A metabolomics pipeline highlights microbial metabolism in bloodstream infections.

The growth of antimicrobial resistance (AMR) highlights an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe infections profoundly alter host metabolism, prior studies have largely ignored microbial metabolism in this context. Here, we describe an iterative, comparative metabolomics pipeline to uncover microbial metabolic features in the complex setting of a host and apply it to investigate gram-negative bloodstream infection (BSI) in patients. We find elevated levels of bacterially derived acetylated polyamines during BSI and discover the enzyme responsible for their production (SpeG). Blocking SpeG activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity also enhances bacterial membrane permeability and increases intracellular antibiotic accumulation, allowing us to overcome AMR in culture and in vivo. This study highlights how tools to study pathogen metabolism in the natural context of infection can reveal and prioritize therapeutic strategies for addressing challenging infections.

Identification and targeting of microbial putrescine acetylation in bloodstream infections.

The growth of antimicrobial resistance (AMR) has highlighted an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe bacterial infections profoundly alter host metabolism, prior studies have largely ignored alterations in microbial metabolism in this context. Performing metabolomics on patient and mouse plasma samples, we identify elevated levels of bacterially-derived N-acetylputrescine during gram-negative bloodstream infections (BSI), with higher levels associated with worse clinical outcomes. We discover that SpeG is the bacterial enzyme responsible for acetylating putrescine and show that blocking its activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity enhances bacterial membrane permeability and results in increased intracellular accumulation of antibiotics, allowing us to overcome AMR of clinical isolates both in culture and in vivo. This study highlights how studying pathogen metabolism in the natural context of infection can reveal new therapeutic strategies for addressing challenging infections.

Power of testing for exposure effects under incomplete mediation.

Mediation analysis studies situations where an exposure may affect an outcome both directly and indirectly through intervening variables called mediators. It is frequently of interest to test for the effect of the exposure on the outcome, and the standard approach is simply to regress the latter on the former. However, it seems plausible that a more powerful test statistic could be achieved by also incorporating the mediators. This would be useful in cases where the exposure effect size might be small, which for example is common in genomics applications. Previous work has shown that this is indeed possible under complete mediation, where there is no direct effect. In most applications, however, the direct effect is likely nonzero. In this paper we study linear mediation models and find that under certain conditions, power gain is still possible under this incomplete mediation setting for testing the null hypothesis that there is neither a direct nor an indirect effect. We study a class of procedures that can achieve this performance and develop their application to both low- and high-dimensional mediators. We then illustrate their performances in simulations as well as in an analysis using DNA methylation mediators to study the effect of cigarette smoking on gene expression.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. SIGNIFICANCE: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Cross-species systems analysis of evolutionary toolkits of neurogenomic response to social challenge.

Social challenges like territorial intrusions evoke behavioral responses in widely diverging species. Recent work has showed that evolutionary "toolkits"-genes and modules with lineage-specific variations but deep conservation of function-participate in the behavioral response to social challenge. Here, we develop a multispecies computational-experimental approach to characterize such a toolkit at a systems level. Brain transcriptomic responses to social challenge was probed via RNA-seq profiling in three diverged species-honey bees, mice and three-spined stickleback fish-following a common methodology, allowing fair comparisons across species. Data were collected from multiple brain regions and multiple time points after social challenge exposure, achieving anatomical and temporal resolution substantially greater than previous work. We developed statistically rigorous analyses equipped to find homologous functional groups among these species at the levels of individual genes, functional and coexpressed gene modules, and transcription factor subnetworks. We identified six orthogroups involved in response to social challenge, including groups represented by mouse genes Npas4 and Nr4a1, as well as common modulation of systems such as transcriptional regulators, ion channels, G-protein-coupled receptors and synaptic proteins. We also identified conserved coexpression modules enriched for mitochondrial fatty acid metabolism and heat shock that constitute the shared neurogenomic response. Our analysis suggests a toolkit wherein nuclear receptors, interacting with chaperones, induce transcriptional changes in mitochondrial activity, neural cytoarchitecture and synaptic transmission after social challenge. It shows systems-level mechanisms that have been repeatedly co-opted during evolution of analogous behaviors, thus advancing the genetic toolkit concept beyond individual genes.

