Faculty Bibliography

During self-renewal, cell-type-defining features are drastically perturbed in mitosis and must be faithfully reestablished upon G1 entry, a process that remains largely elusive. Here, we characterized at a genome-wide scale the dynamic transcriptional and architectural resetting of mouse pluripotent stem cells (PSCs) upon mitotic exit. We captured distinct waves of transcriptional reactivation with rapid induction of stem cell genes and transient activation of lineage-specific genes. Topological reorganization at different hierarchical levels also occurred in an asynchronous manner and showed partial coordination with transcriptional resetting. Globally, rapid transcriptional and architectural resetting associated with mitotic retention of H3K27 acetylation, supporting a bookmarking function. Indeed, mitotic depletion of H3K27ac impaired the early reactivation of bookmarked, stem-cell-associated genes. However, 3D chromatin reorganization remained largely unaffected, suggesting that these processes are driven by distinct forces upon mitotic exit. This study uncovers principles and mediators of PSC molecular resetting during self-renewal.

A fundamental, evolutionarily conserved biological mechanism required for long-term memory formation is rapid induction of gene transcription upon learning in relevant brain areas. For episodic types of memories, two regions undergoing this transcription are the dorsal hippocampus (dHC) and prelimbic (PL) cortex. Whether and to what extent these regions regulate similar or distinct transcriptomic profiles upon learning remains to be understood. Here, we used RNA sequencing in the dHC and PL cortex of male rats to profile their transcriptomes in untrained conditions (baseline) and at 1 hour and 6 days after inhibitory avoidance learning. We found that, out of 33,713 transcripts, over 14,000 were significantly expressed at baseline in both regions and approximately 3,000 were selectively enriched in each region. Gene Ontology biological pathway analyses indicated that commonly expressed pathways included synapse organization, regulation of membrane potential, and vesicle localization. The enriched pathways in the dHC were gliogenesis, axon development, and lipid modification, while in the PL cortex included vesicle localization and synaptic vesicle cycle. At 1 hour after learning, 135 transcripts changed significantly in the dHC and 478 in the PL cortex; of these, only 34 were shared. Biological pathways most significantly regulated by learning in the dHC were protein dephosphorylation, glycogen and glucan metabolism, while in the PL cortex were axon development and axonogenesis. The transcriptome profiles returned to baseline by 6 days after training. Thus, a significant portion of dHC and PL cortex transcriptomic profiles is divergent and their regulation upon learning is largely distinct and transient.Significance StatementLong-term episodic memory formation requires gene transcription in several brain regions including the hippocampus and prefrontal cortex. The comprehensive profiles of the dynamic mRNA changes that occur in these regions following learning are not well understood. Here, we performed RNA sequencing in the dorsal hippocampus (dHC) and prelimbic (PL) cortex, a prefrontal cortex subregion, at baseline, 1 hour, and 6 days after episodic learning in rats. We found that at baseline, dHC and PL cortex differentially express a significant portion of mRNAs. Moreover, learning produces a transient regulation of region-specific profiles of mRNA, indicating that unique biological programs in different brain regions underlie memory formation.

During the germinal center (GC) reaction, B cells undergo extensive redistribution of cohesin complex and three-dimensional reorganization of their genomes. Yet, the significance of cohesin and architectural programming in the humoral immune response is unknown. Herein we report that homozygous deletion of Smc3, encoding the cohesin ATPase subunit, abrogated GC formation, while, in marked contrast, Smc3 haploinsufficiency resulted in GC hyperplasia, skewing of GC polarity and impaired plasma cell (PC) differentiation. Genome-wide chromosomal conformation and transcriptional profiling revealed defects in GC B cell terminal differentiation programs controlled by the lymphoma epigenetic tumor suppressors Tet2 and Kmt2d and failure of Smc3-haploinsufficient GC B cells to switch from B cell- to PC-defining transcription factors. Smc3 haploinsufficiency preferentially impaired the connectivity of enhancer elements controlling various lymphoma tumor suppressor genes, and, accordingly, Smc3 haploinsufficiency accelerated lymphomagenesis in mice with constitutive Bcl6 expression. Collectively, our data indicate a dose-dependent function for cohesin in humoral immunity to facilitate the B cell to PC phenotypic switch while restricting malignant transformation.

