Quantifying Target Occupancy of Small Molecules Within Living Cells.

The binding affinity and kinetics of target engagement are fundamental to establishing structure-activity relationships (SARs) for prospective therapeutic agents. Enhancing these binding parameters for operative targets, while minimizing binding to off-target sites, can translate to improved drug efficacy and a widened therapeutic window. Compound activity is typically assessed through modulation of an observed phenotype in cultured cells. Quantifying the corresponding binding properties under common cellular conditions can provide more meaningful interpretation of the cellular SAR analysis. Consequently, methods for assessing drug binding in living cells have advanced and are now integral to medicinal chemistry workflows. In this review, we survey key technological advancements that support quantitative assessments of target occupancy in cultured cells, emphasizing generalizable methodologies able to deliver analytical precision that heretofore required reductionist biochemical approaches.

Revealing operando surface defect-dependent electrocatalytic performance of Pt at the subparticle level.

Understanding the operando defect-tuning performance of catalysts is critical to establish an accurate structure-activity relationship of a catalyst. Here, with the tool of single-molecule super-resolution fluorescence microscopy, by imaging intermediate CO formation/oxidation during the methanol oxidation reaction process on individual defective Pt nanotubes, we reveal that the fresh Pt ends with more defects are more active and anti-CO poisoning than fresh center areas with less defects, while such difference could be reversed after catalysis-induced step-by-step creation of more defects on the Pt surface. Further experimental results reveal an operando volcano relationship between the catalytic performance (activity and anti-CO ability) and the fine-tuned defect density. Systematic DFT calculations indicate that such an operando volcano relationship could be attributed to the defect-dependent transition state free energy and the accelerated surface reconstructing of defects or Pt-atom moving driven by the adsorption of the CO intermediate. These insights deepen our understanding to the operando defect-driven catalysis at single-molecule and subparticle level, which is able to help the design of highly efficient defect-based catalysts.

Dissection of the structure-function relationship of Nav channels.

Voltage-gated sodium channels (Nav) undergo conformational shifts in response to membrane potential changes, a mechanism known as the electromechanical coupling. To delineate the structure-function relationship of human Nav channels, we have performed systematic structural analysis using human Nav1.7 as a prototype. Guided by the structural differences between wild-type (WT) Nav1.7 and an eleven mutation-containing variant, designated Nav1.7-M11, we generated three additional intermediate mutants and solved their structures at overall resolutions of 2.9-3.4 Å. The mutant with nine-point mutations in the pore domain (PD), named Nav1.7-M9, has a reduced cavity volume and a sealed gate, with all voltage-sensing domains (VSDs) remaining up. Structural comparison of WT and Nav1.7-M9 pinpoints two residues that may be critical to the tightening of the PD. However, the variant containing these two mutations, Nav1.7-M2, or even in combination with two additional mutations in the VSDs, named Nav1.7-M4, failed to tighten the PD. Our structural analysis reveals a tendency of PD contraction correlated with the right shift of the static inactivation I-V curves. We predict that the channel in the resting state should have a "tight" PD with down VSDs.

Unraveling the molecular mechanism for enhanced gas adsorption in mixed-metal MOFs via solid-state NMR spectroscopy.

The incorporation of multiple metal ions in metal-organic frameworks (MOFs) through one-pot synthesis can induce unique properties originating from specific atomic-scale spatial apportionment, but the extraction of this crucial information poses challenges. Herein, nondestructive solid-state NMR spectroscopy was used to discern the atomic-scale metal apportionment in a series of bulk Mg1-xCox-MOF-74 samples via identification and quantification of eight distinct arrangements of Mg/Co ions labeled with a 13C-carboxylate, relative to Co content. Due to the structural characteristics of metal-oxygen chains, the number of metal permutations is infinite for Mg1-xCox-MOF-74, making the resolution of atomic-scale metal apportionment particularly challenging. The results were then employed in density functional theory calculations to unravel the molecular mechanism underlying the macroscopic adsorption properties of several industrially significant gases. It is found that the incorporation of weak adsorption sites (Mg2+ for CO and Co2+ for CO2 adsorption) into the MOF structure counterintuitively boosts the gas adsorption energy on strong sites (Co2+ for CO and Mg2+ for CO2 adsorption). Such effect is significant even for Co2+ remote from Mg2+ in the metal-oxygen chain, resulting in a greater enhancement of CO adsorption across a broad composition range, while the enhancement of CO2 adsorption is restricted to Mg2+ with adjacent Co2+. Dynamic breakthrough measurements unambiguously verified the trend in gas adsorption as a function of metal composition. This research thus illuminates the interplay between atomic-scale structures and macroscopic gas adsorption properties in mixed-metal MOFs and derived materials, paving the way for developing superior functional materials.

