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.     

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]     

A Non-canonical MEK/ERK Signaling Pathway Regulates Autophagy via Regulating Beclin 1

Autophagy-essential proteins are the molecular basis of protective or destructive autophagy machinery. However, little is known about the signaling mechanisms governing these proteins and the opposing consequences of autophagy in mammals. Here we report that a non-canonical MEK/ERK module, which is positioned downstream of AMP-activated protein kinase (AMPK) and upstream of tuberous sclerosis complex (TSC), regulates autophagy by regulating Beclin 1. Depletion of ERK partially inhibited autophagy, whereas specific inhibition on MEK completely inhibited autophagy. MEK could bypass ERK to promote autophagy. Basal MEK/ERK activity conferred basal Beclin 1 by preventing disassembly of mammalian target of rapamycin complex 1 (mTORC1) and mTORC2. Activation of MEK/ERK by AMPK upon autophagy stimuli disassembled mTORC1 via binding to and activating TSC but disassembled mTORC2 independently of TSC. Inhibition of mTORC1 or mTORC2 by transiently or moderately activated MEK/ERK caused moderately enhanced Beclin 1 resulting in cytoprotective autophagy, whereas inhibition of both mTORC1 and mTORC2 by sustained MEK/ERK activation caused strongly pronounced Beclin 1 leading to cytodestructive autophagy. Our findings thus propose that the AMPK-MEK/ERK-TSC-mTOR pathway regulation of Beclin 1 represents different thresholds responsible for a protective or destructive autophagy.Autophagy is an evolutionally conserved machinery involving the degradation and turnover of cytoplasmic material in lysosomes. Autophagy plays a role in cellular homeostasis (1), antiaging (2–4), development (1, 5), protection of the genome (6), and regulation of cell size (7). Autophagy may act as a means of defense against bacterium and virus invasion and be linked to various diseases including cancer (8–10), cardiomyopathy (11), and neurodegenerative disorders (12).Autophagy starts with the formation of an autophagosome, enclosed within a double membrane that engulfs part of the cytoplasm. During periods of autophagy stimuli, cells respond to either maintain the metabolism essential for survival or execute cell death. Autophagy-essential proteins (Atg)² are the molecular basis of autophagy machinery. About 30 Atg proteins in yeast and 10 in mammals have been identified. In yeast, the protein kinase target of rapamycin (TOR) mediates autophagy via Atg1-Atg13 kinase complex. Atg1 interacts with multiple components of the autophagic machinery through direct association, phosphorylation, and/or intracellular localization (13, 14).In mammalian systems, autophagosomes fuse with lysosomes to generate autophagolysosomes, which undergo a maturation process by fusing with endocytic compartments and lysosomes (15). Because it is not known how the Atg1 homolog acts in mammals, a different mechanism may be involved in regulating autophagy. Beclin 1/Atg6, microtubule-associated protein 1 light chain 3 (LC3)/Atg8, Atg5, Atg12, and Atg13 are essential for autophagosome formation in mammalian species (5, 16–20). Atg7 and Atg3 are required in the conjugation reaction between Atg12 and Atg5 and in the lipidation of LC3. During the formation of autophagosomes in mammalian cells, LC3 is lipidated via a ubiquitylation-like system (17, 21), generating a soluble form, LC3-I. LC3-I is further modified to a membrane-bound form, LC3-II, which is subsequently localized to autophagosomes and autolysosomes until being degraded by the lysosome.Beclin 1 was initially isolated as a B-cell lymphoma-2 (Bcl2)-interacting tumor suppressor in mammalian cells (22). Overexpression of Bcl2 attenuates the formation of the kinase complex Beclin 1-class III phosphatidylinositol 3-kinase (PI3KC3) essential for the formation of autophagosomes (23). The UV radiation resistance-associated gene tumor suppressor and the activating molecule in Beclin 1-regulated autophagy protein 1 (Ambra 1) were identified as new Beclin 1-binding partners that also regulate autophagy by regulating the Beclin 1-PI3KC3 kinase complex. Association of Beclin 1 with PI3KC3 is negatively regulated by Bcl2 (22) and positively regulated by UV radiation resistance-associated gene tumor suppressor and Ambra 1 (24, 25). Beclin 1 is homoallelically deleted in many human tumors. A decreased Beclin 1 level causes defective autophagy and breast cancer, but restoration of Beclin 1 induces autophagy and inhibits tumorigenicity of human breast cancer cells (18). These reports evidence the dependence on Beclin 1 for a functional autophagy mechanism.Diverse signaling pathways have been reported in the regulation of autophagy in mammalian cells (26, 27). In contrast to yeast, mammalian cells regulate autophagy via both class I and class III PI3K. Class I PI3K plays an inhibitory role, whereas class III PI3K kinase complex, which includes Beclin 1, plays a stimulatory role in autophagy by promoting the nucleation of autophagic vesicles (28, 29). A recent study also indicates that hVps15 is required in regulation of class III PI3K in mammalian cells (30). However, the signaling mechanisms controlling autophagy-essential proteins, in particular Beclin 1, and the opposing consequences of autophagy remain to be resolved.Our present studies identified and positioned a non-canonical MEK/ERK pathway downstream of AMPK and upstream of TSC and mTOR. This MEK/ERK module regulated autophagy via regulating the Beclin 1 level through the AMPK-MEK/ERK-TSC-mTOR pathway. Moderately enhanced Beclin 1 by transient or moderate activation of MEK/ERK and subsequent inhibition on mTORC1 and mTORC2 individually caused protective autophagy. Strongly pronounced Beclin 1 by sustained or strong activation of MEK/ERK followed by dual inhibition on mTORC1 and mTORC2 caused destructive autophagy. Our results thus reveal interesting Beclin 1 thresholds in regulating autophagy.     

