Intake of B vitamins and the risk of developing islet autoimmunity and type 1 diabetes in the TEDDY study.

PURPOSE: The aim was to study the association between dietary intake of B vitamins in childhood and the risk of islet autoimmunity (IA) and progression to type 1 diabetes (T1D) by the age of 10 years. METHODS: We followed 8500 T1D-susceptible children born in the U.S., Finland, Sweden, and Germany in 2004 -2010 from the Environmental Determinants of Diabetes in the Young (TEDDY) study, which is a prospective observational birth cohort. Dietary intake of seven B vitamins was calculated from foods and dietary supplements based on 24-h recall at 3 months and 3-day food records collected regularly from 6 months to 10 years of age. Cox proportional hazard models were adjusted for energy, HLA-genotype, first-degree relative with T1D, sex, and country. RESULTS: A total of 778 (9.2) children developed at least one autoantibody (any IA), and 335 (3.9%) developed multiple autoantibodies. 280 (3.3%) children had IAA and 319 (3.8%) GADA as the first autoantibody. 344 (44%) children with IA progressed to T1D. We observed that higher intake of niacin was associated with a decreased risk of developing multiple autoantibodies (HR 0.95; 95% CI 0.92, 0.98) per 1 mg/1000 kcal in niacin intake. Higher intake of pyridoxine (HR 0.66; 95% CI 0.46, 0.96) and vitamin B12 (HR 0.87; 95% CI 0.77, 0.97) was associated with a decreased risk of IAA-first autoimmunity. Higher intake of riboflavin (HR 1.38; 95% CI 1.05, 1.80) was associated with an increased risk of GADA-first autoimmunity. There were no associations between any of the B vitamins and the outcomes "any IA" and progression from IA to T1D.  CONCLUSION: In this multinational, prospective birth cohort of children with genetic susceptibility to T1D, we observed some direct and inverse associations between different B vitamins and risk of IA.

Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes.

The etiology of type 1 diabetes (T1D) foreshadows the pancreatic islet beta-cell autoimmune pathogenesis that heralds the clinical onset of T1D. Standardized and harmonized tests of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A), and ZnT8 transporter (ZnT8A) allowed children to be followed from birth until the appearance of a first islet autoantibody. In the Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter (Finland, Germany, Sweden, and the United States) observational study, children were identified at birth for the T1D high-risk HLA haploid genotypes DQ2/DQ8, DQ2/DQ2, DQ8/DQ8, and DQ4/DQ8. The TEDDY study was preceded by smaller studies in Finland, Germany, Colorado, Washington, and Sweden. The aims were to follow children at increased genetic risk to identify environmental factors that trigger the first-appearing autoantibody (etiology) and progress to T1D (pathogenesis). The larger TEDDY study found that the incidence rate of the first-appearing autoantibody was split into two patterns. IAA first peaked already during the first year of life and tapered off by 3-4 years of age. GADA first appeared by 2-3 years of age to reach a plateau by about 4 years. Prior to the first-appearing autoantibody, genetic variants were either common or unique to either pattern. A split was also observed in whole blood transcriptomics, metabolomics, dietary factors, and exposures such as gestational life events and early infections associated with prolonged shedding of virus. An innate immune reaction prior to the adaptive response cannot be excluded. Clarifying the mechanisms by which autoimmunity is triggered to either insulin or GAD65 is key to uncovering the etiology of autoimmune T1D.

High serum levels of inflammatory markers are associated with early recurrence in patients with high-grade serous ovarian cancer after platinum therapy.

OBJECTIVE: To determine if inflammatory biomarkers can predict the long-term outcome of platinum therapy in patients with high-grade serous ovarian cancer. METHODS: Women diagnosed with high-grade serous epithelial ovarian cancer (n = 70) at a single institution were enrolled in a prospective serum collection study between 2005 and 2020. Seventeen markers of inflammation and oxidative stress were measured in serum samples on a chemistry analyzer. Association was tested for serum levels with progression-free survival (PFS), time to recurrence (TTR), overall survival (OS), and time to death (TTD) using Cox proportional hazards and Kaplan-Meier curves. Patient survival was censored at 10 years. RESULTS: Higher serum levels of LDH were associated with worse PFS (HR 2.57, p = 0.028). High serum levels of BAP (HR 0.38, p = 0.025), GSP (HR 0.40, p = 0.040), HDL-c (HR 0.27, p = 0.002), and MG (HR 0.36, p = 0.017) were associated with improved PFS. Higher expression of LDH was associated with worse OS (HR 2.16, p = 0.023). Higher levels of CK.nac (HR 0.39, p = 0.033) and HDL-c (HR 0.35, p = 0.029) were associated with improved OS. Similar outcomes were found with TTR and TTD analyses. CONCLUSION: General inflammatory biomarkers may serve as a guide for prognosis and treatment benefit. Future studies needed to further define their role in predicting prognosis or how these markers may affect response to therapy.