Lack of cellular differentiation is a hallmark of many human cancers, including acute myeloid leukemia (AML). Strategies to overcome such a differentiation blockade are an approach for treating AML. To identify targets for differentiation-based therapies, we applied an integrated cell surface-based CRISPR platform to assess genes involved in maintaining the undifferentiated state of leukemia cells. Here we identify the RNA-binding protein ZFP36L2 as a critical regulator of AML maintenance and differentiation. Mechanistically, ZFP36L2 interacts with the 3' untranslated region of key myeloid maturation genes, including the ZFP36 paralogs, to promote their mRNA degradation and suppress terminal myeloid cell differentiation. Genetic inhibition of ZFP36L2 restores the mRNA stability of these targeted transcripts and ultimately triggers myeloid differentiation in leukemia cells. Epigenome profiling of several individuals with primary AML revealed enhancer modules near ZFP36L2 that associated with distinct AML cell states, establishing a coordinated epigenetic and post-transcriptional mechanism that shapes leukemic differentiation.

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning (ML) in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.

Linker histone H1 proteins bind to nucleosomes and facilitate chromatin compaction¹, although their biological functions are poorly understood. Mutations in the genes that encode H1 isoforms B-E (H1B, H1C, H1D and H1E; also known as H1-5, H1-2, H1-3 and H1-4, respectively) are highly recurrent in B cell lymphomas, but the pathogenic relevance of these mutations to cancer and the mechanisms that are involved are unknown. Here we show that lymphoma-associated H1 alleles are genetic driver mutations in lymphomas. Disruption of H1 function results in a profound architectural remodelling of the genome, which is characterized by large-scale yet focal shifts of chromatin from a compacted to a relaxed state. This decompaction drives distinct changes in epigenetic states, primarily owing to a gain of histone H3 dimethylation at lysine 36 (H3K36me2) and/or loss of repressive H3 trimethylation at lysine 27 (H3K27me3). These changes unlock the expression of stem cell genes that are normally silenced during early development. In mice, loss of H1c and H1e (also known as H1f2 and H1f4, respectively) conferred germinal centre B cells with enhanced fitness and self-renewal properties, ultimately leading to aggressive lymphomas with an increased repopulating potential. Collectively, our data indicate that H1 proteins are normally required to sequester early developmental genes into architecturally inaccessible genomic compartments. We also establish H1 as a bona fide tumour suppressor and show that mutations in H1 drive malignant transformation primarily through three-dimensional genome reorganization, which leads to epigenetic reprogramming and derepression of developmentally silenced genes.

Epilepsy is a heterogenous group of disorders defined by recurrent seizure activity due to abnormal synchronized activity of neurons. A growing number of epilepsy cases are believed to be caused by genetic factors and copy number variants (CNV) contribute to up to 5% of epilepsy cases. However, CNVs in epilepsy are usually large deletions or duplications involving multiple neurodevelopmental genes. In patients who underwent seizure focus resection for treatment-resistant epilepsy, whole genome DNA methylation profiling identified 3 main clusters of which one showed strong association with receptor tyrosine kinase (RTK) genes. We identified focal copy number gains involving epidermal growth factor receptor (EGFR) and PDGFRA loci. The dysplastic neurons of cases with amplifications showed marked overexpression of EGFR and PDGFRA, while glial and endothelial cells were negative. Targeted sequencing of regulatory regions and DNA methylation analysis revealed that only enhancer regions of EGFR and gene promoter of PDGFRA were amplified, while coding regions did not show copy number abnormalities or somatic mutations. Somatic focal copy number gains of noncoding regulatory represent a previously unrecognized genetic driver in epilepsy and a mechanism of abnormal activation of RTK genes. Upregulated RTKs provide a potential avenue for therapy in seizure disorders.

PURPOSE/OBJECTIVE:Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma. EXPERIMENTAL DESIGN/METHODS:We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400). RESULTS:= 0.03 for the Leica SCN400). CONCLUSIONS:Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers.