Such alterations often occur during early postnatal neurodevelopment. Among the neurons most affected in ASD as well as in schizophrenia are those expressing the Ca2+-binding protein parvalbumin (PV). These mainly inhibitory interneurons present in many different brain regions in humans and rodents are characterized by rapid, non-adaptive firing and have a high energy requirement. PV expression is often reduced at both messenger RNA (mRNA) and protein levels in human ASD brain samples and mouse ASD (and schizophrenia) models. Although the human PVALB gene is not a high-ranking susceptibility/risk gene for either disorder and is currently only listed in the SFARI Gene Archive, we propose and present supporting evidence for the Parvalbumin Hypothesis, which posits that decreased PV level is causally related to the etiology of ASD (and possibly schizophrenia).The prevalence of autism spectrum disorder (ASD)—a type of neurodevelopmental disorder—is increasing and is around 2% in North America, Asia, and Europe. Besides the known genetic link, environmental, epigenetic, and metabolic factors have been implicated in ASD etiology. Although highly heterogeneous at the behavioral level, ASD comprises a set of core symptoms including impaired communication and social interaction skills as well as stereotyped and repetitive behaviors. This has led to the suggestion that a large part of the ASD phenotype is caused by changes in a few and common set of signaling pathways, the identification of which is a fundamental aim of autism research. Using advanced bioinformatics tools and the abundantly available genetic data, it is possible to classify the large number of ASD-associated genes according to cellular function and pathways. Cellular processes known to be impaired in ASD include gene regulation, synaptic transmission affecting the excitation/inhibition balance, neuronal Ca Complex mechanisms underlie neurodevelopmental disorders (NDDs) and neuropsychiatric conditions. From the molecular to the system level, subtle changes at any point during development can lead to impairment of brain functions including cognition, learning, memory, and behavior, which are dynamic and subject to developmental and environmental influences and activity/experience-dependent mechanisms. Genome-wide association studies, investigation of rare genetic disorders, and transcriptome analyses aim to identify genes implicated in the etiology of NDDs, and bioinformatics approaches are used to elucidate dysregulated processes/pathways. The interplay between altered genes, circuits, and networks giving rise to clinical symptoms in NDDs is illustrated in Figure 1 of Krol et al. .+ interneurons; Hu et al., + neurons are defined as those labeled by anti-PV antibody. A case where the number of PV-immunoreactive neurons is lower than in control animals as a result of experimental treatment or genetic manipulation is either correctly described as a reduction in the number of PV+ neurons or otherwise as a loss of PV+ neurons. The latter implies that these neurons either died or did not properly migrate to a specific brain region during development as a result of the manipulation, while a third possibility is that PV was downregulated to a level below the limit of detection by immunohistochemistry (IHC). Thus, distinguishing between PV+ neuron loss vs. decreased PV expression is critical for accurately interpreting previous studies. For the remainder of this article, we use the term PV+ neurons only in reference to neurons identified solely by PV IHC; the class of interneurons defined by PV expression as well as additional features  is referred to as Pvalb neurons irrespective of PV expression level. As an example, brains of PV−/− mice completely lacking PV expression were found to harbor the same number of Pvalb neurons as wild-type (WT) mice  expressed by a subpopulation of neurons (sometimes referred to as fast-spiking interneurons (FSIs) or PVPVALB mRNA is the most strongly downregulated transcript in human ASD samples  or absent (PV−/−) expression exhibit the core ASD-like symptoms  the function of Pvalb neurons is often impaired in NDDs and neuropsychiatric disorders Marin, ; (2) PVAmet-type) and transcriptomic (t-type) datasets  Pvalb neurons. Given the different algorithms used to stratify the datasets, it is not surprising that the reported number of Pvalb neuron subtypes varies across studies. Pvalb neurons in the cortex include several types of basket cell, chandelier cell, and translaminar neurons. Basket cells localized in layers 2–6 constitute the majority; chandelier cells are abundant at the boundary between layers 1 and 2 and in layer 6; and the relatively rare translaminar Pvalb neurons are mostly present in layers 5 and 6 , potassium voltage-gated channel modifier subfamily S member 3 KV9.3 (KCNS3), or components of perineuronal nets (PNNs), which are a set of extracellular matrix molecules surrounding Pvalb neurons in several brain regions including the cortex —e.g., GAD67 (GAD1)—are regulated in an experience/activity-dependent manner  neurons in the posterior cingulate cortex and fusiform gyrus of postmortem ASD brains  and control (n = 4) samples, although the number of Purkinje cells was reduced in two or three ASD samples  from dorsolateral prefrontal area 9, a significant reduction in the number of PV+ neurons was observed in ASD brains; the ratio of PV+/CB+ interneurons was 0.65 as compared to ~1 in controls. The lower ratio was assumed to reflect a decreased number of PV+ interneurons  were separately analyzed in the same samples  enabled the differentiation of basket and chandelier cells, as only the former are surrounded by VVA+ PNNs in human brain. The number of PV+ chandelier cells was consistently lower in the PFC of ASD specimens (n = 10); the number of PV+/VVA+ basket cells was also reduced but to a lesser degree. As the authors did not use a second marker to unambiguously identify chandelier cells, they concluded that the reduction in the number of PV+ cells represents either a true loss of Pvalb neurons (mostly chandelier cells) or a decrease in PV protein level.In order to conclude that PV+ FSIs  pathway is implicated in Ca2+/activity-dependent gene expression  to induce cAMP response element-binding protein (CREB) phosphorylation and expression of prototypical activity-driven genes  as well as Gad1 and Pvalb  significantly downregulated mRNAs detected in combined gene array datasets of human ASD samples  of a lower number/density of Pvalb neurons in human ASD brains. Even if this was demonstrated, an outstanding question would be whether this is due to the death of true Pvalb neurons , defects in migration or maturation of immature Pvalb neurons during development , with genes putatively associated with ASD ranked according to several criteria. More models  are listed in the AutDB database (http://autism.mindspec.org/autdb/Welcome.do). The number of (mostly mouse) ASD models is constantly increasing, with the models themselves becoming more sophisticated as a result of the possibility of inactivating/deleting genes in a brain region- and neuron type-specific and temporally regulated manner. The last aspect is particularly important for elucidating the etiology of NDDs. Although many mouse models currently used in basic research lack robust construct validity, their strong face validity  make them highly useful for mechanistic studies on ASD etiology. In this paper, we focus on NDD models affecting Pvalb neurons in terms of neuron number and function and PV expression level, irrespective whether they are currently classified as models of validated (syndromic) ASD risk genes.Various animal models have been used to investigate NDDs including ASD. ASD risk genes are defined as those that are more frequently mutated in ASD patients than is expected by chance. Large-scale exome sequencing has identified 102 such genes  and vasoactive intestinal polypeptide-positive (VIP+) interneurons] was analyzed in parallel; the results showed an uneven distribution of the three major interneuron types in the isocortex. The density of the different interneuron subtypes varies in different cortical layers , medial (m)PFC, striatum, hippocampus, thalamic reticular nucleus (TRN), and cerebellum. Importantly, the choice of brain region for investigating PV expression has been somewhat arbitrary and likely dictated by other types of experiments . Thus, if a significant difference in PV expression is observed in a particular brain region, it cannot be assumed that differences also exist in other brain regions. Therefore, systematic analyses are warranted. In some cases, changes in the number/distribution of PV+ cells in a particular brain region are correlated with altered electrophysiological properties. For example, in Ambra1+/− mice, a likely reduction in PV—observed as a decrease in PV+ cell number and PV protein level and interpreted as a loss of hippocampal PV interneurons  imbalance (+/− mice—a model for delayed circuit maturation—evidenced by a reduced number of PV+ neurons at early age (PND14) in several cortical regions and characterized by impaired E/I balance . In many brain regions, the overlap between PV+ and PNN+ neurons is >90% , the number of VVA+ neurons did not differ from that in WT mice. While there is a global reduction in PV expression in PV+/− mice, in other ASD models, the decrease is brain region specific, yet the number of VAA+ neurons in these regions is unchanged. Reduced PV level and fewer PV+ neurons were observed in the SSC of Shank1−/− (−30% PV+ cells) mice and in the striatum of Shank3B−/− (−30%), Cntnap2−/− (−30%), and in utero valproic acid (VPA)-treated (−15%) mice, suggesting that the striatum is a hotspot for the ASD-associated downregulation of PV. Notably, in the abovementioned mouse models, there are other brain regions with unaltered numbers of PV+ neurons . Thus, the Shank1−/−, Shank3B−/−, Cntnap2−/−, VPA, and PV−/− ASD mouse models are characterized by PV downregulation and not the loss of Pvalb neurons. However, such information is unavailable for most of the ASD mouse models listed in + neurons has been reported in most cases. Nonetheless, such information can provide mechanistic insights as a decreased number or functional impairment of Pvalb neurons due to a reduction in PV is expected to alter the functioning of the neuronal network in distinct ways.It is unclear whether the previously reported reduction in the number of PV+ neurons, the mouse models listed in Acute Reduction in PV Expression Induces ASD-Like Behavior in Juvenile Mice, While Promoting PV Expression in PV+/−Mice Attenuates Social Behavioral Impairment. Permanent changes in the firing properties of Pvalb neurons, their inputs, or their targets in ASD mouse models alter the steady-state equilibrium . It was initially proposed that an increase in the E/I ratio could be causally related to ASD  show an increase in inhibitory synaptic transmission in SSC pyramidal cells—that is, increases in spontaneous inhibitory postsynaptic current (sIPSC) and evoked (e)IPSC frequency - and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-type glutamate receptors is increased. As the former shows greater involvement, the NMDA/AMPA ratio as well as E/I ratio are increased. The increase in NMDA/AMPA ratio does not occur in layer 2/3 pyramidal neurons of the SSC  and eIPSC amplitude are decreased, while miniature excitatory postsynaptic current (mEPSC) frequency and amplitude are unchanged  of spontaneous action potential (AP)-driven IPSC is increased in mutant mice, whereas mIPSC is unchanged (both in frequency and amplitude). Strongly reduced layer 4 stimulation-evoked feedforward inhibition  and a smaller decrease in feedforward excitation  onto layer 2/3 SSC pyramidal cells results in a higher E/I ratio in several ASD mouse models including Cntnap2−/− and Fmr1y/− mice  is reduced in these two ASD models  or inhibition (Mecp2−/−), respectively. This suggests that an optimal E/I circuit balance depends on precise Pvalb neuronal circuit function , and repetitive and stereotyped behaviors  was behavior following social behavior  approach to investigate the role of PV in the occurrence of ASD-like core symptoms in mice. We previously showed that PV+/− mice, we assumed the same temporal course as in WT mice; however, PV level reaching only 50% of WT at PND25  from PND5 to PND15, resulting in a PV expression level that was ~90% that of WT mice at PND25 1Swal mouse line (abbreviated as shPV22 or shPV), which permits temporally controlled PV downregulation upon isopropyl-β-d-thiogalactoside (IPTG) administration  expressing PV  PV+/− rescue, in which PV expression was enhanced by E2 treatment; and (4) shPV22 (downregulation), in which PV expression declined starting from PND18 to 50–60% at PND25. Results from the first three groups have been published  and p = 0.179 in the second cohort (n = 4)]. However, when results from the two cohorts were combined, a preference (p = 0.0049) for the stranger mouse is maintained—as to a lesser extent (n.s.)—in constitutive PV+/− mice  WT; (2) constitutive PV2+ buffer  like PV is simple in theory: binding Ca2+ ions at high intracellular Ca2+ concentration ([Ca2+]i) and releasing/unbinding Ca2+ when local Ca2+ concentration decreases. PV-specific biophysical properties include Ca2+ as well as Mg2+ affinity and kinetics. Only the most pertinent findings are discussed here; additional details can be found in Schwaller  of 11 nM and 41 μM, respectively . Together with PV mobility determined in neuron somata i in PV-loaded chromaffin cells   that subsequently affects short-term modulation of synaptic plasticity. This has been observed in a model system based on PV-loaded chromaffin cells  the effect of PV is dependent on the time interval between stimuli and is strongest at the time point when the [Ca2+]i decay curve with or without PV shows the largest difference; (3) if stimuli are long enough i decay following such a train is slowed/prolonged by Ca2+-loaded PV acting as a Ca2+ source (“on” mechanism). Evidence for a direct role of PV under these conditions is summarized below, although only a few selected examples are discussed; additional details can be found in the chapter on PV in Schwaller  at the synapses between MLI and Purkinje cells  PV that acts as a fast Ca2+ buffer. Therefore, Mg2+ binding/unbinding may serve as a metabuffering (buffering of buffering) mechanism that maintains the concentration of apo-PV during rapid, repetitive activity in fast-spiking Pvalb neurons  by modulating intrinsic oscillations via SK channel activation  reticulum Ca2+ ATPase (SERCA) pumps enables the generation of Ca2+ signaling-dependent oscillatory activity in the TRN  may represent the extreme end of decreased PV-induced changes because reduced but not complete loss of PV expression is usually observed in human NDDs and corresponding animal models. The functional consequences of a direct or indirect  decrease in PV level are discussed here. A small hairpin (sh)RNA was used to assess the role of PV in cortical circuits of adolescent rats (PND34–38)  neurons and EPSCs determined ex vivo. Moreover, behavioral data from PV-shRNA rats are sparse. In the trace fear conditioning and extinction test, acquisition of a cue-mediated fear response was unaffected by a reduction in PV level, while fear extinction was prolonged. Compared to controls, PV-shRNA rats showed increased freezing times lasting until the last trial of cue presentation  specifically in Pvalb neurons [PV-Lmo4 knockout (KO) mice] show increased Pvalb neuron-mediated perisomatic feedforward inhibition (likely by basket cells) onto pyramidal cells  . Changes in proteins involved in synaptic transmission have been reported in these mice and are briefly summarized here. The level of Kcnc1 transcript encoding the Kv3.1b channel necessary for maintaining the fast-spiking phenotype of Pvalb neurons was decreased slightly in PV−/− mouse and significantly (−40%) in VPA mouse forebrain lysates    in the forebrain of PND25 VPA mice that showed no difference in forebrain PV expression relative to the control  alterations may be responsible for  homeostatic mechanisms and are observed in several ASD models, mostly those with decreased PV level; some of these are likely cell autonomous and linked to Pvalb neuron hypofunction , as are the downstream consequences of reduced (absent) PV expression such as increases in the number of mitochondria and dendritic branching . Altered expression of HCN family members Hcn1–4 or potassium calcium-activated channel subfamily N members 1–4 (SK channels) that are not specifically expressed in or even absent from Pvalb neurons reflect a Pvalb neuronal circuit phenotype caused by PV downregulation-mediated functional changes in Pvalb neurons. However, the putative changes  have not been fully elucidated.As discussed above and shown in +/− mice, in which a ~50% reduction in PV level was sufficient to influence PPF at this corticostriatal synapse. Further experiments have suggested that changes in short-term plasticity at the cortical neuron–FSI synapse involve a presynaptic adaptation mechanism, possibly resulting from homeostatic plasticity in the cortical neuron caused by the absence/reduction in PV in the postsynaptic striatal Pvalb neuron. However, the lack of a direct link (synapse) between Pvalb neurons and their cortical inputs indicate a retrograde or circuit (network) effect. In summary, the removal/decrease in PV in Pvalb neurons has complex effects on intracellular Ca2+ signaling and synaptic transmission even without considering the changes induced at the morphological level .Adding yet another level of complexity, the absence of PV in striatal PV neurons has a presumed retrograde effect on the cortical glutamatergic inputs onto Pvalb neurons. In the time window (10–50 ms) when Pvalb neuron–MSN synapses show stronger facilitation in the absence of PV, PPF of EPSCs between cortical neurons and striatal Pvalb neurons—mostly mediated by AMPA receptors—is significantly reduced . These processes involve the cortico–basal ganglia–thalamocortical loop in which cortical pyramidal cells project to the striatum, with striatal neurons giving rise to direct and indirect pathways. In the former, the neurons project to the internal segment of the globus pallidus, which sends efferents to the thalamus; projections from the thalamus to the cortex close the loop. In the indirect pathway, a fiber bundle from the striatum reaches the external segment of the external globus pallidus, which forms synapses with neurons of the subthalamic nucleus (STN). The STN projects to the thalamus and from there back to the cortex. The term “disclosed loop” has been used to describe a closed circuit that is open to outside influence at the initial stage of cortical input  and ASD (CHD2 and PTCHD1) are strongly expressed in TRN, and as they are also thought to be linked to Pvalb neurons, results obtained in the mutant mice are discussed here. TRN neurons comprise two (mainly non-overlapping) populations characterized by expression of either PV (~60% of neurons) or SST (~40%)  mediated by CaV3.2 and CaV3.3  in CaV3.3−/− mice decreases IT by ~80% and almost completely abolishes the bursting properties of TRN neurons   neurons were more abundant than the long-bursting type (III), indicating a decrease in rebound bursting in the absence of PV. Additionally, the ISI within a burst was longer, while the number of spikes was unaffected  show a strong effect on the function of TRN neurons, which most likely involves SK channels  of mitochondria  to compensate for a loss/decrease in PV. Moreover, while SERCA pumps and PMCAs are not obviously upregulated in PV-deficient cells, they are presumed to contribute more extensively to Ca2+ removal in the absence of PV . Thus, PV-deficient Pvalb neurons likely need to produce more ATP to accommodate the increased activity of Ca2+ extrusion systems. Ca2+ uptake by mitochondria is energetically costly and can only be maintained by increasing ATP production , mitochondrial calcium uniporter regulator 1 (Mcur1), and mitochondrial cytochrome c oxidase subunit 1 (mt-Co1) are upregulated in PV−/− mice  are higher in PV-negative CG4 cells than in those overexpressing PV   are elevated in control (PV−) CG4 cells (similar to the condition in PV−/− mice) compared to PV-expressing CG4 cells  . This is in line with RNA-seq results from cortical samples of ASD patients demonstrating that UCP2 is among the most strongly upregulated transcripts  cells than in those overexpressing PV , no differences in bursting behavior existed between genotypes. While membrane potential-dependent differences in firing behavior of TRN neurons are correlated with decreased PV level in Gclm−/− TRN neurons, a causal relationship between these events has yet to be established. Notably, a decrease in the number of PV+ neurons in the TRN has been reported in other ASD models showing behavioral phenotypes such as the Engrailed-2 KO mice (En2−/−) and abnormal spindle-like microcephaly-associated gene (Aspm1–7) mutants  have been causally linked to schizophrenia etiology and pathology  may trigger a cascade of events that leads to NDDs such as ASD or schizophrenia  . This hyThe raw data of novel experiments described in the article supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by the Cantonal Veterinary Office . Experiments were performed according to institutional guidelines of the present Swiss law and the European Communities Council Directive of 24 November 1986 (86/609/EEC). The authorization number for housing of mice is H-04.2012-Fr and for the experiments 2016_37_FR and 2019_24_FR.FF, LJ, TH, AB, and BS contributed to data collection (literature research). FF, TH, and AB carried out the experiments and performed data analysis. BS conceived the concept of the article. All authors were involved in writing the paper and read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling, an ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach highlighted the unanticipated role for CDK1 in rear retraction, a prediction we confirmed experimentally. Moreover, our models led to a novel prediction regarding the potential existence of a ‘set point’ in local stiffness gradients that promotes polarisation and rapid rear retraction. Cell migration is a fundamentally important process which underpins development, the immune system, wound healing as well as the harmful spread of diseases such as cancer. Efficient migration in 3D environments requires the co-ordinated input of both protrusions at the front and retraction of the rear. While the dynamics at the cell front have been well characterised, the rear is by comparison a much less well understood system. We have therefore interrogated the signalling dynamics which lead to rapid, efficient retraction of the rear with three different mathematical modelling approaches with the aim to (i) derive new knowledge of how the rear retracts and (ii) compare and contrast models of different complexity in the same, relatively understudied area. By working through simple On/Off Boolean logic, kinetic models