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Long recognized as an evolutionarily ancient cell type involved in tissue homeostasis and immune defense against pathogens, macrophages are being rediscovered as regulators of several diseases, including cancer. Here we show that in mice, mammary tumor growth induces the accumulation of tumor-associated macrophages (TAMs) that are phenotypically and functionally distinct from mammary tissue macrophages (MTMs). TAMs express the adhesion molecule Vcam1 and proliferate upon their differentiation from inflammatory monocytes, but do not exhibit an \"alternatively activated\" phenotype. TAM terminal differentiation depends on the transcriptional regulator of Notch signaling, RBPJ; and TAM, but not MTM, depletion restores tumor-infiltrating cytotoxic T cell responses and suppresses tumor growth. These findings reveal the ontogeny of TAMs and a discrete tumor-elicited inflammatory response, which may provide new opportunities for cancer immunotherapy.

Senescence is a state of stable cell cycle exit with important implications for development and disease. Here, we demonstrate that the chromatin remodeling enzyme ATRX is required for therapy-induced senescence. ATRX accumulates in nuclear foci and is required for therapy-induced senescence in multiple types of transformed cells exposed to either DNA damaging agents or CDK4 inhibitors. Mobilization into foci depends on the ability of ATRX to interact with H3K9me3 histone and HP1. Foci form soon after cells exit the cell cycle, before other hallmarks of senescence appear. Eliminating ATRX in senescent cells destabilizes the senescence-associated heterochromatic foci. Additionally, ATRX binds to and suppresses expression from the HRAS locus; repression of HRAS is sufficient to promote the transition of quiescent cells into senescence and preventing repression blocks progression into senescence. Thus ATRX is a critical regulator of therapy-induced senescence and acts in multiple ways to drive cells into this state. Therapy induced senescence (TIS) is a growth suppressive program activated by cytostatic agents in some cancer cells. Here the authors show that the chromatin remodeling enzyme ATRX is a regulator of TIS and drives cells into this state via multiple mechanisms.

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) ( n  = 739). We mapped thousands of cis -regulatory domains (CRDs), revealing fine-grained, 10 4 –10 6 -bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains. Girdhar et al. constructed chromosomal domains from prefrontal histone acetylation and methylation maps and discovered, in a large cohort of schizophrenia and bipolar brains, converging alignment by genetic risk, neuronal function and three-dimensional genomics.

Schahram Akbarian and colleagues report that mutation of the gene encoding the SETDB1 (KMT1E) histone methyltransferase in mouse neurons leads to dissolution of chromosome conformations and a topologically associated domain at the clustered protocadherin locus. They show that SETDB1 prevents excess CTCF binding and is important for maintaining developmentally important higher-order chromatin organization. We report locus-specific disintegration of megabase-scale chromosomal conformations in brain after neuronal ablation of Setdb1 (also known as Kmt1e ; encodes a histone H3 lysine 9 methyltransferase), including a large topologically associated 1.2-Mb domain conserved in humans and mice that encompasses >70 genes at the clustered protocadherin locus (hereafter referred to as cPcdh). The cPcdh topologically associated domain (TAD cPcdh ) in neurons from mutant mice showed abnormal accumulation of the transcriptional regulator and three-dimensional (3D) genome organizer CTCF at cryptic binding sites, in conjunction with DNA cytosine hypomethylation, histone hyperacetylation and upregulated expression. Genes encoding stochastically expressed protocadherins were transcribed by increased numbers of cortical neurons, indicating relaxation of single-cell constraint. SETDB1-dependent loop formations bypassed 0.2–1 Mb of linear genome and radiated from the TAD cPcdh fringes toward cis -regulatory sequences within the cPcdh locus, counterbalanced shorter-range facilitative promoter–enhancer contacts and carried loop-bound polymorphisms that were associated with genetic risk for schizophrenia. We show that the SETDB1 repressor complex, which involves multiple KRAB zinc finger proteins, shields neuronal genomes from excess CTCF binding and is critically required for structural maintenance of TAD cPcdh .

