Much randomness occurs at the molecular level, which enables higher level functions to select lower level outcomes according to higher level needs. Intelligent causation occurs when organisms engage in deduction, enabling prediction and planning. This is possible because ion channels enable action potentials to propagate in axons. The further key feature is that such branching biological behavior acts down to cause the underlying physical interactions to also exhibit a contextual branching behavior.Biology differs fundamentally from the physics that underlies it. This paper The interactions proceed in a remorseless impersonal way as described by these laws, with no hint of function or purpose. They can exhibit branching behavior in phase changes, as discussed in section 2.2 below, but there is again no trace of purpose or choice in that behavior. Quantum physics has a branching behavior, but that again is nothing to do with choice or function: it is to do with irreducible randomness of quantum outcomes (section 2.3).When applied to large collections of particles, statistical physics emerges from these interactions and describes how ensembles of particles behave Penrose, . This giMany characterizations of life have been given. They include,All life exhibits function or purpose  rather than evolutionary ones , because if one goes the latter route it is not easily possible to discuss the issue of drift raised in Kimura  and discAfter discussing the options in depth, Mossio et al.  in effecAccording to the OA, a trait type T has a function if, and only if, it is submitted to organizational closure C in a differentiated self-maintaining system S. This definition implies the fulfillment of three different conditions. Accordingly, a trait T has a function if and only if:“C1. T contributes to the maintenance of the organization O of S;C2. T is produced and maintained under some constraints exerted by O;C3. S is organizationally differentiated.”If such a trait exists, its function will tend to lead to evolutionary success and hence to selection for this trait, which will explain its existence (up to the issue of drift).function to describe a process (an action) and a trait to be a property of a biological system at one level which may enable a function to be performed in relation to another level. This is consistent with the above. Second, the organizational closure mentioned is conditional on top-down constraint or realization occurring as well as bottom-up emergence in the modular hierarchy  acting on cell-surface receptors relay information through intracellular signaling pathways that can have a number of components. They usually begin with the activation of transducers that use amplifiers to generate internal messengers that either act locally or can diffuse throughout the cell. These messengers then engage sensors that are coupled to the effectors that are responsible for activating cellular responses. .. cell signaling is a dynamic process consisting of ON mechanisms during which information flows down the pathway, opposed by the OFF mechanisms that switch off the different steps of the signaling pathway” “This is an example of the kind of contextual branching that takes place in biology (section 3.1) and distinguishes it from physics.Note that this is not the same as saying that biological processes can be considered as computational processes, because it is not implying there is a computation or program of some kind determining the branching choices that are madeHowever, there is also a major random element at the molecular level introducing statistical variation in happenings at that level. It is then remarkable that these lower level processes produce reliable physiological outcomes at higher levels, such as regular heartbeats and breathing  logic, despite the statistical nature of molecular processes.How that happens is the concern of this paper.This paper focuses initially on the voltage gated ion channels that underlie neuronal functioning, although the same applies for example to the active sites of enzyme molecules which are complementary to the shape of the substrate. We first consider the difference between the logic of physics (section 2) and the logic of biology (section 3), then the biomolecules that make this difference possible (section 4), and finally how such molecules have come into being (section 5). The processes of deductive causation are discussed in section (6). The conclusion (section 7) clarifies first the three general kinds of causation that occur in biology, and second how contextual biological dynamics causes branching behavior at the underlying physical level. Overall, this is a view of how physics underlies integrative physiology , and so does not display any branching behavior. However, two aspects of physical laws do exhibit branching: phase changes (section 2.2) and quantum wave function collapse (section 2.3); but neither of these relate to function as characterized above, enabled by branching dynamics. How then does physics enable such branching to emerge? Through symmetry breaking (section 2.4), which is how quite different behavior can emerge from the underlying physics. In a biological context where higher level branching dynamics occurs, that leads to branching physical behavior at the electron level, as discussed in section 7.2.C consists of boundary and constraint conditions. The dynamical law uniquely determines later states of the relevant variable X from suitable initial conditions X(t1) . Thus there is a specific unique outcome: no branching takes place as in (9).on the possible values of the variables, with control parameters  Arnold, , and the Arnold, . The dyn Arnold, , p. 8  represents the solutions that necessarily follow from (3–5), proceeding purposelessly on the basis of the context C. These equations are time symmetric and imply energy conservation. Bifurcations can occur in some cases when a small change in a contextual parameter or initial data occurs, but the outcomes are still determined uniquely by the dynamical equations  for states q. They may also have stochastic elements due to random environmental effects, leading to stochastical dynamics represented by coupling deterministic equations of motion to “noise” that mimics the effect of many unknown variables. Then a stochastic term η(t) must be added to (5)  happen in physics when phase changes takes place, for example solid/liquid/gas transitions for a substance P and temperature T, and SP, V, LP, V and GP, V are the subsets of the  plane for solids, liquids, and gases respectively. At first glance this looks like it has the biological branching form (9). However, the regions SP, V, LP, V, and GP, V are fixed by the physics of the substance. Thus this is physical logic, determined purely by the laws of physics; no historical or evolutionary factor enters. Note for example the contrast with the homeostatic process governing core body temperature, where the setpoint of 98.4oF is not determined by physical laws; it was determined through evolutionary processes related to physiological optimization.Here the context is represented by the pressure t1)〉 (an “event”) is not deterministic. It has the logicThe Schrödinger evolution is Hamiltonian, but wave function collapse, as occurs when a measurement takes place, is a branching operation. However, such wave function collapse of a wave function |Ψ are not. Contextless physics is Hamiltonian, but physics in a biomolecular context is not (section 7.2). Hence in the end this is what enables the difference between physics and biology.The key physical effect enabling the existence of the biomolecules discussed here, with their functional properties arising out of complex molecular structures, is the existence of Again the underlying physics relevant to biological functioning is time symmetric, but biological effects such as cell signaling  is the truth value of arbitrary evaluative statements depending on X. It can arise from any combination of Boolean logical operations , perhaps combined with mathematical operations, while F1(Y) and F2(Z) are outcomes tending to further the function α. Thus they might be the homeostatic response“If blood sugar levels are too high, release insulin,” or the conscious “If the calculated range of the aircraft as presently fueled is <500 km, add more fuel” .Here Together with (8), the crucial point isIndependence of physics: The evaluative function T(X) and the outcome options F1(Y) and F2(Z) are not determined by the underlying physical laws, despite being enabled by them.conceived by human thought so as to result in planned outcomes  and it is the higher level dynamics emerging out of combinations of such operations that need description as in (10). In particular (9) is true for the cell signaling networks described by Berridge Berridge . Furthermore, there is no Hamiltonian or Lagrangian that leads to (10), and in the physics case there is no function α associated with the dynamics, as in (8). Physics per se is not teleonomic and does not show branching behavior related to function (section 2). That is the import of the plethora of existence and uniqueness theorems for fundamental physics , so (10) is really not different from (1), but as discussed in detail in Binder and Ellis , physicaModule 8), in the case of molecular machines ; probabilities may be the best description of the branching logic at play. In particular, chance plays a key role in evolutionary theory Glymour,  and molehomeostasis  is some operation that increases the value of the target variable X through changing the value of the control variable Y, and XDEC(Z) is some operation that decreases the value of X through changing the value of Z (which may or may not be the same as Y). The default is to leave the situation as is. Note that this is not a simple ON/OFF effect  and XMAX(C), with dynamics described by the equations of feedback control systems  and XMAX(C) are in general dependent on the context .where This is a particular case of (9). Note that this is just one part of the complex interacting processes generating their own constraints, immersed in many dimensional interactions. However, (11) undoubtedly occurs at both macro and micro levels as part of this larger set of interactions. Thus such processes control blood pressure and core body temperature at the macro level, and potassium and sodium levels in axons and glucose concentration in extracellular fluid at the micro level structural hierarchy of life . This is local branching within the module. However, the higher level regulator TF1 is a higher level variable). The function is production of Xn when and only when it is needed. Thus lower level branching circuits such as (12) can be used to build up higher level branching logic such as (13). This is how abstraction occurs in a modular hierarchy ]. From the system view, what matters is the emerging logic (13) where transcription factor TF1 controls conversion of metabolite X1 to Xn. Regulation of lower levels through higher level conditions is possible between any adjacent levels in the hierarchy. Through it, metabolic regulation can control gene expression in a top-down way , but occurring at a higher level  can result in the same phenotype . So how is it possible that they can be realized through the functioning of the underlying physical levels? We will now focus on the brain to give the discussion a specific biological context.All these branching operations emerge from the underlying physics, but are of a quite different nature than the deterministic function of physical laws Module 10). Spike trains proceed via dendrites to the neuron soma where a summation operation is performed. Spike trains then proceed from the cell body down axons to synapses, where another summation process occurs; signals are passed on to other neurons if the sum is above an activation threshold  plus statistical relations and diffusion equations. In a neuronal context, these lead to the e Scott, .voltage gated ion channels imbedded in axon and dendrite membranes  that emerges is enabled by particular proteins: namely The ion channels result in branching dynamics with the following logical structure:ion channels, specifically its tertiary and quaternary structure , where a branching logic.which is a specific case of the form (9). The function is to facilitate the propagation of action potentials in axons, and so enable functioning of the nervous system  which underlie the functioning of all cells , once epigenetic processes have selected which specific DNA segments will be read in what order.The question then is, how did that genetic information get written? As stated before, we do not enter here into the discussion of how life started: we assume here that somehow cells came into existence, allowing metabolism and the existence and reading of genetic information. In that context, how was it that the genotype for the specific proteins that actually occur } is a projection operation ΠS that gives the output ensemble {Ê(X):Here C. The basic physics case is Maxwell's Demon  actually occursreproduction with variation (20), which is where directed sexual selection and differential reproductive success enters, followed by elimination (18), which is where differential survival rates matter  may involve logical operations AND, OR, NOT, and their combinations, or mathematical equalities or inequalities, or both logical and mathematical relations in any combination. Thus one might have a conjunction of conditionswhere X, Y and Z may or may not be the same variables. These are of the same logical form as (9), but the key difference is that in that case, the context was the logic implicitly embodied in biological processes, whereas here the relations refer to explicit logical thought patterns. They may be realized at some moment in a brain, or written down on paper, or recorded in some other way (such as on a black board or a computer screen), but the patterns themselves are abstract relations with their own internal logic that is independent of whatever specific realization may occur.where Mathematical examples are the relationsalgebraic argumentation, and the partial differential equation resultwhich is proved by .Logical examples are the relationsand the combinatorial rules of Boolean logic involving AND, OR, NOT, and so on.Probabilistic logical arguments deal with likely outcomes on the basis of statistical evidence, for example:T1 means T1 is valid with probability P1, and so on. A key example is Bayes' Theorem  and P(B) are the probabilities of observing events A and B independent of each other, P(A|B) is the conditional probability of observing event A given that B is true, and P(B|A) is the conditional probability of observing event B given that A is true. This relation, which is of the form (26), underlies the learning processes of the predictive brain  becomesT2(Z) necessarily follows from T1(X), and we know T1(X) to be true either because we have seen it to be true (there is a dog in the room), or it is common knowledge (England is near France), or it is an established scientific fact (DNA is a key molecule underlying genetic inheritance), or at least it is a best explanation . For examplewhere E = mc2) relates deductive argumentation to real world possibilities. Similarly an extension of a simple case of (26) becomesIn other words, because we know special relativity is true, we know we can in principle make nuclear power stations and nuclear bombs. Thus reliable data  and (30), we can deductively determine that specific actions will inevitably or probably have specific outcomes:This leads to the basis of deductive choice of best actions:O*, and A(V) is some action chosen to alter O via a control variable V. The purpose is to produce an optimal outcome O on the basis of a representation of the situation founded on the best available evidence  to ensure that temperature T1, to optimize its design O by running computer aided design packages A(V) representing the aircraft design via variables V. We call When we carry out such deductive argumentation, the abstract logic of the argument Menzies,  that if C. Overall, the import of this section is thatIn practice  the argument is often probabilistic because we can never be absolutely certain of the outcome, due to uncertainty concerning the contextual effects Deductive causation: Logical deductions about scientific, engineering, and social issues can lead to action plans that are causally effective in terms of altering the world. In these cases it is explicit abstract logicrealized in brains and/or computers that guides and shapes what happens in highly productive ways  and hence may be said to be the essential cause of what happens.This is all possible because of the properties of brains as prediction machines that are also able to make choices between alternatives. The logical operations of deduction n Ellis, , while bn Ellis, . Each leC Ellis, .Deductive causation depends on being able to choose between options, which is where imagination comes in. There must be a process in the brain that generates the options that are taken into account when a choice between various options is made:Given this ensemble of choices, one can choose between them using selection criteria as above (section 6.3): a process of adaptive selection takes place whereby an option is chosen, whether it be physical  or mental . This generation of options to choose from takes place at the psychological level Byrne, , assisteThe deductive processes of section 6.1 are determined as valid by the brain through adaptive learning processes leading to logical understanding , provide the physical link between physics and biological causation by allowing branching dynamics at the molecular level, which can then underlie emergence of macro-scale branching dynamics and even deductive causation when incorporated in adaptive modular hierarchical networks. Both the networks and the proteins must have been shaped through processes of adaptive selection; however some of their aspects (that do not hinder their proper function) may be due to drift.excitatory or inhibitory receptors in neurons  and the branching functional logic of biological causation (9), enabled by biomolecules in general and proteins in particular (section 4.2).adaptive evolutionary processes first came into being. We do not know how that occurred.The progression of emergence is illustrated in Table per se to have causal powers. Emergence of this kind of causation is a major transition in evolution  occurs, enabling deductive logic Note that we are able to say this without having to make any specific comments on the relation between the brain and consciousness. What is indisputable is that deductive causation does indeed take place in the real world, as demonstrated by many examples (such as the existence of aircraft and computers), and is crucially different than the kind of causation characteristic of physics (section 2), although it is enabled by that kind of causation ; but physics in a biological context must do so, in order to allow the biological branching processes discussed here to emerge.There is however a key underlying question: it is clear that branching dynamics takes place at the biomolecular level, so how then does the underlying physics allow this branching to take place? The physics The solution Figure  is that One of the most remarkable aspects of an animal's behavior is the ability to modify that behavior by learning”. He then identifies how this happens at the molecular level as what he calls “A dialogue between genes and synapses” . Physical outcomes are determined by context, which break the symmetries of the underlying physical laws . Second, though signaling processes  originating from higher levels  that shape  what electron flows actually take place. This enables branching dynamics occurring in these signaling networks to cause branching outcomes at the electron level.This enables physiological processes such as those occurring in the heart  to the underlying physics level is provided by the fact that the branching biological logic at higher levels, including cellular , and mental  functioning, induces congruent branching dynamics at the underlying physical level by changing constraints at that level.Equation (2) has to be replaced bywhere the time-dependent nature of the physics constraints derives from the time-dependent biological context, and means that the physics evolution is no longer subject to the uniqueness theorems mentioned in sections 2.1, 3.1. This has to be so in order that the biology-physics relation be consistent.m that is constrained to move on a circular arc by a string of length L(t) that varies with time (this is the constraint C(t) governing possible motions of the bob) , which controls outcomes at both macro and micro levels in a way that cannot be predicted from a knowledge of the initial data (starting position X0 and speed v0) alone. The dynamics can be controlled by an experimental protocol for L(t) designed by a scientist (which is top down causation L(t) is controlled by a computer receiving signals from a detector of particles emitted by decay of a radioactive element (cf. section 2.3).A physics analogy is a pendulum made of a bob of mass Feldman,  see the GE provided the main idea and drafted the main text. JK contributed further ideas and helped develop the text.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.


Escherichia coli water isolates collected bimonthly from the Chobe River in Northern Botswana  in relation to water quality dynamics , land use, season, and AR in wildlife and humans within this system. No commercial agricultural or large medical facilities exist within this region. Here, we identify widespread AR in surface water, with land use and season significant predicators of AR levels. Mean AR was significantly higher in the wet season than the dry season (p = 0.003), and highest in the urban landscape  than the protected landscape . In-water E. coli concentrations were significantly positively associated with mean AR in the wet season (p < 0.001) but not in the dry season (p = 0.110), with TSS negatively associated with mean AR across seasons (p = 0.016 and p = 0.029), identifying temporal and spatial relationships between water quality variables and AR. Importantly, when human, water, and wildlife isolates were examined, similar AR profiles were identified (p < 0.001). Our results suggest that direct human inputs are sufficient for extensive dispersal of AR into the environment, with landscape features, season, and water quality variables influencing AR dynamics. Focused and expensive efforts to minimize pollution from agricultural sources, while important, may only provide incremental benefits to the management of AR across complex landscapes. Controlling direct human AR inputs into the environment remains a critical and pressing challenge.Little is known about the role of surface water in the propagation of antibiotic resistance (AR), or the relationship between AR and water quality declines. While healthcare and agricultural sectors are considered the main contributors to AR dissemination, few studies have been conducted in their absence. Using linear models and Bayesian kriging, we evaluate AR among  Antibiotic resistance (AR) is recognized as a critical global health emergency, with expected increases in deaths worldwide from diseases that were once treatable , fecal coliform  and TSS (mg/L) concentrations in the Chobe River following methodology previously described in Fox and Alexander , then aseptically transferred to RAPID' E.coli2 agar plates  and incubated at 37°C for 24 h prior to being enumerated. Up to six putative E. coli colonies were picked from each water sample and grown overnight in tryptic soy broth. DNA was extracted from all samples using the standard boiling method involving detergent/heat lysis followed by ethanol precipitation. DNA samples were screened by PCR to confirm presence of the E. coli malB locus, as previously described for water and wildlife samples  were collected bimonthly from transect sites , and clinical human fecal data (n = 113), previously reported , hippopotamus (Hippopotamus amphibius), African clawless otter (Aonyx capensis), and waterbuck (Kobus ellipsiprymnus)], as well as non-water-associated animals [African elephant (Loxodonta africana), banded mongoose (Mungos mungo), bushbuck (Tragelaphus scriptus), Cape buffalo (Syncerus caffer), Chacma baboon (Papio ursinus), domestic cattle (Bos primigenius), giraffe , greater kudu (Tragelaphus strepsciseros), guineafowl (Numida meleagris), impala (Aepyceros melampus), leopard (Panthera pardus), sable (Hippotragus niger), spotted hyena (Crocuta crocuta), vervet monkey (Chlorocebus pygerythrus), and warthog (Phacochoerus africanus) , or “Mixed Use” for transects in the growing, peri-urban area of Kazungula that incorporates land used for farming, wildlife corridors, and human settlement. All water-sampling observations were recorded between July 2011 and April 2012 at 15-day intervals. Based on rainfall during our study period, we classified the dry season as 13th July 2011 to 28th November 2011 and the wet season as 16th December 2011 to 25th April 2012. Statistical analyses were carried out using R 3.1.3  linear regression was used to estimate the effect of environmental predictors on AR isolates in a given sample (Table E. coli and TSS) to better approximate a normal distribution prior to fitting the regression models. FC was not included in the analysis because it strongly covaried with E. coli. We assessed for spatial autocorrelation (SAC) in model residuals using the Global Moran's I statistic with row-standardized weights, which was not significant for either the dry or wet season indicating SAC was not influential in the model residuals.To investigate the association between AR in le Table . SeparatAdditional linear regressions were run to determine whether similar trends