Antisense Oligonucleotides Targeting Jagged 1 Reduce House Dust Mite-induced Goblet Cell Metaplasia in the Adult Murine Lung.

Goblet cell metaplasia, excessive mucus production, and inadequate mucus clearance accompany and exacerbate multiple chronic respiratory disorders, such as asthma and chronic obstructive pulmonary disease. Notch signaling plays a central role in controlling the fate of multiple cell types in the lung, including goblet cells. In the present study, we explored the therapeutic potential of modulating the Notch pathway in the adult murine lung using chemically modified antisense oligonucleotides (ASOs). To this end, we designed and characterized ASOs targeting the Notch receptors Notch1, Notch2, and Notch3 and the Notch ligands Jag1 (Jagged 1) and Jag2 (Jagged 2). Pulmonary delivery of ASOs in healthy mice or mice exposed to house dust mite, a commonly used mouse model of asthma, resulted in a significant reduction of the respective mRNAs in the lung. Furthermore, ASO-mediated knockdown of Jag1 or Notch2 in the lungs of healthy adult mice led to the downregulation of the club cell marker Scgb1a1 and the concomitant upregulation of the ciliated cell marker FoxJ1 (forkhead box J1). Similarly, ASO-mediated knockdown of Jag1 or Notch2 in the house dust mite disease model led to reduced goblet cell metaplasia and decreased mucus production. Because goblet cell metaplasia and excessive mucus secretion are a common basis for many lung pathologies, we propose that ASO-mediated inhibition of JAG1 could provide a novel therapeutic path for the treatment of multiple chronic respiratory diseases.

A modular analysis of microglia gene expression, insights into the aged phenotype.

BACKGROUND: Microglia are multifunctional cells that are key players in brain development and homeostasis. Recent years have seen tremendous growth in our understanding of the role microglia play in neurodegeneration, CNS injury, and developmental disorders. Given that microglia show diverse functional phenotypes, there is a need for more precise tools to characterize microglial states. Here, we experimentally define gene modules as the foundation for describing microglial functional states. RESULTS: In an effort to develop a comprehensive classification scheme, we profiled transcriptomes of mouse microglia in a stimulus panel with 96 different conditions. Using the transcriptomic data, we generated fine-resolution gene modules that are robustly preserved across datasets. These modules served as the basis for a combinatorial code that we then used to characterize microglial activation under various inflammatory stimulus conditions. CONCLUSIONS: The microglial gene modules described here were robustly preserved, and could be applied to in vivo as well as in vitro conditions to dissociate the signaling pathways that distinguish acutely inflamed microglia from aged microglia. The microglial gene modules presented here are a novel resource for classifying and characterizing microglial states in health and disease.

Viable RNaseH1 knockout mice show RNaseH1 is essential for R loop processing, mitochondrial and liver function.

Viable constitutive and tamoxifen inducible liver-specific RNase H1 knockout mice that expressed no RNase H1 activity in hepatocytes showed increased R-loop levels and reduced mitochondrial encoded DNA and mRNA levels, suggesting impaired mitochondrial R-loop processing, transcription and mitochondrial DNA replication. These changes resulted in mitochondrial dysfunction with marked changes in mitochondrial fusion, fission, morphology and transcriptional changes reflective of mitochondrial damage and stress. Liver degeneration ensued, as indicated by apoptosis, fibrosis and increased transaminase levels. Antisense oligonucleotides (ASOs) designed to serve as substrates for RNase H1 were inactive in the hepatocytes from the RNase H1 knockout mice and in vivo, demonstrating that RNase H1 is necessary for the activity of DNA-like ASOs. During liver regeneration, a clone of hepatocytes that expressed RNase H1 developed and partially restored mitochondrial and liver function.

Hepatotoxicity of high affinity gapmer antisense oligonucleotides is mediated by RNase H1 dependent promiscuous reduction of very long pre-mRNA transcripts.

High affinity antisense oligonucleotides (ASOs) containing bicylic modifications (BNA) such as locked nucleic acid (LNA) designed to induce target RNA cleavage have been shown to have enhanced potency along with a higher propensity to cause hepatotoxicity. In order to understand the mechanism of this hepatotoxicity, transcriptional profiles were collected from the livers of mice treated with a panel of highly efficacious hepatotoxic or non-hepatotoxic LNA ASOs. We observed highly selective transcript knockdown in mice treated with non-hepatotoxic LNA ASOs, while the levels of many unintended transcripts were reduced in mice treated with hepatotoxic LNA ASOs. This transcriptional signature was concurrent with on-target RNA reduction and preceded transaminitis. Remarkably, the mRNA transcripts commonly reduced by toxic LNA ASOs were generally not strongly associated with any particular biological process, cellular component or functional group. However, they tended to have much longer pre-mRNA transcripts. We also demonstrate that the off-target RNA knockdown and hepatotoxicity is attenuated by RNase H1 knockdown, and that this effect can be generalized to high affinity modifications beyond LNA. This suggests that for a certain set of ASOs containing high affinity modifications such as LNA, hepatotoxicity can occur as a result of unintended off-target RNase H1 dependent RNA degradation.

