Does sweetness exposure drive 'sweet tooth'?

It is widely believed that exposure to sweetened foods and beverages stimulates the liking and desire for sweetness. Here we provide an updated review of the empirical evidence from human research examining whether exposure to sweet foods or beverages influences subsequent general liking for sweetness ('sweet tooth'), based on the conclusions of existing systematic reviews and more recent research identified from a structured search of literature. Prior reviews have concluded that the evidence for a relationship between sweet taste exposure and measures of sweet taste liking is equivocal, and more recent primary research generally does not support the view that exposure drives increased liking for sweetness, in adults or children. In intervention trials using a range of designs, acute exposure to sweetness usually has the opposite effect (reducing subsequent liking and desire for sweet taste), while sustained exposures have no significant effects or inconsistent effects. Recent longitudinal observational studies in infants and children also report no significant associations between exposures to sweet foods and beverages with measures of sweet taste preferences. Overall, while it is widely assumed that exposure to sweetness stimulates a greater liking and desire for sweetness, this is not borne out by the balance of empirical evidence. While new research may provide a more robust evidence base, there are also a number of methodological, biological and behavioural considerations that may underpin the apparent absence of a positive relationship between sweetness exposure and liking.

Gastrointestinal tolerance of D-allulose in children: an acute, randomised, double-blind, placebo-controlled, cross-over study.

D-Allulose, a low-calorie sugar, provides an attractive alternative to added sugars in food and beverage products. There is however limited data on its gastrointestinal (GI) tolerance, with only two studies in adults, and no studies in children to date. We therefore performed an acute, randomised, double-blind, placebo-controlled, cross over study designed to determine, for the first time, the GI tolerance of 2 doses of D-allulose (2.5 g per 120 ml and 4.3 g per 120 ml) in young children. The primary tolerance endpoint was the difference in the number of participants experiencing at least one stool that met a Type 6 or Type 7 description on the Bristol Stool Chart, within 24 hours after study product intake. Secondary endpoints included the assessment of stool frequency, stool consistency, and the presence of GI symptoms. Only one participant in the low dose group experienced a stool type 6 or 7, while no participants experienced a stool type 6 or 7 in the high dose group. A statistically significant difference in the change in stool frequency compared to placebo in the high dose group (p = 0.044) was found, with no significant difference between the groups for stool consistency and no participants experienced unusual stool frequency. All the encountered adverse events were non-serious, either mild or moderate, and there were no serious adverse events. All in all, D-allulose was tolerated well in children, making this ingredient a good candidate to reformulate commercially produced goods by replacing added sugars with lower caloric content.

A Global Transcriptional Atlas of the Effect of Sleep Deprivation in the Mouse Frontal Cortex.

Sleep deprivation (SD) has negative effects on brain function. Sleep problems are prevalent in neurodevelopmental, neurodegenerative and psychiatric disorders. Thus, understanding the molecular consequences of SD is of fundamental importance in neuroscience. In this study, we present the first simultaneous bulk and single-nuclear (sn)RNA sequencing characterization of the effects of SD in the mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of thousands of transcripts. At both the global and cell-type specific level, SD has a large repressive effect on transcription, down-regulating thousands of genes and transcripts; underscoring the importance of accounting for the effects of sleep loss in transcriptome studies of brain function. As a resource we provide extensive characterizations of cell types, genes, transcripts and pathways affected by SD; as well as tutorials for data analysis.

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA.

Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns. However, adequate statistical tools that exploit the previously unavailable spatial information to more coherently group cells and genes are still lacking. In this work, we introduce SpaRTaCo, a new statistical model that clusters the spatial expression profiles of the genes according to a partition of the tissue. This is accomplished by performing a co-clustering, i.e., inferring the latent block structure of the data and inducing two types of clustering: of the genes, using their expression across the tissue, and of the image areas, using the gene expression in the spots where the RNA is collected. Our proposed methodology is validated with a series of simulation experiments and its usefulness in responding to specific biological questions is illustrated with an application to a human brain tissue sample processed with the 10X-Visium protocol.

Curated single cell multimodal landmark datasets for R/Bioconductor.

BACKGROUND: The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. RESULTS: We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data. CONCLUSIONS: We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Low-no-calorie sweeteners exert marked compound-specific impact on the human gut microbiota ex vivo.