held when considering the mean number of antibiotics to which an isolate was resistant (ranging from 0 to 10). A generalized linear model (GLM) was used to determine the effect of environmental predictors on the outcome of AR for each isolate. Once non-significant predictors were eliminated, a linear mixed model was used to account for the potential correlation between isolates within a given water sample through the assignment of random intercepts associated with each of the 414 water samples used in the regression.E. coli, FC and TSS) were interpolated and mapped using empirical Bayesian kriging (EBK) in ESRI ArcGIS v.10.2 . EBK makes predictions through automated subsetting and simulation using a restricted maximum likelihood approach to obtain an optimal empirical semivariogram and calculate weights such that the interpolation mean square error is minimized  to AR in E. coli isolates from fecal samples collected from humans (n = 113), non-water-associated animals (n = 382), and water-associated animals [n = 58, previously published data, see  was 1.39 (SD = 0.051) in the Park, 2.15 (SD = 0.098) in Town, and 1.76 (SD = 0.079) in the Mixed land use areas.To explore AR dynamics, we examined the distribution and frequency of resistance phenotypes in t = 2.938; p = 0.003). Isolates from the Park exhibited lower levels of AR than Mixed Use  and Town, which exhibited the highest levels of AR  distributed among water samples (n = 414). Similar to the results of the OLS, Analysis of Variance (ANOVA) of the most parsimonious model revealed significance of both land use type (F = 18.633) and season (F = 4.144) in the mean number of antibiotics to which an isolate exhibited resistance.When examining differences in mean AR, ordinary linear models identified both land use and season as being statistically significant predictors, with the wet season resulting in higher mean AR than the dry season  was compared to mean AR in E. coli isolates from fecal samples collected from non-water-associated animals (n = 382), water-associated animals [n = 58, previously published data, see . A similar rank order of resistance to antibiotics was observed across sample types and land use  indicating that the river is well-mixed and relatively homogeneous along this stretch, and between human isolates and water isolates . Antibiotic resistance in water-associated animals demonstrated the strongest positive association with water AR profiles in the Park , followed by Mixed  then Town . Non-water-associated wildlife AR values in the Town and Park land areas were highly correlated with that observed in water , while non-water-associated wildlife in the Mixed land type was not associated with water AR profiles .Mean AR in isolates from human fecal samples . Our results suggest that even in the absence of agricultural inputs, human source AR movement may be sufficient to create widespread AR dispersal across surface water even in protected areas upstream of urban centers.In this study, we identify widespread movement of AR in surface water across land use in the absence of commercial livestock production systems and large medical facilities. This suggests that direct human inputs together with other environmental factors may be sufficient for the extensive dispersal of AR observed in this ecosystem across protected and unprotected landscapes. Season and land use were both important predictors of mean AR in surface water with greater AR levels observed in the wet season than in the dry season . The area with the highest levels of AR was located along a segment of the river in Town associated with a peak density of low-income residential properties (264 residences/ha). In this area, an estimated 69% of households use traditional, self-built pit latrines, instead of flush toilets  had the highest level of AR in vitro with ARG transfer occurring between environmental E. coli isolates, Citrobacter freundii, and Klebsiella pneumonia  than in Town  carnivores , and water-associated species   and E. coli abundances in the top 20 cm of wet sand have been known to exceed the amount in the water column by a factor of 38  and AR levels in the wet season. Across all land uses and both seasons, Town exhibited the best water quality perimeters overall (lowest E. coli, FC and TSS concentrations; Figure Water quality variables and surface water AR are significantly influenced by season and land use. In our study system, water quality declines are known to be influenced by wildlife distribution in the dry season, and rainfall and river hydrology in the wet season (Fox and Alexander, on Table . IncreasE. coli. In addition, human effluent from boats entering the park may enter the system if the effluent is not disposed of properly or if existing holding tanks over flow, potentially providing a mechanism for additional AR inputs into the system. Numerous studies have shown that tourists can aid in the contraction and distribution of pathogens and AR microbes worldwide (reviewed in Okeke and Edelman, E. coli isolates were found in fecal samples from tourists visiting Mexico without ever having consumed prophylactic antibiotics prior to traveling (Murray et al., Classified as a “biodiversity hotspot,” the abundance of wildlife in the Chobe region attracts more than 20,000 tourists annually. The concentrated recreational use of the Chobe River in the Chobe National Park and high density of boats may contribute to the spread of AR into this protected area. This boat activity, combined with low water levels in the dry season, may result in the increased re-suspension of river sediment reservoirs of AR E. coli from surface water, humans and wildlife populations from the Chobe region were highly similar (Figure AR profiles of r Figure . These dr Figure  and highr Figure . This shr Figure , increasThe ingestion of surface water harboring AR microbes has been identified as a likely route of AR movement in naïve wildlife populations (Mariano et al., The dissemination of AR in the environment is a key component of the growing global health threat, however, surveillance of environmental antimicrobial hot spots is absent from action plans. Perennial surface water affords ecosystem level surveillance, allowing early detection and control of AR before spreading into clinically important pathogens. Indeed, water quality declines represent the penultimate expression of system degradation. It is clear from our study that surveillance focused on ARG in human and animal pathogens may have limited effectiveness. This surveillance approach simply alerts us to the ultimate failure in controlling AR among pathogens that threaten human health.Our study provides clear indication that humans have the potential to directly influence the movement and accumulation of AR across protected and unprotected landscapes in the absence of commercial livestock production and extensive health facilities. Focused and expensive efforts to minimize pollution from agricultural sources, while important, may only have incremental benefits to the management of antimicrobial resistance across complex landscapes. AR control efforts might benefit from sentinel surveillance systems that identify ARG movement and accumulation in the environment before invasion into pathogenic microbial communities. Controlling direct human AR input into the environment remains a critical challenge.CS conducted the laboratory work, interpreted the data, and drafted and revised the manuscript. ED conducted the statistical analyses, aided in data interpretation and wrote the method and results sections corresponding to the statistical analyses. JF conducted the spatial analysis and wrote the corresponding sections in the manuscript. AC interpreted the data and assisted in manuscript revision. KA developed the study concept and design, interpreted the data and conducted critical revision of the manuscript, and secured funding for the study.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.


Daily precipitation in California has been projected to become less frequent even as precipitation extremes intensify, leading to uncertainty in the overall response to climate warming. Precipitation extremes are historically associated with Atmospheric Rivers (ARs). Sixteen global climate models are evaluated for realism in modeled historical AR behavior and contribution of the resulting daily precipitation to annual total precipitation over Western North America. The five most realistic models display consistent changes in future AR behavior, constraining the spread of the full ensemble. They, moreover, project increasing year-to-year variability of total annual precipitation, particularly over California, where change in total annual precipitation is not projected with confidence. Focusing on three representative river basins along the West Coast, we show that, while the decrease in precipitation frequency is mostly due to non-AR events, the increase in heavy and extreme precipitation is almost entirely due to ARs. This research demonstrates that examining meteorological causes of precipitation regime change can lead to better and more nuanced understanding of climate projections. It highlights the critical role of future changes in ARs to Western water resources, especially over California. These “rivers in the sky” deliver intense pulses of water vapor onshore and largely drive the hydroclimate of this region5. This is particularly true in California, where against the backdrop of recent dryness and persistently mounting anomalous warmth, the notorious volatility of the state’s water resources3 has been on display. Only four wet years have occurred so far in the 21st century . The most recent period included five years of historic drought (2012–2016) with the first three years constituting an exceptionally dry period (water years 2012–2014) in the instrumental record spanning over 120 years6. Water year 2014, which tied for the driest year on record, was followed by 2015 – the year of unprecedented warmth and snow drought7 . These dry years were followed by the wettest water year on record for much of California11 – 2017 – a wet season marked by widespread flooding12 and AR activity unprecedented in seven decades of record5. This deluge was then followed by a dry water year 2018, especially in Southern California, parts of which received less than 1/3 of normal precipitation. Yet, even 2018 was punctuated with anomalously warm flood-producing storms13. Currently, a long series of alternating cold frontal and warm AR storms is resulting in a very wet water year 2019 in California featuring AR-driven flooding14.Coastal western North America (West Coast) receives much of its annual precipitation in the form of orographic heavy rain and snow produced by atmospheric rivers (ARs)3. Recent extreme hydroclimatic variation over the region was notably marked with unprecedented warmth. The heightened water resource volatility and associated impacts exemplify expectations from a warming climate in Mediterranean California, where precipitation will be delivered with progressively declining frequency but increasing intensity16. In climate-change projections, shrinking numbers of storms producing the region’s annual total precipitation result in increasing year-to-year variability of water resources17. Specifically, as the Hadley Cell widens19, California, along with the other Mediterranean climate regions, becomes more subtropical20. Frequency of precipitation declines mainly in fall and spring21, which results in decreased shoulder-season precipitation22, while heavy precipitation becomes preferentially more extreme in winter16 – the AR season in California5. The meteorological mechanisms of this regional precipitation regime change, however, have not been quantified and are currently inadequately understood.This volatility is associated with California’s Mediterranean climate, which generates annual precipitation in a narrow window of opportunity during the cool season5 and are historically associated with extreme precipitation and flooding23. They can also penetrate far inland along preferred topographic corridors26. Consequently, the majority of western floods are orchestrated by ARs land-falling upon the West Coast22 and so are flood insurance claims27. Thermodynamic enhancement of vertically integrated horizontal water vapor transport (IVT) in a warmer future28 will lead to wetter, wider and longer ARs globally29, exacerbating their impacts. For the West Coast, evidence of increasing IVT30 as well as of AR landfalling activity associated with observed long-term Pacific sea surface temperature (SST) warming has been reported with impacts on precipitation over the West, although this trend has not heretofore clearly emerged from the envelope of natural variability5. Increasing IVT and associated AR activity have been clearly projected over the West Coast for later in the century35. Although some of these model studies linked changes in AR activity to precipitation