Hyperactive HRAS dysregulates energetic metabolism in fibroblasts from patients with Costello syndrome via enhanced production of reactive oxidizing species

Germline-activating mutations in HRAS cause Costello syndrome (CS), a cancer prone multisystem disorder characterized by reduced postnatal growth. In CS, poor weight gain and growth are not caused by low caloric intake. Here, we show that constitutive plasma membrane translocation and activation of the GLUT4 glucose transporter, via reactive oxygen species-dependent AMP-activated protein kinase α and p38 hyperactivation, occurs in primary fibroblasts of CS patients, resulting in accelerated glycolysis and increased fatty acid synthesis and storage as lipid droplets. An accelerated autophagic flux was also identified as contributing to the increased energetic expenditure in CS. Concomitant inhibition of p38 and PI3K signaling by wortmannin was able to rescue both the dysregulated glucose intake and accelerated autophagic flux. Our findings provide a mechanistic link between upregulated HRAS function, defective growth and increased resting energetic expenditure in CS, and document that targeting p38 and PI3K signaling is able to revert this metabolic dysfunction.n

Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure

Impact of Insulin Degludec/Liraglutide Fixed Combination on the Gut Microbiomes of Elderly Patients With Type 2 Diabetes: Results From A Subanalysis of A Small Non-Randomised Single Arm Study

In elderly Type 2 Diabetes (T2D) patients the relationship between the destabilization of gut microbiome and reversal of dysbiosis via glucose lowering drugs has not been explored. We investigated the effect of 6 months therapy with a fixed combination of Liraglutide and Degludec on the composition of the gut microbiome and its relationship with Quality of Life, glucose metabolism, depression, cognitive function, and markers of inflammation in a group of very old T2D subjects (n=24, 5 women, 19 men, mean age=82 years). While we observed no significant differences in microbiome biodiversity or community among study participants (N = 24, 19 men, mean age 82 years) who responded with decreased HbA1c (n=13) versus those who did not (n=11), our results revealed a significant increase in Gram-negative Alistipes among the former group (p=0.013). Among the responders, changes in the Alistipes content were associated directly with cognitive improvement (r=0.545, p=0.062) and inversely with TNF alpha levels (r=-0.608, p=0.036). Our results suggest that this combination drug may have a significant impact on both gastrointestinal microbes and cognitive function in elderly T2D individuals

Transcriptomic Characterization of Cow, Donkey and Goat Milk Extracellular Vesicles Reveals Their Anti-Inflammatory and Immunomodulatory Potential

Milk extracellular vesicles (mEVs) seem to be one of the main maternal messages delivery systems. Extracellular vesicles (EVs) are micro/nano-sized membrane-bound structures enclosing signaling molecules and thus acting as signal mediators between distant cells and/or tissues, exerting biological effects such as immune modulation and pro-regenerative activity. Milk is also a unique, scalable, and reliable source of EVs. Our aim was to characterize the RNA content of cow, donkey, and goat mEVs through transcriptomic analysis of mRNA and small RNA libraries. Over 10,000 transcripts and 2000 small RNAs were expressed in mEVs of each species. Among the most represented transcripts, 110 mRNAs were common between the species with cow acting as the most divergent. The most represented small RNA class was miRNA in all the species, with 10 shared miRNAs having high impact on the immune regulatory function. Functional analysis for the most abundant mRNAs shows epigenetic functions such as histone modification, telomere maintenance, and chromatin remodeling for cow; lipid catabolism, oxidative stress, and vitamin metabolism for donkey; and terms related to chemokine receptor interaction, leukocytes migration, and transcriptional regulation in response to stress for goat. For miRNA targets, shared terms emerged as the main functions for all the species: immunity modulation, protein synthesis, cellular cycle regulation, transmembrane exchanges, and ion channels. Moreover, donkey and goat showed additional terms related to epigenetic modification and DNA maintenance. Our results showed a potential mEVs immune regulatory purpose through their RNA cargo, although in vivo validation studies are necessary

Intestinal Taxa Abundance and Diversity in Inflammatory Bowel Disease Patients: An Analysis including Covariates and Confounders

Intestinal dysbiosis has been widely documented in inflammatory bowel diseases (IBDs) and is thought to influence the onset and perpetuation of gut inflammation. However, it remains unclear whether such bacterial changes rely in part on the modification of an IBD-associated lifestyle (e.g., smoking and physical activity) and diet (e.g., rich in dairy products, cereals, meat and vegetables). In this study, we investigated the impact of these habits, which we defined as confounders and covariates, on the modulation of intestinal taxa abundance and diversity in IBD patients. 16S rRNA gene sequence analysis was performed using genomic DNA extracted from the faecal samples of 52 patients with Crohn’s disease (CD) and 58 with ulcerative colitis (UC), which are the two main types of IBD, as well as 42 healthy controls (HC). A reduced microbial diversity was documented in the IBD patients compared with the HC. Moreover, we identified specific confounders and covariates that influenced the association between some bacterial taxa and disease extent (in UC patients) or behaviour (in CD patients) compared with the HC. In particular, a PERMANOVA stepwise regression identified the variables “age”, “eat yogurt at least four days per week” and “eat dairy products at least 4 days per week” as covariates when comparing the HC and patients affected by ulcerative proctitis (E1), left-sided UC (distal UC) (E2) and extensive UC (pancolitis) (E3). Instead, the variables “age”, “gender”, “eat meat at least four days per week” and “eat bread at least 4 days per week” were considered as covariates when comparing the HC with the CD patients affected by non-stricturing, non-penetrating (B1), stricturing (B2) and penetrating (B3) diseases. Considering such variables, our analysis indicated that the UC extent differentially modulated the abundance of the Bifidobacteriaceae, Rikenellaceae, Christensenellaceae, Marinifilaceae, Desulfovibrionaceae, Lactobacillaceae, Streptococcaceae and Peptostreptococcaceae families, while the CD behaviour influenced the abundance of Christensenellaceae, Marinifilaceae, Rikenellaceae, Ruminococcaceae, Barnesiellaceae and Coriobacteriaceae families. In conclusion, our study indicated that some covariates and confounders related to an IBD-associated lifestyle and dietary habits influenced the intestinal taxa diversity and relative abundance in the CD and UC patients compared with the HC. Indeed, such variables should be identified and excluded from the analysis to characterize the bacterial families whose abundance is directly modulated by IBD status, as well as disease extent or behaviour

Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders

In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables