Motivation: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. Results: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. Availability: Source code written in R is available from Contact:  hongyu.zhao@yale.edu Supplementary Information: Supplementary Data are available at

This study, designed and conducted as part of the International Life Sciences Institute working group on the Application of Genomics and Proteomics, examined the changes in the expression profile of genes associated with the administration of three different nephrotoxicants--cisplatin, gentamicin, and puromycin--to assess the usefulness of microarrays in the understanding of mechanism(s) of nephrotoxicity. Male Sprague-Dawley rats were treated with daily doses of puromycin (5-20 mg/kg/day for 21 days), gentamicin (2-240 mg/kg/day for 7 days), or a single dose of cisplatin (0.1-5 mg/kg). Groups of rats were sacrificed at various times after administration of these compounds for standard clinical chemistry, urine analysis, and histological evaluation of the kidney. RNA was extracted from the kidney for microarray analysis. Principal component analysis and gene expression-based clustering of compound effects confirmed sample separation based on dose, time, and degree of renal toxicity. In addition, analysis of the profile components revealed some novel changes in the expression of genes that appeared to be associated with injury in specific portions of the nephron and reflected the mechanism of action of these various nephrotoxicants. For example, although puromycin is thought to specifically promote injury of the podocytes in the glomerulus, the changes in gene expression after chronic exposure of this compound suggested a pattern similar to the known proximal tubular nephrotoxicants cisplatin and gentamicin; this prediction was confirmed histologically. We conclude that renal gene expression profiling coupled with analysis of classical end points affords promising opportunities to reveal potential new mechanistic markers of renal toxicity.

Macrophage colony-stimulating factor (M-CSF) is a hematopoietic growth factor that is responsible for the survival and proliferation of monocytes and the differentiation of monocytes into macrophages, including Kupffer cells (KCs) in the liver. KCs play an important role in the clearance of several serum enzymes, including aspartate aminotransferase and creatine kinase, that are typically elevated as a result of liver or skeletal muscle injury. We used three distinct animal models to investigate the hypothesis that increases in the levels of serum enzymes can be the result of decreases in KCs in the apparent absence of hepatic or skeletal muscle injury. Specifically, neutralizing M-CSF activity via a novel human monoclonal antibody reduced the CD14 ؉ CD16 ؉ monocyte population, depleted KCs, and increased aspartate aminotransferase and creatine kinase serum enzyme levels in cynomolgus macaques. In addition, the treatment of rats with clodronate liposomes depleted KCs and led to increased serum enzyme levels, again without evidence of tissue injury. Finally, in the osteopetrotic (Csf1 op /Csf1 op ) mice lacking functional M-CSF and having reduced levels of KCs, the levels of serum enzymes are higher than in wild-type littermates. Together, these findings support a mechanism for increases in serum enzyme levels through M-CSF regulation of tissue macrophage homeostasis without concomitant histopathological changes in either the hepatic or skeletal system.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.