Genomic landscape of advanced prostate cancer patients with BRCA1 versus BRCA2 mutations as detected by comprehensive genomic profiling of cell-free DNA.

BRCA1-mutated prostate cancer has been shown to be less responsive to poly (ADP-ribose) polymerase (PARP) inhibitors as compared to BRCA2-mutated prostate cancer. The reason for this differential response is not clear. We hypothesized this differential sensitivity to PARP inhibitors may be explained by distinct genomic landscapes of BRCA1 versus BRCA2 co-segregating genes. In a large dataset of 7,707 men with advanced prostate cancer undergoing comprehensive genomic profiling (CGP) of cell-free DNA (cfDNA), 614 men harbored BRCA1 and/or BRCA2 alterations. Differences in the genomic landscape of co-segregating genes was investigated by Fisher's exact test and probabilistic graphical models (PGMs). Results demonstrated that BRCA1 was significantly associated with six other genes, while BRCA2 was not significantly associated with any gene. These findings suggest BRCA2 may be the main driver mutation, while BRCA1 mutations tend to co-segregate with mutations in other molecular pathways contributing to prostate cancer progression. These hypothesis-generating data may explain the differential response to PARP inhibition and guide towards the development of combinatorial drug regimens in those with BRCA1 mutation.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.

The history and geographic distribution of a KCNQ1 atrial fibrillation risk allele.

The genetic architecture of atrial fibrillation (AF) encompasses low impact, common genetic variants and high impact, rare variants. Here, we characterize a high impact AF-susceptibility allele, KCNQ1 R231H, and describe its transcontinental geographic distribution and history. Induced pluripotent stem cell-derived cardiomyocytes procured from risk allele carriers exhibit abbreviated action potential duration, consistent with a gain-of-function effect. Using identity-by-descent (IBD) networks, we estimate the broad- and fine-scale population ancestry of risk allele carriers and their relatives. Analysis of ancestral migration routes reveals ancestors who inhabited Denmark in the 1700s, migrated to the Northeastern United States in the early 1800s, and traveled across the Midwest to arrive in Utah in the late 1800s. IBD/coalescent-based allele dating analysis reveals a relatively recent origin of the AF risk allele (~5000 years). Thus, our approach broadens the scope of study for disease susceptibility alleles to the context of human migration and ancestral origins.

A Poisson binomial-based statistical testing framework for comorbidity discovery across electronic health record datasets.

Discovering the concomitant occurrence of distinct medical conditions in a patient, also known as comorbidities, is a prerequisite for creating patient outcome prediction tools. Current comorbidity discovery applications are designed for small datasets and use stratification to control for confounding variables such as age, sex or ancestry. Stratification lowers false positive rates, but reduces power, as the size of the study cohort is decreased. Here we describe a Poisson binomial-based approach to comorbidity discovery (PBC) designed for big-data applications that circumvents the need for stratification. PBC adjusts for confounding demographic variables on a per-patient basis and models temporal relationships. We benchmark PBC using two datasets to compute comorbidity statistics on 4,623,841 pairs of potentially comorbid medical terms. The results of this computation are provided as a searchable web resource. Compared with current methods, the PBC approach reduces false positive associations while retaining statistical power to discover true comorbidities.

De novo and recessive forms of congenital heart disease have distinct genetic and phenotypic landscapes.

The genetic architecture of sporadic congenital heart disease (CHD) is characterized by enrichment in damaging de novo variants in chromatin-modifying genes. To test the hypothesis that gene pathways contributing to de novo forms of CHD are distinct from those for recessive forms, we analyze 2391 whole-exome trios from the Pediatric Cardiac Genomics Consortium. We deploy a permutation-based gene-burden analysis to identify damaging recessive and compound heterozygous genotypes and disease genes, controlling for confounding effects, such as background mutation rate and ancestry. Cilia-related genes are significantly enriched for damaging rare recessive genotypes, but comparatively depleted for de novo variants. The opposite trend is observed for chromatin-modifying genes. Other cardiac developmental gene classes have less stratification by mode of inheritance than cilia and chromatin-modifying gene classes. Our analyses reveal dominant and recessive CHD are associated with distinct gene functions, with cilia-related genes providing a reservoir of rare segregating variation leading to CHD.

Antigenic evolution of H9N2 chicken influenza viruses isolated in China during 2009-2013 and selection of a candidate vaccine strain with broad cross-reactivity.

