Human cholangiocyte cultures were exposed to periods of warm and cold ischemia to mimic DCD liver donation. EMT was tested by assays of cell migration, cell morphology, and differential gene expression. Transplantation of syngeneic rat livers recovered under DCD conditions were evaluated for EMT changes by immunohistochemistry. Human cholangiocytes exposed to DCD conditions displayed migratory behavior and gene expression patterns consistent with EMT. E-cadherin and CK-7 expressions fell while N-cadherin, vimentin, TGFβ, and SNAIL rose, starting 24 hours and peaking 1–3 weeks after exposure. Cholangiocyte morphology changed from cuboidal  before to spindle shaped  a week after ischemia. These changes were blocked by pretreating cells with the Transforming Growth Factor beta (TGFβ) receptor antagonist Galunisertib (1 μM). Finally, rats with liver isografts cold stored for 20 hours in UW solution and exposed to warm ischemia (30 minutes) at recovery had elevated plasma bilirubin 1 week after transplantation and the liver tissue showed immunohistochemical evidence of early cholangiocyte EMT. Our findings show EMT occurs after exposure of human cholangiocytes to DCD conditions, which may be initiated by upstream signaling from autocrine derived TGFβ to cause mesenchymal specific morphological and migratory changes.Donation after circulatory death (DCD) has expanded the donor pool for liver transplantation. However, ischemic cholangiopathy (IC) after DCD liver transplantation causes inferior outcomes. The molecular mechanisms of IC are currently unknown but may depend on ischemia-induced genetic reprograming of the biliary epithelium to mesenchymal-like cells. The main objective of this study was to determine if cholangiocytes undergo epithelial to mesenchymal transition (EMT) after exposure to DCD conditions and if this causally contributes to the phenotype of IC.  Liver transplantation is the only potentially curative treatment for patients with end stage liver disease. However, due to the continued shortage of organ donors, only 50 liver transplants per 100 patient-years of wait time are performed annually in the United States resulting in significant pre-transplant waitlist mortality. In 2017, 1334 patients died while waiting for a liver and 1177 were removed from the wait list for becoming “too sick to transplant” [Relative to donation after brain death (DBD), DCD grafts have a 4-fold increased odds of organ discard with livers recovered but not transplanted (1). Nevertheless, the increased warm ischemia time (WIT) with DCD transplants leads to significantly lower graft and recipient survival rates compared to DBD. This is due to higher rates of early allograft dysfunction, acute organ failure, and severe biliary complications 2–5). Ischemic cholangiopathy (IC), defined as non-anastomotic strictures of the intra- or extrahepatic biliary tree in the presence of a patent hepatic artery –5. Ische. IC is aWhile the etiology of IC is likely multifactorial, causative factors include ischemic events in the peri-transplant period, bile salt toxicity, and immune-mediated injury . The iscBecause of its significant impact on post-transplant outcomes, IC has been called the Achilles heel of DCD liver transplantation, limiting the maximal utilization of livers from DCD donors. Although there are several techniques to minimize the risk of IC, including reducing ischemia times, thrombolytic therapy, and machine perfusion –16, littEpithelial to mesenchymal transition (EMT), a cellular phenomenon that results in tissue fibrosis, may have a role in the pathogenesis of IC . During β receptor activation and ischemia-induced cholangiocyte morphological changes characteristic of EMT. This holds promise for the goal of therapeutically targeting EMT circuits to prevent or reverse ischemic cholangiopathy in patients receiving an expanded criteria liver allograft.Given that EMT is associated with a loss of epithelial cells in response to IRI as well as a pro-fibrotic phenotype, it is reasonable to conclude that EMT may be involved in the development of IC. However, data regarding hypoxia-induced EMT in biliary epithelial cells is lacking. Thus, the goal of this study was to demonstrate the presence of an IRI-induced EMT in an in vitro model of preservation injury using primary human cholangiocytes. We hypothesize that cells exposed to ischemia and reperfusion will undergo an epithelial-to-mesenchymal transition, and that this effect will be more pronounced in cholangiocytes that are subjected to warm ischemia compared to cold storage alone. The long-term goal is to understand mechanisms of ischemic cholangiopathy after DCD liver transplantation to later serve as potential therapeutic targets to expand the liver donation pool and improve transplantation success. The data presented in this study were successful in demonstrating strong associations of EMT attributes  with known initiators of IC in DCD liver transplantation (prolonged preservation ischemia). We also demonstrate a causal link between TGF2 for 5 minutes followed by placing the box first in a 37 degree water bath for 60 minutes and then in a cold room on melting ice for cold storage at 2–4 C for 20 hours. After storage, the cells were returned to the 37 C incubator, exposed to atmospheric oxygen, and cultured as usual. Cells were removed from culture at 1, 5, and 7 days after storage ischemia and reoxygenation for study and analysis. Some cells were pretreated with the selective TGFβ receptor antagonist Galunisertib in DMSO (1μM) or DMSO (0.03%) for an hour prior to exposure to ischemia.Human primary biliary epithelial cells  were cultured on collagen plates using Human Cholangiocyte growth media (Celprogen). Cells at 80% confluency were used for studies. To simulate DCD recovery and preservation conditions, cell cultures were subjected to both periods of warm and cold ischemia. Cell culture plates were placed in a plastic airtight box (Tupperware) purged with 95% nitrogen and 5% COTo both measure migratory and invasive behavior and to select out cells with such behavior, normal or preserved cholangiocytes were placed on 24-well cell invasion assay plates coated with collagen  and allowed to culture for 48 hours. After culture, cells that may have migrated through the collagen matrix and attached onto the fiber pad below were recovered, washed, and expanded in cell culture to select out specific migratory phenotypes for later analysis. Cells on the top that refuse to migrate were also collected and expanded in cell culture to serve as non-reactive controls. Finally, Naïve colangiocytes were also used as non-stimulated controls since they never experienced DCD conditions nor did they migrate through the collagen migration chambers.Morphology of cultured cholangiocytes was assessed using light microscope at 400x magnification in normal (Naive) cells before and after exposure to 24 hours of cold storage (CS) with or without 1 hour of warm ischemia prior to the CS. Cell morphology is graded on a scale of 1–4 in a blinded manner. A score of (1) refers to normal cuboidal appearing epithelial cells, (2) are mostly cuboidal appearing cells, (3) refers to most cells having a spindle appearing mesenchymal cell morphology, and (4) refers to all cells having a spindle mesenchymal appearance.Cells were grown on cover slips, fixed in 10% formalin, permeabilized with citrate buffer microwaving, and stained with specific primary antibodies to one of the following targets: cytokeratin-7 (CK-7), E-Cadherin, SNAIL, N-Cadherin, and Vimentin. After incubating for 2 hours at 37 C, the primary antibodies were washed away and the cells were probed with a secondary antibody directed at the primary and labeled with Alexa Fluor-488 or -647. Imaging was performed using a fluorescent microscope and images processed using ImageJ . These analyses were performed and compared between 3 groups of cholangiocytes: normal control cholangiocytes, (Fresh), cells exposed to 24 hours of cold ischemia storage (CS) and cells exposed to 60 minutes of warm ischemia conditions followed by 24 hours of cold ischemia storage (WI+CS). Analyses were repeated for cells cultured for 24 hours, and 1, 2 and 3 weeks after the corresponding storage conditions.For the initial RNA isolation procedures, the RNeasy Mini Kit  was utilized according to manufacturer’s instructions. Briefly, 1 x 107 cells were pelleted and lysed with RLT buffer from the kit. 70% ethanol was added, the sample was transferred to the RNeasy Mini spin column, and the column centrifuged for 15 seconds at > 10,000 rpm. After several column wash steps, the RNA was eluted from the column membrane with 30 μl RNase-free water. Residual DNA contamination was removed by a DNase treatment protocol (Qiagen RNase-free DNase Set). Purified RNA was analyzed on the NanoDrop ONE for purity and concentration.Purified, quantitated RNA samples were converted into complementary DNA (cDNA) for compatibility with subsequent qRT-PCR. A commercially available reverse transcriptase (RT) cDNA synthesis kit  was used on RNA samples, producing a presumed 1:1 cDNA product.