Augustin Luna

The Systems Biology Graphical Notation

Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.

The BioPAX community standard for pathway data sharing

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma

Dysregulated metabolism is a hallmark of cancer, manifested through alterations in metabolites. We performed metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs and found that ccRCC is characterized by broad shifts in central carbon metabolism, one-carbon metabolism, and antioxidant response. Tumor progression and metastasis were associated with metabolite increases in glutathione and cysteine/methionine metabolism pathways. We develop an analytic pipeline and visualization tool (metabolograms) to bridge the gap between TCGA transcriptomic profiling and our metabolomic data, which enables us to assemble an integrated pathway-level metabolic atlas and to demonstrate discordance between transcriptome and metabolome. Lastly, expression profiling was performed on a high-glutathione cluster, which corresponds to a poor-survival subgroup in the ccRCC TCGA cohort.

Further evidence for association between ErbB4 and schizophrenia and influence on cognitive intermediate phenotypes in healthy controls

Further evidence for association between ErbB4 and schizophrenia and influence on cognitive intermediate phenotypes in healthy controls

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures.

BackgroundTumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.ResultsWe compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.ConclusionsOur analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types. We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number. Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.

Software support for SBGN maps

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. Contact: sbgn-libsbgn@lists.sourceforge.net

A 5′ Promoter Region SNP in NRG1 is Associated with Schizophrenia Risk and Type III Isoform Expression

NRG1 is a schizophrenia candidate gene which regulates brain development and neural function. The minor allele of rs7014762 in the NRG1 5′ core promoter was associated with schizophrenia (p=0.031) and significant predicted reduced NRG1 Type III isoform expression in postmortem human brain of schizophrenia cases (p=0.001). Our results provide additional evidence for transcriptional dysregulation as a biological mechanism implicating NRG1 in schizophrenia risk. Association between NRG1 and schizophrenia was originally discovered via haplotype analysis in an Icelandic sample (HAPICE) at the 5′ end of the gene1, further replicated in a Scottish population2. In the present report, we examined association between schizophrenia and the NRG1 SNP rs7014762 because it is situated within a core promoter region3 and is physically proximal (87bp) to a functional SNP which has been shown to influence NRG1 type IV isoform expression4. In addition to clinical association analyses, we validated the functional effects of rs7014762 by testing for effects on mRNA expression in postmortem human brain. Cases (N=296) and controls (N=365) were ascertained as part of the Clinical Brain Disorders Branch Sibling Study. Probands met DSM-IV criteria for a broad diagnosis category including schizophrenia, schizoaffective disorder, psychosis NOS, delusional disorder, schizotypal, schizoid, or paranoid personality disorder. Control individuals were screened to exclude individuals with psychiatric diagnoses. All participants gave informed consent and self-identified as Caucasian. Blood was collected and DNA was extracted using standard methods. Genotypes were obtained using the Taqman 5′-exonuclease allelic discrimination assay. Postmortem brain tissue was collected with informed consent from the legal next-of-kin. The sample was previously described in detail along with the NRG1 primer and probe sets3,4. Briefly, hippocampi from 84 normal controls (22 females/62 males, 53 African American/25 American Caucasian/5 Hispanic and 1 Asian individual, mean age 40.5 ±(SD) 15.4 years, post mortem interval (PMI) 30.7 ± 13.9 hrs, pH 6.59 ± 0.32); and 44 schizophrenic patients (15 females/29 males, 24 African Americans/20 Caucasians, mean age 49.7 ± 17.2 years, PMI, 36.3 ± 17.7 hrs, pH 6.48 ± 0.28) were available for study. Diagnoses were determined by independent reviews of clinical records and family interviews by two psychiatrists using DSM-IV criteria. Macro- and microscopic neuropathological examinations and toxicology screening were performed prior to inclusion. No differences were observed on variables that potentially affect gene expression in human postmortem brain (i.e. age, PMI, pH and RIN) by rs7014762 genotype group. NRG1 (types I-IV) mRNA splice isoform expression was measured by real-time quantitative RT-PCR using an ABI Prism 7900 sequence detection system with 384-well format (Applied Biosystems, Foster City, CA, USA). Case-control analyses used unconditional logistic regression. Effects of rs7014762 on NRG1 isoform mRNA expression were examined using ANOVA with genotype and diagnosis as independent factors, controlling for race. Where there was a significant genotype bydiagnosis interaction, individual group post hoc tests were examined. P-values were not adjusted for multiple testing. rs7014762 was in Hardy Weinberg equilibrium in cases and controls (p > 0.05). The minor allele of rs7014762 showed significant association with schizophrenia case status (minor allele (T) carrier OR = 1.49 (1.04, 2.15), p-value = 0.031). mRNA expression analysis revealed a significant diagnosis by genotype interaction on type III isoform expression in the hippocampus (F (5, 106) = 5.98; p-value = 0.003). Post hoc analysis showed the effect of rs7014762 was significant only in patients, whereby individuals heterozygous (LSD; p-value = 0.001) or homozygous (LSD p-value = 0.002) for the minor allele exhibited significantly lower levels of NRG1 type III expression compared to major allele homozygotes (Figure 1). No effects of race or race by genotype interactions were observed. No effects of genotype were observed for any other NRG1 isoform. Figure 1 Normalized NRG1 Type III Expression by rs7014762 Genotype in Schizophrenia Cases and Healthy Controls We report association between schizophrenia and rs7014762 in a case-control sample and show that the same allele of rs7014762 in the NRG1 5′ promoter region significantly predicts lower type III isoform expression levels in patients. NRG1 is expressed throughout the human brain, including the hippocampus and prefrontal cortex5, two areas implicated in schizophrenia, and individuals with schizophrenia show abnormalities in ErbB4–NRG1 signaling in the brain versus healthy controls6. In animal models, NRG1 type III isoform has been associated with axonal myelination7, lateral ventricle enlargement, and reduced function in the prefrontal cortex and hippocampus8. Disturbances in myelination/oligodendroglial density in individuals with schizophrenia have been observed9 and suggest reduced structural connectivity may be part of the neurobiology of schizophrenia. The same allele at this SNP has been shown to be associated with increased risk for bipolar disorder10. In addition, rs7014752 is 87 bp from rs6994992 (HAPICE SNP8NRG1243177) and is in moderate LD with this HAPICE SNP (D′ = 0.96, r2 = 0.21). SNP rs6994992 has previously been reported to be a functional promoter variant associated with schizophrenia and the regulated expression of a novel brain-specific isoform of NRG1, type IV, in humans3,4 and was associated with alterations in activation in frontal/temporal lobes, higher risk of psychotic symptomology and reduction in premorbid IQ in schizophrenia patients11. Together these observations suggest that variation in the HAPICE region may impact risk for schizophrenia via transcriptional regulation of multiple NRG1 isoforms.