We previously demonstrated that H9N2 subtype avian influenza viruses (AIVs) isolated from 1994 to 2008 evolved into distinct antigenic groups (C, D, and E) and then underwent antigenic drift from commercial vaccines, causing a country-wide outbreak during 2010-2013. In this study, H9N2 AIVs isolated from chickens during 2009-2013 were antigenically analyzed by performing hemagglutination inhibition and neutralization assays using a panel of polyclonal antibodies. Our findings confirmed the antigenic drift of recent H9N2 viruses from the commercial vaccine and showed that most of these antigenic variants form a novel HI antigenic group, F, with a few belonging to groups D and E. Slight antigenic variation was observed in group F viruses. Genetic analysis of amino acid sequences deduced from hemagglutinin (HA) gene sequences indicated that 9 of 15 mutations predominant in the 2009-2013 viruses can be mapped to known antigenic sites, which might be responsible for the novel antigenicity of group F. These antigenic changes make it necessary to modify the influenza vaccine to ensure efficient protection. A vaccine candidate, Ck/HeB/YT/10, was selected and provided significant protection against viruses from different antigenic groups in terms of reduction in virus shedding, suggesting broad cross-reactivity. Taken together, our results indicate that the H9N2 chicken influenza viruses in China have evolved from distinct antigenic groups into a novel group F that became dominant during the country-wide outbreak and now seems to be undergoing new antigenic divergence. Systematic surveillance and timely updating of vaccine strains are important for viral prevention and control in the future.

Evolution of the H9N2 influenza genotype that facilitated the genesis of the novel H7N9 virus.

The emergence of human infection with a novel H7N9 influenza virus in China raises a pandemic concern. Chicken H9N2 viruses provided all six of the novel reassortant's internal genes. However, it is not fully understood how the prevalence and evolution of these H9N2 chicken viruses facilitated the genesis of the novel H7N9 viruses. Here we show that over more than 10 y of cocirculation of multiple H9N2 genotypes, a genotype (G57) emerged that had changed antigenicity and improved adaptability in chickens. It became predominant in vaccinated farm chickens in China, caused widespread outbreaks in 2010-2013 before the H7N9 viruses emerged in humans, and finally provided all of their internal genes to the novel H7N9 viruses. The prevalence and variation of H9N2 influenza virus in farmed poultry could provide an important early warning of the emergence of novel reassortants with pandemic potential.

Genomic landscape of Ewing sarcoma defines an aggressive subtype with co-association of STAG2 and TP53 mutations.

UNLABELLED: Ewing sarcoma is a primary bone tumor initiated by EWSR1-ETS gene fusions. To identify secondary genetic lesions that contribute to tumor progression, we performed whole-genome sequencing of 112 Ewing sarcoma samples and matched germline DNA. Overall, Ewing sarcoma tumors had relatively few single-nucleotide variants, indels, structural variants, and copy-number alterations. Apart from whole chromosome arm copy-number changes, the most common somatic mutations were detected in STAG2 (17%), CDKN2A (12%), TP53 (7%), EZH2, BCOR, and ZMYM3 (2.7% each). Strikingly, STAG2 mutations and CDKN2A deletions were mutually exclusive, as confirmed in Ewing sarcoma cell lines. In an expanded cohort of 299 patients with clinical data, we discovered that STAG2 and TP53 mutations are often concurrent and are associated with poor outcome. Finally, we detected subclonal STAG2 mutations in diagnostic tumors and expansion of STAG2-immunonegative cells in relapsed tumors as compared with matched diagnostic samples. SIGNIFICANCE: Whole-genome sequencing reveals that the somatic mutation rate in Ewing sarcoma is low. Tumors that harbor STAG2 and TP53 mutations have a particularly dismal prognosis with current treatments and require alternative therapies. Novel drugs that target epigenetic regulators may constitute viable therapeutic strategies in a subset of patients with mutations in chromatin modifiers.

The landscape of somatic mutations in epigenetic regulators across 1,000 paediatric cancer genomes.

Studies of paediatric cancers have shown a high frequency of mutation across epigenetic regulators. Here we sequence 633 genes, encoding the majority of known epigenetic regulatory proteins, in over 1,000 paediatric tumours to define the landscape of somatic mutations in epigenetic regulators in paediatric cancer. Our results demonstrate a marked variation in the frequency of gene mutations across 21 different paediatric cancer subtypes, with the highest frequency of mutations detected in high-grade gliomas, T-lineage acute lymphoblastic leukaemia and medulloblastoma, and a paucity of mutations in low-grade glioma and retinoblastoma. The most frequently mutated genes are H3F3A, PHF6, ATRX, KDM6A, SMARCA4, ASXL2, CREBBP, EZH2, MLL2, USP7, ASXL1, NSD2, SETD2, SMC1A and ZMYM3. We identify novel loss-of-function mutations in the ubiquitin-specific processing protease 7 (USP7) in paediatric leukaemia, which result in decreased deubiquitination activity. Collectively, our results help to define the landscape of mutations in epigenetic regulatory genes in paediatric cancer and yield a valuable new database for investigating the role of epigenetic dysregulations in cancer.

Computational design of protein-small molecule interfaces.

The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation-π, and π-π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein-ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement.

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions.

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with ROSETTA. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several ROSETTA protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Towards ligand docking including explicit interface water molecules.

