Mutations in DDX58, which encodes RIG-I, cause atypical Singleton-Merten syndrome.

Singleton-Merten syndrome (SMS) is an autosomal-dominant multi-system disorder characterized by dental dysplasia, aortic calcification, skeletal abnormalities, glaucoma, psoriasis, and other conditions. Despite an apparent autosomal-dominant pattern of inheritance, the genetic background of SMS and information about its phenotypic heterogeneity remain unknown. Recently, we found a family affected by glaucoma, aortic calcification, and skeletal abnormalities. Unlike subjects with classic SMS, affected individuals showed normal dentition, suggesting atypical SMS. To identify genetic causes of the disease, we performed exome sequencing in this family and identified a variant (c.1118A>C [p.Glu373Ala]) of DDX58, whose protein product is also known as RIG-I. Further analysis of DDX58 in 100 individuals with congenital glaucoma identified another variant (c.803G>T [p.Cys268Phe]) in a family who harbored neither dental anomalies nor aortic calcification but who suffered from glaucoma and skeletal abnormalities. Cys268 and Glu373 residues of DDX58 belong to ATP-binding motifs I and II, respectively, and these residues are predicted to be located closer to the ADP and RNA molecules than other nonpathogenic missense variants by protein structure analysis. Functional assays revealed that DDX58 alterations confer constitutive activation and thus lead to increased interferon (IFN) activity and IFN-stimulated gene expression. In addition, when we transduced primary human trabecular meshwork cells with c.803G>T (p.Cys268Phe) and c.1118A>C (p.Glu373Ala) mutants, cytopathic effects and a significant decrease in cell number were observed. Taken together, our results demonstrate that DDX58 mutations cause atypical SMS manifesting with variable expression of glaucoma, aortic calcification, and skeletal abnormalities without dental anomalies.

Reevaluation of the evolutionary events within recA/RAD51 phylogeny.

BACKGROUND: The recA/RAD51 gene family encodes a diverse set of recombinase proteins that affect homologous recombination, DNA-repair, and genome stability. The recA gene family is expressed across all three domains of life - Eubacteria, Archaea, and Eukaryotes - and even in some viruses. To date, efforts to resolve the deep evolutionary origins of this ancient protein family have been hindered by the high sequence divergence between paralogous groups (i.e. ~30% average pairwise identity). RESULTS: Through large taxon sampling and the use of a phylogenetic algorithm designed for inferring evolutionary events in highly divergent paralogs, we obtained a robust, parsimonious and more refined phylogenetic history of the recA/RAD51 superfamily. CONCLUSIONS: In summary, our model for the evolution of recA/RAD51 family provides a better understanding of the ancient origin of recA proteins and the multiple events that lead to the diversification of recA homologs in eukaryotes, including the discovery of additional RAD51 sub-families.

Conserved GXXXG- and S/T-like motifs in the transmembrane domains of NS4B protein are required for hepatitis C virus replication.

Hepatitis C virus (HCV) nonstructural protein 4B (NS4B) is an integral membrane protein, which plays an important role in the organization and function of the HCV replication complex (RC). Although much is understood about its amphipathic N-terminal and C-terminal domains, we know very little about the role of the transmembrane domains (TMDs) in NS4B function. We hypothesized that in addition to anchoring NS4B into host membranes, the TMDs are engaged in intra- and intermolecular interactions required for NS4B structure/function. To test this hypothesis, we have engineered a chimeric JFH1 genome containing the Con1 NS4B TMD region. The resulting virus titers were greatly reduced from those of JFH1, and further analysis indicated a defect in genome replication. We have mapped this incompatibility to NS4B TMD1 and TMD2 sequences, and we have defined putative TMD dimerization motifs (GXXXG in TMD2 and TMD3; the S/T cluster in TMD1) as key structural/functional determinants. Mutations in each of the putative motifs led to significant decreases in JFH1 replication. Like most of the NS4B chimeras, mutant proteins had no negative impact on NS4B membrane association. However, some mutations led to disruption of NS4B foci, implying that the TMDs play a role in HCV RC formation. Further examination indicated that the loss of NS4B foci correlates with the destabilization of NS4B protein. Finally, we have identified an adaptive mutation in the NS4B TMD2 sequence that has compensatory effects on JFH1 chimera replication. Taken together, these data underscore the functional importance of NS4B TMDs in the HCV life cycle.

Phylogenetic profiles reveal evolutionary relationships within the "twilight zone" of sequence similarity.

