Olivia Janice Ogilvie

Celiac disease is a lifelong immune-mediated disease triggered by dietary gluten and managed exclusively through a strict gluten-free diet. Differences in the gut microbiome are consistently observed in celiac disease, yet the associated metabolic consequences remain insufficiently defined. This study evaluated the stool microbiome-metabolome relationship in children from Canterbury, New Zealand, a region with a notably high prevalence of celiac disease (∼ 1 in 82). Eleven children with untreated celiac disease and 10 controls were assessed, with nine children resampled after 6 months of gluten-free diet adherence. Stool samples were subjected to 16S rRNA sequencing, targeted and untargeted metabolomic profiling, and multi-omics integration using cross-block and machine-learning approaches. Celiac disease was characterized by consistent reproducible changes in a small set of resampled-stable taxa and metabolites, rather than a uniform shift. The most prominent finding was rearrangement within Bacteroides rather than a uniform shift: B. ovatus increased reproducibly in both untreated and treated disease, while integration analyses associated B. fragilis with celiac disease and B. vulgatus with controls. Metabolite changes were observed in two linked axes: a butyrate-led fermentation signature and a redox and organic acid signature, both elevated in untreated disease and shifting toward control levels after 6 months of gluten-free diet. Recovery was partial, and the diet imposed its own signature, notably an increase in the fat-responsive pathobiont Bilophila wadsworthia . Machine learning discrimination was modest (ROC-AUC = 0.66–0.68, PR-AUC = 0.73–0.79), but the microbiome and metabolome aligned along one reproducible cross-omic axis. The results support prioritizing a concise list of reproducible candidate features for mechanistic validation and longitudinal treatment monitoring in more extensive cohorts, rather than for immediate diagnostic application.

Celiac disease affects ~1-2% of people and remains incurable, requiring lifelong dietary restriction. The gut microbiome is thought to contribute to the development and progression of celiac disease. However, findings across previous studies are fragmented, making it difficult to understand exactly how the gut microbiome is involved. We integrate over 900 samples from global datasets spanning different disease stages (before onset, during active disease, and after treatment), body sites, and research methods. Datasets produced using both 16S rRNA gene sequencing and shotgun metagenomics profile the gut microbiome. Alpha and beta diversity analyses and differential abundance testing identify consistent changes in bacterial communities linked to celiac disease. Machine learning tests how well microbiome data predicts disease status. Here, we show that celiac disease is not marked by large changes in gut microbiome diversity. Instead, there are subtle, consistent changes in specific bacteria, including a reduction in beneficial butyrate producers (Faecalibacterium, Prevotella, Agathobacter, Gemmiger), changes in mucin-associated microbes (Akkermansia muciniphila), and an increase in potentially harmful bacteria (Helicobacter, Campylobacter, Haemophilus parainfluenzae). These changes are seen before and during active disease and persist on a gluten-free diet. Microbiome-based disease prediction is moderately accurate for active disease and weaker for prospective performance, likely constrained by training data. Our findings suggest that celiac disease is linked to specific changes in gut bacteria that are not fully resolved by diet alone. Future treatments may need to focus on restoring healthy gut bacteria, not just avoiding gluten, to better manage the disease.

Celiac disease is an autoimmune condition where the gut microbiome is increasingly recognised as a key environmental factor. While high-throughput sequencing has led to a surge in celiac-related gut microbiome profiling data, these datasets remain fragmented, heterogeneous, and often lack the metadata required for large-scale integration into pooled, cross-cohort datasets. To address this, we developed the Celiac Microbiome Repository (CMR), a curated, open-access collection of celiac-related 16S rRNA gene and shotgun metagenomic sequencing datasets. We employed a systematic curation workflow to identify datasets across the NCBI Sequence Read Archive (SRA) and Scopus, followed by manual metadata extraction and direct author engagement. All 16S data was reprocessed through DADA2 and shotgun data through MetaPhlAn4 to facilitate comparison across studies. The CMR version 1.0 comprises 28 datasets containing 3,245 samples from 13 countries and 5 body sites. Our analysis reveals that while publicly available celiac microbiome samples have accumulated at a rate of approximately 140 per year, significant barriers to accessibility exist. Just 20 of 58 eligible datasets were found to have both raw data and essential metadata readily available within public archives. The repository features a dual-interface design, consisting of a GitHub backend for programmatic access and an R Shiny frontend for interactive data exploration. By providing this curated and harmonised resource, the CMR enables the research community to leverage public data for global meta-analyses and machine learning applications. Ultimately, this work provides the foundation needed to move beyond isolated, small-scale studies toward high-powered discoveries in celiac disease research. Database URLs: https://github.com/CeliacMicrobiomeRepo/celiac-repository | https://celiac.shinyapps.io/celiac-webapp

