Portopulmonary hypertension (PoPH) is a type of pulmonary vascular disease due to portal hypertension that exhibits high morbidity and mortality. The mechanisms driving disease are unknown, and transcriptional characteristics unique to the PoPH liver remain unexplored. Here, we apply single nuclear RNA sequencing to compare cirrhotic livers from patients with and without PoPH. We identify characteristics unique to PoPH in cells surrounding the central hepatic vein, including increased growth differentiation factor signaling, enrichment of the arginine biosynthesis pathway, and differential expression of the bone morphogenic protein type II receptor and estrogen receptor type I genes. These results provide insight into the transcriptomic characteristics of the PoPH liver and mechanisms by which PoPH cellular dysfunction might contribute to pulmonary vascular remodeling. Single-nucleus RNA-sequencing is used to investigate cirrhotic liver with PoPH and identifies diminished circulating BMP9, excess estrogen levels, altered arginine metabolism, and dysregulated GDF15 signaling may contribute to pulmonary vascular remodeling in PoPH Portopulmonary hypertension (PoPH) is a particularly lethal subtype of PAH occurring in the setting of portal hypertension, associated with a 5-year mortality in excess of 60%, that is believed to afflict up to 6% of all chronic liver disease patients6. PoPH is known to exhibit pulmonary arterial vasculopathy and hemodynamic characteristics that are similar to other forms of PAH. Clinical studies comparing PoPH to non-PoPH portal hypertensive liver disease have previously implicated a number of potential biomarkers and genetic risk factors that may be involved in PoPH pathogenesis, including a deficiency in circulating bone morphogenic protein type 9 (BMP9) and genetic variation in estrogen signaling and metabolism11. To date, the biological and transcriptional characteristics unique to the PoPH liver have yet to be explored, and the mechanisms linking portal hypertension to pulmonary vascular remodeling in PoPH remain poorly defined.Pulmonary arterial hypertension (PAH) encompasses a diverse group of vascular diseases that lead to progressive right heart failure and death15. Despite the widespread adoption of this platform to provide biological information about human pulmonary vascular disease at the resolution of a single cell, to our knowledge single cell technology has never been applied to the study of pulmonary vascular disease due to liver disease.Single nucleus RNA sequencing (snRNAseq) provides detailed gene expression profiles of individual cellular populations from dissociated segments of whole tissue. This technology has provided novel insights into the functions and interactions of resident cell populations in human tissue in health and disease, including PAH and liver cirrhosisWe anticipated that the transcriptomic profiles of PoPH human liver tissue cells would differ from non-PoPH human liver tissue derived from patients with cirrhosis, and would provide insight into the mechanisms of PoPH. We performed snRNAseq on human PoPH and non-PoPH cirrhosis liver tissue, using a combination of differential gene expression, pathway analysis, immunohistochemistry, and circulating biomarker assessments to identify and validate biological characteristics unique to PoPH.N = 5, 56%). The subjects with PoPH (N = 4) had moderate to severe precapillary pulmonary vascular disease, with mean pulmonary arterial pressure (mPAP) ranging from 45 mmHg to 76 mmHg, and pulmonary vascular resistance (PVR) ranging from 4.8 Wood units to 16.1 Wood units, at the time of diagnosis.We applied snRNAseq to liver tissue samples from nine patients, acquired at the time of liver transplantation Table . The majN = 49,339, 60.8%) contributed to the full dataset relative to PoPH liver nuclei , all samples contributed nuclei to all clusters for both PoPH and non-PoPH cirrhosis 10 or the estrogen signaling pathway 8. We identified significant downregulation of BMPR2 expression across multiple PoPH hepatocyte clusters, and significant upregulation of ESR1 across PoPH hepatocyte, cholangiocyte, and macrophage clusters  across multiple clusters, but this pattern aligned with the overrepresentation of female sex in PoPH compared to the non-PoPH cohort  in our dataset, and although multiple HSC and LSEC PoPH clusters differentially expressed the ENG gene , this pattern of ENG expression did not remain significant after false discovery rate correction17.Differentially expressed genes for each cluster were generated from the full dataset   as a promising candidate biomarker for further validation27  signaling and HIF-1 Signaling)BMPR2, ESR1, and FLT1 genes, we then sought to validate this pattern of gene expression using immunohistochemistry staining of human liver tissue from PoPH, non-PoPH cirrhosis, and normal liver tissue samples. PoPH samples demonstrated relatively more intense staining for both the ESR1 and FLT1 proteins . We first grouped PoPH cell clusters into zones by using differential expression of known marker genes for the peri-central  and peri-portal  zones of the human liver32. Using this approach, we identified the Hepatocyte 1, Hepatocyte 2, HSC 4, LSEC 3, Cholangiocyte 3, and Macrophage 1 clusters as likely to be peri-portal  relative to non-PoPH cirrhosis . We first constructed CellChat predicted signaling networks for the PoPH peri-central clusters, PoPH peri-portal clusters, and non-PoPH cirrhosis peri-central clusters. Focusing on the PoPH peri-central clusters  and the intermediate zone cluster Hepatocyte S1  in PoPH clusters, we then sought to validate and confirm low circulating BMP9 levels as characteristic of PoPH in subjects with underlying liver cirrhosis, and to assess a possible discrepancy between BMP9 liver transcriptomic signature and circulating protein levels, which may have implications for PoPH disease pathogenesis11. Given differential expression across multiple PoPH clusters, we also examined circulating FLT1 as a candidate biomarker of PoPH. In the initial validation cohort, subjects across groups had a median age of 58, and the majority were White  Table , d. We tThere was no significant association between biomarker levels  and hemodynamic disease severity  in either cohort , is thought to promote pulmonary vascular remodeling in PAH by disrupting the normal balance of antiproliferative and pro-proliferative signaling that regulates the growth of pulmonary vascular smooth muscle and endothelial cells35. Increased GDF15 expression has been shown in lung vascular endothelial cells from PAH relative to non-PAH, and increased circulating GDF15 has been shown to have prognostic value in pulmonary hypertension subtypes38. In non-hereditary PAH subjects, BMPR2 gene expression has been shown to be lower in both peripheral blood mononuclear cells as well as lung vasculature as compared to healthy controls40. Evidence also suggests BMPR2 expression is modulated by estrogen levels, as the BMPR2 promoter binds to the estrogen receptor, and increased exogenous estrogen has been shown to reduce cell BMPR2 expression in vitro41. Relevant to PoPH, clinical studies have shown increased circulating estrogen metabolites in PoPH relative to non-PoPH cirrhosis, and genetic variation in estrogen signaling genes has been associated with the risk of PoPH in patients with underlying liver disease8. Increased expression of the estrogen receptor has also been observed in the pulmonary arterioles of cirrhotic patients with histopathologic evidence of pulmonary vascular disease as compared to cirrhotic controls without pulmonary arterial vasculopathy, implying estrogen signaling may play a direct role in the process of pulmonary vascular remodeling in PoPH25. Estrogen signaling has been closely linked to apelin signaling, and in experimental PAH, estrogen-apelin interactions appear to exert a BMPR2-dependent cardioprotective effect on the right ventricle42. Studies have also identified BMP9, shown to circulate at diminished levels in PoPH plasma as compared to non-PoPH cirrhosis, as able to rescue BMPR2 deficiency in animal models43. These data, coupled with the findings from our study, suggests a plausible mechanism by which peri-central PoPH cellular dysfunction results in excess estrogen, GDF15 release, and a deficiency of BMP9 entering the central venous circulation. When combined with altered apelin signaling, these circulating intermediaries may disrupt the balance between BMPR2 and GDF ligands (such as GDF15) that control growth of pulmonary artery endothelial and smooth muscle cells, shifting towards a pro-proliferative state that stimulates pulmonary vascular remodeling and PoPH disease pathogenesis. Further confirmation of the importance of these proteins and pathways to PoPH disease pathogenesis is clearly warranted.The application of snRNAseq to human PoPH liver tissue identified multiple distinct clusters of hepatocytes, cholangiocytes, macrophages, LSEC’s, and HSC’s. Differential gene expression analysis showed relative increase in IL32 and IL34 was observed, along with increased expression of IL4R and IL6R across PoPH clusters. In addition, PoPH macrophage clusters displayed a unique inflammatory signature, characterized by increased expression of the pro-inflammatory CXCL2 gene (a potent neutrophil chemoattractant), the activation marker CD163, and diminished MIF gene expression (which has been shown to have both pro- and anti-inflammatory effects on macrophages across different disease states)46. Taken together, this suggests PoPH liver tissue may be characterized by a distinct pattern of inflammatory signaling as compared to non-PoPH cirrhosis liver tissue, and macrophages may play an important role as effector cells in promoting a pro-inflammatory state. This finding is of particular interest given the strong association between pro-inflammatory cytokines (in particular IL6 and IL6R levels), pathobiology, and clinical outcomes in other forms of PAH47. However, the simultaneous finding of diminished expression of the pro-inflammatory IL32 and IL34 genes across multiple PoPH clusters, the lack of information regarding differential gene expression of other inflammatory immune cell populations , and the absence of pulmonary vascular tissue gene expression profiles in our study, all raise an important note of caution when interpreting these findings. Although our results provide a useful guide for understanding how key cell populations and signaling pathways might be involved in PoPH inflammation, additional transcriptomic and phenotypic comparison between lung and liver inflammatory cell populations in PoPH, and robust confirmation of the differential expression of key pro- and anti-inflammatory cytokines and chemokines in PoPH relative to non-PoPH cirrhosis, is needed to better clarify the role and importance of inflammation in PoPH pathogenesis.A distinct pattern of inflammatory signaling emerged when analyzing our snRNAseq differential gene expression data. Specifically, diminished expression of 48. In our study, the differential expression pattern of ESR1, BMPR2, and FLT1 in PoPH liver tissue as compared to non-PoPH liver tissue appeared to correlate with protein expression based on immunohistochemical staining of human liver tissue specimens. In particular, BMPR2 expression appeared to be reduced, and both ESR1 and FLT1 expression appeared to be increased, in PoPH peri-central hepatocytes as compared to non-PoPH cirrhosis liver tissue. In addition, PoPH peri-central cells showed unique enhancement for GDF15 signaling and the arginine biosynthesis pathways, patterns absent when examining non-PoPH cells around the central vein and PoPH peri-portal cells. This latter finding is particularly notable because arginine is an amino acid precursor for the synthesis of the vasodilator nitric oxide (NO), which has long been implicated in the pathogenesis of pulmonary vascular disease50. Observations from experimental PAH animal models support a role for arginine in PAH, including that pulmonary artery endothelial cells from a PAH rodent model differentially expressed genes involved in the arginine biosynthesis pathway relative to cells from control rodents, and a deficiency in circulating arginine (with excess arginine intermediates) constitutes a plasma metabolite signature unique to the PAH monocrotaline rat model52. Human studies have corroborated this association, showing enrichment of arginine biosynthesis predicted by pathway analysis of intestinal microbiome gene expression in PAH subjects relative to non-PAH controls, and distinct levels of arginine metabolites and ARG1 gene expression in human PAH whole lung tissue as compared to non-PAH