ARAX: a graph-based modular reasoning tool for translational biomedicine.

MOTIVATION: With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine-supporting that language-that federates semantically integrated knowledge-bases. RESULTS: We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface (API). ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user's query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX's application and utility in specific disease contexts, we present several use-case examples. AVAILABILITY AND IMPLEMENTATION: The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web API endpoint at arax.rtx.ai/api/arax/v1.3/ui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.

BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Genomic Analysis of MSM Rectal Chlamydia trachomatis Isolates Identifies Predicted Tissue-Tropic Lineages Generated by Intraspecies Lateral Gene Transfer-Mediated Evolution.

Chlamydia trachomatis is an obligate intracellular bacterium that causes serious diseases in humans. Rectal infection and disease caused by this pathogen are important yet understudied aspects of C. trachomatis natural history. The University of Washington Chlamydia Repository has a large collection of male-rectal-sourced strains (MSM rectal strains) isolated in Seattle, USA and Lima, Peru. Initial characterization of strains collected over 30 years in both Seattle and Lima led to an association of serovars G and J with male rectal infections. Serovar D, E, and F strains were also collected from MSM patients. Genome sequence analysis of a subset of MSM rectal strains identified a clade of serovar G and J strains that had high overall genomic identity. A genome-wide association study was then used to identify genomic loci that were correlated with tissue tropism in a collection of serovar-matched male rectal and female cervical strains. The polymorphic membrane protein PmpE had the strongest correlation, and amino acid sequence alignments identified a set of PmpE variable regions (VRs) that were correlated with host or tissue tropism. Examination of the positions of VRs by the protein structure-predicting Alphafold2 algorithm demonstrated that the VRs were often present in predicted surface-exposed loops in both PmpE and PmpH protein structure. Collectively, these studies identify possible tropism-predictive loci for MSM rectal C. trachomatis infections and identify predicted surface-exposed variable regions of Pmp proteins that may function in MSM rectal versus cervical tropism differences.

Predicting chemotherapy response using a variational autoencoder approach.

BACKGROUND: Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma. RESULTS: We found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor's gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested. CONCLUSIONS: Given high-dimensional "omics" data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.

Function of C/EBPdelta in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals.

The innate immune system is like a double-edged sword: it is absolutely required for host defense against infection, but when uncontrolled, it can trigger a plethora of inflammatory diseases. Here we use systems-biology approaches to predict and confirm the existence of a gene-regulatory network involving dynamic interaction among the transcription factors NF-kappaB, C/EBPdelta and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of the genes encoding interleukin 6 and C/EBPdelta and experimentally confirmed the prediction that the combination of an initiator (NF-kappaB), an amplifier (C/EBPdelta) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4-induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately.

Exosomal proteomic signatures correlate with drug resistance and carboplatin treatment outcome in a spontaneous model of canine osteosarcoma.

BACKGROUND: Osteosarcoma patients often experience poor outcomes despite chemotherapy treatment, likely due in part to various mechanisms of tumor cell innate and/or acquired drug resistance. Exosomes, microvesicles secreted by cells, have been shown to play a role in drug resistance, but a comprehensive protein signature relating to osteosarcoma carboplatin resistance has not been fully characterized. METHODS: In this study, cell lysates and exosomes from two derivatives (HMPOS-2.5R and HMPOS-10R) of the HMPOS osteosarcoma cell line generated by repeated carboplatin treatment and recovery, were characterized proteomically by mass spectrometry. Protein cargos of circulating serum exosomes from dogs with naturally occurring osteosarcoma, were also assessed by mass spectrometry, to identify biomarkers that discriminate between good and poor responders to carboplatin therapy. RESULTS: Both cell lysates and exosomes exhibited distinct protein signatures related to drug resistance. Furthermore, exosomes from the resistant HMPOS-2.5R cell line were found to transfer drug resistance to drug-sensitive HMPOS cells. The comparison of serum exosomes from dogs with a favorable disease-free interval [DFI] of > 300 days, and dogs with < 100 days DFI revealed a proteomic signature that could discriminate between the two cohorts with high accuracy. Furthermore, when the patient's exosomes were compared to exosomes isolated from carboplatin resistant cell lines, several putative biomarkers were found to be shared. CONCLUSIONS: The findings of this study highlight the significance of exosomes in the potential transfer of drug resistance, and the discovery of novel biomarkers for the development of liquid biopsies to better guide personalized chemotherapy treatment.