Elevated levels of CO 2 and temperature can both affect plant growth and development, but the signalling pathways regulating these processes are still obscure. MicroRNAs function to silence gene expression, and environmental stresses can alter their expressions. Here we identify, using the small RNA-sequencing method, microRNAs that change significantly in expression by either doubling the atmospheric CO 2 concentration or by increasing temperature 3–6 °C. Notably, nearly all CO 2 -influenced microRNAs are affected inversely by elevated temperature. Using the RNA-sequencing method, we determine strongly correlated expression changes between miR156/157 and miR172, and their target transcription factors under elevated CO 2 concentration. Similar correlations are also found for microRNAs acting in auxin-signalling, stress responses and potential cell wall carbohydrate synthesis. Our results demonstrate that both CO 2 and temperature alter microRNA expression to affect Arabidopsis growth and development, and miR156/157- and miR172-regulated transcriptional network might underlie the onset of early flowering induced by increasing CO 2 . An increase in the concentration of atmospheric carbon dioxide and warmer temperatures can alter plant growth and development. Here the authors show that these conditions can also elicit significant changes in microRNAs expression, including some which might induce early flowering in Arabidopsis.

Dopaminergic neurons are critical to movement, mood, addiction, and stress. Current techniques for generating dopaminergic neurons from human induced pluripotent stem cells (hiPSCs) yield heterogenous cell populations with variable purity and inconsistent reproducibility between donors, hiPSC clones, and experiments. Here, we report the rapid (5 weeks) and efficient (~90%) induction of induced dopaminergic neurons (iDANs) through transient overexpression of lineage-promoting transcription factors combined with stringent selection across five donors. We observe maturation-dependent increase in dopamine synthesis and electrophysiological properties consistent with midbrain dopaminergic neuron identity, such as slow-rising after- hyperpolarization potentials, an action potential duration of ~3 ms, tonic sub-threshold oscillatory activity, and spontaneous burst firing at a frequency of ~1.0–1.75 Hz. Transcriptome analysis reveals robust expression of genes involved in fetal midbrain dopaminergic neuron identity. Specifically expressed genes in iDANs, as well as those from isogenic induced GABAergic and glutamatergic neurons, were enriched in loci conferring heritability for cannabis use disorder, schizophrenia, and bipolar disorder; however, each neuronal subtype demonstrated subtype-specific heritability enrichments in biologically relevant pathways, and iDANs alone were uniquely enriched in autism spectrum disorder risk loci. Therefore, iDANs provide a critical tool for modeling midbrain dopaminergic neuron development and dysfunction in psychiatric disease.

Differentiation of oligodendrocytes (OL) from progenitor cells (OPC) is the result of a unique program of gene expression, which is further regulated by the formation of topological domains of association with the nuclear lamina. In this study, we show that cultured OPC were characterized by progressively declining levels of endogenous Lamin B1 (LMNB1) during differentiation into OL. We then identify the genes dynamically associated to the nuclear lamina component LMNB1 during this transition, using a well established technique called DamID, which is based on the ability of a bacterially-derived deoxyadenosine methylase (Dam), to modify genomic regions in close proximity. We expressed a fusion protein containing Dam and LMNB1 in OPC (OPC LMNB1-Dam ) and either kept them proliferating or differentiated them into OL (OL LMNB1-Dam ) and identified genes that were dynamically associated to LMNB1 with differentiation. Importantly, we identified Lss , the gene encoding for lanosterol synthase, a key enzyme in cholesterol synthesis, as associated to the nuclear lamina in OL LMNB1-Dam . This finding could at least in part explain the lipid dysregulation previously reported for mouse models of ADLD characterized by persistent LMNB1 expression in oligodendrocytes.

