Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis.

Brepocitinib is an oral selective dual TYK2/JAK1 inhibitor and based on its cytokine inhibition profile is expected to provide therapeutic benefit in the treatment of plaque psoriasis. Efficacy data from a completed Phase 2a study in patients with moderate-to-severe plaque psoriasis were utilized to develop a population exposure-response model that can be employed to inform dose selection decisions for further clinical development. A modeling approach that employs the zero-inflated beta distribution was used to account for the bounded nature and distributional characteristics of the Psoriasis Area and Severity Index (PASI) score data. The developed exposure-response model provided an adequate description of the observed PASI scores across all the treatment arms tested and across both the induction and maintenance dosing periods of the study. In addition, the developed model exhibited a good predictive capacity with regard to the derived responder metrics (e.g., 75%/90%/100% improvement in PASI score [PASI75/90/100]). Clinical trial simulations indicated that the induction/maintenance dosing paradigm explored in this study does not offer any advantages from an efficacy perspective and that doses of 10, 30, and 60 mg once-daily may be suitable candidates for clinical evaluation in subsequent Phase 2b studies.

Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes.

Knowledge of locations and activities of cis -regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our V al i dated S ystematic I ntegrati on (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have revolutionized the study of gene regulation. A lack of standardization in the analysis of the highly dimensional datasets generated by these techniques has made reproducibility difficult to achieve, leading to discrepancies in the published, processed data. Part of this problem is due to the diverse range of bioinformatic tools available for the analysis of these types of data. Secondly, a number of different bioinformatic tools are required sequentially to convert raw data into a fully processed and interpretable output, and these tools require varying levels of computational skills. Furthermore, there are many options for quality control that are not uniformly employed during data processing. We address these issues with a complete assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) upstream pipeline (CATCH-UP), an easy-to-use, Python-based pipeline for the analysis of bulk ChIP-seq and ATAC-seq datasets from raw fastq files to visualizable bigwig tracks and peaks calls. This pipeline is simple to install and run, requiring minimal computational knowledge. The pipeline is modular, scalable, and parallelizable on various computing infrastructures, allowing for easy reporting of methodology to enable reproducible analysis of novel or published datasets.

Ancient genomic linkage couples metabolism with erythroid development.

Generation of mature cells from progenitors requires tight coupling of differentiation and metabolism. During erythropoiesis, erythroblasts are required to massively upregulate globin synthesis then clear extraneous material and enucleate to produce erythrocytes1-3. Nprl3 has remained in synteny with the α-globin genes for >500 million years4, and harbours the majority of the α-globin enhancers5. Nprl3 is a highly conserved inhibitor of mTORC1, which controls cellular metabolism. However, whether Nprl3 itself serves an erythroid role is unknown. Here, we show that Nprl3 is a key regulator of erythroid metabolism. Using Nprl3-deficient fetal liver and adult competitive bone marrow - fetal liver chimeras, we show that NprI3 is required for sufficient erythropoiesis. Loss of Nprl3 elevates mTORC1 signalling, suppresses autophagy and disrupts erythroblast glycolysis and redox control. Human CD34+ progenitors lacking NPRL3 produce fewer enucleated cells and demonstrate dysregulated mTORC1 signalling in response to nutrient availability and erythropoietin. Finally, we show that the α-globin enhancers upregulate NprI3 expression, and that this activity is necessary for optimal erythropoiesis. Therefore, the anciently conserved linkage of NprI3, α-globin and their associated enhancers has enabled coupling of metabolic and developmental control in erythroid cells. This may enable erythropoiesis to adapt to fluctuating nutritional and environmental conditions.

Inferring causal genes at type 2 diabetes GWAS loci through chromosome interactions in islet cells.

Background: Resolving causal genes for type 2 diabetes at loci implicated by genome-wide association studies (GWAS) requires integrating functional genomic data from relevant cell types. Chromatin features in endocrine cells of the pancreatic islet are particularly informative and recent studies leveraging chromosome conformation capture (3C) with Hi-C based methods have elucidated regulatory mechanisms in human islets. However, these genome-wide approaches are less sensitive and afford lower resolution than methods that target specific loci. Methods: To gauge the extent to which targeted 3C further resolves chromatin-mediated regulatory mechanisms at GWAS loci, we generated interaction profiles at 23 loci using next-generation (NG) capture-C in a human beta cell model (EndoC-ßH1) and contrasted these maps with Hi-C maps in EndoC-ßH1 cells and human islets and a promoter capture Hi-C map in human islets. Results: We found improvements in assay sensitivity of up to 33-fold and resolved ~3.6X more chromatin interactions. At a subset of 18 loci with 25 co-localised GWAS and eQTL signals, NG Capture-C interactions implicated effector transcripts at five additional genetic signals relative to promoter capture Hi-C through physical contact with gene promoters. Conclusions: High resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.

GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.

Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.

Direct correction of haemoglobin E ß-thalassaemia using base editors.

