Metabolic model of necrotizing enterocolitis in the premature newborn gut resulting from enteric dysbiosis

Necrotizing enterocolitis (NEC) is a leading cause of premature newborn morbidity and mortality. The clinical features of NEC consistently include prematurity, gut dysbiosis and enteral inflammation, yet the pathogenesis remains obscure. Herein we combine metagenomics and targeted metabolomics, with functional in vivo and in vitro assessment, to define a novel molecular mechanism of NEC. One thousand six hundred and forty seven publicly available metagenomics datasets were analyzed (NEC = 245; healthy = 1,402) using artificial intelligence methodologies. Targeted metabolomic profiling was used to quantify the concentration of specified fecal metabolites at NEC onset (n = 8), during recovery (n = 6), and in age matched controls (n = 10). Toxicity assays of discovered metabolites were performed in vivo in mice and in vitro using human intestinal epithelial cells. Metagenomic and targeted metabolomic analyses revealed significant differences in pyruvate fermentation pathways and associated intermediates. Notably, the short chain fatty acid formate was elevated in the stool of NEC patients at disease onset (P = 0.005) dissipated during recovery (P = 0.02) and positively correlated with degree of intestinal injury (r2 = 0.86). In vitro, formate caused enterocyte cytotoxicity in human cells through necroptosis (P \u3c 0.01). In vivo, luminal formate caused significant dose and development dependent NEC-like injury in newborn mice. Enterobacter cloacae and Klebsiella pneumoniae were the most discriminatory taxa related to NEC dysbiosis and increased formate production. Together, these data suggest a novel biochemical mechanism of NEC through the microbial production of formate. Clinical efforts to prevent NEC should focus on reducing the functional consequences of newborn gut dysbiosis associated metabolic pathways

Metagenomic insights of the infant microbiome community structure and function across multiple sites in the United States

The gut microbiome plays an important role in early life, protecting newborns from enteric pathogens, promoting immune system development and providing key functions to the infant host. Currently, there are limited data to broadly assess the status of the US healthy infant gut microbiome. To address this gap, we performed a multi-state metagenomic survey and found high levels of bacteria associated with enteric inflammation (e.g. Escherichia, Klebsiella), antibiotic resistance genes, and signatures of dysbiosis, independent of location, age, and diet. Bifidobacterium were less abundant than generally expected and the species identified, including B. breve, B. longum and B. bifidum, had limited genetic capacity to metabolize human milk oligosaccharides (HMOs), while B. infantis strains with a complete capacity for HMOs utilization were found to be exceptionally rare. Considering microbiome composition and functional capacity, this survey revealed a previously unappreciated dysbiosis that is widespread in the contemporary US infant gut microbiome

A Spitzer Space Telescope far-infrared spectral atlas of compact sources in the Magellanic Clouds. I. The Large Magellanic Cloud

[abridged] We present 52-93 micron spectra obtained with Spitzer in the MIPS-SED mode, of a representative sample of luminous compact far-IR sources in the LMC. These include carbon stars, OH/IR AGB stars, post-AGB objects and PNe, RCrB-type star HV2671, OH/IR red supergiants WOHG064 and IRAS05280-6910, B[e] stars IRAS04530-6916, R66 and R126, Wolf-Rayet star Brey3a, Luminous Blue Variable R71, supernova remnant N49, a large number of young stellar objects, compact HII regions and molecular cores, and a background galaxy (z~0.175). We use the spectra to constrain the presence and temperature of cold dust and the excitation conditions and shocks within the neutral and ionized gas, in the circumstellar environments and interfaces with the surrounding ISM. Evolved stars, including LBV R71, lack cold dust except in some cases where we argue that this is swept-up ISM. This leads to an estimate of the duration of the prolific dust-producing phase ("superwind") of several thousand years for both RSGs and massive AGB stars, with a similar fractional mass loss experienced despite the different masses. We tentatively detect line emission from neutral oxygen in the extreme RSG WOHG064, with implications for the wind driving. In N49, the shock between the supernova ejecta and ISM is revealed by its strong [OI] 63-micron emission and possibly water vapour; we estimate that 0.2 Msun of ISM dust was swept up. Some of the compact HII regions display pronounced [OIII] 88-micron emission. The efficiency of photo-electric heating in the interfaces of ionized gas and molecular clouds is estimated at 0.1-0.3%. We confirm earlier indications of a low nitrogen content in the LMC. Evidence for solid state emission features is found in both young and evolved object; some of the YSOs are found to contain crystalline water ice.Comment: Accepted for publication in The Astronomical Journal. This paper accompanies the Summer 2009 SAGE-Spec release of 48 MIPS-SED spectra, but uses improved spectrum extraction. (Fig. 2 reduced resolution because of arXiv limit.

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women\u27s physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI):[0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC= 0.87, 95% CI:[0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC= 0.90, 95% CI:[0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia

High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers

Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin’s lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL).Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling.Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients.Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL

Notch signaling inhibits hepatocellular carcinoma following inactivation of the RB pathway

Mice lacking all three Rb genes in the liver develop tumors resembling specific subgroups of human hepatocellular carcinomas, and Notch activity appears to suppress the growth and progression of these tumors

Representing Where along with What Information in a Model of a Cortical Patch

Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects