Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

Epigenetic profiling reveals key genes and cis-regulatory networks specific to human parathyroids.

In all terrestrial vertebrates, the parathyroid glands are critical regulators of calcium homeostasis and the sole source of parathyroid hormone (PTH). Hyperparathyroidism and hypoparathyroidism are clinically important disorders affecting multiple organs. However, our knowledge regarding regulatory mechanisms governing the parathyroids has remained limited. Here, we present the comprehensive maps of the chromatin landscape of the human parathyroid glands, identifying active regulatory elements and chromatin interactions. These data allow us to define regulatory circuits and previously unidentified genes that play crucial roles in parathyroid biology. We experimentally validate candidate parathyroid-specific enhancers and demonstrate their integration with GWAS SNPs for parathyroid-related diseases and traits. For instance, we observe reduced activity of a parathyroid-specific enhancer of the Calcium Sensing Receptor gene, which contains a risk allele associated with higher PTH levels compared to the wildtype allele. Our datasets provide a valuable resource for unraveling the mechanisms governing parathyroid gland regulation in health and disease.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations.

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients. The increasing sizes of ultra-rare, "N-of-1" disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We apply existing and introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We make our gene-level findings and variant-level information across the cohort available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton.

Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it is not known how well estimates of growth milestones can be obtained from cross-sectional samples. The Craniofacial Growth Consortium Study (CGCS) contains longitudinal growth data for approximately 2000 individuals. Single samples from the CGCS for individuals representing cross-sectional data were used to test the ability to predict growth parameters in linear trait measurements separately by sex. Testing across a range of cross-sectional sample sizes from 5 to the full sample, we found that means from repeated samples were able to approximate growth rates determined from the full longitudinal CGCS sample, with mean absolute differences below 1 mm at cross-sectional sample sizes greater than ~ 200 individuals. Our results show that growth parameters and milestones can be accurately estimated from cross-sectional data compared to population-level estimates from complete longitudinal data, underscoring the utility of such datasets in growth modeling. This method can be applied to other forms of growth (e.g., stature) and to cases in which repeated radiographs are not feasible (e.g., cone-beam CT).

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

A molecular glue approach to control the half-life of CRISPR-based technologies.

Cas9 is a programmable nuclease that has furnished transformative technologies, including base editors and transcription modulators (e.g., CRISPRi/a), but several applications of these technologies, including therapeutics, mandatorily require precision control of their half-life. For example, such control can help avert any potential immunological and adverse events in clinical trials. Current genome editing technologies to control the half-life of Cas9 are slow, have lower activity, involve fusion of large response elements (> 230 amino acids), utilize expensive controllers with poor pharmacological attributes, and cannot be implemented in vivo on several CRISPR-based technologies. We report a general platform for half-life control using the molecular glue, pomalidomide, that binds to a ubiquitin ligase complex and a response-element bearing CRISPR-based technology, thereby causing the latter's rapid ubiquitination and degradation. Using pomalidomide, we were able to control the half-life of large CRISPR-based technologies (e.g., base editors, CRISPRi) and small anti-CRISPRs that inhibit such technologies, allowing us to build the first examples of on-switch for base editors. The ability to switch on, fine-tune and switch-off CRISPR-based technologies with pomalidomide allowed complete control over their activity, specificity, and genome editing outcome. Importantly, the miniature size of the response element and favorable pharmacological attributes of the drug pomalidomide allowed control of activity of base editor in vivo using AAV as the delivery vehicle. These studies provide methods and reagents to precisely control the dosage and half-life of CRISPR-based technologies, propelling their therapeutic development.

Joint estimation and imputation of variant functional effects using high throughput assay data.

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (Functional Substitution Estimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1, p=2.24×10-51), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53, FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10-6). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we have substantially improved the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

Peptide fusion improves prime editing efficiency.

Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, the Peptide Self-Editing sequencing assay (PepSEq), to measure how fusion of 12,000 85-amino acid peptides influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites. Top prime-enhancing peptides function by increasing translation efficiency and serve as broadly useful tools to improve prime editing efficiency.

Structural, Morphological, Optical and Electrical Characterization of Gahnite Ferroan Nano Composite Derived from Fly Ash Silica and ZnO Mixture.

The synthesis of a high value-added product, gahnite ferroan nano composite, from a mixture of fly ash silica and ZnO is a low-cost and non-expensive technique. The XRD pattern clearly reveals the synthesized product from fly ash after leaching is a product of high-purity gahnite ferroan composite. The grains are mostly cubical in shape. The optical band gap of powdered gahnite ferroan nano composite is 3.37 eV, which acts as a UV protector. However, the bulk sample shows that the 500 to 700 nm wavelength of visible light is absorbed, and UV light is allowed to pass through. So, the bulk sample acts as a band pass filter of UV light which can be used in many optical applications for conducting UV-irradiation activity. Dielectric permittivity and dielectric loss increase with a rise in temperature. The increase in the ac conductivity at higher temperatures denotes the negative temperature coefficient resistance (NTCR) behavior of the material.

