Fate Mapping and Quantitation of Hematopoiesis In Vivo.

Hematopoietic stem cells (HSCs) and downstream progenitors have long been studied based on phenotype, cell purification, proliferation, and transplantation into myeloablated recipients. These experiments, complemented by data on expression profiles, mouse mutants, and humans with hematopoietic defects, are the foundation for the current hematopoietic differentiation tree. However, there are fundamental gaps in our knowledge of the quantitative and qualitative operation of the HSC/progenitor system under physiological and pathological conditions in vivo. The hallmarks of HSCs, self-renewal and multipotency, are observed in in vitro assays and cell transplantation experiments; however, the extent to which these features occur naturally in HSCs and progenitors remains uncertain. We focus here on work that strives to address these unresolved questions, with emphasis on fate mapping and modeling of the hematopoietic flow from stem cells toward myeloid and lymphoid lineages during development and adult life.

A "data sharing trust" model for rapid, collaborative science.

Complex datasets provide opportunities for discoveries beyond their initial scope. Effective and rapid data sharing and management practices are crucial to realize this potential; however, they are harder to implement than post-publication access. Here, we introduce the concept of a "data sharing trust" to maximize the value of large datasets.

Global Trends in Marine Plankton Diversity across Kingdoms of Life.

The ocean is home to myriad small planktonic organisms that underpin the functioning of marine ecosystems. However, their spatial patterns of diversity and the underlying drivers remain poorly known, precluding projections of their responses to global changes. Here we investigate the latitudinal gradients and global predictors of plankton diversity across archaea, bacteria, eukaryotes, and major virus clades using both molecular and imaging data from Tara Oceans. We show a decline of diversity for most planktonic groups toward the poles, mainly driven by decreasing ocean temperatures. Projections into the future suggest that severe warming of the surface ocean by the end of the 21st century could lead to tropicalization of the diversity of most planktonic groups in temperate and polar regions. These changes may have multiple consequences for marine ecosystem functioning and services and are expected to be particularly significant in key areas for carbon sequestration, fisheries, and marine conservation. VIDEO ABSTRACT.

Trans Effects on Gene Expression Can Drive Omnigenic Inheritance.

Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits.

Repressive Gene Regulation Synchronizes Development with Cellular Metabolism.

Metabolic conditions affect the developmental tempo of animals. Developmental gene regulatory networks (GRNs) must therefore synchronize their dynamics with a variable timescale. We find that layered repression of genes couples GRN output with variable metabolism. When repressors of transcription or mRNA and protein stability are lost, fewer errors in Drosophila development occur when metabolism is lowered. We demonstrate the universality of this phenomenon by eliminating the entire microRNA family of repressors and find that development to maturity can be largely rescued when metabolism is reduced. Using a mathematical model that replicates GRN dynamics, we find that lowering metabolism suppresses the emergence of developmental errors by curtailing the influence of auxiliary repressors on GRN output. We experimentally show that gene expression dynamics are less affected by loss of repressors when metabolism is reduced. Thus, layered repression provides robustness through error suppression and may provide an evolutionary route to a shorter reproductive cycle.

Combinatorial Genetics Reveals a Scaling Law for the Effects of Mutations on Splicing.

Despite a wealth of molecular knowledge, quantitative laws for accurate prediction of biological phenomena remain rare. Alternative pre-mRNA splicing is an important regulated step in gene expression frequently perturbed in human disease. To understand the combined effects of mutations during evolution, we quantified the effects of all possible combinations of exonic mutations accumulated during the emergence of an alternatively spliced human exon. This revealed that mutation effects scale non-monotonically with the inclusion level of an exon, with each mutation having maximum effect at a predictable intermediate inclusion level. This scaling is observed genome-wide for cis and trans perturbations of splicing, including for natural and disease-associated variants. Mathematical modeling suggests that competition between alternative splice sites is sufficient to cause this non-linearity in the genotype-phenotype map. Combining the global scaling law with specific pairwise interactions between neighboring mutations allows accurate prediction of the effects of complex genotype changes involving >10 mutations.

Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity.

Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts" of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed "noise" in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell's ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Reconstructing the Deep Population History of Central and South America.

We report genome-wide ancient DNA from 49 individuals forming four parallel time transects in Belize, Brazil, the Central Andes, and the Southern Cone, each dating to at least ∼9,000 years ago. The common ancestral population radiated rapidly from just one of the two early branches that contributed to Native Americans today. We document two previously unappreciated streams of gene flow between North and South America. One affected the Central Andes by ∼4,200 years ago, while the other explains an affinity between the oldest North American genome associated with the Clovis culture and the oldest Central and South Americans from Chile, Brazil, and Belize. However, this was not the primary source for later South Americans, as the other ancient individuals derive from lineages without specific affinity to the Clovis-associated genome, suggesting a population replacement that began at least 9,000 years ago and was followed by substantial population continuity in multiple regions.

Anti-CRISPR Phages Cooperate to Overcome CRISPR-Cas Immunity.

