Disease Mutations in the Human Mitochondrial DNA Polymerase Thumb Subdomain Impart Severe Defects in Mitochondrial DNA Replication

Forty-five different point mutations in POLG, the gene encoding the catalytic subunit of the human mitochondrial DNA polymerase (pol γ), cause the early onset mitochondrial DNA depletion disorder, Alpers syndrome. Sequence analysis of the C-terminal polymerase region of pol γ revealed a cluster of four Alpers mutations at highly conserved residues in the thumb subdomain (G848S, c.2542g→a; T851A, c.2551a→g; R852C, c.2554c→t; R853Q, c.2558g→a) and two Alpers mutations at less conserved positions in the adjacent palm subdomain (Q879H, c.2637g→t and T885S, c.2653a→t). Biochemical characterization of purified, recombinant forms of pol γ revealed that Alpers mutations in the thumb subdomain reduced polymerase activity more than 99% relative to the wild-type enzyme, whereas the palm subdomain mutations retained 50–70% wild-type polymerase activity. All six mutant enzymes retained physical and functional interaction with the pol γ accessory subunit (p55), and none of the six mutants exhibited defects in misinsertion fidelity in vitro. However, differential DNA binding by these mutants suggests a possible orientation of the DNA with respect to the polymerase during catalysis. To our knowledge this study represents the first structure-function analysis of the thumb subdomain in pol γ and examines the consequences of mitochondrial disease mutations in this region.As the only DNA polymerase found in animal cell mitochondria, DNA polymerase γ (pol γ)³ bears sole responsibility for DNA synthesis in all replication and repair transactions involving mitochondrial DNA (1, 2). Mammalian cell pol γ is a heterotrimeric complex composed of one catalytic subunit of 140 kDa (p140) and two 55-kDa accessory subunits (p55) that form a dimer (3). The catalytic subunit contains an N-terminal exonuclease domain connected by a linker region to a C-terminal polymerase domain. Whereas the exonuclease domain contains essential motifs I, II, and III for its activity, the polymerase domain comprising the thumb, palm, and finger subdomains contains motifs A, B, and C that are crucial for polymerase activity. The catalytic subunit is a family A DNA polymerase that includes bacterial pol I and T7 DNA polymerase and possesses DNA polymerase, 3′ → 5′ exonuclease, and 5′-deoxyribose phosphate lyase activities (for review, see Refs. 1 and 2). The 55-kDa accessory subunit (p55) confers processive DNA synthesis and tight binding of the pol γ complex to DNA (4, 5).Depletion of mtDNA as well as the accumulation of deletions and point mutations in mtDNA have been observed in several mitochondrial disorders (for review, see Ref. 6). mtDNA depletion syndromes are caused by defects in nuclear genes responsible for replication and maintenance of the mitochondrial genome (7). Mutation of POLG, the gene encoding the catalytic subunit of pol γ, is frequently involved in disorders linked to mutagenesis of mtDNA (8, 9). Presently, more than 150 point mutations in POLG are linked with a wide variety of mitochondrial diseases, including the autosomal dominant (ad) and recessive forms of progressive external ophthalmoplegia (PEO), Alpers syndrome, parkinsonism, ataxia-neuropathy syndromes, and male infertility (tools.niehs.nih.gov/polg) (9).Alpers syndrome, a hepatocerebral mtDNA depletion disorder, and myocerebrohepatopathy are rare heritable autosomal recessive diseases primarily affecting young children (10–12). These diseases generally manifest during the first few weeks to years of life, and symptoms gradually develop in a stepwise manner eventually leading to death. Alpers syndrome is characterized by refractory seizures, psychomotor regression, and hepatic failure (11, 12). Mutation of POLG was first linked to Alpers syndrome in 2004 (13), and to date 45 different point mutations in POLG (18 localized to the polymerase domain) are associated with Alpers syndrome (9, 14, 15). However, only two Alpers mutations (A467T and W748S, both in the linker region) have been biochemically characterized (16, 17).During the initial cloning and sequencing of the human, Drosophila, and chicken pol γ genes, we noted a highly conserved region N-terminal to motif A in the polymerase domain that was specific to pol γ (18). This region corresponds to part of the thumb subdomain that tracks DNA into the active site of both Escherichia coli pol I and T7 DNA polymerase (19–21). A high concentration of disease mutations, many associated with Alpers syndrome, is found in the thumb subdomain.Here we investigated six mitochondrial disease mutations clustered in the N-terminal portion of the polymerase domain of the enzyme (Fig. 1A). Four mutations (G848S, c.2542g→a; T851A, c.2551a→g; R852C, c.2554c→t; R853Q, c.2558g→a) reside in the thumb subdomain and two (Q879H, c.2637g→t and T885S, c.2653a→t) are located in the palm subdomain. These mutations are associated with Alpers, PEO, mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), ataxia-neuropathy syndrome, Leigh syndrome, and myocerebrohepatopathy (

Nickel-based Enzyme Systems

Of the eight known nickel enzymes, all but glyoxylase I catalyze the use and/or production of gases central to the global carbon, nitrogen, and oxygen cycles. Nickel appears to have been selected for its plasticity in coordination and redox chemistry and is able to cycle through three redox states (1+, 2+, 3+) and to catalyze reactions spanning ∼1.5 V. This minireview focuses on the catalytic mechanisms of nickel enzymes, with an emphasis on the role(s) of the metal center. The metal centers vary from mononuclear to complex metal clusters and catalyze simple hydrolytic to multistep redox reactions.Seven of the eight known nickel enzymes (1). CODH² interconverts CO and CO₂; ACS utilizes CO; the nickel ARD produces CO; hydrogenase generates/utilizes hydrogen gas; MCR generates methane; urease produces ammonia; and SOD generates O₂.

TABLE 1

Nickel-containing enzymes

Molecular and Biochemical Characterization of the Protein Template Controlling Biosynthesis of the Lipopeptide Lichenysin

Lichenysins are surface-active lipopeptides with antibiotic properties produced nonribosomally by several strains of Bacillus licheniformis. Here, we report the cloning and sequencing of an entire 26.6-kb lichenysin biosynthesis operon from B. licheniformis ATCC 10716. Three large open reading frames coding for peptide synthetases, designated licA, licB (three modules each), and licC (one module), could be detected, followed by a gene, licTE, coding for a thioesterase-like protein. The domain structure of the seven identified modules, which resembles that of the surfactin synthetases SrfA-A to -C, showed two epimerization domains attached to the third and sixth modules. The substrate specificity of the first, fifth, and seventh recombinant adenylation domains of LicA to -C (cloned and expressed in Escherichia coli) was determined to be Gln, Asp, and Ile (with minor Val and Leu substitutions), respectively. Therefore, we suppose that the identified biosynthesis operon is responsible for the production of a lichenysin variant with the primary amino acid sequence l-Gln–l-Leu–d-Leu–l-Val–l-Asp–d-Leu–l-Ile, with minor Leu and Val substitutions at the seventh position.Many strains of Bacillus are known to produce lipopeptides with remarkable surface-active properties (11). The most prominent of these powerful lipopeptides is surfactin from Bacillus subtilis (1). Surfactin is an acylated cyclic heptapeptide that reduces the surface tension of water from 72 to 27 mN m⁻¹ even in a concentration below 0.05% and shows some antibacterial and antifungal activities (1). Some B. subtilis strains are also known to produce other, structurally related lipoheptapeptides (Table ​(Table1),1), like iturin (32, 34) and bacillomycin (3, 27, 30), or the lipodecapeptides fengycin (50) and plipastatin (29).

TABLE 1

Lipoheptapeptide antibiotics of Bacillus spp.

Mode of Action of cGMP-dependent Protein Kinase-specific Inhibitors Probed by Photoaffinity Cross-linking Mass Spectrometry

