Macrophages (MØs) are a key cell type of both the innate and the adaptive immune response and can tailor their response to prevailing conditions. To sense the host’s status, MØs employ two classes of receptors: Toll-like receptors (TLRs), which are sensors for pathogen-derived material, and Fcγ receptors (FcγRs) that are detectors of the adaptive immune response. How MØs integrate the input from these various sensors is not understood and is the focus of active study. Here, we review the recent literature on the molecular mechanisms of TLR and FcgR crosstalk and synergy, and discuss the implications of these findings. This overview suggests a multilayered mechanism of receptor synergy that allows the MØ to fine-tune its response to prevailing conditions and provides ideas for future investigation. Macrophages (MØs) are among the key effector cells of host defense and have the ability to tailor their response to the host’s immune status. To accomplish this feat, MØs use two classes of immune receptors: Toll-like receptors (TLRs), which are sensors that drive the innate immune response, and Fcγ receptors (FcγRs) that are detectors of the adaptive immune response. How MØs integrate the inputs from these two distinct signaling pathways is not fully understood and is the focus of active investigation.in vitro studies directly engaging single receptor types with saturating doses of strong ligands . However, during infection, it is likely that conditions will lead to less than 100% receptor engagement, meaning that physiologically MØs will be responding to submaximal receptor signaling. Moreover, the presence of natural antibodies able to opsonize incoming pathogens3 suggests that most pathogens will engage both TLRand FcγR, even in naïve hosts. As the host response is thus driven by the integration of the TLR and FcγR inputs, our comprehension of the mechanisms of TLR–FcγR crosstalk is critical to our understanding of host defense.Much of our knowledge of the signaling pathways activated by TLR and FcγR has been determined fromIn broad terms, in the issue of receptor signal integration, crosstalk between the two receptor-driven signaling pathways could regulate messenger RNA (mRNA) transcription or mRNA translation into protein or both. In the case of transcriptional control, integration of TLR and FcγR signals would regulate the levels and activity of transcription factors that control the production of cytokine mRNA. Alternatively, signal integration could occur post-transcriptionally, whereby signals from either of the two pathways would modulate the efficiency of mRNA translation. Because of the need for tight regulation of MØ activation and cytokine production, it would not be surprising if both pre- and post-transcriptional mechanisms were at work.Although the mechanisms of TLR–FcγR signal integration are incompletely understood, recent advances provide clues as to how MØs might integrate these signals for the tight regulation of cytokine production. With respect to FcγR, we will focus on crosstalk involving the activating (as opposed to inhibitory) FcγR.4. In contrast, signaling by activating FcγR occurs via src family kinase-mediated phosphorylation of immunoreceptor tyrosine-based activation motifs (ITAMs), which results in the recruitment of the tyrosine kinase Syk for triggering of downstream pathways, including activation of phospholipase C, generation of an intracellular calcium flux, and NADPH activation6.Although it is well accepted that there is crosstalk between the TLR and FcγR signaling pathways, the underlying cellular and molecular mechanisms are far from clear. A primary signaling pathway for most TLR is through MyD88, TIRAP, and TRAF6, which ultimately leads to the activation of transcription factors such as nuclear factor kappa B (NF-κB) and activator protein 1 (AP-1)7, suggesting multiple nodes for TLR regulation of FcγR signaling.Even though there are shared signaling molecules between the two pathways, how these two distinct signaling tracks communicate is not completely clear. Interestingly, a recent unbiased analysis of the TLR2/4-induced phosphoproteome in MØs revealed multiple targets within the FcγR-mediated phagocytosis pathway that are phosphorylated in response to TLR2/4 ligation and demonstrated an ability of TLR2/4 signaling to augment FcR-mediated phagocytosis8. Here, the authors were able to co-immunoprecipitate TLR4 and Syk8, suggesting either a direct interaction between the two molecules or the presence of a signaling complex. Subsequent publications have demonstrated a similar TLR–Syk interaction in MØs  in, or immediately adjacent to, the TLR transmembrane domain  or filipin profoundly changes  the MØ interleukin-6 (IL-6) cytokine response to TLR2–FcγR co-engagement . Thus, although there is intriguing evidence that TLR–FcγR crosstalk may occur at a receptor proximal stage of signaling, perhaps through assembly of signaling platforms within lipid rafts, the molecular basis for that interaction and the mechanism of TLR–FcγR–Syk communication require much additional investigation.Although TLR–FcγR crosstalk may occur at the level of shared downstream signaling molecules such as MAPK and components of the NF-κB pathway, it is also possible that the interaction occurs more proximal to the receptor at the level of Syk activation. Though the role of Syk in FcγR signaling is well known, Syk’s involvement downstream of TLR engagement is less well appreciated. The first evidence linking Syk and TLR was from Arndt and colleagues, who reported the LPS–TLR4-driven activation of Syk in neutrophils19. Subsequent studies, recently summarized by Suthers and Sarantopoulos20, have identified several potential points of communication between TLR signaling pathways and BCR ITAM signaling. For example, one aspect of TLR–BCR crosstalk involves Syk-driven upregulation of the TLR signaling molecule TRAF621. However, this mechanism requires induction of new gene expression, a process that would take minutes to hours and thus cannot account for receptor synergy that occurs within seconds of receptor engagement. However, a recent publication by Schweighoffer and colleagues22 revealed a faster-acting potential molecular mechanism. Here, it appears that in B cells TLR4 engagement drives Syk activationthrough the BCR (in the absence of a BCR ligand) via a mechanism that doesnot involve MyD88. Thus, ligand binding to the TLR is influencing the neighboring unligated BCR via an unknown mechanism to drive Syk activation. It is possible that a similar mechanism underlies TLR–FcγR synergy.FcγRs are not the only ITAM-bearing receptors that can interact with TLR, as both the B-cell receptor (BCR) on B lymphocytes (discussed below) and IgE FcR on basophils/mast cells . SCIMP is a member of the family of transmembrane adaptor proteins (TRAPs) and is known to be involved in Src family kinase signaling in B cellsTaking all of this into consideration, we would argue that TLR ligation drives two distinct downstream signaling pathways. One is the canonical MyD88-dependent pathway that leads to the activation of NF-κB. The second is a Src–Syk-dependent pathway downstream of an ITAM. In this model, the strength of the MyD88 versus ITAM inputs would tailor the cell’s ultimate response. “Strength of signal” can vary in response to changes in parameters such as the percentage of bound receptors , the degree of pathogen opsonization, the avidity/affinity of the TLR for the pathogen-associated molecular pattern (PAMP) or FcγR for the opsonizing IgG subtype or the size of the particle or both. For example, conditions of partial TLR engagement, such as would occur at the early stages of infection with few bacteria avidly binding to TLR but only low circulating IgG levels, could lead to robust activation of the MyD88-dependent arm of TLR signaling but weak activation of Syk-dependent signaling. This would lead to partial/incomplete MØ activation. In contrast, MØ interaction with the same pathogen opsonized with many IgG molecules (at the peak of infection when IgG levels are high) would promote TLR–FcγR synergy to drive robust MØ activation. Here, bacterial PAMPs such as LPS would engage the TLR to drive MyD88-dependent signaling, while IgG binding to FcγR would directly drive ITAM-dependent Syk signaling. This model of TLR–FcγR synergy in MØs is derived from information gained from many experiments using disparate cell types and receptor agonists and provides a framework for future studies.Regulation of mRNA translation/stability is a general mechanism to fine-tune cellular responses such as cytokine production, and microRNAs (miRNAs) are one tool that the cell can use to this end. Although to the best of our knowledge there are no published studies on the role of miRNA in the crosstalk between MØ TLR and FcγR, recent reports on the interplay between miRNA and TLR/FcγR signaling raise some interesting possibilities for future investigation. Here, we will discuss those findings in a general framework of MØ IL-6 and tumor necrosis factor-alpha (TNF-α) production, as these cytokines are two key MØ-produced inflammatory mediators.25.miRNAs are short noncoding RNAs of about 21 nucleotides in length and can regulate protein production in multiple ways. In addition to binding mRNA molecules and targeting them for degradation via the RNA-induced silencing complex (RISC), miRNAs can interfere with mRNA translation as well as nascent polypeptide stabilityEscherichia coli LPS, numerous miRNAs are downregulated26, including those that inhibit production of the inflammatory cytokines TNF-α (miR-24) and IL-6 (miR-24 and Let-7c)28. Consequently, robust TLR signaling would lead toboth the upregulation of cytokine mRNAs via a MyD88/NF-κB-dependent pathwayand the downregulation of miRNAs that would block the translation of cytokine mRNA into protein. One of the questions raised by this observation is how these miRNAs might block cytokine protein production. Here, recent work from the Nares lab suggests an interesting possibility29. Their report shows that, in human monocytic cells, ectopic overexpression of a miR-24 mimetic results in a significant decrease in both TNF-α and IL-6 cytokine production butno downregulation of either mRNA species. This indicates that the blockade in inflammatory cytokine production is most likely occurring via a miRNA-based inhibition of mRNA translation. In regard to the role of miRNA in TLR signaling, it is currently unclear whether the signals that elicit TLR-driven miRNA downregulation are MyD88 or Syk dependent. However, the finding that FcγR signaling can also lead to the downregulation of the same miRNA species (see below) suggests that it may be the Syk-dependent pathway.When MØs are activated with strong TLR ligands such as purified30. In addition, it has been shown in tumor cells that Src kinase signaling can drive the downregulation of Let-7a 31, suggesting that FcγR ITAM Src signaling in MØs might also downregulate levels of Let-7 miRNAs. This would mean that MØ interaction with even low numbers of antibody-opsonized bacteria could result in robust inflammatory cytokine production. Here, TLR signaling would be responsible for driving the transcription of cytokine mRNA molecules, while FcγR ITAM signaling (driven by FcγR recognition of the opsonizing antibodies) would downregulate miR-24/Let-7 miRNA to allow efficient cytokine mRNA translation. Hence, TLR–FcγR synergy may be occurring at a post-transcriptional stage in the cytokine response and be mediated by miRNA. However, this intriguing possibility requires additional investigation.Under physiological conditions of TLR engagement, such as MØ interaction with an individual bacterium, it is unclear whether the levels of induction of cytokine mRNA and downregulation of miRNA would be similar. It is possible that some conditions of TLR engagement lead to robust cytokine mRNA induction but little or no downregulation of the miRNA that would block the translation of these mRNA molecules, resulting in little or no cytokine production. Under these conditions, TLR–FcγR synergy could become very important to the development of a robust cytokine response. In human monocyte-derived MØs, FcγR signaling has been shown to downregulate the level of miR-24, which is known to block the translation of IL-6 and TNF-α mRNAThe ability of MØs to integrate and respond to multiple signals from their ever-changing environment is critical for MØ function and the health of the host. Although it is clear that TLR and FcγR are two key MØ receptors that allow the cell to discern the immune state of the host, the molecular mechanisms that underlie this feat are incompletely understood. At the pre-transcriptional step, there are multiple nodes of interaction between the TLR–MyD88 signaling pathway and FcγR–ITAM–Syk signaling pathways that represent potential points of crosstalk. At the post-transcriptional step, TLR and FcγR regulation of inhibitory miRNA are also likely involved. This sophisticated regulatory web would allow precise tailoring of the MØ response to environmental cues. Dendritic cells also express TLR and FcR, and it is known that crosstalk occurs in these cells as well. However, the underlying molecular mechanisms and impact of crosstalk on immune function appear to be different (recently reviewed in


Hyperuricemia is associated with metabolic syndrome (MetS), but the association is often confounded by the shared background of obesity. We sought to explore the modifying effects of obesity on the association between uric acid (UA), MetS components, and nonalcoholic fatty liver disease (NAFLD).2) and elevated UA was assessed using an interaction term in a logistic regression analysis. The presence of additive interaction was assessed based on the relative excess risk due to the interaction (RERI) and the attributable proportion due to the interaction (AP).We conducted a cross-sectional study in a Chinese population of 10,069 participants aged ≥20 years. Multiplicative interaction between obesity  and hypertriglyceridemia (RERI of 1.38 [0.57–2.20] for men and 1.38 [0.08–2.67] for women). In addition, 42% and 36% of the increased odds of NAFLD for men and women, respectively, can be explained by an interaction between obesity and elevated UA (AP of 0.42 [95% CI (0.30–0.54)] for men and 0.36 [0.17–0.55] for women). Similarly, the interaction accounted for 27% and 26% of the increased risk of hypertriglyceridemia for men and women (AP of 0.27 [0.14–0.41] for men and 0.26 [0.06–0.47] for women).In this population, obesity and elevated UA synergistically interacted to increase the risk of NAFLD and hypertriglyceridemia. The prevalence of MetS dramatically increased worldwide, ranging from 10% to 84% depending on the age, gender, and ethnicity/race of the population. The studies performed in China indicate it is experiencing an epidemic of MetS, with a high prevalence of 33.9% among general Chinese population. The pathogenesis of MetS remains unclear, although the possible involvement of insulin resistance has been implicated as a linking factor. Determination of the risk factors of MetS is essential for identification and intervention of MetS.Metabolic syndrome (MetS) is a cluster of interrelated cardiometabolic risk factors Increasing evidence indicates that elevated UA levels, even within the normal range, are related to MetS. Cardiovascular disease is a major comorbidity of hyperuricemia; however, whether elevated UA levels are independently associated with cardiovascular disease risk is still controversial. It is likely that the relative importance of UA, per se, as a cardiovascular disease risk factor may involve an interaction with the other metabolic disorders.Serum levels of uric acid (UA), the end-product of purine metabolism, are maintained by a balance between the production and excretion of UA. With the rapid socio-economic growth, the prevalence of obesity has increased rapidly among Chinese adults during the past decades, from 2.9% and 5.0% to 11.4% and 10.1% among men and women, respectively. A shared background of obesity usually confounds the relationship observed between UA and MetS. Several studies have investigated the role of obesity in the association of UA and MetS, but the results were inconsistent.–12 The possible interaction between obesity and UA with regard to certain components of MetS has not been well studied. In addition, nonalcoholic fatty liver disease (NAFLD), which is characterized by excessive fat accumulation in the liver without excessive alcohol consumption, has been regarded as the hepatic manifestation of MetS. NAFLD has frequently been associated with many metabolic abnormalities, such as insulin resistance, obesity, type 2 diabetes mellitus, and hyperlipidemia, which are the main features of MetS. It has been reported that approximately 90% of the NAFLD patients present more than 1 component of MetS, and about 33% of the patients meet the criteria of MetS. Recent studies have demonstrated a close relationship between serum UA and NAFLD.–18 However, few studies have explored whether obesity, as a major risk factor for NAFLD, can modify the association between UA and NAFLD.Obesity is a global public health problem and is associated with MetS and hyperuricemia.In the present study, we sought to explore the modifying effect of obesity on the association between elevated UA, MetS components, and NAFLD in a Chinese population.22.1–21 Questionnaires were used to collect data regarding demographic characteristics, including age, sex, medical history, and drinking status. We excluded individuals who were taking medicines for diabetes, hypertension, dyslipidemia, or hyperuricemia; who had missing information on their age, sex, body mass index (BMI), fasting plasma glucose (FPG), blood pressure (BP), triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), UA or liver ultrasonography data; and who had chronic kidney disease (defined as an estimated glomerular filtration rate <60 mL/min per 1.73 m2). Finally, 10,069 participants, 6378 males and 3691 females, were included in the present study. The fact that male participants accounted for 63.3% of the entire cohort was consistent with the sex percentage at WISCO. Our study was approved by the Institutional Review Board of the WISCO General Hospital, and the informed consent requirement was exempted because of our retrospective estimation of a de-identified database.The participants in the present study were from the Wuhan Iron and Steel Company (WISCO), consisting of Chinese employee aged ≥20 years. The data derived from a health examination of all of the employees and retirees at the WISCO General Hospital in 2009.2.2Physical examination was performed and anthropometric parameters were obtained including weight, height, and BP. Weight was measured with the participants wearing light clothing and height was measured without shoes. BMI was calculated as weight (in kilograms)/height square (in meters). BP was measured twice every 5 minutes on the right arm after 5 minutes of rest with the participants seated. The mean of the 2 measures was taken for data analysis. Ln [TG (mg/dL) × FPG (mg/dL)/2].Blood samples were collected from the antecubital vein of all participants after fasting at least 10 hours overnight and biochemically analyzed for FPG, UA, creatinine, alanine aminotransferase, TG, and HDL-C levels. All the measurements were determined using an auto-analyzer . The triglycerides and glucose index (TyG) were calculated using the published formula:2.3Given that the UA levels differed substantially by gender, sex-specific quartiles of UA levels were established . We defined an elevated UA level as the highest UA quartile (≥6.4 mg/dL in men and ≥4.9 mg/dL in women), and a normal UA level as the lower 3 quartiles. obese status is defined as a BMI ≥25 kg/m2 and nonobese status is a BMI <25 kg/m2.According to the World Health Organization (WHO) expert consulate for Asians, (1) abdominal obesity: waist circumference ≥90/80 cm for men/women); (2) elevated BP: systolic/diastolic BP ≥130/85 mm Hg; (3) elevated FPG: FPG ≥5.6 mmol/L (100 mg/dL); (4) elevated TG: TG ≥1.7 mmol/L (150 mg/dL); and (5) low HDL-C: HDL-C ≤1.0/1.3 mmol/L (40/50 mg/dL) for men/women. Of these components, we assessed the associations between elevated BP, elevated FPG, elevated TG, and low HDL-C with UA levels in obese and nonobese subjects. As waist measurement was not available for the study population, a BMI of ≥25 kg/m2 for all patients was substituted as an index of obesity which was taken as a component of MetS. Previous studies have confirmed the validation of this definition.Components of MetS are defined based on the following definitions of the Adult Treatment Panel-III (ATP III):As NAFLD was regarded as a hepatic manifestation of metabolic syndrome, we also evaluated the relationship between NAFLD with UA levels in obese and nonobese subjects. NAFLD is diagnosed based on the presence of fatty liver, as assessed using ultrasonography, ruling out excessive alcohol intake , hepatic virus infection, or the use of steatogenic or hepatotoxicity medications.According to the guideline of the Asia-Pacific Working Party,2.4U test or chi-square test. The age-adjusted association between UA and MetS components was determined using logistic regression analysis. The multiplicative interaction between BMI and UA was assessed using a cross-product interaction term included in the logistic regression model.All statistical analyses were conducted using SPSS . Men and women were both stratified into 4 mutually exclusive groups based on BMI and UA levels, namely, nonobese with normal UA or elevated UA level and obese with normal UA or elevated UA level. The basic characteristics of the participants were presented as a median