and ‘single cell’ simulations based on probabilities, we defined an interconnected signalling network that controls Rho GTPase activation and rear retraction, and a new role for CDK1 in driving the cell rear forward. Cell migration is a key physiological process which underpins development, immune responses, wound healing, and the metastatic progression of diseases such as cancer. Archetypal mesenchymal motility has been conceptualised as a cyclic process depending on the regulated activity of four key stages ,2: polarRecently, we identified a mechanism for efficient rear retraction in fast moving, highly polarised cells in 3D-matrix and on 2D rigidity gradients . We founWhile the dynamics leading to cell rear retraction specifically has not been widely modelled mathematically, other signalling/mechanical events leading to the potentiation of the cell motility machinery have been interrogated successfully using the models based on Boolean logic, ODEs and stochastic simulations, particularly regarding the formation of leading edge protrusions –20. Likea priori, the most appropriate modelling approach is employed to fully interrogate the system of interest in each study. For large systems (> 20 variables) with little quantitative time-course data, a Boolean approach is ideal for ascertaining whether the overall model topography is correct and for making predictions regarding the complete removal of variables or alteration of initial conditions [a priori quantitative knowledge is available, kinetic, ODE based models can be of great value [in silico and in vitro results.Generally, particularly in more well-characterised systems with more known nditions . Due to at value . Their uat value . Provideat value ,30, behaat value ,32, for Using these complementary approaches in tandem, we aim to both elucidate more predictions and underlying biological knowledge regarding the precise spatiotemporal dynamics which lead to efficient rear retraction and identify possible intervention strategies to prevent this. We also aimed to highlight the strengths, potential pitfalls, and points of agreement and of divergence of the varying modelling approaches when studying a mid-level sized network in more general terms.We theorised the modelled domain as tightly and specifically associated to caveolae at the rear bounding membrane. We rationalised this point model approach with the facts that caveolae persist at the cell rear for several hours  , pure OR + OR + OR schemes with and without Src → Cav subject to synchronous updates were chosen (as these simulations exhibited plausible levels of rear retraction in the unperturbed state and are the least stringent). Interestingly, perturbing the system in the absence of the Src → Cav directed link revealed predictions which were almost entirely consistent with our previous experimental findings  however, this feedback loop is bypassed to keep caveolae ON, which in turn keeps RhoA ON. This results in caveolae becoming the only vital variable upstream of RhoA, while just RhoA becomes necessary for subsequent retraction (in silico knockouts) that Src → Cav should not be included in the model hence giving rise to a Src/Cav paradox: asynchronous updates suggest the presence of the reaction is vital while in silico knockouts (with synchronous updates) suggest that it is vital the reaction is not included.Next, we sought to make predictions using the Boolean model via traction , right. We further sought to interrogate the effect of the initial conditions (ICs) used on synchronous outputs: previously  we modelTaken together these data suggest the following conclusions can be drawn from the various simulations of the Boolean model:Dynamics of key variables RhoA, F-actin alignment and p-MLC depend partially on the precise balance between their immediate upstream activators and inhibitors in a manner which cannot be fully described using Boolean logic alone.This formulation of the model relies on some level of caveolae ‘activation’ by Src, however Boolean logic cannot incorporate this interaction in a way which agrees with previous experimental findings.Initial conditions are not important to the steady state reached by the model.in vitro results, suggesting stochastic models may be of greater value.Incorporating stochasticity into the simulations via random asynchronous updates alters outputs and in many cases leads to more agreement with previous While Boolean logic can model some aspects of the signalling/mechanical events which lead to efficient cell rear retraction, more quantitative approaches to incorporate the balance of certain reactions are required to precisely model the signalling network that controls rear retraction.in vitro. We therefore increased in silico complexity by adopting a kinetic approach based on ODEs with mass action reactions, informed by previous findings of the Boolean model  in comparison with caveolae activation by decreased membrane tension) and based on our previous kinetic ODE model [rd and final phase of steady state high rear retraction  and 1% (red lines) levels as well as 10x overexpression (blue lines) compared to unperturbed simulations (black lines) . This liIn silico DRF knockdowns showed a graded response of rear retraction speed; ROCK1 knockdowns showed more switch-like responses of rear retraction, whereby knockdown to 1% levels abrogates all rear retraction while 10% levels exhibit a severe retardation of transition to high steady-state, but a low decrease in this steady-state value. Knockdown of PKN-2 resulted in a graded increase in transition time to steady state (at the 1% level a marked ~4 fold increase) but also a slight increase in steady-state rear retraction speed . Contrary to the logical expectation that overexpression will have the opposite effect to knockdowns, in silico overexpression to 10x levels of DRF, PKN-2 and, to a much lesser extent, ROCK1 all reduced rear retraction. This can be explained by the fact that RhoA acts through these 3 pathways to promote activity of 2 outputs: F-actin alignment and MLC phosphorylation. Overexpression of any one of these effectors however sequesters RhoA away from the other 2 pathways, which leads to a deficiency of either F-actin alignment or p-MLC, thus preventing rear retraction. Finally, as with the Boolean model predictions, Src activating caveolae formation was deemed an important interaction since either knockdown level of Src renders rear retraction speed negligible; overexpression of Src meanwhile also significantly reduced rear retraction speed via the inhibitory effect on p190RhoGAP.The 1%, 10% and 10x overexpression simulations of ROCK1, PKN-2 and DRF reveal that efficient rear retraction relies on the precise balance of RhoA binding to effectors. We further analysed the dynamics of the cell rear model with sensitivity analysis to determine the key kinetic parameters which had the strongest scaled effect on the rear retraction output . Upon thThe systematic perturbations of the ODE model in Informed by the importance of Src perturbation in the ODE model, and observations concerning caveolae activation by Src in Boolean simulations, we next sought to establish the importance of the balance between Cav activation via sensing decreased membrane tension embedded in the positive feedback loop and bypassing direct protein-protein interaction activation. While for the Boolean formulation the only options were for the operation Src → Cav to be present or absent, with the ODE model we could tune the rate of this parameter simultaneously with the input of ‘polarised substrate stiffness’ to view the effect on retraction speed. Partitioning the parameter space domain into a 5x5 logarithmic square  revealedin silico knockdown of DRF, ROCK1 and PKN-2 all had some negative effect on retraction speed and efficiency in agreement with the suggestions of previous experimental findings, the theoretical over-expression of the proteins suggested that the balance of active RhoA binding was crucial for aligned actin and/or p-MLC: 10x increase in PKN-2 or DRF levels retarded retraction to negligible levels. Motivated by these findings, we sought to further ascertain the effects of different RhoA binding partners by simultaneously altering the rates of RhoA binding to DRF, ROCK1 and PKN-2 on a logarithmic scale, thus producing multiple 5x5 heatmaps in silicoe manner . Furthere manner . Perturbe manner . Furthere manner , highligHaving established that CDK1 activity is important for efficient rear retraction, we next sought to compare the relative importance of Ect-2 activation by CDK1 and caveolae activation by Src directly. We used previously established specific inhibitors of CDK1 and Src as these permit a dose response to treatment with low and high levels of inhibition . In in silico overexpression of PKN-2 or increased RhoA-PKN-2 binding rates reduces directional retraction to negligible levels. Experimental findings show that reduction of PKN-2 protein levels reduce cell speed and abrogate F-actin alignment at the rear [in silico knockdowns now had a big effect on the steady-state rear retraction speed and transition time to steady state , such that the rear . The OR dy state , left: libition; ,40. ThisIn vitro, migrating cells exhibit a whole range of speeds and rear retraction efficiencies as well as protein expression and signalling levels. Starting from the initial model . We have seen previously that while CDMs are reasonably chaotic and show variability in substrate stiffness, cells often move in shallow stiffness gradients . Translaapparent , thus unapparent . This shBy adopting different distinct modelling approaches to study the dynamics of directional rear retraction in 3D non-uniform environments, we sought to satisfy two main aims: (i) to interrogate, further understand and inform future experiments regarding our biological system of interest, i.e. the signalling and mechanical hallmarks which lead to efficient cell rear contractility; and (ii) to analyse the value of Boolean, ODE and stochastic models on exactly the same system. Both of these lines of interrogation have been under-represented in the past: cell rear dynamics in 3D remains a poorly elucidated area, while the adoption of multiple modelling approaches within the same study is not common.in vitro data regarding the variability of individual migrating cells. In more general terms, the disparate modelling approaches seem to complement each other: Boolean logic interrogates general topology, deterministic ODE simulations are capable of a exploring a diverse range of reaction schemes which Boolean logic is unable to recapitulate, while the stochastic simulations finally offers the ability to predict the behaviour of individual ‘cells’ within a population. Moreover, with a ‘working’ ODE model, further subsequent analyses and perturbation analyses can be generated quickly and automatically. This suggests that, particularly in medium sized ~20 variables systems where the ‘ideal’ modelling approach is unclear, using a multi-pronged approach as adopted here should be of value in other studies.Overall, the three-pronged modelling approach helped to further interrogate cell rear dynamics, whereby each model/simulation approach generated some interesting and novel ideas, while the increasing complexity involved in moving from Boolean logic to the stochastic simulation via the ODE model increased biological plausibility with each step. For example, the Boolean approach highlighted a lack of dependence on initial conditions, and highlighted the quandary of Src/caveolae dynamics in