiome in transplant recipients are well documented, largely because of prophylactic antibiotic use, and the potential for DMI is high. There is evidence that the gut microbiome may alter the pharmacokinetic disposition of tacrolimus and result in microbiome-specific tacrolimus metabolites. The gut microbiome also impacts the enterohepatic recirculation of mycophenolate, resulting in substantial changes in pharmacokinetic disposition and systemic exposure. The mechanisms of these DMI and the specific bacteria or communities of bacteria are under investigation. There are little or no human DMI data for cyclosporine A, corticosteroids, and sirolimus. The available evidence in transplantation is limited and driven by small studies of heterogeneous designs. Larger clinical studies are needed, but the potential for future clinical application of the pharmacomicrobiome in avoiding poor outcomes is high.

Meeting the New AACP Competencies in Genetics and Clinical Pharmacogenomics at the University of Minnesota.

Objective: Pharmacogenomics (PGx) is increasingly being used for creating individualized treatments for patient care. Healthcare professionals, especially pharmacists, need to understand how genetic variation impacts the efficacy and toxicity of medications. Due to the breadth and complexity of PGx-related information, it has been challenging to determine what information should be included in pharmacy curricula and how best to educate students. Methods: The University of Minnesota College of Pharmacy recently began the process of incorporating into the curriculum expanded competencies for PGx from the American Association of Colleges of Pharmacy (AACP) Pharmacogenomics Special Interest Group (PGx-SIG). We evaluated our curriculum for PGx content, determined what was currently being taught and identified educational gaps. Results: A review of our Doctor of Pharmacy curriculum showed substantial PGx content, although it was inconsistently taught throughout the required courses and in some courses absent. We revised the content of existing courses incorporating content that meet most of the PGx-SIG recommended competencies. Conclusion: There are and will be major changes in our understanding of the influences of PGx on individualized medical treatment. As our understanding grows, information on PGx in pharmacy curriculums will need to keep pace with these changes. We have begun this process at the University of Minnesota by doing a full review of PGx related information and making appropriate revisions in the pharmacy curriculum.