We have developed and analytically validated a next-generation sequencing (NGS) assay to classify microsatellite instability (MSI) in formalin-fixed paraffin-embedded (FFPE) tumor specimens.The assay relies on DNA-seq evaluation of insertion/deletion (indel) variability at 29 highly informative genomic loci to estimate MSI status without the requirement for matched-normal tissue. The assay has a clinically relevant five-day turnaround time and can be conducted on as little as 20 ng genomic DNA with a batch size of up to forty samples in a single run.n = 94) consisting of 22 MSI-H, 24 MSS, and 47 matched normal samples and tested on an independent test set of 24 MSI-H and 24 MSS specimens. Assay performance with respect to accuracy, reproducibility, precision as well as control sample performance was estimated across a wide range of FFPE samples of multiple histologies to address pre-analytical variability (percent tumor nuclei), and analytical variability . Analytical precision studies demonstrated that the assay is highly reproducible and accurate as compared to established gold standard PCR methodology and has been validated through NYS CLEP.The MSI detection method was developed on a training set (This assay provides clinicians with robust and reproducible NGS-based MSI testing without the need of matched normal tissue to inform clinical decision making for patients with solid tumors. Microsatellite instability (MSI) is a well-described phenomenon characterized by the altered length of short repetitive regions of DNA referred to as microsatellites. The usual setting of microsatellite instability is deactivation of a mismatch repair system protein . TypicalApproximately 20% of colorectal adenocarcinomas (CRA) and 30% of endometrial endometrioid adenocarcinomas (EEM) are microsatellite unstable, with the majority being sporadic in nature. A minority of cases are associated with Lynch syndrome, which is characterized by early-onset CRA with right-sided predominance that can be synchronous or metachronous, and an increased incidence of extracolonic neoplasms, including EEM. Although CRA and EEM account for the majority of microsatellite unstable tumors, microsatellite instability has a low but substantial incidence in various other tumors .Beyond its function as a screening test to identify patients with Lynch syndrome, MSI status is a critical biomarker of response to checkpoint inhibitors , 9. MSI An important weakness of the traditional fragment analysis approach for the detection of MSI status is its inherent need to test a tumor sample in parallel with matched normal tissue, which is often not clinically available. Consequently, the requirement of normal DNA can limit the number of patients that can have testing performed due to the difficulty to obtain normal tissue leading to suboptimal turnaround time or an inability to complete microsatellite testing. Although there are additional published studies that use NGS testing to evaluate MSI status –15 and tP value < 0.0001 that were present in ≥ 80% (at least 6 out of 7) MSI-H cases but not in MSS cases were identified. The resultant set of 40 loci representing 21 chromosomes were included in the MSI NGS panel design (Supplementary Table 1).A broad pool of microsatellite instability markers previously identified by NGS in > 300 solid tumors of various histologies was considered as potential targets for inclusion in the MSI NGS assay . To confMSI NGS Caller was developed using a training dataset of 94 FFPE samples which included 22 MSI-H and 25 MSS samples, and matched normal, as previously determined by gold standard MSI-PCR , 20. IndAs with all assays that require a set threshold to determine outcome of reporting, true clinical samples that reside close to this decision boundary have the ability to be inaccurately called. Specifically, to the development of this MSI NGS assay, an inconclusive range is necessary to protect against both false positive reporting of MSS cases as MSI-H (subjecting patients to unnecessary treatment) and false negative reporting (lending to missed therapy). As the centroid distances for cluster 1 (MSI-H) and cluster 3 (MSS) get closer together, the ability of the assay to resolve MSS from MSI-H decreases. Comparing MSI-PCR and MSI NGS calls and reviewing the proximity of the cluster 1 (MSI-H) and cluster 3 (MSS) centroid distances to each other allowed for the identification of discordant reporting compared to the MSI-PCR gold standard. Of the 24 samples with MSI-PCR data, 11 cases resided between a ±3 centroid distance between cluster 1 and 3. Of these 11 samples, 4 had discordant calls when comparing MSI-PCR to MSI NGS accounting for an approximate discordance of 37% within the ±3 centroid range . A critiTo evaluate the performance of the MSI NGS caller, it was first applied to the training set of 94 samples (Supplementary Table 5). Within this cohort eight samples fell into the pre-defined inconclusive category accounting for ~8.5% of all samples tested . Of these eight inconclusive samples, two (25%) were MSI-H by MSI-PCR . For the remaining 86 samples, the MSI caller performed with an accuracy of 100% on this cohort with no false reporting . Perform10 BAT-25 and BAT-26 included in the NGS assay for samples with matched normal available. The number of peaks by NGS and PCR were calculated for tumor and matched normal cases determined as unstable for BAT-25 by PCR assay (Supplementary Table 7). This analysis showed 17 out of 18 (94%) BAT-25 unstable cases with difference in number of peaks (or unique indels present at each loci) for both PCR and NGS assay, demonstrating very high concordance between the two assays. Similarly, 19 out of 20 (95%) BAT-26 unstable cases showed difference in number of peaks for both PCR and NGS assay (Supplementary Table 8). The numerical shift in difference in number of peaks by both methods can be attributed to greater sensitivity of the NGS assay coupled with a calling algorithm designed to accurately call indels in repeat regions. The average number of peaks by NGS and PCR for both BAT-25 and BAT-26 for MSI-H, MSS and Normal groups supports a potential increased sensitivity offered by the NGS assay  was evaluated using serial dilutions of MSI-H Control cell line DNA with MSS Control cell line DNA as well varying levels of tumor DNA with matched normal DNA to assess proportion of malignant tissue required for testing. Additionally, studies to evaluate potential interferents, including variable nucleic acid input and batch size, on microsatellite instability detection were performed . These sTo begin to determine the level of detection of the MSI NGS assay serial dilutions of MSI-H positive DNA extracted from commercially available cell lines were diluted with normal; i.e., non-MSI DNA. The MSI-H DNA contribution ranged from 0.0098% to 100% with the MSI NGS assay having the ability to identify and call MSI-H status down to 2.5% input levels . As expeAs the clinical LOD for MSI-H is based on the tumor content of the sample we further determined the LOD of the MSI NGS assay in regards to tumor content using four samples selected from the 50 MSI-H gold-standard samples with sections containing areas of both tumor (70–90% tumor nuclei content) and adjacent normal tissue. To perform this evaluation, MSI-H tumor samples with abundant adjacent normal for which both elements were independently processed for DNA isolation. Tumor content was defined through pathological assessment of the tumor fraction, representing a range of neoplastic cells. The non-tumor DNA was mixed with the tumor DNA to represent a function of decreasing tumor content for two MSI-H samples. A series of seven different percent tumor nuclei dilutions were carried out on the four samples (Supplementary Table 9). For each sample, at the varying percent tumor nuclei amounts, QC data was collected and the number of peaks and mean indel length per loci were calculated to determine the MSI status (Supplementary Table 9). Correlation values were high between the sample specific indel peak number (mean r = 0.932) and mean indel lengths (mean r = 0.924) for the different percent tumor nuclei values, with the MSI NGS calls for three of the four samples within the dilution series maintaining 100% concordance down to 7–9% tumor content . As the Inconsistency in the DNA input amounts can be expected in normal practice due to potential variability at the lab bench. To evaluate the potential impact of such variability, the effect of DNA input at 50, 10, and 5 ng compared to standard input of 20ng was performed on five samples . Correlation values were calculated for both number of peaks and mean indel length when comparing the 20 ng input for each sample (Supplementary Table 10). When comparing the MSI NGS call for each sample across the four DNA input amounts there was 100% concordance . AlthougIn routine clinical testing, variability in batch size can be expected. To demonstrate that the MSI NGS assay results are unaffected by the number of samples included in a batch (sequencing run), the concordance of MSI NGS calling with varying numbers of samples included per run  was characterized. Forty samples representing both MSS and MSI-H status were run on a single flow cell, followed by a subset of 20, 10, and 5 samples, with each subset run on a single flow cell. As the twenty sample run size is considered optimal for the workflow in the laboratory, correlation values were calculated and shown to be very high for both number of peaks and mean indel length when comparing the 20 sample batch size to the 5, 10, and 40 sample batch sizes for each sample (Supplementary Table 11). When comparing MSI calling for each sample run within the different batch sizes, there was a very high concordance (97.5%) across all runs . Sample Precision of the MSI NGS assay was determined through a series of reproducibility experiments to ensure the test’s ability to make concordant calling across variables typically found in routine clinical testing . Furthermore, this study defined the utility and thresholds associated with a common set of control samples  which are to be included in every run . To measThe intra-run variance reproducibility study was completed by operator 1 on day 1 using DNA from 6 samples (3 MSI-H and 3 MSS) processed in triplicate with rotating barcodes . For allFor each sample of the intra-run reproducibility study the number of peaks and mean indel length per loci were calculated to determine the MSI status and correlations for these parameters were calculated across the replicate sets (Supplementary Table 13). Although for three individual replicates within RD-5312 and RD-5345 an inconclusive call was made due to centroid cluster distance residing very close to the cut-off (Between run reproducibility was performed on 20 samples (10 MSI-H and 10 MSS) across 2 operators, 3 different days, on 3 different runs with each replicate using a different barcode. Briefly, on day 2, operator 1 completed libraries for 20 samples sequenced without replication. On day 3, operator 2 completed a second library for the 20 samples that was again sequenced without replication. On day 3, operator 1 completed a third library that was sequenced without replication for a total of three inter-run DNA replicates for the 20 samples.For each sample of the inter-run and barcode reproducibility study the number of peaks and mean indel length per loci were calculated to determine the MSI status including correlation across the replicate sets (Supplementary Table 13). For the MSI NGS assay, the inter-run and barcode reproducibility study had 100% concordance when comparing the MSI NGS cluster designations (Supplementary Table 13), however four of the twenty samples had disparity in final calling due to inconclusive status related to the cluster boundary . As withBetween operator reproducibility was performed on 20 samples across 2 operators, and 2 different runs. Briefly, operator 1 completed libraries for the 20 samples that were sequenced without replication. Operator 2 completed libraries for the same 20 samples that were again sequenced without replication.For each sample of the inter-operator reproducibility study the number of peaks and mean indel length per loci were calculated to determine the MSI status (Supplementary Table 13). For the MSI NGS assay, the inter-operator reproducibility demonstrated a 100% concordance when comparing the MSI NGS cluster designations between the two replicates of twenty samples, however two samples had individual calls of inconclusive due to centroid distances residing extremely close to the decision boundary . As prevBetween day reproducibility was evaluated on 20 samples across a single operator, and 2 different days. Briefly, operator 1 completed libraries for the 20 samples that was sequenced without replication on day 2. On a second day, operator 1 completed libraries for the 20 samples that was again sequenced without replication on day 3.For each sample of the inter-day reproducibility study the number of peaks and mean indel length per loci were calculated to determine the MSI status (Supplementary Table 13). For the MSI NGS assay, the inter-day reproducibility demonstrated a 100% concordance when comparing the MSI NGS cluster designations between the two replicates of twenty samples sequenced on different days . Three oThe NTC (water) library preparation was included in each run to monitor assay contamination. NTC libraries cannot be sequenced as they gravely effect cluster performance, therefore nM library quantitation (qPCR) and library length  are assessed and used as contamination threshold metrics. During the validation, the NTC library was generated 14 times as individual replicates producing an average concentration of 4.0 nM (range: 1.6–8.6 nM) with an average library length of 161 bp (range: 147–218) (Supplementary Table 14). As the expected library size of this assay is ~300–350 bp (171 average insert size plus 130 bp of adapter and oligo sequence) and the average library length for the MSI-CTL and MSS-CTL were measured at 321 bp and 327 bp respectively it is evident that the NTC sample is not integrating DNA into the library preparation. Therefore the NTC library thresholds were defined as 8.5 nM  and 206 bp  to monitor the assay for gDNA contamination.The MSI-CTL and MSS-CTL were included in each MSI NGS run as positive and negative run controls. During the validation, the MSI-CTL and MSS-CTL were run a total of 14 times. The correlation of each MSI-CTL and MSS-CTL replicate was calculated by comparing each subsequent run to the first MSI-CTL or MSS-CTL result, with an average correlation of 0.9467 (MSI-CTL) and 0.9265 (MSS-CTL) for number of peaks and 0.9833 (MSI-CTL) and 0.9230 (MSS-CTL) for average indel length demonstrating high correlation and reproducibility at these metrics (Supplementary Table 15). The accurate MSI-H and MSS calls for the MSI-CTL and MSS-CTL, along with other sample level metrics, including percent mapped reads and percent singletons, single-end (non-paired) reads, as well as run level parameters such as Cluster PF%, Total Reads PF, and % ≥Q30 which have been previously defined by Illumina, Inc.  are utilMSI status by NGS and PCR were compared to assess the accuracy of DNA-seq from fifty MSI-H and fifty MSS samples of multiple histologies (Supplementary Table 18). Twenty samples were included per run (10 MSI-H and 10 MSS);for each sample the number of peaks, mean indel length per loci and centroid cluster distances were calculated to determine the MSI status . Although 15 of 100 samples  fell into the inconclusive range predefined during the development of the NGS assay, the overall concordance between MSI NGS and MSI-PCR for the 100 samples was 98% due to two MSI-PCR MSI-H samples being called MSS by MSI NGS . The two false negative cases can be attributed to the fact that these cases are on the edge of the predefined decision boundary of 3.0 between cluster centroid 1 (MSI-H) and cluster centroid 3 (MSS), which is an expected observation when decision boundaries are defined in clinical testing.The high concordance and absence of any false positive calls of the MSI NGS assay confers the high accuracy of this sequencing workflow and pipeline. From these MSI NGS results, assay sensitivity, specificity, PPV, NPV, and several sequencing level metrics for use as future sample level quality control thresholds were calculated. The sensitivity and specificity of the accuracy study are 96% and 100% respectively with a PPV of 100% and an NPV of 96% with an inconclusive rate of 15% .Traditionally the clinical significance of microsatellite instability status rested on the observations that a subset of MSI-H cancers is associated with Lynch syndrome , necessiThe ability to accurately identify MSI status without the need of matched normal tissue, a major hurdle in clinical testing, relies on the use of our MSI caller which utilizes 29 loci targeting homopolymer tandem repeat regions within the genome, integrating both mean indel length and number of unique indel peaks at each loci to define Euclidian distance and its association to cluster centroids to define MSI status. Utilizing a gold standard sample set that has defined MSI status using PCR-based clinical testing as the training and test set we developed the MSI caller which showed 100% sensitivity and 100% specificity in both groups with an inconclusive status in reporting of approximately 10%. As described, the inconclusive range is determined by a defined Euclidean distance between MSI-H cluster 1 and MSS cluster 3. The integration of an inconclusive status helps to protect against false positive and false negative reporting which may lead to detrimental pharmacotherapy. To this end, a sample which resides in the inconclusive range will be reported clinically as inconclusive, which would indicate therapy should not be pursued in this small subset of patients.Utilizing the MSI Caller algorithm and a defined set of gold-standard clinical samples, where MSI status has previously been reported using a PCR based clinical assay, we have carried out an analytical validation of a MSI NGS assay that can be utilized to determine MSI status in solid tumors for stratification of treatment to CPI therapy. As part of the analytical validation and assay robustness, precision and accuracy were determined. The MSI NGS assays robustness, or measure of the assays capacity to remain unaffected by small variations in procedural parameters, defined a tolerance to a minimum DNA input of 5 ng , the sensitivity to detect genomic instability in tumors from 10%–100% neoplastic nuclei content, and the ability to run various batch sizes from 5 to 40 samples per run all of which are critical to help mitigate the vast sample variances identified within the clinical laboratory workflow. Precision of the MSI NGS assay was determined through a series of reproducibility experiments to ensure the test’s ability to make concordant calling across intra-run, inter-run, inter-operator, inter-day, and inter-barcode variance. Furthermore, during the reproducibility studies, the utility and thresholds associated with a common set of control samples  which are to be included in every clinical run were defined. To measure accuracy of the MSI NGS assay 100 gold-standard solid tumor samples previously tested using our NYS CLEP approved MSI-PCR assay (Project ID:709) were utilized. The reported sensitivity and specificity of the accuracy study are 96% and 100% respectively with a PPV of 100% and an NPV of 96% with an inconclusive rate of 15%.While the PCR-based Bethesda markers were designed for MSI profiling in CRC, the large number of target regions included in the MSI NGS assay allows for increased confidence in pan-cancer testing without the requirement for matched normal tissue, a key component to the significant advantage over PCR. Prior to MSI NGS testing in our laboratory performing MSI in a pan-cancer setting up to 40% of clinical orders lacked matched normal tissue and required subsequent requests for alternate blocks or blood to perform MSI-PCR. Of the cases where alternate material were requested slightly less than one-half yielded matched normal material for testing with an excessive wait time of typically 10 to12 days leading to delayed reporting and an overall inability to complete 20% of all clinical requests for MSI testing. Since the activation of the MSI NGS assay within the clinical laboratory the need to request and wait for matched normal tissue has been alleviated and all cases that meet the requirements set during the analytical validation can be tested, greatly reducing turnaround time and failure rates within the lab.Overall, we have developed the first NYS-CLEP approved, analytically validated MSI assay that requires minimum input of tumor DNA without the need for matched normal and is histology agnostic. Although it has an equivalent cost and turnaround times to MSI-PCR, it is much more scalable at a high-volume laboratory. Although this assay is NGS-based, the workflow is performed similarly to a single analyte test and can be efficiently and inexpensively integrated into any molecular diagnostic laboratory.Samples were procured with informed patient consent under an institutional banking policy (IRB Protocol I115707) and the study was approved by the internal review board at Roswell Park Comprehensive Cancer Center (IRB Protocol # BRD 073116). For assay validation, 100 fixed paraffin embedded (FFPE) human clinical specimens collected from 2009–2017 and a subset of matched normal tissues from colorectal cancer (73 cases), endometrioid carcinoma (18), uterine (4), small intestine (2), prostate (1), stomach adenocarcinoma (1), and female genital (1) cancer patients stored at the OmniSeq, Inc.  and Roswell Park Comprehensive Cancer Center  remnant tissue biobanks were used to evaluate the performance of the MSI NGS assay. Two human cell lines, non-small cell lung cancer (NSCLC) HCC-78 cells , and colorectal cancer (CRC) HCT-116 cells  processed as FFPE blocks were also used for development and as internal run controls.2 of tumor surface area per slide, with tumor cellularity ≥ 10% and necrosis ≤ 50%. For level of detection studies, non-malignant tissue was also identified for isolation and reviewed to exclude any neoplastic tissue. Genomic DNA was extracted from areas identified by the pathologist using 3–5 unstained slides with the truXTRAC FFPE extraction kit , as described previously. DNA was eluted in 100 µL water, and yield was determined by the Qubit DNA HS Assay , as per manufacturer’s recommendation. To ensure adequate library preparation, a predefined yield of 20 ng DNA was used.A hematoxylin and eosin (H&E) stained tumor and normal tissue sections were reviewed by a board-certified anatomical pathologist to establish tissue QC parameters. Criteria for neoplastic testing was ≥ 2 mmTo establish thresholds and daily QC parameters, run controls were identified and included in each library preparation batch and NGS run. They included both MSI positive (HCT-116) and MSS (HCC-78) controls, as well as a no template control . Positive controls provide templates for all targets for MSI-H interpretations, while negative controls monitor assay specificity. The NTC is used to monitor assay contamination.MSI NGS libraries were prepared from 20 ng DNA using the TruSeq Custom Amplicon Low Input Kit . The panel content is detailed in section “Results: Assay development”. Following hybridization, oligos were extended, ligated, and unique indexes were added. Libraries were amplified, purified, quantitated and normalized to 4 nM. Up to 40 equimolar libraries were pooled, denatured and further diluted to 7 pM. Pooled libraries were sequenced on a MiSeq (Illumina) sequencer using a 300 cycle paired end sequencing kit to obtain 500X mean depth per sample.MSI NGS accuracy was evaluated by comparison with a PCR based NYS CLEP-approved assay for all samples. For gold standard PCR analysis, 4 sets of fluorescently-labeled primers were used for amplification of five markers . Internal lane size standards added to PCR products assured accurate sizing of alleles and to adjust for run-to-run variation. PCR products were separated by capillary electrophoresis using ABI PRISM 3500xl Genetic Analyzer and output data was analyzed with GeneMapper Software 5 .QC metrics were established and defined at the run and sample level to ensure high quality results and to monitor any run to run variance or long term drift (Supplementary Table 20). Sequencing data from the Illumina platform were first processed using custom bioinformatics pipeline for reference mapping and indel calling, during which validation-defined quality control (QC) specifications for depth of coverage were used as acceptance criteria. To ensure high quality results, a QC system was developed based on NGS data generated at validation. The QC criteria were established for several metrics at the run, sample, and run control thresholds, with defined values to accept or reject one or more aspects of sequencing. Likewise, specific QC metrics were monitored over time to detect any potential long-term assay drift. Quality filters were used at the amplicon level to remove counts below the threshold for detection, and at the base-pair level for low-quality indel calls.n = 94; MSI-H = 22, MSS = 24) and test set  of gold-standard MSI-PCR samples collected from the clinical laboratory archive . In general, the pipeline was designed to read “.fastq” files of all samples from a sequencing run and conduct sequence alignment, variant calling, indel extraction, indel length and number of peaks calculation and MSI prediction . Specifically, in the first part of the algorithm fastq file of each sample within a run was aligned to human genome (hg19) using bwa resulting in SAM genome alignment file which was further sorted and converted to BAM format and indexed for faster processing. This was followed by the crucial variant calling step wherein, all the aligned reads within a sample specific indexed BAM file were then used for custom variant calling  to generate VCF  files. This was followed by extraction of indel calls from the VCF files which were then used to calculate number of peaks (number of unique indels at each loci) and average indel length for 29 loci identified in the MSI call development as follows: For each locus X, Average indel Length(L)=∑i=1nallelic_lengthiTodal Number of alleles at loci"X" and For each locus X, Number of Peaks (nPeaks(X)) = Total number of alleles at loci “X” Finally, MSI classification was then performed using these 58 features where, Euclidean distance was calculated between each new sample and the centroid of original training kmeans clusters. The closest cluster by Euclidean distance was then used to assign a MSI NGS prediction for the sample .A custom MSI NGS caller and pipeline was developed to predict the MSI status from NGS data utilizing a training set ((TP[TP+FN]), specificity (TN[TN+FP]), PPV (TP[TP+FP]), NPV (TP(TN+FN)) and accuracy (TP+TN(TP+FP+TN+FN)).Principal component analysis (FactoMineR v1.41 in R v3.4.2) was performed on 58 features to visualize the separation between MSI-H and MSS/Normal samples. To develop a predictive model, Kmeans clustering (kmeans stats package in R v3.4.2) was performed to identify three centroids (k = 3) that represent two MSI-H clusters and a combined MSS/Normal cluster. This led to an intuitive and simplistic method of assigning future samples to each of the original clusters using simple Euclidean distance measure. Correlation measure used throughout the study is Pearson’s correlation coefficient denoted as “r”. Standard performance metrics are defined as Sensitivity


Ascaphus truei) is endemic to the Pacific Northwest of North America and is listed as a species of Special Concern under the Canadian Species at Risk Act. Its range is limited to British Columbia where it occurs widely west of the Coast Mountain Ranges extending north almost to the Alaskan Panhandle. The present study focused on surveying within the Cayoosh, Bridge (Shulaps), Seton, Anderson, Carpenter, and Downton Lake drainages. Four years of previous inventory efforts using conventional time-constrained search (TCS) methods detected tailed frog at 23/292 discrete sites (7.9% detection rate) in seven watersheds. Non-invasive environmental DNA (eDNA) methods hold promise for cryptic and low-abundance species detection. We rigorously validated a quantitative real-time polymerase chain reaction (qPCR)-based tool for detecting coastal tailed frog eDNA in water samples. This eASTR4 test is highly specific and sensitive. We applied a two-step targeted eDNA analysis approach on duplicate filtered water samples from a total of 72 sites collected over five days. The first IntegritE-DNA step mitigates false negative results and tests all DNA samples for the ability to support amplification from endogenous plant chloroplast DNA as a measure of sample viability. Three DNA samples failed this step even after inhibitor clean up suggesting that these samples were poor quality and not reliable for targeted species’ DNA analyses. All other DNA samples were deemed viable and were then tested for species-specific DNA. Coastal tailed frog eDNA was detected in 55/72 (76%) discrete stream reaches; nine sites with historical known occurrence were all eDNA positive. The false negative rate for TCS compared to eDNA methods was 58%. The results expand known coastal tailed frog distribution to 24 watersheds effectively more than tripling extant occurrences and confirm a previously suspected, apparently isolated coastal tailed frog metapopulation in the Shulaps drainage.The coastal tailed frog ( Ascaphus montanus) and coastal tailed frog , both occurring in British Columbia (BC) [There are two species of tailed frog in the amphibian family Ascaphidae, Rocky Mountain tailed frog (bia (BC) . Tailed bia (BC) . Adult, bia (BC)  and theibia (BC) . Movemenbia (BC) ,6; howevbia (BC) . Adults via internal delayed fertilization. The following summer the female lays up to 85 colorless eggs, which hatch six weeks later. Embryos feed on yolk sacs through their first winter. By the following spring, the eggs transform into tadpoles with a white spot on their tail (ocellus) that may help distract predators by drawing attention to the waving tail. Tadpoles are approximately 11 mm total length upon hatching and can grow up to 6.5 cm before metamorphosis into a terrestrial form [Coastal tailed frog breeding occurs in early fall ial form . TadpoleMetamorphosis into a frog generally occurs within four years (minimum one year) of hatching . Since m2 and stream reaches used for breeding are generally <10 km2. Ideal lotic habitats feature step-pool or cascade-pool morphology. Terrestrial forms require mature forests that provide retreat sites  within a stable and moist microclimate as coastal tailed frogs have a narrow temperature tolerance (from 6 to 18°C) [Suitable aquatic habitat occurs within lotic systems that feature a boulder or cobble substrate with abundant interstitial spaces, which provide secure habitat for tadpoles and adults  ,9. Occupto 18°C) . Globallto 18°C) .Between the periods of 2000 and 2013, four studies using traditional time constrained surveys (TCS) were performed west of Lillooet, British Columbia in the Cayoosh, Bridge (Shulaps), Seton, Anderson, Carpenter, and Downton Lake drainages; tributaries in the Shuswap Range ; and around the headwaters of Shulaps Creek  . The latAscaphus montanus [Given the difficult terrain and elusive nature of the coastal tailed frog, we decided to compare TCS