Increasing interest in determining the effects of genetic variation for bioengineering, human health and basic biological research has propelled the development of technologies for high-throughput mutagenesis and selection. However, since designing functional assays is challenging and systematic testing of combinations of mutations is intractable, there is a parallel need to develop more accurate computational predictions.. Most computational methods have relied significantly on the signal of evolutionary conservation, but do not account for dependencies between positions in a sequence. We present an unsupervised method for predicting the effects of mutations (EVmutation) that explicitly captures residue dependencies between positions. We find that it improves the prediction accuracies of a comprehensive collection of recent high-throughput experimental fitness landscapes, biochemical measurements and human disease mutations. We suggest EVmutation may be useful to assess the quantitative effects of mutations in genes of any organism and provide precomputed predictions for ~ 7000 human proteins.

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology—the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.

Because of the constant threat posed by emerging infectious diseases and the limitations of existing approaches used to identify new pathogens, there is a great demand for new technological methods for viral discovery. We describe herein a DNA microarray-based platform for novel virus identification and characterization. Central to this approach was a DNA microarray designed to detect a wide range of known viruses as well as novel members of existing viral families; this microarray contained the most highly conserved 70mer sequences from every fully sequenced reference viral genome in GenBank. During an outbreak of severe acute respiratory syndrome (SARS) in March 2003, hybridization to this microarray revealed the presence of a previously uncharacterized coronavirus in a viral isolate cultivated from a SARS patient. To further characterize this new virus, approximately 1 kb of the unknown virus genome was cloned by physically recovering viral sequences hybridized to individual array elements. Sequencing of these fragments confirmed that the virus was indeed a new member of the coronavirus family. This combination of array hybridization followed by direct viral sequence recovery should prove to be a general strategy for the rapid identification and characterization of novel viruses and emerging infectious disease.

In eukaryotic cells, most mRNAs are exported from the nucleus by the transcription export (TREX) complex, which is loaded onto mRNAs after their splicing and capping. We have studied in mammalian cells the nuclear export of mRNAs that code for secretory proteins, which are targeted to the endoplasmic reticulum membrane by hydrophobic signal sequences. The mRNAs were injected into the nucleus or synthesized from injected or transfected DNA, and their export was followed by fluorescent in situ hybridization. We made the surprising observation that the signal sequence coding region (SSCR) can serve as a nuclear export signal of an mRNA that lacks an intron or functional cap. Even the export of an intron-containing natural mRNA was enhanced by its SSCR. Like conventional export, the SSCR-dependent pathway required the factor TAP, but depletion of the TREX components had only moderate effects. The SSCR export signal appears to be characterized in vertebrates by a low content of adenines, as demonstrated by genome-wide sequence analysis and by the inhibitory effect of silent adenine mutations in SSCRs. The discovery of an SSCR-mediated pathway explains the previously noted amino acid bias in signal sequences and suggests a link between nuclear export and membrane targeting of mRNAs.