The inhibitor peptide DT-2 (YGRKKRRQRRRPPLRKKKKKH) is the most potent and selective inhibitor of the cGMP-dependent protein kinase (PKG) known today. DT-2 is a construct of a PKG tight binding sequence (W45, LRKKKKKH, K_I = 0.8 μm) and a membrane translocating sequence (DT-6, YGRKKRRQRRRPP, K_I = 1.1 μm), that combined strongly inhibits PKG catalyzed phosphorylation (K_I = 12.5 nm) with ∼1000-fold selectivity toward PKG over protein kinase A, the closest relative of PKG. However, the molecular mechanism behind this inhibition is not entirely understood. Using a combination of photoaffinity labeling, stable isotope labeling, and mass spectrometry, we have located the binding sites of PKG-specific substrate and inhibitor peptides. Covalent linkage of a PKG-specific substrate analogue was localized in the catalytic core on residues 356–372, also known as the glycine-rich loop, essential for ATP binding. By analogy, the individual inhibitor peptides W45 and DT-6 were also found to cross-link near the glycine-rich loop, suggesting these are both substrate competitive inhibitors. A bifunctional photoreactive analogue of DT-2 was found to generate dimers of PKG. This cross-linking induced covalent PKG dimerization was not observed for an N-terminal deletion mutant of PKG, which lacks the dimerization domain. In addition, non-covalent mass spectrometry was used to determine binding stoichiometry and binding order of the inhibitor peptides. Dimeric PKG binds two W45 and DT-6 peptides, whereas only one DT-2 molecule was observed to bind to the dimeric PKG. Taken together, these findings imply that (i) the two individual components making up DT-2 are both targeted against the substrate-binding site and (ii) binding of a single DT-2 molecule inactivates both PKG monomers simultaneously, which is an indication that (iii) in cGMP-activated PKG the catalytic centers of both subunits may be in each other''s proximity.Among the superfamily of protein kinases the two cyclic nucleotide-regulated protein kinases, cAMP-dependent protein kinase and cGMP-dependent protein kinase, form a closely related subfamily of serine/threonine protein kinases (1–4). Both proteins share several structural elements, such as the N-terminal dimerization domain, an autoinhibition site, two in-tandem cyclic nucleotide-binding sites, and a highly conserved catalytic core (Fig. 1, A and B). Despite these similarities, these two enzymes display differences, which account for their unique properties. Whereas PKA² is nearly ubiquitous, PKG is primarily found in the lung, cerebellum, and smooth muscles (5, 6). From a structural point of view these cyclic nucleotide-dependent protein kinases differ as well. The holoenzyme of PKA is a tetramer composed of two regulatory and two catalytic subunits. The catalytic subunits are non-covalently attached to the regulatory subunit dimer. Upon interaction with cAMP, the catalytic subunits dissociate from the holoenzyme and are free to catalyze heterophosphorylation (Fig. 1C). The mammalian type I PKGs are homodimeric cytosolic proteins containing two identical polypeptides of ∼76 kDa. Alternative mRNA splicing produces type Iα and type Iβ PKG, which are identical proteins apart from their first ∼100 N-terminal residues (7). Each PKG subunit is composed of a regulatory and a catalytic domain on a single polypeptide chain. Consequently, when cGMP activates PKG, the catalytic and regulatory components remain physically attached (Fig. 1D). Within the catalytic domain PKA and PKG share a strong primary sequence homology (8). Not surprisingly, these enzymes also exhibit overlapping substrate specificities, a feature that often interferes with efforts to elucidate their distinct biological pathways. Peptide substrates with a primary amino acid sequence motif RRX(S/T)X are in general recognized by both PKA and PKG (9). Besides this strong overlapping substrate specificity, several studies report on subtle differences in determinants that discriminate for PKA and PKG substrate specificity (10–16). To specifically discriminate between PKG and PKA activity in biological assays a highly specific PKG peptide inhibitor was developed (17). This peptide, YGRKKRRQRRRPPLRKKKKKH (DT-2), is the most potent and selective PKG inhibitor known today. Recently, the validity of DT-2 as a superior inhibitor of PKG in terms of potency, selectivity, and membrane permeability has been demonstrated (18–24). The inhibitor is a construct of a substrate competitive sequence, LRKKKKKH (W45), derived from a library screen that selected for tight PKG binding sequences, with a significant specificity toward PKG over PKA, and a membrane translocating signal peptide, YGRKKRRQRRRPP (DT-6). DT-2 strongly inhibits PKG-catalyzed phosphorylation (K_i = 12.5 nm), however, the molecular nature of DT-2 inhibition is not entirely understood (25). Because high resolution structural data are not available for PKG, one of our goals is to elucidate binding sites for PKG-specific substrates and inhibitors in more detail using a combination of mass spectrometric techniques and photoaffinity labeling. To further delineate the nature of inhibition we have developed photoaffinity analogues of DT-2 and related inhibitory peptides, as well as a high affinity peptide substrate. The method of photoaffinity labeling enables the direct probing of target proteins through a covalent bond, which is photochemically introduced between a ligand and its specific receptor (26). In combination with modern mass spectrometric techniques this is a powerful approach for the characterization of peptide-protein interactions (27). Substrate and inhibitor peptides containing photoactivatable analogues of phenylalanine, 4-benzoyl-l-phenylalanine (Phe(Bz)) or 4′-(3-(trifluoromethyl)-3H-diazirin-3-yl)-l-phenylalanine (Phe(Tmd)) were synthesized and used to locate their substrate/inhibitor-binding sites on PKG. These measurements indicate that the substrate peptide resides near the glycine-rich loop within the catalytic domain and that the inhibitor peptides are directed similarly toward this substrate-binding site, thereby acting as competitive inhibitors. In addition, nanoflow electrospray ionization time of flight mass spectrometry (ESI-TOF-MS) was performed to study the interaction between DT-2 and PKG in more detail. ESI-MS has proven to be a useful tool to analyze the non-covalent interaction of proteins with ligands, oligonucleotides, peptides, or other proteins (28–31). Using this technique, important information on conformational changes (32–35), measurement of relative dissociation constants (36, 37), and sequential binding order and cooperativity (38, 39) can be obtained. ESI-MS confirms that PKG is primarily a homodimer and is able to bind four cGMP molecules. Binding of DT-2 was strongly enhanced in the presence of cGMP. Surprising is the observation that only one DT-2 molecule binds to dimeric PKG. The information derived from these measurements allows for molecular modeling and structural refinements of the next generation of PKG-selective inhibitors.Open in a separate windowFIGURE 1.Linear arrangement of the functional domains of the regulatory and catalytic subunit of PKA (A) and PKG (B) type I and schematic representation of the current working models of the activation process of PKA (C) and PKG (D) type 1. Binding of cAMP to the PKA induces a conformational change that results in the dissociation of the catalytic subunits. Binding of cGMP to PKG also induces a conformational change, which exposes the catalytic domains, but both catalytic domains remain near each other via the N-terminal dimerization domain. (Images adapted from Scholten et al. (4).)

