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Time scale for cAMP-dependent pathway cascades


What is the time scale for cAMP-dependent pathway cascades that start at the level of ligand binding to a G-protein receptor and finish at the level of gene transcription regulation?

For example, when corticotropin releasing hormone binds to CRH receptor 1, a cAMP-dependent pathway cascade is initiated, which ultimately leads to an upregulation in the transcription of proopiomelanocortin (POMC) mRNA.

I recognize that this is a very broad question, that is likely highly case dependent (i.e. different from receptor-ligand pair to receptor-ligand pair). Nonetheless, are we looking at several hundred milliseconds? Several seconds? Several minutes?

In the case of CRH, how quickly does CRH-CRH receptor 1 binding lead to the upregulated transcription of POMC mRNA?

If there are any good sources to read about this, I would greatly appreciate it!


Frontiers in Microbiology

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Annette E. Kaiser

Faculty of Medicine, University of Duisburg-Essen, Germany

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Matthew G. Gold

University College London, United Kingdom

Enno Klussmann

Max Delbrück Center for Molecular Medicine, Helmholtz Association of German Research Centers (HZ), Germany

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PDK-1 Signaling Pathway

As a member of AGC kinases family, PDK-1, a protein of 556 amino acids, is composed of serine and threonine kinases. The catalytic domain of these serine and threonine kinases show a sequential similarity with cAMP-dependent protein kinase 1 (PKA), cGMP-dependent protein kinase (PKG) and protein kinase C (PKC). There are two phosphorylation sites that many AGC kinases and they are to regulate the activity of AGC kinases. The one which is located within the kinase domain is called activation loop, while the other one named hydrophobic motif is located in a region which is adjacent to the catalytic domain. The enzymatic full activation is triggered by phosphorylation of activation loop and hydrophobic motif which is catalyzed by an autophosphorylation reaction. n addition, PDK-1 kinase also has a PH domain. The PH domain is used for mainly interacting with phosphatidylinositol (3,4)-bisphosphate and phosphatidylinositol (3,4,5)-trisphosphate that is essential in localization and activation of some of membrane associated PDK-1's substrates such as AKT. The kinase domain has three ligand binding sites, namely the substrate binding site, the ATP binding site, and the docking site which is also known as PIF pocket. The PIF pocket interacts and binds some PDK-1 substrates such as S6K and Protein kinase C . Several small molecule allosteric activators of PDK-1 in previous studies were shown to inhibit the activation of substrates selectively. Instead of binding to the active site, these small molecules enable PDK-1 to activate other substrates which do not require docking site interaction. So far, there is no well-defined inhibitor for PDK-1. One of the most important substrate of PDK-1 is AKT. The activation of AKT requires a proper orientation of the kinase and PH domains of PDK-1 and AKT at the membrane. Many proteins that interact with PDK-1 via a hydrophobic motif named PDK-1 interacting fragment (PIF). This fragment lies in a conserved Phe-Xaa-Xaa-Phe/Tyr-Ser/Thr-Phe/Tyr sequence where phosphorylation occurs in serine or threonine. Although PDK-1 is the only member of the AGC kinase family that lacks the hydrophobic motif, it has a hydrophobic pocket that is also called PDK1 interacting fragment pocket (PIF pocket). This plays an important role in the interaction between PDK-1 and the hydrophobic motif of the targeted protein kinases. PIF pocket binds the PIF hydrophobic motif via a docking site, which promotes the phosphorylation of the targeted kinase at the activation loop. PDK-1 is significant for the activation of many other AGC kinases including PKC , S6K , SGK and AKT /PKB via phosphorylation. An important role for PDK-1 is in the signaling pathways activated by several growth factors and hormones including insulin signaling.

Figure 2. PDK-1 primary structure

PDK-1 signaling pathway

PDK-1 signaling cascade

Class IA phosphoinositide 3 kinases (PI3Ks) which is composed of p110α–p85, p110β–p85 and p110δ–p85 are recruited to the membrane by direct interaction of the p85 subunit with the activated receptors such as platelet derived growth factor receptor or by the association with adaptor proteins interacted with the receptors like insulin receptor substrate 1 after the stimulation by growth factor and subsequent activation of receptor tyrosine kinases (RTKs). Next, the activated p110 catalytic subunit converts phosphatidylinositol 4,5 bisphosphate (PtdIns(4,5)P2) to phosphatidylinositol 3,4,5 trisphosphate (PtdIns(3,4,5)P3) at the membrane, which offers docking sites for downstream signaling proteins, namely putative 3 phosphoinositide dependent kinase 1 (PDK-1) and serine–threonine protein kinase AKT (also known as protein kinase B). PDK-1 phosphorylates AKT, which activates AKT. This activation initiates a wide range of downstream signaling events. The interaction of G protein coupled receptors (GPCRs) and Gβγ subunit of trimeric G proteins can lead to the direct activation of the class IB PI3K (p110γ–p101). GPCR can activate the p110β and p110δ subunits as well. At the same time, PTEN (phosphatase and tensin homologue) antagonizes the PI3K action through the dephosphorylation of PtdIns(3,4,5)P3.

Figure 3. PDK-1 signaling cascade

Downstream signaling

In previous studies, PDK-1 has shown its function to activate many other members of AGC kinase family like p70S6K, SGK, p90RSK and the members of PKC family via phosphorylation. Different from some mechanisms of other kinases cascades that it is linear and consecutive activation events, the phosphorylation through PDK-1 requires to be coupled with another convergent signal. For example, the activation of AKT requires a proper orientation of the kinase and PH domains of PDK-1 and AKT at the membrane. At the same time, other substrates need the phosphorylation of their hydrophobic motif by other kinases. In addition to the conventional substrates among AGC kinase family, PDK-1 also show the phosphorylation ability to other proteins. Polo-like kinase 1, p21- activated kinase (PAK) and adhesions proteins like β3 integrin are also targets of PDK-1. In addition, some downstream effectors can also be regulated by PDK-1 via kinase independent mechanisms, including myotonic dystrophy kinase-related CDC42-binding kinase alpha (MRCKα) and Rho-associated protein kinase 1 (ROCK1). At the first time that PDK-1 was discovered, scientists found that it was for phosphorylating the AKT activation loop at residue Thr308, which was important for the activation of AKT. There would be further phosphorylation of AKT at residue Thr308 that was dependent on PtdIns(3,4,5)P3. Even if AKT is the primary target of PDK-1, plenty of other kinases are also downstream targets. For instance, serum glucocorticoid-dependent kinase (SGK) which is one member of AGC family kinases, p90 ribosomal protein S6 kinase (RSK), p70 ribosomal protein S6 kinases (S6K) and atypical protein kinase C (PKC) isoforms are known to be a direct target of PDK-1. In these interactions, PDK-1 phosphorylates specific serine/threonine residues of target proteins’ activation loop. Therefore, PDK-1 has been regarded as the master regulator in AGC-related signaling pathways and plays a significant role in controlling cell motility proliferation and survival.

Pathway regulation

The regulation of signaling activated by PDK-1 involves different mechanisms
Since the PH domain of PDK-1 interacts with phosphatidylinositol 3,4-bisphosphate PtdIns(3,4)P2, PtdIns(4,5)P2 and phosphor-nositides PtdIns(3,4,5)P3 with high affinity, PDK-1 is anchored at the plasma membrane. This close association shows a potential PI3K-dependent PDK1 membrane translocation. Even though the membrane localization has been greatly studied, the dynamic mechanism is still not well-described. Especially, with the stimulation of growth factors, whether PDK-1 is constitutively localized to the plasma membrane or it trans-locates to the plasma membrane is unknown yet. The mechanism of PDK-1 membrane localization is important for the phosphorylation of AKT at the residue Thr308. The PI3K activation induces the membrane translocation of AKT, which results in the co-localization of PDK-1 at the plasma membrane. The membrane recruitment of AKT directly leads to the conformational changes and these changes promote the phosphorylation of AKT at Thr308 by PDK-1. When the PH domain of AKT is mutated, the membrane recruitment of AKT is severely destroyed, which leads to the significant decrease of AKT phosphorylation by PDK-1. The deletion of the PDK1-PH domain significantly decreases AKT phosphorylation as well. PDK-1 also shows a manner of dimeric conformation via PH domain interaction of two PDK-1 monomers. This interaction plays an important role in the phosphorylation of AKT. If the formation of the homodimer is inhibited by destroying the PH domain, the more active form monomeric PDK-1 is released, which promote the phosphorylation of AKT.

Relationship with diseases

The hyperactivation of the PI3K/PDK-1/AKT pathway has a close association with many human cancers. PDK-1 is of paramount importance in breast cancer initiation and progress. The bigger copy number of PDK-1 is universal among patients with breast cancer. This high level of PDK-1 is related to the activation of PI3K pathway. At the same time, the mutant PI3K class 1 p110 seems to accumulate with the increasing PDK-1 copy number. The overexpression is shown to promote the transformation of mammary epithelial cells. More than 45% patients with acute myeloid leukemia (AML) have been reported that their PDK-1 level was higher. Compared to primary tumors, the locus where PDK-1 gene locates is more amplified in lymph node metastases and castration-resistant prostate cancer. At the same time, the expression level of PDK-1 protein is much higher in tumor cells compared to adjacent non-cancerous tissues in esophageal squamous cell carcinoma. In gastric carcinoma, both mRNA and protein expression level of PDK-1 are higher in tumor samples than those in adjacent normal tissues. Shorter survival has also been shown in patients with higher PDK-1 expression. The similar situation was observed in hepatocellular carcinoma. Many studies have shown that the overexpression of PDK-1 contributes a lot to a variety of cancers and is highly associated with advanced tumor stages.

Figure 4. PDK-1 alterations in cancer

However, whether PDK-1 is crucial to tumorigenesis or is just required in later stages is still not well-defined. Some studies suggested that PI3K can promote cancer via both AKT-dependent and AKT-independent regulation. Not like other AGC kinases, the phosphorylation on the activation loop of PDK-1 at serine 241, instead of by other kinase proteins, is catalyzed by itself. The de-phosphorylation of phospho-serine 241 is little efficient since phosphatases have no access to it. In such case, PDK1 has been considered constitutively active. In addition, homo-dimerization is highly related to the regulation of PDK-1 activity. The drugs targeted at PDK-1 are promising in therapies of many related cancers.


Results

Reconstructed Fg cAMP–PKA pathway based on DEGs and phenome data

Key regulators of the cAMP–PKA pathway

We reconstructed the Fg cAMP–PKA signalling pathway based on Δfac1 and Δcpk1 mutant expression data, previously characterized Fg TFs (Son et al., 2011 ) and kinases (Wang et al., 2011 ), and information from the Pathogen–Host Interaction Database (Winnenburg et al., 2008 ). FAC1- and/or CPK1-dependent key regulators were defined as TFs and PKs that were differentially expressed in one or both Δfac1 and Δcpk1 mutants. We identified 65 TFs and 22 PKs with a false discovery rate (FDR) of less than 0.05 (Table S2, see Supporting Information). According to previous phenotypic characterizations of all TF and PK knockout lines (Son et al., 2011 Wang et al., 2011 ), the mutants of 13 TFs and 15 PKs exhibited aberrant phenotypes, including defects in virulence, DON and zearalenone (ZEA) production, sporulation, sexual reproduction and the stress response (Fig. 1A). To study the transcriptional regulation of this set of key regulators, we further characterized their expression patterns using 60 Fg transcriptomics datasets available at PLEXdb (Dash et al., 2012 ). These 60 experiments included four datasets that captured: (i) the active pathogen–host interaction during the course of wheat coleoptile infection (Guenther et al., 2009 Zhang et al., 2012 ) (ii) conidial germination (Seong et al., 2008 ) (iii) sexual reproduction (Hallen et al., 2007 ) and (iv) DON induction (Gardiner et al., 2009 Jonkers et al., 2012 ).

Functional affiliation of the differentially expressed regulators shared in Fusarium graminearum Δfac1 and Δcpk1 mutants. (A) Gene–phenotype networks depict the association of known phenotypes (red nodes) with cyclic adenosine monophosphate (cAMP)-dependent transcription factors (green nodes) and protein kinases (blue nodes). Genes are differentially expressed in the Δfac1 and/or Δcpk1 mutants. Phenotype information is derived from FgTFPD (Son et al., 2011 ), a previous kinome analysis (Wang et al., 2011 ) and the Pathogen–Host Interaction (PHI) Database (Winnenburg et al., 2008 ). Phenotypes include ‘Conidia’ (conidiation), ‘Stress’ (fungal growth under stress conditions, such as oxidative and osmotic stresses), ‘Veg_growth’ [vegetative growth on potato dextrose agar (PDA) or minimal medium], ‘Sex_dev’ (perithecium and ascospore development), ‘DON’ (deoxynivalenol production), ‘ZEA’ (zearalenone production) and ‘Virulence’ (wheat head blight). (B) Heatmap of gene expression profile (fold changes) of cAMP-dependent transcription factors (green) and protein kinases (blue) during wheat infection, sexual development, conidial germination and DON-inducing conditions. Fold changes are log2 values and scale bar shows down-regulation (blue), up-regulation (yellow) and no change (black). Genes are divided into three groups (I, II and III) via hierarchical clustering of expression levels.