with a new environmental DNA (eDNA) method. The term “eDNA” refers to any trace fragment of exogenous DNA that is released by an organism into the environment . The relmontanus ,19 usingmontanus  as it ismontanus .A critical component to eDNA surveillance methods is the eDNA assay. The assay consists of ex-situ testing for the presence of a species’ DNA using quantitative polymerase chain reaction (qPCR) and it requires the development of a set of species-specific primers and a hydrolysis probe. These primers and probe must be carefully designed and rigorously validated in order to achieve the greatest confidence in the assay . To datehttps://www.ncbi.nlm.nih.gov). These selected gene sequences included coastal tailed frog as well as sympatric species and human: possible confounders of eDNA detection tools. These gene sequences were aligned using ClustalW  to determine where the sequences share homology. Using the output aln file with BioEdit  and Primer Premier Version 6  primers and probes were designed for coastal tailed frog targeting unique regions where there was limited identity to the other species in the alignment. Particular care was taken to ensure the designed primers and probes would not cross-react with human. Multiple primer candidates were designed and tested and the successful primer/probe combination is in TaqMan qPCR primers and probes were developed using mitochondrial gene sequences obtained from the National Center for Biotechnology Information database  then the replicate was scored as positive.To determine specificity, the primers were first tested against coastal tailed frog and other species’ total DNA  with twoThe best primer/probe set was then further tested if it exhibited specificity for coastal tailed frog. At this stage, total DNA from coastal tailed frog and nine other select species plus human were run with 23 additional technical replicates. Therefore n = 25 technical replicates were run overall.After specificity testing, sensitivity was initially determined using a five-fold dilution series of the coastal tailed frog total DNA between 0.008 and 5 μg/L. Twenty-five technical replicate qPCR reactions were run for each DNA concentration in addition to the no DNA template negative control. The binomial standard error was calculated as in .The sensitivity of the eASTR4 qPCR test was further empirically evaluated using a synthetic DNA fragment and methods adapted and modified from . This me2O . This tRNA stock was stored in aliquots at -20°C before use. The working tRNA solution was prepared immediately before use by performing a 10x dilution of 100 ng/μL tRNA in Tris-EDTA Buffer pH 8.0 .In order to stabilize the synthetic DNA and its subsequent dilutions in solution, we prepared a stock solution of tRNA  in diethylcarbonate (DEPC)-H10 copies/μL superstock. This superstock was then diluted to 107 copies/μL using working tRNA solution. One μL of this dilution was added to 31 μL of working tRNA solution to produce a temporary stock containing 312,500 copies/μL.The lyophilized synthetic DNA was suspended in an appropriate volume of working tRNA solution to create a 10This stock was then serially diluted five-fold to produce a range of ten synthetic DNA concentrations from 31,250 copies/μL to 0.016 copies/μL. Two μL of each dilution were run in qPCR reactions with eight technical replicates using the same reaction conditions as  and the 2) are located in the xeric rain-shadow of the southern coastal mountains. The study area is located in the Interior Transition Ranges ecoregion within the Southern Interior Ecoprovince [The Bridge River and Seton, Anderson, Carpenter, and Downton Lake drainages  bio-geoclimatic (BEC) zone and are characterized by warm, dry summers, a fairly long growing season, and cool winters . The CoaAbies lasiocarpa). Coastal tailed frog is commonly occurs in the MS, CWH, and ESSF BEC zones, and occurs with lower reported frequency in the IDF BEC zone.Upper elevation habitats occur within the Montane Spruce (MS) and Engelmann Spruce-Sub-alpine fir (ESSF) BEC zones before transitioning into the Interior Mountain-heather Alpine (IMA) BEC zone . The ESSversus glacier fed) was also included to ensure representation of habitat heterogeneity in sample site placement. Details regarding the site attributes are presented in Seventy-two sites were selected based on the consideration of several criteria including results from previously applied TCS methods. As these sites were on Crown Land, no permits were necessary. Forty-five eDNA sampling sites were within 2 km of TCS sites documented in one or more of the four previous surveys within the study area . Nine ofOne hundred and forty-four water samples were collected from 72 sites between August 13 and 18, 2016. Duplicate 1 L water samples were collected in polypropylene bottles at each sample location or ‘site’. No permits were necessary for water collection on Crown Land. Sites near a stream confluence were sampled (~50 m) upstream of the confluence to ensure samples were not contaminated by water from effluent immediately downstream and eliminate ambiguity regarding eDNA source. Each sample was collected as close to the thalweg of the stream as possible, as thalwegs concentrate particulate matter, including DNA, into a narrow stream channel; thereby theoretically raising the probability of a positive test if the targeted species is present [DNA was isolated from the “A” replicate samples from each site and tested first. If the sample passed the two-step evaluation process described below  and resuPrior to isolating DNA from the filter membranes, the filter samples were randomized and assigned a DNA Processing Number (DPN) to eliminate processing bias. DNA was isolated using an established protocol  with thePrior to analyzing each sample for the target taxa, an “IntegritE-DNA” DNA viability control was run to ensure the quality of DNA extracted was suitable for qPCR analysis and that there were no inhibitors present . To testEach isolated DNA sample that passed the IntegritE-DNA test was then assessed using the eASTR4 test using eight technical replicate qPCR reactions with each qPCR plate including two positive and eight negative PCR reaction controls . The posA qPCR tool was successfully designed for coastal tailed frog detection after consideration of multiple primer/probe combinations derived from the mitochondrially encoded 12S RNA (MT-RNR1), 16S RNA (MT-RNR2), NADH:ubiquinone oxidoreductase core subunit 1 (MT-ND1) and cytochrome B (MT-CYB) genes. This tool is designated as eASTR4 and targets a 202 bp amplicon from the MT-CYB gene; see Analysis of the dilution curves generated with the synthetic DNA template revealed the eASTR4 tool to have consistent and reliable detection . The LOQTo date, we have run 265 no template controls (NTCs) in the context of 11 separate projects with 129 directly relating to the eASTR4 test validation and the present study. We have never had a positive hit in any of these technical replicates demonstrating that the background noise for this test is zero.t for identifying a good quality sample [t from 117 field sample filters was 22.1±0.18. Three samples had Ct values >30, thus failing the IntegritE-DNA test (All isolated eDNA samples were subjected to the IntegritE-DNA test. We have experienced a consistent background level of 36.32±0.15 (n = 56) from the ePlant5 tool within this study and previously . For thiy sample . The aveDNA test . These sDNA test . They stt <50). A site was deemed positive if either of the two replicate samples yielded a positive qPCR score.Keeping the results of the serial dilutions of total and synthetic DNA in mind and the observation that all of the distilled water field sample controls returned 0/8 for the eASTR4 test, we analyzed and interpreted the eASTR4 test results for each sample as follows. A positive result on a sample was assigned if ≥1 (of 8) technical replicates returned a positive result (CFifty-five of the 72 (76%) sites tested positive, resulting in confirmation of coastal tailed frog DNA within all higher-order watersheds in the study area . DetailsFour BEC zones were sampled including: CWH (5 sites), ESSF (17 sites), IDF (23 sites) and MS (27 sites) . CoastaleDNA methods are becoming increasingly popular as a surveillance tool, particularly for application on species that are difficult to detect using traditional surveillance methods. These include species with low population densities, inconspicuous species with secretive ecologies  and species that occupy habitats that are difficult to survey .Despite advances being made to improve rigor and ensure consistency in laboratory methods and interpretation, there still is a dire need for improved and consistent application of eDNA techniques ,20,24. FThe TCS detection probability within the context of the present study was enhanced substantially by approximately an order of magnitude using eDNA methods for detecting coastal tailed frog eDNA. The study area was particularly conducive to such a comparison given the challenging terrain and high likelihood of coastal tailed frog presence. Other studies on amphibians have realized enhanced detection probabilities; the magnitude of which was most influenced by the method of detection used; gel-based, semi-quantitative methods perform less well than qPCR-based methods ,18,27,28Prior to the present application of eDNA in the Bridge River system, there had been four extensive previous studies over a 13 year period using conventional methods to document presence and distribution of coastal tailed frog within the study area . We collThe results of the present study, when compared to results from previous studies using conventional methods, provide compelling support for the use of eDNA as an alternative method for detecting the presence of aquatic taxa, including coastal tailed frog. The rapid field collection associated with eDNA studies , the relatively low cost of filter materials, the elimination of observer bias, and relatively high efficacy  suggest that eDNA methods are more efficient and more effective for coastal tailed frog inventory than standard TCS methods.S1 Table(PDF)Click here for additional data file.S2 TableIntegritE-DNA tests were performed in four technical replicates and the eASTR4 test was done in eight technical replicates. Samples that failed the IntegritE-DNA test are indicated in orange.(PDF)Click here for additional data file.S3 TableHistorical data from four previous surveys within the region of interest (see (PDF)Click here for additional data file.