Integrated Analysis of Genetic Ancestry and Genomic Alterations across Cancers.

Disparities in cancer care have been a long-standing challenge. We estimated the genetic ancestry of The Cancer Genome Atlas patients, and performed a pan-cancer analysis on the influence of genetic ancestry on genomic alterations. Compared with European Americans, African Americans (AA) with breast, head and neck, and endometrial cancers exhibit a higher level of chromosomal instability, while a lower level of chromosomal instability was observed in AAs with kidney cancers. The frequencies of TP53 mutations and amplification of CCNE1 were increased in AAs in the cancer types showing higher levels of chromosomal instability. We observed lower frequencies of genomic alterations affecting genes in the PI3K pathway in AA patients across cancers. Our result provides insight into genomic contribution to cancer disparities.

Genetic Reduction in Left Ventricular Protein Kinase C-α and Adverse Ventricular Remodeling in Human Subjects.

BACKGROUND: Inhibition of PKC-α (protein kinase C-α) enhances contractility and cardioprotection in animal models, but effects in humans are unknown. Genotypes at rs9912468 strongly associate with PRKCA expression in the left ventricle, enabling genetic approaches to measure effects of reduced PKC-α in human populations. METHODS AND RESULTS: We analyzed the cis expression quantitative trait locus for PRKCA marked by rs9912468 using 313 left ventricular specimens from European Ancestry patients. The forward strand minor allele (G) at rs9912468 is associated with reduced PKC-α transcript abundance (1.7-fold reduction in minor allele homozygotes, P=1×10-41). This association was cardiac specific in expression quantitative trait locus data sets that span 16 human tissues. Cardiac epigenomic data revealed a predicted enhancer in complete (R2=1.0) linkage disequilibrium with rs9912468 within intron 2 of PRKCA. We cloned this region and used reporter constructs to verify cardiac-specific enhancer activity in vitro in cardiac and noncardiac cells and in vivo in zebrafish. The PRKCA enhancer contains 2 common genetic variants and 4 haplotypes; the haplotype correlated with the rs9912468 PKC-α-lowering allele (G) showed lowest activity. In contrast to previous reports in animal models, the PKC-α-lowering allele is associated with adverse left ventricular remodeling (higher mass, larger diastolic dimension), reduced fractional shortening, and higher risk of dilated cardiomyopathy in human populations. CONCLUSIONS: These findings support PKC-α as a regulator of the human heart but suggest that PKC-α inhibition may adversely affect the left ventricle depending on timing and duration. Pharmacological studies in human subjects are required to discern potential benefits and harms of PKC-α inhibitors as an approach to treat heart disease.

Deep evolutionary conservation of autism-related genes.

E. O. Wilson proposed in Sociobiology that similarities between human and animal societies reflect common mechanistic and evolutionary roots. When introduced in 1975, this controversial hypothesis was beyond science's ability to test. We used genomic analyses to determine whether superficial behavioral similarities in humans and the highly social honey bee reflect common molecular mechanisms. Here, we report that gene expression signatures for individual bees unresponsive to various salient social stimuli are significantly enriched for autism spectrum disorder-related genes. These signatures occur in the mushroom bodies, a high-level integration center of the insect brain. Furthermore, our finding of enrichment was unique to autism spectrum disorders; brain gene expression signatures from other honey bee behaviors do not show this enrichment, nor do datasets from other human behavioral and health conditions. These results demonstrate deep conservation for genes associated with a human social pathology and individual differences in insect social behavior, thus providing an example of how comparative genomics can be used to test sociobiological theory.

Comprehensive Genomic Characterization of Long Non-coding RNAs across Human Cancers.

The discovery of long non-coding RNA (lncRNA) has dramatically altered our understanding of cancer. Here, we describe a comprehensive analysis of lncRNA alterations at transcriptional, genomic, and epigenetic levels in 5,037 human tumor specimens across 13 cancer types from The Cancer Genome Atlas. Our results suggest that the expression and dysregulation of lncRNAs are highly cancer type specific compared with protein-coding genes. Using the integrative data generated by this analysis, we present a clinically guided small interfering RNA screening strategy and a co-expression analysis approach to identify cancer driver lncRNAs and predict their functions. This provides a resource for investigating lncRNAs in cancer and lays the groundwork for the development of new diagnostics and treatments.

