DNAJB1-PRKACA fusion protein-regulated LINC00473 promotes tumor growth and alters mitochondrial fitness in fibrolamellar carcinoma.

Fibrolamellar carcinoma (FLC) is a rare liver cancer that disproportionately affects adolescents and young adults. Currently, no standard of care is available and there remains a dire need for new therapeutics. Most patients harbor the fusion oncogene DNAJB1-PRKACA (DP fusion), but clinical inhibitors are not yet developed and it is critical to identify downstream mediators of FLC pathogenesis. Here, we identify long noncoding RNA LINC00473 among the most highly upregulated genes in FLC tumors and determine that it is strongly suppressed by RNAi-mediated inhibition of the DP fusion in FLC tumor epithelial cells. We show by loss- and gain-of-function studies that LINC00473 suppresses apoptosis, increases the expression of FLC marker genes, and promotes FLC growth in cell-based and in vivo disease models. Mechanistically, LINC00473 plays an important role in promoting glycolysis and altering mitochondrial activity. Specifically, LINC00473 knockdown leads to increased spare respiratory capacity, which indicates mitochondrial fitness. Overall, we propose that LINC00473 could be a viable target for this devastating disease.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning.

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Aberrant miR-29 is a predictive feature of severe phenotypes in pediatric Crohn's disease.

Crohn's disease (CD) is a chronic inflammatory gut disorder. Molecular mechanisms underlying the clinical heterogeneity of CD remain poorly understood. MicroRNAs (miRNAs) are important regulators of gut physiology, and several have been implicated in the pathogenesis of adult CD. However, there is a dearth of large-scale miRNA studies for pediatric CD. We hypothesized that specific miRNAs uniquely mark pediatric CD. We performed small RNA-Seq of patient-matched colon and ileum biopsies from treatment-naive pediatric patients with CD (n = 169) and a control cohort (n = 108). Comprehensive miRNA analysis revealed 58 miRNAs altered in pediatric CD. Notably, multinomial logistic regression analysis revealed that index levels of ileal miR-29 are strongly predictive of severe inflammation and stricturing. Transcriptomic analyses of transgenic mice overexpressing miR-29 show a significant reduction of the tight junction protein gene Pmp22 and classic Paneth cell markers. The dramatic loss of Paneth cells was confirmed by histologic assays. Moreover, we found that pediatric patients with CD with elevated miR-29 exhibit significantly lower Paneth cell counts, increased inflammation scores, and reduced levels of PMP22. These findings strongly indicate that miR-29 upregulation is a distinguishing feature of pediatric CD, highly predictive of severe phenotypes, and associated with inflammation and Paneth cell loss.

Molecular and Metabolic Analysis of Arsenic-Exposed Humanized AS3MT Mice.

BACKGROUND: Chronic exposure to inorganic arsenic (iAs) has been associated with type 2 diabetes (T2D). However, potential sex divergence and the underlying mechanisms remain understudied. iAs is not metabolized uniformly across species, which is a limitation of typical exposure studies in rodent models. The development of a new "humanized" mouse model overcomes this limitation. In this study, we leveraged this model to study sex differences in the context of iAs exposure. OBJECTIVES: The aim of this study was to determine if males and females exhibit different liver and adipose molecular profiles and metabolic phenotypes in the context of iAs exposure. METHODS: Our study was performed on wild-type (WT) 129S6/SvEvTac and humanized arsenic +3 methyl transferase (human AS3MT) 129S6/SvEvTac mice treated with 400 ppb of iAs via drinking water ad libitum. After 1 month, mice were sacrificed and the liver and gonadal adipose depots were harvested for iAs quantification and sequencing-based microRNA and gene expression analysis. Serum blood was collected for fasting blood glucose, fasting plasma insulin, and homeostatic model assessment for insulin resistance (HOMA-IR). RESULTS: We detected sex divergence in liver and adipose markers of diabetes (e.g., miR-34a, insulin signaling pathways, fasting blood glucose, fasting plasma insulin, and HOMA-IR) only in humanized (not WT) mice. In humanized female mice, numerous genes that promote insulin sensitivity and glucose tolerance in both the liver and adipose are elevated compared to humanized male mice. We also identified Klf11 as a putative master regulator of the sex divergence in gene expression in humanized mice. DISCUSSION: Our study underscored the importance of future studies leveraging the humanized mouse model to study iAs-associated metabolic disease. The findings suggested that humanized males are at increased risk for metabolic dysfunction relative to humanized females in the context of iAs exposure. Future investigations should focus on the detailed mechanisms that underlie the sex divergence. https://doi.org/10.1289/EHP12785.

SELfies and CELLfies: Whole Genome Sequencing and Annotation of Five Antibiotic Resistant Bacteria Isolated from the Surfaces of Smartphones, An Inquiry Based Laboratory Exercise in a Genomics Undergraduate Course at the Rochester Institute of Technology.

Are touchscreen devices a public health risk for the transmission of pathogenic bacteria, especially those that are resistant to antibiotics? To investigate this, we embarked on a project aimed at isolating and identifying bacteria that are resistant to antibiotics from the screens of smartphones. Touchscreen devices have become ubiquitous in society, and it is important to evaluate the potential risks they pose towards public health, especially as it pertains to the harboring and transmission of pathogenic bacteria that are resistant to antibiotics. Sixteen bacteria were initially isolated of which five were unique (four Staphylococcus species and one Micrococcus species). The genomes of the five unique isolates were subsequently sequenced and annotated. The genomes were analyzed using in silico tools to predict the synthesis of antibiotics and secondary metabolites using the antibiotics and Secondary Metabolite Analysis SHell (antiSMASH) tool in addition to the presence of gene clusters that denote resistance to antibiotics using the Resistance Gene Identifier (RGI) tool. In vivo analysis was also done to assess resistance/susceptibility to four antibiotics that are commonly used in a research laboratory setting. The data presented in this manuscript is the result of a semester-long inquiry based laboratory exercise in the genomics course (BIOL340) in the Thomas H. Gosnell School of Life Sciences/College of Science at the Rochester Institute of Technology.