SUMMARY

Although both natural and induced regulatory T (nTreg and iTreg) cells can enforce tolerance, the mechanisms underlying their synergistic actions have not been established. We examined the functions of nTreg and iTreg cells by adoptive transfer immunotherapy of newborn Foxp3-deficient mice. As monotherapy, only nTreg cells prevented disease lethality, but did not suppress chronic inflammation and autoimmunity. Provision of Foxp3-sufficient conventional T cells with nTreg cells reconstituted the iTreg pool and established tolerance. In turn, acute depletion of iTreg cells in rescued mice resulted in weight loss and inflammation. Whereas the transcriptional signatures of nTreg and in vivo derived iTreg cells were closely matched, there was minimal overlap in their T cell receptor (TCR) repertoires. Thus, iTreg cells are an essential non-redundant regulatory subset that supplements nTreg cells, in part by expanding TCR diversity within regulatory responses.

OBJECTIVE

Inflammatory mediators associated with type 1 diabetes are dilute and difficult to measure in the periphery, necessitating development of more sensitive and informative biomarkers for studying diabetogenic mechanisms, assessing preonset risk, and monitoring therapeutic interventions.

RESEARCH DESIGN AND METHODS

We previously utilized a novel bioassay in which human type 1 diabetes sera were used to induce a disease-specific transcriptional signature in unrelated, healthy peripheral blood mononuclear cells (PBMCs). Here, we apply this strategy to investigate the inflammatory state associated with type 1 diabetes in biobreeding (BB) rats.

RESULTS

Consistent with their common susceptibility, sera of both spontaneously diabetic BB DRlyp/lyp and diabetes inducible BB DR+/+ rats induced transcription of cytokines, immune receptors, and signaling molecules in PBMCs of healthy donor rats compared with control sera. Like the human type 1 diabetes signature, the DRlyp/lyp signature, which is associated with progression to diabetes, was differentiated from that of the DR+/+ by induction of many interleukin (IL)-1–regulated genes. Supplementing cultures with an IL-1 receptor antagonist (IL-1Ra) modulated the DRlyp/lyp signature (P < 10−6), while administration of IL-1Ra to DRlyp/lyp rats delayed onset (P = 0.007), and sera of treated animals did not induce the characteristic signature. Consistent with the presence of immunoregulatory cells in DR+/+ rats was induction of a signature possessing negative regulators of transcription and inflammation.

CONCLUSIONS

Paralleling our human studies, serum signatures in BB rats reflect processes associated with progression to type 1 diabetes. Furthermore, these studies support the potential utility of this approach to detect changes in the inflammatory state during therapeutic intervention.

Background

In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.

Results

In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.

Conclusions

Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

Activated protein C (aPC) therapy reduces mortality in adult patients with severe sepsis. In mouse endotoxemia and sepsis models, mortality reduction requires the cell signaling function of aPC, mediated through protease-activated receptor–1 (PAR1) and endothelial protein C receptor (EPCR; also known as Procr). Candidate cellular targets of aPC include vascular endothelial cells and leukocytes. Here, we show that expression of EPCR and PAR1 on hematopoietic cells is required in mice for an aPC variant that mediates full cell signaling activity but only minimal anticoagulant function (5A-aPC) to reduce the mortality of endotoxemia. Expression of EPCR in mature murine immune cells was limited to a subset of CD8+ conventional dendritic cells. Adoptive transfer of splenic CD11chiPDCA-1– dendritic cells from wild-type mice into animals with hematopoietic EPCR deficiency restored the therapeutic efficacy of aPC, whereas transfer of EPCR-deficient CD11chi dendritic cells or wild-type CD11chi dendritic cells depleted of EPCR+ cells did not. In addition, 5A-aPC inhibited the inflammatory response of conventional dendritic cells independent of EPCR and suppressed IFN-γ production by natural killer–like dendritic cells. These data reveal an essential role for EPCR and PAR1 on hematopoietic cells, identify EPCR-expressing dendritic immune cells as a critical target of aPC therapy, and document EPCR-independent antiinflammatory effects of aPC on innate immune cells.