and interquartile range for continuous variables and as a percentage for categorical variables. Differences between any 2 groups were assessed using the Mann–Whitney  Therefore, we used a method proposed by Rothman to test for additive interaction between BMI and UA levels. To quantify the amount of additive interaction, we calculated 2 measures using the approach of Andersson et al, namely, the relative excess risk due to the interaction (RERI) and the attributable proportion due to the interaction (AP). An RERI and AP of zero indicate the absence of an additive interaction.The additive interaction may better reflect a biological interaction.P-value <0.05 was considered to be significant.A 2-tailed 33.12, respectively. Clinical characteristics of the population stratified by BMI and UA levels are presented in Table P < 0.01), compared with those with normal UA level for both the nonobese and obese groups. The difference in level of FPG between the elevated and normal UA group was statistically significant for nonobese men, but not for obese men. For women, subjects with high levels of UA were older than those with normal UA, and had significantly higher BMI, systolic BP, FPG, TG, TG/HDL-C, TyG, and ALT, and lower HDL-C , regardless of their obesity status.The average age and BMI of this study were 49.5 ± 14.7 years and 23.6 ± 3.1 kg/mP < 0.0001; NAFLD 8.9 ± 0.7, non-NAFLD 8.3 ± 0.6, P < 0.0001) and women , as compared with their counterparts. For men, the OR and 95% CI of TyG for MetS and NAFLD was 12.5 (10.8–14.6) and 5.3 (4.8–5.9), respectively. For women, the corresponding figures were 16.2 (12.6–20.7) and 6.5 (5.6–7.5), respectively.The TyG index was significantly elevated in MetS and NAFLD participants for men , but not an elevated FPG. For women, the prevalence of all evaluated components of MetS and NAFLD were higher among those with an elevated UA level compared to those with a normal UA level in both BMI groups, albeit the prevalence of a low HDL-C was marginally different between individuals with an elevated or normal UA level in the obese group (P = 0.07).The prevalence of MetS components in UA groups according to sex and BMI is shown in Table P-value of the interaction term was used to evaluate the multiplicative interaction. Compared with nonobese individuals with a normal UA level, obese subjects with an elevated UA level had a significantly increased risk of MetS components and NAFLD in both men and women. In particular, ORs for NAFLD (15.49 [95% CI 12.57–19.09] for men and 16.25 [12.39–21.31] for women) and hypertriglyceridemia (5.05 [4.27–5.96] for men and 5.25 [4.07–6.77] for women) were significantly greater than other MetS components in obese subjects with elevated UA levels. However, there was no evidence of a multiplicative interaction between obesity and UA for increasing the risk of these assessed metabolic disorders.The age-adjusted ORs for MetS components and NAFLD are shown in Table 3.3 Therefore, we further conducted an additive interaction analysis to detect whether the combined influence of obesity and UA on the risk of MetS components exceeded the sum of their individual impact. The results are presented in Table An additive interaction better reflects a biological interaction.4In the present study, we found evidence that being obese modified the association of UA with NAFLD and hypertriglyceridemia. The magnitude of the effect of modification was large; obese men and women with elevated UA had 15.49 and 16.25 times the risk of having NAFLD, and 5.05 and 5.25 times the risk for hypertriglyceridemia, which were much higher than the increased risks among obese individuals with normal UA levels or nonobese persons with elevated UA levels. These findings suggest a synergistic effect of obesity and an elevated UA level for increasing the risk of NAFLD and hypertriglyceridemia. No statistically significant interaction was observed with regard to other MetS components. A recent study from Norway conducted by Norvik et al also demonstrated that BMI could modify the association of UA and some MetS components. They observed that elevated UA predicted the onset of elevated BP and elevated FPG in subjects with a BMI >25 kg/m2, but not in those with a BMI < 25 kg/m2, but this was not the case for other MetS components. In contrast, another study in a general population from Korea reported that UA was associated with an increased risk of MetS in a subgroup with a BMI <25 kg/m2, but not in a subgroup with a BMI >25 kg/m2. This discrepancy may be due to differences in study design and the study population. In addition, a study of a prospective cohort of American Indians with a high prevalence of obesity suggests that UA levels do not predict MetS when taking fat-free mass into account, indicating that the body composition that is altered in the context of obesity has an important role in the relationship between UA and MetS. This finding contradicts the evidence from a study conducted in Korea that showed that the relationship between UA and MetS remained significant after adjusting for multiple confounders, including body composition. The difference in the obesity status of the 2 cohorts is likely to be a major factor responsible for the discordant results. Taken together, these findings suggest that obesity may be an essential confounder in the relation between UA and metabolic disorders, although some may disagree.Obesity is a common condition related to hyperuricemia and MetS components. However, the modifying effect of obesity on the association between UA with MetS and NAFLD has not been well studied. Several lines of evidence suggest that obesity could modify the relationship between UA and MetS, but there is still a plenty of controversy. A large-sample study of European participants suggested that elevated BMI is an essential confounding factor to the observational relationship analyses of UA and related conditions. Furthermore, we observed in this study that the TyG index, including lipid parameter and proposed as a surrogate marker for insulin resistance,,31 was increased in both MetS and NAFLD patients and was associated with a higher risk for the diseases, which indicating the close relationship between lipid metabolism, insulin resistance, and NAFLD. In accordance, the association between TyG and fatty liver has also been demonstrated in previous studies.,33 The combined effect of obesity and elevated UA levels on the risk of NAFLD and hypertriglyceridemia may share a common pathogenesis. Obesity could result in elevated UA levels through a reduction in urinary urate excretion and overproduction of UA. Notably, visceral adiposity is more tightly linked to overproduction of UA than subcutaneous fat. In turn, UA becomes a strong pro-oxidant in the presence of obesity. Hyperuricemia induces alternations in oxidative homeostasis in adipocytes, including a decrease in nitric oxide bioavailability and an increase in lipid oxidation, which may play an important role in the subsequent development of hepatic steatosis and hypertriglyceridemia. In addition, recent studies indicate that fructose is associated with hepatic steatosis. In addition to directly generating triglycerides, fructose also stimulates triglycerides synthesis via increasing UA production. Hence, fructose may be a crucial link between obesity, elevated UA, NAFLD, and hypertriglyceridemia.Our findings show that obesity and elevated UA levels have a pronounced synergistic effect on the development of NAFLD and hypertriglyceridemia. NAFLD is widely regarded as a liver manifestation of MetS and is also closely related to central obesity. Hypertriglyceridemia is a major metabolic abnormality associated with the presence of NAFLD.,38–40 have shown that elevated UA levels are associated with MetS components, especially hypertriglyceridemia, elevated BP, and low HDL-C; however, the relationship varied based on the study. This variation, to some extent, was due to the differences in the selected population and specific demographics of the participants. Nonetheless, the relationship between UA and hypertriglyceridemia was strong and stable. The mechanism that links UA to TG has not been elucidated. One experimental study demonstrated that lowering serum UA correlated directly with a reduction of TG levels and hypertriglyceridemia was completely blocked by the decrease of UA levels with allopurinol. Although the role of UA in TG metabolism remains unknown, UA might be implicated in either the reduction of clearance or the overproduction of TGs. Our data provide another possible explanation. Obesity, as a common context of hyperuricemia and MetS and a major risk factor for the development of abnormal TG, may interact with UA and confer an additional risk of hypertriglyceridemia. The biological mechanism underlying this problem needs to be further investigated.The additive interaction between obesity and UA on hypertriglyceridemia provides a possible explanation for the finding that high levels of UA have a stronger association with hypertriglyceridemia than other MetS components. Previous studies Several important limitations of the present study should be noted. First, due to a cross-sectional design, we could not explore the causality of the associations. Second, we identified NAFLD using only ultrasonography, which is a reasonably accurate method for diagnosing NAFLD in patients with only modest amounts of liver fat (>30% liver fat infiltration). Third, the participants in the present study were from a selected population  with a preponderance of men; therefore, we should be cautious in extrapolating the findings to the general Chinese population or to other ethnicities. Nonetheless, the large cohort of the present investigation ensures sufficient power in determining the significance of the interaction between UA and BMI. Fourth, this study did not evaluate the dietary profile of the studied population due to the lack of diet information, which might have influenced UA concentrations. However, high UA levels are mainly due to abnormal metabolism which is revealed that high UA levels are associated with various metabolic risk factors, such as insulin resistance, obesity, type 2 diabetes, hypertension, and dyslipidemia, rather than dietary profile in adults.In a conclusion, obesity and elevated UA levels have a pronounced synergistic effect on the development of NAFLD and hypertriglyceridemia. The clinical significance of our finding is substantial because a large proportion of cases of NAFLD (42% for men and 36% for women) and hypertriglyceridemia (27% for men and 26% for women) can be explained based on an interaction between obesity and elevated levels of UA. This evidence indicates that the burden of NAFLD and hypertriglyceridemia in obese patients may be markedly reduced after improving serum UA levels.We thank all the participants for their contribution and participation.


The Wnt signaling pathway is intricately involved in many aspects of development and is the root cause of an increasing number of diseases. For example, colorectal cancer is the second leading cause of death in the industrialized world and aberration of Wnt signaling within the colonic stem cell is the cause of more than 90% of these cancers. Despite our advances in successfully targeting other pathways, such as Human Epidermal Growth Factor Receptor 2 (HER2), there are no clinically relevant therapies available for Wnt-related diseases. Here, we investigated where research activities are focused with respect to Wnt signaling modulators by searching the United States Patent and Trade Office (USPTO) for patents and patent applications related to Wnt modulators and compared this to clinical trials focusing on Wnt modulation. We found that while the transition of intellectual property surrounding the Wnt ligand-receptor interface to clinical trials is robust, this is not true for specific inhibitors of β-catenin, which is constitutively active in many cancers. Considering the ubiquitous use of the synthetic T-cell Factor/Lymphoid Enhancer Factor (TCF/Lef) reporter system and its success in identifying novel modulators in vitro, we speculate that this model of drug discovery does not capture the complexity of in vivo Wnt signaling that may be required if we are to successfully target the Wnt pathway in the clinic. Notwithstanding, increasingly more complex models are being developed, which may not be high throughput, but more pragmatic in our pursuit to control Wnt signaling. The Wnt signaling pathway is one of the oldest signaling pathways in multicellular eukaryotes and is involved in many aspects of development and in the maintenance of stem cells ,2,3. DerWe focused on the USPTO Patents and Patent Application (P/PA) databases searching abstracts for “Wnt AND cancer” or “-catenin AND cancer” and combined these results with the general search term “Wnt inhibitor”. This produced a combined total of 674 P/PA . These wSearch for “Wnt” or “Catenin” or “DKK” resulted in 84 search results. Studies that incorporated non-specific inhibitors  or looking for biomarkers were removed, resulting in 34 Clinical Trials referencing the inhibition or activation of Wnt signaling.We focused primarily on screens that have been completed since 2005, searching the PubMed database for “Wnt[Title/Abstract] AND Inhibitor*[Title/Abstract] AND screen*[Title/Abstract] AND ‘2005’[Date-Publication]: ‘3000’[Date-Publication]” which returned 242 articles as of 22 June 2016. We scanned the abstracts of these 242 articles to identify papers that specifically screened for inhibitors of Wnt signaling, removing screens that identified inhibitors of Wnt signaling via other routes  among others.The objective of the search was to obtain a broad overview on where researchers were focusing their efforts. While we attempted to be as comprehensive as possible, our search criteria had some limitations. First, our search and filtering criteria missed several well-known inhibitors. For example, a search of the P/PA databases for LGK974, a potent inhibitor of the Wnt secretion protein Porcupine  identifiAfter filtering we ended up with 151 P/PA that were ultimately combined into 103 unique P/PA with high confidence as Wnt signaling modulators . The majThe Wnt ligands were the next most popular extracellular subject (19%) followed by the Frizzled and LRP5/6 receptors . The intracellular protein β-catenin tied with DKK as a popular choice for modulating the Wnt pathway, accounting for 24% of the P/PA .Perhaps one of the most surprising finds was the effort put towards identifying enhancers, which was not part of our original search efforts. A subsequent search for Wnt enhancers or activators turned up a few more patents and patent applications (not shown), but the subjects of these patents were already covered under our original search and therefore do not offer any further information on the research space. Of the 103 P/PA, 74% were inhibitors of Wnt signaling, while 26% were Wnt signaling enhancers (27/103) . Twenty-The intracellular Wnt agonists concentrated around β-catenin stability by either inhibiting GSK3 (2/5); affecting the β-catenin-E-cadherin interaction; inhibition of its phosphorylation; or overexpression β-catenin itself. The vast majority of agonists appeared to be targeting bone growth, such as possible treatments for osteoporosis, although a couple of these P/PA targeted hair loss.The ligand-receptor interface was also very popular for antagonizing Wnt signaling, most likely because of its accessibility by biomolecules. Of the 76 inhibitors identified in our search, 45 (59%) were targeted to inhibiting activation of the receptor. Methods used to antagonize receptor activation were fairly equally divided among antibodies to Wnt, Frizzled or LRP6; or the overexpression of soluble frizzled fragments or Wnt ligand peptides . While tSome other interesting findings included the ratio of P/PA focused on chemicals compared to ones focusing on biomolecules . Based oWe identified 34 clinical trials registered with ClinicalTrials.gov that involve treatments to either inhibit or activate Wnt signaling . Of thesOf the 19 trials focusing on inhibition of the Wnt pathway, none are using DKK proteins , even thWe identified 76 screens that were specifically searching for modifiers of Wnt signaling . These wThe involvement of Wnt signaling in so many biological processes and diseases makes this pathway a difficult yet obvious target for therapeutic intervention and there are numerous reviews on this subject ,94,95,96Our first observation is that most of the intellectual property is focused around the ligand-receptor interaction. This was somewhat unexpected considering that mutations in the APC and β-catenin genes account for greater than 90% of all colorectal cancers, which is the second leading cause of death in the developed world . The reaPerhaps the most interesting finding was that inhibition of Wnt signaling via β-catenin was the focus of the majority of P/PA (22/103), but there are only five clinical trials in this area, one of which is a non-specific inhibitor and four using the same compound, PRI-724, to inhibit the β-catenin-CBP interaction ,14. The β-catenin transcriptional activity is central to the majority of the assays used to identify compounds that alter Wnt signaling . The useWe hypothesize that there are two distinct, yet overlapping mechanisms at play. The first is rather obvious, that a synthetic reporter cannot simply capture the complexity of the Wnt signaling pathway. The second mechanism revolves around the Goldilocks hypothesis and the “just-right” amount of Wnt signaling required to induce or maintain tumorigenesis ,106,107.Other screening approaches are being proposed to tackle the first problem of complexity. For example, antagonists of specific Wnt-FZD combinations might be more selective for certain cancers compared to general β-catenin antagonists. Indeed, the FZD receptor has many advantages that could be used for high-throughput in vitro screening . AnotherOur comparison of the patenting and clinical field with respect to modulators of Wnt signaling has yielded some insight. First, inhibiting or enhancing Wnt signaling at the ligand-receptor level appears to have more traction. Considering the strong potential for clinical efficacy, we predict that the number and combination of ligands and receptors available for intellectual property protection will make this an active and attractive area for further investigation and investment. Second, and perhaps more importantly, the intellectual protection of real estate covering inhibitors of β-catenin is extensive, but this has not proven to be clinically viable, yet. We argue that the current methods used to identify modulators of Wnt signaling for clinical use do not capture the complexity of Wnt signaling that is intricately involved in many biological processes. More effective second generation screening mechanisms are required to capture this complexity and indeed some are already in use ,82,83,86


In vivo, HTLV-1 is mostly detected in CD4+ T-cells, and to a lesser extent in CD8+ T cells and dendritic cells. There is a strong correlation between HTLV-1 proviral load (PVL) and clinical status of infected individuals. Thus, reducing PVL could be part of a strategy to prevent or treat HTLV-1-associated diseases among carriers. Treatment of ATLL patients using conventional chemotherapy has very limited benefit. Some chronic and acute ATLL patients are, however, efficiently treated with a combination of interferon α and zidovudine (IFN-α/AZT), to which arsenic trioxide is added in some cases. On the other hand, no efficient treatment for TSP/HAM patients has been described yet. It is therefore crucial to develop therapies that could either prevent the occurrence of HTLV-1-associated diseases or at least block the evolution of the disease in the early stages. In vivo, reverse transcriptase (RT) activity is low in infected cells, which is correlated with a clonal mode of viral replication. This renders infected cells resistant to nucleoside RT inhibitors such as AZT. However, histone deacetylase inhibitors (HDACi) associated to AZT efficiently induces viral expression and prevent de novo cellular infection. In asymptomatic STLV-1 infected non-human primates, HDACi/AZT combination allows a strong decrease in the PVL. Unfortunately, rebound in the PVL occurs when the treatment is stopped, highlighting the need for better antiviral compounds. Here, we review previously used strategies targeting HTLV-1 replication. We also tested a series of HIV-1 RT inhibitors in an in vitro anti-HTLV-1 screen, and report that bis-POM-PMEA (adefovir dipivoxil) and bis-POC-PMPA (tenofovir disoproxil) are much more efficient compared to AZT to decrease HTLV-1 cell-to-cell transmission in vitro. Our results suggest that revisiting already established antiviral drugs is an interesting approach to discover new anti-HTLV-1 drugs.Five to ten million individuals are infected by Human T-cell Leukemia Virus type 1 (HTLV-1). HTLV-1 is transmitted through prolonged breast-feeding, by sexual contacts and by transmission of infected T lymphocytes through blood transfusion. One to ten percent of infected carriers will develop a severe HTLV-1-associated disease: Adult-T-cell leukemia/lymphoma (ATLL), or a neurological disorder named Tropical Spastic Paraparesis/HTLV-1 Associated Myelopathy (TSP/HAM).  Interestingly, the differences in the number of HTLV-1-infected cells among distinct cell types are not due to the ability of HTLV-1 to infect these cells, but rather to its ability to persist and eventually transform the infected cells. Indeed, in vitro, human monocyte-derived dendritic cells are more susceptible to HTLV-1 infection than autologous CD4+ T-cells  . This re T-cells . In anim T-cells . Neverths clones , and thes clones . A bioma+ T lymphoproliferation of very poor prognosis with a mean survival time of 6 months in the acute form, or a progressive neurological disorder named Tropical Spastic Paraparesis/HTLV-1-Associated Myelopathy (TSP/HAM) (in vitro (ex vivo or in vivo.One to ten percent of infected carriers develop a severe HTLV-1-associated disease during their life: Adult-T-cell leukemia/lymphoma (ATLL), a CD4TSP/HAM) . About 1TSP/HAM) . In the TSP/HAM) . BecauseTSP/HAM) . In JapaTSP/HAM) . In EuroTSP/HAM) . It is wTSP/HAM) . Howeverin vitro . While oin vitro , treatmein vitro . Differein vitro ] have shin vitro , IFN-β   and that this was linked to some properties of the viral core and to post-entry processes that are still unclear . In contft part) . Of noteeriments .Figure 1, right part)  . Becauseht part) . Interesht part)  and has ht part) , althougNowadays, 25 antiretroviral agents classified in six classes have been approved to treat HIV infections . The antNucleotide reverse transcriptase inhibitors were the first class of compounds to be used in HIV therapy, by the approval of zidovudine (AZT) in 1987 , initialin vitro  were first shown to block HIV replication in T cells  and subsin vitro . Other nin vitro , or carbin vitro , were thin vitro . However in vivo , and in To circumvent this drawback, acyclic nucleoside phosphonates (ANPs) have revolutionized the antiviral drug field . ANPs arUndesirable emergence of viral resistance, toxicity, drug-related adverse effects and drug-drug interactions associated with treatment using NRTIs have challenged the antiviral therapy. Over the last 30 years, it has led to the discovery and approval of several new compounds  with new® , Videx® , Zerit® , Epivir® , Combivir® , Ziagen® , Trizivir® , Viread® , Emtriva® , Epzicom® , Truvada® , Atripla® (TDF + FTC + Efavirenz (NNRTI), 2006), Complera® , Triumeq® , Genvoya® , Odefsey® , Descovy® . Hivid®  was discontinued due to genotoxicity. This antiviral success prompted clinician and researchers to test whether some of the anti-HIV drugs, and especially the acyclic phosphonates ones, could also be active against infections in which viral replication use a viral DNA polymerase enzyme, such as HBV, HSV, EBV, or CMV   requires RNA reverse transcription performed by phylogenetically related RTs, it is tempting to hypothesize that some anti-HIV-1 drugs targeting HIV-1 RT might also be efficient against HTLV-1 RT. Indeed, using cellular assays of viral transmission, it was shown that AZT, 3TC, phosphonated carbocyclic 2′-Oxa-3′-Aza-nucleoside and Tenofovir each reduced HTLV-1 transmission through cell-cell contact (see Table 1) . Furtherf HTLV-1 . The firf HTLV-1 . Howeverf HTLV-1 . Furtherf HTLV-1 .50 are consistently higher when inhibition assays are performed using cell-to-cell infection settings instead of cell-free virus  compared to in vitro RT inhibition assays performed on virions from supernatant of infected cells (inhibition range of around 1 nM) , consistent with the results on HIV replication for which IC50 is threefold higher than that of the parental derivative (p < 0.01), an effect that is lost when the concentration is increased to 50 μM , probably due to the higher toxicity of 7 at 50 μM .Consistent with previous reports, AZT (1) had a significant but limited effect on HTLV-1 transmission, even at the high concentration of 50 μM . bis-POM-PMEA (8) showed a statistically better, although modest, inhibition of LTR activation at 1 μM than the parent compound PMEA (4) tested at the same concentration . Although S-PMEA (6) had a significant yet modest inhibitory effect at 1 μM , its bis-POM modification (9) results in a loss of this effect when used at the same concentration. This suggests that although the cell-membrane penetration is increased, it does not translate into a better efficacy of the compound. This could be explained by the higher toxicity of the pro-drug (6) at 1 μM , which within the 42 h of treatment could impair the metabolism of the pro-drug after its penetration into the cells. Higher concentration could not be used because of compound 9 toxicity .We then tested ANP pro-drugs since they were reported to display an active concentration that was better than that of their parent compound due to the higher ability in passing through cell membranes . However, when used at 10 μM, prodrugs (10–12) had a higher effect than PMPA (5) itself and induced a significant reduction  of HTLV-1 replication.For the PMPA compound family, no effect was observed when pro-drugs  were used at 1 μM. This is consistent with the absence of inhibition observed with the parent PMPA (5) compound . This could be explained by the lower de-protection rate of (13) in cell, thus leading to a lower concentration of active S-PMPA within cells  did not show any improvement over S-PMPA (7) and even had a statistically significant positive effect on viral transmission (in cells . Surpris120-fold  by bis-Pin vitro enzymatic assays (see Table 1). However, we demonstrate that several pro-drugs of PMPA are efficient , confirming earlier studies  is not active when HTLV-1 infection occurs through cell–cell contacts, although (5) inhibits HTLV-1 RT in  studies  and suggin vivo, suggesting that the virus mostly replicates through clonal expansion , specific anti-HTLV-1 drugs have not been developed yet. In addition, and contrary to the HIV-1 field, asymptomatic HTLV-1-infected individuals are not treated, although a high PVL is established to be a valuable risk factor that could be used as a biomarker to identify individuals at risk for developing HTLV-1-associated diseases. Rather, patients are treated once they develop the disease, which is probably too late. Clonal populations of infected T-cells are detected xpansion , althougxpansion . In addiividuals .in vivo viral transmission mode. Our results using stavudine d4T and lamivudine 3TC are consistent with the literature and show that, for some unexplained reasons, these drugs have no effect on HTLV-1 RT. More interestingly, we show here for the first time that Adefovir (PMEA) as well as its prodrug (Adefovir dipivoxil) work much better than AZT. In the case of Tenofovir (PMPA), the drug has no effect, however, the prodrug (Tenofovir disoproxil) has a significant inhibitory effect when used at 10 μM. This encourages us to validate its inhibitory potential in vivo, in naturally infected and currently asymptomatic STLV-1 infected non-human primates. The ongoing pre-clinical test combines Tenofovir disoproxil fumarate (Viread) to valproate, in a protocol similar to that used in the Afonso study (Anti-HIV-1 drugs were first tested using cell-free infection systems in which it is easy to quantify viral production. This is the reason why reverse-transcriptase inhibitors have also been tested using HTLV-1 purified virions , 2008, oso study , in whicAP and SA performed the experiments. LR and KA synthesized all phosphonates derivatives except the bis-POC-PMPA fumarate and PMPA Alafenamide which were purchased from Selleckchem. M-IT contributed with technical developments for some experiments and discussion. RM, HD, and KA designed the experiments. RM, CJ, HD, and KA wrote the article.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.