β were added to each well. The plate was sealed, centrifuged for 5 minutes on a plate-spinner, and then analyzed in the CFX Connect according to manufacturer’s instructions. CFX Maestro software collects, compiles, and analyzes the resulting data, including amplification cycle and melting curves. Data were expressed relative to the GAPDH reference product.A CFX Connect Real-Time PCR Detection System (Bio-Rad) was used for real-Time PCR. cDNA samples were transferred to a 96-well PCR plate and iTaq Universal SYBR Green Supermix (Bio-Rad), and forward / reverse primer pairs for human TGFWith the approval of the VCU Institutional Animal Care and Use Committee (IACUC), syngeneic liver transplants between adult male Brown Norway rats  were performed as previously described by our group , 26. AllAll data were tested for distribution normality. Some data were analyzed by parametric One-way ANOVA with Bonferroni multiple comparison correction. Cell counts, histological scores, and relative intensity staining or expression ratios were analyzed by the non-parametric Kruskal-Wallis one-way ANOVA followed by the Mann-Whitney test for stochastic dominance. Most data are expressed as mean plus or minus the standard deviation. IHC and other cell results were derived from 4 independent experiments usually run in triplicate. Liver transplants were repeated 6 times per group. Statistical analysis were performed using GraphPad InState and Prism software. A P value less than 0.05 was considered statistically significant.Fig 1. This was demonstrated by significantly higher cell staining of cells being measured on the fiber pads under the collagen matrix, indicating cells from above physically moved from top to bottom through the matrix. Fresh control cholangiocytes without exposure to DCD conditions  did not migrate to reach the lower fiber pad.Cholangiocytes exposed to DCD conditions (DCD Cells) had a significantly increased migratory behavior on the cell invasion assay as shown in Fig 2 with DCD cells exposed to both WI and CS expressing morphology similar to mesenchymal cells.Cells that were exposed to prolonged cold storage for 24 hours with or without prior warm ischemia for one hour displayed a mesenchymal morphology compared to their baseline cuboidal epithelial cell appearance. These morphological changes are clearly seen in Fig 3 (representative fluorescent cell images) and Fig 4 (curated data). Protein expression was measured using immunocytochemistry techniques. Early expression 24 hours after ischemia exposure for the epithelial-specific cell markers CK-7 and E-cadherin are shown in Changes in protein expression profiles in cholangiocytes exposed to DCD ischemia conditions compared to fresh non-ischemic control cells are shown in Fig 5 (panel D). The relative expression of these markers significantly increased in a time-dependent manner over the baseline values prior to ischemic exposure (panels A-C). Specifically, as the time from DCD exposure progresses from 24 hours to 3 weeks, the expression of these markers increased. Furthermore, there was a clearly higher expression response of these markers in the warm ischemic group, relative to cold ischemia alone (panels A-D).A western blot analysis of changes in mesenchymal specific cell markers  over time in cholangiocytes isolated after exposure to DCD ischemic conditions is shown in β from cells exposed to DCD conditions that remained on the top of the migration chamber after 24 hours compared to cells that migrated through the chamber to the bottom are shown in Fig 6. Gene expression was measured by RT-PCR and shown as the relative expression from the GAPDH control gene (Δ Ct). DCD conditioned cells migrating to the bottom express multiple fold more TGFβ compared to DCD conditioned cells that don’t migrate (Top).The differential expression of cholangiocyte TGFFig 7. Most cholangiocyte cultures were exposed to DCD ischemic conditions and either pretreated with 0.01% DMSO in the culture media (Vehicle) or the selective TGFβ receptor-1 antagonist (RA) Galunisertib  one hour prior to exposure to DCD conditions (Panel A). Then cells were immediately counted and scored for the degree of morphometric change at time 0 or placed into the tissue culture incubator and scored at days-1, -5, and -7 after exposure to DCD conditions. As a control, naïve cells (not exposed to DCD conditions) were plated and cultured for 7 days and their cell morphology was determined as before on days 1, 5, and 7 . All liver isografts were recovered from the recipients 7 days after transplantation. The appearance of SNAIL expression is clearly seen in the biliary epithelial cells with liver grafts that were exposed to cold storage ischemia and more so with exposure to warm ischemia (panel A upper row). Conversely, CK-7 expression weakened significantly in biliary epithelial cells in sections from livers with prior exposure to DCD conditions . The signal intensity for these stains are shown in Immunohistochemical detection of expression of the mesenchymal marker SNAIL and the epithelial marker CK-7 in fresh control liver tissue sections, sections from liver isografts with prior exposure to cold storage for 24 hours, and sections from liver isografts with prior exposure to warm ischemia for 30 minutes are shown in Fig 8 (panel C). Transaminase levels rose dramatically on the first post-operative day in the DCD grafted recipients, compared to the controls but the levels normalized thereafter. Total bilirubin, however, stayed low in both groups until day 7 after transplantation, when it significantly spiked in the DCD group but not the control group, which were exposed only to cold storage for 24 hours.Finally, plasma transaminase (ALT) and total bilirubin values from rats with isografts exposed to DCD conditions Vs. sham surgery controls are shown in β receptor-1, which is a canonical upstream activator of the EMT pathway in response to ischemic stress. What is lacking is evidence demonstrating detailed downstream signaling in cholangiocyte EMT, further causative evidence, and validation that EMT induced in human cholangiocyte cultures in response to DCD conditions can predict disease in human or animal models of ischemic cholangiopathy.Ischemic cholangiopathy following DCD liver transplantation is characterized by loss of biliary epithelium, loss of intrahepatic microscopic bile ducts, biliary constriction, and fibrosis. Although the molecular mechanisms of these phenotypic changes are unknown, it is hypothesized that the biliary epithelia first undergo an Epithelial to Mesenchymal Transition (EMT) that establishes the genetic reprogramming of the cells to transform them into cells with a mesenchymal phenotype. This is supported by data showing the loss of epithelialization and the establishment of fibroblast like cells with both smooth muscle and migratory behaviors in our cholangiocyte cell model of IC. In this model, we demonstrate the migratory behavior in the invasion assays, the smooth muscle appearance from the morphological assessments, and the genotypic shift from epithelial-specific to mesenchymal-specific gene expression. These EMT-specific cellular changes are correlated to exposure of these cells to DCD preservation conditions and establish a strong correlation between these two events. We further hypothesize that these events are causative in that DCD conditions cause both EMT and IC because we can prevent or reverse morphological EMT changes in human cholangiocytes pretreated with a specific inhibitor of TGFThe hypothesis that EMT serves as a molecular mechanism of ischemic cholangiopathy is supported by data from this study and by similar events seen in kidney transplantation. Renal allografts can undergo chronic allograft nephropathy (CAN) or tubular atrophy with interstitial fibrosis (TA/IF) in some patients , 23. Thi, followed by surface receptor ligation of the TGFβ receptor, and downstream SMAD signaling often initiates the EMT cascade. Other non-Smad signaling may also be involved. These could include stress activation of MAP kinase pathways such as p38, either dependent on upstream TGFβ signaling or independently through other upstream signaling through TNFα or growth factors like Bone Morphogensis Proteins (BMP) and Hepatocyte Growth Factor (HGF) [The initiators, modifiers, and downstream pathways of EMT in this model are not known, but we suspect activation of TGFβ synthesis and receptor signaling is involved. In canonical EMT signaling, early synthesis of TGFβor (HGF) . The p38or (HGF) . Hepatocor (HGF) .This sigβ may have initiated the phenotypic shift in cytoskeletal protein