Dietary Intake and Body Mass Index Influence the Risk of Islet Autoimmunity in Genetically At-Risk Children: A Mediation Analysis Using the TEDDY Cohort.

Background/Objective: Growth and obesity have been associated with increased risk of islet autoimmunity (IA) and progression to type 1 diabetes. We aimed to estimate the effect of energy-yielding macronutrient intake on the development of IA through BMI. Research Design and Methods: Genetically at-risk children (n = 5,084) in Finland, Germany, Sweden, and the USA, who were autoantibody negative at 2 years of age, were followed to the age of 8 years, with anthropometric measurements and 3-day food records collected biannually. Of these, 495 (9.7%) children developed IA. Mediation analysis for time-varying covariates (BMI z-score) and exposure (energy intake) was conducted. Cox proportional hazard method was used in sensitivity analysis. Results: We found an indirect effect of total energy intake (estimates: indirect effect 0.13 [0.05, 0.21]) and energy from protein (estimates: indirect effect 0.06 [0.02, 0.11]), fat (estimates: indirect effect 0.03 [0.01, 0.05]), and carbohydrates (estimates: indirect effect 0.02 [0.00, 0.04]) (kcal/day) on the development of IA. A direct effect was found for protein, expressed both as kcal/day (estimates: direct effect 1.09 [0.35, 1.56]) and energy percentage (estimates: direct effect 72.8 [3.0, 98.0]) and the development of GAD autoantibodies (GADA). In the sensitivity analysis, energy from protein (kcal/day) was associated with increased risk for GADA, hazard ratio 1.24 (95% CI: 1.09, 1.53), p = 0.042. Conclusions: This study confirms that higher total energy intake is associated with higher BMI, which leads to higher risk of the development of IA. A diet with larger proportion of energy from protein has a direct effect on the development of GADA.

Thymus-derived regulatory T cells contribute to tolerance to commensal microbiota.

Peripheral mechanisms preventing autoimmunity and maintaining tolerance to commensal microbiota involve CD4(+) Foxp3(+) regulatory T (Treg) cells generated in the thymus or extrathymically by induction of naive CD4(+) Foxp3(-) T cells. Previous studies suggested that the T-cell receptor repertoires of thymic Treg cells and induced Treg cells are biased towards self and non-self antigens, respectively, but their relative contribution in controlling immunopathology, such as colitis and other untoward inflammatory responses triggered by different types of antigens, remains unresolved. The intestine, and especially the colon, is a particularly suitable organ to study this question, given the variety of self-, microbiota- and food-derived antigens to which Treg cells and other T-cell populations are exposed. Intestinal environments can enhance conversion to a regulatory lineage and favour tolerogenic presentation of antigens to naive CD4(+) T cells, suggesting that intestinal homeostasis depends on microbiota-specific induced Treg cells. Here, to identify the origin and antigen-specificity of intestinal Treg cells, we performed single-cell and high-throughput sequencing of the T-cell receptor repertoires of CD4(+) Foxp3(+) and CD4(+) Foxp3(-) T cells, and analysed their reactivity against specific commensal species. We show that thymus-derived Treg cells constitute most Treg cells in all lymphoid and intestinal organs, including the colon, where their repertoire is heavily influenced by the composition of the microbiota. Our results suggest that thymic Treg cells, and not induced Treg cells, dominantly mediate tolerance to antigens produced by intestinal commensals.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions.

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

The Influence of Pubertal Development on Autoantibody Appearance and Progression to Type 1 Diabetes in the TEDDY Study.

