Plastid genome of Passiflora tripartita var. mollissima (poro-poro) from Huánuco, Peru

Passiflora tripartita var. mollissima, known locally as poro-poro, is an important native fruit used in traditional Peruvian medicine with relevant agro-industrial and pharmaceutical potential for its antioxidant capacity for human health. However, to date, only a few genetic data are available, which limits exploring its genetic diversity and developing new genetic studies for its improvement. We report the poro-poro plastid genome to expand the knowledge of its molecular markers, evolutionary studies, molecular pathways, and conservation genetics. The complete chloroplast (cp) genome is 163,451 bp in length with a typical quadripartite structure, containing a large single-copy region of 85,525 bp and a small single-copy region of 13,518 bp, separated by a pair of inverted repeat regions (IR) of 32,204 bp, and the overall GC content was 36.87%. This cp genome contains 128 genes (110 genes were unique and 18 genes were found duplicated in each IR region), including 84 protein-coding genes, 36 transfer RNA-coding genes, eight ribosomal RNA-coding genes, and 13 genes with introns (11 genes with one intron and two genes with two introns). The inverted repeat region boundaries among species were similar in organization, gene order, and content, with a few revisions. The phylogenetic tree reconstructed based on single-copy orthologous genes and maximum likelihood analysis demonstrates poro-poro is most closely related to Passiflora menispermifolia and Passiflora oerstedii. In summary, our study constitutes a valuable resource for studying molecular evolution, phylogenetics, and domestication. It also provides a powerful foundation for conservation genetics research and plant breeding programs. To our knowledge, this is the first report on the plastid genome of Passiflora tripartita var. mollissima from Peru.

Consequences of Genetic Recombination on Protein Folding Stability

Genetic recombination is a common evolutionary mechanism that produces molecular diversity. However, its consequences on protein folding stability have not attracted the same attention as in the case of point mutations. Here, we studied the effects of homologous recombination on the computationally predicted protein folding stability for several protein families, finding less detrimental effects than we previously expected. Although recombination can affect multiple protein sites, we found that the fraction of recombined proteins that are eliminated by negative selection because of insufficient stability is not significantly larger than the corresponding fraction of proteins produced by mutation events. Indeed, although recombination disrupts epistatic interactions, the mean stability of recombinant proteins is not lower than that of their parents. On the other hand, the difference of stability between recombined proteins is amplified with respect to the parents, promoting phenotypic diversity. As a result, at least one third of recombined proteins present stability between those of their parents, and a substantial fraction have higher or lower stability than those of both parents. As expected, we found that parents with similar sequences tend to produce recombined proteins with stability close to that of the parents. Finally, the simulation of protein evolution along the ancestral recombination graph with empirical substitution models commonly used in phylogenetics, which ignore constraints on protein folding stability, showed that recombination favors the decrease of folding stability, supporting the convenience of adopting structurally constrained models when possible for inferences of protein evolutionary histories with recombination.

Microbial mercury transformations: Molecules, functions and organisms

Mercury (Hg) methylation, methylmercury (MeHg) demethylation, and inorganic redox transformations of Hg are microbe-mediating processes that determine the fate and cycling of Hg and MeHg in many environments, and by doing so influence the health of humans and wild life. The discovery of the Hg methylation genes, hgcAB, in the last decade together with advances in high throughput and genome sequencing methods, have resulted in an expanded appreciation of the diversity of Hg methylating microbes. This review aims to describe experimentally confirmed and recently discovered hgcAB gene-carrying Hg methylating microbes; phylogenetic and taxonomic analyses are presented. In addition, the current knowledge on transformation mechanisms, the organisms that carry them out, and the impact of environmental parameters on Hg methylation, MeHg demethylation, and inorganic Hg reduction and oxidation is summarized. This knowledge provides a foundation for future action toward mitigating the impact of environmental Hg pollution.

SEQUENCE-FREE PHYLOGENETICS WITH MASS SPECTROMETRY

An alternative, more rapid, sequence-free approach to build phylogenetic trees has been conceived and implemented. Molecular phylogenetics has continued to mostly focus on improvement in tree construction based on gene sequence alignments. Here protein-based phylogenies are constructed using numerical data sets ("phylonumerics") representing the masses of peptide segments recorded in a mass mapping experiment. This truly sequence-free method requires no gene sequences, nor their alignment, to build the trees affording a considerable time and cost-saving to conventional phylogenetics methods. The approach also calculates single point amino acid mutations from a comparison of mass pairs from different maps in the data set and displays these at branch nodes across the tree together with their frequency. Studies of the consecutive, and near-consecutive, ancestral and descendant mutations across interconnected branches of a mass tree allow putative adaptive, epistatic, and compensatory mutations to be identified in order to investigate mechanisms associated with evolutionary processes and pathways. A side-by-side comparison of this sequence-free approach and conventional gene sequence phylogenetics is discussed.

ProteinEvolverABC: coestimation of recombination and substitution rates in protein sequences by approximate Bayesian computation

MOTIVATION

The evolutionary processes of mutation and recombination, upon which selection operates, are fundamental to understand the observed molecular diversity. Unlike nucleotide sequences, the estimation of the recombination rate in protein sequences has been little explored, neither implemented in evolutionary frameworks, despite protein sequencing methods are largely used.

RESULTS

In order to accommodate this need, here I present a computational framework, called ProteinEvolverABC, to jointly estimate recombination and substitution rates from alignments of protein sequences. The framework implements the approximate Bayesian computation approach, with and without regression adjustments and includes a variety of substitution models of protein evolution, demographics and longitudinal sampling. It also implements several nuisance parameters such as heterogeneous amino acid frequencies and rate of change among sites and, proportion of invariable sites. The framework produces accurate coestimation of recombination and substitution rates under diverse evolutionary scenarios. As illustrative examples of usage, I applied it to several viral protein families, including coronaviruses, showing heterogeneous substitution and recombination rates.

AVAILABILITY AND IMPLEMENTATION

ProteinEvolverABC is freely available from https://github.com/miguelarenas/proteinevolverabc, includes a graphical user interface for helping the specification of the input settings, extensive documentation and ready-to-use examples. Conveniently, the simulations can run in parallel on multicore machines.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PhylomeDB V5: an expanding repository for genome-wide catalogues of annotated gene phylogenies

PhylomeDB is a unique knowledge base providing public access to minable and browsable catalogues of pre-computed genome-wide collections of annotated sequences, alignments and phylogenies (i.e. phylomes) of homologous genes, as well as to their corresponding phylogeny-based orthology and paralogy relationships. In addition, PhylomeDB trees and alignments can be downloaded for further processing to detect and date gene duplication events, infer past events of inter-species hybridization and horizontal gene transfer, as well as to uncover footprints of selection, introgression, gene conversion, or other relevant evolutionary processes in the genes and organisms of interest. Here, we describe the latest evolution of PhylomeDB (version 5). This new version includes a newly implemented web interface and several new functionalities such as optimized searching procedures, the possibility to create user-defined phylome collections, and a fully redesigned data structure. This release also represents a significant core data expansion, with the database providing access to 534 phylomes, comprising over 8 million trees, and homology relationships for genes in over 6000 species. This makes PhylomeDB the largest and most comprehensive public repository of gene phylogenies. PhylomeDB is available at http://www.phylomedb.org.

Protein phylogenetics with mass spectrometry. A comparison of methods

Advances in protein mass spectrometry have provided the ability to identify and sequence proteins with unprecedented speed, sensitivity and accuracy. These benefits now offer advantages for studies of protein evolution and phylogeny avoiding the need to generate and align DNA sequences which can prove time consuming, costly and difficult in the case of large genomes and for highly diverse organisms. The methods of phylogenetic analysis using protein mass spectrometry can be classified into three categories: (1) de novo protein sequencing followed by multiple sequence alignment for classical phylogenetic reconstruction, (2) direct phylogenetic reconstruction using expressed protein mass profiles exploited in microbial biotyping applications, and (3) the construction of trees using proteolytic peptide mass map or fingerprint data. This review describes the three approaches together with the relevant tools and algorithms required to implement them. It also compares each of these alternative protein based methods alongside conventional gene sequence based phylogenetics.

