Identified Protein Family Research Articles

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method’s applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment.

Fucose as a nutrient ligand for Dikarya and a building block of early diverging lineages

Fucose is a deoxyhexose sugar present and studied in mammals. The process of fucosylation has been the primary focus in studies relating to fucose in animals due to the presence of fucose in Lewis antigens. Very few studies have reported its presence in Fungi, mostly in Mucoromycotina. The constitution of 25% and 12% of this sugar in the carbohydrates of cell wall in the respective Umbelopsis and Mucorales strains boosts the need to bridge the gap of knowledge on fucose metabolism across the fungal tree of life. In the absence of a network map involving fucose proteins, we carried out an in-silico approach to construct the fucose metabolic map in Fungi. We analyzed the taxonomic distribution of 85 protein families in Fungi including diverse early diverging fungal lineages. The expression of fucose-related protein-coding genes proteins was validated with the help of transcriptomic data originating from representatives of early diverging fungi. We found proteins involved in several metabolic activities apart from fucosylation such as synthesis, transport and binding. Most of the identified protein families are shared with Metazoa suggesting an ancestral origin in Opisthokonta. However, the overall complexity of fucose metabolism is greater in Metazoa than in Fungi. Massive gene loss has shaped the evolutionary history of these metabolic pathways, leading to a repeated reduction of these pathways in most yeast-forming lineages. Our results point to a distinctive mode of utilization of fucose among fungi belonging to Dikarya and the early diverging lineages. We speculate that, while Dikarya used fucose as a source of nutrients for metabolism, the early diverging group of fungi depended on fucose as a building block and signaling compound.Graphical abstract

Chasing long-range evolutionary couplings in the AlphaFold era.

Coevolution between protein residues is normally interpreted as direct contact. However, the evolutionary record of a protein sequence contains rich information that may include long-range functional couplings, couplings that report on homo-oligomeric states or even conformational changes. Due to the complexity of the sequence space and the lack of structural information on various members of a protein family, it has been difficult to effectively mine the additional information encoded in a multiple sequence alignment (MSA). Here, taking advantage of the recent release of the AlphaFold (AF) database we attempt to identify coevolutionary couplings that cannot be explained simply by spatial proximity. We propose a simple computational method that performs direct coupling analysis on a MSA and searches for couplings that are not satisfied in any of the AF models of members of the identified protein family. Application of this method on 2012 protein families suggests that ~12% of the total identified coevolving residue pairs are spatially distant and more likely to be disordered than their contacting counterparts. We expect that this analysis will help improve the quality of coevolutionary distance restraints used for structure determination and will be useful in identifying potentially functional/allosteric cross-talk between distant residues.

Integrated Omic Approaches Reveal Molecular Mechanisms of Tolerance during Soybean and Meloidogyne incognita Interactions.

The root-knot nematode (RKN), Meloidogyne incognita, is a devastating soybean pathogen worldwide. The use of resistant cultivars is the most effective method to prevent economic losses caused by RKNs. To elucidate the mechanisms involved in resistance to RKN, we determined the proteome and transcriptome profiles from roots of susceptible (BRS133) and highly tolerant (PI 595099) Glycine max genotypes 4, 12, and 30 days after RKN infestation. After in silico analysis, we described major defense molecules and mechanisms considered constitutive responses to nematode infestation, such as mTOR, PI3K-Akt, relaxin, and thermogenesis. The integrated data allowed us to identify protein families and metabolic pathways exclusively regulated in tolerant soybean genotypes. Among them, we highlighted the phenylpropanoid pathway as an early, robust, and systemic defense process capable of controlling M. incognita reproduction. Associated with this metabolic pathway, 29 differentially expressed genes encoding 11 different enzymes were identified, mainly from the flavonoid and derivative pathways. Based on differential expression in transcriptomic and proteomic data, as well as in the expression profile by RT–qPCR, and previous studies, we selected and overexpressed the GmPR10 gene in transgenic tobacco to assess its protective effect against M. incognita. Transgenic plants of the T2 generation showed up to 58% reduction in the M. incognita reproduction factor. Finally, data suggest that GmPR10 overexpression can be effective against the plant parasitic nematode M. incognita, but its mechanism of action remains unclear. These findings will help develop new engineered soybean genotypes with higher performance in response to RKN infections.

Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between ‘effector’ molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

Effects of TDP-43 overexpression on neuron proteome and morphology in vitro

TDP-43 is pathologically and genetically with associated amyotrophic lateral sclerosis and frontotemporal lobar degeneration. These diseases are characterized by significant neurite defects, including cytoskeletal pathology. The involvement of TDP-43 in the degeneration of neurons in these diseases are not yet well understood, however accumulating evidence shows involvement in neurite outgrowth, remodelling and in regulation of many components of the neuronal cytoskeleton. In order to investigate how alterations to TDP-43 expression levels may exert effects on the neuronal cytoskeleton, primary cortical neurons from transgenic mice overexpressing one or two copies of human wildtype TDP-43 under the prion promoter were examined. Label-free quantitative proteomic analysis, followed by functional annotation clustering to identify protein families that clustered together within up- or down-regulated protein groups, revealed that actin-binding proteins were significantly more abundant in neurons overexpressing TDP-43 compared to wildtype neurons. Morphological analysis demonstrated that during early development neurons expressing one copy of human TDP-43 had an increased number of neurite branches and alterations to growth cone morphology, while no changes were observed in neurons expressing two copies of TDP-43. These developmental processes require specific expression and organization of the cytoskeleton. The results from these studies provide further insight into the normal function of TDP-43 and how alterations in TDP-43 expression may impact the cytoskeleton.

A Distinct Motif in a Prokaryotic Small Ras-Like GTPase Highlights Unifying Features of Walker B Motifs in P-Loop NTPases

A hallmark of the catalytically essential Walker B motif of P-loop NTPases is the presence of an acidic residue (aspartate/glutamate) for efficient Mg2+ coordination. Although the Walker B motif has been identified in well-studied examples of P-loop NTPases, its identity is ambiguous in many families, for example, in the prokaryotic small Ras-like GTPase family of MglA. MglA, belonging to TRAFAC class of P-loop NTPases, possesses a threonine at the position equivalent to Walker B aspartate in eukaryotic Ras-like GTPases. To resolve the identity of the Walker B residue in MglA, we carried out a comprehensive analysis of Mg2+ coordination on P-loop NTPase structures. Atoms in the octahedral coordination of Mg2+ and their interactions comprise a network including water molecules, Walker A, Walker B and switch motifs of P-loop NTPases. Based on the conserved geometry of Mg2+ coordination, we confirm that a conserved aspartate functions as the Walker B residue of MglA, and validate it through mutagenesis and biochemical characterization. Location of the newly identified aspartate is spatially equivalent to the Walker B residue of the ASCE division of P-loop NTPases. Furthermore, similar to the allosteric regulation of the Walker B aspartate conformation in MglA, we identify protein families in which large conformational changes involving Walker B motif potentially function as allosteric regulators. The study unravels conserved features of Mg2+ coordination among divergent families of P-loop NTPases, especially between ancient Ras-like GTPases and ASCE family of ATPases. The conserved geometric features provide a foundation for design of nucleotide-hydrolyzing enzymes.

Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes.

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.

The distinction of CPR bacteria from other bacteria based on protein family content

Candidate phyla radiation (CPR) bacteria separate phylogenetically from other bacteria, but the organismal distribution of their protein families remains unclear. Here, we leveraged sequences from thousands of uncultivated organisms and identified protein families that co-occur in genomes, thus are likely foundational for lineage capacities. Protein family presence/absence patterns cluster CPR bacteria together, and away from all other bacteria and archaea, partly due to proteins without recognizable homology to proteins in other bacteria. Some are likely involved in cell-cell interactions and potentially important for episymbiotic lifestyles. The diversity of protein family combinations in CPR may exceed that of all other bacteria. Over the bacterial tree, protein family presence/absence patterns broadly recapitulate phylogenetic structure, suggesting persistence of core sets of proteins since lineage divergence. The CPR could have arisen in an episode of dramatic but heterogeneous genome reduction or from a protogenote community and co-evolved with other bacteria.