accumulations, particularly from heavy precipitation events assessed at the coarse GCM resolution35, the existing body of work on IVT and atmospheric rivers in climate model projections has not so far fully considered impacts on precipitation regimes, especially at the scales relevant for driving regional water resources. Since much of the precipitation produced by ARs is orographic, such information is essential at a fine enough scale to resolve spatial precipitation patterns influenced by the region’s complex topography, which importantly includes its Coastal Ranges as part of the greater North American Cordillera.Atmospheric rivers along the West Coast can produce in excess of 50% of total annual precipitation36, how these changes play into the precipitation regime change projected for this region17 by a set of 16 GCMs under the “business-as-usual” emissions scenario. Validating these models with respect to their realism in simulating historical regional AR activity and its contribution to Western precipitation proves an essential exercise and turns out to constrain projection uncertainty. Focusing on three flood-prone coastal river basins — Chehalis in Washington State, Russian in Northern California, and Santa Ana in Southern California — we examine the changing contribution of ARs to daily precipitation for different intensity categories. Regionalizing these detailed local results to clarify impacts on the precipitation regime over the entire West, we highlight the meteorological causes and nuances of the projected trends in the regional hydroclimate, which are particularly noteworthy over California.We here explore how landfalling ARs are projected to change along the West Coast and, using statistically downscaled bias-corrected precipitation5 (ARDT) was applied daily to an ensemble of 16 Global Climate Models 37 product and referred to here as the SIO R1 catalog5 and precipitation is from gridded station data38. GCM precipitation data were downscaled daily using a state-of-the-art statistical downscaling method36. The five most realistic GCMs, hereinafter referred to as “Real-5”, were identified and highlighted in Table An automated AR detection schemehistorical period) and that of the 21st (2051–2100: future period), although changes occurring by and in the current (2001–2050) half-century and throughout the 150 years of the CMIP5 record are also assessed. The years referred to here encompass the AR year along the West Coast (from July 1 to June 30). These are numbered consistently with Water Years, i.e. an AR landfalling in November of year n contributes to AR Year n + 1.Projected changes are mainly quantified as differences between the second half of the 20th century (1951–2000 water years: 5), however, has declined over the West Coast, significantly with respect to duration, during the historical period. This is likely due to natural decadal variability and may explain the lack of increasing trends in observed intense precipitation in the West39. The models’ internal variability, which largely determines trends during the historical period, is obviously not expected to be in phase with that observed and can result in trends of different sign in different model runs40. The observed negative trend is consistent with 6 of the 16 GCM historical realizations  activity peaking in the late summer/early fall, while California AR activity peaks in winterter Fig. , even asase Fig. . This isth %-ile of all wet days. Daily precipitation thus binned was further classified as AR- and non-AR-related in both the historical and future periods using the ARDT results. The projected changes of precipitation frequency in each bin are displayed in percent of historical frequencies in Fig. We now consider the contribution of atmospheric rivers to projected changes in precipitation frequency as a function of daily precipitation intensity. The Chehalis, Russian, and Santa Ana river basins were chosen to represent projected conditions in the northern, middle and southern reaches of our coastal domain, where we have classified daily precipitation into intensity bins ranging from dry days to drizzle, to low, medium and heavy intensity bins defined by deciles of the historical daily precipitation probability distribution, to extreme precipitation above the 99th and 90th %-iles) is mostly accounted for by strong declines in non-AR events, while ARs are projected to partially counteract this decrease, i.e. contribute more medium intensity precipitation. As far as heavy (between the 90th and 99th %-iles) and extreme (>99th %-ile) precipitation, frequency of days in these categories increases due to ARs. In fact, AR contributions increase in all precipitation bins, with the increases becoming greater at higher intensities, while non-AR precipitation contribution is projected to become more frequent in the drizzle and other low-intensity precipitation categories, and decreasing at higher intensities.In the Chehalis River basin Fig. , the pro5 - are projected to greatly increase the frequency of heavy and extreme precipitation events. Extreme precipitation in the Russian River basin becomes more than twice as likely in the second half of the 21st century compared to the second half of the 20th. This projected change is due essentially to ARs.Further south in the Russian (and Santa Ana) River basin, the contribution of ARs also progressively increases in all precipitation intensity categories up to extreme. In the Russian River basin Fig. , an overOver the Santa Ana River basin Fig.  in coast41.Figure 43. Generally speaking, the probability density function of daily precipitation is projected to favor heavy and extreme events due to reinforced AR activity in a warmer climate, while the frequency of medium-low-intensity precipitation dwindles as the wintertime circulation over the north-eastern Pacific generally becomes more anticyclonic45, the storm track migrates poleward46, and mid-latitude cyclones respond by generally diminishing in frequency46. Other results indicate a strengthening and SE-ward extension of the NE Pacific jet stream in the historical jet exit region west of California47. This is consistent with a stronger and more SE-ward extensive Aleutian Low, which, in combination with a more poleward-extensive Subtropical High, promotes stronger vapor transport into California16 bolstering AR activity there but displacing cold-frontal convection northward. While examination of these somewhat competing dynamical mechanisms in the Real-5 GCMs is beyond the scope of the current work, we reiterate that the largest decrease in precipitation frequency occurs in the shoulder seasons21, while the largest increase in extreme precipitation should occur at the peak of the AR season  as suggested by our bootstrap resampling test (see Section 5e). Given the leading role of ARs in driving the region’s annual precipitation, it is reasonable to consider ability to realistically simulate AR behavior as prerequisite to realistic projections of future regional hydroclimate. This, however, is only part of the story. In the Real-5 GCMs, the stronger increasing trends in AR-related precipitation combine with weaker decreasing trends in non-AR-related precipitation to produce an overall increase in total California precipitation as compared to the rest of the full ensemble of 16 GCMs examined here. In any case, notwithstanding the challenges stemming from a more volatile precipitation regime, California can take solace in the fact that it is not projected to dry as are the other Mediterranean climate regions around the world16.Our most robust conclusions with respect to total annual precipitation point at increasing year-to-year precipitation variability due to decreasing frequency of storms making up California’s annual total precipitation and greater contribution of AR-related extreme events. The Real-5 GCMs, however, also project increasing mean annual precipitation, in contrast to the full model ensemble, which is unclear on the sign of the future annual precipitation change. This result is seemingly in agreement with another recent study52 as they culled GCMs based on their abilities to simulate several general features of regional climates, while more targeted recent studies conclude that weighting all models equally is suboptimal55. Our results highlight the importance of focusing GCM validation on the mechanistic response that matters to the study at hand. Our results also highlight the outstanding need to understand why many models have trouble realistically simulating the observed climatology of AR behavior as well as AR contribution to total precipitation.These results highlight the importance of validating models with respect to their realism in simulating the salient regional weather and climate features that are central to the study at hand. In this study, the validation exercise yielded a significant reduction in the range of projected AR behavior, resulting in a more constrained precipitation regime projection. Past studies have not been successful in reducing projection uncertainty56 from rain on snow events58. The climate warming could thus operate via an additional mechanism of warmer and stronger ARs to enhance snowpack erosion from lower elevations progressively upwards. Enhanced runoff generated by boosted and warmer rainfall extremes in a flashier precipitation regime will likely also adversely impact groundwater recharge and storage59. The impacts of hazards such as floods27, debris flows60, and coastal water quality61 will be exacerbated by the changing precipitation regime alongside the more regionally extensive impacts of drought62.Understanding the driving mechanisms of hydroclimatic change leads to a more nuanced perspective on West Coast impacts of climate warming. The emerging trends will challenge water resource and risk management. The challenges faced by West Coast reservoir managers in balancing the mandates for water storage and flood control will escalate with an increasingly volatile precipitation regime. Exacerbating these mounting challenges, ARs tend to be warm storms, which in a warmer world will yield progressively higher rain/snow ratios, resulting in an additional boost to runoff in winter and added threat to an already dwindling snowpack63 and their important role in driving the water resources of the West as well as the numerous other societal impacts associated with AR landfalls or lack thereof. Beyond California and western North America, ARs can be expected to play increasingly important roles in all AR-prone regions of the world: mainly west- and equatorward-facing coasts of subtropical, mid- and high-latitude land masses from Greenland to Antarctica66. In the warming climate, thermodynamics will ensure the increasing role of ARs in the hydroclimates of all these regions with dynamical drivers of change possibly adding important regional detail to this evolving global picture.As ARs and their associated precipitation become more extreme, the need for improved predictions of AR activity on timescales ranging from days to seasons will grow in order to better manage the consequences of the changing climate. This is given limited current predictability of AR landfallsPhase 5 of Coupled Model Intercomparison Project (CMIP567), for which the daily data was available for computing the vertically integrated horizontal vapor transport (IVT) – the key variable for defining and detecting atmospheric rivers (ARs). For the projected period, we focus on the Representative Concentration Pathway 8.567 (RCP8.5) - essentially the “business-as-usual” scenario from CMIP5. The daily simulations of historical (1950–2005) and projected (2006–2100) specific humidity, zonal and meridional wind components at four standard pressure levels spanning 1000–500 millibars  were used to estimate IVT [1] and detect ARs landfalling along the west coast of North America (20–60°N).q is specific humidity, u is zonal wind, v is meridional wind, p is pressure, and g is acceleration due to gravity.The study considers 16 GCMs Table  from theTwelve of the sixteen GCMs did not consistently report atmospheric data extrapolated to 1000 mb during synoptic low-pressure conditions. In these models, specific humidity and wind vectors near the surface (10 m) were substituted for 1000 mb-level data. These GCMs are marked by an asterisk (*) in Table et al.5 (SIO-R1), which has been developed at the Scripps Institution of Oceanography (SIO) and applied to the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis37 (R1) and validated with independent observed precipitation data38. The SIO-R1 catalog is recognized for its relevance for West Coast precipitation studies and it compares favorably with other available AR catalogs69. Specifically, this 2.5° × 2.5° R1 catalog is in good agreement with those based on more finely-resolved reanalyses68 and provides a longer historical record for GCM validation (1948-present) than most other reanalysis products. The SIO-R1 catalog