TABLE 1

Inhibition contants (K_I) of PKA- or PKG-specific peptide inhibitors and the PKA/PKG specificity index

Critical Factors Determining Dimerization of Human Antizyme Inhibitor

Ornithine decarboxylase (ODC) is the first enzyme involved in polyamine biosynthesis, and it catalyzes the decarboxylation of ornithine to putrescine. ODC is a dimeric enzyme, whereas antizyme inhibitor (AZI), a positive regulator of ODC that is homologous to ODC, exists predominantly as a monomer and lacks decarboxylase activity. The goal of this paper was to identify the essential amino acid residues that determine the dimerization of AZI. The nonconserved amino acid residues in the putative dimer interface of AZI (Ser-277, Ser-331, Glu-332, and Asp-389) were substituted with the corresponding residues in the putative dimer interface of ODC (Arg-277, Tyr-331, Asp-332, and Tyr-389, respectively). Analytical ultracentrifugation analysis was used to determine the size distribution of these AZI mutants. The size-distribution analysis data suggest that residue 331 may play a major role in the dimerization of AZI. Mutating Ser-331 to Tyr in AZI (AZI-S331Y) caused a shift from a monomer configuration to a dimer. Furthermore, in comparison with the single mutant AZI-S331Y, the AZI-S331Y/D389Y double mutant displayed a further reduction in the monomer-dimer K_d, suggesting that residue 389 is also crucial for AZI dimerization. Analysis of the triple mutant AZI-S331Y/D389Y/S277R showed that it formed a stable dimer (K_d value = 1.3 μm). Finally, a quadruple mutant, S331Y/D389Y/S277R/E332D, behaved as a dimer with a K_d value of ∼0.1 μm, which is very close to that of the human ODC enzyme. The quadruple mutant, although forming a dimer, could still be disrupted by antizyme (AZ), further forming a heterodimer, and it could rescue the AZ-inhibited ODC activity, suggesting that the AZ-binding ability of the AZI dimer was retained.Polyamines (putrescine, spermidine, and spermine) have been shown to have both structural and regulatory roles in protein and nucleic acid biosynthesis and function (1–3). Ornithine decarboxylase (ODC,³ EC 4.1.1.17) is a central regulator of cellular polyamine synthesis (reviewed in Refs. 1, 4, 5). This enzyme catalyzes the pyridoxal 5-phosphate (PLP)-dependent decarboxylation of ornithine to putrescine, and it is the first and rate-limiting enzyme in polyamine biosynthesis (2, 3, 6, 7). ODC and polyamines play important roles in a number of biological functions, including embryonic development, cell cycle, proliferation, differentiation, and apoptosis (8–15). They also have been associated with human diseases and a variety of cancers (16–26). Because the regulation of ODC and polyamine content is critical to cell proliferation (11), as well as in the origin and progression of neoplastic diseases (23, 24), ODC has been identified as an oncogenic enzyme, and the inhibitors of ODC and the polyamine pathway are important targets for therapeutic intervention in many cancers (6, 11).ODC is ubiquitously found in organisms ranging from bacteria to humans. It contains 461 amino acid residues in each monomer and is a 106-kDa homodimer with molecular 2-fold symmetry (27, 28). Importantly, ODC activity requires the formation of a dimer (29–31). X-ray structures of the ODC enzyme reveal that this dimer contains two active sites, both of which are formed at the interface between the N-terminal domain of one monomer, which provides residues involved in PLP interactions, and the C-terminal domain of the other subunit, which provides the residues that interact with substrate (27, 32–41).ODC undergoes a unique ubiquitin-independent proteasomal degradation via a direct interaction with the regulatory protein antizyme (AZ). Binding of AZ promotes the dissociation of the ODC homodimers and targets ODC for degradation by the 26 S proteasome (42–46). Current models of antizyme function indicate that increased polyamine levels promote the fidelity of the AZ mRNA translational frameshift, leading to increased concentrations of AZ (47). The AZ monomer selectively binds to dimeric ODC, thereby inactivating ODC by forming inactive AZ-ODC heterodimers (44, 48–50). AZ acts as a regulator of polyamine metabolism that inhibits ODC activity and polyamine transport, thus restricting polyamine levels (4, 5, 51, 52). When antizymes are overexpressed, they inhibit ODC and promote ubiquitin-independent proteolytic degradation of ODC. Because elevated ODC activity is associated with most forms of human malignancies (1), it has been suggested that antizymes may function as tumor suppressors.In contrast to the extensive studies on the oncogene ODC, the endogenous antizyme inhibitor (AZI) is less well understood. AZI is homologous to the enzyme ODC. It is a 448-amino acid protein with a molecular mass of 50 kDa. However, despite the homology between these proteins, AZI does not possess any decarboxylase activity. It binds to antizyme more tightly than does ODC and releases ODC from the ODC-antizyme complex (53, 54). Both the AZI and AZ proteins display rapid ubiquitin-dependent turnover within a few minutes to 1 h in vivo (5). However, AZ binding actually stabilizes AZI by inhibiting its ubiquitination (55).AZI, which inactivates all members of the AZ family (53, 56), restores ODC activity (54), and prevents the proteolytic degradation of ODC, may play a role in tumor progression. It has been reported that down-regulation of AZI is associated with the inhibition of cell proliferation and reduced ODC activity, presumably through the modulation of AZ function (57). Moreover, overexpression of AZI has been shown to increase cell proliferation and promote cell transformation (58–60). Furthermore, AZI is capable of direct interaction with cyclin D1, preventing its degradation, and this effect is at least partially independent of AZ function (60, 61). These results demonstrate a role for AZI in the positive regulation of cell proliferation and tumorigenesis.It is now known that ODC exists as a dimer and that AZI may exist as a monomer physiologically (62). Fig. 1 shows the dimeric structures of ODC (Fig. 1A) and AZI (Fig. 1B). Although structural studies indicate that both ODC and AZI crystallize as dimers, the dimeric AZI structure has fewer interactions at the dimer interface, a smaller buried surface area, and a lack of symmetry of the interactions between residues from the two monomers, suggesting that the AZI dimer may be nonphysiological (62). In this study, we identify the critical amino acid residues governing the difference in dimer formation between ODC and AZI. Our preliminary studies using analytical ultracentrifugation indicated that ODC exists as a dimer, whereas AZI exists in a concentration-dependent monomer-dimer equilibrium. Multiple sequence alignments of ODC and AZI from various species have shown that residues 277, 331, 332, and 389 are not conserved between ODC and AZI (Open in a separate windowFIGURE 1.Crystal structure and the amino acid residues at the dimer interface of human ornithine decarboxylase (hODC) and mouse antizyme inhibitor (mAZI). A, homodimeric structure of human ODC with the cofactor PLP analog, LLP (Protein Data Bank code 1D7K). B, putative dimeric structure of mouse AZI (Protein Data Bank code 3BTN). The amino acid residues in the dimer interface are shown as a ball-and-stick model. The putative AZ-binding site is colored in cyan. This figure was generated using PyMOL (DeLano Scientific LLC, San Carlos, CA).