Rapamycin Rescues TDP-43 Mislocalization and the Associated Low Molecular Mass Neurofilament Instability

TDP-43 is a nuclear protein involved in exon skipping and alternative splicing. Recently, TDP-43 has been identified as the pathological signature protein in frontotemporal lobar degeneration with ubiquitin-positive inclusions and in amyotrophic lateral sclerosis. In addition, TDP-43-positive inclusions are present in Parkinson disease, dementia with Lewy bodies, and 30% of Alzheimer disease cases. Pathological TDP-43 is redistributed from the nucleus to the cytoplasm, where it accumulates. An ∼25-kDa C-terminal fragment of TDP-43 accumulates in affected brain regions, suggesting that it may be involved in the disease pathogenesis. Here, we show that overexpression of the 25-kDa C-terminal fragment is sufficient to cause the mislocalization and cytoplasmic accumulation of endogenous full-length TDP-43 in two different cell lines, thus recapitulating a key biochemical characteristic of TDP-43 proteinopathies. We also found that TDP-43 mislocalization is associated with a reduction in the low molecular mass neurofilament mRNA levels. Notably, we show that the autophagic system plays a role in TDP-43 metabolism. Specifically, we found that autophagy inhibition increases the accumulation of the C-terminal fragments of TDP-43, whereas inhibition of mTOR, a key protein kinase involved in autophagy regulation, reduces the 25-kDa C-terminal fragment accumulation and restores TDP-43 localization. Our results suggest that autophagy induction may be a valid therapeutic target for TDP-43 proteinopathies.TDP-43 (transactive response DNA-binding protein 43) is a conserved and ubiquitously expressed nuclear protein with a theoretical molecular mass of ∼44 kDa. It is encoded by the TARDBP gene on chromosome 1, which is made of six exons that can be alternatively spliced to yield 11 different isoforms, with the mRNA encoding TDP-43 being the major species (1). Functionally, TDP-43 appears to be involved in exon skipping and alternative splicing (2, 3), and it has also been shown to link different types of nuclear bodies (4). Structural studies have confirmed the presence of two RNA recognition motifs (RRM1 and RRM2) and a glycine-rich C-terminal tail, which is thought to mediate protein-protein interaction (5).Recently, TDP-43 has been shown to be the major pathological protein in a wide range of disorders referred to as TDP-43 proteinopathies (6–8). These include frontotemporal lobar degeneration with ubiquitin-positive inclusions (FTLD-U),² motor neuron disease, and amyotrophic lateral sclerosis (ALS). These last two disorders have been directly linked to mutations in TDP-43 (9, 10). In addition, TDP-43-positive inclusions are present in Parkinson disease, dementia with Lewy bodies, and 30% of Alzheimer disease cases (11–14). Sporadic and familial forms of FTLD-U and ALS are characterized by cytoplasmic accumulation of insoluble, hyperphosphorylated, ubiquitinated, and proteolytically cleaved C-terminal fragments in affected brain and spinal cord regions. The cytoplasmic accumulation of TDP-43 is associated with a depletion of nuclear TDP-43 (8, 15–21). These data suggest that some of these TDP-43 proteinopathies may share common mechanisms of pathogenesis.FTLD-U is caused by loss-of-function mutations in the progranulin gene, which lead, by an unknown mechanism, to the accumulation of cytoplasmic TDP-43 inclusions (22, 23). Notably, the TDP-43 inclusions in the ALS and FTLD-U brains are enriched with TDP-43 C-terminal fragments (8, 19). It has been suggested that the C-terminal fragments can be obtained by caspase-dependent cleavage of the full-length protein (24). However, it remains to be established if these fragments play a role in the disease pathogenesis.TDP-43 proteinopathies are characterized by the accumulation of abnormally modified TDP-43, suggesting that dysfunction in the intracellular quality control systems (ubiquitin-proteasome system and the autophagy-lysosome system) may be involved in the disease pathogenesis. The autophagic system is a conserved intracellular system designed for the degradation of long-lived proteins and organelles in lysosomes (25, 26). Three types of autophagy have been described: macroautophagy, microautophagy, and chaperon-mediated autophagy. Whereas macroautophagy and microautophagy involve the “in bulk” degradation of regions of the cytosol (27, 28), chaperon-mediated autophagy is a more selective pathway, and only proteins with a lysosomal targeting sequence are degraded (29). Cumulative evidence has suggested that an age-dependent decrease in the autophagy-lysosome system may account for the accumulation of abnormal proteins during aging (30, 31).Macroautophagy is induced when an isolation membrane is formed surrounding cytosolic components, forming an autophagic vacuole, which will eventually fuse with lysosomes for protein/organelle degradation. Induction of the isolation membrane is negatively regulated by mTOR (mammalian target of rapamycin) (32). It has been shown that increasing autophagy activation by mTOR inhibitors has beneficial effects in neurodegeneration (33–35).     

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]     

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]     

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]     

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]