Using hierarchical clustering, we clustered the regulators of the cAMP–PKA pathway into three groups (Fig. 1B). Interestingly, nine of the 15 PKs (60%) were grouped into Group I, whereas 11 of the 13 TFs were grouped into Groups II and III. In agreement with the mutant data showing that most PKs involved in the cAMP signalling pathway contribute to virulence (Fig. 1A), Group I genes were highly expressed throughout the course of infection and during conidial germination. This finding may reflect the direct contribution of PKs to fungal pathogenesis. The other two groups, and particularly Group III, included most of the regulators controlling sexual reproduction. Mutants in this group of PKs, such as FGSG_04484 (FgSrb10), FGSG_04947 (FgSak1), FGSG_05734 (FgKic1) and FGSG_09274 (FgKin1), had pleiotropic defects in growth, conidiation, sexual reproduction and plant infection, indicating that the corresponding wild-type genes are involved in basic cellular processes (Wang et al., 2011 ).

We observed more inconsistencies between gene expression and phenotypes for TFs than for PKs. For example, the only two TFs (FGSG_10286, FGSG_00930) in Group I lacked a direct association with virulence, as observed for the PKs. The FGSG_00930 mutant exhibited defects in the stress response, toxin production and sexual reproduction. Only sexual reproduction was affected in the FGSG_10286 mutant. In agreement with this finding, FGSG_10286 expression was induced during sexual reproduction. Several deletion mutants of genes that are up-regulated during infection, such as FGSG_11799 and FGSG_05604, showed normal virulence. This suggests that, unlike PKs, which initiate a chain of reactions with direct functional consequences, TFs influence cell function by reprogramming the transcriptional network, which results in pleiotropic effects. Overall, our data suggest that condition-specific transcriptional regulation of cAMP-dependent regulators has a significant impact on fungal responses to environmental signals.

The Fg cAMP–PKA pathway

To decipher the gene regulatory networks involved in cAMP signalling in Fg, we measured the expression profiles of two loss-of-function mutants, Δfac1 and Δcpk1, which lack key components of the cAMP signalling pathway. We identified genes that were differentially expressed between each mutant and the wild-type strain PH1 (Table S2). A total of 1239 genes with FDR < 0.05 showed at least a two-fold change in expression in the Δfac1 mutant, including 1005 genes that were down-regulated and 234 genes that were up-regulated. A total of 294 genes were differentially expressed in the Δcpk1 mutant, including 219 that were down-regulated and 75 that were up-regulated. Considering the functional properties of the key regulators and the functional enrichment of all DEGs in these two Fg mutants, we reconstructed the Fg cAMP–PKA signalling pathway, which includes portions controlled by both FAC1 and CPK1 (the FAC1–CPK1 subpathway) and portions controlled only by FAC1 (the FAC1-unique subpathway) or CPK1 (the CPK1-unique subpathway Fig. 2).

Proposed model for cyclic adenosine monophosphate–protein kinase A (cAMP–PKA)-mediated transcriptional and signalling pathways in Fusarium graminearum based on transcriptomics analysis of the Δfac1 and Δcpk1 mutants. Environmental cues and first messengers are perceived by G protein-coupled receptors, and G proteins are activated to generate cAMP through adenylate cyclase FAC1. cAMP then binds to the regulatory subunit of PKA (PKR: FGSG_09908) and releases catalytic subunits of PKA (CPK1, CPK2) that regulate downstream cellular functions. cAMP-dependent processes can be FAC1 dependent (FAC1-unique and FAC1CPK1) or independent (CPK1-unique), regulating a variety of functions (blue boxes) that are both basic housekeeping and specialized. All genes downstream of and including FAC1 and CPK1 in this network are differentially expressed in the Δfac1 and Δcpk1 mutants based on microarray analysis. The correlation of phenotypes and key regulators (protein kinases and transcription factors) is primarily based on F. graminearum phenome data described in FgTFPD (Son et al., 2011 ), previous kinome phenotype data (Wang et al., 2011 ) and the Pathogen–Host Interaction (PHI) Database (Winnenburg et al., 2008 ). Cellular function association is based on functional enrichment of differentially expressed genes (DEGs) in the Δfac1 and Δcpk1 mutants (Table S2) using FunCat (Munich Information Center for Protein Sequences, MIPS). The F. graminearum gene annotation is derived from the F. graminearum genome annotation database (MIPS) and yeast homology information (Saccharomyces Genome Database). DON, deoxynivalenol ZEA, zearalenone.

The FAC1–CPK1 subpathway

About 60% of DEGs identified in the Δcpk1 mutant (180) were also identified in the Δfac1 mutant, including 137 and 43 that were down-regulated and up-regulated, respectively (Table S2), consistent with the finding that AC and CPKA are two key components of the same cAMP–PKA signalling pathway (D'Souza and Heitman, 2001 ).

Functional enrichment analysis suggested that the FAC1CPK1 subpathway positively regulates several essential housekeeping functions, including regulation of the S-phase of the mitotic cell cycle, tRNA processing, homeostasis and nitrogen metabolism, by modulating the expression of different sets of genes (Fig. 2). By contrast, this subpathway suppressed 16 ion transport genes, including seven siderophore transport genes, three phosphate transport genes and two genes involved in chromatin remodelling (i.e. UME6, a key regulator of meiosis and chromatin remodelling, and CBF1, a centromere-binding factor) (Fig. 2). In addition, this subpathway regulated mycotoxin production, sexual growth and virulence by controlling a TF (FGSG_08617) and an FgHog1 mitogen-activated protein kinase (MAPK) (FGSG_09612) (a homologue of the S. cerevisiae osmoregulator HOG1). The link between the FAC1CPK1 subpathway and the HOG1 homologue suggests that cAMP signalling and the MAPK cascade are interconnected, at least in Fg.

The FAC1-unique subpathway

We identified more DEGs in Δfac1 than in Δcpk1, indicating that FAC1 acts upstream of CPK1. Furthermore, this finding suggests the existence of additional cAMP-dependent signalling components that are independent of CPK1. PKR (FGSG_09908), but not CPK1, was detected among the Δfac1 DEGs. Indeed, no other CPK genes were found among the Δfac1 DEGs, suggesting that the transcription of CPK1 and other potential CPKs was not directly regulated by cAMP. Fg and many other filamentous fungi contain two paralogous PKA catalytic subunit genes: CPK1 and CPK2. Although deletion of CPK2 had no detectable phenotype, the Δcpk1Δcpk2 double mutant exhibited more severe defects than Δcpk1 (Hu et al., 2014 ). Therefore, it is possible that many downstream targets are co-regulated by CPK1 and CPK2. Functional enrichment analysis showed that the FAC1-unique subpathway is involved in a broad spectrum of biological functions, including metabolism, transcription, protein synthesis, cell cycle control and cell signalling (Fig. 2). This observation is consistent with the fact that AC and its product cAMP are important in many fundamental cellular processes. Based on phenomics and transcriptomics data, we identified 17 and 10 regulators that control virulence and sexual reproduction, respectively (Figs 1A and 2). The regulators that contributed most to the phenotype were FGSG_00362, FGSG_05734 (Kic1), FGSG_09274 (FgKin1) and FGSG_08719 (TF), and each of these contributed to different phenotypes. This finding is highly consistent with the phenotypes of Δfac1 in Fg, which included reduced growth, absence of sexual reproduction, decreased DON production and reduced pathogenicity. All of these FAC1-dependent regulators of key biological processes were co-regulated in this pathway.

The CPK1-unique subpathway

The CPK1-unique subpathway included 114 genes that were only differentially expressed in the Δcpk1 mutant, and these genes were mostly involved in the homeostasis of cations and metal ions, ion transport and cell defence. Both up-regulated and down-regulated DEGs were associated with each biological process, suggesting that CPK1 is a dual regulator of these processes (Fig. 2). The detection of the CPK1-unique subpathway suggests that Fg CPK1 may function independently of FAC1, because intracellular cAMP levels can also be influenced by PDE activities.

In contrast with the other two subpathways of the cAMP–PKA pathway, the CPK1-unique subpathway was strongly linked to organismal defence. For instance, this subpathway regulated the expression of three genes involved in DNA repair and five genes involved in detoxification. CPK1 suppressed a heat shock response gene, FGSG_11457 (BRT1), probably by contributing to the increased heat tolerance phenotype observed in Δcpk1, but not Δfac1 (Hu et al., 2014 ). CPK1 also regulated the expression of a non-ribosomal peptide synthase gene, FGSG_03747 (NPS6). In Alternaria brassicicola and Cochiliobolus heterostrophus (Oide et al., 2006 ), NPS6 is involved in extracellular siderophore biosynthesis, tolerance to H2O2 stress and fungal virulence. Therefore, NPS6 appears to act downstream of CPK1 in Fg and may regulate iron acquisition, oxidative stress tolerance and virulence.

In summary, our pathway analysis correlated key regulators (TFs and PKs) with many phenotypes (Figs 1 and 2). However, the identification of direct links between genes regulated by the cAMP–PKA signalling pathway and specific biological functions remains challenging, and will require further functional characterization of the genes identified here. Overall, the FAC1-dependent components control many essential biological functions, including housekeeping functions and host–pathogen interaction processes. By contrast, the CPK1-dependent components were mostly involved in cell homeostasis, ion transport and cell defence. However, some functions were regulated by both FAC1-dependent and CPK1-dependent components. For instance, cAMP was reported to regulate iron transport in S. cerevisiae (Robertson et al., 2000 ). CPK1 suppressed the expression of 10 genes involved in siderophore transport, including three genes via the CPK1-unique subpathway and seven genes via the FAC1CPK1 subpathway.

Conservation of the cAMP–PKA pathway between Fg and Fv

As the cAMP–PKA pathway is a key signalling cascade that regulates cellular systems in response to environmental conditions, we anticipated that this pathway would be functionally conserved in Fg and Fv, two fungal species that diverged less than 40 million years ago (Ma et al., 2013 ). Based on a total of 8750 orthologues identified between these two phytopathogenic fungi (Ma et al., 2010 ), we compared the transcriptomics profiles of orthologous genes in the Δfac1 and Δcpk1 mutants of Fg and Fv.

Among the 298 and 153 DEGs detected in the Fv Δfac1 and Δcpk1 mutants (Table S3, see Supporting Information), 215 (72%) and 105 (68%), respectively, have Fg orthologues (Fig. S5, see Supporting Information). Surprisingly, only about one-third of these orthologues exhibited the same expression pattern in both fungal species when a maximal FDR of 0.05 and a minimal fold change of two were used as thresholds to define DEGs. A total of 63 DEGs, including 39 that were down-regulated, 21 that were up-regulated and three that were up-regulated in one species, but down-regulated in the other, were shared between the Fg and Fv Δfac1 mutants (Fig. S5, Table S4, see Supporting Information). Thirty-one DEGs exhibited the same expression patterns in the Fg and Fv Δcpk1 mutants, including seven commonly down-regulated and 24 commonly up-regulated genes (Fig. S5, Table S4). The predicted functions of these common DEGs aligned with those of genes that function in the conserved portions of the cAMP–PKA signalling pathway in these two species, including genes involved in key cellular functions, such as primary and secondary metabolism, protein synthesis, mitotic cell cycle control, the stress response, homeostasis and ion transport. Similar to the case in Fg, the DEGs in the conserved cAMP–PKA pathway could be divided into three different categories based on their dependence on FAC1, CPK1 or both FAC1 and CPK1 in Fv (Fig. 3).

Proposed conserved cyclic adenosine monophosphate–protein kinase A (cAMP-PKA) pathway for Fusarium graminearum and F. verticillioides. Endogenous cAMP produced by FAC1 activates CPK1 and other CPKs to regulate many conserved biological functions (blue boxes), such as metabolism, protein synthesis, the cell cycle, homeostasis, ion transport and chromatin remodelling via orthologous genes differentially expressed in both species (ovals). These processes are dependent on FAC1 (blue arrows) or FAC1CPK1 (black arrows), and cAMP can activate CPK1 and regulate detoxification and iron transport, including siderophore transport, independently of FAC1 (green arrows). Cellular function association is based on functional enrichment of differentially expressed genes (DEGs) in the Δfac1 and Δcpk1 mutants (Tables S2 and S3) using FunCat (Munich Information Center for Protein Sequences, MIPS). Fusarium graminearum and F. verticillioides gene annotations are derived from the Fusarium genome annotation database (MIPS) and yeast homology information (Saccharomyces Genome Database). GPCR, G protein-coupled receptor.

Orthologues regulated by both FAC1 and CPK1 in both Fg and Fv included key regulators of meiosis (FgUME6), chromatin remodelling (FgCBF1), calcium and manganese homeostasis (FGSG_07832, the CCC1 protein), and nitrogen (FGSG_08402) and phosphate (FGSG_10404 and FGSG_07894) metabolism. In the FAC1-unique pathway in both species, FAC1 positively regulated a kinesin protein (FGSG_06334, FVEG_05009) involved in the mitotic cell cycle, consistent with our current knowledge of the roles of the cAMP–PKA pathway in the regulation of cell cycles. Two orthologues encoding a tRNA ligase (FGSG_07436, FVEG_07820) and tRNA synthetase (FGSG_08614, FVEG_02055) were present in both the Fg and Fv pathways, suggesting that cAMP-mediated regulation of protein synthesis is conserved in these two species. Orthologues involved in the stress response, such as oxidative and heat stress (FGSG_08721, FGSG_12890), were also detected. Orthologues regulated by the CPK1-unique subpathway in both Fg and Fv included genes responsible for iron uptake (Fig. 3), such as S. cerevisiae SIT1 (Robertson et al., 2000 ).

The conservation of the cAMP–PKA signalling pathway in the regulation of protein synthesis, ion transport and the stress response can be further extended from the genus Fusarium to Saccharomyces. For example, it is known that S. cerevisiae TPK genes (CPK1 homologues) suppress environmental stress response events by activating the expression of ribosomal protein genes (Gasch, 2003 ). In Fg Δfac1 and Δcpk1 mutants, down-regulated genes were highly enriched for ribosomal protein genesis and translation processes, confirming the regulatory role of the cAMP–PKA pathway in protein synthesis in Fg. Similarly, genes involved in protein synthesis were also down-regulated in the Fv Δfac1 and Δcpk1 mutants. Consistent with the finding that S. cerevisiae TPK regulates SIT1, a siderophore iron transporter (Robertson et al., 2000 ), SIT1 homologues in Fg and Fv (FGSG_05848 and FVEG_04458) were also negatively regulated by FAC1–CPK1. The other regulatory function in the cAMP–PKA pathway shared by Fg and S. cerevisiae was the organism stress response (Fig. 2). In Fg, a key process regulated by cAMP was the cellular response to oxidative and heat stresses. In S. cerevisiae, a constitutively activated Ras/cAMP pathway resulted in a decreased response to stress conditions, suggesting that there is a negative correlation between endogenous cAMP levels and the cell's tolerance to stress responses (Jones et al., 2003 ).

Functional divergence of the cAMP–PKA pathway in Fg and Fv

Noise reduction in microarray data

We were concerned that about two-thirds of the orthologous DEGs identified in the Δfac1 and Δcpk1 mutants in these two closely related species did not show similar expression patterns. This could either reflect the rapid functional divergence of this signalling pathway or errors caused by factors such as the stochastic nature of expression data and the randomness of the hard, arbitrary cut-off of two-fold expression changes. In an effort to differentiate true functional divergence of orthologous genes from errors, we developed a program that combined sequence conservation and patterns of orthologous gene expression in two different species, using the ratio of expression level (ROEL) (see Experimental procedures).

In contrast with standard expression analysis, which identifies DEGs on the basis of expression fold changes, this method compares the expression patterns of orthologous genes directly using the level of expression. We compared the expression profiles of 8750 orthologues identified in a comparative genomics study (Ma et al., 2010 ) in the wild-type, Δfac1 and Δcpk1 strains in both species. Consistent with their phylogenetic relatedness, we observed a strong correlation in expression level between the orthologues (Pearson's correlation coefficients of 0.80, 0.80 and 0.79 for the wild-type, Δfac1 and Δcpk1 strains, respectively) (Fig. 4A). We define a pair of orthologues as being functionally diverged if the ROEL score is either above 1.5 or below 0.66, indicating that the expression level is at least 50% higher in one genome than in the other (Table S5, see Supporting Information).

Expression correlation analysis of identified conserved and diverged orthologous gene expression in Fusarium graminearum and F. verticillioides. (A) Scatter plots and Pearson correlation coefficient (r) of orthologous gene expression levels in the two Fusarium species in three genetic backgrounds: wild-type and Δfac1 and Δcpk1 mutants. Red lines indicate the cut-offs of the ratio of expression levels (ROELs) (Fg/Fv) of 1.5, 1.0 and 0.66 as indicated. (B) Heatmap of ROELs in the wild-type (CM) and Δfac1 and Δcpk1 mutants. Functionally diverged orthologues (FDO) have a ROEL of larger than 1.5 or less than 0.66 in all three genetic backgrounds. Conditionally diverged orthologues (CDO) have a ROEL of larger than 1.5 or less than 0.66 in only one or two backgrounds. Functionally conserved orthologues (FCO) have a ROEL of larger than 0.66 and less than 1.5 in all three backgrounds. (C) Venn diagrams showing common differentially expressed genes (DEGs) and distinct DEGs in the two Fusarium species. True diverged DEGs (in red) were identified by removing functionally conserved orthologues (FCO) and conditionally diverged orthologues (CDO).

Combining data from all three genetic backgrounds, we found that most orthologues (79%) had ROEL scores of between 0.66 and 1.5, and were thus defined as functionally conserved orthologues. Approximately 15.5% of the orthologues had diverged expression profiles (ROELs of above 1.5 or below 0.66) under one or two genetic backgrounds, and were considered to be conditionally diverged orthologues (Fig. 4B). The expression levels of about 6% (482) of orthologous genes consistently varied (ROEL of above 1.5 or below 0.66) between these two species among all three tested backgrounds (Fig. 4B), and these genes were defined as functionally diverged orthologues.

Among the DEGs identified in individual mutants of each species, over 70% had orthologous genes in the respective genome and a number were functionally conserved orthologues (Table 1). By removing these conserved orthologues and conditionally diverged orthologues from the distinct DEGs from both species, we defined species-specific DEGs, which were unique to a species and functionally diverged orthologues (Fig. 4C Table 1). The diverged orthologue rate was calculated to determine the divergence of each component of the cAMP–PKA pathway between these two species (Table 1). For Fg, the diverged rates were 23% (FAC1) and 40% (CPK1), whereas the diverged rates of Fv were 32% (FAC1) and 49% (CPK1). The consistently higher diverged rates in the Δcpk1 mutant suggested that, compared with FAC1, CPK1 makes a greater contribution to species-specific functions (Table 1).

DEGs Δfvfac1 Δfgfac1 Δfvcpk1 Δfgcpk1
Shareda a ‘Shared’ DEGs between Δfvfac1 and Δfgfac1, and between Δfvcpk1 and Δfgcpk1.
63 63 31 31
Species-specificb b True diverged DEGs shown in Venn diagrams of Fig. 4C.
100 220 58 71
Functionally diverged orthologues (FDO)b b True diverged DEGs shown in Venn diagrams of Fig. 4C.
11 24 3 9
Conditionally diverged orthologues (CDO) 54 217 44 81
Functionally conserved orthologues (FCO) 133 778 48 133
Total DEGs (TD) 298 1239 153 294
Total orthologues (TO) 198 1019 95 223
Orthologous DEG ratec c Orthologous DEG rate is calculated as the frequency of DEGs that are total orthologues (TO) amongst all DEGs (total DEGs, TD).
(TO/TD) (%)
76 82 62 75
Diverged orthologue rated d Diverged orthologue rate is calculated as the frequency of diverged orthologues (FDO + CDO) amongst total orthologues (TO).
(TO − FCO)/TO (%)
32 23 49 40
  • a ‘Shared’ DEGs between Δfvfac1 and Δfgfac1, and between Δfvcpk1 and Δfgcpk1.
  • b True diverged DEGs shown in Venn diagrams of Fig. 4C.
  • c Orthologous DEG rate is calculated as the frequency of DEGs that are total orthologues (TO) amongst all DEGs (total DEGs, TD).
  • d Diverged orthologue rate is calculated as the frequency of diverged orthologues (FDO + CDO) amongst total orthologues (TO).

The diverged cAMP–PKA pathway controls species-specific biological processes

Using the identified Fg- or Fv-specific DEGs (Fig. 4C Table 1), we reconstructed the unique components of the cAMP–PKA pathway in Fg and Fv, respectively (Fig. 5). In addition to the functional conservation described above, we found that the cAMP–PKA pathway evolved independently in Fg and Fv to control species-specific functions, such as the production of some species-specific secondary metabolites, as well as certain primary metabolites used as precursors for these species-specific secondary metabolites.

Proposed diverged cAMP–PKA pathways of Fusarium graminearum and F. verticillioides. Blue boxes represent significantly enriched biological processes regulated by the cAMP–PKA pathway. The number of differentially expressed genes (DEGs) involved in different biological processes is indicated in red circles. Blue arrows denote the FAC1-unique subpathway. Black arrows denote the FAC1–CPK1 subpathway. Green arrows denote the FAC1-independent activation of CPK1. GPCR, G protein-coupled receptor.

In Fg, the genes responsible for the biosynthesis of aurofusarin, an Fg-specific secondary metabolite, were upregulated in Δfac1 (Fig. S4, see Supporting Information). In addition, the Fg-specific component of the cAMP–PKA pathway regulated a primary metabolic pathway that produces isoprenoids used as precursors for the biosynthesis of the Fg-specific mycotoxin DON (Scott et al., 2004 ). The isoprenoid metabolic pathway is also regulated by Tri6, a TF that is part of the trichothecene biosynthesis gene cluster (TRI cluster) and regulates DON production (Seong et al., 2009 ). Indeed, the expression of several TRI genes, including Tri4, Tri5, Tri8 and Tri9, was down-regulated (Table S2) in Δfac1 and ΔcpkA mutants, under a less stringent cut-off (P < 0.1). We anticipate that a more significant fold change of the TRI genes may be observed under DON-inducible conditions. Indeed, DON production was decreased in both Δfac1 and ΔcpkA mutants (Hu et al., 2014 ), further confirming our prediction.

In Fv, genes responsible for the biosynthesis of bikaverin, a polyketide structurally similar to fatty acids (Hopwood and Sherman, 1990 ), were up-regulated in the Δfac1 mutant (Table S2). Bikaverin is one of the main toxic secondary metabolites with antibiotic activity produced by Fv (Lazzaro et al., 2012 ). Interestingly, the Fv-unique component of the cAMP–PKA pathway also regulated several genes involved in fatty acid metabolism (Fig. 5). However, no significant difference in expression was observed in the Δfac1 and Δcpk1 mutants for genes involved in the biosynthesis of another key toxic secondary metabolite, fumonisin, even when a less stringent cut-off was used. The Δfac1 and Δcpk1 mutants were reported to have normal fumonisin production, but increased production of bikaverin (Choi and Xu, 2010 ), which suggests that fumonisin biosynthesis is independent of the cAMP–PKA pathway.

A few shared annotation terms, such as primary and secondary metabolism, detoxification, stress responses and virulence, were regulated by unique components of the cAMP–PKA pathway in Fg and Fv. Nevertheless, genes involved in these functions were mostly unique for each species and orthologues with diverged functions (Fig. 5). For example, in the ‘detoxification’ category, nine of 12 Fg genes were Fg specific, whereas all 11 Fv genes were unique to Fv. For ‘stress response’, five of six Fg genes and six of eight Fv genes were unique to each species, respectively. Similarly, nine genes in Fg and seven genes in Fv that were annotated as being related to ‘disease and virulence process’ were regulated by unique pathways in each genome. Six of these nine Fg genes were Fg specific, including an integral membrane protein similar to PTH11 (FGSG_07792), a known virulence factor reported in M. oryzae (DeZwaan et al., 1999 ). Six of the seven Fv genes were unique to Fv, including two pectin lyase genes (FVEG_13545 and 13546), a gene encoding pisatin demethylase (FVEG_12563) and a cell surface ferroxidase gene (FVEG_01690). We also observed the regulatory divergence of orthologous genes. For instance, FvPKS13 (FVEG_10535), which is potentially involved in an unknown polyketide synthesis process, was down-regulated in the Fv Δfac1 mutant. As a functionally diverged orthologue, the expression of its Fg orthologue (FGSG_03340) was not affected in the Δfac1 and Δcpk1 mutants.

In conclusion, both the genomics and transcriptomics data presented here illustrate that unique components of the cAMP–PKA pathway may have evolved in these two species to regulate virulence and mycotoxin biosynthesis. Functional divergence of orthologues and the acquisition of novel genes occurred during the evolution of cAMP–PKA pathways in both species.


Developmental cascades

There is a long history of interest in developmental theory and research in the processes by which function in one domain or level or system influences another system or level of function over time to shape the course of ontogenesis and epigenesis. Theoretically, these effects reflect the processes (transactions coactions) by which interactions influence development in complex living systems (Ford & Lerner, Reference Ford and Lerner 1992 Gottlieb, Reference Gottlieb, Damon and Lerner 1998, Reference Gottlieb 2007 Sameroff, Reference Sameroff 2000 Thelen & Smith, Reference Thelen, Smith and Damon 1998 Ward, Reference Ward 1995). Developmental cascades refer to the cumulative consequences for development of the many interactions and transactions occurring in developing systems that result in spreading effects across levels, among domains at the same level, and across different systems or generations. Theoretically these effects may be direct and unidirectional, direct and bidirectional, or indirect through various pathways, but the consequences are not transient: developmental cascades alter the course of development. Such effects have gone by different names in the literature, including chain reactions, and snowball, amplification, spillover or progressive effects, as well as developmental cascades (Burt et al., Reference Burt, Obradović, Long and Masten 2008 Cicchetti & Cannon, Reference Cicchetti and Cannon 1999 Cicchetti & Tucker, Reference Cicchetti and Tucker 1994 Dodge et al., Reference Dodge, Malone, Lansford, Miller, Pettit and Bates 2009 Dodge & Pettit, Reference Dodge and Pettit 2003 Fry & Hale, Reference Fry and Hale 1996 Hanson & Gottesman, Reference Hanson, Gottesman and Masten 2007 Hinshaw, Reference Hinshaw 1992 Hinshaw & Anderson, Reference Hinshaw, Anderson, Mash and Barkley 1996 Kagan, Reference Kagan 2005 Masten & Coatsworth, Reference Masten and Coatsworth 1998 Masten et al., Reference Masten, Roisman, Long, Burt, Obradovic and Riley 2005 Patterson, Reid, & Dishion, Reference Patterson, Reid and Dishion 1992 Rutter, Reference Rutter 1999 Rutter, Kim-Cohen, & Maughan, Reference Rutter, Kim-Cohen and Maughan 2006 Rutter & Sroufe, Reference Rutter and Sroufe 2000).

The articles in this two-part Special Issue of Development and Psychopathology examine cumulative or progressive effects across levels and areas of function in development that have implications for adaptive behavior. At the same time, these papers illustrate diverse methods for testing developmental cascade models of change.

In developmental psychopathology, there has been keen interest in the possibility that adaptive and maladaptive functions and behaviors spread over time to promote or undermine development because of the profound implications for causal theory, prevention, and treatment (Cicchetti, Reference Cicchetti, Hartup and Weinberg 2002a Cicchetti & Curtis, Reference Cicchetti, Curtis, Cicchetti and Cohen 2006 Masten, Burt, & Coatsworth, Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006). Cascade effects could explain why some problems in childhood predict widespread difficulties in adulthood, whereas others do not. In their classic review of longitudinal data on adjustment, Kohlberg, LaCrosse, and Ricks ( Reference Kohlberg, LaCrosse, Ricks and Wolman 1972) observed decades ago that some indicators of childhood success or problems consistently forecasted adult adjustment across multiple domains of outcome. In particular, they noted the robust and broad predictive validity of general cognitive competence (often indexed by academic achievement or intellectual ability) and socialized conduct versus antisocial behavior (often indexed by persistent rule-breaking behavior). Cascade effects also offer a possible explanation for some of the comorbidity that is observed so often for some disorders, such as conduct disorder (Angold, Costello, & Erkanli, Reference Angold, Costello and Erkanli 1999 Caron & Rutter, Reference Caron and Rutter 1991 Jensen, Reference Jensen 2003).

Given effects that spread over time for some kinds of psychopathology, well-timed and targeted interventions could interrupt negative or promote positive cascades these efforts may work by counteracting negative cascades, by targeting the reduction of problems in domains that often cascade to cause other problems, or by targeting improvements in competence in domains that increase the probability of better function in other domains (Cicchetti & Curtis, Reference Cicchetti, Curtis, Cicchetti and Cohen 2006 Cicchetti & Gunnar, Reference Cicchetti and Gunnar 2008 Masten et al., Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006 Masten, Long, Kuo, McCormick, & Desjardins, Reference Masten, Long, Kuo, McCormick and Desjardins 2009). Moreover, if developmental cascades are common and often begin with adaptive behavior in early childhood, then it would explain why the evidence in prevention science indicates a higher return on investment in early childhood interventions, such as high quality preschool programs (Heckman, Reference Heckman 2006 Reynolds & Temple, Reference Reynolds, Temple, Zigler, Gilliam and Jones 2006).

Intervention designs that target mediating processes for change represent cascade models. The “theory of the intervention” represents a hypothesis that the intervention will change the mediating process that will in turn change the outcome of the person in key adaptive domains. In other words, the intervention is designed to initiate change in the form of a cascade from the intervention to the mediator to the outcome (Cicchetti & Hinshaw, Reference Cicchetti and Hinshaw 2002 Hinshaw, Reference Hinshaw 2002).

Cascade effects encompass a broad array of phenomena studied in developmental science within and across multiple levels of function, from the molecular level to the macrolevel. Cascade models may account for the pathways by which gene–environment interplay unfolds over time in epigenesis to shape development, linking genes to neural levels of function to behavior to social experience, through processes of multilevel dynamics (Cicchetti, Reference Cicchetti, Hartup and Weinberg 2002a Cicchetti & Cannon, Reference Cicchetti and Cannon 1999 Gottlieb, Reference Gottlieb, Damon and Lerner 1998, Reference Gottlieb 2007 Hanson & Gottesman, Reference Hanson, Gottesman and Masten 2007). These cascades may flow downward across levels, upward, or across domains of function in a developing system and its interactions with other systems as development proceeds. The effects of pharmacological interventions on behavior can be viewed as intentional efforts to induce an upward cascade by means of neurochemical changes that influence neural function in the brain and subsequently adaptive behavior (Charney, Reference Charney 2004 Cicchetti & Curtis, Reference Cicchetti and Curtis 2007). The processes by which genetic disorders result in the development of behavioral anomalies have been described as cascades (e.g., Järvinen-Pasley et al., Reference Järvinen-Pasley, Bellugi, Reilly, Mills, Galaburda and Reiss 2008). Biological embedding of experience, as might happen when traumatic or negative experiences alter gene expression or the stress–response systems of a developing child, may begin as a downward cascade (experience alters functional systems in the child) and then subsequently these altered systems may have cascading upward consequences for brain development, stress reactivity, and symptoms of psychopathology through a complex sequence of processes (Cicchetti, Reference Cicchetti 2002b Cicchetti & Cannon, Reference Cicchetti and Cannon 1999 Gunnar & Quevedo, Reference Gunnar and Quevedo 2007 Lupien et al., Reference Lupien, Ouellet-Morin, Hupbach, Tu, Buss, Walker, Cicchetti and Cohen 2006 McEwen & Stellar, Reference McEwen and Stellar 1993 Meaney, Reference Meaney 2010 Shonkoff, Boyce, & McEwen, Reference Shonkoff, Boyce and McEwen 2009). Deterioration in marital relationships has been described as cascades to divorce (Gottman, Reference Gottman 1993). Contagion effects observed in social networks of peers or treatment effects that spread in families also can be viewed as cascade effects (Dishion & Patterson, Reference Dishion, Patterson, Cicchetti and Cohen 2006). Intergenerational transmission of behavior (mediated by genes, experience, and their interplay) or risk can also be viewed as cascade effects linking multiple generations and multiple behaviors or levels of analysis (Meaney, Reference Meaney 2010). Research on the intergenerational transmission of parenting (e.g., Belsky, Conger, & Capaldi, Reference Belsky, Conger and Capaldi 2009 Conger, Neppl, Kim, & Scaramella, Reference Conger, Neppl, Kim and Scaramella 2003 Shaffer, Burt, Obradovic, Herbers, & Masten, Reference Shaffer, Burt, Obradovic, Herbers and Masten 2009) or psychosocial risk (Serbin & Karp, Reference Serbin and Karp 2004) offer examples of generation to generation cascades.

Developmental cascades may be positive or negative in their consequences with respect to adaptive behavior. Developmental theories about competence (see Masten et al., Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006) invoke cascades in the fundamental assertion that effectiveness in one domain of competence in one period of life becomes the scaffold on which later competence in newly emerging domains develop: in other words, competence begets competence. Success in early developmental tasks of childhood are expected to foster competence in subsequent and later emerging developmental tasks (Cicchetti & Schneider-Rosen, Reference Cicchetti, Schneider-Rosen, Rutter, Izard and Read 1986 Cicchetti & Tucker, Reference Cicchetti and Tucker 1994 Masten & Coatsworth, Reference Masten and Coatsworth 1998 Masten & Wright, Reference Masten, Wright, Reich, Zautra and Hall 2009 Sroufe, Reference Sroufe 1979). This idea also has been described in terms of positive chain reactions (Rutter, Reference Rutter 1999) and skill formation (Heckman, Reference Heckman 2006). Cognitive scientists have described a variety of cascade processes where an early advantage or disadvantage in one cognitive domain influences another later developing and high order domain. Fry and Hale ( Reference Fry and Hale 1996) examined the influence of increasing speak of information processing on working memory, which in turn, was associated with improvements in fluid intelligence. Indirect connections linking early infant behaviors to later intellectual function have been observed in developmental research (e.g., Bornstein et al., 2006 Colombo et al., Reference Colombo, Kannass, Shaddy, Kundurthi, Maikranz and Anderson 2004).

Developmental models of competence also explicitly or implicitly propose that failures in key developmental task domains have important consequences for psychological well being or function in other domains (Masten et al., Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006). One of the most important cascade models of this kind was proposed by Patterson and colleagues at the Oregon Social Learning Center in the “dual failure” model, which is based on their developmental theory of antisocial behavior (Capaldi, Reference Capaldi 1992 Patterson, DeBaryshe, & Ramsey, Reference Patterson, DeBaryshe and Ramsey 1989 Patterson et al., Reference Patterson, Reid and Dishion 1992). In this cascade model, behavior problems arising in the family prior to the school years in relation to inept parenting are carried forward into the school context by the child, leading to problems in two new domains of academic and social competence at school. Failures in these two domains of adaptive behavior were expected to contribute in turn to depressive affect. Rejection by normative peers also raised the risk for drifting into relationships with deviant peers who would reinforce further antisocial behavior. Dual cascade models of this kind have been supported in empirical work from the Oregon Social Learning Center (e.g., Capaldi, Reference Capaldi 1992 Capaldi & Stoolmiller, Reference Capaldi and Stoolmiller 1999 Patterson & Stoolmiller, Reference Patterson and Stoolmiller 1991), as well as in recent analyses from the Fast Track study (Dodge, Greenberg, Malone, & the Conduct Problems Prevention Research Group, Reference Dodge, Greenberg and Malone 2008) and Project Competence (Obradović, Burt, & Masten, Reference Obradović, Burt and Masten 2010). Cole ( Reference Cole 1990, Reference Cole 1991 Cole, Martin, Powers, & Truglio, Reference Cole, Martin, Powers and Truglio 1996) proposed and tested a similar competency-based model of depression wherein cumulative effects of academic and social incompetence contributed to depressive symptoms.

The cascading consequences of conduct problems for subsequent school success, social competence, and internalizing problems appear to be one of the most widely corroborated patterns in the literature on developmental cascades to date, and articles in these Special Issues contribute additional supportive evidence. Although this general sequence is supported in different prospective studies, beginning in preschool and later, the timing of assessments that are conducted and cascades that are observed varies across studies. It is conceivable that these cumulative cascade effects continue over an extended age span beginning in the preschool years and extending across the years into adolescence. Nonetheless, it will be important for intervention and prevention efforts to understand more precisely when and how the processes underlying the externalizing problem cascade initiate and accelerate and, concomitantly, when the processes that undermine academic or social competence begin to have lasting consequences for the future. Cascade timing has important implications for when it makes most developmental sense to implement preventive interventions (Masten et al., Reference Masten, Long, Kuo, McCormick and Desjardins 2009).

Research and classification of mental health disorders also have focused attention on the potential consequences of symptoms for adaptive behavior (Masten et al., Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006). Symptoms of psychopathology are hypothesized to undermine function in important domains of adaptive behavior. Evidence is compelling that symptoms predict impairment in adaptive function, and the criteria defining mental disorders often encompass impairment criteria. However, it is not easy to establish the direct and indirect pathways by which impairment occurs or whether the association of symptoms and impairment is the result of common risks, one problem causing the other, or an artifact of confounded measurement (Masten et al., Reference Masten, Burt, Coatsworth, Cicchetti and Cohen 2006).

From a methodological standpoint, cascade models are challenging to test for several reasons. Testing cascade models requires longitudinal data that are often difficult and time consuming to collect. Strong tests of cascade models also require repeated assessment of multiple domains or levels of function over time, and the application of complex statistical approaches. The most stringent tests of cascade models have similar requirements to those for testing mediating effects (Cole & Maxwell, 2006 Masten et al., Reference Masten, Roisman, Long, Burt, Obradovic and Riley 2005). It is important to control for the continuity or stability over time that one would expect within most any domain of adaptive behavior. At the same time, it is important to control for the covariance of key measures assessed in the same time frame during an assessment window. If continuity and covariance are not controlled, then it is difficult to establish whether there is a unique and cumulative cascade effect from one domain to another over time and when this might be occurring. Without accounting for within time covariance and across time stability, an apparent cascade effect may reflect correlations that were already present at the beginning of the assessments or that represent an artifact of unmeasured outcome covariance. In effect, the role of within-time correlation and also across-time stability are controlled in the most informative designs.

A related challenge for research on developmental cascades occurs when a model to be tested includes newly emerging domains of function or organization. In developmental task theory, for example, the domains of work and romantic competence emerge in adolescence in many developed nations, and subsequently become salient tasks of adulthood (Roisman, Masten, Coatsworth, & Tellegen, Reference Roisman, Masten, Coatsworth and Tellegen 2004). These emerging tasks are believed to have roots in academic and social competence earlier in childhood success in earlier tasks builds the skills for success in these later tasks. Therefore, cascades might be expected from success (or failure) in age-salient domains of childhood competence to these newly emerging domains in adolescence. In the situation of emerging domains of behavior like these, or newly developing levels of function (e.g., higher level cognitive skills), it may be difficult to differentiate a cascade effect from developmental manifestations of progress within a broader underlying domain of function or the development of a higher order of organization in a system.