TABLE 1

Amino acid residues at the dimer interface of human ODC and AZI

Protein Identification Using Top-Down Spectra

In the last two years, because of advances in protein separation and mass spectrometry, top-down mass spectrometry moved from analyzing single proteins to analyzing complex samples and identifying hundreds and even thousands of proteins. However, computational tools for database search of top-down spectra against protein databases are still in their infancy. We describe MS-Align+, a fast algorithm for top-down protein identification based on spectral alignment that enables searches for unexpected post-translational modifications. We also propose a method for evaluating statistical significance of top-down protein identifications and further benchmark various software tools on two top-down data sets from Saccharomyces cerevisiae and Salmonella typhimurium. We demonstrate that MS-Align+ significantly increases the number of identified spectra as compared with MASCOT and OMSSA on both data sets. Although MS-Align+ and ProSightPC have similar performance on the Salmonella typhimurium data set, MS-Align+ outperforms ProSightPC on the (more complex) Saccharomyces cerevisiae data set.In the past two decades, proteomics was dominated by bottom-up mass spectrometry that analyzes digested peptides rather than intact proteins. Bottom-up approaches, although powerful, do have limitations in analyzing protein species, e.g. various proteolytic forms of the same protein or various protein isoforms resulting from alternative splicing. Top-down mass spectrometry focuses on analyzing intact proteins and large peptides (1–10) and has advantages in localizing multiple post-translational modifications (PTMs)¹ in a coordinated fashion (e.g. combinatorial PTM code) and identifying multiple protein species (e.g. proteolytically processed protein species) (11). Until recently, most top-down studies were limited to single purified proteins (12–15). Top-down studies of protein mixtures were restricted by difficulties in separating and fragmenting intact proteins and a shortage of robust computational tools.In the last two years, because of advances in protein separation and top-down instrumentation, top-down mass spectrometry moved from analyzing single proteins to analyzing complex samples containing hundreds and even thousands of proteins (16–21). Because algorithms for interpreting top-down spectra are still in their infancy, many recent developments include computational innovations in protein identification.Because top-down spectra are complex, the first step in top-down spectral interpretation is usually spectral deconvolution, which converts a complex top-down spectrum to a list of monoisotopic masses (a deconvolved spectrum). Every protein (possibly with modifications) can be scored against a top-down deconvoluted spectrum, resulting in a Protein-Spectrum-Match (PrSM). The top-down protein identification problem is finding a protein in a database with the highest scoring PrSM for a top-down spectrum and further output the PrSM if it is statistically significant. There are several software tools for top-down protein identification (SoftwareIdentification of unexpected modificationsProteogenomics search against 6-frame translationSpeedEstimation of statistical significanceProSightPC+/−^a+Fast/Slow^b+PIITA+/−−Fast−UStag++Fast−MS-TopDown+−Slow−MS-Align+++Fast+Open in a separate window^a ProSightPC has various search modes that contribute to bridging the gap between blind and restrictive modes of MS/MS database search. It can identify truncated proteins by using biomarker search and identify unexpected modifications by using Δm mode and setting the error tolerance of precursor mass to a large value (e.g., 1999 Da). However, it is not designed for identifying truncated proteins with unexpected PTMs which are not represented in the “shotgun annotated” database.^b In its most advances mode, ProSightPC can search the annotated top-down database that contains various protein species. However, ProSightPC searches in this mode become an order of magnitude slower.

