Environmental signatures in proteome properties

Metals in Motion: Understanding Labile Metal Pools in Bacteria

Metal ions are essential for all life. In microbial cells, potassium (K+) is the most abundant cation and plays a key role in maintaining osmotic balance. Magnesium (Mg2+) is the dominant divalent cation and is required for nucleic acid structure and as an enzyme cofactor. Microbes typically require the transition metals manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn), although the precise set of metal ions needed to sustain life is variable. Intracellular metal pools can be conceptualized as a chemically complex mixture of rapidly exchanging (labile) ions, complemented by those reservoirs that exchange slowly relative to cell metabolism (sequestered). Labile metal pools are buffered by transient interactions with anionic metabolites and macromolecules, with the ribosome playing a major role. Sequestered metal pools include many metalloproteins, cofactors, and storage depots, with some pools redeployed upon metal depletion. Here, I review the size, composition, and dynamics of intracellular metal pools and highlight the major gaps in understanding.

The genome of Candidatus phytoplasma ziziphi provides insights into their biological characteristics

Phytoplasmas are obligate cell wall-less prokaryotic bacteria that primarily multiply in plant phloem tissue. Jujube witches' broom (JWB) associated with phytoplasma is a destructive disease of jujube (Ziziphus jujuba Mill.). Here we report the complete 'Candidatus Phytoplasma ziziphi' chromosome of strain Hebei-2018, which is a circular genome of 764,108-base pairs with 735 predicted CDS. Notably, extra 19,825 bp (from 621,995 to 641,819 bp) compared to the previously reported one complements the genes involved in glycolysis, such as pdhA, pdhB, pdhC, pdhD, ackA, pduL and LDH. The synonymous codon usage bias (CUB) patterns by using comparative genomics analysis among the 9 phytoplasmas were similar for most codons. The ENc-GC3s analysis among the 9 phytoplasmas showed a greater effect under the selection on the CUBs of phytoplasmas genes than mutation and other factors. The genome exhibited a strongly reduced ability in metabolic synthesis, while the genes encoding transporter systems were well developed. The genes involved in sec-dependent protein translocation system were also identified.The expressions of nine FtsHs encoding membrane associated ATP-dependent Zn proteases and Mn-SodA with redox capacity in the Ca. P. ziziphi was positively correlated with the phytoplasma concentration. Taken together, the genome will not only expand the number of phytoplasma species and provide some new information about Ca. P. ziziphi, but also contribute to exploring its pathogenic mechanism.

Survival and Energy Producing Strategies of Alkane Degraders Under Extreme Conditions and Their Biotechnological Potential

Many petroleum-polluted areas are considered as extreme environments because of co-occurrence of low and high temperatures, high salt, and acidic and anaerobic conditions. Alkanes, which are major constituents of crude oils, can be degraded under extreme conditions, both aerobically and anaerobically by bacteria and archaea of different phyla. Alkane degraders possess exclusive metabolic pathways and survival strategies, which involve the use of protein and RNA chaperones, compatible solutes, biosurfactants, and exopolysaccharide production for self-protection during harsh environmental conditions such as oxidative and osmotic stress, and ionic nutrient-shortage. Recent findings suggest that the thermophilic sulfate-reducing archaeon Archaeoglobus fulgidus uses a novel alkylsuccinate synthase for long-chain alkane degradation, and the thermophilic Candidatus Syntrophoarchaeum butanivorans anaerobically oxidizes butane via alkyl-coenzyme M formation. In addition, gene expression data suggest that extremophiles produce energy via the glyoxylate shunt and the Pta-AckA pathway when grown on a diverse range of alkanes under stress conditions. Alkane degraders possess biotechnological potential for bioremediation because of their unusual characteristics. This review will provide genomic and molecular insights on alkane degraders under extreme conditions.

