Jeremy L. Praissman

Microbial metalloproteomes are largely uncharacterized

Metal ion cofactors afford proteins virtually unlimited catalytic potential, enable electron transfer reactions and have a great impact on protein stability. Consequently, metalloproteins have key roles in most biological processes, including respiration (iron and copper), photosynthesis (manganese) and drug metabolism (iron). Yet, predicting from genome sequence the numbers and types of metal an organism assimilates from its environment or uses in its metalloproteome is currently impossible because metal coordination sites are diverse and poorly recognized. We present here a robust, metal-based approach to determine all metals an organism assimilates and identify its metalloproteins on a genome-wide scale. This shifts the focus from classical protein-based purification to metal-based identification and purification by liquid chromatography, high-throughput tandem mass spectrometry (HT-MS/MS) and inductively coupled plasma mass spectrometry (ICP-MS) to characterize cytoplasmic metalloproteins from an exemplary microorganism (Pyrococcus furiosus). Of 343 metal peaks in chromatography fractions, 158 did not match any predicted metalloprotein. Unassigned peaks included metals known to be used (cobalt, iron, nickel, tungsten and zinc; 83 peaks) plus metals the organism was not thought to assimilate (lead, manganese, molybdenum, uranium and vanadium; 75 peaks). Purification of eight of 158 unexpected metal peaks yielded four novel nickel- and molybdenum-containing proteins, whereas four purified proteins contained sub-stoichiometric amounts of misincorporated lead and uranium. Analyses of two additional microorganisms (Escherichia coli and Sulfolobus solfataricus) revealed species-specific assimilation of yet more unexpected metals. Metalloproteomes are therefore much more extensive and diverse than previously recognized, and promise to provide key insights for cell biology, microbial growth and toxicity mechanisms.

The functional O-mannose glycan on α-dystroglycan contains a phospho-ribitol primed for matriglycan addition

Multiple glycosyltransferases are essential for the proper modification of alpha-dystroglycan, as mutations in the encoding genes cause congenital/limb-girdle muscular dystrophies. Here we elucidate further the structure of an O-mannose-initiated glycan on alpha-dystroglycan that is required to generate its extracellular matrix-binding polysaccharide. This functional glycan contains a novel ribitol structure that links a phosphotrisaccharide to xylose. ISPD is a CDP-ribitol (ribose) pyrophosphorylase that generates the reduced sugar nucleotide for the insertion of ribitol in a phosphodiester linkage to the glycoprotein. TMEM5 is a UDP-xylosyl transferase that elaborates the structure. We demonstrate in a zebrafish model as well as in a human patient that defects in TMEM5 result in muscular dystrophy in combination with abnormal brain development. Thus, we propose a novel structure—a ribitol in a phosphodiester linkage—for the moiety on which TMEM5, B4GAT1, and LARGE act to generate the functional receptor for ECM proteins having LG domains. DOI: http://dx.doi.org/10.7554/eLife.14473.001

Mammalian O-mannosylation pathway: glycan structures, enzymes, and protein substrates.

The mammalian O-mannosylation pathway for protein post-translational modification is intricately involved in modulating cell–matrix interactions in the musculature and nervous system. Defects in enzymes of this biosynthetic pathway are causative for multiple forms of congenital muscular dystophy. The application of advanced genetic and biochemical technologies has resulted in remarkable progress in this field over the past few years, culminating with the publication of three landmark papers in 2013 alone. In this review, we will highlight recent progress focusing on the dramatic expansion of the set of genes known to be involved in O-mannosylation and disease processes, the concurrent acceleration of the rate of O-mannosylation pathway protein functional assignments, the tremendous increase in the number of proteins now known to be modified by O-mannosylation, and the recent progress in protein O-mannose glycan quantification and site assignment. Also, we attempt to highlight key outstanding questions raised by this abundance of new information.

