text:
Modulation of the secretory pathway by amino-acid starvation

van Leeuwen et al. discuss how the cellular secretory pathway adapts and contributes to stress-induced pathways during nutrient starvation.

Introduction
The secretory pathway in growing cells The secretory pathway is highly conserved in the eukaryotic kingdom. It comprises a series of membrane-bound compartments that mediates the export of proteins and lipids from the lumen or the membrane of the ER to the extracellular medium, the plasma membrane, and nearly all cellular membrane-bound compartments. In the ER, newly synthesized proteins are folded, assembled, glycosylated, and exported from the ER in COP II-coated transport vesicles that bud at specialized cup-shaped regions of the ER, the ER exit sites (ERES). Proteins reach the Golgi complex, where they are further processed, sorted, and dispatched to their correct destination (Bonifacino and Glick, 2004;Gomez-Navarro and Miller, 2016).
COP II vesicle formation is initiated through activation of the GTPase Sar1 (GTP-bound state) by the guanine nucleotide exchange factor (GEF) Sec12, which mediates Sar1 recruitment and insertion at the ERES membrane. Sar1-GTP recruits the inner coat Sec23/Sec24 heterodimer. This recruitment is coupled to cargo selection and recruitment through binding to Sec24 either directly (Miller et al., 2002;Barlowe, 2015) or indirectly via the cargo receptor (Otte and Barlowe, 2004;Dancourt and Barlowe, 2010). This is followed by the recruitment of the outer coat Sec13/ Sec31 heterotetramer, leading to the formation of a COP II-coated bud and the release of a COP II-coated vesicle that is loaded with cargo (Miller and Schekman, 2013).
The large hydrophilic ERES protein, Sec16, plays an important role in COP II dynamics. Sec16 directly binds nearly all COP II subunits, and it contributes to ERES stability (Sprangers and Rabouille, 2015). As shown in Pichia pastoris (Connerly et al., 2005), mammalian cells (Bhattacharyya and Glick, 2007;Hughes et al., 2009), Caenorhabditis elegans (Witte et al., 2011), and Drosophila melanogaster (Ivan et al., 2008), Sec16 acts as a scaffold required for proper COP II coat assembly at ERES. In addition, studies in Saccharomyces cerevisiae (that do not exhibit clear focused ERES) have shown that Sec16 also regulates the rate of budding and uncoating of COP II vesicles by inhibiting the Sec23 GTPase-activating protein activity toward Sar1 (Kung et al., 2012;Bharucha et al., 2013).
In mammalian cells, 30% of open reading frames use the secretory pathway to reach their cellular destination (Sharpe et al., 2010). It is therefore considered a major anabolic pathway. To adapt to the ever-changing environment, the secretory pathway must also respond to extracellular cues . In this context, the early secretory pathway is directly regulated by proliferative signaling pathways. For instance, Sec16 is a protein required for cell proliferation  and has been shown to be phosphorylated on threonine 415 by ERK2 downstream of Ras signaling that is stimulated by EGF. This phosphorylation increases Sec16 recruitment to ERES and enhances the rate of COP II vesicle budding (Farhan et al., 2010).
The early secretory pathway under cellular stress Conversely, the early secretory pathway is also sensitive to cellular stress as demonstrated by the Golgi fragmentation observed upon heat stress (Petrosyan and Cheng, 2014), DNA damage (Farber-Katz et al., 2014), oxidative stress (Catara et al., 2017), and cancer (Makowski et al., 2017). Less is known, however, about the regulation of the secretory pathway and secretion in general during changes in metabolism.
In this review, we focus on nutrient stress (i.e., starvation of amino acids and serum) and report on how it modulates and/or redirects secretion, with a focus on ER export via COP II-coated vesicles. First, we discuss the ever-growing relationship between autophagy and the COP II-coated vesicles. Second, we outline how serum and amino-acid starvation also trigger a large remodeling of ERES, at least in Drosophila, and blocks protein trafficking in the secretory pathway. In the third part, we discuss sensing of amino-acid starvation in vivo and how secretion is modulated in response to this stress.
Autophagy requires COP II-coated vesicle budding A major response to amino-acid starvation is the stimulation of the degradative pathway, macroautophagy (hereafter referred to as autophagy) resulting from mechanistic target of rapamycin complex 1 (mTORC1) inhibition. mTORC1 is a central regulator of nutrient-sensing pathways and is activated by nutrient availability (see mTORC1 activation text box). Active mTORC1 stimulates anabolic pathways leading to cell and organismal growth (Saxton and Sabatini, 2017). Conversely, mTORC1 inhibition (such as upon nutrient deprivation) leads to a series of compensatory events (Huang and Fingar, 2014) including the activation of autophagy (see Molecular players of autophagy text box). Autophagy is a complex catabolic process involved in degradation of cytoplasmic materials comprising several biochemical phases supported by 40 gene products (autophagy-related genes [Atgs]; Mizushima et al., 2011). Autophagy starts at the phagophore assembly site (PAS) with the formation of an isolation membrane that expands into a phagophore, a flattened cisterna that starts engulfing a portion of the cytoplasm. It then expands and eventually closes to form a double membrane autophagosome. This fuses with lysosomes, leading to the degradation of the inner membranes and their content (Suzuki et al., 2007;Kawamata et al., 2008;Suzuki and Ohsumi, 2010; see Molecular players of autophagy text box and enclosed figure).

ERES contribute to autophagosome biogenesis via COP II vesicle budding both in yeast and mammalian cells
Even though the nature of the phagophore is still under debate, COP II-coated vesicle budding is clearly necessary for the autophagic process (Ge et al., 2017). The general consensus is that COP II-coated vesicles help build the phagophore or lead to its expansion and maturation into an autophagosome (Farhan et al., 2017).
The first indication of an interaction between autophagy and COP II vesicle budding comes from S. cerevisiae. When these harbor defective mutations in COP II subunits, they also display impaired autophagosome formation upon nutrient starvation (Ishihara et al., 2001). This was confirmed using a temperature-sensitive Sec12-4, where the autophagosome flux was mTORC1 activation on the lysosome by amino acids and the AKT pathway The evolutionary conserved protein kinase mTORC1 is a major nutrient sensor, and as such is a key regulator of growth ensuring that cells adapt to different environmental and nutritional cues like amino-acid availability (Wolfson and Sabatini, 2017). mTORC1 is activated via multiple pathways, including cholesterol (Castellano et al., 2017), growth factors (Inoki et al., 2002) such as the insulin-AKT pathway, and, consistent with its role as a key integrator of the cellular nutrient status, amino acids (Hara et al., 1998). Overall, when amino acids are present, mTORC1 is active and drives anabolic pathways such as translation and growth. When amino acids are absent, mTORC1 is inactivated, anabolic pathways are inhibited, and catabolic pathways, such as autophagy, are stimulated (Wolfson and Sabatini, 2017). Inhibition of mTORC1 leads to activation of nutrient stress-responsive transcription factors in yeast (Crespo et al., 2002). mTORC1 inhibition leads to increased lifespan, translation inhibition, inhibition of lipid synthesis, inhibition of lysosome biogenesis, inhibition of ribosome biogenesis, and down-regulation of transcription (Huang and Fingar, 2014).
At the molecular level, the kinase AKT is activated when insulin/IGF binds to the insulin receptor (InR). Activation of InR subsequently activates phosphoinositide-dependent kinase 1 via PI3K. Phosphoinositide-dependent kinase 1 activates AKT, which phosphorylates and inhibits the tuberous sclerosis complex (TSC). TSC is a GTPase-activating protein for Rheb, a small GTPase that is active in the GTP-bound state (Long et al., 2005;Buerger et al., 2006). As a result, the nucleotide-loading state of Rheb is controlled by both Akt and the TSC complex (Wullschleger et al., 2006). When Rheb is activated, it activates mTORC1 (Dibble and Cantley, 2015).
Parallel to the insulin-Akt pathway, amino-acid levels (in the extracellular medium, the cytoplasm, and/or the lysosome) also minutely modulate the activity of the mTORC1 pathway as they promote the formation of a heterodimer of active RagA −GTP /C −GDP or RagB −GTP /D −GDP . Rag GTPases form a complex with Ragulator, which is anchored to the lysosomal membrane (Kim et al., 2008;Sancak et al., 2008Sancak et al., , 2010. This leads to the lysosomal recruitment of mTORC1 (Sancak et al., 2010;Manifava et al., 2016) and to its activation (see enclosed figure).
Interestingly, Rag GTPases further modulate Rheb activity. Ultimately, the nucleotide loading state of the Rag heterodimer determines whether mTORC1 or TSC is recruited to the lysosome and whether mTORC1 is active (Sancak et al., 2010;Demetriades et al., 2014; see enclosed figure).
Conversely, when amino acids are absent, the nucleotide status of the Rag heterodimer switches to RagA −GDP /C −GTP or RagB −GDP /D −GTP , mTORC1 dissociates from the lysosome, and TSC is recruited to the lysosome by the Rag complex. There, TSC, which is inhibited by the PI3K-Akt pathway (Manning et al., 2002;Dibble and Cantley, 2015), is activated and catalyzes the hydrolysis of Rheb-GTP to Rheb-GDP (Demetriades et al., 2014), and therefore abrogates activation of mTORC1 by Rheb (Inoki et al., 2003). found to be markedly reduced (Graef et al., 2013). Similar to the role of COP II vesicles in yeast, knockdown of COP II subunits in HeLa cells by siRNA also resulted in loss of autophagosomes (Ge et al., 2014).
The notion that the COP II machinery is necessary for the initiation of autophagy was reinforced by the finding that yeast Atg proteins bind COP II subunits (Sar1, Sec23, and Sec24) and Sec16 after treatment with the mTORC1 inhibitor rapamycin (Graef et al., 2013). The functional relevance of these interactions was further demonstrated using tagged Atg8 (marking the PAS, phagophores, and autophagosomes; see Molecular players of autophagy text box) that was visualized very close to ERES markers (Sec16 and Sec13) in rapamycin-treated cells (Graef et al., 2013;Suzuki et al., 2013;Fig. 1). Collectively, these data suggest that COP II vesicle budding contributes to phagophore formation.
In mammalian cells, COP II-coated vesicles are also required to feed the expanding phagophore (or contribute to its formation). However, they appear to bud from the ER-Golgi intermediate compartment (ERG IC), not from the ERES as during the transport to the Golgi during normal growth (Fig. 1). Indeed, upon starvation, Sec12 appears to be relocated from the ERES to the ERG IC as observed by superresolution microscopy (Ge et al., 2017). This is modulated by CTA GE5, an ERES scaffold protein, and FIP200, a subunit of the ULK1 protein kinase complex. Depletion of either prevents ERES remodeling upon starvation and COP II complex assembly at ERG IC. The relocation of the COP II budding activity to the ERG IC appears to be regulated by a phosphoinositide 3-kinase (PI3K), the activity of which is stimulated by starvation. This leads to an enrichment of phosphatidylinositol 3-phosphate (PI(3)P) on the ERG IC leading to the recruitment of the COP II coat. This is in agreement with earlier data that showed that autophagosomes form at PI(3)P-rich sites (Axe et al., 2008). The ERG IC-derived COP II vesicles recruit LC3 and lead to its lipidation, a key event in phagophore expansion (Ge et al., 2014(Ge et al., , 2015 Fig. 1).

