HEART®, addressing these limitations. To assure pre-analytical quality and consider interrelated cfDNA measures, plasma preparation was optimized and total cfDNA (TCF) concentration, DNA fragmentation, and DF quantification were validated in parallel for integration into myTAIHEART reporting. Analytical validations employed individual and reconstructed mixtures of human blood-derived genomic DNA (gDNA), cfDNA, and gDNA sheared to apoptotic length. Precision, linearity, and limits of blank/detection/quantification were established for TCF concentration, DNA fragmentation ratio, and DF determinations. For DF, multiplexed high-fidelity amplification followed by quantitative genotyping of 94 SNP targets was applied to 1168 samples to evaluate donor options in staged simulations, demonstrating DF call equivalency with/without donor genotype. Clinical validation studies using 158 matched endomyocardial biopsy-plasma pairs from 76 pediatric and adult heart transplant recipients selected a DF cutoff (0.32%) producing 100% NPV for ≥2R ACR. This supports the assay’s conservative intended use of stratifying low versus increased probability of ≥2R ACR. myTAIHEART is clinically validated for heart transplant recipients ≥2 months old and ≥8 days post-transplant, expanding opportunity for noninvasive transplant rejection assessment to infants and children and to all recipients >1 week post-transplant.Lifelong noninvasive rejection monitoring in heart transplant patients is a critical clinical need historically poorly met in adults and unavailable for children and infants. Cell-free DNA (cfDNA) donor-specific fraction (DF), a direct marker of selective donor organ injury, is a promising analytical target. Methodological differences in sample processing and DF determination profoundly affect quality and sensitivity of cfDNA analyses, requiring specialized optimization for low cfDNA levels typical of transplant patients. Using next-generation sequencing, we previously correlated elevated DF with acute cellular and antibody-mediated rejection (ACR and AMR) in pediatric and adult heart transplant patients. However, next-generation sequencing is limited by cost, TAT, and sensitivity, leading us to clinically validate a rapid, highly sensitive, quantitative genotyping test, myTAI Noninvasive risk assessment for rejection in heart transplant recipients, both adult and pediatric, is an imperative and urgent clinical need. Organ-transplant patients require life-long immunosuppression that must be controlled carefully to balance risk of allograft rejection and loss with equally life-threatening immunosuppression-induced risks of infection, cancer, and other maladies. In heart transplant patients, this balance traditionally has been monitored through a multitude of diagnostic modalities. These include assessments of clinical symptomology, viral loads and other microbiological indicators, immunosuppressive drug and procalcitonin blood levels , echocar™ test  and the AlloMap® test . Heartsbreath™ is a test for biomarkers of oxidative stress in breath and received FDA approval in 2004 through humanitarian device exception. That approval was based on a single, non-randomized three-year multicenter National Heart Lung and Blood Institute-sponsored study of 539 heart transplantation patients in which breath was analyzed by gas chromatography/mass spectroscopy for alkanes and monomethyl alkanes and correlated with histological grading of subsequent EMB [Two early clinical offerings for noninvasive monitoring of heart transplant rejection included the Heartsbreathuent EMB ,31. Thisuent EMB ,32. Allouent EMB , making uent EMB . Transpluent EMB . AlloMapuent EMB , and is uent EMB .A more widely applicable and direct biomarker for risk stratification of transplant rejection, the donor-specific fraction (DF) of recipient plasma cell free DNA (cfDNA), has also been explored –39 and ie.g., SRY) in sex-mismatched donor-recipient pairs, of clearly limited application, and HLA genes, limited by poor informativity of target sequences in some recipient-donor pairs [Early iterations of cfDNA-based assays for determination of plasma DF in transplant recipients were challenged by the very low levels of cfDNA in plasma of healthy transplant recipients, which closely overlap those of un-transplanted healthy controls, much lower than levels typically observed in cancer patients and during pregnancy. Accordingly, some have targeted known donor-recipient discriminative loci, such as Y chromosome-specific sites  in addition to higher grade ACR (ISHLT 2R and 3R), AMR, and graft vasculopathy. We herein report results of analytical and clinical validation studies performed to support clinical diagnostic use of this non-invasive test for quantitative determination of total cfDNA (TCF) concentration and cfDNA DF with subsequent stratification of risk of moderate to severe ACR in adult and pediatric heart transplant patients 2 months of age or older and 8 or more days post-transplant. Donor genotyping is not required. This provides an unmet clinical need for virtually all heart transplant patients in the most critical period for rejection risk as well as throughout life.Methodological differences in DF measurement define the performance characteristics, speed, cost, and practicality of clinical application and contribute importantly to its clinical utility. Digital PCR has been explored to improve sensitivity of DF determination and lower cost, but is hampered by limited multiplexing capacity . Using NHEART test uses multiplexed, high-fidelity amplification followed by allele-specific qPCR of a panel of 94 highly informative bi-allelic single nucleotide polymorphisms (SNPs) and two controls in a heart transplant recipient’s plasma, thereby distinguishing “donor specific” cfDNA originating from the engrafted heart from “self-specific” cfDNA originating from the recipient’s own native cells. The myTAIHEART testing on any given transplant recipient, a sample of recipient whole blood, either pre- or post-transplant, is collected in an EDTA tube and without further processing sent to TAI Diagnostics for basic genotyping (bGT) of the recipient’s genomic (leukocyte) DNA (gDNA) at the 94 SNPs of interest. Plasma cfDNA DF for that recipient at clinically indicated time points post-transplant is then determined in subsequent blood samples by multiplexed, high-fidelity amplification and allele-specific quantitative genotyping (qGT) of those same targets. The blood samples for post-transplant qGT are collected as whole blood in a K2EDTA plasma gel separator tube (BD Vacutainer® PPT™ Plasma Preparation Tube) and require subjection to two brief low speed centrifugation steps at the collection site within 2 hours of phlebotomy. This allows quick and effective separation of the plasma (cfDNA) phase from contaminating leukocytes and erythrocytes prior to freezing and shipping overnight to TAI Diagnostics for subsequent qGT to determine TCF concentration (ng/ml) and cfDNA DF (%). qGT is performed each time a plasma sample is submitted for myTAIHEART testing, producing a unique longitudinal post-transplant time point for determination of plasma TCF concentration and DF. DF provides a point estimation of selective injury to donor-specific cells of the transplanted heart that is used to categorize the patient’s probability of transplant rejection.As a one-time clinical requirement for initiation of myTAIHEART DNA fragmentation test. The myTAIHEART DNA Fragmentation Assay is based on fragment length-dependent real time qPCR amplification of multi-copy Alu gene sequence and is used as a quality control step to rule out significant contribution to the cfDNA pool by leukocyte lysis or contamination during sample collection and plasma separation that may necessitate specimen rejection. The one-time bGT result is used for interrogating qGT findings to generate the cfDNA DF using the proprietary myTAIHEART software.Upon receipt in the TAI Clinical Reference Laboratory, whole blood and frozen plasma samples are extracted and processed through bGT and/or qGT workflows as appropriate. Prior to entering these workflows, gDNA from the recipient whole blood sample (for bGT) is extracted and quantitated by UV-spectroscopy. cfDNA is extracted from the plasma sample (for qGT), quantified by reference gene quantitative PCR = RNase P qPCR) and subjected to a proprietary, internally controlled myTAIHEART testing, a signed test report is generated resulting plasma TCF in ng/ml and DF (%). Based on the DF result, an interpretation is further assigned, according to validated reference ranges , for low versus increased probability of moderate or severe ACR. Additional report content includes test performance characteristics, intended use, historical DF results for the patient, warnings and limitations, and optional comments from the Medical Director. All positive results for increased risk of rejection are critical results and directly communicated to the ordering physician or other responsible health care provider. Copies of the final report are sent to the referring laboratory and ordering physician by secure fax and or email as specified on the test requisition, followed by hard copy. The entire workflow of this Laboratory Developed Test (LDT) was documented formally and locked prior to performance of clinical analytical validation studies described herein in the Clinical Laboratory Improvement Act (CLIA) and College of American Pathologists (CAP)-accredited TAI Diagnostics Clinical Reference Laboratory.For each incidence of clinical myTAIHEART genotyping panel would function well without availability of the donor genotype and would be highly informative for a broad range of transplant recipients and donors with diverse ancestral and ethnic genetic backgrounds. These backgrounds included all major human supergroups of which seven were defined by gnomAD [HEART panel was designed initially by filtering from over 240 million known variant, strictly biallelic, sites for minor allele frequency cutoffs of > 25% in each subpopulation of the 1000 Genomes, also requiring that they be greater than 1000 base pair (bp) apart to minimize linkage. Potentially problematic genomic regions in the initially selected group were removed prior to final SNP selection by excluding syndromic regions likely to be abnormal in heart transplant patients (such as trisomy 21 and CNVs associated with disease) [Targets were chosen from among SNPs exhibiting variation across the entire range of known human genetic diversity in order to ensure that a single myTAIy gnomAD ,77 and fy gnomAD . The myTdisease) , low comdisease) ,81, alloin silico to select compatible candidate pools. A pool of 400 candidate primer pairs [HEART panel selection representing the upper quartile of this pool. Median lengths of final library and allele specific primers were 42 bp and 21bp, respectively.Multiple 20–26 bp primer pairs within each selected SNP window were designed to amplify targets. To ensure specificity, all candidate primers were queried with BLAST to Human Reference Genome GCRh37 to check for possible cross amplification of non-pairing primers to non-target sites in the human genome. Multiplex amplification was evaluated er pairs  was chosHEART DNA Fragmentation Assay, plasma was spiked with short fragments of DNA obtained by Covaris ME220  focused ultrasonication (“shearing”) of gDNA from the paired cellular component (buffy coat) to a size distribution primarily in the range of 130–180 bp, approximating that of cfDNA. Resultant fragment lengths were evaluated on an Agilent 2100 Bioanalyzer  with a high sensitivity DNA chip to confirm production of the targeted range as determined by base pair size of maximum fluorescence values  associated with increased probability of rejection. The TAI Protocol for cfDNA sample handling and processing requires separation of plasma from leukocytes with two low speed spins performed within 2 hours of collection, extended to 4 hrs in Streck BCT tubes , whether for clinical trial or clinical diagnostic use. One hundred and fifty eight biopsy-associated heart transplant patient samples collected at Froedtert