Robustness of the results is confirmed using simulation-based error propagation. The structural changes resulting from functionally biasing mutations centered around the DRY, NPxxY, and PIF motifs, selectively linking these micro-switches to unique signaling profiles. Our data identify different receptor regions that are important for the stabilization of distinct conformations underlying functional selectivity.Functional selectivity of G-protein-coupled receptors is believed to originate from ligand-specific conformations that activate only subsets of signaling effectors. In this study, to identify molecular motifs playing important roles in transducing ligand binding into distinct signaling responses, we combined in silico evolutionary lineage analysis and structure-guided site-directed mutagenesis with large-scale functional signaling characterization and non-negative matrix factorization clustering of signaling profiles. Clustering based on the signaling profiles of 28 variants of the β Ligand-induced biased signaling is thought to result in part from ligand-specific receptor conformations that cause the engagement of distinct effectors. Here the authors trace and evaluate the impact of mutations of the β2–adrenergic receptor on multiple signaling outputs to provide structural-level insight into the determinants of GPCR functional selectivity. In addition, G-protein-independent signaling of GPCRs has been reported; primarily through βarrestins, which are also part of the desensitization/endocytosis machinery4. It follows that individual GPCRs have greater repertoires of cognate downstream signaling partners than originally anticipated, making their signaling more akin to a network than to a linear cascade. Furthermore, ligands have been identified that bind to the same GPCR but activate distinct and specific subsets of potential signaling pathways9. This phenomenon, called ligand-biased signaling or functional selectivity, has important implication for drug discovery as it paves the way for the identification of ligands that selectively target signaling pathways with therapeutic relevance while sparing pathways that could underlie undesirable effects. Currently, however, the possibility to rationally design compounds with intended signaling profiles is limited by the poorly understood molecular and structural determinants of functional selectivity.G-protein-coupled receptors (GPCR) form the largest family of receptors involved in cellular signaling. Diverse external stimuli such as hormones, neurotransmitters, metabolites, ions, and fatty acids are translated into a cellular response via GPCR activation. The wide range of GPCR-regulated cellular responses and disease associations with GPCR dysregulation make this system a prime target for drug discovery and development. In recent years, it has become evident that a single GPCR can couple not only to one but to several different G-protein subtypes leading to complex signaling profiles13. It is thought that these conformational changes are mediated by a conserved network of non-covalent contacts and that these allosteric rearrangements define activation pathways17. Such activation pathways include small groups of structurally neighboring amino acids within the seven-helical transmembrane domain common to GPCR that are called microswitches. These include the DRY motif , the toggle switch (W2866.48), the NPxxY motif 20, and the PIF/connector motif 21. However, in recent years, molecular dynamic simulations and structural studies of GPCRs in solution have shown that GPCR conformations are very dynamic and ligand dependent26. These observations suggest that different ensembles of receptor conformations might engage different effectors and thereby induce functional selectivity.Translating extracellular ligand binding to intracellular signaling relies on numerous small structural rearrangements of receptors, and functional selectivity is believed to result from differences in these rearrangements. Structural changes accompanying GPCR activation include an elongation and rotation of TM5, an outward movement of TM6 and an inward movement of TM729. Such mutagenesis-directed functional selectivity has even been introduced in M3 muscarinic-based designer receptors exclusively activated by designer drug (DREADD), resulting in a receptor that could engage βarrestin but could no longer activate Gq in response to the designer drug CNO30. However, connections between structural perturbation and signaling bias are still poorly understood. The available 3D structures of the β2-adrenergic receptor (β2AR) in both active and inactive states21 provide initial descriptions of receptor activation, which in combination with site-directed mutagenesis, could inform us on the structure–function basis of functional selectivity. The impact of mutations depends on the sensitivity of the site that is mutated and on the magnitude of the mutational substitution31. The effect of both is captured by evolutionary action (EA), an equation that models the sensitivity by the evolutionary trace (ET) method32, and the substitution magnitude using amino acid transition log-odds31. In theory, EA quantifies the evolutionary effect of genotype variations on functional responses. In practice, and spanning molecular, clinical, and population genetic observations, mutations with low EA value are mostly neutral when tested experimentally, those with high EA value drastically affect protein function, and those in between modulate function35. This approach generalizes prior observations that ET identifies functionally important  positions40. In GPCRs, mutations specifically targeted to the most evolutionary sensitive positions rewired ligand specificity of the D2 dopamine receptor by single- and multiple-site mutations41 and separated G-protein activation from βarrestin signaling by a triple-site mutation42. By considering the substitution magnitude in addition to site sensitivity, EA can now quantify the effect of specific mutations more precisely, suggesting that we may select a mutation’s site and substitution to finely tune the expected perturbation up or down.Mutations in GPCRs, including both synthetic and naturally occurring ones associated to diseases, have been shown to selectively alter subsets of the signaling repertoire2AR 3D structures to identify residues that could underlie the specific conformational rearrangements involved in the engagement of distinct signaling effectors, thereby determining the structural basis of β2AR functional selectivity. For the nine chosen residues, we utilized EA to select mutations intended to differentially perturb function, resulting in 28 single-site mutations. Their signaling profiles in response to isoproterenol (ISO) were characterized via cell-based assays on five different signaling pathways. Based solely on these experimental assays, non-negative matrix factorization followed by k-means clustering groups the mutations into three different phenotypical clusters, which are not only defined by common signaling signatures but also demonstrate shared conformational alterations, and EA scores. These observations point to multiple motifs in determining signaling specificity toward the different pathways engaged by the β2AR, thus providing insights in the molecular determinants of functional selectivity.Here, we used ET and a visual examination of the β2AR residues using the previously described ET method43. Given that intramolecular water molecules are known to play a key role in regulating protein activity46, we considered ET values, as well as their distances to the water molecules involved in hydrogen bond networks within the receptor structure to identify the residues to be mutated  with the highest ET ranking that are located close to intra-protein water molecules and, for four of them, distant from the ligand binding pocket were selected. In order to assess the differential effects of mutation with a range of impacts on these residues47, we selected substitutions with a wide range of EA scores from low (<30), to moderate (30–70), and to high impact   or threonine (T) to assess their ability to interfere with G-protein selective coupling.In addition to the ET-based selection, visual inspection of the crystal structures of inactive and active β2AR, we analyzed G-protein-dependent and -independent signaling pathways. We monitored basal and ISO-induced Gs activation, Gi activation and βarrestin engagement as well as their downstream events 49 and k-means clustering to partition the mutations into the fewest groups within which the assay profiles were most alike and between which they were most distinct  differs significantly from WT β2AR. Whereas Gs signaling and cAMP production were not appreciably affected, and βarrestin engagement and endocytosis were only slightly reduced, the constitutive activity toward Gi activation was considerably increased. The change in constitutive activity was accompanied by a complete loss of ISO-stimulated activity. This lack of responsiveness did not result from a loss of binding ability of the ligand since ISO-binding affinity was not affected , cluster 1, displays only minor changes compared to WT βp value = 1.34 × 10−05). As expected, cluster 1 is comprised of low, cluster 2 of medium, and cluster 3 of high EA scores. The correlation between EA scores and phenotypic mutational impact can be quantified by calculating the overall phenotypic effect of each mutation compared to wild type signaling, leading to R2 value of 0.74  were predicted using the Molecular Operating Environment (MOE) structure-based design package2AR Fig. . The pos2AR Fig.  receptorThe perturbations promoted by mutations of cluster 1 were dispersed throughout the receptor for both the inactive and the active structure without any appearance of structural grouping. The average number of residues affected by each mutation was smaller in cluster 1 than either cluster 2 or 3 Fig. , consistThe activation process of GPCRs is conveyed by conformational rearrangements of key residues in microswitches. Hence, we investigated whether the predicted clustered changes in cluster 2 and 3 neighbor known microswitches . For cluster 2, the majority of the changes centered around the G-protein binding site, which is flanked by the DRY and NPxxY motifs. The alterations in cluster 3 occur both around the PIF motif as well as the NPxxY motif Fig. .Fig. 6InThe prediction of structural changes is consistent with the phenotypic clustering since mutations in the different clusters resulted in distinct structural perturbations. Receptor variants in cluster 2 that displayed increased constitutive Gi activity and loss of ISO-induced Gi activation are characterized mainly by changes around the G-protein binding site (DRY/NPxxY). For cluster 3 variants, which showed increased basal activity for both G-proteins and also lost ISO-promoted Gi activation and βarrestin engagement, mutations were predicted to affect the PIF and toggle switches in