Von Hippel-Lindau syndrome, a familiar predisposition to develop cancer is a clear example. Here, we present our efforts to decipher the role of von Hippel-Lindau tumor suppressor protein (pVHL) in cancer insurgence. We collected high quality information about both pVHL mutations and interactors to investigate the association between patient phenotypes, mutated protein surface and impaired interactions. Our data suggest that different phenotypes correlate with localized perturbations of the pVHL structure, with specific cell functions associated to different protein surfaces. We propose five different pVHL interfaces to be selectively involved in modulating proteins regulating gene expression, protein homeostasis as well as to address extracellular matrix (ECM) and ciliogenesis associated functions. These data were used to drive molecular docking of pVHL with its interactors and guide Petri net simulations of the most promising alterations. We predict that disruption of pVHL association with certain interactors can trigger tumor transformation, inducing metabolism imbalance and ECM remodeling. Collectively taken, our findings provide novel insights into VHL-associated tumorigenesis. This highly integrated  Cancer is generally caused by a series of mutations accumulating over time in a healthy tissue, which becomes re-programmed to proliferate at the expense of the hosting organism. This process is difficult to follow and understand as events in a multitude of different genes can lead to similar outcomes without apparent cause. The von Hippel-Lindau (VHL) tumor suppressor is one of the few genes harboring a familiar cancer syndrome, i.e. VHL mutations are known to cause a predictable series of events leading cancer in the kidneys and a few selected other tissues. This article describes a large-scale analysis to relate known VHL mutations to specific cancer pathways by looking at the molecular interactions. Different cancer types appear to be caused by mutations changing the surface of specific parts of the VHL protein. By looking at the VHL interactors involved, it is therefore possible to identify other candidate genes for mutations leading to very similar cancer types. Familial cancers are rare, accounting for about 5–10% of all cancers  and geneAlthough the best known pVHL function is its role in degrading the HIF-1α transcription factor , a numbeth quantile of the mutation dataset and identified 18 frequently mutated pVHL positions  sites may play a role in VHL progression. We found multiple missense mutations overlapping a total of 14 PTM sites distributed on all of the pVHL binding surfaces , S3 Fig.Although a single mutation is never associated to either paraganglioma and cystadenoma as the sole phenotype observed in patients, we found specific mutations which can improve our knowledge on their onset. The five mutations p.Arg161Gln, p.Gln164His, p.Val166Phe, Arg167Trp and Arg167Gln localize in a small area forming the ELAVL1 binding interface . IncreasWe wondered whether the binding interface data may be used to perform protein docking studies of pVHL with its multiple binding partners. We modeled the pVHL interaction with ELAVL1 and AR. Association with ELAVL1 is sustained by the pVHL α-domain and the RRM1 domain of ELAVL1 . As the The docking model of the pVHL-AR complex shows pVHL surface C interacting with α-helices H3 and H11  of the ATo better clarify how cell modifications due to differentially impaired pVHL interactors prompt tumor transformation, we simulated the most promising impaired associations using a Petri net description of the pVHL interaction network . ConsideAR is a steroid hormone receptor playing a pivotal role in cancer through androgen-induced gene transcription and kinase-signaling cascade activation ,77. pVHLWe also wondered whether this approach can be used to simulate loss of pVHL interaction with different kinases. We selected GSK3β and ATK-1, as representatives of phosphorylation-mediated pVHL modulation and pVHL-dependent kinase inhibition respectively. GSK3β is known to phosphorylate pVHL at Ser72 . This moWe investigated the effect of mutations affecting the pVHL tumor suppressor and their correlation with different phenotypes described for VHL patients. Our efforts in deciphering pVHL functions arise from the consideration that clinical manifestations of this familiar predisposition to develop cancers may vary among patients harboring the same mutation. The best known pVHL function is its role in degrading the HIF-1α transcription factor . Its rolA schematic representation of the analysis workflow is presented in http://www.R-project.org). In particular all mutations accounting for a number of patients greater than the 95th quantile (p = 0.05), computed considering the vector of patient numbers, were selected for further analysis. Renal VHL manifestations  were collectively presented as renal disease due to many case report papers describing novel VHL mutations lack a precise typing of cancer sub-type, e.g. generically referring to either benign renal cancer or renal carcinoma. The pVHL surfaces were defined as previously described [Germline and somatic pVHL mutations were retrieved from VHLdb  and enriescribed ,29. Missescribed  on threeescribed . Variatiescribed .Manually curated pVHL interactors with known pVHL binding surface were retrieved from VHLdb   are for an estimated spectrum of pathogenicity with C65 most likely to interfere with function and C0 least likely.(XLSX)Click here for additional data file.S2 TableMutations were ranked by number of affected patients.(XLSX)Click here for additional data file.S3 TableResidue-residue interactions in wild type pVHL and the corresponding mutated residue. HBOND is for hydrogen bond, vdW is for van der Waals interaction, while PIPISTACK identifies cation π-π interaction. MC and SC refer to amino acid main and side chain respectively.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 TableList of manually curated pVHL interactors extracted from VHLdb.(XLSX)Click here for additional data file.S6 TableMutations are mapped for affected interface and divided for each associated cancer.(XLSX)Click here for additional data file.S7 TableInteractors binding interfaces are represented as yellow blocks according with bibliographic data.(XLSX)Click here for additional data file.S8 TableList of mutation affecting PTM sites and their predicted phenotype effects.(XLSX)Click here for additional data file.S9 TableList of scientific works used to retrieve detailed information about VHL phenotypes.(XLSX)Click here for additional data file.S1 FileClusters of interactors of interactors associated to each pVHL surface.(PDF)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S2 FigPrediction of tissue-specific interactions among pVHL binding proteins.(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 FigElectrostatic surfaces of pVHL in complex with ELAVL1 (up) and AR (down), with red representing negatively charged areas, while blue is for positive region. Proteins are alternatively presented as transparent views and rotated around vertical axis to better highlight complementary surfaces.(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.


Formation of mineral-organic associations is a key process in the global carbon cycle. Recent concepts propose litter quality-controlled microbial assimilation and direct sorption processes as main factors in transferring carbon from plant litter into mineral-organic associations. We explored the pathways of the formation of mineral-associated organic matter (MOM) in soil profiles along a 120-ky ecosystem gradient that developed under humid climate from the retreating Franz Josef Glacier in New Zealand. We determined the stocks of particulate and mineral-associated carbon, the isotope signature and microbial decomposability of organic matter, and plant and microbial biomarkers  in MOM. Results revealed that litter quality had little effect on the accumulation of mineral-associated carbon and that plant-derived carbon bypassed microbial assimilation at all soil depths. Seemingly, MOM forms by sorption of microbial as well as plant-derived compounds to minerals. The MOM in carbon-saturated topsoil was characterized by the steady exchange of older for recent carbon, while subsoil MOM arises from retention of organic matter transported with percolating water. Overall, MOM formation is not monocausal but involves various mechanisms and processes, with reactive minerals being effective filters capable of erasing chemical differences in organic matter inputs. Knowledge on processes involved in the formation of mineral-associated organic matter (MOM) has been identified as key in understanding soil carbon sequestration and climate change mitigation2. Two general pathways of MOM formation have been advocated, either the sorption of microbial assimilates3 or the sorption of plant-derived compounds5. The individual contribution of these pathways and the controlling factors are still not fully resolved.Up to 90% of the organic matter in soil is tightly bound to minerals, particularly in deeper soil layerset al.3 proposed the ‘microbial efficiency-matrix stabilization’ framework to conceptualize the nexus from input and decomposition of plant litter to the formation of MOM. The framework assumes that the microbial community processes plant litter and then microbial assimilates become sorbed by minerals  supply and increasing productivity until 12 ky, and retrogression stages, with exhausted nutrient reservoirs, especially of phosphorus25. The oceanic temperate climate 25 caused the development of podzolized soils with eluvial mineral topsoil horizons overlaying horizons being enriched in metal oxides and soil organic matter , a greater portion of iron and aluminum resided in metal‒humus complexes, whereas older soils were enriched in poorly crystalline iron and aluminum oxide phases  and Normalized Density Vegetation Index (NDVI) values obtained for June 2017 (winter) and December 2018 (summer) tended to decrease along the gradient, with differences among sites being more pronounced during winter , i.e., was associated with minerals . The light fraction carbon, representing mainly fine root debris, accounted on average for 17 ± 8% of total carbon in topsoils and 11 ± 10% in subsoils; and it was virtually absent in half of the subsoil horizons.Stocks of organic carbon and nitrogen in organic soil layers increased towards the 5 ky site, and then, declined Fig. . Most of28. The declining resource availability in the organic layers along the Franz Josef chronosequence is also reflected by changing invertebrate diversity and trophic organization29, microbial