Lutheran Memorial Hospital (FLMH) and Children’s Hospital of Wisconsin (CHW) in Milwaukee were used in clinical validation of the myTAIHEART DF test . Whole blood was drawn into 10 ml Cell-Free DNA BCT® tubes  tubes at FLMH and CHW, hand-walked to the adjacent Children’s Research Institute (CRI) Nucleic Acid Extraction Lab from centrifugation to separate plasma  prior to freezing at -80°C for delayed extraction. DNA Based Transplant Rejection Test (DTRT) prospective blinded multicenter study samples  were used to establish the TCF “normal” reference range for the myTAIHEART test in healthy, asymptomatic heart transplant patients. These samples were also collected in 10 ml Streck BCT tubes and rapidly processed per protocol prior to submission to TAI Diagnostics for analysis . For myTAIHEART clinical diagnostic testing, the TAI protocol requests that samples shipped distantly to TAI be collected in 8.5 ml BD Vacutainer® PPT™ Plasma Preparation Tubes  then subjected to two brief low speed (1100xg) centrifugation steps before freezing of purified plasma for shipment overnight on dry ice to TAI Diagnostics. The PPT processing procedure was formally beta-tested in two hospital clinical laboratories to collect feedback from laboratory staff regarding ease of integration into standard laboratory workflows.Standardized sample collection and processing protocols that minimize leukocyte lysis and produce plasma free of leukocyte contamination are important for cfDNA analyses of all types. This is particularly important for determinations of TCF concentration and DF that accurately reflect HEART test submission according to standard clinical laboratory procedures. Shipment frozen or on ice was considered acceptable, but not considered superior to standard ambient temperature shipment.For one-time basic genotyping (bGT) of each recipient, which targets the overwhelming abundance of leukocyte genomic nuclear  DNA in whole blood, a simple K2-EDTA (purple top) whole blood collection without centrifugation was sent to TAI prior to or coincident with the first myTAIHEART DNA Fragmentation Assay is a proprietary modification of the method of Utemani et al [Alu multi-copy gene consensus sequence, with a shared TaqMan probe for both short and long amplicons . Alu elements are short stretches of DNA originally characterized by the action of the Arthrobacter luteus (Alu) restriction endonuclease. Alu sequences are classified as short interspersed nucleotide elements, approximately 300 nucleotides in length, and account for more than 10% of the human genome at a copy number of about 1.4 million per genome [HEART DNA Fragmentation Assay produces amplification product from Alu fragments of almost all lengths, including the short fragments of a modal size of about 166 bp (140–200 bp) characteristically derived from cellular apoptosis [Alu fragments, essentially the entire cfDNA complement. In contrast, only those longer fragments derived from non-apoptotic cellular death mechanisms, such as those occurring from ex vivo lysis of leukocytes during whole blood sample processing, are detected by amplification of a 247 bp fragment of the Alu sequence (Alu ratio (247 bp:115 bp) provides a useful tool to detect levels of leukocyte lysis which might produce a false negative result for increased probability of rejection in samples evaluated for DF.TAI sample processing protocols were designed to prevent significant leukocyte lysis and/or contamination that could reasonably dilute the DF of cfDNA through contamination by recipient-specific gDNA after sample collection. As a quality control measure, a sensitive DNA fragmentation assay based upon analysis of a multi-copy gene was developed to identify, prior to further cfDNA analysis, if unintended gDNA contamination did occur due to unrecognized sample preparation deviations. The myTAIni et al  and usesr genome ,85,86. Tpoptosis ,87, as wsequence . The ratHEART DNA Fragmentation Assay was performed on cfDNA extract as part of the overall myTAIHEART workflow after quantification of extract TCF concentration by RNase P qPCR. Input was 50 pg, run in triplicate for both Alu fragment length amplifications against a five-point human gDNA standard curve. ALU115 and ALU247 amplifications were performed individually for each primer pair on a Roche Lightcycler 480 (LC280) using a shared proprietary TaqMan™ probe. The Lightcycler software was used to calculate a standard curve for the run by plotting the known DNA concentration of each standard dilution on the x-axis and the mean crossing point (Cp) value for those dilutions on the y-axis, also calculating the slope and amplification efficiency for each run. TCF concentrations of patient samples were individually determined by the Lightcycler software for the ALU115 and ALU247 amplifications using the calculated standard curve equation and the mean Cp as input. Results generated by the LC480 Abs Quant/2nd Derivative Max algorithm captured in a report were used to determine Alu ratio by dividing the ALU247 concentration by the ALU115 concentration. That ratio provides a quality indicator of potentially significant leukocyte lysis/contamination of the patient sample that could potentially influence DF results and/or cause specimen rejection . Analytical quality metrics developed to ensure validity of a myTAIHEART DNA Fragmentation Assay run included required ranges for ALU115 and ALU247 amplification efficiency, standard deviations of standard curve points, quantifications in pg/μl of low, medium, and high ALU115 and ALU247 controls, fragment ratios of specified standards, no template control (NTC) mean Cp, and specified standard Cp. For detailed analytical validation data, see Analytical Validation Methods below.The myTAI® Copy Number Reference Assay RNase P, Applied Biosystems, Foster City, CA). gDNA for bGT analysis was extracted from buffy coat of whole blood collected in a standard K2-EDTA tube either manually using a Qiagen kit or in an automated fashion using the TECAN. The concentrations and A260/A280 ratios of gDNA extracts were determined by NanoDrop™ One  spectroscopy.cfDNA for determination of DF by qGT was extracted from 4.0 ml aliquots of patient plasma or contrived reference samples manufactured at TAI Diagnostics as described above. The extraction was performed in a standardized, automated fashion using a high throughput TECAN liquid handling platform to minimize inter-sample variability in extraction efficiency. Proprietary chemistry and scripts were employed to optimize extraction efficiency appropriate to the range of DNA fragment lengths typical of patient cfDNA mixtures. TCF concentration in extracts of plasma and contrived reference samples was determined by RNase P qPCR  of gDNA extracted from a blood sample was performed at each of the 94 highly informative target allelic sites (and 2 control sites) prior to qGT. Additionally, donor whole blood samples were available for separate genotyping of donors for 158 recipients providing the matched endomyocardial biopsy—blood sample pairs used in the clinical validation study. This provided opportunity to validate the evolved “without donor genotype” version of the myTAIHEART target sites. Heterozygous (HET), homozygous reference (HOM REF) and homozygous variant (HOM VAR) calibrators were pre-prepared from pooled mixtures of sequence-verified, double-stranded DNA linear constructs and representing each of the myTAIHEART target alleles. Accompanied by a negative template control (NTC), calibrators were amplified using Q5 Hot Start High-Fidelity DNA Polymerase  in each multiplexed library run alongside patient/validation samples on Eppendorf Mastercyclers®. Subsequent enzymatic cleanup  was employed to remove excess primers and unincorporated deoxynucleotide triphosphates (dNTPs), preventing interference with downstream amplification steps. Adequacy of library amplification was monitored by post-amplification automated microfluidic electrophoresis using an Agilent 2100 Bioanalyzer . Passing pre-amplification library mixtures were advanced into the genotyping phase of bGT workflow, there subjected alongside HET, HOM REF, HOM VAR calibrators and NTC to robotically-controlled real-time Roche LC480 PCR amplification and product detection using AptaTaq Genotyping Master Mix  and the proprietary myTAIHEART primers and probes (see Target Selection and Primer Design). Post-amplification data was analyzed by the myTAIHEART software to provide genotype calls and associated quality metrics.Samples for bGT entered the workflow by input of 15 ng of extracted gDNA into a multiplexed high-fidelity library amplification reaction containing 96 library primer pairs , each amplifying a region including one of the myTAISample quality control (QC) measures applied to ensure accurate bGT results included requirement for ≥ 0.5 mL volume of whole blood, shipment frozen or according to sender’s standard procedure, time from sample receipt to extraction ≤ 3 days, gDNA quantity ≥ 15 ng, and quality . Genotyping QC acceptance criteria included a valid run with a minimum of 89% (84/94) of targets with passing calibrator data, and a valid sample with a specified minimum percentage of genotype calls and a specified maximum of heterozygous genotype calls.HEART software . The qGT approach was also applied without modification to extracts of reconstructive genotypic mixtures used as reference materials in DF validation studies.DF was determined by multiplexed, high-fidelity amplification followed by allele-specific qPCR of 94 SNP targets and 2 control targets also targeted by one-time bGT of the recipient’s native “self” gDNA, but for qGT followed by algorithmic minor species determination of DF using the myTAIHEART informative and control target sites, performed on Eppendorf Mastercyclers® followed by enzymatic cleanup. The qGT library amplification differed from the bGT library amplification in inclusion of low (0.2%), mid (1.0%), and high (10%) positive template controls (PTC’s) and a singular (HET) calibrator, but was otherwise equivalent. After Agilent 2100 Bioanalyzer amplification verification and pre-determined dilution, the amplified libraries, along with calibrators and controls, were subjected in duplicate to robotically controlled real-time Roche 480 PCR amplification/product detection using the allele-specific primers and probes also used for bGT.Before entering the qGT workflow, as for the bGT workflow, 15 ng extracted DNA (in this case post-transplant plasma cfDNA) was input into multiplexed high-fidelity library pre-amplification of regions including the myTAIThe qGT step uses standard curves of heterozygous DNA sources to quantify alleles at each target. Quality metrics were developed to establish threshold acceptance criteria for a valid qGT run. These included comparisons to historic amplification shape, ≥ 75 targets with passing calibrators, log RNase P values ≥ 8 to ≤ 10, 70 passing quantifiable targets, ≥ 27 informative targets, a robust CV (rCV) ≤ 100%, skew (a metric designed to detect possible relatedness between donor and recipient) ≤ 1.01, and lot-specific acceptance criteria for PTC’s. Elevated rCV or skew would activate implementation of sample swap checks, and any high NTC’s would flag the reference lab manager to either institute the lab wipe testing protocol or possibly replenish with fresh reagents and repeat run.HEART software. The original developmental version of this software (Method 1) integrated results of separate bGT reads for both recipient and donor at the 94 highly informative SNP targets of the myTAIHEART test, using them to label the recipient and donor with three possible genotypes at each target . The donor and recipient bGT information was then inserted into the qGT analysis, along with standard curve results, to quantitate the allelic ratio, as a minor species proportion, at each target at the time of myTAIHEART testing. The median of all informative and QC-passed allelic ratios was then used to determine DF.Validated output from the bGT and qGT runs was analyzed using the proprietary myTAIHEART software algorithm, relying upon amplification results at the 94 highly informative