addition to the NPxxY, consistent with a greater functional impact.These observations suggest that different regions of the receptor may be important for the stabilization of different receptor conformations causing preferential signaling through various effectors.51. To better characterize the structure–function relationship in GPCR signaling bias, we combined complementary approaches including: computational predictions via ET and EA analyses, experimental receptor signaling profiling via BRET-based biosensors and molecular modeling. First, ET and EA were combined to select and tune the intensity of mutational perturbations at sequence positions that are themselves predicted to be more or less functionally sensitive. Second, in order to disentangle the heterogeneous signaling outputs typical of GPCR signaling, we utilized BRET biosensors to characterize the diversity of these pathways. Third, computational comparison, through nnmf and clustering analyses, grouped the complexity of each mutation’s response profile into a simpler and robust classification of the mutation’s global effect on the entirety of receptor signaling profile rather than being limited to a specific pathway-based analysis. Taken together, these steps therefore form a comprehensive computational and experimental pipeline to predict, test and validate mutationally induced receptor bias allowing targeted site-specific mutagenesis and reengineering of receptor function. While we have demonstrated this approach within the β2AR, all the methodologies used were designed for application in any protein and therefore represent a high throughput methodology to interrogate structure–function relationships.GPCR signaling is pleiotropic, and while the basis of GPCR functional selectivity is not fully characterized, perturbations to the structural conformation have been implicated in biased signaling2AR signaling profile using 28 single-site mutations at nine different structural positions important to receptor function. These 28 mutations clustered into three different major phenotypic outcomes: (i) minimal perturbation compared to WT signaling, (ii) increased basal activity for Gi activation together with loss of ligand-induced Gi activation, and (iii) increased basal activity for G-protein activation and loss of both ligand-induced Gi and βarrestin responses. Noticeably, only a few mutations affected agonist-stimulated Gs activity so that it did not contribute to the clustering , V86A (53), V86Y (73), and V86D (90)  only perturb specific aspects of protein function rather than have a universally deleterious effect. Interestingly, the predicted structural changes for these mutations are centered around the active conformation of the G-protein binding site, including the NPxxY and DRY motif. While the R1312AR23, 5HT13, μOR46). So far, these rearrangements have been loosely associated with G-protein signaling13, however, no in depth functional analysis on this motif has been performed. The NPxxY motif in TM7 was shown to be important for normal receptor function, including G-protein signaling and receptor internalization54. Furthermore, the conformation of TM7 was linked to βarrestin engagement55. Our results are in agreement with these analyses and implicate both the PIF and NPxxY motifs in disruption of receptor signaling, constitutively active G-protein activation, and the elimination of βarrestin signaling.High impact mutations in cluster 3 share a signaling bias against agonist-promoted Gi- and βarrestin-signaling and an increased basal Gs and Gi activity. Cluster 3 mutations are predicted to promote similar alterations in the active state G-protein binding site  as cluster 2, which likely contributes to the increased basal activity and reduced ISO-induced activity towards Gi activation. However, the drastically increased constitutive Gi and Gs activity and loss of agonist-promoted βarrestin engagement suggest other causes. Across all mutations in cluster 3, we note a significantly altered structural conformations around the PIF and NPxxY motifs, which are not present in cluster 1 or 2. Structural rearrangements in the PIF motif during activation have been described for various receptors  to the vasopressin type 2 receptor (V2R) (I276), we found that, as was the case for the β2AR, the I276V/L variants had no or modest effects on either Gs activation, cAMP production or βarrestin recruitment whereas I276Q considerably and selectively affected βarrestin recruitment  at position Ie β2AR I28 to the 57. Based on NMR signals, Liu et al.24 reported that β-arrestin-biased ligands predominantly impact the conformational states of TM7. Interestingly, mutations selectively affecting βarrestin engagement (cluster 3) are predicted to have more impact on residues close to the PIF and NPXXY than the DRY motif. Assessing how these mutations may affect the movement of TM7 upon binding of balanced and biased ligands will be of considerable interest.The approach followed in the present study has obvious implications for a possible understanding of the structural determinants of ligand-biased signaling. Further studies assessing the effects of mutations selectively affecting Gi or βarrestin engagement on the activity of βarrestin- or G-protein-biased ligands should shed new light on the specific and/or local conformational rearrangements required to engage specific signaling effectors. Structural studies have already highlighted that ligands with distinct functional selectivity profiles result in distinct receptor conformationIn summary, we have developed a comprehensive methodology capable of guiding site-directed mutagenesis studies and protein reengineering by coupling in silico evolutionary lineage analysis with biological characterization and collaborative filtering clustering techniques. Using this approach, we demonstrate a strong correlation between our computational predictions stemming from evolutionary history, the actual biological perturbations and structural determinants thereof. Additionally, EA enables the design of mutations at the same position, which vary gradually in functional effect and magnitude, in effect tuning them like a turn of a rheostat. Even though our designed mutations did not occur directly within the functional motifs, the resulting structural changes altered key functional microswitches, further demonstrating the power of this approach to arrive at non-obvious solutions to signaling perturbation. Through this validation, we have gained a better understanding of the molecular determinants of biased GPCR conformations, a crucial first step for the development of biased ligands that activate only beneficial pathways thereby reducing pharmacological side effects and predicting how clinically relevant mutations result in altered cellular signaling leading to disease progression.(-)-Isoproterenol hydrochloride (ISO) was purchased from Sigma Aldrich . Coelenterazine 400a was purchased from NanoLight Technology . All cell culture reagents were bought from Wisent .2AR and V2R were introduced via PCR with QuickChange Site-directed Mutagenesis Kit  following the manufacturer’s instructions. All β2AR variants were verified by sequencing. Receptor constructs contained an Ha tag at their N-terminus, as previously described59, to facilitate the quantification of cell surface expression by ELISA.Single-site mutations in the β10 coding sequence (cds) without stop codon was PCR amplified with a forward primer encoding the plasma-membrane targeting sequence from the Lyn Kinase and a small linker (MGCIKSKGKDSLSNA), RlucII cds without stop codon was PCR amplified and both fragments assembled in pCDNA 3.1 Zeo(+) using In-Fusion , creating pCDNA3.1 Zeo(+) Lyn-GFP10-RlucII. The cds of a structurally disorganized 300 residues-long linker, previously described (Dis300LNK)60, was subcloned in between GFP10 and RlucII, creating pCDNA3.1 Zeo(+) Lyn-GFP10-Dis300LNK-RlucII. The cds of βarrestin2 was PCR amplified using forward primers encoding a small flexible linker (GSGSAGTA) and inserted at the C-terminus of RlucII, creating pCDNA3.1 Zeo(+)βarr2 trans constructs.For creating a unimolecular biosensor for monitoring βarrestin recruitment, GFPBCM3 is a clone of the HEK293T cell line in which BRET-based biosensors have been developed in Dr Bouvier’s laboratory and was used for all the BRET and ELISA experiments. Cells were regularly tested for mycoplasma contamination  and only mycoplasma-negative cells were used for the assays.2 in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum. HEK293T cells were transiently transfected  in six-well plates with biosensors for bioluminescence resonance energy transfer (BRET) assays and ELISA with X-treme GENE 9 DNA Transfection Reagent . Cells were re-plated  24 h post transfection into white 96 well Culture Plates  coated with poly-l-ornithine. The day of the experiment, cells were washed twice with stimulation buffer : 137 mM NaCl, 5.4 mM KCl, 0.25 mM Na2HPO4, 0.44 mM KH2PO4, 1.8 mM CaCl2, 0.8 mM MgSO4, 4.2 mM NaHCO3, 0.2% (w/v) d-glucose, pH 7.4).HEK293T cells were grown at 37 °C with 5% COThe activity of each signaling pathway was measured in living cells using BRET-based assays described below. Optimal times of measurement following agonist stimulation were determined from time-course experiments carried out for each assay for the WT receptor. Times for which maximal responses was achieved and stable for a given assay were selected and were 5, 15 and 30 min for G-protein activity, βarrestin recruitment and cAMP production assays, respectively. The ELISA-based endocytosis assay was carried out 60 min following agonist stimulation.2AR variants (250 ng per well) and a three-component G-protein activation biosensor composed of Gα-RlucII (Gαs-67-RlucII (5 ng per well)61 for Gs activation or Gαi2-99-RlucII (1 ng per well)62 for Gi activation), Gβ1 (100 ng per well), and GFP10-Gγ1 (25 ng per well) as described above. Coelenterazine 400a, diluted in stimulation buffer  was added to the wells for 6 min, then ISO, diluted in stimulation buffer, was added at the indicated concentrations to the wells for 5 min. Plates were read on the Mithras LB 940 , for 1 s per well, with filters set at 410 ± 70 nm (RlucII) and 515 ± 20 nm (GFP10) and BRET ratios were calculated as GFP10 divided by RlucII. Upon formation of the Gα-RlucII:Gβ1:GFP10-Gγ1 hetero-trimer, a significant BRET signal is detected resulting from the proximity between Gα-RlucII and GFP10-Gγ1. Upon activation of Gα, the separation between the Gα and Gβγ subunits leads to a decrease in BRET61.HEK293T cells were co-transfected with varying amounts of WT or β2AR variants (200 ng per well) and BRET-cAMP biosensor 63. The indicated concentrations of ISO, diluted in stimulation buffer, were added to the wells for 30 min, then coelenterazine 400a, diluted in stimulation buffer  was added to the wells for 5 min. Plates were read on the Mithras LB 940, as described above. Upon binding of cAMP, EPAC undergoes a conformational change leading to a significant decrease in the observed BRET signal.HEK293T cells were co-transfected with varying amounts of WT or β2AR variants (250 ng per well) and the BRET-βarrestin2-recruitment biosensor . The indicated concentrations of ISO, diluted in stimulation buffer, were added to the wells for 15 min, then coelenterazine 400a, diluted in stimulation buffer  was added to the wells for 5 min. Plates were read on the Mithras LB 940, as described above. Upon recruitment of βarrestin2 to the receptor, a significant increase in BRET signal is observed resulting from the increased proximity between RlucII and GFP10 upon translocation of the βarrestin to the stimulated receptor.HEK293T cells were co-transfected with varying amounts of WT or β2AR variants (50 ng per well) as described above. The indicated concentrations of ISO, diluted in stimulation buffer, were added to the wells for 60 min, then a cell surface ELISA was performed. In short, cells were fixed with 3% paraformaldehyde for 10 min. Cells were washed three times in washing buffer (modified HBSS (see above) +0.5% BSA), then cells were incubated for 60 min with anti-HA-peroxidase antibody  (1:1000 in washing buffer). Cells were washed three times in washing buffer. ECL  was added to the wells and plates were read on the Mithras LB 940, for 1 s per well, with no filters.HEK293T cells were transfected with WT or β2AR transfected cells used for BRET or endocytosis assays, cell surface ELISA was performed on cells re-plated 24 h post transfection, as for endocytosis experiments but without ISO stimulation.In order to monitor cell surface expression of WT or variants β2AR-unspecific response, the value of cells only transfected with the biosensor (no receptor added) was subtracted from the experimental value. For each repeat of the assays, data were normalized as percentage of WT β2AR maximal ISO-stimulated response ,,2), n wa2AR using 5–150% of DNA amount were prepared and the cell surface expression was measured. The signaling parameters  were determined and their correlation to the expression level was calculated  and included sequences from mammals, amphibians, reptiles, fish, birds, echinoderms, and protostomes. To select key functional amino acids, the ET results were projected on to the protein structure for β2AR (pdb: 2RH1). Targets were chosen based on their ET scores, their distance to the ligand and to the intramolecular waters.To identify functional residues, ET takes a set of homologs as input. Due to the variation in the GPCR loop regions, we focused solely on the transmembrane domains, a total of 195 residues. The multiple sequence alignment of the transmembrane region was made up of 2512 Class A GPCRs, excluding olfactory receptors, and constructed as previously describedEA represents a first principle equation describing the fundamental basis for evolution by providing an evaluable Eq.  connectiφ) is equal to the evolutionary fitness gradient at that position (dγ), calculated by ET, multiplied by the magnitude of the substitution made, calculated by the log odds of the substitution  with the available secondary structure information . When structure is not available, a purely sequence-based EA score is used.Formally the change in phenotype  by randomly sampling, for each phenotype data point, a single value from the normal distribution with the mean and standard deviation estimated from the mutation replicates. Each point was then normalized using the normalized difference detailed above. We then performed the clustering method, detailed below, independently on each of the 1000 sampled matrices. Finally, we quantified how frequently each mutation clustered together in each of these 1000 runs, which resulted in a clustering frequency matrix (28 mutations×28 mutations). To obtain final cluster assignments and create the similarity dendrogram, the frequency matrix was converted to a distance matrix using Pearson’s correlation as a measure of similarity with final cluster designation assigned via hierarchical clustering.In order to establish robustness of the clusters, we propagated random experimental error through the clustering procedure. To accomplish this, we generated 1000 matrices  on a scale of 0–1 using Eq. sing Eq. 5\documen2AR variant and max and min represent the maximal and minimal values for each column. This normalization was necessary to prevent differences in phenotype scale from biasing the feature reduction step. The resulting mutant×phenotype was deconstructed into its basis vectors   using the multiplicative algorithm of nnmf with 500 replicates. Cluster assignment was performed using k-means on the W basis vector where k equaled the number of features used by nnmf. This methodology was then repeated 100 times to measure how frequently any two mutations clustered together, resulting in a mutation by mutation frequency matrix with each value being the frequency that two mutants co-clustered , the automated structure preparation protocol Protonate3D66 was run. Protonate3D calculates the optimal protonation states, including titration, rotamer and “flips” using a large-scale combinatorial search. Using Residue Scanning in the Protein Design panel, the intended mutations were inserted. The selection of side chain conformations is made from a rich rotamer library followed by a refinement protocol based on force field energy minimization. For this purpose, the AMBER12EHT force field was used.Protein structure prediction of the mutated receptors was performed using the MOE structure-based design package67. Residues within 4.5 Å of the mutations sites were identified via chimera both in WT and mutated receptors. By comparing these environments, changes in their amino acid composition (both entering and existing amino acids within this 4.5 Å radius) were detected. Additionally, it was analyzed with chimera which of these changes appeared within 4.5 Å of known microswitches. Detected changes were back projected on the 3D structure and highlighted as indicated in the figure legends.Structural predictions were then visualized using the chimera visualization system68.Data analysis was performed using Microsoft Excel , GraphPad Prism  MATLAB and Statistics Toolbox Release 2014b , and NumPy and SciPy packages2AR using the Gibson Assembly Kit  according to manufacturer’s instruction. Both domains were joined by an eight amino acid Gly-Ser-linker. In order to improve expression, the reported export signal69 was added to the construct. The signaling and binding properties of the construct were verified  was inserted N-terminally into WT β2AR WT or variants (100 ng per well) as described above. Cells were re-plated  24 h post transfection into white 96 well cellGrade Plates  coated with poly-l-ornithine. Cells were washed twice with stimulation buffer (Modified HBSS: mentioned above). 100 nM (S)-propranololol-green  and increasing concentrations of ISO, diluted in stimulation buffer, were added to the wells for 30 min, then coelenterazine 400a, diluted in stimulation buffer  was added to the wells directly before reading. Plates were read on the GloMax® 96 Microplate Luminometer , for 0.5 s per well, with filters set at 465 ± 25 nm (Nluc) and 530 longpass (BODIPY-FL). BRET ratios were calculated as BODIPY-FL divided by Nluc. Upon binding of the fluorescent ligand to the Nluc-tagged receptor, a significant BRET signal is detected. The non-labeled ligand displaces the fluorescent ligand leading to a decrease in the BRET signal. The binding affinity of ISO was determined from the IC50s of the ISO-promoted decrease in propranolol-green binding for each variant.HEK293T cells were transfected with Nluc-βThe authors declare that all data supporting the findings in this study are presented within the article and its Supplementary Information Files, and are available from the corresponding author upon request.Supplementary Information


Atypical Chronic Myeloid Leukemia (aCML) is a myeloproliferative neoplasm characterized by neutrophilic leukocytosis and dysgranulopoiesis. From a genetic point of view, aCML shows a heterogeneous mutational landscape with mutations affecting signal transduction proteins but also broad genetic modifiers and chromatin remodelers, making difficult to understand the molecular mechanisms causing the onset of the disease. The JAK-STAT, MAPK and ROCK pathways are known to be responsible for myeloproliferation in physiological conditions and to be aberrantly activated in myeloproliferative diseases. Furthermore, experimental evidences suggest the efficacy of inhibitors targeting these pathways in repressing myeloproliferation, opening the way to deep clinical investigations. However, the activation status of these pathways is rarely analyzed when genetic mutations do not occur in a component of the signaling cascade. Given that mutations in functionally unrelated genes give rise to the same pathology, it is tempting to speculate that alteration in the few signaling pathways mentioned above might be a common feature of pathological myeloproliferation. If so, targeted therapy would be an option to be considered for aCML patients. BCR-ABL and PDGFRA, PDGFRB or FGFR1 rearrangements are minimal diagnostic criteria for aCML [9/L with ≥10% of immature granulocytes and ≤20% blasts in the blood and the bone marrow [Atypical Chronic Myeloid Leukemia (aCML) is an aggressive and genetically heterogeneous disease for which no standard of care exists. The classification of aCML, which is included in the group of Philadelphia-negative myeloid neoplasms, has been a matter of debate for years. While the identification of the translocation t9;22)q34;q11) in a patient with accumulation of mature granulocytes and their precursors is sufficient for the diagnosis of Chronic Myeloid Leukemia (CML) [1 in a pa2q34;q11)for aCML . Howevere marrow . These de marrow –11. For e marrow . Howevere marrow , while iA big effort has been made in the last decades to elucidate the molecular mechanisms leading to myeloproliferation. The identification of oncogenic mutations in signal transduction proteins pointed to the role of specific pathways in inducing excessive proliferation of myeloid lineages . The subJAK2 is a tyrosine kinase that plays an essential role in myelopoiesis by transducing cytokine signals from several receptors, like receptors for erythropoietin (EPO-R), thrombopoietin (TPO-R) and granulocyte colony-stimulating factor (G-CSF-R). JAKs associate with cytoplasmic domains of different cytokine and growth factor receptors. The binding of extracellular ligands causes changes in the receptors that permit the associated intracellular JAKs to phosphorylate one another. Trans-phosphorylated JAKs then phosphorylate downstream substrates, including STATs. Activated STATs enter the nucleus and bind to specific enhancer sequences in target genes, thus regulating their transcription .JAK2 V617F mutation is found rarely in aCML cases [JAK2 V617F mutated cases could benefit of the JAK2 inhibitor ruxolitinib, already in clinical use for the treatment of intermediate or high-risk MF [JAK2 V617F mutation, which is present in 50% of the patients [JAK2 mutational status or allele burden have been related to clinical signs of the disease like splenomegaly, transformation to Acute Myeloid Leukemia (AML) and overall survival [JAK2 V617F positive MPN [JAK2 V617F positive or JAK2 V617F negative primary MF, post–essential thrombocythemia MF, or post–PV MF were enrolled. 