Integrated Safety Assessment of 2'-O-Methoxyethyl Chimeric Antisense Oligonucleotides in NonHuman Primates and Healthy Human Volunteers.

The common chemical and biological properties of antisense oligonucleotides provide the opportunity to identify and characterize chemical class effects across species. The chemical class that has proven to be the most versatile and best characterized is the 2'-O-methoxyethyl chimeric antisense oligonucleotides. In this report we present an integrated safety assessment of data obtained from controlled dose-ranging studies in nonhuman primates (macaques) and healthy human volunteers for 12 unique 2'-O-methoxyethyl chimeric antisense oligonucleotides. Safety was assessed by the incidence of safety signals in standardized laboratory tests for kidney and liver function, hematology, and complement activation; as well as by the mean test results as a function of dose level over time. At high doses a number of toxicities were observed in nonhuman primates. However, no class safety effects were identified in healthy human volunteers from this integrated data analysis. Effects on complement in nonhuman primates were not observed in humans. Nonhuman primates predicted safe doses in humans, but over predicted risk of complement activation and effects on platelets. Although limited to a single chemical class, comparisons from this analysis are considered valid and accurate based on the carefully controlled setting for the specified study populations and within the total exposures studied.

Synthesis, biophysical properties and biological activity of second generation antisense oligonucleotides containing chiral phosphorothioate linkages.

Bicyclic oxazaphospholidine monomers were used to prepare a series of phosphorothioate (PS)-modified gapmer antisense oligonucleotides (ASOs) with control of the chirality of each of the PS linkages within the 10-base gap. The stereoselectivity was determined to be 98% for each coupling. The objective of this work was to study how PS chirality influences biophysical and biological properties of the ASO including binding affinity (Tm), nuclease stability, activity in vitro and in vivo, RNase H activation and cleavage patterns (both human and E. coli) in a gapmer context. Compounds that had nine or more Sp-linkages in the gap were found to be poorly active in vitro, while compounds with uniform Rp-gaps exhibited activity very similar to that of the stereo-random parent ASOs. Conversely, when tested in vivo, the full Rp-gap compound was found to be quickly metabolized resulting in low activity. A total of 31 ASOs were prepared with control of the PS chirally of each linkage within the gap in an attempt to identify favorable Rp/Sp positions. We conclude that a mix of Rp and Sp is required to achieve a balance between good activity and nuclease stability.

Transfection of siRNAs can alter miRNA levels and trigger non-specific protein degradation in mammalian cells.

Sequence-non-specific effects of siRNAs that alter the expression of non-targeted genes have been reported, including competition of siRNAs with endogenous RISC components. However, the detailed mechanisms and subsequent effects of such competition are not well documented. Here we analyze the competition of miRNAs in mammalian cells with low concentrations of siRNAs, and found that: 1) transfection of different siRNAs in the low nanomolar range used to deplete target RNAs can reduce the levels of miRNAs in different cell types, 2) siRNA transfection results in rapid reduction of Ago2-associated miRNAs concurrent with accumulation of Ago2-bound siRNAs and a significant change in the expression levels of many miRNAs, 3) competition largely depends on Ago2 and not Dicer, 4) microarray analysis showed that the majority of highly expressed miRNAs are reduced, in a siRNA concentration dependent manner, and low abundant miRNAs may be unchanged or repressed and a few miRNAs appear to have increased levels, and 5) consistent with previous studies, the expression levels of mRNAs that are targeted by highly repressed miRNAs are preferentially increased. As a consequence of such competition, we observed that α-tubulin, a substrate of two up-regulated proteases, granzyme B and granzyme M, was rapidly degraded at the protein level upon siRNA transfection. Our results support a model in which transfection of siRNAs can change the levels of many miRNAs by competition for Ago2, leading to altered expression of many miRNA target genes, which can in turn affect downstream gene expression even at the protein level.

A novel 'social contract' - An attempt to harmonize a sponsor's exploratory research with a clinical study participant's data rights.

BACKGROUND: Pharmaceutical drug development rarely addresses a study participant's control of their genomic data, how to return individual incidental findings, and how to make use of genomic data more efficiently for exploratory research purposes. Mutually beneficial solutions to these issues are needed, as whole genome sequencing (WGS) is increasingly adopted in human research and as access to such information could provide impactful health-related information for a participant. METHODS: In this paper, we offer a novel framework to align a trial sponsor's need for broad exploratory research of the human genome with the study participant's right to data access and access control. The Exploratory Genetic Research Project (EGRP) aims to gather WGS on all participants of a sponsor's clinical trials. It is set up as a separate umbrella protocol to facilitate the consenting process, as well as the delineation between clinical trial endpoints versus exploratory future research. CONCLUSION: This concept establishes a participant's autonomy regarding access to genomic data and the disclosure of actionable incidental findings. The feasibility of EGRP will be tested and reassessed as it is deployed over the next few years.

