Leukocyte infiltration plays an important role in the pathogenesis and progression of myositis, and is highly associated with disease severity. Currently, there is a lack of: efficacious therapies for myositis; understanding of the molecular features important for disease pathogenesis; and potential molecular biomarkers for characterizing inflammatory myopathies to aid in clinical development.
In this study, we developed a simple model and predicted that 1) leukocyte-specific transcripts (including both protein-coding transcripts and microRNAs) should be coherently overexpressed in myositis muscle and 2) the level of over-expression of these transcripts should be correlated with leukocyte infiltration. We applied this model to assess immune cell infiltration in myositis by examining mRNA and microRNA (miRNA) expression profiles in muscle biopsies from 31 myositis patients and 5 normal controls.
Several gene signatures, including a leukocyte index, type 1 interferon (IFN), MHC class I, and immunoglobulin signature, were developed to characterize myositis patients at the molecular level. The leukocyte index, consisting of genes predominantly associated with immune function, displayed strong concordance with pathological assessment of immune cell infiltration. This leukocyte index was subsequently utilized to differentiate transcriptional changes due to leukocyte infiltration from other alterations in myositis muscle. Results from this differentiation revealed biologically relevant differences in the relationship between the type 1 IFN pathway, miR-146a, and leukocyte infiltration within various myositis subtypes.
Results indicate that a likely interaction between miR-146a expression and the type 1 IFN pathway is confounded by the level of leukocyte infiltration into muscle tissue. Although the role of miR-146a in myositis remains uncertain, our results highlight the potential benefit of deconvoluting the source of transcriptional changes in myositis muscle or other heterogeneous tissue samples. Taken together, the leukocyte index and other gene signatures developed in this study may be potential molecular biomarkers to help to further characterize inflammatory myopathies and aid in clinical development. These hypotheses need to be confirmed in separate and sufficiently powered clinical trials.
Myositis; Genomics; Leukocyte infiltration; Type 1 interferon; miR-146a
Genetic variation may contribute to differential gene expression in the brain of individuals with psychiatric disorders. To test this hypothesis, we identified genes that were differentially expressed in individuals with bipolar disorder, along with nearby single nucleotide polymorphisms (SNPs) that were associated with expression of the same genes. We then tested these SNPs for association with bipolar disorder in large case-control samples.
We used the Stanley Genomics Database to extract gene expression and SNP microarray data from individuals with bipolar disorder (n = 40) and unaffected controls (n = 43). We identified 367 genes that were differentially expressed in the prefrontal cortex of cases vs. controls (fold change > 1.3 and FDR q-value < .05) and 45 nearby SNPs that were associated with expression of those same genes (FDR q-value < .05). We tested these SNPs for association with bipolar disorder in a meta-analysis of genome-wide association studies (GWAS) including 4,936 cases and 6,654 healthy controls.
We identified 45 SNPs that were associated with expression of differentially expressed genes, including HBS1L (15 SNPs), HLA-DPB1 (15 SNPs), AMFR (8 SNPs), PCLO (2 SNPs) and WDR41 (2 SNPs). Of these, one SNP (rs13438494), in an intron of the piccolo (PCLO) gene, was significantly associated with bipolar disorder (FDR adjusted p < .05) in the meta-analysis of GWAS.
These results support the previous findings implicating PCLO in mood disorders and demonstrate the utility of combining gene expression and genetic variation data to improve our understanding of the genetic contribution to bipolar disorder.
Allelic expression; expression quantitative trait loci (eQTL); genetic variants; functional genomics; risk factors
Dermatomyositis (DM) is an autoimmune disease that mainly affects the skin, muscle, and lung. The pathogenesis of skin inflammation in DM is not well understood.
Methodology and Findings
We analyzed genome-wide expression data in DM skin and compared them to those from healthy controls. We observed a robust upregulation of interferon (IFN)-inducible genes in DM skin, as well as several other gene modules pertaining to inflammation, complement activation, and epidermal activation and differentiation. The interferon (IFN)-inducible genes within the DM signature were present not only in DM and lupus, but also cutaneous herpes simplex-2 infection and to a lesser degree, psoriasis. This IFN signature was absent or weakly present in atopic dermatitis, allergic contact dermatitis, acne vulgaris, systemic sclerosis, and localized scleroderma/morphea. We observed that the IFN signature in DM skin appears to be more closely related to type I than type II IFN based on in vitro IFN stimulation expression signatures. However, quantitation of IFN mRNAs in DM skin shows that the majority of known type I IFNs, as well as IFN g, are overexpressed in DM skin. In addition, both IFN-beta and IFN-gamma (but not other type I IFN) transcript levels were highly correlated with the degree of the in vivo IFN transcriptional response in DM skin.