bi-allelic SNP targets developed for Method 1, uses recipient bGT information in concert with the recipient’s qGT (post-transplant) cfDNA results to evaluate donor options in staged Monte Carlo simulations . Preliminary random selections of candidate donor genotypes simulate what DFs a given qGT sample could represent. Statistical analyses provide evidence of the most probable donor genotypes. Secondary Monte Carlo simulations further explore these likely donor genotypes and yield a range of probable qGT outcomes. Each simulation produces a candidate median DF value, along with quality control metrics. The final DF call provided on the myTAIHEART test report is derived from the distribution of candidate DF values by a linear adjustment to the optimal midpoint. Validation Design and Results of the Method 2 “no donor” algorithmic approach are described in sections below.The “Method 1” approach, with its utilization of independently determined recipient and donor genotypes, is not always practical given inconsistent availability of donor tissue/blood/DNA samples for genotyping. It served as an important gold standard for validation of an evolved method, Method 2, in which bioinformatics analysis of circulating post-transplantation cfDNA is able to distinguish “self” from “non-self” cfDNA without independent availability of donor genotyping. The evolved “no donor” Method 2 myTAIHEART bioinformatic algorithm was predicated on validation of the original “with donor genotype” algorithm, both achieved using a cohort of 1168 heart transplant recipient samples. Within that cohort, 568 samples fell within the linear, quantifiable range of the original method (0.125–10% DF), and were randomly divided into two subsets , one subset to hone the new, “no donor” algorithm to the original “with donor” algorithm and the other subset to revalidate the results. This ensured a uniform distribution of high and low DF in each bin, while also distributing patient genotypes randomly. Comparison of DF results obtained using the previously validated “with donor” myTAIHEART (Method1) algorithm versus the “no donor” (Method 2) algorithm was accomplished by Passing-Bablok regression analysis using R software to test hypothesis that the “no donor” method is equivalent to the predicate “with donor” method.Validation of the definitive “no-donor genotype” myTAIHEART assay of DF as an index of rejection probability. Organ procurement and transplantation arrangements were managed independently of the study for medical purposes through the United Network for Organ Sharing (UNOS) system in the United States under governance of the Uniform Anatomical Gift Act. Study approval was provided by the CHW Institutional Review Board (IRB), with approval for adult participants deferred to the CHW IRB by the Institutional Review Board of the Medical College of Wisconsin (MCW). Patients were recruited into this study from June 2010 through March 2013 with transplant procedures performed from June 2010 through July 2013. Patients were followed by biopsy and myTAI HEART testing through from June 2010 through Aug 2016. A preliminary account of this study population and initial results of application of the myTAIHEART test to it were reported by members of our group as a Clinical Letter in the Journal of the American College Cardiology in 2018 [A total of 452 whole blood samples obtained with prior informed consent from 88 volunteer adult and pediatric heart transplant recipient study participants between June of 2010 and Aug 2016 from two Milwaukee transplant centers—CHW and FMLH—were considered for inclusion in clinical validation of the myTAI in 2018 . Mean suHEART protocol. Inclusion criteria for the clinical validation study included availability of clinical encounter and historical data, a properly collected and processed blood sample, a concurrent EMB specimen submitted for routine histopathologic evaluation and ISHLT 2004 grading by board-certified pathologists at the participating institutions, and access to the institutional EMB pathology report. The collection date and time of all blood samples paired with EMB were monitored to ensure blood samples were drawn immediately prior to any intra-cardiac access, not thereafter. Clinical exclusion criteria included blood sample collection less than 8 days after cardiac transplantation [HEART protocol; and insufficient cfDNA yield for analysis. Analytical exclusion criteria included library failures and failure to pass analytical quality specifications. The clinical data content associated with all samples remained blinded to TAI Diagnostics personnel until all analytical data was generated and quality control metrics for inclusion/exclusion in the clinical validation data set were applied. A final set of 158 matched pairs of endomyocardial biopsy-plasma samples collected from 76 heart transplant recipients, both pediatric and adult, met study inclusion criteria and passed exclusion criteria.Clinical data was collected longitudinally on all study subjects, with focus on admission for transplant, treatment episodes for rejection, and all endomyocardial biopsy (EMB) procedures. All candidate whole blood samples for cfDNA analysis were collected in Streck BCT tubes and walked to the CRI Nucleic Acid Extraction Lab, where the two required serial 1400 x g x 10 min centrifugations were performed. Final supernatants immediately frozen and stored at -80°C were encoded by unique study identifiers prior to extraction per the TAI-approved TECAN automated protocol and transfer to the TAI Diagnostics Clinical Reference Lab for qGT according to the analytically validated myTAIantation  or withiHEART protocol (Method 1) that would later be used to demonstrate equivalency of results produced by the “no donor genotype” myTAIHEART protocol (Method 2) as described in the preceding section.Either the buffy coat from the cfDNA Streck tube collection or a separate EDTA tube for each study participant afforded gDNA for recipient bGT. A blood or tissue sample from the donor for each recipient participating in the study was available and similarly submitted for basic genotyping. This step allowed validation first of a “with donor genotype” myTAIMethods, patient blood sample collection and plasma separation. From a starting number of 2537 quality-controlled blood samples from 241 post-heart transplant recipient subjects [For use in determination of the “normal” TCF reference range, Aim 1 of the DTRT prospective blinded multicenter study (NIH/NHLBI) provided a unique set of samples from asymptomatic heart transplant recipients. These samples were shipped to TAI Diagnostics and processed as described in subjects ,90, clinsubjects ,90. SampHEART testing protocol were performed according to recommendations of CLSI EP17-A2 Evaluation of Detection Capability for Clinical Laboratory Management Measurement Procedures [All analytical validation protocols, covering multiple aspects of the myTAIocedures  whereverPrecision/reproducibility of extraction and quantification of TCF concentration was performed using five plasma samples prepared at cfDNA concentrations of 2ng/ml, 25ng/ml, 50ng/ml 100ng/ml and 200ng/ml. Extraction was performed in duplicate in each of 18 extraction runs using two lots of proprietary TAI extraction chemistry on two TECAN Freedom EVO 150 instruments by two technologists. Each extraction run contained a positive control, consisting of previously characterized plasma, and a negative extraction control; input sample plasma volumes were four ml. Resultant cfDNA was quantified by reference gene (RNase P) qPCR performed by two technologists using two reagent lots and two Roche LC 480 thermocycler systems. For the run to be valid, yield of the positive extraction control in ng cfDNA/mL plasma was required to fall within a previously established range. Runs that did not meet this requirement were removed from the analysis.Limit of blank (LoB), limit of detection (LoD), and limit of quantification (LoQ) of TCF quantification were determined according to recommendations of CLSI EP17-A2 Evaluation of Detection Capability for Clinical Laboratory Management Measurement Procedures . LoB wasLinearity of TCF concentration quantification was assessed following recommendations provided in CLSI EP06-A Evaluation of the Linearity of Quantitative Measurement Procedures . ContrivHEART DNA Fragmentation Assay was designed to individually establish performance characteristics of the short (≥115bp) and long (≥247bp) fragment Alu amplification tests that together comprise the myTAIHEART DNA Fragmentation Assay. LoB, LoD, LoQ, precision, accuracy, and linear range of the ALU115 and ALU247 amplifications were determined such that those characteristics in the resultant Alu ratio could be implied.Analytical validation of the myTAIHEART DNA Fragmentation Assay validation studies, as described above in Reference Materials, Covaris-sheared human buffy coat gDNA spiked back into aliquots of paired plasma was used to produce plasma samples at targeted long to short DNA fragmentation ratios of 0.2 to 0.5, yielding final actual ratios of 0.19 to 0.490. Additionally, for studies not linked to extraction, gDNA was introduced into 0.1X Tris-EDTA buffer. Samples with extraction were quantified by RNase P qPCR on a Roche LC480 prior to use in determinations of precision, DNA fragmentation assay LoB, LoD, LoQ, and linearity according to CLSI guidelines [To support myTAIidelines ,92, see Reproducibility of the bGT protocol was assessed using a panel of 6 patient whole blood samples from 6 different individuals that were each aliquoted and extracted in five DNA extraction runs followed by library amplification and running of each library through six bGT runs targeting 94 SNP targets + 2 controls performed by two technologists over a span of 6 days.HEART test that generates plasma cfDNA DF were determined using a series of studies designed to determine Limit of Blank (LoB), Limit of Detection (LoD), Limit of Quantification (LoQ), Linearity, and Precision. Also evaluated were potential interfering effects of substances commonly present in the circulation of heart transplant patients, as well as carryover and cross contamination.Analytical performance characteristics of the essential qGT portion of the myTAIHEART algorithm using eighteen different donor pairings resulting in 18 data points per library tested. Samples that did not meet QC criteria were removed from analysis.For LoB, sheared gDNA isolated from blood of 16 different individuals was used to simulate 16 transplant patients with no donor DNA present. Overall assay LoB was determined by testing between one to four libraries for the 16 samples across two runs. Each sample library was analyzed through the myTAIFor LoD, nine distinct reconstruction samples were made at a theoretical 0.1% donor fraction, a low-level fraction just above the LoB. Each sample, at an input of 15ng cfDNA was amplified in triplicate in each of three library runs and then subjected to qGT. Each library run of 27 samples (nine reconstruction samples run in triplicate) was tested on each of three days with a distinct reagent lot, for a total of three days and three reagent lots. Samples that did not meet QC criteria were removed from the analysis. LoD was determined for each reagent lot by using the equation LoD = LoB + Cp*SDl as outlined in CLSI EP17-A2 .For LoQ, nine additional distinct reconstruction samples were made at theoretical DFs of 0.2% and 0.3% using the same sample reconstruction pairings used in the LoD study above. Samples of each were amplified in triplicate in three separate library runs over three days, with a distinct reagent lot each day, followed by quantification. Samples that did not meet QC criteria were removed from the analysis. Data generated from the LoQ runs were combined with the data from the LoD runs (at 0.1% DF). For each reconstructed sample, data from all three library runs was pooled to generate the %CV of DF. The average %CV for each DF fraction was plotted to generate a precision profile curve to estimate the concentration at which the precision of the assay is ≤ 20%CV.HEART testing workflow in