Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Neurodegenerative diseases are known for their wide range of harmful conditions related to progressive cell damage, nervous system connections and neuronal death. These pathologies promote the loss of essential motor and cognitive functions, such as mobility, learning and sensation. Neurodegeneration affects millions of people worldwide, and no integral cure has been created yet. Here, bioactive compounds have been proven to exert numerous beneficial effects due to their remarkable bioactivity, so they could be considered as great options for the development of new neuroprotective strategies. Phenolic bioactives have been reported to be found in edible part of plants; however, over the last years, a large amount of research has focused on the phenolic richness that plant by-products possess, which sometimes even exceeds the content in the pulp. Thus, their possible application as an emergent neuroprotective technique could also be considered as an optimal strategy to revalorize these agricultural residues (those originated from plant processing). This review aims to summarize main triggers of neurodegeneration, revise the state of the art in plant extracts and their role in avoiding neurodegeneration and discuss how their main phenolic compounds could exert their neuroprotective effects. For this purpose, a diverse search of studies has been conducted, gathering a large number of papers where by-products were used as strong sources of phenolic compounds for their neuroprotective properties. Finally, although a lack of investigation is quite remarkable and greatly limits the use of these compounds, phenolics remain attractive for research into new multifactorial anti-neurodegenerative nutraceuticals.

Intestinal microbiota has been associated with systemic autoimmune diseases, yet the functional consequences of these associations are elusive. We characterized the fecal microbiota (16S rRNA gene amplification and sequencing) and the plasma metabolome (high‐performance liquid chromatography coupled to mass spectrometry) in 59 patients with systemic sclerosis (SSc) and 28 healthy controls (HCs). Microbial and metabolic data were cross‐correlated to find meaningful associations after extensive data mining analysis and internal validation. Our data show that a reduced model of nine bacteria is capable of differentiating HCs from SSc patients. SSc gut microbiota is characterized by a reduction in protective butyrate‐producing bacteria and by an increase in proinflammatory noxious genera, especially Desulfovibrio. From the metabolic point of view, a multivariate model with 17 metabolite intermediates well distinguished cases from controls. The most interesting peaks we found were identified as glycerophospholipid metabolites and benzene derivatives. The microbial and metabolic data showed significant interactions between Desulfovibrio and alpha‐N‐phenylacetyl‐l‐glutamine and 2,4‐dinitrobenzenesulfonic acid. Our data suggest that in SSc, intestinal microbiota is characterized by proinflammatory alterations subtly entwined with the metabolic state. Desulfovibrio is a relevant actor in gut dysbiosis that may promote intestinal damage and influence amino acid metabolism.

A phase 2 trial of neoadjuvant metformin in combination with trastuzumab and chemotherapy in women with early HER2-positive breast cancer: the METTEN study A phase 2 trial of neoadjuvant metformin in combination with trastuzumab and chemotherapy in women with early HER2-positive breast cancer: the METTEN study

Dysbiosis has been described in systemic autoimmune diseases (SADs), including systemic lupus erythematosus (SLE), Sjögren’s syndrome (SjS), and primary anti-phosholipid syndrome (PAPS), however the biological implications of these associations are often elusive. Stool and plasma samples from 114 subjects, including in SLE (n = 27), SjS (n = 23), PAPs (n = 11) and undifferentiated connective tissue (UCTD, n = 26) patients, and geographically-matched healthy controls (HCs, n = 27), were collected for microbiome (16s rRNA gene sequencing) and metabolome (high-performance liquid chromatography coupled to mass spectrometry) analysis to identify shared characteristics across diseases. Out of 130 identified microbial genera, a subset of 29 bacteria was able to differentiate study groups (area under receiver operating characteristics (AUROC) = 0.730 ± 0.025). A fair classification was obtained with a subset of 41 metabolic peaks out of 254 (AUROC = 0.748 ± 0.021). In both models, HCs were well separated from SADs, while UCTD largely overlapped with the other diseases. In all of the SADs pro-tolerogenic bacteria were reduced, while pathobiont genera were increased. Metabolic alterations included two clusters comprised of: (a) members of the acylcarnitine family, positively correlating with a Prevotella-enriched cluster and negatively correlating with a butyrate-producing bacteria-enriched cluster; and (b) phospholipids, negatively correlating with butyrate-producing bacteria. These findings demonstrate a strong interaction between intestinal microbiota and metabolic function in patients with SADs.