reorganization towards an invasive phenotype. This is further supported by our ability to block these morphological changes in the exposed cholangiocyte by first pre-treating them with a selective TGFβ receptor-1 antagonist. These later data not only implicate autocrine TGFβ as an upstream initiator of EMT and IC-like behavior but supports the hypothesis that the descriptive changes in protein expression associated with EMT may be causatively involved in the IC phenotype. These data provide reassurance that if EMT in cholangiocytes exposed to DCD conditions significantly causes clinical IC in DCD liver allografts, then rational pharmacological manipulation of these initiator or downstream circuits could prevent or reverse clinical disease. The role of other early initiators in the EMT cascade in DCD exposed cholangiocytes such as BMP and HGF or modifiers like HDAC or HIF1α also need to be explored. These pathways may represent attractive targets to either prevent or arrest an existing EMT and IC since small molecule biologically active and stable drug candidates already exist for these targets. That is the ultimate goal in studying these pathways.While the data involving TGFβ and its associated signaling components or modulators are not yet understood in our models, we know they are causally operational since TGFβ mRNA expression dramatically increases in human cholangiocytes after DCD conditions. Furthermore, the molecule is only expressed in the cells exposed to DCD conditions that also assume a migratory behavior, suggesting that TGFThe EMT changes observed in our primary human cholangiocyte model of DCD preservation injury support the hypothesis that EMT is a causal factor in the development of ischemic cholangiopathy in human liver allografts recovered from DCD donors. This is supported by preliminary data obtained from our early rat liver transplant model. In these studies, we observed that rat liver isografts exposed to 30 min of prior warm ischemia before cold storage began to show signs of biliary complications seven days after transplantation, compared to rat liver isografts without the prior exposure to warm ischemia. This is based on the rapid spike in total bilirubin values measured in the plasma of the recipient rats at day 7 after transplantation compared to the normal baseline values seen in the first 6 days after transplantation or in the rats with just exposure to cold storage. This suggests that something changed in the DCD livers that was causing complications in the normal handling of bile acids in the liver, which is an early clinical feature of IC in patients with DCD liver grafts. Furthermore, immunohistochemical analysis of these tissues in the two groups of rat liver isografts indicated an increased expression of SNAIL in the cholangiocytes in DCD livers, relative to livers with only cold storage. Conversely, we observed a decrease in the expression of the epithelial cell marker CK-7 in the DCD livers over time compared to livers with only cold storage. These early changes in gene expression in the DCD liver isografts at the time we observed biliary changes is consistent with the hypothesis that these livers were in the early stages of IC and that this may have been caused by the pre-existing exposure to warm ischemia at the time of liver recovery. To test this hypothesis directly, future studies will pharmacologically interfere with early EMT signaling after transplantation to see the effect this has on liver function and differential gene expression at longer reperfusion times.In conclusion, primary human cholangiocytes exposed to warm ischemia and cold storage displayed morphologic, genetic, and phenotypic changes consistent with EMT. The expression of TGFβ is involved in morphological changes characteristic of IC. The EMT signaling pathway may be a targetable mechanism to treat post-transplant biliary complications in DCD liver allografts or to prevent IC from occurring by acting in the organ recovery or preservation period.S1 File(PPTX)Click here for additional data file.S2 File(PZF)Click here for additional data file.S3 File(PZF)Click here for additional data file.S4 File(PZFX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(PZFX)Click here for additional data file.S7 File(PZFX)Click here for additional data file.S8 File(PZF)Click here for additional data file.


We also examined the effect of antioxidant treatment on the regional expression of thromboxane synthase (TXAS) mRNA using a microdissection method. Increases in the glomerular expression of TXAS mRNA were observed in the SHRSP at 15 weeks of age compared with those in the age-matched normotensive control Wistar–Kyoto (WKY) rats: 2.4-fold and 3.1-fold in the superficial and juxtamedullary glomeruli, respectively (P < 0.05). The heme oxygenase-1 mRNA expression was markedly increased  in both the superficial and juxtamedullary glomeruli in the SHRSP compared with the expression in the WKY rats. In contrast to our expectations, the treatment of SHRSP with tempol (a superoxide dismutase mimetic) significantly (P < 0.05) increased the TXAS mRNA expression in the superficial glomeruli and did not improve the histological injury or albuminuria, which were both aggravated. Moreover, ozagrel (a TXAS inhibitor) had a suppressive effect on the TXAS mRNA expression and significantly (P < 0.05) improved the histological injury. These results indicated that although TXA2 and oxidative stress are linked to each other, TXA2 rather than oxidative stress may be a better therapeutic target to improve hypertensive glomerular injury.We examined the potential contributions of oxidative stress and thromboxane A Despite recent advances in antihypertensive therapy, hypertension is an important risk factor for the progression of chronic kidney disease and can lead to end-stage kidney disease (ESKD) , 2. Thus2 (TXA2), a vasopressor eicosanoid that frequently contributes to impaired renal hemodynamics under pathological conditions and potently stimulates reactive oxygen species (ROS) generation, to the development of regional heterogeneity in hypertensive glomerular injury  causes platelet aggregation, the contraction of vascular smooth muscle cells and increases the expression of adhesion molecules in endothelial cells [2 decreases the glomerular filtration rate (GFR) under pathological conditions [2–TPR pathway and oxidative stress form a vicious circle, i.e., TXA2 stimulates the generation of ROS and O2− [2 synthesis by TXAS upregulation [The importance of oxidative stress in the pathogenesis of hypertensive organ damage has been extensively studied , 26–29. r damage . It is wr damage . Howeverin SHRSP . TXA2 isal cells , 33. In nditions –36. Gelonditions  have rep and O2− , 12, whigulation . This ba2 together with oxidative stress contribute to the development of hypertensive glomerular injury. However, tempol showed a tendency to cause marked increases in the UAE and significantly reduced renal oxidative stress as estimated by the urinary 8-OHdG excretion (Fig. 2 inhibition is a better therapeutic target than ROS inhibition to inhibit the aggravation of hypertensive glomerular injury at an advanced stage.We found that both the TXAS mRNA expression and oxidative stress increased in SHRSP particularly in the juxtamedullary glomeruli, which showed more severe hypertensive injury than that in the superficial glomeruli. These findings support the idea that TXAion Fig. , althoug2− production in SHR enhances the TGF response, which is blunted by tempol, leading to the development of glomerular hypertension [2 production and a further increase in the glomerular capillary pressure [2 is known to accelerate glomerular injury through the activation of adhesion molecules or the stimulation of mesangial cell proliferation under pathological conditions [2 synthesis with ozagrel may increase the UAE via the elevation of the glomerular ultrafiltration coefficient under conditions with advanced hypertensive glomerular injury. Consistent with this concept, TXA2 is known to decrease the ultrafiltration coefficient and GFR [2–TPR pathway in the pathophysiology of hypertensive renal injury are clearly required.The reason why tempol exacerbated UAE remains unclear. Sugama et al.  hypothesrtension . It has pressure . These fnditions , 44, 45.Our study has several limitations. First, although we selected the concentration of tempol and ozagrel based on the reasons described, the dose-dependent effects of these drugs must be elucidated. Second, the potential involvement of other prostanoids, such as prostacyclin, in hypertensive glomerular injury must also be verified.2–TPR pathway and oxidative stress participate and interact together to promote hypertensive glomerular injury. Our results indicated that antioxidant therapy through TXA2 inhibition may be a better therapeutic target than ROS inhibition to inhibit the aggravation of hypertensive glomerular injury at an advanced stage.In conclusion, the TXA


