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Joel S Bader

Publications and source records attributed to Joel S Bader.

At least 19 recordsLinked to original sources
PubMed2026

Human Immunodeficiency Virus-Associated Proteomic Signature of Myocardial Fibrosis and Incident Heart Failure.

BACKGROUND: People with human immunodeficiency virus (HIV) (PWH) are at higher risk of myocardial fibrosis and subsequent heart failure (HF) compared to people without HIV (PWOH). Mechanisms underlying this risk and its specificity to PWH are unclear. METHODS: We measured 2594 proteins in plasma obtained concurrently with cardiovascular magnetic resonance imaging among 342 PWH and PWOH. We estimated associations with HIV serostatus and myocardial fibrosis (elevated extracellular volume fraction [ECV] &#x2265;30% among women, &#x2265;28% among men) using multivariable regression. Among an independent community-based cohort, we estimated associations between the identified signature and time to incident HF. RESULTS: Mean age of participants was 55 (standard deviation [SD], 6) years, 25% were female, 61% were PWH (88% on antiretroviral therapy, 74% with undetectable HIV RNA), and 52% had elevated ECV. We identified 39 proteins and 1 cluster of 42 proteins that were higher among PWH versus PWOH and positively associated with elevated ECV, independent of risk factors (false discovery rate <0.05). Among an independent cohort of 3223 PWOH (mean age, 68 [SD, 9] years; 52% female; 118 incident HF cases over a mean of 9.8 [SD, 1.4] years), we found that this protein cluster and 34 of 39 individual proteins were associated with time to incident HF. This signature was statistically enriched for T-cell activation, tumor necrosis factor signaling, ephrin signaling, and tissue maintenance and repair. CONCLUSIONS: We identified an HIV-related proteomic signature associated with myocardial fibrosis regardless of HIV serostatus and that predicted incident HF among the general population. Our results identify several novel associations related to specific immune processes that may contribute to risk of myocardial fibrosis and subsequent HF among both PWH and PWOH.

Humans↗
PubMed2025

Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.

MOTIVATION: Genome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions. RESULTS: We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNet method builds on existing methods that focus on individual loci with evidence from gene distance and expression quantitative trait loci (eQTL) by sharing information across loci using protein-protein and gene regulatory interaction network data. In an application to cardiac electrophysiology with 226 GWAS loci, only 46 (20%) have within-locus evidence from Mendelian genes, protein-coding changes, or colocalization with eQTL signals. At the remaining 180 loci lacking functional information, SigNet selects 56 genes other than the minimum distance gene, equal to 31% of the information-poor loci and 25% of the GWAS loci overall. Assessment by pathway enrichment demonstrates improved performance by SigNet. Review of individual loci shows literature evidence for genes selected by SigNet, including PMP22 as a novel causal gene candidate.

Genome-Wide Association Study↗
PubMed2006

Phosphoproteomic analysis of Her2/neu signaling and inhibition.

Her2/neu (Her2) is a tyrosine kinase belonging to the EGF receptor (EGFR)/ErbB family and is overexpressed in 20-30% of human breast cancers. We sought to characterize Her2 signal transduction pathways further by using MS-based quantitative proteomics. Stably transfected cell lines overexpressing Her2 or empty vector were generated, and the effect of an EGFR and Her2 selective tyrosine kinase inhibitor, PD168393, on these cells was characterized. Quantitative measurements were obtained on 462 proteins by using the SILAC (stable isotope labeling with amino acids in cell culture) method to monitor three conditions simultaneously. Of these proteins, 198 showed a significant increase in tyrosine phosphorylation in Her2-overexpressing cells, and 81 showed a significant decrease in phosphorylation. Treatment of Her2-overexpressing cells with PD168393 showed rapid reversibility of the majority of the Her2-triggered phosphorylation events. Phosphoproteins that were identified included many known Her2 signaling molecules as well as known EGFR signaling proteins that had not been previously linked to Her2, such as Stat1, Dok1, and delta-catenin. Importantly, several previously uncharacterized Her2 signaling proteins were identified, including Axl tyrosine kinase, the adaptor protein Fyb, and the calcium-binding protein Pdcd-6/Alg-2. We also identified a phosphorylation site in Her2, Y877, which is located in the activation loop of the kinase domain, is distinct from the known C-terminal tail autophosphorylation sites, and may have important implications for regulation of Her2 signaling. Network modeling, which combined phosphoproteomic results with literature-curated protein-protein interaction data, was used to suggest roles for some of the previously unidentified Her2 signaling proteins.

Amino Acid Sequence↗
PubMed2006

RNA interference analysis of Legionella in Drosophila cells: exploitation of early secretory apparatus dynamics.

