BACKGROUND: Maple syrup urine disease (MSUD) is an autosomal recessive genetic disorder caused by defects in the catabolism of the branched-chain amino acids (BCAAs). However, the clinical and metabolic screening is limited in identifying all MSUD patients, especially those patients with mild phenotypes or are asymptomatic. This study aims to share the diagnostic experience of an intermediate MSUD case who was missed by metabolic profiling but identified by genetic analysis.
METHODS: This study reports the diagnostic process of a boy with intermediate MSUD. The proband presented with psychomotor retardation and cerebral lesions on magnetic resonance imaging scans at 8 mo of age. Preliminary clinical and metabolic profiling did not support a specific disease. However, whole exome sequencing and subsequent Sanger sequencing at 1 year and 7 mo of age identified bi-allelic pathogenic variants of the BCKDHB gene, confirming the proband as having MSUD with non-classic mild phenotypes. His clinical and laboratory data were retrospectively analyzed. According to his disease course, he was classified into an intermediate form of MSUD. His management was then changed to BCAAs restriction and metabolic monitoring conforming to MSUD. In addition, genetic counseling and prenatal diagnosis were provided to his parents.
CONCLUSIONS: Our work provides diagnostic experience of an intermediate MSUD case, suggesting that a genetic analysis is important for ambiguous cases, and alerts clinicians to avoid missing patients with non-classic mild phenotypes of MSUD.

Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Jellyfish represent an important component of marine food webs characterized by large fluctuations of population density, with the ability to abruptly form outbreaks, followed by rarity periods. In spite of considerable efforts to investigate how jellyfish populations are responding globally to anthropogenic change, available evidence still remains unclear. In the last 50 years, jellyfish are seemingly on the rise in a number of coastal areas, including the Mediterranean Sea, where jellyfish blooms periodically become an issue to marine and maritime human activities. Their impacts on marine organism welfare have been poorly quantified. The jellyfish, Rhizostoma pulmo, is an outbreak-forming scyphomedusa whose large populations spread across the Mediterranean, with increasing periodicity and variable abundance. Studies on cnidarian jellyfish suggested being important vectors of bacterial pathogens. In the present study, by combination of conventional culture-based methods and a high-throughput amplicon sequencing (HTS) approach, we characterized the diversity of the bacterial community associated with this jellyfish during their summer outbreak. Three distinct jellyfish compartments, namely umbrella, oral arms, and the mucus secretion obtained from whole specimens were screened for specifically associated microbiota. A total of 17 phyla, 30 classes, 73 orders, 146 families and 329 genera of microbial organisms were represented in R. pulmo samples with three major clades (i.e. Spiroplasma, Mycoplasma and Wolinella) representing over 90% of the retrieved total sequences. The taxonomic microbial inventory was then combined with metabolic profiling data obtained from the Biolog Eco-Plate system. Significant differences among the jellyfish compartments were detected in terms of bacterial abundance, diversity and metabolic utilization of 31 different carbon sources with the highest value of abundance and metabolic potential in the mucus secretion compared to the umbrella and oral arms. Results are discussed in the framework of the species ecology as well as the potential health hazard for marine organisms and humans.

It is challenging to establish the mechanisms involved in the variety of well-defined clinical phenotypes in autism spectrum disorder (ASD) and the pathways involved in their pathogeneses.
The aim of the present study was to evaluate the metabolomic profiles of children with ASD subclassified by mental regression (AR) phenotype and with no regression (ANR).
The present study was a cross-sectional case-control study. Thirty children aged 2-6 years with ASD were included: 15 with ANR and 15 with AR. In addition, a control group of 30 normally developing children was selected and matched to the ASD group by sex and age. Plasma samples were analyzed with a metabolomics single platform methodology based on liquid chromatography-mass spectrometry. Univariate and multivariate analysis, including orthogonal partial least squares-discriminant analysis modeling and Shared-and-Unique-Structures plots, were performed using MetaboAnalyst 4.0 and SIMCA-P 15. The primary endpoint was the metabolic signature profiling among healthy children and autistic children and their subgroups.
Metabolomic profiles of 30 healthy children, 15 ANR and 15 AR were compared. Several differences between healthy children and children with ASD were detected, involving mainly amino acid, lipid and nicotinamide metabolism. Furthermore, we report subtle differences between the ANR and AR groups.
In this study, we report, for the first time, the plasmatic metabolomic profiles of children with ASD, including two different phenotypes based on mental regression status. The use of a liquid chromatography-mass spectrometry platform approach for metabolomics in ASD children using plasma appears to be very efficient and adds further support to previous findings in urine. Furthermore, the present study documents several changes related to amino acid, NAD+ and lipid metabolism that, in some cases, such as arginine and glutamate pathway alterations, seem to be associated with the AR phenotype. Further targeted analyses are needed in a larger cohort to validate the results presented herein.