Use of the dTAG system in vivo to degrade CDK2 and CDK5 in adult mice and explore potential safety liabilities.

The degradation tag (dTAG) system for target protein degradation can remove proteins from biological systems without the drawbacks of some genetic methods, such as slow kinetics, lack of reversibility, low specificity, and the inability to titrate dosage. These drawbacks can make it difficult to compare toxicity resulting from genetic and pharmacological interventions, especially in vivo. Because the dTAG system has not been studied extensively in vivo, we explored the use of this system to study the physiological sequalae resulting from CDK2 or CDK5 degradation in adult mice. Mice with homozygous knock-in of the dTAG sequence onto CDK2 and CDK5 were born at Mendelian ratios despite decreased CDK2 or CDK5 protein levels in comparison with wild-type mice. In bone marrow cells and duodenum organoids derived from these mice, treatment with the dTAG degrader dTAG-13 resulted in rapid and robust protein degradation but caused no appreciable change in viability or the transcriptome. Repeated delivery of dTAG-13 in vivo for toxicity studies proved challenging; we explored multiple formulations in an effort to maximize degradation while minimizing formulation-related toxicity. Degradation of CDK2 or CDK5 in all organs except the brain, where dTAG-13 likely did not cross the blood brain barrier, only caused microscopic changes in the testis of CDK2dTAG mice. These findings were corroborated with conditional CDK2 knockout in adult mice. Our results suggest that the dTAG system can provide robust protein degradation in vivo and that loss of CDK2 or CDK5 in adult mice causes no previously unknown phenotypes.

Effects of donor source on transcriptomic profiles of human kidney tissue.

Normal human tissue is a critical reference control in biomedical research. However, the type of tissue donor can significantly affect the underlying biology of the samples. We investigated the impact of tissue donor source type by performing transcriptomic analysis on healthy kidney tissue from three donor source types: cadavers, organ donors, and normal-adjacent tissue from surgical resections of clear cell renal cell carcinomas, and we compared the gene expression profiles to those of clear cell renal cell carcinoma samples. Comparisons among the normal samples revealed general similarity, with notable differences in gene expression pathways involving immune system and inflammatory processes, response to extracellular stimuli, ion transport, and metabolism. When compared to tumors, the transcriptomic profiles of the normal adjacent tissue were highly similar to the profiles from cadaveric and organ donor tissue samples, arguing against the presence of a field cancerization effect in clear cell renal cell carcinoma. We conclude that all three normal source types are suitable for reference kidney control samples, but important differences must be noted for particular research areas and tissue banking strategies.

Nonclinical Safety Signals in PharmaPendium Improve the Predictability of Human Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a leading cause of candidate attrition during drug development in the pharmaceutical industry. This study evaluated liver toxicity signals for 249 approved drugs (114 of "most-DILI concern" and 135 of "no-DILI concern") using PharmaPendium and assessed the association between nonclinical and clinical injuries using contingency table analysis. All animal liver findings were combined into eight toxicity categories based on nature and severity. Together, these analyses revealed that cholestasis [odds ratio (OR): 5.02; 95% confidence interval (CI) 1.04-24.03] or liver aminotransferase increases (OR: 1.86; 95% CI 1.09-3.09) in rats and steatosis (OR-1.9; 95% CI 1.03-3.49) or liver aminotransferase increases (OR-2.57; 95% CI 1.4-4.7) in dogs were significant predictors of human liver injury. The predictive value further improved when the liver injury categories were combined into less severe (steatosis, cholestasis, liver aminotransferase increase, hyperbilirubinemia, or jaundice) and more-severe (liver necrosis, acute liver failure, or hepatotoxicity) injuries. In particular, less-severe liver injuries in the following pairs of species predicted human hepatotoxicity {[dog and mouse] (OR: 2.70; 95% CI 1.25-5.84), [dog and rat] (OR-2.61; 95% CI 1.48-4.59), [monkey and mouse] (OR-4.22; 95% CI 1.33-13.32), and [monkey and rat] (OR-2.45; 95% CI 1.15-5.21)} were predictive of human hepatotoxicity. Meanwhile, severe liver injuries in both [dog and rat] (OR-1.9; 95% CI 1.04-3.49) were significant predictors of human liver toxicity. Therefore, we concluded that the occurrence of DILI in humans is highly likely if liver injuries are observed in one rodent and one nonrodent species and that liver aminotransferase increases in dogs and rats can predict DILI in humans. Together, these findings indicate that the liver safety signals observed in animal toxicity studies indicate potential DILI risk in humans and could therefore be used to prioritize small molecules with less potential to cause DILI in humans.

Annotation depth confounds direct comparison of gene expression across species.

