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Uridine is a pyrimidine nucleoside that exerts restorative functions in tissues under stress. Short-term co-administration of uridine with multiple unrelated drugs prevents drug-induced liver lipid accumulation. Uridine has the ability to modulate liver metabolism; however, the precise mechanism has not been delineated. In this study, long-term effects of uridine on liver metabolism were examined in both HepG2 cell cultures and C57BL/6J mice. We report that uridine administration was associated with O-GlcNAc modification of FOXO1, increased gluconeogenesis, reduced insulin signaling activity, and reduced expression of a liver-specific fatty acid binding protein FABP1. Long-term uridine feeding induced systemic glucose intolerance and severe liver lipid accumulation in mice. Our findings suggest that the therapeutic potentials of uridine should be designed for short-term acute administration.

The nematode Caenorhabditis elegans has been employed as a model organism to study human obesity due to the conservation of the pathways that regulate energy metabolism. To assay for fat storage in C. elegans, a number of fat-soluble dyes have been employed including BODIPY, Nile Red, Oil Red O, and Sudan Black. However, dye-labeled assays produce results that often do not correlate with fat stores in C. elegans. An alternative label-free approach to analyze fat storage in C. elegans has recently been described with coherent anti-Stokes Raman scattering (CARS) microscopy. Here, we compare the performance of CARS microscopy with standard dye-labeled techniques and biochemical quantification to analyze fat storage in wild type C. elegans and with genetic mutations in the insulin/IGF-1 signaling pathway including the genes daf-2 (insulin/IGF-1 receptor), rict-1 (rictor) and sgk-1 (serum glucocorticoid kinase). CARS imaging provides a direct measure of fat storage with unprecedented details including total fat stores as well as the size, number, and lipid-chain unsaturation of individual lipid droplets. In addition, CARS/TPEF imaging reveals a neutral lipid species that resides in both the hypodermis and the intestinal cells and an autofluorescent organelle that resides exclusively in the intestinal cells. Importantly, coherent addition of the CARS fields from the C-H abundant neutral lipid permits selective CARS imaging of the fat store, and further coupling of spontaneous Raman analysis provides unprecedented details including lipid-chain unsaturation of individual lipid droplets. We observe that although daf-2, rict-1, and sgk-1 mutants affect insulin/IGF-1 signaling, they exhibit vastly different phenotypes in terms of neutral lipid and autofluorescent species. We find that CARS imaging gives quantification similar to standard biochemical triglyceride quantification. Further, we independently confirm that feeding worms with vital dyes does not lead to the staining of fat stores, but rather the sequestration of dyes in lysosome-related organelles. In contrast, fixative staining methods provide reproducible data but are prone to errors due to the interference of autofluorescent species and the non-specific staining of cellular structures other than fat stores. Importantly, both growth conditions and developmental stage should be considered when comparing methods of C. elegans lipid storage. Taken together, we confirm that CARS microscopy provides a direct, non-invasive, and label-free means to quantitatively analyze fat storage in living C. elegans.

Obesity is often associated with a chronic, low-grade inflammatory state affecting the entire body. This sustained inflammatory state disrupts the coordinated communication between the periphery and the brain, which has a crucial role in maintaining homeostasis through humoural, nutrient-mediated, immune and nervous signalling pathways. The inflammatory changes induced by obesity specifically affect communication interfaces, including the blood–brain barrier, glymphatic system and meninges. Consequently, brain areas near the third ventricle, including the hypothalamus and other cognition-relevant regions, become susceptible to impairments, resulting in energy homeostasis dysregulation and an elevated risk of cognitive impairments such as Alzheimer’s disease and dementia. This Review explores the intricate communication between the brain and the periphery, highlighting the effect of obesity-induced inflammation on brain function. Le Thuc and García-Cáceres discuss the effect of obesity-induced systemic inflammation on the brain, including hypothalamic circuits for whole-body energy homeostasis as well as cognitive function.

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.