Insights into the sequence parameters for halophilic adaptation

The sequence parameters for halophilic adaptation are still not fully understood. To understand the molecular basis of protein hypersaline adaptation, a detailed analysis is carried out, and investigated the likely association of protein sequence attributes to halophilic adaptation. A two-stage strategy is implemented, where in the first stage a supervised machine learning classifier is build, giving an overall accuracy of 86 % on stratified tenfold cross validation and 90 % on blind testing set, which are better than the previously reported results. The second stage consists of statistical analysis of sequence features and possible extraction of halophilic molecular signatures. The results of this study showed that, halophilic proteins are characterized by lower average charge, lower K content, and lower S content. A statistically significant preference/avoidance list of sequence parameters is also reported giving insights into the molecular basis of halophilic adaptation. D, Q, E, H, P, T, V are significantly preferred while N, C, I, K, M, F, S are significantly avoided. Among amino acid physicochemical groups, small, polar, charged, acidic and hydrophilic groups are preferred over other groups. The halophilic proteins also showed a preference for higher average flexibility, higher average polarity and avoidance for higher average positive charge, average bulkiness and average hydrophobicity. Some interesting trends observed in dipeptide counts are also reported. Further a systematic statistical comparison is undertaken for gaining insights into the sequence feature distribution in different residue structural states. The current analysis may facilitate the understanding of the mechanism of halophilic adaptation clearer, which can be further used for rational design of halophilic proteins.

Alzheimer's and Parkinson's diseases: an environmental proteomic point of view

Alzheimer's and Parkinson's diseases are severe neurodegenerative conditions triggered by complex biochemical routes. Many groups are currently pursuing the search for valuable biomarkers to either perform early diagnostic or to follow the disease's progress. Several studies have reported relevant findings regarding environmental issues and the progression of such diseases. Here the etiology and mechanisms of these diseases are briefly reviewed. Approaches that might reveal candidate biomarkers and environmental stressors associated to the diseases were analyzed under a proteomic perspective. This article is part of a Special Issue entitled: Environmental and structural proteomics.

Evolution of complete proteomes: guanine-cytosine pressure, phylogeny and environmental influences blend the proteomic architecture

Background

Guanine-cytosine (GC) composition is an important feature of genomes. Likewise, amino acid composition is a distinct, but less valued, feature of proteomes. A major concern is that it is not clear what valuable information can be acquired from amino acid composition data. To address this concern, in-depth analyses of the amino acid composition of the complete proteomes from 63 archaea, 270 bacteria, and 128 eukaryotes were performed.

Results

Principal component analysis of the amino acid matrices showed that the main contributors to proteomic architecture were genomic GC variation, phylogeny, and environmental influences. GC pressure drove positive selection on Ala, Arg, Gly, Pro, Trp, and Val, and adverse selection on Asn, Lys, Ile, Phe, and Tyr. The physico-chemical framework of the complete proteomes withstood GC pressure by frequency complementation of GC-dependent amino acid pairs with similar physico-chemical properties. Gln, His, Ser, and Val were responsible for phylogeny and their constituted components could differentiate archaea, bacteria, and eukaryotes. Environmental niche was also a significant factor in determining proteomic architecture, especially for archaea for which the main amino acids were Cys, Leu, and Thr. In archaea, hyperthermophiles, acidophiles, mesophiles, psychrophiles, and halophiles gathered successively along the environment-based principal component. Concordance between proteomic architecture and the genetic code was also related closely to genomic GC content, phylogeny, and lifestyles.

Conclusions

Large-scale analyses of the complete proteomes of a wide range of organisms suggested that amino acid composition retained the trace of GC variation, phylogeny, and environmental influences during evolution. The findings from this study will help in the development of a global understanding of proteome evolution, and even biological evolution.

GC content-independent amino acid patterns in bacteria and archaea

Every organism can be characterized by the amino acid composition of its proteome. So far it was assumed that these compositions are determined by the GC content of the DNA or, in some cases, by extreme lifestyles, like thermophily or halophily. Here, we focussed our analysis on eight amino acids, each of which is encoded by both, GC and AT rich codons, to identify finer amino acid patterns beyond the GC dominance. We investigated the conceptually translated proteomes of 1029 bacterial and archaeal strains with sequenced genomes for amino acid composition. Using correspondence analysis, we found that phylogenetic groups within bacteria and archaea generally can be discriminated from other groups due to their amino acid composition. In some cases, single organisms, e.g. Treponema pallidum strains or Mycoplasma penetrans, are characterized by extreme amino acid compositions. We assume that our data could provide a basis for a new approach to analyze evolution of bacterial and archaeal groups. Furthermore, for single organisms, the detailed knowledge of the amino acid composition of the entire proteome encoded in the genome could lead to a better understanding, important for pharmaceutical or biotechnological applications. We recommend that information about amino acid compositions should be provided in databases, comparable to the GC content of genomes.