with adjustment for temporal resolution consistency with daily GCMs data is used as the historical “observational” data for GCM validation.The methodology chosen to detect landfalling ARs is that of Gershunov et al.5 to daily resolution, as follows:At each daily time step, spatial patterns of contiguous grid cells with IVT in excess of 250 kg/m/s and IWV in excess of 15 mm are identified.Those patterns that cross the coastline and are at least 1500 km long in their entirety (including inland penetration) are retained.The grid cell corresponding to the IVT maximum along the coast is considered the “central land-falling location”.The central land-falling location of a continuous AR event may not move more than 1000 km (8.9° north or south) from one day to the next. The number of grid cells depends on the GCM/R1 spatial resolution.Two AR events making landfall in the same coastal vicinity are considered distinct if they are separated by at least 24 hours.For consistency with GCM data, we adjusted the 6-hourly AR detection methodology of Gershunov Daily catalogs of AR landfalling activity were thus created for R1 and each of the 16 GCMs.38 is used to assess observed AR-related precipitation. Specifically, precipitation associated with ARs is defined as that occurring during AR landfalls and on the subsequent day in regions demarcated by the AR footprint, defined as in Gershunov et al.5. Localized Constructed Analog statistical downscaling36, trained on the Livneh et al.38 data, was used for the same purpose in the 16 GCMs historical simulations and projections. Importantly, LOCA downscaling includes bias correction70.Daily precipitation data on a 6 × 6 km gridet al.5 for calculation details). Second, the climatological contribution of ARs to total annual precipitation . The agreement between GCM- and observed AR climatology patterns was quantified using spatial correlation and root mean square error (RMSE) and summarized in Fig. The ability of GCMs to accurately represent the historical AR climatology was quantified by comparison against SIO-R1. We validate the GCMs with respect to two metrics. First, the AR landfalling climatology patterns along the West Coast (20–60°N) over 50 years (from July 1950 through June 2000) are assessed via the average frequency of AR days detected by month and landfalling latitude  teleconnections.The models with the most realistic climatologies with respect to AR day frequency and AR precipitation contribution to the annual total are those with the highest spatial correlation and lowest RMSE  from each GCM ensemble. These were the only integrations from each GCM that were entirely downscaled via LOCA downscaling. This, together with the fact that daily vertically-resolved humidity and wind data needed to compute IVT were made available only for the first run of most GCMs for download from the World Climate Research Programme73, dictated our choice of GCM realizations. For two of the GCMs, however – CanESM2 and MIROC5, the requisite data are available for five realizations each. To partially validate our choice of the Real-5 GCMs, we applied our AR detection methodology and AR catalog validation algorithm to 4 additional realizations  available for CanESM2  and MIROC5 (Other-11 category), each. We assessed the realism of these two GCMs, in all five available realizations each, with respect to their ability to simulate the AR landfalling climatology along the West Coast. The results from these additional realizations (not shown) are much more similar to the first realization from the respective model than they are to results from other GCMs. We therefore conclude that our choice of Real-5 GCMs is unlikely to have been affected by the choice of realization from each GCM’s ensemble.Since we are mindful of results highlighting uncertainty due to internal model variability74. This involved generating a distribution of standard deviations in the projected changes among 2000 random combinations  = 4,368 possible combinations) of 5 randomly selected models out of the full ensemble of 16 GCMs and comparing the Real-5 result to that bootstrapped distribution. We found that the standard deviation associated with the Real-5 AR landfall frequency changes corresponded to the 2nd percentile of the bootstrapped distribution, conservatively concluding that the reduction in uncertainty was statistically significant with 95% confidence.Real-5 results showing reduction in uncertainty in projected AR event frequency compared to projected full ensemble of all 16 GCMs Table  were testh percentile of the bootstrapped distribution of trends, prompting us to conclude that it was not significant in a two-tailed test with 95% confidence. Specifically, although significant as trends go, we cannot confidently rule out the possibility that it could have resulted from random sampling of GCMs.We applied a similar approach to testing whether the ensemble average trend in total precipitation projected by the Real-5 GCMs Fig.  were signd order) trend75 fitted to the full 150-year record of LOCA-downscaled water-year precipitation in each of the Real-5 models and at each 6 × 6 km grid cell. For this purpose, the R function “poly” was used.In order to assess changes in natural variability in projected downscaled precipitation Fig. , we remoSupplementary Materials


Pulldown assay shows that both of Full-length (FL)-AR and AR-V were able to interact with GABARAPL1, suggesting that GABARAPL1 may play its role through directly scaffolding AR. The further analysis from Oncomine database showed that negative correlation between GABARAPL1 expression and 5-years survival in CaP cases. Our findings have identified GABARAPL1 as critical regulator of FL-AR/AR-V, suggesting the potential benefit of targeting GABARAPL1 to treat AR-positive CaP that is resistant to next generation AR inhibitors.The next generation Androgen receptor (AR)-targeted therapies are now in widespread clinical use and prolong prostate cancer (CaP) patient survival. However, the therapies are not curative due to diverse range of resistance mechanisms. AR variants (AR-V), one major mechanism of resistance, has recently gained momentum. Here, we found that GABARAPL1 knockdown inhibits the growth of AR-positive LNCaP and CWR22rv1 CaP cells  Prostate cancer (CaP) is the most common cancer in men and the second leading cause of male cancer mortality in the United States . DespiteThe production of c-terminally truncated AR variant (AR-V) proteins lacking the AR ligand binding domain (LBD) has been proposed to be a major mechanism driving resistance to conventional and next-generation ADT . These lThe GABARAPL1 (known as GEC1 or ATG8L) was initially identified as an early estrogen-induced gene in quiescent guinea-pig endometrial glandular epithelial cells (GEC). Reduced GABARAPL1 expression was observed in various cancer cell lines . A studyin vitro and in vivo, decreases the transcriptional activity of FL-AR/AR-V and their nuclear translocation through directly scaffolding, suggesting that GABARAPL1 is a critical regulator of FL-AR/AR-V. Our findings indicate that GABARAPL1 may be a potential therapeutic target in FL-AR/AR-V-positive CRPC that is resistant to the next generation AR inhibitors.We previously demonstrated that GABARAPL1 mediates androgen-regulated autophagy in CaP cells . In currThe following primary antibodies (Ab) were used: rabbit polyclonal for AR, tubulin and GST , GABARAPL1 . Mouse monoclonal for AR , β-Actin , Myc . CellTiter 96® AQueous MTS Reagent Powder was obtained from Promega , charcoal-stripped fetal bovine serum (CS-FBS) was from Gibco , BD MartigelTM Matrix  and p3XFLAG-Myc-CMV™-24 Expression Vector .2. PC-3 cells were cultured in DMEM/F12 media supplemented with 10% FBS. DU145 cells were cultured in DMEM media supplemented with 10% FBS. For androgen-addition culture medium, CS-FBS was used as a substitute for FBS, and cells were cultured overnight prior to the addition of 10 nM Dihydrotestosterone (DHT).LNCaP, CWR22Rv1, PC-3, DU145, and HEK293T cells were purchased from the Chinese Academy of Cell Bank. LNCaP and CWR22Rv1 cells were cultured in RPMI1640 media supplemented with 10% FBS and incubated at 37°C in a humidified incubator containing 5% COGST-GABARAPL1 fusion plasmid was made by PCR amplification and then confirmed by sequencing. Forward and revere primers contain an EcoR I and Sal I site to facilitate cloning into the pGEX-5X vectors. The p3XFLAG-Myc-CMV™-24-AR-NTD or DBD truncated construct was produced by PCR amplification with forward primer containing EcoRI site and reverse primer containing Sal I site. The p3XFLAG-Myc-CMV™-24-AR-LBD construct was produced by PCR amplification with forward primer containing Not I site and reverse primer containing Sal I site. The primers were listed in Lentiviruses expressing pLKO.1-shRNA-GABARAPL1 (shGab1) or control vectors were generated by transfection into HEK293A cells as previously published . ProstatThe growth of prostate cancer cells infected with shRNA-GABARAPL1 or control shRNA were evaluated using MTS assay following the manufacturer's protocol.Cell lysates were generated and Western blotting with AR, GABARAPL1, GST, Myc, and βactin antibodies was carried out as described previously .Male BALB/c nude mice (4–6 weeks) were purchased from Beijing HFK Bioscience Company  and maintained under specific pathogen-free (SPF) condition. CWR22rv1 cells (106) with stably infection of shRNA-GABARAPL1 or control shRNA were suspended in 50 μL RPMI1640 and 50 μL Martigel were randomly injected s.c. into the left and right flank of mice (5 mice/group). Tumor volume (cubic millimeters) was measured weekly using a caliper, applying the formula [volume = 0.52 × (width)2 × (length)] for approximating the volume of a spheroid. When the experiment ends, mice were terminally narcotized and sacrificed. This study was performed in accordance with animal use protocols approved by the Committee for the Ethics of Animal Experiments, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center (SPHMC) (protocol number 2011-004). All animals were handled in accordance with the guidelines of the Committee for the Ethics of Animal Experiments, SPHMC.LNCaP or CWR22rv1 cells harboring shRNA-GABARAPL1 were transiently transfected with AR-responsive pMMTV-luc reporter plasmid, together with pRL-CMV plasmid to normalize the transfection efficiency. The relative luciferase activities were calculated by normalizing the firefly luciferase activity to Renilla luciferase activity.Total RNA was isolated using TRIZOL Reagent  and reverse transcripted into cDNA using RevertAid First Strand cDNA Synthesis Kit . Real-time PCR was performed using SYBR premix EX Taq  and analyzed with CFX96 Real-Time System (BIO-RAD). Real-time primer sequences were listed in Lysates containing 500 μg of protein from LNCaP or CWR22rv1 cells were incubated with glutathione sepharose 4B beads bound to GST or GST-GABARAPL1 fusion proteins. After washing with RIPA buffer, the beads were subjected to immunoblot analysis probed with anti-AR antibody. Lysates of HEK293T cells transiently transfected with p3XFLAG-Myc-CMV™-24-truncated AR plasmids were incubated with glutathione sepharose 4B beads bound to GST or GST-GABARAPL1 fusion proteins. After washing with RIPA lysis buffer, the beads were subjected to immunoblot analysis probed with Myc antibody.LNCaP cells were plated on glass cover-slips in 6-well plates in medium containing 10% CS-FBS overnight prior to the addition of 10 nM DHT for six additional hour. Cells were then fixed in 4% paraformaldehyde for 30 min and permeabilized by 0.1% Triton X-100 for 10 min. The cover slips were rinsed in PBS, blocked in 5% BSA for 2 h, and then incubated with mouse anti-AR antibody (1:500) and rabbit anti-tubulin . The cover slips were washed three times in PBS and incubated with CY3 anti-mouse secondary antibody (red) and Alexa Fluor488 (green) for 2 h. After three washes in PBS, cover-slips were mounted and cells visualized using a Zeiss LSM 510 fluorescence microscope with a ×60 objective.t-test. All statistical analyses were performed using SPSS 16.0 software program. A p < 0.05 was considered significant.Statistical significances between groups were determined by two-tailed student's in vitro and in vivo    and kapp(GABAAR) . GABARAP(GABAAR) . These f(GABAAR) .The AR is structurally composed of an amino-terminal domain (NTD), a DNA-binding domain (DBD), and a carboxy-terminal ligand-binding domain (LBD)  (protocol number 2011-004). All animals were handled in accordance with the guidelines of the Committee for the Ethics of Animal Experiments, SPHMC.BS conceived of the study and participated in its design. LZ carried out the molecular and cellular studies, participated in the animal experiments. SL performed the statistical analysis. XC revised the manuscripts and the statistical analysis. WZ drafted the manuscripts. All authors read and approved the final manuscripts.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.


