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Abstract Background The relationship between the gut microbiome and diet has been the focus of numerous recent studies. Such studies aim to characterize the impact of diet on the composition of the microbiome, as well as the microbiome’s ability to utilize various compounds in the diet and produce metabolites that may be beneficial for the host. Consumption of dietary fibers (DFs)—polysaccharides that cannot be broken down by the host’s endogenous enzymes and are degraded primarily by members of the microbiome—is known to have a profound effect on the microbiome. Yet, a comprehensive characterization of microbiome compositional and functional shifts in response to the consumption of specific DFs is still lacking. Results Here, we introduce a computational framework, coupling metagenomic sequencing with careful annotation of polysaccharide degrading enzymes and DF structures, for inferring the metabolic ability of a given microbiome sample to utilize a broad catalog of DFs. We demonstrate that the inferred fiber degradation profile (IFDP) generated by our framework accurately reflects the dietary habits of various hosts across four independent datasets. We further demonstrate that IFDPs are more tightly linked to the host diet than commonly used taxonomic and functional microbiome-based profiles. Finally, applying our framework to a set of ~700 metagenomes that represents large human population cohorts from 9 different countries, we highlight intriguing global patterns linking DF consumption habits with microbiome capacities. Conclusions Combined, our findings serve as a proof-of-concept for the use of DF-specific analysis for providing important complementary information for better understanding the relationship between dietary habits and the gut microbiome.

Differences between the local value of the Hubble constant measured via the distance ladder versus the value inferred from the cosmic microwave background with the assumption of the standard ΛCDM model have reached over 5 σ significance. To determine if this discrepancy is due to new physics or more mundane systematic errors, it is essential to remove as many sources of systematic uncertainty as possible, by developing high-precision distance ladders that are independent of the traditional Cepheid and Type Ia supernova route. Here, we present JWST observations of three early-type Fornax Cluster galaxies, the first of 14 observations from a Cycle 2 JWST program. Our modest integration times allow us to measure highly precise tip of the red giant branch (TRGB) distances and they will also be used to perform measurements of surface brightness fluctuations (SBFs). From these three galaxies, we determine an average TRGB distance modulus to the Fornax Cluster of μ = 31.424 ± 0.077 mag or D = 19.3 ± 0.7 Mpc. With 11 more scheduled observations in nearby elliptical galaxies, our program will allow us to set the zero-point of the SBF scale to better than 2% for more distant measurements, charting a path toward a high-precision measurement of H _0 that is independent of the traditional Cepheid–supernova Ia distance ladder.

Longitudinal 'omics analytical methods are extensively used in the evolving field of precision medicine, by enabling 'big data' recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representation of multiway data may prove useful to the biological research community by their advertised ability to tackle this challenge. In this study, we present tcam-a new unsupervised tensor factorization method for the analysis of multiway data. Building on top of cutting-edge developments in the field of tensor-tensor algebra, we characterize the unique mathematical properties of our method, namely, 1) preservation of geometric and statistical traits of the data, which enable uncovering information beyond the inter-individual variation that often takes over the focus, especially in human studies. 2) Natural and straightforward out-of-sample extension, making tcam amenable for integration in machine learning workflows. A series of re-analyses of real-world, human experimental datasets showcase these theoretical properties, while providing empirical confirmation of tcam's utility in the analysis of longitudinal 'omics data.