A microbiome-dependent gut-brain pathway regulates motivation for exercise.

Exercise exerts a wide range of beneficial effects for healthy physiology1. However, the mechanisms regulating an individual's motivation to engage in physical activity remain incompletely understood. An important factor stimulating the engagement in both competitive and recreational exercise is the motivating pleasure derived from prolonged physical activity, which is triggered by exercise-induced neurochemical changes in the brain. Here, we report on the discovery of a gut-brain connection in mice that enhances exercise performance by augmenting dopamine signalling during physical activity. We find that microbiome-dependent production of endocannabinoid metabolites in the gut stimulates the activity of TRPV1-expressing sensory neurons and thereby elevates dopamine levels in the ventral striatum during exercise. Stimulation of this pathway improves running performance, whereas microbiome depletion, peripheral endocannabinoid receptor inhibition, ablation of spinal afferent neurons or dopamine blockade abrogate exercise capacity. These findings indicate that the rewarding properties of exercise are influenced by gut-derived interoceptive circuits and provide a microbiome-dependent explanation for interindividual variability in exercise performance. Our study also suggests that interoceptomimetic molecules that stimulate the transmission of gut-derived signals to the brain may enhance the motivation for exercise.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology. MATERIALS AND METHODS: We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC. RESULTS: The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence. CONCLUSION: Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Immune pathway upregulation and lower genomic instability distinguish EBV-positive nodal T/NK-cell lymphoma from ENKTL and PTCL-NOS.

Primary Epstein-Barr virus (EBV)-positive nodal T/NK-cell lymphoma (PTCL-EBV) is a poorly understood disease which shows features resembling extranodal NK/T-cell lymphoma (ENKTL) and is currently not recognized as a distinct entity but categorized as a variant of primary T-cell lymphoma not otherwise specified (PTCL-NOS). Herein, we analyzed copynumber aberrations (n=77) with a focus on global measures of genomic instability and homologous recombination deficiency and performed gene expression (n=84) and EBV miRNA expression (n=24) profiling as well as targeted mutational analysis (n=16) to further characterize PTCL-EBV in relation to ENKTL and PTCL-NOS. Multivariate analysis revealed that patients with PTCL-EBV had a significantly worse outcome compared to patients with PTCL-NOS (P=0.002) but not to those with ENKTL. Remarkably, PTCL-EBV exhibited significantly lower genomic instability and homologous recombination deficiency scores compared to ENKTL and PTCL-NOS. Gene set enrichment analysis revealed that many immune-related pathways, interferon α/γ response, and IL6_JAK_STAT3 signaling were significantly upregulated in PTCLEBV and correlated with lower genomic instability scores. We also identified that NFκB-associated genes, BIRC3, NFKB1 (P50) and CD27, and their proteins are upregulated in PTCL-EBV. Most PTCL-EBV demonstrated a type 2 EBV latency pattern and, strikingly, exhibited downregulated expression of most EBV miRNA compared to ENKTL and their target genes were also enriched in immune-related pathways. PTCL-EBV also showed frequent mutations of TET2, PIK3CD and STAT3, and are characterized by microsatellite stability. Overall, poor outcome, low genomic instability, upregulation of immune pathways and downregulation of EBV miRNA are distinctive features of PTCL-EBV. Our data support the concept that PTCL-EBV could be considered as a distinct entity, provide novel insights into the pathogenesis of the disease and offer potential new therapeutic targets for this tumor.

SLIDE - a web-based tool for interactive visualization of large-scale - omics data.

Summary: Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDE's interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen. Availability and implementation: SLIDE is publicly available under BSD license both as an online version as well as a stand-alone version at https://github.com/soumitag/SLIDE. Supplementary information: Supplementary data are available at Bioinformatics online.

Cockayne syndrome group A and B proteins converge on transcription-linked resolution of non-B DNA.

