Quantitative BioSciences

Dietary fat composition modulates host physiology and the gut microbiome, but the long-term effects of specific fat sources and the extent to which these changes resolve after dietary reversal remain incompletely defined. Here, we present a longitudinal multi-omic resource of mice maintained for one year on a purified control diet, seven high-fat diets differing in predominant fat source, or reversal regimens in which animals were switched from high-fat to control diet after 4 or 9 months. We further incorporated two cohorts with distinct pre-existing microbiome configurations to determine how baseline community structure shapes diet-induced remodeling of the gut microbiome ecosystem. By integrating longitudinal phenotyping, fecal metagenomics, fecal metabolomics, plasma metabolomics and lipidomics, and intestinal single-cell RNA sequencing, we defined the shared and dietary fat-specific responses across host and microbiome compartments. Baseline microbiome composition strongly influenced microbial responses to diet, indicating that pre-existing community structure is a major determinant of dietary ecosystem remodeling. Although many altered features shifted toward baseline after dietary reversal, only approximately half of diet-associated microbial changes recovered within the study window. A subset of taxa exhibited persistent alterations, including sustained depletion of Lactobacillus johnsonii and Bifidobacterium pseudolongum and sustained enrichment of Alistipes finegoldii, consistent with a "microbiome memory" of prior high-fat diet exposure. This memory effect is mirrored in the host, by sustained suppression of major histocompatibility complex class II (MHC-II) gene expression in intestinal epithelial cells after dietary reversal. These findings indicate that dietary fats leave a lasting imprint on the host-microbiome interactome that survives dietary intervention. Together, these data establish a resource for defining how dietary fat source, baseline microbiome composition, and dietary history shape host-microbiome states. The entire resource is available online as an RShiny app.

Experimental evolution has been a useful tool for investigating long-term temporal evolutionary dynamics and molecular mechanisms underlying adaptation. However, extracting fundamental principles and predictive features of evolutionary outcomes from these datasets remains challenging. Here we sought to circumvent these challenges by comparing distant yeast species that share several evolutionary features but differ in evolutionary history and genome architecture, that is Saccharomyces cerevisiae and Schizosaccharomyces pombe. We evolved ten populations of the fission yeast for 10,000 generations in the same conditions as a pre-existing budding yeast dataset, allowing us to observe repeatable evolutionary outcomes within species but diverse molecular targets of adaptation across species. The most frequent route of adaptation was through changes in carbon flux metabolism, which was previously unseen in S. cerevisiae evolved populations, but similar evolutionary paths have been observed in wild populations. This suggests that parallelism is pervasive and that mechanisms of adaptation can be shared among closely related or distant species. Despite similar gene content and identical environments, recurrent adaptation across S. pombe populations involved different genes than in S. cerevisiae and was detectable mostly at the transcriptomic level. This indicates that trans-regulatory effects and contingency may contribute to differences in evolutionary outcomes between these species.

Rapid, aqueous macrocyclization strategies that proceed in high conversion are valuable for ultra-large macrocyclic peptide library synthesis via display and DNA-encoded library (DEL) technology. Here we report an on-DNA macrocyclization based on sulfur(VI) fluoride exchange (SuFEx) that unites above features. This approach embeds a phenol and an aryl sulfonyl fluoride within a DNA-tagged peptide to accelerate SuFEx and trigger intramolecular cyclization immediately upon dissolution in basic aqueous buffer. Macrocycles ranging from 15-to 52-membered rings bearing diverse amino acids were synthesized efficiently. The reaction integrates seamlessly into DEL workflows and enabled discovery of potent, de novo macrocyclic RIPK1 inhibitors. The on-DNA cyclization conditions readily translate off-DNA to furnish sulfonate-and sulfonamide-linked macrocycles. NMR analyses showed SuFEx-derived biaryl linkers act as conformational elements: changes in aryl substitution and linker length tune backbone conformations from extended strands to rigid turns. These findings establish SuFEx cyclization as a robust, biocompatible strategy for programmable macrocycle design and drug discovery.

