Suggestive Evidence for Darwinian Selection against Asparagine-Linked Glycans of Plasmodium falciparum and Toxoplasma gondii

We are interested in asparagine-linked glycans (N-glycans) of Plasmodium falciparum and Toxoplasma gondii, because their N-glycan structures have been controversial and because we hypothesize that there might be selection against N-glycans in nucleus-encoded proteins that must pass through the endoplasmic reticulum (ER) prior to threading into the apicoplast. In support of our hypothesis, we observed the following. First, in protists with apicoplasts, there is extensive secondary loss of Alg enzymes that make lipid-linked precursors to N-glycans. Theileria makes no N-glycans, and Plasmodium makes a severely truncated N-glycan precursor composed of one or two GlcNAc residues. Second, secreted proteins of Toxoplasma, which uses its own 10-sugar precursor (Glc₃Man₅GlcNAc₂) and the host 14-sugar precursor (Glc₃Man₉GlcNAc₂) to make N-glycans, have very few sites for N glycosylation, and there is additional selection against N-glycan sites in its apicoplast-targeted proteins. Third, while the GlcNAc-binding Griffonia simplicifolia lectin II labels ER, rhoptries, and surface of plasmodia, there is no apicoplast labeling. Similarly, the antiretroviral lectin cyanovirin-N, which binds to N-glycans of Toxoplasma, labels ER and rhoptries, but there is no apicoplast labeling. We conclude that possible selection against N-glycans in protists with apicoplasts occurs by eliminating N-glycans (Theileria), reducing their length (Plasmodium), or reducing the number of N-glycan sites (Toxoplasma). In addition, occupation of N-glycan sites is markedly reduced in apicoplast proteins versus some secretory proteins in both Plasmodium and Toxoplasma.Animals, fungi, and plants synthesize Asn-linked glycans (N-glycans) by means of a lipid-linked precursor containing 14 sugars (dolichol-PP-Glc₃Man₉GlcNAc₂) (26). Recently we used bioinformatics and experimental methods to show that numerous protists are missing sets of glycosyltransferases (Alg1 to Alg14) and so make truncated N-glycan precursors containing 0 to 11 sugars (46). For example, Entamoeba histolytica, which causes dysentery, makes N-glycan precursors that contain seven sugars (Man₅GlcNAc₂) (33). Giardia lamblia, a cause of diarrhea, makes N-glycan precursors that contain just GlcNAc₂ (41). N-glycan precursors may be identified by metabolic labeling with radiolabeled mannose (Entamoeba) or glucosamine (Giardia) (46). Unprocessed N-glycans of each protist may be recognized by wheat germ agglutinin 1 (WGA-1) (GlcNAc₂ of Giardia) or by the antiretroviral lectin cyanovirin-N (Man₅GlcNAc₂ of Entamoeba) (2, 33, 41).N-glycans are transferred from lipid-linked precursors to sequons (Asn-Xaa-Ser or Asn-Xaa-Thr, where Xaa cannot be Pro) on nascent peptides by an oligosaccharyltransferase (OST) (28). For the most part, transfer of N-glycans by the OST is during translocation, although there are human and Trypanosoma OSTs that transfer N-glycans after translocation (34, 45).N-glycan-dependent quality control (QC) systems for protein folding and endoplasmic reticulum (ER)-associated degradation (ERAD), which are present in most eukaryotes, are missing from Giardia and a few other protists that make truncated N-glycans (5, 26, 53). There is positive Darwinian selection for sequons (sites of N-glycans) that contain Thr in secreted and membrane proteins of organisms that have N-glycan-dependent QC (12). This selection occurs for the most part by an increased probability that Asn and Thr will be present in sequons rather than elsewhere in secreted and membrane proteins. In contrast, there is no selection on sequons that contain Ser, and there is no selection on sequons in the secreted proteins of organisms that lack N-glycan-dependent QC.For numerous reasons, we are interested in the N-glycans of Plasmodium falciparum and Toxoplasma gondii, which cause severe malaria and disseminated infections, respectively.(i) There has been controversy for a long time as to whether Plasmodium makes N-glycans. While some investigators identified a 14-sugar Plasmodium N-glycan resembling that of the human host (29), others identified no N-glycans (6, 22).(ii) There is also controversy concerning whether the N-glycans of Toxoplasma, after removal of Glc by glucosidases in the ER lumen, contain either 7 sugars (Man₅GlcNAc₂), like Entamoeba (32, 33), or 11 sugars (Man₉GlcNAc₂), like the human host (16, 19, 26). If it is Man₅GlcNAc₂, then Toxoplasma uses the dolichol-PP-linked glycan predicted by its set of Alg enzymes (32, 46). If it is Man₉GlcNAc₂, then Toxoplasma uses the dolichol-PP-linked glycan of the host cell (16, 19, 26).