Amyloid fibrils formed from different proteins, each associated with a particular disease, contain a common cross-beta spine. The atomic architecture of a spine, from the fibril-forming segment GNNQQNY of the yeast prion protein Sup35, was recently revealed by X-ray microcrystallography. It is a pair of beta-sheets, with the facing side chains of the two sheets interdigitated in a dry 'steric zipper'. Here we report some 30 other segments from fibril-forming proteins that form amyloid-like fibrils, microcrystals, or usually both. These include segments from the Alzheimer's amyloid-beta and tau proteins, the PrP prion protein, insulin, islet amyloid polypeptide (IAPP), lysozyme, myoglobin, alpha-synuclein and beta(2)-microglobulin, suggesting that common structural features are shared by amyloid diseases at the molecular level. Structures of 13 of these microcrystals all reveal steric zippers, but with variations that expand the range of atomic architectures for amyloid-like fibrils and offer an atomic-level hypothesis for the basis of prion strains.

Determining protein functions from genomic sequences is a central goal of bioinformatics. We present a method based on the assumption that proteins that function together in a pathway or structural complex are likely to evolve in a correlated fashion. During evolution, all such functionally linked proteins tend to be either preserved or eliminated in a new species. We describe this property of correlated evolution by characterizing each protein by its phylogenetic profile, a string that encodes the presence or absence of a protein in every known genome. We show that proteins having matching or similar profiles strongly tend to be functionally linked. This method of phylogenetic profiling allows us to predict the function of uncharacterized proteins.The fully sequenced genomes of numerous organisms offer large amounts of information about cellular biology (see the genomes listed at the web site of The Institute for Genome Research: www.tigr.org). It is a central challenge of bioinformatics to use this information in discovering the function of proteins. Functional assignments of genes come primarily from biochemical experimentation, which can be extended by matching recently sequenced proteins to those that have already been characterized (1). For the exceptionally well studied genome of Escherichia coli (2), these and related techniques (3, 4) have lead to tentative functional assignments of slightly more than half of its proteins (5). The problem of assigning functions to the remaining proteins is addressed here.Our computational method detects proteins that participate in a common structural complex or metabolic pathway. Proteins within these groups are defined as functionally linked. The underlying hypothesis is that functionally linked proteins evolve in a correlated fashion, and, therefore, they have homologs in the same subset of organisms. For instance, we expect to find flagellar proteins in bacteria that possess flagella but not in other organisms. In short, we show that if two proteins have homologs in the same subset of fully sequenced organisms, they are likely to be functionally linked. We exploit this property systematically to map links between all the proteins coded by a genome. In general, pairs of functionally linked proteins have no amino acid sequence similarity with each other and, therefore, cannot be linked by conventional sequence-alignment techniques. METHODSTo represent the subset of organisms that contain a homolog, we constructed a phylogenetic profile for each protein. This profile is a string with n entries, each one bit, where n corresponds to the number of genomes (16 in the present article). We indicate the presence of a homolog to a given protein in the nth genome with an entry of unity at the nth position. If no homolog is found, the entry is zero. Proteins are clustered according to the similarity of their phylogenetic profiles. Similar profiles show a correlated pattern of inheritance and, by implication, functional linkage. The method predicts that the functions of u...

Tristetraprolin (TTP) is a widely expressed potential transcription factor that contains two unusual CCCH zinc fingers and is encoded by the immediate-early response gene, Zfp-36. Mice made deficient in TTP by gene targeting appeared normal at birth, but soon manifested marked medullary and extramedullary myeloid hyperplasia associated with cachexia, erosive arthritis, dermatitis, conjunctivitis, glomerular mesangial thickening, and high titers of anti-DNA and antinuclear antibodies. Myeloid progenitors from these mice showed no increase in sensitivity to growth factors. Treatment of young TTP-deficient mice with antibodies to tumor necrosis factor alpha (TNF alpha) prevented the development of essentially all aspects of the phenotype. These results indicate a role for TTP in regulating TNF alpha synthesis, secretion, turnover, or action. TTP-deficient mice may serve as useful models of the autoimmune inflammatory state resulting from chronic effective TNF alpha excess.

Faithful segregation of replicated chromosomes is essential for maintenance of genetic stability and seems to be monitored by several mitotic checkpoints. Various components of these checkpoints have been identified in mammals, but their physiological relevance is largely unknown. Here we show that mutant mice with low levels of the spindle assembly checkpoint protein BubR1 develop progressive aneuploidy along with a variety of progeroid features, including short lifespan, cachectic dwarfism, lordokyphosis, cataracts, loss of subcutaneous fat and impaired wound healing. Graded reduction of BubR1 expression in mouse embryonic fibroblasts causes increased aneuploidy and senescence. Male and female mutant mice have defects in meiotic chromosome segregation and are infertile. Natural aging of wild-type mice is marked by decreased expression of BubR1 in multiple tissues, including testis and ovary. These results suggest a role for BubR1 in regulating aging and infertility.

The availability of over 20 fully sequenced genomes has driven the development of new methods to find protein function and interactions. Here we group proteins by correlated evolution, correlated messenger RNA expression patterns and patterns of domain fusion to determine functional relationships among the 6,217 proteins of the yeast Saccharomyces cerevisiae. Using these methods, we discover over 93,000 pairwise links between functionally related yeast proteins. Links between characterized and uncharacterized proteins allow a general function to be assigned to more than half of the 2,557 previously uncharacterized yeast proteins. Examples of functional links are given for a protein family of previously unknown function, a protein whose human homologues are implicated in colon cancer and the yeast prion Sup35.

Based on the crystal structure of the cross-␤ spine formed by the peptide NNQQNY, we have developed a computational approach for identifying those segments of amyloidogenic proteins that themselves can form amyloid-like fibrils. The approach builds on experiments showing that hexapeptides are sufficient for forming amyloid-like fibrils. Each six-residue peptide of a protein of interest is mapped onto an ensemble of templates, or 3D profile, generated from the crystal structure of the peptide NNQQNY by small displacements of one of the two intermeshed ␤-sheets relative to the other. The energy of each mapping of a sequence to the profile is evaluated by using ROSETTADESIGN, and the lowest energy match for a given peptide to the template library is taken as the putative prediction. If the energy of the putative prediction is lower than a threshold value, a prediction of fibril formation is made. This method can reach an accuracy of Ϸ80% with a P value of Ϸ10 ؊12 when a conservative energy threshold is used to separate peptides that form fibrils from those that do not. We see enrichment for positive predictions in a set of fibril-forming segments of amyloid proteins, and we illustrate the method with applications to proteins of interest in amyloid research.amyloid ͉ prediction ͉ ROSETTADESIGN ͉ lysozyme ͉ myoglobin A myloid-like fibrils of protein are common to deposition diseases such as Alzheimer's, the spongiform encephalopathies including Creutzfeldt-Jakob disease and bovine spongiform encephalopathy, and the protein-based heredity of [PSI ϩ ] and other prions in yeast. Thus understanding the range of protein sequences that can undergo fibrillization and the basis for stability of fibrils could have wide significance. We address these problems with a computational method for predicting which segments of a given protein might form the cross-␤ spine in the fibrillar form.The ability to form amyloid fibers is not restricted to those proteins associated with amyloid or prion disease. Otherwise innocuous proteins can be fibrillized by altering the pH, temperature, or composition of their native solvent (1-3). In addition, numerous short peptides (e.g., four to seven residues) are found to form amyloid-like fibrils in isolation from the rest of the protein (4-11). De novo-designed synthetic peptides have also been shown to form fibers (12)(13)(14).The question of how both full proteins and short peptides can form fibrils was illuminated by the crystal structures (11) of NNQQNY and GNNQQNY, which showed that the fundamental structure of the protofibril is a pair of ␤-sheets, which mate at a dry interface where their side chains tightly interdigitate in a ''steric zipper.'' To form this steric zipper, the strands in the sheets need be only four to six residues in length. Therefore, we would expect that short peptides with a tendency to fibrillize can do so, either when cleaved from the rest of the protein chain, as for the ␤-amyloid (Abeta) peptide of Alzheimer's disease, or when they are unmasked from an inaccessible ...

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or ''Bar-seq,'' outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that ;20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Human DNA-methylation data have been used to develop highly accurate biomarkers of aging ("epigenetic clocks"). Recent studies demonstrate that similar epigenetic clocks for mice (Mus Musculus) can be slowed by gold standard anti-aging interventions such as calorie restriction and growth hormone receptor knock-outs. Using DNA methylation data from previous publications with data collected in house for a total 1189 samples spanning 193,651 CpG sites, we developed 4 novel epigenetic clocks by choosing different regression models (elastic net- versus ridge regression) and by considering different sets of CpGs (all CpGs vs highly conserved CpGs). We demonstrate that accurate age estimators can be built on the basis of highly conserved CpGs. However, the most accurate clock results from applying elastic net regression to all CpGs. While the anti-aging effect of calorie restriction could be detected with all types of epigenetic clocks, only ridge regression based clocks replicated the finding of slow epigenetic aging effects in dwarf mice. Overall, this study demonstrates that there are trade-offs when it comes to epigenetic clocks in mice. Highly accurate clocks might not be optimal for detecting the beneficial effects of anti-aging interventions.