Small molecule docking predicts the interaction of a small molecule ligand with a protein at atomic-detail accuracy including position and conformation the ligand but also conformational changes of the protein upon ligand binding. While successful in the majority of cases, docking algorithms including RosettaLigand fail in some cases to predict the correct protein/ligand complex structure. In this study we show that simultaneous docking of explicit interface water molecules greatly improves Rosetta's ability to distinguish correct from incorrect ligand poses. This result holds true for both protein-centric water docking wherein waters are located relative to the protein binding site and ligand-centric water docking wherein waters move with the ligand during docking. Protein-centric docking is used to model 99 HIV-1 protease/protease inhibitor structures. We find protease inhibitor placement improving at a ratio of 9:1 when one critical interface water molecule is included in the docking simulation. Ligand-centric docking is applied to 341 structures from the CSAR benchmark of diverse protein/ligand complexes [1]. Across this diverse dataset we see up to 56% recovery of failed docking studies, when waters are included in the docking simulation.

Small-molecule ligand docking into comparative models with Rosetta.

Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models.

Prediction of HIV-1 protease/inhibitor affinity using RosettaLigand.

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

Rosetta Ligand docking with flexible XML protocols.

RosettaLigand is premiere software for predicting how a protein and a small molecule interact. Benchmark studies demonstrate that 70% of the top scoring RosettaLigand predicted interfaces are within 2Å RMSD from the crystal structure [1]. The latest release of Rosetta ligand software includes many new features, such as (1) docking of multiple ligands simultaneously, (2) representing ligands as fragments for greater flexibility, (3) redesign of the interface during docking, and (4) an XML script based interface that gives the user full control of the ligand docking protocol.

RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite.

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

Practically useful: what the Rosetta protein modeling suite can do for you.

The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 A. In addition to de novo structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem.

Predicting the sensitivity and specificity of published real-time PCR assays.

BACKGROUND: In recent years real-time PCR has become a leading technique for nucleic acid detection and quantification. These assays have the potential to greatly enhance efficiency in the clinical laboratory. Choice of primer and probe sequences is critical for accurate diagnosis in the clinic, yet current primer/probe signature design strategies are limited, and signature evaluation methods are lacking. METHODS: We assessed the quality of a signature by predicting the number of true positive, false positive and false negative hits against all available public sequence data. We found real-time PCR signatures described in recent literature and used a BLAST search based approach to collect all hits to the primer-probe combinations that should be amplified by real-time PCR chemistry. We then compared our hits with the sequences in the NCBI taxonomy tree that the signature was designed to detect. RESULTS: We found that many published signatures have high specificity (almost no false positives) but low sensitivity (high false negative rate). Where high sensitivity is needed, we offer a revised methodology for signature design which may designate that multiple signatures are required to detect all sequenced strains. We use this methodology to produce new signatures that are predicted to have higher sensitivity and specificity. CONCLUSION: We show that current methods for real-time PCR assay design have unacceptably low sensitivities for most clinical applications. Additionally, as new sequence data becomes available, old assays must be reassessed and redesigned. A standard protocol for both generating and assessing the quality of these assays is therefore of great value. Real-time PCR has the capacity to greatly improve clinical diagnostics. The improved assay design and evaluation methods presented herein will expedite adoption of this technique in the clinical lab.

ABCB1 and GST polymorphisms associated with TP53 status in breast cancer.

BACKGROUND AND OBJECTIVE: Many environmental and genetic factors influence the development of chemoresistance. The goal of this study was to characterize the genetic variation in the ABCB1, GSTM1, GSTT1 and GSTP1 genes, as well as the haplotype structure in the ABCB1 gene. METHODS: Variants in these genes were studied in 109 healthy controls and 93 breast cancer cases, both of Caucasian origin. The cases were analyzed in relation to TP53 mutation status and response to doxorubicin. Both single and multiple single nucleotide polymorphism analyses were performed. RESULTS: Chi-square analyses revealed a significant association between TP53 mutation status and both the GA genotype of ABCB1 exon 11 (Ser400Asn) and the GG genotype of GSTP1 (Ile105Val; P<0.01 and P<0.05, respectively). Multifactor dimensionality reduction showed that carriers of the combined GG genotype for GSTP1 and the GG for ABCB1 exon 11 had the highest chance of acquiring a mutation in the TP53 gene (P<0.02). Haplotype analysis of ABCB1 revealed a significantly different distribution of haplotypes between the breast cancer cases and the controls (P<0.01). A specific haplotype association to TP53 mutation (P<0.01) distant metastases (P<0.05) and estrogen receptor status (P<0.05) was also observed in the case group. CONCLUSION: An association between polymorphisms in GSTP1 and ABCB1 and risk of acquiring intratumoral TP53 mutations suggests the existence of putative predisposing genotype backgrounds. The degree of linkage disequilibrium in the ABCB1 gene was higher in healthy individuals, whereas haplotypes in the cases seemed degenerated by a number of low frequency variants. This observation may either point to the existence of a protective haplotype in the controls or may underline the importance of the accumulation of low frequency variants as susceptibility factors.