Philip C Rosenstiel

This study shows that PPI-associated microbiome changes cause systemic metabolic and transcriptional responses, affecting metabolism and inflammation in multiple tissues.

The paper identifies a symbiont-derived biosurfactant that links host neuroimmune control of intestinal peristalsis with bacterial susceptibility to expulsion.

This study shows that STING acts as a genome-integrity checkpoint in epithelial cells, independent of interferon, and its loss leads to chromosomal instability and tumor development.

This study identifies microbial-specific Trp metabolism signatures in colitis by analyzing metabolites across multiple compartments in mice treated with DSS and antibiotics.

This study investigates long-term intestinal epithelial changes after acute protein-energy malnutrition using multi-omics approaches and identifies microbial lipid metabolites and epigenetic factors as key drivers of persistent Paneth cell dysfunction.

The paper investigates how industrialization affects microbial and physiological traits in the human gut across diverse populations, finding homogenized microbiomes, reduced diversity, and impaired disease prediction accuracy in less industrialized groups.

This study compares trio, singleton, and standard genome sequencing in rare disease diagnosis, finding trio sequencing has the highest diagnostic yield.

This study shows ATF6 activation in colonic epithelial cells alters lipid metabolism, promoting tumor-associated microbial adaptation in mice and human colorectal cancer patients.

This study uses metabolic modeling to uncover host-microbiome metabolic dysregulation in IBD, identifying key pathways and potential therapeutic interventions.

This paper constructs and compares computational disease maps of ulcerative colitis and atopic dermatitis to identify shared and unique molecular mechanisms and potential therapeutic targets.

This study shows that neonatal microbiota colonization sequentially programs primary and secondary goblet cell functions in the colon, with MyD88 and Duox2 playing key roles in mucus barrier and sentinel cell development.

This study uses multi-omics and metabolic modeling in mice to show that aging reduces host-microbiome metabolic interactions, affecting key host pathways involved in intestinal barrier function and homeostasis.

This study shows that deleting HKDC1 in intestinal epithelial cells reduces tumor growth and protects against colorectal cancer in mice by impairing mitochondrial function and cell proliferation.

This study examines how Atg16l1 deletion in intestinal epithelial cells affects fecal microbiota shifts during pregnancy in mice, identifying altered microbial composition and functional potential.

The paper examines the relationship between HK2 expression and inflammation severity in IBD patients using bulk transcriptome data and confirms findings at the cellular and protein levels.

FixNCut is a reversible fixation and dissociation method that preserves RNA integrity and cellular composition in human and mouse tissues, enabling robust single-cell and spatial analyses.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

This study performs a genome-wide association analysis of hemorrhoidal disease in 944,133 individuals, identifying 102 risk loci and linking them to vascular and tissue development pathways.

The paper uses a multi-parental recombinant inbred line population in C. elegans to study gene expression variation and identifies eQTLs and trans-bands affecting gene expression.

This study identifies host-symbiont interaction mechanisms in Riftia pachyptila by analyzing transcriptome and metaproteomic data under different energy conditions.

The paper integrates single cell RNA-seq with bulk epigenomic data to identify distinct regulatory states in bidirectional promoters and their associations with structural features and TF binding.

This study develops a multi-parent recombinant inbred line population of C. elegans to improve QTL mapping resolution and identify novel QTLs for life-history traits.

normR normalizes ChIP-seq data to improve enrichment calling in different histone modification regimes.

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.