B4GAT1 is the priming enzyme for the LARGE-dependent functional glycosylation of α-dystroglycan

Recent studies demonstrated that mutations in B3GNT1, an enzyme proposed to be involved in poly-N-acetyllactosamine synthesis, were causal for congenital muscular dystrophy with hypoglycosylation of α-dystroglycan (secondary dystroglycanopathies). Since defects in the O-mannosylation protein glycosylation pathway are primarily responsible for dystroglycanopathies and with no established O-mannose initiated structures containing a β3 linked GlcNAc known, we biochemically interrogated this human enzyme. Here we report this enzyme is not a β-1,3-N-acetylglucosaminyltransferase with catalytic activity towards β-galactose but rather a β-1,4-glucuronyltransferase, designated B4GAT1, towards both α- and β-anomers of xylose. The dual-activity LARGE enzyme is capable of extending products of B4GAT1 and we provide experimental evidence that B4GAT1 is the priming enzyme for LARGE. Our results further define the functional O-mannosylated glycan structure and indicate that B4GAT1 is involved in the initiation of the LARGE-dependent repeating disaccharide that is necessary for extracellular matrix protein binding to O-mannosylated α-dystroglycan that is lacking in secondary dystroglycanopathies. DOI: http://dx.doi.org/10.7554/eLife.03943.001

Novel Multiprotein Complexes Identified in the Hyperthermophilic Archaeon Pyrococcus furiosus by Non-denaturing Fractionation of the Native Proteome

Virtually all cellular processes are carried out by dynamic molecular assemblies or multiprotein complexes, the compositions of which are largely undefined. They cannot be predicted solely from bioinformatics analyses nor are there well defined techniques currently available to unequivocally identify protein complexes (PCs). To address this issue, we attempted to directly determine the identity of PCs from native microbial biomass using Pyrococcus furiosus, a hyperthermophilic archaeon that grows optimally at 100 °C, as the model organism. Novel PCs were identified by large scale fractionation of the native proteome using non-denaturing, sequential column chromatography under anaerobic, reducing conditions. A total of 967 distinct P. furiosus proteins were identified by mass spectrometry (nano LC-ESI-MS/MS), representing ∼80% of the cytoplasmic proteins. Based on the co-fractionation of proteins that are encoded by adjacent genes on the chromosome, 106 potential heteromeric PCs containing 243 proteins were identified, only 20 of which were known or expected. In addition to those of unknown function, novel and uncharacterized PCs were identified that are proposed to be involved in the metabolism of amino acids (10), carbohydrates (four), lipids (two), vitamins and metals (three), and DNA and RNA (nine). A further 30 potential PCs were classified as tentative, and the remaining potential PCs (13) were classified as weakly interacting. Some major advantages of native biomass fractionation for PC identification are that it provides a road map for the (partial) purification of native forms of novel and uncharacterized PCs, and the results can be utilized for the recombinant production of low abundance PCs to provide enough material for detailed structural and biochemical analyses.

The high-throughput protein-to-structure pipeline at SECSG.

Using a high degree of automation, the crystallography core at the Southeast Collaboratory for Structural Genomics (SECSG) has developed a high-throughput protein-to-structure pipeline. Various robots and automation procedures have been adopted and integrated into a pipeline that is capable of screening 40 proteins for crystallization and solving four protein structures per week. This pipeline is composed of three major units: crystallization, structure determination/validation and crystallomics. Coupled with the protein-production cores at SECSG, the protein-to-structure pipeline provides a two-tiered approach for protein production at SECSG. In tier 1, all protein samples supplied by the protein-production cores pass through the pipeline using standard crystallization screening and optimization procedures. The protein targets that failed to yield diffraction-quality crystals (resolution better than 3.0 A) become tier 2 or salvaging targets. The goal of tier 2 target salvaging, carried out by the crystallomics core, is to produce the target proteins with increased purity and homogeneity, which would render them more likely to yield well diffracting crystals. This is performed by alternative purification procedures and/or the introduction of chemical modifications to the proteins (such as tag removal, methylation, surface mutagenesis, selenomethionine labelling etc.). Details of the various procedures in the pipeline for protein crystallization, target salvaging, data collection/processing and high-throughput structure determination/validation, as well as some examples, are described.