Genetic influences on dentognathic morphology in the Jirel population of Nepal.

Patterns of genetic variation and covariation impact the evolution of the craniofacial complex and contribute to clinically significant malocclusions in modern human populations. Previous quantitative genetic studies have estimated the heritabilities and genetic correlations of skeletal and dental traits in humans and nonhuman primates, but none have estimated these quantitative genetic parameters across the dentognathic complex. A large and powerful pedigree from the Jirel population of Nepal was leveraged to estimate heritabilities and genetic correlations in 62 maxillary and mandibular arch dimensions, incisor and canine lengths, and post-canine tooth crown areas (N ≥ 739). Quantitative genetic parameter estimation was performed using maximum likelihood-based variance decomposition. Residual heritability estimates were significant for all traits, ranging from 0.269 to 0.898. Genetic correlations were positive for all trait pairs. Principal components analyses of the phenotypic and genetic correlation matrices indicate an overall size effect across all measurements on the first principal component. Additional principal components demonstrate positive relationships between post-canine tooth crown areas and arch lengths and negative relationships between post-canine tooth crown areas and arch widths, and between arch lengths and arch widths. Based on these findings, morphological variation in the human dentognathic complex may be constrained by genetic relationships between dental dimensions and arch lengths, with weaker genetic correlations between these traits and arch widths allowing for variation in arch shape. The patterns identified are expected to have impacted the evolution of the dentognathic complex and its genetic architecture as well as the prevalence of dental crowding in modern human populations.

Craniofacial growth and morphology among intersecting clinical categories.

Differential patterns of craniofacial growth are important sources of variation that can result in skeletal malocclusion. Understanding the timing of growth milestones and morphological change associated with adult skeletal malocclusions is critical for developing individualized orthodontic growth modification strategies. To identify patterns in the timing and geometry of growth, we used Bayesian modeling of cephalometrics and geometric morphometric analyses with a dense, longitudinal sample consisting of 15,407 cephalograms from 1,913 individuals between 2 and 31 years of age. Individuals were classified into vertical facial types (hyper-, normo-, hypo-divergent) and anteroposterior (A-P) skeletal classes (Class I, Class II, Class III) based on adult mandibular plane angle and ANB angle, respectively. These classifications yielded eight facial type-skeletal class categories with sufficient sample sizes to be included in the study. Four linear cephalometrics representing facial heights and maxillary and mandibular lengths were fit to standard double logistic models generating type-class category-specific estimates for age, size, and rate of growth at growth milestones. Mean landmark configurations were compared among type-class categories at four time points between 6 and 20 years of age. Overall, morphology and growth patterns were more similar within vertical facial types than within A-P classes and variation among A-P classes typically nested within variation among vertical types. Further, type-class-associated variation in the rate and magnitude of growth in specific regions identified here may serve as targets for clinical treatment of complex vertical and A-P skeletal malocclusion and provide a clearer picture of the development of variation in craniofacial form.

Estimating Craniofacial Growth Cessation: Comparison of Asymptote- and Rate-Based Methods.

OBJECTIVE: To identify differences between asymptote- and rate-based methods for estimating age and size at growth cessation in linear craniofacial measurements. DESIGN: This is a retrospective, longitudinal study. Five linear measurements were collected from lateral cephalograms as part of the Craniofacial Growth Consortium Study (CGCS). Four estimates of growth cessation, including 2 asymptote- (GCasym, GCerr) and 2 rate-based (GCabs, GC10%) methods, from double logistic models of craniofacial growth were compared. PARTICIPANTS: Cephalometric data from participants in 6 historic longitudinal growth studies were included in the CGCS. At least 1749 individuals (870 females, 879 males), unaffected by craniofacial anomalies, were included in all analyses. Individuals were represented by a median of 11 images between 2.5 and 31.3 years of age. RESULTS: GCasym consistently occurred before GCerr and GCabs consistently occurred before GC10% within the rate-based approaches. The ordering of the asymptote-based methods compared to the rate-based methods was not consistent across measurements or between males and females. Across the 5 measurements, age at growth cessation ranged from 13.56 (females, nasion-basion, GCasym) to 24.39 (males, sella-gonion, GCerr). CONCLUSIONS: Adolescent growth cessation is an important milestone for treatment planning. Based on our findings, we recommend careful consideration of specific definitions of growth cessation in both clinical and research settings since the most appropriate estimation method may differ according to patients' needs. The different methods presented here provide useful estimates of growth cessation that can be applied to raw data and to a variety of statistical models of craniofacial growth.

Tibial Bone Quality in Former Bariatric Surgery Patients with Osteoarthritis.

