Genomic Plasticity of the Human Fungal Pathogen Candida albicans

The genomic plasticity of Candida albicans, a commensal and common opportunistic fungal pathogen, continues to reveal unexpected surprises. Once thought to be asexual, we now know that the organism can generate genetic diversity through several mechanisms, including mating between cells of the opposite or of the same mating type and by a parasexual reduction in chromosome number that can be accompanied by recombination events (2, 12, 14, 53, 77, 115). In addition, dramatic genome changes can appear quite rapidly in mitotic cells propagated in vitro as well as in vivo. The detection of aneuploidy in other fungal pathogens isolated directly from patients (145) and from environmental samples (71) suggests that variations in chromosome organization and copy number are a common mechanism used by pathogenic fungi to rapidly generate diversity in response to stressful growth conditions, including, but not limited to, antifungal drug exposure. Since cancer cells often become polyploid and/or aneuploid, some of the lessons learned from studies of genome plasticity in C. albicans may provide important insights into how these processes occur in higher-eukaryotic cells exposed to stresses such as anticancer drugs.The purpose of this review is to describe the tools used to detect genome changes, to highlight recent advances in our understanding of large-scale chromosome changes that arise in Candida albicans, and to discuss the role of specific stresses in eliciting these genome changes. The types of genomic diversity that have been characterized suggest that C. albicans can undergo extreme genomic changes in order to survive stresses in the human host. We propose that C. albicans and other pathogens may have evolved mechanisms not only to tolerate but also to generate large-scale genetic variation as a means of adaptation.C. albicans is a polymorphic yeast with a 16-Mb (haploid) genome organized in 8 diploid chromosomes (140, 154, 203). The C. albicans genome displays a very high degree of plasticity. This plasticity includes the types of genomic changes frequently observed with cancer cells, including gross chromosomal rearrangements, aneuploidy, and loss of heterozygosity (reviewed in references 100, 117, and 157). Similar to somatic cancer cells, C. albicans reproduces primarily through asexual clonal division (65, 84). Nonetheless, it has retained much of the machinery needed for mating and meiosis (189), yet meiosis has never been observed (13, 120).C. albicans has two mating-type-like (MTL) alleles, MTLa and MTLα (76). The MTL locus is on the left arm of chromosome 5 (Chr5), approximately 80 kbp from the centromere. Most C. albicans isolates are heterozygous for the MTL locus, but approximately 3 to 10% of clinical isolates are naturally homozygous at MTL (104, 108). Mating can occur between strains carrying the opposite MTL locus, and most strains that were found to be naturally MTL homozygous are mating competent (104, 108). MTL-homozygous strains were also constructed from MTL-heterozygous strains by deletion of either the MTLa or MTLα locus (77) or by selection for Chr5 loss on sorbose (87, 115).Mating between these diploid strains of opposite mating type can occur both in vitro (115) and in vivo (77, 97). The products are tetraploid and do not undergo a conventional meiotic reduction in ploidy (12, 120). Rather, they undergo random loss of multiple chromosomes, a process termed “concerted chromosome loss,” until they reach a near-diploid genome content (2, 12, 53, 85). A subset of these cells also undergoes multiple gene conversion events reminiscent of meiotic recombination, and most remain trisomic for one to several chromosomes (53). While mating and concerted chromosome loss have been induced in the laboratory, the role of the parasexual cycle during the host-pathogen interaction and in the response to stresses, such as exposure to antifungal drugs, remains unclear. The prevailing model is that adaptive mutations (such as those that occur with the acquisition of drug resistance) evolve through somatic events, including point mutations, recombination, gene conversion, loss of heterozygosity, and/or aneuploidy (13).     

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]     

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.     