AR‐V) 7 in circulating tumor cells (CTCs) is a predictor for resistance to anti‐AR‐targeted treatment, but not to taxane‐based chemotherapy in metastatic castration‐resistant prostate cancer (mCRPC). In this study, we investigated whether the presence of two constitutively active variants  and two other conditionally activated variants  vs full‐length androgen receptor (AR‐FL) measured in CTCs from patients with mCRPC were associated with outcome to therapy with the taxane cabazitaxel. Blood was collected at baseline and after two cycles of cabazitaxel from 118 mCRPC patients starting cabazitaxel in a prospective phase II trial. CellSearch‐enriched CTCs were enumerated and in parallel characterized for the presence of the AR‐Vs by reverse transcription quantitative polymerase chain reaction. Correlations with CTC and prostate‐specific antigen response to cabazitaxel as well as associations with overall survival (OS) were investigated. All AR‐Vs were frequently present and co‐expressed at frequencies of 31–48% at baseline and at 19–40% after two cycles of cabazitaxel. No specific directions of change in the measured variants were detected between the start of treatment and after two cycles of cabazitaxel. No associations between the presence of AR‐V3 and AR‐V7 and outcome to cabazitaxel were observed. While a reduction in CTCs to < 5 CTCs during treatment (CTC5‐response) was less often observed in patients with AR‐V9‐positive CTCs at baseline (P = 0.004), the CTC5‐adjusted detection of AR‐V1 after two cycles of cabazitaxel was an independent prognostic factor for OS [HR 2.4 ]. These novel findings are expected to contribute to more personalized treatment approaches in mCRPC patients.The androgen receptor splice variant ( ARandrogen receptorAR‐FLfull‐length androgen receptorAR‐V(1/3/7/9)androgen receptor splice variant (1/3/7/9)cDNAcomplementary deoxyribonucleic acidCqcycle threshold for quantificationCRPCcastration-resistance prostate cancerCTCcirculating tumor cellHRhazard ratioIQRinterquartile rangeLODlimit of detectionLOQlimit of quantificationmCRPCmetastatic castration‐resistant prostate cancermRNAmessenger ribonucleic acidOSoverall survivalPSAprostate‐specific antigenRRresponse rateRT–qPCRreverse transcription quantitative polymerase chain reactionSDstandard deviation1AR‐V) 7 in circulating tumor cells (CTCs) was recently shown to predict resistance to new‐generation anti‐AR‐targeted treatments , but not to taxane‐based chemotherapy in patients with metastatic castration‐resistant prostate cancer (mCRPC)  splice variant  are conditionally activated variants, have been described  and two, like AR‐FL, conditionally activated variants (AR‐V1 and AR‐V9) in CTCs at baseline and after two cycles of cabazitaxel. Finally, we investigated their possible clinical relevance.In previous reports, we demonstrated in cases included in the prospective CABARESC trial  [log‐rank (Mantel–Cox) P = 0.41] or incidence or severity of cabazitaxel‐induced diarrhea . The Erasmus MC and the local Institutional Review Boards at sites that collaborated in the study lent approval. All patients provided written informed consent.As reported before , Italy] and EDTA tubes before the first and after the second cycle of cabazitaxel. As described before  can be found in Table Cq) value: −3.38 to −3.82]. The limit of detection (LOD), defined as the lowest input of RNA isolated from VCaP cells that could be reliably identified as being qualitatively present in the sample, was calculated to be the RNA equivalent of 0.03 (for AR‐WT) to 0.5 (for AR‐V3) VCaP cells in the final reverse transcription qPCR (RT–qPCR). The limit of quantification (LOQ), defined as the lowest input of RNA isolated from VCaP cells that could be reproducibly quantified, was set at a CV of less than 25% and calculated to be equal to the RNA equivalent of 0.02 (for AR‐V1) to 12 (for AR‐V9) VCaP cells in the final RT–qPCR (Table P > 0.05) (Table AR status were exchanged between two laboratories (Rotterdam and Antwerp). For both the cell line and clinical samples, similar outcomes for AR and its splice variants were obtained  Table . For theed Table .2.4et al., Cq of the reference genes GUSB, HMBS, and HPRT1 < 26.5  and (b) an average Cq of the epithelial genes EPCAM and KRT19 < 26.0  were selected for the analyses. All Cq values were normalized to the average Cq value of the two epithelial genes to avoid confounding of the analyses by the number of CTCs present in a sample. As established before for AR‐V7  expression level of the target, the delta–delta Cq (ddCq) method was used.Evaluation of the RT–qPCR data was performed as described in full detail before ‐RR (30% and/or 50% decline from baseline to 12 weeks) and OS by the different AR‐Vs. No formal sample size calculations were performed since we were restricted to the inclusion of patients from the CABARESC trial who consented for additional blood sampling for CTCs. Therefore, our analyses should be considered exploratory.The primary endpoint of this study was the difference in CTC response rate (CTC‐RR), either defined as a conversion from ≥ 5 CTCs/7.5 mL to < 5 CTCs during treatment (CTC5) .Standard statistical tests were applied to address the objectives. Differences between patient groups were tested by chi‐square or Fisher’s exact test. Survival was calculated from the registration date in the CABARESB trial, that is, start with cabazitaxel, until the date of death or last contact in the case of baseline parameters and from the time of second blood sampling until date of death or last contact for the on‐study parameters. The survival analyses were conducted using univariate and multivariate (with backward stepwise selection) Cox regression models. For the post‐treatment OS analyses, we conducted landmark analyses to compensate for the 6‐week treatment by removing the patients which experienced an event prior to the 6‐week landmark and then resetting the time to zero at the landmark. All statistical tests were two‐sided. Bootstrapping and a stringent 33.1n = 124 patients that had been included in the prospective CABARESC trial  of the evaluable baseline samples. Of the 12 patients previously treated with abiraterone or enzalutamide, 6 (50%) expressed AR‐V7 at baseline compared with 19/40 patients (48%) who had not received prior abiraterone or enzalutamide  patients positive for one AR‐V co‐expressed at least one other AR‐V. After two cycles of cabazitaxel, 96% of the patients remained positive for AR‐FL, the variants AR‐V1, AR‐V3, AR‐V7, and AR‐V9 were detected in 30%, 19%, 40%, and 32% of the patients, respectively. Twenty‐six of the 36 patients positive for one AR‐V co‐expressed at least one other AR‐V  was observed between AR‐Vs and any of the baseline clinical parameters, including prior anti‐AR‐targeted treatment  to 0 during treatment, respectively). The overall CTC5‐RR in the 43 evaluable patients was 35%; the overall CTC0‐RR for these patients was 16%. No significant differences at our multiple correction adjusted 3.5AR‐Vs, at baseline and after two cabazitaxel cycles, respectively. Median OS for the n = 52 baseline patients was 8.3 months (95% CI: 7.1–13.0) and 7.7 (95% CI: 7.0–10.6) for the n = 47 patients at follow‐up.Lastly, we investigated the impact on OS of the presence of the AR‐V1 status and CTC count associated with decreased OS in the univariate model. Multivariate Cox regression analyses showed that at baseline only CTC count remained an independent poor prognostic factor for OS  and two more cell–context‐dependent variants (AR‐V1 and AR‐V9)  were with the equivalent of 0.11 cells comparable for the two assays, the LOQ was the equivalent of 0.02 cells for AR‐V1 and 12 cells for AR‐V9; see also Table AR‐V1 in CTCs was a poor prognostic factor for OS at baseline, but this might merely have been a reflection of CTC count. After the second cabazitaxel cycle, the presence of AR‐V1 kept its prognostic value. And at this time point, the presence of AR‐V1 was, together with high CTC count, an independent factor for worse OS in our multivariate analysis.Confirming our prior observations . For the conditionally active AR‐V1 and AR‐V9, these fractions were less with 45% and 9%, respectively. Given the negative impact of the presence of AR‐V7 on outcome to anti‐AR‐targeted compounds  is that it provides the possibility to obtain a CTC count in parallel to the CTC characterization. Importantly, our qPCR‐based characterization assay incorporates a correction for the number of CTCs present in a sample by use of the expression levels of epithelial genes to limit confounding of the analyses by the CTC count. Both the CTC count and CTC dynamics during treatment have robustly been shown to be strongly associated with outcome .In conclusion, we confirm that the presence of AR‐V7 assay has been patented (#WO2016133387 A1) by WO, AMS, and SS.The described AMS and WO involved in manuscript preparation, design of the work, data acquisition and data analysis; JK, CMB, NMV, and BdL acquired the data; LD, PH, AB, HM, and GJC included the patient; WMW, GWJ, AJN, and AHM designed the study; JWM supervised the project: SS supervised the project and designed the study. All co‐authors have read and approved of the manuscript and acknowledge their contributions.Table S1. Details Taqman gene expression assays.Table S2. Limit of detection and limit of quantification for the AR‐V assays. Experiments were performed as described before for AR‐V7 . In brief, the limit of detection (LOD), defined as the lowest input of RNA isolated from VCaP cells that could be reliably identified as being qualitatively present in the sample, was calculated to be the RNA equivalent of 0.03 (for AR‐WT) to 0.5 (for AR‐V3) VCaP cells in the final RT–qPCR. The limit of quantification (LOQ), defined as the lowest input of RNA isolated from VCaP cells that could be reproducibly quantified, was set at a CV of less than 25% and calculated to be equal to the RNA equivalent of 0.02 (for AR‐V1) to 12 (for AR‐V9) VCaP cells in the final RT–qPCR.Table S3. Intralaboratory validation for the AR‐V assays on paired clinical samples. Experiments were performed as described before for AR‐V7 .Table S4. Interlaboratory validation for the AR‐V assays. Experiments were performed as described before for AR‐V7 .Table S5. Patient characteristics of all 124 patients participating to the CTC study as shown in Fig. 1.Table S6. Extension of Table 1, now also including comparisons between the patient groups with CTCs negative or positive for the other AR‐Vs besides AR‐V7.Click here for additional data file.Data S1. In‐ and exclusion criteria for participation in CABARESC trial.Click here for additional data file. Click here for additional data file.