lung tissue54. Although portal hypertension is required for PoPH disease pathogenesis, there is no known association between the degree of portal hypertension nor the extent of liver synthetic dysfunction and either the presence or severity of pulmonary arterial hypertension in PoPH; and the so-called “liver-lung” axis of molecular communication in PoPH remains incompletely defined56. Our work, in which transcriptomic, immunohistochemical, and biochemical pathway differences between PoPH and non-PoPH cirrhosis tissue were clustered around the central vein, suggests this region of the liver may play an outsized role in PoPH disease pathogenesis. Dysregulated peri-central hepatic arginine biosynthesis might result in a deficiency of pulmonary arterial NO production, augmenting and accelerating pulmonary vascular remodeling caused by disinhibited pulmonary arterial cells; arginine along with altered estrogen, GDF15, BMP9, and BMPR2 signaling may be key players in the “liver-lung” molecular crosstalk that drives PoPH pathogenesis. Further characterization of this region’s particular anatomic and functional characteristics will help clarify the true significance of the liver peri-central region to the development and progression PoPH, potentially identifying targetable mediators capable of disrupting this pathogenic process and ameliorating PoPH disease.The central hepatic vein, which drains directly into the vena cava, is anatomically the closest avenue of communication between the hepatic and pulmonary circulation. In addition, the liver lobule is known to be composed of distinct sub-populations of cells  that demonstrate molecular and transcriptomic heterogeneity, consistent with spatially distinct cellular physiologic functions (liver “zones”) and responses to liver injury57. Our work, in a larger cohort than previously studied, provides additional validation that circulating BMP9 levels may help discriminate PoPH from both non-PoPH cirrhosis and IPAH. Our results also support a potential mechanism by which low BMP9 could interact with GDF15 and the BMPR2 signaling pathway to promote vascular remodeling in PoPH. Using snRNAseq and pathway analysis, we failed to find strong evidence that low circulating BMP9 levels in PoPH were due to hepatic differential expression of the GDF2 or ENG genes themselves. Further studies, examining the longitudinal changes in plasma BMP9 levels in PoPH both after targeted therapy and following liver transplantation, will be required to fully define both the significance and clinical utility of this biomarker in PoPH.BMP9, a circulating vascular quiescence factor produced primarily by hepatic stellate cells, has been shown to be diminished in plasma from PoPH subjects as compared to those with non-PoPH cirrhosis60.We did not observe a significant difference in circulating FLT1 levels between PoPH and non-PoPH cirrhosis groups in the second independent validation cohort, despite observing increased FLT1 immunohistochemistry staining in the PoPH liver peri-central region and elevated plasma FLT1 levels in PoPH subjects from the initial validation cohort. Given that a subset of plasma samples from the initial cohort were depleted and unable to be used for FLT1 measurement, and plasma samples from the initial validation cohort had been stored longer and had undergone more processing and freeze/thaw cycles than those from the second independent validation cohort, the differences in plasma FLT1 levels between the two cohorts may have been due to measurement error. In addition, as circulating FLT1 can be generated by either alternatively spliced mRNA that is directly translated into soluble FLT1 or post-translational cleavage of membrane-bound FLT1, and both the etiology and severity of liver disease may affect the levels of metalloproteinases and other FLT1 cleavage proteins, it is possible that the differences in plasma FLT1 levels between cohorts may be confounded by the differences in underlying etiology and severity of liver disease between PoPH and non-PoPH cirrhosis subjects across the two cohorts14. Although our work provides valuable insights into the transcriptomic characteristics of the human PoPH liver, we do not examine gene expression patterns of the pulmonary vasculature unique to PoPH. True characterization of the “liver-lung” axis in PoPH would require an integrative approach examining lung and liver tissue in PoPH simultaneously, which is a vanishingly rare clinical opportunity.Although our study is the first to describe the transcriptomic patterns of the human PoPH liver observed using single nuclear RNA sequencing, we acknowledge several limitations that are important to consider when interpreting the results of our work. Due to the rare nature of PoPH and the paucity of those affected to successfully undergo liver transplantation, we were only able to perform snRNAseq and IHC on a small number of human PoPH liver tissue samples. The resulting lack of diversity in race/ethnicity and sex in our cohort, and the heterogeneity in etiologies of underlying liver disease, limits the generalizability of our findings and may have affected our results. The limited sample availability of existing PAH biorepository resources precluded validation of all candidate biomarkers identified in the snRNAseq pathway analysis, and in isolation our transcriptomic data should be viewed as hypothesis-generating and in need of further validation. We measured plasma biomarkers in subjects with PoPH, IPAH, and non-PoPH cirrhosis, but did not include unaffected controls and cannot compare our circulating biomarker results to this group. We applied snRNAseq to our liver tissue samples to improve the yield of parenchymal (Hepatocyte) and mesenchymal cell (HSC’s and LSEC’s) transcripts that would have eluded capture with single-cell RNA sequencing, due to the difficulty in liberating cells embedded in the fibrotic extracellular matrix with single cell techniques. Because the single nuclear sequencing approach is known to partially obscure hepatic immune cell clusters , however, we may have missed important PoPH-specific differentially expressed genes, pathways, and biomarkers from these clustersBMPR2 and ESR1 genes, enhanced signaling through the GDF15 signaling pathway, and enrichment of the arginine biosynthesis pathway are characteristics that are unique to PoPH cells surrounding the central hepatic vein. Taken together, our findings provide a framework for understanding how the liver peri-central region might influence liver-lung crosstalk and promote PoPH pathogenesis, and may ultimately lead to new targets and strategies for clinical trials.In conclusion, data presented in this study provides evidence that the PoPH liver demonstrates unique patterns of gene expression as compared to the non-PoPH cirrhosis liver, and differential expression of the 4. Non-PoPH Cirrhosis was defined as patients with liver cirrhosis and sequelae of portal hypertension who had PAH ruled out by pre-operative echocardiography (estimated right ventricular systolic pressure < 30 mmHg) and confirmed by either pre-transplant or intra-operative RHC . After removal of the capsule, multiple samples of 5-mm cubed segments of tissue were obtained from different lobes of the explant and snap frozen in liquid nitrogen within 30 min of operative removal from the patient. Single nucleus suspensions were then generated using a modification of the “Humphrey’s” protocol for human kidney tissue (https://www.protocols.io/view/nuclei-isolation-from-human-kidney-for-single-nucl-81wgbobnlpko/v1)61. Nuclear lysis buffer 0 (NLB0) was created, composed of 10 ml of Nuclei EZ lysis buffer  and 1 tablet of cOmplete ULTRA protease inhibitor . Nuclear lysis buffer 1 (NLB1) was created, composed of 4 ml of NLB0, 20 μl of Promega RNAsin Plus , and 20 μl of Invitrogen SUPERaseIN , suspended in 0.1% Bovine Serum Albumin solution. Nuclei lysis buffer 2 was created, composed of 4 ml of Nuclei EZ lysis buffer , 4 μl of Promega RNAsin Plus , and 4 μl of Invitrogen SUPERaseIN , suspended in 0.1% Bovine Serum Albumin solution. Nuclei suspension buffer (NSB) was created, composed of 2 ml of Dulbecco’s phosphate buffered saline  and 40 units/ml of Promega RNAsin Plus , suspended in 1% Bovine Serum Albumin solution. Briefly, liver tissue cubes were placed on a 60 mm dish, 1 ml of NLB1 was added, and tissue was mechanically dissociated via mincing with a fresh razor blade. After transfer to a Dounce tissue grinder over ice, and addition of 1 ml of NLB1, further mechanical dissociation was performed via grinding 15–20 times with a loose pestle. The homogenate was passed through a 200 μl strainer and collected in a 50 ml conical tube. The homogenate was returned to a Dounce tissue grinder, and ground 7–10 times with a tight pestle. Following this, 2 ml of NLB1 was added, and the solution was incubated over ice for 5 min. The homogenate was passed through a 40 μm strainer into a 50 ml conical tube, then transferred to a 15 ml conical tube. This tube was centrifuged at 500 × g for 5 min at 4 °C. After discarding the supernatant, the resulting pellet was suspended in 4 ml of NLB2 and incubated on ice for 5 min. The tube was again centrifuged at 500 × g for 5 min at 4 °C. After again discarding the supernatant, the pellet was resuspended in 2 ml of NSB. The resulting suspension was passed through a 10 μm filter  into a 50 ml conical tube.Explanted human liver tissue was obtained at the time of liver transplantation. Samples were collected with institutional ethics approval from the University of Cincinnati IRB (UC IRB 2013–8157). All relevant ethical regulations were followed. PoPH was defined as precapillary PAH on resting right heart catheterization  ≥25 mmHg, pulmonary capillary wedge pressure ≤ 15 mmHg, pulmonary vascular resistance (PVR) ≥3 Wood Units) that occurred in the context of portal hypertension  without other etiologies of PAH identified Single nucleus samples were prepared per instructions provided in the 10X Genomics Single-Cell 3’v3.1 Reagent Kit. Following generation of single nucleus suspensions, examination was performed using trypan blue dye exclusion, to determine concentration and intact percentage of nuclei. For each sample, nuclei concentration was adjusted to 700–1200 nuclei/ul, 11200 nuclei were loaded into the 10X Chromium controller for 10X single nuclei sequencing (targeting recovery of 7000 nuclei), and a Gel Beads in Emulsion was generated. cDNA libraries were prepared as per instructions provided in the 10X Genomics Single-Cell 3’v3.1 Reagent kit, 18 cycles were used for cDNA amplification, and single nuclear libraries were sequenced using Illumina NovaSeq 6000. Single nuclear data were processed using the 10X Cell Ranger software version 6.1, mapping reads to a modified GRCh38 human genome which included intronic regions to ensure quantification of reads derived from immature messenger RNA (mRNA) present in the nucleus. Sequencing QC summaries for each liver profiled are found in Supplementary Table Seurat program version 4.0.6, the scrublet platform, and R version 4.1.2, data were processed. We excluded all genes detected in 10 or fewer nuclei, nuclei with fewer than 50 detected genes, nuclei with fewer than 1000 unique expressed genes (based on Barcode Rank Plots generated by the 10X CellRanger platform) and nuclei with greater than 10% of UMIs mapped to mitochondrial genome. Doublets were detected using the scrublet platform predicted doublet scores, with an expected doublet rate of 6%, and removed64. Each sample was size-factor normalized to 10,000 UMI per cell/nucleus, then log2 transformed and scaled using Seurat. Using data from all samples together, integration features were identified based on the top 2000 most highly variable features across all datasets. Ribosomal genes were removed from this list of highly variable features, and previously described canonical marker genes for human liver tissue single cell and single nuclear RNA sequencing , hepatic stellate cells , macrophages , cholangiocytes , and hepatocytes  were added28. Each sample was then scaled and the top principal components were identified from individually scaled datasets. Finally, integration anchors across all datasets were identified using the FindIntegrationAnchors function in Seurat63, and samples were integrated via an anchor-based canonical correlation method. The integrated dataset was then scaled and the top 30 principal components were identified. An elbow plot was then constructed, which identified the top 15 principal components as optimal for clustering, and the integrated dataset was clustered using Seurat’s shared nearest neighbor (SNN)-Louvain clustering algorithm with 15 dimensions65. Using the Louvain modularity optimization in Seurat, the clustree package, and JackStraw plots, we explored clusters at several resolution values. With 15 principal components and a resolution of 1.8 we generated a total of 35 unique clusters for further analysis. Differentially expressed “marker” genes for a given cluster were identified using Seurat’s FindAllMarkers function, applying a likelihood ratio test and selecting upregulated genes expressed in a minimum of 25% of nuclei with a minimum log-fold difference in expression of 0.25 or greater between clusters. Clusters were named based on a combination of previously reported canonical marker genes, subjecting the top 500 differentially expressed genes to Gene Set Enrichment Analysis (GSEA), and manual curation66. GSEA analysis involved cross-referencing differentially expressed genes with known cell type signature gene sets, the degree of overlap based on false discovery adjusted hypergeometric testing65. Two clusters expressed a mix of marker genes, one identified with both hepatocyte and HSC markers, and another with a combination of cholangiocyte, HSC, and LSEC markers. These clusters of mixed marker genes were subsequently sub-clustered with 15 principal components at a resolution of 0.2, yielding sub-clusters which were subsequently named with the suffix “S1” based on the aforementioned marker genes. The final dataset consisted of 36 unique clusters, including multiple macrophage, hepatocyte, LSEC, and HSC cell clusters (Supplementary Data Using the p < 0.05) were identified for each cluster by comparing PoPH and non-PoPH cirrhosis clusters using Seurat’s findMarkers function  was examined across various cell clusters9. Gene Ontology enrichment analysis was performed across clusters using Cytoscape version 3.9.1 and STRING enrichment67. Individual genes in a cluster were first scaled to the intensity of differential gene expression in PoPH relative to non-PoPH cirrhosis (log-fold-change between groups), STRING networks were constructed based on protein queries, functional enrichment was obtained for the largest discrete network, and significantly  affected KEGG pathways corresponding to this network were obtained. Canonical zonation markers were used to identify peri-central and peri-portal cells based on marker gene expression31. Predicted communication networks between cells and between groups were compared using the CellChat platform68. Briefly, CellChat produces predicted cell-cell communication networks by integrating gene expression data with known receptor-ligand interactions contained in the “CellChatDB” , assigning significance of predicted enrichment based on the likelihood of differentially expressing a given receptor-ligand interaction pair when compared to a random permutation of receptor-ligand pairs. The resulting output assigns cell clusters as senders (ligands), receivers (receptors), or mediators/influencers .Significant differentially expressed genes  as a candidate biomarker for further validation. Biomarkers known to be associated with PAH (serum NTproBNP) and PoPH (plasma BMP9) were also selected as reference comparators to help quantify the potential diagnostic and/or prognostic utility of novel candidate PoPH-specific biomarkers10.The results of the KEGG pathway analysis was used to curate a genes that contributed to upregulated KEGG pathways of interest based on suspected mechanisms of PAH and PoPH vascular pathogenesis: estrogen signaling (hsa04915), TGF-B signaling (hsa04350), fluid shear stress and atherosclerosis (hsa05418), Hippo signaling (hsa04390), VEGF signaling (hsa04370), and HIF-1 signaling (hsa04066)69. All relevant ethical regulations were followed. PAH was defined hemodynamically as a mPAP > 25 mmHg, pulmonary capillary wedge pressure or left ventricular end-diastolic pressure ≤15 mmHg, and PVR ≥ 3 Wood units (240 dyne•s•cm−5) on resting RHC. As part of the original repository protocol, site principal investigators classified PoPH as PAH occurring in the context of underlying portal hypertensive liver disease, and Idiopathic PAH (IPAH) as PAH occurring with no identifiable cause. Portal hypertensive liver disease was determined either clinically based on the presence of classic sequelae  or by direct measurement of an elevated hepatic venous pressure gradient. Demographic information  and body mass index  were collected upon enrollment. Non-fasting blood samples were collected at the time of study enrollment via peripheral venous phlebotomy using serum separator tubes or sodium heparin plasma tubes. Following centrifugation, aliquots of serum and plasma were stored at −80 °C. From the PAH biobank, we randomly selected 80 out of the total of 192 PoPH subjects with a full complement of biological samples and clinical data. We also selected 40 IPAH subjects as controls, to be included in the initial validation cohort.The National Biological Sample and Data Repository for PAH (“PAH Biobank”) is a National Institutes of Health-funded repository of biological samples and clinical data collected from 37 enrolling PAH centers across the United States. Patients with World Health Organization Group 1 PAH, based on right heart catheterization (RHC) hemodynamics and reviewed by pulmonary hypertension specialists, were screened for enrollment. Biorepository data collection was approved by the institutional review board at each participating center, and all patients gave informed consent at the time of enrollment2. Non-fasting blood samples were collected at the time of study enrollment via peripheral venous phlebotomy, using serum separator tubes or sodium heparin plasma tubes. Following centrifugation, aliquots of serum and plasma were stored at −80 °C. We selected 50 non-PoPH Cirrhosis subjects from the UC Cirrhosis Biobank matched on age and sex to PoPH subjects, and obtained blood samples (serum and plasma) and clinical data from these subjects, to be included in the initial validation cohort.Our institution routinely collects biological samples and clinical data from adult patients with known or suspected liver disease (“UC Cirrhosis Biobank”) . All participants provide informed consent at the time of enrollment. All relevant ethical regulations were followed. Occult pulmonary vascular disease was assessed by transthoracic echocardiography, and non-PoPH cirrhosis defined by liver cirrhosis in the absence of echocardiographic pulmonary hypertension  value greater than 2 cm, and ratio of right-to-left ventricular diastolic diameter below 0.8)−5)) without another etiology of pulmonary hypertension present. Patients with a significant obstructive or restrictive ventilatory defect on pulmonary function testing performed according to American Thoracic Society and European Respiratory Society recommendations (forced expiratory volume in one second (FEV1) divided by forced vital capacity (FVC) below 70%, with FEV1 of less than 50% predicted or FVC less than 60% predicted, using standard sex-specific, age-specific, and race-specific reference equations) were excluded8. The Model for End-stage Liver Disease (MELD) score was calculated for patients using the formulas: MELD = 10*{[0.957*ln(creatinine] + [0.378*ln(bilirubin)] + [1.12*ln] − [0.033*MELD*(137-sodium)]70. Patients with HIV infection and those with significant valvular heart disease  or left ventricular diastolic dysfunction on transthoracic echocardiography were excluded. Non-PoPH cirrhosis patients were those with known portal hypertension  with transthoracic echocardiographic testing showing a right ventricular systolic pressure less than 40 mmHg and the absence of right ventricular dysfunction. A single standardized echocardiographic protocol was applied to all subjects, and the Echocardiography Core Laboratory at the Mayo Clinic evaluated all individual echocardiograms. The institutional review boards at each of the participating centers approved biorepository data collection, and informed consent was obtained from all patients at the time of enrollment. Blood samples were collected at time of study enrollment via peripheral venous phlebotomy after an overnight fast, using serum separator tubes or sodium heparin plasma tubes. Following centrifugation, aliquots of serum and plasma were stored at −80 °C. Out of the full cohort of 454 subjects enrolled in the PVCLD2 biobank, a total of 327 subjects had available data and were included in the final biobank cohort, of which 37 had PoPH and 290 were non-PoPH cirrhosis controls. We created a second independent validation cohort comprised of 25 PoPH and 75 non-PoPH cirrhosis subjects randomly selected from the PVCLD2 Biobank.The Pulmonary Vascular Complications of Liver Disease 2 (PVCLD2) study enrolled patients evaluated for liver transplantation or pulmonary hypertension across 8 centers in the United States between 2013 and 2017 (“PVCLD2 Biobank”). Study participation was approved by the institutional review board at each participating center, and all patients gave informed consent at the time of enrollment. All relevant ethical regulations were followed. The only inclusion criterion was the presence of chronic portal hypertension with or without intrinsic liver disease. Patients with evidence of active infection, gastrointestinal bleeding in the two weeks prior to study enrollment, or prior liver or lung transplantation were excluded. The diagnosis of PoPH in liver cirrhosis was based on a combination of transthoracic echocardiography, pulmonary function testing, imaging, and clinical assessment by PAH specialists. Specifically, PoPH was defined by RHC hemodynamics . Plasma BMP9 was measured using a magnetic bead immunoassay kit  in the initial cohort. NTproBNP serum levels were measured using a commercially available multiplex magnetic bead immunoassay kit .Unstained formalin-fixed paraffin-embedded (FFPE) liver tissue sections (2 μm thickness) were obtained from archival tissue obtained at the time of surgical resection or transplantation. Liver tissue was obtained from three PoPH and five non-PoPH cirrhotic patients, and three non-cirrhotic control patients who had liver tissue removed for bleeding, trauma, and a ruptured adenoma. The same hemodynamic and clinical definitions used to identify PoPH and non-PoPH cirrhosis subjects for snRNAseq analysis of liver tissue were used to characterize subjects for archival liver tissue immunohistochemistry. Non-cirrhosis controls were defined by the absence of known pulmonary vascular or liver disease at the time of surgery. Immunohistochemical study of the expression levels of molecular markers FLT1 (VEGFR1), BMPR2, and the estrogen receptor ESR1 was carried out using the following primary antibodies: (i) recombinant rabbit anti-VEGF Receptor 1 monoclonal IgG antibody , (ii) recombinant rabbit anti-Estrogen Receptor alpha monoclonal IgG antibody , (iii) recombinant rabbit anti-BMPR2 polyclonal IgG antibody ; and secondary antibody: (iv) unconjugated polyclonal rabbit anti-goat IgG (H + L) secondary antibody . Tissue samples were first deparaffinized by washing in distilled water, followed by antigen retrieval using a citrate buffer solution. Sections were then blocked first with BLOXALL endogenous peroxide blocker . Primary staining  was applied overnight, followed by secondary staining using the rabbit anti-goat antibody listed above at a dilution of 1:500. Slides were then developed using DAPI , countered with hematoxylin, and dehydrated.cellSens software and an Olympus DP72 digital camera . Images of both the portal triad region and the central vein region were obtained at 20x magnification for each subject. Limited tissue samples prevented us from testing BMPR2 and ESR1 in NL3 samples. Semi-quantitative immunohistochemistry was performed using the Fiji software analysis platform ImageJ2 version 2.9.0/1.53t72 , categorical variables summarized using number and percentage (%). Mann–Whitney–Wilcoxon tests and Chi-Squared tests were used as appropriate.Missing biomarker levels were removed in a pairwise fashion prior to analysis. All biomarker levels were measured in duplicate , and PVR obtained on right heart catheterization) were assessed using the non-parametric Kendall Correlation. Analyses of biomarker relationships with disease etiology (PoPH or non-PoPH cirrhosis) were performed using logistic regression models. Biomarker levels were log-transformed prior to logistic regression analysis. Univariate regression models were adjusted in a multivariable fashion for clinical covariates ) in the initial validation cohort comprised of PoPH, non-PoPH cirrhosis, and IPAH subjects.N = 3), PoPH (N = 3), non-PoPH cirrhosis (N = 5) for the peri-portal region and peri-central region of liver tissue, without statistical testing performed.Semi-quantitative immunohistochemistry staining values were compared between tissue types .Further information on research design is available in the Supplemental MaterialDescription of Additional Supplementary FilesSupplemental Data 1Reporting Summary


