A chronic myeloid leukemia-like syndrome case with del (12) (p12) in a Li-Fraumeni syndrome family

Li-Fraumeni syndrome is a familial cancer syndrome characterized by different tumors and hereditary p53 mutations. Here, a chronic myeloid leukemia-like syndrome case in a Li-Fraumeni syndrome family with del (12) (p12) cytogenetic abnormality was presented. A hereditary p53 mutation (pro309ser) supported the Li-Fraumeni syndrome diagnosis in this family. This syndrome was characterized by the clonal myeloproliferative accumulation in bone marrow and peripheral blood with negative bcr/abl gene rearrangement finding. The etiology of this rare syndrome is still unclear. This is the only chronic myeloid leukemia-like syndrome case reported in a Li-Fraumeni syndrome family. Del (12) (p12) was observed in leukemias except chronic myeloid leukemia-like syndrome. The deletion in chromosome 12pl2 with hereditary p53 mutation should have a critical role in chronic myeloid leukemia-like syndrome etiology in our case. © 2005 Blackwell Publishing Ltd

Identification of Type 1 Diabetes-Associated DNA Methylation Variable Positions That Precede Disease Diagnosis

Monozygotic (MZ) twin pair discordance for childhood-onset Type 1 Diabetes (T1D) is similar to 50%, implicating roles for genetic and non-genetic factors in the aetiology of this complex autoimmune disease. Although significant progress has been made in elucidating the genetics of T1D in recent years, the non-genetic component has remained poorly defined. We hypothesized that epigenetic variation could underlie some of the non-genetic component of T1D aetiology and, thus, performed an epigenome-wide association study (EWAS) for this disease. We generated genome-wide DNA methylation profiles of purified CD14(+) monocytes (an immune effector cell type relevant to T1D pathogenesis) from 15 T1D-discordant MZ twin pairs. This identified 132 different CpG sites at which the direction of the intra-MZ pair DNA methylation difference significantly correlated with the diabetic state, i.e. T1D-associated methylation variable positions (T1D-MVPs). We confirmed these T1D-MVPs display statistically significant intra-MZ pair DNA methylation differences in the expected direction in an independent set of T1D-discordant MZ pairs (P = 0.035). Then, to establish the temporal origins of the T1D-MVPs, we generated two further genome-wide datasets and established that, when compared with controls, T1D-MVPs are enriched in singletons both before (P = 0.001) and at (P = 0.015) disease diagnosis, and also in singletons positive for diabetes-associated autoantibodies but disease-free even after 12 years follow-up (P = 0.0023). Combined, these results suggest that T1D-MVPs arise very early in the etiological process that leads to overt T1D. Our EWAS of T1D represents an important contribution toward understanding the etiological role of epigenetic variation in type 1 diabetes, and it is also the first systematic analysis of the temporal origins of disease-associated epigenetic variation for any human complex disease

Glycotoxin and autoantibodies are additive environmentally determined predictors of type 1 diabetes: a twin and population study.

