Single-Cell Profiling of Ebola Virus Disease In Vivo Reveals Viral and Host Dynamics.

Ebola virus (EBOV) causes epidemics with high mortality yet remains understudied due to the challenge of experimentation in high-containment and outbreak settings. Here, we used single-cell transcriptomics and CyTOF-based single-cell protein quantification to characterize peripheral immune cells during EBOV infection in rhesus monkeys. We obtained 100,000 transcriptomes and 15,000,000 protein profiles, finding that immature, proliferative monocyte-lineage cells with reduced antigen-presentation capacity replace conventional monocyte subsets, while lymphocytes upregulate apoptosis genes and decline in abundance. By quantifying intracellular viral RNA, we identify molecular determinants of tropism among circulating immune cells and examine temporal dynamics in viral and host gene expression. Within infected cells, EBOV downregulates STAT1 mRNA and interferon signaling, and it upregulates putative pro-viral genes (e.g., DYNLL1 and HSPA5), nominating pathways the virus manipulates for its replication. This study sheds light on EBOV tropism, replication dynamics, and elicited immune response and provides a framework for characterizing host-virus interactions under maximum containment.

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments.

Understanding the mechanisms of HIV tissue persistence necessitates the ability to visualize tissue microenvironments where infected cells reside; however, technological barriers limit our ability to dissect the cellular components of these HIV reservoirs. Here, we developed protein and nucleic acid in situ imaging (PANINI) to simultaneously quantify DNA, RNA, and protein levels within these tissue compartments. By coupling PANINI with multiplexed ion beam imaging (MIBI), we measured over 30 parameters simultaneously across archival lymphoid tissues from healthy or simian immunodeficiency virus (SIV)-infected nonhuman primates. PANINI enabled the spatial dissection of cellular phenotypes, functional markers, and viral events resulting from infection. SIV infection induced IL-10 expression in lymphoid B cells, which correlated with local macrophage M2 polarization. This highlights a potential viral mechanism for conditioning an immunosuppressive tissue environment for virion production. The spatial multimodal framework here can be extended to decipher tissue responses in other infectious diseases and tumor biology.

Tree diversity increases decadal forest soil carbon and nitrogen accrual.

Increasing soil carbon and nitrogen storage can help mitigate climate change and sustain soil fertility1,2. A large number of biodiversity-manipulation experiments collectively suggest that high plant diversity increases soil carbon and nitrogen stocks3,4. It remains debated, however, whether such conclusions hold in natural ecosystems5-12. Here we analyse Canada's National Forest Inventory (NFI) database with the help of structural equation modelling (SEM) to explore the relationship between tree diversity and soil carbon and nitrogen accumulation in natural forests. We find that greater tree diversity is associated with higher soil carbon and nitrogen accumulation, validating inferences from biodiversity-manipulation experiments. Specifically, on a decadal scale, increasing species evenness from its minimum to maximum value increases soil carbon and nitrogen in the organic horizon by 30% and 42%, whereas increasing functional diversity enhances soil carbon and nitrogen in the mineral horizon by 32% and 50%, respectively. Our results highlight that conserving and promoting functionally diverse forests could promote soil carbon and nitrogen storage, enhancing both carbon sink capacity and soil nitrogen fertility.

Native diversity buffers against severity of non-native tree invasions.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

LC3-associated phagocytosis of neutrophils triggers tumor ferroptotic cell death in glioblastoma.

Necrosis in solid tumors is commonly associated with poor prognostic but how these lesions expand remains unclear. Studies have found that neutrophils associate with and contribute to necrosis development in glioblastoma by inducing tumor cell ferroptosis through transferring myeloperoxidase-containing granules. However, the mechanism of neutrophilic granule transfer remains elusive. We performed an unbiased small molecule screen and found that statins inhibit neutrophil-induced tumor cell death by blocking the neutrophilic granule transfer. Further, we identified a novel process wherein neutrophils are engulfed by tumor cells before releasing myeloperoxidase-containing contents into tumor cells. This neutrophil engulfment is initiated by integrin-mediated adhesion, and further mediated by LC3-associated phagocytosis (LAP), which can be blocked by inhibiting the Vps34-UVRAG-RUBCN-containing PI3K complex. Myeloperoxidase inhibition or Vps34 depletion resulted in reduced necrosis formation and prolonged mouse survival in an orthotopic glioblastoma mouse model. Thus, our study unveils a critical role for LAP-mediated neutrophil internalization in facilitating the transfer of neutrophilic granules, which in turn triggers tumor cell death and necrosis expansion. Targeting this process holds promise for improving glioblastoma prognosis.

iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images.

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.

Linking fine root lifespan to root chemical and morphological traits-A global analysis.

Fine root lifespan is a critical trait associated with contrasting root strategies of resource acquisition and protection. Yet, its position within the multidimensional "root economics space" synthesizing global root economics strategies is largely uncertain, and it is rarely represented in frameworks integrating plant trait variations. Here, we compiled the most comprehensive dataset of absorptive median root lifespan (MRL) data including 98 observations from 79 woody species using (mini-)rhizotrons across 40 sites and linked MRL to other plant traits to address questions of the regulators of MRL at large spatial scales. We demonstrate that MRL not only decreases with plant investment in root nitrogen (associated with more metabolically active tissues) but also increases with construction of larger diameter roots which is often associated with greater plant reliance on mycorrhizal symbionts. Although theories linking organ structure and function suggest that root traits should play a role in modulating MRL, we found no correlation between root traits associated with structural defense (root tissue density and specific root length) and MRL. Moreover, fine root and leaf lifespan were globally unrelated, except among evergreen species, suggesting contrasting evolutionary selection between leaves and roots facing contrasting environmental influences above vs. belowground. At large geographic scales, MRL was typically longer at sites with lower mean annual temperature and higher mean annual precipitation. Overall, this synthesis uncovered several key ecophysiological covariates and environmental drivers of MRL, highlighting broad avenues for accurate parametrization of global biogeochemical models and the understanding of ecosystem response to global climate change.

Robust single-cell matching and multimodal analysis using shared and distinct features.

The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

The Phytophthora sojae nuclear effector PsAvh110 targets a host transcriptional complex to modulate plant immunity.

Plants have evolved sophisticated immune networks to restrict pathogen colonization. In response, pathogens deploy numerous virulent effectors to circumvent plant immune responses. However, the molecular mechanisms by which pathogen-derived effectors suppress plant defenses remain elusive. Here, we report that the nucleus-localized RxLR effector PsAvh110 from the pathogen Phytophthora sojae, causing soybean (Glycine max) stem and root rot, modulates the activity of a transcriptional complex to suppress plant immunity. Soybean like-heterochromatin protein 1-2 (GmLHP1-2) and plant homeodomain finger protein 6 (GmPHD6) form a transcriptional complex with transcriptional activity that positively regulates plant immunity against Phytophthora infection. To suppress plant immunity, the nuclear effector PsAvh110 disrupts the assembly of the GmLHP1-2/GmPHD6 complex via specifically binding to GmLHP1-2, thus blocking its transcriptional activity. We further show that PsAvh110 represses the expression of a subset of immune-associated genes, including BRI1-associated receptor kinase 1-3 (GmBAK1-3) and pathogenesis-related protein 1 (GmPR1), via G-rich elements in gene promoters. Importantly, PsAvh110 is a conserved effector in different Phytophthora species, suggesting that the PsAvh110 regulatory mechanism might be widely utilized in the genus to manipulate plant immunity. Thus, our study reveals a regulatory mechanism by which pathogen effectors target a transcriptional complex to reprogram transcription.

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Histamine H2 receptor deficit in glutamatergic neurons contributes to the pathogenesis of schizophrenia.

Schizophrenia is a serious mental disorder, and existing antipsychotic drugs show limited efficacy and cause unwanted side effects. The development of glutamatergic drugs for schizophrenia is currently challenging. Most functions of histamine in the brain are mediated by the histamine H1 receptor; however, the role of the H2 receptor (H2R) is not quite clear, especially in schizophrenia. Here, we found that expression of H2R in glutamatergic neurons of the frontal cortex was decreased in schizophrenia patients. Selective knockout of the H2R gene (Hrh2) in glutamatergic neurons (CaMKIIα-Cre; Hrh2 fl/fl) induced schizophrenia-like phenotypes including sensorimotor gating deficits, increased susceptibility to hyperactivity, social withdrawal, anhedonia, and impaired working memory, as well as decreased firing of glutamatergic neurons in the medial prefrontal cortex (mPFC) in in vivo electrophysiological tests. Selective knockdown of H2R in glutamatergic neurons in the mPFC but not those in the hippocampus also mimicked these schizophrenia-like phenotypes. Furthermore, electrophysiology experiments established that H2R deficiency decreased the firing of glutamatergic neurons by enhancing the current through hyperpolarization-activated cyclic nucleotide-gated channels. In addition, either H2R overexpression in glutamatergic neurons or H2R agonism in the mPFC counteracted schizophrenia-like phenotypes in an MK-801-induced mouse model of schizophrenia. Taken together, our results suggest that deficit of H2R in mPFC glutamatergic neurons may be pivotal to the pathogenesis of schizophrenia and that H2R agonists can be regarded as potentially efficacious medications for schizophrenia therapy. The findings also provide evidence for enriching the conventional glutamate hypothesis for the pathogenesis of schizophrenia and improve the understanding of the functional role of H2R in the brain, especially in glutamatergic neurons.

Methionine biosynthesis enzyme MoMet2 is required for rice blast fungus pathogenicity by promoting virulence gene expression via reducing 5mC modification.