Proteome sequence features carry signatures of the environmental niche of prokaryotes

Background

Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.

Results

We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests.

Conclusions

To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments.

Simple proteomic checks for detecting noncoding RNA

Proper validation can accelerate sequence-based discovery of proteins and protein-coding genes. Databases currently contain a backlog of experimentally unverified gene models and tentative assignments of observed transcripts to coding or noncoding RNA. We present and apply a general principle, founded on base composition and the genetic code and validated here by bulk 2-D gels, that can improve the reliability of such classifications and of the algorithms or pipelines that lead to them.

Evolution of proteomes: fundamental signatures and global trends in amino acid compositions

BACKGROUND

The evolutionary characterization of species and lifestyles at global levels is nowadays a subject of considerable interest, particularly with the availability of many complete genomes. Are there specific properties associated with lifestyles and phylogenies? What are the underlying evolutionary trends? One of the simplest analyses to address such questions concerns characterization of proteomes at the amino acids composition level.

RESULTS

In this work, amino acid compositions of a large set of 208 proteomes, with significant number of representatives from the three phylogenetic domains and different lifestyles are analyzed, resorting to an appropriate multidimensional method: Correspondence analysis. The analysis reveals striking discrimination between eukaryotes, prokaryotic mesophiles and hyperthemophiles-themophiles, following amino acid usage. In sharp contrast, no similar discrimination is observed for psychrophiles. The observed distributional properties are compared with various inferred chronologies for the recruitment of amino acids into the genetic code. Such comparisons reveal correlations between the observed segregations of species following amino acid usage, and the separation of amino acids following early or late recruitment.

CONCLUSION

A simple description of proteomes according to amino acid compositions reveals striking signatures, with sharp segregations or on the contrary non-discriminations following phylogenies and lifestyles. The distribution of species, following amino acid usage, exhibits a discrimination between [high GC]-[high optimal growth temperatures] and [low GC]-[moderate temperatures] characteristics. This discrimination appears to coincide closely with the separation of amino acids following their inferred early or late recruitment into the genetic code. Taken together the various results provide a consistent picture for the evolution of proteomes, in terms of amino acid usage.

Subcellular pH and predicted pH-dependent features of proteins

A characteristic of two-dimensional proteomics gels is a general bimodal distribution of isoelectric (pI) values. Discussion of this feature has focussed on the balance of acidic and basic ionisable residues, and potential relationships between pI distributions and organism classification or protein subcellular location. Electrostatics calculations on a set of protein structures with known subcellular location show that predicted folded state pI are similar to those calculated from sequence alone, but adjusted according to a general stabilising effect from interactions between ionisable groups. Bimodal distributions dominate both pI and the predicted pH of maximal stability. However, there are significant differences between these features. The average pH of maximal stability generally follows organelle pH. Average pI values are well removed from organelle pH in most subcellular environments, consistent with the view that proteins have evolved to carry (on average) net charge in a given subcellular location, and relevant to discussion of solubility in crowded environments. Correlation of the predicted pH of maximum stability with subcellular pH suggests an evolutionary pressure to adjust folded state interactions according to environment. Finally, our analysis of ionisable group contributions to stability suggests that Golgi proteins have the largest such term, although this dataset is small.

Amino acids as placeholders: base-composition pressures on protein length in malaria parasites and prokaryotes

BACKGROUND

The composition and sequence of amino acids in a protein may serve the underlying needs of the nucleic acids that encode the protein (the genome phenotype). In extreme form, amino acids become mere placeholders inserted between functional segments or domains, and--apart from increasing protein length--playing no role in the specific function or structure of a protein (the conventional phenotype).

METHODS

We studied the genomes of two malarial parasites and 521 prokaryotes (144 complete) that differ widely in GC% and optimum growth temperature, comparing the base compositions of the protein coding regions and corresponding lengths (kilobases).