duplicate and processed in the same run with a single lot of reagents. DF results were plotted against the theoretical DF intended by the reconstructions and assessed for linearity according to CLSI EP06-A Evaluation of the Linearity of Quantitative Measurement Procedures. Samples that did not meet QC criteria were removed from analysis.For testing of linearity of DF determination, three distinct series of contrived samples were constructed at 0.10, 0.20, 0.30, 0.40, 0.50, 0.75, 1.00, 1.50, 2.00, 4.00, 6.00, 8.00, and 10.00% DF using gDNA as described in Materials and Methods. Within each series, samples were tested through the myTAIFor evaluation of precision of DF determination, three reconstruction samples were generated at low (~0.20%), medium (~1.0%), and high (~10%) DF. Samples were mixed and aliquoted into single use aliquots and tested in a single replicate up to two times a day over 31 days for a total of 46 runs (46 replicates of each samples). Multiple operators tested samples across several reagent lots and equipment lines. Samples that did not meet QC criteria were removed from the analysis. The %CV within run date and across all samples was determined for each sample.HEART bGT and qGT results was verified by comparison to results obtained by Illumina sequencing as an independent second method. For bGT, eight amplified DNA libraries across four control samples were used for comparison, and for qGT six amplified DNA libraries across three control samples . Ten substances were chosen for the study, including bilirubin, hemoglobin, EDTA, prednisone, tacrolimus, sirolimus, mycophenolate, cyclosporine A, triglycerides, and IVIg, these representing endogenous substances commonly elevated in plasma samples from heart transplant recipients as well as exogenous substances commonly introduced by standard medical therapies. Additionally, for extraction and genotyping validations, potential interference by two viruses, cytomegalovirus (CMV) and BK virus (BKV) was tested. Concentrations for each substance/virus were selected according to CLSI EP07-A2 [e.g., nuclease free water, ethanol, DMSO) were tested separately in the absence of that substance. Samples were then extracted in triplicate using the myTAIHEART automated extraction procedure prior to processing through the intended myTAIHEART workflow. Any samples not passing QC criteria during testing were removed from analysis. Passing results were analyzed using the statistical software package JMP, version 14  using the Tukey-Kramer HSD test following one-way ANOVA testing to determine if the results from exposed samples deviated significantly from those of paired samples extracted and tested without spiked-in substance.Parallel sets of studies, identical in fundamental design, were executed to assess the effects of potentially interfering substances on four individual aspects of the myTAI EP07-A2 , or prevHEART DNA Fragmentation Assay analyses were performed in concert using three separate contrived human plasma samples prepared at TCF concentrations targeted at 2ng/ml, 25ng/ml, and 50ng/ml, each possessing a slightly different Alu ratio. bGT interference testing was performed using blood samples from two individuals. Interference testing of the qGT DF determination step employed two separate contrived plasma samples prepared by mixing plasma from two previously genotyped individuals in different proportions to simulate plasma from two transplant patients with DFs near the cut point for increased risk of rejection. All prepared samples were immediately frozen in single use aliquots, then thawed and immediately spiked, extracted in triplicate and processed through the intended workflows and statistical analyses of results as outlined above.Sample preparation protocols and logistics unique to each interfering substance application are as follows: Interference studies for TCF quantification and myTAIHEART DNA Fragmentation analyses was assessed by testing high positive contrived samples  generated at ~200 ng cfDNA/mL alongside negative (nuclease free water) samples in a 32 position checkboard pattern on the TECAN instrument across two independent runs. The extracted samples maintained the same sample positioning during subsequent RNase P quantification of TCF and myTAIHEART DNA Fragmentation testing (ALU115 and ALU247).Potential carryover/cross-contamination during extraction and downstream analytical workflows that could impact results of TCF quantification and myTAIHEART qGT workflow to produce DF results.Potential carryover/cross-contamination during extraction and all downstream analytical workflows that could influence DF results was assessed using contrived plasma generated by mixing plasma from two unique individuals together in two different proportions to simulate plasma samples from two transplant patients, one with low (~0.2%) DF and one with high (~2.0%) DF, aliquoted into single use, 2mL portions and frozen at -80°C. Samples were subsequently thawed and extracted in a checkboard pattern in two independent TECAN runs. The extracted samples maintained the same sample positioning during subsequent processing in duplicate through the full myTAIHEART is a laboratory developed test (LDT) developed for clinical diagnostic performance exclusively in the College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendment (CLIA)—accredited TAI Diagnostics Clinical Reference Laboratory. The entire assay and workflow presented here, from whole blood sample collection and plasma separation through bioinformatics analysis and result reporting, depicted at high level in myTAIThe test uses a panel of 94 highly informative SNPs to quantitatively genotype cfDNA in the patient’s plasma, accurately distinguishing “donor specific” cfDNA originating from the engrafted heart from “self-specific” cfDNA originating from the recipient’s native cells and reporting the cfDNA DF as a direct marker of selective injury to the transplanted organ. It is intended to aid in categorization of the patient as at low or increased risk of moderate (grade 2R) to severe (grade 3R) ACR at the time of testing in conjunction with standard clinical assessment. This test is indicated for use in heart transplant recipients who are 2 months of age or older and ≥ 8 days post-transplant based upon study populations of wide age range that extend into the very early transplant period demonstrating lack of age and sex bias and typical rapid return of DF level to baseline within 4–7 days post-transplant . It is care pregnantcurrently have or in the past have had another transplanted organ have post-transplant lymphoproliferative diseasehave cancer or have had cancer within the previous 2 yearsare on mechanical circulatory supportare closely related to the transplant donorHEART result should continue to be monitored according to standard clinical care, with all results interpreted in the context of the patient’s overall clinical findings, history, and laboratory results. A conservative cut-off value with 100% NPV for increased probability for moderate to severe ACR, established using 158 matched pairs of endomyocardial biopsy-plasma samples collected from 76 heart pediatric and adult transplant recipients, as described in Methods, Clinical Validation Plan and Study Populations, was purposefully selected. Clinical and analytical test performance characteristics . The myTAIHEART SNP target panel is statistically indistinguishable from optimal. In reference to HEART panel is less than 11% frequent in any major population group identified by the largest genomic dataset to date. In more finely divided groups  was established based upon a study of 241 heart transplant patients, 106 of which were determined to be “healthy” and asymptomatic at the time of testing , tuning of the myTAIHEART software for use without requirement for donor genotyping was performed on a training data set to hone the “without donor genotype” (Method 2) algorithm to the previously validated “with donor genotype” (Method 1) algorithm. The Method 1 and honed Method 2 algorithms were then individually applied to a separate validation data set. Results for 1128 of 1168 samples passed QC for both methods and are shown in As described in Methods & Materials (myTAILow percentages of donor-specific cfDNA (<0.3%) within an already minute total population of cfDNA molecules are observed in most heart transplant recipients  and necessitate rigorous prevention of leukocyte gDNA contamination when preparing plasma for cfDNA DF analysis to prevent underestimation of DF. Potential causes of contamination include leukocyte lysis or exocytosis during prolonged or otherwise stressful exposures during sample collection and processing; incomplete removal of leukocytes from the plasma phase of whole blood prior to plasma freezing and shipping for off-site cfDNA analysis; and, more rarely, immediate pre-phlebotomy events, such as drug infusions, that may in some instances cause transient leukocyte lysis. Failure to exert the needed pre-analytical precautions in cfDNA DF testing will result in underestimation of DF and, potentially, false negative reporting of rejection risk. A consistent recommendation for effective leukocyte elimination from plasma is inclusion of two sequential centrifugation steps. –97Minimization of leukocyte gDNA contamination is also important in cfDNA analyses applicable in fetomaternal medicine and oncology, although the impact of low-level leukocyte contamination is generally better tolerated in those non-transplant applications due to the higher TCF levels and higher minor species excluding early or minimally residual tumors very low fetal cfDNA representations. For instance, TCF levels average as much as 20x higher in unselected cancer patients  than in ex vivo DNase activity [For all cfDNA applications, the plasma cfDNA component of diagnostic interest can be protected from both degradation and leukocyte gDNA contamination by select blood collection additives. EDTA is preferred over citrate and heparin as an anticoagulant, in part, because EDTA salts, in addition to protecting cfDNA by preventing release of cellular DNA through coagulative stress, inhibit activity . Many stactivity ,106,107.activity ,108. Aftactivity , thus re®)  in 2010 [® Blood ccfDNA Tube , LBgard™ Blood Tube , cf-DNA Preservative Tube , Blood Stasis™ 21-ccfDNA Tube , and Blood Exo DNA ProTeck® tubes  among others, recently reviewed [To extend the window of stability of EDTA anticoagulated whole blood samples for cfDNA analysis and add the convenience of shipping to reference laboratories prior to shipping, a plethora of commercially available cell preservation tubes containing proprietary cell membrane stabilizing additives that inhibit release of cellular DNA for prolonged periods over a wide temperature range have been developed. Pre-dating these developments, simply adding formaldehyde, a widely used cross-linking fixative, not surprisingly was found to be effective ,111. For in 2010 . These hreviewed . Some tyWe previously studied the effect of 72 hr temperature-monitored shipping of whole blood by air courier in Streck BCT tubes for prenatal testing purposes, finding them generally acceptable, but noting an increase in TCF concentration and a decrease in fetal fraction in samples shipped on cool packs that lowered temperature within insulated containers into the 4–7°C range . UnexpecHEART DF results were observed with shipped whole blood samples. In contrast, more consistent results were obtained using shipped samples for which plasma was separated from BCT tubes and frozen quickly at the collection site before shipping on dry ice according to TAI protocol [HEART protocol. This was accomplished using manufactured “post-transplant” whole blood samples from four healthy donors drawn into Streck BCT tubes and promptly spiked within 20 min of phlebotomy with approximately 1 ng of “donor” cfDNA previously isolated from a commercial blood lot , the temperature sensitivities of BCT tubes, while potentially tolerable in some cfDNA applications not requiring sensitive assessment of minor species proportions, can have significant impact on measurement