SIRT1/PARP1 crosstalk: connecting DNA damage and metabolism

An intricate network regulates the activities of SIRT1 and PARP1 proteins and continues to be uncovered. Both SIRT1 and PARP1 share a common co-factor nicotinamide adenine dinucleotide (NAD+) and several common substrates, including regulators of DNA damage response and circadian rhythms. We review this complex network using an interactive Molecular Interaction Map (MIM) to explore the interplay between these two proteins. Here we discuss how NAD + competition and post-transcriptional/translational feedback mechanisms create a regulatory network sensitive to environmental cues, such as genotoxic stress and metabolic states, and examine the role of those interactions in DNA repair and ultimately, cell fate decisions.

Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0

Abstract People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

Gene Expression Profiles of the NCI-60 Human Tumor Cell Lines Define Molecular Interaction Networks Governing Cell Migration Processes

Although there is extensive information on gene expression and molecular interactions in various cell types, integrating those data in a functionally coherent manner remains challenging. This study explores the premise that genes whose expression at the mRNA level is correlated over diverse cell lines are likely to function together in a network of molecular interactions. We previously derived expression-correlated gene clusters from the database of the NCI-60 human tumor cell lines and associated each cluster with function categories of the Gene Ontology (GO) database. From a cluster rich in genes associated with GO categories related to cell migration, we extracted 15 genes that were highly cross-correlated; prominent among them were RRAS, AXL, ADAM9, FN14, and integrin-beta1. We then used those 15 genes as bait to identify other correlated genes in the NCI-60 database. A survey of current literature disclosed, not only that many of the expression-correlated genes engaged in molecular interactions related to migration, invasion, and metastasis, but that highly cross-correlated subsets of those genes engaged in specific cell migration processes. We assembled this information in molecular interaction maps (MIMs) that depict networks governing 3 cell migration processes: degradation of extracellular matrix, production of transient focal complexes at the leading edge of the cell, and retraction of the rear part of the cell. Also depicted are interactions controlling the release and effects of calcium ions, which may regulate migration in a spaciotemporal manner in the cell. The MIMs and associated text comprise a detailed and integrated summary of what is currently known or surmised about the role of the expression cross-correlated genes in molecular networks governing those processes.