RESULTS

Malarial parasites show distinctive responses to base-compositional pressures that increase as protein lengths increase. A low-GC% species (Plasmodium falciparum) is likely to have more placeholder amino acids than an intermediate-GC% species (P. vivax), so that homologous proteins are longer. In prokaryotes, GC% is generally greater and AG% is generally less in open reading frames (ORFs) encoding long proteins. The increased GC% in long ORFs increases as species' GC% increases, and decreases as species' AG% increases. In low- and intermediate-GC% prokaryotic species, increases in ORF GC% as encoded proteins increase in length are largely accounted for by the base compositions of first and second (amino acid-determining) codon positions. In high-GC% prokaryotic species, first and third (non-amino acid-determining) codon positions play this role.

CONCLUSION

In low- and intermediate-GC% prokaryotes, placeholder amino acids are likely to be well defined, corresponding to codons enriched in G and/or C at first and second positions. In high-GC% prokaryotes, placeholder amino acids are likely to be less well defined. Increases in ORF GC% as encoded proteins increase in length are greater in mesophiles than in thermophiles, which are constrained from increasing protein lengths in response to base-composition pressures.

Comparison of theoretical proteomes: identification of COGs with conserved and variable pI within the multimodal pI distribution

BACKGROUND

Theoretical proteome analysis, generated by plotting theoretical isoelectric points (pI) against molecular masses of all proteins encoded by the genome show a multimodal distribution for pI. This multimodal distribution is an effect of allowed combinations of the charged amino acids, and not due to evolutionary causes. The variation in this distribution can be correlated to the organisms ecological niche. Contributions to this variation maybe mapped to individual proteins by studying the variation in pI of orthologs across microorganism genomes.

RESULTS

The distribution of ortholog pI values showed trimodal distributions for all prokaryotic genomes analyzed, similar to whole proteome plots. Pairwise analysis of pI variation show that a few COGs are conserved within, but most vary between, the acidic and basic regions of the distribution, while molecular mass is more highly conserved. At the level of functional grouping of orthologs, five groups vary significantly from the population of orthologs, which is attributed to either conservation at the level of sequences or a bias for either positively or negatively charged residues contributing to the function. Individual COGs conserved in both the acidic and basic regions of the trimodal distribution are identified, and orthologs that best represent the variation in levels of the acidic and basic regions are listed.

CONCLUSION

The analysis of pI distribution by using orthologs provides a basis for resolution of theoretical proteome comparison at the level of individual proteins. Orthologs identified that significantly vary between the major acidic and basic regions maybe used as representative of the variation of the entire proteome.

Uracil content of 16S rRNA of thermophilic and psychrophilic prokaryotes correlates inversely with their optimal growth temperatures

We report here the finding of a highly significant inverse correlation of the uracil content of 16S rRNA and the optimum growth temperature (T_opt) of cultured thermophilic and psychrophilic prokaryotes. This correlation was significantly different from the weaker correlations between the contents of other nucleotides and T_opt. Analysis of the 16S rRNA secondary structure regions revealed a fall in the A:U base-pair content in step with the increase in T_opt that was much steeper than that of mismatched base-pairs, which are thermodynamically less stable. These findings indicate that the 16S rRNA sequences of thermophiles and psychrophiles are under a strong thermo-adaptive pressure, and that structure–function constraints play a crucial role in determining their 16S rRNA nucleotide composition. The derived relationship between uracil content and T_opt was used to develop an algorithm to predict the T_opt values of uncultured prokaryotes lacking cultured close relatives and belonging to the phyla predominantly containing thermophiles. This algorithm may be useful in guiding the design of cultivation conditions for hitherto uncultured microbes.

Classifying G-protein coupled receptors with bagging classification tree

G-protein coupled receptors (GPCRs) play a key role in different biological processes, such as regulation of growth, death and metabolism of cells. They are major therapeutic targets of numerous prescribed drugs. However, the ligand specificity of many receptors is unknown and there is little structural information available. Bioinformatics may offer one approach to bridge the gap between sequence data and functional knowledge of a receptor. In this paper, we use a bagging classification tree algorithm to predict the type of the receptor based on its amino acid composition. The prediction is performed for GPCR at the sub-family and sub-sub-family level. In a cross-validation test, we achieved an overall predictive accuracy of 91.1% for GPCR sub-family classification, and 82.4% for sub-sub-family classification. These results demonstrate the applicability of this relative simple method and its potential for improving prediction accuracy.