SUMMARY

We describe a patient with an autoinflammatory disease in which the main clinical features are pustular rash, marked osteopenia, lytic bone lesions, respiratory insufficiency, and thrombosis. Genetic studies revealed a 175-kb homozygous deletion at chromosome 2q13, which encompasses several interleukin-1 family members, including the gene encoding the interleukin-1–receptor antagonist (IL1RN). Mononuclear cells, obtained from the patient and cultured, produced large amounts of inflammatory cytokines, with increasing amounts secreted after stimulation with lipopolysaccharide. A similar increase was not observed in peripheral-blood mononuclear cells from a patient with neonatal-onset multisystem inflammatory disorder (NOMID). Treatment with anakinra completely resolved the symptoms and lesions.

TCDD is a reproductive toxicant and endocrine disruptor, yet the mechanisms by which it causes these reproductive alterations are not fully understood. In order to provide additional insight into the molecular mechanisms that underlie TCDD’s reproductive toxicity, we assessed TCDD-induced transcriptional changes in the ovary as they relate to previously described impacts on serum estradiol concentrations and altered follicular development in zebrafish. In-silico computational approaches were used to correlate candidate regulatory motifs with observed changes in gene expression. Our data suggest that TCDD inhibits follicle maturation via attenuated gonadotropin responsiveness and/or depressed estradiol biosynthesis, and that interference of estrogen-regulated signal transduction may also contribute to TCDD’s impacts on follicular development. TCDD may also alter ovarian function by disrupting various signaling pathways such as glucose and lipid metabolism, and regulation of transcription. Furthermore, events downstream from initial TCDD molecular-targets likely contribute to ovarian toxicity following chronic exposure to TCDD. Data presented here provide further insight into the mechanisms by which TCDD disrupts follicular development and reproduction in fish, and can be used to formulate new hypotheses regarding previously documented ovarian toxicity.

Background

Providing quantitative microarray data that is sensitive to very small differences in target sequence would be a useful tool in any number of venues where a sample can consist of a multiple related sequences present in various abundances. Examples of such applications would include measurement of pseudo species in viral infections and the measurement of species of antibodies or T cell receptors that constitute immune repertoires. Difficulties that must be overcome in such a method would be to account for cross-hybridization and for differences in hybridization efficiencies between the arrayed probes and their corresponding targets. We have used the memory T cell repertoire to an influenza-derived peptide as a test case for developing such a method.

Results

The arrayed probes were corresponded to a 17 nucleotide TCR-specific region that distinguished sequences differing by as little as a single nucleotide. Hybridization efficiency between highly related Cy5-labeled subject sequences was normalized by including an equimolar mixture of Cy3-labeled synthetic targets representing all 108 arrayed probes. The same synthetic targets were used to measure the degree of cross hybridization between probes. Reconstitution studies found the system sensitive to input ratios as low as 0.5% and accurate in measuring known input percentages (R2 = 0.81, R = 0.90, p < 0.0001). A data handling protocol was developed to incorporate the differences in hybridization efficiency. To validate the array in T cell repertoire analysis, it was used to analyze human recall responses to influenza in three human subjects and compared to traditional cloning and sequencing. When evaluating the rank order of clonotype abundance determined by each method, the approaches were not found significantly different (Wilcoxon rank-sum test, p > 0.05).

Conclusion

This novel strategy appears to be robust and can be adapted to any situation where complex mixtures of highly similar sequences need to be quantitatively resolved.

Background

Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations.

Results

Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium.

Conclusions

Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer.

Background

Global gene expression studies with microarrays can offer biological insights never before possible. However, the technology possesses many sources of technical variability that are an obstacle to obtaining high quality data sets. Since spotted microarrays offer design/content flexibility and potential cost savings over commercial systems, we have developed prehybridization quality control strategies for spotted cDNA and oligonucleotide arrays. These approaches utilize a third fluorescent dye (fluorescein) to monitor key fabrication variables, such as print/spot morphology, DNA retention, and background arising from probe redistributed during blocking. Here, our labeled cDNA array platform is used to study, 1) compression of array data using known input ratios of Arabidopsis in vitro transcripts and arrayed serial dilutions of homologous probes; 2) how curing time of in-house poly-L-lysine coated slides impacts probe retention capacity; and 3) the retention characteristics of 13 commercially available surfaces.

Results

When array element fluorescein intensity drops below 5,000 RFU/pixel, gene expression measurements become increasingly compressed, thereby validating this value as a prehybridization quality control threshold. We observe that the DNA retention capacity of in-house poly-L-lysine slides decreases rapidly over time (~50% reduction between 3 and 12 weeks post-coating; p < 0.0002) and that there are considerable differences in retention characteristics among commercially available poly-L-lysine and amino silane-coated slides.

Conclusions

High DNA retention rates are necessary for accurate gene expression measurements. Therefore, an understanding of the characteristics and optimization of protocols to an array surface are prerequisites to fabrication of high quality arrays.

Background

Once specific genes are identified through high throughput genomics technologies there is a need to sort the final gene list to a manageable size for validation studies. The triaging and sorting of genes often relies on the use of supplemental information related to gene structure, metabolic pathways, and chromosomal location. Yet in disease states where the genes may not have identifiable structural elements, poorly defined metabolic pathways, or limited chromosomal data, flexible systems for obtaining additional data are necessary. In these situations having a tool for searching the biomedical literature using the list of identified genes while simultaneously defining additional search terms would be useful.

Results

We have built a tool, BEAR GeneInfo, that allows flexible searches based on the investigators knowledge of the biological process, thus allowing for data mining that is specific to the scientist's strengths and interests. This tool allows a user to upload a series of GenBank accession numbers, Unigene Ids, Locuslink Ids, or gene names. BEAR GeneInfo takes these IDs and identifies the associated gene names, and uses the lists of gene names to query PubMed. The investigator can add additional modifying search terms to the query. The subsequent output provides a list of publications, along with the associated reference hyperlinks, for reviewing the identified articles for relevance and interest. An example of the use of this tool in the study of human prostate cancer cells treated with Selenium is presented.

Conclusions

This tool can be used to further define a list of genes that have been identified through genomic or genetic studies. Through the use of targeted searches with additional search terms the investigator can limit the list to genes that match their specific research interests or needs. The tool is freely available on the web at [1], and the authors will provide scripts and database components if requested mdatta@mcw.edu

Background

Spotted 70-mer oligonucleotide arrays offer potentially greater specificity and an alternative to expensive cDNA library maintenance and amplification. Since microarray fabrication is a considerable source of data variance, we previously directly tagged cDNA probes with a third fluorophore for prehybridization quality control. Fluorescently modifying oligonucleotide sets is cost prohibitive, therefore, a co-spotted Staphylococcus aureus-specific fluorescein-labeled "tracking" oligonucleotide is described to monitor fabrication variables of a Mycobacterium tuberculosis oligonucleotide microarray.

Results

Significantly (p < 0.01) improved DNA retention was achieved printing in 15% DMSO/1.5 M betaine compared to the vendor recommended buffers. Introduction of tracking oligonucleotide did not effect hybridization efficiency or introduce ratio measurement bias in hybridizations between M. tuberculosis H37Rv and M. tuberculosis mprA. Linearity between the mean log Cy3/Cy5 ratios of genes differentially expressed from arrays either possessing or lacking the tracking oligonucleotide was observed (R2 = 0.90, p < 0.05) and there were no significant differences in Pearson's correlation coefficients of ratio data between replicates possessing (0.72 ± 0.07), replicates lacking (0.74 ± 0.10), or replicates with and without (0.70 ± 0.04) the tracking oligonucleotide. ANOVA analysis confirmed the tracking oligonucleotide introduced no bias. Titrating target-specific oligonucleotide (40 μM to 0.78 μM) in the presence of 0.5 μM tracking oligonucleotide, revealed a fluorescein fluorescence inversely related to target-specific oligonucleotide molarity, making tracking oligonucleotide signal useful for quality control measurements and differentiating false negatives (synthesis failures and mechanical misses) from true negatives (no gene expression).