Cockayne syndrome is a neurodegenerative accelerated aging disorder caused by mutations in the CSA or CSB genes. Although the pathogenesis of Cockayne syndrome has remained elusive, recent work implicates mitochondrial dysfunction in the disease progression. Here, we present evidence that loss of CSA or CSB in a neuroblastoma cell line converges on mitochondrial dysfunction caused by defects in ribosomal DNA transcription and activation of the DNA damage sensor poly-ADP ribose polymerase 1 (PARP1). Indeed, inhibition of ribosomal DNA transcription leads to mitochondrial dysfunction in a number of cell lines. Furthermore, machine-learning algorithms predict that diseases with defects in ribosomal DNA (rDNA) transcription have mitochondrial dysfunction, and, accordingly, this is found when factors involved in rDNA transcription are knocked down. Mechanistically, loss of CSA or CSB leads to polymerase stalling at non-B DNA in a neuroblastoma cell line, in particular at G-quadruplex structures, and recombinant CSB can melt G-quadruplex structures. Indeed, stabilization of G-quadruplex structures activates PARP1 and leads to accelerated aging in Caenorhabditis elegans In conclusion, this work supports a role for impaired ribosomal DNA transcription in Cockayne syndrome and suggests that transcription-coupled resolution of secondary structures may be a mechanism to repress spurious activation of a DNA damage response.

Absence of cannabinoid 1 receptor in beta cells protects against high-fat/high-sugar diet-induced beta cell dysfunction and inflammation in murine islets.

AIMS/HYPOTHESIS: The cannabinoid 1 receptor (CB1R) regulates insulin sensitivity and glucose metabolism in peripheral tissues. CB1R is expressed on pancreatic beta cells and is coupled to the G protein Gαi, suggesting a negative regulation of endogenous signalling in the beta cell. Deciphering the exact function of CB1R in beta cells has been confounded by the expression of this receptor on multiple tissues involved in regulating metabolism. Thus, in models of global genetic or pharmacological CB1R blockade, it is difficult to distinguish the indirect effects of improved insulin sensitivity in peripheral tissues from the direct effects of inhibiting CB1R in beta cells per se. To assess the direct contribution of beta cell CB1R to metabolism, we designed a mouse model that allows us to determine the role of CB1R specifically in beta cells in the context of whole-body metabolism. METHODS: We generated a beta cell specific Cnr1 (CB1R) knockout mouse (ß-CB1R-/-) to study the long-term consequences of CB1R ablation on beta cell function in adult mice. We measured beta cell function, proliferation and viability in these mice in response to a high-fat/high-sugar diet and induction of acute insulin resistance with the insulin receptor antagonist S961. RESULTS: ß-CB1R-/- mice had increased fasting (153 ± 23% increase at 10 weeks of age) and stimulated insulin secretion and increased intra-islet cAMP levels (217 ± 33% increase at 10 weeks of age), resulting in primary hyperinsulinaemia, as well as increased beta cell viability, proliferation and islet area (1.9-fold increase at 10 weeks of age). Hyperinsulinaemia led to insulin resistance, which was aggravated by a high-fat/high-sugar diet and weight gain, although beta cells maintained their insulin secretory capacity in response to glucose. Strikingly, islets from ß-CB1R-/- mice were protected from diet-induced inflammation. Mechanistically, we show that this is a consequence of curtailment of oxidative stress and reduced activation of the NLRP3 inflammasome in beta cells. CONCLUSIONS/INTERPRETATION: Our data demonstrate CB1R to be a negative regulator of beta cell function and a mediator of islet inflammation under conditions of metabolic stress. Our findings point to beta cell CB1R as a therapeutic target, and broaden its potential to include anti-inflammatory effects in both major forms of diabetes. DATA AVAILABILITY: Microarray data have been deposited at GEO (GSE102027).

Metabolomic changes in vertebrate host during malaria disease progression.