Downstream effects of bariatric weight-loss surgery have been associated with bone resorption, potentially jeopardizing total knee arthroplasty (TKA) implant fixation/ingrowth. PURPOSE: This case-control study sought to determine if TKA patients with history of bariatric surgery exhibit altered microanatomy of subchondral bone quality in the tibial plateau compared to controls. MATERIALS AND METHODS: With IRB approval, 41 bone samples were evaluated from 12 former bariatric surgery patients and 10 sex-, age-, weight-, height-, and BMI-matched controls. Patient-Reported Outcomes Measurement Information System (PROMIS) surveys were completed prior to TKA. Tibial plateau osteochondral tissues were recovered during the TKA procedure, and samples from the medial and lateral plateaus were dissected into 1 × 2 cm sections, scanned using microcomputed tomography (µCT), and plastic-embedded for histologic sectioning/staining of undecalcified bone. Paired t tests with Bonferroni correction were performed to assess group differences. RESULTS: Female bariatric surgery patients had reduced osteoid/total area and greater osteoclast number asymmetry than female controls (p < 0.03). No differences were noted in µCT or histologic bone parameters between bariatric and control patients when the sexes were combined. Bariatric patients self-reported worse preoperative PROMIS pain interference and physical function scores than controls (p < 0.04). CONCLUSIONS: Similarities of subchondral bone between former bariatric surgery patients and matched controls indicate OA disease progression dominates the bone landscape in both patient groups.

Small molecule inhibition of ATM kinase increases CRISPR-Cas9 1-bp insertion frequency.

Mutational outcomes following CRISPR-Cas9-nuclease cutting in mammalian cells have recently been shown to be predictable and, in certain cases, skewed toward single genotypes. However, the ability to control these outcomes remains limited, especially for 1-bp insertions, a common and therapeutically relevant class of repair outcomes. Here, through a small molecule screen, we identify the ATM kinase inhibitor KU-60019 as a compound capable of reproducibly increasing the fraction of 1-bp insertions relative to other Cas9 repair outcomes. Small molecule or genetic ATM inhibition increases 1-bp insertion outcome fraction across three human and mouse cell lines, two Cas9 species, and dozens of target sites, although concomitantly reducing the fraction of edited alleles. Notably, KU-60019 increases the relative frequency of 1-bp insertions to over 80% of edited alleles at several native human genomic loci and improves the efficiency of correction for pathogenic 1-bp deletion variants. The ability to increase 1-bp insertion frequency adds another dimension to precise template-free Cas9-nuclease genome editing.

Geometric morphometric analysis of growth patterns among facial types.

INTRODUCTION: Extreme patterns of vertical facial divergence are of great importance to clinicians because of their association with dental malocclusion and functional problems of the orofacial complex. Understanding the growth patterns associated with vertical facial divergence is critical for clinicians to provide optimal treatment. This study evaluates and compares growth patterns from childhood to adulthood among 3 classifications of vertical facial divergence using longitudinal, lateral cephalograms from the Craniofacial Growth Consortium Study. METHODS: Participants (183 females, 188 males) were classified into 1 of 3 facial types on the basis of their adult mandibular plane angle (MPA): hyperdivergent (MPA >39°; n = 40), normodivergent (28° ≤ MPA ≤ 39°; n = 216), and hypodivergent (MPA <28°; n = 115). Each individual had 5 cephalograms between ages 6 and 20 years. A set of 36 cephalometric landmarks were digitized on each cephalogram. Landmark configurations were superimposed to align 5 homologous landmarks of the anterior cranial base and scaled to unit centroid size. Growth trajectories were calculated using multivariate regression for each facial type and sex combination. RESULTS: Divergent growth trajectories were identified among facial types, finding more similarities in normodivergent and hypodivergent growth patterns than either share with the hyperdivergent group. Through the use of geometric morphometric methods, new patterns of facial growth related to vertical facial divergence were identified. Hyperdivergent growth exhibits a downward rotation of the maxillomandibular complex relative to the anterior cranial base, in addition to the increased relative growth of the lower anterior face. Conversely, normodivergent and hypodivergent groups exhibit stable positioning of the maxilla relative to the anterior cranial base, with the forward rotation of the mandible. Furthermore, the hyperdivergent maxilla and mandible become relatively shorter and posteriorly positioned with age compared with the other groups. CONCLUSIONS: This study demonstrates how hyperdivergent growth, particularly restricted growth and positioning of the maxilla, results in a higher potential risk for Class II malocclusion. Future work will investigate growth patterns within each classification of facial divergence.

Using CRISPR to understand and manipulate gene regulation.

Understanding how genes are expressed in the correct cell types and at the correct level is a key goal of developmental biology research. Gene regulation has traditionally been approached largely through observational methods, whereas perturbational approaches have lacked precision. CRISPR-Cas9 has begun to transform the study of gene regulation, allowing for precise manipulation of genomic sequences, epigenetic functionalization and gene expression. CRISPR-Cas9 technology has already led to the discovery of new paradigms in gene regulation and, as new CRISPR-based tools and methods continue to be developed, promises to transform our knowledge of the gene regulatory code and our ability to manipulate cell fate. Here, we discuss the current and future application of the emerging CRISPR toolbox toward predicting gene regulatory network behavior, improving stem cell disease modeling, dissecting the epigenetic code, reprogramming cell fate and treating diseases of gene dysregulation.

Detection of gene cis-regulatory element perturbations in single-cell transcriptomes.

We introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.