Conclusions and Significance
As in the blood and muscle, DM skin is characterized by an overwhelming presence of an IFN signature, although it is difficult to conclusively define this response as type I or type II. Understanding the significance of the IFN signature in this wide array of inflammatory diseases will be furthered by identification of the nature of the cells that both produce and respond to IFN, as well as which IFN subtype is biologically active in each diseased tissue.
Insulin-like growth factor (IGF) signaling through human insulin receptor isoform A (IR-A) contributes to tumorigenesis and intrinsic resistance to anti-IGF1R therapy. In the present study, we (a) developed quantitative TaqMan real time-PCR-based assays (qRT-PCR) to measure human insulin receptor isoforms with high specificity, (b) evaluated isoform expression levels in molecularly-defined breast cancer subtypes, and (c) identified the IR-A:IR-B mRNA ratio as a potential biomarker guiding patient stratification for anti-IGF therapies.
mRNA expression levels of IR-A and IR-B were measured in 42 primary breast cancers and 19 matched adjacent normal tissues with TaqMan qRT-PCR assays. The results were further confirmed in 165 breast cancers. The tumor samples were profiled using whole genome microarrays and subsequently subtyped using the PAM50 breast cancer gene signature. The relationship between the IR-A:IR-B ratio and cancer subtype, as well as markers of proliferation were characterized.
The mRNA expression levels of IR-A in the breast tumors were similar to those observed in the adjacent normal tissues, while the mRNA levels of IR-B were significantly decreased in tumors. The IR-A:IR-B ratio was significantly higher in luminal B breast cancer than in luminal A. Strong concordance between the IR-A:IR-B ratio and the composite Oncotype DX proliferation score was observed for stratifying the latter two breast cancer subtypes.
The reduction in IR-B expression is the key to the altered IR-A:IR-B ratio observed in breast cancer. The IR-A:IR-B ratio may have biomarker utility in guiding a patient stratification strategy for an anti-IGF therapeutic.
OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antibiotics over a time course of hours to days. In order to manage the large amount of data produced from the OmniLog™ instrument, PheMaDB (Phenotype Microarray DataBase), a web-based relational database, was designed. PheMaDB enables efficient storage, retrieval and rapid analysis of the OmniLog™ PM data.
PheMaDB allows the user to quickly identify records of interest for data analysis by filtering with a hierarchical ordering of Project, Strain, Phenotype, Replicate, and Temperature. PheMaDB then provides various statistical analysis options to identify specific growth pattern characteristics of the experimental strains, such as: outlier analysis, negative controls analysis (signal/background calibration), bar plots, pearson's correlation matrix, growth curve profile search, k-means clustering, and a heat map plot. This web-based database management system allows for both easy data sharing among multiple users and robust tools to phenotype organisms of interest.
PheMaDB is an open source system standardized for OmniLog™ PM data. PheMaDB could facilitate the banking and sharing of phenotype data. The source code is available for download at http://phemadb.sourceforge.net.
Dermatomyositis (DM) is an autoimmune disease involving muscle and skin. Perifascicular atrophy (PFA) of myofibers is a specific and characteristic DM pathological lesion. Interferon-stimulated gene 15 (ISG15) is a ubiquitin-like modifier with a poorly understood immunological role.
We generated microarray data measuring the expression of approximately 18,000 genes in each of 113 human muscle biopsy specimens. Biopsy specimens and cultured skeletal muscle were further studied using immunohistochemistry, immunoblotting, proteomic profiling by liquid chromatography/mass spectrometry, real-time quantitative PCR, and laser capture microdissection.