There are other challenges as well. If the assessment windows are close together in time, then the cumulative direction of effects may be lost in the directionally indeterminate covariance within an assessment wave. In other words, a directional cascade effect could be obscured by close waves of data collection that result in high correlations among variables at each assessment wave, with the result that no cascades are evident. In this situation, omitting data or waves to allow more time to elapse between assessment intervals may result in the emergence of a cascade effect across time and domains. Generally, it is important to consider the issue of the interval between assessments and the effect under investigation. The design must allow adequate time for the effects of interest to be manifested (Cole, Reference Cole 2006 Cole & Maxwell, Reference Cole and Maxwell 2003).

It is also important to remember that correlations within time are consistent with cascade influences, even though the direction(s) of effect cannot be tested with clarity. Thus, given three variables that show high stability over three waves of assessment and that are also intercorrelated within each wave, even in the absence of cross-domain cascade effects, the correlations across the domains may nonetheless increase over time as a result of bidirectional or unidirectional cascade effects occurring among the variables within the time frame of each assessment wave.

In addition, even with powerful statistical model testing of cascade effects in longitudinal designs, there still may be many alternative explanations for observed cascade effects and it is not possible to establish causal effects. Some studies in these Special Issues test alternative models or replicate earlier studies, two compelling approaches for building a convincing evidence base for cascade effects. Some also test potential “third cause variables” that might explain what appears to be a cascade effect. Considering plausible third variable causes is another effective strategy for strengthening the evidence that a cascade effect reflects a causal process linking two aspects of function, rather than an artifact. Nonetheless, the most convincing evidence of a cascade effect will be built on experiments with random assignment to intervention that are designed to produce a particular cascade sequence.

The articles in these Special Issues highlight the range of developmental cascade models and methods under investigation in developmental psychopathology, although as a group these papers emphasize behavioral levels of analysis. Fewer articles test cascades across level, linking context to behavior or behavior to neural or biologic function. Most of the work represented in these Special Issues has focused on naturally occurring cascades rather than experiments to induce cascades however, prevention studies are represented among the contributions to the Issues. There is intriguing convergence across some of the findings, although clearly these articles represent the initial stages of rigorous testing of cascade models. As a collective, the articles in these Special Issues represent a highly promising foundation for future research on cascades in developmental science. Developmental cascade research has the potential to inform, test, and refine theories of change that are integral to understanding pathways of adaptation, maladaptation, psychopathology, and resilience. Such knowledge is central to the goal of designing preventive interventions with strategic timing and targets.


Results

Testicular Expression of SRC

The expression of SRC during spermatogenesis was assessed by immunohistochemistry on human testis sections. This tyrosine kinase is expressed in both the seminiferous tubules and the interstitial tissues ( Fig. 1A). This result is not surprising, since this protein is known to be ubiquitously expressed. Within the seminiferous tubules, SRC is strongly expressed in round and elongating spermatids in a crescent shape surrounding the nucleus, suggesting an association with acrosomal structures or its presence within the acrosome ( Fig. 1B). This tyrosine kinase is barely detectable at earlier stages of spermatogenesis, in spermatocytes or spermatogonia. To confirm the localization of SRC at the acrosomal region of round and elongating spermatids, the same protocol was used with an affinity-purified antibody directed against the ACRBP that specifically localizes to the acrosome [ 45] ( Fig. 1, C and D).

Expression of SRC in human testis. Immunohistochemistry was performed using the SRC or the sp32 polyclonal antibody and revealed using a biotinylated secondary antibody and horseradish peroxidase-conjugated streptavidin. The cellular localization of SRC and ACRBP was visualized with DAB and appears as a brown precipitate. A and B show SRC expression at ×200 and ×1000, and C and D show sp32 at ×200 and ×1000, respectively. No signal was observed when commercial rabbit IgGs were used as a negative control (original magnifications ×200 [E] and ×1000 [F]). The immunodetection of SRC was performed on testicular sections from three different donors with identical results. Scale bars at ×200 and ×1000 represent 50 μm and 10 μm, respectively.

Expression of SRC in human testis. Immunohistochemistry was performed using the SRC or the sp32 polyclonal antibody and revealed using a biotinylated secondary antibody and horseradish peroxidase-conjugated streptavidin. The cellular localization of SRC and ACRBP was visualized with DAB and appears as a brown precipitate. A and B show SRC expression at ×200 and ×1000, and C and D show sp32 at ×200 and ×1000, respectively. No signal was observed when commercial rabbit IgGs were used as a negative control (original magnifications ×200 [E] and ×1000 [F]). The immunodetection of SRC was performed on testicular sections from three different donors with identical results. Scale bars at ×200 and ×1000 represent 50 μm and 10 μm, respectively.

The Tyrosine Kinase SRC in Human Ejaculated Spermatozoa

Cellular localization of SRC in mature human sperm was next assessed by indirect immunofluorescence. As shown in Figure 2A, a positive signal is observed in the acrosomal region of the sperm head and the entire flagellum. Preabsorption of the antibody with the immunizing peptide showed no signal, confirming the specificity of the antibody ( Fig. 2E). Commercial nonimmune rabbit IgGs were also used as a negative control and showed no positive signal (data not shown). Western blot analysis on sperm subcellular extracts clearly showed that SRC is detected mostly in the plasma membrane-enriched fraction ( Fig. 3A). Furthermore, when spermatozoa were solubilized with nonionic detergents (IP buffer), which will be used for the specific IP and tyrosine kinase assay of SRC, this kinase was partially extracted ( Fig. 3B). Although SRC was still detected in the head and flagellum after detergent extraction, most of the signal present in the acrosomal region was lost ( Fig. 2C). This suggests that in the sperm acrosomal region, SRC is associated with the membranes.

Immunolocalization of SRC in human spermatozoa. A) Washed spermatozoa were fixed/permeabilized and SRC was localized using the SRC polyclonal antibody. C) Spermatozoa were first extracted with the IP buffer before fixation, and the detection of SRC was next processed as in A. No signal was observed when the antibody was preadsorbed with the immunizing peptide prior to sperm immunodetection (E) or when commercial rabbit IgGs were used as a negative control (not shown). Arrows point to the positive signal to the acrosomal region of the sperm head and the entire flagellum (A, C). Corresponding fields observed by phase-constrast microscopy are shown (B, D, F). Picture is representative of more than three replicates with identical results shown. Bar =10 μm.

Immunolocalization of SRC in human spermatozoa. A) Washed spermatozoa were fixed/permeabilized and SRC was localized using the SRC polyclonal antibody. C) Spermatozoa were first extracted with the IP buffer before fixation, and the detection of SRC was next processed as in A. No signal was observed when the antibody was preadsorbed with the immunizing peptide prior to sperm immunodetection (E) or when commercial rabbit IgGs were used as a negative control (not shown). Arrows point to the positive signal to the acrosomal region of the sperm head and the entire flagellum (A, C). Corresponding fields observed by phase-constrast microscopy are shown (B, D, F). Picture is representative of more than three replicates with identical results shown. Bar =10 μm.

Subcellular distribution of the tyrosine kinase SRC. A) Washed sperm were subjected to subcellular fractionation as described in Materials and Methods. A total of 3 μg of proteins from each fraction were subjected to SDS-PAGE. B) Washed spermatozoa were extracted in the IP buffer containing nonionic detergents. Each lane contains the proteins from 1 × 10 6 spermatozoa that were separated by SDS-PAGE. Gels from these two experiments were electrotransferred to PVDF and probed with SRC monoclonal antibody. These experiments were carried out on sperm samples from three separate donors with similar results.

Subcellular distribution of the tyrosine kinase SRC. A) Washed sperm were subjected to subcellular fractionation as described in Materials and Methods. A total of 3 μg of proteins from each fraction were subjected to SDS-PAGE. B) Washed spermatozoa were extracted in the IP buffer containing nonionic detergents. Each lane contains the proteins from 1 × 10 6 spermatozoa that were separated by SDS-PAGE. Gels from these two experiments were electrotransferred to PVDF and probed with SRC monoclonal antibody. These experiments were carried out on sperm samples from three separate donors with similar results.

SRC Kinase Activity in Human Spermatozoa

Human sperm SRC tyrosine kinase activity was next investigated after IP. Using denatured rabbit muscle enolase as a substrate, no or weak kinase activity was detected when the assay was performed upon sperm incubation and SRC IP in the absence of Ca 2+ ( Fig. 4, lanes 1 and 2). However, when sperm were incubated in the presence of Ca 2+ ( Fig. 4, lanes 3 and 4), the phosphorylation of enolase by immunoprecipitated SRC was easily detected. This difference in SRC tyrosine kinase activity could not be attributed to a lesser amount of the kinase itself, as none of the treatments affected the solubilization of SRC (data not shown). This supports the idea that sperm SRC kinase activity is Ca 2+ dependent.

Effect of Ca 2+ on sperm SRC kinase activity. Percoll-washed spermatozoa were incubated for 1 h at 37°C in BWW medium containing no added CaCl2 and supplemented with 1 mM of the Ca 2+ chelator BAPTA (lanes 1 and 2) or in complete BWW (1.7 mM CaCl2 lanes 3 and 4). After the incubation, sperm protein extraction and IP with the SRC monoclonal antibody (mab327 lanes 2 and 4) or the mouse commercial IgGs (lanes 1 and 3) was performed as described in Materials and Methods. The immunokinase assay was performed on the immune complex in the presence of enolase. Once the reaction was stopped with electrophoresis sample buffer, the proteins were separated by SDS-PAGE, and the gels were stained by Coomassie Brilliant Blue, dried, and exposed to x-ray films after a 3-h exposure to phosphor screens. An experiment representative of three with similar results is shown. a-SRC, Anti-SRC.

Effect of Ca 2+ on sperm SRC kinase activity. Percoll-washed spermatozoa were incubated for 1 h at 37°C in BWW medium containing no added CaCl2 and supplemented with 1 mM of the Ca 2+ chelator BAPTA (lanes 1 and 2) or in complete BWW (1.7 mM CaCl2 lanes 3 and 4). After the incubation, sperm protein extraction and IP with the SRC monoclonal antibody (mab327 lanes 2 and 4) or the mouse commercial IgGs (lanes 1 and 3) was performed as described in Materials and Methods. The immunokinase assay was performed on the immune complex in the presence of enolase. Once the reaction was stopped with electrophoresis sample buffer, the proteins were separated by SDS-PAGE, and the gels were stained by Coomassie Brilliant Blue, dried, and exposed to x-ray films after a 3-h exposure to phosphor screens. An experiment representative of three with similar results is shown. a-SRC, Anti-SRC.

The specificity of the tyrosine kinase assay toward SRC was next assessed using different tyrosine kinase inhibitors. As shown in Figure 5A, when human spermatozoa were incubated for 4 h under capacitating conditions, an increase in the phosphotyrosine content of specific sperm proteins, such as p81 and p105, was observed. However, when the incubation was done in the presence of SU6656 or PP1, specific inhibitors of the SRC kinase family, the rise in the phosphotyrosine content of those proteins was blocked, whereas P6, a pan-JAK kinase inhibitor which inhibits JAK family kinases at a low nanomolar range (IC50, 1–15 nM) [ 51], had no effect, even at concentrations up to 5 μM. No effect on sperm motility was observed in any of these treatments (data not shown). This suggests that this increase in sperm protein tyrosine phosphorylation is due to the activity of the SRC kinase family. Moreover, when the IP kinase assay was performed in the presence of SU6656, a significant decrease in SRC activity was observed (P < 0.05), whereas no effect was observed when the assay was done in the presence of P6 (negative control Fig. 5B). A decrease in SRC activity was also significant in the presence of PP1 (P < 0.001 Fig. 5C). This correlates with the decrease in the phosphotyrosine content shown in Figure 5A and supports our hypothesis that SRC plays a role in the increase of the phosphotyrosine content of specific human sperm proteins.

Effect of the SRC kinase inhibitors on human sperm protein phosphotyrosine content and SRC activity. A) Sperm were incubated at 37°C in calcium-containing BWW in the absence (B, lanes 1 and 2) or presence of 10 μM SU6656 (SU, lane 3), 10 μM PP1 (lane 4), or 100 nM P6 (lane 5), a specific inhibitor of the JAK family of tyrosine kinases. Aliquots were collected before (lane 1) or after (lanes 2–5) 4 h of incubation. The proteins were solubilized, separated by SDS-PAGE, and transferred onto PVDF. The phosphotyrosine-containing proteins were detected using a monoclonal anti-phosphotyrosine antibody. The anti-TUBA was used to monitor the amount of proteins loaded on the gel for each condition. An experiment representative of three with similar results is shown. No effect of P6 was observed on sperm protein phosphotyrosine content at concentrations up to 100 μM (data not shown). B) Sperm were incubated in complete BWW medium at 37°C for 1 h in the absence (white bar) or presence of 10 μM SU6656 (gray bar) or 100 nM P6 (black bar) and processed for SRC IP and kinase assay in the presence of the same inhibitors. The rest of the procedure was performed as described in Figure 4. The graph shows the relative SRC kinase activity under these conditions as evaluated using the Quantity One software after scanning/digitalizing of the phosphor screens. Values represent mean ± SEM of four different experiments. Statistical significance was assessed using a Dunnett multiple comparison test following ANOVA. *Significantly different (P < 0.05) from the value measured in BWW medium. C) Sperm were incubated 1 h at 37°C in BWW in the absence or presence of 10 μM PP1 and processed as described above. The graph illustrates the quantification of SRC kinase activity in the absence (open bar) or presence (closed bar) of PP1. Values represent mean ± SEM of three different experiments. Statistical significance was assessed by t-test. *Significantly different (P < 0.001) from the value measured in BWW incubation medium.

