Collection of WHO technical guidance on COVID-19, updated based on new scientific findings as the epidemic evolves.

The hologenome concept of evolution is a hypothesis explaining host evolution in the context of the host microbiomes. As a hypothesis, it needs to be evaluated, especially with respect to the extent of fidelity of transgenerational coassociation of host and microbial lineages and the relative fitness consequences of repeated associations within natural holobiont populations. Behavioral ecologists are in a prime position to test these predictions because they typically focus on animal phenotypes that are quantifiable, conduct studies over multiple generations within natural animal populations, and collect metadata on genetic relatedness and relative reproductive success within these populations. Regardless of the conclusion on the hologenome concept as an evolutionary hypothesis, a hologenomic perspective has applied value as a systems-level framework for host biology, including in medicine. Specifically, it emphasizes investigating the multivarious and dynamic interactions between patient genomes and the genomes of their diverse microbiota when attempting to elucidate etiologies of complex, noninfectious diseases.

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[Following is an excerpt of a comprehensive discussion with NC State scientists and other subject-matter experts conducted by NC State Applied Ecology Professor Rob Dunn. We highly recommend you read the full article from NC State News.] Many words have been written (or at least typed) about coronavirus SARS-CoV-2, the virus that causes COVID-19. Yet, for as ...

Climate change, water, and land management affect the terrestrial water cycle and river flow. They do so through changes in precipitation and evaporation, aside from a multitude of other land surface processes. Earth system models are routinely used to simulate and detect globally observed changes and attribute these changes to climate change. Attribution is based on an assessment of the consistency or inconsistency of change signatures by including or excluding hypothesized drivers of change in process-based models ([ 1 ][1]). On page 1159 of this issue, Gudmundsson et al. ([ 2 ][2]) compare the consistency that globally observed trend-patterns in mean river flow and hydrological extremes exhibit with regard to a set of model simulations. Gudmundsson et al. conclude, on the basis of the ESM output, that the simulated effects of water and land management cannot reproduce the observed change pattern in river flow. Rather, the modeled changes in river flow are only consistent with the observed changes in climatic variables if historical radiative forcing that accounts for climate change is used. This finding is distinct and important, although Gudmundsson et al. 's attribution of changing river flow patterns to anthropogenic climate change is made by a simple quantitative line of arguments. For instance, if the model is driven by observational atmospheric forcing and it reproduces the observed global change pattern, the authors concluded that the observed trends are related to changes in the radiative forcing. If the observed changes are only consistent with model output driven with historical atmospheric forcing, then these trends are attributed to that driver. Although the attribution statement in Gudmundsson et al. is logical and likely in terms of process understanding of climate dynamics, technically that evidence is still circumstantial. Indeed, different causal pathways could still lead to a similar outcome, that is, the same trend observed in the data could have emerged from a different process, even though not accounted for in the models. Additionally, owing to the presence of internal variability, such attribution will always have some degree of uncertainty (even with complete consistency between models and data) ([ 1 ][1]). To improve the explanatory power of such important studies and to generate more confidence in such attribution statements, we need to move beyond these first-order assessments that involve simple proof of consistency and inconsistency when investigating the effects of climatic change. The key for a more robust way to elicit the most likely driving mechanisms resides in characterizing the information transfer between potential drivers and the process of interest (e.g., between climatic change and river flows). Those providing strongest information transfer can be attributed as dominant drivers. Additionally, these information transfer metrics are probabilistic, hence internal variability and uncertainties are natively incorporated. This strengthens the process of attribution and makes it more realistic and reliable. To achieve robust attribution, several measures of information transfer are already used elsewhere, including transfer entropy ([ 3 ][3]), traditional Bayesian approaches ([ 4 ][4]), and network connectivity metrics with time directionality ([ 5 ][5], [ 6 ][6]). Attribution procedures by information transfer and Bayesian approaches are traditionally perceived as indicators of causality. However, they only allow quantifying the ability to infer the state of a process given the knowledge of another. Whether or not there is a cause-effect relation remains elusive, because no physical causation mechanism can be retrieved from these inferential statistics alone. More recently, dynamical system metrics have been proposed with the aim to assess causal codependencies between drivers and processes ([ 7 ][7]) by evaluating whether there is a deterministic link between them (connection in phase space). This brings the added value of dynamic connectivity and allows for seamless integration with modeling approaches. However, even with these more advanced measures, a true cause-effect diagnostic is still elusive because the phase spatial diagnostics are basically correlative. The connected variables can simply be dynamically correlated effects of a common third-party cause. The way forward is therefore to combine information transfer and dynamical system approaches, with fundamental principles and methodological understanding in mind. Such a combined approach allows bridging the best of both worlds while overcoming the respective caveats. This brings us to the emerging pathways of information physics ([ 8 ][8]), reconciling and generalizing statistical, geometric, and mechanistic information metrics ([ 9 ][9]). The use of information physics enables the retrieval of physically consistent information attributes and dependencies in coevolutionary systems such as in hydrology and Earth system dynamics in a changing climate. Information physics can pave the way for bringing physical meaning to inferential metrics, and a dynamic coevolving flexibility to the statistical metrics of information transfer, bringing new pathways for causal discovery and attribution. Exploring such pathways may thus provide further validation to the findings presented by Gudmundsson et al. and might also bring out unknown unknowns to add to the discussion of drivers of change in the hydrological system. This may thus complement any measure of causality that entails the development of multiple working hypotheses based on a thorough process-based understanding to avoid overlooking potential drivers of change that might cause the same signature ([ 10 ][10]). The findings by Gudmundsson et al. allow one to infer that climate change has affected low, mean, and high flows at the global scale. Whether the retrieved drivers are the real causes or just predictors requires further investigation, and the development and application of causal discovery methods grounded on information physics offer encouraging pathways to further that quest for attribution. 