Functional Proteomics Identifies Acinus L as a Direct Insulin- and Amino Acid-Dependent Mammalian Target of Rapamycin Complex 1 (mTORC1) Substrate

The serine/threonine kinase mammalian target of rapamycin (mTOR) governs growth, metabolism, and aging in response to insulin and amino acids (aa), and is often activated in metabolic disorders and cancer. Much is known about the regulatory signaling network that encompasses mTOR, but surprisingly few direct mTOR substrates have been established to date. To tackle this gap in our knowledge, we took advantage of a combined quantitative phosphoproteomic and interactomic strategy. We analyzed the insulin- and aa-responsive phosphoproteome upon inhibition of the mTOR complex 1 (mTORC1) component raptor, and investigated in parallel the interactome of endogenous mTOR. By overlaying these two datasets, we identified acinus L as a potential novel mTORC1 target. We confirmed acinus L as a direct mTORC1 substrate by co-immunoprecipitation and MS-enhanced kinase assays. Our study delineates a triple proteomics strategy of combined phosphoproteomics, interactomics, and MS-enhanced kinase assays for the de novo-identification of mTOR network components, and provides a rich source of potential novel mTOR interactors and targets for future investigation.The serine/threonine kinase mammalian target of rapamycin (mTOR)¹ is conserved in all eukaryotes from yeast to mammals (1). mTOR is a central controller of cellular growth, whole body metabolism, and aging, and is frequently deregulated in metabolic diseases and cancer (2). Consequently, mTOR as well as its upstream and downstream cues are prime candidates for targeted drug development to alleviate the causes and symptoms of age-related diseases (3, 4). The identification of novel mTOR regulators and effectors thus remains a major goal in biomedical research. A vast body of literature describes a complex signaling network around mTOR. However, our current comparatively detailed knowledge of mTOR''s upstream cues contrasts with a rather limited set of known direct mTOR substrates.mTOR exists in two structurally and functionally distinct multiprotein complexes, termed mTORC1 and mTORC2. Both complexes contain mTOR kinase as well as the proteins mLST8 (mammalian lethal with SEC thirteen 8) (5–7), and deptor (DEP domain-containing mTOR-interacting protein) (8). mTORC1 contains the specific scaffold protein raptor (regulatory-associated protein of mTOR) (9, 10), whereas mTORC2 contains the specific binding partners rictor (rapamycin-insensitive companion of mTOR) (5–7), mSIN1 (TORC2 subunit MAPKAP1) (11–13), and PRR5/L (proline rich protein 5/-like) (14–16). The small macrolide rapamycin acutely inhibits mTORC1, but can also have long-term effects on mTORC2 (17, 18). More recently, ATP-analogs (19) that block both mTOR complexes, such as Torin 1 (20), have been developed. As rapamycin has already been available for several decades, our knowledge of signaling events associated with mTORC1 as well as its metabolic inputs and outputs is much broader as compared with mTORC2. mTORC1 responds to growth factors (insulin), nutrients (amino acids, aa) and energy (ATP). In response, mTORC1 activates anabolic processes (protein, lipid, nucleotide synthesis) and blocks catabolic processes (autophagy) to ultimately allow cellular growth (21). The insulin signal is transduced to mTORC1 via the insulin receptor (IR), and the insulin receptor substrate (IRS), which associates with class I phosphoinositide 3-kinases (PI3Ks). Subsequent phosphatidylinositol 3,4,5 trisphosphate (PIP3) binding leads to relocalization of the AGC kinases phosphoinositide-dependent protein kinase 1 (PDK1) and Akt (also termed protein kinase B, PKB) to the plasma membrane, where PDK1 phosphorylates Akt at T308 (22, 23). In response, Akt phosphorylates and inhibits the heterocomplex formed by the tuberous sclerosis complex proteins 1 and 2 (TSC1-TSC2) (24, 25). TSC1-TSC2 is the inhibitory, GTPase-activating protein for the mTORC1-inducing GTPase Ras homolog enriched in brain (rheb) (26–30), which activates mTORC1 at the lysosome. mTORC1 localization depends on the presence of aa, which in a rag GTPase-dependent manner induce mTORC1 relocalization to lysosomes (31, 32). Low energy levels are sensed by the AMP-dependent kinase (AMPK), which in turn phosphorylates the TSC1-TSC2 complex (33) and raptor (34), thereby inhibiting mTORC1.mTORC1 phosphorylates its well-described downstream substrate S6-kinase (S6K) at T389, the proline-rich Akt substrate of 40 kDa (PRAS40) at S183, and the translational repressor 4E-binding protein (4E-BP) at T37/46 (35–41). Unphosphorylated 4E-BP binds and inhibits the translation initiation factor 4G (eIF4G), which within the eIF4F complex mediates the scanning process of the ribosome to reach the start codon. Phosphorylation by mTORC1 inhibits 4E-BP''s interaction with eIF4E, thus allowing for assembly of eIF4F, and translation initiation (42, 43). More recently, also the IR-activating growth factor receptor-bound protein 10 (Grb10) (44, 45), the autophagy-initiating Unc-51-like kinase ULK1 (46), and the trifunctional enzymatic complex CAD composed of carbamoyl-phosphate synthetase 2, aspartate transcarbamoylase, and dihydroorotase (47, 48), which is required for nucleotide synthesis, have been described as direct mTORC1 substrates.mTORC2 activation is mostly described to be mediated by insulin, and this is mediated by a PI3K variant that is distinct from the PI3K upstream of mTORC1 (49, 50). Furthermore, mTORC2 responds to aa (5, 51). In response, mTORC2 phosphorylates the AGC kinases Akt at S473 (52–55), and serum and glucocorticoid kinase SGK (56) and protein kinase C alpha (PKCalpha) (7) within their hydrophobic motifs (57, 58), to control cellular motility (5–7), hepatic glycolysis, and lipogenesis (59). In addition, mTOR autophosphorylation at S2481 has been established as an mTORC2 readout in several cell lines including HeLa cells (49).Given the multiplicity of effects via which mTOR controls cellular and organismal growth and metabolism, it is surprising that only relatively few direct mTOR substrates have been established to date. Proteomic studies are widely used to identify novel interactors and substrates of protein kinases. Two studies have recently shed light on the interaction of rapamycin and ATP-analog mTOR inhibitors with TSC2 inhibition in mammalian cells (44, 45), and one study has analyzed the effects of raptor and rictor knockouts in non-stimulated cells (48).In this work, we report a functional proteomics approach to study mTORC1 substrates. We used an inducible raptor knockdown to inhibit mTORC1 in HeLa cells, and analyzed the effect in combination with insulin and aa induction by quantitative phosphoproteomics using stable isotope labeling by amino acids in cell culture (SILAC) (60). In parallel, we purified endogenous mTOR complexes and studied the interactome of mTOR by SILAC-MS. Through comparative data evaluation, we identified acinus L as a potential novel aa/insulin-sensitive mTOR substrate. We further validated acinus L by co-immunoprecipitation and MS-enhanced kinase assays as a new direct mTORC1 substrate.     

Biomathematical Description of Synthetic Peptide Libraries

Libraries of randomised peptides displayed on phages or viral particles are essential tools in a wide spectrum of applications. However, there is only limited understanding of a library''s fundamental dynamics and the influences of encoding schemes and sizes on their quality. Numeric properties of libraries, such as the expected number of different peptides and the library''s coverage, have long been in use as measures of a library''s quality. Here, we present a graphical framework of these measures together with a library''s relative efficiency to help to describe libraries in enough detail for researchers to plan new experiments in a more informed manner. In particular, these values allow us to answer-in a probabilistic fashion-the question of whether a specific library does indeed contain one of the "best" possible peptides. The framework is implemented in a web-interface based on two packages, discreteRV and peptider, to the statistical software environment R. We further provide a user-friendly web-interface called PeLiCa (Peptide Library Calculator, http://www.pelica.org), allowing scientists to plan and analyse their peptide libraries.     

Dissecting Long-Term Glucose Metabolism Identifies New Susceptibility Period for Metabolic Dysfunction in Aged Mice

Metabolic disorders, like diabetes and obesity, are pathogenic outcomes of imbalance in glucose metabolism. Nutrient excess and mitochondrial imbalance are implicated in dysfunctional glucose metabolism with age. We used conplastic mouse strains with defined mitochondrial DNA (mtDNA) mutations on a common nuclear genomic background, and administered a high-fat diet up to 18 months of age. The conplastic mouse strain B6-mt^FVB, with a mutation in the mt-Atp8 gene, conferred β-cell dysfunction and impaired glucose tolerance after high-fat diet. To our surprise, despite of this functional deficit, blood glucose levels adapted to perturbations with age. Blood glucose levels were particularly sensitive to perturbations at the early age of 3 to 6 months. Overall the dynamics consisted of a peak between 3–6 months followed by adaptation by 12 months of age. With the help of mathematical modeling we delineate how body weight, insulin and leptin regulate this non-linear blood glucose dynamics. The model predicted a second rise in glucose between 15 and 21 months, which could be experimentally confirmed as a secondary peak. We therefore hypothesize that these two peaks correspond to two sensitive periods of life, where perturbations to the basal metabolism can mark the system for vulnerability to pathologies at later age. Further mathematical modeling may perspectively allow the design of targeted periods for therapeutic interventions and could predict effects on weight loss and insulin levels under conditions of pre-diabetic obesity.     

In Vivo Volatile Organic Compound Signatures of Mycobacterium avium subsp. paratuberculosis

Mycobacterium avium ssp. paratuberculosis (MAP) is the causative agent of a chronic enteric disease of ruminants. Available diagnostic tests are complex and slow. In vitro, volatile organic compound (VOC) patterns emitted from MAP cultures mirrored bacterial growth and enabled distinction of different strains. This study was intended to determine VOCs in vivo in the controlled setting of an animal model. VOCs were pre-concentrated from breath and feces of 42 goats (16 controls and 26 MAP-inoculated animals) by means of needle trap microextraction (breath) and solid phase microextraction (feces) and analyzed by gas chromatography/ mass spectrometry. Analyses were performed 18, 29, 33, 41 and 48 weeks after inoculation. MAP-specific antibodies and MAP-specific interferon-γ-response were determined from blood. Identities of all marker-VOCs were confirmed through analysis of pure reference substances. Based on detection limits in the high pptV and linear ranges of two orders of magnitude more than 100 VOCs could be detected in breath and in headspace over feces. Twenty eight substances differed between inoculated and non-inoculated animals. Although patterns of most prominent substances such as furans, oxygenated substances and hydrocarbons changed in the course of infection, differences between inoculated and non-inoculated animals remained detectable at any time for 16 substances in feces and 3 VOCs in breath. Differences of VOC concentrations over feces reflected presence of MAP bacteria. Differences in VOC profiles from breath were linked to the host response in terms of interferon-γ-response. In a perspective in vivo analysis of VOCs may help to overcome limitations of established tests.     

Unusual evolution of a catalytic core element in CCA-adding enzymes

CCA-adding enzymes are polymerases existing in two distinct enzyme classes that both synthesize the C-C-A triplet at tRNA 3′-ends. Class II enzymes (found in bacteria and eukaryotes) carry a flexible loop in their catalytic core required for switching the specificity of the nucleotide binding pocket from CTP- to ATP-recognition. Despite this important function, the loop sequence varies strongly between individual class II CCA-adding enzymes. To investigate whether this loop operates as a discrete functional entity or whether it depends on the sequence context of the enzyme, we introduced reciprocal loop replacements in several enzymes. Surprisingly, many of these replacements are incompatible with enzymatic activity and inhibit ATP-incorporation. A phylogenetic analysis revealed the existence of conserved loop families. Loop replacements within families did not interfere with enzymatic activity, indicating that the loop function depends on a sequence context specific for individual enzyme families. Accordingly, modeling experiments suggest specific interactions of loop positions with important elements of the protein, forming a lever-like structure. Hence, although being part of the enzyme’s catalytic core, the loop region follows an extraordinary evolutionary path, independent of other highly conserved catalytic core elements, but depending on specific sequence features in the context of the individual enzymes.     

A dual role of extracellular DNA during biofilm formation of Neisseria meningitidis

Major pathogenic clonal complexes (cc) of Neisseria meningitidis differ substantially in their point prevalence among healthy carriers. We show that frequently carried pathogenic cc (e.g. sequence type ST‐41/44 cc and ST‐32 cc) depend on extracellular DNA (eDNA) to initiate in vitro biofilm formation, whereas biofilm formation of cc with low point prevalence (ST‐8 cc and ST‐11 cc) was eDNA‐independent. For initial biofilm formation, a ST‐32 cc type strain, but not a ST‐11 type strain, utilized eDNA. The release of eDNA was mediated by lytic transglycosylase and cytoplasmic N‐acetylmuramyl‐l ‐alanine amidase genes. In late biofilms, outer membrane phospholipase A‐dependent autolysis, which was observed in most cc, but not in ST‐8 and ST‐11 strains, was required for shear force resistance of microcolonies. Taken together, N. meningitidis evolved two different biofilm formation strategies, an eDNA‐dependent one yielding shear force resistant microcolonies, and an eDNA‐independent one. Based on the experimental findings and previous epidemiological observations, we hypothesize that most meningococcal cc display a settler phenotype, which is eDNA‐dependent and results in a stable interaction with the host. On the contrary, spreaders (ST‐11 and ST‐8 cc) are unable to use eDNA for biofilm formation and might compensate for poor colonization properties by high transmission rates.