Lysosomal degradation of cytoplasmic components by autophagy is essential for cellular survival and homeostasis under nutrient-deprived conditions1–4. Acute regulation of autophagy by nutrient-sensing kinases is well defined3, 5–7, but longer-term transcriptional regulation is relatively unknown. Here we show that the fed-state sensing nuclear receptor FXR8, 9 and the fasting transcriptional activator CREB10, 11 coordinately regulate the hepatic autophagy gene network. Pharmacological activation of FXR repressed many autophagy genes and inhibited autophagy even in fasted mice and feeding-mediated inhibition of macroautophagy was attenuated in FXR-knockout mice. From mouse liver ChIP-seq data12–15, FXR and CREB binding peaks were detected at 178 and 112, respectively, of 230 autophagy-related genes, and 78 genes showed shared binding, mostly in their promoter regions. CREB promoted lipophagy, autophagic degradation of lipids16, under nutrient-deprived conditions, and FXR inhibited this response. Mechanistically, CREB upregulated autophagy genes, including Atg7, Ulk1, and Tfeb, by recruiting the coactivator CRTC2. After feeding or pharmacological activation, FXR trans-repressed these genes by disrupting the functional CREB/CRTC2 complex. This study identifies the novel FXR/CREB axis as a key physiological switch regulating autophagy, resulting in sustained nutrient regulation of autophagy during feeding/fasting cycles.

A low-cost yet highly sensitive colorimetric sensor array for the detection and identification of toxic industrial chemicals (TICs) has been developed. The sensor consists of a disposable array of cross-responsive nanoporous pigments whose colors are changed by diverse chemical interactions with analytes. Clear differentiation among 20 different TICs has been easily achieved at both their IDLH (immediately dangerous to life or health) concentration within 2 min of exposure and PEL (permissible exposure limit) concentration within 5 min of exposure with no errors or misclassifications. Detection limits are generally well below the PEL (in most cases below 5% of PEL) and are typically in the low ppb range. The colorimetric sensor array is not responsive to changes in humidity or temperature over a substantial range. The printed arrays show excellent batch to batch reproducibility and long shelf life (greater than 3 months).

Matlab programs are available upon request from the authors.

Molecular recognition of sugars and a practical method to detect and discriminate among a large number of such similar analytes remain substantial scientific challenges. We report here a low-cost, simple colorimetric sensor array capable of identification and quantification of sugars and related compounds. Fifteen different monosaccharides, disaccharides, and artificial sweeteners were differentiated without error in 80 trials. Limits of detection at pH 7.4 for glucose were <1mM, which is below physiologically important levels.Array-based sensing has emerged as a powerful tool for the detection of chemically diverse analytes. Based on cross-responsive sensor elements, these systems mimic the mammalian gustatory and olfactory systems by producing specificity, not from any single sensor, but as a unique composite response for each analyte. 1,2 For example, electronic tongue technology for the detection of aqueous analytes has generally employed arrays of sensors based on polymer absorption, electrochemical reactions, or oxidations of analytes on metal oxides. 3 We have previously reported on the development of a rather different, but quite simple, optoelectronic approach using a colorimetric sensor array of chemically responsive dyes for identification and quantification of a wide range of analytes both in gas phase and in aqueous solutions. 4-6 The colors of the dyes are affected by a wide range of analyte-dye interactions (e.g., pH, Lewis acid-base, dipolar, π-π, etc.), and the arrays made by simply printing hydrophobic dyes on a hydrophobic membrane. Although this approach is very effective for the detection of volatile organics in the gas phase 5 and more hydrophobic organics in water, 6 hydrophilic analytes Molecular recognition of carbohydrates (which differ primarily in the conformation of multiple hydroxyl groups) poses a particularly difficult challenge and generally requires the use of preexisting protein-saccharide interactions. 9 The approach used here for the identification of carbohydrates is nonenzymatic and relies in part on the differences in association constants of boronic acids with diols (e.g., sugars), leading to changes in solution pH. 10-13 Arylboronic acids specifically show discrimination among saccharides. For example, Chang and coworkers recently reported discrimination among sugars using pH indicators and boric and arylboronic acids. 12 This approach is cumbersome, however, requiring the addition of individual analytes to multiple separate liquid solutions of each pH indicator/boronic mixture. In contrast, we report here that the single addition of analyte to a printed array of immobilized pH indicators in a sol-gel matrix offers advantages in ease of use, sensitivity, expense, and reusability.Even more importantly, we find that the presumption 11h,12 that this discrimination is due exclusively to changes in pH is incorrect: even for the class of closely related sugars, the colorimetric array data proves to be highly multidimensional, with six independent dimensions necessary f...

Human fungal infections have gained recent notoriety following contamination of pharmaceuticals in the compounding process. Such invasive infections are a more serious global problem, especially for immunocompromised patients. While superficial fungal infections are common and generally curable, invasive fungal infections are often life-threatening and much harder to diagnose and treat. Despite the increasing awareness of the situation’s severity, currently available fungal diagnostic methods cannot always meet diagnostic needs, especially for invasive fungal infections. Volatile organic compounds produced by fungi provide an alternative diagnostic approach for identification of fungal strains. We report here an optoelectronic nose based on a disposable colorimetric sensor array capable of rapid differentiation and identification of pathogenic fungi based on their metabolic profiles of emitted volatiles. The sensor arrays were tested with 12 human pathogenic fungal strains grown on standard agar medium. Array responses were monitored with an ordinary flatbed scanner. All fungal strains gave unique composite responses within 3 hours and were correctly clustered using hierarchical cluster analysis. A standard jackknifed linear discriminant analysis gave a classification accuracy of 94% for 155 trials. Tensor discriminant analysis, which takes better advantage of the high dimensionality of the sensor array data, gave a classification accuracy of 98.1%. The sensor array is also able to observe metabolic changes in growth patterns upon the addition of fungicides, and this provides a facile screening tool for determining fungicide efficacy for various fungal strains in real time.