The continuous growth in both the number of phenotypic records and the range of traits included in beef cattle genetic evaluations poses substantial statistical and computational challenges for the estimation of genetic and residual (co)variance matrices required for breeding value estimation. Structural equation models (SEM), implemented using either factor analysis (FA) or recursive model (REC) structures, provide a flexible framework to model genetic and residual (co)variance matrices while yielding more parsimonious and computationally efficient parameterizations. Here, SEM was applied to estimate parameters for growth and ultrasound-measured carcass traits in beef cattle. The dataset comprised 2942 animals, and six traits were evaluated using standard multiple-trait mixed models (SMTM) and SEM. We considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Relative to the SMTM, both the model with two factors in the genetic covariance matrix (FA2G) and the model in which six recursive effects were constrained to zero in the residual covariance matrix (REC1) demonstrated a strong ability to capture genetic variability, as reflected by comparable heritability estimates. Correlations between estimated breeding values (EBV) for the same traits across models were consistently high, ranging from 0.94 to 1.00, indicating strong agreement among model estimates. The FA2G model was the most parsimonious in terms of the effective number of parameters (pD), with 431.2 pD, corresponding to a reduction of 25.3 parameters relative to the SMTM. The REC1 model also emerged as a competitive alternative for this dataset, exhibiting a lower pD (443.6) than the SMTM (456.5) and the most favorable deviance information criterion among all models evaluated (e.g., 37,868.6 for REC1 versus 37,874.7 for SMTM). Overall, these results demonstrate that mixed-effects multi-trait models for beef cattle genetic evaluation can be effectively implemented using FA or REC structures, which provide parsimonious representations of the underlying covariance patterns while maintaining high agreement in EBV.

Accurate assessment of fusion status following spinal fusion surgery is paramount, as it closely relates to patient outcomes and quality of life. Multiple imaging modalities have been developed to evaluate fusion, each with distinct advantages and limitations. Plain radiography remains the most accessible and practical method, with dynamic flexion-extension radiographs providing reasonably reliable information on segmental motion and stability. Computed tomography offers superior spatial resolution and enables direct visualization of trabecular bone bridging between vertebral segments, making it the most accurate modality for confirming fusion. Magnetic resonance imaging and radionuclide imaging can serve as complementary techniques, particularly when conventional methods are inconclusive or contraindicated. This review summarizes the strengths and limitations of each imaging modality used for assessing spinal fusion. It also highlights recent technological advances and emerging diagnostic approaches that may enhance the objectivity, reliability, and accuracy of fusion assessment in the future.

This paper introduces the Entrepreneurship in the Population (EPOP) survey, a nationally representative data source for understanding entrepreneurial activities across the United States since 2022. The EPOP survey is distinguished by its focus on a broad set of entrepreneurial activities and collects a wealth of information on the behaviors, challenges, and resources available to entrepreneurs. This data brief provides information on the design and measurement approach of the survey, discusses how to work with EPOP data, and outlines future plans for the survey, including a longitudinal data collection to support research on trajectories of current and prospective entrepreneurs.

Abstract This study used data from the National Longitudinal Study of Adolescent to Adult Health (Add Health, N = 11,054) to assess whether increases in screen-based leisure during adolescence (Wave II, from 1996) predicted adult well-being (Wave IV, from 2008-09), adjusting for a wide range of covariates (Wave I, from 1995). Using an outcome-wide analytic approach, we examined associations between screen time and 38 adult outcomes, adjusting for prior screen time, values of most outcomes, and confounders. Most associations were null. Modest evidence was found for links between screen time (continuous) and reduced sense of control, illicit drug use, and allostatic load. High screen time (14 h/week) or more also showed weak associations with lower depression and preventive care use. Because the data predate widespread internet use, the findings help establish a baseline for the long-term effects of non-internet screen activities, which appeared to behave had limited impact on adult health and well-being.

In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status. A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team. Meta-analyses evaluating the performance of AI-based models developed with CT-derived radiomics features alone or combined with clinical data were performed. A meta-regression to analyze the influence of different predictors was also conducted. Of 890 studies identified, 124 evaluating models for the prediction of epidermal growth factor-1 (EGFR), anaplastic lymphoma kinase (ALK), and Kirsten rat sarcoma virus (KRAS) mutations were included in the systematic review, of which 51 were meta-analyzed. The AI algorithms' sensitivity/false positive rate (FPR) in predicting mutation status using radiomics-based models was 0.754 (95% CI 0.727-0.780)/0.344 (95% CI 0.308-0.381) for EGFR, 0.754 (95% CI 0.638-0.841)/0.225 (95% CI 0.163-0.302) for ALK and 0.475 (95% CI 0.153-0.820)/0.181 (95% CI 0.054-0.461) for KRAS. A meta-analysis of combined models was possible for EGFR mutation, revealing a sensitivity of 0.806 (95% CI 0.777-0.833) and a FPR of 0.315 (95% CI 0.270-0.364). No statistically significant results were obtained in the meta-regression. Radiomics-based models may offer a non-invasive alternative for determining oncogene mutation status in NSCLC. Further research is required to analyze whether clinical data might boost their performance. Question Can imaging-based radiomics and artificial intelligence non-invasively predict oncogene mutation status to improve diagnosis in non-small cell lung cancer (NSCLC)? Findings Radiomics-based models achieved high performance in predicting mutation status in NSCLC; adding clinical data showed limited improvement in predictive performance. Clinical relevance Radiomics and AI tools offer a non-invasive strategy to support molecular profiling in NSCLC. Validation studies addressing clinical and methodological aspects are essential to ensure their reliability and integration into routine clinical practice.