Next-generation sequencing (NGS) technologies have matured considerably since their introduction and a focus has been placed on developing sophisticated analytical tools to deal with the amassing volumes of data. Chromatin immunoprecipitation sequencing (ChIP-seq), a major application of NGS, is a widely adopted technique for examining protein-DNA interactions and is commonly used to investigate epigenetic signatures of diffuse histone marks. These datasets have notoriously high variance and subtle levels of enrichment across large expanses, making them exceedingly difficult to define. Windows-based, heuristic models and finite-state hidden Markov models (HMMs) have been used with some success in analyzing ChIP-seq data but with lingering limitations. To improve the ability to detect broad regions of enrichment, we developed a stochastic Bayesian Change-Point (BCP) method, which addresses some of these unresolved issues. BCP makes use of recent advances in infinite-state HMMs by obtaining explicit formulas for posterior means of read densities. These posterior means can be used to categorize the genome into enriched and unenriched segments, as is customarily done, or examined for more detailed relationships since the underlying subpeaks are preserved rather than simplified into a binary classification. BCP performs a near exhaustive search of all possible change points between different posterior means at high-resolution to minimize the subjectivity of window sizes and is computationally efficient, due to a speed-up algorithm and the explicit formulas it employs. In the absence of a well-established \"gold standard\" for diffuse histone mark enrichment, we corroborated BCP's island detection accuracy and reproducibility using various forms of empirical evidence. We show that BCP is especially suited for analysis of diffuse histone ChIP-seq data but also effective in analyzing punctate transcription factor ChIP datasets, making it widely applicable for numerous experiment types.

Background Midbrain dopaminergic neurons (MDN) represent 0.0005% of the brain’s neuronal population and mediate cognition, food intake, and metabolism. MDN are also posited to underlay the neurobiological dysfunction of schizophrenia (SCZ), a severe neuropsychiatric disorder that is characterized by psychosis as well as multifactorial medical co-morbidities, including metabolic disease, contributing to markedly increased morbidity and mortality. Paradoxically, however, the genetic risk sequences of psychosis and traits associated with metabolic disease, such as body mass, show very limited overlap. Methods We investigated the genomic interaction of SCZ with medical conditions and traits, including body mass index (BMI), by exploring the MDN’s “spatial genome,” including chromosomal contact landscapes as a critical layer of cell type-specific epigenomic regulation. Low-input Hi-C protocols were applied to 5–10 × 10 3 dopaminergic and other cell-specific nuclei collected by fluorescence-activated nuclei sorting from the adult human midbrain. Results The Hi-C-reconstructed MDN spatial genome revealed 11 “Euclidean hot spots” of clustered chromatin domains harboring risk sequences for SCZ and elevated BMI. Inter- and intra-chromosomal contacts interconnecting SCZ and BMI risk sequences showed massive enrichment for brain-specific expression quantitative trait loci (eQTL), with gene ontologies, regulatory motifs and proteomic interactions related to adipogenesis and lipid regulation, dopaminergic neurogenesis and neuronal connectivity, and reward- and addiction-related pathways. Conclusions We uncovered shared nuclear topographies of cognitive and metabolic risk variants. More broadly, our PsychENCODE sponsored Hi-C study offers a novel genomic approach for the study of psychiatric and medical co-morbidities constrained by limited overlap of their respective genetic risk architectures on the linear genome.