The yeast TRA PPI II complex appears to redirect COP II vesicles to the PAS to mediate phagophore expansion
The second mechanism supporting the role of ERES activity in phagophore formation involves the yeast TRA PP complexes. These are GEFs of the small yeast Rab GTPase Ypt1 that is present on COP II vesicles and is necessary for the tethering of COP II vesicles, through Uso1p, to the Golgi during secretion (Cao et al., 1998;Weide et al., 2001;Cai et al., 2007). Three TRA PP complexes have been described. TRA PPI would play a role in ER-Golgi trafficking (Cai et al., 2007), TRA PPII would mediate intra-Golgi trafficking (Cai et al., 2007;Yamasaki et al., 2009), and the TRA PPI II complex would be required for autophagy (Lynch-Day et al., 2010), but not in ER-Golgi transport.
However, this view has recently been challenged. Tandem affinity purification of the TRA PP complexes from log-phase yeast revealed the absence of TRA PPI. Yeast appear to only display TRA PPII and TRA PPI II (Thomas et al., 2018). The use of transport assays in the early secretory pathway lead to the conclusion that TRA PPI II functions as the GEF for Ypt1 in ER-Golgi trafficking (Thomas et al., 2018). Interestingly, a very similar conclusion was drawn from studies in Drosophila (Riedel et al., 2018). CRI SPR/Cas9 knockout mutants for TRA PPC1 encoding a subunit of the TRA PPI II complex in metazoa, leads to the absence of Rab1 (orthologue of yeast Ypt1) activation in the secretory pathway (Riedel et al., 2018). Thus, TRA PPI II is required for the ER-Golgi transport, both in yeast and metazoa (Fig. 1).
In conditions that trigger autophagy, yeast TRA PPI II also acts as a GEF for Ypt1 (Lynch-Day et al., 2010;Tan et al., 2013). Both the loss of function of Ypt1 and Trs85 (a key subunit of TRA PPI II) result in impaired autophagy (Meiling-Wesse et al., 2005;Nazarko et al., 2005). In agreement, TRA PPI II and Ypt1 localize to the PAS upon starvation (Lynch-Day et al., 2010). Furthermore, TRA PPI II recruitment to the PAS appears dependent on Atg17 . Once at the PAS, activated Ypt1 recruits the Atg1 kinase complex that triggers phagophore formation , perhaps via tethering and fusion of Golgi-derived Atg9 vesicles (Rao et al., 2016).
As mentioned, Ypt1 is present on COP II-coated vesicles, and in vitro, TRA PPI II also interacts with Sec23 via its Trs33 subunit (Tan et al., 2013). The model proposed, at least in yeast, is that

Molecular players of autophagy
Autophagy is a catabolic process involved in degradation of cytoplasmic materials to maintain cellular homeostasis. It is efficiently triggered by amino-acid starvation and mTORC1 inhibition to quickly obtain new building supplies.
Autophagy is a conserved complex process compromising several biochemical phases supported by 40 gene products of the so-called Atg genes (Mizushima et al., 2011). It starts at the PAS and the formation of a phagophore (Suzuki et al., 2007;Kawamata et al., 2008;Suzuki and Ohsumi, 2010). As it engulfs portions of cytoplasm tagged for degradation, phagophores expand and eventually close to form a double membrane autophagosome. These fuse with lysosomes, leading to degradation of the inner membranes and their contents.
In yeast, the autophagic pathway is very well understood. The kinase Atg1 (ULK1/2 in mammals) is recruited at the PAS through its interaction with upstream proteins Atg13, Atg17, Atg29, and Atg31. This results in the formation of a phagophore (Suzuki et al., 2007;Kawamata et al., 2008;Suzuki and Ohsumi, 2010). Thereafter, the catalytically active Atg1 recruits Atg9 and the class III PI3K, the Vps34 complex (Kawamata et al., 2008). Atg9 is a transmembrane protein that is transported from the Golgi to the phagophore in Atg9-containing vesicles. Vps34 phosphorylates phosphatidylinositol phosphate to PI(3)P. Atg18 (WIPI in mammals) and Atg2 bind to PI(3) P and play a role in Atg9 turnover (Reggiori et al., 2004). The expanded phagophore acquires Atg8 (LC3 in mammals) that is lipidated and matures into an autophagosome. Atg proteins disassemble (Cebollero et al., 2012) before they fuse with lysosomes, forming an autolysosome (see enclosed figure).
TRA PPI II binds to COP II vesicles to activate Ypt1, which directs COP II vesicles either toward the Golgi or to the PAS depending on whether the cell is in a growing condition or undergoing autophagy. COP II vesicle redirection to the PAS provides the membranes required for phagophore expansion (Fig. 1).
COP II vesicle redirection by the yeast kinase Hrr25 provides membrane for phagophore expansion A third mechanism supporting a role for COP II-coated vesicles in phagophore expansion in yeast involves the kinase Hrr25 that is found necessary for both ER-Golgi trafficking and autophagy (Murakami et al., 1999;Lord et al., 2011;Wang et al., 2015) depending on the growing conditions. On one hand, Hrr25 phosphorylates Sec23 and Sec24 in vitro (Lord et al., 2011) and loss of Hrr25 kinase activity results in impaired fusion of COP II vesicles with the Golgi. This indicates that Hrr25-mediated phosphorylation of Sec23/Sec24 is essential for ER-Golgi trafficking (Fig. 1). On the other hand, Hrr25 is also involved in autophagosome formation (Wang et al., 2015).
How does Hrr25 activity regulate both processes? When the Sec24 threonines T324, T325, and T328 (the latter being also conserved in mammals) are mutated to alanine, autophagy is inhibited but not ER-Golgi transport (Davis et al., 2016), indicating that these specific phosphorylation events are critical for the autophagic process. Indeed, this form of phosphorylated Sec24 binds Atg9, resulting in an increase in the autophagosome number upon nutrient starvation (Fig. 1). Conversely, expression of a kinase-dead Hrr25 lowers the interaction between Sec24 and Atg9 (Davis et al., 2016). Of note, Hrr25 does not appear to directly phosphorylate Sec24 whether in growing conditions or upon autophagy induction, and it is also unclear how it is activated.  (1), Ypt1 is present on COP II vesicles in an inactive GDP-bound state. TRA PPI II catalyses the nucleotide exchange on Ypt1 (GTP-bound) that recruits Uso1p. In metazoa (1′), TRA PPI II has the same role on Rab1 that recruits p115. (2) In yeast, Hrr25-dependent Sec24 phosphorylation is necessary for fusion of the COP II-derived vesicle with the Golgi. (3) In mammalian cells, COP II vesicle formation is regulated by TEC PR2, which protects Sec24 against proteasomal degradation and helps maintain ERES integrity. Interestingly, TEC PR2 interacts with lipidated LC3C. (B) Under conditions in which autophagy is activated, ERES provide membranes for phagophore biogenesis and extension. (4) In yeast (S. cerevisiae), TRA PPI II-activated Ypt1 present on COP II-coated vesicles recruits Atg17 and the Atg1 complex. This allows the binding of Atg9-containing vesicles that in yeast derive from the Golgi. Collectively, this forms the PAS that matures into a phagophore. (5) Upon autophagy stimulation, Sec24 is specifically phosphorylated on three threonines in a Hrr25-dependent manner. This phosphorylation allows the binding to Atg9 vesicles and redirects COP II-coated vesicles to the growing phagophore. (6) Phagophores (Atg8-positive) have also been observed in close proximity to Sec13 and to Sec16. (7) In mammalian cells, autophagy induction leads to the remodeling of ERES and the relocalization of Sec12 to ERG IC in a CTA GE5-and FIB200-dependent manner. This leads to COP II vesicle budding from ERG IC, recruitment of LC3, and binding to Atg9-containing vesicles. Together, they form and/or feed the growing phagophore. (8) In mammalian cells, the TEC PR2 interaction with LC3C somehow leads to the redirection of COP II vesicles to the phagophore.
Collectively, the specific Hrr25-dependent phosphorylation of Sec24 is proposed to mediate the redirection of COP II vesicles to the nascent phagophore, leading to an increase in autophagosome number (Fig. 1).
In addition, the level of the autophagosome marker protein WIPI2 (Atg18) is also decreased upon TEC PR2 depletion (Stadel et al., 2015). At the moment, it is not clear whether TEC PR2 is directly involved in autophagosome formation or indirectly by maintaining an efficient COP II vesicle budding.
Collectively, data from yeast and mammalian cells show a strong cross talk between the early secretory pathway and autophagy. Evidence indicates that COP II vesicles are necessary for the early step of autophagy. Upon nutrient starvation or mTORC1 inhibition, COP II vesicles are proposed to be redirected and provide membranes for phagophore expansion using different mechanisms. How those are coordinated remains to be better understood.
Effects of amino-acid starvation on the early secretory pathway in Drosophila As discussed, amino-acid starvation triggers autophagy via mTORC1 inhibition. However, amino-acid starvation is sensed as a stress and as such also triggers other pathways. The hallmarks of stress are the stalling of anabolic pathways (for instance, protein translation [Aguilera-Gomez et al., 2017]), transcriptional and translational up-regulation of factors helping the cells to deal with stress such as Hsp (Ashburner and Bonner, 1979) and ATF4 (Harding et al., 2000), posttranslational modifications elicited by stress signaling (Duncan and Hershey, 1989), changes in cytoplasmic biophysical properties (Munder et al., 2016), and the formation of membraneless stress assemblies (Rabouille and Alberti, 2017).
The best-documented stress assemblies are stress granules (Anderson and Kedersha, 2009), reversible coalescences that form upon liquid-liquid phase separation with features of liquid droplets (Molliex et al., 2015;Patel et al., 2015;Protter and Parker, 2016;Franzmann et al., 2018) in a manner that depends both on low complexity sequences (Boeynaems et al., 2017;Franzmann et al., 2018) and on mRNA (Van Treeck et al., 2018). Stress granules form in response to protein translation inhibition or stalling imposed by many stresses, such as heat shock, oxidative stress, and ER stress (Aulas et al., 2017). As a result, untranslated mRNAs accumulate and bind RNA-binding proteins that coalesce (Anderson and Kedersha, 2002;Aulas et al., 2017). Interestingly, the stress of amino-acid starvation leads to the formation of stress granules in mammalian cells (Damgaard and Lykke-Andersen, 2011) and in Drosophila (Zacharogianni et al., 2014;Aguilera-Gomez et al., 2017).
In addition, at least in Drosophila, amino-acid starvation triggers the formation of a novel stress assembly, the Sec body. Indeed, amino-acid starvation inhibits not only protein translation but also protein transport in the secretory pathway (Zacharogianni et al., 2014), and Sec bodies provide a cytoprotective mechanism for ERES components. There is therefore an interesting parallel between the inhibition of protein translation and the formation of stress granules, and the inhibition of protein transport through the early secretory pathway and the formation of Sec bodies.
Sec bodies are membraneless assemblies that form after 3-4 h of amino-acid starvation and are quickly and efficiently reversible upon refeeding. They are in part made of COP II subunits as well as the ERES component Sec16, the level of which increases during starvation. Sec bodies appear to be associated with ER membranes but are distinct from COP II-coated vesicles, autophagosomes, endosomes, and lipid droplets. Instead, Sec bodies have liquid droplet properties and appear to form by phase separation that is driven by proteins exhibiting low complexity sequences, such as Sec16 and Sec24. As such, they have properties very similar to those of stress granules except that they are not RNA-based (Zacharogianni et al., 2014). Sec bodies appear to protect COP II subunits against degradation, and they are necessary for survival during stress and fitness upon stress relief (Zacharogianni et al., 2014).