controlling rear retraction. The ODE approach meanwhile made many population level predictions Figs –5 and hiNon-spatially dependent ‘point’ models are useful tools when studying the signalling dynamics during cellular processes such as migration. For example, non-spatially dependent kinetic models of GTPase control and crosstalk of similar scope to our study have been published over recent years ,20,25. SBetween our three approaches, we have made many new predictions regarding the key events which lead to efficient rear retraction in fast moving cells and potential mechanisms to abrogate such potentially metastatic-inducing contractility, and we have highlighted several potential future avenues of investigation. Specifically, the model points us to areas where we can increase our understanding of the system as a whole, for example elucidating the role of Src on caveolae/general rear dynamics using inhibitors/activators; testing the speed of directional rear retraction in different gradients of polarised substrate stiffness to ascertain further whether there exists a ‘critical gradient’ as predicted and implied by our experimental data; refining the roles of PKN-2 and DRFs in rear retraction.in silico predictions to the biological setting, exciting predictions emerge. At shallow/non-existent gradient levels of substrate stiffness polarity, little directional rear retraction will occur, as was observed with cells on uniform plastic . Furthermore, much of the variability of the outputs in the stochastic simulation approach are likely linked to this . Considering the modelled domain is very tightly and specifically located to just the membrane bound region right at the rear of polarised migrating cells  it naturally follows that all proteins at play in our system will be bound to finite levels, which may be relatively low. In the absence of localised abundance data (which would be incredibly difficult to ascertain), we therefore decided to adopt this 0–100 levels approach. While this is certainly a viable approach and is akin to normalising protein/biophysical entity expression, it may be interesting to alter this 0–100 formulation in the future.in silico inclusion is an alternative means by which RhoA is activated at the rear (in addition to the decreased membrane tension feedback loop). Indeed, we saw that CDK1 inhibition had a stronger effect on cell migration than Src inhibition, and moreover that the combination of CDK1 and Src inhibition at low doses resulted in an additive slowing of cells compared to either inhibitor alone, as we predicted with our improved model. We also tested CDK1 perturbation on rear retraction, caveolae/Ect-2 localisation and RhoA activity and found this is vital for rear retraction via the membrane tension based positive feedback loop . Although CDK1 is more usually associated with control of cell division [By refining the ODE model further  to includivision , interphdivision . Given tdivision , our resIn conclusion, both deterministic and stochastic approaches were able to fit many of our known priors, making the possibilities of their future use in further interrogating cell rear retraction, and other spatial signalling contexts, extremely promising. Indeed, we previously successfully used the ROCK1/PKN-2 inhibitor to show the unexpected loss of localised caveolae accumulation as guided by the ODE simulations . We hopeThe general starting point topology of all the models used, as shown in the wiring diagram in In the signalling network , each noSimple reactions in the model, i.e. nodes with one directed input, were coded as single activations or inhibitions. Where protein nodes are affected by two upstream activators but no inhibitors , interactions were taken as OR reactions due to the absence of any reported protein complex formation. Where biophysical entities are affected by two upstream activators , these interactions were implemented as AND reactions based on biological findings . Other ambiguous relationships are dealt with explicitly in Figs The Boolean model is including as a supplementary zip file ‘S1 Boolean Model’. Upon extraction, instructions for loading and simulating the model can be found in .In silico knockouts and subsequent node classification were performed by removing all edges which lead into a single node and observing the effects on the output variable rear retraction. In silico knockouts were performed individually for all nodes in the model as stated.All Boolean simulations were performed in CellNetAnalyzer , a MatlaRandom initial conditions  were creThe formulation and hallmarks that the kinetic model was built on, and the estimation of the parameter values used, was previously outlined in detail in . DiffereAll parameters in the model were iteratively, manually estimated for optimised qualitative and quantitative agreement with previously known experimental data, i.e. in . For exaThe values of the 10 most important parameters  were halved and doubled  to confihttp://copasi.org/Download/), using the import SBML option . The full list of ODEs of each model are also included as image.png files in the supplementary material  used were the input ‘polarised substrate stiffness’ set to the maximum 100, active Ect-2 set to 50, active Src set to 50, active Cofilin and MLCP set to 100, while the active form of all other is set to 0. The polarised substrate stiffness and Ect-2 ICs were taken for the same reasons as in the Boolean formulation section above ; Cofilin and MLCP were set to 100 since these are inhibitory variables with no explicit activators, such that the only way these can be active is when they start active, while Src was set at 50% active initially since the role of Src in the model is ambiguous as previously discovered in the Boolean section.The model was built and simulated using COPASI software version 4.27 . DetermiParameter sensitivity analysis was performed using the built-in COPASI tool with Retraction/Recoil as the target and all rates in the model as the analysed objects. Scaled parameter analysis was run, such that the concurrent rates used in the model were factored in according to the apparent importance of each parameter, while the total of the scaled parameter sensitivity summed to zero.Heatmaps in Stochastic simulations were performed in COPASI version 4.27. All reactions in the previous kinetic models were first converted to irreversible. The stochastic τ-Leap method was used for simulation with Epsilon = 0.001, max internal steps set at 10000 and no random seed selected. Simulations were run to t = 20,000s with 20,000 intervals and an interval size of 1s, or to t = 50,000s with 25,000 intervals and an interval size of 2s. 6 simulations were manually performed (to take into account simulation variability while still being visible on one graph) per condition analysed, where computational time per simulation was ~10s. Throughout, data was exported and plotted using GraphPad Prism. As with deterministic simulations, throughout the main figures, only the rear retraction output of the model is displayed.2. Images were acquired using a Retiga R6 (Q-Imaging) camera. 4–6 randomly chosen positions per cell were captured every 10 minutes over 16h. 5 randomly chosen cells per position (meaning 20–30 cells tracked per condition per experiment) were individually manually tracked using the ImageJ plugin MTrackJ every 3 frames .A2780 human ovarian cancer cells were maintained in RPMI-1640 medium (Sigma-Aldrich) supplemented with 10% (v/v) foetal calf serum, 1% (v/v) L-Glutamine and (v/v) 1% Antibiotic-antimycotic (both Sigma-Aldrich), incubated at 37°C in a humidified 5% (v/v) CO2 atmosphere as previously described. Knockdown of CDK1 was achieved with an individual siRNA (Dharmacon J-003224-15) using the nucleofection method and imaged 48h later. SiRNA efficiency was determined by standard SDS-PAGE western blot of lysates as described previously ) and imaFor rear movement, control/CDK1 siRNA cells were transfected by nucleofection with Lifeact-Emerald 24h before imaging (24h post siRNA nucleofection) and spread on CDM for 4 hours prior to imaging. Cells were imaged on a CSU-X1 spinning disc confocal (Yokagowa) on a Zeiss Axio-Observer Z1 microscope with a 63x/1.40 Plan-Apochromat objective. An Evolve EMCCD camera (Photometrics) and motorised XYZ stage (ASI) was used with 488 nm laser excitation. Images were captured using SlideBook 6.0 software (3i). Randomly chosen representative control/CDK1 siRNA cells were chosen and imaged for 5 minutes using point visiting while being maintained at 37°C. The distances moved by the rear of each cell during the 5 minute imaging interval were manually analysed using the line tool in ImageJ.For ratiometric FRET RhoA activity, control/CDK1 siRNA cells were transfected by nucleofection with the Raichu-RhoA biosensor  24h prioA2780 cells were cultured, spread on CDMs and fixed in 4% PFA as previously described [A2780 cells were seeded on CDM and imaged within 1 hour of seeding, such that most cells were in situ but not spread or fully polarised yet (to try to get an approximate for time zero corresponding to the time zero used in the model). Cells were imaged on a widefield Nikon microscope, acquiring confocal images every 300s (5 minutes) for up to 20,000s. Cells were automatically tracked using Imaris software, where Wavelet software identified individual cells as different objects. Tracks were manually checked for accuracy. Instantaneous cell speeds were then automatically determined based on this tracking data, and 10 cells randomly plotted from 3 experimental repeats.S1 FigBetween the 9 combinations of reaction schemes as in (TIF)Click here for additional data file.S2 FigA. Upon node removal (or constitutive activity for inhibitory variables), effects could be grouped into 4 functional sets as summarised in B. Heatmaps of the activity of all variables in the model with the reaction schemes OR + OR + OR (left), Hybrid + Hybrid + Hybrid (centre) and AND + AND + AND (right) given the same set of random initial conditions. Note the set of initial conditions used can be seen by the activity of all the variables at the initial time point .(TIF)Click here for additional data file.S3 FigPlots show the activity of the active form of all variables in the model during the first 20,000s. For visual convenience, curves are separated into 5 separate graphs: ranging from RhoA and effectors (top), to biophysical entities (bottom), colours or each curve corresponds to a variable as indicated. Note simulations are all corresponding to the unperturbed simulations in (TIF)Click here for additional data file.S4 FigB. Deterministic simulation outputs up to t = 50000s given setting each positive regulator reactions with rates less than < 0.0001 to 0. Rates set to 0 shown in red, both rates display the same output dynamics on each graph; curve indistinguishable from x-axis shows that recoil amount = 0 throughout the entire timecourse.Range of rear retraction temporal dynamics given halving and doubling of the 10 most influential rates in the model. Time course curves of rear recoil amount given halving and doubling of the rate parameters (as shown above each graph) were plotted for the first 20,000 timepoints, then the region in between shaded red on the assumption that this whole region will be covered upon continuous alteration of the rate parameter between these two extremes. (TIF)Click here for additional data file.S5 FigA. Efficiency of knockdown of CDK1 using individual siRNA . B. Left: A2780 cells were treated with DMSO (left) or CDK1 inhibitor (right) and seeded in CDM and imaged by high-end widefield microscopy across 16 hours, representative individual cells shown across t = 400 minutes, Right: Quantification of average speed of control or CDK1 inhibited cells during 16h timecourse, .