Moreover, angiotensin II (Ang II) -induced CF in rat cardiac fibroblasts could also be attenuated by Lnc RNF7 silence. Furthermore, miR-543 could simultaneously target lnc RNF7 and 3' UTR of TSP1. Lnc RNF7 silence suppressed, while miR-543 inhibition promoted TSP1 protein and TGFβ activation, as well as ECM markers expression. The effects of lnc RNF7 silence was significantly reversed by miR-543 inhibition. In conclusion, CF progression might be regulated by lnc RNF7/miR-543 axis via TSP1-mediated TGFβ activation.Cardiac fibrosis (CF) is regulated by multiple factors, including transforming growth factor β1 (TGFβ1) and non-coding RNAs. Thrombospondin 1 (TSP1) is a physiologic regulator of TGFβ activation. Here, we performed microarray analyses on mRNAs and lncRNAs differentially-expressed in the CF and normal rat hearts. KEGG signaling annotation and GO enrichment analyses were performed to validate the roles of extracellular matrix (ECM) and TSP1-enhanced TGFβ activation in CF. The co-expression network between differentially-expressed lncRNAs and ECM-related factors was constructed to identify candidate lncRNAs and miRNAs. We found that lncRNA Homo sapiens ring finger protein 7 (lnc RNF7) was significantly correlated with TSP1 and ECM. Lnc RNF7 silence could attenuate isoproterenol (ISP)-induced CF in rat heart  Cardiac fibrosis (CF) is characterized by increased activity of cardiac fibroblasts and the production of excessive extracellular matrix (ECM) in the myocardium . Fibrosiin vitro and in many homeostatic and pathologic conditions in vivo [CF is regulated by multiple factors, including transforming growth factor β1 (TGFβ1) , 4, angi in vivo –15. The Non-coding RNAs are involved in the regulation of CF. Several long noncoding RNAs (lncRNAs), which are longer than 200 nucleotides and not translated into proteins, are dysregulated in patients with CF. By acting as competing endogenous RNAs (ceRNAs), lncRNAs post-transcriptionally regulate microRNA (miRNA) levels by homologous base pairing, therefore modulating mRNA stability and translation . As for in vivo and in vitro. Furthermore, miRNAs that might simultaneously target lnc RNF7 and TSP1 (THBS1) were identified and the predicted bindings between miR-543 and RNF7, between miR-543 and THBS1, were confirmed. Finally, the effects of the combination of lnc RNF7 and miR-543 on TSP1 and TGFβ activation were evaluated. In summary, we provided a novel mechanism of RNF7/miR-543 axis modulating CF in rats via TSP1-induced TGFβ activation.In the present study, we performed microarray profiling analyses on differentially-expressed mRNAs and lncRNAs in CF and normal control rat hearts. Next, differentially-expressed mRNAs were applied to the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling annotation and Gene Ontology (GO) enrichment analyses to validate the essential roles of ECM and TSP1-enhanced TGFβ activation in CF. Co-expression network between differentially-expressed lncRNAs and ECM-related factors was constructed and lncRNA Homo sapiens ring finger protein 7 variant 5 and variant 6 (lnc RNF7), which was conservative in rat, mouse, and human, was selected and examined for its effects on CF The rat CF model was first constructed via ISP induction. After induction, Masson staining and Immunoblotting assays were performed to validate the model. As represented by Masson staining, the fibrotic area was dramatically increased by ISP induction , 1B. MorTo investigate the factors involved in the pathogenic processes, we performed microarray profiling analyses to identify differentially-expressed mRNAs and lncRNAs in CF and normal rat hearts. As represented by the hierarchical clustering diagram, a total of 55 mRNAs were significantly downregulated  and 239 mRNAs were significantly upregulated  . These dR >0.8, p <0.05)  and 46 were upregulated  . Since Cp <0.05) . Among tTo investigate the effects of lnc RNF7 on CF, CF rats were injected with Lv-sh-lnc RNF7 to achieve lnc RNF7 silence and examined by Masson staining, real-time PCR, and Immunoblotting. Lnc RNF7 silence was verified by real-time PCR . Masson To investigate the underlying mechanism of lnc RNF7 function on rat CF, we used ISP or Ang II to construct a fibrosis cell model on primary rat cardiac fibroblasts and then infected these cells with Lv-sh-lnc RNF7, as confirmed by real-time PCR . Next, Iin vitro and in many homeostatic and pathologic conditions in vivo [As we have mentioned, the matricellular protein TSP1 is a physiologic regulator of latent TGFβ activation  in vivo –15. HereLncRNAs act as endogenous sponges for other types of RNAs such as mRNAs and miRNAs –23. SincTo further verify the prediction and selection of miR-543, we achieved miR-543 inhibition in rat cardiac fibroblasts by transfection of miR-543 inhibitor, as confirmed by real-time PCR . In respAs above-described, miR-543 targets lnc RNF7 and THBS1 and may be involved in the process of lnc RNF7 affecting rat CF via TSP1. Next, rat cardiac fibroblasts were co-transfected with miR-543 inhibitor and Lv-sh-lnc RNF7 and examined for the dynamic effects of lnc RNF7 and miR-543 on ECM-related factors and TGFβ activation in rat cardiac fibroblasts. Lnc RNF7 silence significantly decreased the protein levels of TSP1, Collagen I, and CTGF, while miR-543 inhibition exerted an opposing effect on these proteins; the effect of lnc RNF7 silence could be significantly reversed by miR-543 inhibition , 7B. ConTo further confirm the above findings, the expression of miR-543 and THBS1 in tissue samples and their correlations with lnc RNF7 were detected. The expression of THBS1 was significantly higher in the CF group compared to the normal group ; howeverin vivo. Lnc RNF7 silence could also mitigate ISP or Ang II-induced rat cardiac fibroblasts activation. Furthermore, as predicted by online tools and confirmed by RIP and luciferase reporter assays, miR-543 could simultaneously target lnc RNF7 and TSP1 (THBS1). Lnc RNF7 silence suppressed, while miR-543 inhibition promoted TSP1 protein and TSP1-mediated TGFβ activation, as well as ECM marker expression. The effects of lnc RNF7 silence could be significantly reversed by miR-543 inhibition.Herein, microarray profiling analysis revealed that a total of 294 mRNAs and 107 lncRNAs were differentially-expressed in CF rat hearts. The KEGG signaling annotation and GO enrichment analyses further confirmed the essential roles of ECM and TSP1-enhanced TGFβ activation in CF pathogenesis. Co-expression network construction and investigation revealed that lnc RNF7 was significantly correlated with TSP1 and ECM. Lnc RNF7 silence could attenuate ISP-induced CF in the rat heart In the remodeling, infarcted and pressure-overloaded myocardium TSP1 may modulate fibrotic responses by activating TGFβ  and by iin vivo. As for the cellular functions, lnc RNF7 silence in rat cardiac fibroblasts significantly reduced ISP or Ang II-upregulated protein levels of Collagen I, TGFβ, and CTGF, the proliferation of rat cardiac fibroblasts, as well as TSP1 protein levels and TSP1-enhanced TGFβ1 activity in vitro. These data indicate that lnc RNF7 silence could improve ISP or Ang II-induced cardiac fibrosis in rats, in vivo and in vitro.Non-coding transcripts are functionally active as physiological and pathological regulatory molecules in heart disease, including CF. For example, lncRNA CHRF was reported to regulate cardiac hypertrophy together with miR-489 and Myd88 . CardiacConcerning the molecular mechanism, lncRNAs act as endogenous sponges for other types of RNAs such as mRNAs and miRNAs –23. It hMoreover, THBS1 mRNA expression was significantly upregulated in ISP-treated cardiac fibroblasts while downregulated in lnc RNF7-silenced cardiac fibroblasts upon ISP treatment. On the contrary, miR-543 expression was downregulated by ISP treatment while rescued by lnc RNF7 silence upon ISP treatment. In tissue samples, miR-543 was negatively correlated with lnc RNF7 and THBS1, respectively; lnc RNF7 and THBS1 were positively correlated with each other. We provided a solid experimental basis for a novel mechanism that the lnc RNF7/miR-543 