Biochemical, clinical and genetic characteristics in adults with persistent hypophosphatasaemia; Data from an endocrinological outpatient clinic in Denmark

BACKGROUND: Hypophosphatasia (HPP) is an inborn disease caused by pathogenic variants in ALPL. Low levels of alkaline phosphatase (ALP) are a biochemical hallmark of the disease. Scarce knowledge about the prevalence of HPP in Scandinavia exists, and the variable clinical presentations make diagnostics challenging. The aim of this study was to investigate the prevalence of ALPL variants as well as the clinical and biochemical features among adults with endocrinological diagnoses and persistent hypophosphatasaemia. METHODS: A biochemical database containing ALP measurements of 26,121 individuals was reviewed to identify adults above 18 years of age with persistently low levels of ALP beneath range (≤ 35 ± 2.7 U/L). ALPL genetic testing, biochemical evaluations and assessment of clinical features by a systematic questionnaire among included patients, were performed. RESULTS: Among 24 participants, thirteen subjects (54.2%) revealed a disease-causing variant in ALPL and reported mild clinical features of HPP, of which musculoskeletal pain was the most frequently reported (n = 9). The variant c. 571G > A; p.(Glu191Lys) was identified in six subjects, and an unreported missense variant (c.1019A > C; p.(His340Pro)) as well as a deletion of exon 2 were detected by genetic screening. Biochemical analyses showed no significant differences in ALP (p = 0.059), the bone specific alkaline phosphatase (BALP) (p = 0.056) and pyridoxal-5′-phosphate (PLP) (p = 0.085) between patients with an ALPL variant and negative genetic screening. Patients with a variant in ALPL had significantly higher PLP levels than healthy controls (p = 0.002). We observed normal ALP activity in some patients classified as mild HPP, and slightly increased levels of PLP in two subjects with normal genetic screening and four healthy controls. Among 51 patients with persistent hypophosphatasaemia, fifteen subjects (29.4%) received antiresorptive treatment. Two patients with unrecognized HPP were treated with bisphosphonates and did not show complications due to the treatment. CONCLUSIONS: Pathogenic variants in ALPL are common among patients with endocrinological diagnoses and low ALP. Regarding diagnostics, genetic testing is necessary to identify mild HPP due to fluctuating biochemical findings. Antiresorptive treatment is a frequent reason for hypophosphatasaemia and effects of these agents in adults with a variant in ALPL and osteoporosis remain unclear and require further studies

APIM-peptide targeting PCNA improves the efficacy of docetaxel treatment in the TRAMP mouse model of prostate cancer

publishedVersio

A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs

Chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (ChIP-seq) is rapidly becoming the method of choice for discovering cell-specific transcription factor binding locations genome wide. By aligning sequenced tags to the genome, binding locations appear as peaks in the tag profile. Several programs have been designed to identify such peaks, but program evaluation has been difficult due to the lack of benchmark data sets. We have created benchmark data sets for three transcription factors by manually evaluating a selection of potential binding regions that cover typical variation in peak size and appearance. Performance of five programs on this benchmark showed, first, that external control or background data was essential to limit the number of false positive peaks from the programs. However, >80% of these peaks could be manually filtered out by visual inspection alone, without using additional background data, showing that peak shape information is not fully exploited in the evaluated programs. Second, none of the programs returned peak-regions that corresponded to the actual resolution in ChIP-seq data. Our results showed that ChIP-seq peaks should be narrowed down to 100–400 bp, which is sufficient to identify unique peaks and binding sites. Based on these results, we propose a meta-approach that gives improved peak definitions

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

Background: Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer. Methods: DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan–Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT. Results: DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049). Conclusion: Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.publishedVersio

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.publishedVersio

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome

A ChIP-Seq Benchmark Shows That Sequence Conservation Mainly Improves Detection of Strong Transcription Factor Binding Sites

Transcription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks. Several methods for predicting TFBS exist, but there are no standard genome-wide datasets on which to assess the performance of these prediction methods. Also, it is believed that information about sequence conservation across different genomes can generally improve accuracy of motif-based predictors, but it is not clear under what circumstances use of conservation is most beneficial.Here we use published ChIP-seq data and an improved peak detection method to create comprehensive benchmark datasets for prediction methods which use known descriptors or binding motifs to detect TFBS in genomic sequences. We use this benchmark to assess the performance of five different prediction methods and find that the methods that use information about sequence conservation generally perform better than simpler motif-scanning methods. The difference is greater on high-affinity peaks and when using short and information-poor motifs. However, if the motifs are specific and information-rich, we find that simple motif-scanning methods can perform better than conservation-based methods.Our benchmark provides a comprehensive test that can be used to rank the relative performance of transcription factor binding site prediction methods. Moreover, our results show that, contrary to previous reports, sequence conservation is better suited for predicting strong than weak transcription factor binding sites