Effect of the SRC kinase inhibitors on human sperm protein phosphotyrosine content and SRC activity. A) Sperm were incubated at 37°C in calcium-containing BWW in the absence (B, lanes 1 and 2) or presence of 10 μM SU6656 (SU, lane 3), 10 μM PP1 (lane 4), or 100 nM P6 (lane 5), a specific inhibitor of the JAK family of tyrosine kinases. Aliquots were collected before (lane 1) or after (lanes 2–5) 4 h of incubation. The proteins were solubilized, separated by SDS-PAGE, and transferred onto PVDF. The phosphotyrosine-containing proteins were detected using a monoclonal anti-phosphotyrosine antibody. The anti-TUBA was used to monitor the amount of proteins loaded on the gel for each condition. An experiment representative of three with similar results is shown. No effect of P6 was observed on sperm protein phosphotyrosine content at concentrations up to 100 μM (data not shown). B) Sperm were incubated in complete BWW medium at 37°C for 1 h in the absence (white bar) or presence of 10 μM SU6656 (gray bar) or 100 nM P6 (black bar) and processed for SRC IP and kinase assay in the presence of the same inhibitors. The rest of the procedure was performed as described in Figure 4. The graph shows the relative SRC kinase activity under these conditions as evaluated using the Quantity One software after scanning/digitalizing of the phosphor screens. Values represent mean ± SEM of four different experiments. Statistical significance was assessed using a Dunnett multiple comparison test following ANOVA. *Significantly different (P < 0.05) from the value measured in BWW medium. C) Sperm were incubated 1 h at 37°C in BWW in the absence or presence of 10 μM PP1 and processed as described above. The graph illustrates the quantification of SRC kinase activity in the absence (open bar) or presence (closed bar) of PP1. Values represent mean ± SEM of three different experiments. Statistical significance was assessed by t-test. *Significantly different (P < 0.001) from the value measured in BWW incubation medium.

Effect of the cAMP-Dependent Pathway on SRC Kinase Activity

Immunoprecipitation kinase assays were next performed on spermatozoa incubated for different periods of time under capacitating conditions. A transient increase in SRC kinase activity was detected at 1 h ( Fig. 6, A and B). When spermatozoa were incubated with the phosphodiesterase inhibitor IBMX to prevent cAMP catabolism, a treatment that has been shown to induce increases in sperm capacitation, motility, and protein phosphotyrosine content [ 5, 32], a higher SRC kinase activity was measured (P < 0.01 Fig. 6B). The difference in the kinase activity could not be attributed to differences in the amounts of the kinase itself, as none of the treatments affected the solubilization of SRC subjected to the IP kinase assay (data not shown). The IBMX effect was rapid ( Fig. 6A, lane 1 vs. lane 6), as the increase in SRC activity was significant (P < 0.01) immediately at the beginning of the incubation. For this first measure of kinase activity, the cells were in the presence of IBMX for ∼7 min due to the centrifugation and extraction procedures. As Western blot analyses revealed no increase in the SRC kinase content in the protein extract subjected to the IP kinase assay, the increase in enolase phosphorylation most likely results from a higher SRC kinase activity. To confirm the role of cAMP on SRC tyrosine kinase activity, the sperm cells were incubated under capacitating conditions in the presence of dbcAMP, a cell-permeant cAMP analog. As shown in Figure 6C, even though we did not observe any differences in sperm motility after 1 h of incubation (data not shown), dbcAMP significantly increased SRC activity (P < 0.05). These results suggest that SRC activity is positively modulated by cAMP.

Effect of the cAMP-dependent pathway on SRC kinase activity in human spermatozoa. A) Sperm were incubated at 37°C in BWW medium in the absence (lanes 1–5) or presence (lanes 6–10) of the phosphodiesterase inhibitor IBMX. At different times during the incubation, aliquots were collected and proteins were processed for SRC IP and kinase assay in the presence of Ca 2+ as described in Figure 4. An experiment representative of three with similar results is shown. B) Quantification of SRC kinase activity in the absence (open bars) or presence (closed bars) of IBMX. Values represent mean ± SEM of three different experiments. Statistical significance (P < 0.01) of the IBMX effect over the duration of incubation was assessed by two-way ANOVA. *Significantly different (P < 0.01) from the value measured at the same incubation time in BWW medium as assessed by a Bonferroni posttest following a two-way ANOVA. C) Sperm were incubated 1 h at 37°C in BWW in the absence (open bar) or presence (closed bar) of 1 mM dbcAMP and processed as described above for the quantification of SRC kinase activity. Values represent mean ± SEM of four different experiments. *Significantly different (P < 0.05) from the value measured in BWW medium using a t-test analysis. D) Sperm were incubated in complete BWW medium at 37°C for 1 h in the absence (white bar) or presence of 10 (gray bar) or 50 μM H-89 (black bar) and processed for SRC IP and kinase assay. The graph shows the relative SRC kinase activity under these conditions. Values represent mean ± SEM of five different experiments. *Significantly different (P < 0.01) from the value measured in BWW medium using a Dunnett multiple comparison test following ANOVA.

Effect of the cAMP-dependent pathway on SRC kinase activity in human spermatozoa. A) Sperm were incubated at 37°C in BWW medium in the absence (lanes 1–5) or presence (lanes 6–10) of the phosphodiesterase inhibitor IBMX. At different times during the incubation, aliquots were collected and proteins were processed for SRC IP and kinase assay in the presence of Ca 2+ as described in Figure 4. An experiment representative of three with similar results is shown. B) Quantification of SRC kinase activity in the absence (open bars) or presence (closed bars) of IBMX. Values represent mean ± SEM of three different experiments. Statistical significance (P < 0.01) of the IBMX effect over the duration of incubation was assessed by two-way ANOVA. *Significantly different (P < 0.01) from the value measured at the same incubation time in BWW medium as assessed by a Bonferroni posttest following a two-way ANOVA. C) Sperm were incubated 1 h at 37°C in BWW in the absence (open bar) or presence (closed bar) of 1 mM dbcAMP and processed as described above for the quantification of SRC kinase activity. Values represent mean ± SEM of four different experiments. *Significantly different (P < 0.05) from the value measured in BWW medium using a t-test analysis. D) Sperm were incubated in complete BWW medium at 37°C for 1 h in the absence (white bar) or presence of 10 (gray bar) or 50 μM H-89 (black bar) and processed for SRC IP and kinase assay. The graph shows the relative SRC kinase activity under these conditions. Values represent mean ± SEM of five different experiments. *Significantly different (P < 0.01) from the value measured in BWW medium using a Dunnett multiple comparison test following ANOVA.

To further investigate the effect of the cAMP on sperm SRC kinase activity, the PRKA inhibitor H-89 was used to determine whether cAMP actions are mediated by PRKA. As shown in Figure 6D, a significant dose-dependent decrease in SRC kinase activity is measured when H-89 is present at 10 and 50 μM (P < 0.01). No difference in sperm motility was observed in these conditions (data not shown). These results strongly suggest that cAMP increases SRC tyrosine kinase activity in a PRKA-dependent manner.

The interaction of the tyrosine kinase SRC with PRKA was next assessed by coimmunoprecipitation. The catalytic subunit of PRKA (PRKAC) is present within the SRC immunoprecipitated complex from human sperm ( Fig. 7A). Furthermore, when PRKAC was added to the sperm lysate prior to SRC IP under conditions sustaining PRKAC activity, SRC was detected by an anti-phospho-PRKA substrate antibody ( Fig. 7B, lane 2). This suggests that in human sperm, SRC is phosphorylated by PRKAC. After such a procedure, when SRC activity was assessed, a significant increase in enolase phosphorylation was measured when the sperm lysate was added with PRKAC prior to SRC IP ( Fig. 7C). As an alternative, to determine whether PRKA affects SRC's activity indirectly through phosphorylation of the C-terminal SRC kinase (CSK), the presence of the latter was next assessed in human spermatozoa and Western blot analysis clearly shows its presence in sperm extracts ( Fig. 7D).

Association of the cAMP-dependent protein kinase with SRC in human spermatozoa. A) Washed sperm proteins were extracted in the Ca 2+ -containing IP buffer, precleared using commercial mouse IgGs and immunoprecipitated with a SRC monoclonal antibody or a monoclonal antibody directed against the catalytic subunit of PRKA (PRKAC) and processed for Western blot using anti-PRKAC antibody after probing the membrane with the horseradish peroxidase-coupled anti-mouse secondary antibody which only revealed IgG's heavy and light chains (data not shown). The arrows indicate the mouse light and heavy IgG chains. The position of PRKAC is indicated on the right. IP, Antibody used for the immunoprecipitation WB, antibody used for the Western blot. B) Washed sperm were extracted as in A, and the proteins were subjected to in vitro phosphorylation in the absence or presence of 5 units of PRKAC as described in the experimental procedures. The proteins were next precleared and immunoprecipitated with SRC monoclonal antibody as in A, except that the Western blot was performed with a monoclonal phospho-PRKA substrate antibody. ctrl, Control. C) Sperm were processed as described in B, and kinase assay was as described in Figure 4. The graph illustrates the quantification of SRC kinase activity when the sperm lysate was subjected to in vitro phosphorylation in the absence (open bar) or presence (closed bar) of 5 units PRKAC prior to SRC IP and kinase assay. Values represent mean ± SEM of four different experiments. *Significantly different (P < 0.05) from the value measured in the absence of PRKAC using a t-test analysis. D) Detection of CSK by Western blot on total sperm extracts by Western blot analysis. An equivalent of 1 × 10 6 spermatozoa was loaded on the gel.

Association of the cAMP-dependent protein kinase with SRC in human spermatozoa. A) Washed sperm proteins were extracted in the Ca 2+ -containing IP buffer, precleared using commercial mouse IgGs and immunoprecipitated with a SRC monoclonal antibody or a monoclonal antibody directed against the catalytic subunit of PRKA (PRKAC) and processed for Western blot using anti-PRKAC antibody after probing the membrane with the horseradish peroxidase-coupled anti-mouse secondary antibody which only revealed IgG's heavy and light chains (data not shown). The arrows indicate the mouse light and heavy IgG chains. The position of PRKAC is indicated on the right. IP, Antibody used for the immunoprecipitation WB, antibody used for the Western blot. B) Washed sperm were extracted as in A, and the proteins were subjected to in vitro phosphorylation in the absence or presence of 5 units of PRKAC as described in the experimental procedures. The proteins were next precleared and immunoprecipitated with SRC monoclonal antibody as in A, except that the Western blot was performed with a monoclonal phospho-PRKA substrate antibody. ctrl, Control. C) Sperm were processed as described in B, and kinase assay was as described in Figure 4. The graph illustrates the quantification of SRC kinase activity when the sperm lysate was subjected to in vitro phosphorylation in the absence (open bar) or presence (closed bar) of 5 units PRKAC prior to SRC IP and kinase assay. Values represent mean ± SEM of four different experiments. *Significantly different (P < 0.05) from the value measured in the absence of PRKAC using a t-test analysis. D) Detection of CSK by Western blot on total sperm extracts by Western blot analysis. An equivalent of 1 × 10 6 spermatozoa was loaded on the gel.


Plain Language Summary

We created multiple blueprints for the United States to reach zero or negative CO2 emissions from the energy system by 2050 to avoid the most damaging impacts of climate change. By methodically increasing energy efficiency, switching to electric technologies, utilizing clean electricity (especially wind and solar power), and deploying a small amount of carbon capture technology, the United States can reach zero emissions without requiring changes to behavior. Cost is about $1 per person per day, not counting climate benefits this is significantly less than estimates from a few years ago because of recent technology progress. Models with more detail than used in the past revealed unexpected synergies, counterintuitive results, and tradeoffs. The lowest-cost electricity systems get >80% of energy from wind and solar power but need other resources to provide reliable service. Eliminating fossil fuel use altogether is possible but higher cost. Restricting biomass use and land for renewables is possible but could require nuclear power to compensate. All blueprints for the United States agree on the key tasks for the 2020s: increasing the capacity of wind and solar power by 3.5 times, retiring coal plants, and increasing electric vehicle and electric heat pump sales to >50% of market share.