Context: The 2 peaks of type 1 diabetes incidence occur during early childhood and puberty. Objective: We sought to better understand the relationship between puberty, islet autoimmunity, and type 1 diabetes. Methods: The relationships between puberty, islet autoimmunity, and progression to type 1 diabetes were investigated prospectively in children followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Onset of puberty was determined by subject self-assessment of Tanner stages. Associations between speed of pubertal progression, pubertal growth, weight gain, homeostasis model assessment of insulin resistance (HOMA-IR), islet autoimmunity, and progression to type 1 diabetes were assessed. The influence of individual factors was analyzed using Cox proportional hazard ratios. Results: Out of 5677 children who were still in the study at age 8 years, 95% reported at least 1 Tanner Stage score and were included in the study. Children at puberty (Tanner Stage ≥2) had a lower risk (HR 0.65, 95% CI 0.45-0.93; P = .019) for incident autoimmunity than prepubertal children (Tanner Stage 1). An increase of body mass index Z-score was associated with a higher risk (HR 2.88, 95% CI 1.61-5.15; P < .001) of incident insulin autoantibodies. In children with multiple autoantibodies, neither HOMA-IR nor rate of progression to Tanner Stage 4 were associated with progression to type 1 diabetes. Conclusion: Rapid weight gain during puberty is associated with development of islet autoimmunity. Puberty itself had no significant influence on the appearance of autoantibodies or type 1 diabetes. Further studies are needed to better understand the underlying mechanisms.

Ovarian recurrence risk assessment using machine learning, clinical information, and serum protein levels to predict survival in high grade ovarian cancer.

In ovarian cancer, there is no current method to accurately predict recurrence after a complete response to chemotherapy. Here, we develop a machine learning risk score using serum proteomics for the prediction of early recurrence of ovarian cancer after initial treatment. The developed risk score was validated in an independent cohort with serum collected prospectively during the remission period. In the discovery cohort, patients scored as low-risk had a median time to recurrence (TTR) that was not reached at 10 years compared to 10.5 months (HR 4.66, p < 0.001) in high-risk patients. In the validation cohort, low-risk patients had a median TTR which was not reached compared to 4.7 months in high-risk patients (HR 4.67, p = 0.009). In advanced-stage patients with a CA125 < 10, low-risk patients had a median TTR of 68 months compared to 6 months in high-risk patients (HR 2.91, p = 0.02). The developed risk score was capable of distinguishing the duration of remission in ovarian cancer patients. This score may help guide maintenance therapy and develop innovative treatments in patients at risk at high-risk of recurrence.

HLA Genotype and Probiotics Modify the Association Between Timing of Solid Food Introduction and Islet Autoimmunity in the TEDDY Study.

OBJECTIVE: To study the interaction among HLA genotype, early probiotic exposure, and timing of complementary foods in relation to risk of islet autoimmunity (IA). RESEARCH DESIGN AND METHODS: The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,676 children with increased genetic risk of type 1 diabetes. We used a Cox proportional hazards regression model adjusting for potential confounders to study early feeding and the risk of IA in a sample of 7,770 children. RESULTS: Any solid food introduced early (<6 months) was associated with increased risk of IA if the child had the HLA DR3/4 genotype and no probiotic exposure during the 1st year of life. Rice introduced at 4-5.9 months compared with later in the U.S. was associated with an increased risk of IA. CONCLUSIONS: Timing of solid food introduction, including rice, may be associated with IA in children with the HLA DR3/4 genotype not exposed to probiotics. The microbiome composition under these exposure combinations requires further study.

Physical Activity and the Development of Islet Autoimmunity and Type 1 Diabetes in 5- to 15-Year-Old Children Followed in the TEDDY Study.

OBJECTIVE: This study investigated physical activity and its association with the development of islet autoimmunity and type 1 diabetes in genetically at-risk children aged 5-15 years. RESEARCH DESIGN AND METHODS: As part of the longitudinal Environmental Determinants of Diabetes in the Young (TEDDY) study, annual assessment of activity using accelerometry was conducted from age 5 years. Time-to-event analyses using Cox proportional hazard models were used to assess the association between time spent in moderate to vigorous physical activity per day and the appearance of one or several autoantibodies and progression to type 1 diabetes in three risk groups: 1) 3,869 islet autoantibody (IA)-negative children, of whom 157 became single IA positive; 2) 302 single IA-positive children, of whom 73 became multiple IA positive; and 3) 294 multiple IA-positive children, of whom 148 developed type 1 diabetes. RESULTS: No significant association was found in risk group 1 or risk group 2. A significant association was seen in risk group 3 (hazard ratio 0.920 [95% CI 0.856, 0.988] per 10-min increase; P = 0.021), particularly when glutamate decarboxylase autoantibody was the first autoantibody (hazard ratio 0.883 [95% CI 0.783, 0.996] per 10-min increase; P = 0.043). CONCLUSIONS: More daily minutes spent in moderate to vigorous physical activity was associated with a reduced risk of progression to type 1 diabetes in children aged 5-15 years who had developed multiple IAs.