Metabolomics refers to top-down systems biological analysis of metabolites in biological specimens. Phenotypic proximity of metabolites makes them interesting candidates for studying biomarkers of environmental stressors such as parasitic infections. Moreover, the host-parasite interaction directly impinges upon metabolic pathways since the parasite uses the host metabolite pool as a biosynthetic resource. Malarial infection, although not recognized as a classic metabolic disorder, often leads to severe metabolic changes such as hypoglycemia and lactic acidosis. Thus, metabolomic analysis of the infection has become an invaluable tool for promoting a better understanding of the host-parasite interaction and for the development of novel therapeutics. In this review, we summarize the current knowledge obtained from metabolomic studies of malarial infection in rodent models and human patients. Metabolomic analysis of experimental rodent malaria has provided significant insights into the mechanisms of disease progression including utilization of host resources by the parasite, sexual dimorphism in metabolic phenotypes, and cellular changes in host metabolism. Moreover, these studies also provide proof of concept for prediction of cerebral malaria. On the other hand, metabolite analysis of patient biofluids generates extensive data that could be of use in identifying biomarkers of infection severity and in monitoring disease progression. Through the use of metabolomic datasets one hopes to assess crucial infection-specific issues such as clinical severity, drug resistance, therapeutic targets, and biomarkers. Also discussed are nascent or newly emerging areas of metabolomics such as pre-erythrocytic stages of the infection and the host immune response. This review is organized in four broad sections-methodologies for metabolomic analysis, rodent infection models, studies of human clinical specimens, and potential of immunometabolomics. Data summarized in this review should serve as a springboard for novel hypothesis testing and lead to a better understanding of malarial infection and parasite biology.

SOFAST-HMQC-an efficient tool for metabolomics.

Nuclear magnetic resonance (NMR)-based metabolomics relies mostly on 1D NMR; however, the technique is limited by overlap of the signals from the metabolites. In order to circumvent this problem, 2D 1H-13C correlation spectroscopy techniques are often used. However owing to poorer natural abundance and gyromagnetic ratio of 13C, the acquisition time for 2D 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) is long. This makes it almost impossible to be used in high throughput study. We have reported the application of selective optimized flip angle short transient (SOFAST) technique coupled to heteronuclear multiple quantum correlation (HMQC) along with nonlinear sampling (NUS) in urine and serum samples. This technique takes sevenfold less experimental time than the conventional 1H-13C HSQC experiment with retention of almost all molecular information. Hence, this can be used for high throughput study. Graphical abstract SOFAST-HMQC is a two-dimensional NMR technique that significantly decreases experimental time without loss of information. This technique is applied in complex biofluid samples that are used for high throughput metabolomics studies and shows promise of better information recovery than conventional two-dimensional NMR technique in shorter time.

Early prediction of cerebral malaria by (1)H NMR based metabolomics.

BACKGROUND: Cerebral malaria (CM) is a life-threatening disease, caused mainly by Plasmodium falciparum in humans. In adults only 1-2% of P. falciparum-infected hosts transit to the cerebral form of the disease while most exhibit non-cerebral malaria (NCM). The perturbed metabolic pathways of CM and NCM have been reported. Early marker(s) of CM is(are) not known and by the time a patient exhibits the pathological symptoms of CM, the disease has progressed. Murine CM, like the human disease, is difficult to assign to specific animals at early stage and hence the challenge to treat CM at pre-clinical stage of the disease. This is the first report of prediction of CM in mice using a novel strategy based on (1)H nuclear magnetic resonance (NMR)-based metabolomics. METHODS: Mice were infected with malarial parasites, and serum was collected from all the animals (CM/NCM) before CM symptoms were apparent. The assignment of mice as NCM/CM at an early time point is based on their symptoms at days 8-9 post-infection (pi). The serum samples were subjected to (1)H NMR-based metabolomics. (1)H NMR spectra of the serum samples, collected at various time points (pi) in multiple sets of experiments, were subjected to multivariate analyses. RESULTS: The results from orthogonal partial least square discriminant analyses (OPLS-DA) suggest that the animals with CM start to diverge out in metabolic profile and were distinct on day 4 pi, although by physical observation they were indistinguishable from the NCM. The metabolites that appeared to contribute to this distinction were serum lipids and lipoproteins, and 14-19% enhancement was observed in mice afflicted with CM. A cut-off of 14% change of total lipoproteins in serum predicts 54-71% CM in different experiments at day 4 pi. CONCLUSION: This study clearly demonstrates the possibility of differentiating and identifying animals with CM at an early, pre-clinical stage. The strategy, based on metabolite profile of serum, tested with different batches of animals in both the sex and across different times of the year, is found to be robust. This is the first such study of pre-clinical prognosis of CM.

