DMDM: domain mapping of disease mutations

Functional characterization of SORL1 variants in cell-based assays to investigate variant pathogenicity

SORLA, the protein encoded by the SORL1 gene, has an important role in recycling cargo proteins to the cell surface. While SORLA loss-of-function variants occur almost exclusively in Alzheimer's disease cases, the majority of SORL1 variants are missense variants that are individually rare and can have individual mechanisms how they impair SORLA function as well as have individual effect size on disease risk. However, since carriers mostly come from small pedigrees, it is challenging to determine variant penetrance, leaving clinical significance associated with most missense variants unclear. In this article, we present functional approaches to evaluate the pathogenicity of a SORL1 variant, p.D1105H. First, we generated our mutant receptor by inserting the D1105H variant into the full-length SORLA-WT receptor. Then using western blot analysis we quantified the effect of the mutation on maturation and shedding of the receptor for transfected cells, and finally applied a flow cytometry approach to quantify SORLA expression at the cell surface. The results showed decreased maturation, decreased shedding, and decreased cell surface expression of D1105H compared with wild-type SORLA. We propose how these approaches can be used to functionally assess the pathogenicity of SORL1 variants in the future. This article is part of a discussion meeting issue 'Understanding the endo-lysosomal network in neurodegeneration'.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures

MOTIVATION

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis.

RESULTS

To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vicruiser/3Dmapper.

Calculating and comparing codon usage values in rare disease genes highlights codon clustering with disease-and tissue- specific hierarchy

We designed a novel strategy to define codon usage bias (CUB) in 6 specific small cohorts of human genes. We calculated codon usage (CU) values in 29 non-disease-causing (NDC) and 31 disease-causing (DC) human genes which are highly expressed in 3 distinct tissues, kidney, muscle, and skin. We applied our strategy to the same selected genes annotated in 15 mammalian species. We obtained CUB hierarchical clusters for each gene cohort which showed tissue-specific and disease-specific CUB fingerprints. We showed that DC genes (especially those expressed in muscle) display a low CUB, well recognizable in codon hierarchical clustering. We defined the extremely biased codons as "zero codons" and found that their number is significantly higher in all DC genes, all tissues, and that this trend is conserved across mammals. Based on this calculation in different gene cohorts, we identified 5 codons which are more differentially used across genes and mammals, underlining that some genes have favorite synonymous codons in use. Since of the muscle genes clear clusters, and, among these, dystrophin gene surprisingly does not show any "zero codon" we adopted a novel approach to study CUB, we called "mapping-on-codons". We positioned 2828 dystrophin missense and nonsense pathogenic variations on their respective codon, highlighting that its frequency and occurrence is not dependent on the CU values. We conclude our strategy consents to identify a hierarchical clustering of CU values in a gene cohort-specific fingerprints, with recognizable trend across mammals. In DC muscle genes also a disease-related fingerprint can be observed, allowing discrimination between DC and NDC genes. We propose that using our strategy which studies CU in specific gene cohorts, as rare disease genes, and tissue specific genes, may provide novel information about the CUB role in human and medical genetics, with implications on synonymous variations interpretation and codon optimization algorithms.

Protein domain-based approaches for the identification and prioritization of therapeutically actionable cancer variants

The tremendous number of cancer variants that can be detected by NGS analyses has required the development of computational approaches to prioritize mutations on the basis of their biological and clinical significance. Standard strategies take a gene-centric approach to the problem, allowing exclusively the identification of highly frequent variants. On the contrary, protein domain (PD)-based approaches allow to identify functionally relevant low frequency variants by searching for mutations that recur on analogous residues across homologous proteins (i.e. containing the same PD). Such approaches enable to transfer information about the effects and druggability from one known mutation to unknown ones. Here we describe how PD-based strategies work, and discuss how they could be exploited for mutation prioritization. The principle that mutations clustered on specific residues of PDs have the same functional consequences and are therapeutically actionable in a similar manner could help the choice of patient-specific targeted drugs, eventually improving the management of cancer patients.

Glycosylation of Serum Clusterin in Wild-Type Transthyretin-Associated (ATTRwt) Amyloidosis: A Study of Disease-Associated Compositional Features Using Mass Spectrometry Analyses

Wild-type transthyretin-associated (ATTRwt) amyloidosis is an age-related disease that causes heart failure in older adults. This disease frequently features cardiac amyloid fibril deposits that originate from dissociation of the tetrameric protein, transthyretin (TTR). Unlike hereditary TTR (ATTRm) amyloidosis, where amino acid replacements destabilize the native protein, in ATTRwt amyloidosis, amyloid-forming TTR lacks protein sequence alterations. The initiating cause of fibril formation in ATTRwt amyloidosis is unclear, and thus, it seems plausible that other factors are involved in TTR misfolding and unregulated accumulation of wild-type TTR fibrils. We believe that clusterin (CLU, UniProtKB P10909), a plasma circulating glycoprotein, plays a role in the pathobiology of ATTRwt amyloidosis. Previously, we have suggested a role for CLU in ATTRwt amyloidosis based on our studies showing that (1) CLU codeposits with non-native TTR in amyloid fibrils from ATTRwt cardiac tissue, (2) CLU interacts only with non-native (monomeric and aggregated) forms of TTR, and (3) CLU serum levels in patients with ATTRwt are significantly lower compared to healthy controls. In the present study, we provide comprehensive detail of compositional findings from mass spectrometry analyses of amino acid and glycan content of CLU purified from ATTRwt and control sera. The characterization of oligosaccharide content in serum CLU derived from patients with ATTRwt amyloidosis is novel data. Moreover, results comparing CLU oligosaccharide variations between patient and healthy controls are original and provide further evidence for the role of CLU in ATTRwt pathobiology, possibly linked to disease-specific structural features that limit the chaperoning capacity of CLU.

PMEPA1 Gene Isoforms: A Potential Biomarker and Therapeutic Target in Prostate Cancer

The identification of prostate transmembrane protein androgen induced 1 (PMEPA1), an androgen responsive gene, came initially from the studies of androgen regulatory gene networks in prostate cancer. It was soon followed by the documentation of the expression and functional analysis of transmembrane prostate androgen-induced protein (TMEPAI)/PMEPA1 in other solid tumors including renal, colon, breast, lung, and ovarian cancers. Further elucidation of PMEPA1 gene expression and sequence analysis revealed the presence of five isoforms with distinct extracellular domains (isoforms a, b, c, d, and e). Notably, the predicted amino acid sequences of PMEPA1 isoforms show differences at the N-termini, a conserved membrane spanning and cytoplasmic domains. PMEPA1 serves as an essential regulator of multiple signaling pathways including androgen and TGF-β signaling in solid tumors. Structure-function studies indicate that specific motifs present in the cytoplasmic domain (PY, SIM, SH3, and WW binding domains) are utilized to mediate isoform-specific functions through interactions with other proteins. The understanding of the “division of labor” paradigm exhibited by PMEPA1 isoforms further expands our knowledge of gene’s multiple functions in tumorigenesis. In this review, we aim to summarize the most recent advances in understanding of PMEPA1 isoform-specific functions and their associations with prostate cancer progression, highlighting the potentials as biomarker and therapeutic target in prostate cancer.

Structure of Urocanate Hydratase from the protozoan Trypanosoma cruzi

The enzyme Urocanate Hydratase (UH) participates in the catabolic pathway of L-histidine. Trypanosoma cruzi Urocanate Hydratase (TcUH) is identified as a therapeutic molecular target in the WHO/TDR Targets Database. We report the 3D structure determination and number of features of TcUH, and compared it to other few available bacterial UH structures. Each monomer presents two domains and one NAD⁺ molecule. Superpositions revealed differences in the relative orientation of domains within monomers, such that TcUH monomer A resembles Urocanate Hydratase from Geobacillus kaustophilus (GkUH) (open conformation), while monomer C resembles Urocanate Hydratase from Pseudomonas putida (PpUH) and Urocanate Hydratase from Bacillus subtilis (BsUH) (closed conformations). We use the structure of TcUH to make considerations about 3 non-deleterious and 2 deleterious mutations found in human UHs: non-deleterious mutations could be accommodated without large displacements or interaction interruptions, whereas deleterious mutations in one case might disrupt an α-helix (as previously suggested) and in the other case, besides disrupting the enzyme interaction with the substrate, might interfere with interdomain movement.

Human COQ10A and COQ10B are distinct lipid-binding START domain proteins required for coenzyme Q function

Coenzyme Q (CoQ or ubiquinone) serves as an essential redox-active lipid in respiratory electron and proton transport during cellular energy metabolism. CoQ also functions as a membrane-localized antioxidant protecting cells against lipid peroxidation. CoQ deficiency is associated with multiple human diseases; CoQ₁₀ supplementation in particular has noted cardioprotective benefits. In Saccharomyces cerevisiae, Coq10, a putative START domain protein, is believed to chaperone CoQ to sites where it functions. Yeast coq10 deletion mutants (coq10Δ) synthesize CoQ inefficiently during log phase growth and are respiratory defective and sensitive to oxidative stress. Humans have two orthologs of yeast COQ10, COQ10A and COQ10B Here, we tested the human co-orthologs for their ability to rescue the yeast mutant. We showed that expression of either human ortholog, COQ10A or COQ10B, rescues yeast coq10Δ mutant phenotypes, restoring the function of respiratory-dependent growth on a nonfermentable carbon source and sensitivity to oxidative stress induced by treatment with PUFAs. These effects indicate a strong functional conservation of Coq10 across different organisms. However, neither COQ10A nor COQ10B restored CoQ biosynthesis when expressed in the yeast coq10Δ mutant. The involvement of yeast Coq10 in CoQ biosynthesis may rely on its interactions with another protein, possibly Coq11, which is not found in humans. Coexpression analyses of yeast COQ10 and human COQ10A and COQ10B provide additional insights to functions of these START domain proteins and their potential roles in other biologic pathways.

Functional Annotation and Analysis of Dual Oxidase 1 (DUOX1): a Potential Anti-pyocyanin Immune Component

Dual Oxidase 1 (DUOX1) is a prominent immune system component primarily expressed in esophagus, lungs, skin, and urinary bladder including others. DUOX1 is involved in lactoperoxidase-mediated innate immunity at mucosal surfaces by generation of antimicrobial hypothiocyanite at the apical surface of epithelial lining. Upon detection of bacterial pathogens mainly Pseudomonas aeruginosa, DUOX1 is activated in bronchial epithelial cells. Both the host and pathogen enter a redox dual with DUOX1 and hypothiocyanite from host and Pyocyanin (PCN) as a redox active virulence factor from P. aeruginosa. The synergy of the both enzymes permanently oxidizes PCN and thus holds the potential to prevent PCN-induced virulence, which otherwise paves the way for establishment of persistent chronic infection. In this study, we structurally and functionally annotated the DUOX1, predicted its 3d structure, physio-chemical properties, post-translational modifications, and genetic polymorphism analysis with subsequent disease-associated single-nucleotide variations and their impact on DUOX1 functionality by employing in silico approaches. DUOX1 holds greater homology with gorilla and chimpanzee than other primates. The localization signal peptide was present at the beginning of the peptide with cleavage site at 22 aa position. Three distinct functional domains were observed based on homology: An_peroxidase, FRQ1, and oxido-reductase domains. Polymorphism analysis revealed > 60 SNPs associated with different cancers with probable damaging effects. No cancer-associated methylated island was observed for DUOX1. Three-dimensional structure was developed via homology modeling strategy. The proper annotation will help in characterization of DUOX1 and enhance our knowledge of its functionality and biological roles.

Identification and analysis of mutational hotspots in oncogenes and tumour suppressors

Background

The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes.

Results

In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains We find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain.

Conclusions

By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation. We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/extra/MOKCa).

Empirical null estimation using zero-inflated discrete mixture distributions and its application to protein domain data

In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. This article aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. One main assumption is that the mutation counts follow a zero-inflated model in order to account for the true zeros in the count model and the excess zeros. The class of models considered is the Zero-inflated Generalized Poisson (ZIGP) distribution. Furthermore, we assumed that there exists a cut-off value such that smaller counts than this value are generated from the null distribution. We present several data-dependent methods to determine the cut-off value. We also consider a two-stage procedure based on screening process so that the number of mutations exceeding a certain value should be considered as significant mutations. Simulated and protein domain data sets are used to illustrate this procedure in estimation of the empirical null using a mixture of discrete distributions. Overall, while maintaining control of the FDR, the proposed two-stage testing procedure has superior empirical power.