Discussion

Our previous findings on the presence of membrane rafts in the plasma membrane overlying the sperm head region and the functional importance of these domains in mammalian sperm motivated us to investigate the membrane raft's role in regulating the signaling pathways leading to AR in response to physiological stimulation. Our results showed that membrane rafts play an important role in AR induction through the regulation of the cAMP-dependent pathways, including PKA. These findings add to our knowledge of the mechanisms by which AR is induced in avian sperm, highlighting the similarities and differences between mammalian and avian sperm in terms of the behavior of the signal transduction events that precede AR.

In previous studies, sterol removal by sterol acceptors resulted in disorganization of membrane rafts [ 41, 42]. In this study, we found that phosphorylation of PKA substrate proteins and IPVL-induced AR were inhibited in response to sterol removal in chicken sperm. Previous pharmacological experiments on chicken sperm demonstrated that inhibition of PKA activity resulted in diminished AR without affecting the population of motile sperm [ 19]. In concert with these results, we confirmed no change in percentage of motile sperm after sterol removal. Together with the present finding that dbcAMP supplementation abolished the inhibition caused by sterol removal, our results suggest that the disorganization of the membrane rafts inhibits PKA activity, thereby reducing the AR ability in chicken sperm. 2-OHCD treatment is known to result in the elevation of acrosomal responsiveness to physiological stimulation by lowering sterol content in human [ 43, 44] and murine sperm [ 45]. This is contrary to the present finding that the ability to undergo AR was diminished by sterol removal. This discrepancy might be the result of a distinction between signal transduction pathways associated with AR induction in mammals and birds. In mammalian sperm, sterol removal stimulates the phosphorylation of protein tyrosine followed by PKA activation, leading to an increase in acrosomal responsiveness [ 45, 46]. In contrast, sterol removal in chicken sperm did not affect the phosphorylation of protein tyrosine, but inhibited both PKA activity and AR, suggesting that the activation of tyrosine kinase(s) is not involved in the cAMP-dependent pathway in avian sperm. This is supported by evidence that stimulators for the cAMP-dependent pathway did not increase protein tyrosine phosphorylation in quail sperm [ 47]. In mammalian sperm, protein tyrosine phosphorylation is a major event that signals capacitation and is largely regulated by sAC activity. Our results will provide a foundation for investigating why capacitation is not necessary for avian sperm to undergo AR.

Despite the distinction between mammalian and avian sperm in terms of the signaling pathways that precede AR, it is intriguing that, similar to that in mammalian sperm, sterol removal stimulated spontaneous AR in chicken sperm [ 11, 43]. A previous study on murine sperm showed that extracellular Ca 2+ potentiates the initiation of spontaneous AR [ 48]. In addition, our results showed that [Ca 2+ ]i significantly increases in response to sterol removal in chicken sperm. These results suggest that 2-OHCD–induced spontaneous AR results from an increase in [Ca 2+ ]i.

A previous study performed on murine sperm demonstrated that PKA activity is suppressed by Ser/Thr phosphatases during the noncapacitated state [ 39]. Multiple Ser/Thr phosphatases were found in chicken sperm and appeared to be involved in AR [ 17, 49], which brought up the possibility of the involvement of Ser/Thr phosphatases in the inhibition of PKA substrate protein phosphorylation by sterol removal therefore, changes in cytosolic cAMP content were examined in chicken sperm after sterol removal. The results showed that PKA inhibition was a result of a decrease in cAMP content. Cytosolic cAMP levels depend on the concerted action of both synthesis and degradation. In other cells, several isoforms of ACs were found to be tethered to membrane rafts and this is of importance for compartmentalized localization of regulatory modules for cAMP-dependent signaling pathways [ 50, 51]. A study on ciona sperm demonstrated that a disruption of the membrane rafts by sterol removal inhibited cytosolic cAMP synthesis by suppressing AC activity [ 14]. These reports and our results suggest that a regulatory module for the cAMP-dependent pathway might target the membrane rafts in chicken sperm.

Although multiple tmACs were shown to exist in mammalian sperm based on mRNA expression [ 22], their presence and roles remain controversial because there is a discrepancy in the effect of forskolin, an tmAC stimulator, on cAMP production in mammalian sperm [ 40]. On the other hand, sAC is insensitive to forskolin but is regulated by bicarbonate ion. A previous study performed using mutant mice devoid of sAC and a specific inhibitor demonstrated that sAC contributes to protein tyrosine phosphorylation and motility however, we found no reports of studies on the presence of either tmAC or sAC in avian sperm. Previous studies reported in mammalian sperm that bicarbonate and forskolin elevated cytosolic cAMP [ 52, 53] as well as stimulated AR [ 54, 55] however, we found that neither stimulators increased AR in response to physiological stimulation, suggesting that, unlike in mammalian sperm, an increase in cAMP is not necessary for chicken sperm to undergo AR. This suggestion is supported by a previous study that showed that stimulation of sAC by bicarbonate did not increase AR in chicken sperm [ 56]. This difference might be a result of a distinction in sperm functions. In mammalian sperm, cAMP regulates the initiation of capacitation, which then stimulates AR. Avian sperm does not undergo this process to prepare for AR induction however, it is intriguing that inhibitors for both isoforms decreased AR, concomitant to the disappearance of the phosphorylation of PKA substrate proteins, in a dose-dependent manner. Considering that our transcriptome analysis of AC isoforms identified testicular expression of ADCY1, 2, 3, 5, 7, 8, 9, and 10, this suggests the involvement of both isoforms in chicken sperm AR. In mammalian sperm, several lines of evidence have implicated that AR does not appear to need sAC activity [ 21, 57], but does rely on tmAC. Recently, it was reported that the cAMP synthetic pathway is compartmentalized in sperm as follows: sAC in the sperm tail for motility and tmAC in the sperm head for AR [ 22]. Our results showed a distinction between mammalian and avian sperm functions and provide a foundation to promote the characterization of both AC isoforms in avian sperm.

In a previous study, sAC activity was found to be associated with the plasma membranes in mammalian sperm, but the mechanism of this activity remains unclear [ 58]. On the other hand, tmAC consists of nine members, five of which were found to be localized in membrane rafts through biochemical interactions in other cells [ 50], suggesting that membrane rafts might regulate tmAC function. These results, combined with our findings of the functional involvement of AC isoforms in chicken sperm, led to a hypothesis that inhibition of PKA substrate protein phosphorylation and AR in response to sterol removal might be restored by either bicarbonate or forskolin treatment. In agreement with this, we observed that both bicarbonate and forskolin abolished the inhibition of phosphorylation of PKA substrate proteins and AR in 2-OHCD-treated chicken sperm. Interestingly, we found that stimulation with forskolin restored AR more potently than bicarbonate. This is consistent with recent results that AR is predominantly regulated by tmAC and that sAC is not needed for AR in murine sperm [ 20, 22, 57]. In a previous study, the G protein subunit responsible for tmAC activation was localized in the plasma membrane overlying the acrosome [ 22]. In fact, we have shown, using live cell imaging, that this membrane region contains multiple membrane rafts [ 7, 8]. Similarly, membrane rafts were found in the plasma membrane overlaying the sperm head in chicken sperm [ 15]. These reports taken in conjunction with a strict linkage identified between sterol removal and phosphorylation of PKA substrate proteins in mammalian and bird sperm our results suggest the need for further investigations to more fully characterize the functional and spatial distinctions between tmAC and sAC in avian sperm.


Biological pathways to psychopathology

Ahmad R. Hariri, PhD, is Professor of Psychology & Neuroscience at Duke University, where he is also an Investigator in the Institute for Genome Sciences & Policy. Dr. Hariri’s program of research encompasses magnetic resonance imaging, positron emission tomography, pharmacology and molecular genetics. Through the integration of complementary technologies Dr. Hariri’s research has begun to illuminate the neurobiological mechanisms mediating individual differences in complex behavioral traits. This work represents a critical foundation for the identification of risk markers that interact with unique environmental factors to predict neuropsychiatric disorders as well as for the development of more effective and individually tailored treatments for these same disorders. Findings from Dr. Hariri’s program of research have been published in Science, Nature, Nature Neuroscience, Journal of Neuroscience, Archives of General Psychiatry, Biological Psychiatry, Trends in Cognitive Sciences and the Annual Review of Neuroscience. In August 2009, Dr. Hariri’s contributions to the science of individual differences were recognized by the American Psychological Association who presented him with the Distinguished Scientific Award for Early Career Contribution to Psychology.

Individual differences in trait affect, personality and temperament are critical in shaping complex human behaviors, such as those involved in successfully navigating social interactions and overcoming challenges from our ever changing environments. Such individual differences may also serve as important predictors of vulnerability to neuropsychiatric disorders including depression, anxiety and addiction, especially upon exposure to environmental adversity. Accordingly, identifying the biological mechanisms that give rise to these differences can afford a unique opportunity to develop a deeper understanding of complex human behaviors, disease liability and treatment. Having established a number of neural processes that support complex behavioral processes, human neuroimaging studies, especially those employing BOLD fMRI, have now begun to reveal the neural substrates of inter-individual variability in these and related constructs. Moreover, recent studies have established that BOLD fMRI measures represent temporally stable and reliable indices of brain function. Thus, much like their behavioral counterparts, such patterns of brain activation are increasingly thought to represent enduring, trait-like phenomenon, which in and of themselves may serve as important markers of individual differences related to disease liability and pathophysiology.

As neuroimaging studies continue to illustrate the predictive relation between regional brain activation and trait-like behaviors (e.g., increased amygdala reactivity predicts trait anxiety), an important next step is to systematically identify the underlying mechanisms driving variability in brain circuit function. In this regard, recent neuroimaging studies employing pharmacological challenge paradigms, principally targeting monoamine neurotransmission, have revealed that even subtle alterations in dopaminergic, noradrenergic and serotonergic signaling can have a profound impact on the functional response of brain circuitries supporting affect, personality and temperament. Similarly, multimodal neuroimaging approaches have provided evidence for directionally specific relations between key components of monoaminergic signaling cascades, assessed with radiotracer PET, and brain function, assessed with BOLD fMRI. Together, pharmacological challenge neuroimaging and multimodal PET/fMRI are revealing how variability in behaviorally-relevant brain activation emerges as a function of underlying variability in key brain signaling pathways (e.g., increased serotonin signaling predicting increased amygdala reactivity). One key next step is to identify the sources of inter-individual variability in these key neurochemical signaling mechanisms.

In the modern era of human molecular genetics, this step includes identifying common variation in the genes that influence the functioning or availability of components in these pathways. As DNA sequence variation across individuals represents the ultimate wellspring of variability in emergent molecular, neurobiological and related behavioral processes, understanding the relations between genes, brain and behavior is important for establishing a mechanistic foundation for individual differences in behavior and related psychiatric disease. Moreover, such genetic polymorphisms can be readily identified from DNA collected via cells from individual blood or even saliva samples using relatively well-tolerated, inexpensive and standardized laboratory protocols. Once collected and isolated, an individual’s DNA can be amplified repeatedly providing an almost endless reservoir of material for genotyping of additional candidate polymorphisms as they are identified. When precise cascades of related neurobiological and behavioral effects are clearly established, common polymorphisms could become powerful, readily accessible predictive markers of such emergent properties. DNA samples can be collected in doctor’s offices from everyone, even newborns, and genetic assays cost only tens of dollars per sample in comparison to the hundreds and even thousands required for fMRI, PET and drug studies. Of course, arriving at this ultimate reduction requires intensive and expansive efforts wherein all these technologies as well as epidemiological and clinical studies are first brought to bear on explicating the detailed biological mechanisms mediating individual differences in trait behaviors and related risk for neuropsychiatric disease.

In the last five years, significant progress has been made in describing the contributions of multiple common genetic polymorphisms to individual differences in complex behavioral phenotypes and disease liability – in particular, by identifying effects of functional genetic variation on the neural processes that mediate behavioral responses to environmental challenge (Caspi & Moffitt, 2006 Hariri & Holmes, 2006). The potential of this approach is highlighted by recent studies demonstrating how common polymorphisms affecting brain chemistry bias brain circuitry that helps shape individual differences in behaviors such as temperamental anxiety and impulsivity (Figure 1). With increased utilization and continued expansion each level of analysis in this integrative strategy - brain circuit function, neural signaling cascades and molecular genetics – also has the potential to uniquely illuminate clinically relevant information that can be used in efforts to devise individually tailored treatment regimes and establish predictive disease markers. Three specific examples, summarized in Table 1, illustrate the effectiveness of this integrated strategy to parse biological mechanisms mediating individual differences in complex behaviors. In each, subjects were retrospectively genotyped for the candidate functional polymorphisms of interest from stored samples of DNA and this information was used to group subjects based on their individual genotypes. Notably, the behavioral assessments in all three examples were conducted as a component of a larger parent protocol that preceded measurement of task-related regional brain function with BOLD fMRI by an average interval of 29 weeks. The fact that robust brain-behavior correlations were observed despite the separation in time is consistent with the suggestion that both metrics (i.e., brain function and behavior) are remarkably stable, possibly indicative of trait-related variation. Such a relation further underscores the likelihood that inter-individual variability in brain-behavior associations are influenced by functional genetic polymorphisms.