Legionella pneumophila translocates multiple bacterial effector proteins into host cells to direct formation of a replication vacuole for the bacterium. The emerging consensus is that formation of this compartment involves recruitment of membrane material that traffics between the endoplasmic reticulum (ER) and Golgi. To investigate this model, a targeted approach was used to knock down expression of proteins involved in membrane trafficking, using RNA interference in Drosophila cells. Surprisingly, few single knockdowns of ER-Golgi transport proteins decreased L. pneumophila replication. By analyzing double-stranded RNAs in pairs, combinations were identified that together caused defects in intracellular replication, consistent with the model that membrane traffic funnels into the replication vacuole from multiple sources. In particular, simultaneous depletion of the intermediate compartment and Golgi-tethering factor transport protein particle together with the ER SNARE protein Sec22 reduced replication efficiency, indicating that introduction of lesions at distinct sites in the secretory system reduces replication efficiency. In contrast to knockdowns in secretory traffic, which required multiple simultaneous hits, knockdown of single cytosolic components of ER-associated degradation, including Cdc48/p97 and associated cofactors, was sufficient to inhibit intracellular replication. The requirement for the Cdc48/p97 complex was conserved in mammalian cells, in which replication vacuoles showed intense recruitment of ubiquitinated proteins, the preferred substrates of Cdc48/p97. This complex promoted dislocation of both ubiquitinated proteins and bacterial effectors from the replication vacuole, consistent with the model that maintenance of high-level replication requires surveillance of the vacuole surface. This work demonstrates that L. pneumophila has the ability to gain access to multiple sites in the secretory system and provides the first evidence for a role of the Cdc48/p97 complex in promoting intracellular replication of pathogens and maintenance of replication vacuoles.

Adenosine Triphosphatases↗
PubMed2006

A DNA integrity network in the yeast Saccharomyces cerevisiae.

A network governing DNA integrity was identified in yeast by a global genetic analysis of synthetic fitness or lethality defect (SFL) interactions. Within this network, 16 functional modules or minipathways were defined based on patterns of global SFL interactions. Modules or genes involved in DNA replication, DNA-replication checkpoint (DRC) signaling, and oxidative stress response were identified as the major guardians against lethal spontaneous DNA damage, efficient repair of which requires the functions of the DNA-damage checkpoint signaling and multiple DNA-repair pathways. This genome-wide genetic interaction network also identified novel components (DIA2, NPT1, HST3, HST4, and the CSM1 module) that potentially contribute to mitotic DNA replication and genomic stability and revealed novel functions of well-studied genes (the CTF18 module) in DRC signaling. This network will guide more detailed characterization of mechanisms governing DNA integrity in yeast and other organisms.

Chromosomal Proteins, Non-Histone↗
PubMed2006

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets.

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.

Animals↗
PubMed2005

Genetic Interaction Motif Finding by expectation maximization--a novel statistical model for inferring gene modules from synthetic lethality.

BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. RESULTS: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. CONCLUSION: GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway.

Algorithms↗
PubMed2005

Global synthetic-lethality analysis and yeast functional profiling.

The Saccharomyces genome-deletion project created >5900 'molecularly barcoded' yeast knockout mutants (YKO mutants). The YKO mutant collections have facilitated large-scale analyses of a multitude of mutant phenotypes. For example, both synthetic genetic array (SGA) and synthetic-lethality analysis by microarray (SLAM) methods have been used for synthetic-lethality screens. Global analysis of synthetic lethality promises to identify cellular pathways that 'buffer' each other biologically. The combination of global synthetic-lethality analysis, together with global protein-protein interaction analyses, mRNA expression profiling and functional profiling will, in principle, enable construction of a cellular 'wiring diagram' that will help frame a deeper understanding of human biology and disease.

Chromosome Mapping↗
PubMed2005

Gene function prediction from congruent synthetic lethal interactions in yeast.

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein-dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans.

Alleles↗
PubMed2005

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast.

BACKGROUND: In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. RESULTS: We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). CONCLUSION: Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed).

Protein Interaction Mapping↗
PubMed2005

Genome sequencing in microfabricated high-density picolitre reactors.

The proliferation of large-scale DNA-sequencing projects in recent years has driven a search for alternative methods to reduce time and cost. Here we describe a scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments. The apparatus uses a novel fibre-optic slide of individual wells and is able to sequence 25 million bases, at 99% or better accuracy, in one four-hour run. To achieve an approximately 100-fold increase in throughput over current Sanger sequencing technology, we have developed an emulsion method for DNA amplification and an instrument for sequencing by synthesis using a pyrosequencing protocol optimized for solid support and picolitre-scale volumes. Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine.

Electrophoresis, Capillary↗
PubMed2004

A robust toolkit for functional profiling of the yeast genome.