Quantitative metabolic profiling of NMR spectral signatures of branched chain amino acids in blood serum.

Branched Chain Amino Acids (BCAAs) are related to different aspects of diseases like pathogenesis, diagnosis and even prognosis. While in some diseases, levels of all the BCAAs are perturbed; in some cases, perturbation occurs in one or two while the rest remain unaltered. In case of ischemic heart disease, there is an enhanced level of plasma leucine and isoleucine but valine level remains unaltered. In 'Hypervalinemia', valine is elevated in serum and urine, but not leucine and isoleucine. Therefore, identification of these metabolites and profiling of individual BCAA in a quantitative manner in body-fluid like blood plasma/serum have long been in demand. (1)H NMR resonances of the BCAAs overlap with each other which complicates quantification of individual BCAAs. Further, the situation is limited by the overlap of broad resonances of lipoprotein with the resonances of BCAAs. The widely used commercially available kits cannot differentially estimate the BCAAs. Here, we have achieved proper identification and characterization of these BCAAs in serum in a quantitative manner employing a Nuclear Magnetic Resonance-based technique namely T2-edited Correlation Spectroscopy (COSY). This approach can easily be extended to other body fluids like bile, follicular fluids, saliva, etc.

Identification of biochemical differences between different forms of male infertility by nuclear magnetic resonance (NMR) spectroscopy.

PURPOSE: The aim of this study was to analyze the seminal plasma of patients with idiopathic/male factor infertility and healthy controls with proven fertility by NMR spectroscopy, with a hope of establishing difference in biomarker profiles, if any, between the groups. METHODS: A total of 103 subjects visiting the infertility clinic of Manipal University with normozoospermic parameters, oligozoospermia, asthenozoospermia, azoospermia and teratozoospermia were included. Semen characteristics were analysed by standard criteria. Seminal plasma was subjected to NMR spectroscopy at a 700 MHz (1)H frequency. The resultant data was analyzed by appropriate software. RESULTS: The analysis revealed significant differences between the fertile control group and other forms of male infertility. Interestingly, seminal plasma profile of the idiopathic infertility group showed distinct segregation from the control population as well as other infertile groups. The difference in biomarker profiles between the idiopathic infertility and the rest of the groups combined could originate from either the up-regulation or down regulation of a several compounds, including lysine, arginine, tyrosine, citrate, proline and fructose. CONCLUSION: Our data suggests the presence of a metabolic reason behind the origin of idiopathic infertility. (1)H NMR based metabonomic profiling based on concentration of biomarker lysine has the potential to aid in the detection and diagnosis of idiopathic infertility in an efficient manner.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Degree of Cyclooxygenase-2 Inhibition Modulates Blood Pressure Response to Celecoxib and Naproxen.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) increase the risk of adverse cardiovascular events via suppression of cyclooxygenase (COX)-2-derived prostacyclin (PGI2) formation in heart, vasculature, and kidney. The Prospective Randomized Evaluation of Celecoxib Integrated Safety versus Ibuprofen Or Naproxen (PRECISION) trial and other large clinical studies compared the cardiovascular risk of traditional NSAIDs (i.e. naproxen), which inhibit both COX isozymes, with NSAIDs selective for COX-2 (i.e. celecoxib). However, whether pharmacologically equipotent doses were used - that is, whether a similar degree of COX-2 inhibition was achieved - was not considered. We compared drug target inhibition and blood pressure response to celecoxib at the dose used by most patients in PRECISION with the lowest recommended naproxen dose for osteoarthritis, which is lower than the dose used in PRECISION. Methods: Sixteen healthy participants (19-61 years) were treated with celecoxib (100 mg every 12h), naproxen (250 mg every 12h), or placebo administered twice daily for seven days in a double-blind, crossover design randomized by order. On Day 7 when drug levels had reached steady state, the degree of COX inhibition was assessed ex vivo and in vivo. Ambulatory blood pressure was measured throughout the final 12h dosing interval. Results: Both NSAIDs inhibited COX-2 activity relative to placebo, but naproxen inhibited COX-2 activity to a greater degree (62.9±21.7%) than celecoxib (35.7±25.2%; p<0.05). Similarly, naproxen treatment inhibited PGI2 formation in vivo (48.0±24.9%) to a greater degree than celecoxib (26.7±24.6%; p<0.05). Naproxen significantly increased blood pressure compared to celecoxib (differences in least-square means of mean arterial pressure: 2.5 mm Hg (95% CI: 1.5, 3.5); systolic blood pressure: 4.0 mm Hg (95% CI: 2.9, 5.1); diastolic blood pressure: 1.8 mm Hg (95% CI: 0.8, 2.8); p<0.05 for all). The difference in systolic blood pressure relative to placebo was associated with the degree of COX-2 inhibition (p<0.05). Conclusions: Celecoxib 200 mg/day inhibited COX-2 activity to a lesser degree than naproxen 500 mg/day, resulting in a less pronounced blood pressure increase. While the PRECISION trial concluded the non-inferiority of celecoxib regarding cardiovascular risk, this is based on a comparison of doses that are not equipotent.ClinicalTrials.gov identifier: NCT02502006 (https://clinicaltrials.gov/study/NCT02502006).