abundance, and extracellular enzyme activities27. Nonetheless, the organic layers as well as the mineral topsoil horizons along the sequence comprised comparable ratios of Fungi to Bacteria as based on SSU rRNA gene copy numbers, whereas Archaea were enriched in the oligotrophic subsoils, especially at older sites30.The organic carbon-to-organic nitrogen ratio of organic layers (Oe and Oa) increased from 15 ± 1 at the youngest to 35 ± 6 at the oldest site Fig. , which cN = 7), suggesting that already at the youngest sites the topsoil (including the OA horizon at the youngest site) offers a similar capacity for organic matter accrual as the topsoil of older sites. Given that transfer of carbon to MOM should be controlled by litter quality, topsoil at younger sites, with higher litter quality and phosphorus availability, should feature more mineral-associated carbon than those of older sites. However, the increasing carbon-to-nitrogen ratios of above- and belowground litter were not related to mineral-associated carbon  contained a similar ratio of mineral-associated carbon to clay stocks . This also indicates there was no reduced accumulation of mineral-associated carbon at older sites with lower litter quality  due to sufficient nitrogen and phosphorus supply, there was no consistent trend in the stocks of topsoil MOM . This finding points to sources of subsoil MOM other than microbial ones.Contents of microbial biomass carbon and nitrogen increased in topsoil along the gradient despite of declining litter quality Fig. , withoutter Fig.  than sub4, microbial recycling and direct sorption likely reflect a continuum, with the relative contribution depending on microbial activity in a given soil volume, the predominant hydrological regime, and the availability of mineral sorption sites. The oceanic temperate climate along the Franz Josef chronosequence facilitates water leaching, and thus, vertical transport of dissolved organic matter within the soil profile. It is well established that certain organic compounds bind directly and more strongly to minerals than others35 and even displace them from binding sites36. Several studies showed that lignin-derived aromatic compounds sorb preferentially to soil minerals due to their high content of carboxyl groups . Sanderman et al.32 demonstrated that the composition of organic matter derived from chemically diverse litter was astonishingly similar after reaction with soil clay fractions  because of preferential uptake of lignin and exclusion of proteins. Such mechanistic experiments support the idea of minerals acting as filters and being capable of erasing the chemical differences in the source materials  horizons support the hypothesis of preferential binding of strongly sorbing components in the topsoils Fig. . Strong 13C-depleted aromatic moieties in topsoil, results in 13C-enriched, less aromatic and less strongly sorbing dissolved organic matter42 that then becomes transported deeper into soil where it interacts with less organically coated mineral surfaces. In line with this, MOM revealed distinct compositional changes with soil depth from topsoil to subsoil, i.e., declining lignin, amino sugar and amino acid proportions  as related to pedogenesis and the hydrological regime would certainly be a major leap forward.25. Soils formed in glacial and fluvio-glacial sediments from schist and greywacke deposited during repeated glacial advance and retreat over 120 ky. Chemical and physical soil properties are given in Table 30. Stock data were calculated based on horizon depth, stone content, and bulk density27.Triplicate soil profiles 0‒1 m depth) including organic layers per site were sampled in January 2012 and February 2014 at seven sites along the Franz Josef soil chronosequence  located on the west coast of the South Island of New Zealand. Woody plant diversity, tree height, and vegetation cover increase during ecosystem progression, and decline at the oldest sites during retrogression m depth et al.46 using sodium polytungstate adjusted to a density of 1.6 g cm‒3. The mean recovery of organic carbon with the fractions was 90 ± 10% . Analysis of organic carbon and total nitrogen as well as isotopic composition in organic layers and heavy fractions were carried out in duplicates on ground samples using an Elementar IsoPrime 100 IRMS  coupled to an Elementar vario MICRO cube EA C/N analyzer . The δ13C values are given relative to the Pee Dee-Belemnite standard while the δ15N values are presented relative to the atmospheric isotopic signature of nitrogen gas. The 14C content in heavy fractions was analyzed by accelerator mass spectrometry  at the Max Planck Institute of Biogeochemistry . Data were analyzed according to Steinhof et al.47 and expressed as percent modern carbon (100% = 1950 AD)48.The light and heavy fractions were separated for 80 mineral soils according to Golchin et al.27.Carbon and nitrogen in microbial biomass was determined by the fumigation-extraction method as described in Turner 13C-NMR spectra of homogenized litter samples were recorded on a BRUKER AVANCE III at 100.5 MHz with a proton spin-lock and decoupling frequency of 400 MHz. The proton nutation frequency was 80 kHz corresponding to a π/2 pulse duration of 3.12 µs. The cross-polarization time was 500 µs. Samples were spun with 10,000 Hz and between 2,000 and 4,000 scans were recorded. After baseline correction, the intensities of spectral regions were corrected for different CP efficiencies at different spectral regions49. For this purpose, for one sample, 1H T1ρ as well as TCH were estimated selectively for the regions.Solid-state CPMAS 43 whereas amino sugars and amino acids were determined after acid hydrolysis50. Lignin phenols were derivatized with a 1:1 mixture of pyridine and N,O-bis(trimethylsilyl) trifluoroacetamide and measured on an ion-trap GC-MS system  equipped with a VF-5ms column (Agilent). The recovery of the internal ethylvanillin standard averaged 80%. Amino sugars  were analyzed by aldonitril acetate derivates using a GC equipped with a flame ionization detector  and a SPB-5 fused silica column. The average recovery of the myo-inositol internal standard was 76%. Amino acids were measured as N-pentafluoropropionyl-amino acid isopropyl esters and concentrations of individual 26 compounds (including enantiomers) were analyzed using an ion-trap GC-MS system  equipped with a Chirasil-L-Val column (Agilent). The mean recovery of the L-norvaline internal standard was 66%. More analytical details are given in the Supporting Information.Lignin-derived phenols in organic layers and heavy fractions were analyzed after alkaline CuO oxidationet al.51. In another set, we incubated light fraction material isolated from topsoils over 125 days at 15 °C as described in Turner et al.30. More details are provided in the Supporting Information.Carbon and nitrogen respiration of bulk soils were determined in 90-day incubation experiments conducted under oxic and anoxic conditions at 25 °C by using bulk soil samples (<2 mm) of four chronosequence sites . Concentrations of carbon dioxide were analyzed by GC-ECD  because data lacked homogeneity of variances and were not normally distributed (even after log transformation). For topsoil horizons, we used site age as a group variable  and different variables representing MOM chemistry or stocks as dependent variables. Spearman rank correlation was used to test for correlations between variables. Differences were considered significant at Supplementary information


Predicting disease progression in chronic lymphocytic leukemia (CLL) remains challenging particularly in patients with Rai Stage 0/I disease that have an unmutated immunoglobulin heavy chain variable region (UM IGHV). Even though patients with UM IGHV have a poor prognosis and generally require earlier treatment, not all UM IGHV patients experience more rapid disease progression with some remaining treatment free for many years. This observation suggests biologic characteristics other than known prognostic factors influence disease progression. Alterations in long non-coding RNA (lncRNA) expression levels have been implicated in diagnosis and prognosis of various cancers, however, their role in disease progression of early Rai stage UM CLL is unknown. Here we use microarray analysis to compare lncRNA and mRNA profiles of Rai 0/I UM IGHV patients who progressed in <2 years relative to patients who had not progressed for >5 years. Over 1,300 lncRNAs and 940 mRNAs were differentially expressed . Of interest, the differentially expressed lncRNAs T204050, NR_002947, and uc.436+, have known associated genes that have been linked to CLL. Thus, our study reveals differentially expressed lncRNAs in progressive early stage CLL requiring therapy versus indolent early Rai stage UM CLL. These lncRNAs have the potential to impact relevant biological processes and pathways that influence clinical outcome in CLL. TP53 status, IGHV mutation status, and serum β2-microglobulin has been shown to better predict progression in early CLL [Chronic lymphocytic leukemia (CLL) remains a clinically heterogeneous disease despite recent advances in disease classification centered upon cellular and molecular markers. Prognostic models based on Rai stage, immunoglobulin heavy chain variable region mutation status (IGHV), cytogenetic abnormalities, gene mutations and expression of ZAP-70, CD38 and CD49d proteins have been developed and show efficacy in predicting disease outcome in some cases of CLL –10. A rearly CLL . Despitearly CLL , 13, somGene expression profiling and genome-wide association studies have identified important genes and alleles that may play a pathobiological role in CLL –17. HoweLncRNAs are transcribed RNA molecules greater than 200 nucleotides long and have been traditionally defined as having no open reading frame, although many lncRNAs associate with ribosomes and may encode short peptides , 32. StuIn this study, we used a microarray based approach to analyze the lncRNA and mRNA expression profiles of Rai 0/I UM CLL patients with progressive disease requiring therapy  ≤2 years) compared to those with more stable, indolent disease . Our comprehensive profiling has allowed us to identify specific lncRNAs that are differentially expressed and may contribute to the clinical heterogeneity observed in this subset of CLL patients. Moreover, the data provide a crucial foundation for future studies investigating the utility of any of these lncRNAs to serve as biomarkers that may allow earlier prediction of disease progression.LncRNA and mRNA expression profiling was performed on 34 PD and 29 ID Rai 0/I UM IGHV CLL samples using a microarray based