Pan-tissue transcriptome analysis of long noncoding RNAs in the American beaver Castor canadensis.

BACKGROUND: Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver (Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species. RESULTS: Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such as XIST, MEG3, TINCR, and NIPBL-DT. We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs. CONCLUSIONS: To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs-both novel and those with known orthologs-are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.

Loss of 2 Akt (Protein Kinase B) Isoforms in Hematopoietic Cells Diminished Monocyte and Macrophage Survival and Reduces Atherosclerosis in Ldl Receptor-Null Mice.

Objective- Macrophages express 3 Akt (protein kinase B) isoforms, Akt1, Akt2, and Akt3, which display isoform-specific functions but may be redundant in terms of Akt survival signaling. We hypothesize that loss of 2 Akt isoforms in macrophages will suppress their ability to survive and modulate the development of atherosclerosis. Approach and Results- To test this hypothesis, we reconstituted male Ldlr-/- mice with double Akt2/Akt3 knockout hematopoietic cells expressing only the Akt1 isoform (Akt1only). There were no differences in body weight and plasma lipid levels between the groups after 8 weeks of the Western diet; however, Akt1only→ Ldlr-/- mice developed smaller (57.6% reduction) atherosclerotic lesions with more apoptotic macrophages than control mice transplanted with WT (wild type) cells. Next, male and female Ldlr-/- mice were reconstituted with double Akt1/Akt2 knockout hematopoietic cells expressing the Akt3 isoform (Akt3only). Female and male Akt3only→ Ldlr-/- recipients had significantly smaller (61% and 41%, respectively) lesions than the control WT→ Ldlr-/- mice. Loss of 2 Akt isoforms in hematopoietic cells resulted in markedly diminished levels of white blood cells, B cells, and monocytes and compromised viability of monocytes and peritoneal macrophages compared with WT cells. In response to lipopolysaccharides, macrophages with a single Akt isoform expressed low levels of inflammatory cytokines; however, Akt1only macrophages were distinct in expressing high levels of antiapoptotic Il10 compared with WT and Akt3only cells. Conclusions- Loss of 2 Akt isoforms in hematopoietic cells, preserving only a single Akt1 or Akt3 isoform, markedly compromises monocyte and macrophage viability and diminishes early atherosclerosis in Ldlr-/- mice.

CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features.

BACKGROUND: We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP "radius" as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures. RESULTS: We expanded the set of reference SNPs to 39,083 (the OSU18 set) and extracted CERENKOV SNP feature data. We computed radius empirical likelihoods and likelihood densities for rSNPs and control SNPs, and found significant likelihood differences between rSNPs and control SNPs. We fit parametric models of likelihood distributions for five different distance measures to obtain ten log-likelihood features that we combined with the 248-dimensional CERENKOV feature matrix. On the OSU18 SNP set, we measured the classification accuracy of CERENKOV with and without the new distance-based features, and found that the addition of distance-based features significantly improves rSNP recognition performance as measured by AUPVR, AUROC, and AVGRANK. Along with feature data for the OSU18 set, the software code for extracting the base feature matrix, estimating ten distance-based likelihood ratio features, and scoring candidate causal SNPs, are released as open-source software CERENKOV2. CONCLUSIONS: Accounting for the locus-specific geometry of SNPs in data-space significantly improved the accuracy with which noncoding rSNPs can be computationally identified.

An LXR-NCOA5 gene regulatory complex directs inflammatory crosstalk-dependent repression of macrophage cholesterol efflux.

LXR-cofactor complexes activate the gene expression program responsible for cholesterol efflux in macrophages. Inflammation antagonizes this program, resulting in foam cell formation and atherosclerosis; however, the molecular mechanisms underlying this antagonism remain to be fully elucidated. We use promoter enrichment-quantitative mass spectrometry (PE-QMS) to characterize the composition of gene regulatory complexes assembled at the promoter of the lipid transporter Abca1 following downregulation of its expression. We identify a subset of proteins that show LXR ligand- and binding-dependent association with the Abca1 promoter and demonstrate they differentially control Abca1 expression. We determine that NCOA5 is linked to inflammatory Toll-like receptor (TLR) signaling and establish that NCOA5 functions as an LXR corepressor to attenuate Abca1 expression. Importantly, TLR3-LXR signal crosstalk promotes recruitment of NCOA5 to the Abca1 promoter together with loss of RNA polymerase II and reduced cholesterol efflux. Together, these data significantly expand our knowledge of regulatory inputs impinging on the Abca1 promoter and indicate a central role for NCOA5 in mediating crosstalk between pro-inflammatory and anti-inflammatory pathways that results in repression of macrophage cholesterol efflux.

