Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease‐associated non‐synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site‐specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non‐interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease‐associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome‐wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease. Proteins 2015; 83:428–435. © 2014 Wiley Periodicals, Inc.     

Human germline and pan-cancer variomes and their distinct functional profiles

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.     

Single nucleotide variations: Biological impact and theoretical interpretation

Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.     

A simple method for analyzing exome sequencing data shows distinct levels of nonsynonymous variation for human immune and nervous system genes

To measure the strength of natural selection that acts upon single nucleotide variants (SNVs) in a set of human genes, we calculate the ratio between nonsynonymous SNVs (nsSNVs) per nonsynonymous site and synonymous SNVs (sSNVs) per synonymous site. We transform this ratio with a respective factor f that corrects for the bias of synonymous sites towards transitions in the genetic code and different mutation rates for transitions and transversions. This method approximates the relative density of nsSNVs (rdnsv) in comparison with the neutral expectation as inferred from the density of sSNVs. Using SNVs from a diploid genome and 200 exomes, we apply our method to immune system genes (ISGs), nervous system genes (NSGs), randomly sampled genes (RSGs), and gene ontology annotated genes. The estimate of rdnsv in an individual exome is around 20% for NSGs and 30-40% for ISGs and RSGs. This smaller rdnsv of NSGs indicates overall stronger purifying selection. To quantify the relative shift of nsSNVs towards rare variants, we next fit a linear regression model to the estimates of rdnsv over different SNV allele frequency bins. The obtained regression models show a negative slope for NSGs, ISGs and RSGs, supporting an influence of purifying selection on the frequency spectrum of segregating nsSNVs. The y-intercept of the model predicts rdnsv for an allele frequency close to 0. This parameter can be interpreted as the proportion of nonsynonymous sites where mutations are tolerated to segregate with an allele frequency notably greater than 0 in the population, given the performed normalization of the observed nsSNV to sSNV ratio. A smaller y-intercept is displayed by NSGs, indicating more nonsynonymous sites under strong negative selection. This predicts more monogenically inherited or de-novo mutation diseases that affect the nervous system.     

Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.     

SNVDis: A Proteome-wide Analysis Service for Evaluating nsSNVs in Protein Functional Sites and Pathways

Amino acid changes due to non-synonymous variation are included as annotations for individual proteins in UniProtKB/Swiss-Prot and RefSeq which present biological data in a protein-or gene-centric fashion. Unfortunately, proteome-wide analysis of non-synonymous singlenucleotide variations (nsSNVs) is not easy to perform because information on nsSNVs and functionally important sites are not well integrated both within and between databases and their search engines. We have developed SNVDis that allows evaluation of proteome-wide nsSNV distribution in functional sites, domains and pathways. More specifically, we have integrated human-specific data from major variation databases (UniProtKB, dbSNP and COSMIC), comprehensive sequence feature annotation from UniProtKB, Pfam, RefSeq, Conserved Domain Database (CDD) and pathway information from Protein ANalysis THrough Evolutionary Relationships (PANTHER) and mapped all of them in a uniform and comprehensive way to the human reference proteome provided by UniProtKB/Swiss-Prot. Integrated information of active sites, pathways, binding sites, domains, which are extracted from a number of different sources, provides a detailed overview of how nsSNVs are distributed over the human proteome and pathways and how they intersect with functional sites of proteins. Additionally, it is possible to find out whether there is an over-or under-representation of nsSNVs in specific domains, pathways or user-defined protein lists. The underlying datasets are updated once every 3 months. SNVDis is freely available at http://hive.biochemistry.gwu.edu/tool/snvdis.     