Models that estimate rates of energy flow in complex food webs often fail to account for species-specific prey selectivity of diverse consumer guilds. While DNA metabarcoding is increasingly used for dietary studies, methodological biases have limited its application for food web modeling. Here, we used data from dietary metabarcoding studies of zooplankton to calculate prey selectivity indices and assess energy fluxes in a pelagic resource-consumer network. We show that food web dynamics are influenced by prey selectivity and temporal match-mismatch in growth cycles and that cyanobacteria are the main source of primary production in the investigated coastal pelagic food web. The latter challenges the common assumption that cyanobacteria are not supporting food web productivity, a result that is increasingly relevant as global warming promotes cyanobacteria dominance. While this study provides a method for how DNA metabarcoding can be used to quantify energy fluxes in a marine food web, the approach presented here can easily be extended to other ecosystems. A flux-based model incorporating molecular diet data reveals the importance of feeding selectivity in shaping food web dynamics. Energetic food web models are used by decision-makers globally to manage marine populations, predict the effects of environmental change on commercial fish stocks, and inform control of algal blooms  needed to quantify energy fluxes in highly resolved food webs and thereby assess the role of cyanobacteria would require running selectivity laboratory experiments for all pairwise interactions in a food web . This approach provides a model with high trophic resolution and enables us to identify the main sources and pathways of primary production in a pelagic food web. The DNA analyses and the model revealed that cyanobacteria are incorporated into pelagic food webs, a result that urges us to reconsider the structural and functional role of cyanobacteria in present-day and future marine food webs.In this study, we used diet information from DNA metabarcoding to calculate the relative feeding selectivity of zooplankton . We usedTemora, Centropages, Pseudocalanus, and Acartia), cladocerans (Evadne and Bosmina), and rotifers (Synchaeta and Keratella) (16S rRNA gene (16S) metabarcoding and being abundant in the Baltic Sea biomass monitoring. The phytoplankton orders included diatoms  and dinoflagellates  that together constitute the spring bloom; filamentous cyanobacteria  and picocyanobacteria  that together constitute the summer bloom; as well as chlorophytes , haptophytes , cryptophytes , and chrysophytes   . We incllinales) .2 per year. Among this flux, filamentous cyanobacteria  and picocyanobacteria  together constituted the major source of primary production in the food web. Spring bloom-forming dinoflagellates  and diatoms , as well as a diversity of other small-sized phytoplankton (6%), contributed to a lesser extent to the detected annual secondary production . Temora accounted for most of the total detected predation on Synechococcales (65%) and other groups of small phytoplankton . At the same time, Temora accounted for a relatively small proportion of the detected predation on filamentous cyanobacteria  and chain-forming diatoms . The copepods Centropages and Pseudocalanus accounted for 13 and 10% of the detected secondary production, respectively, and appeared to split their prey equally between available resources . Although cladoceran and rotifer zooplankton  had comparatively low-energy consumption due to relatively low biomass throughout the year, they together constituted 17% of the detected annual predation on primary producers  had a higher preference for filamentous cyanobacteria  compared to the copepods Temora and Centropages. Similarly, copepods had a generally higher preference for diatoms than the non-copepod zooplankton  over the season between 2017 and 2020 (fig. S1). Zooplankton samples were collected with vertical hauls from 0 to 30 m and 30 to 60 m using a 90-μm WP2 plankton net and preserved immediately in 95% ethanol. Individuals of zooplankton were identified and isolated under a stereomicroscope (table S2). To remove contamination of external DNA, all individuals were rinsed five times in Milli-Q water, screened for visible epibionts, soaked for 30 s in a 1% bleach solution, and rinsed another five times in Milli-Q water. Five to 12 individuals of the same species were pooled randomly into the same sample tube and stored with 180 μl of alanine transaminase lysis buffer . For estimating available prey composition, water samples were collected with 10-liter Niskin bottles with 5-m depth intervals above the thermocline of 30-m depth or with a 20-m long hose according to the Helsinki Commission’s guidelines for plankton monitoring in the Baltic Sea , including 1 μg of carrier RNA following the manufacturer’s instructions for tissue samples. DNA from water samples was extracted from the water filters using the DNeasy Plant Mini Kit (QIAGEN) with an additional step of bead beating with 1-mm glass beads and an overnight incubation at 56°C with proteinase K (QIAGEN). Illumina sequencing library preparation was performed according to best practices (16S rRNA gene (16S) using the primers 341F-Adapter1 and 805R-Adapter2 (table S3) (We amplified a 500–base pair (bp)–long fragment of the V3-V4 region of the An outer PCR step was added to attach unique index sequences, to facilitate sample pooling. Reactions contained 14 μl of KAPA HiFi HotStart Ready Mix , 1 μl of Handle1-index-Adapter1 (10 μM), 1 μl of Handle2-index-Adapter2 (10 μM), and 12 μl of cleaned PCR product from the previous step (table S3). The thermocycling conditions were 98°C for 2 min, followed by 10 cycles of 98°C for 20 s, 62°C for 30 s, 72°C for 30 s, and a final extension step of 2 min at 72°C. PCR products were pooled at equimolar amounts and purified using Agencourt AMPure XP (Beckman Coulter). DNA concentration and quality were determined using a Qubit fluorometer  and a Bioanalyzer assay . Libraries were sequenced on MiSeq (MSC 2.5.0.5/RTA 1.18.54) pair-end setup  with the addition of 10% genomic PhiX.. All downstream analyses were conducted in R . Quality control and filtering, error rate modeling, sequence dereplication, ribosomal sequence variant inference, and taxonomic assignment were done using the DADA2 R package  using “bcl2fastq2” from the Casava software. Primers and adapters were truncated in the Cutadapt software, which also removes sequences where primers are missing iS) for each prey taxon (i) of a consumer, we used a standardized forage ratio (i)Bg and the prey availability in the environment (iBe). The selectivity index for each prey can be calculated using the following equation (To assess the selectivity index (equation Si=BgiBeiiRg for gut samples and iRe for the environment) and prey biomass (iBg for gut samples and iBe for the environment)  {Bgi=Rgi∗iP is a factor representing biases linked to the identity of the prey taxa (i), such as the ratio between tissue biomass and gene copy number  are constant for both gut and environmental samples and that library-related biases (L) are independent of prey taxa, the selectivity index is not influenced by the values of either P or L  with sampling intensity ranging between monthly and weekly samples. Individual body masses of phytoplankton and zooplankton were retrieved from the Helsinki Commission’s guidelines for plankton monitoring in the Baltic Sea  is derived from species’ body masses iM and an allometric scaling constant, i.e., a normalization constant a = 17.17 for invertebrates. iBe represents the total population biomass per area unit (grams per square meter) and is multiplied by the metabolic rate (flux per gram biomass) to obtain a metabolic rate estimate at the population level. Metabolic rates are also adjusted to ambient temperature, where E is the activation energy, K is Boltzmann’s constant, and T is the absolute temperature in Kelvin.The metabolic rate for each node was calculated on the basis of metabolic scaling theory , where tiG = iL). In this model, losses of each population (Li) were defined as the sum of metabolic losses (iX) for the population and the energy flux lost to predation (iF) (iG) were calculated as the sum of all fluxes from prey to predator multiplied with the assimilation efficiency, i.e., conversion of consumed biomass into energy (je)  . Gains frgy (ej)  and an a to 0.77 . The fluiF = ∑jFij (Joules per day per meter squared), normalized predation pressure as iP = iF/iBe (Joules per gram per day) (jF = ∑iFji (Joules per day per meter squared). We also calculated annual food web metrics by summarizing all daily flux networks (Joules per year per meter squared).To account for annual variations in biomass of prey and predators, we estimated fluxes (Joules per day per meter squared) between primary producers and primary consumers for each day of the year and each station . One iteration of the bootstrap procedure consisted of random sampling with replacement of in replicates for each consumer taxa i (using “slice_sample” from dplyr R package). We used this dataset to recalculate average preferences and corresponding energy fluxes. The procedure was repeated 100 times to characterize a distribution of fluxes showing the uncertainty around our predictions. Last, we compared the distributions against a null model that assumes no preferences (for each consumer the differences in foraging intensity depend only on its prey’s relative biomasses).To test for the consistency of our results in regard to the variability in consumer preferences, we ran a bootstrapping analysis. For each consumer taxa 16S sequences (Our model assumes a steady-state ecosystem where the energy required by zooplankton equals their resting state respiration. This assumption leads to an underestimation of secondary consumption when the population grows and overestimates the requirements during population decline. Furthermore, limited access to dietary data from DNA metabarcoding limited us to only include the most abundant taxa in the model. Including less abundant taxa and more trophic levels and life stages would influence the model output. Because of a limited resolution in the current taxonomic database used to annotate the equences , nonsyst


ANKRD26-related thrombocytopenia is a rare inherited disorder associated with an increased risk of malignancy. While the genetic mutations underlying this condition are well understood, there is limited knowledge regarding its contribution to myeloid neoplasms, such as acute myeloid leukemia (AML). We present a case of ANKRD26-related thrombocytopenia with a variant of uncertain significance in a patient with AML and review the pathogenesis and implications of hereditary germline mutations in disease management. ANKRD26-related thrombocytopenia is a rare genetic disorder that affects platelet production and is associated with an increased risk of developing acute myeloid leukemia (AML). The ANKRD26 gene encodes proteins containing N-terminal ankyrin repeats, which regulates proteins in mitogen-activated protein kinase (MAPK) signaling pathways involved in megakaryocyte maturation and platelet formation [ANKRD26 are found in the 5' untranslated region, result in a gain-of-function effect, and cause ANKRD26-related thrombocytopenia, also known as thrombocytopenia-2 [ANKRD26 [ormation . Pathoge[ANKRD26 . Penetra[ANKRD26 .p53 mutation, that was diagnosed following a workup for menorrhagia. At the time of admission, complete blood count revealed white blood cell count 2.6x109/L , hemoglobin 10.2 g/dL , and platelet count 11x109/L . During evaluation for potential hematopoietic stem cell transplantation genomic analysis was performed with pertinent results shown in Table A 20-year-old female with past medical history of childhood Diamond-Blackfan anemia in remission presented to our hospital for initiation of induction chemotherapy for high-risk AML without maturation with complex cytogenetics, including ANKRD26 gene.Genomic DNA was extracted directly from a peripheral blood sample in EDTA (ethylenediaminetetraacetic acid). The DNA was enriched for the complete coding regions and splice junctions of most genes of the human genome using a proprietary capture system developed by GeneDx  for next-generation sequencing with CNV calling (NGS-CNV). The enriched targets were simultaneously sequenced with paired-end reads on an Illumina platform. Bi-directional sequence reads were assembled and aligned to reference sequences based on National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) transcripts and human genome build GRCh37/UCSC hg19. Using a custom-developed analysis tool , data were filtered and analyzed to identify sequence variants and most deletions and duplications involving three or more coding exons in the selected genes or regions of interest. Genomic DNA testing of peripheral blood revealed a heterozygous c.5106 G>C p.