(TIF)Click here for additional data file.S6 FigA. Alternative formulation of model concerning RhoA signalling to aligned actin where F-actin alignment now requires coordinated activity of both DRF and PKN-2 together. B. Timecourse plots of rear retraction amounts in response to in silico knockdown/reductions in levels of PKN-2 between 0 and 10% (left), ROCK1 between 3 and 5%  or DRF between 0 and 10% .(TIF)Click here for additional data file.S7 FigA. 6 stochastic simulations showing rear recoil amount with respect to time for the first 20,000s when run with exactly the same reactions and parameters as in the CDK1-included ODE model in B. Timecourse plots of the original model of rear recoil during stochastic simulations for the first 1,000,000s with polarised substrate stiffness set to 4%. C. Proportion of time of the 1,000,000s timecourses rear recoil amount spent in the high state (defined as rear recoil > 50%), the low state (< 20%) or transitioning (trans.) between  of 3 simulations with substrate stiffness polarity set at 2% or 4% with the CDK1 model.(TIF)Click here for additional data file.S1 MovieRepresentative A2780 cell expressing GFP-Cavin-1 (green) and BFP-Lifeact (blue) migrating in 3D CDM over 3.5 hours.(AVI)Click here for additional data file.S2 MovieRepresentative A2780 cells (moving left to right) migrating in 3D CDM over 7h treated with DMSO ; CDK1 inhibitor at 1 μM (top centre) or 10 μM (top right); Src inhibitor at 0.3 μM (bottom left) or 3 μM (bottom centre); or both CDK1 inhibitor at 1 μM and Src inhibitor at 0.3 μM together (bottom right).(AVI)Click here for additional data file.S3 MovieRepresentative A2780 cells (moving left to right) migrating in 3D CDM over 10h treated with DMSO ; or SMIFH2 at 5 μM (top right), 12.5 μM (bottom left) or 50 μM (bottom right).(AVI)Click here for additional data file.S4 MovieWhole field of view of A2780 cells moving in CDM (left) or on uniform stiff plastic (right) over 16 hours, with 5 randomly chosen cells highlighted and tracked for each condition.(AVI)Click here for additional data file.S1 Boolean modelCompatible with CellNetAnalyzer (a MatLab plug-in), see (ZIP)Click here for additional data file.S1 ModelCompatible with Copasi, see (XML)Click here for additional data file.S2 ModelCompatible with Copasi, see (XML)Click here for additional data file.S1 Equations(PNG)Click here for additional data file.S2 Equations(PNG)Click here for additional data file.


Cancer therapeutics is a rapidly changing field which offers patients the prospect of a better quality of life and cure. Immunotherapy has become a unique approach for select metastatic solid tumours. While initial results do show durable responses in select patients, there are concerns on how best to utilise this expensive resource which can result in costly side effects and in whom the use of biomarkers to stratify patients is still in its infancy. Given the ageing population and extreme challenges on healthcare, economic modelling with regards to immunotherapy is imperative especially now when it is being considered for further cancer types. Paralleling this rise is the increasing sales of oncology medications which in 2000 represented 3.5% of global pharmaceutical sales and 7% in 2011. A major contribution to this increase is the emergence of targeted therapies which can offer superior outcomes in select patients.24 Whilst radiotherapy and chemotherapy remain mainstay in the oncologist’s arsenal and act by indiscriminately killing normal and cancerous cells alike, with dose limiting toxicities. Immunotherapy works indirectly by promoting the immune system to recognise and destroy cancer cells to produce clinical benefit even in late stage and metastatic disease.5Cancer is a process whereby normal cells acquire mechanisms to circumvent the normally controlled processes of proliferation and apoptosis.6 While the immune system is good at detecting and eradicating tumorigenic cells through immune surveillance, established tumours employ many mechanisms to evade detection including the production of immunosuppressive cytokines and reduced surface major histocompatibility complex (MHC) expression. Immunotherapies encompass a wide range of biologically active molecules including interferons, interleukins, and immune checkpoint inhibitors (ICIs) of these the latter are the most studied, utilised, and researched.5 ICIs work to enhance T-cell activation which are essential for anti-tumour activity and requires two distinct signals to be present. The first of these is the interaction between the T-cell receptor (TCR) and the peptides being presented by the MHC on antigen presenting cells (APC) at the draining lymph node while the second signal is a confirmatory co-stimulation signal involving interaction between the T-cell and APC by CD28:CD80/CD86 respectively. In the absence of this costimulatory signal T-cell anergy occurs. One method by which T-cell activation is limited to avoid hyperstimulation of the immune system is by the T-cell CD28 homolog, Cytotoxic T-lymphocyte -Associated Protein 4 (CTLA-4) which is upregulated on initial T-cell activation and competitively and with greater affinity than CD28 binds to CD80/CD86 on APCs and downregulates their expression. CTLA-4 also plays an essential role in regulatory T-cells function to maintain immune tolerance.8 Therefore by inhibiting the action of CTLA-4 one potentiates T-cell activation, proliferation and infiltration of the tumour leading to potentially robust anti-tumour responses. CTLA-4 is the target of the humanized IgG monoclonal antibody, Ipilimumab, which since 2011 has been licensed for the treatment of malignant melanoma. Another key target of ICI therapy is Programmed Death Receptor (PD-1R) blockade. This is expressed on a plethora of immune cells including B, T and NK cells whose normal functions are altered in the setting of cancer. The ligand of this receptor, PD-L1 is expressed on stromal and tumour cells and upon binding downregulates T-cell activation, proliferation and IL-2 production thereby limiting anti-tumour responses. This pathway is the target of the ICIs Pembrolizumab and Nivolumab which block PD-1R.7The origins of Immunotherapy derive from the 1800 s where William Coley utilised mixtures of live and inactivated Strep.Pyogenes and Serratia to induce remission in sarcoma, lymphomas, and testicular cancer with varying success. Recently immunotherapy is being employed in select solid cancer management including malignant melanoma and non-small cell lung cancer (NSCLC) however in non-invasive bladder cancer it has been used for decades in the form of the Baccile Calmette-Guerin vaccine.9 In 2011 the first phase III trial to demonstrate Ipilimumab’s and immunotherapies superiority was reported. This involved 676 patients of good functional status with previously treated stage III-IV melanoma who had progressed despite best standard care. Participants were randomized to three groups each receiving four treatments: Ipilimumab with glycoprotein 100 (gp100) a peptide vaccine and adjuvant (n-403), Ipilimumab monotherapy (n-137) or gp100 alone (n-136). The median overall survival of these groups was 10.0, 10.1 and 6.4 months respectively at follow-up (p < 0.001). In addition, 45.6% and 23.5% of Ipilimumab monotherapy recipients were alive at 12 and 24 months respectively with a 36% reduction in progression noted. Importantly, on re-exposure to Ipilimumab clinical responses were still seen.11 As a result of this pivotal study Ipilimumab was approved in 2011 for advanced/unresectable melanoma as well as an adjuvant therapy for fully resected stage III disease.4 The National Institute of Clinical Excellence (NICE) appraisal however concluded that while Ipilimumab was clearly beneficial that only 30% of recipients may show some benefit and of these a minority show durable responses. As such it was deemed that without manufacturer discount with total treatment Ipilimumab would not be cost-effective. In addition, there was methodological concerns with the lack of clinical difference reported between escalating doses of Ipilimumab and the usefulness of the gp100 adjuvant.12 More recent work published in 2018 has highlighted the power and synergistic effect combination immunotherapy can have. In the pivotal CheckMate-067 trial looking at 3-year outcomes in those with untreated grade III/IV malignant melanoma, patients (n=945) were randomized equally to receive either monotherapy or combination therapy involving Nivolumab and Ipilimumab until either progression or unacceptable toxicity. Patients who received combination therapy had a higher objective and complete response (58% and 19% respectively) than those on monotherapy after only a median of 4 doses. This correlated with a progression free survival (PFS) of 11.5 months and an overall survival (OS) rate of 58%. This study highlighted that Nivolumab therapy was more efficacious in all patient groups than Ipilimumab with a complete response of 16% and 5% and OS of 52% and 34% respectively. Importantly these responses were durable and reinforced observations seen in earlier trials and highlighted the superior outcomes that combination therapy holds even upon extremely limited exposure. It is because of such data and the concept of cure that many patients often embrace the use of immunotherapy. However, it remains unknown if these patients are cured or if they represent immunotherapy induced tumour dormancy.13Historically Malignant melanoma had a median survival of 6 months with Dacarbazine the standard of care in patients with inoperable disease despite showing no conclusive survival benefit.14 Unlike traditional therapies immunotherapies do not commonly cause hair loss, nausea, and infertility.1 Instead by leading to unopposed stimulation of the immune system, ICIs can lead to autoimmune reactions and multi-organ dysfunction through an uncontrolled, dysregulated immune response which paradoxically may lead to enhanced anti-tumour activity. Every organ system has been implicated as part of immune related adverse effects (iRAEs) with dermatological complications, hypothyroidism and hypopituitarism well described in the literature occurring up to 3 years post-exposure.15 Currently up to two-thirds of patients receiving anti-CTLA-4 therapy experience irAEs of which one third are gastrointestinal in nature ranging from diarrhoea to colitis and can necessitate hospital admission and therefore be resource intensive to manage. In many recipients however grade I/II toxicities are observed with 80% of side effects resolving within 4–6 weeks of corticosteroid therapy. It is these toxicities which are often underreported in clinical trials and reported economic analyses.17 Evidently understanding and identifying patients in whom immunotherapies will work is of paramount importance given their costs and side effect profile. At present the only biomarker used in clinical practice is tumour PD-L1 expression, which is used in NSCLC to determine the likelihood of response. However, this has several disadvantages with PD-L1 expression known to vary with time, previous treatment exposure, and underlying method of detection.17 In addition, there has been reports of a 20-30% response rate to anti-PD-1R therapy in patients who were PD-L1 negative making its validity questionable. It is important therefore for formal