Daniel R. Richards

Genomic responses in mouse models poorly mimic human inflammatory diseases

A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.

A network-based analysis of systemic inflammation in humans

Oligonucleotide and complementary DNA microarrays are being used to subclassify histologically similar tumours, monitor disease progress, and individualize treatment regimens. However, extracting new biological insight from high-throughput genomic studies of human diseases is a challenge, limited by difficulties in recognizing and evaluating relevant biological processes from huge quantities of experimental data. Here we present a structured network knowledge-base approach to analyse genome-wide transcriptional responses in the context of known functional interrelationships among proteins, small molecules and phenotypes. This approach was used to analyse changes in blood leukocyte gene expression patterns in human subjects receiving an inflammatory stimulus (bacterial endotoxin). We explore the known genome-wide interaction network to identify significant functional modules perturbed in response to this stimulus. Our analysis reveals that the human blood leukocyte response to acute systemic inflammation includes the transient dysregulation of leukocyte bioenergetics and modulation of translational machinery. These findings provide insight into the regulation of global leukocyte activities as they relate to innate immune system tolerance and increased susceptibility to infection in humans.

Genome-wide mapping with biallelic markers in Arabidopsis thaliana

Single-nucleotide polymorphisms, as well as small insertions and deletions (here referred to collectively as simple nucleotide polymorphisms, or SNPs), comprise the largest set of sequence variants in most organisms. Positional cloning based on SNPs may accelerate the identification of human disease traits and a range of biologically informative mutations. The recent application of high-density oligonucleotide arrays to allele identification has made it feasible to genotype thousands of biallelic SNPs in a single experiment. It has yet to be established, however, whether SNP detection using oligonucleotide arrays can be used to accelerate the mapping of traits in diploid genomes. The cruciferous weed Arabidopsis thaliana is an attractive model system for the construction and use of biallelic SNP maps. Although important biological processes ranging from fertilization and cell fate determination to disease resistance have been modelled in A. thaliana, identifying mutations in this organism has been impeded by the lack of a high-density genetic map consisting of easily genotyped DNA markers. We report here the construction of a biallelic genetic map in A. thaliana with a resolution of 3.5 cM and its use in mapping Eds16, a gene involved in the defence response to the fungal pathogen Erysiphe orontii. Mapping of this trait involved the high-throughput generation of meiotic maps of F2 individuals using high-density oligonucleotide probe array-based genotyping. We developed a software package called InterMap and used it to automatically delimit Eds16 to a 7-cM interval on chromosome 1. These results are the first demonstration of biallelic mapping in diploid genomes and establish means for generalizing SNP-based maps to virtually any genetic organism.

Global Analysis of the Meiotic Crossover Landscape

Tight control of the number and distribution of crossovers is of great importance for meiosis. Crossovers establish chiasmata, which are physical connections between homologous chromosomes that provide the tension necessary to align chromosomes on the meiotic spindle. Understanding the mechanisms underlying crossover control has been hampered by the difficulty in determining crossover distributions. Here, we present a microarray-based method to analyze multiple aspects of crossover control simultaneously and rapidly, at high resolution, genome-wide, and on a cell-by-cell basis. Using this approach, we show that loss of interference in zip2 and zip4/spo22 mutants is accompanied by a reduction in crossover homeostasis, thus connecting these two levels of crossover control. We also provide evidence to suggest that repression of crossing over at telomeres and centromeres arises from different mechanisms. Lastly, we uncover a surprising role for the synaptonemal complex component Zip1 in repressing crossing over at the centromere.

Cell-specific expression and pathway analyses reveal alterations in trauma-related human T cell and monocyte pathways.

Monitoring genome-wide, cell-specific responses to human disease, although challenging, holds great promise for the future of medicine. Patients with injuries severe enough to develop multiple organ dysfunction syndrome have multiple immune derangements, including T cell apoptosis and anergy combined with depressed monocyte antigen presentation. Genome-wide expression analysis of highly enriched circulating leukocyte subpopulations, combined with cell-specific pathway analyses, offers an opportunity to discover leukocyte regulatory networks in critically injured patients. Severe injury induced significant changes in T cell (5,693 genes), monocyte (2,801 genes), and total leukocyte (3,437 genes) transcriptomes, with only 911 of these genes common to all three cell populations (12%). T cell-specific pathway analyses identified increased gene expression of several inhibitory receptors (PD-1, CD152, NRP-1, and Lag3) and concomitant decreases in stimulatory receptors (CD28, CD4, and IL-2Rα). Functional analysis of T cells and monocytes confirmed reduced T cell proliferation and increased cell surface expression of negative signaling receptors paired with decreased monocyte costimulation ligands. Thus, genome-wide expression from highly enriched cell populations combined with knowledge-based pathway analyses leads to the identification of regulatory networks differentially expressed in injured patients. Importantly, application of cell separation, genome-wide expression, and cell-specific pathway analyses can be used to discover pathway alterations in human disease.