44% of them showed reduction of splenomegaly and the majority of them, who received the drug at a dose of 10 mg twice daily to 25 mg twice daily, had more than 50% improvement in total or individual symptom scores according to the Myelofibrosis Symptom Assessment Form (MFSAF) [The mutation that causes the substitution V617F results in the activation of JAK2 signaling even without receptor stimulation, leading to ligand-independent granulocyte proliferation . The JAKML cases , 24, whiML cases . Althoug-risk MF . There apatients . JAK2 musurvival –29, thustive MPN , a phasetive MPN  and two tive MPN , 32. In  (MFSAF) . In both (MFSAF) , patient (MFSAF) –37. 5-ye (MFSAF)  and 4.1  (MFSAF) –41. Howe (MFSAF) , 41. Con (MFSAF) –44. ThisJAK2 V617F mutated cells causes reduction of both JAK2 and PI3K mediated STAT5 phosphorylation, impairment of the clonogenic potential of JAK2 V617F-mutated hematopoietic progenitors cells and reduced splenomegaly and myeloid cells infiltration in Jak2 V617F knock-in mice [The lack of a complete response in MPN patients treated with ruxolitinib might be due to the activation of collateral oncogenic pathways, like the one of c-Jun N-terminal kinase (JNK) or PI 3-kinase (PI3K)/AKT serine/threonine kinase (AKT)/ mammalian target of rapamycin (mTOR) pathway . Consist-in mice . These sJAK2 mutations, MPN cells use different strategies to induce JAK2 hyperactivation and trigger myeloid expansion [CALR gene, encoding for calreticulin, is mutated in the vast majority of JAK2 V617F negative MPN patients [++ binding protein with chaperone activity located in the endoplasmic reticulum [CALR mutations have been found rarely in aCML patients [CSF3R gene encodes for the Granulocyte colony-stimulating factor receptor (G-CSF-R), the more relevant JAK2-upstream regulator in aCML. This receptor provides the signal for growth and differentiation of granulocytes through the binding to its ligand: the granulocyte colony-stimulating factor 3  [CSF3R mutations in 59% of patients with CNL or aCML, while subsequent analysis indicated that the activating CSF3R T618I mutation is present in < 10% of cases of aCML [CSF3R T618I in the context of neutrophilic leukocytosis is now strongly associated with a diagnosis of CNL, where it is present in approximately 80% of patients [CSF3R T618I develop a CNL-like disease characterized by neutrophil expansion in the peripheral blood and bone marrow and neutrophil infiltration in spleen and liver [CSF3R mutations  in a patient progressing from MPN unclassifiable to aCML [CSF3R T618I, and sensitivity to ruxolitinib treatment [CSF3R T618I-mutated patients was demonstrated with two case reports: a 75-years old man and a 11-years old girl with aCML. The man, who was refractory to hydroxyurea treatment, displayed decreased WBC, reduction of the spleen volume, increased haemoglobin and platelets count after ruxolitinib treatment [JAK2 or CSF3R mutations in aCML patients could open the way for specific therapeutic interventions  –61. Two F; CSF3) : membranF; CSF3) –64. BesiF; CSF3) , 66, G-CF; CSF3) , 68. WheF; CSF3) . In a fi of aCML , 69, 70.nd liver . Adminisnd liver . Another to aCML . An altereatment . The youHRAS, KRAS, NRAS) which, in their GTP-bound form, signal through the MAPK and the PI3K pathways [KRAS and NRAS genes (but rarely in the HRAS gene) are frequently identified in myeloid disorders (15%–60%), including AML [RAS-mutant cancers even if targeting RAS itself has proven to be difficult [RAS-mutant cancers [NRAS/KRAS mutated leukemic cells have been tested using two different mouse models: a Mx1-Cre, KrasLSL-G12D mice, which develop a fatal myeloproliferative neoplasm [NRAS mutated AML cells (NrasG12D AML cells) [BRAF V600E/V600 K mutation [ClinicalTrials.gov identifier NCT00920140) [KRAS mutations, and 54 patients had NRAS mutations. Among RAS mutated patients, the overall response rate was 21% with reduction in bone marrow and peripheral blasts. However, the response did not translate into survival advantage, probably due to the fact that RAS mutations emerge late during leukemogenesis and that many of the patients enrolled were already resistant to previous therapies, possibly due to the presence of different subclones with various levels of dependence on the MAPK pathway [KRAS/NRAS mutations were identified in 7/20 patients (35%) [RAS proteins are small GTPases involved in signal transduction and frequently mutated in human cancers. There are three major isoforms  . In the L cells)  which prL cells) . In the mutation . Both MEmutation . Trametits (35%) . A case ts (35%) .RAS-mutated aCML patients  after 18 months of treatment with the first line tyrosine kinase inhibitor imatinib. Low morgana expression levels confer resistance to imatinib in in vitro treatment of BCR-ABL positive bone marrow cells from CML patients. The combination of imatinib with the ROCK inhibitor fasudil is sufficient, in vitro, to restore an optimal apoptotic response demonstrating that targeting BCR-ABL and ROCK signaling simultaneously could be a therapeutic strategy for BCR-ABL positive patients expressing low morgana levels. The beneficial effect of fasudil on CML and aCML patients still need to be tested.ROCK1/2 are two multifunctional proteins with 65% of overall identity . They plferation , 109–111man aCML , 112. Moman aCML  and ROCKman aCML , and recman aCML . Chordc1Interestingly, JAK2, RAS and ROCK pathways are strictly interconnected: RAS binds to and activates PI3K, while JAK2 activates RAS and PI3K pathways  and in tJAK2 V617F negative neoplasm [ASXL1 (28%), TET2 (16%), NRAS (16%), SETBP1 (12%), RUNX1 (12%), ETNK1 (8%), PTPN11 (4%), CSF3R (3%) [NRAS, JAK2 and CSF3R mutations, have been previously discussed and since they directly impact on the MAPK and JAK/STAT pathways, they are obvious candidates for targeted therapies. However, other two mutations are predicted to activate the signaling pathways discussed above.In the past few years, scientists’ attention focused on genetic alterations characterizing aCML. The development of next generation sequencing (NGS) techniques allowed the identification of low recurrent mutations, translocations, indels and splicing variants in a number of genes , 115–119neoplasm , 121 witneoplasm , 70. HowF3R (3%) , 123. ThSETBP1 encodes a protein named SET binding protein 1 (SEB) for which the precise function is still to be discovered. Mutations in a particular 12 bp hot spot disrupt a degron signal, leading to SEB overexpression [pression . It has pression . In partpression , 127 thapression .PTPN11 gene encodes for SHP2 (Src-homology-2 domain containing protein tyrosine phosphatase), a protein tyrosine phosphatase (PTPase) acting downstream to growth factor receptors. Mutations in the PTPN11 gene result in constitutively activated RAS. In fact, when SHP2 is mutated it activates guanine nucleotide exchange factors (GEFs), necessary for the conversion of GDP-RAS into GTP-RAS [JAK2, PTPN11 and RAS mutations were identified as mutually exclusive in MDS, suggesting their participation to the same pathway [SETBP1, PTPN11 and JAK2 encoded proteins on MAPK pathway overactivation, patients carrying mutations in these genes could benefit from treatment with MEK inhibitors. GTP-RAS , 128. In GTP-RAS . Moreove pathway . Given tHowever, a number of genes mutated in aCML encodes for biosynthetic enzymes, transcription factors and epigenetic modifiers. These proteins are apparently unrelated with the signal transduction molecules previously discussed and their exact role in the onset of the pathology is still unclear.ETNK1, for example, encodes an ethanolamine kinase (EKI 1) which phosphorylates ethanolamine to phosphoethanolamine in the phosphatidylethanolamine biosynthesis pathway. Two recurrent point mutations impairing the catalytic activity of the kinase have been described in ETNK1 gene in aCML [ in aCML . The pho in aCML , 133. DuRUNX1 encodes the alpha subunit of the core binding factor (CBF) complex. This complex activates and represses transcription of genes involved in growth, survival and differentiation pathways in hematopoietic cells, maintaining the proper balance among different lineage progenitors [RUNX1 mutations have been described in aCML [RUNX1 induces myeloid expansion is still to be understood.genitors . This ge in aCML . Some mu in aCML . HoweverTET2 is frequently mutated in both myeloid and lymphoid malignancies [TET2 mutational status) with some positive results, even if on small cohorts of patients, and deserves better investigations [The TET dioxygenases, TET1, TET2, and TET3, catalyze the transfer of an oxygen atom to the methyl group of 5-methylcytocine (5-mC), converting it to 5-hydroxymethylcytocine (5-hmC) , 137. Thgnancies , 139–142gnancies , but, agigations –147.ASXL1 mutations include: loss of PRC2-mediated gene repression, global loss of H3K27 trimethylation (H3K27me3) and derepression of the posterior HOXA cluster genes, including HOXA5–9, known to play a role in leukemogenesis [ASXL1  plays a role in the recruitment of the Polycomb Repressive Complex 2 (PRC2) to its target sequences and takes part in the complex involved in deubiquitination of histone H2A lysine 119 (H2AK119) , 149. Muogenesis .All these proteins have in common a functional pleiotropy, since they can modify the expression of hundreds of genes or the functionality of many proteins in the cell. However, it is conceivable that, among the several deregulated events and pathways, few are responsible for leukemogenesis. In this view, it would be very useful to analyze the signaling pathways known to play a role in myeloproliferation in these mutational contexts in the final attempt to exploit targeted therapies with available inhibitors. Moreover, given that two or more mutations often occur simultaneously in aCML patients  combinatDNMT3A, TET2, and ASXL1 mutant cells. Of note, somatic mutations were found to be associated with increased risk of hematological malignancies, as well as other adverse events [Recently, two studies demonstrated that the percentage of healthy people showing clonal expansion of somatic mutations associated with hematologic diseases increases with age. The authors found that clonal haematopoiesis frequently involves e events , 151. Ite events , 151.aCML is a rare hematological disease for which no standard of care exists. NGS techniques have allowed in the past few years to highlight mutations in signal transduction proteins but also in proteins with pleiotropic functions, like transcription factors and chromatin-modifying enzymes . These p