The genetic architecture of behavioral canalization.

Behaviors are components of fitness and contribute to adaptive evolution. Behaviors represent the interactions of an organism with its environment, yet innate behaviors display robustness in the face of environmental change, which we refer to as 'behavioral canalization'. We hypothesize that positive selection of hub genes of genetic networks stabilizes the genetic architecture for innate behaviors by reducing variation in the expression of interconnected network genes. Robustness of these stabilized networks would be protected from deleterious mutations by purifying selection or suppressing epistasis. We propose that, together with newly emerging favorable mutations, epistatically suppressed mutations can generate a reservoir of cryptic genetic variation that could give rise to decanalization when genetic backgrounds or environmental conditions change to allow behavioral adaptation.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits.

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

Triadic influence as a proxy for compatibility in social relationships.

Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric-the "triadic influence"-that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks-functions of the triadic influence and the personalities of the students-control the evolution of real social networks, opening an avenue for the quantitative study of these systems.

Machine learning of spectra-property relationship for imperfect and small chemistry data.

Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.

Hemodynamic transient and functional connectivity follow structural connectivity and cell type over the brain hierarchy.

The neural circuit of the brain is organized as a hierarchy of functional units with wide-ranging connections that support information flow and functional connectivity. Studies using MRI indicate a moderate coupling between structural and functional connectivity at the system level. However, how do connections of different directions (feedforward and feedback) and regions with different excitatory and inhibitory (E/I) neurons shape the hemodynamic activity and functional connectivity over the hierarchy are unknown. Here, we used functional MRI to detect optogenetic-evoked and resting-state activities over a somatosensory pathway in the mouse brain in relation to axonal projection and E/I distribution. Using a highly sensitive ultrafast imaging, we identified extensive activation in regions up to the third order of axonal projections following optogenetic excitation of the ventral posteriomedial nucleus of the thalamus. The evoked response and functional connectivity correlated with feedforward projections more than feedback projections and weakened with the hierarchy. The hemodynamic response exhibited regional and hierarchical differences, with slower and more variable responses in high-order areas and bipolar response predominantly in the contralateral cortex. Electrophysiological recordings suggest that these reflect differences in neural activity rather than neurovascular coupling. Importantly, the positive and negative parts of the hemodynamic response correlated with E/I neuronal densities, respectively. Furthermore, resting-state functional connectivity was more associated with E/I distribution, whereas stimulus-evoked effective connectivity followed structural wiring. These findings indicate that the structure-function relationship is projection-, cell-type- and hierarchy-dependent. Hemodynamic transients could reflect E/I activity and the increased complexity of hierarchical processing.

A genealogical estimate of genetic relationships.

The application of genetic relationships among individuals, characterized by a genetic relationship matrix (GRM), has far-reaching effects in human genetics. However, the current standard to calculate the GRM treats linked markers as independent and does not explicitly model the underlying genealogical history of the study sample. Here, we propose a coalescent-informed framework, namely the expected GRM (eGRM), to infer the expected relatedness between pairs of individuals given an ancestral recombination graph (ARG) of the sample. Through extensive simulations, we show that the eGRM is an unbiased estimate of latent pairwise genome-wide relatedness and is robust when computed with ARG inferred from incomplete genetic data. As a result, the eGRM better captures the structure of a population than the canonical GRM, even when using the same genetic information. More importantly, our framework allows a principled approach to estimate the eGRM at different time depths of the ARG, thereby revealing the time-varying nature of population structure in a sample. When applied to SNP array genotypes from a population sample from Northern and Eastern Finland, we find that clustering analysis with the eGRM reveals population structure driven by subpopulations that would not be apparent via the canonical GRM and that temporally the population model is consistent with recent divergence and expansion. Taken together, our proposed eGRM provides a robust tree-centric estimate of relatedness with wide application to genetic studies.

Optimization of quenched fluorescent peptide substrates of SARS-CoV-2 3CLpro main protease (Mpro) from proteomic identification of P6-P6' active site specificity.