Elucidating the transcriptional program of feline injection-site sarcoma using a cross-species mRNA-sequencing approach.

BACKGROUND: Feline injection-site sarcoma (FISS), an aggressive iatrogenic subcutaneous malignancy, is challenging to manage clinically and little is known about the molecular basis of its pathogenesis. Tumor transcriptome profiling has proved valuable for gaining insights into the molecular basis of cancers and for identifying new therapeutic targets. Here, we report the first study of the FISS transcriptome and the first cross-species comparison of the FISS transcriptome with those of anatomically similar soft-tissue sarcomas in dogs and humans. METHODS: Using high-throughput short-read paired-end sequencing, we comparatively profiled FISS tumors vs. normal tissue samples as well as cultured FISS-derived cell lines vs. skin-derived fibroblasts. We analyzed the mRNA-seq data to compare cancer/normal gene expression level, identify biological processes and molecular pathways that are associated with the pathogenesis of FISS, and identify multimegabase genomic regions of potential somatic copy number alteration (SCNA) in FISS. We additionally conducted cross-species analyses to compare the transcriptome of FISS to those of soft-tissue sarcomas in dogs and humans, at the level of cancer/normal gene expression ratios. RESULTS: We found: (1) substantial differential expression biases in feline orthologs of human oncogenes and tumor suppressor genes suggesting conserved functions in FISS; (2) a genomic region with recurrent SCNA in human sarcomas that is syntenic to a feline genomic region of probable SCNA in FISS; and (3) significant overlap of the pattern of transcriptional alterations in FISS with the patterns of transcriptional alterations in soft-tissue sarcomas in humans and in dogs. We demonstrated that a protein, BarH-like homeobox 1 (BARX1), has increased expression in FISS cells at the protein level. We identified 11 drugs and four target proteins as potential new therapies for FISS, and validated that one of them (GSK-1059615) inhibits growth of FISS-derived cells in vitro. CONCLUSIONS: (1) Window-based analysis of mRNA-seq data can uncover SCNAs. (2) The transcriptome of FISS-derived cells is highly consistent with that of FISS tumors. (3) FISS is highly similar to soft-tissue sarcomas in dogs and humans, at the level of gene expression. This work underscores the potential utility of comparative oncology in improving understanding and treatment of FISS.

Differential gene regulatory networks in development and disease.

Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.

Cross-species analysis of the canine and human bladder cancer transcriptome and exome.

We investigated the correspondence between transcriptome and exome alterations in canine bladder cancer and the correspondence between these alterations and cancer-driving genes and transcriptional alterations in human bladder cancer. We profiled canine bladder tumors using mRNA-seq and exome-seq in order to investigate the similarity of transcriptional alterations in bladder cancer, in humans and canines, at the levels of gene functions, pathways, and cytogenetic regions. We found that the transcriptomes of canine and human bladder cancer are remarkably similar at the functional and pathway levels. We demonstrated that canine bladder cancer involves coordinated differential expression of genes within cytogenetic bands, and that these patterns are consistent with those seen in human bladder cancer. We found that genes that are mutated in canine bladder cancer are more likely to be transcriptionally downregulated than non-mutated genes, in the tumor. Finally we report three novel mutations (FAM133B, RAB3GAP2, and ANKRD52) for canine bladder cancer.

Multiscale representation of genomic signals.

Genomic information is encoded on a wide range of distance scales, ranging from tens of bases to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as G+C content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations. By integrating the information across all scales, we demonstrated improved prediction of gene expression from polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements, and we observed that gene expression differences in colorectal cancer are related to methylation patterns that extend beyond the single-gene scale. Our software is available at https://github.com/tknijnen/msr/.

Epigenome-guided analysis of the transcriptome of plaque macrophages during atherosclerosis regression reveals activation of the Wnt signaling pathway.