A Computational Framework for Proteome-Wide Pursuit and Prediction of Metalloproteins using ICP-MS and MS/MS Data

BackgroundMetal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICP-MS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations and complement the metal-directed protein purification, we developed a computational infrastructure and statistical methodology to aid in the pursuit and identification of novel metalloproteins.ResultsWe demonstrate that our methodology enables predictions of metal-protein interactions using an experimental data set derived from a chromatography fractionation experiment in which 870 proteins and 10 metals were measured over 2,589 fractions. For each of the 10 metals, cobalt, iron, manganese, molybdenum, nickel, lead, tungsten, uranium, vanadium, and zinc, clusters of proteins frequently occurring in metal peaks (of a specific metal) within the fractionation space were defined. This resulted in predictions that there are from 5 undiscovered vanadium- to 13 undiscovered cobalt-containing proteins in Pyrococcus furiosus. Molybdenum and nickel were chosen for additional assessment producing lists of genes predicted to encode metalloproteins or metalloprotein subunits, 22 for nickel including seven from known nickel-proteins, and 20 for molybdenum including two from known molybdo-proteins. The uncharacterized proteins are prime candidates for metal-based purification or recombinant approaches to validate these predictions.ConclusionsWe conclude that the largely uncharacterized extent of native metalloproteomes can be revealed through analysis of the co-occurrence of metals and proteins across a fractionation space. This can significantly impact our understanding of metallobiochemistry, disease mechanisms, and metal toxicity, with implications for bioremediation, medicine and other fields.

A Novel Role for Progesterone Receptor Membrane Component 1 (PGRMC1): A Partner and Regulator of Ferrochelatase

Heme is an iron-containing cofactor essential for multiple cellular processes and fundamental activities such as oxygen transport. To better understand the means by which heme synthesis is regulated during erythropoiesis, affinity purification coupled with mass spectrometry (MS) was performed to identify putative protein partners interacting with ferrochelatase (FECH), the terminal enzyme in the heme biosynthetic pathway. Both progesterone receptor membrane component 1 (PGRMC1) and progesterone receptor membrane component 2 (PGRMC2) were identified in these experiments. These interactions were validated by reciprocal affinity purification followed by MS analysis and immunoblotting. The interaction between PGRMC1 and FECH was confirmed in vitro and in HEK 293T cells, a non-erythroid cell line. When cells that are recognized models for erythroid differentiation were treated with a small molecule inhibitor of PGRMC1, AG-205, there was an observed decrease in the level of hemoglobinization relative to that of untreated cells. In vitro heme transfer experiments showed that purified PGRMC1 was able to donate heme to apo-cytochrome b5. In the presence of PGRMC1, in vitro measured FECH activity decreased in a dose-dependent manner. Interactions between FECH and PGRMC1 were strongest for the conformation of FECH associated with product release, suggesting that PGRMC1 may regulate FECH activity by controlling heme release. Overall, the data illustrate a role for PGRMC1 in regulating heme synthesis via interactions with FECH and suggest that PGRMC1 may be a heme chaperone or sensor.

Improved dimensionally-reduced visual cortical network using stochastic noise modeling

In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance of firing rates in the original large-scale simulations while still accurately predicting the orientation preference distribution.

Parameter-space screening: a powerful tool for high-throughput crystal structure determination. Corrigendum

Fig. 4 in the article by Liu et al. [(2005), Acta Cryst. D61, 520–527] was labelled incorrectly. A corrected version of the figure is given here. Also in §3.1.3 of the original article the Cr Kα wavelength was given incorrectly. It should be 2.29 A.