Low-no-calorie sweeteners (LNCS) are used as sugar substitutes as part of strategies to reduce the risk of chronic diseases related to high sugar intake (e.g. type 2 diabetes (T2D)). This study investigated how a range of sweeteners [tagatose (TA)/maltitol (MA)/sorbitol (SO)/stevia (ST)/sucralose (SU)/acesulfame K (ACK)] impact the gut microbiota of T2D subjects and healthy human adults using the ex vivo SIFR® technology (n = 12). The cohort covered clinically relevant interpersonal and T2D-related differences. ACK/SU remained intact while not impacting microbial composition and metabolite production. In contrast, TA/SO and ST/MA were respectively readily and gradually fermented. ST and particularly TA/SO/MA increased bacterial density and SCFA production product-specifically: SO increased acetate (∼Bifidobacterium adolescentis), whilst MA/ST increased propionate (∼Parabacteroides distasonis). TA exerted low specificity as it increased butyrate for healthy subjects, yet propionate for T2D subjects. Overall, LNCS exerted highly compound-specific effects stressing that results obtained for one LNCS cannot be generalised to other LNCS.

Effect of packaging in preventing cholesterol autoxidation in milk chocolates for a higher quality and safer shelf-life.

Non-enzymatic cholesterol oxidation products (COPs) are nowadays receiving increasing attention in food technology for their potential use as biomarkers of freshness and safety in raw materials and complex food matrices, as well as markers of cholesterol oxidation during the production and shelf-life of end products. Here reported is the investigation of how long three prototype milk chocolates containing whole milk powders (WMPs) of increasing shelf-lives (i.e. 20, 120, and 180 days), could be safely stored in the market by adopting the non-enzymatic COPs as a quality markers. In addition, the protective effect of two different primary packaging, sealed and unsealed ones, in mitigating the generation of non-enzymatic COPs in three prototype milk chocolates after 3, 6, 9, 12 months of shelf-life was assessed to simulate two real storage conditions. Quantifying oxysterols' levels by mass spectrometry, the oxygen impermeable packaging (PLUS) resulted to significantly quench the non-enzymatic COPs production up to 34% as to that found in the same product but with unsealed standard packaging (STD). This study represents one practical application of non-enzymatic COPs as a reliable tool for corrective strategies to prevent food oxidation.

Activation Profile of TAS2R2, the 26th Human Bitter Taste Receptor.

SCOPE: To avoid ingestion of potentially harmful substances, humans are equipped with about 25 bitter taste receptor genes (TAS2R) expressed in oral taste cells. Humans exhibit considerable variance in their bitter tasting abilities, which are associated with genetic polymorphisms in bitter taste receptor genes. One of these variant receptor genes, TAS2R2, is initially believed to represent a pseudogene. However, TAS2R2 exists in a putative functional variant within some populations and can therefore be considered as an additional functional bitter taste receptor. METHODS AND RESULTS: To learn more about the function of the experimentally neglected TAS2R2, a functional screening with 122 bitter compounds is performed. The study observes responses with eight of the 122 bitter substances and identifies the substance phenylbutazone as a unique activator of TAS2R2 among the family of TAS2Rs, thus filling one more gap in the array of cognate bitter substances. CONCLUSIONS: The comprehensive characterization of the receptive range of TAS2R2 allows the classification into the group of TAS2Rs with a medium number of bitter agonists. The variability of bitter taste and its potential influences on food choice in some human populations may be even higher than assumed.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data.

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

Age-Dependent Prebiotic Effects of Soluble Corn Fiber in M-SHIME® Gut Microbial Ecosystems.

Soluble corn fiber (SCF) has demonstrated prebiotic effects in clinical studies. Using an in vitro mucosal simulator of the human intestinal microbial ecosystem (M-SHIME®) model, the effects of SCF treatment on colonic microbiota composition and metabolic activity and on host-microbiome interactions were evaluated using fecal samples from healthy donors of different ages (baby [≤ 2 years], n = 4; adult [18-45 years], n = 2; elderly [70 years], n = 1). During the 3-week treatment period, M-SHIME® systems were supplemented with SCF daily (baby, 1.5, 3, or 4.5 g/d; adult, 3 or 8.5 g/d; and elderly, 8.5 g/d). M-SHIME® supernatants were evaluated for their effect on the intestinal epithelial cell barrier and inflammatory responses in lipopolysaccharide. (LPS)-stimulated cells. Additionally, short-chain fatty acid (SCFA) production and microbial community composition were assessed. In the baby and adult models, M-SHIME® supernatants from SCF treated vessels protected Caco-2 membrane integrity from LPS-induced damage. SCF treatment resulted in the expansion of Bacteroidetes, Firmicutes, and Bifidobacterial, as well as increased SCFA production in all age groups. SCF tended to have the greatest effect on propionate production. These findings demonstrate the prebiotic potential of SCF in babies, adults, and the elderly and provide insight into the mechanisms behind the observed prebiotic effects.

benchdamic: benchmarking of differential abundance methods for microbiome data.

SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bartlett-type correction for likelihood ratio tests with application to testing equality of Gaussian graphical models.

This work defines a new correction for the likelihood ratio test for a two-sample problem within the multivariate normal context. This correction applies to decomposable graphical models, where testing equality of distributions can be decomposed into lower dimensional problems.

Differential detection workflows for multi-sample single-cell RNA-seq data.

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

Production of xylo-oligosaccharides (XOS) of tailored degree of polymerization from acetylated xylans through modelling of enzymatic hydrolysis.

Xylo-oligosaccharides (XOS) are emerging prebiotics that have recently been gained a great interest in the market of functional foods. Since their beneficial activity strictly depends on their chemical structure and on their degree of polymerization (DP), in this work an enzymatic method was developed to produce XOS with variable and modellable DPs, involving a combination of a commercial endo-ß-1,4-xylanase M3 from Trichoderma longibrachiatum and a deacetylase, using a commercial acetylated standard xylan as substrate. A Design of Experiment (DoE) was developed and through the variation of some hydrolysis conditions, some experiments allowed to obtain significant amounts of XOS with DP 7-10, up to 11%, despite XOS with DP 2-4 were always the most abundant (60-96% of total XOS). The most impacting parameter on the XOS distribution was the order of addition of the xylanase and deacetylating enzyme, while pH showed to have a great influence on the total yield. The method was also tested on an acetylated xylan extracted from grape stalks, structurally similar to the commercial standard xylan. The model was found to work in a very similar way also on the non-purified xylan sample, allowing the manipulation of enzymatic hydrolysis on a low-cost by-product, with the potential to obtain on a large scale XOS with high added value and with a specific DP, depending on the final application.

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

Moderate intakes of soluble corn fibre or inulin do not cause gastrointestinal discomfort and are well tolerated in healthy children.

We investigated the gastrointestinal (GI) tolerance of soluble corn fibre (SCF) compared with inulin in children 3-9 years old. SCF (3-8 g/d for 10d) was tolerated as well as inulin: no differences were identified in stool frequency and consistency, proportion of subjects with at least one loose stool or reporting symptoms during bowel movement. Compared to inulin, 6 g/d of SCF lowered gas severity in children aged 3-5 years old. No differences were noted for alpha and beta diversity, relative abundance of Bacteroidota, Firmicutes, Ruminococcaceae, or the Firmicutes to Bacteroidota ratio. Relative abundance of some specific strains (i.e. Anaerostipes, Bifidobacterium, Fusicatenibacter, Parabacteroides) varied depending on the fibre type and dose level. Fortification at a level of 6-8 g/d of SCF and/or inulin could help addressing the fibre gap without any GI discomfort.

Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data.

Statistical evaluation of diagnostic tests, and, more generally, of biomarkers, is a constantly developing field, in which complexity of the assessment increases with the complexity of the design under which data are collected. One particularly prevalent type of data is clustered data, where individual units are naturally nested into clusters. In these cases, Bias can arise from omission, in the evaluation process, of cluster-level effects and/or individual covariates. Focusing on the three-class case and for continuous-valued diagnostic tests, we investigate how to exploit the clustered structure of data within a linear-mixed model approach, both when the assumption of normality holds and when it does not. We provide a method for the estimation of covariate-specific receiver operating characteristic surfaces and discuss methods for the choice of optimal thresholds, proposing three possible estimators. A proof of consistency and asymptotic normality of the proposed threshold estimators is given. All considered methods are evaluated by extensive simulation experiments. As an application, we study the use of the Lysosomal Associated Membrane Protein Family Member 5 gene expression as a biomarker to distinguish among three types of glutamatergic neurons.

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor.

SUMMARY: SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages. AVAILABILITY AND IMPLEMENTATION: The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.