Inferring evolutionary relationships among highly divergent protein sequences is a daunting task. In particular, when pairwise sequence alignments between protein sequences fall <25% identity, the phylogenetic relationships among sequences cannot be estimated with statistical certainty. Here, we show that phylogenetic profiles generated with the Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) are capable of deriving, ab initio, phylogenetic relationships for highly divergent proteins in a quantifiable and robust manner. Notably, the results from our computational case study of the highly divergent family of retroelements accord with previous estimates of their evolutionary relationships. Taken together, these data demonstrate that GDDA-BLAST provides an independent and powerful measure of evolutionary relationships that does not rely on potentially subjective sequence alignment. We demonstrate that evolutionary relationships can be measured with phylogenetic profiles, and therefore propose that these measurements can provide key insights into relationships among distantly related and/or rapidly evolving proteins.

Efficacy of salivary versus subgingival bacterial sampling for the detection and quantification of periodontal pathogens.

PURPOSE: The aim of this study was to investigate the efficacy and validity of subgingival bacterial sampling using a retraction cord, and to evaluate how well this sampling method reflected changes in periodontal conditions after periodontal therapy. METHODS: Based on clinical examinations, 87 subjects were divided into a healthy group (n=40) and a periodontitis group (n=47). Clinical measurements were obtained from all subjects including periodontal probing depth (PD), bleeding on probing (BOP), clinical attachment loss (CAL), and the plaque index. Saliva and gingival crevicular fluid (GCF) as a subgingival bacterial sample were sampled before and 3 months after periodontal therapy. The salivary and subgingival bacterial samples were analyzed by reverse-transcription polymerase chain reaction to quantify the following 11 periodontal pathogens: Aggregatibacter actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), Tannerella forsythus (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Pavimonas micra (Pm), Campylobacter rectus (Cr), Prevotella nigrescens (Pn), Eikenella corrodens (Ec), and Eubacterium nodatum (En). RESULTS: Non-surgical periodontal therapy resulted in significant decreases in PD (P<0.01), CAL (P<0.01), and BOP (P<0.05) after 3 months. Four species (Pg, Tf, Pi, and Pm) were significantly more abundant in both types of samples in the periodontitis group than in the healthy group. After periodontal therapy, Cr was the only bacterium that showed a statistically significant decrease in saliva, whereas statistically significant decreases in Cr, Pg, and Pn were found in GCF. CONCLUSIONS: Salivary and subgingival bacterial sampling with a gingival retraction cord were found to be equivalent in terms of their accuracy for differentiating periodontitis, but GCF reflected changes in bacterial abundance after periodontal therapy more sensitively than saliva.

Revising a personal genome by comparing and combining data from two different sequencing platforms.

For the robust practice of genomic medicine, sequencing results must be compatible, regardless of the sequencing technologies and algorithms used. Presently, genome sequencing is still an imprecise science and is complicated by differences in the chemistry, coverage, alignment, and variant-calling algorithms. We identified ~3.33 million single nucleotide variants (SNVs) and ~3.62 million SNVs in the SJK genome using SOLiD and Illumina data, respectively. Approximately 3 million SNVs were concordant between the two platforms while 68,532 SNVs were discordant; 219,616 SNVs were SOLiD-specific and 516,080 SNVs were Illumina-specific (i.e., platform-specific). Concordant, discordant, and platform-specific SNVs were further analyzed and characterized. Overall, a large portion of heterozygous SNVs that were discordant with genotyping calls of single nucleotide polymorphism chips were highly confident. Approximately 70% of the platform-specific SNVs were located in regions containing repetitive sequences. Such platform-specificity may arise from differences between platforms, with regard to read length (36 bp and 72 bp vs. 50 bp), insert size (~100-300 bp vs. ~1-2 kb), sequencing chemistry (sequencing-by-synthesis using single nucleotides vs. ligation-based sequencing using oligomers), and sequencing quality. When data from the two platforms were merged for variant calling, the proportion of callable regions of the reference genome increased to 99.66%, which was 1.43% higher than the average callability of the two platforms, representing ~40 million bases. In this study, we compared the differences in sequencing results between two sequencing platforms. Approximately 90% of the SNVs were concordant between the two platforms, yet ~10% of the SNVs were either discordant or platform-specific, indicating that each platform had its own strengths and weaknesses. When data from the two platforms were merged, both the overall callability of the reference genome and the overall accuracy of the SNVs improved, demonstrating the likelihood that a re-sequenced genome can be revised using complementary data.

PHYRN: a robust method for phylogenetic analysis of highly divergent sequences.