Some phages encode anti-CRISPR (acr) genes, which antagonize bacterial CRISPR-Cas immune systems by binding components of its machinery, but it is less clear how deployment of these acr genes impacts phage replication and epidemiology. Here, we demonstrate that bacteria with CRISPR-Cas resistance are still partially immune to Acr-encoding phage. As a consequence, Acr-phages often need to cooperate in order to overcome CRISPR resistance, with a first phage blocking the host CRISPR-Cas immune system to allow a second Acr-phage to successfully replicate. This cooperation leads to epidemiological tipping points in which the initial density of Acr-phage tips the balance from phage extinction to a phage epidemic. Furthermore, both higher levels of CRISPR-Cas immunity and weaker Acr activities shift the tipping points toward higher initial phage densities. Collectively, these data help elucidate how interactions between phage-encoded immune suppressors and the CRISPR systems they target shape bacteria-phage population dynamics.

Diverse Spatial Expression Patterns Emerge from Unified Kinetics of Transcriptional Bursting.

How transcriptional bursting relates to gene regulation is a central question that has persisted for more than a decade. Here, we measure nascent transcriptional activity in early Drosophila embryos and characterize the variability in absolute activity levels across expression boundaries. We demonstrate that boundary formation follows a common transcription principle: a single control parameter determines the distribution of transcriptional activity, regardless of gene identity, boundary position, or enhancer-promoter architecture. We infer the underlying bursting kinetics and identify the key regulatory parameter as the fraction of time a gene is in a transcriptionally active state. Unexpectedly, both the rate of polymerase initiation and the switching rates are tightly constrained across all expression levels, predicting synchronous patterning outcomes at all positions in the embryo. These results point to a shared simplicity underlying the apparently complex transcriptional processes of early embryonic patterning and indicate a path to general rules in transcriptional regulation.

Systematic Nanoscale Analysis of Endocytosis Links Efficient Vesicle Formation to Patterned Actin Nucleation.

Clathrin-mediated endocytosis is an essential cellular function in all eukaryotes that is driven by a self-assembled macromolecular machine of over 50 different proteins in tens to hundreds of copies. How these proteins are organized to produce endocytic vesicles with high precision and efficiency is not understood. Here, we developed high-throughput superresolution microscopy to reconstruct the nanoscale structural organization of 23 endocytic proteins from over 100,000 endocytic sites in yeast. We found that proteins assemble by radially ordered recruitment according to function. WASP family proteins form a circular nanoscale template on the membrane to spatially control actin nucleation during vesicle formation. Mathematical modeling of actin polymerization showed that this WASP nano-template optimizes force generation for membrane invagination and substantially increases the efficiency of endocytosis. Such nanoscale pre-patterning of actin nucleation may represent a general design principle for directional force generation in membrane remodeling processes such as during cell migration and division.

Polar Positioning of Phase-Separated Liquid Compartments in Cells Regulated by an mRNA Competition Mechanism.

P granules are non-membrane-bound RNA-protein compartments that are involved in germline development in C. elegans. They are liquids that condense at one end of the embryo by localized phase separation, driven by gradients of polarity proteins such as the mRNA-binding protein MEX-5. To probe how polarity proteins regulate phase separation, we combined biochemistry and theoretical modeling. We reconstitute P granule-like droplets in vitro using a single protein PGL-3. By combining in vitro reconstitution with measurements of intracellular concentrations, we show that competition between PGL-3 and MEX-5 for mRNA can regulate the formation of PGL-3 droplets. Using theory, we show that, in a MEX-5 gradient, this mRNA competition mechanism can drive a gradient of P granule assembly with similar spatial and temporal characteristics to P granule assembly in vivo. We conclude that gradients of polarity proteins can position RNP granules during development by using RNA competition to regulate local phase separation.

Models that matter.

Models, like the Central Dogma, are a core feature of many papers we publish, yet their utility can be undermined when they are either overly simplistic or overly complicated, and it can contribute to misunderstandings when key details or limitations are left out. In this editorial, we connect different versions of the Central Dogma and how we think about scientific models more generally.

Contribution of adaptive immunity to human COPD and experimental models of emphysema.

The pathophysiology of chronic obstructive pulmonary disease (COPD) and the undisputed role of innate immune cells in this condition have dominated the field in the basic research arena for many years. Recently, however, compelling data suggesting that adaptive immune cells may also contribute to the progressive nature of lung destruction associated with COPD in smokers have gained considerable attention. The histopathological changes in the lungs of smokers can be limited to the large or small airways, but alveolar loss leading to emphysema, which occurs in some individuals, remains its most significant and irreversible outcome. Critically, however, the question of why emphysema progresses in a subset of former smokers remained a mystery for many years. The recognition of activated and organized tertiary T- and B-lymphoid aggregates in emphysematous lungs provided the first clue that adaptive immune cells may play a crucial role in COPD pathophysiology. Based on these findings from human translational studies, experimental animal models of emphysema were used to determine the mechanisms through which smoke exposure initiates and orchestrates adaptive autoreactive inflammation in the lungs. These models have revealed that T helper (Th)1 and Th17 subsets promote a positive feedback loop that activates innate immune cells, confirming their role in emphysema pathogenesis. Results from genetic studies and immune-based discoveries have further provided strong evidence for autoimmunity induction in smokers with emphysema. These new findings offer a novel opportunity to explore the mechanisms underlying the inflammatory landscape in the COPD lung and offer insights for development of precision-based treatment to halt lung destruction.