Transcripts encoding ISG15-conjugation pathway proteins were upregulated in DM with PFA (DM-PFA) muscle, with marked elevation of ISG15 (339-fold), HERC5 (62-fold), and USP18 (68-fold) present in all DM-PFA patients but none of 99 non-DM samples. Combined analysis with publicly available microarray datasets further showed marked ISG15 and USP18 transcript elevation had 100% sensitivity and specificity for 28 biopsies from adult DM-PFA and juvenile DM compared to 199 other muscle samples from a wide range of muscle diseases. Free ISG15 and ISG15-conjugated proteins were found by immunoblot only in DM-PFA muscle. Cultured human skeletal muscle exposed to type 1 interferons produced similar transcripts and both ISG15 protein and ISG15 conjugates. Laser capture microdissection followed by proteomic analysis showed deficiency of titin in DM perifascicular atrophic myofibers.
A large-scale microarray study of muscle samples from a diverse collection of muscle diseases revealed that the autoimmune disease dermatomyositis was uniquely associated with overactivation of the ISG15 conjugation pathway. Exposure of human skeletal muscle cell culture to type 1 interferons produces a molecular picture highly similar to that of human DM muscle biopsy specimens. Perifascicular atrophic myofibers in DM are deficient in a number of skeletal muscle proteins, most markedly titin.
Donor- and third-party-induced proliferation of T-helper (Th) and T-cytotoxic (Tc) cells, and their naïve and memory subsets was evaluated simultaneously in single blood samples from 77 children who received steroid-free liver transplantation (LTx) after induction with rabbit anti-human thymocyte globulin. Proliferation was measured by dilution of the intravital dye carboxy-flourescien-succinimidyl-ester (CFSE) in 3–4 day MLR co-culture. The ratio of donor: third-party-induced proliferated, (CFSElow) T-cells was reported as the immunoreactivity index (IR) for each subset. Rejectors were defined as those who experienced biopsy-proven acute cellular rejection within 60 days of the assay. IR>1 signified increased risk of rejection and IR<1 implied decreased risk.
Demographics for 32 Rejectors and 45 Non-Rejectors were similar. Proliferated CFSElow T-cells and subsets were significantly higher among Rejectors, compared with Non-Rejectors. In 33 of 77 randomly selected children, logistic regression, leave-one-out cross-validation and ROC analyses showed that the IR of Tc associated best with biopsy-proven rejection (sensitivity>75%, specificity>88%). Sensitivity/specificity were replicated in the remaining 44 children, comprising the validation cohort. IR of CFSElow Tc correlated significantly with IR of pro-inflammatory, allospecific CD154+Tc (r=0.664, p=0.0005), and inversely with IR of allospecific, anti-inflammatory, CTLA4+Tc (r=−0.630, p=0.007).
Proliferative alloresponses of T-cytotoxic cells can identify rejection-prone children receiving LTx. (200)
Antigen-specific T-cells, which express CD154 rapidly, but remain untested in alloimmunity, were measured with flow cytometry in 16-hour MLR of 58 identically-immunosuppressed children with liver transplantation (LTx), to identify Rejectors (who had experienced biopsy-proven rejection within 60 days post-transplantation). Thirty one children were sampled once, cross-sectionally. Twenty seven children were sampled longitudinally, pre-LTx, and at 1–60 and 61–200 days after LTx. Results were correlated with proliferative alloresponses measured by CFSE-dye dilution (n=23), and CTLA4, a negative T-cell costimulator, which antagonizes CD154-mediated effects (n=31). In cross-sectional observations, logistic regression and leave-one-out cross-validation identified donor-specific, CD154+T-cytotoxic (Tc)-memory cells as best associated with rejection outcomes. In the longitudinal cohort, 1) the association between CD154+Tc-memory cells and rejection outcomes was replicated with sensitivity/specificity 92.3%/84.6% for observations at 1–60 days, and 2) elevated pre-LTx CD154+Tc-memory cell responses were associated with significantly increased incidence (p=0.02) and hazard (HR=7.355) of rejection in survival/proportional hazard analysis. CD154 expression correlated with proliferative alloresponses (r=0.835, p=7.1e-07), and inversely with CTLA4 expression of allospecific CD154+Tc-memory cells (r=−0.706, p=3.0e-05). Allospecific CD154+T-helper-memory cells, not CD154+Tc-memory, were inhibited by increasing Tacrolimus concentrations (p=0.026). Collectively, allospecific CD154+T-cells provide an estimate of rejection risk in children with LTx.
Limited access to large samples and independent replication cohorts precludes genome-wide association (GWA) studies of rare but complex traits. To localize candidate genes with family-based GWA, a novel exploratory analysis was first tested on 1,774 major histocompatibility complex single nucleotide polymorphisms (SNPs) in 240 DNA samples from 80 children with primary liver transplantation (LTx), and their biological parents.
Initially, 57 SNPs with large differences (p<0.05) in minor allele frequencies were selected, when parents of children with early rejection (Rejectors) were compared with parents of Non-Rejectors. In hypothesis-testing of selected SNPs, the gamete competition statistic identified the minor allele G (ancestral allele T) of the SNP rs9296068, near HLA-DOA, as being significantly different (p=0.018) in parent-to-child transmission between outcome groups. Subsequent simple association testing confirmed over- and under-transmission of rs9296068 based on 1) the most significant differences between outcome groups, of 1,774 SNPs tested (p=0.002), and 2) allele (G) frequencies that were greater among Rejectors (51.4 vs. 36.8%, p=0.015), and lower among Non-Rejectors (26.8 vs. 36.8%, p=0.074), compared with 400 normal control Caucasian children. In early functional validation, a) Rejectors demonstrated significant repression of the first HLA-DOA exon closest to rs9296068, and b) Rejectors with the risk allele showed 3-fold greater intragraft content of B-lymphocytes, whose antigen-presenting function is inhibited selectively by HLA-DOA, compared with Rejectors without the allele.
The minor allele of the SNP rs9296068 is significantly associated with LTx rejection, and with enhanced B-lymphocyte participation in rejection, likely due to a dysfunctional HLA-DOA gene product.
Dendritic cells (DC) play an important role in the induction and regulation of immune responses.
Myeloid CD11c+DC (MDC), which may have inflammatory functions, and plasmacytoid CD123+ DC (PDC), which may have tolerogenic potential, were measured by flow cytometric analysis, cross-sectionally, once, in 48 children, and longitudinally (pre-transplant, and at days 1–60, 61–200, 201–400 post transplant) in 30 children following liver transplantation (LTx). All children received 53/25 cadaveric/live donor liver allografts with rabbit anti-human thymocyte globulin (rATG) induction, and steroid-free Tacrolimus therapy. Rejectors in both groups were those children (n=35), who experienced biopsy-proven acute cellular rejection (ACR) within 60 days of DC monitoring.
Among rejectors in the longitudinal and cross-sectional cohorts, the MDC: PDC ratio was higher, and was associated with decreased PDC frequencies. Logistic regression analysis, leave-one out cross-validation, and receiver operating characteristic analysis applied to 30 cross-sectional subjects revealed that an MDC:PDC ratio 1.78 was associated with rejector status with sensitivity/specificity of 76.9/88.2%. Sensitivity and specificity were replicated in the 18 remaining cross-sectional subjects (88.8 and 78.8%, respectively), but not in longitudinally-monitored subjects, during the early, 60-day period after LTx (30.76 and 62.50%, respectively). A significant negative correlation was observed between Tacrolimus whole blood concentrations and PDC frequencies (Spearman r = −0.370, p=0.005) in 48 cross-sectional subjects in whom DC subsets were monitored 1–3 years after LTx, but not during the early post-LTx period.
We conclude that an elevated MDC: PDC ratio associates with liver graft rejection, which occurs after first year in children induced with rATG.