A robust method for the estimation and visualization of IgE cross-reactivity likelihood between allergens belonging to the same protein family

Among the vast number of identified protein families, allergens emanate from relatively few families which translates to only a small fraction of identified protein families. In allergy diagnostics and immunotherapy, interactions between immunoglobulin E and allergens are crucial because the formation of an allergen-antibody complex is necessary for triggering an allergic reaction. In allergic diseases, there is a phenomenon known as cross-reactivity. Cross-reactivity describes a situation where an individual has produced antibodies against a particular allergenic protein, but said antibodies fail to discriminate between the original sensitizer and other similar proteins that usually belong to the same family. To expound the concept of cross-reactivity, this study examines ten protein families that include allergens selected specifically for the analysis of cross-reactivity. The selected allergen families had at least 13 representative proteins, overall folds that differ significantly between families, and include relevant allergens with various potencies. The selected allergens were analyzed using information on sequence similarities and identities between members of the families as well as reports on clinically relevant cross-reactivities. Based on our analysis, we propose to introduce a new A-RISC index (Allergens’–Relative Identity, Similarity and Cross-reactivity) which describes homology between two allergens belonging to the same protein family and is used to predict the likelihood of cross-reactivity between them. Information on sequence similarities and identities, as well as on the values of the proposed A-RISC index is used to introduce four categories describing a risk of a cross-reactive reaction, namely: high, medium-high, medium-low and low. The proposed approach can facilitate analysis in component-resolved allergy diagnostics, generation of avoidance guidelines for allergic individuals, and help with the design of immunotherapy.

Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

MotivationBiological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.ResultsWe propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein–protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.Availability and implementation https://github.com/bio-ontology-research-group/onto2vec Supplementary information Supplementary data are available at Bioinformatics online.

Tea plant SWEET transporters: expression profiling, sugar transport, and the involvement of CsSWEET16 in modifying cold tolerance in Arabidopsis.

Thirteen SWEET transporters were identified in Camellia sinensis and the cold-suppression gene CsSWEET16 contributed to sugar compartmentation across the vacuole and function in modifying cold tolerance in Arabidopsis. The sugars will eventually be exported transporters (SWEET) family of sugar transporters in plants is a recently identified protein family of sugar uniporters that contain seven transmembrane helices harbouring two MtN3 motifs. SWEETs play important roles in various biological processes, including plant responses to environmental stimuli. In this study, 13 SWEET transporters were identified in Camellia sinensis and were divided into four clades. Transcript abundances of CsSWEET genes were detected in various tissues. CsSWEET1a/1b/2a/2b/2c/3/9b/16/17 were expressed in all of the selected tissues, whereas the expression of CsSWEET5/7/9a/15 was not detected in some tissues, including those of mature leaves. Expression analysis of nine CsSWEET genes in leaves in response to abiotic stresses, natural cold acclimation and Colletotrichum camelliae infection revealed that eight CsSWEET genes responded to abiotic stress, while CsSWEET3 responded to C. camelliae infection. Functional analysis of 13 CsSWEET activities in yeast revealed that CsSWEET1a/1b/7/17 exhibit transport activity for glucose analogues and other types of hexose molecules. Further characterization of the cold-suppression gene CsSWEET16 revealed that this gene is localized in the vacuolar membrane. CsSWEET16 contributed to sugar compartmentation across the vacuole and function in modifying cold tolerance in Arabidopsis. Together, these findings demonstrate that CsSWEET genes play important roles in the response to abiotic and biotic stresses in tea plants and provide insights into the characteristics of SWEET genes in tea plants, which could serve as the basis for further functional identification of such genes.