Sec body formation and autophagy
How is Sec body formation compatible with the induction of autophagy that, as discussed above, requires ERES function and COP II vesicle budding? First, autophagosome formation in Drosophila S2 cells occurs earlier than Sec bodies. It peaks ∼1.5 h after starvation and appears to slow down after 3-4 h, at least in S2 cells (Zacharogianni et al., 2014). Second, Sec body formation does not appear to interfere with autophagy. Conversely, inhibiting autophagy leads to the premature formation of Sec bodies (Zacharogianni et al., 2014). Autophagy leads to the replenishment of amino acids in the cytoplasm (Carroll et al., 2015), and the amino-acid level in the cytoplasm may need to drop below a certain threshold for ERES components to coalesce into Sec bodies.
Because amino starvation leads to strong inhibition of mTORC1 (see mTORC1 activation text box), we tested whether mTORC1 inhibition on its own (via rapamycin and depletion of its Raptor subunit) would result in Sec body formation. However, this is not the case (Zacharogianni et al., 2014). This shows that although it happens during amino-acid starvation, mTORC1 inhibition is not sufficient to trigger Sec body formation. Collectively, we propose that the ERES remodeling into Sec bodies is compatible with the initiation of autophagy, at least in Drosophila S2 cells, and that Sec body formation is mediated by signaling pathways that do not solely depend upon mTORC1 inhibition.

Posttranslational modifications on Sec16 and Sec body formation
As discussed, formation of membraneless liquid droplets (and more generally, phase-separated higher assemblies) is driven by stress that changes the properties of the cytoplasm, such as a drop in yeast cytoplasmic pH upon energy depletion (Alberti, 2017). In addition, stress can elicit posttranslational modifications of key proteins, leading to their coalescence through their low complexity sequences that can engage into multivalent low-affinity interactions (Rabouille and Alberti, 2017).
One of the interesting posttranslational modifications involved in the formation of stress assemblies is ADP-ribosylation. ADP-ribosylation (either mono or poly) is catalyzed by 17 poly-ADP-ribose-polymerases (PARPs) in mammals and 3 in Drosophila (Hottiger et al., 2010), and has been shown to be involved in the composition of stress granules during oxidative stress of mammalian cells (Leung et al., 2011;Leung, 2014;Catara et al., 2017).
Interestingly, ADP-ribosylation via dPARP16 is involved in Sec body formation in Drosophila, as dPARP16 overexpression in the absence of stress leads to Sec body formation and its depletion prevents their formation upon amino-acid starvation. Furthermore, dPARP16 is a survival factor during amino-acid starvation and is required for cell fitness upon stress release (Aguilera-Gomez et al., 2016). During amino-acid starvation, dPARP16 is proposed to mono-ADP-ribosylate Sec16 on a small 44-residue conserved sequence localized toward its C terminus, SRDC (serum starvation domain conserved). Interestingly, overexpression of this sequence leads to the formation of Sec bodies in a dPARP16-dependent manner, suggesting a model whereby dPARP16 dependent Sec16 mono-ADP-ribosylation leads to the coalescence of ERES components into Sec bodies (Aguilera-Gomez et al., 2016; Fig. 2).
It is still unknown whether yeast and C. elegans form Sec bodies during amino-acid starvation. Furthermore, although Sec bodies have so far not been reported in mammalian cells, amino-acid starvation of mammalian cells induces remodeling of their ERES and formation of structures that are reminiscent of Sec bodies (unpublished data). Which features of mammalian Sec16 and PARP16 are necessary for Sec body formation needs to be further investigated. Furthermore, the response to amino-acid starvation in mammalian cells might be more geared toward autophagy. Because this process requires COP II vesicle budding, this might impede the formation of ERES components-based cytoprotective assemblies.

ERK7: Another pathway intersecting with Sec bodies?
Serum starvation also remodels the early secretory pathway, in both Drosophila and mammalian cells (Zacharogianni et al., 2011), as it also changes the dynamics of Sec16. However, during serum starvation, Sec16 disperses away from the ERES instead of forming Sec bodies.
The unconventional ERK kinase ERK7 appears to have a role in the serum starvation response, with ERK7 overexpression leading to dispersion of Sec16 away from ERES akin to serum starvation (Zacharogianni et al., 2011). Consistently, ERK7 is necessary for cells to sense serum starvation (in both Drosophila and HeLa cells), and ERK7 is strongly stabilized by starvation (Fig. 2). This leads to the model that upon nutrient starvation, ERK7 protein levels increase leading to Sec16 release from ERES and cessation of protein transport in the secretory pathway (Zacharogianni et al., 2011). Whether ERK7 is involved in Sec body formation remains to be defined. This will help specify if ERK7 and dPARP16 cooperate.
Collectively, these results indicate that amino-acid starvation does more than stimulate autophagy, at least in Drosophila cells. It leads to the cessation of secretion and to the strong remodeling of ERES into a membraneless Sec body. The signaling events Figure 2. Amino-acid starvation of Drosophila cells leads to protein transport inhibition and Sec body formation. In growing conditions, COP II-coated vesicles in Drosophila S2 cells bud from the ERES in a Sec16-dependent manner, leading to active transport through the secretory pathway. ERK7 levels are low and dPARP16 is inactive. Upon amino-acid starvation, protein transport from the ERES to the Golgi is inhibited. ERK7 level increases, leading to Sec16 release away from the ERES membrane. dPARP16 is activated and mono-ADP-ribosylates Sec16 on its SRDC, leading to the coalescence of Sec16 and COP II subunits into Sec bodies after 3-4-h starvation. MARylated, mono-ADP-ribosylated. triggered by amino-acid starvation resulting in the activation of both ERK7 and dPARP16, as well as their interplay, will be important to discover.

Amino-acid starvation modulates secretion in vivo via humoral and cell-autonomous responses
Nutrient availability needs to be sensed at the organismal level to couple nutrient availability to organismal growth. Accordingly, nutrient restrictions in vivo affect secretion, and this is well understood in Drosophila, especially in larvae. In brief, Drosophila embryogenesis is followed by three larval molts during which larvae mostly feed and grow. The third-instar larvae then stop feeding and start the process of pupation that ultimately leads to the eclosion of a new fly.
Feeding Drosophila larvae possess a sensory mechanism that measures amino-acid availability and integrates it into systemic insulin signaling, which adjusts the growth rate of the larva. This sensory mechanism is made of two very important tissues that communicate with one another, the fat body and the insulin-producing cells (IPCs). The insulin/insulin-like growth factor-signaling pathway is conserved from Drosophila to vertebrates, but it became functionally differentiated: in Drosophila, Drosophila insulin-like peptides (Dilps) control both metabolism and growth, whereas in vertebrates, insulin controls metabolism and insulin-like growth factors control growth (Nakae et al., 2001;Garofalo, 2002;Wu and Brown, 2006).

The Drosophila fat body senses the circulating amino-acid level through the amino-acid transporter Slimfast
The fat body is a larval organ with features of both mammalian liver and adipose tissue. Nutrients originating from the gut, including amino acids, are imported into fat body cells, where they are stored and stimulate mTORC1, resulting in larval growth.
Conversely, the larval fat body serves as an amino-acid sensor and detects the level of dietary circulating amino acids. Larvae that are deprived of amino acids are smaller and give rise to significantly smaller adults (Colombani et al., 2003). Strikingly, ablation of the amino-acid transporter encoded by the slimfast gene solely in the fat body is sufficient to phenocopy the effects of starvation on wild-type larvae-that is, smaller larvae and smaller adults. This strongly suggests that Slimfast is the major amino-acid transporter in fat body cells (Colombani et al., 2003;Fig. 3). Accordingly, Slimfast activity impacts mTORC1 signaling. In the presence of circulating amino acids, mTORC1 is activated in a Slimfast-dependent manner (Colombani et al., 2003).

When amino acids are available, fat body-secreted SunA and B trigger Dilp secretion from IPCs
The sensing of amino-acid availability by the fat body is transduced into a systemic growth response. When the amino-acid level is high, the fat body secretes fed signals, such as SunA/B. SunA and SunB are two peptide isoforms encoded by the Drosophila stunted gene, their expression and secretion in the fat Figure 3. The secretion-based signaling between the Drosophila fat body and IPCs upon sensing the level of circulating amino acids. Top: When amino acids are available, they are transported into the fat body cells by Slimfast, where they activate mTORC1 (see mTORC1 activation text box). The nutrient responsive coactivator PGC-1 stimulates stunted expression, giving rise to the SunA and SunB peptides that are secreted into the hemolymph. SunA and SunB subsequently bind to their receptor Methuselah on the plasma membrane of IPCs in the brain. The IPCs transcribe the Dilp genes, leading to the accumulation of Dilps 3, 2, and 5. Activation of Methuselah triggers intracellular calcium release, which stimulates Dilps secretion to the hemolymph. In turn, Dilps activate the InR in the fat body, which increases mTORC1 activity further via the insulin-Akt pathway (see mTORC1 activation text box). Bottom: Upon amino-acid starvation, mTORC1 activation is reduced, leading to a reduction of SunA and SunB. It is also possible that their secretion is inhibited through the increase of the ERK7 level. Decreased SunA and SunB secretion leads to a decreased stimulation of Methuselah, resulting in a reduced Dilp secretion from IPCs where they accumulate. ERK7 level in the IPCs is increased, possibly contributing to secretion inhibition. Reduced Dilp secretion during amino-acid starvation effectively reduces mTORC1 activation and overall growth. body being strongly stimulated by amino acids . Interestingly, Sun secretion depends on mTORC1 activity, but not stunted expression that is enhanced by the nutrient-responsive transcription coactivator PGC-1, independently of mTORC1 .
After being secreted into the hemolymph by fat body cells, SunA/B binds to the Methuselah receptor at the surface of IPCs. Activation of Methuselah by SunA/B triggers the release of intracellular calcium, which leads to the fusion of Dilp-containing vesicles with the plasma membrane, releasing Dilps into the hemolymph Fig. 3). Dilps, in turn, bind to the fat body cell plasma membrane and enhance/reinforce mTORC1 activation via Akt (see mTORC1 activation text box).
The loop described above clearly establishes that fat body cells secrete a signal in response to amino acids, which triggers voltage-dependent Dilp secretion from IPCs. This relay mechanism allows the secretion of Dilps from IPCs when amino acids are available, which therefore has a permissive effect on systemic growth. The homology of the fat body with mammalian liver and adipose tissue emphasizes the need of assessing how amino-acid starvation modulates secretion by these two tissues.
In addition to Dilp secretion from IPCs, Dilp expression by IPCs is regulated at the transcriptional level. Upon starving conditions, the transcriptional regulation of dilp genes is inhibited in the Drosophila larvae by the release of fat body factors, including TACE-dependent cleaved Eiger, the Drosophila TNF-α that is sensed by the IPC receptor, Grindelwald Fig. 3). Interestingly, Eiger function is conserved in mice, as TNF-α inhibits insulin gene expression in mouse insulinoma-derived cells and islets . This suggests that the physiological response to amino-acid starvation in Drosophila is conserved and has become pathological by causing insulin resistance in obese mammals . However, whether the TNF-α-dependent reduction of insulin gene expression in mice is specific for amino-acid starvation remains unclear.