Proteome-wide analysis of single-nucleotide variations in the N-glycosylation sequon of human genes

N-linked glycosylation is one of the most frequent post-translational modifications of proteins with a profound impact on their biological function. Besides other functions, N-linked glycosylation assists in protein folding, determines protein orientation at the cell surface, or protects proteins from proteases. The N-linked glycans attach to asparagines in the sequence context Asn-X-Ser/Thr, where X is any amino acid except proline. Any variation (e.g. non-synonymous single nucleotide polymorphism or mutation) that abolishes the N-glycosylation sequence motif will lead to the loss of a glycosylation site. On the other hand, variations causing a substitution that creates a new N-glycosylation sequence motif can result in the gain of glycosylation. Although the general importance of glycosylation is well known and acknowledged, the effect of variation on the actual glycoproteome of an organism is still mostly unknown. In this study, we focus on a comprehensive analysis of non-synonymous single nucleotide variations (nsSNV) that lead to either loss or gain of the N-glycosylation motif. We find that 1091 proteins have modified N-glycosylation sequons due to nsSNVs in the genome. Based on analysis of proteins that have a solved 3D structure at the site of variation, we find that 48% of the variations that lead to changes in glycosylation sites occur at the loop and bend regions of the proteins. Pathway and function enrichment analysis show that a significant number of proteins that gained or lost the glycosylation motif are involved in kinase activity, immune response, and blood coagulation. A structure-function analysis of a blood coagulation protein, antithrombin III and a protease, cathepsin D, showcases how a comprehensive study followed by structural analysis can help better understand the functional impact of the nsSNVs.     

The pathogen-host interactions database (PHI-base) provides insights into generic and novel themes of pathogenicity

Fungal and oomycete pathogens of plants and animals are a major global problem. In the last 15 years, many genes required for pathogenesis have been determined for over 50 different species. Other studies have characterized effector genes (previously termed avirulence genes) required to activate host responses. By studying these types of pathogen genes, novel targets for control can be revealed. In this report, we describe the Pathogen-Host Interactions database (PHI-base), which systematically compiles such pathogenicity genes involved in pathogen-host interactions. Here, we focus on the biology that underlies this computational resource: the nature of pathogen-host interactions, the experimental methods that exist for the characterization of such pathogen-host interactions as well as the available computational resources. Based on the data, we review and analyze the specific functions of pathogenicity genes, the host-specific nature of pathogenicity and virulence genes, and the generic mechanisms of effectors that trigger plant responses. We further discuss the utilization of PHI-base for the computational identification of pathogenicity genes through comparative genomics. In this context, the importance of standardizing pathogenicity assays as well as integrating databases to aid comparative genomics is discussed.     

水稻条斑病细菌hrp基因诱导表达系统的建立

水稻条斑病细菌(Xanthomonas oryzae pv.oryzicola,Xooc)决定在非寄主植物上激发过敏反应(hypersensitive response)和在寄主水稻上具致病性(pathogenicity)的hrp基因簇是诱导表达的。为研究hrp基因的功能,利用hpa1和hrpX基因的启动子与gfp基因进行融合,构建了hrp基因诱导表达系统。绿色荧光蛋白表达揭示,Xooc的hrp基因在营养丰富的NB培养基上不能有效表达,在hrp诱导培养基XOM3上可有效表达。以hrpX和hrpG突变体为参照,RT-PCR研究结果提示,Xooc野生型菌株hpa1基因在NB上不能有效表达,在XOM3培养基上可有效表达。相应地,hrpX突变体中hpa1基因不能被诱导表达,而在hrpG突变体中hpa1基因转录表达水平低于野生菌。研究结果还证实,水稻悬浮细胞能高效诱导Xooc的hrp基因表达。Xooc hrp基因诱导表达系统的建立为研究hrp基因功能、发掘T3SS效应分子以及开展Xooc致病性研究奠定了基础。     

A domain-centric analysis of oomycete plant pathogen genomes reveals unique protein organization

Oomycetes comprise a diverse group of organisms that morphologically resemble fungi but belong to the stramenopile lineage within the supergroup of chromalveolates. Recent studies have shown that plant pathogenic oomycetes have expanded gene families that are possibly linked to their pathogenic lifestyle. We analyzed the protein domain organization of 67 eukaryotic species including four oomycete and five fungal plant pathogens. We detected 246 expanded domains in fungal and oomycete plant pathogens. The analysis of genes differentially expressed during infection revealed a significant enrichment of genes encoding expanded domains as well as signal peptides linking a substantial part of these genes to pathogenicity. Overrepresentation and clustering of domain abundance profiles revealed domains that might have important roles in host-pathogen interactions but, as yet, have not been linked to pathogenicity. The number of distinct domain combinations (bigrams) in oomycetes was significantly higher than in fungi. We identified 773 oomycete-specific bigrams, with the majority composed of domains common to eukaryotes. The analyses enabled us to link domain content to biological processes such as host-pathogen interaction, nutrient uptake, or suppression and elicitation of plant immune responses. Taken together, this study represents a comprehensive overview of the domain repertoire of fungal and oomycete plant pathogens and points to novel features like domain expansion and species-specific bigram types that could, at least partially, explain why oomycetes are such remarkable plant pathogens.     

Replication and pathogenicity of human immunodeficiency virus type 1 accessory gene mutants in SCID-hu mice.

The functional roles of the human immunodeficiency virus type 1 (HIV-1) accessory genes (nef, vpr, vpu, and vif) are as yet unclear. Using the SCID-hu model system, we have examined the infectivity, replicative capacity, and pathogenicity of strains of the molecular clone HIV-1NL4-3 that contain deletion mutations in these individual accessory genes. We determined that deletion of these genes had differential effects on both infectivity and pathogenicity. Deletion of vpr had little or no effect on viral infectivity, replication, and pathogenicity; however, deletion of vpu or vif had a significant effect on infectivity and moderate effects on pathogenicity. nef-minus strains were the most attenuated in this system, demonstrating significantly lower levels of infectivity and pathogenicity. However, deletion of these individual genes attenuated but did not abrogate the pathogenic properties of HIV-1. Mutant viruses still retained the ability to induce thymocyte depletion to various degrees if implants were infected with higher doses of virus or observed for longer periods of time. The relative contributions of these genes to in vivo pathogenic potential should be taken into consideration when one is contemplating a live attenuated vaccine for HIV-1.     

Restriction enzyme-mediated integration used to produce pathogenicity mutants of Colletotrichum graminicola

We have developed a restriction enzyme-mediated insertional mutagenesis (REMI) system for the maize pathogen Colletotrichum graminicola. In this report, we demonstrate the utility of a REMI-based mutagenesis approach to identify novel pathogenicity genes. Use of REMI increased transformation efficiency by as much as 27-fold over transformations with linearized plasmid alone. Ninety-nine transformants were examined by Southern analysis, and 51% contained simple integrations consisting of one copy of the vector integrated at a single site in the genome. All appeared to have a plasmid integration at a unique site. Sequencing across the integration sites of six transformants demonstrated that in all cases the plasmid integration occurred at the corresponding restriction enzyme-recognition site. We used an in vitro bioassay to identify two pathogenicity mutants among 660 transformants. Genomic DNA flanking the plasmid integration sites was used to identify corresponding cosmids in a wild-type genomic library. The pathogenicity of one of the mutants was restored when it was transformed with the cosmids.     