We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy--leveraging macrophage epigenomic measurements--to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5' regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5' regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that ß-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in macrophage emigration from the plaque during lipid lowering-induced regression, and they illustrate the discovery potential of an epigenome-guided, systems approach to understanding atherosclerosis regression.

Investigation of Reagent Delivery Formats in a Multivalent Malaria Sandwich Immunoassay and Implications for Assay Performance.

Conventional lateral flow tests (LFTs), the current standard bioassay format used in low-resource point-of-care (POC) settings, have limitations that have held back their application in the testing of low concentration analytes requiring high sensitivity and low limits of detection. LFTs use a premix format for a rapid one-step delivery of premixed sample and labeled antibody to the detection region. We have compared the signal characteristics of two types of reagent delivery formats in a model system of a sandwich immunoassay for malarial protein detection. The premix format produced a uniform binding profile within the detection region. In contrast, decoupling the delivery of sample and labeled antibody to the detection region in a sequential format produced a nonuniform binding profile in which the majority of the signal was localized to the upstream edge of the detection region. The assay response was characterized in both the sequential and premix formats. The sequential format had a 4- to 10-fold lower limit of detection than the premix format, depending on assay conjugate concentration. A mathematical model of the assay quantitatively reproduced the experimental binding profiles for a set of rate constants that were consistent with surface plasmon resonance measurements and absorbance measurements of the experimental multivalent malaria system.

A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites.

MOTIVATION: The position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure ('shape') is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites. RESULTS: We compared a traditional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential score, for accuracy in detecting binding sites for 75 vertebrate transcription factors. The PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significant loss of accuracy for the remaining TFs. AVAILABILITY AND IMPLEMENTATION: The shape-based model is available as an open-source R package at that is archived on the GitHub software repository at https://github.com/ramseylab/regshape/. CONTACT: stephen.ramsey@oregonstate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cholesterol loading reprograms the microRNA-143/145-myocardin axis to convert aortic smooth muscle cells to a dysfunctional macrophage-like phenotype.

OBJECTIVE: We previously showed that cholesterol loading in vitro converts mouse aortic vascular smooth muscle cells (VSMC) from a contractile state to one resembling macrophages. In human and mouse atherosclerotic plaques, it has become appreciated that ≈40% of cells classified as macrophages by histological markers may be of VSMC origin. Therefore, we sought to gain insight into the molecular regulation of this clinically relevant process. APPROACH AND RESULTS: VSMC of mouse (or human) origin were incubated with cyclodextrin-cholesterol complexes for 72 hours, at which time the expression at the protein and mRNA levels of contractile-related proteins was reduced and of macrophage markers increased. Concurrent was downregulation of miR-143/145, which positively regulate the master VSMC differentiation transcription factor myocardin. Mechanisms were further probed in mouse VSMC. Maintaining the expression of myocardin or miR-143/145 prevented and reversed phenotypic changes caused by cholesterol loading. Reversal was also seen when cholesterol efflux was stimulated after loading. Notably, despite expression of macrophage markers, bioinformatic analyses showed that cholesterol-loaded cells remained closer to the VSMC state, consistent with impairment in classical macrophage functions of phagocytosis and efferocytosis. In apoE-deficient atherosclerotic plaques, cells positive for VSMC and macrophage markers were found lining the cholesterol-rich necrotic core. CONCLUSIONS: Cholesterol loading of VSMC converts them to a macrophage-appearing state by downregulating the miR-143/145-myocardin axis. Although these cells would be classified by immunohistochemistry as macrophages in human and mouse plaques, their transcriptome and functional properties imply that their contributions to atherogenesis would not be those of classical macrophages.

Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast.

Transcriptional noise is known to be an important cause of cellular heterogeneity and phenotypic variation. The extent to which molecular interaction networks may have evolved to either filter or exploit transcriptional noise is a much debated question. The yeast genetic network regulating galactose metabolism involves two proteins, Gal3p and Gal80p, that feed back positively and negatively, respectively, on GAL gene expression. Using kinetic modeling and experimental validation, we demonstrate that these feedback interactions together are important for (i) controlling the cell-to-cell variability of GAL gene expression and (ii) ensuring that cells rapidly switch to an induced state for galactose uptake.

Asymmetric positive feedback loops reliably control biological responses.

Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.