(iii) Both Plasmodium and Toxoplasma are missing proteins involved in N-glycan-dependent QC of protein folding (5).(iv) We hypothesize that there may be negative selection against N-glycans in Plasmodium and Toxoplasma, because the N-glycans added in the ER lumen during translocation will likely interfere with threading of nucleus-encoded apicoplast proteins into a nonphotosynthetic, chloroplast-derived organelle called the apicoplast (21, 35, 37, 48, 52, 54). Nucleus-encoded apicoplast proteins have a bipartite signal at the N terminus, which targets proteins first to the lumen of the ER and second to lumen of the apicoplast. This bipartite signal has been used in transformed plasmodia where green fluorescent protein (GFP) is targeted to the apicoplast with the bipartite signal of the acyl carrier protein (ACP_leader-GFP), to the secretory system with the signal sequence only (ACP_signal-GFP), and to the cytosol with the organelle-targeting transit peptide only (ACP_transit-GFP) (55). Similar constructs have been used to characterize signals that target nucleus-encoded proteins of Toxoplasma to the apicoplast (11, 25).Here we use a combination of bioinformatic, biochemical, and morphological methods to characterize the N-glycans of Plasmodium and Toxoplasma and to test our hypothesis that there is negative selection against N-glycans in protists with apicoplasts.     

F-Actin Dynamics in Neurospora crassa

This study demonstrates the utility of Lifeact for the investigation of actin dynamics in Neurospora crassa and also represents the first report of simultaneous live-cell imaging of the actin and microtubule cytoskeletons in filamentous fungi. Lifeact is a 17-amino-acid peptide derived from the nonessential Saccharomyces cerevisiae actin-binding protein Abp140p. Fused to green fluorescent protein (GFP) or red fluorescent protein (TagRFP), Lifeact allowed live-cell imaging of actin patches, cables, and rings in N. crassa without interfering with cellular functions. Actin cables and patches localized to sites of active growth during the establishment and maintenance of cell polarity in germ tubes and conidial anastomosis tubes (CATs). Recurrent phases of formation and retrograde movement of complex arrays of actin cables were observed at growing tips of germ tubes and CATs. Two populations of actin patches exhibiting slow and fast movement were distinguished, and rapid (1.2 μm/s) saltatory transport of patches along cables was observed. Actin cables accumulated and subsequently condensed into actin rings associated with septum formation. F-actin organization was markedly different in the tip regions of mature hyphae and in germ tubes. Only mature hyphae displayed a subapical collar of actin patches and a concentration of F-actin within the core of the Spitzenkörper. Coexpression of Lifeact-TagRFP and β-tubulin–GFP revealed distinct but interrelated localization patterns of F-actin and microtubules during the initiation and maintenance of tip growth.Actins are highly conserved proteins found in all eukaryotes and have an enormous variety of cellular roles. The monomeric form (globular actin, or G-actin) can self-assemble, with the aid of numerous actin-binding proteins (ABPs), into microfilaments (filamentous actin, or F-actin), which, together with microtubules, form the two major components of the fungal cytoskeleton. Numerous pharmacological and genetic studies of fungi have demonstrated crucial roles for F-actin in cell polarity, exocytosis, endocytosis, cytokinesis, and organelle movement (6, 7, 20, 34, 35, 51, 52, 59). Phalloidin staining, immunofluorescent labeling, and fluorescent-protein (FP)-based live-cell imaging have revealed three distinct subpopulations of F-actin-containing structures in fungi: patches, cables, and rings (1, 14, 28, 34, 60, 63, 64). Actin patches are associated with the plasma membrane and represent an accumulation of F-actin around endocytic vesicles (3, 26, 57). Actin cables are bundles of actin filaments stabilized with cross-linking proteins, such as tropomyosins and fimbrin, and are assembled by formins at sites of active growth, where they form tracks for myosin V-dependent polarized secretion and organelle transport (10, 16, 17, 27, 38, 47, 48). Cables, unlike patches, are absolutely required for polarized growth in the budding yeast