Background:The mechanisms by which human PUF proteins repress target mRNAs remain unknown. Results: PUM1 and PUM2 reduce protein and mRNA levels of targets by recruiting the CNOT deadenylase complex and by a poly(A)-independent mechanism. Conclusion: PUMs employ deadenylation-dependent and -independent mechanisms of repression. Significance: Deadenylation is a conserved means of PUF repression but additional mechanism(s) contribute to mRNA regulation.

Human Pumilio proteins, PUM1 and PUM2, are sequence specific RNA-binding proteins that regulate protein expression. We used RNA-seq, rigorous statistical testing and an experimentally derived fold change cut-off to identify nearly 1000 target RNAs—including mRNAs and non-coding RNAs—that are functionally regulated by PUMs. Bioinformatic analysis defined a PUM Response Element (PRE) that was significantly enriched in transcripts that increased in abundance and matches the PUM RNA-binding consensus. We created a computational model that incorporates PRE position and frequency within an RNA relative to the magnitude of regulation. The model reveals significant correlation of PUM regulation with PREs in 3′ untranslated regions (UTRs), coding sequences and non-coding RNAs, but not 5′ UTRs. To define direct, high confidence PUM targets, we cross-referenced PUM-regulated RNAs with all PRE-containing RNAs and experimentally defined PUM-bound RNAs. The results define nearly 300 direct targets that include both PUM-repressed and, surprisingly, PUM-activated target RNAs. Annotation enrichment analysis reveal that PUMs regulate genes from multiple signaling pathways and developmental and neurological processes. Moreover, PUM target mRNAs impinge on human disease genes linked to cancer, neurological disorders and cardiovascular disease. These discoveries pave the way for determining how the PUM-dependent regulatory network impacts biological functions and disease states.

Surfactant protein A (SP-A) is an innate immune molecule that binds foreign organisms that invade the lungs and targets them for phagocytic clearance by the resident pulmonary phagocyte, the alveolar macrophage (AM). We hypothesized that SP-A binds to and enhances macrophage uptake of other nonself particles, specifically apoptotic polymorphonuclear neutrophils (PMNs). PMNs are recruited into the lungs during inflammation, but as inflammation is resolved, PMNs undergo apoptosis and are phagocytosed by AMs. We determined that SP-A increases AM phagocytosis of apoptotic PMNs 280 ± 62% above the no protein control value. The increase is dose dependent, and heat-treated SP-A still enhanced uptake, whereas deglycosylated SP-A had significantly diminished ability to enhance phagocytosis. Surfactant protein D also increased phagocytosis of apoptotic PMNs by ∼125%. However, other proteins that are structurally homologous to SP-A, mannose-binding lectin and complement protein 1q, did not. SP-A enhances phagocytosis via an opsonization-dependent mechanism and binds apoptotic PMNs ∼4-fold more than viable PMNs. Also, binding of SP-A to apoptotic PMNs does not appear to involve SP-A’s lectin domain. These data suggest that the pulmonary collectins SP-A and SP-D facilitate the resolution of inflammation by accelerating apoptotic PMN clearance.

No abstract

The human PUF-family proteins, PUM1 and PUM2, post-transcriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3' UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a 'rulebook' of key contextual features that differentiate functional vs. non-functional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.

No abstract

Surfactant protein A (SP-A), a pulmonary lectin, plays an important role in regulating innate immune cell function. Besides accelerating pathogen clearance by pulmonary phagocytes, SP-A also stimulates alveolar macrophage chemotaxis and directed actin polymerization. We hypothesized that SP-A would also stimulate neutrophil chemotaxis. With the use of a Boyden chamber assay, we found that SP-A (0.5-25 microg/ml) did not stimulate chemotaxis of rat peripheral neutrophils or inflammatory bronchoalveolar lavage (BAL) neutrophils isolated from LPS-treated lungs. However, SP-A affected neutrophil chemotaxis toward the bacterial peptide formyl-met-leu-phe (fMLP). Surprisingly, the effect was different for the two neutrophil populations: SP-A reduced peripheral neutrophil chemotaxis toward fMLP (49 +/- 5% fMLP alone) and enhanced inflammatory BAL neutrophil chemotaxis (277 +/- 48% fMLP alone). This differential effect was not seen for the homologous proteins mannose binding lectin and complement protein 1q but was recapitulated by type IV collagen. SP-A bound both neutrophil populations comparably and did not alter formyl peptide binding. These data support a role for SP-A in regulating neutrophil migration in pulmonary tissue.

The human PUF-family proteins, PUM1 and PUM2, post-transcriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3' UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a 'rulebook' of key contextual features that differentiate functional vs. non-functional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.