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Even though desert dust is the most abundant aerosol by mass in Earth’s atmosphere, atmospheric models 25 struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused byfundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, 30 extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global 35 basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current 40 model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

SETDB1 is an essential histone methyltransferase that deposits histone H3 lysine 9 trimethylation (H3K9me3) to transcriptionally repress genes and repetitive elements. The function of differential H3K9me3 enrichment between cell-types remains unclear. Here, we demonstrate mutual exclusivity of H3K9me3 and CTCF across mouse tissues from different developmental timepoints. We analyze SETDB1 depleted cells and discover that H3K9me3 prevents aberrant CTCF binding independently of DNA methylation and H3K9me2. Such sites are enriched with SINE B2 retrotransposons. Moreover, analysis of higher-order genome architecture reveals that large chromatin structures including topologically associated domains and subnuclear compartments, remain intact in SETDB1 depleted cells. However, chromatin loops and local 3D interactions are disrupted, leading to transcriptional changes by modifying pre-existing chromatin landscapes. Specific genes with altered expression show differential interactions with dysregulated cis -regulatory elements. Collectively, we find that cell-type specific targets of SETDB1 maintain cellular identities by modulating CTCF binding, which shape nuclear architecture and transcriptomic networks. Here, the authors show how the histone methyltransferase SETDB1 is involved in cell-type specific regulation of chromatin landscape by catalyzing H3K9me3, which antagonizes CTCF binding. They further define the subsequent transcriptomic impact.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression; the results annotate candidate regulatory elements in diverse tissues and cell types, their candidate regulators, and the set of human traits for which they show genetic variant enrichment, providing a resource for interpreting the molecular basis of human disease. Epigenomics of human disease The goal of the NIH Roadmap Epigenomics Consortium was to generate a reference collection of human epigenomes for primary cells and tissues. This study describes the integrative analysis of 111 reference human epigenomes, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. The results show that disease and trait-associated genetic variants are enriched in predicted tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits and providing a resource for interpreting the molecular basis of human disease.

We investigated whether circulating osteopontin (OPN) could be used as a biomarker for cervical cancer. We employed a monoclonal antibody (mAb 659) specific for the unique and intact thrombin-sensitive site in OPN using an inhibition ELISA. We found significantly higher levels of OPN in 33 cervical cancer patients in both the plasma (mean +/- SD, 612 +/- 106 ng/mL) and serum (424 +/- 121 ng/mL) compared to healthy subjects [409 +/- 56 ng/mL, from 31 plasma samples (P < 0.0001), and 314 +/- 98 ng/mL, from 32 serum samples (P = 0.0002), respectively]. Similar results were obtained when the plasma from a bigger group (147 individuals) of cervical cancer patients (560 +/- 211 ng/mL) were compared with the same plasma samples of the healthy individuals (P = 0.0014). More significantly, the OPN level was highest in stage III-IV disease (614 +/- 210 ng/mL, from 52 individuals; P = 0.0001) and least and non-discriminatory in stage I (473 +/- 110 ng/mL, from 40 individuals; P = 0.5318). No such discrimination was found when a mAb of a different specificity (mAb 446) was used in a similar inhibition ELISA to compare the two groups in the first study; a commercial capture ELISA also failed. The possibility that the target epitope recognized by the antibody probe in these assays was absent from the circulating OPN due to protein truncation was supported by gel fractionation of the OPN found in patients' plasma: 60-64 kDa fragments were found instead of the presumably full-length OPN (68 kDa) seen in healthy people. How these fragments are generated and what possible role they play in cancer biology remain interesting questions.

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small “climate benefit”, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

Sedimentary records indicate that atmospheric dust has increased substantially since preindustrial times. However, state-of-the-art global Earth system models (ESMs) are unable to capture this historical increase, posing challenges in assessing the impacts of desert dust on Earth's climate. To address this issue, we construct a globally gridded dust emission dataset (DustCOMMv1) spanning 1841–2000. We do so by combining 19 sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived emission dataset contains interdecadal variability of dust emissions as forced by the deposition flux records, which increased by approximately 50 % from 1851–1870 to 1981–2000. We further provide future dust emission datasets for 2000–2100 by assuming three possible scenarios for how future dust emissions will evolve. We evaluate the historical dust emission dataset and illustrate its effectiveness in enforcing a historical dust increase in ESMs by conducting a long-term (1851–2000) dust cycle simulation with the Community Earth System Model (CESM2). The simulated dust depositions are in reasonable agreement with the long-term increase in most sedimentary dust deposition records and with measured long-term trends in dust concentration at sites in Miami and Barbados. This contrasts with the CESM2 simulations using a process-based dust emission scheme and with simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends in dust deposition, concentration, and optical depth. The DustCOMM emissions thus enable ESMs to account for the historical radiative forcings (RFs), including due to dust direct interactions with radiation (direct RF). Our CESM2 simulations estimate a 1981–2000 minus 1851–1870 direct RF of −0.10 W m−2 by dust aerosols up to 10 µm in diameter (PM10) at the top of atmosphere (TOA). This global dust emission dataset thus enables models to more accurately account for historical aerosol forcings, thereby improving climate change projections such as those in the Intergovernmental Panel on Climate Change (IPCC) assessment reports.