Obesity is an established risk factor for metabolic disease. This study explores the functional complementation of anti-adipogenic phytonutrients for obesity prevention and management. Nine phytonutrients were selected based on their ability to affect the expression of one or more selected adipogenic biomarker proteins. The phytonutrients include berberine, luteolin, resveratrol, fisetin, quercetin, fucoidan, epigallocatechin gallate, hesperidin, and curcumin. The selected adipogenic biomarker proteins include PPARɣ, SREBP1c, FASN, PLIN1, FABP4, and β-catenin. Individually, phytonutrients had variable effects on the expression level of selected adipogenic biomarker proteins. Collectively, the functional complementation of nine phytonutrients suppressed de novo fatty acid biosynthesis via the negative regulation of PPARɣ, FASN, PLIN1, and FABP4 expression; activated glycolysis via the positive regulation of SREBP1c expression; and preserved cell–cell adhesion via the inhibition of β-catenin degradation. In primary human subcutaneous preadipocytes, the composition of nine phytonutrients had more potent and longer lasting anti-adipogenic effects compared to individual phytonutrients. In a diet-induced obesity murine model, the composition of nine phytonutrients improved glucose tolerance and reduced weight gain, liver steatosis, visceral adiposity, circulating triglycerides, low-density lipoprotein cholesterol, and inflammatory cytokines and chemokines. The functional complementation of anti-adipogenic phytonutrients provides an effective approach toward engineering novel therapeutics for the prevention and management of obesity and metabolic syndrome.

Purines and pyrimidines are complementary bases of the genetic code. The roles of purines and their derivatives in cellular signal transduction and energy metabolism are well-known. In contrast, the roles of pyrimidines and their derivatives in cellular function remain poorly understood. In this study, the roles of uridine, a pyrimidine nucleoside, in liver metabolism are examined in mice. We report that short-term uridine administration in C57BL/6J mice increases liver protein glycosylation profiles, reduces phosphorylation level of insulin signaling proteins, and activates the HRI-eIF-2α-ATF4 heme-deficiency stress response pathway. Short-term uridine administration is also associated with reduced liver hemin level and reduced ability for insulin-stimulated blood glucose removal during an insulin tolerance test. Some of the short-term effects of exogenous uridine in C57BL/6J mice are conserved in transgenic UPase1-/- mice with long-term elevation of endogenous uridine level. UPase1-/- mice exhibit activation of the liver HRI-eIF-2α-ATF4 heme-deficiency stress response pathway. UPase1-/- mice also exhibit impaired ability for insulin-stimulated blood glucose removal. However, other short-term effects of exogenous uridine in C57BL/6J mice are not conserved in UPase1-/- mice. UPase1-/- mice exhibit normal phosphorylation level of liver insulin signaling proteins and increased liver hemin concentration compared to untreated control C57BL/6J mice. Contrasting short-term and long-term consequences of uridine on liver metabolism suggest that uridine exerts transient effects and elicits adaptive responses. Taken together, our data support potential roles of pyrimidines and their derivatives in the regulation of liver metabolism.

A valid machine model is predictive, but a predictive model may not be valid. The gap between these two can be larger than many practitioners may expect.

In this study, the effects of cinnamaldehyde and curcumin on Akt2, a serine/threonine protein kinase central to the insulin signaling pathway, were examined in preadipocytes. Cinnamaldehyde or curcumin treatment increased Akt2 phosphorylation at multiple sites including T450 and Y475, but had no effect on Akt2 phosphorylation at S474, which is critical for Akt2 activation. Surprisingly, insulin treatment with cinnamaldehyde or curcumin increased p-Akt2 (S474) by 3.5-fold versus insulin treatment alone. Furthermore, combined cinnamaldehyde, curcumin, and insulin treatment increased p-Akt2 (S474) by 7-fold versus insulin treatment alone. Interestingly, cinnamaldehyde and curcumin inhibited both serine/threonine phosphatase 2A (PP2A) and protein tyrosine phosphatase 1B (PTP1B). Akt2 activation is a multistep process that requires phosphorylation at T450 for proper folding and maturation, and phosphorylation of both Y475 and S474 for stabilization of the catalytic domain. It is plausible that by inhibiting PP2A and PTP1B, cinnamaldehyde and curcumin increase phosphorylation at T450 and Y475, and prime Akt2 for insulin-stimulated phosphorylation at S474. Notably, the combination of a PP2A inhibitor, okadaic acid, and a PTP1B inhibitor increased p-Akt2 (S474), even in the absence of insulin. Future combinations of PP2A and PTP1B inhibitors provide a rational platform to engineer new therapeutics for insulin resistance syndrome.

A large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the number of samples. Propensity score is a common way to deal with a large covariate set, but the accuracy of propensity score estimation (normally done by logistic regression) is also challenged by the large number of covariates. In this paper, we prove that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation. The theoretical result enables effective data-driven algorithms for causal effect estimation. Supported by the result, we develop an algorithm that employs a supervised kernel dimension reduction method to learn a lower dimensional representation from the original covariate space, and then utilises nearest neighbour matching in the reduced covariate space to impute the counterfactual outcomes to avoid the large sized covariate set problem. The proposed algorithm is evaluated on two semisynthetic and three real-world datasets and the results show the effectiveness of the proposed algorithm.