Upon amino-acid starvation, IPCs stop secreting Dilps in both a cell-nonautonomous and -autonomous manner
Conversely, amino-acid starvation leads to a reduced secretion of SunA/B by fat body cells, leading in turn to a diminished stimulation of Methuselah on the IPCs. Dilp release from stored secretory vesicles is diminished, leading to their accumulation. A reduced amount of circulating Dilps would prevent mTORC1 activation and allows the larvae to resist starvation by reducing growth (Agrawal and Subramani, 2016;Delanoue et al., 2016;Fig. 3).
In addition, the IPCs also inhibit Dilp secretion in a cell-autonomous manner. Indeed, similar to amino acid-starved Drosophila S2 cells (see Effects of amino-acid starvation on the early secretory pathway in Drosophila), the level of ERK7 in the IPCs increases when larvae are starved (Hasygar and Hietakangas, 2014). This ERK7 increase is proposed to lead to a cessation of Dilp2 secretion in a cell-autonomous manner. Whether the ERK7 increase specifically inhibits Dilp secretion or secretion in general needs to be established (Fig. 3). Whether ERK7 also works in fat bodies to slow down SunA/B secretion remains to be investigated.
In conclusion, it appears that the humoral loop originating from the fat body to control Dilp release by the IPC is backed up by an ERK7-dependent cell-autonomous response. When the amino-acid level is high, the fat body secretes fed signals (humoral signals) in the hemolymph that reach the brain, where they activate the secretion of Dilps. Dilps, in addition to amino-acid levels, are then sensed in the fat body, stimulating mTORC1 and the release of more fed signals. mTORC1 is also overall stimulated to allow larvae to grow (Géminard et al., 2009;Delanoue et al., 2016;Fig. 3). Conversely, when the amino-acid level is low, the fat body stops secreting fed signals, and Dilp release is inhibited in the IPCs. This is accompanied by an increase in ERK7 levels that down-regulates secretion efficiency. Whether this is linked to the dispersion of Sec16 or ERES remodeling occurs remains to be elucidated.

Conclusions and perspectives
Over the last 10 years, research on the early secretory pathway upon nutrient starvation has brought to light the extent of its modulation. One lesson that has been learned is that nutrient starvation remodels the ERES and their function to stimulate survival and adaptive strategies. First, the ER-Golgi transport machinery and COP II vesicle budding appear to be diverted to fuel the growth of phagophores, the first membrane compartment of the autophagic pathway, a clear survival pathway aiming to replenish the amino-acid level in the cell cytoplasm. At least three distinct mechanisms governing this redirection have been described, but how they are integrated and what the additional layers of controls are still need to be investigated. It is also unclear whether the ERES also contribute to autophagy in other ways.
Interestingly, at least in Drosophila cells, amino-acid starvation also stimulates autophagy and leads to an inhibition of protein export from the ER. Given that autophagy requires trafficking out of the ER via COP II-coated vesicles, this raises the question of whether a pool of ERES is marked at the molecular level and dedicated to the phagophore expansion, whereas the ERES functioning in the classical secretory pathway are shut down. This remains to be further studied as it would help reconcile this apparent contradiction.
Nutrient starvation also results in ERES remodeling and coalescence of ERES components into membraneless Sec bodies, where they are protected against degradation, thus an adaptive response providing a fitness advantage upon stress relief. The question is whether Sec bodies form in vivo and in mammalian cells and whether they also provide a fitness advantage there. In Drosophila larvae, it is possible that Sec body formation might function as a mechanism regulating secretion of signaling peptides. As discussed, ERK7, a factor that modulates Sec16 dynamics, is a negative regulator of Dilp secretion from IPCs in response to amino-acid starvation. How it cooperates with dPARP16 in vivo also remains to be investigated.
Overall, the regulation of secretion upon nutrient stress is essential for cellular fitness, and as such it is an exciting field for future research in the context of whole-organism fitness and tumor growth.


extfieldsofstudy: Biology Medicine

text:
A Point Mutation V419L in the Sodium Channel Gene from Natural Populations of Aedes aegypti Is Involved in Resistance to λ-Cyhalothrin in Colombia

Resistance to pyrethroids in mosquitoes is mainly caused by target site insensitivity known as knockdown resistance (kdr). In this work, we examined the point mutations present in portions of domains I, II, III, and IV of the sodium channel gene in Aedes aegypti mosquitoes from three Colombian municipalities. A partial region coding for the sodium channel gene from resistant mosquitoes was sequenced, and a simple allele-specific PCR-based assay (AS-PCR) was used to analyze mutations at the population level. The previously reported mutations, V1016I and F1534C, were found with frequencies ranging from 0.04 to 0.41, and 0.56 to 0.71, respectively, in the three cities. Moreover, a novel mutation, at 419 codon (V419L), was found in Ae. aegypti populations from Bello, Riohacha and Villavicencio cities with allelic frequencies of 0.06, 0.36, and 0.46, respectively. Interestingly, the insecticide susceptibility assays showed that mosquitoes from Bello were susceptible to λ-cyhalothrin pyrethroid whilst those from Riohacha and Villavicencio were resistant. A positive association between V419L and V1016I mutations with λ-cyhalothrin resistance was established in Riohacha and Villavicencio. The frequency of the F1534C was high in the three populations, suggesting that this mutation could be conferring resistance to insecticides other than λ-cyhalothrin, particularly type I pyrethroids. Further studies are required to confirm this hypothesis.

Introduction
Aedes aegypti (L.) is the main vector for Zika, dengue, and chikungunya viruses. The diseases caused by these viruses are a rapidly growing public health problem worldwide, due to an increase in the mosquito's population, to rapid human urbanization with population growth, climate parameters, and human migration [1,2]. During 2016, there was an alarming number of new cases for Zika, dengue, and chikungunya in Colombia, with incidences around 100,000, 83,000, and 23,000 cases, respectively [3].
With no suitable vaccines currently available, strategies to control these diseases are focused on reducing the mosquito population through the use of insecticides. In Colombia, these strategies have been mainly based on using insecticides such as DDT, temephos, malathion, and, more recently, pyrethroids [4]. Unfortunately, most dengue vector control programs underway are poorly implemented, resulting in the development of insecticide resistance. Insecticide resistance in mosquitoes is caused by a number of mechanisms, with two in particular being the focus of the majority of studies: metabolic resistance and target site alterations. Metabolic resistance involves large multi-gene enzyme families: cytochrome P450s, glutathione S-transferases, and carboxyl esterases. Cytochrome P450s, in particular, have been implicated in conferring resistance in mosquitoes [5,6]. The role of cuticular resistance and behavioral avoidance of insecticides are less studied. Target site insensitivity, known as knockdown resistance (kdr), where several point mutations in the gene encoding the sodium channel have been associated with resistance to pyrethroids. To date, at least 10 resistance-associated sodium channel mutations have been identified in pyrethroid-resistant Ae. aegypti and four of them, S989P, I1011M, V1016G, and F1534C, have been functionally confirmed to confer sodium channel resistance to pyrethroids [6,7]. The mutations G923W, L982W, I1011M, and V1016G in domain II, were found in permethrin/DDT-resistant populations from Asia and Brazil [8], while substitutions I1011V and V1016I were found in Ae. aegypti populations from Latin America [9]. Also, the F1534C substitution in domain III-S6 was discovered in Vietnam and Thailand [10][11][12], and more recently in Brazil [13], Venezuela [14], and Colombia [15]. Finally, two additional mutations, S989P and D1763Y, were found in Asiatic countries [16,17]. In addition, double mutations, V1016G and F1534C, were reported in Singapore [18] and triple mutations S989P, V1016G, and F1534C in Ae. aegypti from China, Indonesia, and Thailand [11]. In America, the mutation V1016I was discovered to coexist with F1534C in Venezuela [14] and Brazil [19][20][21]. Interestingly, Vera-Maloof et al., in 2015, proposed that the F1534C mutation was selected first and confers a low level of pyrethroid resistance, and that the V1016I haplotype cannot be selected in the absence of F1534C [22]. Regular monitoring has revealed that many Colombian populations of Ae. aegypti have become resistant to pyrethroid insecticides [23][24][25][26]. However, the underlying mechanism for this resistance is not yet known. Recently, a valine to isoleucine substitution at position 1016 (V1016I) within the second domain of the Ae. aegypti sodium channel was observed in Colombian Caribbean populations with allelic frequencies ranging from 0.07 to 0.35 [4] and 0.03 [27]. Unfortunately, these last studies only investigated mutations previously reported in mosquitoes from other countries around the world. Thus, the identification of known and novel mutations present in Ae. aegypti populations from Colombia and their association with insecticide resistance should be studied in more detail.
Therefore we aimed to measure resistance level to λ-cyhalothrin and to investigate the association between kdr mutation and resistance to this insecticide in mosquitoes from three Colombian cities that have different epidemiological characteristics for dengue. For this, the partial coding region of the sodium channel gene from resistant mosquitoes was sequenced and a simple allele-specific PCR-based assay (AS-PCR) was used to detect these mutations at the population level. We report a new mutation in the coding region of the sodium channel from wild mosquitoes involved in the resistance to this pyrethroid.

Mosquito Collection
Both adult mosquitoes and immature stages were collected during 2013-2014 in three cities of Colombia with the assistance of staff involved in vector-borne diseases programs for each municipality. Larvae and pupae were collected from the deployment of ovitraps, and adults were collected using sweep nets, in 20 randomized houses from four neighborhoods [28]. The cities sampled are localized in three eco-geographical regions showing distinct weather and dengue incidence, as was reported previously [28]. Bello (BE), located in the central Andean region of Colombia at an altitude of 1250 m, is characterized by the reduced use of chemical insecticides, while Riohacha (RH) and Villavicencio (VI), located at an altitude of 2 and 467 m, respectively, are frequently exposed to vector control using chemical insecticides (Figure 1). The adult mosquitoes were individually placed in absolute ethanol (99.8%) and stored in a freezer at −20 °C until molecular analysis was performed. The immature stages were reared until adults emerged and maintained under controlled conditions of temperature (28 ± 1 °C), relative humidity (80 ± 5%), and photoperiod (12 h light: 12 h dark).