Polymorphic integrations of endogenous retroviruses (ERVs) have been previously detected in mouse and human genomes. While most are inert, a subset can influence the activity of the host genes. However, the molecular mechanism underlying how such elements affect the epigenome and transcriptome and their roles in driving intra-specific variation remain unclear. Here, by utilizing wildtype murine embryonic stem cells (mESCs) derived from distinct genetic backgrounds, we discover a polymorphic MMERGLN (GLN) element capable of regulating H3K27ac enrichment and transcription of neighboring loci. We demonstrate that this polymorphic element can enhance the neighboring Klhdc4 gene expression in cis , which alters the activity of downstream stress response genes. These results suggest that the polymorphic ERV-derived cis -regulatory element contributes to differential phenotypes from stimuli between mouse strains. Moreover, we identify thousands of potential polymorphic ERVs in mESCs, a subset of which show an association between proviral activity and nearby chromatin states and transcription. Overall, our findings elucidate the mechanism of how polymorphic ERVs can shape the epigenome and transcriptional networks that give rise to phenotypic divergence between individuals. How polymorphic elements can drive intra-species variation has not been clearly examined. Here the authors show that the polymorphic GLN endogenous retrovirus (ERV) functions as a cis -regulatory element for neighboring genes, leading to differences in how two mouse strains respond to stress.

As part of the Epigenome Roadmap project, this study uses a chromosome-spanning haplotype reconstruction strategy to construct haplotype-resolved epigenomic maps for a diverse set of human tissues; the maps reveal extensive allelic biases in chromatin state and transcription, which vary across individuals due to genetic backgrounds. Haplotype-resolved epigenomic maps As part of the Epigenome Roadmap project, Bing Ren and colleagues use a chromosome-spanning haplotype reconstruction strategy to construct haplotype-resolved epigenomic maps for a diverse set of human tissues. The maps reveal extensive allelic biases in chromatin state and transcription, which vary across tissues and between individuals. They authors investigate cis -regulatory relationships between genes and their control sequences and tissue-restricted activities of repetitive elements. Allelic differences between the two homologous chromosomes can affect the propensity of inheritance in humans; however, the extent of such differences in the human genome has yet to be fully explored. Here we delineate allelic chromatin modifications and transcriptomes among a broad set of human tissues, enabled by a chromosome-spanning haplotype reconstruction strategy 1 . The resulting large collection of haplotype-resolved epigenomic maps reveals extensive allelic biases in both chromatin state and transcription, which show considerable variation across tissues and between individuals, and allow us to investigate cis -regulatory relationships between genes and their control sequences. Analyses of histone modification maps also uncover intriguing characteristics of cis -regulatory elements and tissue-restricted activities of repetitive elements. The rich data sets described here will enhance our understanding of the mechanisms by which cis -regulatory elements control gene expression programs.

Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes.

The Origin Recognition Complex (ORC) is an evolutionarily conserved six-subunit protein complex that binds specific sites at many locations to coordinately replicate the entire eukaryote genome. Though highly conserved in structure, ORC’s selectivity for replication origins has diverged tremendously between yeasts and humans to adapt to vastly different life cycles. In this work, we demonstrate that the selectivity determinant of ORC for DNA binding lies in a 19-amino acid insertion helix in the Orc4 subunit, which is present in yeast but absent in human. Removal of this motif from Orc4 transforms the yeast ORC, which selects origins based on base-specific binding at defined locations, into one whose selectivity is dictated by chromatin landscape and afforded with plasticity, as reported for human. Notably, the altered yeast ORC has acquired an affinity for regions near transcriptional start sites (TSSs), which the human ORC also favors. In most model yeast species the Origin Recognition Complex (ORC) binds defined and species-specific base sequences while in humans what determines the binding appears to be more complex. Here the authors reveal that the yeast’s ORC complex binding specificity is dependent on a 19-amino acid insertion helix in the Orc4 subunit which is lost in human.