In type 1 diabetes, diabetes-associated autoantibodies, including islet cell antibodies (ICAs), reflect adaptive immunity, while increased serum N(ε)-carboxymethyl-lysine (CML), an advanced glycation end product, is associated with proinflammation. We assessed whether serum CML and autoantibodies predicted type 1 diabetes and to what extent they were determined by genetic or environmental factors. Of 7,287 unselected schoolchildren screened, 115 were ICA(+) and were tested for baseline CML and diabetes autoantibodies and followed (for median 7 years), whereas a random selection (n = 2,102) had CML tested. CML and diabetes autoantibodies were determined in a classic twin study of twin pairs discordant for type 1 diabetes (32 monozygotic, 32 dizygotic pairs). CML was determined by enzyme-linked immunosorbent assay, autoantibodies were determined by radioimmunoprecipitation, ICA was determined by indirect immunofluorescence, and HLA class II genotyping was determined by sequence-specific oligonucleotides. CML was increased in ICA(+) and prediabetic schoolchildren and in diabetic and nondiabetic twins (all P < 0.001). Elevated levels of CML in ICA(+) children were a persistent, independent predictor of diabetes progression, in addition to autoantibodies and HLA risk. In twins model fitting, familial environment explained 75% of CML variance, and nonshared environment explained all autoantibody variance. Serum CML, a glycotoxin, emerged as an environmentally determined diabetes risk factor, in addition to autoimmunity and HLA genetic risk, and a potential therapeutic target.J.C.H. was supported by the Children’s Diabetes Foundation in Denver, the University of Colorado Denver Diabetes and Endocrinology Research Center (National Institutes of Health [NIH] Grant P30-DK-57516), NIH Grant R01-DK-052068, and the Juvenile Diabetes Research Foundation International Autoimmunity Center Consortium; B.O.B. was supported by Deutsche Forschungsgemeinschaft (DFG SFB 518/ GRK 1041) and State Baden-Wuerttemberg Centre of Excellence “Metabolic Disorders”; and R.D.L. was supported by grants from the British Diabetic Twin Research Trust and the Juvenile Diabetes Research Foundation International. H.Be. was in receipt of an Eli Lilly award

Assessment of dried blood spots for DNA methylation profiling

Background: DNA methylation reflects health-related environmental exposures and genetic risk, providing insights into aetiological mechanisms and potentially predicting disease onset, progression and treatment response. An increasingly recognised need for large-scale, longitudinally-profiled samples collected world-wide has made the development of efficient and straightforward sample collection and storage procedures a pressing issue. An alternative to the low-temperature storage of EDTA tubes of venous blood samples, which are frequently the source of the DNA used in such studies, is to collect and store at room temperature blood samples using purpose built filter paper, such as Whatman FTA® cards. Our goal was to determine whether DNA stored in this manner can be used to generate DNA methylation profiles comparable to those generated using blood samples frozen in EDTA tubes. Methods: DNA methylation profiles were obtained from matched EDTA tube and Whatman FTA® card whole-blood samples from 62 Generation Scotland: Scottish Family Health Study participants using the Infinium HumanMethylation450 BeadChip. Multiple quality control procedures were implemented, the relationship between the two sample types assessed, and epigenome-wide association studies (EWASs) performed for smoking status, age and the interaction between these variables and sample storage method. Results: Dried blood spot (DBS) DNA methylation profiles were of good quality and DNA methylation profiles from matched DBS and EDTA tube samples were highly correlated (mean r = 0.991) and could distinguish between participants. EWASs replicated established associations for smoking and age, with no evidence for moderation by storage method. Conclusions: Our results support the use of Whatman FTA® cards for collecting and storing blood samples for DNA methylation profiling. This approach is likely to be particularly beneficial for large-scale studies and those carried out in areas where freezer access is limited. Furthermore, our results will inform consideration of the use of newborn heel prick DBSs for research use

An RDF based semantic approach to model temporal relations in health records

Progression of diseases may vary for each patient due to genetic makeup, life style, or previous health history. Even for well-known medical conditions, temporal signatures can be different for specific genotypes. Secondary use of health records can help us to identify these signatures. We propose an RDF based approach for modelling the temporal relations in health records. RDF graphs compared to relational data representations provide advantages with their inherent notion of a hierarchy and a temporal model. In this work, we suggest a new approach to representing temporal relations in RDF graphs. The proposed approach will help to improve the efficiency of data mining by including a more relevant set of patient attributes

Exploring Diffusion Models for Unsupervised Video Anomaly Detection

This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used. As being sparse, diverse, contextual, and often ambiguous, detecting abnormal events precisely is a very ambitious task. To this end, we rely only on the information-rich spatio-temporal data, and the reconstruction power of the diffusion models such that a high reconstruction error is utilized to decide the abnormality. Experiments performed on two large-scale video anomaly detection datasets demonstrate the consistent improvement of the proposed method over the state-of-the-art generative models while in some cases our method achieves better scores than the more complex models. This is the first study using a diffusion model and examining its parameters’ influence to present guidance for VAD in surveillance scenarios