Insecticide Bioassays
The insecticide being evaluated was the pyrethroid λ-cyhalothrin. Around 20 Ae. aegypti thirdor fourth-instar F1 larvae, were exposed to five concentrations of the insecticide (0.0031 ppm to 0.5 ppm) to determine larval mortality 24 h after exposure, following the methods proposed by the World Health Organization (WHO) [12]. The insecticide susceptible Rockefeller strain (of Caribbean origin, that has been in colony since the early 1930s [29,30]) (kindly provided by Grupo de Sistemática Molecular of Universidad Nacional de Colombia), was used as a control. Three replicates were performed at each concentration and control for each wild population and the Rockefeller strain.
The lethal concentrations (LC50) and confidence intervals (p < 0.05) were calculated for every mosquito population. Concentration-mortality data were subjected to Probit analysis and 95% CIs for resistance ratio (RRs) were estimated. A resistance ratio (RR50) was obtained by dividing the lethal concentration of the population by the equivalent lethal concentration of the Rockefeller reference strain. The RR was interpreted as susceptible (<5-fold), tolerant or moderate resistance (5-to 10-fold) and high resistance (>10-fold) [18]. p-values < 0.05 were considered statistically significant.

RNA Extraction and cDNA Synthesis
The surviving larvae to λ-cyhalothrin treatment were reared to adults, and total RNA was extracted using Qiagen's Trizol kit (QIAGEN, Duesseldorf, Germany). cDNA synthesis was performed using 2 μg of RNA, 0.5 μg oligo dT and retrotranscriptase Biolab (New England BioLabs Inc., Ipswich, MA, USA). The cDNA was stored to −20 °C.

cDNA Sequencing
In order to identify the mutations present in the sodium channel gene, we amplified the partial coding region of this gene from Villavicencio population (with the highest resistance ratio) and Rockefeller strain (reference susceptible). Portions of domains IS4-S6, IIS1-S6, and IIIS4-S6 of this gene were amplified using the primer sets previously reported by Yanola et al., 2011 [12]. The PCR The adult mosquitoes were individually placed in absolute ethanol (99.8%) and stored in a freezer at −20 • C until molecular analysis was performed. The immature stages were reared until adults emerged and maintained under controlled conditions of temperature (28 ± 1 • C), relative humidity (80 ± 5%), and photoperiod (12 h light: 12 h dark).

Insecticide Bioassays
The insecticide being evaluated was the pyrethroid λ-cyhalothrin. Around 20 Ae. aegypti third-or fourth-instar F1 larvae, were exposed to five concentrations of the insecticide (0.0031 ppm to 0.5 ppm) to determine larval mortality 24 h after exposure, following the methods proposed by the World Health Organization (WHO) [12]. The insecticide susceptible Rockefeller strain (of Caribbean origin, that has been in colony since the early 1930s [29,30]) (kindly provided by Grupo de Sistemática Molecular of Universidad Nacional de Colombia), was used as a control. Three replicates were performed at each concentration and control for each wild population and the Rockefeller strain.
The lethal concentrations (LC 50 ) and confidence intervals (p < 0.05) were calculated for every mosquito population. Concentration-mortality data were subjected to Probit analysis and 95% CIs for resistance ratio (RRs) were estimated. A resistance ratio (RR50) was obtained by dividing the lethal concentration of the population by the equivalent lethal concentration of the Rockefeller reference strain. The RR was interpreted as susceptible (<5-fold), tolerant or moderate resistance (5-to 10-fold) and high resistance (>10-fold) [18]. p-values < 0.05 were considered statistically significant.

RNA Extraction and cDNA Synthesis
The surviving larvae to λ-cyhalothrin treatment were reared to adults, and total RNA was extracted using Qiagen's Trizol kit (QIAGEN, Duesseldorf, Germany). cDNA synthesis was performed using 2 µg of RNA, 0.5 µg oligo dT and retrotranscriptase Biolab (New England BioLabs Inc., Ipswich, MA, USA). The cDNA was stored to −20 • C.

cDNA Sequencing
In order to identify the mutations present in the sodium channel gene, we amplified the partial coding region of this gene from Villavicencio population (with the highest resistance ratio) and Rockefeller strain (reference susceptible). Portions of domains IS4-S6, IIS1-S6, and IIIS4-S6 of this gene were amplified using the primer sets previously reported by Yanola et al., 2011 [12]. The PCR products were purified and then sequenced by Macrogen Inc. (Seoul, South Korea). The sequences were edited using the Biological Sequence Alignment Editor (BioEdit version 7.2.1, Ibis Biosciences, Carlsbad, CA, USA) program, and aligned using the MEGA5 program, with the aim of identifying the mutations present in this region. The sequences were also compared with others available on GenBank using tblastx to verify the amplified fragment and identify the point mutations.

Allele-Specific PCR (AS-PCR) Genotyping
A minimum of 57 adult mosquitoes from each city was processed for genotyping. Genomic DNA from individual mosquitoes was extracted using the protocol described by Collins et al. [31]. The samples were genotyped at positions 1255, 3046, and 4673 of the sodium channel gene using allele-specific PCR (AS-PCR). For this, we designed an allele-specific primer to 419 mutation (G1255T); and we used allele-specific primers previously developed to 1016 (G3046A) and 1534 (T4673G) mutations [12,32] (Table 1) . PCR amplification products were separated on a 2% agarose gel in a TE buffer, at 100 V for 60 min. The Rockefeller strain of Ae. aegypti was used as a reference for the wild-type alleles (419L, 1016V and 1534C) of the sodium channel gene. The allelic frequencies were calculated from the gel results by summing the genotypic frequency of the heterozygotes divided by two and the frequency of the homozygotes. GENEPOP v.4.6 (Laboratoire de Genetique et Environment, Montpellier, France, http://genepop.curtin.edu.au/) was used to calculate allelic and genotypic frequencies and the data were tested for conformity to Hardy-Weinberg equilibrium [33].

Correlation of Resistance with the Frequencies of Different Mutations
To evaluate whether the frequency of point mutations observed in Colombian populations was correlated with resistance to λ-cyhalothrin, a descriptive analysis was conducted comparing the kdr allele frequency and the resistance ratio values. In order to verify the role of these mutations in the resistance to λ-cyhalothrin, mosquitoes from Villavicencio were maintained in the laboratory without insecticide pressure until F8 filial generation. The allelic frequencies in 52 mosquitoes were also calculated.

λ-Cyhalothrin Bioassays and Adult Genotyping
The mosquitoes from the three cities presented different levels of resistance to λ-cyhalothrin. The resistance ratios (RR) ranged from 1.97 for mosquitoes from Bello to 10.96 and 11.34-fold for mosquitoes from Riohacha and Villavicencio, respectively. Table 2 shows the values of LC 50 and RR obtained for this insecticide.

Comparison of cDNA Region from Susceptible and Resistant Mosquitoes and Identification of Sodium Channel Gene Mutations
The mosquitoes with highest RR were analyzed for the presence of mutations. The partial coding region of the sodium channel gene from nine resistant mosquitoes was sequenced and aligned with sequences from susceptible mosquitoes ( Figure 2). Interestingly, all the mosquitoes analyzed contained the three point mutations, two of which, V1016I and F1534C, have been previously reported, whilst the novel mutation V419L, is being reported here for the first time in Colombian mosquitoes. This new mutation was identified in the domain I of segment S4-S6 (IS4-6) from resistant mosquitoes of Villavicencio ( Figure 2). All three mutations were used to design an AS-PCR to identify the genotypic and allelic frequencies at the population level.

λ-Cyhalothrin Bioassays and Adult Genotyping
The mosquitoes from the three cities presented different levels of resistance to λ-cyhalothrin. The resistance ratios (RR) ranged from 1.97 for mosquitoes from Bello to 10.96 and 11.34-fold for mosquitoes from Riohacha and Villavicencio, respectively. Table 2 shows the values of LC50 and RR obtained for this insecticide.

Comparison of cDNA Region from Susceptible and Resistant Mosquitoes and Identification of Sodium Channel Gene Mutations
The mosquitoes with highest RR were analyzed for the presence of mutations. The partial coding region of the sodium channel gene from nine resistant mosquitoes was sequenced and aligned with sequences from susceptible mosquitoes ( Figure 2). Interestingly, all the mosquitoes analyzed contained the three point mutations, two of which, V1016I and F1534C, have been previously reported, whilst the novel mutation V419L, is being reported here for the first time in Colombian mosquitoes. This new mutation was identified in the domain I of segment S4-S6 (IS4-6) from resistant mosquitoes of Villavicencio ( Figure 2). All three mutations were used to design an AS-PCR to identify the genotypic and allelic frequencies at the population level.

AS-PCR for the V419L, V1016I and F1534C Mutations
In order to determine the frequency of three mutations in each city, a minimum of 57 mosquitoes were genotyped using AS-PCR ( Figure S1). The genotypic frequencies of V419L, V1016I and F1534C mutations are shown in Table 3. For the V419L mutation, all three genotypes were observed (V/V, V/L and L/L) in the three cities, although the heterozygote (V419/419L) and homozygote (419L/419L) genotypes were rare in the Bello municipality. Similarly, the 419L mutant allele frequency was found at 0.06 in Bello, whilst in Villavicencio and Riohacha, was 0.46 and 0.30, respectively ( Figure 3). All three genotypes at position 1016 were observed in the three cities (V/V, V/I and I/I), and the 1016I mutant allele frequency in Villavicencio and Riohacha, was 0.41 and 0.25, respectively, in comparison to 0.04 observed in Bello. Conversely, there was no clear difference in the frequency of allele 1534C, between mosquitoes from the three cities with values of 0.63, 0.71 and 0.56 in Villavicencio, Riohacha and Bello, respectively. All genotype frequencies across the three different populations conformed to Hardy-Weinberg equilibrium ( Table 3). A comparative analysis between allelic frequencies and resistance ratio of these three alleles in the three cities studied is shown in Figure 3.

AS-PCR for the V419L, V1016I and F1534C Mutations
In order to determine the frequency of three mutations in each city, a minimum of 57 mosquitoes were genotyped using AS-PCR ( Figure S1). The genotypic frequencies of V419L, V1016I and F1534C mutations are shown in Table 3. For the V419L mutation, all three genotypes were observed (V/V, V/L and L/L) in the three cities, although the heterozygote (V419/419L) and homozygote (419L/419L) genotypes were rare in the Bello municipality. Similarly, the 419L mutant allele frequency was found at 0.06 in Bello, whilst in Villavicencio and Riohacha, was 0.46 and 0.30, respectively ( Figure 3). All three genotypes at position 1016 were observed in the three cities (V/V, V/I and I/I), and the 1016I mutant allele frequency in Villavicencio and Riohacha, was 0.41 and 0.25, respectively, in comparison to 0.04 observed in Bello. Conversely, there was no clear difference in the frequency of allele 1534C, between mosquitoes from the three cities with values of 0.63, 0.71 and 0.56 in Villavicencio, Riohacha and Bello, respectively. All genotype frequencies across the three different populations conformed to Hardy-Weinberg equilibrium ( Table 3). A comparative analysis between allelic frequencies and resistance ratio of these three alleles in the three cities studied is shown in Figure 3.

