Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics.

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

Prediction of efficiencies for diverse prime editing systems in multiple cell types.

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

High-precision coding in visual cortex.

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

A Structure-Informed Atlas of Human-Virus Interactions.

While knowledge of protein-protein interactions (PPIs) is critical for understanding virus-host relationships, limitations on the scalability of high-throughput methods have hampered their identification beyond a number of well-studied viruses. Here, we implement an in silico computational framework (pathogen host interactome prediction using structure similarity [P-HIPSTer]) that employs structural information to predict ∼282,000 pan viral-human PPIs with an experimental validation rate of ∼76%. In addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings: the discovery of shared and unique machinery employed across human-infecting viruses, a likely role for ZIKV-ESR1 interactions in modulating viral replication, the identification of PPIs that discriminate between human papilloma viruses (HPVs) with high and low oncogenic potential, and a structure-enabled history of evolutionary selective pressure imposed on the human proteome. Further, P-HIPSTer enables discovery of previously unappreciated cellular circuits that act on human-infecting viruses and provides insight into experimentally intractable viruses.

Single-cell analysis of FOXP3 deficiencies in humans and mice unmasks intrinsic and extrinsic CD4+ T cell perturbations.

FOXP3 deficiency in mice and in patients with immune dysregulation polyendocrinopathy enteropathy X-linked (IPEX) syndrome results in fatal autoimmunity by altering regulatory T (Treg) cells. CD4+ T cells in patients with IPEX syndrome and Foxp3-deficient mice were analyzed by single-cell cytometry and RNA-sequencing, revealing heterogeneous Treg-like cells, some very similar to normal Treg cells, others more distant. Conventional T cells showed no widespread activation or helper T cell bias, but a monomorphic disease signature affected all CD4+ T cells. This signature proved to be cell extrinsic since it was extinguished in mixed bone marrow chimeric mice and heterozygous mothers of patients with IPEX syndrome. Normal Treg cells exerted dominant suppression, quenching the disease signature and revealing in mutant Treg-like cells a small cluster of genes regulated cell-intrinsically by FOXP3, including key homeostatic regulators. We propose a two-step pathogenesis model: cell-intrinsic downregulation of core FOXP3-dependent genes destabilizes Treg cells, de-repressing systemic mediators that imprint the disease signature on all T cells, furthering Treg cell dysfunction. Accordingly, interleukin-2 treatment improved the Treg-like compartment and survival.

Clonally expanded EOMES+ Tr1-like cells in primary and metastatic tumors are associated with disease progression.

Regulatory T (Treg) cells are a barrier for tumor immunity and a target for immunotherapy. Using single-cell transcriptomics, we found that CD4+ T cells infiltrating primary and metastatic colorectal cancer and non-small-cell lung cancer are highly enriched for two subsets of comparable size and suppressor function comprising forkhead box protein P3+ Treg and eomesodermin homolog (EOMES)+ type 1 regulatory T (Tr1)-like cells also expressing granzyme K and chitinase-3-like protein 2. EOMES+ Tr1-like cells, but not Treg cells, were clonally related to effector T cells and were clonally expanded in primary and metastatic tumors, which is consistent with their proliferation and differentiation in situ. Using chitinase-3-like protein 2 as a subset signature, we found that the EOMES+ Tr1-like subset correlates with disease progression but is also associated with response to programmed cell death protein 1-targeted immunotherapy. Collectively, these findings highlight the heterogeneity of Treg cells that accumulate in primary tumors and metastases and identify a new prospective target for cancer immunotherapy.

High-Definition Medicine.

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of medicine.

Parsing the Interferon Transcriptional Network and Its Disease Associations.

Type 1 interferon (IFN) is a key mediator of organismal responses to pathogens, eliciting prototypical "interferon signature genes" that encode antiviral and inflammatory mediators. For a global view of IFN signatures and regulatory pathways, we performed gene expression and chromatin analyses of the IFN-induced response across a range of immunocyte lineages. These distinguished ISGs by cell-type specificity, kinetics, and sensitivity to tonic IFN and revealed underlying changes in chromatin configuration. We combined 1,398 human and mouse datasets to computationally infer ISG modules and their regulators, validated by genetic analysis in both species. Some ISGs are controlled by Stat1/2 and Irf9 and the ISRE DNA motif, but others appeared dependent on non-canonical factors. This regulatory framework helped to interpret JAK1 blockade pharmacology, different clusters being affected under tonic or IFN-stimulated conditions, and the IFN signatures previously associated with human diseases, revealing unrecognized subtleties in disease footprints, as affected by human ancestry.