Functional Evolution of Proteins

The functional evolution of proteins advances through gene duplication followed by functional drift, whereas molecular evolution occurs through random mutational events. Over time, protein active-site structures or functional epitopes remain highly conserved, which enables relationships to be inferred between distant orthologs or paralogs. In this study, we present the first functional clustering and evolutionary analysis of the RCSB Protein Data Bank (RCSB PDB) based on similarities between active-site structures. All of the ligand-bound proteins within the RCSB PDB were scored using our Comparison of Protein Active-site Structures (CPASS) software and database (http://cpass.unl.edu/). Principal component analysis was then used to identify 4431 representative structures to construct a phylogenetic tree based on the CPASS comparative scores (http://itol.embl.de/shared/jcatazaro). The resulting phylogenetic tree identified a sequential, step-wise evolution of protein active-sites and provides novel insights into the emergence of protein function or changes in substrate specificity based on subtle changes in geometry and amino acid composition.

Identification, Structural Characterization and Gene Expression Analysis of Members of the Nuclear Factor-Y Family in Chickpea (Cicer arietinum L.) under Dehydration and Abscisic Acid Treatments

In plants, the Nuclear Factor-Y (NF-Y) transcription factors (TFs), which include three distinct types of NF-YA, NF-YB, and NF-YC TFs, have been identified to play key roles in the regulation of various plant growth and developmental processes under both normal and environmental stress conditions. In this work, a total of 40 CaNF-Y-encoding genes, including eight CaNF-YAs, 21 CaNF-YBs, and 11 CaNF-YCs, were identified in chickpea, and their major gene and protein characteristics were subsequently obtained using various web-based tools. Of our interest, a phylogenetically-based analysis predicted 18 CaNF-Ys (eight CaNF-YAs, seven CaNF-YBs, and three CaNF-YCs) that potentially play roles in chickpea responses to dehydration according to their close relationship with the well-characterized GmNF-Ys in soybean. These results were in good agreement with the enrichment of drought-responsive cis-regulatory motifs and expression patterns obtained from in silico analyses using publically available transcriptome data. Most of the phylogenetically predicted drought-responsive CaNF-Y genes (15 of 18) were quantitatively validated to significantly respond to dehydration treatment in leaves and/or roots, further supporting the results of in silico analyses. Among these CaNF-Y genes, the transcript levels of CaNF-YA01 and CaNF-YC10 were the most highly accumulated in leaves (by approximately eight-fold) and roots (by approximately 18-fold), respectively, by dehydration. Furthermore, 12 of the 18 CaNF-Y genes were found to be responsive to the most well-known stress hormone, namely abscisic acid (ABA), in leaves and/or roots, suggesting that these genes may act in chickpea response to dehydration in ABA-dependent manner. Taken together, our study has provided a comprehensive and fundamental information for further functional analyses of selected CaNF-Y candidate genes, ultimately leading to the improvement of chickpea growth under water-limited conditions.

Proteomic identification and purification of seed proteins from native Amazonian species displaying antifungal activity

Seeds of native species from the rain forest (Amazon) are source of chitinases and their protein extracts exhibited strong and broad antifungal activity. Numerous plant species native to the Amazon have not yet been chemically studied. Studies of seeds are scarcer, since adversities in accessing study areas and seasonality pose constant hurdles to systematic research. In this study, proteins were extracted from seeds belonging to endemic Amazon species and were investigated for the first time. Proteolytic activity, peptidase inhibitors, and chitinases were identified, but chitinolytic activity predominated. Four proteins were purified through chromatography and identified as lectin and chitinases by MS/MS analyses. The proteins were examined for inhibition of a phytopathogen (Fusarium oxysporum). Analyses by fluorescence microscopy suggested binding of propidium iodide to DNA of fungal spores, revealing that spore integrity was lost when accessed by the proteins. Further structural and functional analyses of defensive proteins belonging to species facing highly complex ecosystems such as Amazonia should be conducted, since these could provide new insights into specificity and synergism involving defense proteins of plants submitted to a very complex ecosystem.

