Samuel K. Handelman

The structural basis of cyclic diguanylate signal transduction by PilZ domains.

The second messenger cyclic diguanylate (c‐di‐GMP) controls the transition between motile and sessile growth in eubacteria, but little is known about the proteins that sense its concentration. Bioinformatics analyses suggested that PilZ domains bind c‐di‐GMP and allosterically modulate effector pathways. We have determined a 1.9 Å crystal structure of c‐di‐GMP bound to VCA0042/PlzD, a PilZ domain‐containing protein from Vibrio cholerae. Either this protein or another specific PilZ domain‐containing protein is required for V. cholerae to efficiently infect mice. VCA0042/PlzD comprises a C‐terminal PilZ domain plus an N‐terminal domain with a similar β‐barrel fold. C‐di‐GMP contacts seven of the nine strongly conserved residues in the PilZ domain, including three in a seven‐residue long N‐terminal loop that undergoes a conformational switch as it wraps around c‐di‐GMP. This switch brings the PilZ domain into close apposition with the N‐terminal domain, forming a new allosteric interaction surface that spans these domains and the c‐di‐GMP at their interface. The very small size of the N‐terminal conformational switch is likely to explain the facile evolutionary diversification of the PilZ domain.

Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of mediating interprotein interactions and is not strongly influenced by overall thermodynamic stability. We identify specific sequence features that correlate with crystallization propensity and that can be used to estimate the crystallization probability of a given construct. Analyses of entire predicted proteomes demonstrate substantial differences in the amino acid–sequence properties of human versus eubacterial proteins, which likely reflect differences in biophysical properties, including crystallization propensity. Our thermodynamic measurements do not generally support previous claims regarding correlations between sequence properties and protein stability.

Identification of Eukaryotic and Prokaryotic Methylthiotransferase for Biosynthesis of 2-Methylthio-N6-threonylcarbamoyladenosine in tRNA

Bacterial and eukaryotic transfer RNAs have been shown to contain hypermodified adenosine, 2-methylthio-N6-threonylcarbamoyladenosine, at position 37 (A37) adjacent to the 3′-end of the anticodon, which is essential for efficient and highly accurate protein translation by the ribosome. Using a combination of bioinformatic sequence analysis and in vivo assay coupled to HPLC/MS technique, we have identified, from distinct sequence signatures, two methylthiotransferase (MTTase) subfamilies, designated as MtaB in bacterial cells and e-MtaB in eukaryotic and archaeal cells. Both subfamilies are responsible for the transformation of N6-threonylcarbamoyladenosine into 2-methylthio-N6-threonylcarbamoyladenosine. Recently, a variant within the human CDKAL1 gene belonging to the e-MtaB subfamily was shown to predispose for type 2 diabetes. CDKAL1 is thus the first eukaryotic MTTase identified so far. Using purified preparations of Bacillus subtilis MtaB (YqeV), a CDKAL1 bacterial homolog, we demonstrate that YqeV/CDKAL1 enzymes, as the previously studied MTTases MiaB and RimO, contain two [4Fe-4S] clusters. This work lays the foundation for elucidating the function of CDKAL1.

Crystal Structures of Phd-Doc, HigA, and YeeU Establish Multiple Evolutionary Links between Microbial Growth-Regulating Toxin-Antitoxin Systems.

Bacterial toxin-antitoxin (TA) systems serve a variety of physiological functions including regulation of cell growth and maintenance of foreign genetic elements. Sequence analyses suggest that TA families are linked by complex evolutionary relationships reflecting likely swapping of functional domains between different TA families. Our crystal structures of Phd-Doc from bacteriophage P1, the HigA antitoxin from Escherichia coli CFT073, and YeeU of the YeeUWV systems from E. coli K12 and Shigella flexneri confirm this inference and reveal additional, unanticipated structural relationships. The growth-regulating Doc toxin exhibits structural similarity to secreted virulence factors that are toxic for eukaryotic target cells. The Phd antitoxin possesses the same fold as both the YefM and NE2111 antitoxins that inhibit structurally unrelated toxins. YeeU, which has an antitoxin-like activity that represses toxin expression, is structurally similar to the ribosome-interacting toxins YoeB and RelE. These observations suggest extensive functional exchanges have occurred between TA systems during bacterial evolution.