BackgroundClearance of circulating tumor DNA (ctDNA) following checkpoint blockade (CB) can precede radiographic response,1 2 though current state of the art ctDNA detection via targeted panels faces limited sensitivity in low burden disease (figure 1). We previously showed that whole genome sequencing (WGS) of plasma can overcome low input of ctDNA to dynamically track low volume malignancy using matched tumor tissue.3 We therefore sought to evaluate ctDNA for tracking early response to checkpoint blockade (CB) in melanoma, and developed a novel classifier that allows us to track disease without matched tumor tissue for expanded applicability in immunotherapy.MethodsTo identify ctDNA sparsely diluted in noncancerous plasma cell free DNA (cfDNA), we developed Phoenix, a deep-learning classifier that uses genomic and epigenomic features to distinguish single nucleotide variants (SNVs) in melanoma from sequencing noise. We evaluated Phoenix on a retrospective cohort of serially sampled plasma from patients with advanced cutaneous melanoma on CB (nivolumab alone or with ipilimumab). Plasma was collected at 0, 3, 6 and 12 weeks after first dose of immunotherapy. ctDNA dynamics were compared to radiographic imaging results at 12 weeks.ResultsWe trained Phoenix on tumor-confirmed SNVs in plasma from a single patient with high tumor mutational burden (TMB) melanoma and cfDNA from age-matched patients without known cancer. Overall ctDNA signal-to-noise enrichment ranged from 100 - 260x in validation patients (n=2) with bulky disease. Phoenix learned key features of melanoma ctDNA including the UV mutational signature and short fragment size (figure 2), and sensitively tracked persistent low burden disease seen on imaging (figure 3). To validate these findings, we expanded our cohort (n= 15) of serially tracked tumors. In our preliminary analysis of 12 patients, Phoenix detected pretreatment ctDNA in 92% of patients at a specificity of 97% (figure 4), compared with only 17% with the benchmark in the field (iChorCNA, a plasma-based WGS liquid biopsy tool; table 1). Phoenix detected a decrease in ctDNA 3 weeks after initiation of CB in 80% of patients (figure 5) with an objective response on imaging. No change in ctDNA was seen in patients who did not respond to treatment.ConclusionsPhoenix successfully identified pretreatment melanoma ctDNA without matched tumor tissue and identified response to CB as early as 3 weeks after treatment. Our ongoing studies aim to optimize this technology for early identification of CB response in clinical practice.Abstract 33 Figure 1WGS of plasma increases sensitivity in low-burden diseaseLikelihood of ctDNA SNV detection is a function of tumor fraction, depth, and breadth (number of candidate sites). Because the limited number of genomic equivalents exhausts depth in targeted sequencing, detection sensitivity is limited by the relatively small number of sites in a clinical panel. In contrast, WGS at modest depth (35x) can detect low tumor fraction by integrating signal from thousands of SNVs across the genome.Abstract 33 Figure 2Phoenix learns key covariates for melanoma ctDNAPhoenix was trained on tumor-confirmed SNVs in plasma from patients with high burden melanoma and cfDNA from age-matched patients without known cancer. We aggregated Phoenix positive (ctDNA, blue) and negative (cfDNA, red) predictions on SNVs from a held out validation melanoma plasma sample. Phoenix ctDNA predictions correctly reflect important melanoma SNV attributes including UV-signature (C>T trinucleotide context, a), low DNase accessibility (b), late replication timing (c), and short fragment length (d).Abstract 33 Figure 3Phoenix sensitively tracks response to nivolumabPlasma samples were collected to monitor treatment response to nivolumab. Treatment monitoring by computed tomography (CT) shows response to therapy but residual disease after 3 months of therapy (a). Phoenix quantifies tumor response, matching radiographic changes, in higher temporal resolution than what is feasible with imaging (b). IchorCNA sensitivity captures initial treatment response dynamics but does not detect residual disease after 3 months of treatment (c). Log z score is calculated from a single plasma sample for each timepoint compared to a panel of control samples (n = 37).Abstract 33 Table 1Characteristics of patients at baseline and ctDNA dynamicsAbstract 33 Figure 4Phoenix detects pre- and intratreatment melanoma ctDNAWe evaluated Phoenix post-filter sample-level detection rate. Phoenix detects ctDNA in 92% of pretreatment melanoma plasma samples (green, n=12) at a specificity of 97% relative to held-out noncancerous controls (blue, n=38). Phoenix detected ctDNA in 84% of postreatment plasma samples (n=38, yellow), indicating full ctDNA clearance in 7/38 samples.Abstract 33 Figure 5ctDNA response to checkpoint blockade after 3 weeksSerial plasma samples were taken from patients on checkpoint blockade (nivolumab alone or with ipilimumab). ctDNA burden was measured as detection rate among post-filter candidate SNVs and compared to a 97% specificity boundary among a panel of healthy controls. Phoenix detects a response to checkpoint blockade, measured as a decrease in ctDNA detection rate, as early as 3 weeks as shown in 3 patients (MSK-38, MSK-40, MSK 42).AcknowledgementsThanks to support from the Conquer Cancer FoundationEthics ApprovalUse of human data in this study was approved by Memorial Sloan Kettering’s IRB, Assurance Number FWA0000499ReferencesZhang Q, Luo J, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov 2020 pp: CD-20-0047. doi:10.1158/2159-8290.CD-20-0047Bratman SV, Yang SYC., Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-0096-53.Zviran A, Schulman RC, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med 2020;26(7):1114–1124. doi:10.1038/s41591-020-0915-3Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun2017;8(1):1324. Published 2017 Nov 6. doi:10.1038/s41467-017-00965-y