The stunning ancient symbols shed light on a forgotten chapter in religious history. The site of Jesus's Last Supper itself likely originally held a synagogue. But after what Heritage Daily describes as “cycles of destruction and reconstruction,” there came to be, in the time of the Crusades, a structure built referred to as the Cenacle, which still stands to this day. The Cenacle has attracted religious pilgrims for centuries, from impoverished worshippers to kings and conquerors. And now, a study published in Studium Biblicum Franciscanum has revealed that some of those pilgrims left messages behind on the very walls that surround this sacred site. The scientists also identified “coats of arms belonging to Tristram von Teuffenbach, a Styrian nobleman who was part of a pilgrimage to Jerusalem in 1436, led by Archduke Frederick of Habsburg (later the Holy Roman Emperor),” according to Heritage Daily, as well as an inscription that read “Christmas 1300.” They assessed it as being written “in a style typical of Armenian nobility.” But, as study coauthor Ilya Berkovich points out, a discovery like this can broaden our understanding of the places made sacred by centuries of tradition. Michale Natale is a News Editor for the Hearst Enthusiast Group. As a writer and researcher, he has produced written and audio-visual content for more than fifteen years, spanning historical periods from the dawn of early man to the Golden Age of Hollywood. Ancient Plants May Show the Site of Jesus's Tomb Time Could Be Flowing in Reverse All Around Us

You are using a browser version with limited support for CSS. Please note there may be errors present which affect the content, and all legal disclaimers apply. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif7 (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. This is a preview of subscription content, access via your institution Prices may be subject to local taxes which are calculated during checkout PhD Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA Rachel A. Silverstein, Nahye Kim, Ann-Sophie Kroell, Russell T. Walton, Blaire K. Smith, Kathleen A. Christie, Leillani L. Ha & Benjamin P. Kleinstiver Rachel A. Silverstein, Nahye Kim, Ann-Sophie Kroell, Russell T. Walton, Blaire K. Smith, Kathleen A. Christie, Leillani L. Ha & Benjamin P. Kleinstiver Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Hematology, St. Jude Children's Research Hospital, Memphis, TN, USA Department of Systems Biology, Harvard Medical School, Boston, MA, USA Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Standing nine stories above the waterline, complete with a five-acre flight deck and capacity for four squadrons of fighter jets, the ship instantly became the largest and most expensive to ever sail—a formidable platform projecting American naval superiority and American naval vulnerability. Despite all of her technical prowess, Ford is faced with a harsh reality: over the last two decades, China's hypersonic missile technology has far outpaced America's. Beijing has “the world's leading hypersonic missile arsenal,” according to a December 2024 report from the U.S. Department of Defense—and it only takes one successful anti-ship missile strike to destroy even the most powerful floating air base. In the event that China makes an amphibious landing on Taiwan, which experts expect will take place by 2027, China's suite of hypersonic missiles “can take out our 10 aircraft carriers in the first 20 minutes of a conflict,” U.S. Secretary of Defense Pete Hegseth said in a November interview that has recently resurfaced. “China is building an army specifically designed to destroy the U.S.,” Hegseth continued. These assertions fall in line with the Pentagon's priority of developing Navy laser weapons that can neutralize the hypersonic threat. Consider the DF-17, a medium-range Chinese hypersonic missile that can reportedly reach speeds of Mach 10 (10 times the speed of sound) and attack from more than 1,500 miles away. This missile does not fly in a conventional arc, but remains highly maneuverable as it reenters the atmosphere, meaning it can change its trajectory and direction while in flight. A weapon of this caliber is unpredictable and, therefore, more difficult to intercept. But a laser weapon powered by Ford's advanced A1B nuclear reactors could fire thousands or tens of thousands of times at a weapon like DF-17, tracking and targeting it better than conventional missiles. That said, the United States is still years away from a reliable, high-powered laser weapon capable of taking out hypersonic missiles. In one test, the laser tracked and locked in on a small inflatable speedboat with replica cannons and a dummy driver before silently firing an invisible ray of dancing atoms at the speed of light, causing the cannons to explode. In another test, LaWs destroyed a drone launched from the deck of a nearby ship, sending its fiery body toward the sea. However, due to operational issues, LaWs never saw mass production. In sea trials, the laser successfully engaged a static training target. Known as HELIOS (short for High-Energy Laser with Integrated Optical Dazzler and Surveillance), the 60-kilowatt laser integrates with the ship's AEGIS radar and weapons control system. During a weapons test in 2024, HELIOS zapped an aerial drone, according to a January report from the Office of the Director, Operational Test and Evaluation. Enter HELCAP, the High Energy Laser Counter-Anti-Ship Cruise Missile Program. Even HELCAP won't be strong enough to destroy an anti-ship hypersonic missile, though. That will take a 1-megawatt laser, the Pentagon believes—and it can't come soon enough. For her part, Ford is a transformational aircraft carrier that will serve well into the 2050s with next-generation technologies that we haven't yet seen. But given the proliferation of weapons like DF-17, she is perhaps more vulnerable now than her contemporaries have been in the last 80 years. If the Navy can expedite development of a high-power ship-mounted laser weapon—a HELCAP+, if you will—before China invades Taiwan, Ford may sail the seas without a worthy opponent to fear. Her favorite topics include, but are not limited to: the giant squid, punk rock, and robotics. How Tech Bros Almost Killed America's New Fighter We're Already Fighting the World's First AI War Is the U.S. Ready to Fight a New Kind of War?

You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. In recent years, the gut microbiota and derived metabolites have emerged as relevant players in modulating several brain functions, including energy balance control1,2,3. This form of distant communication mirrors that of metabolic hormones (for example, leptin, ghrelin), which convey information about the organism's energy status by exerting effects on diverse brain regions, including the master homeostatic centre, the hypothalamus4. However, whether the hypothalamus is also able to influence gut microbiota composition remains enigmatic. Here we present a study designed to unravel this challenging question. Subsequently, we conducted microbiota composition analysis throughout the gut using 16S rRNA gene sequencing. Our results showed that these brain interventions significantly changed the gut microbiota in an anatomical and short-term (2–4 h) fashion. Transcriptomic analysis indicated that these changes were associated with the reconfiguration of neuronal and synaptic pathways in the duodenum concomitant with increased sympathetic tone. Interestingly, diet-induced obesity attenuated the brain-mediated changes triggered by leptin in gut microbiota communities and sympathetic activation. Our findings reveal a previously unanticipated brain–gut axis that acutely attunes microbiota composition on fast timescales, with potential implications for meal-to-meal adjustments and systemic energy balance control. Therefore, the gut microbiota is emerging as a crucial determinant for the maintenance and promotion of host homeostasis and health1. This bacterial ecosystem is a complex and dynamic entity that produces many active compounds that are involved in a wide range of organismal functions. In the context of energy balance, the availability and abundance of different bacterial-derived metabolites (for example, short-chain fatty acids, bile acid derivatives, endocannabinoids) may profoundly influence host metabolism and feeding behaviour via diverse brain mechanisms1,2,6. Indeed, the interaction of these metabolites with G protein-coupled receptors of enteroendocrine cells promotes indirect signalling to the brain through the release of gut hormones (glucagon-like peptide-1, peptide YY) and neurotransmitters (γ-aminobutyric acid, serotonin)1. Furthermore, growing evidence suggests that many bacterial-derived metabolites reach the bloodstream and exert direct effects on distal organs, including the brain7. Collectively, these data indicate that certain metabolites produced by intestinal bacterial communities contribute to fine-tuning inflammation, glucose and lipid metabolism, and appetite via the so-called microbiota–gut–brain axis3. This mode of communication used by the gut microbiota mirrors the strategies used by metabolic organs (stomach, white adipose tissue or pancreatic beta cells, among others), which secrete hormones (ghrelin, leptin and insulin, respectively) that signal the whole-body energy status to the brain4. In turn, the brain integrates this information and orchestrates a range of adaptive mechanisms. For example, pro-opiomelanocortin (POMC) and agouti-related peptide (AgRP) neurons located in the arcuate nucleus (ARC) of the hypothalamus can sense and amalgamate multiple dynamic cues conveyed by metabolic hormones (ghrelin, leptin), nutrients and sensory inputs4. According to this information, these neurons co-ordinately modulate appetite, energy expenditure and glucose and lipid metabolism via the autonomic regulation of peripheral tissues, thus adjusting overall energy balance and metabolism4. On this basis, the brain also communicates with the gut and regulates key gastrointestinal functions (motility, permeability, pH, mucus production, immune response)8. However, whether the brain is also able to influence gut microbiota composition remains enigmatic. Our results unequivocally show that acute modulation of POMC or AgRP neuronal activity via chemogenetics, as well as the central delivery of the metabolic hormones ghrelin and leptin, changes gut microbiota composition in a short-term and anatomically specific fashion. Together, our findings reveal a new and unexpected brain–gut axis that acutely attunes microbiota composition on rapid timescales, with potential implications for meal–meal adjustments and systemic energy balance control. These observations represent a paradigm shift because they introduce the brain as an active modulator of microbiome plasticity and composition in response to metabolic signals. To initially assess whether the brain influences gut microbiota composition, we chemogenetically modulated the activity of hypothalamic AgRP and POMC neurons. These populations of neurons are plausible candidates to mediate brain–gut communication because of their crucial role in systemic metabolic control4. AgRPCre/+9 or POMCCre/+10 mice, as well as AgRP+/+ or POMC+/+ as controls, were bilaterally injected with an adeno-associated virus (AAV) vector encoding excitatory hM3Dq (AAV8-hSYN-DIO-hM3D(Gq)-mCherry) or inhibitory hM4Di (AAV8-hSYN-DIO-hM4D(Gi)-mCherry) DREADDs in the ARC (Fig. Subsequent studies were conducted 3 weeks later to ensure adequate AAV expression. Validation of the fidelity of AAV infection and the effectiveness of DREADD performance on AgRP and POMC neurons was confirmed via immunofluorescence studies (Extended Data Fig. a, Diagram illustrating the approach to express the excitatory (hM3Dq) or inhibitory (hM4Di) DREADDs in AgRP and POMC neurons. b, Schematic representation of the data processing and analysis workflow. c,d, Microbial α-diversity (Shannon index) of gut microbiota from vehicle and AgRP neuron-activated mice and vehicle (c) and AgRP neuron-inhibited mice 2 or 4 h after CNO injection (d). Sample sizes in d: 2 h control n = 7 per gut section; 2 h inhibited n = 9 per gut section; 4 h control n = 7 per gut section; 4 h inhibited n = 8 in the duodenum, jejunum and ileum, and n = 7 in the caecum. e,f, Microbial α-diversity (Shannon index) of gut microbiota from vehicle and POMC neuron-activated mice (e) and vehicle and POMC neuron-inhibited mice (f) 2 or 4 h after CNO injection. Sample sizes in e: 2 h control n = 7 per gut section; 2 h activated n = 8 per gut section; 4 h control n = 8 per gut section; 4 h activated n = 8 per gut section. Sample sizes in f: 2 h control n = 7 in the duodenum, jejunum and ileum, and n = 6 in the caecum; 2 h inhibited n = 9 per gut section; 4 h control n = 7 per gut section; 4 h inhibited n = 7 in the duodenum, jejunum and ileum, and n = 6 in the caecum. Statistical significance was determined using a one-way analysis of variance (ANOVA), followed by false discovery rate (FDR) adjustment. Data are represented by the effect size (log fold change). Microbial taxa with statistically significant differences between the control and treated group at 2 and 4 h after CNO injection in specific gut sections are shown. Stars indicate statistically significant differences compared to the control group; q values for the differences depicted in the heatmaps are provided in Supplementary Table 1. The DREADD ligand clozapine N-oxide (CNO) was administered to selectively activate or inhibit POMC or AgRP neurons; gut luminal contents from four anatomically distinct segments (duodenum, jejunum, ileum and caecum) were collected 2 and 4 h later, followed by gut microbiota analysis (Fig. To assess how the composition of microbial communities changed upon brain manipulations, we implemented a tailored metagenomic profiling and statistical analysis pipeline (Fig. Microbiota community richness and evenness was evaluated according to the α-diversity using the Shannon index. We observed that modulation of the activity of AgRP or POMC neurons did not significantly modify this parameter (Fig. Beta diversity analysis was performed to obtain the differential abundant taxa using the analysis of compositions of microbiomes with bias correction (ANCOM-BC) method11. 1g), while inhibition also induced slight changes in the gut microbiota composition of the jejunum and caecum (Fig. In contrast, activation of POMC neurons predominantly increased or decreased several bacterial families in the duodenum, while inhibition significantly affected bacterial families in the jejunum, ileum and caecum (Fig. These changes in microbiome composition were not due to alterations in gut motility (Extended Data Fig. Collectively, our findings suggest that the acute chemogenetic manipulation of key populations of hypothalamic neurons (particularly POMC neurons) results in rapid changes in gut microbiota composition independent of food intake. To investigate these findings under a more physiological context, we implemented two complementary strategies that progressed with the modulation of hypothalamic AgRP and POMC neurons: (1) sensory perception of food12,13,14,15 and (2) central administration of metabolic hormones (that is, ghrelin and leptin)4,12,16. Regarding the sensory aspect, overnight fasted C57BL/6J mice were exposed either to a caged inedible object (a wood dowel) or a caged food pellet that could be seen and smelled but not consumed (Fig. Mice were euthanized at a shorter time interval (1 h later), compatible with the acute food sensory effects on hypothalamic neurons12,13,14,15; luminal contents were obtained from the diverse gut regions for microbiota analysis. Similarly, sensory detection of food did not cause changes in gut microbiota composition in any of the intestinal segments assessed (Fig. These results indicate that transient fluctuations in AgRP and POMC neuron activity in response to food perception are insufficient to influence the gut microbiome, suggesting that more sustained or intense stimuli may be required to induce noticeable changes. Nevertheless, we cannot exclude that the timing of sample collection could be contributing to the absence of changes in microbiota composition under these experimental conditions. a, Schematic representation of the food sensory perception paradigm. b, Microbial α-diversity (Shannon index) of gut microbiota from mice exposed to an inedible object (n = 6 samples per gut section) or inaccessible food pellet for 60 min (n = 7 samples per gut section). Statistical significance was determined using a one-way ANOVA, followed by FDR adjustment. Data are represented according to the effect size (log fold change). Stars indicate microbial taxa with statistically significant differences (*q = 0.0144) between an inedible object and an inaccessible food pellet after 