BACKGROUND: Comparisons of the molecular framework among organisms can be done on both structural and functional levels. One of the most common top-down approaches for functional comparisons is RNA sequencing. This estimation of organismal transcriptional responses is of interest for understanding evolution of molecular activity, which is used for answering a diversity of questions ranging from basic biology to pre-clinical species selection and translation. However, direct comparison between species is often hindered by evolutionary divergence in structure of molecular framework, as well as large difference in the depth of our understanding of the genetic background between humans and other species. Here, we focus on the latter. We attempt to understand how differences in transcriptome annotation affect direct gene abundance comparisons between species. RESULTS: We examine and suggest some straightforward approaches for direct comparison given the current available tools and using a sample dataset from human, cynomolgus monkey, dog, rat and mouse with a common quantitation and normalization approach. In addition, we examine how variation in genome annotation depth and quality across species may affect these direct comparisons. CONCLUSIONS: Our findings suggest that further efforts for better genome annotation or computational normalization tools may be of strong interest.

T cells and monocyte-derived myeloid cells mediate immunotherapy-related hepatitis in a mouse model.

BACKGROUND & AIMS: Immune checkpoint inhibitors (ICIs) are associated with immune-related adverse events (irAEs) which are more severe when ICIs are used in combination. We aimed to use a mouse model to elucidate the molecular mechanisms of immune-related hepatitis, one of the common irAEs associated with ICIs. METHODS: Immune phenotyping and molecular profiling were performed on Pdcd1-/- mice treated with anti-CTLA4 and/or the IDO1 inhibitor epacadostat or a 4-1BB agonistic antibody. RESULTS: ICI combination-induced hepatitis and 4-1BB agonist-mediated hepatitis share similar features yet maintain distinct immune signatures. Both were characterized by an expansion of periportal infiltrates and pan-zonal inflammation albeit with different morphologic characteristics. In both cases, infiltrates were predominantly CD4+ and CD8+ T cells with upregulated T-cell activation markers, ICOS and CD44. Depletion of CD8+ T cells abolished ICI-mediated hepatitis. Single-cell transcriptomics revealed that the hepatitis induced by combination ICIs is associated with a robust immune activation signature in all subtypes of T cells and T helper 1 skewing. Expression profiling revealed a central role for IFNγ and liver monocyte-derived macrophages in promoting a pro-inflammatory T-cell response to ICI combination and 4-1BB agonism. CONCLUSION: We developed a novel mouse model which offers significant value in yielding deeper mechanistic insight into immune-mediated liver toxicity associated with various immunotherapies. LAY SUMMARY: Hepatitis is one of the common immune-related adverse events in cancer patients receiving immune checkpoint inhibitor (ICI) therapy. The mechanisms of ICI-induced hepatitis are not well understood. In this paper, we identify key molecular mechanisms mediating immune intracellular crosstalk between liver T cells and macrophages in response to ICI in a mouse model.

Evaluating the Sensitivity and Specificity of Promising Circulating Biomarkers to Diagnose Liver Injury in Humans.

Early diagnosis of drug-induced liver injury (DILI) continues to be a major hurdle during drug development and postmarketing. The objective of this study was to evaluate the diagnostic performance of promising biomarkers of liver injury-glutamate dehydrogenase (GLDH), cytokeratin-18 (K18), caspase-cleaved K18 (ccK18), osteopontin (OPN), macrophage colony-stimulating factor (MCSF), MCSF receptor (MCSFR), and microRNA-122 (miR-122) in comparison to the traditional biomarker alanine aminotransferase (ALT). Biomarkers were evaluated individually and as a multivariate model in a cohort of acetaminophen overdose (n = 175) subjects and were further tested in cohorts of healthy adults (n = 135), patients with liver damage from various causes (n = 104), and patients with damage to the muscle (n = 74), kidney (n = 40), gastrointestinal tract (n = 37), and pancreas (n = 34). In the acetaminophen cohort, a multivariate model with GLDH, K18, and miR-122 was able to detect DILI more accurately than individual biomarkers alone. Furthermore, the three-biomarker model could accurately predict patients with liver injury compared with healthy volunteers or patients with damage to muscle, pancreas, gastrointestinal tract, and kidney. Expression of K18, GLDH, and miR-122 was evaluated using a database of transcriptomic profiles across multiple tissues/organs in humans and rats. K18 mRNA (Krt18) and MiR-122 were highly expressed in liver whereas GLDH mRNA (Glud1) was widely expressed. We performed a comprehensive, comparative performance assessment of 7 promising biomarkers and demonstrated that a 3-biomarker multivariate model can accurately detect liver injury.

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.

BACKGROUND: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines. METHODS: Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles. FINDINGS: By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs. INTERPRETATION: These associations provide a comprehensive resource to support drug development and human biology studies. FUNDING: This study was not supported by any formal funding bodies.