SARS-CoV-2 3C-like main protease (3CLpro) is essential for protein excision from the viral polyprotein. 3CLpro inhibitor drug development to block SARS-CoV-2 replication focuses on the catalytic non-prime (P) side for specificity and potency, but the importance of the prime (P') side in substrate specificity and for drug development remains underappreciated. We determined the P6-P6' specificity for 3CLpro from >800 cleavage sites that we identified using Proteomic Identification of Cleavage site Specificity (PICS). Cleavage occurred after the canonical P1-Gln and non-canonical P1-His and P1-Met residues. Moreover, P3 showed a preference for Arg/Lys and P3' for His. Essential H-bonds between the N-terminal Ser1 of protomer-B in 3CLpro dimers form with P1-His, but not with P1-Met. Nonetheless, cleavage occurs at P1-Met456 in native MAP4K5. Elevated reactive oxygen species in SARS-CoV-2 infection oxidize methionines. Molecular simulations revealed P1-MetOX forms an H-bond with Ser1 and notably, strong positive cooperativity between P1-Met with P3'-His was revealed, which enhanced peptide-cleavage rates. The highly plastic S3' subsite accommodates P3'-His that displays stabilizing backbone H-bonds with Thr25 lying central in a "'threonine trio" (Thr24-Thr25-Thr26) in the P'-binding domain I. Molecular docking simulations unveiled structure-activity relationships impacting 3CLpro-substrate interactions, and the role of these structural determinants was confirmed by MALDI-TOF-MS cleavage assays of P1'- and P3'-positional scanning peptide libraries carrying a 2nd optimal cut-site as an internal positive control. These data informed the design of two new and highly soluble 3CLproquenched-fluorescent peptide substrates for improved FRET monitoring of 3CLpro activity with 15× improved sensitivity over current assays.IMPORTANCEFrom global proteomics identification of >800 cleavage sites, we characterized the P6-P6' active site specificity of SARS-CoV-2 3CLpro using proteome-derived peptide library screens, molecular modeling simulations, and focussed positional peptide libraries. In P1', we show that alanine and serine are cleaved 3× faster than glycine and the hydrophobic small amino acids Leu, Ile, or Val prevent cleavage of otherwise optimal non-prime sequences. In characterizing non-canonical non-prime P1 specificity, we explored the unusual P1-Met specificity, discovering enhanced cleavage when in the oxidized state (P1-MetOX). We unveiled unexpected amino acid cooperativity at P1-Met with P3'-His and noncanonical P1-His with P2-Phe, and the importance of the threonine trio (Thr24-Thr25-Thr26) in the prime side binding domain I in defining prime side binding in SARS-CoV-2 3CLpro. From these analyses, we rationally designed quenched-fluorescence natural amino acid peptide substrates with >15× improved sensitivity and high peptide solubility, facilitating handling and application for screening of new antiviral drugs.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet.

Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging. Computational methods have been developed to predict surface protein expression using gene expression information such as from single-cell RNA sequencing (scRNA-seq) data. Existing methods however are computationally demanding and lack the interpretability to reveal underlying biological processes. We propose CrossmodalNet, an interpretable machine learning model, to predict surface protein expression from scRNA-seq data. Our model with a customized adaptive loss accurately predicts surface protein abundances. When samples from multiple time points are given, our model encodes temporal information into an easy-to-interpret time embedding to make prediction in a time-point-specific manner, and is able to uncover noise-free causal gene-protein relationships. Using three publicly available time-resolved CITE-seq data sets, we validate the performance of our model by comparing it with benchmarking methods and evaluate its interpretability. Together, we show that our method accurately and interpretably profiles surface protein expression using scRNA-seq data, thereby expanding the capacity of CITE-seq experiments for investigating molecular mechanisms involving surface proteins.

A novel strategy for designing the magic shotguns for distantly related target pairs.

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

Application of mendelian randomization in ocular diseases: a review.

Ocular disorders can significantly lower patients' quality of life and impose an economic burden on families and society. However, for the majority of these diseases, their prevalence and mechanisms are yet unknown, making prevention, management, and therapy challenging. Although connections between exposure factors and diseases can be drawn through observational research, it is challenging to rule out the interference of confounding variables and reverse causation. Mendelian Randomization (MR), a method of research that combines genetics and epidemiology, has its advantage to solve this problem and thus has been extensively utilized in the etiological study of ophthalmic diseases. This paper reviews the implementation of MR in the research of ocular diseases and provides approaches for the investigation of related mechanisms as well as the intervention strategies.

Diosgenyl glucosamine conjugates increase pro-apoptotic and selective activities in cancer cell lines.