The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics. The viruses responsible for influenza evolve rapidly during infection. Changes typically emerge in two key ways: through random mutations in the genetic sequence of the virus, or by reassortment. Reassortment can occur when two or more strains infect the same cell. Once in a cell, viral particles ‘open up’ to release their genetic material so it can make copies of itself using the cell’s machinery. The new copies of the genetic material of the virus are used to make new viral particles, which then envelop the genetic material and are released from the cell to infect other cells. If several strains of a virus infect the same cell, a new viral particle may pick up genetic segments from each of the infecting strains, creating a new strain via reassortment.Several factors are known to affect the success of the reassortment process. For example, if the new strain acquires a genetic defect that hinders its replication cycle, it is likely to die out quickly. Other times, this trading of genetic information can create a strain that is more resistant to the human immune system, allowing it to sweep across the globe and cause a deadly pandemic. However, a key part of the reassortment process that still remains unclear is how genome segments from two different influenza strains recognize each other before merging together to create hybrid daughter viruses.To explore this further, Jones et al. used a technique called fluorescence microscopy. They found that genome segments that evolved along similar paths were more likely to cluster in the same area inside infected cells, and therefore, more likely to be reassorted together into a new strain during assembly of daughter viruses. This suggests that assembly may guide the evolutionary path taken by individual genomic segments. Jones et al. also looked at the evolution of different genome segments collected from patients suffering from seasonal influenza, and found that these segments had a distinct evolutionary path to those in pandemic-causing strains.This research provides new insights into the role of reassortment in the evolution of influenza viruses during infection. In particular, it suggests that how the genome segments interact with one another may have a previously unknown and important role in guiding this evolution. These insights could be used to predict future reassortment events based on evolutionary relationships between influenza virus genomic segments, and may in the future be used as part of risk assessment tools to predict the emergence of new pandemic strains. Inordinately high genetic variation is a hallmark of RNA viruses. The rapid evolution underlying this variation can occur as a result of mutation, recombination, or reassortment, with major consequences for human disease . In the Public health measures to limit the impact of influenza virus outbreaks prioritize emerging viruses based on perceived risk factors such as the potential for reassortment between circulating influenza viruses. Reassortment of vRNA segments must occur during selective assembly of all eight genomic segments, which occurs after export of vRNA segments from the nucleus . GenomicComplex intersegmental RNA-RNA interactions could be governed by epistasis, the phenomenon by which a mutation in one gene is impacted by the presence or absence of mutations in other genes . A numbeIn this study, we set out to combine phylogenetics and molecular biology to examine parallel evolution across vRNA segments genome-wide in seasonal human influenza viruses to identify potential epistatic relationships. Unlike previous studies, our objective was to identify relationships between vRNA segments that might play key roles specifically in genomic assembly. To evaluate phylogenetic relationships among vRNA segments, we relied upon the Robinson-Foulds distance (RF), a widely used measure of topological distance between trees . This meH1N1 and H3N2 viruses have cocirculated in the human population since 1977 . In ordeOur approach outlined in The primary objective behind clustering was to minimize variation between trees that was not statistically supported by bootstrapping. The cutoff for sequence identity during clustering of the species tree was therefore an important consideration because it controlled how much unsupported variation remained in our trees. Higher cutoffs (98–99% sequence identity) yielded species trees with more clusters containing fewer members while lower cutoffs (95–96% sequence identity) contained increasingly fewer clusters with more members grouped in each cluster. We selected a cutoff of 97% sequence identity based on the observation that it produced vRNA trees with an intermediate number of clusters (16–17 clusters in each species tree). We selected several high-quality sequences from each cluster to build replicate vRNA trees for comparison . Using tTree similarity among different vRNA segments is expected to be highest when there are strong epistatic interactions between encoded protein and/or RNA complexes . We expeGenome-wide inferences of tree similarity can distinguish the relative degree of parallel evolution of all eight genomic segments to each other and capture the strongest overall relationships between segments. To examine the extent of parallel evolution between all vRNA segments, we measured tree distances in all sets of vRNA trees from H3N2 viruses from 2005 to 2014. Recent studies have identified a highly plastic and redundant network of interactions between vRNA segments in influenza virus particles produced during productive infection, many of which may be transient . Based oOur results suggest that vRNA relationships are remarkably consistent across H3N2 viruses from a period spanning two decades. To examine whether our approach captures anticipated changes in vRNA relationships in seasonal human influenza viruses of other subtypes and lineages, we assessed these relationships in H1N1 viruses from 2000 to 2008 and 2010 to 2018. Human H1N1 viruses from these time periods represent distinct lineages before and after the 2009 pandemic. This pandemic was caused by an antigenically shifted H1N1 virus that emerged from reassortment of two swine-origin viruses, the North American triple reassortant swine H1N1 virus and Eurasian swine H1N1 virus . TherefoSpecies trees comprising full-length concatenated H1N1 virus genomes from 2000 to 2008 or 2010 to 2018 were constructed and clusters were defined using the same approach described for H3N2 viruses . While tHeatmaps comparing tree distances between vRNA pairs further suggested that vRNA relationships are not conserved across H1N1 viruses of different lineages . Linear As discussed previously, shared evolutionary trajectories could arise from either protein-protein or RNA-RNA interactions. We have already shown that known protein relationships between PB1 and PA, two members of the polymerase complex, are captured by our approach . HoweverTo compare parallel evolution between influenza proteins to that of the parent vRNA segments, the mean RF from the gene trees were plotted against the mean RF from the protein trees . In the To address whether parallel evolution between the PB2 and NA segments corresponds with their behavior during influenza virus infection, we examined whether these vRNA segments preferentially colocalize in infected cells . During Quantification of colocalized vRNA segments was performed using fluorescence in situ hybridization (FISH) and immunofluorescence (IF) in productively infected cells . Lung epWhole cytoplasmic analysis of vRNP colocalization in 15 individually analyzed cells revealed that the majority of cytoplasmic foci contained all three vRNA segments . These dIn this study, we used phylogenetics and molecular biology methods to investigate genome-wide relationships between vRNA segments in seasonal human IAV. We found that parallel evolution varies considerably between vRNA segments, with distinct relationships forming in different influenza virus subtypes (H1N1 vs. H3N2) and between H1N1 virus lineages arising from distinct evolutionary paths. We further demonstrate that evolutionary relatedness between vRNA segments in H3N2 viruses is largely conserved over time. Importantly, our data suggest that parallel evolution cannot be attributed solely to protein interactions, and we successfully predicted intracellular colocalization between two coevolved vRNA segments during infection with an H3N2 virus. Thus, we present a phylogenetic approach for interrogating putative RNA associations that could be broadly applied toward the study of genomic assembly and reassortment in segmented viruses.Selective assembly of all eight genomic segments is fundamental to the production of fully infectious virus particles. We and others have used a variety of biochemical approaches to investigate the mechanisms that promote selective assembly . We prevThe approach we present here differs from other experimental approaches in that we identify a novel, conserved RNA-driven relationship between vRNA segments in H3N2 viruses. For example, we found that relationships between PB1, PA, NP, and NA are enriched over other segments in H3N2 viruses and conserved over time. One might expect PB1, PA, and NP to coevolve because of the functions of the proteins they encode: the polymerase subunits PB2, PB1, and PA form a supramolecular complex around each vRNA segment with NP . HoweverPrevious pandemic influenza viruses emerged through reassortment . Risk ashttp://www.fludb.org) (FASTA files of each genomic segment of human IAV sequences of H1N1 and H3N2 viruses were downloaded from the IRD (udb.org)  on June Sequences were read into R (version 3.5.2) using the DECIPHER (version 2.18.1) package  and subsMaximum-likelihood trees were reconstructed under the Tanglegrams, or back-to-back trees with intersecting lines matching tips from the left tree to the right tree, were built from pairs of vRNA phylogenies within replicates using the phytools package (version 0.7–70) . RF was U test with a Benjamini-Hochberg post hoc correction was used to identify statistically significant differences between RF from two time periods.Linear regression was used to determine the overall association between tree distances from different sets of viruses (RF or CID). A set of 1000 randomly sampled, unrooted trees with 8, 9, or 12 tips were built using the ape package to determine confidence intervals for the RF between phylogenetic trees. RF was calculated for all pairs of trees and these were fit to a linear regression model. For visualization purposes, null RF values were either log-transformed or transformed by the Yeo-Johnson method , as indi50) using the endpoint titration method  were maintained in high-glucose Dulbecco’s modified Eagle medium (Sigma) supplemented with 10% fetal bovine serum (HyClone), 2% L-glutamine, and 1% penicillin/streptomycin. Recombinant virus was rescued as previously described . Virus tn method . ValidatCustom Stellaris RNA FISH oligonucleotide probes specific for the H3N2 virus NS, NA, and PB2 vRNA segments were purchased from BioSearch Technologies (refer to 2O) and then incubated at 28°C overnight in hybridization buffer  with anti-IAV NP antibody  and FISH probes. After hybridization, cells were washed and incubated with Alexa Fluor 594 goat anti-mouse  and DAPI  in wash buffer. Coverslips were mounted on slides in ProLong Diamond antifade mountant (Thermo Fisher).Three independent FISH-IF experiments were performed . A549 ceex) with 0.5% laser power and a detection range of 415–470 nm , 488 nm λex with 10% laser power and a detection range of 493–540 nm with time gating of 1–6 nanoseconds (ns) , 582 λex with 15% laser power and a detection range of 590–635 nm with time gating of 1.5–6 ns , 545 nm λex with 5% laser power and a detection range of 545–568 nm with time gating of 1.5–6 ns , 647 nm λex with 5% laser power and a detection range of 670–730 nm with time gating of 1.5–6 ns . In each experiment, five volumetric z-stacks were imaged of infected cells and one z-stack was imaged of mock-infected cells.Microscope slides were imaged on a Leica SP8 confocal microscope equipped with a pulsed white light laser as an excitation source and an acousto-optical beam splitter and Leica Hybrid Detectors. All imaging was performed with a 100× oil immersion objective with a numerical aperture of 1.4. Sequential scanning with a line averaging of 3 between frames was used. To obtain Nyquist sampling, z-stacks of each cell were taken with a step size of 170 nm to achieve a pixel size of 45 nm × 45 nm × 170 nm. The following custom parameters were established using single-color infected controls for sensitive detection of all five fluorophores: 405 nm excitation wavelength . In each experiment, images taken of mock-infected cells were deconvolved using the same parameters as those of infected cells. 3D reconstruction and colocalization analysis of the resulting images were performed using Imaris software  as previously described . Brieflyhttps://github.com/Lakdawala-Lab/Parallel-Evolution-Of-Influenza-Viral-RNA/ (copy swh:1:rev:27dc83b8eec1f461bbf9ef3f1dbeba61f0514fb3)).Custom code for analysis of parallel evolution in concatenated, full-length genomic influenza virus sequences is available on GitHub  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:This interesting study provides phylogenetic and molecular evidence for novel RNA-RNA interactions driving the genomic coevolution of Influenza virus subtypes. The authors make important contributions towards understanding how certain genetic combinations can lead to emerging pathogen variants with the possibility of new antigenic properties and spillover.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by George Perry as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Debapriyo Chakraborty (Reviewer #2).Thank you for submitting your article "Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:In this study, the authors combined phylogenetics and molecular assays to demonstrate parallel evolution across vRNA segments in influenza A virus subtypes, highlighting the importance of RNA-RNA interaction as a novel driver of viral genomic evolution. While reviewers agree that the work is important and likely to have implications for pathogen evolution and emerging infections, there are several major concerns that have been raised in terms of clarity of working hypotheses, methods and interpretations.To cite a few, for example, the rationale behind (1) leaving out sequences that did not resolve well in the phylogenic analyses; (2) relating RNA segment co-localization to similar phylogenetic patterns was unclear and thus, requires more qualification.Unless all the queries raised by the reviewers are addressed, at present, it is difficult to truly judge on its content and what is supported by the data produced.Reviewer #1 (Recommendations for the authors ):In this paper, authors did a fine job of combining phylogenetics and molecular methods to demonstrate the parallel evolution across vRNA segments in two seasonal influenza A virus subtypes. They first estimated phylogenetic relationships between vRNA segments using Robinson-Foulds distance and identified the possibility of parallel evolution of RNA-RNA interactions driving the genomic assembly. This is indeed an interesting mechanism in addition to the traditional role for proteins for the same. Subsequently, they used molecular biology to validate such RNA-RNA driven interaction by demonstrating co-localization of vRNA segments in infected cells. They also showed that the parallel evolution between vRNA segments might vary across subtypes and virus lineages isolated from distinct host origins. Overall, I find this to be excellent work with major implications for genome evolution of infectious viruses; emergence of new strains with altered genome combination.I am wondering if leaving out sequences (not resolving well) in the phylogenic analysis interferes with the true picture of the proposed associations. What if they reflect the evolutionary intermediates, with important implications for the pathogen evolution which is lost in the analyses?Lines 50-51: Can you please elaborate? I think this might be useful for the reader to better understand the context. Also, a brief description on functional association between different known fragments might instigate curiosity among the readers from the very beginning. At present, it largely caters to people already familiar with the biology of influenza virus.Lines 95-96 Were these strains all swine-origin? More details on these lineages will be useful for the readers.Lines 128-132: I think it will be nice to talk about these hypotheses well in advance, may be in the Introduction, with more functional details of viral segments.Lines 134-136: Please rephrase this sentence to make it more direct and explain the why. E.g. "… parallel evolution between PB1 and HA is likely to be weaker than that of PB1 and PA".Lines 222-223: Please include a set of hypotheses to explain you results? Please add a perspective in the discussion on how this contribute might to the pandemic potential of H1N!?.Lines 287-288: I am wondering how likely is this to be true for H1N1.Reviewer #2 (Recommendations for the authors):Abstract: Please define parallel evolution and mention the methods used, without details, at the beginning.Line 29: "Genetic variation is ubiquitious in RNA viruses." I find this sentence vague. I would prefer a clear statement about high genetic variation in RNA viruses.Line 38: Typo, concomitantLine 38: "Thus, the emergence of pandemic strains is marked by a concommittent alteration in the influenza virus genome triggered by new genetic diversity." – This is a very complex sentence; either simplify or break it into two, please.Line 41: "Genetic mutation is driven by stochastic processes and is therefore difficult to predict ." – The relevance is not clear. Either remove or provide more context.Line 50-52: "It is consequently imperative to identify the evolutionary constraints imposed by intersegmental vRNA interactions, as this may help predict future influenza pandemics." – This is a huge jump of reductionism. It's highly unlikely that we will be able to predict future influenza pandemics even after knowing all the evolutionary constraints of its genome.Line 53: Please, define epistasis.Line 61: Please define parallel evolution.Lines 76-84: Authors should keep the hypothesis and predictions in the intro but the methodological details such as the actual RF measure should go to methods section.Line 425: What is a tanglegram? What substitution model was used to reconstruct phylogenies for each vRNA segment? Same model for all?Reviewer #3 (Recommendations for the authors):I have no further specific suggestions or critiques that are not in the other sections. Essential revisions:In this study, the authors combined phylogenetics and molecular assays to demonstrate parallel evolution across vRNA segments in influenza A virus subtypes, highlighting the importance of RNA-RNA interaction as a novel driver of viral genomic evolution. While reviewers agree that the work is important and likely to have implications for pathogen evolution and emerging infections, there are several major concerns that have been raised in terms of clarity of working hypotheses, methods and interpretations.To cite a few, for example, the rationale behind (1) leaving out sequences that did not resolve well in the phylogenic analyses; (2) relating RNA segment co-localization to similar phylogenetic patterns was unclear and thus, requires more qualification.Unless all the queries raised by the reviewers are addressed, at present, it is difficult to truly judge on its content and what is supported by the data produced.Reviewer #1 (Recommendations for the authors ):In this paper, authors did a fine job of combining phylogenetics and molecular methods to demonstrate the parallel evolution across vRNA segments in two seasonal influenza A virus subtypes. They first estimated phylogenetic relationships between vRNA segments using Robinson-Foulds distance and identified the possibility of parallel evolution of RNA-RNA interactions driving the genomic assembly. This is indeed an interesting mechanism in addition to the traditional role for proteins for the same. Subsequently, they used molecular biology to validate such RNA-RNA driven interaction by demonstrating co-localization of vRNA segments in infected cells. They also showed that the parallel evolution between vRNA segments might vary across subtypes and virus lineages isolated from distinct host origins. Overall, I find this to be excellent work with major implications for genome evolution of infectious viruses; emergence of new strains with altered genome combination.I am wondering if leaving out sequences (not resolving well) in the phylogenic analysis interferes with the true picture of the proposed associations. What if they reflect the evolutionary intermediates, with important implications for the pathogen evolution which is lost in the analyses?, specifically provided for the reviewers, illustrates the resultant collapsed PB2 tree, with bootstrap values of 70 or higher shown in red and individual strains coded by cluster and replicate. As expected, the majority of internal nodes on such a tree are largely unsupported by bootstrapping, indicating that relaxing our constraint of 97% sequence identity increases the uncertainty in our trees.We fully appreciate this concern and have explored this extensively. One principle assumption underlying the approach we outline in this manuscript is that the trees analyzed are robust and well-resolved. We use tree similarity as a correlate for relationships between genomic segments, so the trees must be robust enough to support our claims, as we have clarified in lines 127-130. We initially set out to examine a broader range of viral isolates in each set of trees, but larger trees containing more isolates consistently failed to be supported by bootstrapping. Bootstrapping is by far the most widely used methodology for demonstrating support for tree nodes. We provided the closest possible example to the trees presented in this manuscript for comparison. We took all 84 H3N2 strains from 2005-2014 analyzed in replicate trees 1-7 and collapsed these sequences into one tree for each vRNA segment. Author response image 1A2 = 0.6408; P = 3.1E-07; Because we agree with Reviewers #1 and #3 on the critical importance of validating our approach, we determined the distances between these new collapsed trees using a complementary approach, Clustering Information Distances (CID), that is independent of tree size .Lines 95-96 Were these strains all swine-origin? More details on these lineages will be useful for the readers.We have clarified that all strains analyzed were isolated from humans, but were of different lineages (lines 115-120).Lines 128-132: I think it will be nice to talk about these hypotheses well in advance, may be in the Introduction, with more functional details of viral segments.We incorporated our hypotheses regarding tree similarity into the existing discussion of epistasis in the Introduction (lines 74-75 and 89-106).Lines 134-136: Please rephrase this sentence to make it more direct and explain the why. E.g. "… parallel evolution between PB1 and HA is likely to be weaker than that of PB1 and PA".The text has been modified (lines 164-167).Lines 222-223: Please include a set of hypotheses to explain you results? Please add a perspective in the discussion on how this contribute might to the pandemic potential of H1N!?.We have added in our interpretation of the results (lines 266-268) and expanded upon this in the Discussion (lines 403-407).Lines 287-288: I am wondering how likely is this to be true for H1N1.We have expanded on this in the Discussion (lines 394-395).Reviewer #2 (Recommendations for the authors):Abstract: Please define parallel evolution and mention the methods used, without details, at the beginning.We described methods within word count limitations (lines 18-20 and 24-26). A definition of parallel evolution is provided in the Introduction (lines 70-71).Line 29: "Genetic variation is ubiquitous in RNA viruses." I find this sentence vague. I would prefer a clear statement about high genetic variation in RNA viruses.This sentence has been modified (line 30).Line 38: Typo, concomitantThis has been corrected (line 40).Line 38: "Thus, the emergence of pandemic strains is marked by a concomitant alteration in the influenza virus genome triggered by new genetic diversity." – This is a very complex sentence; either simplify or break it into two, please.This sentence has been simplified (lines 39-40).Line 41: "Genetic mutation is driven by stochastic processes and is therefore difficult to predict ." – The relevance is not clear. Either remove or provide more context.This statement has been removed.Line 50-52: "It is consequently imperative to identify the evolutionary constraints imposed by intersegmental vRNA interactions, as this may help predict future influenza pandemics." – This is a huge jump of reductionism. It's highly unlikely that we will be able to predict future influenza pandemics even after knowing all the evolutionary constraints of its genome.We modified this sentence to better reflect our meaning (lines 55-57).Line 53: Please, define epistasis.We added a definition of epistasis (lines 58-60).Line 61: Please define parallel evolution.We added a definition of parallel evolution (lines 70-71).Lines 76-84: Authors should keep the hypothesis and predictions in the intro but the methodological details such as the actual RF measure should go to methods section.We believe it is helpful to explain tree distance in the Introduction since we are targeting a wide audience of readers and, therefore, we respectfully disagree with the reviewer on this point. A brief explanation of the theory behind both distance measures in the resubmission (RF and CID) is included in the Introduction to make this article more accessible to audiences without expertise in phylogenetics.Line 425: What is a tanglegram? What substitution model was used to reconstruct phylogenies for each vRNA segment? Same model for all?We have added a definition of a tanglegram (lines 450-451). The substitution model is stated on lines 441-442; we found that this model is optimal for all trees tested based on AIC and log likelihood criteria.