of low cfDNA DF’s generally observed within already low TCF complements in heart transplant patients. Adding to this concern, we became aware during recent clinical trials employing BCT tubes for air transport shipment of whole blood samples from heart transplant patients, that even with protection afforded by Styrofoam-insulated and gel-pack-protected shipping containers, inconsistent myTAIprotocol . To inve lot see .HEART test is designed to provide high-level sensitivity required for detection of acute cellular rejection in heart transplant patients, a task more demanding than detection of acute antibody-mediated rejection in these patients [The myTAIpatients . This reAlu ratio for quantitative quality control use in detection of significant leukocyte lysis in clinical samples submitted for cfDNA analysis, it was necessary to construct combinations of un-sheared and sheared gDNA to produce clinically relevant target Alu ratios in a range of ALU115 and ALU247 concentrations . For some validation studies  for which extraction was not required, human gDNA from a commercial vendor was used directly to make defined gDNA concentrations ranging from 0.25 pg/μl to 400 pg/μl in 0.1X TE Buffer. For other validation studies requiring DNA extraction, contrived samples prepared by spiking combinations of sheared and unsheared gDNA into aliquots of human plasma were employed  for the ALU115 and ALU247 amplifications. Resultant distributions of blanks for both ALU115  was diluted in 0.1X TE to concentrations of 4, 2, 1, 0.5 and 0.25 pg/μl. Each resultant sample was tested in five wells per run and two runs per day for four days yielding a total of 40 separate measurements collected across eight runs for each fragment length. Two lots of primers and probe were tested. LoD values for each assay (ALU115 and ALU247) were determined using the parametric approach as outlined in CLSI EP17-A2, pages 16–17 [LoB values for the myTAIh ALU115  and ALU2h ALU115  did not h ALU115 . For detes 16–17 . As the HEART DNA Fragmentation Assay, human gDNA prepared as described in Materials and Methods  was diluted in 0.1X TE to concentrations of 4, 2, 1, 0.5 and 0.25 pg/μl. Each sample dilution was tested in five wells per run and two runs per day for four days, producing 40 measurements collected across eight runs for each fragment length. Two lots of primers and probe were tested (Reagent Lot A and Reagent Lot B). LoQ for each assay was determined according guidelines outlined in CLSI EP17-A2 [For determination of LoQ for the short and long fragment components of the myTAI EP17-A2 . The LoQHEART DNA Fragmentation Assay amplifications for ALU115 and ALU247, gDNA was diluted in 0.1X TE to the following concentrations: 400, 200, 100, 50, 25, 12.5, 6.25, 3.13, 1.56 and 0.78 pg/μl. Each linearity sample was tested in duplicate wells per run, two runs per day for one day. The resulting ALU115 and ALU247 amplification measurements, quantitated against a standard curve in units of pg/μl as described in Materials and Methods, was plotted against the theoretical concentration and assessed for linearity according to CLSI EP06-A [For linearity assessment of myTAII EP06-A  Fig 6)..HEART DNAlu ratio. For each substance at each background cfDNA concentration, Alu ratio results were analyzed in JMP using the Tukey-Kramer HSD test following an ANOVA test to determine if the mean of the test case significantly differed from that of an “unspiked” control sample extracted and tested without added test substance or substance diluting solvent . Results were also compared to those of a “solvent only” control sample spiked with a matched volume of the relevant diluting solvent without added substance. This allowed differentiation, within the bounds of the intrinsic variability of the ALU test, of any effect due to the substance itself versus any effect of the solvent required to dilute the substance for in vitro testing. “Solvent only” effects are not relevant to clinical test substance exposure. Importantly, for all tested substances, no statistically significant differences in Alu ratio of test samples compared to controls spiked only with diluting solvent were seen at any background cfDNA concentration. At 25 ng/ml TCF concentration, which falls well within the 95% confidence level clinical reference range for healthy heart transplant patients, small, but statistically significant differences compared to unspiked control were seen for Sirolimus, EDTA, and bilirubin, but not compared to the respective diluting solvent controls for these three substances  using three contrived human plasma samples prepared at three different clinically relevant TCF concentrations  of variable bstances . At verymoglobin , but notmoglobin . No statml cfDNA . These fHEART DNA Fragmentation Assay is designed to flag presence of excessive gDNA released from recipient leukocytes lysed during sample processing and shipping due to poor technique or extreme environmental exposures. Many published studies have demonstrated elevated TCF levels and/or increased proportions of long fragment DNA in plasma samples exposed to those conditions or delayed in separation of plasma from the cellular components of blood [HEART DNA Fragmentation Assay in detecting leukocyte lysis, we performed a leukocyte titration study using multiple 1.5 ml aliquots of a contrived sample prepared by spiking plasma from one healthy “recipient” blood donor sourced from blood bags provided by a commercial vendor with “donor” plasma from a second healthy subject. This yielded a theoretical DF of 0.4% in “post-transplant” plasma aliquots that were then spiked with specific numbers of leukocytes from the “recipient” donors buffy coat (enumerated by Cell Dyne cytometry), ranging from 0–2500 cells per 1.5 ml aliquot. After freezing at -80°C to lyse the leukocytes, samples were placed into the Method 2 myTAIHEART workflow to determine DF and DNA fragmentation (ALU247/115) ratio per validated protocols. Results depicted graphically in Alu ratio and DF changes are linear relative to quantitated addition of lysed cells. These results further show that the myTAIHEART DNA Fragmentation Assay can detect elevations of Alu ratio by DNA derived from presence of as few as 300 lysed cells/ml of plasma, this representing roughly 0.003% of the leukocytes in whole blood from which that plasma is purified, based on normal reference range clinical leukocyte counts. Within the tested range of leukocyte contamination/lysis (300–1667 lysed cells /ml plasma), DF can drop from roughly 0.45% to as low as 0.275%. Even low levels of leukocyte lysis or contamination during sample processing have potential to shift DF from the high probability rejection range into the low probability range  if not monitored by DNA fragmentation analysis. Plasma samples most sensitive to risk for potential production of a false negative DF result due to leukocyte lysis are those with low TCF concentration and relatively low DF. Mathematical modeling to estimate that sensitivity is shown in The myTAIof blood ,108,116.e.g., Agilent Bioanalyzer) electropherograms, as previously shown for a contrived cfDNA reference sample in Alu PCR-based myTAIHEART DNA Fragmentation Assay to those of this independent method. The unusually high cfDNA level, with low DF, in this patient stemmed from acute renal tubular injury at time of myTAIHEART blood sample collection following an episode of cardiac arrest and resuscitation prior to eventual recovery. The sample was processed through using the standard dual low-speed spin myTAIHEART plasma preparation protocol, followed by automated extraction per Methods. It is informative to contrast the resultant electropherogram of the patient cfDNA extract collected by TAI protocol  in a checkboard pattern maintained throughout TECAN extraction and myTAImination . PositivIn the LoB study of cfDNA extraction and quantification, which incorporated nine replicates of nuclease free water tested in each of six extraction and detection runs across four days using two reagent lots, there was no amplification in any sample. A non-normal distribution of the results requires the use of the nonparametric option when calculating the LoB. The LoB is 0 ng/mL. This predicts that whenever no DNA is present in a sample, TCF concentration results will be 0 ng/mL since no cross-reactivity of the primers or probe with themselves or any other component in the test system was detected in this study.Numbers used to calculate LoD for TCF quantification according to CLSI EP17-A2, based upon testing of four distinct plasma samples in replicates of five in each of six extraction/detection runs across four days using two reagent lots, are detailed in The study data exhibited a normal distribution; the parametric approach therefore was applied, assigning LoD for TCF quantification as 0.608ng/mL, the greater LoD of the two reagent lots.Results and statistics used to determine the LOQ for TCF quantification are shown in Samples that generated RNase P values above the highest standard curve point were diluted and retested such that they fell within the standard curve. The resulting quantifications were then multiplied by the appropriate dilution factor to back-calculate the starting concentration. The linear, second order and third order fits were assessed across the entire data set . In this range, there were significant fits for both the linear and second order polynomial. The second order polynomial fit is not statistically significant when the range is reduced to 2–1,000 ng/mL. HEART DNA Fragmentation Assay previously described. For the included reconstructed plasma samples (TCF concentrations of 25 ng/mL and 50 ng/mL), results of Tukey-Kramer analysis indicated that samples spiked with each tested substance were statistically in the same group as those spiked only with the diluting solvents  in parallel with and without deviation from these assessments performed for the myTAIsolvents . TherefoCarryover/cross contamination was assessed in parallel with and without deviation from the checkerboard-style testing of positive and negative samples applied to the DNA Fragmentation Assay above. No data was removed from analysis. All negative samples remained negative, indicating the extraction and quantification workflow was not affected by carryover/cross contamination .Using TCF concentration measurements determined for 264 samples from 106 asymptomatic heart transplant patients from Aim 1 of the DTRT study ,90, the HEART bGT results was verified in comparison to paired Illumina sequencing of 14 amplified DNA libraries across four control samples. On average, 82/94 SNPs were called with confidence on both platforms, and in every instance the genotype call was equal.Reproducibility/precision testing of the basic-genotyping (bGT) test was performed using six separate patient whole blood samples in six different extraction/library amplification runs performed by two different technologists over a span of seven days. Across all samples, runs, and targets , 3123/3420 (91.3%) generated concordant calls, 293/3420 (8.6%) did not generate a call, and 4/3420 (0.12%) generated discordant calls. Overall, all samples generated concordant genotype calls across all runs with exception of a single target in one sample and another single target in another sample. Accuracy of myTAIAnalytical performance characteristics of the critical quantitative genotyping (qGT) portion of the assay that determines post-transplant DF were evaluated per CLSI EP17-A2 Evaluation of Detection Capability for Clinical Laboratory Management Measurement Procedures  as descrLoD for DF quantification was determined using the equation LoD = LoB + Cp*SDI for each of 9 distinct reconstructed samples made at a theoretical DF of 0.1% in triplicate, each amplified in triplicate at an input of 15 ng DNA on each of three days on each day using a different mastermix reagent lot. Two sample replicates failing library amplification were removed from the analysis. LoDs for the three different reagent lots were 0.165%, 0.149%, and 0.153% . The oveLoQ for DF quantification was determined for nine distinct reconstructed samples made