• ProSightPC—ProSightPC is the most commonly used tool for top-down protein identification (22, 23). ProSightPC searches spectra against a “shotgun annotated” protein database, which is generated by considering all expected PTMs. The “shotgun annotated” protein database is much larger than the original protein database. ProSightPC can identify some (but not all) proteins with unexpected PTMs using advanced search options, such as biomarker search and Δm mode, but it is not designed for identifying truncated proteins with unexpected PTMs that are not represented in the “shotgun annotated” database. ProSightPC is a fast tool that reports the statistical significance of PrSMs.
• PIITA—Unlike ProSightPC, PIITA (19) is a precursor independent method that uses only fragment ions for protein identification. It is capable of identifying protein species with unexpected PTMs on N- or C-termini, but it cannot directly identify protein species with PTMs on both N- and C-termini. PIITA is a fast tool that provides FIT scores and Δ scores rather than statistical significance estimates.
• USTag—Unique Sequence Tag (USTag) (17) generates long (6 amino acids or longer) peptide sequence tags to identify PrSMs. This approach, although fast, relies on long peptide sequence tags that may be difficult to obtain for some spectra. It also does not provide an estimate of the statistical significance of PrSMs.
• MS-TopDown—MS-TopDown (24) is based on spectral alignment (25). MS-TopDown allows one to match top-down spectra to proteins with unexpected PTMs, i.e. without knowing which PTMs are present in the sample. However, MS-TopDown is rather slow when searching against large proteomes and does not provide the statistical significance of PrSMs, making it difficult to select good PrSMs.
• In addition, MASCOT, SEQUEST, and OMSSA (16, 26, 27) have been used for top-down protein identification.

We describe MS-Align+, a fast software tool for top-down protein identification. MS-Align+ shares the spectral alignment approach with MS-TopDown, but greatly improves on speed, statistical analysis (providing E-values of PrSMs), and the number of identified PrSMs (e.g. by finding spectral alignments between spectra and truncated proteins). We benchmarked various tools for top-down protein identification on two data sets from Saccharomyces cerevisiae (SC) and Salmonella typhimurium (ST). We demonstrate that MS-Align+ significantly increase the number of identified spectra as compared with MASCOT and OMSSA on both data sets. Although MS-Align+ and ProSightPC have similar performance on the ST data set, MS-Align+ outperforms ProSightPC on the more complex SC data set.     

The Atrazine Catabolism Genes atzABC Are Widespread and Highly Conserved

Pseudomonas strain ADP metabolizes the herbicide atrazine via three enzymatic steps, encoded by the genes atzABC, to yield cyanuric acid, a nitrogen source for many bacteria. Here, we show that five geographically distinct atrazine-degrading bacteria contain genes homologous to atzA, -B, and -C. The sequence identities of the atz genes from different atrazine-degrading bacteria were greater than 99% in all pairwise comparisons. This differs from bacterial genes involved in the catabolism of other chlorinated compounds, for which the average sequence identity in pairwise comparisons of the known members of a class ranged from 25 to 56%. Our results indicate that globally distributed atrazine-catabolic genes are highly conserved in diverse genera of bacteria.Atrazine [2-chloro-4-(ethylamino)-6-(isopropylamino)- 1,3,5-triazine] is a herbicide used for controlling broad-leaf and grassy weeds and is relatively persistent in soils (51). Atrazine and other s-triazine compounds have been detected in ground and surface waters at levels exceeding the Environmental Protection Agency’s maximum contaminant level of 3 ppb (30).Microbial populations exposed to synthetic chlorinated compounds, such as atrazine, often respond by producing enzymes that degrade these molecules. Most of our current understanding of the genes and enzymes involved in atrazine degradation derives from studies using Pseudomonas strain ADP, in which the first three enzymatic steps in atrazine degradation have been defined (6, 14, 15, 48). The genes atz A, -B, and -C, which encode these enzymes, have been cloned and sequenced. Atrazine chlorohydrolase (AtzA), hydroxyatrazine ethylaminohydrolase (AtzB), and N-isopropylammelide isopropylaminohydrolase (AtzC) sequentially convert atrazine to cyanuric acid (6, 14, 15, 48) (Fig. ​(Fig.1).1). Cyanuric acid and related compounds are catabolized by many soil bacteria (10, 11, 17, 24, 26, 61), and by Pseudomonas sp. ADP, to carbon dioxide and ammonia (35). This provides the evolutionary pressure for the atzA, -B, and -C genes to permit bacterial growth on the more than one billion pounds of atrazine that have been applied to soils globally (20). Here we used a knowledge of the atzA, -B, and -C gene sequences to investigate the presence of homologous genes in other atrazine-degrading bacteria. In this study, we report that five atrazine-degrading microorganisms, which were recently isolated from geographically separated sites exposed to atrazine, contained nearly identical atzA, -B, and -C genes. Open in a separate windowFIG. 1Pathway for atrazine catabolism to cyanuric acid in Pseudomonas sp. strain ADP.

Atrazine-catabolizing bacteria used in this study.

Until recently, attempts at isolating bacteria (18) or fungi (27) that completely degrade atrazine to carbon dioxide, ammonia, and chloride were unsuccessful. While several microorganisms were shown to dealkylate atrazine, they were unable to displace the chlorine atom (41, 54). Since 1994, several research groups have independently isolated atrazine-degrading bacteria that displaced the chlorine atom and mineralized atrazine (3, 7, 13, 35, 39, 46). Six of these bacterial cultures, listed in Table ​Table1,1, were studied here, and the Clavibacter strain had been investigated previously (13).

TABLE 1

Recently isolated atrazine-catabolizing bacteria

Comparative Analysis of Myxococcus Predation on Soil Bacteria

Predator-prey relationships among prokaryotes have received little attention but are likely to be important determinants of the composition, structure, and dynamics of microbial communities. Many species of the soil-dwelling myxobacteria are predators of other microbes, but their predation range is poorly characterized. To better understand the predatory capabilities of myxobacteria in nature, we analyzed the predation performance of numerous Myxococcus isolates across 12 diverse species of bacteria. All predator isolates could utilize most potential prey species to effectively fuel colony expansion, although one species hindered predator swarming relative to a control treatment with no growth substrate. Predator strains varied significantly in their relative performance across prey types, but most variation in predatory performance was determined by prey type, with Gram-negative prey species supporting more Myxococcus growth than Gram-positive species. There was evidence for specialized predator performance in some predator-prey combinations. Such specialization may reduce resource competition among sympatric strains in natural habitats. The broad prey range of the Myxococcus genus coupled with its ubiquity in the soil suggests that myxobacteria are likely to have very important ecological and evolutionary effects on many species of soil prokaryotes.Predation plays a major role in shaping both the ecology and evolution of biological communities. The population and evolutionary dynamics of predators and their prey are often tightly coupled and can greatly influence the dynamics of other organisms as well (1). Predation has been invoked as a major cause of diversity in ecosystems (11, 12). For example, predators may mediate coexistence between superior and inferior competitors (2, 13), and differential trajectories of predator-prey coevolution can lead to divergence between separate populations (70).Predation has been investigated extensively in higher organisms but relatively little among prokaryotes. Predation between prokaryotes is one of the most ancient forms of predation (27), and it has been proposed that this process may have been the origin of eukaryotic cells (16). Prokaryotes are key players in primary biomass production (44) and global nutrient cycling (22), and predation of some prokaryotes by others is likely to significantly affect these processes. Most studies of predatory prokaryotes have focused on Bdellovibrionaceae species (e.g., see references 51, 55, and 67). These small deltaproteobacteria prey on other Gram-negative cells, using flagella to swim rapidly until they collide with a prey cell. After collision, the predator cells then enter the periplasmic space of the prey cell, consume the host cell from within, elongate, and divide into new cells that are released upon host cell lysis (41). Although often described as predatory, the Bdellovibrionaceae may also be considered to be parasitic, as they typically depend (apart from host-independent strains that have been observed [60]) on the infection and death of their host for their reproduction (47).In this study, we examined predation among the myxobacteria, which are also deltaproteobacteria but constitute a monophyletic clade divergent from the Bdellovibrionaceae (17). Myxobacteria are found in most terrestrial soils and in many aquatic environments as well (17, 53, 74). Many myxobacteria, including the model species Myxococcus xanthus, exhibit several complex social traits, including fruiting body formation and spore formation (14, 18, 34, 62, 71), cooperative swarming with two motility systems (64, 87), and group (or “wolf pack”) predation on both bacteria and fungi (4, 5, 8, 9, 15, 50). Using representatives of the genus Myxococcus, we tested for both intra- and interspecific variation in myxobacterial predatory performance across a broad range of prey types. Moreover, we examined whether prey vary substantially in the degree to which they support predatory growth by the myxobacteria and whether patterns of variation in predator performance are constant or variable across prey environments. The latter outcome may reflect adaptive specialization and help to maintain diversity in natural populations (57, 59).Although closely related to the Bdellovibrionaceae (both are deltaproteobacteria), myxobacteria employ a highly divergent mode of predation. Myxobacteria use gliding motility (64) to search the soil matrix for prey and produce a wide range of antibiotics and lytic compounds that kill and decompose prey cells and break down complex polymers, thereby releasing substrates for growth (66). Myxobacterial predation is cooperative both in its “searching” component (6, 31, 82; for details on cooperative swarming, see reference 64) and in its “handling” component (10, 29, 31, 32), in which secreted enzymes turn prey cells into consumable growth substrates (56, 83). There is evidence that M. xanthus employs chemotaxis-like genes in its attack on prey cells (5) and that predation is stimulated by close contact with prey cells (48).Recent studies have revealed great genetic and phenotypic diversity within natural populations of M. xanthus, on both global (79) and local (down to centimeter) scales (78). Phenotypic diversity includes variation in social compatibility (24, 81), the density and nutrient thresholds triggering development (33, 38), developmental timing (38), motility rates and patterns (80), and secondary metabolite production (40). Although natural populations are spatially structured and both genetic diversity and population differentiation decrease with spatial scale (79), substantial genetic diversity is present even among centimeter-scale isolates (78). No study has yet systematically investigated quantitative natural variation in myxobacterial predation phenotypes across a large number of predator genotypes.Given the previous discovery of large variation in all examined phenotypes, even among genetically extremely similar strains, we anticipated extensive predatory variation as well. Using a phylogenetically broad range of prey, we compared and contrasted the predatory performance of 16 natural M. xanthus isolates, sampled from global to local scales, as well as the commonly studied laboratory reference strain DK1622 and representatives of three additional Myxococcus species: M. flavescens (86), M. macrosporus (42), and M. virescens (63) (Table ​(Table1).1). In particular, we measured myxobacterial swarm expansion rates on prey lawns spread on buffered agar (31, 50) and on control plates with no nutrients or with prehydrolyzed growth substrate.