Antisense oligonucleotide treatment ameliorates IFN-γ-induced proteinuria in APOL1-transgenic mice.

African Americans develop end-stage renal disease at a higher rate compared with European Americans due to 2 polymorphisms (G1 and G2 risk variants) in the apolipoprotein L1 (APOL1) gene common in people of African ancestry. Although this compelling genetic evidence provides an exciting opportunity for personalized medicine in chronic kidney disease, drug discovery efforts have been greatly hindered by the fact that APOL1 expression is lacking in rodents. Here, we describe a potentially novel physiologically relevant genomic mouse model of APOL1-associated renal disease that expresses human APOL1 from the endogenous human promoter, resulting in expression in similar tissues and at similar relative levels as humans. While naive APOL1-transgenic mice did not exhibit a renal disease phenotype, administration of IFN-γ was sufficient to robustly induce proteinuria only in APOL1 G1 mice, despite inducing kidney APOL1 expression in both G0 and G1 mice, serving as a clinically relevant "second hit." Treatment of APOL1 G1 mice with IONIS-APOL1Rx, an antisense oligonucleotide (ASO) targeting APOL1 mRNA, prior to IFN-γ challenge robustly and dose-dependently inhibited kidney and liver APOL1 expression and protected against IFN-γ-induced proteinuria, indicating that the disease-relevant cell types are sensitive to ASO treatment. Therefore, IONIS-APOL1Rx may be an effective therapeutic for APOL1 nephropathies and warrants further development.

Chemical modification of PS-ASO therapeutics reduces cellular protein-binding and improves the therapeutic index.

The molecular mechanisms of toxicity of chemically modified phosphorothioate antisense oligonucleotides (PS-ASOs) are not fully understood. Here, we report that toxic gapmer PS-ASOs containing modifications such as constrained ethyl (cEt), locked nucleic acid (LNA) and 2'-O-methoxyethyl (2'-MOE) bind many cellular proteins with high avidity, altering their function, localization and stability. We show that RNase H1-dependent delocalization of paraspeckle proteins to nucleoli is an early event in PS-ASO toxicity, followed by nucleolar stress, p53 activation and apoptotic cell death. Introduction of a single 2'-O-methyl (2'-OMe) modification at gap position 2 reduced protein-binding, substantially decreasing hepatotoxicity and improving the therapeutic index with minimal impairment of antisense activity. We validated the ability of this modification to generally mitigate PS-ASO toxicity with more than 300 sequences. Our findings will guide the design of PS-ASOs with optimal therapeutic profiles.

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data.

Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (http://woldlab.caltech.edu/compClust/). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein-DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein-DNA interaction data to identify G1 regulatory modules.

Mining gene expression data by interpreting principal components.

BACKGROUND: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. RESULTS: We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. CONCLUSION: We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

Partial Hepatectomy Induced Long Noncoding RNA Inhibits Hepatocyte Proliferation during Liver Regeneration.

Liver regeneration after partial hepatectomy (PHx) is a complex and well-orchestrated biological process in which synchronized cell proliferation is induced in response to the loss of liver mass. To define long noncoding RNAs (lncRNAs) that participate in the regulation of liver regeneration, we performed microarray analysis and identified more than 400 lncRNAs exhibiting significantly altered expression. Of these, one lncRNA, LncPHx2 (Long noncoding RNA induced by PHx 2), was highly upregulated during liver regeneration. Depletion of LncPHx2 during liver regeneration using antisense oligonucleotides led to a transient increase in hepatocyte proliferation and more rapid liver regeneration. Gene expression analysis showed that LncPHx2 depletion resulted in upregulation of mRNAs encoding proteins known to promote cell proliferation, including MCM components, DNA polymerases, histone proteins, and transcription factors. LncPHx2 interacts with the mRNAs of MCM components, making it a candidate to regulate the expression of MCMs and other genes post-transcriptionally. Collectively, our data demonstrate that LncPHx2 is a key lncRNA that participates in a negative feedback loop modulating hepatocyte proliferation through RNA-RNA interactions.

Helicos single-molecule sequencing of bacterial genomes.

With the advent of high-throughput sequencing technologies, multiple bacterial genomes can be sequenced in days. While the ultimate goal of de novo assembly of bacterial genomes is progressing, changes in the genomic sequence of closely related bacterial strains and isolates are now easily monitored by comparison of their sequences to those of a reference genome. Such studies can be applied to the fields of bacterial evolution, epidemiology, and diagnostics. We present a protocol for single-molecule sequencing of bacterial DNA whose end result is the identification of single nucleotide variants, and various size insertions and deletions relative to a reference genome. The protocol is characterized by the simplicity of sample preparation and the lack of amplification-related sequencing bias.