Cloning of the Arabidopsis RSF1 Gene by Using a Mapping Strategy Based on High-Density DNA Arrays and Denaturing High-Performance Liquid Chromatography

Mapping genes by chromosome walking is a widely used technique applicable to cloning virtually any gene that is identifiable by mutagenesis. We isolated the gene responsible for the recessive mutation rsf1 (for reduced sensitivity to farred light) in the Arabidopsis Columbia accession by using classical genetic analysis and two recently developed technologies: genotyping high-density oligonucleotide DNA array and denaturing high-performance liquid chromatography (DHPLC). The Arabidopsis AT412 genotyping array and 32 F2 plants were used to map the rsf1 mutation close to the top of chromosome 1 to an interval of ∼500 kb. Using DHPLC, we found and genotyped additional markers for fine mapping, shortening the interval to ∼50 kb. The mutant gene was directly identified by DHPLC by comparing amplicons generated separately from the rsf1 mutant and the parent strain Columbia. DHPLC analysis yielded polymorphic profiles in two overlapping polymorphic amplicons attributable to a 13-bp deletion in the third of five exons of a gene encoding a 292–amino acid protein with a basic helix-loop-helix (bHLH) domain. The mutation in rsf1 results in a truncated protein consisting of the first 129 amino acids but lacking the bHLH domain. Cloning the RSF1 gene strongly suggests that numerous phytochrome A–mediated responses require a bHLH class transcription factor.

Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases.

Knowledge of immune system and host-pathogen pathways can inform development of targeted therapies and molecular diagnostics based on a mechanistic understanding of disease pathogenesis and the host response. We investigated the feasibility of rapid target discovery for novel broad-spectrum molecular therapeutics through comprehensive systems biology modeling and analysis of pathogen and host-response pathways and mechanisms. We developed a system to identify and prioritize candidate host targets based on strength of mechanistic evidence characterizing the role of the target in pathogenesis and tractability desiderata that include optimal delivery of new indications through potential repurposing of existing compounds or therapeutics. Empirical validation of predicted targets in cellular and mouse model systems documented an effective target prediction rate of 34%, suggesting that such computational discovery approaches should be part of target discovery efforts in operational clinical or biodefense research initiatives. We describe our target discovery methodology, technical implementation, and experimental results. Our work demonstrates the potential for in silico pathway models to enable rapid, systematic identification and prioritization of novel targets against existing or emerging biological threats, thus accelerating drug discovery and medical countermeasures research.

Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases

BackgroundNext-generation sequencing is widely used to identify disease-causing variants in patients with rare genetic disorders. Identifying those variants from whole-genome or exome data can be both scientifically challenging and time consuming. A significant amount of time is spent on variant annotation, and interpretation. Fully or partly automated solutions are therefore needed to streamline and scale this process.ResultsWe describe Phenotype Driven Ranking (PDR), an algorithm integrated into Ingenuity Variant Analysis, that uses observed patient phenotypes to prioritize diseases and genes in order to expedite causal-variant discovery. Our method is based on a network of phenotype-disease-gene relationships derived from the QIAGEN Knowledge Base, which allows for efficient computational association of phenotypes to implicated diseases, and also enables scoring and ranking.ConclusionsWe have demonstrated the utility and performance of PDR by applying it to a number of clinical rare-disease cases, where the true causal gene was known beforehand. It is also shown that PDR compares favorably to a representative alternative tool.

Functional modularity in a large-scale mammalian molecular interaction network

The Ingenuity/spl trade/ Pathways Knowledge Base (IPKB) contains over one million findings manually curated from the scientific literature. Highly-structured content from the IPKB forms the basis for a large-scale molecular network of direct interactions observed between mammalian orthologs, which is used in Ingenuitys Pathway Analysis (IPA) system. In this study we explore the relationship between this global network and known functional annotations of genes. In particular we show that (a) subnetworks formed by genes annotated with the same functional category have significantly more edges than equivalent random subnetworks, and (b) highly-interconnected subnetworks are significantly enriched in genes with specific functional annotations.