Bile acids (BAs) are signalling molecules that mediate various cellular responses in both physiological and pathological processes. Several studies report that BAs can be detected in the brain1, yet their physiological role in the central nervous system is still largely unknown. Here we show that postprandial BAs can reach the brain and activate a negative-feedback loop controlling satiety in response to physiological feeding via TGR5, a G-protein-coupled receptor activated by multiple conjugated and unconjugated BAs2 and an established regulator of peripheral metabolism3-8. Notably, peripheral or central administration of a BA mix or a TGR5-specific BA mimetic (INT-777) exerted an anorexigenic effect in wild-type mice, while whole-body, neuron-specific or agouti-related peptide neuronal TGR5 deletion caused a significant increase in food intake. Accordingly, orexigenic peptide expression and secretion were reduced after short-term TGR5 activation. In vitro studies demonstrated that activation of the Rho-ROCK-actin-remodelling pathway decreases orexigenic agouti-related peptide/neuropeptide Y (AgRP/NPY) release in a TGR5-dependent manner. Taken together, these data identify a signalling cascade by which BAs exert acute effects at the transition between fasting and feeding and prime the switch towards satiety, unveiling a previously unrecognized role of physiological feedback mediated by BAs in the central nervous system.Macrophages generate mitochondrial reactive oxygen species and mitochondrial reactive electrophilic species as antimicrobials during Toll-like receptor (TLR)-dependent inflammatory responses. Whether mitochondrial stress caused by these molecules impacts macrophage function is unknown. Here, we demonstrate that both pharmacologically driven and lipopolysaccharide (LPS)-driven mitochondrial stress in macrophages triggers a stress response called mitohormesis. LPS-driven mitohormetic stress adaptations occur as macrophages transition from an LPS-responsive to LPS-tolerant state wherein stimulus-induced pro-inflammatory gene transcription is impaired, suggesting tolerance is a product of mitohormesis. Indeed, like LPS, hydroxyoestrogen-triggered mitohormesis suppresses mitochondrial oxidative metabolism and acetyl-CoA production needed for histone acetylation and pro-inflammatory gene transcription, and is sufficient to enforce an LPS-tolerant state. Thus, mitochondrial reactive oxygen species and mitochondrial reactive electrophilic species are TLR-dependent signalling molecules that trigger mitohormesis as a negative feedback mechanism to restrain inflammation via tolerance. Moreover, bypassing TLR signalling and pharmacologically triggering mitohormesis represents a new anti-inflammatory strategy that co-opts this stress response to impair epigenetic support of pro-inflammatory gene transcription by mitochondria.Cancer metabolism adapts the metabolic network of its tissue of origin. However, breast cancer is not a disease of a single origin. Multiple epithelial populations serve as the culprit cell of origin for specific breast cancer subtypes, yet our knowledge of the metabolic network of normal mammary epithelial cells is limited. Using a multi-omic approach, here we identify the diverse metabolic programmes operating in normal mammary populations. The proteomes of basal, luminal progenitor and mature luminal cell populations revealed enrichment of glycolysis in basal cells and of oxidative phosphorylation in luminal progenitors. Single-cell transcriptomes corroborated lineage-specific metabolic identities and additional intra-lineage heterogeneity. Mitochondrial form and function differed across lineages, with clonogenicity correlating with mitochondrial activity. Targeting oxidative phosphorylation and glycolysis with inhibitors exposed lineage-rooted metabolic vulnerabilities of mammary progenitors. Bioinformatics indicated breast cancer subtypes retain metabolic features of their putative cell of origin. GABA Receptor antagonist Thus, lineage-rooted metabolic identities of normal mammary cells may underlie breast cancer metabolic heterogeneity and targeting these vulnerabilities could advance breast cancer therapy.GeoBioMed – a new transdisciplinary approach that integrates the fields of geology, biology and medicine – reveals that kidney stones composed of calcium-rich minerals precipitate from a continuum of repeated events of crystallization, dissolution and recrystallization that result from the same fundamental natural processes that have governed billions of years of biomineralization on Earth. This contextual change in our understanding of renal stone formation opens fundamentally new avenues of human kidney stone investigation that include analyses of crystalline structure and stratigraphy, diagenetic phase transitions, and paragenetic sequences across broad length scales from hundreds of nanometres to centimetres (five Powers of 10). This paradigm shift has also enabled the development of a new kidney stone classification scheme according to thermodynamic energetics and crystalline architecture. Evidence suggests that ≥50% of the total volume of individual stones have undergone repeated in vivo dissolution and recrystallization. Amorphous calcium phosphate and hydroxyapatite spherules coalesce to form planar concentric zoning and sector zones that indicate disequilibrium precipitation. In addition, calcium oxalate dihydrate and calcium oxalate monohydrate crystal aggregates exhibit high-frequency organic-matter-rich and mineral-rich nanolayering that is orders of magnitude higher than layering observed in analogous coral reef, Roman aqueduct, cave, deep subsurface and hot-spring deposits. This higher frequency nanolayering represents the unique microenvironment of the kidney in which potent crystallization promoters and inhibitors are working in opposition. These GeoBioMed insights identify previously unexplored strategies for development and testing of new clinical therapies for the prevention and treatment of kidney stones.The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.