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Background: Gallstone disease (GD) is no longer an exclusive condition of adulthood, and its prevalence is increasing in pediatric age. The management and the extent of the etiological investigation of GD in children and adolescents remains controversial. This study aimed to analyze the difficulties in the work-up and management of pediatric GD patients., Methods: A retrospective study performed in a single tertiary center enrolled sixty-five patients with GD followed from January 2014 to June 2021. Patients were categorized conveniently according to their age at diagnosis: Group A (<10years, n=35) and Group B (≥10years, n=30). We analyzed demographic, clinical and laboratory data, ultrasonographic findings at presentation, therapeutics and complications., Results: Symptoms were more frequent in patients >10years old (p=0.001). Cholecystectomy was performed in 31 patients (47.7%). A multivariate regression logistic model identified the age >10years (OR=6.440, p=0.005) and underlying entities (OR=6.823, p=0.017) as independent variables to perform surgery. Spontaneous resolution of GD was more common in children <2years old. A multivariate regression logistic model showed a trend for those >10years old to develop more complications. Two out of 18 patients were diagnosed with ABCB4 gene mutations in heterozygosity., Conclusions: Decision-making on cholecystectomy remains challenging in asymptomatic patients. Identifying predictive factors for the development of complications has proven difficult. However, we found a trend toward the development of complications in individuals older than 10years., (Copyright © 2024 Elsevier España, S.L.U. All rights reserved.)

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Bacterial natural products have historically been a deep source of new medicines, but their slowed discovery in recent decades has put a premium on developing strategies that enhance the likelihood of capturing novel compounds. Here, we used a straightforward approach that capitalizes on the interactive ecology of "rare" actinomycetes. Specifically, we screened for interactions that triggered the production of antimicrobials that inhibited the growth of a bacterial strain with exceptionally diverse natural antimicrobial resistance. This strategy led to the discovery of a family of antimicrobials we term the dynaplanins. Heterologous expression enabled identification of the dynaplanin biosynthetic gene cluster, which was missed by typical algorithms for natural product gene cluster detection. Genome sequencing of partially resistant mutants revealed a 2-oxo acid dehydrogenase E2 subunit as the likely molecular target of the dynaplanins, and this finding was supported by computational modeling of the dynaplanin scaffold within the active site of this enzyme. Thus, this simple strategy, which leverages microbial interactions and natural antibiotic resistance, can enable discovery of molecules with unique antimicrobial activity. In addition, these results indicate that primary metabolism may be a direct target for inhibition via chemical interference in competitive microbial interactions. IMPORTANCE Many antibiotics were originally discovered from microbes. However, in recent decades, resistance to current treatments has risen, while novel antibiotic discovery has become increasingly challenging. Thus, there is a need to develop new strategies to find novel antimicrobials. Here, we incorporated three levels of innovation into a single, simple discovery pipeline: focusing on understudied bacteria with a high potential for producing antibiotics, growing these bacteria in binary microbial interactions, and screening for activity against a multidrug-resistant bacterium. This led us to discover a family of antimicrobials that we call the dynaplanins, which are synthesized by genes that were not detected by typical prediction algorithms. We found that dynaplanins likely block the function of one of three related enzymes called 2-oxo acid dehydrogenases, which are vital to cellular metabolism. Overall, our strategy based on bacterial competition led to discovery of a novel antibiotic that inhibits the ability to metabolize nutrients.