1. [↵][11]1. T. F. Stocker et al. 1. N. L. Bindoff et al ., in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds. (Cambridge Univ. Press, 2013), p. 872. 2. [↵][12]1. L. Gudmundsson et al ., Science 371, 1159 (2021). [OpenUrl][13][Abstract/FREE Full Text][14] 3. [↵][15]1. T. Schreiber , Phys. Rev. Lett. 85, 461 (2000). [OpenUrl][16][CrossRef][17][PubMed][18][Web of Science][19] 4. [↵][20]1. N. Najibi et al ., NPJ Clim. Atmos. Sci 2, 19 (2019). [OpenUrl][21] 5. [↵][22]1. J. Runge et al ., Nat. Commun. 6, 8502 (2015). [OpenUrl][23] 6. [↵][24]1. A. E. Goodwell et al ., Proc. Natl. Acad. Sci. U.S.A. 115, E8604 (2018). [OpenUrl][25][Abstract/FREE Full Text][26] 7. [↵][27]1. S. Vannitsem, 2. P. Ekelmans , Earth Syst. Dynam 9, 1063 (2018). [OpenUrl][28] 8. [↵][29]1. R. A. P. Perdigão et al ., Water Resour. Res. 56, e2019WR025270 (2020). [OpenUrl][30] 9. [↵][31]1. R. A. P. Perdigão , Entropy 20, 26 (2018). [OpenUrl][32] 10. [↵][33]1. S. Harrigan, 2. C. Murphy, 3. J. Hall, 4. R. L. Wilby, 5. J. Sweeney , Hydrol. Earth Syst. Sci. 18, 1935 (2014). [OpenUrl][34] Acknowledgments: We acknowledge the Meteoceanics research programs MR-220617 “Mathematical Physics and Predictability of Complex Coevolutionary Systems” and MR-010319 “Synergistic Dynamics of Complex Socio-Natural Systems.” R.A.P.P. also acknowledges the “Fundação para a Ciência e Tecnologia” through projects UIDB/00329/2020, UIDP/00329/2020, and UID/EEA/50008/2019. Both authors contributed equally to this work. 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The discovery in 2018 of superconductivity when two layers of graphene are stacked on top of each other at a “magic angle” has opened a new paradigm for studying electronic phenomena ([ 1 ][1]). Now, a pair of studies, one on page 1133 of this issue by Hao et al. ([ 2 ][2]) and the other by Park et al. ([ 3 ][3]), take the twisting magic trick one step further. More robust and tunable superconductivity was realized in three-layer stacks of graphene arranged at an alternating magic twist angle that is a factor of ![Graphic][4] greater than the magic angle for bilayers. The authors also present evidence that superconductivity in twisted graphene is not caused by the conventional weak-coupling Bardeen-Cooper-Schrieffer (BCS) electron-pairing mechanism. The mechanism of pairing remains unknown, but the experiments suggest that the electrons form tightly bound pairs at temperatures above those at which superconductivity is macroscopically detected. ![Figure][5] Alternating twists Superconductivity in twisted graphene layers arises from flat-band structures near zero energy. GRAPHIC: C. BICKEL/ SCIENCE Early theoretical work predicted that the moiré superlattice created by stacking two twisted layers of graphene, at a magic angle of ∼1°, creates electronic bands with vanishing bandwidths ([ 4 ][6], [ 5 ][7]). The quenched kinetic energy of electrons occupying such flat bands creates strong interactions and would make magic angle–twisted bilayer graphene (TBG) a spectacular platform for collective phenomena. for sighting new quantum phases ([ 6 ][8]) including unexpected interaction-driven topological insulators ([ 7 ][9]). Although steady progress is being made in understanding TBG, the mechanism and nature of its superconducting phase remains a mystery. It is tempting to think in terms of a simple weak-coupling BCS scenario in which a large density of states of a nearly flat band can enhance superconductivity. However, superconductivity occurs in the presence of strong Coulomb interactions that are comparable with or larger than the bandwidth of TBG in the noninteracting limit ([ 8 ][10]), and other flat-band moiré systems do not show reliable signs of superconductivity, despite exhibiting other strongly correlated behavior. Twisting three-layer graphene was predicted to possess flat bands if it was constructed with a curious alternating magic twist angle ![Graphic][11] greater than that of the bilayer system ([ 9 ][12]). This trilayer differs from the bilayer in several respects. For example, its flat bands coexist with dispersing Dirac bands, and a perpendicular displacement field can be used to tune its band structure (see the figure). The two experimental studies fabricated trilayer near the predicted greater alternating magic angle and explored trilayer properties as a function of carrier density and perpendicular displacement field. The presence of the Dirac bands circumvents the formation of correlated insulating states, which also slightly screens the interaction between electrons in the flat band of the trilayer. However, the interactions within the trilayer flat bands give rise to cascades of transitions at several of the integer filling (ν) of carriers per moiré unit cell of its flat bands, similar to those observed in TBG ([ 10 ][13], [ 11 ][14]). These transitions signal the propensity for flavor (spin or valley isospin) symmetry–breaking, near-integer fillings, including at ν = ±2, from which superconductivity emerges upon doping. The trilayer appears at a superconducting transition temperature ( T c) of 2 K (twice that of the bilayer) system. The cause of this enhancement is unclear, but the moiré superlattice constant is smaller in the trilayer, which would increase the Coulomb-interaction scale at the same electron density Several experimental findings in the magic trilayer signal unconventional superconductivity. The superconducting coherence length is about the same as the interparticle distance, and T c increases almost linearly with doping. The ratio of T c to the Fermi temperature is also large (0.1). The superconducting state appears to be in the strong-coupling regime and likely driven by Bose condensation of tightly bound Cooper pairs and limited by their density. Pairing might