PPNs were also found to exhibit rich community structure revealing inherent clustering of prescribers as a result of patient movement, and were markedly different from those generated by random graph models. The magnitude of deviation from random graphs decreased Post-HB1 relative to Pre-HB1. The proposed network approach provides system-level insights into prescribers with potential to complement classical reductionist approaches and aggregate statistical measures used in assessing changes in prescription patterns pre- and post- policy implementation. It can provide preliminary cues into drug seeking behavior, and facilitate targeted surveillance of prescriber communities.The present study investigated variations in patient movement patterns between prescribers before and after House Bill 1 (HB1) implementation in Kentucky using network abstractions (PPN: prescriber-prescriber networks) from a one-month cross-sectional Schedule III prescription data in a Medicaid population. Network characteristics such as degree centrality distribution of PPN was positively skewed and revealed Dental Practitioners to be the highly connected specialty with opioid analgesic hydrocodone-acetaminophen to be the most commonly prescribed drug. Taxonomy enrichment of the prescriber specialties in PPN using chi-square test revealed a reduction in the enriched taxonomies Post-HB1 compared to Pre-HB1 with Dental practitioners being constitutively enriched ( CDC WONDER also reported deaths from prescription opioids  to be higher compared to that from heroin , cocaine , benzodiazepines , psychostimulants  and antidepressants (5269) in 2017. Non-medical use of opioid analgesics has especially gained attention when it comes to overdose deaths2. Non-medical use3 can be stratified under five broad categories 4. Prescription drugs fall under the schedules  with varying physical and psychological dependence (https://www.dea.gov/drug-scheduling). A recent study reported considerable misuse of prescription opioids in a large population5 with a majority of the misuse attributed to relieving pain. Pain relievers such as hydrocodone-acetaminophen are also one of the commonly prescribed medications by dental practitioners6 during the acute post-operative period, after invasive dental procedures7. Opioid analgesic hydrocodone-acetaminophen was initially classified under Schedule III but were later rescheduled under Schedule II/IIN analgesic opioids by the Drug Enforcement Association  with increased regulatory, criminal and civil sanctions. While recent studies have reported non-medical use of pain medications in dental settings from patient self-reported questionnaire8, others have demonstrated increasing trends (2010–2015) in opioid prescribing by dental practitioners from commercial claims data9. The American Dental Association official statement in response to the opioid crisis recommended a limit on opioid usage with a duration of seven days for treating acute pain in an effort to reduce overprescribing practices. The newer guidelines suggest shorter limits on prescribing duration.The Centers for Disease Control and Prevention (CDC) provided compelling evidence of increasing trends (2009–2017) in drug overdose deaths including those from prescription opioids 12. However, increasing evidence of patients seeking drugs across multiple prescribers for non-medical use 13 demands developing novel approaches that can complement traditional characterization by incorporating patient-movement patterns. More specifically, such an understanding has the potential to provide system-level insights into the concerted behavior of prescribers that may not be readily apparent from a reductionist representation where patients and prescribers are treated as independent entities14. As shown in the present study, it can also provide insights into prescriber communities that represent clustering of prescribers into groups and quantify its variation pre- and post-intervention.Classical approaches for investigating prescription patterns have focused traditionally on aggregate statistical measures with emphasis on the number of prescriptions dispensed and temporal trends across specific drugs, drug class, demographics, prescriber specialties and policy implementationHB1) in April, 2012 requiring mandatory use of the prescription drug monitoring program (KASPER: Kentucky All Schedule Prescription Electronic Reporting) for prescribing controlled substances. A recent scoping review summarized the impact of PDMPs across diverse settings including opioid misuse, mortality, and across dental urgent care centers15.Kentucky was ranked at number eight in prescription drug overdose mortality with 27.9 deaths per 100,000 in 2017 and had at that time seen an increasing prescription drug abuse problem for several years (2017 Opioid Summaries from drugabuse.gov). The state was an early adopter of electronic prescription drug monitoring program (PDMP). PDMPs have been implemented across 49 states (pdmpassist.org) for combating prescription drug (Schedule II–IV) abuse and diversion. Kentucky passed House Bill 1 (Pre-HB1) and after HB1  implementation with a focus on Medicaid population using traditional aggregate measures as well as network abstractions (PPN: prescriber-prescriber network)14. The results presented demonstrate a marked decrease Post-HB1 relative to Pre-HB1 in aggregate measures such as prescription counts. PPN characteristics including degree centrality distributions, taxonomy enrichment and strength of the community structure using random graphs as internal controls were also shown to exhibit marked variations between Pre-HB1 and Post-HB1. These results elucidate the usefulness of network analytics approaches in complementing more traditional characterization of prescription patterns and their potential to serve as evaluation tools, visualizing patient movement patterns and assessing changes pre- and post-policy implementation.The present study investigated Schedule III prescription patterns from Medicaid claims data from Kentucky in a single month before HB1 . Prescribers were classified under their respective broad specialties using the first four letters of the 10 digit Taxonomy Code provided by Medicaid and the drug names were identified from their NDC description. The taxonomy codes of General Practice Dentists, Endodontist, Prosthodontist, Oral & Maxillofacial Surgeons and Pediatric Dentists were merged under the common term “Dental Practitioners” with taxonomy code (1223). The taxonomy codes were not available for 126 prescribers in 2011 and 86 prescribers in 2012, therefore they were eliminated from further consideration. All the approaches in the present study were implemented in open-source environment R using publicly available R libraries such as 17 for network analysis and community structure detection. Open-source package Gephi18 was used to generate geo-spatial visualization of the PPNs.The data investigated in the present study are outpatient pharmaceutical claims data retrieved from Kentucky Medicaid Services consisting of encrypted prescriber IDs , encrypted patient IDs, National Drug Code (NDC) description of Schedule III drugs, and GIS locations of the prescribers and specialty of the prescribers in the month of September in 2011 (Pre-HB1) and 2012 (Post-HB1). The time period is restricted to the same month across Pre-HB1 and Post-HB1 in order to minimize the effect of potential confounders . The time period also was restricted to a single month (30-day period) to focus on a snapshot of typical prescribing patterns in a controlled time frame with minimal likelihood of patients seeking multiple prescriptions of Schedule III drugs. The study was approved by the University of Kentucky Institutional Review Board and the data can be requested from the Cabinet for Health and Family Services (The mean and standard deviation of patient’s age who were prescribed Schedule III prescriptions were 39.1 ± 14.6 years (Pre-HB1) and 41.3 ± 13.9 years (Post-HB1) respectively. Their corresponding sex ratios  were 46% Pre-HB1 and 48% Post-HB1. The mean and standard deviation of patient’s age who were prescribed Schedule III prescriptions by dentists were 28.7.1 ± 11.7 years (Pre-HB1) and 30.0 ± 11.9 years (Post-HB1) respectively. Their corresponding sex ratios were 43% Pre-HB1 and 40% Post-HB1.14. An edge from a patient to a prescriber represents a patient who was prescribed Schedule III prescription by that prescriber. Multiple prescriptions from distinct prescribers are represented by multiple edges , Fig. Classical reductionist representation that treat the patients and prescribers as independent entities is shown in Fig. 19 of a provider in PPN is given by the number of distinct providers that the given provider is connected to as a result of patient movement. Degree centrality distribution has the potential to provide novel insights into the PPN topology20. Of interest is to note that targeted intervention of prescribers with high-degree centrality from the network can considerably fragment the network and in turn impact patient movement patterns. For instance, targeted removal of P2 in Fig. Degree centralityT) in the PPN was determined from a 2 × 2 contingency table in conjunction with chi-square test (α = 0.05). The elements of the 2 × 2 table comprised of four categories corresponding to (1) number of unique prescribers with taxonomy T in PPN, (2) number of unique prescribers with taxonomy T not in PPN, (3) number of unique prescribers with other taxonomies in PPN, and (4) number of unique prescribers with other taxonomies not in PPN. Since the above procedure involves multiple testing, Bonferroni correction was used to control for family-wise error rate with the corrected significance level given by (α* = α/n) where n represented