at each of three theoretical donor fractions , each amplified in triplicate libraries then quantified for DF. Each of these library runs was tested across multiple days with distinct reagent lots. For each reconstructed sample, DF data from the triplicate library runs was pooled to generate the %CV for each of the nine reconstruction samples . The aveHEART assay relied upon use of three reconstruction control samples generated at low (~0.20%), medium (~1.0%), and high (~10%) donor fractions. These were aliquoted into single use portions that were tested as a single replicate up to two times a day over 31 days for a total of 46 replicates per sample, tested by multiple operators across several reagent lots and equipment lines as described in Materials and Methods. Input DNA mass was 15 ng for all samples for a total of 46 replicates per sample. The %CV within run date and across all samples was determined for each sample  of myTAIHEART DF compared to sequencing DF was less than one standard deviation, indicating good concordance between methodologies.Accuracy of myTAILinearity results for DF determination, as described in Materials and Methods and performed in accordance with CLSI EP06-A Evaluation of the Linearity of Quantitative Measurement Procedures, were based on use of three reconstruction series manufactured to represent DF’s of 0.10, 0.20, 0.30, 0.40, 0.50, 0.75, 1.00, 1.50, 2.00, 4.00, 6.00, 8.00, and 10.00%. These were all tested in duplicate and processed in the same run with a single lot of reagents. One sample in one series failed RNase P quantification, indicating failed library amplification, and was removed from analysis. Effects of 10 clinically relevant, potentially interfering substances and two viruses (CMV and BKV) on determination of DF by qGT were evaluated individually, in triplicate, as described in Materials and Methods for two unique contrived plasma samples. These were prepared by mixing plasma from two individuals in different proportions to simulate plasma from two transplant patients with donor fractions of ~0.6% and ~1.0%. For each contrived sample, un-spiked aliquots and aliquots spiked only by solvents used to dilute each tested substance were run alongside as comparators. Two replicates (one spiked with Cyclosporine and one spiked with DMSO) failed library amplification and were removed from analysis. Tukey-Kramer HSD analysis indicated that the mean DF result of all substance-spiked replicates was statistically equivalent to the mean DF result of their paired samples spiked with solvent only as well as that of the un-spiked sample. All potentially interfering substances were also equivalent to each other. The results of interfering substance testing at both DF levels showed that prescription drugs, endogenous substances, and pathogens common in the heart transplant population do not significantly affect DF results, shown for the 0.6% DF reconstruction in HEART qGT workflow to produce DF results  generated at low DF  and high DF . These were extracted on duplicate TECAN runs in a checkboard pattern that was maintained throughout subsequent processing through the full myTAI results . All samHEART assay is to aid in identification of heart transplant recipients who have a low versus increased risk of moderate/severe ACR (ISHLT 2005 grade 2R or higher) at time of testing in conjunction with standard clinical assessments. The final clinical validation dataset selected according to the defined exclusion and inclusion criteria described in Methods included 158 matched pairs of endomyocardial biopsy-plasma samples collected from 76 heart transplant recipients, both pediatric and adult . The age range of these patients was 0.28–28.00 years (mean 10.85 years), including five patients aged less than 1 year, 16 from 1–5 years, 20 from 5–10 years, 13 from 10–15 years, 10 from 15–20 years, and 12 from 20–28 years. Of the 158 biopsies, 148 were asymptomatic surveillance biopsies. Racial/ethnic composition of the 76 subjects was 3% Asian, 9% Black or African American, 84% white, 1% Native American, 3% unreported. Using this dataset, a DF cutoff of 0.32% was selected to maximize the negative predictive value (NPV) for grade 2R or higher ACR by establishing a cutoff for grades 1R or higher  vs grade 0R (no rejection). DF increased across rejection grades: the median DF in 0R (n = 134) was 0.12% (IQR 0.09–0.23%), in 1R (n = 21) was 0.84% (IQR 0.21–4.64%), and in 2R (n = 3) was 1.04% (IQR 0.85–3.13%). Note that these DF mean values, and their interquartile ranges, are slightly different than previously reported for this sample set [HEART bioinformatics algorithm here and a developmental version previously.The intended clinical use of the myTAImple set  due to aBased upon ACR grade as defined by the 2004 ISHLT classification, Receiver Operating Characteristic (ROC) analysis was performed to evaluate diagnostic accuracy across all possible cutoffs. To maximize diagnostic accuracy, Youden’s Index was used to select the optimal cutoff, found to correspond to a DF value of 0.32%. Using this cutoff, clinical performance characteristics of the assay included an NPV of 100.00% for grade 2R or higher ACR, with 100.00% sensitivity and 75.48% specificity; Area under the Curve (AUC) for this analysis was 0.842, indicative of robust ability of the DF assay to rule out 2R or greater ACR for DF values less than 0.32% HEART DF cut-off threshold for increased risk of moderate to severe ACR. To test this observation in a larger population of heart transplant patients and samples, we correlated myTAIHEART-determined DF values patient age at time of sample collection from three combined QC-passed study cohorts which included the current clinical validation study set, the AIM1 cohort from the previously cited DTRT study [We found no statistically significant correlation of DF with age across the broad developmental age range of our study. Predictably, there was some variability in distribution of cases of significant rejection across age subgroups, since each subgroup was of relatively small N and influenced by intrinsic weighting of shorter interval between transplantation and sample collection in younger age groups. There was no evidence to suggest that age differences would challenge the low, highly conservative myTAIRT study , and theThe intended use of this highly sensitive, clinically accredited PCR-based assay for selective damage to a donor heart is designed conservatively to stratify low versus increased probability of moderate to severe ACR in heart transplant recipients with 100% NPV based on a DF cut-off value. The analytical and clinical validation data reported herein strongly supports its validity in doing so. This test is validated for clinical diagnostic use in heart transplant recipients who are 2 months of age or older and as early as 8 days post-transplant. It significantly expands the window of opportunity for noninvasive transplant rejection assessment to infants and young children and to all recipients, adult or pediatric, into the critical early post-transplant period in which rejection is most common.HEART results require correlation with other clinical indicators to guide patient care interventions. Although the most compelling value of the test is a negative myTAIHEART test result (DF < 0.32%), which with its 100% NPV for 2R/3R ACR confidently assures absence of significant acute rejection, rare false negative results are possible due to unusual clinical conditions. Heart transplant recipients with a negative result should continue to be monitored according to standard clinical care. Heart transplant recipients with a positive result should receive further evaluations, guided by clinical judgement and results of other testing modalities, to determine the cause. “Biopsy negative” rejection is a distinct possibility. In all instances of cfDNA analysis, regardless of target or methodology, it is essential and achievable to address the universal concern for potential false negatives caused by artefactual contamination of plasma with leukocyte gDNA. Such contamination can originate from leukocytes not removed completely or lysed during improper sample processing, or more rarely through in vivo acute leukocyte injury from concurrent non-cardiac biological insult or therapeutic intervention . The myTAIHEART test addresses these challenges in part by instituting a simple, rapid sample shipping and processing protocol easily performed in collecting clinical laboratories that assures high plasma quality at the point of collection. It also employs a clinically validated DNA fragmentation “safety net” assay at the point of testing that verifies absence of an unacceptable level of long fragment, non-apoptotic, cfDNA which would be indicative of problematic leukocyte contamination or lysis. It is also important to acknowledge that patients recently treated for rejection may have variably affected DF’s. The latter variability is not yet well investigated, but is avoided by the 28-day post-treatment warning of the myTAIHEART assay. Accrued clinical experience with use of the test will likely narrow this post-treatment interval. Trending through relatively frequent longitudinal serial testing may be helpful in some patients.It is important to realize that cfDNA DF elevation within the reportable range of this highly sensitive assay can also be caused by other forms of selective cellular injury to the donor heart such as ACR 1R, acute antibody-mediated rejection (AMR), and CAV. Therefore, as with any single test, myTAIHEART testing, a number of scenarios have rapidly surfaced that particularly benefit from myTAIHEART cfDNA DF analysis, for example:In real-life clinical application of myTAIPatients with limited vascular access precluding or complicating biopsyStable patients past the first post-transplant year who would benefit from reduced biopsy incidencePatients shifted to monotherapy, requiring more frequent monitoringPatents too ill for anesthesia and biopsyPatients needing closer non-invasive serial follow-up to a recent positive biopsyPatients with clinical suspicion of rejection, but negative biopsyPatients with shortness of breath of respiratory versus cardiac originAdditional and refined applications will presumably develop over time as clinical experience in daily practice with cfDNA testing accrues. This noninvasive testing option is particularly critical in meeting unmet clinical needs for infants and young children not eligible for other noninvasive interventions, but is also applicable to adolescents and adults.HEART test is not intended to completely replace EMB, which can provide uniquely advantageous morphological, immunohistochemical, and molecular diagnostic information, but rather to complement and/or reduce the incidence of invasive biopsy while providing its own game-changing advantages of sampling uniformity, objectivity, cost-effectiveness, and safety. The validation studies presented here comprehensively document the analytical validity and clinical performance characteristics of the myTAIHEART test, including its DF and TCF determinations and accompanying DNA Fragmentation quality control analysis. These, together, support reliable clinical utility in conservative stratification of probability of moderate to severe ACR, providing an immediate alternative to invasive endomyocardial biopsy for pediatric and adult patients with contraindications to biopsy as well as for all who would benefit from more frequent monitoring. Additional prospective studies evaluating long-term outcomes across a variety of clinical scenarios using the test versus biopsy alone are needed to establish its clinical utility further in reducing the incidence of need for invasive endomyocardial biopsy and to evaluate its performance in following response to therapy. This is a precise, quantitative test and has potential in the future to provide quantitative measures of transplant health beyond the current binary risk stratification. Larger scale clinical studies over coming years will more fully enlighten the myriad potential clinical utilities and limitations of cfDNA analysis in the care of heart transplant patients.To be clear, the myTAIS1 Fig(A) At three targeted DF levels of contrived reference materials, the expected TCF concentration of 20 ng/ml was closely approximated, analyzed by the All Pairs, Tukey HSD test . (B) Correlation of measured and expected DF, each error bar constructed using one standard error from the mean.(TIF)Click here for additional data file.S2 FigLoB, calculated according to the CLSI nonparametric option  and deno(TIF)Click here for additional data file.S3 FigLoB, calculated according to the CLSI nonparametric option  and deno(TIF)Click here for additional data file.S4 FigTesting results at 2ng/ml TCF (A) and at 50 ng/ml TCF (B). For results at 25 ng/ml TCF see Text, (TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 FigThe average %CV at each DF is shown, along with the calculated best-fit line. The DF level along that line at which precision of the assay was ≤ 20%CV, is 0.108%, indicated by the vertical marker.(TIF)Click here for additional data file.S10 FigData includes study subjects (N = 180) and clinical registry records (N = 96). See text for statistical interpretation.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