approach Table . Of the The differentially expressed lncRNAs were distributed across all chromosomes with 39.5% located on chromosomes 1, 2, 6, 7, 8 and 10 Figure . Given tLncRNAs can be classified into 5 broad categories based on genomic context and their relationship to protein coding genes . This clEven though the exact target protein coding genes and the biological role of many lncRNAs are unknown, many play a regulatory role in the transcription of protein coding genes . TherefoThirty lncRNAs were selected for validation by real-time quantitative PCR (RT-qPCR) based on FC, associated or nearby protein coding genes, and/or relevance to CLL. Protein coding genes are considered associated if the lncRNA is located within 1 kb of the promoter region of the gene(s). LncRNAs beyond 1 kb but within 300 kb are considered intergenic. Using a subset of samples from the original microarray cohort, 25 of the 30 lncRNAs followed the trend of the microarray analysis although only 20 of the 25 reached statistical significance . Seven lIn this study we identified 1,330 lncRNAs and 940 protein coding transcripts that were differentially expressed in a statistically significant manner between PD and ID Rai 0/I UM IGHV CLL using an array based method. We confirmed the differential expression of selected lncRNAs and their associated mRNAs by RT-qPCR including mRNAs that have previously been shown to play a role in CLL. The majority of differentially expressed lncRNAs and mRNAs had a 2-4 FC with only 5.4% of the lncRNAs and 1.1% of mRNAs having a FC ≥ 4.0. Previous studies of lncRNA expression during B cell development show that B cell subsets can be distinguished by their respective lncRNA pattern , 55–57. Of the 30 lncRNAs selected for validation by RT-qPCR, 25 followed the directional trend of the microarray results with 20 reaching statistical significance by both methods. Seven lncRNAs were of particular interest based on high FC  or known associated or nearby protein coding genes previously linked to cancer  and specifically CLL . The RT-qPCR results of the lncRNAs were consistent with the microarray results. The lncRNAs T347014 and T183853 do not have known associated genes but T183853 does have a nearby coding gene, RNF144A, which has a role in regulating EGF/EGFR signaling and EGF dependent cell proliferation  suggestiThe lncRNAs ENST00000435766 and ENST00000554679 have known associated protein coding genes which have been implicated in cancer. RALA (associated with ENST00000435766) is a GTPase and a downstream signaling molecule of RAS whose overexpression has been shown to result in malignant transformation and progression in chronic myelogenous leukemia. This effect can be attenuated by miR-181a, another type of non-coding RNA . In thisRAD51B (associated with ENST00000554679) is a member of the RAD51 family of genes which play a role in homologous recombination and B-cell development . In gastThe lncRNA T204050 was down-regulated in PD by microarray analysis (FC=3.6) and RT-qPCR. Of interest, a nearby coding gene of the T204050 lncRNA is CD49d. We have previously shown that high expression of CD49d on CLL B-cells is a very strong prognostic indicator for aggressive CLL and is associated with shorter TTT . In thisBiologically relevant in CLL, the lncRNA NR_002947 and mRNA for its associated protein CD79b were found to be differentially expressed by both microarray and RT-qPCR and displayed opposite expression levels suggesting that CD79b expression may be altered by NR_002947. CD79b is a critical component of the B cell receptor and levels of CD79b mRNA have been shown to be higher in UM CLL even though surface expression of CD79b remains low . HoweverThe lncRNA uc.436+ is one of 481 segments longer than 200 base pairs first described by Bejerano et al  as beingThe IGHV mutation status in CLL has long been used as marker of disease progression with much more unfavorable disease kinetics for CLL clones expressing an UM IGHV. However the precise underlying mechanism for the association with biologically aggressive disease is still unknown. It was postulated at one time that CLL was two distinct diseases arising from B cells at different stages of maturation and therefore could account for the disparities in disease progression . Gene ex9/L along with a CD5+ and κ or λ monoclonal phenotype. For this study the cohort was restricted to Rai stage 0/I patients with UM IGHV . ToPeripheral blood from all study subjects was collected within 3 months of diagnosis and prior to any treatment. Mononuclear cells were collected by ficoll density gradient centrifugation  and frozen until needed. RNA was isolated using the Trizol method  and quantitated on a NanoDrop 2000 spectrophotometer .RNA was submitted to Arraystar Inc.  for lncRNA and mRNA expression profiling and data analysis. The Human lncRNA Array v4.0 covering 40,173 lncRNAs and 20,730 protein coding mRNAs was used. Sample preparation and hybridization were done according to Arraystar standard protocols. Agilent Feature Extraction software  was used to analyze array images and data analysis was done with GeneSpring GX v12.1 software (Agilent Technologies). After quantile normalization of the raw data, only those lncRNAs and mRNAs with at least 12 Present or Marginal flags were considered for further analysis. Volcano plot filtering identified lncRNAs and mRNAs with statistically significant differences between the 2 groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify biological processes and pathway clusters associated with differentially expressed mRNAs. The p-value (≤0.05) as calculated by the Fisher’s exact test denotes the significance of the GO term in the list of differentially expressed mRNAs and the significant enrichment of differentially expressed mRNAs in biological pathways in the KEGG analysis. In both cases the enrichment score is the –log10. The greater the enrichment score the more significant the pathway correlations. The complete set of lncRNA and mRNA data obtained in this study are available in the NCBI GEO database, accession number GSE123075.δδCt method against β-actin for normalization. Primers were designed with Primer-Blast (NCBI) and primer sequences are shown in Real-time quantitative PCR (RT-qPCR) was used to confirm the expression of selected lncRNAs and mRNAs in a subset of the microarray samples and in an additional validation set of 10 samples (5 PD and 5 ID). Expression was analyzed using SYBR green reagents  on an ABI ViiA 7 Real-Time PCR System . All RT-qPCR experiments were performed in triplicate and included no template controls. Data were analyzed by the 2


Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts. The application of deep learning has the potential to increase diagnostic accuracy and reduce delays in diagnosis and treatment for better patient outcomes4. Deep learning techniques are not limited to image analysis, but they also can improve image reconstruction for magnetic resonance imaging (MRI)6, computed tomography (CT)8, and photoacoustic tomography (PAT)9. In particular, deep learning approaches have been used to improve image quality for low-dose CT reconstruction by interpolating sparse CT projection data11, denoising sparse-view reconstructed image8, or both12. These prior works demonstrated that deep learning now is a feasible alternative to well-established analytic and iterative methods of image reconstruction17.Continued rapid advancements in algorithms and computer hardware have accelerated progress in automated computer vision and natural language processing. By combining these two factors with the availability of well-annotated large datasets, significant advances have emerged from automated medical image interpretation for the detection of disease and critical findingsHowever, most prior work using deep learning algorithms has focused on image analysis of reconstructed images or as an alternative approach to image reconstruction. Despite this human centric approach, there is no reason that deep learning algorithms must function in image-space. Since all the information in the reconstructed images is present in the raw measurement data, deep learning models could potentially derive features directly from raw data in sinogram-space without intermediary image reconstruction, with possibly even better performance than models trained in image-space.In this study, we determined the feasibility of analyzing computed tomography (CT) projection data — sinograms — through a deep learning approach for human anatomy identification and pathology detection. We proposed a customized convolutional neural network (CNN) called SinoNet, optimized it for interpreting sinograms, and demonstrated its potential by comparing its performance to pre-existing system based on other CNN architectures using reconstructed CT images. This approach accelerates edge computing by making it possible to identify critical findings rapidly from the raw data without time-consuming image reconstruction processes. In addition, this could enable us to develop simplified scanner hardware for the direct detection of critical findings through SinoNet alone.18 on reconstructed CT images and SinoNet with sinograms (Table sino360x729’), which was then uniformly subsampled in the horizontal direction (projection views) and averaged in vertical direction (detector pixels) by factors of 3 and 9 to obtain moderately sampled sinograms with 120 views by 240 pixels (‘sino120x240’) and sparsely sampled sinograms with 40 views by 80 pixels (‘sino40x80’).We retrieved 200 contiguous whole-body CT datasets from combined positron emission tomography-computed tomography (PET/CT) examinations for body part recognition and 720 non-contrast head CT scans for intracranial hemorrhage (ICH) detection with IRB approval from the picture archiving and communication systems (PACS) at Massachusetts General Hospital. Axial slices in the 200 whole body scans were annotated as sixteen different body regions by a physician, and slices of the 720 head scans were annotated with the presence of ICH by a panel of five neuroradiologists by consensus (Methods). We evaluated twelve different classification models developed by training Inception-v3recon360x729), and images reconstructed from the sparse sinograms (‘recon120x240’ and ‘recon40x80’) were generated using a deep learning approach (FBPConvNet8) followed by a conversion from LAC to HU. Reconstructed CT images and sinograms with predefined window-level settings were created to evaluate the effect of windowing: ‘wrecon360x729’, ‘wrecon120x240’, ‘wrecon40x80’; and ‘wsino360x729’, ‘wsino120x240’, ‘wsino40x80’ (Methods). Based on the scanning geometries and window-level settings