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Hematoma Resolution In Vivo Is Directed by Activating Transcription Factor 1.

ABSTRACT: RATIONALE:The efficient resolution of tissue hemorrhage is an important homeostatic function. In human macrophages in vitro, heme activates an AMPK (AMP-activated protein kinase)/ATF1 (activating transcription factor-1) pathway that directs Mhem macrophages through coregulation of HO-1 (heme oxygenase-1; HMOX1) and lipid homeostasis genes. OBJECTIVE:We asked whether this pathway had an in vivo role in mice. METHODS AND RESULTS:Perifemoral hematomas were used as a model of hematoma resolution. In mouse bone marrow-derived macrophages, heme induced HO-1, lipid regulatory genes including LXR (lipid X receptor), the growth factor IGF1 (insulin-like growth factor-1), and the splenic red pulp macrophage gene Spic. This response was lost in bone marrow-derived macrophages from mice deficient in AMPK (Prkab1-/-) or ATF1 (Atf1-/-). In vivo, femoral hematomas resolved completely between days 8 and 9 in littermate control mice (n=12), but were still present at day 9 in mice deficient in either AMPK (Prkab1-/-) or ATF1 (Atf1-/-; n=6 each). Residual hematomas were accompanied by increased macrophage infiltration, inflammatory activation and oxidative stress. We also found that fluorescent lipids and a fluorescent iron-analog were trafficked to lipid-laden and iron-laden macrophages respectively. Moreover erythrocyte iron and lipid abnormally colocalized in the same macrophages in Atf1-/- mice. Therefore, iron-lipid separation was Atf1-dependent. CONCLUSIONS:Taken together, these data demonstrate that both AMPK and ATF1 are required for normal hematoma resolution. Graphic Abstract: An online graphic abstract is available for this article.

SUBMITTER: Seneviratne A

PROVIDER: S-EPMC7478221 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Hematoma Resolution In Vivo Is Directed by Activating Transcription Factor 1.

Seneviratne Anusha A Han Yumeng Y Wong Eunice E Walter Edward R H ERH Jiang Lijun L Cave Luke L Long Nicholas J NJ Carling David D Mason Justin C JC Haskard Dorian O DO Boyle Joseph J JJ

Circulation research 20200701 7

<h4>Rationale</h4>The efficient resolution of tissue hemorrhage is an important homeostatic function. In human macrophages in vitro, heme activates an AMPK (AMP-activated protein kinase)/ATF1 (activating transcription factor-1) pathway that directs Mhem macrophages through coregulation of HO-1 (heme oxygenase-1; <i>HMOX1</i>) and lipid homeostasis genes.<h4>Objective</h4>We asked whether this pathway had an in vivo role in mice.<h4>Methods and results</h4>Perifemoral hematomas were used as a mod ...[more]

PMID: 32611235

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Project description:Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.

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Hematoma Resolution In Vivo Is Directed by Activating Transcription Factor 1.

Publications

Hematoma Resolution In Vivo Is Directed by Activating Transcription Factor 1.

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