Genetic sharing and heritability of paediatric age of onset autoimmune diseases.

Autoimmune diseases (AIDs) are polygenic diseases affecting 7-10% of the population in the Western Hemisphere with few effective therapies. Here, we quantify the heritability of paediatric AIDs (pAIDs), including JIA, SLE, CEL, T1D, UC, CD, PS, SPA and CVID, attributable to common genomic variations (SNP-h(2)). SNP-h(2) estimates are most significant for T1D (0.863±s.e. 0.07) and JIA (0.727±s.e. 0.037), more modest for UC (0.386±s.e. 0.04) and CD (0.454±0.025), largely consistent with population estimates and are generally greater than that previously reported by adult GWAS. On pairwise analysis, we observed that the diseases UC-CD (0.69±s.e. 0.07) and JIA-CVID (0.343±s.e. 0.13) are the most strongly correlated. Variations across the MHC strongly contribute to SNP-h(2) in T1D and JIA, but does not significantly contribute to the pairwise rG. Together, our results partition contributions of shared versus disease-specific genomic variations to pAID heritability, identifying pAIDs with unexpected risk sharing, while recapitulating known associations between autoimmune diseases previously reported in adult cohorts.

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases.

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases.

A functional genomic approach identifies FAL1 as an oncogenic long noncoding RNA that associates with BMI1 and represses p21 expression in cancer.

In a genome-wide survey on somatic copy-number alterations (SCNAs) of long noncoding RNA (lncRNA) in 2,394 tumor specimens from 12 cancer types, we found that about 21.8% of lncRNA genes were located in regions with focal SCNAs. By integrating bioinformatics analyses of lncRNA SCNAs and expression with functional screening assays, we identified an oncogene, focally amplified lncRNA on chromosome 1 (FAL1), whose copy number and expression are correlated with outcomes in ovarian cancer. FAL1 associates with the epigenetic repressor BMI1 and regulates its stability in order to modulate the transcription of a number of genes including CDKN1A. The oncogenic activity of FAL1 is partially attributable to its repression of p21. FAL1-specific siRNAs significantly inhibit tumor growth in vivo.

More powerful genetic association testing via a new statistical framework for integrative genomics.

Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.

Neck dissection after chemoradiotherapy: timing and complications.

OBJECTIVES: To determine the incidence of postchemoradiotherapy (post-CRT) neck dissection (ND) complications; to ascertain whether timing (< 12 vs ≥ 12 weeks) from CRT to ND or other factors are associated with increased complications; and to determine whether ND timing influences disease control or survival. DESIGN: Ten-year retrospective analysis. SETTING: Tertiary care center. PATIENTS: One hundred five patients with head and neck cancer undergoing ND after CRT. MAIN OUTCOME MEASURES: Complications and survival variables compared between groups undergoing ND less than 12 weeks (less-than-12-weeks ND group) and 12 weeks or more (12-weeks-or-more ND group) after CRT. RESULTS: Sixty-seven NDs were performed less than 12 weeks and 38 were performed 12 weeks or more after CRT. Patient characteristics, treatment, and ND pathology results were comparable between the 2 ND groups. The incidence of complications between the less-than-12-weeks and the 12-weeks-or-more ND groups included major wound complications in 8 of 67 (11.9%) vs 1 of 38 (2.6%; P = .15), minor wound complications in 11 of 67 (16.4%) vs 4 of 38 (10.5%; P = .56), airway complications in 7 of 67 (10.4%) vs 2 of 38 (5.3%; P = .48), and systemic complications in 9 of 67 (13.4%) vs 2 of 38 (5.3%; P = .32). The number of patients with at least 1 complication was significantly smaller in the 12-weeks-or-more ND group (P = .04). Multivariate analysis showed that radical ND was significantly associated with an increased number of complications, and higher radiation doses approached significance (P = .05). Induction chemotherapy was associated with fewer wound complications (P = .01). There were no significant differences in overall survival (P = .82), progression-free survival (P = .77), or regional relapse (P = .54) between groups. Positive ND findings were associated with diminished progression-free and overall survival. CONCLUSION: These findings indicate that ND can be safely performed 12 weeks or more after CRT without adversely affecting surgical complications or survival variables.