(Q1702H) (CAG>CAC) in exon 34 of the Notably, the patient had no childhood history of thrombocytopenia. The only hematologic abnormality the patient reported prior to development of AML was hereditary pure red cell aplasia that responded to immunosuppression with ongoing remission since two years of age. The patient experienced relapse of AML after initial induction therapy and there were challenges in finding a donor for hematopoietic stem cell transplant due to her Middle Eastern heritage and lack of HLA-matched family donors. Unfortunately, after developing neutropenic fever for which re-induction therapy was halted, the patient passed away due to complications of fungemia including septic shock and disseminated intravascular coagulation before receiving further cancer-directed treatment.ANKRD26 gene, which has not been previously identified at significant frequency in large population cohorts  nor has it been published as pathogenic or benign. Although the patient had hereditary pure red cell aplasia, she had no known history of platelet disorders or chronic thrombocytopenia prior to AML. The childhood bone marrow evaluation prior to the presence of AML was not available for review. In individuals with active or chronic hematologic neoplasms or conditions, there is a possibility that genomic testing may detect an acquired somatic variant, leading to a false positive result. The clinical sensitivity of the testing performed depends in part on the patient's clinical phenotype and is expected to be highest for individuals with clearly defined disease and/or family history of disease.We present a case of AML in a patient with heterozygous c.5106 G>C p.(Q1702H) (CAG>CAC) in exon 34 of the ANKRD26-related thrombocytopenia is a rare genetic disorder that can increase the risk of developing AML. In a study of 250 consecutive, non-familial, adult AML patients screened for mutations in the first exon of ANKRD26, including the 5’UTR, three patients had variants in the ANKRD26 coding region [ANKRD26 mutations, it is unclear whether cancer risk remains increased in variant phenotypes without prominent childhood thrombocytopenia. Genetic testing was recommended for the patient’s immediate family members, given the unclear significance of this variant. Additionally, genetic evaluation of hematopoietic stem cell transplant donors is essential to avoid transplant from a donor with the same genetic disorder because inadvertent transplant from a donor carrying a germline mutation associated with a hereditary myeloid malignancy syndrome may result in poor engraftment or donor-derived malignancy [lignancy .ANKRD26-related thrombocytopenia is a rare genetic disorder that can increase the risk of developing AML. Genetic testing should be considered in patients with unexplained thrombocytopenia, especially in those with a family history of bleeding or hematologic malignancies. Surveillance for early detection of myeloid neoplasms should include an annual complete blood count with bone marrow examination if abnormalities are noted. Allogeneic stem cell transplantation is a viable treatment option for patients with ANKRD26-related AML with appropriate genetic screening of donors prior to transplant. It remains unclear if the c.5106 G>C p.(Q1702H) (CAG>CAC) in exon 34 of the ANKRD26 gene variant seen in this patient confers an increased risk of malignancy; however, consistent reporting of variants of uncertain significance is essential to accrue patient data on a population level to identify new pathologic variants.


Hydrogen bonding between an amide group and the p- Similar observations of the In addition to their ubiquity, there is some indication of the importance ofthese interactions. In a 1993 review, Perutz (1993)indicated the potentially wide-ranging importance of these interactions,particularly Armstrong et al.'s (1993) demonstration of their role in stabilizingFollowing the example of Tüchsen and Woodward (1987), we seek to use NMR to provide corroborative evidence ofaromatic hydrogen bonds. In this paper, we survey the Biological MagneticResonance Bank (BMRB) for unusual amide proton chemical shifts andamide–aromatic nuclear Overhauser effects.Theoretical models for the geometrical dependence of the ring current shiftinclude parameterization of quantum-mechanical  calculations, semi-classical approximationusing the Biot–Savart law  for the field arising fromcurrent loops , and a dipole approximation. For distances from the ring centerthat are greater than 3 Å above the plane of the ring, and 5 Å inthe plane of the ring, the theories all agree well with a dipoleapproximation . The 2http://api.bmrb.io/v2/mappings/bmrb/pdb?match_type=exact, last access: 15 January 2021). As of January 2021, we found 7750 BMRB/PDB paired entries and retrieved the BMRB entries  and PDB entries  from their respectivedatabases. We filtered out DNA and RNA entries, entries with ligands, oligomers,and protein complexes. In the end we prepared a dataset that consists of 363 686amide protons from 4670 entries. We combined the chemical shift informationfrom BMRB and the geometric information from PDB for each amide proton andits nearest aromatic ring using sequence number and residue name. For eachassigned amide chemical shift, the https://bmrb.io/ref_info/stats.php?restype=aa&set=filt, last access: 15 January 2021). For each assigned amide, thedistance from the amide position to the center of the nearest aromatic ringis computed from the coordinates in the PDB mmCIF file. The distance isdefined as the average of the distance from the amide proton to the centerof the aromatic ring, averaged over the members of the structural ensemblepresent in the PDB entry. For the nearest aromatic ring, we calculated anazimuth angle , defined as the angle between a vector normal tothe aromatic ring plane and the vector between the amide proton and thecenter of the ring. The ring normal vector is computed by calculating thecross product of two vectors on the plane of the ring (say the vector fromthe center of the ring to CG and CD1). The table of assigned chemicalshifts, To investigate the connection between amide proton chemical shifts and thepotential for hydrogen bonding to an aromatic ring, we searched BMRB forassigned amide protons in proteins corresponding to structures deposited inthe PDB. BMRB provides the list of BMRB and PDB entry ID pairs via BMRB API were used.Corroboration of close proximity between an amide proton and an aromaticring observed in PDB structures is found in assigned distance restraintsbased on nuclear Overhauser effects (NOEs) present in the BMRB entries. NMRrestraint files from the PDB were parsed using PyNMRSTAR  for NOE restraints between amide protonsand aromatic ring protons of different residues. Because many files listNOEs under “simple” distance restraints, these were included. Due toinconsistencies prevalent in the restraint data, several criteria wereimplemented to ensure some conformity in the restraints included in ouranalysis. This and other reasons for excluding entries from the restraintsanalysis are described in greater detail in Table S1 in the Supplement. Alsodiscarded were individual distance restraints which reported only a lowerdistance bound or an upper distance bound greater than 6 Å (as this isinconsistent with the nuclear Overhauser effect) and restraints that wereambiguously between more than two different residues . Of the entries that remained, 2573 listed at least onerestraint between an amide proton and an aromatic ring proton, and 848 didnot. For this section of the analysis, the June 2021 ReBoxitory data lakesnapshots of BMRB and PDB available on NMRbox exhibiting these restraints. For both upfield- and downfield-shifted amideprotons, the greater the deviation from the mean, the greater the likelihoodthat corresponding NOE restraints are observed. The trend is noticeably morepronounced for the upfield-shifted amide protons, which is consistent withthe formation of hydrogen bonds between the amide and the p-In Fig. 6 the restrained amide–aromatic pairs are separated by the type ofthe aromatic residue and the number of restraints between the amide protonand the aromatic ring protons. For every aromatic type, a greater proportionof the upfield-shifted pairs have more than one restraint between them thanthe downfield-shifted pairs, which may indicate a hydrogen bond from theamide to the p-3.3Figure 7a and b show the examples of p-Figure 8a shows the amide proton of D28 is approximately in the plane of theY37 aromatic ring in BOLA3 protein (PDB ID:2NCL), causing the amidechemical shift of D28: there is a local minimum in thevan der Waals (VdW) energy with the amide nitrogen 3.3 Å from the ringcenter. The calculations also show that the non-bonded VdW interactions donot preclude adoption of a hydrogen-bonded aromatic ring; however the welldepth is so small that the VdW attraction alone is likely insufficient toyield a favorable H-bond geometry without additional restraints.Lack of assignments are not evidence of the absence of an NOE. Missingassignments . The correlation between secondary shift and order parameters issufficiently strong that it has been used to predict order parameters fromchemical shifts  .Ring current effects in particular are diminished by fluctuations about theAlthough chemical shifts have been used to refine protein NMR structures, forthe most part these approaches leverage the influence of backbone torsionangles on chemical shifts and do not consider the influence of nearbysidechains. Despite evidence that chemical shift refinement software isbeing used more frequently, the pace of chemical shift-refined structuredepositions remains low .Filtering the data plotted in Fig. 3 to include only structures thatreference CS-Rosetta  does not alter the overall distributions. Achallenge confronting a deeper understanding of these effects is that theavailable metadata in BMRB do not articulate workflows , nor does it indicate when ring current shiftrestraints were utilized.4Ring current shifts have a long history of providing structural insightsfrom NMR studies of globular proteins ,especially for methyl groups, whose secondary shifts tend to be dominated byring current shifts. Early studies were largely anecdotal, focusing onindividual proteins or small surveys. While relatively dynamic aromaticrings  and disorder diminish the influence of ring currenteffects on secondary shifts , theaccumulation of data in BMRB for folded proteins has provided a wealth ofamide chemical shifts exhibiting large secondary chemical shifts. Federationof BMRB chemical shift data with structural data from PDB confirms thestrong correlation between proximity to an aromatic ring and extremesecondary shifts. Markedly different secondary shift trends for differentaromatic residue types suggest promising avenues for improving proteinstructure determination by NMR. Though chemical shift refinement has beenrepeatedly demonstrated , ithas not yet been widely adopted.The extreme outlier amide chemical shifts and corroborating NOE effectsexamined here provide strong evidence of the widespread existence ofamide–aromatic hydrogen bonds, but they are not fully conclusive.Nonetheless potential for under-representation in the BMRB data existsbecause of incomplete assignments. Relaxation studies on ring dynamics,contrasting rings where evidence suggests the presence of hydrogen bondingwith rings lacking such evidence, could provide additional corroboration.Molecular mechanics simulations and structure refinement using polarizableforce fields could reveal additional aromatic hydrogen bonds and restrictedring dynamics in folded proteins. We have initiated investigations alongsome of these lines.More broadly, this preliminary investigation highlights the potential forunlocking latent knowledge hidden in BMRB, PDB, and other biologicaldatabases. The challenges posed include curation and validation of the datarepositories and federation of data between repositories. Robust andefficient solutions to these challenges are needed in order to realize thefull promise of emerging methods in machine learning .10.5194/mr-2-765-2021-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/mr-2-765-2021-supplement.