SLC transporters as a novel class of tumour suppressors: identity, function and molecular mechanisms

The role of plasma membrane transporters in cancer is receiving increasing attention in recent years. Several transporters for essential nutrients are up-regulated in cancer and serve as tumour promoters. Transporters could also function as tumour suppressors. To date, four transporters belonging to the SLC gene family have been identified as tumour suppressors. SLC5A8 is a Na(+)-coupled transporter for monocarboxylates. Among its substrates are the bacterial fermentation products butyrate and propionate and the ubiquitous metabolite pyruvate. The tumour-suppressive function of this transporter relates to the ability of butyrate, propionate and pyruvate to inhibit histone deacetylases (HDAC). SLC5A8 functions as a tumour suppressor in most tissues studied thus far, and provides a molecular link to Warburg effect, a characteristic feature in most cancers. It also links colonic bacteria and dietary fibre to the host. SLC26A3 as a tumour suppressor is restricted to colon; it is a Cl(-)/HCO(-) 3 exchanger, facilitating the efflux of HCO(-) 3 The likely mechanism for the tumour-suppressive function of SLC26A3 is related to intracellular pH regulation. SLC39A1 is a Zn(2+) transporter and its role in tumour suppression has been shown in prostate. Zn(2+) is present at high concentrations in normal prostate where it elicits its tumour-suppressive function. SLC22A18 is possibly an organic cation transporter, but the identity of its physiological substrates is unknown. As such, there is no information on molecular pathways responsible for the tumour-suppressive function of this transporter. It is likely that additional SLC transporters will be discovered as tumour suppressors in the future.

A tripeptidyl peptidase 1 is a binding partner of the Golgi pH regulator (GPHR) in Dictyostelium

Mutations in tripeptidyl peptidase 1 (TPP1) have been associated with late infantile neuronal ceroid lipofuscinosis (NCL), a neurodegenerative disorder. TPP1 is a lysosomal serine protease, which removes tripeptides from the N-terminus of proteins and is composed of an N-terminal prodomain and a catalytic domain. It is conserved in mammals, amphibians, fish and the amoeba Dictyostelium discoideum. D. discoideum harbors at least six genes encoding TPP1, tpp1A to tpp1F. We identified TPP1F as binding partner of Dictyostelium GPHR (Golgi pH regulator), which is an evolutionarily highly conserved intracellular transmembrane protein. A region encompassing the DUF3735 (GPHR_N) domain of GPHR was responsible for the interaction. In TPP1F, the binding site is located in the prodomain of the protein. The tpp1F gene is transcribed throughout development and translated into a polypeptide of ∼65 kDa. TPP1 activity was demonstrated for TPP1F-GFP immunoprecipitated from D. discoideum cells. Its activity could be inhibited by addition of the recombinant DUF3735 domain of GPHR. Knockout tpp1F mutants did not display any particular phenotype, and TPP1 activity was not abrogated, presumably because tpp1B compensates as it has the highest expression level of all the TPP1 genes during growth. The GPHR interaction was not restricted to TPP1F but occurred also with TPP1B. As previous reports show that the majority of the TPP1 mutations in NCL resulted in reduction or loss of enzyme activity, we suggest that Dicyostelium could be used as a model system in which to test new reagents that could affect the activity of the protein and ameliorate the disease.

Summary: Interaction of Dictyostelium tripeptidyl peptidase 1 with GPHR could be relevant for studies of the human enzyme, which is associated with a neurodegenerative disorder.

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are 'gene-centric' in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new 'domain-centric' method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots' unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols

In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

Protein Bioinformatics Databases and Resources

Many publicly available data repositories and resources have been developed to support protein-related information management, data-driven hypothesis generation, and biological knowledge discovery. To help researchers quickly find the appropriate protein-related informatics resources, we present a comprehensive review (with categorization and description) of major protein bioinformatics databases in this chapter. We also discuss the challenges and opportunities for developing next-generation protein bioinformatics databases and resources to support data integration and data analytics in the Big Data era.

Connecting common genetic polymorphisms to protein function: A modular project sequence for lecture or lab

Single nucleotide polymorphisms (SNPs) in DNA can result in phenotypes where the biochemical basis may not be clear due to the lack of protein structures. With the growing number of modeling and simulation software available on the internet, students can now participate in determining how small changes in genetic information impact cellular protein structure and function. We have developed a modular series of activities to engage lab or lecture students in examining the basis for common phenotypes. The activities range from basic phenotype testing/observation to DNA sequencing and simulation of protein structure and dynamics. We provide as an example study of the bitterness receptor TAS2R38 and PTC tasting, however these activities are applicable to other SNPs or genomic variants with a direct connection to an observable phenotype. These activities are modular and can be mixed to meet the student capabilities and infrastructure availability. The complete sequence of activities will demonstrate the direct connection between gene structure, protein structure and organism function. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(6):526-536, 2016.