occur at temperatures much higher than when the zero-resistance state is detected. Tuning the trilayer's band structure allows experimental determination of the role of enhanced density of states at the van Hove singularity (vHS) of the flat bands in superconductivity. The new studies monitored the Hall conductivity as a function of doping and displacement field. When the vHS is tuned to the chemical potential, T c in the trilayer is suppressed, opposite of what is expected from the simple BCS weak-coupling mechanism. Superconductivity appears to be strongly tied to flavor-polarized states, which are beginning to be understood in the bilayer [for example, ([ 12 ][15])]. Topological excitations of these states may be responsible for the pairing mechanism ([ 13 ][16]), but the breakdown of weak coupling does not mean phonons are not involved. Some calculations show that in a fully flat band, electrons cannot pair on their own ([ 14 ][17]). The presence of superconductivity in bilayer and alternating trilayer systems, and its absence in the other flat band system, suggests the importance of spatial-time C2 z T symmetry for the emergence of superconductivity. The discovery of superconductivity in this trilayer raises the possibility that the stacking of multilayers of graphene respecting certain symmetry at other magic angles will uncover more and hopefully greater- T c twisted superconductors. The theory that predicted the ![Graphic][18] ratio for trilayer also identified a hierarchy of magic angles for multilayers with alternating layers ([ 8 ][10]). The prediction for quadrilayers with alternating magic angle larger than the bilayer by the golden ratio is that they would have flat bands without dispersing Dirac bands because they have an even number of layers (see the figure). An unspoken rule in the hunt for new superconductors attributed to early Bell Lab pioneer Bernd Matthias ([ 15 ][19]) is to “never listen to the theorists.” However, maybe the signs of symmetry and the elegance of special ratios need to be heeded. Finding superconductivity in twisted matter with a prescribed symmetry related by the golden ratio of twist angles would be pure magic. 1. [↵][20]1. Y. Cao et al ., Nature 556, 43 (2018). [OpenUrl][21][CrossRef][22][PubMed][23] 2. [↵][24]1. Z. Hao et al ., Science 371, 1133 (2021). [OpenUrl][25][Abstract/FREE Full Text][26] 3. [↵][27]1. J. M. Park et al ., Nature 590, 249 (2021). [OpenUrl][28] 4. [↵][29]1. R. Bistritzer, 2. A. H. MacDonald , Proc. Natl. Acad. Sci. U.S.A. 108, 12233 (2011). [OpenUrl][30][Abstract/FREE Full Text][31] 5. [↵][32]1. E. Suárez Morell et al ., Phys. Rev. B Condens. Matter Mater. Phys. 82, 121407 (2010). [OpenUrl][33][CrossRef][34] 6. [↵][35]1. E. Y. Andrei et al ., Nat. Rev. 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Li and funding from the Gordon and Betty Moore Foundation, the U.S. Department of Energy, and the U.S. National Science Foundation. 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The ocean “ twilight zone,” where sunlight hardly penetrates, is located at depths of 200 to 1000 m from the sea surface. Inhabited by iconic species such as lantern fish and giant squid, it has stimulated our collective imagination for generations. To this day it remains largely unexplored and has revealed few of its secrets. Also known as the mesopelagic, this zone occupies 20% of the volume of the world's oceans. It plays a key role in the global carbon cycle ([ 1 ][1], [ 2 ][2]) and may contain both biomass and biodiversity that have been largely underestimated ([ 3 ][3], [ 4 ][4]). On page 1148 of this issue, Boscolo-Gallazzo et al. ([ 5 ][5]) provide evidence that the establishment of the modern twilight zone is, on planetary time scales, a relatively recent phenomenon that has taken place gradually over the past 15 million years. Using an approach that couples ocean biogeochemical simulations and a compilation of sedimentary isotopic data, Boscolo-Galazzo et al. show the role of the gradual cooling of the climate system (by 4° to 6°C for the global surface ocean) in establishing the modern twilight zone. This global decrease in temperature has reduced the decomposition of organic matter that is formed by upper-ocean ecosystems and then sinks to depth. As such, the export of organic carbon to the deeper ocean has become more efficient and has increased the amount of food available to mesopelagic ecosystems. These long-term paleoclimatic changes are likely responsible for the establishment of relatively new deep ecological niches and the modern twilight zone. The role of the twilight zone in the ocean carbon cycle has been recognized for more than 30 years, with the identification of the importance of the biological carbon pump in transporting carbon out of the ocean's surface layers ([ 1 ][1]). Indeed, it is in the twilight zone that most of the organic carbon that is exported from the surface ocean is remineralized back to carbon dioxide through a series of biological, physical, and chemical processes that are not yet well understood. However, what is known is that this input of organic matter is critical to the survival of most organisms living at these depths and to the ecosystems they inhabit. Hence, although a few billion tons of carbon leave the surface ocean every year in the form of particulate organic carbon, only a tiny fraction reaches the bottom of the twilight zone at 1000 m. This fraction nevertheless represents a long-term carbon sink, with this deep carbon remaining isolated from the atmosphere for centuries to millennia. The mean depth at which organic carbon is converted back to CO2, known as the remineralization depth, has a substantial impact on atmospheric CO2. Kwon et al. ([ 6 ][6]) have shown that a deepening or shoaling of the mean remineralization depth by just a few tens of meters could decrease or increase atmospheric CO2 by a few tens of parts per million over long time scales. It is therefore crucial to determine as accurately as possible the mechanisms by which organic matter is used by biota in the twilight zone, so as to better project the response of these mechanisms under changing ocean conditions and their role in altering the capacity of the ocean to hold carbon in the coming decades and centuries. Some of the growing interest in the twilight zone stems from the recognition that it could harbor considerably more fish biomass than previously estimated ([ 3 ][3])—potentially 20 times as much as in the surface ocean. This discovery has led to new prospects for industrial fisheries, which hope to overcome the limitations posed by depleted or overfished surface fish stocks ([ 7 ][7]). Increasing interest in commercial exploitation of mesopelagic stocks for human consumption, fishmeal, and nutraceuticals is