dendritic cell subsets; liver transplantation; pediatric; anti-thymocyte globulin
Type I interferons are implicated in the pathogenesis of systemic lupus erythematosus (SLE). Type I interferon-inducible mRNAs are widely and concordantly overexpressed in the periphery and involved tissues of a subset of SLE patients, and provide utility as pharmacodynamic biomarkers to aid dose selection, as well as potential indicators of patients who might respond favorably to anti-IFNα therapy in SLE. We implemented a three-tiered approach to identify a panel of type I interferon-inducible mRNAs to be used as potential pharmacodynamic biomarkers to aid dose selection in clinical trials of sifalimumab, an anti-IFNα monoclonal antibody under development for the treatment of SLE. In a single-dose escalation phase 1 trial, we observed a sifalimumab-specific and dose-dependent inhibition of the overexpression of type I interferon-inducible mRNAs in the blood of treated subjects. Inhibition of expression of type I interferon-inducible mRNAs and proteins was also observed in skin lesions of SLE subjects from the same trial. Inhibiting IFNα resulted in a profound downstream effect in these SLE subjects that included suppression of mRNAs of B-cell activating factor belonging to the TNF family and the signaling pathways of TNFα, IL-10, IL-1β, and granulocyte-macrophage colony-stimulating factor in both the periphery and skin lesions. A scoring method based on the expression of type I interferon-inducible mRNAs partitioned SLE patients into two distinct subpopulations, which suggests the possibility of using these type I interferon-inducible genes as predictive biomarkers to identify SLE patients who might respond more favorably to anti-type I interferon therapy.
To identify potential pharmacodynamic biomarkers to guide dose selection in clinical trials using anti-interferon-alpha (IFN-α) monoclonal antibody (mAb)
therapy for systemic lupus erythematosus (SLE), we used an Affymetrix human genome array platform and identified 110 IFN-α/β-inducible transcripts significantly upregulated in whole blood (WB) of 41 SLE patients. The overexpression of these genes was confirmed prospectively in 54 additional SLE patients and allowed for the categorization of the SLE patients into groups of high, moderate, and weak overexpressers of IFN-α/β-inducible genes. This approach could potentially allow for an accurate assessment of drug target neutralization in early trials of anti-IFN-α mAb therapy for SLE. Furthermore, ex vivo stimulation of healthy donor peripheral blood mononuclear cells with SLE patient serum and subsequent neutralization with anti-IFN-α mAb or anti-IFN-α receptor mAb showed that anti-IFN-α mAb has comparable effects of neutralizing the overexpression of type I IFN-inducible genes as that of anti-IFNAR mAb. These results suggest that IFN-α, and not other members of type I IFN family in SLE patients, is mainly responsible for the induction of type I IFN-inducible genes in WB of SLE patients. Taken together, these data strengthen the view of IFN-α as a therapeutic target for SLE.
Disruption in normal development of the human prefrontal cortex (PFC) may lead to cognitive dysfunction that manifests in individuals with schizophrenia. We sought to identify genes associated with age that are implicated in schizophrenia.
We generated genome-wide expression profiles for the PFCs of humans ranging in age from 1 month to 49 years using the Affymetrix HG-U133 plus 2.0 microarrays (54 675 transcripts). Based on the criteria of significance (false discovery rate [FDR]-adjusted q < 0.001 and r2 > 0.6), we identified the genes associated with age in the PFC. We then performed functional annotation analyses of age-associated genes using the Gene Ontology and the Genetic Association Database (GAD).
We found robust age-dependent changes in gene expression in the PFCs of humans (2281 transcripts). The GAD analysis revealed that schizophrenia was an over-represented disease class, with 42 susceptibility genes included (p < 0.001, fold enrichment = 1.66, FDR = 1.5%). Among the 42 genes, glutamate receptor genes (GRIA1, GRIK1, GRIK2, GRIN2D, GRIP1, GRM5, GRM7 and SLC1A6) were consistently downregulated across age. We confirmed microarray gene expression changes by the quantitative polymerase chain reaction experiment.
Although numerous genes undergo robust changes in expression during the PFC development, some of the changes may be confounded by known and unknown factors that are intrinsic to the postmortem brain studies.
Multiple schizophrenia susceptibility genes undergo age-dependent expression changes in the human PFC, and any disruption in those genes during the critical period of development may predispose the individuals to schizophrenia.
Although much progress has been made on antipsychotic drug development, precise mechanisms behind the action of typical and atypical antipsychotics are poorly understood.
We performed genome-wide expression profiling to study effects of typical antipsychotics and atypical antipsychotics in the postmortem liver of schizophrenia patients using microarrays (Affymetrix U133 plus2.0). We classified the subjects into typical antipsychotics (n = 24) or atypical antipsychotics (n = 26) based on their medication history, and compared gene expression profiles with unaffected controls (n = 34). We further analyzed individual antipsychotic effects on gene expression by sub-classifying the subjects into four major antipsychotic groups including haloperidol, phenothiazines, olanzapine and risperidone.