Tissue-Specific Trans Regulation of the Mouse Epigenome.

The epigenetic landscape varies greatly among cell types. Although a variety of writers, readers, and erasers of epigenetic features are known, we have little information about the underlying regulatory systems controlling the establishment and maintenance of these features. Here, we have explored how natural genetic variation affects the epigenome in mice. Studying levels of H3K4me3, a histone modification at sites such as promoters, enhancers, and recombination hotspots, we found tissue-specific trans-regulation of H3K4me3 levels in four highly diverse cell types: male germ cells, embryonic stem cells, hepatocytes, and cardiomyocytes. To identify the genetic loci involved, we measured H3K4me3 levels in male germ cells in a mapping population of 59 BXD recombinant inbred lines. We found extensive trans-regulation of H3K4me3 peaks, including six major histone quantitative trait loci (QTL). These chromatin regulatory loci act dominantly to suppress H3K4me3, which at hotspots reduces the likelihood of subsequent DNA double-strand breaks. QTL locations do not correspond with genes encoding enzymes known to metabolize chromatin features. Instead their locations match clusters of zinc finger genes, making these possible candidates that explain the dominant suppression of H3K4me3. Collectively, these data describe an extensive, set of chromatin regulatory loci that control the epigenetic landscape.

Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets.

BACKGROUND: With the global emergence of multi-drug resistant strains of Mycobacterium tuberculosis, new strategies to treat tuberculosis are urgently needed such as therapeutics targeting potential human host factors. RESULTS: Here we performed a statistical meta-analysis of human gene expression in response to both latent and active pulmonary tuberculosis infections from nine published datasets. We found 1655 genes that were significantly differentially expressed during active tuberculosis infection. In contrast, no gene was significant for latent tuberculosis. Pathway enrichment analysis identified 90 significant canonical human pathways, including several pathways more commonly related to non-infectious diseases such as the LRRK2 pathway in Parkinson's disease, and PD-1/PD-L1 signaling pathway important for new immuno-oncology therapies. The analysis of human genome-wide association studies datasets revealed tuberculosis-associated genetic variants proximal to several genes in major histocompatibility complex for antigen presentation. We propose several new targets and drug-repurposing opportunities including intravenous immunoglobulin, ion-channel blockers and cancer immuno-therapeutics for development as combination therapeutics with anti-mycobacterial agents. CONCLUSIONS: Our meta-analysis provides novel insights into host genes and pathways important for tuberculosis and brings forth potential drug repurposing opportunities for host-directed therapies.

Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective.

Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.

A network biology approach to denitrification in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO2), nitric oxide (NO) and nitrous oxide (N2O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O2), nitrate (NO3), and phosphate (PO4) suggests that PO4 concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO4 on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N2O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide.

Microbiome changes in healthy volunteers treated with GSK1322322, a novel antibiotic targeting bacterial peptide deformylase.

GSK1322322 is a novel antibacterial agent under development, and it has known antibacterial activities against multidrug-resistant respiratory and skin pathogens through its inhibition of the bacterial peptide deformylase. Here, we used next-generation sequencing (NGS) of the bacterial 16S rRNA genes from stool samples collected from 61 healthy volunteers at the predosing and end-of-study time points to determine the effects of GSK1322322 on the gastrointestinal (GI) microbiota in a phase I, randomized, double-blind, and placebo-controlled study. GSK1322322 was administered either intravenously (i.v.) only or in an oral-i.v. combination in single- and repeat-dose-escalation infusions. Analysis of the 16S rRNA sequence data found no significant changes in the relative abundances of GI operational taxonomic units (OTUs) between the prestudy and end-of-study samples for either the placebo- or i.v.-only-treated subjects. However, oral-i.v. treatment resulted in significant decreases in some bacterial taxa, the Firmicutes and Bacteroidales, and increases in others, the Betaproteobacteria, Gammaproteobacteria, and Bifidobacteriaceae. Microbiome diversity plots clearly differentiated the end-of-study oral-i.v.-dosed samples from all others collected. The changes in genome function as inferred from species composition suggest an increase in bacterial transporter and xenobiotic metabolism pathways in these samples. A phylogenetic analysis of the peptide deformylase protein sequences collected from the published genomes of clinical isolates previously tested for GSK1322322 in vitro susceptibility and GI bacterial reference genomes suggests that antibiotic target homology is one of several factors that influences the response of GI microbiota to this antibiotic. Our study shows that dosing regimen and target class are important factors when considering the impact of antibiotic usage on GI microbiota. (This clinical trial was registered at the GlaxoSmithKline Clinical Study Register under study identifier PDF 113376.).

Modeling stochasticity and variability in gene regulatory networks.

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.