described above, 12 CNN models were evaluated: 6 were developed by training Inception-v318 with reconstructed CT images and the other 6 were obtained by training SinoNet with sinograms  and height-wise (detector dominant) filters. The customized architecture of SinoNet allowed for significantly improved performance in both body part recognition and ICH detection when compared with Inception-v3 models trained with sinograms, regardless of sampling density. These results imply that non-square filters may be effective in enabling models to learn the interplay between projection views and detector pixels from sinusoidal curves and to extract salient features from the sinogram domain for classification, a task thought to be impossible for human experts to grasp. This approach is similar to the one proposed for learning temporal and frequency features using rectangular convolution filters in spectrograms19.SinoNet, a proposed convolutional neural network, was developed for analyzing sinograms through customized Inception modules with multi-scale convolutional and pooling layersSinoNet, by operating in sinogram-space, can accelerate image interpretation for pathology detection as complex computations for image reconstruction are not required. SinoNet also excels when the projection data was moderately or sparsely sampled, maintaining its AUC at 0.942 on the hemorrhage detection task, while Inceptionv3 dropped from 0.972 to 0.909. Sparsely sampled datasets suggest that radiation dose could be markedly decreased with only a slight degradation in performance for sinogram-space algorithms. The number of projections linearly correlates with radiation dose, theoretically achieving 33% and 89% dose reductions for moderately and sparsely sampled data respectively. Similarly, by reducing the size and number of detectors required for diagnostic CT data, cheaper and simpler CT scanners can be created. At our institution, the average head CT has a CTDIvol of 50 mGy. Sparsely sampled data could have CTDIvol between 6 and 16 mGy. One possible use of this technique would be to use the sinogram model as a first-line screening tool in the field setting without image reconstruction, subsequently prioritizing a patient for potential stroke therapy given no evidence of intracranial hemorrhage. Subsequent full-dose CT could be used to confirm the interpretation from the sinogram method. Another possible use for this technique would be to create “smart-scanners” which allow the CT scanner to adjust the protocol and field of view based on the intended region of the body.20. Our work will need to be further validated by using raw data from clinical scanners as well as raw data from actual low-dose image acquisitions to see if performance remains robust despite increased image noise.Although these results demonstrate the power of the sinogram based approach, several important areas of future investigation remain. Due to their unavailability, the sinograms used in this study were simulated by applying the 2D parallel-beam Radon transform to the reconstructed CT images rather than actual measurement data acquired from CT scanners. Improved simulation data could be acquired by accounting for other advanced projection geometries — cone-beam or fan-beam — and considering Poisson noise when generating projection data. Although SinoNet trained with windowed sinograms achieved comparable or better performance compared with windowed reconstructed images, windowed sinograms were generated from reconstructed images that were postprocessed with predefined window settings; generation of windowed sinograms directly from CT measurement data is not straightforward, but it could be implemented by using energy-resolving, photon-counting detectors from multi-energy CT imaging to acquire measurements in multiple energy binsIn conclusion, sinogram-space deep learning with our proposed CNN called SinoNet is feasible for human anatomy identification and pathology detection (presence of ICH) on sinograms acquired using different scanning geometries in terms of projections and detectors which are not virtually interpretable to human experts like sinograms. In particular, this study showed SinoNet performed better for pathology detection directly from sparse sinograms than reconstructed images, indicating the potential of deep learning to identify critical findings from raw data without expensive image reconstruction processes in field settings for triage, especially where low dose radiation is required.All the images were fully de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). This retrospective study was conducted with the approval of the Institutional Review Board (IRB) of Massachusetts General Hospital and under a waiver of informed consent. All experiments were performed in accordance with relevant guidelines and regulations.A total of 200 contrast-enhanced PET/CT examinations of head, neck, chest, abdomen, and pelvis for 100 female and 100 male patients were retrieved from our institutional PACS between May 2012 and July 2012. A total of 56,334 axial slices in the CT scans were annotated as one of sixteen body regions by a physician  was annotated by five board-certified neuroradiologists  according to presence of ICH by consensus. The examinations included 201 cases without ICH and 519 cases with ICH, which were randomly split into train (141 cases), validation (30 cases), and test (30 cases) datasets at the case-level to ensure slices from the same case were not split across different datasets (Table sino360x729’), using the 2D parallel-beam Radon transform. ‘sino360x729’ were then used to produce sparser sinograms by uniformly subsampling projection views  and averaging projection data from adjacent detectors  by factors of 3 and 9 to obtain sinograms with 120 projection views and 240 detectors (‘sino120x240’) and sinograms with 40 projection views and 80 detectors (‘sino40x80’), respectively  were resized to 360 × 729 pixels using a bilinear interpolation to have a uniform resolution with the corresponding full-view sinograms (‘sino360x729’).Simulated sinograms were utilized in this study instead of raw data obtained by commercial CT scanners as this was a retrospective analysis and access to raw projection data from patient CT scans could not be retrieved. To generate simulated sinograms, the pixel values of 512 × 512 CT images stored in DICOM file were first converted into scaled linear attenuation coefficients (LACs). Any calculated negative LAC was leveled to zero under the assumption that it is physically impossible to have negative LACs, so this result must represent random noise. Subsequently, three different sinograms were generated based on the scaled LAC images. First, we computed sinograms with 360 projection views over 180 degrees and 729 detectors (‘recon360x729’ as fully sampled sinogram data could be completely reconstructed into images using filtered back projection (FBP). However, other complex algorithms are needed to reconstruct high-quality images from sparser datasets, such as model-based iterative reconstruction. Rather than employing complex iterative algorithms, we implemented a deep learning approach to reconstruct sparsely sampled sinograms as this technique has been demonstrated to compare favorably to state-of-the-art iterative algorithms for sparse-view image reconstruction8. We implemented FBPConvNet, a modified U-net21 with multiresolution decomposition and residual learning as proposed by a prior work8. FBPConvNet takes FBP reconstructed images from sparser sinograms (‘sino120x240’ or ‘sino40x80’) as inputs and is trained for regression between the input and the original CT image (converted into LACs) with mean square error (MSE) as the loss function  values on the validation data were employed on ‘sino120x240’ and ‘sino40x80’ to generate ‘recon120x240’ and ‘recon40x80’ respectively  and window-widths (WW) suitable for each task: abdominal window  for body part recognition and brain window  for ICH detection. The windowed sinograms were generated from corresponding windowed CT images. Examples of windowed images and sinograms are shown in Supplementary Fig. 22. As shown in Fig. 22. In this study, SinoNet was used only for interpreting sinograms.A customized convolutional neural network, SinoNet, was designed for analyzing sinograms using customized Inception modules with multiple convolutional and pooling layers and dense connection for efficient use of model parameters18, a validated CNN for object recognition in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)23, was selected as the network architecture to develop classification models trained on reconstructed images. We modified Inception-v3 by replacing the last fully-connected layers with a sequence of a global average pooling (GAP) layer, a fully-connected layer, and a softmax layer with outputs of the same number of categories: 16 multi-class outputs for body part recognition and a binary output for ICH detection. Inception-v3 was also used to classify sinograms when evaluating SinoNet performance at body part recognition and ICH detection when using sinograms as the input data.Inception-v324. For the ICH detection task, models were initialized with corresponding pre-trained weights on the body part recognition with full-view scanning geometry. For example, the Inception-v3 model trained with ‘recon360x729’ for body part recognition was used as the initial weights for Inception-v3 models trained with reconstructed images for ICH detection for all scanning geometries and window levels. Similarly, SinoNet ICH detection models were initialized using the weights from the body part recognition SinoNet model trained with ‘sino360x729’.All models developed using Inception-v3 and SinoNet for body part recognition task were initialized with He normal initialization25) was used to assess the statistical significance of the difference between AUCs of ICH detection models trained with reconstruction images and sinograms using Stata version 15.1 . We employed a non-parametric, bootstrap approach with 2,000 iterations to compute 95% CIs of the metrics including test accuracy and AUC26.Test accuracy was used as the performance metric for comparing body part recognition models, and ROC curves with AUC were used for evaluating performance of models for detection of ICH. All performance metrics were calculated using scikit-learn 0.19.2 available in python 2.7.12. A non-parametric approach  with a Tensorflow backend (version 1.3.0) as the framework for developing deep learning models, and performed experiments using an NVIDIA Devbox  equipped with four TITAN X GPUs with 12 GB of memory per GPU.Supplementary Information