Typical antipsychotics affected genes associated with nuclear protein, stress responses and phosphorylation, whereas atypical antipsychotics affected genes associated with golgi/endoplasmic reticulum and cytoplasm transport. Comparison between typical antipsychotics and atypical antipsychotics further identified genes associated with lipid metabolism and mitochondrial function. Analyses on individual antipsychotics revealed a set of genes (151 transcripts, FDR adjusted p < 0.05) that are differentially regulated by four antipsychotics, particularly by phenothiazines, in the liver of schizophrenia patients.
Typical antipsychotics and atypical antipsychotics affect different genes and biological function in the liver. Typical antipsychotic phenothiazines exert robust effects on gene expression in the liver that may lead to liver toxicity. The genes found in the current study may benefit antipsychotic drug development with better therapeutic and side effect profiles.
Recent studies have shown similarities between schizophrenia and bipolar disorder in phenotypes and in genotypes, and those studies have contributed to an ongoing re-evaluation of the traditional dichotomy between schizophrenia and bipolar disorder. Bipolar disorder with psychotic features may be closely related to schizophrenia and therefore, psychosis may be an alternative phenotype compared to the traditional diagnosis categories.
We performed a cross-study analysis of 7 gene expression microarrays that include both psychosis and non-psychosis subjects. These studies include over 400 microarray samples (163 individual subjects) on 3 different Affymetrix microarray platforms.
We found that 110 transcripts are differentially regulated (p < 0.001) in psychosis after adjusting for confounding variables with a multiple regression model. Using a quantitative PCR, we validated a set of genes such as up-regulated metallothioneins (MT1E, MT1F, MT1H, MT1K, MT1X, MT2A and MT3) and down-regulated neuropeptides (SST, TAC1 and NPY) in the dorsolateral prefrontal cortex of psychosis patients.
This study demonstrates the advantages of cross-study analysis in detecting consensus changes in gene expression across multiple microarray studies. Differential gene expression between individuals with and without psychosis suggests that psychosis may be a useful phenotypic variable to complement the traditional diagnosis categories.
Psoriasis is an immune-mediated disease characterized by aberrant epidermal differentiation, surface scale formation, and marked cutaneous inflammation. To better understand the pathogenesis of this disease and identify potential mediators, we used whole genome array analysis to profile paired lesional and nonlesional psoriatic skin and skin from healthy donors.
We observed robust overexpression of type I interferon (IFN)–inducible genes and genomic signatures that indicate T cell and dendritic cell infiltration in lesional skin. Up-regulation of mRNAs for IFN-α subtypes was observed in lesional skin compared with nonlesional skin. Enrichment of mature dendritic cells and 2 type I IFN–inducible proteins, STAT1 and ISG15, were observed in the majority of lesional skin biopsies. Concordant overexpression of IFN-γ and TNF-α–inducible gene signatures occurred at the same disease sites.
Up-regulation of TNF-α and elevation of the TNF-α–inducible gene signature in lesional skin underscore the importance of this cytokine in psoriasis; these data describe a molecular basis for the therapeutic activity of anti–TNF-α agents. Furthermore, these findings implicate type I IFNs in the pathogenesis of psoriasis. Consistent and significant up-regulation of type I IFNs and their associated gene signatures in psoriatic skin suggest that type I IFNs may be potential therapeutic targets in psoriasis treatment.
San Francisco has the highest rate of tuberculosis (TB) in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures.
Methods and Findings
We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model.
Systematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction.
The lack of detailed understanding of the mechanism of action of many biowarfare agents poses an immediate challenge to biodefense efforts. Many potential bioweapons have been shown to affect the cellular pathways controlling apoptosis [1-4]. For example, pathogen-produced exotoxins such as Staphylococcal Enterotoxin B (SEB) and Anthrax Lethal Factor (LF) have been shown to disrupt the Fas-mediated apoptotic pathway [2,4]. To evaluate how these agents affect these pathways it is first necessary to understand the dynamics of a normally functioning apoptosis network. This can then serve as a baseline against which a pathogen perturbed system can be compared. Such comparisons can expose both the proteins most susceptible to alteration by the agent as well as the most critical reaction rates to better instill control on a biological network.
We explore this through the modeling and simulation of the Fas-mediated apoptotic pathway under normal and SEB influenced conditions. We stimulated human Jurkat cells with an anti-Fas antibody in the presence and absence of SEB and determined the relative levels of seven proteins involved in the core pathway at five time points following exposure. These levels were used to impute relative rate constants and build a quantitative model consisting of a series of ordinary differential equations (ODEs) that simulate the network under both normal and pathogen-influenced conditions. Experimental results show that cells exposed to SEB exhibit an increase in the rate of executioner caspase expression (and subsequently apoptosis) of 1 hour 43 minutes (± 14 minutes), as compared to cells undergoing normal cell death.
Our model accurately reflects these results and reveals intervention points that can be altered to restore SEB-influenced system dynamics back to levels within the range of normal conditions.
The Stanley Medical Research Institute online genomics database (SMRIDB) is a comprehensive web-based system for understanding the genetic effects of human brain disease (i.e. bipolar, schizophrenia, and depression). This database contains fully annotated clinical metadata and gene expression patterns generated within 12 controlled studies across 6 different microarray platforms.
A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications.
The combination of database content, structure, and query speed offers researchers an efficient tool for data mining of brain disease complete with information such as: cross-platform comparisons, biomarkers elucidation for target discovery, and lifestyle/demographic associations to brain diseases.
Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented.
We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison.
Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons.
Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology.
A growing body of evidence suggests that mitochondrial function may be important in brain development and psychiatric disorders. However, detailed expression profiles of those genes in human brain development and fear-related behavior remain unclear. Using microarray data available from the public domain and the Gene Ontology analysis, we identified the genes and the functional categories associated with chronological age in the prefrontal cortex (PFC) and the caudate nucleus (CN) of psychiatrically normal humans ranging in age from birth to 50 years. Among those, we found that a substantial number of genes in the PFC (115) and the CN (117) are associated with the GO term: mitochondrion (FDR qv <0.05). A greater number of the genes in the PFC (91%) than the genes in the CN (62%) showed a linear increase in expression during postnatal development. Using quantitative PCR, we validated the developmental expression pattern of four genes including monoamine oxidase B (MAOB), NADH dehydrogenase flavoprotein (NDUFV1), mitochondrial uncoupling protein 5 (SLC25A14) and tubulin beta-3 chain (TUBB3). In mice, overall developmental expression pattern of MAOB, SLC25A14 and TUBB3 in the PFC were comparable to the pattern observed in humans (p<0.05). However, mice selectively bred for high fear did not exhibit normal developmental changes of MAOB and TUBB3. These findings suggest that the genes associated with mitochondrial function in the PFC play a significant role in brain development and fear-related behavior.
Despite the decades-long use of Bacillus atrophaeus var. globigii (BG) as a simulant for biological warfare (BW) agents, knowledge of its genome composition is limited. Furthermore, the ability to differentiate signatures of deliberate adaptation and selection from natural variation is lacking for most bacterial agents. We characterized a lineage of BGwith a long history of use as a simulant for BW operations, focusing on classical bacteriological markers, metabolic profiling and whole-genome shotgun sequencing (WGS).
Archival strains and two “present day” type strains were compared to simulant strains on different laboratory media. Several of the samples produced multiple colony morphotypes that differed from that of an archival isolate. To trace the microevolutionary history of these isolates, we obtained WGS data for several archival and present-day strains and morphotypes. Bacillus-wide phylogenetic analysis identified B. subtilis as the nearest neighbor to B. atrophaeus. The genome of B. atrophaeus is, on average, 86% identical to B. subtilis on the nucleotide level. WGS of variants revealed that several strains were mixed but highly related populations and uncovered a progressive accumulation of mutations among the “military” isolates. Metabolic profiling and microscopic examination of bacterial cultures revealed enhanced growth of “military” isolates on lactate-containing media, and showed that the “military” strains exhibited a hypersporulating phenotype.
Our analysis revealed the genomic and phenotypic signatures of strain adaptation and deliberate selection for traits that were desirable in a simulant organism. Together, these results demonstrate the power of whole-genome and modern systems-level approaches to characterize microbial lineages to develop and validate forensic markers for strain discrimination and reveal signatures of deliberate adaptation.