Bi-allelic Mutations in PKD1L1 Are Associated with Laterality Defects in Humans

Disruption of the establishment of left-right (L-R) asymmetry leads to situs anomalies ranging from situs inversus totalis (SIT) to situs ambiguus (heterotaxy). The genetic causes of laterality defects in humans are highly heterogeneous. Via whole-exome sequencing (WES), we identified homozygous mutations in PKD1L1 from three affected individuals in two unrelated families. PKD1L1 encodes a polycystin-1-like protein and its loss of function is known to cause laterality defects in mouse and medaka fish models. Family 1 had one fetus and one deceased child with heterotaxy and complex congenital heart malformations. WES identified a homozygous splicing mutation, c.6473+2_6473+3delTG, which disrupts the invariant splice donor site in intron 42, in both affected individuals. In the second family, a homozygous c.5072G>C (p.Cys1691Ser) missense mutation was detected in an individual with SIT and congenital heart disease. The p.Cys1691Ser substitution affects a highly conserved cysteine residue and is predicted by molecular modeling to disrupt a disulfide bridge essential for the proper folding of the G protein-coupled receptor proteolytic site (GPS) motif. Damaging effects associated with substitutions of this conserved cysteine residue in the GPS motif have also been reported in other genes, namely GPR56, BAI3, and PKD1 in human and lat-1 in C. elegans, further supporting the likely pathogenicity of p.Cys1691Ser in PKD1L1. The identification of bi-allelic PKD1L1 mutations recapitulates previous findings regarding phenotypic consequences of loss of function of the orthologous genes in mice and medaka fish and further expands our understanding of genetic contributions to laterality defects in humans.

Self-regulation of functional pathways by motifs inside the disordered tails of beta-catenin

BACKGROUND

Beta-catenin has two major functions: coordinating cell-cell adhesion by interacting with cadherin in cadherin junction formation pathway; and regulating gene expression through Wnt signaling pathway. Accomplishing these two functions requires synergistic action of various sequential regions of the same beta-Catenin molecule, including the N-terminal tail, the middle armadillo domain, and the C-terminal tail. Although the middle armadillo domain is the major functional unit of beta-Catenin, the involvement of tails in the regulation of interaction between beta-Catenin and its partners has been well observed. Nonetheless, the regulatory processes of both tails are still elusive. In addition, it is interesting to note that the three sequential regions have different structural features: The middle armadillo domain is structured, but both N- and C-terminal tails are disordered. This observation leads to another important question on the functions and mechanisms of disordered tails, which is also largely unknown.

RESULTS

In this study, we focused on the characterization of sequential, structural, and functional features of the disordered tails of beta-Catenin. We identified multiple functional motifs and conserved sequence motifs in the disordered tails, discovered the correlation between cancer-associated mutations and functional motifs, explored the abundance of protein intrinsic disorder in the interactomes of beta-Catenin, and elaborated a working model on the regulatory roles of disordered tails in the functional pathways of beta-Catenin.

CONCLUSION

Disordered tails of beta-Catenin contain multiple functional motifs. These motifs interact with each other and the armadillo domain of beta-catenin to regulate the function of beta-Catenin in both cadherin junction formation pathway and Wnt signaling pathway.

Mutational patterns in oncogenes and tumour suppressors

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer.

LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer

Background

The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots.

Results

We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family.

Conclusions

LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis.

LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0935-7) contains supplementary material, which is available to authorized users.

Exploring the complete mutational space of the LDL receptor LA5 domain using molecular dynamics: linking SNPs with disease phenotypes in familial hypercholesterolemia

Familial hypercholesterolemia (FH), a genetic disorder with a prevalence of 0.2%, represents a high-risk factor to develop cardiovascular and cerebrovascular diseases. The majority and most severe FH cases are associated to mutations in the receptor for low-density lipoproteins receptor (LDL-r), but the molecular basis explaining the connection between mutation and phenotype is often unknown, which hinders early diagnosis and treatment of the disease. We have used atomistic simulations to explore the complete SNP mutational space (227 mutants) of the LA5 repeat, the key domain for interacting with LDL that is coded in the exon concentrating the highest number of mutations. Four clusters of mutants of different stability have been identified. The majority of the 50 FH known mutations (33) appear distributed in the unstable clusters, i.e. loss of conformational stability explains two-third of FH phenotypes. However, one-third of FH phenotypes (17 mutations) do not destabilize the LR5 repeat. Combining our simulations with available structural data from different laboratories, we have defined a consensus-binding site for the interaction of the LA5 repeat with LDL-r partner proteins and have found that most (16) of the 17 stable FH mutations occur at binding site residues. Thus, LA5-associated FH arises from mutations that cause either the loss of stability or a decrease in domain's-binding affinity. Based on this finding, we propose the likely phenotype of each possible SNP in the LA5 repeat and outline a procedure to make a full computational diagnosis for FH.

MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer

The MutationAligner web resource, available at http://www.mutationaligner.org, enables discovery and exploration of somatic mutation hotspots identified in protein domains in currently (mid-2015) more than 5000 cancer patient samples across 22 different tumor types. Using multiple sequence alignments of protein domains in the human genome, we extend the principle of recurrence analysis by aggregating mutations in homologous positions across sets of paralogous genes. Protein domain analysis enhances the statistical power to detect cancer-relevant mutations and links mutations to the specific biological functions encoded in domains. We illustrate how the MutationAligner database and interactive web tool can be used to explore, visualize and analyze mutation hotspots in protein domains across genes and tumor types. We believe that MutationAligner will be an important resource for the cancer research community by providing detailed clues for the functional importance of particular mutations, as well as for the design of functional genomics experiments and for decision support in precision medicine. MutationAligner is slated to be periodically updated to incorporate additional analyses and new data from cancer genomics projects.

GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0×10⁻³) from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.

Twenty-One Novel EGFR Kinase Domain variants in Patients with Nonsmall Cell Lung Cancer

Somatic sequence variants in the epidermal growth factor receptor (EGFR) kinase domain are associated with sensitivity to tyrosine kinase inhibitors (TKIs) in patients with nonsmall cell lung cancer (NSCLC). Patients exhibiting sequence variants in this domain that produce kinase activity enhancement, are more likely to benefit from TKIs than patients with EGFR wild-type disease. Although most NSCLC EGFR-related alleles are concentrated in a few positions, established protocols recommend sequencing EGFR exons 18-21. In this study, 21 novel somatic variants belonging to such exons in adult Argentinean patients affected with NSCLC are reported. Of these, 18 were single amino acid substitutions (SASs), occurring alone or in combination with another genetic alteration (complex cases), one was a short deletion, one was a short deletion-short insertion combination, and one was a duplication. New variants and different combinations of previously reported variants were also found. Moreover, two of the reported SASs occurred in previously unreported positions of the EGFR kinase domain. In order to characterize the new sequence variants, physicochemical, sequence and conformational analyses were also performed. A better understanding of sequence variants in NSCLC may facilitate the most appropriate treatment choice for this complex disease.

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Domain mutation analysis also sharpens the functional interpretation of the impact of mutations, as domains more succinctly embody function than entire genes. By mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of domains, we confirm well-known functional mutation hotspots, identify uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in another gene, detect previously unknown mutation hotspots, and provide hypotheses about molecular mechanisms and downstream effects of domain mutations. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis will likely provide many more leads linking mutations in proteins to the cancer phenotype.

Mutations in zinc finger 407 [ZNF407] cause a unique autosomal recessive cognitive impairment syndrome

Background

A consanguineous Arab family is affected by an apparently novel autosomal recessive disorder characterized by cognitive impairment, failure-to-thrive, hypotonia and dysmorphic features including bilateral ptosis and epicanthic folds, synophrys, midface hypoplasia, downturned mouth corners, thin upper vermillion border and prominent ears, bilateral 5th finger camptodactyly, bilateral short 4th metatarsal bones, and limited knee mobility bilaterally.

Methods

The family was studied by homozygosity mapping, candidate gene mutation screening and whole Exome Next Generation Sequencing of a single affected member to identify the offending gene and mutation. The mutated gene product was studied by structural bioinformatics methods.

Results

A damaging c.C5054G mutation affecting an evolutionary highly conserved amino acid p.S1685W was identified in the ZNF407 gene at 18q23. The Serine to Tryptophane mutation affects two of the three ZNF407 isoforms and is located in the last third of the protein, in a linker peptide adjoining two zinc-finger domains. Structural analyses of this mutation shows disruption of an H-bond that locks the relative spatial position of the two fingers, leading to a higher flexibility of the linker and thus to a decreased probability of binding to the target DNA sequence essentially eliminating the functionality of downstream domains and interfering with the expression of various genes under ZNF407 control during fetal brain development.