RELATIVE REACTIVITY OF LYSINE AND OTHER PEPTIDE-BOUND AMINO ACIDS TO OXIDATION BY HYPOCHLORITE

Antibacterial and inflammatory responses of neutrophils and macrophages produce hypochlorite as a major oxidant. Numerous side chains of amino acids found in extracellular proteins can be modified by hypochlorite, including His, Arg, Tyr, Lys, Trp, and Met. We studied the relative reactivity of each of these amino acid residues in short N-blocked peptides, where other residues in the peptide were highly resistant to hypochlorite attack. Hypochlorite treatment led to modified peptides in each case, which were detected by changes in retention on reversed-phase HPLC. A distinct single product, consuming two equivalents of hypochlorite per equivalent of peptide, was obtained from the Lys-containing peptides. UV spectroscopy, nuclear magnetic resonance (NMR), and electrospray/mass spectroscopy identified this product as the dichloramine at the epsilon-amino group of the Lys side chain. The dichloramine at Lys did not decompose to form a detectable amount of carbonyl reactive with dinitrophenylhydrazine. The dichloramine at Lys did however quantitatively revert back to Lys during HCl digestion of the tetrapeptide for amino acid analysis, with simultaneous modification of the adjacent Phe residue. The formation of the dichloramine at Lys was not blocked by peptides or acetylated amino acids that contained Tyr, His, or Arg. In contrast, the presence of equimolar Met-containing peptide, or N-Acetyl-Trp, both inhibited the formation of the dichloramine at Lys. Thus, Met and Trp side chains of proteins might be able to protect Lys from chloramine formation under some circumstances, but this interpretation must consider that Met and Trp are typically found in relatively inaccessible hydrophobic sites, whereas lysine is typically exposed on the protein surface. The hierarchy of amino acid reactivities examined here will aid in the prediction of residues in biological samples most likely to be modified by hypochlorite.

Missing heritability of common diseases and treatments outside the protein-coding exome

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the ‘missing heritability’. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an ‘off-well’ state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common ‘normal’ variants remain hidden in the noise. Second, epistasis (dynamic gene–gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene–gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

Whole transcriptome RNA-Seq allelic expression in human brain

BackgroundMeasuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.ResultsWe find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.ConclusionsThese results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Structural genomics target selection for the New York consortium on membrane protein structure

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space.

Physiological Response to Membrane Protein Overexpression in E. coli

Overexpression represents a principal bottleneck in structural and functional studies of integral membrane proteins (IMPs). Although E. coli remains the leading organism for convenient and economical protein overexpression, many IMPs exhibit toxicity on induction in this host and give low yields of properly folded protein. Different mechanisms related to membrane biogenesis and IMP folding have been proposed to contribute to these problems, but there is limited understanding of the physical and physiological constraints on IMP overexpression and folding in vivo. Therefore, we used a variety of genetic, genomic, and microscopy techniques to characterize the physiological responses of Escherichia coli MG1655 cells to overexpression of a set of soluble proteins and IMPs, including constructs exhibiting different levels of toxicity and producing different levels of properly folded versus misfolded product on induction. Genetic marker studies coupled with transcriptomic results indicate only minor perturbations in many of the physiological systems implicated in previous studies of IMP biogenesis. Overexpression of either IMPs or soluble proteins tends to block execution of the standard stationary-phase transcriptional program, although these effects are consistently stronger for the IMPs included in our study. However, these perturbations are not an impediment to successful protein overexpression. We present evidence that, at least for the target proteins included in our study, there is no inherent obstacle to IMP overexpression in E. coli at moderate levels suitable for structural studies and that the biochemical and conformational properties of the proteins themselves are the major obstacles to success. Toxicity associated with target protein activity produces selective pressure leading to preferential growth of cells harboring expression-reducing and inactivating mutations, which can produce chemical heterogeneity in the target protein population, potentially contributing to the difficulties encountered in IMP crystallization.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real‐world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene–drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.