Christian Cole

The Jpred 3 secondary structure prediction server.

Jpred (http://www.compbio.dundee.ac.uk/jpred) is a secondary structure prediction server powered by the Jnet algorithm. Jpred performs over 1000 predictions per week for users in more than 50 countries. The recently updated Jnet algorithm provides a three-state (α-helix, β-strand and coil) prediction of secondary structure at an accuracy of 81.5%. Given either a single protein sequence or a multiple sequence alignment, Jpred derives alignment profiles from which predictions of secondary structure and solvent accessibility are made. The predictions are presented as coloured HTML, plain text, PostScript, PDF and via the Jalview alignment editor to allow flexibility in viewing and applying the data. The new Jpred 3 server includes significant usability improvements that include clearer feedback of the progress or failure of submitted requests. Functional improvements include batch submission of sequences, summary results via email and updates to the search databases. A new software pipeline will enable Jnet/Jpred to continue to be updated in sync with major updates to SCOP and UniProt and so ensures that Jpred 3 will maintain high-accuracy predictions.

JPred4: a protein secondary structure prediction server

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.

System-Wide Changes to SUMO Modifications in Response to Heat Shock

The small ubiquitin-like modifier protein SUMO is redistributed among many targets to mediate both short- and long-term signaling events. SUMO Status Revealed Posttranslational modification of proteins through their conjugation to small ubiquitin-like modifier (SUMO) proteins is important in the nucleus for the repair of damaged DNA and the maintenance of chromosome structure, as well as for a number of cytoplasmic processes. Although the machinery involved in attaching SUMO moieties to target proteins is well characterized, less is known about the upstream signals that trigger this modification. Golebiowski et al. designed a highly stringent, quantitative approach, involving protein purification and mass spectrometric techniques, to perform a system-wide analysis of the SUMOylation states of hundreds of proteins in HeLa cells in response to heat shock. The authors also analyzed the dynamic nature of SUMOylation in cells during the subsequent recovery phase. In addition to identifying many previously unknown substrates of SUMO-2, this proteome-wide analysis of SUMOylation revealed a rapid and dramatic redistribution of SUMO-2 among proteins involved in short- or long-term responses to heat stress. This new approach should also prove valuable in systems-wide analysis of other posttranslational modifications. Covalent conjugation of the small ubiquitin-like modifier (SUMO) proteins to target proteins regulates many important eukaryotic cellular mechanisms. Although the molecular consequences of the conjugation of SUMO proteins are relatively well understood, little is known about the cellular signals that regulate the modification of their substrates. Here, we show that SUMO-2 and SUMO-3 are required for cells to survive heat shock. Through quantitative labeling techniques, stringent purification of SUMOylated proteins, advanced mass spectrometric technology, and novel techniques of data analysis, we quantified heat shock–induced changes in the SUMOylation state of 766 putative substrates. In response to heat shock, SUMO was polymerized into polySUMO chains and redistributed among a wide range of proteins involved in cell cycle regulation; apoptosis; the trafficking, folding, and degradation of proteins; transcription; translation; and DNA replication, recombination, and repair. This comprehensive proteomic analysis of the substrates of a ubiquitin-like modifier (Ubl) identifies a pervasive role for SUMO proteins in the biologic response to hyperthermic stress.

Filtering of deep sequencing data reveals the existence of abundant Dicer-dependent small RNAs derived from tRNAs

Deep sequencing technologies such as Illumina, SOLiD, and 454 platforms have become very powerful tools in discovering and quantifying small RNAs in diverse organisms. Sequencing small RNA fractions always identifies RNAs derived from abundant RNA species such as rRNAs, tRNAs, snRNA, and snoRNA, and they are widely considered to be random degradation products. We carried out bioinformatic analysis of deep sequenced HeLa RNA and after quality filtering, identified highly abundant small RNA fragments, derived from mature tRNAs that are likely produced by specific processing rather than from random degradation. Moreover, we showed that the processing of small RNAs derived from tRNA(Gln) is dependent on Dicer in vivo and that Dicer cleaves the tRNA in vitro.

Regulation of the miR-212/132 locus by MSK1 and CREB in response to neurotrophins.

Neurotrophins are growth factors that are important in neuronal development and survival as well as synapse formation and plasticity. Many of the effects of neurotrophins are mediated by changes in protein expression as a result of altered transcription or translation. To determine whether neurotrophins regulate the production of microRNAs (miRNAs), small RNA species that modulate protein translation or mRNA stability, we used deep sequencing to identify BDNF (brain-derived neurotrophic factor)-induced miRNAs in cultured primary cortical mouse neurons. This revealed that the miR-212/132 cluster contained the miRNAs most responsive to BDNF treatment. This cluster was found to produce four miRNAs: miR-132, miR-132*, miR-212 and miR-212*. Using specific inhibitors, mouse models and promoter analysis we have shown that the regulation of the transcription of the miR-212/132 miRNA cluster and the miRNAs derived from it are regulated by the ERK1/2 (extracellular-signal-regulated kinase 1/2) pathway, via both MSK (mitogen and stress-activated kinase)-dependent and -independent mechanisms.