Correlation of Resistance with the Frequencies of the Different Mutations
A positive correlation between the frequency of the 419L and 1016I alleles and resistance to λ-cyhalothrin was observed. Mosquitoes from Bello presented low frequency of these alleles. In contrast the frequency of the 1534C mutation was not correlated with the resistance to λ-cyhalothrin

Correlation of Resistance with the Frequencies of the Different Mutations
A positive correlation between the frequency of the 419L and 1016I alleles and resistance to λ-cyhalothrin was observed. Mosquitoes from Bello presented low frequency of these alleles. In contrast the frequency of the 1534C mutation was not correlated with the resistance to λ-cyhalothrin (Figure 4). Mosquitoes from Bello, Villavicencio and Riohacha showed a high frequency of the 1534C mutated allele (0.56, 0.63 and 0.71, respectively).

Mutations in Combination
Individuals with heterozygote genotypes for 419L and 1016I positions were found at high frequency in mosquitoes from Villavicencio and Riohacha. These double heterozygotes were observed together with the mutation F1534C (Haplotype V419L/V1016I/1534C). Triple heterozygotes (V419L/V1016I/F1534C) and triple mutant homozygotes (419L/1016I/1534C) were observed at low frequency in the three cities. Mosquitoes with only the point mutation 419L were observed in Riohacha (Table 4).

Mutations in Combination
Individuals with heterozygote genotypes for 419L and 1016I positions were found at high frequency in mosquitoes from Villavicencio and Riohacha. These double heterozygotes were observed together with the mutation F1534C (Haplotype V419L/V1016I/1534C). Triple heterozygotes (V419L/V1016I/F1534C) and triple mutant homozygotes (419L/1016I/1534C) were observed at low frequency in the three cities. Mosquitoes with only the point mutation 419L were observed in Riohacha (Table 4).

Allelic Frequencies in Absence of Insecticide
For the V419L mutation, 76.92% of mosquitoes presented the wild genotype in comparison with the 32.91% observed in the parental population. In the F8 population, the homozygote-mutated genotype was not observed. The same situation was observed for the 1016I mutation, with a change Insects 2018, 9, 23 8 of 12 of 26.96% to 76.92%. Conversely, for the 1534 mutation, the homozygote-mutated genotype allelic frequencies were not significantly affected (a change of 0.94 to 0.83).

Discussion
Aedes aegypti has been reported in around 700 municipalities from the 32 departments in Colombia [34]. Notably the cases of arboviruses transmitted by this vector have been remained stable or have increased over time, which is probably due to the broad distribution of this mosquito. The main way of preventing virus transmission is by using chemical insecticides. In order to design well-structured and successful insecticide-based control programs, it is important to know the insecticide resistance status of Ae. aegypti in the different regions of the country. Molecular surveillance of insecticide resistance is an important component for the design of effective control campaigns.
In this study, we assessed the levels of resistance to λ-cyhalothrin from three Colombian cities. Riohacha and Villavicencio are cities where use of insecticides is widespread; whilst in Bello they are rarely used. DNA sequencing of domains I, II and III of the sodium channel gene from resistant mosquitoes collected in Villavicencio revealed the presence of three point mutations. To establish an association between kdr mutations and pyrethroid resistance, we compared the resistance ratio with the allelic frequencies of each mutation observed in these populations. Interestingly, the resistant mosquito populations (Villavicencio and Riohacha) presented the V1016I mutation at a higher frequency than susceptible population (Bello). The 1016I allele was reported for the first time in Colombia in 2014, but no association with resistance to λ-cyhalothrin was found [4]. Recently, Aguirre-Obando et al. found that this mutation was present at a low frequency in mosquito populations from Colombia with susceptibility to temephos [27]. Together these results suggest that this allele is circulating in Colombian mosquito populations, and the uncontrolled use of insecticides could increase the frequency of this allele. Moreover, we have demonstrated a clear difference of allele 1016I between resistant and susceptible populations. In line with our results, a V1016G mutation was positively correlated with resistance to pyrethroids in Ae. aegypti populations from China [32], and this mutation appears to be the most important kdr mutation in this region. Our results provide strong evidence that the mutation V1016I also contributes to pyrethroid resistance. However, Du et al. found that V1016I did not alter channel sensitivity when they tested the functionally of this mutation in Xenopus, whilst V1016G mutation did have a significant functional effect [35]. Further studies are therefore needed to demonstrate the role of the 1016I allele.
The frequency of the 1534C allele in mosquito populations from Villavicencio, Riohacha, and Bello was 0.63, 0.71, and 0.56, respectively. There was no clear difference in the frequency of the 1534C allele between resistant and susceptible mosquito populations. This result suggests that this mutation apparently does not have a resistance effect with λ-cyhalothrin, although some studies revealed that this mutation reduces the sensitivity to permethrin [36,37]. Interestingly, permethrin is a type I pyrethroid, while λ-cyhalothrin belongs to type II pyrethroids; this could explain why this mutation, despite being at a high frequency in the Bello population, did not correlate with resistance.
We report for the first time the V419L mutation in natural populations of Ae. aegypti from Colombia. This mutation was positively correlated with resistance to λ-cyhalothrin. The frequency of this mutation was closely related to the frequency of the mutation at position 1016, with values of 0.46, 0.30, and 0.06, for Villavicencio, Riohacha, and Bello, respectively. Recently, Haddi et al. (2017) reported the presence of this mutation in resistant mosquitoes from Brazil, and this mutation together with mutation F1534C increased strongly the resistance to both type I and II pyrethroids [38]. This result suggests that domain I has a significant effect on the resistance to these insecticides. New studies using functional genomics should be performed to describe the role of V419L mutation in the sodium channel function. In light of our current findings and those of Haddi et al. (2017), we propose that V419L mutation should be taken into account in future studies investigating target site mutations in response to the use of pyrethroids.
A number of mosquitoes in this study harbored only one mutation, and others presented two or three mutations. Interestingly, the V419L mutation was found more frequently alone in some individuals, indicating that it is the first to appear in these mosquitoes. This result does not support those reported by Vera-Maloof et al., which indicated that F1534C mutation was selected first [22]. In addition, our results support the origin of this mutation occurring in independent events and on separate haplotypes. Future studies are required to evaluate if this mutation originated in each population or if it was introduced to Colombia. On the other hand, the additive effect of kdr mutations on resistance to pyrethroids, have been previously reported [11,35,39]. The mutations 1016G and 1534C increased the resistance to deltamethrin compared with 1534C homozygote individuals [40]. Likewise in mosquito populations from Asia, the mutations V1016G and F1534C have been reported separately in two cities [16]. However, in Mexico, both mutations (V1016I and F1534C) appear in the same haplotype, with subsequent strong selection for the linked mutations [22]. The effect of the double mutations V419L and V1016I therefore requires further study.
Different genotypes observed between cities are probably due to the populations having different genetic backgrounds and/or being exposed to differential usage of insecticides. The first option is not supported by microsatellite studies as they show that there is genetic flow between the mosquitoes in the three cities [41]. The second option is therefore more likely. Riohacha and Villavicencio have higher incidences of dengue, Zika, and chikungunya than Bello and consequently insecticides are used more widely in these two cities. The differences between the allelic frequencies indicate that selection pressures on Ae. aegypti are occurring at different levels in these cities.
In conclusion, we propose that the observed resistance to λ-cyhalothrin by the populations of Ae. aegypti from Villavicencio and Riohacha is mediated by the presence of mutations at positions 419 and 1016. Recently, Ponce-García et al., reported that the F1534C and V1016I mutations are widespread in Puerto Rico populations of Ae. aegypti, but they are not involved in resistance to permethrin [42]. It is likely that other mutations such as V419L and/or other mechanisms such as metabolic resistance could be involved in these populations.
We are aware that, while evidence of kdr has been found, we cannot discount the role of other resistance mechanisms conferring the phenotype. Most notable is metabolic resistance, such as the involvement of cytochrome P450s, which have been shown to play a role in conferring resistance in both Ae. aegypti and other mosquito vectors particularly Anopheles gambiae [5,19,38,43,44]. We are currently using transcriptomics to assess the role of metabolic resistance and other mechanisms, including cuticular resistance, in resistant populations from Colombia.
Finally, the indiscriminate use of insecticides to control mosquito populations has led to the development of resistance. Therefore, it is vital to carry out continuous and appropriate monitoring of the presence of these mutations and other resistance mechanisms associated with the loss of susceptibility in populations of Ae. aegypti. This would allow for the implementation of an effective control program that could counteract the presence of arboviruses transmitted by this mosquito in Colombia. For successful dengue, chikungunya, and Zika control, two key elements are needed. Firstly, it is important to know the status of resistance in different regions of the country to design bespoke and well-structured strategies. Secondly, it is important to provide training for the poorest communities that are most at risk and in time design sustainable control strategies for these communities.

Conclusions
A novel mutation, V419L, was found in Ae. aegypti from Colombia. A significant association was found between this mutation and pyrethroid resistance, as well as the mutation V1016I. The allele F1534C occurred frequently in the three populations, probably indicating that this mutation could be conferring resistance to other insecticides. Further studies are required to confirm this hypothesis.


s2fieldsofstudy: Biology Medicine

text:
Amenamevir: Studies of Potential CYP2C8‐ and CYP2B6‐Mediated Pharmacokinetic Interactions With Montelukast and Bupropion in Healthy Volunteers

Abstract Amenamevir (formerly ASP2151) induces cytochrome P450 (CYP)2B6 and CYP3A4 and inhibits CYP2C8. We conducted 2 studies, 1 using montelukast as a probe to assess CYP2C8 and the other bupropion to assess CYP2B6. The montelukast study examined the effect of amenamevir on the pharmacokinetics of montelukast in 24 healthy men: each subject received montelukast 10 mg alone, followed by montelukast 10 mg with amenamevir 400 mg, or vice versa after a washout period. In the bupropion study, 24 subjects received a single dose of 150 mg bupropion on days 1, 15, 22, and 29, and repeated once‐daily doses of 400 mg amenamevir on days 6‐15. Amenamevir increased peak concentration and area under the concentration‐time curve of montelukast by about 22% (ratio 121.7%, 90%CI [114.8, 129.1]; 121% [116.2, 128.4], respectively) with a similar increase in hydroxymontelukast (ratio 121.4%, 90%CI [106.4, 138.5]; 125.6 % [111.3, 141.7]). Amenamevir reduced peak concentration and area under the concentration‐time curve of bupropion by 16% (84.29%, 90%CI [78.00, 91.10]; 84.07%, 90%CI [78.85, 89.63]), with recovery after 1 week; the pharmacokinetics of the primary metabolite hydroxybupropion was unaffected. Thus, amenamevir increased plasma concentrations of montelukast and decreased those of bupropion, but it did not do so enough to require dose adjustment of coadministered substrates of either CYP2C8 or CYP2B6.

Amenamevir is an orally available non-nucleoside analogue inhibitor of the helicase-primase enzyme complex essential for herpesvirus DNA replication. It has activity against herpes simplex types 1 and 2 and varicella zoster virus and has shown promise in early clinical trials. 1 Herpes simplex infection is extremely common and can lead to serious complications such as encephalitis and hepatitis, especially in immunocompromised patients. All other currently licensed medicines for herpesvirus infections act by inhibiting DNA polymerase and are first activated by viral enzymes so that they are active only in virus-infected cells. 2 The treatment of choice since the late 1970s has been acyclovir, which is activated by viral thymidine kinase, which some strains lack, leading to resistance in about 1% in the general population and 3.5% to 14% in immunocompromised patients. 3 Alternative treatment options are therefore required.
The pharmacokinetics and metabolism of amenamevir have previously been investigated in preclinical and clinical studies undertaken by Maruho (unpublished data held by the company). In healthy volunteers amenamevir was rapidly absorbed following single oral doses with maximum plasma concentration (C max ) 1.33-2.5 hours independent of dose. During repeated dosing, C max and the area under the concentration-time curve (AUC) decreased between the first dose (day 1), an intermediate dose (day 9), and the last dose (day 16), suggesting autoinduction of metabolism, probably via cytochrome P450 (CYP)3A4/5. AUC and C max increased less than dose proportionally in dose-ranging studies. Plasma protein binding of 14 C-amenamevir in humans was about 75%, and t ½ about 7-8 hours. After a single oral dose of 200 mg 14 C-amenamevir, 74.6% of 14 C-radioactivity was recovered in feces. These data are consistent with later pharmacokinetic analyses of studies in healthy volunteers 4 and patients. 5 The metabolic profile of amenamevir was evaluated in vitro using pooled liver microsomes and cryopreserved hepatocytes of mouse, rat, rabbit, dog, and human origin. The major human metabolite was a monohydroxy derivative (AS1955888-00, Mo4, R5), which was also detected in all other species tested.
The potential for amenamevir to inhibit cytochrome metabolism was evaluated in vitro using human liver microsomes and CYP-selective substrates. Amenamevir had weak direct inhibitory activity against CYP2C8 (IC50 69 μmol/L) but no activity against CYP2B6 in the range of concentrations studied (0.1 to 100 μmol/L) (IC50 >100 μmol/L).
To investigate the potential of amenamevir to induce CYP2B6, CYP2B6 activity and gene expression were measured in human hepatocytes with or without pretreatment for 72 hours with amenamevir. After pretreatment with 2, 20, and 200 μmol/L amenamevir, CYP2B6 activity increased 1.4-, 1.9-, and 2.9-fold, respectively, and CYP2B6 gene expression increased by 1.9-, 4.5-, and 4.8-fold, respectively, compared with the negative control. Those results suggest that amenamevir has the potential to induce CYP2B6.
We report here 2 studies in healthy volunteers that were part of a series of investigations to elucidate potential interactions. Two probe substrates were selected: montelukast to investigate effects of ame-namevir on CYP2C8 and bupropion to test effects on CYP2B6.
(1) Montelukast is an orally available leukotriene receptor antagonist used for the preventive treatment of asthma and seasonal allergic rhinitis. It is metabolized to its primary metabolite methylhydroxymontelukast (M6) via 36-hydroxylation by CYP2C8, which is also responsible for subsequent conversion to the secondary metabolite montelukast dicarboxylic acid (M4). CYP2C8 is thought to account for 70% to 80% of montelukast's metabolism in vivo, with most of the remainder accounted for by CYP3A4-mediated conversion to M5a and M5b metabolites: less than 0.2% is eliminated in urine. [6][7][8][9][10][11] This, together with its benign safety profile, makes it an appropriate choice as a CYP2C8 probe in healthy subjects. (2) Bupropion is an orally available antidepressant and nonnicotine smoking cessation aid that is thought to exert its activity by reuptake inhibition of norepinephrine and dopamine and by noncompetitive antagonism of nicotinic acetylcholine receptors. 12,13 It is metabolized in vivo to 3 primary active metabolites: hydroxybupropion, threohydrobupropion, and erythrohydrobupropion. Hydroxybupropion has around 50% of the activity of bupropion, but, as its C max is 4-7 times greater and AUC about 10-fold greater, it is responsible for most of bupropion's activity. 14,15 Threohydrobupropion concentrations are about 5-fold greater than that of bupropion, and erythrohydrobupropion concentrations are similar to those of bupropion, but they are only 20% as potent as the parent. 16 Hydroxybupropion formation is closely correlated with CYP2B6 activity in human microsomes and with CYP2B6-specific Ndemethylation of S-mephenytoin and can be 95% inhibited by a CYP2B6-specific monoclonal antibody. Bupropion's other metabolites are formed independently of cytochrome activity. 17

Montelukast Study
This was a randomized, single-center, open-label, 2-way crossover drug-drug interaction study to investigate the effect of a single oral dose of amenamevir on the pharmacokinetics of a single oral dose of montelukast in 24 healthy men. Each subject received montelukast 10 mg alone, followed 2 weeks later by montelukast 10 mg with amenamevir 400 mg, or vice versa; 400 mg amenamevir was selected because it was the projected therapeutic dose daily dose in Japan. Likewise, 10 mg montelukast was selected on the basis that it is the adult daily dose for treatment of asthma and seasonal allergies in adults and is well tolerated in healthy volunteers. 18 Subjects were resident on the ward from the afternoon of the day before dosing (day -1) until day 4 and subsequently returned to give blood samples for pharmacokinetic analysis at 7 and 14 days after their dose of amenamevir. Plasma samples for analysis of montelukast and methyl-hydroxymontelukast were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3,4,5,6,8,10,12,16,20,24,30,36,48, and 72 hours after dosing. Subjects returned for a final follow-up visit about 30 days after their last dose.

Bupropion Study
This was a single-center, open-label drug-drug interaction study to investigate the effect of amenamevirmediated CYP2B6 induction on the probe substrate bupropion. Subjects received a single dose of 150 mg bupropion on days 1, 15, 22, and 29 and once-daily doses of 400 mg amenamevir on days 6-15 ( Figure 1). Subjects were resident on the ward on 3 occasions: from the day before the first dose (day -1) until day 19; from day 21 until day 26; and from day 28 until day 33. Subjects returned for an outpatient visit on day 36 and a final follow-up visit on day 45. Blood samples for assay of bupropion and hydroxybupropion were taken before each dose of bupropion and at 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 24, 36, 48, 60, 72, and 96 hours afterward (Supplementary Table S1).
Blood samples for assay of amenamevir were taken at predose on days 6-14, and before and frequently up to 24 hours after dosing with amenamevir on day 15.

Safety and Tolerability Assessments
Safety and tolerability assessments included adverse events, vital signs, 12-lead electrocardiogram, physical examination, and clinical laboratory tests.

Assays
Plasma concentrations of all compounds and metabolites were determined using validated liquid chromatography tandem mass spectrometry by Shin Nippon Biomedical Laboratories, Ltd (Tokyo, Japan) and Analytical Services International (London, UK). The lower limit of quantification (LLQ) was 5 ng/mL for amenamevir, 10 ng/mL for montelukast, 1 ng/mL for methyl-hydroxymontelukast, 2.5 ng/mL for bupropion, and 10 ng/mL for hydroxybupropion.
Blood samples for bupropion, hydroxybupropion, montelukast, and methyl-hydroxymontelukast were collected in lithium heparin tubes. Plasma was separated by centrifugation at ß1500g for 10 minutes at 4°C, then stored at -20°C or below until analysis by Analytical Services International as described below.
Blood samples for amenamevir and AS1955888-00 were collected in sodium heparin tubes and prepared as above before assay by Shin Nippon Biomedical Laboratories, as described by Adeloye et al. 19 Bupropion Preparation of Internal Standard and Calibrators. Bupropion, hydroxybupropion (calibrators), internal standard 1 (bupropion-d 9 ), and internal standard 2 (hydroxybupropion-d 6 ) were extracted from 100 μL of human plasma by protein precipitation. The internal standards were prepared by diluting 50 μL of bupropion-d 9 stock + 200 μL hydroxybupropion-d 6 stock with 20 mL acetonitrile + 20 mL deionized water to yield a final concentration of 125 ng/mL bupropion and 500 ng/mL hydroxybupropion. For calibration, a substock solution containing bupropion and hydroxybupropion was prepared by diluting 50 μL of bupropion stock (1 mg/mL) and 200 μL hydroxybupropion stock (1 mg/mL) in 20 mL of human plasma.   Analytical Method. We then added to a 2-mL polypropylene tube 100 μL of calibrator or plasma sample, 50 μL of internal standard (125 ng/mL bupropion-d 9 and 500 ng/mL hydroxybupropion-d 6 ), 200 μL precipitating agent (acetonitrile), and 200 μL of deionized water.
The sample was vortexed for 2 minutes before centrifugation and 150 μL supernatant was transferred to an autosampler tube and submitted to analysis by liquid chromatography tandem mass spectrometry.
HPLC Conditions. The analytic column was an Onyx Monolithic C18 column (100 mm × 3 mm) (Phe-nomenex, Torrance, California). The mobile phase comprised methanol 1000 mL, deionized water 1000 mL, plus 10 g ammonium formate. Elution was isocratic at a flow rate of 300 μL/min. MS Settings. An Applied Biosystems (Foster City, California) API4000 mass spectrometer was used with turbo ion spray and positive ionization. Mass ratios were 240.0/184.0 amu for bupropion, 249.0/185.0 amu for the bupropion standard, 256.0/167.0 amu for hydroxybupropion, and 282.0/130.0 amu for the hydroxybupropion standard.
Variability. Variabilities of bupropion and hydroxybupropion determinations are shown in Table 2.

Montelukast
Preparation of Internal Standard and Calibrators. Montelukast, montelukast 1,2-diol (calibrators), and montelukast-d 6 (internal standard) were extracted from 100 μL of human plasma by protein precipitation. Working solutions at a concentration of 1000 ng/mL were prepared by diluting an aliquot (25 μL) of stock solution (1000 μg/mL) with analyte-free plasma up to a final volume of 25 mL.
Analytical Method. We added to a 2-mL polypropylene tube 100 μL of calibrator or plasma sample, 25 μL of internal standard (montelukast-d 6 ), and 500 μL of precipitating solution (acetonitrile:methanol, 1:1). The sample was vortexed for 2 minutes before centrifugation. The supernatant was transferred into a polypropylene autosampler tube, and 10 μL was injected onto the high-precision liquid chromatography tandem mass spectrometer. Because the drug, metabolite and internal standard are all light sensitive, steps were taken to minimize exposure to light throughout. HPLC Conditions. The column used was a Monolith PR-18e column (100 mm × 3.0 mm). The mobile phase comprised 1000 mL acetonitrile, 1000 mL deionized water, and ammonium formate 5 g. Elution was isocratic at a flow rate of 300 μL/min. Variability. Variabilities of montelukast and montelukast 1,2-diol are shown in Table 3.