Haemoglobin E (HbE) ß-thalassaemia causes approximately 50% of all severe thalassaemia worldwide; equating to around 30,000 births per year. HbE ß-thalassaemia is due to a point mutation in codon 26 of the human HBB gene on one allele (GAG; glutamatic acid → AAG; lysine, E26K), and any mutation causing severe ß-thalassaemia on the other. When inherited together in compound heterozygosity these mutations can cause a severe thalassaemic phenotype. However, if only one allele is mutated individuals are carriers for the respective mutation and have an asymptomatic phenotype (ß-thalassaemia trait). Here we describe a base editing strategy which corrects the HbE mutation either to wildtype (WT) or a normal variant haemoglobin (E26G) known as Hb Aubenas and thereby recreates the asymptomatic trait phenotype. We have achieved editing efficiencies in excess of 90% in primary human CD34 + cells. We demonstrate editing of long-term repopulating haematopoietic stem cells (LT-HSCs) using serial xenotransplantation in NSG mice. We have profiled the off-target effects using a combination of circularization for in vitro reporting of cleavage effects by sequencing (CIRCLE-seq) and deep targeted capture and have developed machine-learning based methods to predict functional effects of candidate off-target mutations.

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features.

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom's 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia.

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients.

Brepocitinib is a tyrosine kinase 2 and Janus kinase 1 inhibitor in development for treatment of inflammatory autoimmune diseases. This analysis aimed to add to the pharmacokinetic knowledge of the medication, through development of a population pharmacokinetic model and identification of factors that affect drug disposition. Plasma samples from 5 clinical trials were collated, composed of healthy volunteers, patients with psoriasis and patients with alopecia areata taking oral brepocitinib. NONMEM was used to develop a population pharmacokinetic model, and patient demographics were tested as covariates. The final model was a 1-compartment model with first-order absorption. The typical values for apparent clearance and apparent volume of distribution were 18.7 L/h (78% coefficient of variation [CV]) and 136 L (60.5% CV), respectively. Absorption was rapid with an absorption constant of 3.46 h, with an absorption lag of 0.24 hours observed with the oral tablet formulation. The proportional residual error was found to be 52.7% CV in healthy volunteers and 87.5% CV in patients. High-fat meals were associated with a reduction in both the rate (69.9% lower) and extent (28.3% lower) of absorption, while Asian populations had reduced clearance (24.3% lower). Nonlinear pharmacokinetics were observed at doses of 175 mg and above, with a 35.1% higher relative bioavailability at these doses. There were insufficient data to describe this nonlinearity as a continuous relationship. This initial description of the population pharmacokinetics will act as a foundation for the model-informed drug development of brepocitinib and will facilitate future modeling of this medicine. ClinicalTrials.gov numbers NCT02310750 NCT03236493 NCT03656952 NCT02969018 NCT02974868.

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

LanceOtron: a deep learning peak caller for genome sequencing experiments.

MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that the background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling. RESULTS: We present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near-perfect sensitivity. AVAILABILITY AND IMPLEMENTATION: A fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https://pypi.org/project/lanceotron/, and source code and benchmarking tests are available at https://github.com/LHentges/LanceOtron. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of Multiway Interactions with Tri-C.

Tri-C is a chromosome conformation capture (3C) approach that can efficiently identify multiway chromatin interactions with viewpoints of interest. As opposed to pair-wise interactions identified in methods such as Hi-C, 4C, and Capture-C, the detection of multiway interactions allows researchers to investigate how multiple cis-regulatory elements interact together in higher-order structures in single nuclei and address questions regarding structural cooperation between these elements. Here, we describe the procedure for designing and performing a Tri-C experiment.

The chromatin remodeller ATRX facilitates diverse nuclear processes, in a stochastic manner, in both heterochromatin and euchromatin.

The chromatin remodeller ATRX interacts with the histone chaperone DAXX to deposit the histone variant H3.3 at sites of nucleosome turnover. ATRX is known to bind repetitive, heterochromatic regions of the genome including telomeres, ribosomal DNA and pericentric repeats, many of which are putative G-quadruplex forming sequences (PQS). At these sites ATRX plays an ancillary role in a wide range of nuclear processes facilitating replication, chromatin modification and transcription. Here, using an improved protocol for chromatin immunoprecipitation, we show that ATRX also binds active regulatory elements in euchromatin. Mutations in ATRX lead to perturbation of gene expression associated with a reduction in chromatin accessibility, histone modification, transcription factor binding and deposition of H3.3 at the sequences to which it normally binds. In erythroid cells where downregulation of α-globin expression is a hallmark of ATR-X syndrome, perturbation of chromatin accessibility and gene expression occurs in only a subset of cells. The stochastic nature of this process suggests that ATRX acts as a general facilitator of cell specific transcriptional and epigenetic programmes, both in heterochromatin and euchromatin.

Chromatin interaction maps identify Wnt responsive cis-regulatory elements coordinating Paupar-Pax6 expression in neuronal cells.