60 min of exposure. Next, we examined the effects of ghrelin and leptin, which are canonical metabolic hormones with opposite functions regarding energy homeostasis16. To do so, we administered saline, leptin or ghrelin into the lateral ventricle of the brain of 8-week-old C57BL/6J mice (Fig. Validation of hormonal interventions was confirmed via FOS staining in hypothalamic sections (Extended Data Fig. Luminal contents from the duodenum, jejunum, ileum and caecum were collected 2 and 4 h after the injection (Fig. While ghrelin-associated α-diversity showed no significant differences between groups, leptin treatment led to significant changes in this parameter in certain gut regions (Fig. For β-diversity, similar to AgRP neuronal activation, ghrelin resulted in modest alterations in bacterial families (Fig. Indeed, changes were primarily observed in the ileum and caecum after 2 h of treatment, whereas the main changes at 4 h were evident in the duodenum (Fig. Interestingly, while ghrelin did not significantly affect gut motility, central leptin administration strongly inhibited this parameter (Extended Data Fig. Altogether, these results suggest that satiety signals contribute to reshaping the bacterial composition of the gut in a swift and anatomically selective manner. This aligns with previous studies showing that leptin influences gastrointestinal motility17, which can in turn affect microbial communities and dynamics in the gut. a, Schematic representation of the experimental outline for brain delivery of leptin and ghrelin. b,c, Microbial α-diversity (Shannon index) of gut microbiota from mice treated with ICV ghrelin (b) or leptin (c) at 2 or 4 h after treatment. Sample sizes in b: 2 h vehicle n = 9 per gut section; 2 h ghrelin n = 10 per gut section; 4 h vehicle and ghrelin n = 10 per gut section. Sample sizes in c: 2 h vehicle n = 8 per gut section; 2 h leptin n = 7 in the duodenum, jejunum and caecum, and n = 8 in the ileum; 4 h vehicle and leptin n = 8 in the duodenum, jejunum and caecum, and n = 7 in the ileum; 2 h (ileum, **q = 0.0036; caecum, *q = 0.0101); 4 h (duodenum, *q = 0.0152; jejunum, *q = 0.0269). Statistical significance was determined using a one-way ANOVA, followed by FDR adjustment. Microbial taxa with statistically significant differences between control and treated group at 2 and 4 h after treatment in specific gut sections are shown. Data are represented by the effect size (log fold change). For a comprehensive overview and precise identification of interactions between diverse treatments and gut sections, we visualized differentially abundant taxa using a chord diagram (Fig. This analysis revealed that the most significant brain-induced effects on gut microbiota composition were observed in the duodenum and were mediated by both chemogenetic POMC neuron activation and central leptin (Fig. Therefore, we subsequently focused our attention on this specific gut segment and hormonal treatment. Central leptin resistance is a hallmark of obesity16. To assess whether leptin resistance compromises the leptin-driven changes in the gut microbiota profile, we fed C57BL/6J mice with a high-fat diet (HFD) for 12 consecutive weeks (Fig. As expected, this dietary regime caused significant weight gain, hyperglycaemia and plasma hyperleptinaemia (Extended Data Fig. Notably, central leptin delivery in these animals (Fig. 4a) did not trigger the microbiota changes previously observed across the intestine of lean mice (Fig. No changes in α-diversity were observed (Extended Data Fig. Collectively, these results indicate that functional leptin signalling in the brain is required to drive short-term gut microbiota changes. a, Schematic representing the experimental outline of central leptin treatment under HFD conditions. Data are represented by the effect size (log fold change). Microbial taxa with statistically significant differences between vehicle-treated and leptin-treated groups in mice fed with standard diet (SD) (left heatmap) and mice fed with an HFD (right heatmap) at 2 and 4 h after treatment in specific gut sections are shown (n = 7–10 mice per group). Stars indicate statistically significant differences compared to the control group; q values for the differences depicted in the heatmaps are provided in Supplementary Table 1. c, Volcano plot displaying statistically significant differences in metabolites in the duodenal luminal content between mice centrally treated with vehicle or leptin (red, upregulated; blue, downregulated; black, no changes; n = 6 mice per group). d, Bar plot visualization of significantly changed metabolites (log2 fold change). f, Sankey plot of the top 50 overrepresented GO pathways and related biological categories for the leptin versus vehicle groups comparing untreated (water, left) or antibiotic-treated (right) conditions (n = 6–14 mice per group). The GO pathways are ordered according to ascending P value. Details on GO categories and P values are provided in Supplementary Table 2. g,h, Adrenaline (g) and noradrenaline (h) levels in the duodenum of mice fed with standard or HFD 2 h after central leptin treatment. Sample sizes in g: vehicle (n = 6); leptin, vehicle HFD and leptin HFD (n = 8). Sample sizes in h: n = 8 per group. g,h, Data are represented as the mean ± s.e.m. Statistical significance was determined using a two-way ANOVA followed by Tukey's test. Changes in microbiota communities entail alterations in metabolic pathways and the production of metabolites that impact host health3. To define the metabolic and functional signatures of the microbial communities in the duodenum induced by central leptin administration, we used predictive tools of metagenome functions based on 16S rRNA sequencing data18,19. This analysis revealed 472 metabolic pathways, among which 79 exhibited significant differences (Extended Data Fig. Most pathways reflected an enrichment in biosynthetic processes (amino acids, cofactors, carriers, vitamins, carbohydrates, lipids, precursor metabolites and energy) with few representations of degradative categories (Extended Data Fig. It is important to note that some of the biochemical products of these pathways (for example, certain amino acids, acetate) have been associated with central acute effects on appetite in rodents20,21,22,23. To validate these predictions, we performed liquid chromatography (LC)–tandem mass spectrometry (MS/MS) metabolomics on the duodenal content from mice centrally treated with vehicle or leptin. Vehicle and leptin samples formed distinct clusters (Extended Data Fig. We identified 89 metabolites, 19 of which were significantly altered between groups (Fig. Leptin treatment notably increased amino acids and related metabolites (Fig. 4d), partially validating functional predictions for amino acid metabolism (Extended Data Fig. 4d) support microbial contributions to vitamin biosynthesis pathways predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) plug-in (Extended Data Fig. Our findings revealed that intracerebroventricular (ICV) leptin administration was associated with reduced representation of several GBMs, including those involved in neurotransmitter synthesis (for example, γ-aminobutyric acid and glutamate) and the production of neuroactive metabolites (for example, quinolinic acid, vitamin K2 and p-cresol) (Extended Data Fig. In an attempt to unveil potential mechanisms mediating the rapid reconfiguration of gut microbiota composition after central leptin delivery, we conducted RNA sequencing (RNA-seq) analysis of the duodenum under this experimental condition. We also included an antibiotic treatment to isolate the effects of the microbiota on transcriptomic changes. We concentrated on the 2-h time point, assuming that potential transcriptomic changes in the duodenum should precede any alterations in microbiota if they are causally linked (Fig. To gain insights into the functional characteristics and biological implications of central leptin administration across all experimental groups, we conducted a Gene Ontology (GO) analysis of the transcriptomic dataset. Additionally, leptin administration enhanced pathways related to ‘Immune response and antigen processing' in the antibiotic-treated group (Fig. These results indicate that central leptin delivery engages in neuronal communication processes in the duodenum independently of the gut microbiota, underscoring the establishment of dynamic brain–gut communication mechanisms. The sympathetic nervous system (SNS) has a crucial role in regulating diverse metabolic functions that are essential for maintaining energy balance and physiological homeostasis25. Sympathetic neurons signal, via catecholamines (mainly adrenaline and noradrenaline), to α-adrenergic or β-adrenergic receptors in target cells. In this context, we next set out to determine whether central leptin modulates the duodenal content of adrenaline and noradrenaline. We found that while leptin did not change the concentration of noradrenaline in the duodenum 2 h after administration, it increased adrenaline levels (Fig. 4g,h), suggesting increased duodenal sympathetic tone. This is congruent with the transcriptomic data, reflecting the recruitment of pathways related to neuronal signalling and synaptic transmission (Fig. Interestingly, mice fed with an HFD did not exhibit a rise in duodenal adrenaline induced by central leptin (Fig. These results suggest that intact leptin signalling is necessary to mediate brain–gut sympathetic communication. This aligns with the attenuated changes in microbiota composition previously observed under the HFD regime (Fig. This study represents a new standpoint and a paradigm shift in microbiota biology and metabolic research for several reasons. First, we provide evidence that the direct action of metabolic hormones in the brain (for example, leptin) and modulation of key neurons implicated in energy balance control (for example, POMC neurons) result in acute and anatomically specific alterations in gut microbiota composition. Our findings uncover a new and unexpected layer of communication between the brain and the gut microbiota, with potential physiological significance (in managing the postprandial state) and pathophysiological relevance. Second, our work builds on previous studies demonstrating rapid changes in gut microbiota composition induced by diurnal rhythmicity28,29, dietary manipulations30,31 and neurological insults, including stress32,33,34. Notably, our data extend these observations by offering gut-region-specific insights into gut microbiota composition that are independent of food intake and nutrient availability. Indeed, most of the current knowledge on gut microbiota biology stems from studies of faecal samples, which is an appropriate non-invasive strategy that can be sampled easily and repeatedly. This approach precludes the analysis of regional variations and potential rapid changes (for example, 2 h) in microbiota composition, which is important to understand how microbial communities adapt to specific niches and their contribution to host health. Considering that the small intestine serves as the primary site for nutrient digestion and absorption, it is reasonable to anticipate that nutrients can rapidly influence gut microbiota activity and composition in this gut segment. Our experimental design, centred around 2-h and 4-h intervals, reinforces this idea and emphasizes the importance of analysing specific regions and using short time points. Third, our study also underscores the dynamic nature of the gut microbiome in response to immediate metabolic cues. Therefore, it is tempting to speculate that the rapid brain-mediated changes in gut microbiota may have a meal–meal relevance with a direct impact on systemic metabolism. While food intake is vital for the provision of energy and nutrients, it is also a significant stressor that disrupts homeostasis35. Indeed, these microbiome changes could contribute to multiple postprandial physiological aspects, including nutrient absorption, immune function, maintenance of the gastrointestinal barrier and integrity, and production of bioactive metabolites, as well as metabolism and appetite regulation. Our findings indicating that leptin-induced changes in bacterial functional pathways can be associated with changes in metabolites that acutely modulate appetite support this idea. The dynamic brain–gut reshaping of microbial communities could represent an evolutionary strategy to handle the postprandial situation and fine-tune appetite, thus maximizing metabolic benefits and reducing homeostatic stress. Our transcriptomic data suggest that brain–gut communication occurs through the reorganization of the neuronal and synaptic architecture of the duodenum, independent of gut microbiota. In line with this, we also noted a local increase in sympathetic mediators indicating enhanced SNS activity. Notably, this effect was attenuated under HFD feeding, implying that obesogenic states may disrupt adequate brain–gut communication. This would depict a complex scenario in which multiple factors could act in conjunction with neural pathways or independently to influence gut microbiota and function. Despite this study providing a new dimension in the way that the brain communicates with the periphery, it is important to recognize some limitations. We used pharmacological interventions (that is, exogenous administration of hormones and DREADD ligand in line with those commonly used in the literature), aimed at eliciting robust responses. While our studies were consistently conducted at the same time during the light phase, we believe that the pharmacological treatments used would drive the same observed outcomes regardless of time of day. The dissection of the functional relevance and mechanistic insights of brain-mediated variations in gut microbiota are challenged by the inability to exclude confounding factors or define appropriate biological readouts. The gut microbiota is highly responsive to changes in a wide range of environmental factors (for example, diet, drugs, stress). Thus, any manipulation or intervention aimed at studying the hypothalamus–gut microbiota axis may alter the microbiota composition, making it difficult to isolate specific cause-and-effect relationships. Another constraint is that, although our findings are probably applicable also to females, all studies conducted thus far have exclusively involved male mice. The effects of female hormones on gut microbiota have been reported in several species, including rodents and humans36,37,38,39. This poses challenges in controlling hormonal fluctuations and can represent a significant source of variability. In summary, our findings uncover an unsuspected brain–gut connection that rapidly adjusts gut microbiota composition on short timescales, potentially participating in meal–meal adaptations and whole-body energy balance control. The identification of this new brain–gut axis represents an exciting point of departure in the field of neurogastroenterology and paves the way for new research to precisely delve into the mechanisms and their biological implications. Understanding this biological process may offer a potentially tractable target for a rapidly and transiently modulating gut microbiota composition. This approach would leverage the organism's own adaptive responses, holding potential benefits for metabolic disorders such as type 2 diabetes and obesity. Animal studies followed the ethical standards approved by the Ethics Committee of the University of Barcelona (390/19) and the University of Santiago de Compostela (15012/2023/014) in compliance with local, national and European legislation. Mice were group-housed (4–5 animals per cage) under a controlled environment (humidity 30–80%; temperature 22–24 °C) under a 12-h light–dark cycle and with free access to water and standard diet (Teklad maintenance diet, 14% protein; 2014C, Envigo). As controls, Cre− littermates were used and maintained under identical conditions. For the chemogenetic experiments, all mice received CNO to control for potential ligand effects. Mice were randomly assigned to experimental treatments. Samples were consistently collected at the same time of day to ensure the replicability of microbiome analyses40. Eight-week-old C57BL/6J male mice were fasted overnight (16 h) and exposed to an inedible object (a wood dowel) or a standard chow pellet, both caged within a wire mesh that allowed the object or food to be seen and smelled but not consumed, as described previously15. Sixty minutes later, animals were euthanized for sample collection. Eight-week-old C57BL/6J male mice (for the standard chow diet studies) or 18-week-old mice (for the HFD studies) were anaesthetized with 2.5% isoflurane in oxygen and placed in a stereotaxic frame. Head hair was shaved and the area cleaned with chlorhexidine. A stainless steel cannula was implanted in the right lateral ventricle (coordinates from Bregma: anteroposterior: −0.3 mm; mediolateral: +1.0 mm; dorsoventral: −2.5 mm below the surface of the skull) and fixed to the skull with n-butyl cyanoacrylate. To improve recovery, the area was treated with bupivacaine (local infiltration, 1.25 mg ml−1); mice were placed on a heat pad (37 °C) until recovered from the anaesthesia. The ICV leptin (L3772, Sigma-Aldrich) and ghrelin (4033076, Bachem) treatments were performed 3 days after cannula implantation. One hour after the start of the light period, mice were food-deprived for 2 h before treatment. At the time of the injection, the cannula was opened to administer leptin (3 µg per mouse), ghrelin (5 µg per mouse) or sterile saline for the controls, using a 25-µl syringe (Gas Tight Syringe, Hamilton Company). Animals were euthanized 2 or 4 h after the injection for sample collection. For pain relief, mice received buprenorphine intraperitoneally (0.3 mg kg−1) before surgery and every 12 h for 72 h after surgery. The skull was exposed and a small hole was drilled for AAV injection into the ARC. AAVs encoding excitatory (AAV8-hSYN-DIO-hM3D(Gq)-mCherry; 44361, Addgene; 1.10 × 1,013 gc ml−1) or inhibitory (AAV8-hSYN-DIO-hM4D(Gi)-mCherry, 44362, Addgene; 1.10 × 1,013 gc ml−1) DREADDs were injected bilaterally (300 nl per side) using a 33-G needle connected to a 5-μl syringe (NeuroSyringe, Hamilton Company) at 50 nl min−1 at the following coordinates from Bregma: anteroposterior: −1.5 mm; mediolateral: ±0.3 mm; dorsoventral: −5.8 mm below the surface of the skull. After 8 min, the needle was retracted 1 mm and, after waiting 1 min, it was completely withdrawn. The incision was sutured with VetBond (3M) and mice were placed on a 37 °C heat pad until recovered from the anaesthesia. Experiments were conducted at least 3 weeks after the injections. On the experimental days, food was removed one hour after lights-on; 2 h later, mice were injected intraperitoneally with CNO (1 mg kg−1 for activation or 3 mg kg−1 for inhibition; C4936, Tocris Bioscience) dissolved in sterile saline. Mice were euthanized 2 or 4 h later. The correct injection sites were confirmed in every mouse postmortem by assessing the mCherry signal under a fluorescence microscope (Nikon Eclipse Ni-U). POMCCre/+ and AgRPCre/+ mice were anaesthetized with ketamine/xylazine and perfused intracardially with saline (PBS) followed by ice-cold 4% buffered paraformaldehyde. Brains were dissected, post-fixed overnight, cryoprotected with 30% sucrose, sectioned using a cryostat (RWD) at a 25-µm thickness; slices were stored at −20 °C. For double POMC and FOS immunofluorescence in mCherry+ specimens, hypothalamic slices containing the ARC were blocked with 2% donkey serum in KPBS + 0.1% Triton X-100 + 3% BSA and incubated overnight at 4 °C with rabbit anti-FOS antibody (1:300 dilution, 226008, Synaptic Systems) in blocking solution. After washing in KPBS + 0.1% Triton X-100, slices were incubated for 2 h with donkey anti-rabbit Alexa Fluor 647 antibody (1:300 dilution, A32795, Thermo Fisher Scientific) in KPBS + 0.1% Triton X-100 + 3% BSA. After washing in KPBS + 0.1% Triton X-100, slices were blocked with 2% donkey serum in KPBS + 0.4% Triton X-100 and incubated with rabbit anti-POMC precursor antibody (1:1,000 dilution; H-029-30, Phoenix Pharmaceuticals) for 16 h at 4 °C. Finally, slices were incubated with donkey anti-rabbit Alexa Fluor 488 antibody (1:200 dilution, Thermo Fisher Scientific) for 1.5 h at room temperature followed by nucleus counterstaining and mounting with ProLong Diamond Antifade mountant (P36971, Invitrogen). Imaging was performed using a Nikon Eclipse Ni-U fluorescence microscope. Representative images are maximum-intensity projections generated using ImageJ Fiji (National Institutes of Health) and equally adjusted for brightness and contrast. FOS+POMC+ neuron cells were manually counted in a blinded fashion using ImageJ Fiji. Three mice per genotype were analysed by averaging the percentage of FOS + POMC neurons across four images per mouse. For FOS immunofluorescence in mCherry+ specimens, hypothalamic slices containing the ARC were blocked with 2% donkey serum in KPBS + 0.1% Triton X-100 + 3% BSA and incubated with rabbit anti-FOS antibody (1:300 dilution, 226008, Synaptic Systems) in blocking solution overnight at 4 °C. After washing in KPBS + 0.1% Triton X-100, slices were incubated for 2 h with donkey anti-rabbit Alexa Fluor 488 antibody (1:300 dilution, A32790, Thermo Fisher Scientific) in KPBS + 0.1% Triton X-100 + 3% BSA, followed by nucleus counterstaining and mounting with ProLong Diamond Antifade mountant. Imaging was performed using a Nikon Eclipse Ni-U fluorescence microscope. Representative images are maximum-intensity projections generated using ImageJ Fiji and equally adjusted for brightness and contrast. FOS+ neuron cells were manually counted in a blinded fashion using ImageJ Fiji. Three mice per genotype were analysed by averaging the percentage of FOS + POMC neurons across four images per mouse. For the hormonal studies, C57BL/6J male mice were fasted 2 h before the ICV injection with vehicle, leptin or ghrelin. Ninety minutes after the injection, mice were anaesthetized with ketamine/xylazine and perfused intracardially with saline followed by ice-cold 10% neutral-buffered formalin. Brains were dissected and post-fixed overnight, cryoprotected with 30% sucrose, and cut using a cryostat (Epredia) at a 30-μm thickness; slices were stored at −20 °C. Hypothalamic slices containing the ARC were blocked with 2% goat serum in Tris-buffered saline (TBS) + 0.3% Triton X-100 + 0.25% BSA and incubated with chicken anti-FOS antibody (1:4,000 dilution, 226009, Synaptic Systems) in blocking solution overnight at 4 °C. After washing in TBS + 0.1% Triton X-100, slices were incubated for 2 h at room temperature with a goat anti-chicken Alexa Fluor 488 antibody (1:1,000 dilution, ab150169, Abcam) in TBS + 0.3% Triton X-100 + 0.25% BSA. Imaging was performed using a Leica TC-SP5-X-AOBS confocal microscope. Representative images were equally adjusted for brightness and contrast. Neuron cells positive for FOS were manually counted in a blinded fashion using ImageJ Fiji. Body weight was measured using a precision scale. Blood samples were collected from the tail vein after overnight (16 h) fasting. Blood samples were collected in Microvette 500 lithium heparin tubes and centrifuged at 2,000g for 15 min at 4 °C. Plasma was used for the analysis. Motility experiments were conducted according to a previously described protocol41. Briefly, cage food was removed 1 h after lights-on; 2 h later mice were injected intraperitoneally with CNO or ICV injected with ghrelin or leptin. Thirty minutes after CNO injection or 150 min after the hormonal interventions, each mouse received 200 μl of a sterile 5 mg ml−1 fluorescein isothiocyanate-dextran (70,000 MW, 46945, Sigma-Aldrich) solution in PBS, administered via oral gavage. Mice were euthanized 90 min later and gastrointestinal tracts were immediately collected and placed in ice-cold PBS for 30 s to inhibit motility. Tracts were dissected into 12 segments: stomach, small intestine (partitioned into eight equally sized segments) and colon (with caecum first dissected out; the remaining portion was divided into two equally sized segments). The intestinal flushes were subjected to serial dilutions. Fluorescence was measured using an Infinite M Nano+ 200 PRO (Tecan Ibérica Instrumentación). Absolute fluorescence levels were estimated using a dilution series of a fluorescein isothiocyanate-dextran solution of known concentration. Data analysis was performed as described in ref. Mice were treated with a combination of ampicillin and neomycin, two broad-spectrum antibiotics that are poorly (ampicillin) or not (neomycin) absorbed, thus minimizing systemic effects. One day after surgery, mice were divided into water or antibiotic treatment (ampicillin 1 g l−1; neomycin 0.5 g l−1 in drinking water) groups. After 48 h of treatment, saline or leptin (3 µg) was delivered ICV and mice were euthanized 2 h later. The duodenum was collected and snap-frozen in liquid nitrogen. Frozen samples were processed for RNA isolation and RNA-seq as described below. Catecholamines were measured in duodenal sections using an Epinephrine/Norepinephrine ELISA Kit (2-CAT High Sensitive ELISA BA E-5400R, Labor Diagnostika Nord). For extraction, tissues were digested in 0.01 N HCl-0.3 mg ml−1 ascorbic acid buffer using a homogenizer. Samples were centrifuged at 3,400g for 20 min at 4 °C and the supernatants were used for subsequent analysis. Samples of mucus and luminal content were collected from the duodenum, jejunum, ileum and caecum of each mouse at selected time points for subsequent analysis of microbiota composition. 51604, QIAGEN), according to the manufacturer's instructions with minor modifications45. Library preparation and sequencing of the V4 variable region of the 16S rRNA gene was performed by the Mr. DNA Lab, using the illCUs515F (GTGYCAGCMGCCGCGGTAA) and new806RB (GGACTACNVGGGT WTCTAAT) primer pair and 2 × 250 bp paired-end Illumina MiSeq sequencing technology (30 cycles). FASTQ data of demultiplexed samples were downloaded from the Illumina BaseSpace Sequence Hub using the BaseMount tool. Paired-end reads were merged using USEARCH46,47 with a maximum number of ten mismatches in the alignment. VSEARCH48 was used to strip primer sequences, globally trim the reads to 252 bp and filter the reads using an expected error threshold of 1. Reads containing wildcard bases were removed. Dereplication was performed, after which the unique sequences were denoised with UNOISE3 (ref. Trimmed and quality-filtered reads were aligned with VSEARCH using a pairwise 97% sequence identity threshold onto the predicted zero-radius operational taxonomic unit (ZOTU) sequences to generate an OTU table. SINTAX was used to taxonomically classify the ZOTU sequences51 with the Ribosomal Database Project Training Set v.18 and a bootstrap cut-off value of 0.8. We began by merging the working tables into a phyloseq R package object per experiment (v.4.0.3) (https://www.R-project.org/), including sample, taxonomic classification and count data. To minimize host DNA contamination, we preprocessed the data to work with bacteria only. We also removed taxa with Cyanobacteria phylum annotations to avoid food and water contamination biases. One technical outlier (total counts below 30,000) was removed (Supplementary Fig. No biological outliers were based on (1) extremely different α-diversity score and (2) clear dissimilarities in the principal coordinate analysis distribution compared with the rest of the group components (based on Bray–Curtis dissimilarity). The statistical analysis included clustering, α-diversity, β-diversity and effect size analysis; α-diversity was conducted using ZOTUs, completed by first rarefying the samples library sizes in each experiment to the minimum sample depth of that experiment without replacement. This was implemented using the phyloseq R package. The Shannon index was used to represent α-diversity along with the application of a linear model, followed by an ANOVA and FDR post-hoc adjustment (using the stats and car R packages). Data clustering was conducted using principal coordinate analysis of all data (using the phyloseq R package), including gut section, activation or treatment by tissue section or taxa. Differential abundance analysis for each gut segment, time point and treatment was performed to obtain the differential abundant taxa by implementing the ANCOM-BC method (using the ancombc function from the ANCOM-BC R package)11. ANCOM-BC uses log-ratio transformation and corrects the bias induced by the differences among samples. Absolute abundance data were modelled using a linear regression framework. The method also accounted for zero inflation. To control the FDR across tests, the Benjamini–Hochberg correction was applied, with taxa considered significantly different when Padj (q) was less than 0.05. Data visualization was performed using the RColorBrewer, ggplot2, pheatmap and circlize packages in R. Rarefied pathway abundances were further analysed in STAMP53 using a Welch test between groups. A Dionex Ultimate 3000 RS LC system coupled to an Orbitrap mass spectrometer (Q Exactive, ThermoHESI-II Fisher Scientific) equipped with a heated electrospray ionization (HESI-II) probe was used. Solvents were of LC–MS grade quality (Merck). The luminal content from the duodenum was collected 4 h after leptin treatment and stored at −80 °C until analysis. Metabolomic profiling was performed as reported previously with minor variations54. Briefly, 50 µl of water and 700 µl of acetone, acetonitrile and methanol (1:1:1, v/v/v), containing 2.5 µM Metabolomics Amino Acid Mix Standard (Cambridge Isotope Laboratories), were added to each sample (34 ± 8.6 mg). After incubation and centrifugation, 600 µl of the supernatant were dried under vacuum. The leftover supernatants of all samples were pooled and used for the quality control (QC) samples. Dried supernatants were reconstituted in 70 µl of methanol and acetonitrile (1:1, v/v) for LC–MS/MS analysis. Metabolites were separated on a SeQuant ZIC-HILIC column (150 × 2.1 mm, 5 μm; Merck) using water with 5 mM ammonium acetate as eluent A and acetonitrile/eluent A (95:5, v/v) as eluent B, both containing 0.1% formic acid. Data acquisition was carried out with data-dependent MS/MS scans (top ten). Compound Discoverer 3.3 (Thermo Fisher Scientific) was used for data processing. Metabolites were identified based on exact mass, retention time, fragmentation spectra and isotopic pattern. We used an in-house fragmentation library54, a microbiome-specific library55 and the online library mzCloud. QC at four concentrations ensured signal stability and linearity. Partial least squares discriminant analysis was conducted using the mixOmics package (v.6.28.0) in R. Group differences were assessed using the Mann–Whitney U-test and FDR correction. Metabolites were considered significant when Padj (q) was less than 0.05 and the absolute fold change greater than 1.25. Visualizations were generated using the ggplot2 package in R. Gut–brain modules (GBMs) were inferred as described in ref. Briefly, GBMs were inferred from the orthologue abundance table obtained with PICRUSt2, using the web application GOmixer (http://www.raeslab.org/gomixer/). The detection threshold was set at 66% coverage and a minimum median difference of 5.0. Data were scaled according to sample abundance and the mean of observed reactions was used as an abundance estimator. GBMs were considered significant when P < 0.05 and were represented using the ggplot2 package in R. Duodenal mRNA was isolated using the TRIzol reagent (cat. Strand-specific RNA libraries were generated using 150 ng of total RNA with the Illumina Stranded RNA Prep Ligation Kit with Ribo-Zero Plus according to the manufacturer's instructions. Libraries were sequenced on an Illumina NextSeq 2000 in paired-end mode (read length of 2 × 50 bp). Forty million paired-end reads were generated for each sample and condition. Sequencing was conducted at the Genomics Facility of the Institut d'Investigacions Biomèdiques August Pi i Sunyer. The expression read matrix cut-off was set at an average of ten and read counts were normalized to counts per million using the R package EdgeR (v.3.42.0). Differentially expressed genes (DEGs) were calculated using the limma (v.3.60.4) R package from Bioconductor, applying batch correction. Significant DEGs were those with P < 0.05 and an absolute fold change value greater than 1.5. A principal component analysis plot was generated using the ggfortify (v.0.4.16) and ggplot2 (v.3.4.4) R packages. Pathway enrichment analysis of the DEGs was performed using AmiGO 2 (https://amigo.geneontology.org/amigo); significant annotations (P < 0.05) were selected. We used statistical methods (https://www.datarus.eu/en/applications/granmo/) along with previous experience to predetermine sample sizes. Data collection and analysis were not performed blind to the conditions of the experiments, except for image acquisition and analysis. Data exclusion was based on the ROUT method (1% threshold). Data are presented as the mean ± s.e.m. Statistical analyses were performed using Prism 8.0 (GraphPad Software) and R v.4.4.0. Data distribution was assumed to be normal. Statistical significance was determined with an unpaired one-tailed or two-tailed Student's t-test, one-way or two-way ANOVA followed by an appropriate post-hoc test, as indicated in the figure legends. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Source data are provided with this paper. Van Hul, M. et al. What defines a healthy gut microbiome? & Verbeke, K. The role of short-chain fatty acids in microbiota–gut–brain communication. Agus, A., Clément, K. & Sokol, H. Gut microbiota-derived metabolites as central regulators in metabolic disorders. Brüning, J. C. & Fenselau, H. Integrative neurocircuits that control metabolism and food intake. Mirabella, P. N. & Fenselau, H. Advanced neurobiological tools to interrogate metabolism. Cani, P. D. Microbiota and metabolites in metabolic diseases. Gut commensal E. coli proteins activate host satiety pathways following nutrient-induced bacterial growth. Tong, Q., Ye, C.-P., Jones, J. E., Elmquist, J. K. & Lowell, B. B. Synaptic release of GABA by AgRP neurons is required for normal regulation of energy balance. Xu, A. W. et al. PI3K integrates the action of insulin and leptin on hypothalamic neurons. A. Sensory detection of food rapidly modulates arcuate feeding circuits. Betley, J. N. et al. Neurons for hunger and thirst transmit a negative-valence teaching signal. Mandelblat-Cerf, Y. et al. Arcuate hypothalamic AgRP and putative POMC neurons show opposite changes in spiking across multiple timescales. Brandt, C. et al. Food perception primes hepatic ER homeostasis via melanocortin-dependent control of mTOR activation. Timper, K. & Brüning, J. C. Hypothalamic circuits regulating appetite and energy homeostasis: pathways to obesity. Yarandi, S. S., Hebbar, G., Sauer, C. G., Cole, C. R. & Ziegler, T. R. Diverse roles of leptin in the gastrointestinal tract: modulation of motility, absorption, growth, and inflammation. Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. The short-chain fatty acid acetate reduces appetite via a central homeostatic mechanism. Kasaoka, S. et al. Histidine supplementation suppresses food intake and fat accumulation in rats. Alamshah, A. et al. l-arginine promotes gut hormone release and reduces food intake in rodents. & Schwartz, G. J. Mediobasal hypothalamic leucine sensing regulates food intake through activation of a hypothalamus–brainstem circuit. The neuroactive potential of the human gut microbiota in quality of life and depression. The sympathetic nervous system in the 21st century: neuroimmune interactions in metabolic homeostasis and obesity. Differential contribution of POMC and AgRP neurons to the regulation of regional autonomic nerve activity by leptin. The central melanocortin system directly controls peripheral lipid metabolism. Thaiss, C. A. et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Liang, X., Bushman, F. D. & FitzGerald, G. A. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Diet rapidly and reproducibly alters the human gut microbiome. Brain injury induces specific changes in the caecal microbiota of mice via altered autonomic activity and mucoprotein production. Chang, H. et al. Stress-sensitive neural circuits change the gut microbiome via duodenal glands. Woods, S. C. The eating paradox: how we tolerate food. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Sex differences and hormonal effects on gut microbiota composition in mice. & Kang, S. Low-dose brain estrogen prevents menopausal syndrome while maintaining the diversity of the gut microbiomes in estrogen-deficient rats. Nuriel-Ohayon, M. et al. Progesterone increases Bifidobacterium relative abundance during late pregnancy. Allaband, C. et al. Time of sample collection is critical for the replicability of microbiome analyses. Koester, S. T., Li, N., Lachance, D. M. & Dey, N. Marker-based assays for studying gut transit in gnotobiotic and conventional mouse models. The endocannabinoid system links gut microbiota to adipogenesis. Vijay-Kumar, M. et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. Edgar R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Edgar R. C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Folberth, J., Begemann, K., Jöhren, O., Schwaninger, M. & Othman, A. MS2 and LC libraries for untargeted metabolomics: enhancing method development and identification confidence. Han, S. et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. The project leading to these results received funding from ‘la Caixa' Foundation (ID100010434) under project no. ), and a European Research Council ERC-Synergy-Grant-2019-WATCH no. is a recipient of a Beatriu de Pinós postdoctoral fellowship (2018 BP00032), funded by the Secretary of Universities and Research (Government of Catalonia), and by the Horizon 2020 programme of the European Union under a Marie Skłodowska-Curie grant no. 801370, as well as a contract funded by the Instituto de Salud Carlos III with European funds from the Recovery, Transformation, and Resilience Plan, under file code IHMC22/00039 and Funded by the European Union-Next Generation EU. is the recipient of a Miguel Servet contract (no. CP19/00083) funded by the Instituto de Salud Carlos III and co-funded by the European Social Fund ‘Investing in your future'. RYC2022-037070-I) from the Ministerio de Ciencia e Innovación of Spain. is an honorary research director at the Fonds de la Recherche Scientifique/Fonds National de la Recherche Scientifique and recipient of a Fonds de la Recherche Fondamentale Stratégique-Walloon Excellence in Life Sciences and BIO (WELBIO) technology WELBIO grant no. We are indebted to the Functional Genomics Core Facility of the Institut d' Investigacions Biomèdiques August Pi i Sunyer for their technical help. This work was carried out in part at the Esther Koplowitz Centre, Barcelona. These authors contributed equally: Patrice D. Cani, Rubén Nogueiras, Marc Claret. Neuronal Control of Metabolism Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain Míriam Toledo, Berta Laudo, Elena Eyre, Alicia G. Gómez-Valadés, Macarena Pozo, Iñigo Chivite, Maria Milà-Guasch, Roberta Haddad-Tóvolli, Arnaud Obri, Júlia Fos-Domènech, Iasim Tahiri, Sergio R. Llana, Sara Ramírez, Erika Monelli & Marc Claret Metabolism and Nutrition Research Group (MNUT), Louvain Drug Research Institute (LDRI), UCLouvain, Université Catholique de Louvain, Brussels, Belgium Matthias Van Hul, Rudy Pelicaen, Anthony Puel & Patrice D. Cani Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), WELBIO department, WEL Research Institute, Wavre, Belgium Matthias Van Hul, Anthony Puel & Patrice D. Cani Bioanalytic Core Facility, Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma Bionand, Málaga, Spain Institute of Experimental and Clinical Research (IREC), UCLouvain, Université Catholique de Louvain, Brussels, Belgium CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar conducted the biostatistics and bioinformatics analyses. contributed to conducting the experimental procedures and analysis. Correspondence to Patrice D. Cani, Rubén Nogueiras or Marc Claret. was co-founder of the Akkermansia Company and Enterosys. Rubén Nogueiras serves in the Advisory Board of Albor Biotech. The other authors declare no competing interests. Nature Metabolism thanks John Cryan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Schematic of the approach to express the excitatory (hM3Dq) or inhibitory DREADDs (hM4Di) in AgRP and POMC neurons (n = 3 mice per group). The representative images show mCherry reporter expression in ARC coronal sections. Assessment of chemogenetic activation (A) or inhibition (B) of AgRP neurons in AgRP+/+ and AgRPCre/+ mice expressing hM3Dq or hM4Di two hours after CNO injection. Representative images of FOS (green), mCherry (red), and DAPI nuclei counterstaining (blue) are shown. c, d. Assessment of chemogenetic activation (C) or inhibition (D) of POMC neurons in POMC+/+ and POMCCre/+ mice expressing hM3Dq or hM4Di two hours after CNO injection. Representative images of FOS (magenta) and POMC (green) immunostaining, mCherry (red) and DAPI nuclei counterstaining (blue) are shown. e, f. Quantification of FOS-positive cells in AgRP+/+ and AgRPCre/+ mice expressing hM3Dq (n = 3 mice per group) (E; *p = 0.0205 hM3Dq, 2 h; **p = 0.0015 hM3Dq, 4 h) or hM4Di (F; *p = 0.0162 hM4Di, 2 h; *p = 0.0274 hM4Di, 4 h) two or four hours after CNO injection. Statistical significance was determined with a two-tailed t test. Data in panels E-H are represented as mean ± SEM. Loess curve of intestinal FITC distributions after chemogenetic activation of AgRP (B-C; n = 6 mice per group) and POMC (D-E; n = 7 mice in POMC(-) group and n = 8 mice in POMC(+) group) neurons. X-axis represents different gut segments from stomach to colon. Dot plots represent the geometric mean (g) of FITC proportion. Data are represented as mean ± SEM. Statistical significance was determined using the Wilcoxon test, followed by Benjamini-Hochberg correction. Representative immunofluorescence images showing FOS staining (green) in the arcuate nucleus of the hypothalamus after intracerebroventricular delivery of ghrelin (A) or leptin (B) (n = 5 mice per group). Nuclei were stained with DAPI (blue). Data is represented as mean ± SEM. Statistical significance was determined with a one-tailed t test. Loess curve of intestinal FITC distributions after intracerebroventricular ghrelin (b, c) and leptin (d, e) administration (n = 7 mice per group). X-axis represents different gut segments from stomach to colon. Dot plots represent the geometric mean (g) of FITC proportion. Data are represented as mean ± SEM. Statistical significance was determined using the Wilcoxon test, followed by Benjamini-Hochberg correction. Body weight (A), blood glucose (B), and plasma leptin levels (C) of mice fed with a standard diet (SD) or a high-fat diet (HFD) for 12 weeks. Statistical significance was determined with a two-tailed t test. ****p < 0.0000. d. Microbial α-diversity (Shannon index) of the gut microbiota in vehicle- and centrally leptin-treated mice following 12 weeks of HFD, assessed at two- and four-hours post-treatment (n = 10 mice per group). A total of 79 pathways exhibited statistically significant changes in the duodenum between vehicle-treated and leptin-treated mice at four hours post-treatment. Statistical differences between groups were assessed using Welch's t-test, and the significance of each pathway is indicated by the corresponding p-values. Pathways are grouped according to specific functional categories, visually represented by a colour code. Partial Least Squares Discriminant Analysis (PLS-DA) of duodenal metabolomics data showing differential clustering between vehicle- and leptin-treated groups (n = 6 mice per group). Bar plot illustrating statistically significant differences in gut-brain modules (GBMs) between leptin- and vehicle-treated groups (n = 8 mice per group). Data are presented as proportional differences, along with corresponding p-values. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Toledo, M., Martínez-Martínez, S., Van Hul, M. et al. Rapid modulation of gut microbiota composition by hypothalamic circuits in mice. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

The site is Austria's only known Roman bridgehead fort. Experts, according to a translated statement from the Austrian Archaeological Institute of the Austrian Academy of Sciences, believe castle construction occurred in two phases. The second phase of construction, which occurred around 260 A.D., saw a renovation under Emperor Gallienus. Since that time, troop levels manning the fort dropped. During the excavation, archaeologists unearthed stamped bricks from the Roman legion groups XIV and XV, along with small bronze pieces, ceramics, and coins. The Danube River was an important location for Roman border security and control of trade routes. Now, this newly understood castle has been designated part of the Danube Limes, which has been a UNESO World Heritage Site since 2021. “This impressive find proves the importance of Bernsteinstraße [the Amber Road]—and the Lower Austria region—as an important traffic artery,” Johanna Mikl-Leitner, Lower Austria's governor, said in a statement, “and as a center in the midst of various dominions, function that Lower Austria still holds today.” Tim Newcomb is a journalist based in the Pacific Northwest. He covers stadiums, sneakers, gear, infrastructure, and more for a variety of publications, including Popular Mechanics. Messages Found on the Walls of Last Supper Site The Maya Kingdom Collapsed Due to Burning Events Amateurs Found a Hoard of Ancient Silver Treasure Archaeologists Dug Up a Dirty Roman Statue Head

You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Bioelectronic devices hold transformative potential for healthcare diagnostics and therapeutics. Yet, traditional electronic implants often require invasive surgeries and are mechanically incompatible with biological tissues. Injectable hydrogel bioelectronics offer a minimally invasive alternative that interfaces with soft tissue seamlessly. A major challenge is the low conductivity of bioelectronic systems, stemming from poor dispersibility of conductive additives in hydrogel mixtures. We address this issue by engineering doping conditions with hydrophilic biomacromolecules, enhancing the dispersibility of conductive polymers in aqueous systems. This approach achieves a 5-fold increase in dispersibility and a 20-fold boost in conductivity compared to conventional methods. The resulting conductive polymers are molecularly and in vivo degradable, making them suitable for transient bioelectronics applications. These additives are compatible with various hydrogel systems, such as alginate, forming ionically cross-linkable conductive inks for 3D-printed wearable electronics toward high-performance physiological monitoring. Furthermore, integrating conductive fillers with gelatin-based bioadhesive hydrogels substantially enhances conductivity for injectable sealants, achieving 250% greater sensitivity in pH sensing for chronic wound monitoring. Our findings indicate that hydrophilic dopants effectively tailor conducting polymers for hydrogel fillers, enhancing their biodegradability and expanding applications in transient implantable biomonitoring. Bioelectronic devices have transformed the landscape of medical diagnosis and treatment due to their immense potential in sensing biosignals and stimulating impaired tissues1,2. However, interfacing these devices with internal organs often requires invasive surgeries, which, coupled with mechanical mismatches with the tissue microenvironment, lead to major complications in their long-term performance. These complications stem primarily from fibrosis, poor integration, and damage to the surrounding native tissue. Soft injectable bioelectronics3,4 are emerging as a promising solution, enabling favorable tissue interfacing through minimally invasive approaches5,6,7, such as delivery via needles and catheters8,9,10. Hydrogels have shown excellent versatility for seamless integration with injectable platforms, driving the demand for conductive hydrogels as injectable bioelectronics11. Hydrogel bioelectronic devices are created by the incorporation of conductive additives in hydrophilic polymer networks12. These networks can be engineered further to introduce various functionalities, including tissue regenerative effects, stimuli-responsiveness, bioadhesion, and more13,14,15,16. Traditional conductive materials like metals and carbon-based additives, despite their high conductivity, pose risks of immunogenicity and cytotoxicity17,18. In contrast, conductive polymers, particularly poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), offer biocompatibility and tunable chemistry but face challenges with aggregation and poor percolation networks, resulting in low conductivity19. Efforts to enhance hydrogel conductivity with PEDOT have reported impressive results20, yet these methods often involve processes such as drying steps and cytotoxic phase separation triggers11,21,22,23,24,25,26, making them unsuitable for injectable bioelectronics. Consequently, much of the literature has used PEDOT:PSS as fillers to impart conductivity to hydrogel platforms for minimally invasive and 3D printing applications27,28,29,30. The primary limitation of PEDOT hydrogel composites is their poor dispersibility in aqueous systems due to the aggregation driven by hydrophobic groups in the PSS backbone, which restricts their conductivity. To address these challenges and to achieve high-performance injectable bioelectronics, we introduce a strategy to boost PEDOT hydrogel conductivity by using naturally derived hydrophilic dopants27,31,32,33,34,35 instead of PSS (Fig. We hypothesize that hydrophobic polystyrene backbone of PSS contributes to the poor dispersibility of dry PEDOT:PSS. Thus, we chose alginate, with its rich content of polar groups, as a hydrophilic backbone and modified it with sulfonates (sulfonated alginate, AlgS) that serve as a doping agent in lieu of PSS in the PEDOT polymerization process. The enhanced hydrophilicity allows the freeze-dried PEDOT:AlgS to be re-dispersed in hydrogels at concentrations approximately five times higher than PEDOT:PSS, enabling an order of magnitude improvement in achievable conductivity thresholds in hydrogels (Fig. Additionally, this approach provides molecular-level and in