The potential of baicalin to enhance neuroprotection and mitochondrial function in a human neuronal cell model.

Baicalin is a flavone glycoside derived from flowering plants belonging to the Scutellaria genus. Previous studies have reported baicalin's anti-inflammatory and neuroprotective properties in rodent models, indicating the potential of baicalin in neuropsychiatric disorders where alterations in numerous processes are observed. However, the extent of baicalin's therapeutic effects remains undetermined in a human cell model, more specifically, neuronal cells to mimic the brain environment in vitro. As a proof of concept, we treated C8-B4 cells (murine cell model) with three different doses of baicalin (0.1, 1 and 5 µM) and vehicle control (DMSO) for 24 h after liposaccharide-induced inflammation and measured the levels of TNF-α in the medium by ELISA. NT2-N cells (human neuronal-like cell model) underwent identical baicalin treatment, followed by RNA extraction, genome-wide mRNA expression profiles and gene set enrichment analysis (GSEA). We also performed neurite outgrowth assays and mitochondrial flux bioanalysis (Seahorse) in NT2-N cells. We found that in C8-B4 cells, baicalin at ≥ 1 µM exhibited anti-inflammatory effects, lowering TNF-α levels in the cell culture media. In NT2-N cells, baicalin positively affected neurite outgrowth and transcriptionally up-regulated genes in the tricarboxylic acid cycle and the glycolysis pathway. Similarly, Seahorse analysis showed increased oxygen consumption rate in baicalin-treated NT2-N cells, an indicator of enhanced mitochondrial function. Together, our findings have confirmed the neuroprotective and mitochondria enhancing effects of baicalin in human-neuronal like cells. Given the increased prominence of mitochondrial mechanisms in diverse neuropsychiatric disorders and the paucity of mitochondrial therapeutics, this suggests the potential therapeutic application of baicalin in human neuropsychiatric disorders where these processes are altered.

Metergoline Shares Properties with Atypical Antipsychotic Drugs Identified by Gene Expression Signature Screen.

Novel approaches are required to find new treatments for schizophrenia and other neuropsychiatric disorders. This study utilised a combination of in vitro transcriptomics and in silico analysis with the BROAD Institute's Connectivity Map to identify drugs that can be repurposed to treat psychiatric disorders. Human neuronal (NT2-N) cells were treated with a combination of atypical antipsychotic drugs commonly used to treat psychiatric disorders (such as schizophrenia, bipolar disorder, and major depressive disorder), and differential gene expression was analysed. Biological pathways with an increased gene expression included circadian rhythm and vascular endothelial growth factor signalling, while the adherens junction and cell cycle pathways were transcriptionally downregulated. The Connectivity Map (CMap) analysis screen highlighted drugs that affect global gene expression in a similar manner to these psychiatric disorder treatments, including several other antipsychotic drugs, confirming the utility of this approach. The CMap screen specifically identified metergoline, an ergot alkaloid currently used to treat seasonal affective disorder, as a drug of interest. In mice, metergoline dose-dependently reduced MK-801- or methamphetamine-induced locomotor hyperactivity confirming the potential of metergoline to treat positive symptoms of schizophrenia in an animal model. Metergoline had no effects on prepulse inhibition deficits induced by MK-801 or methamphetamine. Taken together, metergoline appears a promising drug for further studies to be repurposed as a treatment for schizophrenia and possibly other psychiatric disorders.

Heterogeneous landscape promotes distinct microbial communities in an imperiled scrub ecosystem.

Habitat heterogeneity is a key driver of biodiversity of macroorganisms, yet how heterogeneity structures belowground microbial communities is not well understood. Importantly, belowground microbial communities may respond to any number of abiotic, biotic, and spatial drivers found in heterogeneous environments. Here, we examine potential drivers of prokaryotic and fungal communities in soils across the heterogenous landscape of the imperiled Florida scrub, a pyrogenic ecosystem where slight differences in elevation lead to large changes in water and nutrient availability and vegetation composition. We employ a comprehensive, large-scale sampling design to characterize the communities of prokaryotes and fungi associated with three habitat types and two soil depths (crust and subterranean) to evaluate (i) differences in microbial communities across these heterogeneous habitats, (ii) the relative roles of abiotic, biotic, and spatial drivers in shaping community structure, and (iii) the distribution of fungal guilds across these habitats. We sequenced soils from 40 complete replicates of habitat × soil depth combinations and sequenced the prokaryotic 16S and fungal internal transcribed spacer (ITS) regions using Illumina MiSeq. Habitat heterogeneity generated distinct communities of soil prokaryotes and fungi. Spatial distance played a role in structuring crust communities, whereas subterranean microbial communities were primarily structured by the shrub community, whose roots they presumably interacted with. This result helps to explain the unexpected transition we observed between arbuscular mycorrhiza-dominated soils at low-elevation habitats to ectomycorrhiza-dominated soils at high-elevation habitats. Our results challenge previous notions of environmental determinism of microbial communities and generate new hypotheses regarding symbiotic relationships across heterogeneous environments.

