tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.
Background Pyrazinamide is an important drug against the latent stage of tuberculosis and is used in both first- and second-line treatment regimens. Pyrazinamide-susceptibility test usually takes a week to have a diagnosis to guide initial therapy, implying a delay in receiving appropriate therapy. The continued increase in multi-drug resistant tuberculosis and the prevalence of pyrazinamide resistance in several countries makes the development of assays for prompt identification of resistance necessary. The main cause of pyrazinamide resistance is the impairment of pyrazinamidase function attributed to mutations in the promoter and/or pncA coding gene. However, not all pncA mutations necessarily affect the pyrazinamidase function. Objective To develop a methodology to predict pyrazinamidase function from detected mutations in the pncA gene. Methods We measured the catalytic constant (k cat ), K M , enzymatic efficiency, and enzymatic activity of 35 recombinant mutated pyrazinamidase and the wild type (Protein Data Bank ID = 3pl1). From all the 3D modeled structures, we extracted several predictors based on three categories: structural stability (estimated by normal mode analysis and molecular dynamics), physicochemical, and geometrical characteristics. We used a stepwise Akaike’s information criterion forward multiple log-linear regression to model each kinetic parameter with each category of predictors. We also developed weighted models combining the three categories of predictive models for each kinetic parameter. We tested the robustness of the predictive ability of each model by 6-fold cross-validation against random models. Results The stability, physicochemical, and geometrical descriptors explained most of the variability (R 2 ) of the kinetic parameters. Our models are best suited to predict k cat , efficiency, and activity based on the root-mean-square error of prediction of the 6-fold cross-validation. Conclusions This study shows a quick approach to predict the pyrazinamidase function only from the pncA sequence when point mutations are present. This can be an important tool to detect pyrazinamide resistance.
Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer affecting adolescents and young adults, with no gender or ethnicity predilection and without history of underlying viral hepatitis, cirrhosis, or other known risk factors. Almost all FLC patients present a somatic heterozygous deletion in chromosome 19p13.12, DNAJB1::PRKACA, which is sufficient to drive FLC in mice. A few studies comparing FLC tumors with adjacent non-transformed liver (normal) samples revealed many transcriptional differences. However, there were done in very small datasets and analyzed using different bioinformatic methods, resulting in just 18-47% agreement between them. This study aims to comprehensively characterize the transcriptome of FLC at bulk and spatial single-cell resolution. The whole transcriptome of 109 FLC frozen patient samples, the largest RNA-seq dataset of FLC to date, was sequenced using different library preparation and ribosomal depletion methods. Only paired tumor and normal tissue samples resected from the same patient were used and divided into two groups: exploration (3 datasets, 67 samples) and testing (2 datasets, 17 samples). Additionally, as external validation datasets, RNA-seq samples from previously published studies were collected, including Sorenson et al. (FLC: 26, normal: 9, paired: 8), the TCGA-LIHC study (FLC: 6, normal: 1, paired: 1), Hirsch et al. (FLC: 15, normal: 3, paired: 0) and Francisco et al. (FLC: 27, normal: 10, paired: 9). All were reanalyzed using state-of-the-art bioinformatic methods: mapped to the Human Genome GRCh38.103 and transcripts quantified using Salmon 1.6.0, unsupervised clustering exploration using PCA, tSNE and UMAP, differential expression calculated using DESeq2 1.28.1, and checking for detectability and consistency among datasets. We found 857 up- and 988 down-regulated genes presenting the same dysregulation in the exploration datasets and confirmed in the testing and external datasets. We call these genes the transcriptional FLC signature. The FLC signature was further characterized by comparing it with the genes differentially expressed in other liver cancers: hepatocellular carcinoma (41 paired samples), hepatoblastoma (22 paired samples), and cholangiocarcinoma (27 paired samples). We found 276 up- and 352 down-regulated genes altered in other liver cancers as well as FLC, but 156 up- and 68 down-regulated only in FLC. The 112 genes with the strongest dysregulation (56 up and 56 down) were used for a MERFISH screening, providing for the first time a single-cell spatial transcriptomic characterization of FLC. This showed clear differential expression patterns in tumor, normal, stromal, and infiltrating immune cells, allowing the identification of how different cell types contribute to the transcriptional FLC signature. Citation Format: David Requena, Aldhair Medico, Luis F. Soto, Mahsa Shirani, James A. Saltsman, Gadi Lalazar, Michael P. LaQuaglia, Sanford M. Simon. Bulk and spatial single-cell transcriptomic characterization of fibrolamellar hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1516.
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