The emergence of fungicide resistance severely threatens crop production by limiting the availability and application of established fungicides. Therefore, it is urgent to identify new fungicidal targets for controlling plant diseases. Here, we characterized the function of a conserved homoserine O-acetyltransferase (HOA) from the rice blast fungus Magnaporthe oryzae that could serve as the candidate antifungal target. Deletion of the MoMET2 and MoCYS2 genes encoding HOAs perturbed the biosynthesis of methionine and S-adenyl methionine, a methyl group donor for epigenetic modifications, and severely attenuated the development and virulence of M. oryzae. The ∆Momet2 mutant is significantly increased in 5-methylcytosine (5mC) modification that represses the expression of genes required for pathogenicity, including MoGLIK and MoCDH-CYT. We further showed that host-induced gene silencing (HIGS) targeting MoMET2 and MoCYS2 effectively controls rice blasts. Our studies revealed the importance of HOA in the development and virulence of M. oryzae, which suggests the potential feasibility of HOA as new targets for novel anti-rice blast measurements.

Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study.

A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

A modified Agrobacterium-mediated transformation for two oomycete pathogens.

Oomycetes are a group of filamentous microorganisms that include some of the biggest threats to food security and natural ecosystems. However, much of the molecular basis of the pathogenesis and the development in these organisms remains to be learned, largely due to shortage of efficient genetic manipulation methods. In this study, we developed modified transformation methods for two important oomycete species, Phytophthora infestans and Plasmopara viticola, that bring destructive damage in agricultural production. As part of the study, we established an improved Agrobacterium-mediated transformation (AMT) method by prokaryotic expression in Agrobacterium tumefaciens of AtVIP1 (VirE2-interacting protein 1), an Arabidopsis bZIP gene required for AMT but absent in oomycetes genomes. Using the new method, we achieved an increment in transformation efficiency in two P. infestans strains. We further obtained a positive GFP transformant of P. viticola using the modified AMT method. By combining this method with the CRISPR/Cas12a genome editing system, we successfully performed targeted mutagenesis and generated loss-of-function mutations in two P. infestans genes. We edited a MADS-box transcription factor-encoding gene and found that a homozygous mutation in MADS-box results in poor sporulation and significantly reduced virulence. Meanwhile, a single-copy avirulence effector-encoding gene Avr8 in P. infestans was targeted and the edited transformants were virulent on potato carrying the cognate resistance gene R8, suggesting that loss of Avr8 led to successful evasion of the host immune response by the pathogen. In summary, this study reports on a modified genetic transformation and genome editing system, providing a potential tool for accelerating molecular genetic studies not only in oomycetes, but also other microorganisms.

A small molecule exerts selective antiviral activity by targeting the human cytomegalovirus nuclear egress complex.

Human cytomegalovirus (HCMV) is an important pathogen for which new antiviral drugs are needed. HCMV, like other herpesviruses, encodes a nuclear egress complex (NEC) composed of two subunits, UL50 and UL53, whose interaction is crucial for viral replication. To explore whether small molecules can exert selective antiviral activity by inhibiting NEC subunit interactions, we established a homogeneous time-resolved fluorescence (HTRF) assay of these interactions and used it to screen >200,000 compound-containing wells. Two compounds, designated GK1 and GK2, which selectively inhibited this interaction in the HTRF assay with GK1 also active in a co-immunoprecipitation assay, exhibited more potent anti-HCMV activity than cytotoxicity or activity against another herpesvirus. At doses that substantially reduced HCMV plaque formation, GK1 and GK2 had little or no effect on the expression of viral proteins and reduced the co-localization of UL53 with UL50 at the nuclear rim in a subset of cells. GK1 and GK2 contain an acrylamide moiety predicted to covalently interact with cysteines, and an analog without this potential lacked activity. Mass spectrometric analysis showed binding of GK2 to multiple cysteines on UL50 and UL53. Nevertheless, substitution of cysteine 214 of UL53 with serine (C214S) ablated detectable inhibitory activity of GK1 and GK2 in vitro, and the C214S substitution engineered into HCMV conferred resistance to GK1, the more potent of the two inhibitors. Thus, GK1 exerts selective antiviral activity by targeting the NEC. Docking studies suggest that the acrylamide tethers one end of GK1 or GK2 to C214 within a pocket of UL53, permitting the other end of the molecule to sterically hinder UL50 to prevent NEC formation. Our results prove the concept that targeting the NEC with small molecules can selectively block HCMV replication. Such compounds could serve as a foundation for development of anti-HCMV drugs and as chemical tools for studying HCMV.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Managing nitrogen to restore water quality in China.

The nitrogen cycle has been radically changed by human activities1. China consumes nearly one third of the world's nitrogen fertilizers. The excessive application of fertilizers2,3 and increased nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen 'boundary'4 in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5 ± 3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated 'safe' nitrogen discharge threshold (5.2 ± 0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7 ± 0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3 ± 0.3 megatonnes of nitrogen per year-about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18-29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.

Author Correction: El Niño-Southern Oscillation complexity.

In this Review, the middle initial of author Kim M. Cobb was omitted. The original Review has been corrected online.