Saccharomyces cerevisiae (34, 38). Contractile actomyosin rings are essential for cytokinesis in budding yeast, whereas in filamentous fungi, actin rings are less well studied but are known to be involved in septum formation (20, 28, 34, 39, 40).Actin cables and patches have been particularly well studied in budding yeast. However, there are likely to be important differences between F-actin architecture and dynamics in budding yeast and those in filamentous fungi, as budding yeasts display only a short period of polarized growth during bud formation, which is followed by isotropic growth over the bud surface (10). Sustained polarized growth during hyphal morphogenesis is a defining feature of filamentous fungi (21), making them attractive models for studying the roles of the actin cytoskeleton in cell polarization, tip growth, and organelle transport.In Neurospora crassa and other filamentous fungi, disruption of the actin cytoskeleton leads to rapid tip swelling, which indicates perturbation of polarized tip growth, demonstrating a critical role for F-actin in targeted secretion to particular sites on the plasma membrane (7, 22, 29, 56). Immunofluorescence studies of N. crassa have shown that F-actin localizes to hyphal tips as “clouds” and “plaques” (7, 54, 59). However, immunolabeling has failed to reveal actin cables in N. crassa and offers limited insights into F-actin dynamics. Live-cell imaging of F-actin architecture and dynamics has not been accomplished in N. crassa, yet it is expected to yield key insights into cell polarization, tip growth, and intracellular transport.We took advantage of a recently developed live-cell imaging probe for F-actin called Lifeact (43). Lifeact is a 17-amino-acid peptide derived from the N terminus of the budding yeast actin-binding protein Abp140 (5, 63) and has recently been demonstrated to be a universal live-cell imaging marker for F-actin in eukaryotes (43). Here, we report the successful application of fluorescent Lifeact fusion constructs for live-cell imaging of F-actin in N. crassa. We constructed two synthetic genes consisting of Lifeact fused to “synthetic” green fluorescent protein (sGFP) (S65T) (henceforth termed GFP) (12) or red fluorescent protein (TagRFP) (33) and expressed these constructs in various N. crassa strains. In all strain backgrounds, fluorescent Lifeact constructs clearly labeled actin patches, cables, and rings and revealed a direct association of F-actin structures with sites of cell polarization and active tip growth. Our results demonstrate the efficacy of Lifeact as a nontoxic live-cell imaging probe in N. crassa.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Comparison of Gas Chromatography and Mineralization Experiments for Measuring Loss of Selected Polychlorinated Biphenyl Congeners in Cultures of White Rot Fungi

Two methods were used to compare the biodegradation of six polychlorinated biphenyl (PCB) congeners by 12 white rot fungi. Four fungi were found to be more active than Phanerochaete chrysosporium ATCC 24725. Biodegradation of the following congeners was monitored by gas chromatography: 2,3-dichlorobiphenyl, 4,4′-dichlorobiphenyl, 2,4′,5-trichlorobiphenyl (2,4′,5-TCB), 2,2′,4,4′-tetrachlorobiphenyl, 2,2′,5,5′-tetrachlorobiphenyl, and 2,2′,4,4′,5,5′-hexachlorobiphenyl. The congener tested for mineralization was 2,4′,5-[U-¹⁴C]TCB. Culture supernatants were also assayed for lignin peroxidase and manganese peroxidase activities. Of the fungi tested, two strains of Bjerkandera adusta (UAMH 8258 and UAMH 7308), one strain of Pleurotus ostreatus (UAMH 7964), and Trametes versicolor UAMH 8272 gave the highest biodegradation and mineralization. P. chrysosporium ATCC 24725, a strain frequently used in studies of PCB degradation, gave the lowest mineralization and biodegradation activities of the 12 fungi reported here. Low but detectable levels of lignin peroxidase and manganese peroxidase activity were present in culture supernatants, but no correlation was observed among any combination of PCB congener biodegradation, mineralization, and lignin peroxidase or manganese peroxidase activity. With the exception of P. chrysosporium, congener loss ranged from 40 to 96%; however, these values varied due to nonspecific congener binding to fungal biomass and glassware. Mineralization was much lower, ≤11%, because it measures a complete oxidation of at least part of the congener molecule but the results were more consistent and therefore more reliable in assessment of PCB biodegradation.

