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Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies. Despite rapid advancements, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization. Phenoxome dissects the phenotypic manifestation of a patient in concert with their genomic profile to filter and then prioritize variants that are likely to affect the function of the gene (potentially pathogenic variants). To validate our method, we have compiled a clinical cohort of 105 positive patient samples that represent a wide range of genetic heterogeneity. Phenoxome identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these exomes, respectively. Furthermore, we show that our method is optimized for clinical testing by outperforming the current state-of-art method. We have demonstrated the performance of Phenoxome using a clinical cohort and showed that it enables rapid and accurate interpretation of clinical exomes. Phenoxome is available at https://phenoxome.chop.edu/.

Recent advances in DNA sequencing have expanded our understanding of the molecular basis of genetic disorders and increased the utilization of clinical genomic tests. Given the paucity of evidence to accurately classify each variant and the difficulty of experimentally evaluating its clinical significance, a large number of variants generated by clinical tests are reported as variants of unknown clinical significance. Population-scale variant databases can improve clinical interpretation. Specifically, pathogenicity prediction for novel missense variants can use features describing regional variant constraint. Constrained genomic regions are those that have an unusually low variant count in the general population. Computational methods have been introduced to capture these regions and incorporate them into pathogenicity classifiers, but these methods have yet to be compared on an independent clinical variant data set. Here, we introduce one variant data set derived from clinical sequencing panels and use it to compare the ability of different genomic constraint metrics to determine missense variant pathogenicity. This data set is compiled from 17,071 patients surveyed with clinical genomic sequencing for cardiomyopathy, epilepsy, or RASopathies. We further use this data set to demonstrate the necessity of disease-specific classifiers and to train PathoPredictor, a disease-specific ensemble classifier of pathogenicity based on regional constraint and variant-level features. PathoPredictor achieves an average precision >90% for variants from all 99 tested disease genes while approaching 100% accuracy for some genes. The accumulation of larger clinical variant training data sets can significantly enhance their performance in a disease- and gene-specific manner.

Rh alloimmunization remains a challenge for patients with sickle cell disease (SCD) despite transfusion of serologic Rh C, E, and K antigen-matched red cells. Inheritance of altered RH alleles contributes to the prevalence of Rh antibodies after blood transfusion in patients with SCD and explains approximately one-third of cases. The remainder seem to be stimulated by altered Rh proteins on African American donor red cells. Matching patients with donors on the basis of RH genotype may mitigate Rh alloimmunization, but the feasibility and resources required are not known. We compared RH allele frequencies between patients with SCD (n = 857) and African American donors (n = 587) and showed that RH allele frequencies are similar. Overall, 29% of RHD and 53% of RHCE alleles are altered in patients and African American donors. We modeled RH genotype matching compared with serologic Rh D, C, and E, along with K antigen matching, and found that approximately twice the number of African American donors would be required for RH genotype vs Rh serologic matching at our institution. We demonstrated that African American donor recruitment is necessary to maintain an adequate supply of C-, E-, and K-negative donor units to avoid depleting the Rh-negative (RhD−) blood supply. Our results suggest that prophylactic RH genetic matching for patients with SCD is feasible with a donor pool comprised primarily of African-Americans and would optimize the use of our existing minority donor inventory. The current cost of RH genotyping all minority donors and management of the data remain limiting factors.

Rh alloimmunization remains a challenge for patients with sickle cell disease (SCD) despite transfusion of serologic Rh C, E, and K antigen-matched red cells. Inheritance of altered RHalleles contributes to the prevalence of Rh antibodies after blood transfusion in patients with SCD and explains approximately one-third of cases. The remainder seem to be stimulated by altered Rh proteins on African American donor red cells. Matching patients with donors on the basis of RHgenotype may mitigate Rh alloimmunization, but the feasibility and resources required are not known. We compared RHallele frequencies between patients with SCD (n = 857) and African American donors (n = 587) and showed that RHallele frequencies are similar. Overall, 29% of RHDand 53% of RHCEalleles are altered in patients and African American donors. We modeled RHgenotype matching compared with serologic Rh D, C, and E, along with K antigen matching, and found that approximately twice the number of African American donors would be required for RHgenotype vs Rh serologic matching at our institution. We demonstrated that African American donor recruitment is necessary to maintain an adequate supply of C-, E-, and K-negative donor units to avoid depleting the Rh-negative (RhD−) blood supply. Our results suggest that prophylactic RHgenetic matching for patients with SCD is feasible with a donor pool comprised primarily of African-Americans and would optimize the use of our existing minority donor inventory. The current cost of RHgenotyping all minority donors and management of the data remain limiting factors.