HNF4A and HNF1A exhibit tissue specific target gene regulation in pancreatic beta cells and hepatocytes.

HNF4A and HNF1A encode transcription factors that are important for the development and function of the pancreas and liver. Mutations in both genes have been directly linked to Maturity Onset Diabetes of the Young (MODY) and type 2 diabetes (T2D) risk. To better define the pleiotropic gene regulatory roles of HNF4A and HNF1A, we generated a comprehensive genome-wide map of their binding targets in pancreatic and hepatic cells using ChIP-Seq. HNF4A was found to bind and regulate known (ACY3, HAAO, HNF1A, MAP3K11) and previously unidentified (ABCD3, CDKN2AIP, USH1C, VIL1) loci in a tissue-dependent manner. Functional follow-up highlighted a potential role for HAAO and USH1C as regulators of beta cell function. Unlike the loss-of-function HNF4A/MODY1 variant I271fs, the T2D-associated HNF4A variant (rs1800961) was found to activate AKAP1, GAD2 and HOPX gene expression, potentially due to changes in DNA-binding affinity. We also found HNF1A to bind to and regulate GPR39 expression in beta cells. Overall, our studies provide a rich resource for uncovering downstream molecular targets of HNF4A and HNF1A that may contribute to beta cell or hepatic cell (dys)function, and set up a framework for gene discovery and functional validation.

Early Perturbations in Red Blood Cells in Response to Murine Malarial Parasite Infection: Proof-of-Concept 1H NMR Metabolomic Study.

BACKGROUND: The major focus of metabolomics research has been confined to the readily available biofluids-urine and blood serum. However, red blood cells (RBCs) are also readily available, and may be a source of a wealth of information on vertebrates. However, the comprehensive metabolomic characterization of RBCs is minimal although they exhibit perturbations in various physiological states. RBCs act as the host of malarial parasites during the symptomatic stage. Thus, understanding the changes in RBC metabolism during infection is crucial for a better understanding of disease progression. METHODS: The metabolome of normal RBCs obtained from Swiss mice was investigated using 1H NMR spectroscopy. Several 1 and 2-dimensional 1H NMR experiments were employed for this purpose. The information from this study was used to investigate the changes in the RBC metabolome during the early stage of infection (~1% infected RBCs) by Plasmodium bergheii ANKA. RESULTS: We identified over 40 metabolites in RBCs. Several of these metabolites were quantitated using 1H NMR spectroscopy. The results indicate changes in the choline/membrane components and other metabolites during the early stage of malaria. CONCLUSIONS: The paper reports the comprehensive characterization of the metabolome of mouse RBCs. Changes during the early stage of malarial infection suggest significant metabolic alteration, even at low parasite content (~1%). GENERAL SIGNIFICANCE: This study should be of use in maximizing the amount of information available from metabolomic experiments on the cellular components of blood. The technique can be directly applied to real-time investigation of infectious diseases that target RBCs.

HNF1A binds and regulates the expression of SLC51B to facilitate the uptake of estrone sulfate in human renal proximal tubule epithelial cells.