This paper summarizes concepts underlying the atmospheric input of phosphorus (P) to ecosystems, published rates of P deposition, measurement methods, and approaches to future monitoring and research. P conveyed through the atmosphere can be a significant nutrient source for some freshwater and marine ecosystems. Particle sources and sinks at the land-air interface produce variation in P deposition from the atmosphere across temporal and spatial scales. Natural plant canopies can affect deposition rates by changing the physical environment and surface area for particle deposition. Land-use patterns can alter P deposition rates by changing particle concentrations in the atmosphere. The vast majority of P in dry atmospheric deposition is conveyed by coarse (2.5 to 10 μm) and giant (10 to 100 μm) particles, and yet these size fractions represent a challenge for long-term atmospheric monitoring in the absence of accepted methods for routine sampling. Most information on P deposition is from bulk precipitation collectors and wet/dry bucket sampling, both with questionable precision and accuracy. Most published annual rates of P deposition are gross estimates derived from bulk precipitation sampling in locations around the globe and range from about 5 to well over 100 mg P m year, although most inland ecosystems receive between 20 and 80 mg P m year. Rates below 30 mg P m year are found in remote areas and near coastlines. Intermediate rates of 30 to 50 mg P m year are associated with forests or mixed land use, and rates of 50 to 100 mg P m year or more are often recorded from urban or agricultural settings. Comparison with other methods suggests that these bulk precipitation estimates provide crude boundaries around actual P deposition rates for various land uses. However, data screening cannot remove all positive bias caused by contamination of bucket or bulk collectors. As a consequence, continued sampling with these standard collectors in a region will not reduce the large uncertainty in rates derived from existing data. Calibrated surface accumulation methods hold promise as a primary means to estimate P flux in future monitoring. New methods for long-term P deposition monitoring will require an intercomparison of P flux estimates from surrogate surfaces, impactor sampling of particle concentrations combined with deposition models, and “throughfall” estimates for natural canopies. With better sampling methods and more long-term monitoring data, the importance of atmospheric P deposition in ecosystem dynamics and management can be better understood and predicted.


The antidiabetic drug metformin has been proposed to affect non-alcoholic fatty liver disease (NAFLD) through its effects on intestinal microbiota and barrier function. However, so far most studies focused on long-term effects and more progressed disease stages. The aim of this study was to assess in two experimental settings, if the onset of NAFLD is associated with changes of intestinal microbiota and barrier function and to determine effects of metformin herein. C57Bl/6J mice were fed a liquid control diet (C) or fat-, fructose- and cholesterol-rich diet (FFC) for four days or six weeks ±300 mg/kg BW/day metformin (Met). Markers of liver health, intestinal barrier function and microbiota composition were assessed. Metformin treatment markedly attenuated FFC-induced NAFLD in both experiments with markers of inflammation and lipidperoxidation in livers of FFC + Met-fed mice being almost at the level of controls. Metformin treatment attenuated the loss of tight junction proteins in small intestine and the increase of bacterial endotoxin levels in portal plasma. Changes of intestinal microbiota found in FFC-fed mice were also significantly blunted in FFC + Met-fed mice. Taken together, protective effects of metformin on the onset of NAFLD are associated with changes of intestinal microbiota composition and lower translocation of bacterial endotoxins. Overweight and insulin resistance have been identified as key risk factors. However, not only a general overnutrition but also an unhealthy dietary pattern e.g. an elevated intake of sugar- and fat-rich foods, a lack of physical activity and genetic predisposition as well as changes of intestinal microbiota and barrier function are discussed to be critical in the development of NAFLD (for overview see2). Indeed, results of several more recent studies suggest that the development of NAFLD is associated with marked changes of fecal microbiota composition and increased bacterial endotoxin levels6. Furthermore, results of animal studies and some human studies suggest that targeting intestinal microbiota and/or barrier function may have protective effects on the development of NAFLD9.With a prevalence of approximately 25% worldwide10). While for many years it was thought that metformin exerts its beneficial effects on type 2 diabetes through mechanisms involving an AMP-activated protein kinase (AMPK)-dependent improvement of hepatic glucose metabolism and increased glucose uptake into muscle cells11, in more current years, studies indicate that metformin also affects intestinal microbiota composition and intestinal barrier function14. Indeed, recently in patients with type 2 diabetes effects of metformin were associated with a decreased prevalence of Bacteroides fragilis15. Furthermore, treating insulin resistant mice with this bacterial strain additionally to metformin abolished the beneficial effects of metformin on glucose tolerance15. Results of animal and human studies also suggest that metformin may at least in part attenuate the onset and progression of NAFLD19. Results of our own group and those of others suggest that the protective effects of metformin on the development of NAFLD are related to its effects on intestinal microbiota composition and barrier function20. However, mechanisms involved as well as if the effects of metformin on intestinal microbiota composition and barrier function are only found after an extended intake of the drug, remains to be determined.Since the early 1960s, the biguanide metformin  is used as an oral antidiabetic drug  for six weeks developed manifest steatosis with beginning signs of inflammation  mRNA in liver was significantly higher in FFC + Met-fed mice than in all other groups  and C-type lectin domain family 10 member A (Clec10a) mRNA expression in liver tissue was similar between groups  and vimentin did not differ between groups (Table phosphoenolpyruvate carboxykinase (Pepck) mRNA expression in liver did not differ between groups  and fatty acid synthase (Fasn) were markedly higher in both FFC-fed groups compared to both control groups , while mRNA expression of acetyl coA carboxylase (Acc) was only significantly higher in livers of FFC + Met-fed mice than in both control groups. Expression of acyl coA oxidase-1 (Acox1) mRNA was similar between groups  mRNA expression was significantly higher than in C + Met-fed mice, whereas similar differences were not found when comparing FFC-fed groups and C-fed mice after six weeks of feeding  were significantly lower in proximal small intestine of FFC-fed mice when compared to C-fed animals . Principal Coordinate Analysis (PCoA) further revealed a clustering of bacterial structure between the different feeding and treatment groups along the primary ordinate axid (OC1) which accounted for ~47.3% of the total variation . Results from animal studies further suggest that it may also be a therapy of NAFLD with rather marked effects (for overview see26); however, data from human trials are contradictory and less promising27. Still, as there is so far no universally accepted treatment for the management of NAFLD available, a better understanding of the beneficial effects of metformin found in animal studies may add to improve its effects in the human situation. In line with the findings of others in animal models29, in the present study, oral treatment with metformin attenuated at least in part the development of both, the early onset of fatty liver and the progression of NAFLD to steatosis accompanied with inflammation. However, in line with previous studies of us and others30, markers of fibrosis were not yet altered in any treatment group as trials were terminated already after six weeks. Metformin markedly protected FFC-fed animals from fat accumulation in liver and the slight increase in number of pro-inflammatory cells after four days, while after six weeks, fat accumulation was almost similar between FFC-fed groups. In line with the findings that metformin treatment protected mice from hepatic fat accumulation when mice were fed the FFC diet for only four days, the induction of Srebp1c mRNA expression found in FFC-fed mice was markedly attenuated in FFC + Met-fed mice. Indeed, metformin has been reported before to diminish de novo lipogenesis by inhibiting proteolytic cleavage and transcriptional activity of SREBP-1c in hepatocytes treated with glucose and insulin31. Expression of genes involved in β-oxidation seemed not to have been affected by either the FFC diet or metformin in the present study. In support of these findings, other groups also found genes involved in β-oxidation to be unchanged in a dietary model of NAFLD after eight weeks of feeding while with the same diet, markers of β-oxidation were found to be markedly changed after 16 weeks of feeding when mice had developed NASH32. Together with the data of the present study these data suggest that alterations of genes involved in β-oxidation in liver maybe a phenomena of later stages of the disease. In line with the lack of effect of metformin on fat accumulation after six weeks of treatment, expression of genes involved in hepatic de novo lipogenesis e.g. Acc, Fasn, Srebp1c were induced to a similar extend in both FFC-fed groups. Still, after six weeks of feeding signs of inflammation in liver were lower in FFC + Met-fed mice being associated with an induction of Il10 mRNA expression in hepatic tissue. In in vitro experiments in LPS-stimulated macrophages metformin treatment has been shown to induce Il10 mRNA expression before33. ALT activities in plasma were not affected by diet and/or metformin treatment at any time point. However, studies suggest that adipose tissue insulin resistance and low circulating adiponectin levels as well as high hepatic triglyceride levels may be a major factor in the elevation of plasma transaminase activity34. In the present study, a pair-feeding model was employed, so even after six weeks of feeding FFC-fed mice stayed normal weight. Furthermore, studies of others suggest that in normal weight rodents fed a high-fat diet, protein to carbohydrate ratio seems to be critical in impairing glucose homeostasis35. Indeed, similar to the findings in the present study, in these studies35 fasting glucose levels remain unaltered further suggesting that dietary composition and body weight may be critical determinants of transaminase activity and glucose homeostasis. Furthermore, Pepck