vivo degradability for transient bioelectronics applications. a Doping of PEDOT via negatively charged macromolecules with hydrophilic and hydrophobic backbones, i.e., sulfonated alginate (AlgS), and poly(styrene sulfonate) (PSS), respectively. b Doping of PEDOT with AlgS as compared with PSS leads to enhanced dispersibility in aqueous solutions, improved molecular degradability, and high ionic integrability with ionically cross-linkable hydrogel matrices. c The PEDOT:AlgS polymers serve as high-concentration dispersible fillers in hydrogel pre-polymers for the development of injectable and 3D-printable hydrogel bioelectronics for wearable physiological recordings as well as wound closure and monitoring. We demonstrate the utility of PEDOT:AlgS in alginate matrices to develop ionically cross-linkable conductive inks for 3D printing of soft bioelectrodes where PEDOT:AlgS enables highly sensitive detection of signals such as physiological outputs and temperature compared to PEDOT:PSS (Fig. Finally, we showcase the ability of PEDOT:AlgS to introduce pH-sensitive conductivity to gelatin-based bioadhesives for the development of smart sealants capable of wound monitoring applications where the pH sensitivity is enhanced by ~250% compared to conventional PEDOT:PSS. We envision that such PEDOT:AlgS will serve as versatile and biocompatible building blocks, leveraging hydrogels as functional electrodes for soft injectable electronic applications. The synthesis of PEDOT:AlgS consists of a two-step reaction: (1) modification of alginate (W201502, ~200 kDa36) with sulfonate groups using chlorosulfonic acid (CSA) to yield negatively charged hydrophilic AlgS dopants (Supplementary Fig. 1a, b); and (2) using obtained AlgS to dope PEDOT during the oxidative polymerization of EDOT, resulting in PEDOT:AlgS conductive polymers (Fig. The Fourier transform infrared (FTIR) spectra of AlgS (Supplementary Fig. The results of sulfonation degree (Supplementary Fig. 1d) suggest approximately 37% conversion of hydroxyl groups in alginate when CSA concentration exceeded 1.5% w/v, which then plateaued. Consequently, a 2% w/v CSA concentration was established as the upper limit for subsequent experiments. Size-exclusion chromatography (SEC) tests (Supplementary Fig. 2a) showed consistent trends, with minor peak shifts to higher retention times as CSA concentration increased, indicating minor chain degradations during sulfonation. Functionalizing alginate with sulfonate groups substantially improved AlgS's water solubility (Supplementary Fig. The hydrogel formation capacity of AlgS with multivalent cations was tested at their highest soluble concentrations (Supplementary Fig. Rapid crosslinking with gelation points within seconds after exposure to ionic solutions was recorded. Storage moduli at 5 min indicated that sulfonation prevented ionic crosslinking of alginate through divalent Ca2+ cations, but the larger valent Fe3+ cations could still form AlgS hydrogels. While sulfonation interrupted alginate ionic associations, possibly due to steric hindrance and conformational changes, comparable mechanical properties could still be achieved by increasing the AlgS content. 3a, b), began with the formation of EDOT dimers and trimers within the first hours, indicated by a sharp peak at 256 nm37. The broad absorption band from 400–600 nm corresponded to π → π∗ transitions in the neutral state of PEDOT37. Near-infrared absorption bands at wavelength ranges of 600–900 nm and 700–1200 nm suggest transitions to the polaronic and bipolaronic states, respectively, due to doping via anionic sulfonate groups of AlgS (Supplementary Fig. This trend aligns with the previous reports on PEDOT:PSS polymerization39. FTIR spectra of PEDOT:AlgS samples showed peaks at 1358 cm−1 due to C—C and C=C stretching vibrations in quinoidal thiophene rings of PEDOT (Supplementary Fig. 3d)40, while C—S stretching vibrations produced a strong peak at 984 cm−1. SEC tests exhibited a peak shift to lower retention times with increasing EDOT content and degree of sulfonation (Fig. 3e), signifying the formation of larger molecular weight PEDOT structures. The AlgS samples are labeled as AlgSx where x represents the % w/v concentration of CSA used in sulfonation reaction. b UV-vis spectra of PEDOT:AlgS2 at different polymerization times. Data in the inset is presented as mean ± standard deviation. c Effect of alginate sulfonation on the molecular weight distribution of freeze-dried PEDOT:AlgS2 tested via size-exclusion chromatography (SEC) (PEDOT polymerized for 1 d). d Freeze-dried PEDOT forming large aggregates when doped with PSS while doping with AlgS2 results in homogeneously distributed small nanoparticles. Values represent the mean (n = 3 independent samples). 4) showed a high correlation with alginate sulfonation degree, implying its doping effect. Given the differences in polymerization kinetics between PSS and AlgS dopants, the polymerization time for PEDOT:AlgS2 was set to 2 days to achieve a dry conductivity comparable to standard PEDOT:PSS controls, which are typically synthesized over one day38,41. Solid-state impedance spectroscopy was used to evaluate the effects of sulfonation degree and EDOT content on the AC performance of polymer films (Supplementary Fig. In the Randles circuit model43, the ohmic resistance of PEDOT, Rp, showed high correlations with sulfonation degree. Overall, the DC and AC electrical characterizations suggest the formation of percolation networks at EDOT contents of greater than 0.5 ml, which is comparable to the formulation of commercially available PEDOT (Clevios PH1000). A higher EDOT content of 0.9 ml was used for subsequent characterizations to demonstrate the capability of AlgS dopants in high-concentration dispersion of larger PEDOT:dopant ratios in aqueous systems. From a microstructural perspective, scanning electron microscopy (SEM) images (Fig. 6) revealed that freeze-dried PEDOT:PSS aggregated excessively, leading to submillimeter-scale flake particles. In these images, the observed phases (PEDOT, PSS, and AlgS) were identified based on their distinct morphologies, with porous structures corresponding to hydrophilic polymer phases (dopant) and aggregates corresponding to PEDOT. While these results align well with dynamic light scattering (DLS) data (Fig. 3a), further chemical analyses are required to validate the identified phase attributions. In the PEDOT:PSS group, inhomogeneous phase separation of PEDOT and PSS was evident, while freeze-dried PEDOT:AlgS foams formed evenly distributed nanoparticles (~100 nm) within AlgS phase. a Facilitated re-dispersion of PEDOT in aqueous solutions via hydrophilic AlgS dopants. b The number size distribution of PEDOT polymers obtained from dynamic light scattering (DLS) tests. The inset represents the average hydrodynamic sizes of PEDOT re-dispersions in water (n = 3 independent samples). c Results of zeta potential for aqueous PEDOT:AlgS (n = 3 independent samples). d Comparison of the colloidal stability of 2% w/v PEDOT:AlgS2 with PEDOT:PSS after 1 week at rest. Tubes represent 10% w/v PEDOT:PSS in water forming self-associated gels and 20% w/v PEDOT:AlgS remaining in liquid phase. g Ionic crosslinking of PEDOT-based polymers with exposure to Fe3+ cations in terms of storage (Gʹ) and loss (Gʹʹ) modulus. h Hematoxylin and eosin (H&E) staining of intradermally injected solutions of PEDOT:AlgS2 and PEDOT:PSS (5% w/v) in vivo after 1-week implantation (n = 4 independent samples with similar results). j Air dry coating of PEDOT doped with PSS and AlgS on I: glass substrate and their II: atomic force microscopy (AFM) phase plots obtained from PEDOT dispersions (5% w/v) (n = 3 independent samples with similar results). While dialysis and freeze-drying are essential for removing toxic byproducts and re-dispersing PEDOT-based polymers at controlled and high concentrations in aqueous systems, these processes exacerbate PEDOT aggregations44. Here, an enhanced dispersibility was achieved by doping with AlgS, which resulted in smaller size and greater hydrophilicity compared to PSS (Fig. DLS data revealed that PEDOT:AlgS has an order of magnitude smaller size distribution than PEDOT:PSS when synthesized at the same EDOT content of 0.9 ml (Fig. The hydrodynamic sizes of PEDOT:AlgS increased with higher degrees of sulfonation and EDOT content (Supplementary Fig. 7a) due to the greater extent of polymerization. 3c) showed that the net negative charges in PEDOT:AlgS increased with the degree of sulfonation, reaching approximately double those of PEDOT:PSS counterparts. These repulsive forces contribute to much better dispersion stability in water, as illustrated in Fig. A longer-term investigation over 3 months (Supplementary Fig. 7b) also confirmed the crucial role of sulfonate conjugates in colloidal stability, which is critical for structural uniformity and ink flow in 3D printing applications. 3e), exceeding those of PEDOT:PSS by about 4–5×, as the hydrophilic backbone of alginate facilitated interactions with water molecules. This hydrophilicity was reflected in the contact angle results of Supplementary Fig. 7c, where a substantially lower contact angle was obtained in PEDOT:AlgS (26°) compared to PEDOT:PSS (48°). Poor dispersibility of PEDOT:AlgS at low sulfonation degrees (i.e., AlgS0.5) highlighted the critical roles of sulfonate groups in achieving PEDOT aqueous dispersibility. Similarly, commercial PEDOT:PSS solutions compared with PEDOT:AlgS synthesized at the same PEDOT to dopant ratio (1:2.5) resulted in ~15× larger viscosity in solutions of similar concentrations (1.3% w/v) as shown in Supplementary Fig. This result not only indicates better dispersibility of PEDOT:AlgS, but also suggests that AlgS dopants allow for more PEDOT:dopant ratios compared to PSS. Evaluation of ionic crosslinkability using alginate-based dopants suggested that PEDOT:AlgS is ionically responsive to Fe3+, a response not observed in PEDOT:PSS (Fig. In vivo injection of 5% w/v PEDOT:PSS solutions resulted in the formation of a fibrous capsule around PEDOT:PSS within a week after implantation (Fig. We attribute the fibrotic capsules around PEDOT:PSS to its higher dispersion viscosity preventing cells from infiltrations. Extended implantation over 11 weeks showed progressive degradation in PEDOT:AlgS with cell infiltration, while PEDOT:PSS remained stable within the fibrotic capsule (Supplementary Fig. No meaningful differences in follicles or accumulation of fatty tissue were seen between the two groups. Immunostaining results suggest the limited presence of macrophages (F4/80+), neutrophils (Ly6G+), and T cells (CD3+) involved among the infiltrated cells in PEDOT:AlgS (Supplementary Fig. Although, the number of immune cells constituted a substantially lower ratio of the present cells in PEDOT:AlgS compared to the PEDOT:PSS, which implies a stronger immune response in PEDOT:PSS (Supplementary Fig. To understand the degradation mechanisms, hydrolysis-driven molecular weight changes of the polymers were tested in vitro (Fig. While PEDOT is generally stable, the byproducts of alginate are primarily alginate backbone broken into oligo- and monosaccharides involving sulfonated mannuronic acid (M) and guluronic acid (G) residues. The PEDOT phase is expected to remain intact due to the stable bonds, however, its smaller size distribution can promote their renal clearance in vivo. While sulfonation increases the hydrolyzability of alginate, as confirmed by Supplementary Fig. 10b (aligning with previous studies46), the enhanced solubility of alginate due to sulfonation can further facilitate its removal from the body. Metabolic pathways of these degradation byproducts are expected to be primarily through renal excretion. We note that although human enzymes do not metabolize sulfonated oligosaccharides, certain bacteria in the gut produce alginate lyases47, which may further contribute to alginate degradation through enzymatic cleavage of the glycosidic bonds. This biodegradability makes PEDOT:AlgS suitable for transient implantable bioelectronics. Crack formation during drying has been a major challenge in drop-cast coatings of PEDOT:PSS48. Coating on solid surfaces showed an increased coverage ratio with polymer concentration for both PEDOT:PSS and PEDOT:AlgS up to 2.5% w/v (Supplementary Fig. Further increases in PEDOT:PSS concentration beyond 2.5% w/v however, led to prominent cracking and islet formation (Fig. 3j), while PEDOT:AlgS achieved nearly complete surface coverage with minimal defects. 11c–e) suggested larger grain sizes and a more homogeneous distribution of PEDOT (light region) within the dopant (dark region) in PEDOT:AlgS compared to PEDOT:PSS. Despite larger grain sizes typically being attributed to better conductivity49 (due to the fewer boundaries and energy barriers), the greater phase separation and thereby interconnectivity of the PEDOT phase in PEDOT:PSS resulted in a comparable conductivity with PEDOT:AlgS (Supplementary Fig. Given its excellent water dispersibility and solution stability, PEDOT:AlgS offers great potential for injectable applications such as 3D-printed bioelectronics (Fig. The highest dispersible amount of PEDOT:PSS in alginate solutions for inks to remain injectable was approximately 4% w/v, translating to an ~8× possible improvement in conductivity of alginate solutions (Fig. This figure reached ~160× when PEDOT:AlgS was used (~20× greater than PEDOT:PSS) due to its much higher dispersion limit of up to ~20% w/v. The relative conductivity changes with incorporating PEDOT into various hydrogels reported previously (Supplementary Fig. 12) show that relative improvement in conductivity PEDOT:AlgS is remarkably higher, by 1–2 orders of magnitude, among PEDOT-based injectable hydrogels. Here, we emphasize relative conductivity changes with respect to hydrogel matrix to highlight the roles of PEDOT in ohmic conductivity, excluding the effects of ionic conduction and secondary dopants. Given the electrically insulating nature of existing hydrogels (e.g., alginate with a conductivity of ~7.1 × 10−4 S m−1), the conductivity of PEDOT:AlgS-incorporated hydrogels (~7.5 × 10−2 S m−1) is lower than reports on pure PEDOT hydrogels (on the order of 10⁻³–10⁻⁵ S m− 122,25). However, it is important to note that processing pure PEDOT hydrogels typically requires drying steps and the use of organic solvents or cytotoxic ions, which restricts their applicability in scenarios where direct injectability is required. a Formulation and crosslinking scheme of conductive inks comprised of alginate with PEDOT fillers doped with AlgS and PSS. c The relative increase in conductivity of alginate (7.1 × 10−4 S m−1) as a result of introducing conductive PEDOT fillers at their dispersibility limit before crosslinking with Fe3+. The p-value is determined from a two-tailed Student's t-test with unequal variances (n = 3 independent samples). e Illustration of the multilayer patterns of PEDOT:AlgS in alginate after crosslinking in 25 mM FeCl3 solutions. f SEM images of the freeze-dried hydrogels based on the mixtures of PEDOT:PSS and PEDOT:AlgS (4 and 20% w/v, respectively) in alginate (n = 3 independent samples with similar results). g Temperature sensitivity of PEDOT-incorporated alginate hydrogels (n = 3 independent samples). h, i Results of electrocardiogram (ECG) and electromyography (EMG) recordings using conductive hydrogels as electrode interfaces, respectively. Values in (c) and (g) represent the mean and the standard deviation (n = 3 independent samples). A more in-depth analysis of the charge-carrying processes was performed via electrochemical impedance spectroscopy (EIS) (Fig. Nyquist plots showed that alginate-PEDOT:AlgS intersects with Zreal at lower impedances compared with alginate-PEDOT:PSS, reiterating greater electrical conductance. Conductivity through PEDOT-based polymers consisted of direct charge transfer through percolation resistance Rp, which was ~3× larger in alginate-PEDOT:PSS compared to alginate-PEDOT:AlgS. 4e), solutions of 4% w/v PEDOT:PSS in alginate experienced multiple clogging events due to aggregation and precipitation (Supplementary Fig. 14a), whereas PEDOT:AlgS solutions (at 20% w/v concentration) were continuously deposited with no visible defects. Crosslinking of 3D-printed constructs was performed in FeCl3 solutions at its cytocompatible concentration limit of 25 mM (Supplementary Fig. The distribution and morphology of PEDOT:PSS and PEDOT:AlgS after integration with alginate (Fig. 14b, c) show aggregation of PEDOT:PSS within the alginate matrix, whereas PEDOT:AlgS formed a highly homogeneous and uniform dispersion within the alginate network, explaining the observed conductivity characteristics. The potential applications of the conductive alginate inks were studied as temperature-sensing elements in medical devices (Fig. The temperature sensitivity of the alginate