Central nervous system-expressed long non-coding RNAs (lncRNAs) are often located in the genome close to protein coding genes involved in transcriptional control. Such lncRNA-protein coding gene pairs are frequently temporally and spatially co-expressed in the nervous system and are predicted to act together to regulate neuronal development and function. Although some of these lncRNAs also bind and modulate the activity of the encoded transcription factors, the regulatory mechanisms controlling co-expression of neighbouring lncRNA-protein coding genes remain unclear. Here, we used high resolution NG Capture-C to map the cis-regulatory interaction landscape of the key neuro-developmental Paupar-Pax6 lncRNA-mRNA locus. The results define chromatin architecture changes associated with high Paupar-Pax6 expression in neurons and identify both promoter selective as well as shared cis-regulatory-promoter interactions involved in regulating Paupar-Pax6 co-expression. We discovered that the TCF7L2 transcription factor, a regulator of chromatin architecture and major effector of the Wnt signalling pathway, binds to a subset of these candidate cis-regulatory elements to coordinate Paupar and Pax6 co-expression. We describe distinct roles for Paupar in Pax6 expression control and show that the Paupar DNA locus contains a TCF7L2 bound transcriptional silencer whilst the Paupar transcript can act as an activator of Pax6. Our work provides important insights into the chromatin interactions, signalling pathways and transcription factors controlling co-expression of adjacent lncRNAs and protein coding genes in the brain.

Natural and Experimental Rewiring of Gene Regulatory Regions.

The successful development and ongoing functioning of complex organisms depend on the faithful execution of the genetic code. A critical step in this process is the correct spatial and temporal expression of genes. The highly orchestrated transcription of genes is controlled primarily by cis-regulatory elements: promoters, enhancers, and insulators. The medical importance of this key biological process can be seen by the frequency with which mutations and inherited variants that alter cis-regulatory elements lead to monogenic and complex diseases and cancer. Here, we provide an overview of the methods available to characterize and perturb gene regulatory circuits. We then highlight mechanisms through which regulatory rewiring contributes to disease, and conclude with a perspective on how our understanding of gene regulation can be used to improve human health.

Dynamic Runx1 chromatin boundaries affect gene expression in hematopoietic development.

The transcription factor RUNX1 is a critical regulator of developmental hematopoiesis and is frequently disrupted in leukemia. Runx1 is a large, complex gene that is expressed from two alternative promoters under the spatiotemporal control of multiple hematopoietic enhancers. To dissect the dynamic regulation of Runx1 in hematopoietic development, we analyzed its three-dimensional chromatin conformation in mouse embryonic stem cell (ESC) differentiation cultures. Runx1 resides in a 1.1 Mb topologically associating domain (TAD) demarcated by convergent CTCF motifs. As ESCs differentiate to mesoderm, chromatin accessibility, Runx1 enhancer-promoter (E-P) interactions, and CTCF-CTCF interactions increase in the TAD, along with initiation of Runx1 expression from the P2 promoter. Differentiation to hematopoietic progenitor cells is associated with the formation of tissue-specific sub-TADs over Runx1, a shift in E-P interactions, P1 promoter demethylation, and robust expression from both Runx1 promoters. Deletion of promoter-proximal CTCF sites at the sub-TAD boundaries has no obvious effects on E-P interactions but leads to partial loss of domain structure, mildly affects gene expression, and delays hematopoietic development. Together, our analysis of gene regulation at a large multi-promoter developmental gene reveals that dynamic sub-TAD chromatin boundaries play a role in establishing TAD structure and coordinated gene expression.

Capture-C: a modular and flexible approach for high-resolution chromosome conformation capture.

Chromosome conformation capture (3C) methods measure the spatial proximity between DNA elements in the cell nucleus. Many methods have been developed to sample 3C material, including the Capture-C family of protocols. Capture-C methods use oligonucleotides to enrich for interactions of interest from sequencing-ready 3C libraries. This approach is modular and has been adapted and optimized to work for sampling of disperse DNA elements (NuTi Capture-C), including from low cell inputs (LI Capture-C), as well as to generate Hi-C like maps for specific regions of interest (Tiled-C) and to interrogate multiway interactions (Tri-C). We present the design, experimental protocol and analysis pipeline for NuTi Capture-C in addition to the variations for generation of LI Capture-C, Tiled-C and Tri-C data. The entire procedure can be performed in 3 weeks and requires standard molecular biology skills and equipment, access to a next-generation sequencing platform, and basic bioinformatic skills. Implemented with other sequencing technologies, these methods can be used to identify regulatory interactions and to compare the structural organization of the genome in different cell types and genetic models.

Making connections: enhancers in cellular differentiation.

Deciphering the process by which hundreds of distinct cell types emerge from a single zygote to form a complex multicellular organism remains one of the greatest challenges in biological research. Enhancers are known to be central to cell type-specific gene expression, yet many questions regarding how these genomic elements interact both temporally and spatially with other cis- and trans-acting factors to control transcriptional activity during differentiation and development remain unanswered. Here, we review our current understanding of the role of enhancers and their interactions in this context and highlight recent progress achieved with experimental methods of unprecedented resolution.