Renal defects in maturity onset diabetes of the young 3 (MODY3) patients and Hnf1a-/- mice suggest an involvement of HNF1A in kidney development and/or its function. Although numerous studies have leveraged on Hnf1α-/- mice to infer some transcriptional targets and function of HNF1A in mouse kidneys, species-specific differences obviate a straightforward extrapolation of findings to the human kidney. Additionally, genome-wide targets of HNF1A in human kidney cells have yet to be identified. Here, we leveraged on human in vitro kidney cell models to characterize the expression profile of HNF1A during renal differentiation and in adult kidney cells. We found HNF1A to be increasingly expressed during renal differentiation, with peak expression on day 28 in the proximal tubule cells. HNF1A ChIP-Sequencing (ChIP-Seq) performed on human pluripotent stem cell (hPSC)-derived kidney organoids identified its genome-wide putative targets. Together with a qPCR screen, we found HNF1A to activate the expression of SLC51B, CD24, and RNF186 genes. Importantly, HNF1A-depleted human renal proximal tubule epithelial cells (RPTECs) and MODY3 human induced pluripotent stem cell (hiPSC)-derived kidney organoids expressed lower levels of SLC51B. SLC51B-mediated estrone sulfate (E1S) uptake in proximal tubule cells was abrogated in these HNF1A-deficient cells. MODY3 patients also exhibit significantly higher excretion of urinary E1S. Overall, we report that SLC51B is a target of HNF1A responsible for E1S uptake in human proximal tubule cells. As E1S serves as the main storage form of nephroprotective estradiol in the human body, lowered E1S uptake and increased E1S excretion may reduce the availability of nephroprotective estradiol in the kidneys, contributing to the development of renal disease in MODY3 patients.

Disruption of Prostaglandin F2α Receptor Signaling Attenuates Fibrotic Remodeling and Alters Fibroblast Population Dynamics in A Preclinical Murine Model of Idiopathic Pulmonary Fibrosis.

Idiopathic Pulmonary Fibrosis (IPF) is a chronic parenchymal lung disease characterized by repetitive alveolar cell injury, myofibroblast proliferation, and excessive extracellular matrix deposition for which unmet need persists for effective therapeutics. The bioactive eicosanoid, prostaglandin F2α, and its cognate receptor FPr (Ptfgr) are implicated as a TGFß1 independent signaling hub for IPF. To assess this, we leveraged our published murine PF model (IER - SftpcI73T) expressing a disease-associated missense mutation in the surfactant protein C (Sftpc) gene. Tamoxifen treated IER-SftpcI73T mice develop an early multiphasic alveolitis and transition to spontaneous fibrotic remodeling by 28 days. IER-SftpcI73T mice crossed to a Ptgfr null (FPr-/-) line showed attenuated weight loss and gene dosage dependent rescue of mortality compared to FPr+/+ cohorts. IER-SftpcI73T/FPr-/- mice also showed reductions in multiple fibrotic endpoints for which administration of nintedanib was not additive. Single cell RNA sequencing, pseudotime analysis, and in vitro assays demonstrated Ptgfr expression predominantly within adventitial fibroblasts which were reprogrammed to an "inflammatory/transitional" cell state in a PGF2α/FPr dependent manner. Collectively, the findings provide evidence for a role for PGF2α signaling in IPF, mechanistically identify a susceptible fibroblast subpopulation, and establish a benchmark effect size for disruption of this pathway in mitigating fibrotic lung remodeling.

PGF2α signaling drives fibrotic remodeling and fibroblast population dynamics in mice.