Study of mutant phenotypes is a fundamental method for understanding gene function. The construction of a near-complete collection of yeast knockouts (YKO) and the unique molecular barcodes (or TAGs) that identify each strain has enabled quantitative functional profiling of Saccharomyces cerevisiae. By using these TAGs and the SGA reporter, MFA1pr-HIS3, which facilitates conversion of heterozygous diploid YKO strains into haploid mutants, we have developed a set of highly efficient microarray-based techniques, collectively referred as dSLAM (diploid-based synthetic lethality analysis on microarrays), to probe genome-wide gene-chemical and gene-gene interactions. Direct comparison revealed that these techniques are more robust than existing methods in functional profiling of the yeast genome. Widespread application of these tools will elucidate a comprehensive yeast genetic network.

Gene Expression Profiling↗
PubMed2004

Probabilistic inference of molecular networks from noisy data sources.

Information on molecular networks, such as networks of interacting proteins, comes from diverse sources that contain remarkable differences in distribution and quantity of errors. Here, we introduce a probabilistic model useful for predicting protein interactions from heterogeneous data sources. The model describes stochastic generation of protein-protein interaction networks with real-world properties, as well as generation of two heterogeneous sources of protein-interaction information: research results automatically extracted from the literature and yeast two-hybrid experiments. Based on the domain composition of proteins, we use the model to predict protein interactions for pairs of proteins for which no experimental data are available. We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources.

Algorithms↗
PubMed2003

Gaining confidence in high-throughput protein interaction networks.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.

Algorithms↗
PubMed2003

Greedily building protein networks with confidence.

MOTIVATION: With genome sequences complete for human and model organisms, it is essential to understand how individual genes and proteins are organized into biological networks. Much of the organization is revealed by proteomics experiments that now generate torrents of data. Extracting relevant complexes and pathways from high-throughput proteomics data sets has posed a challenge, however, and new methods to identify and extract networks are essential. We focus on the problem of building pathways starting from known proteins of interest. RESULTS: We have developed an efficient, greedy algorithm, SEEDY, that extracts biologically relevant biological networks from protein-protein interaction data, building out from selected seed proteins. The algorithm relies on our previous study establishing statistical confidence levels for interactions generated by two-hybrid screens and inferred from mass spectrometric identification of protein complexes. We demonstrate the ability to extract known yeast complexes from high-throughput protein interaction data with a tunable parameter that governs the trade-off between sensitivity and selectivity. DNA damage repair pathways are presented as a detailed example. We highlight the ability to join heterogeneous data sets, in this case protein-protein interactions and genetic interactions, and the appearance of cross-talk between pathways caused by re-use of shared components. SIGNIFICANCE AND COMPARISON: The significance of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E interactions, a single adjustable parameter controls the size of the pathways that are generated, and an associated P-value indicates the statistical confidence that the pathways are enriched for proteins with a coherent function. Previous approaches have focused on extracting sub-networks by identifying motifs enriched in known biological networks. SEEDY provides the complementary ability to perform a directed search based on proteins of interest. AVAILABILITY: SEEDY software (Perl source), data tables and confidence score models (R source) are freely available from the author.

Algorithms↗
PubMed2002

DNA Pooling: a tool for large-scale association studies.

DNA pooling is a practical way to reduce the cost of large-scale association studies to identify susceptibility loci for common diseases. Pooling allows allele frequencies in groups of individuals to be measured using far fewer PCR reactions and genotyping assays than are used when genotyping individuals. Here, we discuss recent developments in quantitative genotyping assays and in the design and analysis of pooling studies. Sophisticated pooling designs are being developed that can take account of hidden population stratification, confounders and inter-loci interactions, and that allow the analysis of haplotypes.

Animals↗
PubMed2002

Optimal selection strategies for QTL mapping using pooled DNA samples.

The cost of large-scale association studies may be reduced substantially by analysis of pooled DNA from multiple individuals. Here we examine the optimal symmetric and asymmetric designs for pooling experiments for quantitative traits under a range of assumptions about the underlying genetic model and the sources of experimental errors in allele frequency estimation. The results indicate that, in the absence of experimental errors and for common alleles with additive effects, a symmetric pooling scheme comparing the top 27% with the bottom 27% of the trait distribution is optimal, extracting 80% the total information available. A symmetric design is not optimal for rare or recessive alleles, which require asymmetric (or other) pooling strategies. Allele frequency measurement errors reduce the optimal pooling fraction as well as the overall efficiency of the pooling design. In contrast, random variation in the amount of DNA contributed by individuals to a pool reduces only the overall efficiency of the pooling design. Our results emphasize the importance of minimising experimental errors and suggest a pooling fraction of around 20%.

Analysis of Variance↗