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Results for “hierarchical feature aggregation”

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PubMed2026

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans↗

PubMed1990

Quantum mechanics and cellular information processing: the self-assembly paradigm.

Biological cells have greater information processing efficiency than the programmable computers used to model them. In part this is due to the larger number of interactions that can contribute to function. General arguments suggest that systems in which quantum features play a prominent role are more powerful than classical physical-dynamical analogs. A hypothetical model, involving macromolecular self-assembly, is used to illustrate how the parallelism inherent in the quantum mechanical wave function could play a role in cellular pattern processing. Signals impinging on the external membrane of the cell trigger the release of specifically shaped macromolecules. These aggregate into a mosaic shape features that reflect different groupings of the signal input patterns. The shape features are in turn read out and connected to effector actions by adaptor molecules. The self-assembly model fits into a more general hierarchical scheme of biological information processing in which macroscopic signals are transduced to mesoscopic and then microphysical representations, processed largely at the microphysical level, and then amplified for macroscopic action. The physical dynamics are controlled by proteins and other macromolecules that are molded through the evolutionary process of variation and selection. The organizational requirements for evolutionary moldability and for efficient information processing function are completely consistent. They include high dimensionality, multiplicity of weak interactions, and hierarchical-compartmental structure.

Biological Evolution↗

PubMed1992

Biological complexity and strategies for finding DNA variations responsible for inter-individual variation in risk of a common chronic disease, coronary artery disease.

Most common chronic diseases of humans aggregate, but do not segregate, in families. The segregation-linkage research paradigm has not provided great insights into their genetic etiology. In this paper, using coronary artery disease as an example, we discuss hierarchical organization, coherence, emergent properties and dynamism as features that characterize the complexity of genotype-phenotype relationships. We summarize a research strategy for evaluating the contribution of genetic and environmental factors to the prediction of inter-individual variation in risk of disease. We then review a statistical strategy that employs cladistic theory to identify individuals carrying mutant DNA sequences responsible for an observed association between marker variation in a gene and inter-individual variation in biological traits that determine risk of a common multifactorial disease. Finding these DNA sequences is a necessary step in our search for an understanding of the nature of the mapping of genetic variation into variation in risk of a disease like coronary artery disease.

Coronary Disease↗

PubMed1990

Genetic studies in the Garfagnana population (Tuscany, Italy).

The relationship between geographic isolation and historical-demographic features and genetic structure and pattern of variation of genetic markers was analyzed in the population of Garfagnana, a semi-isolated mountainous area in the province of Lucca (Italy), taking into account hierarchical subdivisions. A random sample of unrelated individuals, whose parents were both born in this area, was typed for AB0, MN, Kell, Rh, AK, EsD, 6-PGD, AcP and ABH secretor status. The village samples were aggregated into larger population units: Two districts and six subdistricts. Comparisons were performed with population samples of the plain and the coastal area of the same province (Lucca). Phenotype and genetic differentiations among and within subdivisions were studied using G2, R statistic, Nei's method, Harpending & Ward's method and analysis of genetic distance and similarity matrices. The various parameters consistently show significant heterogeneity among the subdivisions, both at district and at subdistrict level. As expected, the gene diversity between and within subdivisions varies according to their distinctive features of isolation.

Adult↗