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

Direct sequencing of Arabidopsis thaliana RNA reveals patterns of cleavage and polyadenylation

It has recently been shown that RNA 3′-end formation plays a more widespread role in controlling gene expression than previously thought. To examine the impact of regulated 3′-end formation genome-wide, we applied direct RNA sequencing to A. thaliana. Here we show the authentic transcriptome in unprecedented detail and describe the effects of 3′-end formation on genome organization. We reveal extreme heterogeneity in RNA 3′ ends, discover previously unrecognized noncoding RNAs and propose widespread reannotation of the genome. We explain the origin of most poly(A)+ antisense RNAs and identify cis elements that control 3′-end formation in different registers. These findings are essential to understanding what the genome actually encodes, how it is organized and how regulated 3′-end formation affects these processes.

Side-chain conformational entropy at protein-protein interfaces.

Protein–protein interactions are the key to many biological processes. How proteins selectively and correctly associate with their required protein partner(s) is still unclear. Previous studies of this “protein‐docking problem” have found that shape complementarity is a major determinant of interaction, but the detailed balance of energy contributions to association remains unclear. This study estimates side‐chain conformational entropy (per unit solvent accessible area) for various protein surface regions, using a self‐consistent mean field calculation of rotamer probabilities. Interfacial surface regions were less flexible than the rest of the protein surface for calculations with monomers extracted from homodimer datasets in 21 of 25 cases, and in 8 of 9 for the large protomer from heterodimer datasets. In surface patch analysis, based on side‐chain conformational entropy, 68% of true interfaces were ranked top for the homodimer set and 66% for the large protomer/heterodimer set. The results indicate that addition of a side‐chain entropic term could significantly improve empirical calculations of protein–protein association.

Tmem79/Matt is the matted mouse gene and is a predisposing gene for atopic dermatitis in human subjects

BACKGROUND Atopic dermatitis (AD) is a major inflammatory condition of the skin caused by inherited skin barrier deficiency, with mutations in the filaggrin gene predisposing to development of AD. Support for barrier deficiency initiating AD came from flaky tail mice, which have a frameshift mutation in Flg and also carry an unknown gene, matted, causing a matted hair phenotype. OBJECTIVE We sought to identify the matted mutant gene in mice and further define whether mutations in the human gene were associated with AD. METHODS A mouse genetics approach was used to separate the matted and Flg mutations to produce congenic single-mutant strains for genetic and immunologic analysis. Next-generation sequencing was used to identify the matted gene. Five independently recruited AD case collections were analyzed to define associations between single nucleotide polymorphisms (SNPs) in the human gene and AD. RESULTS The matted phenotype in flaky tail mice is due to a mutation in the Tmem79/Matt gene, with no expression of the encoded protein mattrin in the skin of mutant mice. Matt(ft) mice spontaneously have dermatitis and atopy caused by a defective skin barrier, with mutant mice having systemic sensitization after cutaneous challenge with house dust mite allergens. Meta-analysis of 4,245 AD cases and 10,558 population-matched control subjects showed that a missense SNP, rs6684514, [corrected] in the human MATT gene has a small but significant association with AD. CONCLUSION In mice mutations in Matt cause a defective skin barrier and spontaneous dermatitis and atopy. A common SNP in MATT has an association with AD in human subjects.

Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis

Background Atopic dermatitis (AD; eczema) is characterized by a widespread abnormality in cutaneous barrier function and propensity to inflammation. Filaggrin is a multifunctional protein and plays a key role in skin barrier formation. Loss-of-function mutations in the gene encoding filaggrin (FLG) are a highly significant risk factor for atopic disease, but the molecular mechanisms leading to dermatitis remain unclear. Objective We sought to interrogate tissue-specific variations in the expressed genome in the skin of children with AD and to investigate underlying pathomechanisms in atopic skin. Methods We applied single-molecule direct RNA sequencing to analyze the whole transcriptome using minimal tissue samples. Uninvolved skin biopsy specimens from 26 pediatric patients with AD were compared with site-matched samples from 10 nonatopic teenage control subjects. Cases and control subjects were screened for FLG genotype to stratify the data set. Results Two thousand four hundred thirty differentially expressed genes (false discovery rate, P < .05) were identified, of which 211 were significantly upregulated and 490 downregulated by greater than 2-fold. Gene ontology terms for “extracellular space” and “defense response” were enriched, whereas “lipid metabolic processes” were downregulated. The subset of FLG wild-type cases showed dysregulation of genes involved with lipid metabolism, whereas filaggrin haploinsufficiency affected global gene expression and was characterized by a type 1 interferon–mediated stress response. Conclusion These analyses demonstrate the importance of extracellular space and lipid metabolism in atopic skin pathology independent of FLG genotype, whereas an aberrant defense response is seen in subjects with FLG mutations. Genotype stratification of the large data set has facilitated functional interpretation and might guide future therapy development.