axis regulates CF progression through ECM and TSP1-mediated TGFβ1 activation.Thirty adult male Sprague Dawley rats were bought from the Changsha SLAC Laboratory Animal Co. Ltd. Rats were subcutaneously injected with isoproterenol (ISP) (10 mg/kg) per day for 2 weeks, while rats were injected with an equal volume of saline solution as a control. The rats were sacrificed 30 days after injection.For the identification of the CF model, rats were anesthetized with 10% chloral hydrate (300 mg/kg). Sternotomy was performed and the hearts were rapidly removed. Tissue from the left ventricle (LV) was rapidly frozen in liquid nitrogen and stored at -80°C for protein extraction, RNA isolation or Masson staining. The animal study was approved by the Animal Care Committee, Xiangya Hospital, Central South University.9 TU/ml) lentivirus was injected into the left ventricular chamber. Fifteen minutes after injection, CF was induced.For the production of lentivirus, lnc RNF7 short hairpin RNA (sh-lnc RNF7) and negative control shRNA (sh-NC) were constructed into the lentiviral vector PHY-LV-KD5.1  and then packed into lentivirus particles. Lentivirus particles were concentrated by centrifuge, and then 100 μl/heart  then further analyzed with hierarchical clustering with average linkage. Finally, these differentially-expressed genes were applied for KEGG signaling annotation (https://www.genome.jp/kegg/), and the visualization was achieved by using Cytoscape [Heart tissues from CF rats or normal control rats were selected for Next Generation Sequencing by illumine Hiseq 2000 (USA). To select the differentially expressed genes (mRNAs and lncRNAs), we used threshold values of |Log2FC| > 0.56 and a Benjamini–Hochberg corrected ytoscape .2). Cells from earlier passages (2-4) were used. Cells were infected with lentivirus particles (Lv-sh-lnc RNF7 or Lv-sh-NC) in the presence of Polybrene . After 48 h, infection efficiency was examined by real-time PCR. For ISO or Ang II treatment, cardiac fibroblasts were treated with ISP (10 μM/L) or Ang II (1 μM/L) for 48 h. The cells were harvested for further experiments.Primary rat cardiac fibroblasts were obtained from Procell . Cells were cultured in DMEM supplemented with 10% FBS in cell culture incubator -supplemented PBS for 1 h and incubated overnight at 4°C or 4 h at 37°C with anti-Collagen I  and anti-CTGF . Later, the cells were labeled with appropriate fluorescein-labeled secondary antibody for 1 h in the dark at room temperature and followed by washing three times in PBS again (5 min each time). Meanwhile, the cell nuclei were counterstained with DAPI for 5 min, and then the samples were mounted on glass slides and examined on a fluorescence microscope.490 nm value was measured. The viability of the non-treatment cells (control) was defined as 100%, and the viability of cells from all other groups was calculated separately from that of the control group.A modified MTT assay was used to evaluate cell viability. 24 h after seeded into 96-well plates (5000 cells per well), cells treated with or without ISP or Ang II for 24, 48, and 72h, 20 μl MTT  was added, and the cells were incubated for an additional 4 h in a humidified incubator. After discarding the supernatant, 200 μl dimethyl sulfoxide was added to dissolve the formazan. ODCulture supernatants were harvested and centrifuged at 1000 rpm for 10 min at 4 °C. Cell-free conditioned media were stored at -80 °C until used. TGFβ activity was performed using the NRK colony formation assay in soft agar, as previously described . 0.5 ml °C with the appropriate antibodies and then washed and incubated for 1 h with horseradish peroxidase-conjugated goat anti-mouse or goat anti-rabbit IgG . The primary antibodies were as follows: anti-TGFβ1 , anti-TSP1 , anti-Collagen I , anti-CTGF , anti-fibronectin , anti-α-SMA , and anti-GAPDH . Protein levels in each lane were normalized to the levels of GAPDH.Protein samples were separated by SDS-PAGE and transferred to nitrocellulose filters . Membranes were incubated overnight at 4-ΔΔCT method was applied for data processing.Total RNA was extracted from cultured cells using Trizol reagent (Invitrogen). A Hairpin-it TM miRNAs qPCR kit  was used to detect mature miRNA expression. The RNU6B expression was used as an endogenous control. The expression of mRNA was measured using an SYBR Green qPCR assay . The expression of β-actin served as an endogenous control. The 2in vitro experiments was transcribed using T7 High YieldRNA Synthesis Kit  according to the manufacturer’s instructions. IgG, miR-543, RNF7, and THBS1 levels in the immunoprecipitates were measured by qRT-PCR.RNA immunoprecipitation was performed using Magna RIP RNA-Binding Protein Immunoprecipitation Kit  according to the manufacturer’s instructions. RNA for The fragment of lncRNA RNF7 or the 3′-untranslated region (3′-UTR) of THBS1 was amplified by PCR and cloned to the downstream of the Renilla psiCHECK2 vector , named wt-lnc RNF1 or wt-THBS1 3′-UTR. To generate the mutant reporter vectors, the study mutated the predicted miR-543 binding site in lncRNA RNF7 or THBS1 3′-UTR to remove the complementarity to miR-543 and named mut-lnc RNF1 or mut-THBS1 3′-UTR. Rat cardiac fibroblasts were co-transfected with the indicated vectors and miR-543 mimics/inhibitor, respectively. Luciferase activity was detected by the Dual-Luciferase Reporter Assay System . Renilla luciferase activity was normalized to firefly luciferase activity for each transfected well.P values of <0.05 were considered statistically significant.Data were exhibited as a mean ± SD of three independent experiments and processed using SPSS 17.0 statistical software . By using the Student’s test, we compared the differences between two groups. The differences among more than two groups were evaluated using the one-way ANOVA.


Addition of carboxylic acid dianions appears to be a potential alternative to the use of aluminium enolates for nucleophilic ring opening of epoxides. These conditions require the use of a sub-stoichiometric amount of amine (10% mol) for dianion generation and the previous activation of the epoxide with LiCl. Yields are good, with high regioselectivity, but the use of styrene oxide led, unexpectedly, to a mixture resulting from the attack on both the primary and secondary carbon atoms. Generally, a low diastereoselectivity is seen on attack at the primary center, however only one diastereoisomer was obtained from attack to the secondary carbon of the styrene oxide. N2 reactions has been studied and important limitations precisely due to its key mechanistic aspect have been detected  behave as ambident nucleophiles through either their α- or γ-carbon atoms, leading to single or predominant adducts when reacted with electrophiles under appropriate conditions  in THF (2 mL), according to the method already described.  The solu*,5R*)-3,5-Diphenyl-3H-dihydro-2-furanone(3R (5b major diastereoisomer): 1H-NMR: δ (ppm) = 7.34 – 7.26 , 5.62 , 3.88 , 2.78 , 2.66 ; 13C-NMR: δ (ppm) = 177.0 (C=O), 139.4 (C-Ar), 136.5 (C-Ar), 129.1, 128.9, 128.4, 127.7, 125.1 (8CH-Ar), 78.8 (CH-O), 45.2 (CHC=O), 39.3 (CH2); MS: m/z (%) = 238 , 194 , 193 , 117 , 116 , 103 , 77 ; HRMS: m/z calcd. for C16H14O2 [M+]: 238.0994 ; found: 238.0992.*,5S*)-3,5-Diphenyl-3H-dihydro-2-furanone: 1H-NMR: δ (ppm) = 7.36 – 7.26 , 5.45 , 3.97 , 3.00 , 2.33 ; 13C-NMR: δ (ppm) = 176.4 (C=O), 138.7 (C-Ar), 136.1 (C-Ar), 129.1, 128.9, 128.7, 128.4, 128.1, 125.6 (8CH-Ar), 79.1 (CH-O), 47.6 (CHC=O), 40.5 (CH2); MS: m/z (%) = 238 , 194 , 193 , 179 (46), 165 (12), 117 , 116 , 115 (63), 104 , 103 , 91 , 77 ; HRMS: m/z calcd. for C16H14O2 [M+]: 238.0994 ; found: 238.0991.*,4S*)-3,4-Diphenyl-3H-dihydro-2-furanone(3R (8): 1H-NMR: δ (ppm) = 7.08 , 6.80 , 4.68 , 4.21 , 3.96 ; 13C-NMR: δ (ppm) = 176.2 (C=O), 136.7 (C-Ar), 133.0 (C-Ar), 129.4 (CH-Ar), 128.9 (CH-Ar), 128.8 (CH-Ar), 128.4 (CH-Ar), 128.3 (CH-Ar), 128.2 (CH-Ar), 127.9 (CH-Ar), 127.4 (CH-Ar), 127.3 (CH-Ar), 125.6 (CH-Ar), 71.3 (OCH2), 52.0 (CHC=O), 47.7 (CHCH2O); MS: m/z (%) = 238 , 194 , 193 , 178 , 77 ; HRMS:m/z calcd. for C16H14O2 [M+]: 238.0994 ; found: 238.1008.