Nichima gen. nov. (Alismataceae) based on reproductive structures from the Oligocene-Miocene of Mexico.

PREMISE: Alismataceae, a sub-cosmopolitan family with ca. 17 genera and 113 species, is a large group of aquatic plants. Compression/impressions and bioinclusions of reproductive parts in amber support the documentation of the lineage in low-latitude North America. In Mexico, fossil aquatic plants have been infrequently documented. The new reproductive structures exhibit characteristics of Alismataceae, whose fossil record is mainly documented in the northern hemisphere through of fruits and seeds. METHODS: We described and compared 150 samples of reproductive structures preserved as impressions/compressions from the Oligocene Los Ahuehuetes locality in the state of Puebla, and two bioinclusions from the Miocene amber of Simojovel de Allende in the state of Chiapas, Mexico with extinct and extant taxa. Using a parsimony analysis based on 29 floral characters of 17 extant genera of the Alismataceae, we evaluated the relationship between the fossil material and potential living relatives. RESULTS: We discovered a new genus Nichima based on a perfect, actinomorphic flower with an expanded receptacle, three persistent sepals with multiple vasculatures, delicate and caducous petals, six stamens, and a gynoecium composed of three to more superior carpels, maturing into achenes. These characteristics resemble flowers of Alismataceae. Nichima represents an extinct member of the family, with two new species described here, Nichima magalloniae L. Hern., Cevallos-Ferriz et Hernández-Damián sp. nov. and Nichima gonzalez-medranoi L. Hern., Cevallos-Ferriz et Hernández-Damián, sp. nov. Their phylogenetic position suggests affinity with a clade that includes Baldiella, Echinodorus, and Alisma. CONCLUSIONS: Reproductive structures from the Cenozoic of Mexico support the identification of a new extinct genus, Nichima, evidencing the extensive history of Alismataceae in North America's low latitudes and suggesting a southern extension of the boreotropical flora.

Low-probability states, data statistics, and entropy estimation.

A fundamental problem in the analysis of complex systems is getting a reliable estimate of the entropy of their probability distributions over the state space. This is difficult because unsampled states can contribute substantially to the entropy, while they do not contribute to the maximum likelihood estimator of entropy, which replaces probabilities by the observed frequencies. Bayesian estimators overcome this obstacle by introducing a model of the low-probability tail of the probability distribution. Which statistical features of the observed data determine the model of the tail, and hence the output of such estimators, remains unclear. Here we show that well-known entropy estimators for probability distributions on discrete state spaces model the structure of the low-probability tail based largely on a few statistics of the data: the sample size, the maximum likelihood estimate, the number of coincidences among the samples, and the dispersion of the coincidences. We derive approximate analytical entropy estimators for undersampled distributions based on these statistics, and we use the results to propose an intuitive understanding of how the Bayesian entropy estimators work.

Multidimensional specialization and generalization are pervasive in soil prokaryotes.

Habitat specialization underpins biological processes from species distributions to speciation. However, organisms are often described as specialists or generalists based on a single niche axis, despite facing complex, multidimensional environments. Here, we analysed 236 environmental soil microbiomes across the United States and demonstrate that 90% of >1,200 prokaryotes followed one of two trajectories: specialization on all niche axes (multidimensional specialization) or generalization on all axes (multidimensional generalization). We then documented that this pervasive multidimensional specialization/generalization had many ecological and evolutionary consequences. First, multidimensional specialization and generalization are highly conserved with very few transitions between these two trajectories. Second, multidimensional generalists dominated communities because they were 73 times more abundant than specialists. Lastly, multidimensional specialists played important roles in community structure with ~220% more connections in microbiome networks. These results indicate that multidimensional generalization and specialization are evolutionarily stable with multidimensional generalists supporting larger populations and multidimensional specialists playing important roles within communities, probably stemming from their overrepresentation among pollutant detoxifiers and nutrient cyclers. Taken together, we demonstrate that the vast majority of soil prokaryotes are restricted to one of two multidimensional niche trajectories, multidimensional specialization or multidimensional generalization, which then has far-reaching consequences for evolutionary transitions, microbial dominance and community roles.