An ancestral TMEM16 homolog from Dictyostelium discoideum forms a scramblase.

TMEM16 proteins are a recently identified protein family comprising Ca2+-activated Cl- channels that generate outwardly rectifying ionic currents in response to intracellular Ca2+ elevations. Some TMEM16 family members, such as TMEM16F/ANO6 are also essential for Ca2+-dependent phospholipid scrambling. TMEM16-like genes are present in the genomes of most eukaryotic species, the function(s) of TMEM16 family members from evolutionary ancient eukaryotes is not completely clear. Here, we provide insight into the evolution of these TMEM16 proteins by similarity searches for ancestral sequences. All eukaryotic genomes contain TMEM16 homologs, but only vertebrates have the full repertoire of ten distinct subtypes. TMEM16 homologs studied so far belong to the opisthokont branch of the phylogenetic tree, which includes the animal and fungal kingdoms. An organism outside this group is Dictyostelium discoideum, a representative of the amoebozoa group that diverged from the metazoa before fungi. We here functionally investigated the TMEM16 family member from Dictyostelium discoideum. When recombinantly expressed in HEK293 cells, DdTMEM16 induces phospholipid scrambling. However, in several electrophysiological experiments we did not find evidence for a Ca2+-activated Cl- channel function of DdTMEM16.

In Planta Determination of the mRNA-Binding Proteome of Arabidopsis Etiolated Seedlings.

RNA binding proteins (RBPs) control the fate and expression of a transcriptome. Despite this fundamental importance, our understanding of plant RBPs is rudimentary, being mainly derived via bioinformatic extrapolation from other kingdoms. Here, we adapted the mRNA-protein interactome capture method to investigate the RNA binding proteome in planta. From Arabidopsis thaliana etiolated seedlings, we captured more than 700 proteins, including 300 with high confidence that we have defined as the At-RBP set. Approximately 75% of these At-RBPs are bioinformatically linked with RNA biology, containing a diversity of canonical RNA binding domains (RBDs). As no prior experimental RNA binding evidence exists for the majority of these proteins, their capture now authenticates them as RBPs. Moreover, we identified protein families harboring emerging and potentially novel RBDs, including WHIRLY, LIM, ALBA, DUF1296, and YTH domain-containing proteins, the latter being homologous to animal RNA methylation readers. Other At-RBP set proteins include major signaling proteins, cytoskeleton-associated proteins, membrane transporters, and enzymes, suggesting the scope and function of RNA-protein interactions within a plant cell is much broader than previously appreciated. Therefore, our foundation data set has provided an unbiased insight into the RNA binding proteome of plants, on which future investigations into plant RBPs can be based.

Recombinant expression, purification, and crystallization of the sterile α-motif/histidine-aspartate domain-containing protein from chicken

The sterile α-motif and HD domain containing protein 1 (SAMHD1) family is a newly identified protein family, involved in innate immunity restriction. This family possesses a broad-spectrum of antiviral activity. The SAMHD1 family in chicken has not been clearly documented. Here, we expressed chicken SAMHD1 (101-614) fused with a SUMO tag in an Escherichia coli (E. coli) system. For the first time, chicken SAMHD1 (101-614) was found to possess dNTPase cleavage activities in vitro. This suggests that chicken SAMHD1 may be a potential antiviral factor against avian viruses. Through a unique purification method, the purity of the protein as estimated by SDS-PAGE was >95% after a double Ni affinity chromatography and gel filtration purification. Using a sitting-drop vapor-diffusion method, protein crystals were obtained. This study provides some essential method and information for further structure and function determinations of chicken SAMHD1.

A combined bioinformatics and functional metagenomics approach to discovering lipolytic biocatalysts.

The majority of protein sequence data published today is of metagenomic origin. However, our ability to assign functions to these sequences is often hampered by our general inability to cultivate the larger part of microbial species and the sheer amount of sequence data generated in these projects. Here we present a combination of bioinformatics, synthetic biology, and Escherichia coli genetics to discover biocatalysts in metagenomic datasets. We created a subset of the Global Ocean Sampling dataset, the largest metagenomic project published to date, by removing all proteins that matched Hidden Markov Models of known protein families from PFAM and TIGRFAM with high confidence (E-value > 10-5). This essentially left us with proteins with low or no homology to known protein families, still encompassing ~1.7 million different sequences. In this subset, we then identified protein families de novo with a Markov clustering algorithm. For each protein family, we defined a single representative based on its phylogenetic relationship to all other members in that family. This reduced the dataset to ~17,000 representatives of protein families with more than 10 members. Based on conserved regions typical for lipases and esterases, we selected a representative gene from a family of 27 members for synthesis. This protein, when expressed in E. coli, showed lipolytic activity toward para-nitrophenyl (pNP) esters. The Km-value of the enzyme was 66.68 μM for pNP-butyrate and 68.08 μM for pNP-palmitate with kcat/Km values at 3.4 × 106 and 6.6 × 105 M-1s-1, respectively. Hydrolysis of model substrates showed enantiopreference for the R-form. Reactions yielded 43 and 61% enantiomeric excess of products with ibuprofen methyl ester and 2-phenylpropanoic acid ethyl ester, respectively. The enzyme retains 50% of its maximum activity at temperatures as low as 10°C, its activity is enhanced in artificial seawater and buffers with higher salt concentrations with an optimum osmolarity of 3,890 mosmol/l.

Large-Scale Evolutionary Analysis of Genes and Supergene Clusters from Terpenoid Modular Pathways Provides Insights into Metabolic Diversification in Flowering Plants.

An important component of plant evolution is the plethora of pathways producing more than 200,000 biochemically diverse specialized metabolites with pharmacological, nutritional and ecological significance. To unravel dynamics underlying metabolic diversification, it is critical to determine lineage-specific gene family expansion in a phylogenomics framework. However, robust functional annotation is often only available for core enzymes catalyzing committed reaction steps within few model systems. In a genome informatics approach, we extracted information from early-draft gene-space assemblies and non-redundant transcriptomes to identify protein families involved in isoprenoid biosynthesis. Isoprenoids comprise terpenoids with various roles in plant-environment interaction, such as pollinator attraction or pathogen defense. Combining lines of evidence provided by synteny, sequence homology and Hidden-Markov-Modelling, we screened 17 genomes including 12 major crops and found evidence for 1,904 proteins associated with terpenoid biosynthesis. Our terpenoid genes set contains evidence for 840 core terpene-synthases and 338 triterpene-specific synthases. We further identified 190 prenyltransferases, 39 isopentenyl-diphosphate isomerases as well as 278 and 219 proteins involved in mevalonate and methylerithrol pathways, respectively. Assessing the impact of gene and genome duplication to lineage-specific terpenoid pathway expansion, we illustrated key events underlying terpenoid metabolic diversification within 250 million years of flowering plant radiation. By quantifying Angiosperm-wide versatility and phylogenetic relationships of pleiotropic gene families in terpenoid modular pathways, our analysis offers significant insight into evolutionary dynamics underlying diversification of plant secondary metabolism. Furthermore, our data provide a blueprint for future efforts to identify and more rapidly clone terpenoid biosynthetic genes from any plant species.

The SWEET family of sugar transporters in grapevine: VvSWEET4 is involved in the interaction with Botrytis cinerea.

During plant development, sugar export is determinant in multiple processes such as nectar production, pollen development and long-distance sucrose transport. The plant SWEET family of sugar transporters is a recently identified protein family of sugar uniporters. In rice, SWEET transporters are the target of extracellular bacteria, which have evolved sophisticated mechanisms to modify their expression and acquire sugars to sustain their growth. Here we report the characterization of the SWEET family of sugar transporters in Vitis vinifera. We identified 17 SWEET genes in the V. vinifera 40024 genome and show that they are differentially expressed in vegetative and reproductive organs. Inoculation with the biotrophic pathogens Erysiphe necator and Plasmopara viticola did not result in significant induction of VvSWEET gene expression. However, infection with the necrotroph Botrytis cinerea triggered a strong up-regulation of VvSWEET4 expression. Further characterization of VvSWEET4 revealed that it is a glucose transporter localized in the plasma membrane that is up-regulated by inducers of reactive oxygen species and virulence factors from necrotizing pathogens. Finally, Arabidopsis knockout mutants in the orthologous AtSWEET4 were found to be less susceptible to B. cinerea. We propose that stimulation of expression of a developmentally regulated glucose uniporter by reactive oxygen species production and extensive cell death after necrotrophic fungal infection could facilitate sugar acquisition from plant cells by the pathogen.

Comparative genomic analysis between typhoidal and non-typhoidal Salmonella serovars reveals typhoid-specific protein families

BackgroundThe genus Salmonella contains more than 2600 serovars. While most cause a self-limiting gastroenteritis, four serovars, S. Typhi, S. Paratyphi A, B and C, elicit typhoid, a potentially fatal systemic infection. Because of the prevalence in certain regions, such as South Asia, and the disease severity of typhoidal Salmonella infections, comprehensive studies are needed to elucidate the pathogenesis of diseases caused by these typhoidal serovars. ResultsWe performed comparative genomic analyses on eight human typhoidal strains and 27 non-human typhoidal Salmonella strains to elucidate their evolutionary relationships and identify the genes specific to the four typhoidal serovars. Our results indicate that Salmonella may have an open pan-genome. A core-genome based phylogeny demonstrated that divergence between S. Paratyphi A and S. Typhi took place not long ago and S. Paratyphi B shared a recent common ancestor with S. Paratyphi C. Of great interest, the divergence between S. Paratyphi B and S. Paratyphi C was shown to be more recent than that between S. Paratyphi A and S. Typhi. Alignment and comparisons of the genomes identified unique complements of protein families to each of the typhoidal serovars. Most of these protein families are phage related and some are candidate virulence factors. Importantly, we found 88 protein families specific to two to three of the four typhoidal serovars. All but two of the 88 genes are present in S. Typhi, with a few in the three paratyphoidal serovars but none in the non-human typhoidal serovars. Most of these genes are predicted to encode hypothetical proteins and some are known to code for virulence factors such as Vi polysaccharide related proteins. ConclusionsBy comprehensive genomic comparisons, we identified protein families specific to the human typhoidal serovars, which will greatly facilitate investigations on typhoid pathogenesis.

Key components of the eight classes of type IV secretion systems involved in bacterial conjugation or protein secretion

Conjugation of DNA through a type IV secretion system (T4SS) drives horizontal gene transfer. Yet little is known on the diversity of these nanomachines. We previously found that T4SS can be divided in eight classes based on the phylogeny of the only ubiquitous protein of T4SS (VirB4). Here, we use an ab initio approach to identify protein families systematically and specifically associated with VirB4 in each class. We built profiles for these proteins and used them to scan 2262 genomes for the presence of T4SS. Our analysis led to the identification of thousands of occurrences of 116 protein families for a total of 1623 T4SS. Importantly, we could identify almost always in our profiles the essential genes of well-studied T4SS. This allowed us to build a database with the largest number of T4SS described to date. Using profile–profile alignments, we reveal many new cases of homology between components of distant classes of T4SS. We mapped these similarities on the T4SS phylogenetic tree and thus obtained the patterns of acquisition and loss of these protein families in the history of T4SS. The identification of the key VirB4-associated proteins paves the way toward experimental analysis of poorly characterized T4SS classes.