SwarmCG, an automatic multiobjective optimization approachfacilitating the development of lipid force fields, to refine specificallythe bonded interaction parameters in building blocks of lipid modelswithin the framework of the general Martini CG FF. As targets of theoptimization procedure, we employ both experimental observables (top-downreferences: area per lipid and bilayer thickness) and all-atom moleculardynamics simulations (bottom-up reference), which respectively informon the supra-molecular structure of the lipid bilayer systems andon their submolecular dynamics. In our training sets, we simulateat different temperatures in the liquid and gel phases up to 11 homogeneouslamellar bilayers composed of phosphatidylcholine lipids spanningvarious tail lengths and degrees of (un)saturation. We explore differentCG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures anda portion of the phase diagram of a DOPC/DPPC mixture. Successfullyoptimizing up to ∼80 model parameters within still limitedcomputational budgets, we show that this protocol allows the obtainmentof improved transferable Martini lipid models. In particular, theresults of this study demonstrate how a fine-tuning of the representationand parameters of the models may improve their accuracy and how automaticapproaches, such as SwarmCG, may be very useful tothis end.After two decadesof continued development of the Martinicoarse-grainedforce field (CG FF), further refinment of the already rather accurateMartini lipid models has become a demanding task that could benefitfrom integrative data-driven methods. Automatic approaches are increasinglyused in the development of accurate molecular models, but they typicallymake use of specifically designed interaction potentials that transferpoorly to molecular systems or conditions different than those usedfor model calibration. As a proof of concept, here, we employ  Nonbondedinteractions between CG beads are described via simple spherical Lennard-Jones(LJ) and Coulomb potentials, which are parametrized according to themiscibility and partitioning of their associated molecular fragmentbetween different solvent environments (top-down route),while bonded interactions are usually calibrated on the basis of equilibriumsimulations of higher-resolution molecular models (bottom-up route). The resulting simplification of the molecular systems enablesa speed up of 2 to 3 orders of magnitude with respect to equivalentall-atom (AA) modeling.2Molecular dynamics (MD)has become a cornerstone tool in the studyof complex molecular systems by providing high-resolution insightsoften inaccessible via experimental techniques. Coarse-grained (CG)molecular modeling, in which atoms are bundled together into supra-atomicparticles, extends the space and time scales accessible via MD simulationsand is increasingly employed to characterize systems of interest instructural biology, drug discovery, biophysics, and nanomaterialsdesign.4) improved the overall balance of nonbonded interactions between beads, as well as the accuracyof the scheme in predicting molecular packing in MD simulations. Notably,enhanced CG representation of molecular volumes can be obtained viathe use of higher-resolution particles .Particular attention was paid to the description of aliphatic andaromatic ringlike structures, which are ubiquitous in small molecules and building blocks constituting macromolecules. Such improvements enable themodeling of increasingly complex systems comprising multiple classesof molecules, such as solvent mixtures, small molecules, polymers,lipid membranes, proteins, and protein–ligand systems, allwithin the framework of a general force field.17The recentreparametrization of the Martini force field  lipids DLPCand DMPC, respectively including 12 and 14 carbons per “tail”and currently represented by identical CG models, while their phosphate-to-phosphatebilayer thicknesses (DHH) values differ by ∼15%at room temperature.20 The exclusive usage of big beadsfor modeling the fatty acids, which are designed to represent fourheavy atoms and their associated hydrogens, does not provide enoughresolution to differentiate between the two lipids. Further refiningthe CG representation of the models, in principle, will allow to furtherenhance thermodynamic properties of lipids and lipid mixtures in Martinisimulations.21 New experimental measurementshave also become available recently for lipids containing polyunsaturatedfatty acids (PUFA) and should be considered for guiding the refinementof their respective models .22In this paper, we focus on lipid models that yet remain tobe updatedto fully take advantage of the new interaction matrix available inMartini 3.0.0.β′) and ripple (Pβ′) phases are not accuratelydescribed using current Martini models,23 as bilayerspreferentially adopt exclusively the straight gel phase (Lβ) at low temperatures. The further refinement of these models couldfocus on enabling better stabilization of the tilted gel phase (Lβ′), as well as improving the phase transitiontemperatures in simulations.24 Lastly, although onlysparse data is available, experimental phase diagrams of lipid mixturesconstitute one of the most information-rich sources usable in thecalibration or validation of a FF. Maximization of the fidelity ofCG simulations to phase diagrams of lipid mixtures, however, remainsa challenging endeavor both in terms of FF calibration effort andcomputational effort .24 After two decades of continued Martini development,further refining the lipid models has become a demanding task thatcould benefit from automatized procedures and machine learning.2Then, among the widevariety of thermotropic phases exhibited bysaturated lipid membranes, the tilted gel . This eliminates uncertainties related to parameters tuning,which in turn allows for the gaining of insights on the relative abilityof each CG representation to further enhance the thermodynamic propertiesof the lipid models in the Martini framework. The two putative CGrepresentations are compared a posteriori using arange of temperatures, an additional PC lipid type that is not includedin the training set, and by simulating a portion of the phase diagramof a DOPC/DPPC mixture known to simultaneously exhibit two phase statesexperimentally.27Here, we employ i 3.0.04 1a–d. The  and AA MD simulations(bottom-up: bond and angle distributions), which,respectively, inform on the supra-molecular structure of the systemsand on their submolecular dynamics . CG modelsare tested iteratively in a set of simulations of small patches ofhomogeneous lamellar bilayers used to measure the discrepancies observedbetween the simulated and reference data . The software allowsfor the modulation of selected parameters of the CG FF that are iterativelyoptimized within predefined boundaries using FST-PSO28  for the minimization of a loss function designed to improve FFaccuracy.The optimization protocol proposed in SwarmCG,26 allowbonded parameters are defined independently oftemperature as general building blocks that are redundant across lipidtypes while at the same time the space of their potential solutionsis restrained by other simulation parameters remaining constant (notably nonbonded parameters set to Martini 3.0.04), the use of a rich training set can be expected to outputa consistent CG lipid FF that generalizes well to other types of lipidsand to other observables than those used as objectives during optimization26 (Section S1). TheCG representations of the lipids, however, might still limit the extentto which reference data can be fitted, depending on the degrees offreedom preserved by the molecular models .The quality and completeness of the information embeddedin the training set directly conditions the accuracy of the outputFF, as well as its ability to generalize to different types of lipidsand to different thermodynamic conditions. In this study, becausethe HH bilayer thickness).30 Among the corresponding models in Martini, the following pairs arecurrently represented identically: DLPC and DMPC (12:0–12:0and 14:0–14:0), DPPC and DSPC (16:0–16:0 and 18:0–18:0),POPC and SOPC (16:0–18:1 and 18:0–18:1), and SDPC andPDPC (16:0–22:6 and 18:0–22:6) saturation, for which experimental measurements are availablefor pure composition lamellar bilayers in the liquid and gel phases(area per lipid and D:0–22:6) 1c. This  , while reproduction within the CG models of the distributionsof bond and angle values calculated from AA MD simulations constitutesa secondary objective that is less emphasized. To this end, we apply a soft penalty whoserole is to ensure large deviations from the reference bottom-up data are forbidden and allow for effective distribution in betweenthe bonded building blocks of the residual errorinherent to the coarse-graining process .The loss function is defined asglobal and ΔDglobalHH are the aggregatedpercentage deviationsfrom experimental data, calculated across the training set asi and Δ%DiHH arethepercentage deviations observed on average within the ith simulation of a given pair of lipid type and temperatureincluded in the training set; ε represents the tolerated measurementserror in Δ%APLi and Δ%DiHH (set to 1.5); w1 is a weight used to prioritize fitting the top-down objectives over the bottom-up objectives (set to10), which means the protocol is allowed to discard structure-basedinformation for better fitting experimental measurements; and n is the number of pairs of lipid types and temperaturesincluded in the training set.The loss function,to be minimized, reformulates our many-objective optimization probleminto a single-objective one by aggregating the distances from both bottom-up component of our loss function . We then project (map) these trajectoriesaccording to our CG representations of interest (Section S3) and compute all AA-mapped bond and angle distributionsto be used as reference during optimization. The bond and angle typesare defined, respectively, as all possible connected pairs and tripletsof beads, which maps atomic connectivity into beads connectivity accordingto our CG representations , a metric based on optimal transport35 . This metric (hereafter referred to as “OT-B metrics”)has been already proven well-suited for parametrizing the bonded terms of CG models of complex and flexible moleculesin a previous version of SwarmCG.25 The bottom-up component of the loss informson how closely a putative set of FF parameters allows matching ofthe AA description of the molecular systems included in the trainingset and is calculated asa,i andOT-Bb,j are the OT-B distances calculatedrespectively for the ith instance of bondtype b and the jth instanceof angle type a, with n and m, respectively, as the number of instances of bond type b and angle type a in all simulations includedin the training set and B and A,respectively, as the number of bond and angle types appearing in theCG models included in the training set (w2 (set to 50) allows for the obtainment of comparable OT-B valueswhether the metrics are applied on bonds or angles and prioritizesadjusting first the distributions of the bonds. When w2 is set to 50, a distance of 0.4 Å between the distributionsof two bonds is considered equivalent to a distance of 20 degreesbetween the distributions of two angles. When w1 is set to 10, fitting of the experimental measurements isprioritized as long as OT-B distances are below 0.2 Å for bondsand below 10 degrees for angles . This setting of w1 also providesthe required flexibility for adequately exploiting even slightly inaccurateAA-MD reference data (Section S2). Theconvergence criterion is defined as 10 swarm iterations without improvingloss.For the calculation of the ntations 1c,e. We rt35 OT, 1f,whichning set 1e. The w33.1SwarmCG(bonded parameters in building blocks of PC lipidmodels using Representation 1 (Section S4), including in the training set lipids spanning tails ofvarious lengths and degrees of unsaturation. The hypothesis motivatingthis CG representation is that mapping the lipid tails as preciselyas possible by using regular beads to represent exactly four heavyatoms and small beads to represent exactly three heavy atoms may allowan enhanced description of their flexibility and dynamics .We build lipid tails according to the following arbitrary rules, byorder of priority: (i) minimize number of beads; (ii) include onlyone unsaturation per bead, ideally a regular one; and (iii) stackregular beads at the start of the lipid tails. We optimize model parametersby iteratively simulating nine different patches of lamellar bilayers,thereby having each of the eight lipid types simulated in the liquidphase and only the DPPC bilayer simulated also in the tilted gel phase(Lβ′) at 293 K. In order to limit bias inthe evolution of the systems toward either the liquid or gel phase,both DPPC simulations start from a configuration in the ripple phase. We calibrate equilibrium values and force constants for all bondsand all angles defined in the lipid models, which totals 77 parametersacross 16 bond types and 27 angle types . The swarm of particles is initialized randomly within the77-dimensional search space, except for the first particle that isprovided with knowledge from AA-mapped MD simulations and from previous Martini lipid models4 .As a first experiment,we employ SwarmCG,26 to cabonded parameters of the models are adapted successfullyand allow approachingof the objectives until the optimization converges at swarm iteration29. At convergence, the models overall correctly reproduce the APLand DHH experimental measurements defined as target . This is alsovisible when calculating the loss separately for each simulation . In terms of computational time, here,the refinement of 77 bonded parameters of the lipidmodels using nine informative simulations required 15 days  to reach convergence (39 swarm iterations) using 27 particlesin the swarm and using 64 CPU cores .The steady decrease of the global loss . Simulating abilayer composed of 1152 randomly dispersed lipids at 10/90% massDOPC/DPPC and starting from a configuration in the liquid phase, thenucleation of the gel phase is observed after ∼100 ns of equilibration,and the equilibrated system exhibits a liquid/gel phase separationafter ∼1 μs . Reiterating this experiment with 20/80% mass DOPC/DPPCin the system, we do not observe gel phase nucleation after 1 μsof equilibration. When the simulation temperature is lowered to 293K, stable phase separation can be observed also at 20/80% mass DOPC/DPPCafter ∼1 μs of equilibration .Altogether these results indicate that an optimalsolution wasfound for parametrizing the ed tails 1c, the lel phase 5. Repeat3.2bonded parameters for calibrating the building blocks of lipid models usingRepresentation 2 (Section S5). The hypothesis motivating this alternative representationis that while attempting to better preserve degrees of freedom duringthe coarse-graining process, the use of exclusively regular beadsin the bulk of all saturated tails remains mandatory for preservingthe thermodynamic properties of the lipids in both the liquid andgel phases . We include in the training set the same eight PClipids as in the previous experiment, for which we simulate patchesof lamellar bilayers in the liquid phase at the same temperaturesas before, but this time, we directly include bilayer simulationsin the tilted gel phase (Lβ′) for DMPC, DPPC,and DSPC . This provides more informationfor guiding the optimization and introduces additional constraintsenforcing the transferability of the bonded buildingblocks across phase states. Here, also, to limit bias in the evolutionof the system toward either the liquid or gel phase, DMPC, DPPC, andDSPC simulations start from a configuration in the ripple phase . Wecalibrate equilibrium values and force constants for all bonds andall angles defined in the lipid models, which totals 48 parametersacross 13 bond types and 12 angle types . The swarm of particles is initialized randomly within the48-dimensional search space, except for the first particle that isprovided with knowledge from AA-mapped MD simulations and from previous Martini lipid models .We then adapt our protocoland perform an automated search of an optimal set of bonded parameters of the models areadapted successfully until the optimizationconverges at swarm iteration 28. The presence of noise in the evaluationof the APL and DHH thickness for DMPC at 273 K and DSPCat 308 K indicates that the sets of parameters explored during optimizationdo not systematically trigger the formation of a gel phase (Section S3). At convergence,the models overall correctly approach the APL and DHH experimentalmeasurements defined as target (bonded parameters that are introduced by addingin the training set the simulations of DMPC and DSPC in the tiltedgel phase (Lβ′), as well as by reducing thenumber of beads in the lipid tails, notably for PUFA-containing lipids. The APL isvery well fitted to experimental data for all pairs of lipid typeand simulation temperature, both in the liquid and gel phases, whilethe DHH thicknesses remain slightly less fitted on average.The OT-B distances here also are minimized effectively, thereby reproducingoverall within the CG models the structural features present in thereference AA-mapped MD trajectories .The steady decrease of the global loss . Calculation ofthe loss separately for each simulation shows that fitting the objectiveswas harder particularly for DSPC at 333 K and PDPC at 303 K  to reach convergence (38 swarm iterations) using23 particles in the swarm and using 64 CPU cores .The error on fitting Dβ′) correspond to measurements obtained from lamellar bilayers exhibitingnon-negligible tilting of the tails with respect to the bilayer normal(∼32 degrees).30 This tilting triggers a reductionof the hydrocarbon and DHH thicknesses of approximately13–15% in the Lβ′ phase with respectto the straight gel phase (Lβ).30 Therefore, when evaluating the matching of bilayer thicknesses inbetween CG simulations and experimental measurements, the optimizationprotocol here implicitly formulates the requirement that models producetilted tails for saturated lipids in the Lβ′ phase. In practice, not all experimental objectives can be perfectlymatched when optimizing the bonded parameters inthe context of Representation 2, and DMPC and DPPC bilayers exhibitonly limited tails tilting (∼10 degrees) when simulated upto 30 K below their respective gel/liquid transition temperatures(β′ phase (HH thicknesses in cyan). Although the size of the molecularsystems used during optimization is small, this tilt angle is stablealso with increased system size (512 lipids) in 1 μs of productionsimulation  using the Martini3.0.04 lipid models compared with the parameters obtainedfor Representations 1 and 2, along with reference experimental data.30 Here, we focus on evaluating bilayer structural properties acrossphase states; hence, simulations at temperatures that correspond experimentallyto gel or liquid phases are started respectively from a configurationin the tilted gel phase (Lβ′) or in the liquidphase . Using Representation 2, we obtain overall improvedparameters with respect to Martini PC lipids version 3.0.0 in thecontext of this training set, which demonstrates the usefulness ofsuch optimization protocols even when exploiting only the APL andDHH thickness measurements during training. For both Representations1 and 2, as expected, the obtained parameters also transferred wellto SOPC at different temperatures in the liquid phase . For Representation 2,the transition temperatures are better respected while the behaviorof lipid mixtures also appear improved as a posterior result of the optimization of the structural properties in purecomposition bilayer simulations.In id phase 5b, even id phase 5b. This 4SwarmCG, we can eliminateuncertainties related to parameters tuning for focusing on evaluatingthe capabilities and limits of the CG representations .Our results indicate Representation 2 allows for overall improvementupon the current version of the PC lipid models in Martini 3.0.04 (on the basis of the nine different lipid typesincluded in the benchmark and on the objectives set), and the protocolimplemented in SwarmCG(bonded parameters(and likely more). Use of mixed bead sizes in Representation 1 allowsfor matching of the experimental objectives well in the liquid phaseand particularly for PUFA-containing lipids. The description of thephase behavior verified a posteriori for DOPC/DPPCmixtures appears relevant, as well, but the DPPC model in this CGrepresentation is a special case that includes enough repeated bigbeads in the tails to enable the formation of a gel phase (which isnot the case for DMPC and DSPC). Conclusively, the philosophy of Representation1 cannot yield versatile building blocks for describing multiple typesof lipids across phase states.In this study, we applythe automated optimization strategy implementedin SwarmCG,26 for tn SwarmCG,26 is efbonded parameters for the versatile buildingblocks of Representation 2 highlights that reproduction of the tiltangle of lipid bilayers in the Lβ′ phase inMartini simulations is possible. This is not trivial because, to ourknowledge, only one example of such CG simulations exists, whereby bonded parameters were manually optimized initially withthe goal of obtaining a relevant modeling of the tilted ripple phase(Pβ′) for DPPC,23 specifically, without considering the impact on the modeling ofother lipid types (FF parameters are unavailable for this study).We envision that further working on the CG representation, potentiallyby slightly tweaking bead choices to further enhance the balance of bonded and nonbonded interactions in betweenlipid heads and tails, may allow for the description of tails tiltingin the Lβ′ phase for relevant saturated lipidsin the Martini framework.Instead, models obtained herewith Representation 2 produce a morerelevant modeling compromise, which allows for a better approach ofall experimental objectives across phase states 5. In parSwarmCG,26 can be successfullyleveraged not only for calibrating force field parameters but alsofor (in)validating modeling philosophies with higher throughput andenhanced certainty than what can be done manually. To this end, wehere relied principally on experimental APL and DHH thicknessvalues for optimizing the bonded parameters of PClipid models by iteratively simulating small patches of bilayers ata limited number of temperatures across phase states. We note, however,that this protocol could be extended with limited efforts to obtain bonded building blocks for a much larger set of lipid modelsin the context of Martini.4 The trainingset can be further enriched by including additional bilayer systems and the lossfunction can also directly evaluate other experimental measurementsproviding transversal information . Other available experimental properties, such as bendingmodulus or diffusion constants, may also be exploited at a slightlyhigher computational cost. The use of annealing simulations directlyin the training set could also enable finer estimation and tuningof the gel/liquid phase transition temperatures.As demonstrated, automated integrativemodeling approaches, suchas SwarmCG(Further developmentsof SwarmCG,26 may a5https://github.com/GMPavanLab/SwarmCGM, together with the models obtained at convergence for each experiment,as well as the configuration files used in this study and all materialnecessary for reproducing the results. Complete data are also availableat: 10.5281/zenodo.8010318.Thecode used in this study for the optimization of the CG lipidmodels is available at:


To determine the role of biopsy experience regarding a potential benefit of additional systematic biopsies and fusion failures during MRI-targeted biopsy of the prostate.We retrospectively evaluated 576 men undergoing transrectal (MRI)-targeted biopsy of the prostate by seven residents in urology between November 2019 and March 2022. Benefit of systematic biopsies ) solely in systematic biopsies) and fusion failure (detection of csPCa during systematic biopsies in the area of a reported MRI-lesion and no detection of csPCa in targeted biopsy) were compared by growing biopsy experience levels. Multivariable regression analyses were calculated to investigate the association with benefit of systematic biopsies and fusion failure.p = 0.026; 13% versus 3%, p = 0.015, respectively). Increasing biopsy experience was associated with less benefit from systematic biopsies  and less fusion failure .The overall PCa detection rate was 72% (413/576). A benefit of systematic biopsies was observed in 11% (63/576); of those, fusion failure was seen in 76% (48/63). Benefit of systematic biopsies and fusion failure were more common among residents with very low experience compared to highly experienced residents (18% versus 4%, The benefit of systematic biopsies following targeted biopsy decreases with growing biopsy experience. The higher risk of fusion failure among inexperienced residents necessitates systematic biopsies to ensure the detection of csPCa. Further prospective trials are warranted before a targeted only approach can be recommended in routine clinical practice. To date, multi-parametric magnetic resonance imaging (mpMRI) of the prostate is recommended for men with suspected prostate cancer (PCa) before undergoing prostate biopsy (bx) , 21, 23.Available data indicate that systematic bx could be omitted in patients with a Prostate Imaging Reporting and Data System (PI-RADS) score of 5 on mpMRI and previous negative bx , 4. HoweTherefore, this real-world study aimed to investigate the role of growing procedural experience regarding a 1) potential benefit of the additional systematic bx as well as regarding 2) potential fusion failures during MRI-targeted bx in a large sample of 576 MRI-targeted bx performed by 7 residents in urology.n = 576 patients undergoing transrectal MRI-targeted and systematic bx of the prostate for suspected PCa  between November of 2019 and March of 2022 at the Department of Urology of the Klinikum rechts der Isar, Technical University of Munich. The study was approved by the Ethics Committee of the Technical University of Munich in February 2022  and followed the Declaration of Helsinki Ethical Principles for Medical Research.The present retrospective analysis included All mpMRI scans were evaluated according to the PI-RADS v2.0 protocol . DetectiThe following sociodemographic and clinical data were obtained for this analysis: age at bx, PSA level at bx, prostate volume, prior bx (yes (negative); yes (positive/active surveillance); no), max. PI-RADS score , number of mpMRI targets, number of total cores, number of MRI-targeted bx cores, number of positive cores, grading.Bx experience was defined individually for each bx as the total number of procedures performed by the resident up until the current procedure.Benefit of systematic bx was defined as the detection of an ISUP ≥ 2 PCa in the systematic bx and either (a) no detection of PCa or (b) detection of ISUP 1 PCa in the MRI-targeted bx.Benefit of targeted bx was defined as detection of an ISUP ≥ 2 PCa in the targeted bx and either (a) no detection of PCa or (b) detection of ISUP 1 PCa in the systematic bx.Fusion failure was defined as the detection of csPCa (ISUP > 1) during systematic bx in the area of a reported mpMRI lesion and no detection of cancer in the MRI-targeted bx supposedly targeting this mpMRI lesion. All prostate regions unilaterally adjacent to the region containing the suspected lesion on mpMRI were considered “areas of the reported mpMRI lesion” .n = 123), low experience: 15th–28th (n = 112), moderate experience: 29th–47th (n = 116), high experience: 48th–71th (n = 112), and very high experience > 71th (n = 113) bx). Chi-Square tests were calculated to determine differences between binned bx experience categories regarding benefit of systematic bx, benefit of targeted bx, fusion failure, and overall PCa detection rate.Descriptive statistics were calculated for all study variables (Mean and standard deviation (SD) or median and interquartile range (IQR) or range). Bx experience levels were clustered into quintiles using visual binning . Graphs were created using Microsoft Office .Univariable and multivariable logistic regression analyses were calculated to identify variables associated with (1) benefit of systematic bx following MRI-targeted bx and (2) fusion failure. Statistical significance was set at A total of 576 men (age (SD): 66 10) years) were evaluated for this retrospective study. Of those, the majority (52% (298/576) had a PI-RADS 4 lesion and 23% (132/576) had a PI-RADS 5 lesion on mpMRI. The overall PCa detection rate was 72% (413/576) (csPCa 90% (373/413)). PCa was found in 92.4%  of men with a PI-RADS 5 lesion, in 79.5%  of men with PI-RADS 4 lesion and 35.7%  in men with a PI-RADS 3 Lesion. Detailed patient characteristics are shown in Table 0 years) A benefit of systematic bx was observed in 11% (63/576) of all cases. In other words, of the 373 men diagnosed with csPCa (> ISUP 1), 63 (17%) men had a csPCa detected only by systematic bx. These were either ISUP 2 ) or ISUP 3 ) PCa. A potential fusion failure was present in 76% (48/63) of these bx. A benefit of targeted bx was observed in a total of 5% (29/576) of all bx, meaning that 8% (29/373) of all csPCa was solely detected by targeted bx.A total of 7 residents in urology performed the bx analyzed in this study. Each resident performed at least 28 bx. The median bx experience (IQR) at the end of the observed time frame was 66 (38–75).P = 0.026, Fig. P = 0.015, Fig. A benefit of systematic bx was observed significantly more often among the least experienced residents as compared to residents with the highest experience .No statistically significant differences in the detection rate of csPCa among men with PI-RADS 4 or 5 lesions stratified by experience levels were found  and lower risk of fusion failure . Higher PSA was associated with lower risk of fusion failure , whereas a higher number of extracted biopsy cores was associated with higher risk of fusion failure (OR 1.23 95% CI 1.01–1.50) . Consistently, fusion failures—evident in three fourths of the cases with the benefit of systematic bx (76% (48/63))—were nearly six times more common in residents with very low bx experience than in residents with very high bx experience . Yet, the overall PCa detection rate was comparable across all experience levels in this study. These findings underscore the pivotal role of experience to minimize fusion failures and emphasize the value of systematic bx in less experienced urologists to ensure the detection of csPCa. In this regard, the most considerable value of the systematic bx seems to be the additional sampling of the prostate quadrant that has been deemed suspicious in the MRI. CsPCa  was detected by systematic bx outside these areas in less than 5% of biopsies. This indicates that MRI-negative csPCa might not be the most important reason for considering systematic bx. One possible future direction could be reducing systematically extracted bx cores by mainly focusing on additionally sampling the quadrant or prostate lobe entailing the suspicious MRI lesion. Interestingly, multi-regression analysis revealed that an increased number of targeted biopsies was associated with a higher likelihood of fusion failure. This might indicate that a higher number of extracted cores during the targeted bx might indicate uncertainty and improper fusion during the targeted bx. This stresses that in unexperienced urologists, a proper systematic bx of the suspicious quadrant or hemi prostate covering all regions might be of more value than a saturation during the targeted bx, which might be off-targeted due to improper software use or misinterpretation of the MRI image.First, in this large sample of 576 men, increasing bx experience was associated with less benefit of systematic bx and lower risk of fusion failure on multivariable regression analysis. Second, a benefit of systematic bx was observed four times more often among men operated by residents in urology with very low procedural experience as compared to very experienced residents  high risk for PCa (PI-RADS score = 5) , 4, 5. ASeveral limitations of this study are noteworthy. First, given its retrospective design the results of this study are prone to selection bias. Still, as no case was excluded during the investigated period, potential selection bias was minimized. Second, all prostate bx included in this analysis were performed transrectally. Thus, the results of this study might not be comparable to other studies investigating a transperineal approach. Third, the obtained experience thresholds were determined by visual binning and are thereby limited to the current sample and cannot be extrapolated to other collectives. Nevertheless, our analysis reflects a real-world scenario including data of 7 urologists in training with no prior bx experience. The analyses are further strengthened from a statistical/methodological perspective: the allocation of dynamic experience levels illustrates the growing procedural experience for every resident after every bx. From a clinical perspective, the detection rate of PCa by PI-RADS 4 or 5 lesions was 85% in this study and is not only comparable, but even higher than in most previous studies (results ranging from 40–80%) , 18, 30.Taken together, the current study shows that bx experience potentially limits the generalizability of recent studies suggesting that systematic bx of the prostate can be omitted in favor of MRI-targeted approaches. The high risk of fusion failures among very inexperienced urologists warrants the addition of systematic bx following MRI-targeted bx to ensure the detection of csPCa in men with suspected PCa on mpMRI. Further prospective studies and the development of novel biopsy strategies are necessary to safely establish an accurate, minimally invasive and targeted only biopsy regime.