exemplified by the recent European Union Blue Growth Strategy position paper ([ 8 ][8], [ 9 ][9]), which is open to the exploration and exploitation of mesopelagic fish resources. However, there are large uncertainties about the extent of these stocks, which are estimated at 1000 to 20,000 million tons ([ 3 ][3], [ 10 ][10]), and little is known about their vulnerability to fishing and other human pressures. Taking advantage of several oceanographic campaigns and many innovative technological developments (autonomous floats, video profilers), several large-scale scientific projects grouped within the JETZON consortium ([ 10 ][10]) aim to explore the ocean twilight zone and unravel some of its mysteries. The questions are numerous: For example, it is not known what species are present at these depths and what the biomass of these organisms is. Also unclear is the role of processes in the mesopelagic zone in the transfer of carbon from the surface ocean to the deep ocean where it is sequestered for long periods. Another question concerns the sensitivity of these mesopelagic ecosystems to the input of matter from the surface, and to variations in local environmental conditions, in particular temperature, oxygenation, and acidification. The work of Boscolo-Galazzo et al. on the “birth” of the current twilight zone is particularly timely and relevant to these questions. Indeed, it shows how mesopelagic ecosystems may be vulnerable to future ocean warming through its impact on the delivery of organic matter from surface waters. Other ocean conditions, such as oxygen concentrations and ocean acidity, may also alter the viability of these specific ecosystems. The climate projections carried out in the framework of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) confirm that future warming, deoxygenation, and acidification may concurrently alter the deep-ocean environment, with unknown consequences for mesopelagic ecosystems ([ 11 ][11]). In Twenty Thousand Leagues Under the Sea ([ 12 ][12]), Jules Verne used his legendary intuition to describe the depths of the ocean, evoking their mysteries and the fascination they exert on humankind: “The sea is the vast reservoir of Nature. The globe began with sea, so to speak; and who knows if it will not end with it? In it is supreme tranquility. The sea does not belong to despots. Upon its surface men can still exercise unjust laws, fight, tear one another to pieces, and be carried away with terrestrial horrors. But at thirty feet below its level, their reign ceases, their influence is quenched, and their power disappears.” Even the prescient Jules Verne had not foreseen how far-reaching human influence may become. More than 150 years after the publication of his classic novel, we are confronted with the realization that the ocean twilight zone now faces several human threats. 1. [↵][13]1. E. T. Sundquist, 2. W. S. Broecker 1. T. Volk, 2. M. I. Hoffert , in The Carbon Cycle and Atmospheric CO2: Natural Variations, Archean to Present, E. T. Sundquist, W. S. Broecker, Eds. (American Geophysical Union, 1985), pp. 99–110. 2. [↵][14]1. C. M. Marsay et al ., Proc. Natl. Acad. Sci. U.S.A. 112, 1089 (2015). [OpenUrl][15][Abstract/FREE Full Text][16] 3. [↵][17]1. X. Irigoien et al ., Nat. Commun. 5, 3271 (2014). [OpenUrl][18][CrossRef][19][PubMed][20] 4. [↵][21]1. C. Robinson et al ., Deep Sea Res. II 57, 1504 (2010). [OpenUrl][22][CrossRef][23] 5. [↵][24]1. F. Boscolo-Galazzo et al ., Science 371, 1148 (2021). [OpenUrl][25][Abstract/FREE Full Text][26] 6. [↵][27]1. E. Y. Kwon, 2. F. Primeau, 3. J. L. Sarmiento , Nat. Geosci. 2, 630 (2009). [OpenUrl][28][CrossRef][29] 7. [↵][30]Food and Agriculture Organization of the United Nations, The State of World Fisheries and Aquaculture 2020: Sustainability in Action (2020). 8. [↵][31]European Union Directorate-General for Maritime Affairs and Fisheries, Blue Bioeconomy Situation Report and Perspectives (2018); [www.eumofa.eu/documents/20178/84590/Blue+bioeconomy_Final.pdf][32]. 9. [↵][33]European Commission, Blue Growth (2020); [https://ec.europa.eu/maritimeaffairs/policy/blue\_growth\_en][34]. 10. [↵][35]1. A. Martin et al ., Nature 580, 26 (2020). [OpenUrl][36][CrossRef][37][PubMed][38] 11. [↵][39]1. L. Kwiatkowski et al ., Biogeosciences 17, 3439 (2020). [OpenUrl][40] 12. [↵][41]1. J. Verne , Vingt mille lieues sous les mers (Hetzel, 1869–1870). 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Chronic intestinal diseases, such as inflammatory bowel disease (IBD), are associated with continual tissue damage that must be repaired. Mucosal restoration is a coordinated process that can be influenced by extrinsic factors, including the commensal bacterial community ([ 1 ][1]–[ 3 ][2]). However, individuals with intestinal disease often receive antibiotics during their care, disrupting these beneficial bacteria. Although fungi comprise a relatively small proportion of the microbial community, they can take over mucosal niches because they are not targeted by antibiotics ([ 4 ][3]). On page 1154 of this issue, Jain et al. ([ 5 ][4]) find that the yeast Debaryomyces hansenii , which is commonly used in the food industry, can colonize wounds in antibiotic-treated mice and is present in the inflamed tissue of individuals with IBD. This colonization enhances inflammation by blocking the signaling required for wound healing. Thus, disruption of commensal bacteria can open niches for fungi that exacerbate disease. ![Figure][5] The yeast Debaryomyces hansenii prevents intestinal wound healing During homeostasis, anaerobic organisms, such as Akkermansia muciniphila , seed the wound bed and, together with bacterial metabolites, such as deoxycholate, promote wound healing. Antibiotic therapy or loss of bacteria owing to chronic inflammation in inflammatory bowel disease can lead to the colonization of wounds in the epithelial lining with environmental organisms such as the yeast Debaryomyces hansenii . This stimulates a type I interferon (IFN) response in macrophages, which leads to production of C-C motif chemokine 5 (CCL5) and chronic inflammation that prevents wound healing. GRAPHIC: MELISSA THOMAS BAUM/ SCIENCE The intestinal microbiota is a consortium of microorganisms, including bacteria, archaea, fungi, and viruses. Many investigations have identified a role for bacterial members of the microbiota in IBD ([ 6 ][6]). Clinical evidence suggests that fungi may also influence IBD, including the presence of serum antibodies to fungal cell surface moieties in patients with certain types of IBD. Additionally, mutations in the gene that encodes the pattern recognition receptor DECTIN-1 (dendritic cell–associated C-type lectin 1), which recognizes fungal cell walls, or the downstream inflammatory signaling molecule CARD9 (caspase recruitment