Development of a genomic site for gene integration and expression in Enterococcus faecalis

Enterococcus faecalis, a gram-positive opportunistic pathogen, has become one of the leading causes of nosocomial infections. Normally a resident of the gastrointestinal tract, extensive use of antibiotics has resulted in the rise of E. faecalis strains that are resistant to multiple antibiotics. This, compounded with the ability to easily exchange antibiotic determinants with other bacteria, has made certain E. faecalis infections difficult to treat medically. The genetic toolbox for the study of E. faecalis has expanded greatly in recent years, but has lacked methodology to stably introduce a gene in single copy in a non-disruptive manner for complementation or expression of non-native genes. In this study, we identified a specific site in the genome of E. faecalis OG1RF that can serve as an expression site for a gene of interest. This site is well conserved in most of the sequenced E. faecalis genomes. A vector has also been developed to integrate genes into this site by allelic exchange. Using this system, we complemented an in-frame deletion in eutV, demonstrating that the mutation does not cause polar effects. We also generated an E. faecalis OG1RF strain that stably expresses the green fluorescent protein and is comparable to the parent strain in terms of in vitro growth and pathogenicity in C. elegans and mice. Another major advantage of this new methodology is the ability to express integrated genes without the need for maintaining antibiotic selection, making this an ideal tool for functional studies of genes in infection models and co-culture systems.     

Agrobacterium-Mediated Transformation and Insertional Mutagenesis in <Emphasis Type="Italic">Colletotrichum acutatum</Emphasis> for Investigating Varied Pathogenicity Lifestyles

Colletotrichum acutatum is a cosmopolitan pathogen causing economically important diseases known as anthracnose on a wide range of hosts. This fungus exhibits varied pathogenicity lifestyles and the tools essential to understand the molecular mechanisms are still being developed. The transformation methods currently available for this species for gene discovery and functional analysis involve protoplast transformation and are laborious and inefficient. We have developed a protocol for efficient Agrobacterium tumefaciens-mediated transformation (ATMT) of C. acutatum. Using this protocol we were able to transform C. acutatum isolates belonging to different genetic groups and originating from different hosts. The transformation efficiency was up to 156 transformants per 10⁴ conidia, with >70% transformants showing single location/single copy integration of T-DNA. Binary vector pBHt2-GFP was constructed, enabling green fluorescence protein tagging of C. acutatum strains, which will be a useful tool for epidemiology and histopathology studies. The ATMT protocol developed was used to identify putative pathogenicity mutants, suggesting the applicability of this technique for rapid generation of a large panel of insertional mutants of C. acutatum leading to the identification of the genes associated with the varied lifestyles.     

Hydrophobic interaction chromatography in dual salt system increases protein binding capacity

Hydrophobic interaction chromatography (HIC) uses weakly hydrophobic resins and requires a salting‐out salt to promote protein–resin interaction. The salting‐out effects increase with protein and salt concentration. Dynamic binding capacity (DBC) is dependent on the binding constant, as well as on the flow characteristics during sample loading. DBC increases with the salt concentration but decreases with increasing flow rate. Dynamic and operational binding capacity have a major raw material cost/processing time impact on commercial scale production of monoclonal antibodies. In order to maximize DBC the highest salt concentration without causing precipitation is used. We report here a novel method to maintain protein solubility while increasing the DBC by using a combination of two salting‐out salts (referred to as dual salt). In a series of experiments, we explored the dynamic capacity of a HIC resin (TosoBioscience Butyl 650M) with combinations of salts. Using a model antibody, we developed a system allowing us to increase the dynamic capacity up to twofold using the dual salt system over traditional, single salt system. We also investigated the application of this novel approach to several other proteins and salt combinations, and noted a similar protein solubility and DBC increase. The observed increase in DBC in the dual salt system was maintained at different linear flow rates and did not impact selectivity. Biotechnol. Bioeng. 2009;103: 930–935. © 2009 Wiley Periodicals, Inc.     

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.     

Fungal gene expression during pathogenesis-related development and host plant colonization

To successfully infect plants, pathogenic fungi must recognize and communicate with their host during different stages of the disease cycle. In past years, techniques such as insertional mutagenesis, sensitive GFP-based reporter systems and microarray techniques have been developed to analyze these processes at the molecular level, and now novel insights into this fascinating aspect of pathogen-plant communication are beginning to emerge. This is exemplified by a number of pathogenicity genes functioning in distinct stages of pathogenic development in Magnaporthe grisea.