with PEDOT:AlgS within the physiologically relevant ranges (20–40 °C) was found to be ~75% greater than those of PEDOT:PSS-incorporated alginate. Additionally, as a proof-of-concept, we explored the capability of alginate-based conductive electrodes in electrophysiological recording. Electrocardiography (ECG) measurements via electrodes attached to the volunteer's wrist (Fig. 4h) resulted in time delays between two contiguous S waves of 0.71 and 0.69 s for electrodes containing PEDOT:AlgS and PEDOT:PSS, respectively. These figures correspond to heart rates of 84 and 87 beats min−1, which fall within the healthy regime of 60–100 beats min−1. While the potential amplitudes for S waves were found to be similar, these amplitudes for T waves were found to be approximately 30% greater than those observed for PEDOT:PSS. Similarly, electromyography (EMG) signals resulted in ~43% greater signal amplitudes when lifting 13 lb weights using PEDOT:AlgS-based electrodes compared to those of PEDOT:PSS (Fig. This potential was further confirmed by the evaluation of immunoactivity for implantable applications in vitro, where results suggested no inflammatory response associated with PEDOT doped with either PSS or AlgS (Supplementary Fig. Injectable bioadhesive hydrogels have enabled robust sealing of bleeding wounds50,51. However, septic control post-wound closure remains a major challenge as it requires continuous real-time monitoring of patients52,53. Incorporating pH-sensing elements such as PEDOT-based polymers54 to bioadhesives is a promising approach to ensure early detection of potential infections and prompt medical intervention. PEDOT polymers were integrated with a biodegradable and ionically cross-linkable bioadhesive platform (catechol-modified gelatin-caffeic acid conjugates, GelCA)55 as shown in Fig. The dispersion limits of PEDOT:AlgS and PEDOT:PSS in GelCA were similar to those reported above for alginate matrices—20% w/v and 4% w/v, respectively—highlighting the substantially better dispersibility of PEDOT:AlgS in aqueous hydrogel systems. Incorporating PEDOT:PSS in GelCA, although at a lower concentration, required much more rigorous mixing than PEDOT:AlgS to attain a homogeneous solution (Fig. We observed an increase in viscosity (Fig. 5c) and gel-sol transition temperature (Supplementary Fig. 17) of GelCA with the addition of 20% w/v PEDOT:AlgS, and these increases were comparable to 4% w/v PEDOT:PSS in GelCA. These effects are primarily due to the intensified dynamic interactions, such as thermosensitive hydrogen bonding in GelCA networks56. a Formulation of bioadhesives involving gelatin-catechol (GelCA), synthesized via coupling caffeic acid to gelatin, as hydrogel bioadhesive matrices. PEDOT doped with PSS and AlgS are incorporated separately within GelCA at their dispersibility limits (4 and 20% w/v, respectively) and crosslinked ionically by Fe3+ for pH sensing in wound monitoring applications. b Hydrogel pre-polymer composites of GelCA (12% w/v) with PEDOT:PSS (4% w/v) and PEDOT:AlgS (20% w/v) after shaking on a vortex for 1 min. c Viscosity-shear rate of injectable bioadhesives. d Impedance spectroscopy of injectable bioadhesive pre-polymers. e Ex vivo porcine lung burst pressure adhesion testing of hydrogels (n = 3 independent samples). f Clotting time assays in terms of relative decrease in coagulation time for the assessment of hemostatic activity after hydrogel crosslinking. Clotting time for blank controls was 21.7 ± 0.6 min. Statistical analysis was performed via one-way ANOVA (n = 3 independent samples). g In vitro pH sensitivity of conductive bioadhesives obtained by chronoamperometric testing of hydrogels in various pH levels. The inset shows current variations with time at pH 7 (n = 3 independent samples). h In vivo monitoring of wound infection using conductive bioadhesive hydrogels. In terms of tensile mechanical properties (Supplementary Fig. 18a), the elastic modulus of GelCA increased more substantially with the addition of PEDOT:AlgS compared to PEDOT:PSS (Supplementary Fig. 18b) at their dispersibility limits, due to denser dynamic interactions introduced at high concentrations of PEDOT:AlgS. The increase in tensile strength with PEDOT:AlgS was also more prominent compared to PEDOT:PSS (Supplementary Fig. 18c), though no substantial difference was seen across all conditions in terms of stretchability (Supplementary Fig. While PEDOT:PSS resulted in a maximum of ~1.5× improvement at its dispersion limit, PEDOT:AlgS showed a considerably larger improvement (~7×) due to its superior dispersibility. Crosslinking of PEDOT:AlgS hydrogels using FeCl3 further increased this figure to ~9.5×, which could be elevated up to over 35× depending on the crosslinking time. The GelCA matrix concentrations in hydrogels were engineered to yield stable wet adhesion to collagen sheet tissue models (Supplementary Fig. Raising the GelCA content to 12% w/v allowed the formation of stable crosslinked hydrogels that remained integral and adhered to collagen substrates. Swelling tests suggested substantial swelling in the GelCA and GelCA+PEDOT:PSS groups, while no swelling was observed in the GelCA+PEDOT:AlgS groups possibly due to the larger content of hydrophobic PEDOT moieties and denser ionic crosslinking enabled by AlgS (Supplementary Fig. Such conductive bioadhesives showed up to 5-fold greater burst pressure than commercial sealants. In addition to physical sealing, the hemostatic efficacy of bioadhesives is crucial for bleeding control and wound healing57. 21) suggested that all hydrogels exhibited hemostatic potency, primarily attributed to the Fe3+ ions used for crosslinking and the high density of electrostatic interactions driven by the positive and negative charges of PEDOT additives58. The pH sensing function of the hydrogels was tested in physiologically relevant pH ranges59 (Supplementary Fig. Chronoamperometric studies showed that current increased with pH where the current changes were substantially greater for GelCA-PEDOT:AlgS compared to GelCA-PEDOT:PSS, translating into ~250% improvement in overall sensitivity (Fig. This enhancement is due to PEDOT:AlgS facilitating the incorporation of larger amounts of pH-responsive PEDOT moieties into the hydrogel. The pH sensing in conductive hydrogels is generally attributed to the synergistic effects of mediated electron and ion mobility, facilitated by hydrogel swelling, as well as catechol-to-quinone transitions induced by oxidation reactions54,60,61. However, we note that swelling effect was found to be negligible in GelCA-PEDOT:AlgS, whereas it was more prominent under other conditions (Supplementary Fig. Stability of conductivity over 1 week in various pH levels suggested the negligible effects of non-reversible reactions such as catechol oxidation (Supplementary Fig. In vivo application of conductive bioadhesives in monitoring wound infection demonstrated that PEDOT:AlgS enables ~3× larger relative current change in response to infection compared to PEDOT:PSS-based bioadhesives (Fig. This resistance change was fully recoverable with antibiotic treatment in PEDOT:AlgS, showing the robust capacity of the PEDOT:AlgS additives for monitoring wound conditions. The current response to wound infection was also tested 3 days after introducing skin wounds where similar trends were observed, indicating sensing sustainability in wound healing timescales (Supplementary Fig. 23a, b) indicated that both PEDOT:AlgS (at 5× higher concentration) and PEDOT:PSS did not induce any evident cytotoxicity in GelCA hydrogels. Accordingly, the addition of PEDOT-based additives did not affect cell proliferation (Supplementary Fig. Likewise, PEDOT additives did not show substantial antibacterial effects nor affect wound healing properties in vivo (Supplementary Fig. PEDOT:PSS is a popular conductive filler for enhancing conductivity in hydrogels for medical devices and applications, including injectable soft bioelectronics for minimally invasive therapy. Doping conditions play a pivotal role in the characteristics of these polymers, such as electrical properties and aqueous dispersion. Doping PEDOT via hydrophilic alternatives is an effective strategy to enhance the dispersion limit of PEDOT in aqueous systems, allowing dispersions of more than 20% w/v in hydrogels, which is over 5× greater than those of PEDOT:PSS. This approach enables increasing conductivity thresholds up to 20× beyond those achievable by PEDOT:PSS fillers at their dispersibility limit due to the formation of more compact percolation networks. Additionally, the long-term dispersion stability of PEDOT was markedly improved, rendering PEDOT:AlgS stable additives for long-term ink storage and applications in 3D printing. Unlike PEDOT:PSS, ionic associations are possible with PEDOT:AlgS (due to using alginate derivatives as dopants) in response to Fe3+ cations, suggesting opportunities for stronger integration with other ionically crosslinking hydrogel systems such as alginate and catecholic bioadhesive hydrogels. These properties, combined with excellent in vivo response due to mitigation of fibrotic capsule formation, make PEDOT:AlgS an ideal candidate for implantable, biodegradable, and injectable bioelectronics. In combination with hydrogel systems, introducing electrical conductivity to gelatin-based bioadhesives via PEDOT:AlgS enables smart sealants capable of pH monitoring for sensing wound conditions such as infection. Since PEDOT:AlgS enables a greater dispersion limit, the pH sensitivity in their corresponding electrodes is enhanced substantially by ~250%. Overall, doping conducting polymers with hydrophilic moieties such as AlgS holds promising potential for applications in injectable bioelectronics. We envision that various natural biomolecules can be modified for doping polymeric semiconductors to design bioactive electrodes that enable more efficient interfacing with tissues and engineering biomaterials such as soft hydrogels. Integrability of these additives with other crosslinking systems, such as free-radical photopolymerization, further expands their utility in the development of minimally invasive theranostics. Besides, despite advances in improving conductivity, hydrogel-based electrodes still exhibit substantially lower conductivity compared to metallic electrodes, highlighting the need for further innovations in this space. Continued efforts in this field will open new avenues for sophisticated injectable bioelectronic systems capable of long-term in vivo monitoring and therapeutic interventions for more applications in real-time health monitoring and neural interfacing. Lastly, long-term in vivo studies of immune response and degradation, along with demonstrations of PEDOT-based hydrogels with active wound healing and antimicrobial properties, are critical to expand their application for wound monitoring. Type A gelatin derived from porcine skin, gelatin from cold water fish skin, sodium alginate (cat. W201502), calcium chloride (CaCl2), iron(III) chloride (FeCl3), caffeic acid (CA), sodium chloride (NaCl), ammonium persulfate (APS), poly(sodium 4-styrenesulfonate) (PSS) average molecular weight 70,000 Da, formamide, dimethyl sulfoxide (DMSO), chlorosulfonic acid (CSA), hydrochloric acid (HCl), and sodium periodate (NaIO4) were acquired from Sigma-Aldrich. 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), N-hydroxysuccinimide (NHS), and 3,4-ethylenedioxythiophene (EDOT) were purchased from TCI Chemicals, USA. Porcine lung tissues were provided by Sierra for Medical Science, USA. Dulbecco's phosphate-buffered saline (PBS) and buffer solutions at pH 4, 7, and 9 were supplied by ThermoFisher Scientific, USA. Ethanol (C2H5OH) was purchased from KOPTEC (King of Prussia, PA, USA), potassium phosphate monobasic (KH2PO4), and potassium phosphate dibasic (K2HPO4) were purchased from Sigma-Aldrich, USA. The commercial PEDOT:PSS (1.3 wt.% dispersion in H2O) was supplied by Sigma-Aldrich, USA. Alginate was modified to obtain AlgS dopants. First, 1000 mg alginate was added to 40 ml formamide and placed in an ice bath while stirring at 250 rpm. Different batches of AlgSx were synthesized by adding various amounts of CSA (i.e., x = 0.5, 1, and 2 ml) to the reaction solutions, and the reactions proceeded for 4 h at 60 °C under stirring at 250 rpm. Then, AlgS was precipitated by adding an equal volume of acetone to the solution. The solution was vortexed and centrifuged at 3000×g for 5 min to form a pellet. The supernatant was discarded, and the AlgS pellets were dissolved in deionized (DI) water at ~2% w/v. The AlgS solution was dialyzed for 24 h against DI water, where the dialysis media was refreshed 3 times and subsequently freeze-dried. To synthesize PEDOT:AlgS, 1000 mg AlgS was dissolved in 50 ml DI water while stirring at 250 rpm. Where not specified, PEDOT:AlgS is synthesized using AlgS2 and the highest EDOT concentration. Then, the reaction solutions were dialyzed for 3 days against 100 mM NaCl (the dialysis media refreshed 3 times a day), followed by freeze-drying. All products were dialyzed and freeze-dried (1) to remove ions/byproducts for biocompatible interfacing with tissues and (2) to allow re-dispersion at controlled concentrations. The synthesis of PEDOT:PSS controls was performed following the same protocol as PEDOT:AlgS, where doping of PEDOT was conducted by equivalent weight ratios of PSS to EDOT monomers (i.e., the amount of EDOT monomers and APS initiators were 0.9 ml and 2160 mg, respectively). Polymerization of PEDOT in PEDOT:PSS was conducted for 24 h (half of the EDOT polymerization time in PEDOT:AlgS) to yield similar electrical conductivity between the two conditions. For modification of gelatin with bioadhesive catechol motifs (gelatin-catechol, GelCA), 212 mg CA (4.25% w/v) was dissolved in a 5 ml solution of 50 %v/v DMSO in DI water. Subsequently, 7.5 mg of NaIO4 (0.15% w/v) was added to the CA solution, and the solution was stirred for 1 h to produce oxidized CA. Following this step, a solution consisting of 20 ml of 50 %v/v DMSO in water along with 191 mg of EDC (0.96% w/v) and 115 mg of NHS (0.58% w/v) was introduced into the mixture and activation reaction was allowed to proceed for 2 h. Simultaneously, 1 g of gelatin is dissolved in a 75 ml solution of 50% v/v DMSO in water (1.33% w/v) at 50 °C for 1 h. The gelatin solutions were mixed with NHS-activated oxidized CA solution, and the conjugation reaction was left to proceed for 24 h at room temperature. Subsequently, the resulting solutions are dialyzed in DI water for 3 days, with the dialysis solution being refreshed three times daily. Finally, the samples were subjected to freeze-drying for 3 days. Hydrogel mixtures containing PEDOT-based polymers were crosslinked through ionic crosslinking. The inks were exposed to 25 mM FeCl3 solution for 5 min and washed with PBS. Bioadhesive hydrogels were prepared similarly by mixing equivalent amounts of PEDOT:PSS and PEDOT:AlgS in 12% w/v GelCA solutions in water at 80 °C followed by shaking until complete dissolution. The pre-polymers were then injected at room temperature, leading to their physical gelation and then, ionic crosslinking was conducted via FeCl3 diffusion (soaking in 25 mM FeCl3 solution for 5 min). The solubility/dispersibility in water was assessed by mixing dry components in DI water at various concentrations, followed by multiple stages of shaking (by vortex) and heating at 80 °C. These samples were then fixed at an angle and held in position for 10 s where the flowability of the solutions under gravity was correlated to stability. The samples were considered dispersible if they flew under gravity and non-dispersible if did not. Water absorption of hydrogels was assessed via swelling experiments. Hydrogels were crosslinked and their initial wet weight was recorded. They were then soaked in DI water at room temperature and their weight was registered at different time points. The swelling ratio was calculated as the ratio of weight change to initial weight. The UV-vis measurements were performed using a NanoDrop OneC microvolume spectrophotometer (ThermoFisher Scientific, USA) on 2 μl volumes taken from the sample solutions. For UV-vis analysis of PEDOT:AlgS after freeze-drying, the polymers were dispersed at 1% w/v in DI water. For the analysis of polymerization kinetics, samples were taken directly from the reaction solution at different time points. The FTIR spectroscopy measurements were conducted via attenuated total reflection (ATR)-FTIR on freeze-dried AlgS and PEDOT:AlgS samples using a Bruker Alpha II Platinum ATR spectrometer. Baseline correction was performed on the data using SpectraGryph software. The samples were dissolved in DI water at 0.5 mg/ml and filtered through 0.8 μm filter papers. For accelerated hydrolytic degradability tests, the products were treated in 20 mM NaOH at 5% w/v concentration and maintained