A novel network based linear model for prioritization of synergistic drug combinations.

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

Rising Hemoglobin A1c in the Nondiabetic Range Predicts Progression of Type 1 Diabetes As Well As Oral Glucose Tolerance Tests.

OBJECTIVE: Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies. RESEARCH DESIGN AND METHODS: Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190). RESULTS: A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7-18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3-7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82). CONCLUSIONS: An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies.

ParaSAM: a parallelized version of the significance analysis of microarrays algorithm.

MOTIVATION: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. SUMMARY: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations. AVAILABILITY: A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.

A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets.

MOTIVATION: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30,000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44,460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1). AVAILABILITY: The program is freely available and can be downloaded from http://www.amdcc.org/bioinformatics/bioinformatics.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mouse models of diabetic nephropathy.

Diabetic nephropathy is a major cause of ESRD worldwide. Despite its prevalence, a lack of reliable animal models that mimic human disease has delayed the identification of specific factors that cause or predict diabetic nephropathy. The Animal Models of Diabetic Complications Consortium (AMDCC) was created in 2001 by the National Institutes of Health to develop and characterize models of diabetic nephropathy and other complications. This interim report and our online supplement detail the progress made toward that goal, specifically in the development and testing of murine models. Updates are provided on validation criteria for early and advanced diabetic nephropathy, phenotyping methods, the effect of background strain on nephropathy, current best models of diabetic nephropathy, negative models, and views of future directions. AMDCC investigators and other investigators in the field have yet to validate a complete murine model of human diabetic kidney disease. Nonetheless, the critical analysis of existing murine models substantially enhances our understanding of this disease process.

Persistent STAT5 activation reprograms the epigenetic landscape in CD4+ T cells to drive polyfunctionality and antitumor immunity.

The presence of polyfunctional CD4+ T cells is often associated with favorable antitumor immunity. We report here that persistent activation of signal transducer and activator of transcription 5 (STAT5) in tumor-specific CD4+ T cells drives the development of polyfunctional T cells. We showed that ectopic expression of a constitutively active form of murine STAT5A (CASTAT5) enabled tumor-specific CD4+ T cells to undergo robust expansion, infiltrate tumors vigorously, and elicit antitumor CD8+ T cell responses in a CD4+ T cell adoptive transfer model system. Integrated epigenomic and transcriptomic analysis revealed that CASTAT5 induced genome-wide chromatin remodeling in CD4+ T cells and established a distinct epigenetic and transcriptional landscape. Single-cell RNA sequencing analysis further identified a subset of CASTAT5-transduced CD4+ T cells with a molecular signature indicative of progenitor polyfunctional T cells. The therapeutic significance of CASTAT5 came from our finding that adoptive transfer of T cells engineered to coexpress CD19-targeting chimeric antigen receptor (CAR) and CASTAT5 gave rise to polyfunctional CD4+ CAR T cells in a mouse B cell lymphoma model. The optimal therapeutic outcome was obtained when both CD4+ and CD8+ CAR T cells were transduced with CASTAT5, indicating that CASTAT5 facilitates productive CD4 help to CD8+ T cells. Furthermore, we provide evidence that CASTAT5 is functional in primary human CD4+ T cells, underscoring its potential clinical relevance. Our results implicate STAT5 as a valid candidate for T cell engineering to generate polyfunctional, exhaustion-resistant, and tumor-tropic antitumor CD4+ T cells to potentiate adoptive T cell therapy for cancer.

Differential gene expressions in the lacrimal gland during development and onset of keratoconjunctivitis sicca in Sjögren's syndrome (SJS)-like disease of the C57BL/6.NOD-Aec1Aec2 mouse.

