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Background: During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. Results: To address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. Conclusions: Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. [ABSTRACT FROM AUTHOR]

Background: Unbalanced translocations can cause developmental delay (DD), intellectual disability (ID), growth problems, dysmorphic features, and congenital anomalies. They may arise de novo or may be inherited from a parent carrying a balanced rearrangement. It is estimated that 1/500 people is a balanced translocation carrier. The outcomes of different chromosomal rearrangements have the potential to reveal the functional consequences of partial trisomy or partial monosomy and can help guide genetic counseling for balanced carriers, and other young patients diagnosed with similar imbalances. Methods: We performed clinical phenotyping and cytogenetic analyses of two siblings with a history of developmental delay (DD), intellectual disability (ID) and dysmorphic features. Results: The proband, a 38-year-old female, has a history of short stature, dysmorphic features and aortic coarctation. She underwent chromosomal microarray analysis, which identified partial monosomy of 4q and partial trisomy of 10p. Her brother, a 37-year-old male, has a history of more severe DD, behavioral problems, dysmorphic features, and congenital anomalies. Subsequently, karyotype confirmed two different unbalanced translocations in the siblings: 46,XX,der(4)t(4;10)(q33;p15.1) and 46,XY,der(10)t(4;10)(q33;p15.1), respectively. These chromosomal rearrangements represent two possible outcomes from a parent who is a carrier for a balanced translocation 46,XX,t(4;10)(q33;p15.1). Conclusion: To our knowledge, this 4q and 10p translocation has not been described in literature. In this report we compare clinical features due to the composite effects of partial monosomy 4q with partial trisomy 10p and partial trisomy 4q with partial monosomy 10p. These findings speak to the relevance of old and new genomic testing, the viability of these segregation outcomes, and need for genetic counseling. [ABSTRACT FROM AUTHOR]

Background: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. Description: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. Conclusions: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/. [ABSTRACT FROM AUTHOR]

Background: The quality of patient-reported outcome measures (PROMs) used to assess the outcomes of primary hyperparathyroidism (PHPT), a common endocrine disorder that can negatively affect patients' health-related quality of life due to chronic symptoms, has not been rigorously examined. This systematic review aimed to summarize and evaluate evidence on the measurement properties of PROMs used in adult patients with PHPT, and to provide recommendations for appropriate measure selection. Methods: After PROSPERO registration (CRD42023438287), Medline, EMBASE, CINAHL Complete, Web of Science, PsycINFO, and Cochrane Trials were searched for full-text articles in English investigating PROM development, pilot studies, or evaluation of at least one PROM measurement property in adult patients with any clinical form of PHPT. Two reviewers independently identified studies for inclusion and conducted the review following the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) Methodology to assess risk of bias, evaluate the quality of measurement properties, and grade the certainty of evidence. Results: From 4989 records, nine PROM development or validation studies were identified for three PROMs: the SF-36, PAS, and PHPQoL. Though the PAS demonstrated sufficient test-retest reliability and convergent validity, and the PHPQoL sufficient test-retest reliability, convergent validity, and responsiveness, the certainty of evidence was low-to-very low due to risk of bias. All three PROMs lacked sufficient evidence for content validity in patients with PHPT. Conclusions: Based upon the available evidence, the SF-36, PAS, and PHPQoL cannot currently be recommended for use in research or clinical care, raising important questions about the conclusions of studies using these PROMs. Further validation studies or the development of more relevant PROMs with strong measurement properties for this patient population are needed. [ABSTRACT FROM AUTHOR]

Background: Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results: Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion: The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. [ABSTRACT FROM AUTHOR]

Background: Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. Results: We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. Conclusion: NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus. [ABSTRACT FROM AUTHOR]

Background: Use of combinations of long-acting β2 agonists/long-acting muscarinic antagonists (LABA/LAMA) in patients with chronic obstructive pulmonary disease (COPD) is increasing. Nevertheless, existing evidence on cardiovascular risk associated with LABA/LAMA versus another dual combination, LABA/inhaled corticosteroids (ICS), was limited and discrepant. Aim: The present cohort study aimed to examine comparative cardiovascular safety of LABA/LAMA and LABA/ICS with a target trial emulation framework, focusing on dual fixed-dose combination (FDC) therapies. Methods: We identified patients with COPD who initiated LABA/LAMA FDC or LABA/ICS FDC from a nationwide Taiwanese database during 2017–2020. The outcome of interest was a hospitalized composite cardiovascular events of acute myocardial infarction, unstable angina, heart failure, cardiac dysrhythmia, and ischemic stroke. Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for composite and individual cardiovascular events after matching up to five LABA/LAMA FDC initiators to one LABA/ICS FDC initiator using propensity scores (PS). Results: Among 75,926 PS-matched patients, use of LABA/LAMA FDC did not show a higher cardiovascular risk compared to use of LABA/ICS FDC, with a HR of 0.89 (95% CI, 0.78–1.01) for the composite events, 0.80 (95% CI, 0.61–1.05) for acute myocardial infarction, 1.48 (95% CI, 0.68–3.25) for unstable angina, 1.00 (95% CI, 0.80–1.24) for congestive heart failure, 0.62 (95% CI, 0.37–1.05) for cardiac dysrhythmia, and 0.82 (95% CI, 0.66–1.02) for ischemic stroke. The results did not vary substantially in several pre-specified sensitivity and subgroup analyses. Conclusion: Our findings provide important reassurance about comparative cardiovascular safety of LABA/LAMA FDC treatment among patients with COPD. [ABSTRACT FROM AUTHOR]

An abstract for the article "A systematic evaluation of left ventricular (LV) models for estimating LV volumes in children using cardiac cine (MRI)," by Jiming Zhang, Carlo Uribe, Jason Liu, Benjamin Cheong, Amol Pednekar, Paolo Angelini and Raja Muthupillai is presented.

Background: Measuring expression profiles of inflammatory biomarkers is important in monitoring the polarization of immune responses; therefore, results should be independent of quantitation methods if they are to be accepted as validated clinical pathology biomarkers. To evaluate effects of differing quantitation methods, the seven major circulating Th1/Th2/Th17 cytokines interleukin 2 (IL-2), interferon γ (IFN-γ), tumor necrosis factor a (TNF-α), IL-4, IL-6, IL-10 and IL-17A were quantified in plasma of lipopolysaccharide (LPS)-treated mice with two different multiplex platforms. Methods: Female C57BL6 mice were treated orally with vehicle or dexamethasone, followed by LPS intravenously. Plasma samples were analyzed 0.5, 1, 2, 4, and 6 h post-LPS challenge with assays at Myriad-RBM and compared to assays performed on a BD Accuri C6 flow cytometer. Results: IL-17A response to LPS was limited but sustained, and the response for the remaining cytokines were early and transient; dexamethasone reduced expression of all 7 cytokines. TNF-α and IL-6 levels were similar across both assays, and IL-4 levels were generally very low. Plasma levels of remaining cytokines were variably lower with BD assays than Myriad-RBM assays. Conclusions: The present findings demonstrate that quantitation of circulating biomarkers of inflammation can be achieved using multiplexed flow cytometry, but careful consideration must be taken for assay validation when cross-referencing with another multiplexed assay. [ABSTRACT FROM AUTHOR]