Organelle mapping in dendrites of human iPSC-derived neurons reveals dynamic functional dendritic Golgi structures.

Secretory pathways within dendrites of neurons have been proposed for local transport of newly synthesized proteins. However, little is known about the dynamics of the local secretory system and whether the organelles are transient or stable structures. Here, we quantify the spatial and dynamic behavior of dendritic Golgi and endosomes during differentiation of human neurons generated from induced pluripotent stem cells (iPSCs). In early neuronal development, before and during migration, the entire Golgi apparatus transiently translocates from the soma into dendrites. In mature neurons, dynamic Golgi elements, containing cis and trans cisternae, are transported from the soma along dendrites, in an actin-dependent process. Dendritic Golgi outposts are dynamic and display bidirectional movement. Similar structures were observed in cerebral organoids. Using the retention using selective hooks (RUSH) system, Golgi resident proteins are transported efficiently into Golgi outposts from the endoplasmic reticulum. This study reveals dynamic, functional Golgi structures in dendrites and a spatial map for investigating dendrite trafficking in human neurons.

A village in a dish model system for population-scale hiPSC studies.

The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and different conditions. Human induced pluripotent stem cells are uniquely suited to study these context-dependent effects but cell lines from hundreds or thousands of individuals are required. Village cultures, where multiple induced pluripotent stem lines are cultured and differentiated in a single dish, provide an elegant solution for scaling induced pluripotent stem experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned to an induced pluripotent stem line using single-cell sequencing and illustrating that the genetic, epigenetic or induced pluripotent stem line-specific effects explain a large percentage of gene expression variation for many genes. We demonstrate that village methods can effectively detect induced pluripotent stem line-specific effects, including sensitive dynamics of cell states.

A sporadic Alzheimer's blood-brain barrier model for developing ultrasound-mediated delivery of Aducanumab and anti-Tau antibodies.

Rationale: The blood-brain barrier (BBB) is a major impediment to therapeutic intracranial drug delivery for the treatment of neurodegenerative diseases, including Alzheimer's disease (AD). Focused ultrasound applied together with microbubbles (FUS+MB) is a novel technique to transiently open the BBB and increase drug delivery. Evidence suggests that FUS+MB is safe, however, the effects of FUS+MB on human BBB cells, especially in the context of AD, remain sparsely investigated. In addition, there currently are no cell platforms to test for FUS+MB-mediated drug delivery. Methods: Here we generated BBB cells (induced brain endothelial-like cells (iBECs) and astrocytes (iAstrocytes)) from apolipoprotein E gene allele E4 (APOE4, high sporadic AD risk) and allele E3 (APOE3, lower AD risk) carrying patient-derived induced pluripotent stem cells (iPSCs). We established mono- and co-culture models of human sporadic AD and control BBB cells to investigate the effects of FUS+MB on BBB cell phenotype and to screen for the delivery of two potentially therapeutic AD antibodies, an Aducanumab-analogue (AduhelmTM; anti-amyloid-ß) and a novel anti-Tau antibody, RNF5. We then developed a novel hydrogel-based 2.5D BBB model as a step towards a more physiologically relevant FUS+MB drug delivery platform. Results: When compared to untreated cells, the delivery of Aducanumab-analogue and RNF5 was significantly increased (up to 1.73 fold), across the Transwell-based BBB models following FUS+MB treatment. Our results also demonstrated the safety of FUS+MB indicated by minimal changes in iBEC transcriptome as well as little or no changes in iBEC or iAstrocyte viability and inflammatory responses within the first 24 h post FUS+MB. Furthermore, we demonstrated successful iBEC barrier formation in our novel 2.5D hydrogel-based BBB model with significantly increased delivery (1.4 fold) of Aducanumab-analogue following FUS+MB. Conclusion: Our results demonstrate a robust and reproducible approach to utilize patient cells for FUS+MB-mediated drug delivery screening in vitro. With such a cell platform for FUS+MB research previously not reported, it has the potential to identify novel FUS+MB-deliverable drugs as well as screen for cell- and patient-specific effects of FUS+MB, accelerating the use of FUS+MB as a therapeutic modality in AD.

Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration.

There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.

Microglial amyloid beta clearance is driven by PIEZO1 channels.

BACKGROUND: Microglia are the endogenous immune cells of the brain and act as sensors of pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimer's disease (AD), toxic amyloid beta (Aß) accumulates in the brain and forms stiff plaques. In late-onset AD accounting for 95% of all cases, this is thought to be due to reduced clearance of Aß. Human genome-wide association studies and animal models suggest that reduced clearance results from aberrant function of microglia. While the impact of neurochemical pathways on microglia had been broadly studied, mechanical receptors regulating microglial functions remain largely unexplored. METHODS: Here we showed that a mechanotransduction ion channel, PIEZO1, is expressed and functional in human and mouse microglia. We used a small molecule agonist, Yoda1, to study how activation of PIEZO1 affects AD-related functions in human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGL) under controlled laboratory experiments. Cell survival, metabolism, phagocytosis and lysosomal activity were assessed using real-time functional assays. To evaluate the effect of activation of PIEZO1 in vivo, 5-month-old 5xFAD male mice were infused daily with Yoda1 for two weeks through intracranial cannulas. Microglial Iba1 expression and Aß pathology were quantified with immunohistochemistry and confocal microscopy. Published human and mouse AD datasets were used for in-depth analysis of PIEZO1 gene expression and related pathways in microglial subpopulations. RESULTS: We show that PIEZO1 orchestrates Aß clearance by enhancing microglial survival, phagocytosis, and lysosomal activity. Aß inhibited PIEZO1-mediated calcium transients, whereas activation of PIEZO1 with a selective agonist, Yoda1, improved microglial phagocytosis resulting in Aß clearance both in human and mouse models of AD. Moreover, PIEZO1 expression was associated with a unique microglial transcriptional phenotype in AD as indicated by assessment of cellular metabolism, and human and mouse single-cell datasets. CONCLUSION: These results indicate that the compromised function of microglia in AD could be improved by controlled activation of PIEZO1 channels resulting in alleviated Aß burden. Pharmacological regulation of these mechanoreceptors in microglia could represent a novel therapeutic paradigm for AD.

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries.

The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein's function based on its sequence, we still do not understand how to accurately predict an organism's behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, we used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.

Inferring a Property of a Large System from a Small Number of Samples.

Inferring the value of a property of a large stochastic system is a difficult task when the number of samples is insufficient to reliably estimate the probability distribution. The Bayesian estimator of the property of interest requires the knowledge of the prior distribution, and in many situations, it is not clear which prior should be used. Several estimators have been developed so far in which the proposed prior us individually tailored for each property of interest; such is the case, for example, for the entropy, the amount of mutual information, or the correlation between pairs of variables. In this paper, we propose a general framework to select priors that is valid for arbitrary properties. We first demonstrate that only certain aspects of the prior distribution actually affect the inference process. We then expand the sought prior as a linear combination of a one-dimensional family of indexed priors, each of which is obtained through a maximum entropy approach with constrained mean values of the property under study. In many cases of interest, only one or very few components of the expansion turn out to contribute to the Bayesian estimator, so it is often valid to only keep a single component. The relevant component is selected by the data, so no handcrafted priors are required. We test the performance of this approximation with a few paradigmatic examples and show that it performs well in comparison to the ad-hoc methods previously proposed in the literature. Our method highlights the connection between Bayesian inference and equilibrium statistical mechanics, since the most relevant component of the expansion can be argued to be that with the right temperature.

Culture Variabilities of Human iPSC-Derived Cerebral Organoids Are a Major Issue for the Modelling of Phenotypes Observed in Alzheimer's Disease.

Apolipoprotein E (APOE) is the most important susceptibility gene for late onset of Alzheimer's disease (AD), with the presence of APOE-ε4 associated with increased risk of developing AD. Here, we reprogrammed human fibroblasts from individuals with different APOE-ε genotypes into induced pluripotent stem cells (iPSCs), and generated isogenic lines with different APOE profiles. Following characterisation of the newly established iPSC lines, we used an unguided/unpatterning differentiation method to generate six-month-old cerebral organoids from all iPSC lines to assess the suitability of this in vitro system to measure APOE, ß amyloid, and Tau phosphorylation levels. We identified variabilities in the organoids' cell composition between cell lines, and between batches of differentiation for each cell line. We observed more homogenous cerebral organoids, and similar levels of APOE, ß amyloid, and Tau when using the CRISPR-edited APOE isogenic lines, with the exception of one site of Tau phosphorylation which was higher in the APOE-ε4/ε4 organoids. These data describe that pathological hallmarks of AD are observed in cerebral organoids, and that their variation is mainly independent of the APOE-ε status of the cells, but associated with the high variability of cerebral organoid differentiation. It demonstrates that the cell-line-to-cell-line and batch-to-batch variabilities need to be considered when using cerebral organoids.

Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma.

To assess the transcriptomic profile of disease-specific cell populations, fibroblasts from patients with primary open-angle glaucoma (POAG) were reprogrammed into induced pluripotent stem cells (iPSCs) before being differentiated into retinal organoids and compared with those from healthy individuals. We performed single-cell RNA sequencing of a total of 247,520 cells and identified cluster-specific molecular signatures. Comparing the gene expression profile between cases and controls, we identified novel genetic associations for this blinding disease. Expression quantitative trait mapping identified a total of 4,443 significant loci across all cell types, 312 of which are specific to the retinal ganglion cell subpopulations, which ultimately degenerate in POAG. Transcriptome-wide association analysis identified genes at loci previously associated with POAG, and analysis, conditional on disease status, implicated 97 statistically significant retinal ganglion cell-specific expression quantitative trait loci. This work highlights the power of large-scale iPSC studies to uncover context-specific profiles for a genetically complex disease.

A framework for studying behavioral evolution by reconstructing ancestral repertoires.

Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual's behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the mechanistic basis of behavioral evolution.

Automation of Organoid Cultures: Current Protocols and Applications.

Organoids are three-dimensional, functional structures that mimic in vivo organs. They offer new opportunities for the modeling of cancer and infectious and rare hereditary diseases. Furthermore, the advent of organoid biobanks opens new avenues for drug screening in a personalized fashion and holds much promise for personalized regenerative medicine. Thus, there is a need for reproducible, large-scale organoid generation with minimal variability, making manual approaches impracticable. Here, we review the current use of automation in organoid culture and analysis, using cerebral and retinal organoids as illustrations of current applications. An increased demand for automated organoid platforms is anticipated.

Generation of a gene-corrected human isogenic iPSC line from an Alzheimer's disease iPSC line carrying the London mutation in APP (V717I).

We report the genome-editing of an existing iPSC line carrying the London mutation in APP (V717I) into an iPSC line in which the pathogenic mutation was corrected. The resulting isogenic iPSC line maintained pluripotent stem cell morphology, a normal karyotype, expression of pluripotency markers and the ability to differentiate into the three germ-layers in vitro.

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells.

BACKGROUND: The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. RESULTS: Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. CONCLUSIONS: This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.

Environmental stress destabilizes microbial networks.

Environmental stress is increasing worldwide, yet we lack a clear picture of how stress disrupts the stability of microbial communities and the ecosystem services they provide. Here, we present the first evidence that naturally-occurring microbiomes display network properties characteristic of unstable communities when under persistent stress. By assessing changes in diversity and structure of soil microbiomes along 40 replicate stress gradients (elevation/water availability gradients) in the Florida scrub ecosystem, we show that: (1) prokaryotic and fungal diversity decline in high stress, and (2) two network properties of stable microbial communities-modularity and negative:positive cohesion-have a clear negative relationship with environmental stress, explaining 51-78% of their variation. Interestingly, pathogenic taxa/functional guilds decreased in relative abundance along the stress gradient, while oligotrophs and mutualists increased, suggesting that the shift in negative:positive cohesion could result from decreasing negative:positive biotic interactions consistent with the predictions of the Stress Gradient Hypothesis. Given the crucial role microbiomes play in ecosystem functions, our results suggest that, by limiting the compartmentalization of microbial associations and creating communities dominated by positive associations, increasing stress in the Anthropocene could destabilize microbiomes and undermine their ecosystem services.