Recently, we reported development of the C57BL/6.NOD-Aec1Aec2 mouse carrying two genetic intervals derived from the NOD mouse. These two genetic regions confer Sjögren's syndrome (SjS)-like disease in SjS-non-susceptible C57BL/6 mice. In an attempt to define the molecular bases underlying onset of dacryoadenitis and subsequently keratoconjunctivitis sicca (or xerophthalmia) in the C57BL/6.NOD-Aec1Aec2 mouse model, we have carried out a study utilizing microarray technology. Using oligonucleotide microarrays, gene expression profiles of lacrimal glands at 4, 8, 12, 16 and 20weeks of age were generated for C57BL/6.NOD-Aec1Aec2 male mice. Analyses using Linear Models for Microarray Analysis package and B-statistics, 552 genes were identified as being differentially expressed (adjusted p-value <0.01 and B <1.5) during the development of SjS-like disease. These 552 genes could be arranged into four clusters, with each cluster defining a unique pattern of temporal expression, while the individual genes within each cluster could be grouped according to related function. Using a pair-wise analysis, temporal changes in gene expressions provided profiles indicating that individual genes were differentially expressed at specific time points during development of SjS. In addition, multiple genes that have been reported to show, either in humans or mouse models, an association with autoimmunity and/or SjS, e.g., ApoE, Baff, Clu, Ctla4, Fas/Fasl, Irf5, Lyzs, Nfkb, Socs3, Stat4, Tap2, Tgfbeta1, Tnfa, and Vcam1 were also found to exhibit differential expressions, both quantitatively and temporally. Selecting a few families of genes, e.g., cystatins, cathepsins, metalloproteinases, lipocalins, complement, kallikreins, carbonic anhydrases and tumor necrosis factors, it was noted that only a limited number of family members showed differential expressions, suggesting a restricted glandular expression. Utilizing these genes, pathways of inter-reactive genes have been constructed for apoptosis and fatty acid homeostasis, leading to modeling of possible underlying events inducing disease. Thus, these different approaches to analyze microarray data permit identification of multiple sets of genes of interest whose expressions and expression profiles may correlate with molecular mechanisms, signaling pathways and/or immunological processes involved in the development and onset of SjS in this mouse model, thereby providing new insight into the underlying cause or regulation of this disease.

Recipes for creating animal models of diabetic cardiovascular disease.

For more than 50 years, investigators have unsuccessfully tried to recreate in experimental animals the cardiovascular complications of diabetes seen in humans. In particular, accelerated atherosclerosis and dilated cardiomyopathy, the major causes of mortality in patients with diabetes, have been conspicuously absent in many mouse models of the disease. Under the auspices of the NIH, the Animal Models of Diabetic Complications Consortium has worked to address this issue. This effort has focused on the development of mouse models because of the high level of genomic information available and the many well-developed genetic manipulations that may be performed in mice. Importantly, the consortium has also worked to standardize many methods to assess metabolic and cardiovascular end points for measurement of the diabetic state and its macrovascular complications. Finally, for maximum benefits from these animal models in the study of atherosclerosis and of other diabetic complications, the consortium has created a system for sharing both the animal models and the accumulated phenotypic data with the greater scientific community.

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use.

BACKGROUND: During the last decade, the use of microarrays to assess the transcriptome of many biological systems has generated an enormous amount of data. A common technique used to organize and analyze microarray data is to perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant decrease in computational performance as the size of the dataset being analyzed becomes very large. For example, clustering 10000 genes from an experiment containing 200 microarrays can be quite time consuming and challenging on a desktop PC. One solution to the scalability problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers. RESULTS: The software described in this paper is a high performance multithreaded application that implements a parallelized version of the K-means Clustering algorithm. Most parallel processing applications are not accessible to the general public and require specialized software libraries (e.g. MPI) and specialized hardware configurations. The parallel nature of the application comes from the use of a web service to perform the distance calculations and cluster assignments. Here we show our parallel implementation provides significant performance gains over a wide range of datasets using as little as seven nodes. The software was written in C# and was designed in a modular fashion to provide both deployment flexibility as well as flexibility in the user interface. CONCLUSION: ParaKMeans was designed to provide the general scientific community with an easy and manageable client-server application that can be installed on a wide variety of Windows operating systems.