A genomic mutational constraint map using variation in 76,156 human genomes.

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.

Mutualisms weaken the latitudinal diversity gradient among oceanic islands.

The latitudinal diversity gradient (LDG) dominates global patterns of diversity1,2, but the factors that underlie the LDG remain elusive. Here we use a unique global dataset3 to show that vascular plants on oceanic islands exhibit a weakened LDG and explore potential mechanisms for this effect. Our results show that traditional physical drivers of island biogeography4-namely area and isolation-contribute to the difference between island and mainland diversity at a given latitude (that is, the island species deficit), as smaller and more distant islands experience reduced colonization. However, plant species with mutualists are underrepresented on islands, and we find that this plant mutualism filter explains more variation in the island species deficit than abiotic factors. In particular, plant species that require animal pollinators or microbial mutualists such as arbuscular mycorrhizal fungi contribute disproportionately to the island species deficit near the Equator, with contributions decreasing with distance from the Equator. Plant mutualist filters on species richness are particularly strong at low absolute latitudes where mainland richness is highest, weakening the LDG of oceanic islands. These results provide empirical evidence that mutualisms, habitat heterogeneity and dispersal are key to the maintenance of high tropical plant diversity and mediate the biogeographic patterns of plant diversity on Earth.

Revealing uncertainty in the status of biodiversity change.

Biodiversity faces unprecedented threats from rapid global change1. Signals of biodiversity change come from time-series abundance datasets for thousands of species over large geographic and temporal scales. Analyses of these biodiversity datasets have pointed to varied trends in abundance, including increases and decreases. However, these analyses have not fully accounted for spatial, temporal and phylogenetic structures in the data. Here, using a new statistical framework, we show across ten high-profile biodiversity datasets2-11 that increases and decreases under existing approaches vanish once spatial, temporal and phylogenetic structures are accounted for. This is a consequence of existing approaches severely underestimating trend uncertainty and sometimes misestimating the trend direction. Under our revised average abundance trends that appropriately recognize uncertainty, we failed to observe a single increasing or decreasing trend at 95% credible intervals in our ten datasets. This emphasizes how little is known about biodiversity change across vast spatial and taxonomic scales. Despite this uncertainty at vast scales, we reveal improved local-scale prediction accuracy by accounting for spatial, temporal and phylogenetic structures. Improved prediction offers hope of estimating biodiversity change at policy-relevant scales, guiding adaptive conservation responses.

The genetic legacy of the expansion of Bantu-speaking peoples in Africa.

The expansion of people speaking Bantu languages is the most dramatic demographic event in Late Holocene Africa and fundamentally reshaped the linguistic, cultural and biological landscape of the continent1-7. With a comprehensive genomic dataset, including newly generated data of modern-day and ancient DNA from previously unsampled regions in Africa, we contribute insights into this expansion that started 6,000-4,000 years ago in western Africa. We genotyped 1,763 participants, including 1,526 Bantu speakers from 147 populations across 14 African countries, and generated whole-genome sequences from 12 Late Iron Age individuals8. We show that genetic diversity amongst Bantu-speaking populations declines with distance from western Africa, with current-day Zambia and the Democratic Republic of Congo as possible crossroads of interaction. Using spatially explicit methods9 and correlating genetic, linguistic and geographical data, we provide cross-disciplinary support for a serial-founder migration model. We further show that Bantu speakers received significant gene flow from local groups in regions they expanded into. Our genetic dataset provides an exhaustive modern-day African comparative dataset for ancient DNA studies10 and will be important to a wide range of disciplines from science and humanities, as well as to the medical sector studying human genetic variation and health in African and African-descendant populations.

Disappearing cities on US coasts.

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities1-3. However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas4-a factor that is often underrepresented in coastal-management policies and long-term urban planning2,5. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.

100 ancient genomes show repeated population turnovers in Neolithic Denmark.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1-4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5-7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Environmental drivers of increased ecosystem respiration in a warming tundra.