mRNA expression, a key enzyme of gluconeogenesis shown before to be induced in livers of rodents fed a high-fat high-fructose diet36 did not differ between groups after six weeks of treatment. Differences between the findings of the present study and those of others may have resulted from differences in experimental design as well as the lack of massive body weight gain of animals in the present study. Also, in in vitro studies Foretz et al. showed that metformin may alter expression of the catalytic subunit of glucose-6-phosphate (G6pase), while in this study Pepck expression was unaltered37. However, when mice were fed the FFC diet for only four days, Pepck mRNA expression was markedly lower in both FFC-fed mouse groups compared to controls further suggesting that expression of Pepck may be adapted to the availability of certain macronutrients. It has been demonstrated in in vitro studies that free fatty acids may exert short-term inhibitory effects on gluconeogenic response through lowering Pepck and G6pase expression38.Metformin is one of the first line medications in the management of type 2 diabetes . Furthermore, in settings of type 2 diabetes, the beneficial effects of metformin have been suggested to be related to intestinal microbiota composition15 and barrier function14. In the present study, the protection against the onset and the more progressed stages of NAFLD found in metformin treated animals were associated with an attenuation of the loss of tight junction proteins in proximal small intestine but not in distal small intestine, and of the induction of MMP13 in proximal small intestine, the latter suggested to be critical in the degradation of tight junction proteins25. In line with previous findings of our group23, mRNA expressions of tight junction proteins were neither altered after the short nor the long-term intervention. Somewhat contrasts the findings of others reporting that the protective effects of metformin on the development of NAFLD were associated with a protection from the loss of occludin and ZO-1 in ileum and colon14, in the present study effects on tight junction proteins were found in proximal small intestine. However, experimental set-up markedly differed between our study and that of Zhou et al.14 Still, in line with the present study and our earlier findings19, Zhou et al. also showed that oral treatment with metformin abolished the translocation of endotoxin into the portal system14 subsequently leading to a protection against induction of TLR4-dependent signaling cascades in liver19. It has been shown by us and others, that the intake of a fat- and fructose-rich diet or high-fat diet in rodents can result in a marked increase in portal endotoxin levels and induction of depending signaling cascade in the liver within days39. Furthermore, while in the present study Tlr4 mRNA expression in liver tissue was unchanged (four days) or only slightly higher in both FFC-fed groups when compared to C + Met-fed mice after six weeks of feeding, MyD88 protein levels in liver were only found to be significantly higher in mice fed the FFC for six weeks. It has been suggested before that the presence of elevated bacterial endotoxin can alter expression of Tlr4 mRNA in liver tissue40; however, it has also been suggested that the abundance of Tlr4 mRNA in liver tissue doesn´t always reflect the presence of bacterial endotoxin as the receptor is not solely regulated at the level of transcription41. Furthermore, FFC + Met-fed mice were significantly protected against the increase in iNOS protein and 4-HNE protein adduct concentration found in livers of FFC-fed mice. Also, while not reaching the level of significance, phosphorylation of IKKα/β shown to be upstream in the regulation of NFκB, was also lower in livers of FFC + Met-fed mice when compared to FFC-fed animals. The induction of iNOS in liver tissue has been shown to be highly dependent upon the presence of bacterial endotoxin and an activation of NFκB in settings of NAFLD42. Taken together, our data lend further support to the hypothesis that the beneficial effects of an oral treatment with metformin on the development of NAFLD may at least in part result from a protection against impairments of intestinal barrier function e.g. the loss of tight junction proteins and subsequently an increased translocation of bacterial endotoxin. Our data also suggest that these changes are already found within days. However, our data by no means preclude that metformin may also activate AMPK in liver and maybe also in other tissues thereby furthering its beneficial effects.Several human and animal studies suggest that alterations of intestinal microbiota composition, barrier function and subsequently elevated bacterial endotoxin levels are critical in the development of NAFLD . Animals (n = 6/group) were then randomly assigned to the following groups: mice receiving a fat-, fructose- and cholesterol-rich diet , mice receiving the FFC enriched with the oral insulin sensitizer metformin , mice receiving C diet or mice receiving C diet enriched with metformin . For six weeks animals were pair-fed these diets with caloric intake being adjusted daily to the group with the lowest caloric intake. Body weight was assessed weekly and metformin treatment was adjusted accordingly. In week four of the feeding trial mice were fasted for 6 h and blood was taken from retrobulbar venous plexus under isoflurane anesthesia. Fasting blood glucose levels were assessed with a glucometer . Short-term experiments: As detailed above mice were first adapted to the consumption of the liquid control diet. Four days prior to starting the feeding of the different diets, some mice (n = 6–8/group) were fed a liquid control diet enriched with metformin (300 mg/kg BW/day). In line with the experimental set-up detailed above, mice were then assigned to the following groups for another four days: control diet (C), control diet enriched with metformin (C + Met), fat-, fructose- and cholesterol-rich diet (FFC) and FFC enriched with metformin (FFC + Met). Again, caloric intake was adjusted daily between groups. Experimental set-ups are shown in Supplementary Fig. For all animal experiments female C57BL/6J mice (6–8 weeks old)  were housed in a specific-pathogen-free barrier facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. All procedures were approved by the local institutional animal care and use committee  and animals were handled in accordance to the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes. All experiments were carried out under controlled conditions and mice had free access to tap water at all times. Long-term experiments: As detailed previously52. Numbers of neutrophilic granulocytes and F4/80 positive cells in liver sections were assessed using the commercially available Naphthol AS-D Chloroacetate kit  and immunohistochemical staining as described previously53. To determine triglyceride concentration in liver homogenates triglycerides were extracted and measured as previously described19. Picrosirius red  staining was used to determine collagen deposition in hepatic sections as previously described21.Paraffin-embedded liver sections (5 µm) were stained with hematoxylin and eosin  to evaluate status of liver damage using the NAFLD Activity Score (NAS) including steatosis, inflammatory alterations and ballooning54. A commercially available TNFα ELISA  was used to determine TNFα protein concentration in liver homogenate of mice.Alanine transaminase (ALT) activity in portal plasma of mice was determined in a routine laboratory . Furthermore, bacterial endotoxin levels in portal plasma were measured using a limulus amebocyte lysate assay  as detailed previously22. In brief, after treating sections with citrate buffer (MMP13) or incubating with protease  and blocking tissue sections with bovine serum albumin solution , they were incubated with specific primary antibodies . Subsequently, sections were incubated with peroxidase-linked secondary antibodies followed by diaminobenzidine . Staining was evaluated using a camera integrated in a microscope  and an analysis system  as described previously42.Immunohistochemical staining methods were used to measure concentration of 4-HNE, iNOS and MyD88 in paraffin embedded liver sections as well as of MMP13, occludin and ZO-1 in small intestinal tissue sections as previously described19. Real-time polymerase chain reaction (PCR) was used to determine expression of the respective genes as detailed before51. Primer sequences are shown in Supplementary Table To determine mRNA expression in liver and intestinal tissue of mice, RNA was extracted from proximal small intestinal and liver tissue using peqGOLD Trifast  and cDNA was synthetized  as detailed elsewhere42. In brief, cytosolic protein from liver tissue was isolated using a lysis buffer  supplemented with protease and phosphatase inhibitors . After separating protein lysates in a sodium dodecyl sulphate-polyacrylamide gel and transferring them to a HybondTM-P polyvinylidene difluoride membrane, membranes were incubated with specific primary antibodies  and respective secondary antibodies. Super Signal Western Dura kit  was used to detect protein bands and densitometric analysis were performed using ChemiDoc XRS System.To determine protein levels of IKKβ and pIKKα/β Western blot analysis were performed as previously describedet al.55, amplifying the V1-2 region of the 16S rRNA gene using the primers 27Fmod and 338R56. A two-step PCR using Taq primer star HS DNA  with an annealing temperature of 55 °C, using 15 cycles for the first PCR and 20 cycles for the second was performed. No negative control corrections were applied as PCR products were discarded if there was any evidence of product formation in the negative control when being checked on an agarose gel. Amplicons were purified and normalized using the SequalPrepTM Normalization Plate Kit . Samples were sequenced on an Illumina MiSeq platform using 250 bp paired-end sequencing chemistry. Raw reads were quality filtered, assembled and aligned using Mothur pipeline57. After quality filtering, a total of 523,785 reads were obtained with an average of 27,568 ± 19,682 reads per sample. Reads were clustered at 97% identity into 697 operative taxonomic units (OTU). Only OTUs present at an average abundance higher than 0.0001% and a sequence length >250 bp were considered for further analysis. The closest representative was manually identified using seqmatch from RDP58. Sequences were submitted to European Nucleotide Archive under the accession number PRJEB30105.Bacterial DNA was extracted from proximal small intestine of 4–6 mice per group using Fast DNA SPIN Kit for Soil  following the manufacturer’s instructions. Illumina Amplicon Sequencing libraries were prepared with the extracted DNA, as previously described by Camarinha-Silva P < 0.05 was defined to be significant. PRIMER-E 7  was used to analyze Illumina amplicon sequencing data59. Samples were standardized by total and a similarity matrix was created using Bray-Curtis coefficient60. Hierarchical clustering and ordination of the community structures were performed using a Principal Coordinate Analysis (PCoA) plot. For analysis of similarity (ANOSIM), a one-way permutation-based hypothesis testing was used with differences defined as significant if P < 0.05. Similarity percentage analysis (SIMPER) was used as detailed by Clarke & Warwick59 to calculate the percentage of similarity and dissimilarity and to determine which OTUs contribute to differences found between the diets. Differences in the abundance of OTUs of interest between groups were evaluated using the unpaired Welch t-test.The data are presented as means ± standard error of mean (SEM). Statistical analysis was performed using PRISM . Grubb´s test was used to determine outliers. Bartlett´s test was applied to test homogeneity of variances and data were log-transformed if unequal before performing further statistical tests. A two-factorial analysis of variance (ANOVA) was used to identify statistical differences between groups followed by Tukey’s post hoc test. Supplementary Information