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]     

N-Terminal-oriented Proteogenomics of the Marine Bacterium Roseobacter Denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) Labeling and Diagonal Chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.Recent developments in mass spectrometry and bioinformatics have established proteomics as a common and powerful technique for identifying and quantifying proteins at a very broad scale, but also for characterizing their post-translational modifications and interaction networks (1, 2). In addition to the avalanche of proteomic data currently being reported, many genome sequences are established using next-generation sequencing, fostering proteomic investigations of new cellular models. Proteogenomics is a relatively recent field in which high-throughput proteomic data is used to verify coding regions within model genomes to refine the annotation of their sequences (2–8). Because genome annotation is now fully automated, the need for accurate annotation for model organisms with experimental data is crucial. Many projects related to genome re-annotation of microorganisms with the help of proteomics have been recently reported, such as for Mycoplasma pneumoniae (9), Rhodopseudomonas palustris (10), Shewanella oneidensis (11), Thermococcus gammatolerans (12), Deinococcus deserti (13), Salmonella thyphimurium (14), Mycobacterium tuberculosis (15, 16), Shigella flexneri (17), Ruegeria pomeroyi (18), and Candida glabrata (19), as well as for higher organisms such as Anopheles gambiae (20) and Arabidopsis thaliana (4, 5).The most frequently reported problem in automatic annotation systems is the correct identification of the translational start codon (21–23). The error rate depends on the primary annotation system, but also on the organism, as reported for Halobacterium salinarum and Natromonas pharaonis (24), Deinococcus deserti (21), and Ruegeria pomeroyi (18), where the error rate is estimated above 10%. Identification of a correct translational start site is essential for the genetic and biochemical analysis of a protein because errors can seriously impact subsequent biological studies. If the N terminus is not correctly identified, the protein will be considered in either a truncated or extended form, leading to errors in bioinformatic analyses (e.g. during the prediction of its molecular weight, isoelectric point, cellular localization) and major difficulties during its experimental characterization. For example, a truncated protein may be heterologously produced as an unfolded polypeptide recalcitrant to structure determination (25). Moreover, N-terminal modifications, which are poorly documented in annotation databases, may occur (26, 27).Unfortunately, the poor polypeptide sequence coverage obtained for the numerous low abundance proteins in current shotgun MS/MS proteomic studies implies that the overall detection of N-terminal peptides obtained in proteogenomic studies is relatively low. Different methods for establishing the most extensive list of protein N termini, grouped under the so-called “N-terminomics” theme, have been proposed to selectively enrich or improve the detection of these peptides (2, 28, 29). Large N-terminome studies have recently been reported based on resin-assisted enrichment of N-terminal peptides (30) or terminal amine isotopic labeling of substrates (TAILS) coupled to depletion of internal peptides with a water-soluble aldehyde-functionalized polymer (31–35). Among the numerous N-terminal-oriented methods (2), specific labeling of the N terminus of intact proteins with N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinamide (TMPP-Ac-OSu)¹ has proven reliable (21, 36–39). TMPP-derivatized N-terminal peptides have interesting properties for further LC-MS/MS mass spectrometry: (1) an increase in hydrophobicity because of the trimethoxyphenyl moiety added to the peptides, increasing their retention times in reverse phase chromatography, (2) improvement of their ionization because of the introduction of a positively charged group, and (3) a much simpler fragmentation pattern in tandem mass spectrometry. Other reported approaches rely on acetylation, followed by trypsin digestion, and then biotinylation of free amino groups (40); guanidination of lysine lateral chains followed by N-biotinylation of the N termini and trypsin digestion (41); or reductive amination of all free amino groups with formaldehyde preceeding trypsin digestion (42). Recently, we applied the TMPP method to the proteome of the Deinococcus deserti bacterium isolated from upper sand layers of the Sahara desert (13). This method enabled the detection of N-terminal peptides allowing the confirmation of 278 translation initiation codons, the correction of 73 translation starts, and the identification of non-canonical translation initiation codons (21). However, most TMPP-labeled N-terminal peptides are hidden among the more abundant internal peptides generated after proteolysis of a complex proteome, precluding their detection. This results in disproportionately fewer N-terminal validations, that is, 5 and 8% of total polypeptides coded in the theoretical proteomes of Mycobacterium smegmatis (37) and Deinococcus deserti (21) with a total of 342 and 278 validations, respectively.An interesting chromatographic method to fractionate peptide mixtures for gel-free high-throughput proteome analysis has been developed over the last years and applied to various topics (43, 44). This technique, known as COmbined FRActional DIagonal Chromatography (COFRADIC), uses a double chromatographic separation with a chemical reaction in between to change the physico-chemical properties of the extraneous peptides to be resolved from the peptides of interest. Its previous applications include the separation of methionine-containing peptides (43), N-terminal peptide enrichment (45, 46), sulfur amino acid-containing peptides (47), and phosphorylated peptides (48). COFRADIC was identified as the best method for identification of N-terminal peptides of two archaea, resulting in the identification of 240 polypeptides (9% of the theoretical proteome) for Halobacterium salinarum and 220 (8%) for Natronomonas pharaonis (24).Taking advantage of both the specificity of TMPP labeling, the resolving power of COFRADIC for enrichment, and the increase in information through the use of multiple proteases, we performed the proteogenomic analysis of a marine bacterium from the Roseobacter clade, namely Roseobacter denitrificans OCh114. This novel approach allowed us to validate and correct 534 unique proteins (13% of the theoretical proteome) with TMPP-labeled N-terminal signatures obtained using high-resolution tandem mass spectrometry. We corrected 41 annotations and detected five new open reading frames in the R. denitrificans genome. We further identified eight distinct proteins showing direct evidence for multiple start sites.     

Role of Partitioning-defective 1/Microtubule Affinity-regulating Kinases in the Morphogenetic Activity of Helicobacter pylori CagA

Helicobacter pylori CagA plays a key role in gastric carcinogenesis. Upon delivery into gastric epithelial cells, CagA binds and deregulates SHP-2 phosphatase, a bona fide oncoprotein, thereby causing sustained ERK activation and impaired focal adhesions. CagA also binds and inhibits PAR1b/MARK2, one of the four members of the PAR1 family of kinases, to elicit epithelial polarity defect. In nonpolarized gastric epithelial cells, CagA induces the hummingbird phenotype, an extremely elongated cell shape characterized by a rear retraction defect. This morphological change is dependent on CagA-deregulated SHP-2 and is thus thought to reflect the oncogenic potential of CagA. In this study, we investigated the role of the PAR1 family of kinases in the hummingbird phenotype. We found that CagA binds not only PAR1b but also other PAR1 isoforms, with order of strength as follows: PAR1b > PAR1d ≥ PAR1a > PAR1c. Binding of CagA with PAR1 isoforms inhibits the kinase activity. This abolishes the ability of PAR1 to destabilize microtubules and thereby promotes disassembly of focal adhesions, which contributes to the hummingbird phenotype. Consistently, PAR1 knockdown potentiates induction of the hummingbird phenotype by CagA. The morphogenetic activity of CagA was also found to be augmented through inhibition of non-muscle myosin II. Because myosin II is functionally associated with PAR1, perturbation of PAR1-regulated myosin II by CagA may underlie the defect of rear retraction in the hummingbird phenotype. Our findings reveal that CagA systemically inhibits PAR1 family kinases and indicate that malfunctioning of microtubules and myosin II by CagA-mediated PAR1 inhibition cooperates with deregulated SHP-2 in the morphogenetic activity of CagA.Infection with Helicobacter pylori strains bearing cagA (cytotoxin-associated gene A)-positive strains is the strongest risk factor for the development of gastric carcinoma, the second leading cause of cancer-related death worldwide (1–3). The cagA gene is located within a 40-kb DNA fragment, termed the cag pathogenicity island, which is specifically present in the genome of cagA-positive H. pylori strains (4–6). In addition to cagA, there are ∼30 genes in the cag pathogenicity island, many of which encode a bacterial type IV secretion system that delivers the cagA-encoded CagA protein into gastric epithelial cells (7–10). Upon delivery into gastric epithelial cells, CagA is localized to the plasma membrane, where it undergoes tyrosine phosphorylation at the C-terminal Glu-Pro-Ile-Tyr-Ala motifs by Src family kinases or c-Abl kinase (11–14). The C-terminal Glu-Pro-Ile-Tyr-Ala-containing region of CagA is noted for the structural diversity among distinct H. pylori isolates. Oncogenic potential of CagA has recently been confirmed by a study showing that systemic expression of CagA in mice induces gastrointestinal and hematological malignancies (15).When expressed in gastric epithelial cells, CagA induces morphological transformation termed the hummingbird phenotype, which is characterized by the development of one or two long and thin protrusions resembling the beak of the hummingbird. It has been thought that the hummingbird phenotype is related to the oncogenic action of CagA (7, 16–19). Pathophysiological relevance for the hummingbird phenotype in gastric carcinogenesis has recently been provided by the observation that infection with H. pylori carrying CagA with greater ability to induce the hummingbird phenotype is more closely associated with gastric carcinoma (20–23). Elevated motility of hummingbird cells (cells showing the hummingbird phenotype) may also contribute to invasion and metastasis of gastric carcinoma.In host cells, CagA interacts with the SHP-2 phosphatase, C-terminal Src kinase, and Crk adaptor in a tyrosine phosphorylation-dependent manner (16, 24, 25) and also associates with Grb2 adaptor and c-Met in a phosphorylation-independent manner (26, 27). Among these CagA targets, much attention has been focused on SHP-2 because the phosphatase has been recognized as a bona fide oncoprotein, gain-of-function mutations of which are found in various human malignancies (17, 18, 28). Stable interaction of CagA with SHP-2 requires CagA dimerization, which is mediated by a 16-amino acid CagA-multimerization (CM)² sequence present in the C-terminal region of CagA (29). Upon complex formation, CagA aberrantly activates SHP-2 and thereby elicits sustained ERK MAP kinase activation that promotes mitogenesis (30). Also, CagA-activated SHP-2 dephosphorylates and inhibits focal adhesion kinase (FAK), causing impaired focal adhesions. It has been shown previously that both aberrant ERK activation and FAK inhibition by CagA-deregulated SHP-2 are involved in induction of the hummingbird phenotype (31).Partitioning-defective 1 (PAR1)/microtubule affinity-regulating kinase (MARK) is an evolutionally conserved serine/threonine kinase originally isolated in C. elegans (32–34). Mammalian cells possess four structurally related PAR1 isoforms, PAR1a/MARK3, PAR1b/MARK2, PAR1c/MARK1, and PAR1d/MARK4 (35–37). Among these, PAR1a, PAR1b, and PAR1c are expressed in a variety of cells, whereas PAR1d is predominantly expressed in neural cells (35, 37). These PAR1 isoforms phosphorylate microtubule-associated proteins (MAPs) and thereby destabilize microtubules (35, 38), allowing asymmetric distribution of molecules that are involved in the establishment and maintenance of cell polarity.In polarized epithelial cells, CagA disrupts the tight junctions and causes loss of apical-basolateral polarity (39, 40). This CagA activity involves the interaction of CagA with PAR1b/MARK2 (19, 41). CagA directly binds to the kinase domain of PAR1b in a tyrosine phosphorylation-independent manner and inhibits the kinase activity. Notably, CagA binds to PAR1b via the CM sequence (19). Because PAR1b is present as a dimer in cells (42), CagA may passively homodimerize upon complex formation with the PAR1 dimer via the CM sequence, and this PAR1-directed CagA dimer would form a stable complex with SHP-2 through its two SH2 domains.Because of the critical role of CagA in gastric carcinogenesis (7, 16–19), it is important to elucidate the molecular basis underlying the morphogenetic activity of CagA. In this study, we investigated the role of PAR1 isoforms in induction of the hummingbird phenotype by CagA, and we obtained evidence that CagA-mediated inhibition of PAR1 kinases contributes to the development of the morphological change by perturbing microtubules and non-muscle myosin II.     

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]