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Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of \"sentinel genes\" adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.

Exposure of rats to kidney toxicants reduces levels of urinary trefoil factor 3 (TFF3) and increases levels of urinary albumin. Whereas urinary albumin outperforms either serum creatinine (SCr) or blood urea nitrogen (BUN) for detecting kidney tubule damage, urinary TFF3 abundance complements the capacity of combined SCr and BUN levels to detect renal injury. The capacities of urinary trefoil factor 3 (TFF3) and urinary albumin to detect acute renal tubular injury have never been evaluated with sufficient statistical rigor to permit their use in regulated drug development instead of the current preclinical biomarkers serum creatinine (SCr) and blood urea nitrogen (BUN). Working with rats, we found that urinary TFF3 protein levels were markedly reduced, and urinary albumin were markedly increased in response to renal tubular injury. Urinary TFF3 levels did not respond to nonrenal toxicants, and urinary albumin faithfully reflected alterations in renal function. In situ hybridization localized TFF3 expression in tubules of the outer stripe of the outer medulla. Albumin outperformed either SCr or BUN for detecting kidney tubule injury and TFF3 augmented the potential of BUN and SCr to detect kidney damage. Use of urinary TFF3 and albumin will enable more sensitive and robust diagnosis of acute renal tubular injury than traditional biomarkers.

A panel of urinary biomarkers enables the progression of renal injury and subsequent repair and recovery to be monitored after exposure of rats to either carbapenem A or gentamicin. The authors complement this study by demonstrating that serum cystatin C is more sensitive and specific than serum creatinine and blood urea nitrogen in monitoring generalized renal function after exposure to nephrotoxicants. The Predictive Safety Testing Consortium's first regulatory submission to qualify kidney safety biomarkers revealed two deficiencies. To address the need for biomarkers that monitor recovery from agent-induced renal damage, we scored changes in the levels of urinary biomarkers in rats during recovery from renal injury induced by exposure to carbapenem A or gentamicin. All biomarkers responded to histologic tubular toxicities to varied degrees and with different kinetics. After a recovery period, all biomarkers returned to levels approaching those observed in uninjured animals. We next addressed the need for a serum biomarker that reflects general kidney function regardless of the exact site of renal injury. Our assay for serum cystatin C is more sensitive and specific than serum creatinine (SCr) or blood urea nitrogen (BUN) in monitoring generalized renal function after exposure of rats to eight nephrotoxicants and two hepatotoxicants. This sensitive serum biomarker will enable testing of renal function in animal studies that do not involve urine collection.

DNA microarrays are an integral part of the process for therapeutic discovery, optimization and clinical validation. At an early stage, investigators use arrays to prioritize a few genes as potential therapeutic targets on the basis of various criteria. Subsequently, gene expression analysis assists in drug discovery and toxicology by eliminating poor compounds and optimizing the selection of promising leads. Integral to this process is the use of sophisticated statistics, mathematics and bioinformatics to define statistically valid observations and to deduce complex patterns of phenotypes and biological pathways. In short, microarrays are redefining the drug discovery process by providing greater knowledge at each step and by illuminating the complex workings of biological systems.

The first formal qualification of safety biomarkers for regulatory decision making marks a milestone in the application of biomarkers to drug development. Following submission of drug toxicity studies and analyses of biomarker performance to the Food and Drug Administration (FDA) and European Medicines Agency (EMEA) by the Predictive Safety Testing Consortium's (PSTC) Nephrotoxicity Working Group, seven renal safety biomarkers have been qualified for limited use in nonclinical and clinical drug development to help guide safety assessments. This was a pilot process, and the experience gained will both facilitate better understanding of how the qualification process will probably evolve and clarify the minimal requirements necessary to evaluate the performance of biomarkers of organ injury within specific contexts.

Infection by the new corona virus strain SARS-CoV-2 and its related syndrome COVID-19 has been associated with more than two million deaths worldwide. Patients of higher age and with preexisting chronic health conditions are at an increased risk of fatal disease outcome. However, detailed information on causes of death and the contribution of pre-existing health conditions to death yet is missing, which can be reliably established by autopsy only. We performed full body autopsies on 26 patients that had died after SARS-CoV-2 infection and COVID-19 at the Charité University Hospital Berlin, Germany, or at associated teaching hospitals. We systematically evaluated causes of death and pre-existing health conditions. Additionally, clinical records and death certificates were evaluated. We report findings on causes of death and comorbidities of 26 decedents that had clinically presented with severe COVID-19. We found that septic shock and multi organ failure was the most common immediate cause of death, often due to suppurative pulmonary infection. Respiratory failure due to diffuse alveolar damage presented as immediate cause of death in fewer cases. Several comorbidities, such as hypertension, ischemic heart disease, and obesity were present in the vast majority of patients. Our findings reveal that causes of death were directly related to COVID-19 in the majority of decedents, while they appear not to be an immediate result of preexisting health conditions and comorbidities. We therefore suggest that the majority of patients had died of COVID-19 with only contributory implications of preexisting health conditions to the mechanism of death.