Qualitative and quantitative information are crucial to a detailed understanding of the function of protein phosphorylation. MS is now becoming a quantitative approach to analyze protein phosphorylation. All methods that have been described either require the elaborate͞expensive use of stable isotopes to compare a limited number of samples or do not provide phosphorylation stoichiometries. Here, we present stable isotope-free MS strategies that allow relative and absolute quantitation of phosphorylation stoichiometries. By using the developed methods, we can normalize to robustly account for run-to-run variations and variations in amounts of starting material. This procedure monitors the unmodified proteolytic peptides derived from the protein of interest and identifies peptides that are suitable for normalization purposes. Also, we can determine changes in phosphorylation stoichiometry by monitoring the changes in the normalized ion currents of the phosphopeptide(s) of interest. Absolute phosphorylation stoichiometry are measured by monitoring the ion currents of a phosphopeptide and its unmodified cognate as the signal intensity changes of both peptide species are correlated. The method is applicable to multiply phosphorylated species (for which one more sample with varying phosphorylation stoichiometry than number of phosphorylation sites is required to correct for the differences in the ionization͞detection efficiencies of the phosphopeptide, its partially phosphorylated and unphosphorylated cognates). Last, we can quantitate species with ragged ends resulting from incomplete proteolysis and measure phosphorylation stoichiometries of single samples by controlled dephosphorylation. These approaches were validated and subsequently applied to the phosphorylation of the yeast transcription factor Pho4.O ne of the most common and important posttranslational protein modification (PTM) is protein phosphorylation (1). It is estimated that Ϸ30% of all proteins in mammalian cells are phosphorylated at any given time and Ϸ5% of a vertebrate genome encodes protein kinases and phosphatases (2), underscoring the importance of this PTM. The presence of various protein kinases and phosphatases permits the use of quickly reversible phosphorylation in a vast number of different, highly regulated pathways and functions, including signal transduction, cell division, and cell differentiation.Knowledge of the phosphorylation site is crucial to a detailed understanding of regulatory processes in cells; this knowledge requires sensitive-analysis methods. Theoretically, the most sensitive methods for the detection of phosphorylation incorporate radioactive phosphorus isotopes before phosphopeptide mapping and͞or Edman degradation (3). However, the incorporation of radioactive isotopes is not possible (e.g., in tissue samples) or is very inefficient in the case of cell culture because of the presence of endogenous unlabeled ATP. Also, high levels of radioactive phosphate incorporation cause cellular damage and, thereby, can alter pho...

Yeast can anticipate the depletion of a preferred nutrient by preemptively activating genes for alternative nutrients; the degree of this preparation varies across natural strains and is subject to a fitness tradeoff.

Background-Brugada syndrome, characterized by ST-segment elevation in the right precordial ECG leads and the development of life-threatening ventricular arrhythmias, has been associated with mutations in 6 different genes. We identify and characterize a mutation in a new gene. Methods and Results-A 64-year-old white male displayed a type 1 ST-segment elevation in V1 and V2 during procainamide challenge. Polymerase chain reaction-based direct sequencing was performed using a candidate gene approach. A missense mutation (L10P) was detected in exon 1 of SCN3B, the ␤3 subunit of the cardiac sodium channel, but not in any other gene known to be associated with Brugada syndrome or in 296 controls. Wild-type (WT) and mutant genes were expressed in TSA201 cells and studied using whole-cell patch-clamp techniques. Coexpression of SCN5A/WTϩSCN1B/WTϩSCN3B/L10P resulted in an 82.6% decrease in peak sodium current density, accelerated inactivation, slowed reactivation, and a Ϫ9.6-mV shift of half-inactivation voltage compared with SCN5A/WTϩSCN1B/ WTϩSCN3B/WT. Confocal microscopy revealed that SCN5A/WT channels tagged with green fluorescent protein are localized to the cell surface when coexpressed with WT SCN1B and SCN3B but remain trapped in intracellular organelles when coexpressed with SCN1B/WT and SCN3B/L10P. Western blot analysis confirmed the presence of Na V ␤3 in human ventricular myocardium. Conclusions-Our

Natural environments are filled with multiple, often competing, signals. In contrast, biological systems are often studied in "wellcontrolled" environments where only a single input is varied, potentially missing important interactions between signals. Catabolite repression of galactose by glucose is one of the best-studied eukaryotic signal integration systems. In this system, it is believed that galactose metabolic (GAL) genes are induced only when glucose levels drop below a threshold. In contrast, we show that GAL gene induction occurs at a constant external galactose:glucose ratio across a wide range of sugar concentrations. We systematically perturbed the components of the canonical galactose/glucose signaling pathways and found that these components do not account for ratio sensing. Instead we provide evidence that ratio sensing occurs upstream of the canonical signaling pathway and results from the competitive binding of the two sugars to hexose transporters. We show that a mutant that behaves as the classical model expects (i.e., cannot use galactose above a glucose threshold) has a fitness disadvantage compared with wild type. A number of common biological signaling motifs can give rise to ratio sensing, typically through negative interactions between opposing signaling molecules. We therefore suspect that this previously unidentified nutrient sensing paradigm may be common and overlooked in biology.nutrient signaling | signal integration | gene regulation | ratio sensing | yeast