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Ecophysiologists have reported a range of relationships, including intrinsic trade-offs across and within species between plant relative growth rate in high resource conditions (RGR) vs adaptation to tolerate cold or arid climates, arising from trait-based mechanisms. Few studies have considered ecotypes within a species, in which the lack of a trade-off would contribute to a wide species range and resilience to climate change. For 15 ecotypes of Arabidopsis thaliana in a common garden we tested for associations between RGR vs adaptation to cold or dry native climates and assessed hypotheses for its mediation by 15 functional traits. Ecotypes native to warmer, drier climates had higher leaf density, leaf mass per area, root mass fraction, nitrogen per leaf area and carbon isotope ratio, and lower osmotic potential at full turgor. Relative growth rate was statistically independent of the climate of the ecotype native range and of individual functional traits. The decoupling of RGR and cold or drought adaptation in Arabidopsis is consistent with multiple stress resistance and avoidance mechanisms for ecotypic climate adaptation and would contribute to the species' wide geographic range and resilience as the climate changes.

Transgenic T-cell receptor (TCR) adoptive cell therapies recognizing tumor antigens are associated with robust initial response rates, but frequent disease relapse. This usually occurs in the setting of poor long-term persistence of cells expressing the transgenic TCR, generated using murine stem cell virus (MSCV) γ-retroviral vectors. Analysis of clinical transgenic adoptive cell therapy products in vivo revealed that despite strong persistence of the transgenic TCR DNA sequence over time, its expression was profoundly decreased over time at the RNA and protein levels. Patients with the greatest degrees of expression suppression displayed significant increases in DNA methylation over time within the MSCV promoter region, as well as progressive increases in DNA methylation within the entire MSCV vector over time. These increases in vector methylation occurred independently of its integration site within the host genomes. These results have significant implications for the design of future viral vector gene–engineered adoptive cell transfer therapies.Significance:Cellular immunotherapies' reliance on retroviral vectors encoding foreign genetic material can be vulnerable to progressive acquisition of DNA methylation and subsequent epigenetic suppression of the transgenic product in TCR adoptive cell therapy. This must be considered in the design of future generations of cellular immunotherapies for cancer.This article is highlighted in the In This Issue feature, p. 1611

Supplementary Figures and Figure Legends

Supplementary Methods

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single- cell RNA-sequencing (scRNA-seq) dataset of ∼7 million cells profiled, and a spatially resolved transcriptomic dataset of ∼4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

The detection of novel stimuli is critical to learn and survive in a dynamic environment. Though novel stimuli powerfully affect brain activity, their impact on specific cell types and circuits is not well understood. Disinhibition is one candidate mechanism for novelty-induced enhancements in activity. Here we characterize the impact of stimulus novelty on disinhibitory circuit components using longitudinal 2-photon calcium imaging of Vip, Sst, and excitatory populations in the mouse visual cortex. Mice learn a behavioral task with stimuli that become highly familiar, then are tested on both familiar and novel stimuli. Mice consistently perform the task with novel stimuli, yet responses to stimulus presentations and stimulus omissions are dramatically altered. Further, we find that novelty modifies coding of visual as well as behavioral and task information. At the population level, the direction of these changes is consistent with engagement of the Vip-Sst disinhibitory circuit. At the single cell level, we identify separate clusters of Vip, Sst, and excitatory cells with unique patterns of novelty-induced coding changes. This study and the accompanying open-access dataset reveals the impact of novelty on sensory and behavioral representations in visual cortical circuits and establishes novelty as a key driver of cellular functional diversity.

Epigenetic clocks, DNA methylation based predictive models of chronological age, are often utilized to study aging associated biology. Despite their widespread use, these methods do not account for other factors that also contribute to the variability of DNA methylation data. For example, many CpG sites show strong sex-specific or cell type specific patterns that likely impact the predictions of epigenetic age. To overcome these limitations, we developed a multidimensional extension of the Epigenetic Pacemaker, the Multi-State Epigenetic Pacemaker (MSEPM). We show that the MSEPM is capable of accurately modeling multiple methylation associated factors simultaneously, while also providing site specific models that describe the per site relationship between methylation and these factors. We utilized the MSEPM with a large aggregate cohort of blood methylation data to construct models of the effects of age, sex and cell type heterogeneity on DNA methylation. We found that these models capture a large faction of the variability at thousands of DNA methylation sites. Moreover, we found modeled sites that are primarily affected by aging and no other factors. Among these, those that lose methylation over time are enriched for CTCF transcription factor chip peaks, while those that gain methylation over time are enriched for REST transcription factor chip peaks. Both transcription factors are associated with transcriptional maintenance and suggest a general dysregulation of transcription with age that is not impacted by sex or cell type heterogeneity. In conclusion, the MSEPM is capable of accurately modeling multiple methylation associated factors and the models produced can illuminate site specific combinations of factors that affect methylation dynamics.

Species that hibernate generally live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor–arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause ageing suppression. Metabolic rate is also a known correlate of longevity; we thus proposed the ‘hibernation–ageing hypothesis’ whereby ageing is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7–8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females was modelled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic ageing over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (two years old). With respect to circannual patterns, the epigenetic age increased during the active season and essentially stalled during the hibernation period. Taken together, our results are consistent with the hibernation–ageing hypothesis and may explain the enhanced longevity in hibernators.

Introduction: Breast tissue age, measured with DNA methylation-based estimates, is accelerated in healthy women compared with matched peripheral blood samples. While we have shown that the degree of acceleration in breast epigenetic age is related to hormonal factors including elevated body mass index and earlier age at menarche, no prior work has examined alterations in gene expression that accompany breast epigenetic aging. In this study, we examine gene expression patterns associated with epigenetic age acceleration in healthy breast using six epigenetic clocks. Methods: Using the AllPrep (Qiagen) DNA/RNA Kit, we extracted DNA and RNA from the same breast tissue specimens from 192 healthy women aged 19-90 years who donated breast tissue to the Susan G. Komen Tissue Bank at the Indiana University Simon Comprehensive Cancer Center. Transcriptome analysis was performed using the QuantSeq 3’MRNA-SeqFWD kit to generate RNA sequencing libraries. DNA methylation was quantified using the Illumina EPIC 850K array platform, and methylation age was estimated using beta-values from these experiments. Age acceleration is defined using the residuals of a linear regression of methylation age on chronologic age, and samples with positive residuals were characterized as exhibiting epigenetic age acceleration. Differential gene expression analysis was performed using DESeq2 bioconductor software to estimate variance-mean dependence in count data and using a negative binomial distribution model to test for differential gene expression in samples with accelerated epigenetic age in 6 epigenetic clocks: the Horvath pan-tissue clock, Hannum Age, Phenotypic Age, Grim Age, Skin and Blood clock, and the Epigenetic Pacemaker clock (EPM) , a newly developed clock that accounts for curvilinearity in methylation-based estimates of age over time. Results: Principal components analysis revealed that a large proportion of the variance in gene expression was explained by the first two components (PC1 8.6%, PC2 8.2%). Using a false discovery rate cutoff of 0.05, we found 17 (pan-tissue), 909 (Hannum), 3573 (Phenotypic), 1459 (Grim), 3081 (Skin and Blood), and 227 (EPM) genes differentially expressed with the presence of epigenetic age acceleration for each clock. Examining overlap of these genes revealed distinct groups of genes differentially expressed with acceleration in each clock, with no genes in common for all clocks, Phenotypic and Skin and Blood clocks sharing 1137 genes; Phenotypic, Grim, and Skin and Blood clocks sharing 726 genes; and few genes shared by 5 out of 6 clocks (41 genes in all clocks except pan-tissue, 5 genes in all clocks except EPM). Gene set enrichment analysis revealed activation of pathways involved in cellular respiration, oxidative phosphorylation, energy derivation, organic acid metabolic processes, and immune response, and suppression of genes involved in embryonic epithelial morphogenesis, epidermal cell differentiation, chromatin organization, and lipoprotein transport. Conclusion: We identified significant alterations in gene expression patterns that accompany breast epigenetic acceleration in healthy female breast, with distinct patterns observed for six epigenetic clocks examined. Pathways involved include epithelial morphogenesis and differentiation, potentially representing a mechanistic link between breast accelerated aging and carcinogenesis. Citation Format: Mary Elizabeth Sehl, Wenbin Guo, Colin Farrell, Natascia Marino, Jill E. Henry, Anna Maria Storniolo, Jeannette Papp, Jingyi Jessica Li, Matteo Pellegrini, Steve Horvath, Patricia A. Ganz. Differential gene expression patterns in healthy breast tissue exhibiting epigenetic age acceleration [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-06.

DNA methylation modulates telomere function. In Arabidopsis thaliana, telomeric regions have a bimodal chromatin organization with unmethylated telomeres and methylated subtelomeres. To gain insight into this organization we have generated TAIR10-Tel, a modified version of the Arabidopsis reference genome with additional sequences at most chromosome ends. TAIR10-Tel has allowed us to analyse DNA methylation at nucleotide resolution level in telomeric regions. We have analysed the wild-type strain and mutants that encode inactive versions of all currently known relevant methyltransferases involved in cytosine methylation. These analyses have revealed that subtelomeric DNA methylation extends 1 to 2 kbp from Interstitial Telomeric Sequences (ITSs) that abut or are very near to telomeres. However, DNA methylation drops at the telomeric side of the telomere-subtelomere boundaries and disappears at the inner part of telomeres. We present a comprehensive and integrative model for subtelomeric DNA methylation that should help to decipher the mechanisms that govern the epigenetic regulation of telomeres. This model involves a complex network of interactions between methyltransferases and subtelomeric DNA sequences.

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease., Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.

Epigenetic clocks, DNA methylation based chronological age prediction models, are commonly employed to study age related biology. The error between the predicted and observed age is often interpreted as a form of biological age acceleration and many studies have measured the impact of environmental and other factors on epigenetic age. Epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age and as a result they reduce the impact of factors that may moderate the relationship between actual and epigenetic age. Here we compare the standard methods used to construct epigenetic clocks to an evolutionary framework of epigenetic aging, the epigenetic pacemaker (EPM) that directly models DNA methylation as a function of a time dependent epigenetic state. We show that the EPM is more sensitive than epigenetic clocks for the detection of factors that moderate the relationship between actual age and epigenetic state (ie epigenetic age). Specifically, we show that the EPM is more sensitive at detecting sex and cell type effects in a large aggregate data set and in an example case study is more sensitive sensitive at detecting age related methylation changes associated with polybrominated biphenyl exposure. Thus we find that the pacemaker provides a more robust framework for the study of factors that impact epigenetic age acceleration than traditional clocks based on linear regression models.