Polychlorinated biphenyls (PCBs) are produced by chlorination of biphenyl, resulting in up to 209 different congeners. Commercial mixtures range from light oily fluids to waxes, and their physical properties make them useful as heat transfer fluids, hydraulic fluids, solvent extenders, plasticizers, flame retardants, organic diluents, and dielectric fluids (1, 21). Approximately 24 million lb are in the North American environment (19). The stability and hydrophobic nature of these compounds make them a persistent environmental hazard.To date, bacterial transformations have been the main focus of PCB degradation research. Aerobic bacteria use a biphenyl-induced dioxygenase enzyme system to attack less-chlorinated congeners (mono- to hexachlorobiphenyls) (1, 5, 7, 8, 22). Although more-chlorinated congeners are recalcitrant to aerobic bacterial degradation, microorganisms in anaerobic river sediments reductively dechlorinate these compounds, mainly removing the meta and para chlorines (1, 6, 10, 33, 34).The degradation of PCBs by white rot fungi has been known since 1985 (11, 18). Many fungi have been tested for their ability to degrade PCBs, including the white rot fungi Coriolus versicolor (18), Coriolopsis polysona (41), Funalia gallica (18), Hirneola nigricans (35), Lentinus edodes (35), Phanerochaete chrysosporium (3, 11, 14, 17, 18, 35, 39, 41–43), Phlebia brevispora (18), Pleurotus ostreatus (35, 43), Poria cinerescens (18), Px strain (possibly Lentinus tigrinus) (35), and Trametes versicolor (41, 43). There have also been studies of PCB metabolism by ectomycorrhizal fungi (17) and other fungi such as Aspergillus flavus (32), Aspergillus niger (15), Aureobasidium pullulans (18), Candida boidinii (35), Candida lipolytica (35), Cunninghamella elegans (16), and Saccharomyces cerevisiae (18, 38). The mechanism of PCB biodegradation has not been definitively determined for any fungi. White rot fungi produce several nonspecific extracellular enzymes which have been the subject of extensive research. These nonspecific peroxidases are normally involved in lignin degradation but can oxidize a wide range of aromatic compounds including polycyclic aromatic hydrocarbons (37). Two peroxidases, lignin peroxidase (LiP) and Mn peroxidase (MnP), are secreted into the environment of the fungus under conditions of nitrogen limitation in P. chrysosporium (23, 25, 27, 29) but are not stress related in fungi such as Bjerkandera adusta or T. versicolor (12, 30).Two approaches have been used to determine the biodegradability of PCBs by fungi: (i) loss of the parent congener analyzed by gas chromatography (GC) (17, 32, 35, 42, 43) and (ii) mineralization experiments in which the ¹⁴C of the universally labeled ¹⁴C parent congener is recovered as ¹⁴CO₂ (11, 14, 18, 39, 41). In the first method, the loss of a peak on a chromatogram makes it difficult to decide whether the PCB is being partly degraded, mineralized, adsorbed to the fungal biomass, or bound to glassware, soil particles, or wood chips. Even when experiments with killed-cell and abiotic controls are performed, the extraction efficiency and standard error can make data difficult to interpret. For example, recoveries can range anywhere from 40 to 100% depending on the congener used and the fungus being investigated (17). On the other hand, recovery of significant amounts of ¹⁴CO₂ from the cultures incubated with a ¹⁴C substrate provides definitive proof of fungal metabolism. There appears to be only one report relating data from these two techniques (18), and in that study, [U-¹⁴C]Aroclor 1254, rather than an individual congener, was used.In this study, we examined the ability of 12 white rot fungal strains to metabolize selected PCB congeners to determine which strains were the most active degraders. Included in this group was P. chrysosporium ATCC 24725, a strain used extensively in PCB studies (3, 14, 18, 35, 39, 41–43). Six PCB congeners were selected to give a range of chlorine substitutions and therefore a range of potential biodegradability which was monitored by GC. One of the chosen congeners was ¹⁴C labeled and used in studies to compare the results from a mineralization method with those from the GC method.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.