domain–containing protein 9) are commonly found in IBD patients ([ 7 ][7]). Furthermore, investigations in animal models have demonstrated that common gut fungi, such as Saccharomyces cerevisiae or Candida tropicalis , can influence IBD severity ([ 8 ][8]–[ 10 ][9]). However, a role for fungi during intestinal epithelial healing has never been evaluated. IBD is characterized by chronic intestinal inflammation, loss of intestinal barrier integrity, and epithelial damage, requiring constant repair of the mucosa. To study the injury process, Jain et al. used forceps to remove areas of the colonic mucosa during endoscopy in mice. As opposed to chemical or infectious models of intestinal injury, this method allows for spatial and temporal control of the wound. Mucosal healing occurs in three discrete and coordinated stages: barrier reestablishment involving neutrophil infiltration, extensive proliferation, and then tissue remodeling. Preventing any of these stages leads to chronic inflammation and a failure to reform the epithelium. Studies have identified a beneficial role for commensal bacteria during mucosal healing ([ 1 ][1]–[ 3 ][2]). In a healthy gut, shortly after tissue injury, neutrophils are recruited to the injured tissue, creating an anaerobic environment that allows specific bacterial members to seed the wound bed. In particular, Akkermansia muciniphila colonizes the injured tissue and influences signaling in epithelial cells to enhance proliferation and wound closure ([ 1 ][1]). Additionally, bacterial metabolites, such as deoxycholate, affect epithelial signaling to coordinate tissue remodeling ([ 3 ][2]). Thus, mucosal wound healing requires signaling from specific commensal bacteria to occur properly. During inflammatory flares associated with IBD, antibiotics are often used to prevent potentially harmful bacteria accessing the bloodstream. Jain et al. found that antibiotic treatment during the biopsy injury model prevented tissue repair. However, known microbiota-dependent pathways were not involved in this phenotype, leading the authors to test other hypotheses. Fungal out-growth is a frequent side effect of antibiotic use ([ 4 ][3]), and Jain et al. observed D. hansenii colonization of the injured tissue in antibiotictreated mice. Treatment with the antifungal agent amphotericin B improved tissue regeneration. Introduction of D. hansenii , but not S. cerevisiae , by oral gavage into non–antibiotic-treated, conventionally raised animals was sufficient to impair the wound healing process. The same species of yeast was also detected in inflamed colonic tissue from two geographically distinct populations of patients with Crohn's disease, a type of IBD. Therefore, D. hansenii can specifically infect intestinal wounds in mice and humans with IBD and restrict the wound healing process in mice. Jain et al. found that macrophages were increased within the wound bed of animals infected with D. hansenii . Moreover, expression of the chemokine CCL5 (C-C motif chemokine 5) was enriched in macrophages isolated from the wound bed. CCL5 promotes inflammation by recruiting other immune cells to the tissue, and its expression is increased in individuals with IBD ([ 11 ][10], [ 12 ][11]). Wound healing was not impaired by D. hansenii colonization in mice in which Ccl5 was deleted. Culture of macrophages with D. hansenii followed by RNA sequencing revealed that activation of the type I interferon–CCL5 pathway in macrophages prevents wound healing (see the figure). D. hansenii is likely not a common resident of the gut, because it was isolated from all samples obtained from individuals with IBD but from only 1 of 10 healthy donors. D. hansenii is an environmental yeast that is distinctive in its ability to tolerate high-salt and pH conditions and is often used in cheese and meat production ([ 13 ][12]). It is possible that the ability of D. hansenii to persist in extreme environments also allows it to survive within inflamed tissue. This suggests that certain dietary recommendations could be made for patients with IBD to prevent colonization with D. hansenii , but this would need to be established with clinical trials. Additionally, use of antibiotics in individuals with chronic intestinal disease should be evaluated more carefully. The study of Jain et al. demonstrates that the loss of commensal microbes can open up niches for potentially harmful opportunistic organisms. Although CCL5 represents an attractive drug target that is currently being explored in IBD patients ([ 14 ][13]), it is one of many factors that is dysregulated during disease. Because commensal microbes can influence multiple host pathways that include preventing inflammation ([ 15 ][14]), colonization of pathogens, and promoting wound healing, specific cocktails of commensal bacteria might prove to be better therapeutic agents that act on several levels to protect from disease. 1. [↵][15]1. A. Alam et al ., Nat. Microbiol. 1, 15021 (2016). [OpenUrl][16] 2. 1. A. Alam et al ., Mucosal Immunol. 7, 645 (2014). [OpenUrl][17][CrossRef][18][PubMed][19] 3. [↵][20]1. U. Jain et al ., Cell Host Microbe 24, 353 (2018). [OpenUrl][21][CrossRef][22][PubMed][23] 4. [↵][24]1. M. C. Noverr, 2. R. M. Noggle, 3. G. B. Toews, 4. G. B. Huffnagle , Infect. Immun. 72, 4996 (2004). [OpenUrl][25][Abstract/FREE Full Text][26] 5. [↵][27]1. U. Jain et al ., Science 371, 1154 (2021). [OpenUrl][28][Abstract/FREE Full Text][29] 6. [↵][30]1. R. Caruso, 2. B. C. Lo, 3. G. Núñez , Nat. Rev. Immunol. 20, 411 (2020). [OpenUrl][31] 7. [↵][32]1. J. J. Limon, 2. J. H. Skalski, 3. D. M. Underhill , Cell Host Microbe 22, 156 (2017). [OpenUrl][33][CrossRef][34][PubMed][35] 8. [↵][36]1. T. R. Chiaro et al ., Sci. Transl. Med. 9, eaaf9044 (2017). [OpenUrl][37][Abstract/FREE Full Text][38] 9. 1. I. D. Iliev et al ., Science 336, 1314 (2012). [OpenUrl][39][Abstract/FREE Full Text][40] 10. [↵][41]1. T. T. Jiang et al ., Cell Host Microbe 22, 809 (2017). [OpenUrl][42][CrossRef][43][PubMed][44] 11. [↵][45]1. A. Mencarelli et al ., Sci. Rep. 6, 30802 (2016). [OpenUrl][46][CrossRef][47] 12. [↵][48]1. X. Ye et al ., Scand. J. Gastroenterol. 52, 551 (2017). [OpenUrl][49] 13. [↵][50]1. U. Breuer, 2. H. Harms , Yeast 23, 415 (2006). [OpenUrl][51][CrossRef][52][PubMed][53][Web of Science][54] 14. [↵][55]1. L. Vangelista, 2. S. Vento , Front. Immunol. 8, 1981 (2018). [OpenUrl][56] 15. [↵][57]1. K. S. Ost, 2. J. L. Round , Annu. Rev. Microbiol. 72, 399 (2018). [OpenUrl][58] [1]: #ref-1 [2]: #ref-3 [3]: #ref-4 [4]: #ref-5 [5]: pending:yes [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-10 [10]: #ref-11 [11]: #ref-12 [12]: #ref-13 [13]: #ref-14 [14]: #ref-15 [15]: #xref-ref-1-1 "View reference 1 in text" [16]: {openurl}?query=rft.jtitle%253DNat.%2BMicrobiol.%26rft.volume%253D1%26rft.spage%253D15021%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: {openurl}?query=rft.jtitle%253DMucosal%2BImmunol.%26rft.volume%253D7%26rft.spage%253D645%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fmi.2013.84%26rft_id%253D...

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When the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019, researchers rapidly recalibrated epidemiological computer models originally developed for other pandemics to serve as decision support tools for policy-makers and health care professionals planning public health responses. However, no current tools can predict the course of disease and help a doctor decide on the most appropriate treatment for an individual COVID-19 patient. “Digital twins” are software replicas of the dynamic function and failure of engineered products and processes. The medical analog, patient-specific digital twins, could integrate known human physiology and immunology with real-time patient-specific clinical data to produce predictive computer simulations of viral infection and immune response. Such medical digital twins could be a powerful addition to our arsenal of tools to fight future pandemics, combining mechanistic knowledge, observational data, medical histories, and the power of artificial intelligence (AI). An industrial digital twin of a device, such as a specific jet engine, combines a predictive template computational model calibrated using historical data aggregated from many devices with regularly collected operational data for the particular device. Digital twins allow continual forecasting, and small-scale interventions to prevent problems before they become serious. This greatly reduces the frequency of critical failures. The most sophisticated engineering digital twins are also self-improving—they continuously monitor divergence between predictions and observations and use these divergences to improve their own accuracy. Although medical digital twins are much more difficult to develop than those for engineered devices, they have begun to find applications in improving human health. Examples include the “artificial pancreas” for type 1 diabetes patients ([ 1 ][1], [ 2 ][2]). In the artificial pancreas model, a template mathematical model of human glucose metabolism and a closed-loop control algorithm modeling insulin delivery and data from an implanted glucose sensor are customized into a patient-specific digital twin that continuously calculates insulin needs and drives an implanted pump that adjusts blood insulin concentrations. Additionally, pediatric cardiac digital twins combine template models of the heart with patient-derived clinical measurements to optimize some heart surgeries ([ 3 ][3]) and assess the risk of thrombosis ([ 4 ][4]). These examples illustrate how current digital twins can operate in real time to maintain health continuously, or they can be used off-line to design personalized medical interventions. The ARCHIMEDES diabetes model expands these technologies by including models not only of the progression of diabetes within individual patients but also of medical diagnosis, treatments, and the functioning of the health care system that is providing the treatment ([ 5 ][5]). These examples provide a vision of the potential of medical digital twins. Medical digital twins that combine mechanistic understanding of physiology and viral replication with AI-based models derived from population and individual clinical data are promising as tools for optimizing the treatment of patients infected with a virus. As clinical outcomes of SARS treatment revealed, therapies such as steroidal anti-inflammatories can be lifesaving but also ineffective or even lethal if they are not adjusted carefully to suit individual patient responses ([ 6 ][6]). Thus, more complex therapies combining antivirals and multiple immune-stimulating and anti-inflammatory drugs would need to be personalized for time of application and dose of each component to be both effective and safe. Validated digital twins could greatly reduce the cost and complexity of such combinatorial clinical applications. Even primitive digital twins could improve diagnosis, prognosis, and treatment by providing a framework to combine patient-specific population data in a consistent framework. Their deployment would enable rapid refinement and improvement, especially if they were designed in a modular fashion to permit the parallel development and optimization of their component submodels. However, a digital twin that can continuously replicate the complexity of an infection and immune responses sufficiently well to guide individual treatment is currently out of reach. To realize the potential of digital twins in the treatment of viral diseases, there are a number of issues that need to be addressed. The spread of infection within the body and the immune response to viral pathogens are still poorly understood, as are the factors determining if and when specific components of the immune response are beneficial (viral clearance) or harmful (hyperinflammation). Viral infections can be complex, with pathologies developing in organs beyond the sites of primary infection, requiring an understanding of the responses of multiple organs. Therapies are also complex, with combinations integrating phased doses of antivirals, anti-inflammatory drugs, antibodies, and immune-stimulating drugs such as interferon (IFN) or interleukin-7 (IL-7). Where to start in building viral-infection digital twins? When considering how to build mechanistic model components for a viral-infection digital twin, many of the necessary submodels of relevant pathways and processes already exist ([ 7 ][7]) or could be developed using existing experimental methodologies (see the figure). For example, at the subcellular scale, transcriptomics data analysis of human macrophages can be used to construct dynamic network models of the interactions of highly expressed genes for each macrophage subtype ([ 8 ][8]), forming the basis for dynamic models of gene regulatory networks. At the multicellular scale, imaging technology reveals spatial aspects of the immune response ([ 9 ][9]). At the tissue scale, a simulation of an alveolar sac can capture the spatiotemporal variability of the immune response. At the organ scale, computational fluid dynamics models can simulate airflow in the lungs ([ 10 ][10]). And at the whole-body scale, computational models calibrated with simultaneous data from different organs, such as respiratory contractions and cerebral blood flow, can be used to integrate different organ systems ([ 11 ][11]). Physiologically based kinetic models ([ 12 ][12]) are widely used in the development and regulation of pharmaceuticals. Virologists have developed template models that capture aspects of viral spread, immune response, viral replication in individual cells, physiology, and dysfunction of specific organ systems, transport in the blood and lymph, airway transport of viruses, and aerosol therapies ([ 13 ][13]). Digital twins describing infection and treatment require the development, validation, and integration of numerous component submodels in the context of a rapidly developing scientific understanding of biological behaviors and continual generation of new experimental and clinical data. Although individual laboratories may construct submodels, the development of comprehensive digital twins will require laboratories and research groups around the world to integrate and validate submodels independently, with only limited central coordination. Enabling such parallel development requires a flexible simulation architecture that uses a multiscale map of all the relevant components of a patient's response to viral infection, as well as responses to available treatments. Community efforts such as the COVID-19 Disease Map Project and Computational Modeling in Biology Network (COMBINE) are working to build such infrastructure, although much work needs to be done to adapt those for use in digital-twin technology. ![Figure][14] Building a personalized digital twin Data from multiple scales are needed to build computational representations of biological processes and body systems that are affected by viral infection. These submodels are integrated and personalized with clinical data from individual patients. The digital twin can then be used to derive predictions about diagnosis, prognosis, and efficacy and optimization of therapeutic interventions. GRAPHIC: N. CARY/ SCIENCE Another substantial challenge is the generation of the heterogeneous data required to both calibrate template models to known human biology and physiology and to personalize template models into digital twins. Data from clinical trials are an important resource for model validation and discovery, as are high-resolution time-series data characterizing the immune response in a variety of individuals and settings. Model construction and validation require collection of synchronous measurements at different physiological scales: 'omics data from tissues and single cells, from diverse experimental systems, including two-dimensional (2D) and 3D cell cultures, in vivo and ex vivo animal models, and patients; at the tissue level, data characterizing immune cell trafficking and patterns of damage and recovery; and biophysical and structural data from tissues and organs, combined with data characterizing transport throughout the body. To personalize a digital twin, the model template must integrate with clinical records and time courses, such as vital signs, immune cell counts, computed tomography scans of infected organs, measured viral loads, and treatment responses. Given the likely complexity of the underlying models, even the abundance of such data will leave considerable challenges in model validation and uncertainty quantification. Data-driven AI digital twins for prognosis and treatment optimization are often viewed as alternatives to mechanistic modeling. However, AI and mechanistic approaches are most valuable when used together. Mechanistic modeling can provide important constraints for training AI algorithms ([ 14 ][15]). Conversely, AI can assist mechanistic modelin...

Many disease-causing bacteria use a molecular syringe to inject dozens of their proteins, called effectors, into intestinal cells, blocking key immune responses. Ruano-Gallego et al. used the mouse pathogen Citrobacter rodentium to model effector function in vivo. They found that effectors work together as a network, allowing the microbe great flexibility in maintaining pathogenicity. An artificial intelligence platform correctly predicted colonization outcomes of alternative networks from the in vivo data. However, the host was able to bypass the obstacles erected by different effector networks and activate complementary immune responses that cleared the pathogen and induced protective immunity. Science , this issue p. [eabc9531][1] ### INTRODUCTION Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on the injection of effectors via type III secretion systems (T3SSs). The effectors hijack cellular processes through multiple mechanisms, including molecular mimicry and diverse enzymatic activities. Although in vitro analyses have shown that individual effectors can exhibit complementary, interdependent, or antagonistic relationships, most in vivo studies have focused on the contribution of single effectors to pathogenesis. Citrobacter rodentium is a natural mouse pathogen that shares infection strategies and virulence factors with the human pathogens enteropathogenic and enterohemorrhagic E. coli (EPEC and EHEC). The ability of these pathogens to colonize the gastrointestinal tract is mediated by the injection of effectors via a T3SS. Although C. rodentium infects 31 effectors, the prototype EPEC strain E2348/69 translocates 21 effectors. ### RATIONALE The aim of this study was to test the hypotheses that, rather than operating individually, the T3SS effectors form robust intracellular networks that can sustain large contractions and that expanded effector repertoires play a role in distinct disease phenotypes and host adaption. ### RESULTS We tested the effector-network paradigm by infecting mice with >100 C. rodentium effector mutant combinations. First, using machine learning prediction algorithms, we discovered additional effectors, NleN and NleO. We then sequentially deleted effector genes from two distinct starting points to reach sustainable endpoints, which resulted in strains missing 19 unrelated effectors (CR14) or 10 effectors involved in the modulation of innate immune responses in intestinal epithelial cells (IECs) (CRi9). Moreover, we deleted Map and EspF, which target the mitochondria and disrupt tight junctions. Unexpectedly, all strains colonized the colon and activated conserved metabolic and antimicrobial processes in the IECs while eliciting distinct cytokine and immune cell infiltration responses. In particular, although infection with C. rodentium Δ map /Δ espF failed to induce secretion of interleukin-22 (IL-22), CR14 and CRi9 triggered heightened secretion of IL-6 and granulocyte-macrophage colony-stimulating factor (GM-CSF) and of IL-22, interferon-γ (IFN-γ), and IL-17 from colonic explants, respectively. Nonetheless, infection with CR14 or CRi9 induced protective immunity against secondary infections. Although Tir, EspZ, and NleA are essential, other effectors exhibit context-dependent essentiality in vivo. Moreover, C. rodentium expressing the effector repertoire of EPEC E2348/69 failed to efficiently colonize mice. We used curated functional information and our in vivo data to train a machine learning model that predicted values for colonization efficiency of previously uncharacterized mutant combinations. Notably, a mutant with a low predicted value, lacking only nleF , nleG8 , nleG1 , nleB , and espL , failed to colonize. ### CONCLUSION Our analysis revealed that T3SS effectors form robust networks, which can sustain substantial contractions while maintaining virulence, and that the composition of the effector network contributes to host adaptation. Alternative effector networks within a single pathogen triggered markedly different immune responses yet induced protective immunity. CR14 did not tolerate any further contraction, which suggests that this network reached its robustness limit with only 12 effectors. As the robustness limits of other effector networks depend on the contraction starting point and the order of the deletions, machine learning models could transform our ability to predict alternative network functions. Together, this study demonstrates the robustness of T3SS effector networks and the ability of IECs to withstand drastic perturbations while maintaining antibacterial functions. ![Figure][2] T3SS effectors form robust intracellular networks. T3SS effector networks can sustain substantial contractions while maintaining virulence. Using C. rodentium as a model showed that although triggering the conserved infection signatures in IECs, distinct networks induce divergent immune responses and affect host adaption. Because the robustness limit depends on the contraction sequence, machine learning models could transform our ability to predict the virulence potential of alternative networks. Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses. [1]: /lookup/doi/10.1126/science.abc9531 [2]: pending:yes

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Although lithium reduces dinitrogen, the other alkali and alkaline Earth metals have proven largely inert to the gas under ambient conditions. Rösch et al. report that with just the right β-diketiminate ligand and an assist from potassium as terminal reductant, calcium can mediate dinitrogen reduction. Crystallography and spectroscopic characterization revealed a product in which doubly reduced dinitrogen adopted a side-on bridging motif between two calcium centers. A subsequent reaction with coordinated tetrahydrofuran appeared to release diazene. Science , this issue p. [1125][1] Here we report that attempted preparation of low-valent CaI complexes in the form of LCa-CaL (where L is a bulky β-diketiminate ligand) under dinitrogen (N2) atmosphere led to isolation of LCa(N2)CaL, which was characterized crystallographically. The N22ˉ anion in this complex reacted in most cases as a very potent two-electron donor. Therefore, LCa(N2)CaL acts as a synthon for the low-valent CaI complex LCa-CaL, which was the target of our studies. The N22ˉ anion could also be protonated to diazene (N2H2) that disproportionated to hydrazine and N2. The role of Ca d orbitals for N2 activation is discussed. [1]: /lookup/doi/10.1126/science.abf2374

Light-emitting diodes (LEDs) that emit circularly polarized light (spin-LEDs) have potential applications in in three-dimensional displays, bioencoding, and tomography. The requisite spin polarization of the charge carriers is usually achieved with ferromagnetic contacts and applied magnetic fields, but Kim et al. report on a room-temperature spin-LED that relies instead on a chiral-induced spin selectivity organic layer. This layer selectively injected spin-polarized holes into metal halide perovskite nanocrystals, where they radiatively recombined with unpolarized electrons with an efficiency of 2.6%. Science , this issue p. [1129][1] In traditional optoelectronic approaches, control over spin, charge, and light requires the use of both electrical and magnetic fields. In a spin-polarized light-emitting diode (spin-LED), charges are injected, and circularly polarized light is emitted from spin-polarized carrier pairs. Typically, the injection of carriers occurs with the application of an electric field, whereas spin polarization can be achieved using an applied magnetic field or polarized ferromagnetic contacts. We used chiral-induced spin selectivity (CISS) to produce spin-polarized carriers and demonstrate a spin-LED that operates at room temperature without magnetic fields or ferromagnetic contacts. The CISS layer consists of oriented, self-assembled small chiral molecules within a layered organic-inorganic metal-halide hybrid semiconductor framework. The spin-LED achieves ±2.6% circularly polarized electroluminescence at room temperature. [1]: /lookup/doi/10.1126/science.abf5291

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B1.1.7 (VOC 202012/01) variant that emerged in late 2020 in the United Kingdom has many changes in the spike protein gene. Three of these are associated with enhanced infectivity and transmissibility, and there are concerns that B.1.1.7 might compromise the effectiveness of the vaccine. Muik et al. compared the neutralization efficacy of sera from 40 subjects immunized with the BioNTech-Pfizer mRNA vaccine BNT162b2 against a pseudovirus bearing the Wuhan reference strain or the lineage B.1.1.7 spike protein (see the Perspective by Altmann et al. ). Serum was derived from 40 subjects in two age groups 21 days after the booster shot. The vaccine remained effective against B.1.1.7 with a slight but significant decrease in neutralization that was more apparent in participants under 55 years of age. Thus, the vaccine provides a significant “cushion” of protection against this variant. Science , this issue p. [1152][1]; see also p. [1103][2] Recently, a new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage called B.1.1.7 (variant of concern: VOC 202012/01), which is reported to spread more efficiently and faster than other strains, emerged in the United Kingdom. This variant has an unusually large number of mutations, with 10 amino acid changes in the spike (S) protein, raising concerns that its recognition by neutralizing antibodies may be affected. In this study, we tested SARS-CoV-2-S pseudoviruses bearing either the Wuhan reference strain or the B.1.1.7 lineage spike protein with sera of 40 participants who were vaccinated in a previously reported trial with the messenger RNA–based COVID-19 vaccine BNT162b2. The immune sera had slightly reduced but overall largely preserved neutralizing titers against the B.1.1.7 lineage pseudovirus. These data indicate that the B.1.1.7 lineage will not escape BNT162b2-mediated protection. [1]: /lookup/doi/10.1126/science.abg6105 [2]: /lookup/doi/10.1126/science.abg7404

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