Pung, R. | Search bioRxiv

bioRxiv2026

The RNA-binding protein Imp1 promotes a Spdef transcriptional program and mucus fucosylation during necrotizing enterocolitis

BackgroundIn the United States over 10% of all neonates are born premature (less than 37 weeks gestational age), and many face complications related to prematurity, including necrotizing enterocolitis (NEC). NEC is the most deadly gastrointestinal disease and the leading cause of death in preterm neonates, with up to 50% mortality. Since there is no cure for NEC, prevention is the best strategy. Enhancing our understanding of intestinal epithelial cell (IEC) responses to NEC damage will provide novel therapeutic targets to prevent NEC. Published evidence suggests that the RNA-binding protein insulin-like growth factor 2 mRNA binding protein 1 (IMP1) plays roles in intestinal development, barrier function, and intestinal repair. Notably, however, roles for IMP1 in NEC are not defined. Goblet cells produce protective mucus in the intestine, and their mature function is dependent on the transcription factor Spdef. Emerging evidence suggests that goblet cell mucus complexity impacts barrier function and inflammation susceptibility. This study aimed to define the role of IMP1 in NEC pathogenesis using neonatal human enteroids and a model of NEC-like intestinal injury in mice with IEC-specific Imp1 overexpression and loss. HypothesisIMP1 expression is protective in NEC. MethodsThis study used mice with intestinal epithelial Imp1 overexpression or loss and corresponding wild-type controls. At post-natal day 3, mice of both sexes were randomly assigned to control or NEC groups. NEC was induced with the well-established experimental NEC-like intestinal injury model that includes stress, formula feeding, and hypoxia. Imp1 effects on experimental NEC were assessed using RNA sequencing, western blotting, and immunostaining. ResultsInflammatory bacteria induced IMP1 expression in neonatal human enteroids. Mice with Imp1 overexpression incur worse intestinal damage during NEC. Pathway analysis of RNA sequencing data revealed a significant enrichment of the Spdef transcriptional network in Imp1IEC-OE during NEC. This included significant upregulation of Spdef target genes such as Agr2 (in NEC WT: 617.8 {+/-} 33.56 vs Imp1IEC-OE: 812.9 {+/-} 111.3, p=0.02) and Fut2 (in NEC WT: 219.4 {+/-} 34 vs Imp1IEC-OE: 396.6 {+/-} 62.9, p=0.05). In silico analysis predicted Imp1 binding to Spdef and mucus glycosylation mediator mRNAs. Although genotype did not affect goblet cell number, Imp1IEC-OE mice with NEC exhibited significant increases (p0.05) in Spdef protein, genes responsible for goblet cell function (Spink4, Klk1, Tspg1) and mucus glycosylation (Gcnt3, B3gnt7, Qsox1). Ultimately, Imp1 overexpression led to increased mucus fucosylation during NEC. ConclusionOur data indicate that during NEC, upregulation of Imp1 promotes goblet cell function via Spdef, including enhanced goblet cell maturation and mucus fucosylation. NEW AND NOTEWORTHYExpression of the RNA-binding protein Imp1 is enhanced in neonatal human enteroids in response to inflammatory bacteria. Imp1 regulates goblet cell function in response to early intestinal inflammation in a mouse model of necrotizing enterocolitis. Specifically, Imp1 promotes a Spdef transcriptional program by upregulating Spdef, resulting in elevated gene expression of Spdef target genes. Additionally, Imp1 increases the gene expression of (1,2)fucosyltransferase, Fut2, and subsequently, production of fucosylated mucus marked by UEA1. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/702645v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@189f615org.highwire.dtl.DTLVardef@7aeeedorg.highwire.dtl.DTLVardef@dc258corg.highwire.dtl.DTLVardef@1252b98_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

Social networks with strong spatial embedding generate non-standard epidemic dynamics driven by higher-order clustering

Some directly transmitted human pathogens such as influenza and measles generate sustained exponential growth in incidence, and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of non-standard epidemic profiles are either abstract, phenomenological or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behaviour using human population density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number R0 for this system analogous to that used for compartmental models. Controlling for R0, we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and thus induce sub-exponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighbourhoods, identifying very strong correlations between 4th order clustering and non-standard epidemic dynamics. Our results motivate the joint observation of incidence and socio-spatial human behaviour during epidemics that exhibit non-standard incidence patterns. Author SummaryEpidemics are typically described using a standard set of mathematical models that do not capture social interactions or the way those interactions are determined by geography. Here we propose a model that can reflect social networks influenced strongly by the way people travel and we show that they lead to very different epidemic profiles. This type of model will likely be useful for forecasting.

systems biology↗