Exome-based panels are becoming the preferred diagnostic strategy in clinical laboratories. This approach enables dynamic gene content update and, if needed, cost-effective reflex to whole-exome sequencing. Currently, no guidelines or appropriate resources are available to support the clinical implementation of exome-based panels. Here, we highlight principles and important considerations for the clinical development and validation of exome-based panels. In addition, we developed ExomeSlicer, a novel, web-based resource, which uses empirical exon-level next-generation sequencing quality metrics to predict and visualize technically challenging exome-wide regions in any gene or genes of interest. Exome sequencing data from 100 clinical epilepsy cases were used to illustrate the clinical utility of ExomeSlicer in predicting poor-quality regions and its impact on streamlining the ad hocSanger sequencing fill in burden. With the use of ExomeSlicer, >2100 low complexity and/or high-homology regions affecting >1615 genes across the exome were also characterized. These regions can be a source of false-positive or false-negative variant calls, which can lead to misdiagnoses in tested patients and/or inaccurate functional annotations. We provide important considerations and a novel resource for the clinical development of exome-based panels.

Inhibitors of bromodomain and extraterminal motif proteins (BETs) are being evaluated for the treatment of cancer and other diseases, yet much remains to be learned about how BET proteins function during normal physiology. We used genomic and genetic approaches to examine BET function in a hematopoietic maturation system driven by GATA1, an acetylated transcription factor previously shown to interact with BETs. We found that BRD2, BRD3, and BRD4 were variably recruited to GATA1-regulated genes, with BRD3 binding the greatest number of GATA1-occupied sites. Pharmacologic BET inhibition impaired GATA1-mediated transcriptional activation, but not repression, genome-wide. Mechanistically, BETs promoted chromatin occupancy of GATA1 and subsequently supported transcriptional activation. Using a combination of CRISPR-Cas9-mediated genomic engineering and shRNA approaches, we observed that depletion of either BRD2 or BRD4 alone blunted erythroid gene activation. Surprisingly, depletion of BRD3 only affected erythroid transcription in the context of BRD2 deficiency. Consistent with functional overlap among BET proteins, forced BRD3 expression substantially rescued defects caused by BRD2 deficiency. These results suggest that pharmacologic BET inhibition should be interpreted in the context of distinct steps in transcriptional activation and overlapping functions among BET family members.

Inhibitors of bromodomain and extraterminal motif proteins (BETs) are being evaluated for the treatment of cancer and other diseases, yet much remains to be learned about how BET proteins function during normal physiology. We used genomic and genetic approaches to examine BET function in a hematopoietic maturation system driven by GATA1, an acetylated transcription factor previously shown to interact with BETs. We found that BRD2, BRD3, and BRD4 were variably recruited to GATA1-regulated genes, with BRD3 binding the greatest number of GATA1-occupied sites. Pharmacologic BET inhibition impaired GATA1-mediated transcriptional activation, but not repression, genome-wide. Mechanistically, BETs promoted chromatin occupancy of GATA1 and subsequently supported transcriptional activation. Using a combination of CRISPR-Cas9-mediated genomic engineering and shRNA approaches, we observed that depletion of either BRD2 or BRD4 alone blunted erythroid gene activation. Surprisingly, depletion of BRD3 only affected erythroid transcription in the context of BRD2 deficiency. Consistent with functional overlap among BET proteins, forced BRD3 expression substantially rescued defects caused by BRD2 deficiency. These results suggest that pharmacologic BET inhibition should be interpreted in the context of distinct steps in transcriptional activation and overlapping functions among BET family members.

Erythroid transcription factors (TFs) control gene expression programs, lineage decisions, and disease outcomes. How transcription factors contact DNA has been studied extensively in vitro, but in vivo binding characteristics are less well understood as they are influenced in a reciprocal manner by chromatin accessibility and neighboring transcription factors. Here, we present a comparative analysis approach that takes advantage of non-coding sequence variation between functionally equivalent erythroid cell lines to conduct an in-depth analysis of erythroid TF binding profiles and chromatin features.

No relevant conflicts of interest to declare.

No relevant conflicts of interest to declare.

The tremendous proliferative capacity of erythroid precursor cells underlies the production of over a million red blood cells per second in adult humans. During every mitosis the mammalian nucleus is disassembled and transcriptionally silent. Genome reactivation after mitosis is a key step in the propagation of transcriptional programs through cell generations, yet how this occurs remains largely unexplored. We carried out the first genome wide survey of transcription in cells emerging from mitosis using RNA polymerase II ChIP seq in purified populations at various post-mitotic time points. Using unsupervised approaches, we discover and classify genome reactivation patterns among genes. A surprisingly large fraction of genes (~25%) displays a post-mitotic spike in transcription. This spike represents the first complete round of transcription and accounts for the greatest gene-to-gene variance in temporal patterns of transcription in G1 phase. Another notable and contrasting pattern is gene activation late in the G1 phase.

Protein S-nitrosylation controls the brain’s use of glutamate for energy or neurotransmission.