Position-dependent function of human sequence-specific transcription factors.

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.

Breakthrough infections by SARS-CoV-2 variants boost cross-reactive hybrid immune responses in mRNA-vaccinated Golden Syrian hamsters.

Hybrid immunity (vaccination + natural infection) to SARS-CoV-2 provides superior protection to re-infection. We performed immune profiling studies during breakthrough infections in mRNA-vaccinated hamsters to evaluate hybrid immunity induction. The mRNA vaccine, BNT162b2, was dosed to induce binding antibody titers against ancestral spike, but inefficient serum virus neutralization of ancestral SARS-CoV-2 or variants of concern (VoCs). Vaccination reduced morbidity and controlled lung virus titers for ancestral virus and Alpha but allowed breakthrough infections in Beta, Delta and Mu-challenged hamsters. Vaccination primed for T cell responses that were boosted by infection. Infection back-boosted neutralizing antibody responses against ancestral virus and VoCs. Hybrid immunity resulted in more cross-reactive sera, reflected by smaller antigenic cartography distances. Transcriptomics post-infection reflects both vaccination status and disease course and suggests a role for interstitial macrophages in vaccine-mediated protection. Therefore, protection by vaccination, even in the absence of high titers of neutralizing antibodies in the serum, correlates with recall of broadly reactive B- and T-cell responses.

Triclosan administration to humanized UDP-glucuronosyltransferase 1 neonatal mice induces UGT1A1 through a dependence on PPARα and ATF4.

Triclosan (TCS) is an antimicrobial toxicant found in a myriad of consumer products and has been detected in human tissues, including breastmilk. We have evaluated the impact of lactational TCS on UDP-glucuronosyltransferase 1A1 (UGT1A1) expression and bilirubin metabolism in humanized UGT1 (hUGT1) neonatal mice. In hUGT1 mice, expression of the hepatic UGT1A1 gene is developmentally delayed resulting in elevated total serum bilirubin (TSB) levels. We found that newborn hUGT1 mice breastfed or orally treated with TCS presented lower TSB levels along with induction of hepatic UGT1A1. Lactational and oral treatment by gavage with TCS leads to the activation of hepatic nuclear receptors constitutive androstane receptor (CAR), peroxisome proliferator-activated receptor alpha (PPARα), and stress sensor, activating transcription factor 4 (ATF4). When CAR-deficient hUGT1 mice (hUGT1/Car-/-) were treated with TCS, TSB levels were reduced with a robust induction of hepatic UGT1A1, leaving us to conclude that CAR is not tied to UGT1A1 induction. Alternatively, when PPARα-deficient hUGT1 mice (hUGT1/Pparα-/-) were treated with TCS, hepatic UGT1A1 was not induced. Additionally, we had previously demonstrated that TCS is a potent inducer of ATF4, a transcriptional factor linked to the integrated stress response. When ATF4 was deleted in liver of hUGT1 mice (hUGT1/Atf4ΔHep) and these mice treated with TCS, we observed superinduction of hepatic UGT1A1. Oxidative stress genes in livers of hUGT1/Atf4ΔHep treated with TCS were increased, suggesting that ATF4 protects liver from excessive oxidative stress. The increase oxidative stress may be associated with superinduction of UGT1A1. The expression of ATF4 in neonatal hUGT1 hepatic tissue may play a role in the developmental repression of UGT1A1.

Clinical Predictors of Major Intrathoracic Injury in Pediatric Blunt Trauma.

