Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

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.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]