Identification of epistatic mutations and insights into the evolution of the influenza virus using a mass-based protein phylogenetic approach

A mass-based protein phylogenetic approach developed in this laboratory has been applied to study mutation trends and identify consecutive or near-consecutive mutations typically associated with positive epistasis. While epistasis is thought to occur commonly during the evolution of viruses, the extent of epistasis in influenza, and its role in the evolution of immune escape and drug resistant mutants, remains to be systematically investigated. Here putative epistatic mutations within H3 hemagglutinin in type A influenza are identified where leading parent mutations were found to predominate within reported antigenic sites of the protein. Frequent subsequent mutations resided exclusively in different antigenic regions, providing the virus with a possible immune escape mechanism, or at other remote sites that drive beneficial protein structural and functional change. The results also enable a "small steps" evolutionary model to be proposed where the more frequent consecutive, or near-consecutive, non-conservative mutations exhibited less structural, and thus functional, change. This favours the evolutionary survival of the virus over mutations associated with more substantive change that may cause or risk its own extinction.

Expansion of biological pathways based on evolutionary inference

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.

Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence

Genetic and biochemical analyses of RNA interference (RNAi) and microRNA (miRNA) pathways have revealed proteins such as Argonaute/PIWI and Dicer that process and present small RNAs to their targets. Well validated small RNA pathway cofactors, such as the Argonaute/PIWI proteins show distinctive patterns of conservation or divergence in particular animal, plant, fungal, and protist species. We compared 86 divergent eukaryotic genome sequences to discern sets of proteins that show similar phylogenetic profiles with known small RNA cofactors. A large set of additional candidate small RNA cofactors have emerged from functional genomic screens for defects in miRNA- or siRNA-mediated repression in C. elegans and D. melanogaster^1,2 and from proteomic analyses of proteins co-purifying with validated small RNA pathway proteins^3,4. The phylogenetic profiles of many of these candidate small RNA pathway proteins are similar to those of known small RNA cofactor proteins. We used a Bayesian approach to integrate the phylogenetic profile analysis with predictions from diverse transcriptional coregulation and proteome interaction datasets to assign a probability for each protein for a role in a small RNA pathway. Testing high-confidence candidates from this analysis for defects in RNAi silencing, we found that about half of the predicted small RNA cofactors are required for RNAi silencing. Many of the newly identified small RNA pathway proteins are orthologues of proteins implicated in RNA splicing. In support of a deep connection between the mechanism of RNA splicing and small RNA-mediated gene silencing, the presence of the Argonaute proteins and other small RNA components in the many species analysed strongly correlates with the number of introns in that species.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Utility of computational methods to identify the apoptosis machinery in unicellular eukaryotes

Apoptosis is the phenotypic result of an active, regulated process of self-destruction. Following various cellular insults, apoptosis has been demonstrated in numerous unicellular eukaryotes, but very little is known about the genes and proteins that initiate and execute this process in this group of organisms. A bioinformatic approach presents an array of powerful methods to direct investigators in the identification of the apoptosis machinery in protozoans. In this review, we discuss some of the available computational methods and illustrate how they may be applied using the identification of a Plasmodium falciparum metacaspase gene as an example.

Computational approaches for the prediction of protein function in the mitochondrion

Understanding a complex biological system, such as the mitochondrion, requires the identification of the complete repertoire of proteins targeted to the organelle, the characterization of these, and finally, the elucidation of the functional and physical interactions that occur within the mitochondrion. In the last decade, significant developments have contributed to increase our understanding of the mitochondrion, and among these, computational research has played a significant role. Not only general bioinformatics tools have been applied in the context of the mitochondrion, but also some computational techniques have been specifically developed to address problems that arose from within the mitochondrial research field. In this review the contribution of bioinformatics to mitochondrial biology is addressed through a survey of current computational methods that can be applied to predict which proteins will be localized to the mitochondrion and to unravel their functional interactions.