OBJECTIVES: Blunt trauma in pediatric patients accounts for a significant proportion of pediatric death from traumatic injury. Currently, there are no clinical decision-making tools available to guide imaging choice in the evaluation of pediatric patients with blunt thoracic trauma (BTT). This study aimed to analyze the rates of missed major intrathoracic injuries on chest x-ray (CXR) and identify clinical risk factors associated with major intrathoracic injuries to formulate a clinical decision-making tool for computed tomography (CT) use in pediatric patients with BTT. METHODS: We performed a retrospective single-center study using an institutional trauma database of pediatric patients. Inclusion criteria included age, blunt trauma, and patients who received a CXR and thoracic CT within 24 hours of presentation. Thoracic CT findings were graded as major, minor, or none, and comparison CXR was used to determine the rate of missed thoracic injuries. Eighty-four patient variables were then collected, and clinically relevant variables associated with major intrathoracic injuries were placed in a logistic regression model to determine the best predictors of major injury in pediatric BTT patients. RESULTS: A total of 180 patients (48.3%) had CXR that missed an injury that was seen on thoracic CT. In our cohort, 20 patients (5.4%) had major injuries that were missed on CXR. Characteristics correlating with major thoracic injuries were older age (odds ratio [OR], 1.125; 95% confidence interval [CI], 1.015-1.247), chest pain (OR, 4.907; 95% CI, 2.173-11.083), abnormal chest auscultation (OR, 3.564; 95% CI, 1.406-9.035), and tachycardia (OR, 2.876; 95% CI, 1.256-6.586). Using these 4 variables, receiver operating characteristic analysis revealed an area under the curve of 0.7903. CONCLUSIONS: Pediatric BTT patients older than 15 years with tachycardia, chest pain, or abnormal chest auscultation are at increased risk for major intrathoracic injuries and may benefit from thoracic CT.

Quality Improvement Project Reducing Sputum Cultures for Pediatric Patients With a Tracheostomy.

BACKGROUND AND OBJECTIVES: Current research implies overuse of diagnostic testing and overtreatment in children with tracheostomies. There are no guidelines for obtaining sputum cultures for these patients, yet they are commonly obtained without significantly affecting management or outcomes. The aim of our quality improvement project was to decrease rate of sputum cultures in this population by 50%, from 64% to 32%. METHODS: This was a single-center quality improvement project conducted in a pediatric emergency department (ED). Key drivers included: Standardized decision-making, appropriate culture collection, knowledge regarding colonization versus clinically relevant growth, and viral versus bacterial infections in this population. The study team developed an algorithm, used modification to electronic medical records orders, and provided education to drive change. Six months of preintervention and 12 months postintervention data were collected. Run charts/statistical process charts were created for the rate of cultures, length of stay, and return to the ED. RESULTS: There were 159 patient encounters and the rate of sputum cultures decreased from 64% at baseline to 25% without change in length of stay or increased rate at which patients returned to the ED, including during local coronavirus disease 2019 and respiratory syncytial virus surges. We observed nonrandom data patterns after introduction of algorithm resulting in centerline shifts. CONCLUSIONS: The study team was able to introduce an algorithm coinciding with a reduction in number of sputum cultures obtained. Next steps would be determining safety and efficacy of such an algorithm over a larger population.

TRIF-IFN-I pathway in Helicobacter-induced gastric cancer in an accelerated murine disease model and patient biopsies.

Helicobacter pylori (H. pylori) infection is a known cause of many digestive diseases, including gastritis, peptic ulcers, and gastric cancer. However, the underlying mechanisms by which H. pylori infection triggers these disorders are still not clearly understood. Gastric cancer is a slow progressing disease, which makes it difficult to study. We have developed an accelerated disease progression mouse model, which leverages mice deficient in the myeloid differentiation primary response 88 gene (Myd88-/-) infected with Helicobacter felis (H. felis). Using this model and gastric biopsy samples from patients, we report that activation of the Toll/interleukin-1 receptor (TIR)-domain-containing adaptor inducing interferon-ß (TRIF)-type I interferon (IFN-I) signaling pathway promotes Helicobacter-induced disease progression toward severe gastric pathology and gastric cancer development. Further, results implicated downstream targets of this pathway in disease pathogenesis. These findings may facilitate stratification of Helicobacter-infected patients and thus enable treatment prioritization of patients.

Drug target prediction through deep learning functional representation of gene signatures.

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.