Conclusions

This novel approach enables prehybridization array visualization for spotted oligonucleotide arrays and sets the stage for more sophisticated slide qualification and data filtering applications.

Construction methodologies for cDNA microarrays lack the ability to determine array integrity prior to hybridization, leaving the array itself a source of uncontrolled experimental variation. We solved this problem through development of a three-color cDNA array platform whereby printed probes are tagged with fluorescein and are compatible with Cy3 and Cy5 target labeling dyes when using confocal laser scanners possessing narrow bandwidths. Here we use this approach to: (i) develop a tracking system to monitor the printing of probe plates at predicted coordinates; (ii) define the quantity of immobilized probe necessary for quality hybridized array data to establish pre-hybridization array selection criteria; (iii) investigate factors that influence probe availability for hybridization; and (iv) explore the feasibility of hybridized data filtering using element fluorescein intensity. A direct and significant relationship (R2 = 0.73, P < 0.001) between pre-hybridization average fluorescein intensity and subsequent hybridized replicate consistency was observed, illustrating that data quality can be improved by selecting arrays that meet defined pre-hybridization criteria. Furthermore, we demonstrate that our three-color approach provides a means to filter spots possessing insufficient bound probe from hybridized data sets to further improve data quality. Collectively, this strategy will improve microarray data and increase its utility as a sensitive screening tool.

Gene expression studies using microarrays have great potential to generate new insights into human disease pathogenesis, but data quality remains a major obstacle. In particular, there does not exist a method to determine prior to hybridization whether an array will yield high quality data, given good study design and target preparation. We have solved this problem through development of a three-color cDNA microarray platform where printed probes are fluorescein labeled, but are spectrally compatible with Cy3 and Cy5 dye-labeled targets when using confocal laser scanners possessing narrow bandwidths. This approach enables prehybridization evaluation of array/spot morphology, DNA deposition and retention and background levels. By using these measurements and the intra-slide coefficient of variation for fluorescence intensity we show that slides in the same batch are not equivalent and measurable prehybridization parameters can be predictive of hybridization performance as determined by replicate consistency. When hybridizing target derived from two cell lines to high and low quality replicate pairs (n = 50 pairs), a direct and significant relationship between prehybridization signal-to-background noise and post-hybridization reproducibility (R2 = 0.80, P < 0.001) was observed. We therefore conclude that slide selection based upon prehybridization quality scores will greatly benefit the ability to generate reliable gene expression data.

Background

Gene expression profiling using microarrays has become an important genetic tool. Spotted arrays prepared in academic labs have the advantage of low cost and high design and content flexibility, but are often limited by their susceptibility to quality control (QC) issues. Previously, we have reported a novel 3-color microarray technology that enabled array fabrication QC. In this report we further investigated its advantage in spot-level data QC.

Results

We found that inadequate amount of bound probes available for hybridization led to significant, gene-specific compression in ratio measurements, increased data variability, and printing pin dependent heterogeneities. The impact of such problems can be captured through the definition of quality scores, and efficiently controlled through quality-dependent filtering and normalization. We compared gene expression measurements derived using our data processing pipeline with the known input ratios of spiked in control clones, and with the measurements by quantitative real time RT-PCR. In each case, highly linear relationships (R2>0.94) were observed, with modest compression in the microarray measurements (correction factor<1.17).

Conclusion

Our microarray analytical and technical advancements enabled a better dissection of the sources of data variability and hence a more efficient QC. With that highly accurate gene expression measurements can be achieved using the cDNA microarray technology.