Arctic and alpine tundra ecosystems are large reservoirs of organic carbon1,2. Climate warming may stimulate ecosystem respiration and release carbon into the atmosphere3,4. The magnitude and persistency of this stimulation and the environmental mechanisms that drive its variation remain uncertain5-7. This hampers the accuracy of global land carbon-climate feedback projections7,8. Here we synthesize 136 datasets from 56 open-top chamber in situ warming experiments located at 28 arctic and alpine tundra sites which have been running for less than 1 year up to 25 years. We show that a mean rise of 1.4 °C [confidence interval (CI) 0.9-2.0 °C] in air and 0.4 °C [CI 0.2-0.7 °C] in soil temperature results in an increase in growing season ecosystem respiration by 30% [CI 22-38%] (n = 136). Our findings indicate that the stimulation of ecosystem respiration was due to increases in both plant-related and microbial respiration (n = 9) and continued for at least 25 years (n = 136). The magnitude of the warming effects on respiration was driven by variation in warming-induced changes in local soil conditions, that is, changes in total nitrogen concentration and pH and by context-dependent spatial variation in these conditions, in particular total nitrogen concentration and the carbon:nitrogen ratio. Tundra sites with stronger nitrogen limitations and sites in which warming had stimulated plant and microbial nutrient turnover seemed particularly sensitive in their respiration response to warming. The results highlight the importance of local soil conditions and warming-induced changes therein for future climatic impacts on respiration.

Ghost roads and the destruction of Asia-Pacific tropical forests.

Roads are expanding at the fastest pace in human history. This is the case especially in biodiversity-rich tropical nations, where roads can result in forest loss and fragmentation, wildfires, illicit land invasions and negative societal effects1-5. Many roads are being constructed illegally or informally and do not appear on any existing road map6-10; the toll of such 'ghost roads' on ecosystems is poorly understood. Here we use around 7,000 h of effort by trained volunteers to map ghost roads across the tropical Asia-Pacific region, sampling 1.42 million plots, each 1 km2 in area. Our intensive sampling revealed a total of 1.37 million km of roads in our plots-from 3.0 to 6.6 times more roads than were found in leading datasets of roads globally. Across our study area, road building almost always preceded local forest loss, and road density was by far the strongest correlate11 of deforestation out of 38 potential biophysical and socioeconomic covariates. The relationship between road density and forest loss was nonlinear, with deforestation peaking soon after roads penetrate a landscape and then declining as roads multiply and remaining accessible forests largely disappear. Notably, after controlling for lower road density inside protected areas, we found that protected areas had only modest additional effects on preventing forest loss, implying that their most vital conservation function is limiting roads and road-related environmental disruption. Collectively, our findings suggest that burgeoning, poorly studied ghost roads are among the gravest of all direct threats to tropical forests.

Biogeographic response of marine plankton to Cenozoic environmental changes.

In palaeontological studies, groups with consistent ecological and morphological traits across a clade's history (functional groups)1 afford different perspectives on biodiversity dynamics than do species and genera2,3, which are evolutionarily ephemeral. Here we analyse Triton, a global dataset of Cenozoic macroperforate planktonic foraminiferal occurrences4, to contextualize changes in latitudinal equitability gradients1, functional diversity, palaeolatitudinal specialization and community equitability. We identify: global morphological communities becoming less specialized preceding the richness increase after the Cretaceous-Palaeogene extinction; ecological specialization during the Early Eocene Climatic Optimum, suggesting inhibitive equatorial temperatures during the peak of the Cenozoic hothouse; increased specialization due to circulation changes across the Eocene-Oligocene transition, preceding the loss of morphological diversity; changes in morphological specialization and richness about 19 million years ago, coeval with pelagic shark extinctions5; delayed onset of changing functional group richness and specialization between hemispheres during the mid-Miocene plankton diversification. The detailed nature of the Triton dataset permits a unique spatiotemporal view of Cenozoic pelagic macroevolution, in which global biogeographic responses of functional communities and richness are decoupled during Cenozoic climate events. The global response of functional groups to similar abiotic selection pressures may depend on the background climatic state (greenhouse or icehouse) to which a group is adapted.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.

Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.