1 Existing self-supervised microbiome models represent taxa as discrete, independent units restricted to fixed vocabularies, disregarding their evolutionary context. Here we present Micro16S, a deep learning approach that embeds 16S ribosomal RNA gene sequences into a continuous vector space according to phylogenetic relationships derived from the Genome Taxonomy Database. Using a combination of triplet and pair loss objectives, the model learns representations where spatial proximity reflects phylogenetic relatedness, while remaining largely invariant to the specific 16S rRNA region. Evaluations demonstrate taxonomically coherent clustering across most ranks and substantially improved region invariance compared to k -mer frequency baselines. A transformer pretrained on 50,418 unlabelled gut microbiome samples using these embeddings captured biologically meaningful community structure, though classical machine learning baselines outperformed Micro16S across six benchmark classification tasks, highlighting the limitations of the current system. These results establish the feasibility of phylogenetic embeddings for microbiome deep learning and identify mining algorithm design and class imbalance as primary targets for future improvement.

For commercial viability, cultivated meats require scientifically informed approaches to identify and manage hazards and risks. Here we discuss food safety in the rapidly developing field of cultivated meat as it shifts from lab-based to commercial scales. We focus on what science-informed risk mitigation processes can be implemented from neighbouring fields. We case-study pre-market safety assessments from UPSIDE Foods, GOOD Meat and Vow Group using publicly available dossiers. Quality control and safety assurance practices need to be established and standardized for cell lines and food-grade cell banks.

Evidence suggests that consumer perceptions and acceptance of cultivated meat (CM) differ between countries, cultures, and consumer groups. Limited research specific to Aotearoa New Zealand (A-NZ) is available. Survey responses from 592 A-NZ residents were analysed to determine CM awareness, willingness to engage with CM, and perceived CM product attributes relative to conventional meat and plant-based meat alternatives (PBMAs). The effects of gender, age, meat/PBMA consumption frequency, CM awareness, and food neophobia on CM perceptions were determined. The statements were rated on a seven-point Likert scale. Half (52%) of the respondents agreed they were aware of CM. The awareness was higher in men compared to women (p = 0.036), higher in Millennials compared to Generation X (p = 0.022), and higher in regular compared to infrequent PBMA consumers (p = 0.0003). The willingness to engage with CM and perceived CM product attributes were consistently more positive in consumers who were aware, compared with consumers not aware of CM (p < 0.05). Being male, Millennial, low neophobic and a low meat, or high PBMA consumer was also associated with higher potential engagement and perceptions of CM to varying extents. Segmentation divided the respondents into three groups. The ‘positive’ cluster (41%) consumed more PBMAs and less meat and was more aware of CM than the ‘neutral’ (50%) and ‘negative’ (9%) clusters. In conclusion, consumers in A-NZ are not a homogenous group with regards to their perceptions and potential engagement with CM. Increasing awareness and familiarity with CM will be an important strategy to increase engagement with CM.

Celiac disease is activated by digestion-resistant gluten peptides that contain immunogenic epitopes. Sourdough fermentation is a potential strategy to reduce the concentration of these peptides within food. However, we currently know little about the effect of partial sourdough fermentation on immunogenic gluten. This study examined the effect of a single sourdough culture (representative of those that the public may consume) on the digestion of immunogenic gluten peptides. Sourdough bread was digested via the INFOGEST protocol. Throughout digestion, quantitative and discovery mass spectrometry were used to model the kinetic release profile of key immunogenic peptides and profile novel peptides, while ELISA probed the gluten’s allergenicity. Macrostructural studies were also undertaken. Sourdough fermentation altered the protein structure, in vitro digestibility, and immunogenic peptide release profile. Interestingly, sourdough fermentation did not decrease the total immunogenic peptide concentration but altered the in vitro digestion profile of select immunogenic peptides. This work demonstrates that partial sourdough fermentation can alter immunogenic gluten digestion, and is the first study to examine the in vitro kinetic profile of immunogenic gluten peptides from sourdough bread.