Nitric oxide controls proliferation of Leishmania major by inhibiting the recruitment of permissive host cells.

Nitric oxide (NO) is an important antimicrobial effector but also prevents unnecessary tissue damage by shutting down the recruitment of monocyte-derived phagocytes. Intracellular pathogens such as Leishmania major can hijack these cells as a niche for replication. Thus, NO might exert containment by restricting the availability of the cellular niche required for efficient pathogen proliferation. However, such indirect modes of action remain to be established. By combining mathematical modeling with intravital 2-photon biosensors of pathogen viability and proliferation, we show that low L. major proliferation results not from direct NO impact on the pathogen but from reduced availability of proliferation-permissive host cells. Although inhibiting NO production increases recruitment of these cells, and thus pathogen proliferation, blocking cell recruitment uncouples the NO effect from pathogen proliferation. Therefore, NO fulfills two distinct functions for L. major containment: permitting direct killing and restricting the supply of proliferation-permissive host cells.

Understanding nucleic acid-ion interactions.

Ions surround nucleic acids in what is referred to as an ion atmosphere. As a result, the folding and dynamics of RNA and DNA and their complexes with proteins and with each other cannot be understood without a reasonably sophisticated appreciation of these ions' electrostatic interactions. However, the underlying behavior of the ion atmosphere follows physical rules that are distinct from the rules of site binding that biochemists are most familiar and comfortable with. The main goal of this review is to familiarize nucleic acid experimentalists with the physical concepts that underlie nucleic acid-ion interactions. Throughout, we provide practical strategies for interpreting and analyzing nucleic acid experiments that avoid pitfalls from oversimplified or incorrect models. We briefly review the status of theories that predict or simulate nucleic acid-ion interactions and experiments that test these theories. Finally, we describe opportunities for going beyond phenomenological fits to a next-generation, truly predictive understanding of nucleic acid-ion interactions.

Quorum Regulation via Nested Antagonistic Feedback Circuits Mediated by the Receptors CD28 and CTLA-4 Confers Robustness to T Cell Population Dynamics.

T cell responses upon infection display a remarkably reproducible pattern of expansion, contraction, and memory formation. If the robustness of this pattern builds entirely on signals derived from other cell types or if activated T cells themselves contribute to the orchestration of these population dynamics-akin to bacterial quorum regulation-is unclear. Here, we examined this question using time-lapse microscopy, genetic perturbation, bioinformatic predictions, and mathematical modeling. We found that ICAM-1-mediated cell clustering enabled CD8+ T cells to collectively regulate the balance between proliferation and apoptosis. Mechanistically, T cell expressed CD80 and CD86 interacted with the receptors CD28 and CTLA-4 on neighboring T cells; these interactions fed two nested antagonistic feedback circuits that regulated interleukin 2 production in a manner dependent on T cell density as confirmed by in vivo modulation of this network. Thus, CD8+ T cell-population-intrinsic mechanisms regulate cellular behavior, thereby promoting robustness of population dynamics.

Tropical Atlantic multidecadal variability is dominated by external forcing.

The tropical Atlantic climate is characterized by prominent and correlated multidecadal variability in Atlantic sea surface temperatures (SSTs), Sahel rainfall and hurricane activity1-4. Owing to uncertainties in both the models and the observations, the origin of the physical relationships among these systems has remained controversial3-7. Here we show that the cross-equatorial gradient in tropical Atlantic SSTs-largely driven by radiative perturbations associated with anthropogenic emissions and volcanic aerosols since 19503,7-is a key determinant of Atlantic hurricane formation and Sahel rainfall. The relationship is obscured in a large ensemble of CMIP6 Earth system models, because the models overestimate long-term trends for warming in the Northern Hemisphere relative to the Southern Hemisphere from around 1950 as well as associated changes in atmospheric circulation and rainfall. When the overestimated trends are removed, correlations between SSTs and Atlantic hurricane formation and Sahel rainfall emerge as a response to radiative forcing, especially since 1950 when anthropogenic aerosol forcing has been high. Our findings establish that the tropical Atlantic SST gradient is a stronger determinant of tropical impacts than SSTs across the entire North Atlantic, because the gradient is more physically connected to tropical impacts via local atmospheric circulations8. Our findings highlight that Atlantic hurricane activity and Sahel rainfall variations can be predicted from radiative forcing driven by anthropogenic emissions and volcanism, but firmer predictions are limited by the signal-to-noise paradox9-11 and uncertainty in future climate forcings.

Assembly theory explains and quantifies selection and evolution.

Scientists have grappled with reconciling biological evolution1,2 with the immutable laws of the Universe defined by physics. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary3-5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an 'object' on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.