Both multiple sequence alignment and phylogenetic analysis are problematic in the "twilight zone" of sequence similarity (≤ 25% amino acid identity). Herein we explore the accuracy of phylogenetic inference at extreme sequence divergence using a variety of simulated data sets. We evaluate four leading multiple sequence alignment (MSA) methods (MAFFT, T-COFFEE, CLUSTAL, and MUSCLE) and six commonly used programs of tree estimation (Distance-based: Neighbor-Joining; Character-based: PhyML, RAxML, GARLI, Maximum Parsimony, and Bayesian) against a novel MSA-independent method (PHYRN) described here. Strikingly, at "midnight zone" genetic distances (~7% pairwise identity and 4.0 gaps per position), PHYRN returns high-resolution phylogenies that outperform traditional approaches. We reason this is due to PHRYN's capability to amplify informative positions, even at the most extreme levels of sequence divergence. We also assess the applicability of the PHYRN algorithm for inferring deep evolutionary relationships in the divergent DANGER protein superfamily, for which PHYRN infers a more robust tree compared to MSA-based approaches. Taken together, these results demonstrate that PHYRN represents a powerful mechanism for mapping uncharted frontiers in highly divergent protein sequence data sets.

Predicting protein folds with fold-specific PSSM libraries.

Accurately assigning folds for divergent protein sequences is a major obstacle to structural studies. Herein, we outline an effective method for fold recognition using sets of PSSMs, each of which is constructed for different protein folds. Our analyses demonstrate that FSL (Fold-specific Position Specific Scoring Matrix Libraries) can predict/relate structures given only their amino acid sequences of highly divergent proteins. This ability to detect distant relationships is dependent on low-identity sequence alignments obtained from FSL. Results from our experiments demonstrate that FSL perform well in recognizing folds from the "twilight-zone" SABmark dataset. Further, this method is capable of accurate fold prediction in newly determined structures. We suggest that by building complete PSSM libraries for all unique folds within the Protein Database (PDB), FSL can be used to rapidly and reliably annotate a large subset of protein folds at proteomic level. The related programs and fold-specific PSSMs for our FSL are publicly available at: http://ccp.psu.edu/download/FSLv1.0/.

Adaptive GDDA-BLAST: fast and efficient algorithm for protein sequence embedding.

A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity). Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments and for incorporation thereof into high-performance PSSM-based alignment profiles.

Phylogenetic profiles reveal structural/functional determinants of TRPC3 signal-sensing antennae.

Biochemical assessment of channel structure/function is incredibly challenging. Developing computational tools that provide these data would enable translational research, accelerating mechanistic experimentation for the bench scientist studying ion channels. Starting with the premise that protein sequence encodes information about structure, function and evolution (SF&E), we developed a unified framework for inferring SF&E from sequence information using a knowledge-based approach. The Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) provides phylogenetic profiles that can model, ab initio, SF&E relationships of biological sequences at the whole protein, single domain and single-amino acid level.1,2 In our recent paper,4 we have applied GDDA-BLAST analysis to study canonical TRP (TRPC) channels1 and empirically validated predicted lipid-binding and trafficking activities contained within the TRPC3 TRP_2 domain of unknown function. Overall, our in silico, in vitro, and in vivo experiments support a model in which TRPC3 has signal-sensing antennae which are adorned with lipid-binding, trafficking and calmodulin regulatory domains. In this Addendum, we correlate our functional domain analysis with the cryo-EM structure of TRPC3.3 In addition, we synthesize recent studies with our new findings to provide a refined model on the mechanism(s) of TRPC3 activation/deactivation.

Phylogenetic Profiles Reveal Structural and Functional Determinants of Lipid-binding.

One of the major challenges in the genomic era is annotating structure/function to the vast quantities of sequence information now available. Indeed, most of the protein sequence database lacks comprehensive annotation, even when experimental evidence exists. Further, within structurally resolved and functionally annotated protein domains, additional functionalities contained in these domains are not apparent. To add further complication, small changes in the amino-acid sequence can lead to profound changes in both structure and function, underscoring the need for rapid and reliable methods to analyze these types of data. Phylogenetic profiles provide a quantitative method that can relate the structural and functional properties of proteins, as well as their evolutionary relationships. Using all of the structurally resolved Src-Homology-2 (SH2) domains, we demonstrate that knowledge-bases can be used to create single-amino acid phylogenetic profiles which reliably annotate lipid-binding. Indeed, these measures isolate the known phosphotyrosine and hydrophobic pockets as integral to lipid-binding function. In addition, we determined that the SH2 domain of Tec family kinases bind to lipids with varying affinity and specificity. Simulating mutations in Bruton's tyrosine kinase (BTK) that cause X-Linked Agammaglobulinemia (XLA) predict that these mutations alter lipid-binding, which we confirm experimentally. In light of these results, we propose that XLA-causing mutations in the SH3-SH2 domain of BTK alter lipid-binding, which could play a causative role in the XLA-phenotype. Overall, our study suggests that the number of lipid-binding proteins is drastically underestimated and, with further development, phylogenetic profiles can provide a method for rapidly increasing the functional annotation of protein sequences.