BACKGROUND INFORMATION: Antiproliferative and apoptotic activities have been attributed to the phytosteroid diosgenin ((25R)-spirost-5-en-3ß-ol; 1). It is known that combining glucose with two rhamnoses (the chacotrioside framework) linked to diosgenin increases its apoptotic activity. However, the effects of diosgenin glucosamine glycosides on different cancer cell types and cell death have not been entirely explored. RESULTS: This study reports the antiproliferative, cytotoxic, and apoptotic activities of diosgenin and its glycosylated derivative ((25R)-spirost-5-en-3ß-yl ß-D-glucopyranoside; 2). It also explores the effects of two diosgenin glucosamine derivates, diosgenin 2-acetamido-2-deoxy-ß-D-glucopyranoside (3), and diosgenin 2-amino-2-deoxy-ß-D-glucopyranoside hydrochloride (4), on different cancer cell lines. We found that all the compounds affected proliferative activity with minimal toxicity. In addition, all cancer cell lines showed morphological and biochemical characteristics corresponding to an apoptotic process. Apoptotic cell death was higher in all cell lines treated with compounds 2, 3 and 4 than in those treated with diosgenin. Moreover, compounds 3 and 4 induced apoptosis better than compounds 1 and 2. These results suggest that combining glucosamine with modified glucosamine attached to diosgenin has a greater apoptotic effect than diosgenin or its glycosylated derivative (compound 2). Furthermore, diosgenin and the abovementioned glycosides had a selective effect on tumour cells since the proliferative capacity of human lymphocytes, keratinocytes (HaCaT) and epithelial cells (CCD841) was not significantly affected. CONCLUSIONS: Altogether, these results demonstrate that diosgenin glucosamine compounds exert an antiproliferative effect on cancer cell lines and induce apoptotic effects more efficiently than diosgenin alone without affecting non-tumour cells. SIGNIFICANCE: This study evidences the pro-apoptotic and selective activities of diosgenyl glucosamine compounds in cancer cell lines.

Who takes the lead in consumer choices within romantic relationships: the evidence from electroencephalography hyperscanning and granger causality analysis.

In real-life scenarios, joint consumption is common, particularly influenced by social relationships such as romantic ones. However, how romantic relationships affect consumption decisions and determine dominance remains unclear. This study employs electroencephalography hyperscanning to examine the neural dynamics of couples during joint-consumption decisions. Results show that couples, compared to friends and strangers, prefer healthier foods, while friends have significantly faster reaction times when selecting food. Time-frequency analysis indicates that couples exhibit significantly higher theta power, reflecting deeper emotional and cognitive involvement. Strangers show greater beta1 power, indicating increased cognitive effort and alertness due to unfamiliarity. Friends demonstrate higher alpha2 power when choosing unhealthy foods, suggesting increased cognitive inhibition. Inter-brain phase synchrony analysis reveals that couples display significantly higher inter-brain phase synchrony in the beta1 and theta bands across the frontal-central, parietal, and occipital regions, indicating more coordinated cognitive processing and stronger emotional bonds. Females in couples may be more influenced by emotions during consumption decisions, with detailed sensory information processing, while males exhibit higher cognitive control and spatial integration. Granger-causality analysis shows a pattern of male dominance and female dependence in joint consumption within romantic relationships. This study highlights gender-related neural synchronous patterns during joint consumption among couples, providing insights for further research in consumer decision-making.

Disentangling direct from indirect relationships in association networks.

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

Connection between water's dynamical and structural properties: Insights from ab initio simulations.

SignificanceFirst-principles calculations, which explicitly account for the electronic structure of matter, can shed light on the molecular structure and dynamics of water in its supercooled state. In this work, we use density functional theory, which relies on a functional to describe electronic exchange and correlations, to evaluate which functional best describes the temperature evolution of bulk water transport coefficients. We also assess the validity of the Stokes-Einstein relation for all the functionals in the temperature range studied, and explore the link between structure and dynamics. Based on these results, we show how transport coefficients can be computed from structural descriptors, which require shorter simulation times to converge, and we point toward strategies to develop better functionals.

Spatial turnover of soil viral populations and genotypes overlain by cohesive responses to moisture in grasslands.

Viruses shape microbial communities, food web dynamics, and carbon and nutrient cycling in diverse ecosystems. However, little is known about the patterns and drivers of viral community composition, particularly in soil, precluding a predictive understanding of viral impacts on terrestrial habitats. To investigate soil viral community assembly processes, here we analyzed 43 soil viromes from a rainfall manipulation experiment in a Mediterranean grassland in California. We identified 5,315 viral populations (viral operational taxonomic units [vOTUs] with a representative sequence ≥10 kbp) and found that viral community composition exhibited a highly significant distance-decay relationship within the 200-m2 field site. This pattern was recapitulated by the intrapopulation microheterogeneity trends of prevalent vOTUs (detected in ≥90% of the viromes), which tended to exhibit negative correlations between spatial distance and the genomic similarity of their predominant allelic variants. Although significant spatial structuring was also observed in the bacterial and archaeal communities, the signal was dampened relative to the viromes, suggesting differences in local assembly drivers for viruses and prokaryotes and/or differences in the temporal scales captured by viromes and total DNA. Despite the overwhelming spatial signal, evidence for environmental filtering was revealed in a protein-sharing network analysis, wherein a group of related vOTUs predicted to infect actinobacteria was shown to be significantly enriched in low-moisture samples distributed throughout the field. Overall, our results indicate a highly diverse, dynamic, active, and spatially structured soil virosphere capable of rapid responses to changing environmental conditions.