Conclusions

ZNF407 is a transcription factor with an essential role in brain development. When specific and limited in number homozygosity intervals exist that harbor the offending gene in consanguineous families, Whole Exome Sequencing of a single affected individual is an efficient approach to gene mapping and mutation identification.

Towards precision medicine: advances in computational approaches for the analysis of human variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

IQCB1 and PDE6B mutations cause similar early onset retinal degenerations in two closely related terrier dog breeds

PURPOSE

To identify the causative mutations in two early-onset canine retinal degenerations, crd1 and crd2, segregating in the American Staffordshire terrier and the Pit Bull Terrier breeds, respectively.

METHODS

Retinal morphology of crd1- and crd2-affected dogs was evaluated by light microscopy. DNA was extracted from affected and related unaffected controls. Association analysis was undertaken using the Illumina Canine SNP array and PLINK (crd1 study), or the Affymetrix Version 2 Canine array, the "MAGIC" genotype algorithm, and Fisher's Exact test for association (crd2 study). Positional candidate genes were evaluated for each disease.

RESULTS

Structural photoreceptor abnormalities were observed in crd1-affected dogs as young as 11-weeks old. Rod and cone inner segment (IS) and outer segments (OS) were abnormal in size, shape, and number. In crd2-affected dogs, rod and cone IS and OS were abnormal as early as 3 weeks of age, progressing with age to severe loss of the OS, and thinning of the outer nuclear layer (ONL) by 12 weeks of age. Genome-wide association study (GWAS) identified association at the telomeric end of CFA3 in crd1-affected dogs and on CFA33 in crd2-affected dogs. Candidate gene evaluation identified a three bases deletion in exon 21 of PDE6B in crd1-affected dogs, and a cytosine insertion in exon 10 of IQCB1 in crd2-affected dogs.

CONCLUSIONS

Identification of the mutations responsible for these two early-onset retinal degenerations provides new large animal models for comparative disease studies and evaluation of potential therapeutic approaches for the homologous human diseases.

M2SG: mapping human disease-related genetic variants to protein sequences and genomic loci

SUMMARY

Online Mendelian Inheritance in Man (OMIM) is a manually curated compendium of human genetic variants and the corresponding phenotypes, mostly human diseases. Instead of directly documenting the native sequences for gene entries, OMIM links its entries to protein and DNA sequences in other databases. However, because of the existence of gene isoforms and errors in OMIM records, mapping a specific OMIM mutation to its corresponding protein sequence is not trivial. Combining computer programs and extensive manual curation of OMIM full-text descriptions and original literature, we mapped 98% of OMIM amino acid substitutions (AASs) and all SwissProt Variant (SwissVar) disease-related AASs to reference sequences and confidently mapped 99.96% of all AASs to the genomic loci. Based on the results, we developed an online database and interactive web server (M2SG) to (i) retrieve the mapped OMIM and SwissVar variants for a given protein sequence; and (ii) obtain related proteins and mutations for an input disease phenotype. This database will be useful for analyzing sequences, understanding the effect of mutations, identifying important genetic variations and designing experiments on a protein of interest.

AVAILABILITY AND IMPLEMENTATION

The database and web server are freely available at http://prodata.swmed.edu/M2S/mut2seq.cgi.

GPViz: dynamic visualization of genomic regions and variants affecting protein domains

GPViz is a versatile Java-based software for dynamic gene-centered visualization of genomic regions and/or variants. User-defined data can be loaded in common formats as resulting from analysis workflows used in sequencing applications and studied in the context of the gene, the corresponding transcript isoforms, proteins and their domains or other protein features. Both the genomic regions and variants can be also defined interactively. Various gene filter options are provided to enable an intersection of variants, genomic regions and affected protein features. Finally, by using GPViz, we identified differentially expressed exons, which could indicate alternative splicing events, and found somatic variants in different cancer types affecting metabolic proteins. GPViz is freely available at http://icbi.at/gpviz (released under GNU general public license), is based on Java 7 and can be used as a stand-alone or Web Start application.

AVAILABILITY

http://icbi.at/gpviz

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations

Background

The body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled with disease association studies. However, genomic association studies are limited by the molecular complexity of the phenotype being studied and the population size needed to have adequate statistical power. One way to circumvent this problem, which is critical for the study of rare diseases, is to study the molecular patterns emerging from functional studies of existing disease mutations. Current gene-centric analyses to study mutations in coding regions are limited by their inability to account for the functional modularity of the protein. Previous studies of the functional patterns of known human disease mutations have shown a significant tendency to cluster at protein domain positions, namely position-based domain hotspots of disease mutations. However, the limited number of known disease mutations remains the main factor hindering the advancement of mutation studies at a functional level. In this paper, we address this problem by incorporating mutations known to be disruptive of phenotypes in other species. Focusing on two evolutionarily distant organisms, human and yeast, we describe the first inter-species analysis of mutations of phenotypic relevance at the protein domain level.

Results

The results of this analysis reveal that phenotypic mutations from yeast cluster at specific positions on protein domains, a characteristic previously revealed to be displayed by human disease mutations. We found over one hundred domain hotspots in yeast with approximately 50% in the exact same domain position as known human disease mutations.

Conclusions

We describe an analysis using protein domains as a framework for transferring functional information by studying domain hotspots in human and yeast and relating phenotypic changes in yeast to diseases in human. This first-of-a-kind study of phenotypically relevant yeast mutations in relation to human disease mutations demonstrates the utility of a multi-species analysis for advancing the understanding of the relationship between genetic mutations and phenotypic changes at the organismal level.

Chapter 15: disease gene prioritization

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.

Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models

The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.

Differential expression of PDZ domain-containing proteins in human diseases - challenging topics and novel issues

The general features of the PDZ domain structure and functions have been extensively studied during the last decade. PDZ domains are generally present in proteins that are involved in multiple interactions to assemble functional protein complexes that control key cellular processes. One of the best characterized functions of PDZ domain-containing proteins is control of epithelial cell polarity and cell-cell contacts. In the present review, we summarize the current knowledge on regulation of expression of certain PDZ polarity proteins localized at the intercellular junctions. In addition, we provide a critical overview of recent findings regarding the role of these proteins during development of human diseases. Complete understanding of these issues is valuable for the design of novel therapeutic intervention for common pathologies, such as cancer.

Domain landscapes of somatic mutations in cancer

Background

Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development.

Results

We mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the “mutational landscapes” of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain “peaks” were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction.

Conclusions

Using our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, themethod shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

Objective

Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

Materials and methods

An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism.

Results

Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001).

Discussion

The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP).

Conclusion

These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

BACKGROUND AND OBJECTIVE

With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants.

METHODS

Disease mutations aggregated from multiple databases were mapped to domains, and were classified as either cancer- or non-cancer-related. The statistical method for identifying significantly disease-associated domain positions was applied to both sets of mutations and to randomly generated mutation sets for comparison. To leverage the known function of protein domain regions, the method optionally distributes significant scores to associated functional feature positions.

RESULTS

Most disease mutations are localized within protein domains and display a tendency to cluster at individual domain positions. The method identified significant disease mutation hotspots in both the cancer and non-cancer datasets. The domain significance scores (DS-scores) for cancer form a bimodal distribution with hotspots in oncogenes forming a second peak at higher DS-scores than non-cancer, and hotspots in tumor suppressors have scores more similar to non-cancers. In addition, on an independent mutation benchmarking set, the DS-score method identified mutations known to alter protein function with very high precision.

CONCLUSION

By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

Hereditary leukemia due to rare RUNX1c splice variant (L472X) presents with eczematous phenotype

Deleterious mutations in the RUNX1 gene cause hereditary leukemia due to a rare syndrome called Familial platelet Disorder with Associated Myeloid Malignancy (FPDMM). We describe the characteristics of a family with FPDMM due to a novel RUNX1 mutation (L472X), located in the most 3-prime end of the gene reported to date. Our 36-year old proband presented with incidentally detected thrombocytopenia and a family history suggestive of FPDMM. Contrary to previously described families, affected members of our kindred express an eczematous phenotype, reportedly most severe in members who develop leukemia. Pedigree analysis shows that the L472X mutation tracks with thrombocytopenia, acute leukemia, and eczema. The L472X mutation produces a stably expressed RUNX1 protein product with a corresponding decrease in wild type RUNX1 expression. Our data supports the inclusion of eczema in the FPDMM phenotype and suggests the possibility that the RUNX1 L472X mutant causes the type of dominant negative affect that is associated with an elevated risk of leukemia in FPDMM families.