TABLE 1.

List of myxobacteria used, with geographical origin

Biological Activity of Nerve Growth Factor Precursor Is Dependent upon Relative Levels of Its Receptors

Nerve growth factor (NGF) is produced as a precursor called pro-nerve growth factor (proNGF), which is secreted by many tissues and is the predominant form of NGF in the central nervous system. In Alzheimer disease brain, cholinergic neurons degenerate and can no longer transport NGF as efficiently, leading to an increase in untransported NGF in the target tissue. The protein that accumulates in the target tissue is proNGF, not the mature form. The role of this precursor is controversial, and both neurotrophic and apoptotic activities have been reported for recombinant proNGFs. Differences in the protein structures, protein expression systems, methods used for protein purification, and methods used for bioassay may affect the activity of these proteins. Here, we show that proNGF is neurotrophic regardless of mutations or tags, and no matter how it is purified or in which system it is expressed. However, although proNGF is neurotrophic under our assay conditions for primary sympathetic neurons and for pheochromocytoma (PC12) cells, it is apoptotic for unprimed PC12 cells when they are deprived of serum. The ratio of tropomyosin-related kinase A to p75 neurotrophin receptor is low in unprimed PC12 cells compared with primed PC12 cells and sympathetic neurons, altering the balance of proNGF-induced signaling to favor apoptosis. We conclude that the relative level of proNGF receptors determines whether this precursor exhibits neurotrophic or apoptotic activity.Nerve growth factor (NGF)³ regulates neuronal survival, neurite outgrowth, and differentiation in the peripheral and central nervous systems (1). The mature form of NGF forms a non-covalent homodimer and binds with high affinity (k_d ≈ 10⁻¹¹ m) to tropomyosin-related kinase A (TrkA) and with low affinity (k_d ≈ 10⁻⁹ m) to the common neurotrophin receptor p75^NTR (p75 neurotrophin receptor) (2). NGF promotes cell survival and growth in cells expressing TrkA through activation of the phosphatidylinositol 3-kinase/AKT pathway and the Ras/mitogen-activated protein kinase (MAPK) pathway (3, 4). p75^NTR plays diverse roles, ranging from cell survival to cell death depending on the cellular context in which it is expressed. Through activation of the NF-κB pathway, p75^NTR can contribute to cell survival in sensory neurons (5), it is involved in axonal growth via regulation of Rho activity (6), and it can interact with Trks to enhance neurotrophin affinity (at low concentration of ligand) and specificity of binding to Trks (7–9). High levels of p75^NTR expression can induce apoptosis when there are low levels of Trk or when Trk is absent (10, 11). Apoptosis occurs through increased ceramide production (12), activation of c-Jun N-terminal kinase (JNK1), and p53 (10, 13). p75^NTR requires a co-receptor called sortilin to induce cell death (14).NGF is produced as a precursor called pro-nerve growth factor (proNGF) (15). ProNGF is secreted by many tissues such as prostate cells, spermatids, hair follicles, oral mucosal keratinocytes, sympathetic neurons, cortical astrocytes, heart, and spleen (16–20). ProNGF is the predominant form of NGF in the central and peripheral nervous systems, whereas little or no mature NGF can be detected (21–24). In Alzheimer disease brain, retrograde transport from the cortex and hippocampus to basal forebrain cholinergic neurons is reduced as these neurons degenerate, with concomitant proNGF accumulation in the cortex and hippocampus (21, 23). This suggested that proNGF mediates biological activity besides its prodomain function of promoting protein folding and regulation of neurotrophin secretion (25–28). To study the role of proNGF protein in vitro, point mutations were inserted at the cleavage site used by furin, a proprotein convertase known to cleave proNGF (29), to minimize the conversion of proNGF to mature NGF. The resulting recombinant, cleavage-resistant proNGFs reportedly exhibit either apoptotic activity (30, 31) or neurotrophic activity (32, 33). These recombinant proteins differ in several ways (ProNGF(R−1G)ProNGFhisProNGFEProNGF123WT-NGFhisMutations−1 (R to G)−2 and −1 (RR to AA), 118 and 119 (RR to AA)−1 and +1 (RS to AA)−73 and −72 (RR to AA), −43 and −42 (KKRR to KAAR), −2 and −1 (KR to AA)None: cleavable proNGFTagNo tagHistidine tagNo tagNo tagHistidine tagExpression systemInsect cellsInsect cells, mammalian cellsBacteriaInsect cellsInsect cells, mammalian cellsPurificationNo purificationNickel columnRefolded from inclusion bodies, FPLCCation exchange chromatography, immunoaffinity chromatographyNickel columnOpen in a separate window