identification of novel, robust, cost-effective, and reliable biomarkers (e.g. microRNAs) to enhance immunotherapy efficacy and safety in a cost-effective manner while avoiding potential treatment delays.19 The cost of cancer care is currently greater than any other chronic condition and this expense cannot be explained alone by the rarity of the underlying cancer or high research and development costs or a given therapeutic agents efficacy. Indeed complicit with this are the high indirect costs including a loss of income in patients and treatment related costs due to often debilitating side-effects.20 A recent systematic review, looking at the cost of immunotherapy treatment complications in 844 elderly melanoma patients, receiving either immunotherapy (n = 528) or target therapy (n = 316) revealed that the overall costs in both groups suffering complications were on average four times higher than those who did not . Moreover versus targeted therapies, immunotherapy recipients were more likely to suffer respiratory (76%), haematologic (71%) and gastrointestinal (79%) complications all of which costed more to manage  and led to greater 30 day treatment costs.21 This therefore represents a very real challenge for the NHS in advocating potentially life changing treatments and ensuring equal access while limiting their potential complications. In addition, despite nearly a decade of use many healthcare professionals are inexperienced in dealing with immunotherapeutics and their potential toxicities and as such often requires tertiary hospital specialist input and admission. This may not only affect service provision but could antagonise existing area specific health inequalities. This emphasises the need therefore to identify methods to risk stratify patients who will likely be older, suffered progressive disease despite past treatments with attendant comorbidities, organ dysfunction and reduced functionality which themselves may be complicated and exacerbated by immunotherapy.Despite these results immunotherapies may be ineffective in up to 50% of patients, explanations include tumour heterogeneity as well resistant subclonal populations of tumour cells produced by prior therapy. Importantly there may be other immune checkpoint pathways yet unappreciated which run parallel to and circumvent those currently targeted.2 To address this one must first consider the challenges of economic modelling in modern oncology, given that for many therapies there is often limited trial data, diverse drug mechanisms of action and often limited applicability in only a subset of patients suffering a given cancer diagnosis, thereby hindering comparison. In addition, longterm data is often absent limiting one to accurately evaluate the total costs of treatment with quantitative quality of life data often not present which is an important consideration.1This therefore is an important consideration for the NHS, a publicly funded body which has competing interests in managing the increasing demands from an ageing population and nation as a whole, in whom cardiovascular and metabolic disease is becoming more prevalent and for whom sustainability is key. This challenge however is not only restricted to the UK. In America, the cost of cancer care will reach between $173-206 billion this year and as a result of the emerging role of ‘maintenance’ cancer treatment, cancer is quickly becoming a chronic disease with costs set to rise.22 At present several outcome measures can be calculated from such models including quality adjusted life years (QALYs) which considers both quality and quantity of life  and those costs associated with gaining an additional QALY the incremental cost effectiveness ratio (ICER) which is compared with a predetermined willingness to pay threshold. Comparison between these values can help demonstrate cost effectiveness. NICE traditionally have a QALY threshold of £20–30,000 which is substantially less than the USA threshold of $100,000. 23 During Ipilimumab’s appraisal NICE estimated the QALY to be ∼£240,000 with an ICER of £54,000–£70,000 while within NSCLC the ICER of Nivolumab and Pembrolizumab was $1,17,000 and $98,000 in primary and recurrent disease translating into a 3.5 month PFS.13 Importantly however when analysing the cost-effectiveness of Nivolumab according to tumour PD-L1 expression levels a trend to cost-effective satisfying the USA QALY threshold was seen, $1,12,311 (≥1%), $72,897 (≥5%), $78,921(≥10%) with similar results found with Pembrolizumab monotherapy. This therefore represents one method by which economic viability with immunotherapy use can be pursued.20 Regardless it is imperative that risk-sharing schemes are sought to both help mitigate these substantial costs and allow for their sustainable use. Given that even with the historical $50,000/QALY cited in the literature then only three of the twenty economic studies used in a 2018 systematic analyses of immunotherapies would demonstrate cost effectiveness.24 Importantly however with identification of suitable biomarkers and therefore improved patient selection, it can be deduced that QALY and ICER measures will decrease markedly while providing maximal clinical benefit.Currently there are two main economic models which can be utilised to assess cost effectiveness. These are the Markov and Partitioned Survival (PS) Models. While the former considers patients to be in flux between different health states (i.e. progression free or progression of disease etc) and is based on assumptions of how likely such transitions are to take place; PS models rely on Kaplen-Meier survival curves and therefore are extrapolations of real life data.In conclusion while immunotherapy offers to otherwise terminal patients a chance of robust durable responses it is associated with both high treatment costs and frequency of expensive adverse side effects which may limit its use. In addition, there is at present no method to differentiate between likely responders who represent the minority and non-responders which impacts their cost effectiveness. Therefore, future aims must be to identify suitable biomarkers and methods to enhance response rates while analysing cohort data to evaluate the long term clinical and financial outcomes of immunotherapy.


We previously reported that precision-cut organotypic tissue slices of pancreatic ductal adenocarcinoma (PDAC) can be successfully cultured ex-vivo for at least 4 days. In order to study how culturing might affect transcription patterns, we now performed genome-wide transcriptome profiling of both baseline (0 h) and explanted tumors at daily intervals  after start of culturing. The total-RNA from five samples of surgically resected human PDAC tumors at baseline and at different time points in culture was sequenced. Differential gene expression analysis of the whole transcriptome, testing 58,713 genes and over 206,000 transcripts, found that only a small number of genes showed significant changes in expression between baseline and cultured samples. The cultured tumor slices showed upregulation of a median of 12, 10 and 15 genes and downregulation of a median of 15, 12 and 25 genes at 24, 48 and 72 h in culture, respectively. One sample had morphologically increasing loss of tissue viability (range 0–18%). The vascular endothelial growth factor A (VEGFA) was significantly upregulated during the entire culture period in this case. Pathway over-representation analysis suggested that VEGFA together with the PTGS2 gene were upregulated at the same time as HIF-1-triggered cell apoptosis via NF-ĸB and the AP-1 activating factor was induced. Indeed, increased areas of apoptotic lesions were visible in this sample after 24 hours of culture. In conclusion, genome-wide transcriptome analysis supports that ex-vivo cultured tissue slices of PDAC may be a representative model of the original tumor. Transcriptome analysis was found to be a valuable complement to morphology for evaluation of ex-vivo cultures of PDAC.Ex-vivo tumor tissue culture systems are used as models to test specific anti-cancer drugs. Their main advantage is that they are closely comparable with the  Discrepancies in pancreatic cancer incidence between countries suggest that environmental and geographical factors may play a substantial role as risk factors for the disease2. Pancreatic cancer is usually diagnosed at advanced stages and characterized by aggressive tumor biology and pronounced resistance to treatment, which result in an overall 5-year survival rate of <7%3. PDAC tumors with distant metastases are generally not resected due to their dismal prognosis and surgical removal is indicated only for localized cancer. Risk factors are a family history of pancreatic cancer, smoking, obesity, chronic pancreatitis and genetic alterations, e.g. in BRCA1, BRCA2, PALB2 and ATM4.Pancreatic cancer is currently the fourth leading cause of cancer-related death in the West and predicted to rank second by 20305. As a result, translated oncoproteins or suppressor cellular regulatory mechanisms force cells into dysregulated growth8.Most pancreatic cancers have sporadic gene alterations, such as amplifications, deletions, translocations, inversions, frame-shifts, and substitutions. Mutations of multiple genes in pancreatic cancer occur in over 70% of all diagnosed cases. Most of the genetic alterations cause overexpression or activate signal transduction pathways10. Since the microenvironment in pancreatic cancer has unique features that directly impact the molecular signatures of the tumor cells, it is important to study the global molecular events that take place in the tumor tissues12.Histopathologically, most PDACs tumors are characterized by the presence of a prominent desmoplastic stroma with microenvironment that is composed of an admixture of acellular and cellular elements, including the extracellular matrix, mesenchymal cells (mainly cancer associated fibroblasts) and immune cells. The tumor microenvironment has pleiotropic tumor-promoting effects and contributes to an immune suppressive milieu, which can limit treatment efficacy15.Transcriptome sequencing uses massive parallel sequencing technologies to measure tissue RNA expression. Sequence data can be aligned to a reference genome to build full-length transcripts. Annotated transcripts are used to study differentially expressed genes while non-mapped reads can be analyzed to study fusion genes or viral transcripts. Transcriptome sequencing data can also be used to define novel transcripts leading to discovery of neoantigenset al., showed that pancreatic cancer cells, the tumor microenvironment and the interspersed remnants of nonneoplastic pancreas in 350 µm thick slices maintained their structural integrity, phenotypic characteristics and functional activity when kept in culture for at least 4 days16.Earlier, Misra and Moro In the present study, we employed deep sequencing to characterize the transcriptome of five cases of PDAC that were selected based on the different degree of cell death in the tissues and tumor grade. We used freely available, open source bioinformatic tools to perform differential gene expression and pathway enrichment analysis. We hypothesized that tissue viability (and its loss) in ex-vivo cultured tissue slices would be reflected in the differences in transcription of genes involved in pathways related to cell death.We sequenced FFPE tissue from five tumor samples at baseline and their matched cultured tissue slices at 24, 48, and 72 h. In total 5 baseline tumor samples and 15 corresponding organotypic (OT) cultures were assessed.et al.) for being histomorphologically comparable (same tumor type and grouped by grade of differentiation) but showing different levels of tissue viability during culture16. In particular, four cultures  showed no or minimal loss of viability (range 0–6%), while one (OT9) showed mild to moderate loss of tissue viability (range 0–18%)  and 206,487 gene transcripts were tested. Transcript isoform test detected no statistically significantly differentially expressed isoforms at any time point.To analyze the differences in gene expression, each FFPE block prepared at time points 24 h, 48 h, and 72 h was compared to the baseline FFPE tumor tissue sample. Using TopHat2/Cuffdiff a limited number of differentially expressed genes were selected when allowing a false discovery rate of 0.05  were associated significantly with certain pathways, over-representation analysis was performed. Those genes that were expected to be in at least in one pathway are presented in Table Over-representation analysis for sample OT9, indicated that the gene sets PTGS2; IGFBP1; PMAIP1; and VEGFA could activate the therapy-induced HIF-1 survival signaling pathway followed by Quercetin, Nf-kB and AP-1 Induced Cell Apoptosis (48 h), the latter being in line with the histological detection of cell death/apoptosis  in all examined samples (data not shown) but there were no overall expression changes of these transcripts during the incubations.High levels of transcripts of long non-coding lncRNAs, MALAT1 and NEAT1 (located on the long arm of chromosome 11) have been described in various cancer types including pancreatic cancerNo possible oncogenic fusions were detected in sample OT5 at any time-point.FusionCatcher detected “likely” fusion of CDKL5-NEAT1 in specimen OT9 after 48 h but this fusion did not contain sequences from exonic regions of the involved genes.et al.), fresh samples of pancreatic cancer tissue were cut into 350 µm thick slices using a vibrating blade microtome. The slicing process yielded between 12 and 17 slices per tissue sample. The first slice  was immediately fixed in formalin and embedded in paraffin. This was used as baseline for morphological  assessments in the cultured tissue slices. The subsequent eight slices were placed on inserts and cultured at 37 °C in a humidified incubator in 41% O2 for up to 96 h. Every 24 h, duplicate tissue slices were harvested, formalin fixed and embedded in paraffin. The tissue slices were subsequently sectioned and processed for histology. Loss of tissue viability was determined on hematoxylin-eosin stained sections according to the percentage of necrotic and apoptotic areas with respect to the entire surface of the tissue slice. Among the duplicate slices for 24, 48 and 72 h, the one with higher tumor tissue content was selected for transcriptome analysis.This study was approved by the Regional Ethical Review Board, Stockholm (IRB diary number 2012/1657-31/4). In brief, as previously described  2×10 µm curls using the Maxwell RSC FFPE RNA kit . Extracted RNA was quantified by Qubit 4.0 and RNA HS Assay Kit . A maximum of fifty nanogram of extracted RNA was used to prepare cDNA libraries skipping the RNA fragmentation step. Whole transcriptome sequencing libraries were prepared using the Takara Smarter total-RNA Seq kit V2 Pico Input Mammalian . Briefly, cDNA was prepared by random hexamer priming while preserving strand of origin information. During a first PCR amplification, full-length Illumina adapters, including barcodes were added. The ribosomal cDNA sequences (originating from rRNA) were cleaved in the presence of RNAse H and the mammalian-specific R-Probes. The remaining fragments were enriched via a second round of PCR amplification using primers universal to all libraries. The final library contained sequences allowing clustering on any Illumina flow cell. All libraries including a non-template control were quantified on Bioanalyzer . To equalize sequencing input amount, each library was quantified by Qubit 4.0 HS Assay Kit .19 to process and analyze RNA data for gene expression. All sequences were quality-checked by FASTQC20, adapters were preprocessed and trimmed if necessary. To estimate the stability of the sequencing reactions insertion/deletion rate values were observed after FASTQ alignment by STAR using Homo sapiens genome version GRCh38.95 as ref. 21 (data not shown).In total twenty normalized libraries from five PDAC were sequenced on a NextSeq. 500 Illumina system . Paired-end cycle sequencing 2 × 75 was run on High Output V2 Kit which in total generated a median of 40 million raw paired-end reads/sample. Indices, included in the Illumina adapters, were used to demultiplex and assign raw sequence reads. Datasets were analyzed using the Chipster virtual bioinformatic interface at CSC Finland22. Multiple GTF files were merged using Cuffmerge and sorted by chromosome and start position.For comparative analysis of gene expression, TopHat2 alignment tool for paired-end reads followed by the Cufflinks algorithm package version 2.1.1 for assembly and Cuffdiff for differential expressionGTF and the corresponding BAM files were compared to the reference baseline sample using the Cuffdiff tool to study the DEGS of the potentially novel transfrags where at least one splice junction was shared with a reference transcript. To calculate the DEGS, Cuffdiff differences in FPKM (Fragment Per Kilobase Million) values were considered significant when p-values were equal or less than 0.05.et al.23. This test searches gene lists for over-represented pathways in the CPDB database at: http://cpdb.molgen.mpg.de/. The p-values of each pathway test reflect the significance of the observed overlap between the input gene list and the module’s members as compared to random expectations. CPDB is an open source database provided by the Max Planck Institute for Molecular Genetics and contains pathway information from 32 publicly available databases, including BioCarta, HumanCyc, Reactome, KEGG and WikiPathways.To predict the possible molecular functional and gene-pathway interactions, Hypergeometric distribution probability test  was used. This pathway analysis is described by the ConsensusPathDB (CPDB) created by Herwig https://github.com/ndaniel/fusioncatcher), using human genome database v95 was installed and used on a local Linux server24. The command line option was used to enable FusionCatcher to search for fusions in raw paired-end FASTQ files, skipping the alignment tool BLAT and adding -V to the command line to engage and store unaligned reads to be able to BLAST for possible viral transcripts. Only fusion transcripts not labeled as “banned” in the output file  or involving intronic sequences and not associated with a protein product were considered as a novel finding.To analyze the RNA sequencing data for detection of somatic fusion transcripts, FusionCatcher_v1.10 . Written informed consent was obtained from all patients prior to surgery. All study methods were performed in accordance with the relevant guidelines and regulations.Sequencing of the total transcriptome from surgically resected human PDAC at four time points in culture in vitro revealed that limited number of genes (median 10 to 25) exhibited altered expression after ex-vivo culture. Among those, the pancreas-specific transcripts (PNLIPRP1: pancreatic lipase related protein 1 and CPA1: carboxypeptidase A1 and CPA2) were suppressed.In this study modern deep sequencing with short reads was used. In this way a very deep transcriptional mapping could be performed, even in specimens that may contain fragmented ribonucleic acids. The fact that we found similar expression levels in the explanted tumor for >200,000 transcripts indicates that the analysis was both deep and comprehensive.Tumor dissection to enrich for certain cell types was not performed, hence it was not possible to study the contribution or representation of the cellular compartment expression in diverse pathways. Rather, we studied the overall expression from all cellular components within the tissue including the tumor, stroma, infiltrating immune cells and nonneoplastic pancreatic tissue. In this way, it was possible to capture a genome-wide image of globally expressed genes, reflecting the molecular events that take place in the tumor tissue and its surrounding microenvironment.et al. also studied precision-cut slices prepared from human and animal models of different cancer types25. They investigated tissue viability and proliferation at different oxygen levels and analyzed the expression of a large set of RNA molecules that are involved in different cellular mechanisms, such as apoptosis, DNA damage and repair. They concluded that ischaemia during transportation from the surgical theatre and mechanical slicing  had little impact on stress gene expression, but that cultivation induced changes in gene and protein expression. They observed that atmospheric oxygen concentration is suitable for tissue slice culture and that the slice thickness has no impact on O2 diffusion and its availability to the tissue. Consistent with that, a previous study from our group showed no significant differences in tissue viability, proliferation, or metabolic activity between matched tissue slices cultured in atmospheric (21% O2) or hyperoxic (41% O2) conditions16. Davies EJ. et al. also showed overexpression of HIF-1α by immunohistochemistry in association with hypoxia and stress of the tissue slice compartments. In line with this observation, VEGFA and the HIF-1α signaling network were upregulated in the cultured slices of sample OT9 that exhibited increased amounts of apoptotic cell death. The vascular growth factor A, encoded by the VEGFA gene, is a key factor in angiogenesis and increased vascular permeability26. It can be speculated that upregulation of VEGFA was induced in hypoxic conditions because “HIF-1-induced” cell apoptosis via NF-ĸB and the AP-1 activating factor was suggested as a possible pathway.Davies EJ. Altogether, these findings support the role and value of assessing HIF-1α activity for the detection of cellular stress in the cultured tissue slices.In the current study, the analysis of the total cellular transcriptome assessed the impact of the new environment on the cultured pancreatic cancer tissue. Ex-vivo model systems are influenced largely by culture conditions such as varying oxygen tension and absence of vital cellular stimulatory cues.Future studies linking transcriptome data and morphological phenotypes of cancer tissue could be of value to shed light on the molecular origins of intratumoral heterogeneity.Genome-wide transcriptome sequencing revealed transcriptional changes only in a limited number of genes during tissue culture ex vivo. Changes were mainly seen in genes involved in biological processes whose molecular function can affect the stabilization or upregulation of cellular compartments. Consistent with histomorphological analysis, upregulation of pathways related to cell death/apoptosis were observed in a cultured tumor with decreased tissue viability, while they were not upregulated in the tumor cultures with minimal tissue loss.The present analysis supports the transcriptional stability of the tissue slices during ex-vivo culture and its ability to reflect changes related to cell death. This is of special relevance in order to successfully address and correctly assess biological processes and drug responses in slice cultures. In addition, these findings support the use of transcriptomic analysis as a valuable tool for evaluation of ex-vivo cultures of PDAC.