Idiopathic pulmonary fibrosis (IPF) is a chronic parenchymal lung disease characterized by repetitive alveolar cell injury, myofibroblast proliferation, and excessive extracellular matrix deposition for which unmet need persists for effective therapeutics. The bioactive eicosanoid, prostaglandin F2α, and its cognate receptor FPr (Ptgfr) are implicated as a TGF-ß1-independent signaling hub for IPF. To assess this, we leveraged our published murine PF model (IER-SftpcI73T) expressing a disease-associated missense mutation in the surfactant protein C (Sftpc) gene. Tamoxifen-treated IER-SftpcI73T mice developed an early multiphasic alveolitis and transition to spontaneous fibrotic remodeling by 28 days. IER-SftpcI73T mice crossed to a Ptgfr-null (FPr-/-) line showed attenuated weight loss and gene dosage-dependent rescue of mortality compared with FPr+/+ cohorts. IER-SftpcI73T/FPr-/- mice also showed reductions in multiple fibrotic endpoints for which administration of nintedanib was not additive. Single-cell RNA-Seq, pseudotime analysis, and in vitro assays demonstrated Ptgfr expression predominantly within adventitial fibroblasts, which were reprogrammed to an "inflammatory/transitional" cell state in a PGF2α /FPr-dependent manner. Collectively, the findings provide evidence for a role for PGF2α signaling in IPF, mechanistically identify a susceptible fibroblast subpopulation, and establish a benchmark effect size for disruption of this pathway in mitigating fibrotic lung remodeling.

Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis.

Lipids may influence cellular penetrance by pathogens and the immune response that they evoke. Here we find a broad based lipidomic storm driven predominantly by secretory (s) phospholipase A 2 (sPLA 2 ) dependent eicosanoid production occurs in patients with sepsis of viral and bacterial origin and relates to disease severity in COVID-19. Elevations in the cyclooxygenase (COX) products of arachidonic acid (AA), PGD 2 and PGI 2 , and the AA lipoxygenase (LOX) product, 12-HETE, and a reduction in the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients, correlate with the inflammatory response and link to disease severity. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflect disease severity in COVID-19. AA and LA metabolites and LPC-O-16:0 linked variably to the immune response. These studies yield prognostic biomarkers and therapeutic targets for patients with sepsis, including COVID-19. An interactive purpose built interactive network analysis tool was developed, allowing the community to interrogate connections across these multiomic data and generate novel hypotheses.

Deep phenotyping of the lipidomic response in COVID-19 and non-COVID-19 sepsis.

BACKGROUND: Lipids may influence cellular penetrance by viral pathogens and the immune response that they evoke. We deeply phenotyped the lipidomic response to SARs-CoV-2 and compared that with infection with other pathogens in patients admitted with acute respiratory distress syndrome to an intensive care unit (ICU). METHODS: Mass spectrometry was used to characterise lipids and relate them to proteins, peripheral cell immunotypes and disease severity. RESULTS: Circulating phospholipases (sPLA2, cPLA2 (PLA2G4A) and PLA2G2D) were elevated on admission in all ICU groups. Cyclooxygenase, lipoxygenase and epoxygenase products of arachidonic acid (AA) were elevated in all ICU groups compared with controls. sPLA2 predicted severity in COVID-19 and correlated with TxA2, LTE4 and the isoprostane, iPF2α-III, while PLA2G2D correlated with LTE4. The elevation in PGD2, like PGI2 and 12-HETE, exhibited relative specificity for COVID-19 and correlated with sPLA2 and the interleukin-13 receptor to drive lymphopenia, a marker of disease severity. Pro-inflammatory eicosanoids remained correlated with severity in COVID-19 28 days after admission. Amongst non-COVID ICU patients, elevations in 5- and 15-HETE and 9- and 13-HODE reflected viral rather than bacterial disease. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflected disease severity in COVID-19. In healthy marines, these lipids rose with seroconversion. Eicosanoids linked variably to the peripheral cellular immune response. PGE2, TxA2 and LTE4 correlated with T cell activation, as did PGD2 with non-B non-T cell activation. In COVID-19, LPS stimulated peripheral blood mononuclear cell PGF2α correlated with memory T cells, dendritic and NK cells while LA and DiHOMEs correlated with exhausted T cells. Three high abundance lipids - ChoE 18:3, LPC-O-16:0 and PC-O-30:0 - were altered specifically in COVID. LPC-O-16:0 was strongly correlated with T helper follicular cell activation and all three negatively correlated with multi-omic inflammatory pathways and disease severity. CONCLUSIONS: A broad based lipidomic storm is a predictor of poor prognosis in ARDS. Alterations in sPLA2, PGD2 and 12-HETE and the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients and correlate with the inflammatory response to link to disease severity.