Figure 1. Integration of complementary technologies can be used to reveal the neurobiology of individual differences in complex behavioral traits. a. Individual differences in personality and temperament are critical in shaping complex human behaviors and may serve as important predictors of vulnerability to neuropsychiatric disorders. b. Neuroimaging technologies, especially BOLD fMRI, can identify relationships between variability in brain circuit function and individual differences in personality and temperament. c. Multimodal PET/fMRI (or pharmacological fMRI) can map individual differences in behaviorally relevant brain circuit function to variability in specific molecular signaling pathways. d. Variability in specific molecular signaling pathways can be mapped to functional genetic polymorphisms which inform their ultimate biological origins and can be used to efficiently model how such emergent variability impacts behaviorally relevant brain function. e. Each level of analysis can potentially inform clinically relevant issues, provide guiding principles for the development of more effective and personalized treatment options and represent predictive risk markers that interact with unique environmental factors to precipitate disease.

Note: data are created for illustration only. Measures of brain circuit function could be derived from fMRI, EEG, MEG. Those for molecular signaling could be derived from PET or Drug Challenge studies.

As detailed in the three studies summarized in Table 1, neuroimaging technologies, especially BOLD fMRI, have begun to identify how variability in the neural substrates underlying information processing contribute to emergent individual differences in stable and enduring aspects of human behaviors such as personality and temperament. In parallel, the application of pharmacological fMRI and multimodal PET/fMRI is improving our understanding of how variability in specific molecular signaling pathways influences individual differences in the function of these behaviorally relevant brain circuitries. Moreover, information on DNA sequence variation in humans (and related identification of functional genetic polymorphisms) is now being utilized to understand the biological origins of variability in component processes of molecular signaling pathways. Furthermore, this information is being used to efficiently model how such emergent variability impacts behaviorally relevant brain function. Such efforts have the potential to inform clinically relevant issues and provide guiding principles for the development of more effective and individually tailored treatment regimes. In addition, mechanisms that have been elucidated, especially those mapped to functional genetic polymorphisms, can lead to identification of predictive risk markers that interact with unique environmental factors to precipitate disease.

Table 1. Summary of studies linking individual differences in complex behavioral traits with underlying variability in brain circuit function, molecular signaling pathways and functional genetic polymorphisms.

Abbreviations: 5-HT – serotonin DA – dopamine eCB – endocannabinoid HTR1A – serotonin 1A receptor gene DAT1 – dopamine transporter polymorphism FAAH – fatty acid amide hydrolase gene.

While the three examples highlighted here are evidence for the potential of this integrated research strategy, much work is left to be done. First, to allow for tractable experimental designs and testable hypotheses in existing samples, the studies highlighted above have focused on the effects of a single signaling pathway on behaviorally relevant brain circuitry. Of course, it is very clear there are numerous complex interactions between signaling pathways and that more than one pathway contributes to the regulation of any brain circuitry. However, existing studies lack the power and sophistication to model such complex interactions while effectively controlling for other important modulatory factors (e.g., age, sex) in the context of BOLD fMRI, pharmacological MRI or multimodal PET/fMRI protocols. To do so, we must aggressively expand the scale and scope of our studies to include hundreds and, preferably, thousands of subjects.

A second important consideration is that existing studies have been largely conducted in small ethnically and racially homogenous populations. Thus, the observed effects may not generalize to other populations. The potential effect of any single genetic variant on a complex biological and behavioral phenotype is likely small against the background of the approximately 20,000-25,000 human genes and the multitude of other neurobiologically relevant functional variants they likely harbor. Thus, it is important to explicitly test the independence of functional polymorphisms through rigorous statistical modeling in larger samples and also to test the validity of any associations derived in one sample population (e.g., Caucasian) to populations with different genetic backgrounds (e.g., Asian or African).

A third important consideration for the future of this research is the need to conduct large-scale prospective studies beginning in childhood to determine any developmental shifts in neurogenetic pathways mediating individual differences in behavior as well as their predictive utility in identifying neuropsychiatric disease risk as a function of environmental or other stressors. All of the studies described above and most of the studies available in the literature as a whole have been conducted in adults carefully screened for the absence of psychopathology. Because of this, these findings identify mechanisms contributing to variability in the normative range of behavior only. As such, the utility of these neural, molecular or genetic markers in predicting vulnerability to neuropsychiatric disorder is unclear. Such predictive utility is ideally tested through prospective studies beginning with premorbid populations that account for the moderating effects of environmental stress in the emergence of clinical disorder over time (Caspi & Moffitt, 2006 Viding, Williamson, & Hariri, 2006).

Finally, there is tremendous potential in developing large databases (again preferably thousands of subjects) with detailed measures of behavioral traits, neuroimaging based measures of multiple brain circuitries, and extensive genotyping. One of the most exciting applications of molecular genetics is in identifying novel biological pathways contributing to the emergence of complex traits (Gibson & Goldstein, 2007 McCarthy et al., 2008). The continued refinement of a detailed map of sequence variation across the entire human genome (i.e., single nucleotide polymorphisms [SNPs] that “tag” every gene) and production of technologies supporting cost-efficient identification of such variation have dramatically accelerated the discovery of genes involved in the emergence of complex disease processes (Fellay et al., 2007 Link et al., 2008) as well as normal variability in continuous traits (Lettre et al., 2008). Many of the genes identified in such studies have illuminated novel pathways not previously implicated in these processes or traits, spurring intensive efforts to understand the potential biological effects of the proteins produced by these genes. As such, these “genome-wide” screens represent an opportunity to leap forward beyond the available pool of candidate molecules and pathways in parsing the mechanisms of complex biological processes. Because neuroimaging based measures of brain function reveal key mechanisms involved in the emergence of individual differences in behavioral traits and are closer to the biological effects of functional genetic polymorphisms, they are ideal substrates for genome-wide screens. For example, BOLD fMRI estimates of amygdala reactivity predicting variability in temperamental anxiety can be used as the continuous trait in a genome-wide screen. Any significant associations that emerge between genetic variation and amygdala reactivity may confirm existing relations (e.g., the importance of genes biasing 5-HT signaling) or, perhaps more importantly, reveal unexpected candidate molecules or pathways (e.g., a gene producing a molecule that is expressed in the brain and may function in second-messenger signaling cascades). Once identified and, ideally, replicated in large-scale databases that effectively address confounds common to genome-wide screens (e.g., controlling for multiple comparisons resulting from testing the association of a phenotype with hundreds of thousands or millions of SNPs), the impact of variation in novel genes associated with amygdala reactivity can be explored at each level of the biological cascade leading to trait anxiety (i.e., be fed back into the discovery loop outlined in Figure 1). In addition to exponentially improving our understanding of neurobiological pathways leading to individual differences in complex behavioral traits these efforts may lead to the discovery of novel therapeutic strategies targeting related disease processes.

In summary, ongoing efforts are beginning to shed light on detailed mechanisms that mediate individual differences in complex behavioral traits and, potentially, related neuropsychiatric diseases. Elaboration of these mechanisms at the level of brain circuit function, molecular signaling pathways and functional genetic polymorphisms could one day inform clinically relevant issues and provide guiding principles for the development of more effective and individually tailored treatment regimes. In addition, an understanding of such mechanisms, especially those mapped to functional genetic polymorphisms, may lead to identification of predictive risk markers that interact with unique environmental factors to predict disease risk.

Adapted, with permission, from the Annual Review of Neuroscience, Volume 32 (c) 2009 by Annual Reviews. www.annualreviews.org

Caspi, A., & Moffitt, T. E. (2006). Gene-environment interactions in psychiatry: joining forces with neuroscience. Nature Reviews Neuroscience, 7(7), 583-590.

Fakra, E., Hyde, L. W., Gorka, A., Fisher, P. M., Muñoz, K. E., Kimak, M., Halder, I., Ferrell, R. E., Manuck, S. B., & Hariri , A.R. (2009) Effects of HTR1A C(-1019)G on amygdala reactivity and trait anxiety. Archives of General Psychiatry . 66(1):33-40.

Fellay, J., Shianna, K. V., Ge, D., Colombo, S., Ledergerber, B., Weale, M., et al. (2007). A whole-genome association study of major determinants for host control of HIV-1. Science, 317( 5840), 944-947.

Gibson, G., & Goldstein, D. B. (2007). Human genetics: the hidden text of genome-wide associations. Current Biology, 17(21), R929-932.

Hariri, A. R., & Holmes, A. (2006). Genetics of emotional regulation: the role of the serotonin transporter in neural function. Trends in Cognitive Sciences, 10(4), 182-191.

Lettre, G., Jackson, A. U., Gieger, C., Schumacher, F. R., Berndt, S. I., Sanna, S., et al. (2008). Identification of ten loci associated with height highlights new biological pathways in human growth. Nature Genetics, 40(5), 584-591.

Link, E., Parish, S., Armitage, J., Bowman, L., Heath, S., Matsuda, F., et al. (2008). SLCO1B1 variants and statin-induced myopathy--a genomewide study. New England Journal of Medicine, 359(8), 789-799.

McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., et al. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.

Viding, E., Williamson, D. E., & Hariri, A. R. (2006). Developmental imaging genetics: challenges and promises for translational research. Development and Psychopathology, 18(3), 877-892.


Losing the “undruggable” label: Recent success and developments in Ras-targeted therapies

After a long bout of setbacks, recent success has generated renewed energy in the field. Promising Phase II results were observed where a KRAS G12C inhibitor called sotorasib/AMG 510 led to rapid and durable tumor shrinkages in previously treated patients with advanced non-small cell lung cancer. 24 “Now that we have seen it is possible,” says Parsons, “I think many efforts to improve this inhibitor will be underway. The 12C inhibitor isn’t perfect and papers already show resistance, but I think the fundamental concepts will open doors to the development of improved inhibitors with increased efficacy. Companies will keep this information under wraps for now, but I think many will build upon the initial findings.” The small molecule drug was the first KRAS G12C inhibitor to enter the clinic and has been shown to inhibit KRAS signaling and tumor growth in cell lines and patient-derived xenograft models. 25

As cells find new ways to develop resistance, researchers look to new tools and approaches – often beyond just a single solution. For instance, the use of MEK inhibitors alongside BRAF inhibitors in melanoma has been shown to slow the acquisition of resistance. 26 Pajic points to a number of other developments, including the production of clinical grade “iExosomes” (mesenchymal stromal cell-derived exosomes with KrasG12D siRNA) for PDAC, and the targeting of KRAS effectors such as PTPN11 with first-in-human studies underway in patients with advanced solid cancers (e.g., NCT04528836 , NCT04252339 ). 27 Parson and colleagues are also exploring combination therapies across a range of preclinical models, and described how a KRAS 12C inhibitor and EGFR inhibitor resulted in cell death in vitro. 28

Some novel approaches to Ras-targeted therapies have involved the revival of compounds that were initially deemed unsuccessful. The development of Raf-MEK inhibitor CH5126766 looked promising at first, but was thwarted by toxicity issues. To explore potential ways of limiting toxicity, the inhibitor was profiled in a Phase I study using intermittent dosing schedules. 29 Overall, it was concluded that antitumor activity was achieved across multiple cancer types, and the inhibitor was deemed tolerable – warranting further evaluation of the Raf-MEK inhibitor.

Persistent, creative thinking is what the field needs, which is what Pajic ’s group is working to deliver. By exploring the use of novel, small molecule, stroma-targeting agents Pajic hopes to deprive cancer cells of the supportive stromal niche in primary tumors and metastases, while supporting chemotherapy and/or immuno-oncology agents. 30 , 31

Precise, novel oncology tools and preclinical models are needed to drive the development of Ras-related therapeutics. For Parsons, her mission is to develop more representative animal models for colorectal cancer, as many current models develop tumors in the small intestine. Meanwhile, colleague and co-author Maria Zafra is using base editing to make distinct alterations in mice with specific KRAS mutations – now published in Cancer Discovery: 28 “The mice and subsequent organoid cultures are a really valuable tool that faithfully recapitulate signaling dynamics, and serve as an important preclinical model. Previously, most studies would use cell lines which removes a lot of the complexities of signaling,” says Parsons. In the same laboratory, a reporter was developed to indicate when precise, inducible base editing has occurred. “This would be really exciting as you could introduce a specific mutation and then “turn off” the delivery system to avoid long term or off-target effects,” explains Parsons. The work opens the doors to implementing CRISPR base editing to create targeted mutations in cell lines, organoids and animal models. 32

From the KRAS G12C inhibitor breakthrough to the exploration of combination therapies, there has been significant progress in Ras-related research. Together, preclinical research tools such as base editing reporters, organoids and precise animal models are paving the way for the development of rational, tailored anticancer strategies.

“Apart from that I think the gene engineering field in general is driving this field forward,” says Parsons. “The way preclinical models are being developed is rapidly changing to make more representative models. The way we can induce mutations in organoids is changing and the CRISPR/base editing field is evolving at such a rapid pace. Many companies are also getting smarter with drug design and building upon existing drugs like the 12C inhibitor.”

References


Watch the video: ΚΑΤΑΡΡΑΚΤΕΣ ΚΑΙ ΦΑΡΑΓΓΙ ΒΟΛΙΝΑΙΟΥ, ΑΧΑΪΑ DRONE. VOLINAIOS GORGE AND WATERFALLS, ACHAEA, GREECE. (January 2022).