Steininger, H. M. | Search bioRxiv

bioRxiv2026

A Dynamic Stem Cell State Links Environmental Cues to Skeletal and Regenerative Fates

Stem cells faithfully generate tissue architecture during development, yet how their identity and regenerative output are reshaped as anatomical niches mature and environmental signals change with aging and injury remains poorly understood. Rare skeletal stem cells (SSCs) across diverse skeletal sites are defined by combinations of cell-surface markers, precluding their genetic interrogation in vivo. Here, using spatially informed single cell transcriptomics, we identify Pannexin-3 (Panx3) to mark a dynamic functional state within bona fide SSCs and establish Panx3-CreERT2 mice for its longitudinal interrogation. Panx3 SSCs exhibit long-term clonal persistence, self-renewal and multipotency, whereas Panx3- SSCs can acquire this state. Successive SSC cohorts engage the Panx3 state during skeletal development, osteoanabolic stimulation expands pre-existing Panx3 SSC clones and injury induces it locally in previously unlabeled progenitors. Thus, anatomically distinct progenitors can converge on a common stem cell state in response to different environmental inputs. Three-dimensional reconstruction of chondrogenic niches further identifies signals that redirect Panx3-lineage cells towards stable cartilage, enabling durable regeneration in aged and osteoarthritic joints and with human SSCs. These findings establish anatomical origin, stem cell state and lineage fate as separable dimensions of stem cell identity and provide genetic access for interrogating and directing endogenous SSC function.

cell biology↗

bioRxiv2026

Carbohydrate Metabolism Differs in Infants by Asthma-risk Status and is Associated with the Functional Potential of Bacteroides cellulosilyticus

2.Childhood atopic disease is linked to delayed gut microbiome development and metabolic dysfunction, however microbial drivers remain unclear. To explore microbial correlates of asthma risk during a time of active gut microbiome development, we analyzed stool from 6-month-old infants at high asthma risk (HR) or healthy controls (HC), using Genome-resolved metagenomics (HR=7; HC=12) and untargeted metabolomics (HR=11; HC=15). We recovered 82 bacterial species-level metagenomic-assembled genomes (MAGs). Global Taxonomic composition did not differ by asthma risk. Anticipating that key differences might associate with specific genomes, a machine-learning approach pinpointed Bacteroides cellulosilyticus, Hungatella effluvii, and Enterocloster aldenensis as linked with asthma risk status. All three species were more abundant in HC infants and the B. cellulosilyticus genome was enriched for carbohydrate metabolism genes relative to other MAGs. Metabolomic profiling revealed variance associated with asthma risk (PERMANOVA, R2 =0.069, p=0.016). HR fecal metabolomes were enriched in simple sugars, whereas HC contained more nitrogenous compounds. Integrative genome-metabolic modeling of compounds that significantly differentiate asthma-risk groups revealed risk-dependent interactions with community-encoded metabolic potential (CEP), for arabinose and agmatine, whose fecal concentrations are linked with B. cellulosilyticus and H. effluvii functional traits respectively. These findings suggest that microbial-influenced metabolic differences associate with asthma risk at 6 months, with B. cellulosilyticus and H. effluvii emerging as candidate bacteria influencing this observed metabolic remodeling. 3. Impact statementLeveraging a random forest classifier, we identified three bacterial species (Bacteroides cellulosilyticus, Hungatella effluvii, and Enterocloster aldenensis) as distinguishing features enriched in healthy 6-month old infant microbiomes compared to those at high risk of asthma development (HR). We developed an approach to integrate metabolomics and metagenomic-derived microbiome community encoded potential (CEP) with clinical outcomes to identify fecal metabolites whose concentrations are likely to be influenced by the microbiome. Fecal arabinose concentrations were positively associated with CEP in healthy infants, but not in HR subjects who exhibited elevated concentrations irrespective of CEP. These data implicate microbial activity as a contributor to the concentration of this metabolite in healthy but not HR infants. With a leave-one-out-cross-validation, we identified B. cellulosilyticus as a contributor to fecal arabinose concentrations. Our data indicate that microbial functional deficits in HR infants is associated with altered gut metabolic dysfunction during microbiome maturation. 4. Data summaryDurack et. al [1] is the source of the metabolomics data utilized in this study. The authors confirm that all other supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗

bioRxiv2026

Metagenomic strain-resolved DNA modification patterns link extrachromosomal genetic elements to host strains

DNA modification is central to microbial defense against extrachromosomal genetic elements (ECEs), consequently ECEs tend to adopt their hosts modification patterns. Shared ECE-host modification patterns enable linking ECEs to their hosts, but modification detection tools are designed for single genomes and are ineffective at metagenome scale. Here, we present MODIFI, software for detecting DNA modifications in metagenomes. MODIFI assumes that each k-mer in a metagenome is mostly unmodified and calculates background signal levels for that k-mer from PacBio HiFi reads, eliminating the need for matched control experiments. MODIFI ECE-host linkages were validated using >1,000 isolate and mock microbiome datasets. Illustrating the approach, we identified 315 strain-resolved, non-redundant ECE-host linkages in environmental and human metagenomes. In infant gut microbiomes, a chromosomal inversion in Enterococcus faecalis alters host and associated plasmid methylation motifs simultaneously. Overall, MODIFI solves a major bottleneck in DNA modification analysis and provides a foundational tool for understanding microbial epigenomics.

microbiology↗