the total number of tests.Taxonomy enrichment was used to investigate statistically significant proportion of a given prescriber specialty or taxonomy in the given PPN. Enrichment of a prescriber specialty or taxonomy  to Post-HB1 . Top five specialties ranked by the total number of prescriptions dispensed Pre-HB1 and Post-HB1 were Family Medicine, Dental Practitioners, Internal Medicine, Emergency Medicine, Nurse Practitioner, Fig. 2025 that often follow policy implementations. Power-law distributions can exhibit interesting characteristics such as scale-free behavior. Power-law distributions are scale-invariant as scaling the degree 27. In the present study, ranking the prescribers by their degree centrality in the PPN revealed Dental Practitioners to be consistently ranked as the most connected specialty in the PPN. More importantly, the number of dental practitioners in the PPN decreased by ~ 19% and their edges decreased by ~ 30% Post-HB1 relative to Pre-HB1. The degree centrality distributions of the entire PPN and those of dental practitioners were statistically different between Pre-HB1 and Post-HB1 as revealed by Wilcoxon rank-sum test (p < 0.05). Taxonomy enrichment of the Top 5 providers, Fig. The degree centrality distributions of the PPN was positively skewed Pre-HB1  and Post-HB1  indicating a small proportion of highly connected prescribers, Fig. Rather than compare the strengths of the communities Pre-HB1 and Post-HB1 directly, the present study used random graph surrogates as internal controls. Surrogate testing of the Schedule III PPN largest connected component resulted in S values  and  rejecting random graph models as potential generative mechanisms of the community structure in the PPN. More importantly, the deviation from the random graph surrogates reflected by the magnitude of the S values, exhibited a marked decrease from Pre-HB1 to Post-HB1.Prescription drug abuse is a major public health concern. A majority of existing approaches have focused on understanding prescription counts across specific drugs, prescriber specialties and their temporal trends using a variety of data sources. While helpful, these studies have been reductionist in nature and treat prescribers and patients as independent entities. Recent studies have provided compelling evidence of patients seeking drugs from multiple prescribers for non-medical purposes. These in turn demand novel approaches that rely on network analytics for understanding patient movement patterns across prescribers. Such a system-level understanding can assist in identifying prescriber communities and specialties for targeted surveillance. Understanding the variations in the properties of these networks can also serve as useful tools in assessing the impact of policies.25 that usually accompany policy implementations. PPN visualization revealed patient-movement patterns across multiple prescribers over considerable geographical distances that decreased Post-HB1 relative to Pre-HB1. While it is uncommon for patients to seek the same schedule prescription drug across multiple prescribers within a single month time frame, it is possible that this may include patients having multiple asynchronous and chronic health conditions. Findings from the present study such as the skewed degree centrality distributions in the PPN indicate the critical role of a small number of prescribers and prescriber specialties such as dental practitioners with very high connectivity in the PPN comprising the tail of the distribution. Skewed degree centrality distributions such as power-law distributions have been attributed to several interesting phenomena including robustness to random attacks or random interventions28. Preliminary investigation revealed a linear trend in the log–log plot of PPN degree centrality distribution. Since a large number of prescribers are not highly connected in the PPN, random attacks of the PPN nodes are likely to have negligible impact whereas targeted intervention and surveillance of the highly connected prescribers and select prescriber communities and specialties may have profound impact on the patient movement patterns including significant fragmentation of these networks. Thus, understanding the properties of PPN has the potential to assist in developing targeted surveillance strategies. In the present study, Dental Practitioners had the highest degree centrality in the PPN, indicating that patients who were prescribed Schedule III drugs by Dental Practitioners were also prescribed Schedule III drugs by other Dental Practitioners and other prescriber specialties. Taxonomy enrichment also revealed Dental Practitioners to be enriched in the PPN Pre-HB1 and Post-HB1 constitutively with hydrocodone-acetaminophen to be the most prescribed Schedule III opioid analgesic.Results in this study revealed a marked decrease in the number of Schedule III prescriptions Post-HB1 relative to Pre-HB1 in the Kentucky Medicaid population, with certain prescriber specialties such as Dental Practitioners to be highly ranked Pre-HB1 as well as Post-HB1. As noted earlier, changes in prescription counts and distributional signatures of PPN Post-HB1 relative to Pre-HB1 may be an outcome of “chilling effect”e) divided by the number of possible connections given by 2e/[n(n − 1)], where n represents the number of nodes. It is important to note that for the PPN networks investigated in this study, the number of edges were markedly smaller than the number of possible connections rendering the network density estimates small Pre-HB1 and Post-HB1. The S estimates (S ≫ 2) from surrogate testing revealed that the community structure of the PPN were markedly different from those random graph models rejecting these as potential generative mechanisms of PPN Pre-HB1 and Post-HB1, i.e. community structures of the PPN may not be sufficiently explained by synthetic random graph models investigated in the present study. However, random graphs did prove to be useful internal controls and comparing the PPN properties to their random graph surrogates may be preferred as opposed to a direct comparison of the PPNs at Pre-HB1 and Post-HB1 since the number of nodes and edges of the PPN are not the same at Pre-HB1 and Post-HB1. The S estimates at Pre-HB1 was relatively higher than that of Post-HB1 indicating that the strength of deviation of the community structure from the random graphs decreased Post-HB1. These essentially may reflect the effectiveness of House Bill 1 in reducing the patient movement patterns. From the results presented, understanding the patient movement patterns and objectively quantifying its changes using rigorous network-based approaches has the potential to assess the effectiveness of policy as well as enable targeted surveillance of prescriber communities.The present study also investigated the presence of community structure in the connected component of the PPN where it is possible to traverse from a given node to any other node in the PPN. Presence of communities essentially reflect inherent clustering in the PPN where sub-groups of prescribers and prescriber specialties are more densely connected to one another as opposed to the rest. Such an understanding can again assist in targeted surveillance of prescriber communities. This was accomplished using overlapping community structure detection algorithms and random graph surrogate models. Overlapping community structure detection is well suited for the present study as it permits a prescriber to be a member of more than one community as a result of patient movement between distinct prescriber specialties. Community structure in the given PPN was compared to those that can be generated using random graph models that retain the degree centrality distribution of the PPN. Rationale behind such a comparison are two folds: (1) determine whether in-silico random graph models can prove to be useful in generating community characteristics of the PPN and (2) assess the deviation of the properties of the PPN from those of random models using these models as internal controls since the random graphs retain the nodes, edges and degree centrality distribution of the PPN. The surrogates also implicitly retain characteristics such as network density given by the ratio of actual edges (29 and assist in future policy interventions. It is also important to note that the geographical distances between the prescribers were not explicitly accommodated in the methodological approaches presented in this study. While we present compelling evidence of characteristic variations in statistical and network characteristics Pre-HB1 and Post-HB1, these may not necessarily imply a causal association between the observed changes and House Bill 1 implementation. The present study also assumes the PPN to be unweighted undirected graph but it is possible to assign weights to edges when a patient seeks multiple prescriptions between pairs of prescribers. While the results are presented in a Medicaid population in Kentucky, a more comprehensive study across distinct settings is required to establish the enhanced generalizability of the findings.There are several limitations to the present study. The cross-sectional data in this study was deliberately restricted to a single month, since the probability of patients seeking drugs of the same schedule from multiple prescribers within this time frame is expected to be minimal. However, incorporating explicit time stamps of the patient movement between prescribers may assist in determining the sequence of prescription patterns across different prescriber specialties and assess temporal evolution of PPNs. Incorporating explicit temporal information may also assist in predicting the patient drug seeking behavior ahead of time using approaches such as link prediction