Sample Size
A sample size of 24 subjects for the montelukast study was selected based on a statistical power calculation. From published studies, estimates of between-subject percentage coefficient of variation (%CV) for C max and AUC 0-Ý were 20% to 30%. 7 Statistical power was estimated by 10,000 simulations using the model below. The model included treatment (montelukast in combination with amenamevir and montelukast alone), sequence, and session as fixed effects and subject as a random effect.
ln PKparameter ijkl = Treatment i + Session j + Sequence k + Subject l + e ijkl Given a %CV of 25, a true mean ratio of 1.0, and an acceptance limit of 80% to 125% for 90%CI, a sample size of 24 subjects would provide at least 86% power to detect an interaction, as defined by the criteria above. In practice, within-subject variability was likely to be less than between-subject variability.
A sample size of 24 subjects was selected for the bupropion study based on a statistical power calculation. Two previous studies of single doses of 150 mg bupropion gave within-subject estimates of %CV for C max and AUC 0-Ý between 15% and 30%. 20,21 Statistical power was estimated by simulation (the number of simulations was 10,000) using the model below. The model included treatment (bupropion in combination with amenamevir and bupropion alone) as a fixed effect and subject as a random effect. ln (PKparameter) ij = Treatment i + Subject j + ε ij Given a %CV of 22.5 (the average of 15% and 30%), a true mean ratio of 1.0, and an acceptance limit of 80% to 125% for the 90%CI, a sample size of 24 subjects would provide 93% power.

Pharmacokinetic and Statistical Analysis
Pharmacokinetic parameters were calculated using WinNonlin (Certara, Princeton, New Jersey) version 6.3. Interactions were tested using an equivalence analysis.
To assess the effect of amenamevir on montelukast, montelukast in combination with amenamevir was compared with montelukast alone. The AUC 0-Ý and C max were logarithmically transformed and subjected to ANOVA with treatment (montelukast alone or montelukast in combination with amenamevir), sequence, and session as fixed effects and subject as a random effect. Absence of a clinically significant effect of amenamevir on the pharmacokinetics of montelukast was concluded if the 90%CI for both AUC 0-Ý and C max ratios fell within the prespecified interval of 80% to 125%. To assess the effect of amenamevir on methylhydroxymontelukast, montelukast in combination with amenamevir was compared with montelukast alone using the method described above.
To assess the effect of amenamevir on CYP2B6 activity, bupropion in combination with amenamevir (Test) was compared with bupropion alone on day 1 (Reference). Briefly, AUC 0-Ý and C max were logarithmically transformed and subjected to ANOVA with treatment (bupropion alone or bupropion in combination with amenamevir) as a fixed effect and subject as a random effect. The least-squares mean of the treatment difference and its 90%CI, were transformed to the original scale in order to obtain the mean AUC 0-Ý and C max ratios (bupropion in combination with amenamevir relative to bupropion alone). Absence of a clinically significant drug-drug effect of amenamevir on bupropion  was concluded if the 90%CI for both AUC 0-Ý and C max ratios fell within the prespecified interval of 80% to 125%. To assess the effect of amenamevir on hydroxybupropion, bupropion in combination with amenamevir (Test) was compared with bupropion alone (Reference) using the method described above.
To assess the recovery of CYP2B6 activity, the effect of amenamevir on bupropion was assessed on days 22 and 29 using the method described above. The 95%CIs for the difference in means (day 22 vs day 1 and day 29 vs day 1) were used to determine how long it took for CYP2B6 activity to return to normal.

Montelukast Study
Mean age, weight, and body mass index were similar across treatment sequences. There were no notable differences in race, ethnicity, or the subjects' usual cigarette smoking or alcohol intake habits.
From about 1 hour after dosing until 30 hours postdose, mean plasma concentrations of montelukast were about 1.2-fold higher when montelukast was coad-ministered with amenamevir than when given alone ( Figure 2, Supplementary Table S1). After both treatments, mean montelukast plasma concentrations approached LLQ at 24 hours after dosing, and were below the limit of quantification by 30-36 hours after dosing.
At all time points from 1 to 30 hours after dosing, mean plasma concentrations of methylhydroxymontelukast were 1.2-to 2.2-fold higher when montelukast was taken with amenamevir than when given alone (Figure 3, Supplementary Table S2).
Both after montelukast alone and combined with amenamevir, mean methyl-hydroxymontelukast plasma concentration approached LLQ at 20 hours after dosing, and was below the limit of quantification by 36-48 hours after dosing.
Both C max and AUC 0-Ý of montelukast increased significantly, by about 22% (Tables 4 and 5   The elimination half-life of montelukast was slightly increased, and apparent total body clearance was slightly reduced after coadministration of amenamevir (neither was statistically significant). Overall, 7 subjects (29.2%) reported 9 adverse events (AEs): each of those subjects reported an AE after 10 mg montelukast alone, and 2 subjects (8.3%) also reported an AE after 10 mg montelukast with 400 mg amenamevir. Most AEs occurred at least 72 hours after dosing, and only 1 subject required concomitant medication within 72 hours after dosing (paracetamol for pharyngitis).
All AEs were of mild or moderate intensity. Moderate AEs were more frequent after 10 mg montelukast alone (16.7% of subjects) than after 10 mg montelukast with 400 mg amenamevir (4.2% of subjects). Mild AEs were reported by more subjects (12.5%) after 10 mg montelukast alone than after 10 mg montelukast with 400 mg amenamevir (4.2% of subjects).

Bupropion Study
From about 2 to 6 hours after dosing, mean plasma concentrations of bupropion were lower when bupropion was taken with amenamevir than when it was taken alone, whether on day 1 (before amenamevir had been given), or on days 22 and 29 (a week or more after repeated amenamevir dosing had ended (Figure 4, Supplementary Table S3).
After all treatments, mean bupropion plasma concentrations were approaching the LLQ at 36 hours after dosing and were below LLQ by 96 hours after dosing.
Bupropion concentrations on day 22 were similar to those on day 1 (ratio 104.07, 95%CI [94.74, 114.32]), indicating that the induction of CYP2B6 by repeated doses of amenamevir had remitted by 1 week after the last dose of amenamevir (Table 7). Figure 5 shows the mean plasma concentrations of hydroxybupropion plotted against time. On day 1, the AUC of the primary metabolite hydroxybupropion was about 15-fold that of the parent molecule ( Table 8). Coadministration of amenamevir with bupropion had no significant effect on plasma concentrations of hydroxybupropion.
Median time to peak concentration of bupropion was 3 hours on days 1, 15, 22, and 29 (Table 9). Mean t ½ was shortened by amenamevir on day 15 by around 2 hours compared with that on day 1; consistent with that finding and with the reduction in AUC of bupropion, apparent total body clearance of bupropion was slightly higher when given with amenamevir than when given alone.
Overall, 12 subjects (50.0%) reported AE. Headache was the most frequently reported AE. Cannula site      pain, rhinitis, back pain, and anxiety were reported by 2 subjects each. No other AE was reported by more than 1 subject.

Discussion Montelukast
Coadministration of amenamevir with montelukast caused a 22% increase in both C max and AUC of montelukast. The data failed to exclude a clinically significant drug-drug effect because the 90%CI of the log ratio montelukast versus amenamevir plus montelukast did not fall within the range 80% to 125% for both C max and AUC. The increase in C max is consistent with a reduction in first-pass intestinal and hepatic extraction. Apparent clearance and t ½ showed a trend toward reduction in rate of elimination of montelukast.
Our results are consistent with the in vitro finding that amenamevir is a weak inhibitor of CYP2C8. An increase of 22% in plasma concentration of a drug that is a substrate of CYP2C8 would be of importance only for medicines with a very narrow therapeutic window, so reduction of the dose of concurrent medication is unlikely to be necessary in clinical practice. Coadministration of amenamevir with montelukast was associated with a mean 22% increase in plasma C max and AUC of montelukast's major metabolite, methyl-hydroxymontelukast. That increase mirrored the 22% increase in C max and AUC of parent montelukast and is likely due to the metabolite also being dependent on CYP2C8 for subsequent conversion. Thus, rather than concentration decreasing as might be expected, it increased in parallel due to inhibition in a manner consistent with the effect on the parent compound.
Montelukast is metabolized not only by CYP2C8 but also by CYP3A4, of which amenamevir is both a substrate and inducer. However, CYP2C8 is believed to account for about 80% of montelukast's metabolism: inhibition of CYP2C8 by gemfibrozil increases the AUC of montelukast 4-fold. 9 The same study also showed that inhibition of CYP3A4 by itraconazole did not affect the metabolism of montelukast, in contrast with later findings 7,9 : in 1 study, inhibition of CYP3A4 increased AUC of montelukast by 144%. 7 Thus, current evidence is contradictory, but it is certainly conceivable that induction of CYP3A4 activity by amenamevir might reduce plasma concentrations of montelukast. However, amenamevir was given only as a single dose together with a single dose of montelukast in this study, and it is unlikely that CYP3A4 induction by amenamevir could have developed quickly enough to have influenced the results.
In respect to safety and tolerability, both single and combined dosing were equally well tolerated in the healthy men in this study.

Bupropion
Amenamevir 400 mg once daily for 10 days decreased both C max and AUC 0-Ý of bupropion by about 16%. The results did not exclude a significant effect of amenamevir on bupropion exposure, as the 90%CI of the least-squares geometric mean ratios (bupropion with amenamevir to bupropion) of C max and AUC 0-Ý did not fall within the prespecified interval of 80% to 125%. At 1 and 2 weeks (days 22 and 29) after the final dose of amenamevir, C max , and AUC 0-Ý of bupropion were similar to those on day 1, indicating that CYP2B6 activity had returned to pretreatment levels within 1 week after the last amenamevir dose. That is consistent with the observation that even extensive induction of CYP2B6 by rifampicin is fully reversed by 2 weeks after cessation of rifampicin treatment. 22 Prior treatment with amenamevir 400 mg once daily for 10 days did not affect C max or AUC 0-Ý of hydroxybupropion, which is bupropion's main metabolite. Because amenamevir treatment did reduce AUC 0-Ý of bupropion parent drug, there was a small change in the ratio of AUC 0-Ý for hydroxybupropion:bupropion; before amenamevir the ratio was 15.2; after treatment with amenamevir, the corresponding ratio was 17.7. Those results are consistent with the findings of Laizure et al, who showed that the more powerful induction of CYP2B6 by rifampicin reduced AUC 0-Ý of bupropion by 3-fold, but doubled AUC 0-Ý of the hydroxybupropion metabolite. 15 The minor reduction (by 16%) in plasma concentrations of bupropion, coupled with no change in concentrations of the active metabolite hydroxybupropion, would be unlikely to warrant dose adjustment when amenamevir is coadministered with CYP2B6 substrates.
Our AE data showed that repeated doses of 400 mg amenamevir, given alone or with a single dose of 150 mg bupropion, were well tolerated in healthy men.

Conclusions
The minor increase (22%) of the concentration of montelukast and the similarly marginal reduction (16%) in plasma concentration of bupropion suggest that dose adjustment is unlikely to be necessary when amenamevir is coadministered with CYP2C8 or CYP2B6 substrates.