Active influenza A virus (IAV) surveillance in wild waterfowl in the United States has revolved around convenience-based sampling methods, resulting in gaps in surveillance during the spring season. We conducted active IAV surveillance in mallards continuously from July 2017 to July 2019 in the coastal marshes of Lake Erie near Port Clinton, Ohio. We aimed to understand ecological and evolutionary dynamics of IAV across multiple seasons, including the under-sampled spring season. We collected 2096 cloacal swabs and estimated a 6.1% : 0.050–0.071) prevalence during the study period. Prevalence was lowest during spring . Time-stamped phylogenetic analyses revealed local persistence of genetic lineages of multiple gene segments. The PA segment consists of a lineage detected in multiple seasons with a time to most recent common ancestor of 2.48 years (95% highest posterior density: 2.16–2.74). Analysis of the H3 and H6 segments showed close relation between IAVs detected in spring and the following autumn migration. Though the mechanisms behind viral persistence in a single location are not well understood, we provide evidence that viruses can persist across several seasons. Current surveillance methods should be evaluated to ensure they are capturing the breadth of genetic diversity of IAV in waterfowl and prepare for IAV outbreaks in both animals and humans. Wild waterfowl, particularly of the orders Anseriformes  and Charadriiformes , are considered the primary reservoir of influenza A virus (IAV) . These oActive IAV surveillance in wild waterfowl has occurred since their establishment as viable hosts for the virus . The majEvidence suggests that, while IAVs are dispersed over a short period of time throughout migratory flyways, certain IAVs are maintained regionally, reappearing in the same locations each autumn migration, though the mechanisms behind this maintenance are not well understood . Some stAnas platyrhynchos, as the study species as they are known to be readily infected by a genetically diverse range of IAVs, allowing them to represent IAVs circulating in the overall waterfowl population [The objective of this study was to fill the gap in IAV surveillance during spring migration to understand ecological and evolutionary dynamics of the virus in waterfowl during a historically under-sampled time of year. We can better understand persistence of IAV on a continuous timeline and provide insight into the mechanisms driving persistence by conducting year-round surveillance in one species of waterfowl at one location. We chose an important migratory stopover site for waterfowl with a rich history of IAV surveillance as the sampling location. We chose wild mallards, pulation . Here, wActive IAV surveillance was conducted in wild mallards July 2017–July 2019 at an impounded coastal wetland complex located in the south–west Lake Erie basin near Port Clinton, Ohio, USA . Cloacal swabs were collected under the Ohio Department of Natural Resources Scientific Collection Permits 19-120 and 20-281, US Fish and Wildlife Services Permit MB66162B-1, and The Ohio State University Institutional Animal Care and Use Committee protocol number 2007A0148 as previously described [RNA extraction was performed on all samples on the MagMAX Express 96 Magnetic Particle Processor  using the Mag-Bind Viral DNA/RNA 96 Kit  following a modified protocol. The modified protocol used 240 μL of TNA lysis buffer, 24 μL 17% sodium sulfite, 280 μL 100% isopropanol, 1.44 μL 50 mg/mL bovine serum albumin, 4 μL carrier RNA, 2 μL Xeno internal positive control template , 10 μL proteinase K, and 10 μL magnetic beads per reaction. Extraction included two washes with 400 μL VHB buffer and two washes with 500 μL SPR buffer. RNA was eluted into 50 μL nuclease-free water. Real-time reverse transcription polymerase chain reaction (rRT-PCR) targeting a segment of the IAV matrix gene was performed on all extracted RNA using SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase  following an optimized protocol. The reaction mixture contained 15 μL 2× reaction buffer, 1 μL reverse transcriptase, 0.1 μL ROX reference dye , 2.7 μL nuclease-free water, 0.5 μL of 6 μM M+64 probe , 0.5 μL Phylogenetic analyses on nucleotide sequences were used to assess potential persistence and evolution of genetic lineages within a single location annually, with specific emphasis throughout spring migration. We constructed large-scale approximate maximum likelihood phylogenetic analyses on all eight gene segments with FastTree 2.1 using a general time reversible (GTR) substitution model  to identp < 0.05 .95% confidence intervals were calculated for the proportion of positive birds using the standard error of the proportion in each season throughout the study period Logistic regressions were performed to determine association of age and sex with IAV infection. Odds ratios were generated between the predictors, age and sex, and the outcome, IAV. Significance was set to Active trapping during the winter and spring months required extensive labor and effort and was most successful during times of high population size . Winter p = 0.008) of having active IAV infection as compared to mature birds and high numbers of immune-naive juvenile birds at the end of breeding season. Additionally, females had 2.43 times the odds of having active IAV infection as compared to males (p < 0.0005). Historical data from active IAV surveillance in wild mallards at this location were compared to the current study period. Seasonal prevalence trends during the study period were comparable to past surveillance data, except for the summers of 2018 and 2019 when the estimated prevalence appeared lower than what has historically been observed  tested positive for IAV by rRT-PCR while 130 had an internal positive control failure. Due to the high number of rRT-PCR internal control failures for samples during the study period and lack of historical rRT-PCR data, we estimated prevalence by virus isolation. The total estimated prevalence during the study period was 6.1% : 0.050–0.071). Within a given year, estimated prevalence peaked in late summer and early autumn and decreased throughout the winter and spring months. Estimated prevalence was highest in summer months  and lowest during spring months  recovered during the spring season uncovered evidence of genetic persistence across multiple seasons in at least one gene segment. Analysis suggests local evolution of some highly genetically conserved internal gene segments of IAV. A phylogeny of the PA segment shows a small monophyletic clade that includes IAVs detected in mallards at the study location in summer of 2017, spring, summer, and autumn of 2018, and spring of 2019. The estimated TMRCA of this clade was 2.48 years (95% highest posterior density (HPD): 2.18–2.76; Whole genomic sequences were obtained from 127 viral isolates and one original sample  and were used for phylogenetic analysis. Analysis revealed instances of local genetic lineages of several gene segments that persisted throughout the spring season. Clades that included viruses detected in the spring and viruses detected in another season at the study location who share an estimated time to most recent common ancestor (TMRCA) of <2.5 years and have a posterior probability of >0.95 were considered to show genetic persistence and evolution throughout the study period. All viruses (Understanding the ecology and epidemiology of IAV in wild waterfowl is pertinent for the protection of animal and public health . Active Though active trapping for surveillance is expensive and labor-intensive , we haveSpring surveillance conducted during the study period has provided insight on prevalence trends during times of year where historical data is limited. Historical surveillance found prevalence of IAV in the wild waterfowl population in North America peaks as high at 60% during late summer and early autumn as breeding season ends, and then falls to approximately 2% during south-bound autumn migration ,2,31. InDifferences in IAV infection rates among ages and sexes of waterfowl have often been detected during routine IAV surveillance. During our study period, juvenile ducks had increased odds of having active IAV infection compared to mature ducks. This is consistent with what has been described previously, where increased rates of IAV infection in juvenile wild waterfowl are believed to be due to a lack of protective immunity gained through prior exposures ,34,35. AAlthough use of active trapping for sample collection during spring migration required extensive effort and was most productive during peak migration, the 11 IAVs isolated during this time provide important genetic information about the virus during a previously under-sampled time. Phylogenetic analyses revealed genetic lineages of IAVs at the sample location that persist over time that include all 11 viruses recovered during spring. Previous surveillance has shown persistence of genetically similar IAVs at several migratory stopover sites throughout the Mississippi Flyway during each autumn migration . AdditioContinued active IAV surveillance and research in waterfowl is essential for the protection of animal and public health. It is imperative to wholly understand IAV in its natural reservoir in order to make science-based decisions and recommendations that reduce the risk to animals and humans. However, the consistent isolation of highly genetically similar IAVs over time at one location has implications for future IAV surveillance in waterfowl. Cross-sectional studies make up a large proportion of IAV surveillance methods in wild birds, where samples are collected at the same location at the same time year after year ,42,43. I