at 37 °C for 3 weeks. Samples were loaded into an Agilent 1200 high-performance liquid chromatography (HPLC) equipped with a UV detector. A Yarra 3 μm SEC-3000 column (Phenomenex, Torrance, CA, USA) at 30 min running time was used with the mobile phase being PBS. The absorbance data measured at 210 nm wavelength was reported over retention times. A Nano ZS (Malvern Panalytical Ltd, UK) zeta sizer was used for the DLS tests for the characterization of zeta potential and size distributions of the dispersions. The test solutions were prepared at 0.5 mg/ml, followed by loading into the DTS1070 disposable folded capillary cuvettes. The data were recorded at room temperature. Contact angle measurements were performed to assess the wettability of the surfaces using a goniometer RemaHart. A volume of 100 µl PEDOT solution in water (2.5 % w/v) was dried on glass slides on hotplates at 60 °C. A droplet of deionized water (~5 µl) was carefully placed on the surface of coated layer using a microsyringe. Images of the droplet were captured upon contacting surface using a high-resolution camera, and the contact angle was determined by analyzing the droplet profile using ImageJ software. To analyze microstructural features, SEM images were captured. Prior to imaging, a thin layer of gold (20 nm) was sputtered onto the surface of each sample. The images were recorded at 2 kV acceleration voltage using a Zeiss Sigma 300 SEM microscope. To analyze the phase images and surface roughness, atomic force microscopy (AFM) was conducted using a Dimension® Icon® atomic force microscope with ScanAsyst™. The PEDOT-based coatings were prepared by drop casting 75 µl of 5% w/v solutions on a 60 °C hotplate. The sulfonation degree of AlgS was quantified using barium sulfate nephelometry62. First, 15 mg AlgS underwent hydrolysis in 5 ml of 0.1 M HCl at 120 °C for 7 h to release the attached sulfonate groups. Subsequently, the solution volume was adjusted to 25 ml by adding DI water. Then, 625 μl of this solution was mixed with 312.5 µl of a gelatin-barium chloride solution consisting of 5% w/v BaCl2 and 5% w/v gelatin from cold water fish skin in DI water. The mixture was added to 175 μl of 8% w/v trichloroacetic acid in DI water and thoroughly mixed before allowing the reaction to proceed for 20 min. The absorbance of the reaction solutions was measured at 350 nm wavelength using a microplate reader (BioTek UV-vis Synergy 2, VT, USA). To attain standard curves (absorbance-SO3 concentration), stock sodium sulfate (NaSO4) solutions of various concentrations, i.e., 2.500–0.005 mg/ml, were prepared in DI water. where DS is the degree of sulfonation, and SAlgS is the corresponding concentration of sulfonate groups for AlgS samples (in mol l−1) as obtained from the standard curves. Rheological studies were conducted using an MCR 302 (Premium Analytical Instruments, Anton Paar, Graz, Austria) rheometer equipped with an 8 mm parallel-plate torque measurement system using RheoCompass software. The tests were performed on 70 μl of liquid solutions at 1 mm gap size. These tests on GelCA-PEDOT composites were performed at 50 °C. For oscillatory experiments, 1 Hz frequency and 0.5% strain amplitude were defined unless noted otherwise. In order to assess the diffusive crosslinking of hydrogels, pre-gel solutions were submerged in crosslinking solutions, and storage modulus (Gʹ) and loss modulus (Gʹʹ) response were monitored through time sweep tests. Subsequently, a linear thermal transition from 50 °C to 10 °C at a rate of 1 °C/min was applied while Gʹ and Gʹʹ were recorded at 10% strain amplitude. Thermosensitivity tests were performed for PEDOT polymers at their dispersibility concentration limits (i.e., 20% w/v for PEDOT:AlgS and 4% w/v for PEDOT:PSS) incorporated in 12% w/v GelCA solutions. Tensile mechanical properties of crosslinked GelCA-based conductive bioadhesives were evaluated using an Instron 5943 (MA, USA) universal testing equipment using BlueHill 3 software. To prepare the samples, 200 μl solutions were pipetted into 25 × 5 mm rectangular polydimethylsiloxane (PDMS) molds and crosslinked diffusively by 5 min soaking in 25 mM FeCl3 solution. Then, crosslinked samples were glued to plain paper using Krazy glue (OH, USA) for better gripping. The force-displacement data was recorded using a 100 N load cell. To evaluate the sealing effectiveness of the bioadhesive hydrogels, ex vivo burst pressure tests were conducted on porcine lung tissues. A 3 mm wide puncture was thoroughly sealed using 100 µl bioadhesive pre-polymer (containing 12% w/v GelCA and certain concentrations of PEDOT:dopants) followed by 5 min diffusive crosslinking using 25 mM FeCl3. Pressure changes were monitored as oxygen gas was introduced into the lungs until the point where the corresponding pressure was registered as burst pressure. The DC electrical conductivity was determined using a standard two-point probe setup via a Reference 600™ (Gamry Instruments, PA, USA) potentiostat. For conductivity of dry PEDOT:PSS and PEDOT:AlgS films, 75 μl samples taken from the PEDOT polymerization reactions were taken before the dialysis step at different time points and pipetted onto a glass slide, followed by dry annealing in an 80 °C oven. The dried coatings were cut into rectangular shapes, and then copper tape electrodes were affixed onto the coatings using silver glue, resulting in ~3 × 7 mm sample sizes. The coating thicknesses were measured to be ~4 μm using atomic force microscopy, based on which conductivity was calculated according to Eq. Here, resistance (R) was calculated based on the average current output in response to 1 V applied voltage measured over 30 s, and t, l, and w are film thickness, length, and width, respectively. The conductivity of hydrogel samples was measured by bridging two gold electrodes placed at a 7 mm distance via injecting pre-polymer solutions on glass substrates. The applied hydrogel width and thickness were ca. 5 mm and 1 mm, respectively, and the DC conductivity measurements were performed following the same procedure for dry coatings. Impedance spectroscopy characteristics were assessed via a two-point probe setup using an AC voltage of VRMS = 100 mV over 100 MHz to 1 MHz frequency range. Gamry Echem Analyst software (version 6.3) was used for fitting the circuit models to the impedance data. The biocompatibility of the synthesized conductive polymers in GelCA matrices was evaluated through a 2D cell culture experiment using human dermal fibroblast cell lines (ATCC PCS201012). First, a pre-gel hydrogel mixture (100 μl) was introduced into PDMS molds with an 8 mm diameter and a 1.5 mm depth to create hydrogel disks. Afterward, the disks were immersed in a DPBS solution and sterilized under UV light for 1 h. The cells were cultured and incubated at 37 °C with 5% CO2, seeding them in a 24-well plate at a cell density of 2500 cells per well for 24 h in a 2 ml medium consisting of Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. Wells without hydrogels were used as a control. To assess the metabolic activity of the cells, a PrestoBlue assay (ThermoFisher Scientific, USA) was performed at various time intervals. The cells were washed with DPBS and then incubated with 1 ml of PrestoBlue reagent (diluted 10× in the cultured medium) for 1.5 h at 37 °C. Subsequently, the PrestoBlue (100 μl per well) was transferred to a fresh 96-well plate, and fluorescence values were recorded at excitation/emission wavelengths of 530/590 nm using a microplate reader (BioTek Synergy 2, USA). A Live/Dead assay (Invitrogen, USA) was conducted to assess cell viability. The Live/Dead solutions were prepared by mixing 5 μl of calcein and 20 μl of ethidium homodimer-1 with 10 ml DPBS, and 500 μl of this solution was added to each well and incubated at 37 °C for 15 min. The cells were then rinsed with DPBS and imaged using a Zeiss fluorescence microscope (Axio Observer 5) with excitation/emission wavelengths of 494/515 nm for calcein (green) and 528/617 nm for ethidium homodimer-1 (red). ImageJ software was used to count live and dead cells, enabling the calculation of cell viability as the ratio of live cells to the total cell count. The in vitro immunoactivity of PEDOT:AlgS was evaluated using bone marrow-derived macrophages (BMDMs), isolated from the tibia and femur bones of 5- to 6-week-old C57BL/6 mice. Approximately 106 cells per dish were isolated and seeded in a mixture of DMEM supplemented with 10% w/v fetal bovine serum, 55 μM β-mercaptoethanol, 5 ng ml−1 of macrophage colony-stimulating factor (MCSF), and 100 U/ml penicillin. After 8 days of culture, the BMDMs were seeded in a 6-well transwell plate at a density of 1 × 106 cells per well. Following overnight incubation, the macrophages were co-cultured with crosslinked disk-shaped PEDOT:AlgS hydrogels in a transwell plate, each having a volume of 100 μl hydrogel material, for 24 h. Subsequently, the cells were collected, and flow cytometry (Attune, ThermoFisher Scientific, USA) was used to assess polarization and activation for the stained macrophages. In a parallel study for microscopy analysis, the following steps were performed: After a 24-h incubation period, the cells were washed three times with PBS to remove any residual media or debris. Subsequently, the cells were fixed with 4% paraformaldehyde for 20 min to preserve their structure and morphology. To prevent non-specific binding of antibodies, the cells were then blocked with 2% bovine serum albumin (BSA) for 90 min. Following the blocking step, the cells were incubated overnight at 4 °C with primary antibodies against F4/80 (a macrophage-specific marker) and CD80 (a co-stimulatory molecule) at a dilution of 1:250. After the overnight incubation, the cells were washed three times with PBS to remove any unbound antibodies. Subsequently, the cells were stained with DAPI (4',6-diamidino-2-phenylindole) at a dilution of 1:1000 for 15 min. Finally, microscopy analysis was performed to visualize and study the stained cells. Biocompatibility of the crosslinked PEDOT:AlgS was assessed in vivo by implantation in mice following an approved protocol at the Lundquist Institute (#22747-01). For each sample, five black male C57BL/6 mice, 6–8 weeks old, obtained from The Jackson Laboratory in the USA, were housed in standard laboratory conditions with a 12-h light/dark cycle, ambient temperature maintained at 25 ± 2 °C, and relative humidity of 50 ± 10%. Animals were provided ad libitum access to laboratory pellets and purified water in pathogen-free facilities. The PEDOT solutions were then injected (100 µl) intradermally. All surgeries were performed under anesthesia, with 1.5% v/v isoflurane in oxygen gas followed by carprofen injection for pain management. Tissues from the mice were subsequently harvested after 1 week following euthanasia via carbon dioxide inhalation. Subsequently, the samples were preserved by immersion in a 10% v/v formalin solution. Then, the preserved organs and tissues were encased in paraffin and cut into 5 μm-thick sections. The cellular morphology was assessed through hematoxylin and eosin (H&E) staining. For the subcutaneous implantation samples, immunofluorescent staining was performed. Anti-mouse F4/80 (BioRad, catalog number: MCA497A488, lot #18567, clone #Cl:A3-1), CD80 (BioLegend, catalog number: cat 600055, lot #b444189, clone W17200C), CD3 (BioLegend, catalog number: cat 100205, lot #B424757, clone 17A2), and Ly6G (BioLegend, catalog number: 127607, lot #B440678, clone 1A8), as well-established marker for identifying mouse macrophages, their pro-inflammatory subtypes, T cells, and neutrophils were used. The tissues were rehydrated by washing them in ethanol (100%, 90%, and 70%, respectively) for 5 min each, followed by a 5-min wash in PBS. The slides were then submerged in an antigen retrieval buffer diluted 1:50 in MilliQ water, placed in a steamer set to 98.5 °C for 15 min, and subsequently incubated at room temperature for an additional 10 min to achieve antigen retrieval. Afterward, we circled the tissues using a hydrophobic pen. The slides were then washed twice in distilled water for 2 min each, followed by a 5 min wash in PBS. After drying, each tissue was covered with 10% normal goat serum for 30 min, placed in a humid chamber, and incubated in primary antibody solution overnight at 4 °C. From this stage onwards, the slides were submerged twice in PBS for 5 min each during the washing process. Subsequently, the slides were counterstained with DAPI, diluted at a ratio of 1:100 in PBS, for 10 min. Following this, the slides underwent a 10-min staining process with Sudan Black to diminish autofluorescence. Subsequently, the slides were coverslipped using an aqueous mounting medium and imaged using a Keyence microscope. The blood clotting tendency of the material was measured in vitro with whole human blood containing 3.8% w/v sodium citrate anticoagulant. Two hundred μl of GelCA-based pre-gel mixtures were crosslinked in a 48-well plate using 25 mM FeCl3. To assess the clot-forming abilities of different hydrogels, 10 ml of Milli-Q water was introduced to each sample after 16 min of clotting time, Afterward, the supernatant was analyzed using a plate reader at 405 nm. Greater clot formation was associated with lower absorbance values. The conductive inks comprising 20% w/v PEDOT:AlgS and 3% w/v alginate were 3D-printed on glass slide substrates (unless noted otherwise) using a three-axis robotic deposition stage (Aerotech). AutoCAD software was used to design the printing paths, and G-code was generated using a custom Python script (available at https://github.com/hmontazerian/DXF-to-GCode.git). The ink was inserted into 15 ml syringe barrels and equipped with 27 GA tapered tips. Extrusion of the ink was regulated by applying the air pressure using an EFD Nordson benchtop fluid dispenser. Before printing the patterns, the printing pressure and speed were optimized to be 5 psi and 2 mm s−1 for stable extrusion. :Ag)/Ink AT as a three-electrode system and Autolab with PGSTAT10 potentiostat/galvanostat from Metrohm, USA, were used for pH sensing experiments. The gold screen-printed electrodes (SPEs) were rinsed with ethanol, followed by distilled (DI) water, and then dried under nitrogen gas flow. To ensure a consistent pH level during chronoamperometry, we meticulously controlled the environmental factors by maintaining a constant temperature throughout the experiment. The chronoamperometry studies were performed at 0.65 V. After anesthesia and analgesia, 5-mm full-thickness wounds were created using a blade, and biosensors were implanted in subcutaneous pockets along the dorsomedial skin. S. aureus and E. coli were used for antimicrobial tests. The animals were tested for a continuous 3-day period. A mixed bacteria solution with a concentration of 1 × 108 CFU of each bacteria species was added after the first day of the test, and TCP-25 (GKYGFYTHVFRLKKWIQKVIDQFGE) (98% purity, acetate salt) (CPC Scientific) was applied after the secondary day of the test. Current measurement was performed at 0.1 V and the average current measured over 30 s was reported for each measurement time increment. The antimicrobial activity of hydrogels was evaluated using an agar well diffusion assay. E. coli microbial strains were cultured in nutrient broth until reaching an optical density (OD600) of 1.24 for E. coli measured with a UV-Vis spectrometer. Pre-gel solutions (100 µl) were pipetted on the agar plates. Crosslinked hydrogels were placed directly on the agar surface. As a positive control, 8 mm filter paper disks impregnated with 15 µg of silver sulfadiazine were placed on the agar plates. Circular full-thickness wounds were created on the dorsal skin of the animal model using an 8 mm biopsy punch. Wound area measurements were taken on days 6 and 12 post-treatment using images captured to track the healing process. The bioadhesive GelCA-based conductive solutions were used to record biopotential signals. The reference electrode was placed on the leg for both electrocardiogram (ECG) and electromyography (EMG) monitoring. Working electrodes were placed on the left and right arms for ECG monitoring. While for EMG monitoring, the working electrodes were placed at both ends of the biceps brachii muscle. For both biopotential signals monitoring, the signal was acquired through an open-source hardware shield (SparkFun, AD8232) and was processed by a digital lowpass filter (50 Hz) through a MATLAB code (available at https://github.com/hmontazerian/biopotential-signals-recording.git). The temperature-sensing characterization was performed on a ceramic hotplate (ThermoFisher Scientific) where the temperature was monitored using a thermometer. A two-probe setup using an amperometric method was used through an electrochemical workstation (CHI 660E) to record resistance changes with temperature under an applied voltage of 1 V. The reported values are presented as means ± standard deviation (SD). GraphPad Prism 10 software was employed for statistical analyses using analysis of variance (ANOVA), with statistical significance considered when p < 0.05. Normal data distribution was assumed for parametric tests. All the experimentally measured data were conducted in triplicate (n = 3) for independent samples unless otherwise specified. Every experiment involving animals, human participants, or clinical samples has been carried out following a protocol approved by an ethical commission. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. All data supporting the findings of this study are available within the article and its supplementary files. Any additional requests for information can be directed to and will be fulfilled by the corresponding authors. Source data are provided with this paper. Shi, J. et al. Monolithic-to-focal evolving biointerfaces in tissue regeneration and bioelectronics. An electronic pill for non-invasive gastric monitoring. Xu, S., Xiao, X., Manshaii, F. & Chen, J. Injectable fluorescent neural interfaces for cell-specific stimulating and imaging. Permanent fluidic magnets for liquid bioelectronics. Flexible shape-memory scaffold for minimally invasive delivery of functional tissues. Zhou, T. et al. Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Zhang, C. et al. A self-doping conductive polymer hydrogel that can restore electrical impulse propagation at myocardial infarct to prevent cardiac arrhythmia and preserve ventricular function. Hong, G. et al. Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Le, Z. et al. Bioactive sucralfate-based microneedles promote wound healing through reprogramming macrophages and protecting endogenous growth factors. Zhou, T. et al. 3D printable high-performance conducting polymer hydrogel for all-hydrogel bioelectronic interfaces. Li, N. et al. Bioadhesive polymer semiconductors and transistors for intimate biointerfaces. Hsieh, J.-C. et al. Design of an injectable, self-adhesive, and highly stable hydrogel electrode for sleep recording. Jin, S. et al. Injectable tissue prosthesis for instantaneous closed-loop rehabilitation. Kim, S. et al. Injection-on-skin granular adhesive for interactive human–machine interface. Inflammatory response to magnesium-based biodegradable implant materials. Toxicity of carbon nanomaterials and their potential application as drug delivery systems: in vitro studies in Caco-2 and MCF-7 cell lines. Intrinsically stretchable block copolymer based on PEDOT:PSS for improved performance in bioelectronic applications. Wang, J. et al. Ultra-high electrical conductivity in filler-free polymeric hydrogels toward thermoelectrics and electromagnetic interference shielding. Won, D. et al. Digital selective transformation and patterning of highly conductive hydrogel bioelectronics by laser-induced phase separation. Wang, H. et al. Multifunctional filler-free PEDOT:PSS hydrogels with ultrahigh electrical conductivity induced by Lewis-acid-promoted ion exchange. Cheng, T. et al. 3D printable conductive polymer hydrogels with ultra-high conductivity and superior stretchability for free-standing elastic all-gel supercapacitors. Mechanically tunable conductive interpenetrating network hydrogels that mimic the elastic moduli of biological tissue. Yuk, H. et al. 3D printing of conducting polymers. In situ forming dual‐conductive hydrogels enable conformal, self-adhesive and antibacterial epidermal electrodes. Highly conductive MXene/PEDOT:PSS-integrated poly(N-isopropylacrylamide) hydrogels for bioinspired somatosensory soft actuators. Zhang, Z. et al. Fatigue-resistant conducting polymer hydrogels as strain sensor for underwater robotics. Roshanbinfar, K. et al. Electroconductive biohybrid hydrogel for enhanced maturation and beating properties of engineered cardiac tissues. Ding, H. et al. Biologically derived soft conducting hydrogels using heparin-doped polymer networks. Xu, Y. et al. Cytocompatible, injectable, and electroconductive soft adhesives with hybrid covalent/noncovalent dynamic network. The development of a highly conductive PEDOT system by doping with partially crystalline sulfated cellulose and its electric conductivity. Novel PEDOT dispersion by in-situ polymerization based on sulfated nanocellulose. Yu, C. et al. Ultra-histocompatible and electrophysiological-adapted PEDOT-based hydrogels designed for cardiac repair. & Zaldua, A. M. A study of the printability of alginate-based bioinks by 3D bioprinting for articular cartilage tissue engineering. Characterization and conversion determination of stable PEDOT latex nanoparticles synthesized by emulsion polymerization. Sakunpongpitiporn, P., Phasuksom, K., Paradee, N. & Sirivat, A. Facile synthesis of highly conductive PEDOT:PSS via surfactant templates. Hofmann, A. I. et al. How to choose polyelectrolytes for aqueous dispersions of conducting PEDOT complexes. Han, M. G. & Foulger, S. H. Crystalline colloidal arrays composed of poly(3,4-ethylenedioxythiophene)-coated polystyrene particles with a stop band in the visible regime. Cho, H., Cho, W., Kim, Y., Lee, J. G. & Kim, J. H. Influence of residual sodium ions on the structure and properties of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate). Vadhva, P. et al. Electrochemical impedance spectroscopy for all-solid-state batteries: theory, methods and future outlook. & Heinke, L. Insights in the ionic conduction inside nanoporous metal-organic frameworks by using an appropriate equivalent circuit. Xin, X. et al. Freeze‐drying and mechanical redispersion of aqueous PEDOT:PSS. Sodium phytate-incorporated gelatin-silicate nanoplatelet composites for enhanced cohesion and hemostatic function of shear-thinning biomaterials. Partially oxidized alginate as a biodegradable carrier for glucose-responsive insulin delivery and islet cell replacement therapy. Rønne Mette, E., Madsen, M., Tandrup, T., Wilkens, C. & Svensson, B. & Vuong, L. T. Solvent retention and crack evolution in dropcast PEDOT:PSS and dependence on surface wetting. Highly conductive PEDOT:PSS with enhanced chemical stability. Montazerian, H. et al. Bio-macromolecular design roadmap towards tough bioadhesives. Xiong, Z. et al. A wireless and battery-free wound infection sensor based on DNA hydrogel. Gao, Y. et al. A flexible multiplexed immunosensor for point-of-care in situ wound monitoring. Odinotski, S. et al. A conductive hydrogel-based microneedle platform for real-time pH measurement in live animals. Montazerian, H. et al. Injectable gelatin-oligo-catechol conjugates for tough thermosensitive bioadhesion. Montazerian, H. et al. Poly-catecholic functionalization of biomolecules for rapid gelation, robust injectable bioadhesion, and near-infrared responsiveness. Engineered hemostatic biomaterials for sealing wounds. Gaharwar, A. K. et al. Shear-thinning nanocomposite hydrogels for the treatment of hemorrhage. Wang, T. et al. Smart composite hydrogels with pH-responsiveness and electrical conductivity for flexible sensors and logic gates. Sheppard, N. F., Lesho, M. J., Tucker, R. C. & Salehi-Had, S. Electrical conductivity of pH-responsive hydrogels. Fan, L. et al. Synthesis and anticoagulant activity of sodium alginate sulfates. These authors contributed equally: Hossein Montazerian, Elham Davoodi. Hossein Montazerian, Yichao Zhao, Robert Langer & Daniel G. Anderson Hossein Montazerian, Tzung K. Hsiai & Paul S. Weiss Terasaki Institute for Biomedical Innovation, Los Angeles, California, USA Hossein Montazerian, Reihaneh Haghniaz, Neda Mohaghegh, Safoora Khosravi, Fatemeh Zehtabi, Negar Hosseinzadeh, Alireza Hassani Najafabadi & Ali Khademhosseini Elham Davoodi, Canran Wang, Jiahong Li & Wei Gao Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA Rohan R. Sampath, Tianhan Liu & Paul S. Weiss Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California, USA You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar H.M. prepared the majority of the original draft and performed data analysis in addition to conducting electrical characterizations, rheological studies of viscosity and thermal gelation, DLS, and testing the degree of sulfonation. led the dispersibility tests, rheological experiments of ionic crosslinking, UV-vis experiments, as well as 3D printing of hydrogels. conducted pH testing of conductive bioadhesives in vitro, and C.W. assessed the wound monitoring capability of hydrogels in vivo. In vitro biocompatibility tests were performed by R.H. R.R.S. contributed to drafting the experimental section and assisted H.M. in sulfonation degree measurements. performed temperature sensitivity, physiological monitoring studies and assisted E.D. The AFM imaging and FTIR tests were performed by T.L. conducted in vitro immunoactivity studies, SEC experiments for biodegradability, and PEDOT polymerization. Correspondence to Alireza Hassani Najafabadi, Paul S. Weiss, Ali Khademhosseini or Wei Gao. The authors declare no competing interests. Nature Communications thanks Jun Wang, Miryam Criado, and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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You are using a browser version with limited support for CSS. You can also search for this author in PubMed Google Scholar You can also search for this author in PubMed Google Scholar You have full access to this article via your institution. Researchers in the United States are seeking career opportunities abroad as President Donald Trump's administration slashes science funding and workforce numbers, finds an analysis of Nature's jobs-board data. Data from the Nature Careers global science jobs platform show that US scientists submitted 32% more applications for jobs abroad between January and March 2025 than during the same period in 2024. More than 200 federal grants for research related to HIV and AIDS were abruptly terminated last month. Cuts to grants from the US National Institutes of Health for COVID-19 research were revealed, and the government began a US$400-million reduction in research grants at Columbia University in New York City, because of campus protests supporting Palestinians in the conflict with Israel. As this article went to press, the board hosted 983 live vacancies. US President Donald Trump has planned sweeping cuts to research instiutions during his time in office.Credit: Ben Curtis/AP/Alamy Chemical engineer Valerie Niemann is one of many looking beyond the United States to develop her career. In the United States, she says, “people don't know how long their postdocs will be. In a 25 March post on the social media platform X, Xiao Wu, a biostatistician at Columbia University, lamented: “My very first NIH grant was abruptly cancelled just three months after receiving funding.” His work focuses on using evidence-based data to mitigate the harms of climate change on health. Wu is not currently looking for work elsewhere, but fears he might eventually have no choice. “Without these grants, my career stability and professional future are directly jeopardized,” he says. “Therefore, this situation is not simply one of ‘seeking other opportunities', but rather one where we are effectively being forced out of US academic institutions.” (See ‘US interest in international jobs'.) Some European institutions are scrambling to attract what might be an exodus of scientific talent from the United States. Seventy per cent of applicants are researchers from the United States and are specialists in their fields, she says, and 20 people will now be selected this year. “What's happening is terrible for American research,” says Aix-Marseille's president, Éric Berton, adding: “We felt it was our duty to do what we could to show scientists there was a little light in the south of France where they could do their research, be a lot freer and where they were wanted.” At the same time, applications to US institutions from researchers in Europe dropped by 41%. The Max Planck institutes in Germany have been fielding requests from some of their researchers who are from the United States but who would like to stay in Germany longer than they'd planned. The initiative sets out plans to create collaborative research centres with US-based institutions, as well as providing further postdoctoral training posts, and extra places for junior investigators at the society's 84 institutes. The entire international jobs market has seen a spike in activity (see 'Lands of opportunity'). NIH cuts triggered a host of lawsuits: Nature's guide to what's next You're only human: a six-step strategy to surviving your PhD International PhD students make emergency plans in fear of US immigration raids Europe must become a research epicentre as US system gets undermined NIH cuts triggered a host of lawsuits: Nature's guide to what's next An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

You are using a browser version with limited support for CSS. You can also search for this author in PubMed Google Scholar You can also search for this author in PubMed Google Scholar You can also search for this author in PubMed Google Scholar You can also search for this author in PubMed Google Scholar Prices may be subject to local taxes which are calculated during checkout ‘All this is in crisis': US universities curtail staff and spending as Trump cuts take hold ‘All this is in crisis': US universities curtail staff and spending as Trump cuts take hold An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

A new way to deliver disease-fighting proteins throughout the brain may improve the treatment of Alzheimer's disease and other neurological disorders, according to University of California, Irvine scientists. By engineering human immune cells called microglia, the researchers have created living cellular "couriers" capable of responding to brain pathology and releasing therapeutic agents exactly where needed. The National Institutes of Health-supported study, published in Cell Stem Cell, demonstrates for the first time that microglia derived from induced pluripotent stem cells can be genetically programmed to detect disease-specific brain changes -- like amyloid plaques in Alzheimer's disease -- and then release enzymes that help break down those toxic proteins. As a result, the cells were able to reduce inflammation, preserve neurons and synaptic connections, and reverse multiple other hallmarks of neurodegeneration in mice. "We've developed a programmable, living delivery system that gets around that problem by residing in the brain itself and responding only when and where it's needed." Using CRISPR gene editing, the team modified human microglia to secrete neprilysin -- an enzyme known to degrade beta-amyloid -- under the control of a promoter that only activates near plaques. In both cases, the engineered cells adopted unique gene expression profiles -- highlighting the potential to tailor them to a variety of central nervous system diseases. "This work opens the door to a completely new class of brain therapies," said Robert Spitale, UC Irvine professor of pharmaceutical sciences and co-corresponding author on the study. "Instead of using synthetic drugs or viral vectors, we're enlisting the brain's immune cells as precision delivery vehicles." The researchers noted that much work remains to translate this platform into human trials, including demonstrating long-term safety and developing methods for scalable manufacturing. However, because the microglia are derived from induced pluripotent stem cells, they could possibly be produced from a patient's own cells, reducing the risk of immune rejection. Hayk Davtyan, Alina L. Chadarevian and Jonathan Hasselmann of UC Irvine, among others, also contributed to the study, which was a collaboration among the university's Department of Neurobiology & Behavior, Institute for Memory Impairments and Neurological Disorders, and Sue & Bill Gross Stem Cell Research Center. Think of microglia as the brain's own surveillance and cleanup crew. They constantly scan the brain for signs of trouble -- like pathogens, damaged cells or toxic proteins -- and respond by engulfing and digesting harmful substances in a process called phagocytosis. Importantly, in diseases like Alzheimer's, microglia are found near amyloid plaques (clumps of toxic proteins), where they become activated and attempt to surround and clear this toxic debris. But in chronic disease, their activity can become dysregulated, contributing to neuroinflammation and further neuronal damage. Because of their central role in both protecting and sometimes harming the brain, microglia are a major focus of neurological research and a promising target for therapies. Stay informed with ScienceDaily's free email newsletter, updated daily and weekly. Keep up to date with the latest news from ScienceDaily via social networks: Tell us what you think of ScienceDaily -- we welcome both positive and negative comments.