Cancers reprogram their metabolism to adapt to environmental changes. In this study, we examined the consequences of altered expression of the mitochondrial enzyme carnitine palmitoyl transferase I (CPT1A) in prostate cancer (PCa) cell models. Using transcriptomic and metabolomic analyses, we compared LNCaP-C4-2 cell lines with depleted (knockdown (KD)) or increased (overexpression (OE)) CPT1A expression. Mitochondrial reactive oxygen species (ROS) were also measured. Transcriptomic analysis identified ER stress, serine biosynthesis and lipid catabolism as significantly upregulated pathways in the OE versus KD cells. On the other hand, androgen response was significantly downregulated in OE cells. These changes associated with increased acyl-carnitines, serine synthesis and glutathione precursors in OE cells. Unexpectedly, OE cells showed increased mitochondrial ROS but when challenged with fatty acids and no androgens, the Superoxide dismutase 2 (SOD2) enzyme increased in the OE cells, suggesting better antioxidant defenses with excess CPT1A expression. Public databases also showed decreased androgen response correlation with increased serine-related metabolism in advanced PCa. Lastly, worse progression free survival was observed with increased lipid catabolism and decreased androgen response. Excess CPT1A is associated with a ROS-mediated stress phenotype that can support PCa disease progression. This study provides a rationale for targeting lipid catabolic pathways for therapy in hormonal cancers.

Lipid catabolism represents an Achilles heel in prostate cancer (PCa) that can be exploited for therapy. CPT1A regulates the entry of fatty acids into the mitochondria for beta-oxidation and its inhibition has been shown to decrease PCa growth. In this study, we examined the pharmacological blockade of lipid oxidation with ranolazine in TRAMPC1 PCa models. Oral administration of ranolazine (100 mg/Kg for 21 days) resulted in decreased tumor CD8⁺ T-cells Tim3 content, increased macrophages, and decreased blood myeloid immunosuppressive monocytes. Using multispectral staining, drug treatments increased infiltration of CD8⁺ T-cells and dendritic cells compared to vehicle. Functional studies with spleen cells of drug-treated tumors co-cultured with TRAMPC1 cells showed increased ex vivo T-cell cytotoxic activity, suggesting an anti-tumoral response. Lastly, a decrease in CD4⁺ and CD8⁺ T-cells expressing PD1 was observed when exhausted spleen cells were incubated with TRAMPC1 Cpt1a-KD compared to the control cells. These data indicated that genetically blocking the ability of the tumor cells to oxidize lipid can change the activation status of the neighboring T-cells. This study provides new knowledge of the role of lipid catabolism in the intercommunication of tumor and immune cells, which can be extrapolated to other cancers with high CPT1A expression.

As regional climates continue to warm, climate-sensitive species might increasingly depend on microclimates to maintain normal activities. The American pika (Ochotona princeps) has been suggested as a sentinel of montane climate change, largely due to its sensitivity to warm temperatures and high exposure to changing climatic conditions. Pikas use talus to thermoregulate and have been shown to persist more often in talus slopes that create stable microclimates. However, many studies of pika behavior and persistence use metrics of climate measured above the talus surface, outside of the proposed microclimates that pikas can access. We paired fine-scale microclimate measurements with behavioral observations of uniquely tagged pikas in the Colorado Rocky Mountains to model how pikas might use talus subsurface habitat as a microrefuge. Our findings suggest that pikas spend more time on the surface of the talus when there is a stronger gradient in temperatures between “shallow” (0.1 m below the surface) and “deep” (1 m below the surface) regions of the talus, especially during the middle of the day when shallow temperatures are warmer. These results underscore the importance of pairing fine-scale data on microclimate with behavioral observations of tagged individuals to more accurately evaluate how animals might use climate refugia to persist in changing conditions.

Climate change in mountain regions has exposed high-elevation species to rapidly changing temperatures. Although climate exposure can be reduced in certain microclimates, the quality of microclimatic refugia might also degrade with climate change. The American pika (Ochotona princeps) often inhabits high elevations, and is considered climate-sensitive due to its narrow thermal tolerance and recent extirpations in some warmer portions of its range. Pikas behaviorally thermoregulate by taking refuge in the subsurface microclimates found in taluses and other rocky habitats, where daily thermal fluctuations are attenuated and somewhat decoupled from free-air temperatures. Changes in microclimate might reduce the efficacy of this behavioral thermoregulation. This study compares recent (2009–2021) subsurface temperatures at a long-term pika study site with a rare instance of historical (1963–1964) data from the same location. We also place historical and recent microclimates in context using long-term data on free-air temperatures from the same area. Recent free-air temperatures were often warmer than historical records, and subsurface temperatures exhibited even stronger warming between periods. Temperatures measured in the talus were often dramatically warmer in recent records, especially at the deeper of two subsurface sensor placements in this study. Winter months showed the greatest changes in both talus and free-air temperatures. Differences between historical and recent microclimates were not explained by the precise placement of sensors, as recent temperatures were similar across a wide variety of subsurface placements, and temporal changes in free-air temperatures at the historical study site were also reflected in data from nearby weather stations. Together, these results suggest that subsurface microclimates important for pika thermoregulation have changed over the past few decades, perhaps even faster than observed changes in free-air temperatures. The generality of these results and their potential ramifications for ecosystem processes and services should be explored.

Motivation High-dimension cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with potential for biological relevance being overlooked. Here we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. Design PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes, and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments such as identifying parent populations of interest, filtering of low-frequency populations, and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with local computer or server-based use. Results In testing of PhenoComb’s performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimension, single-cell data. Availability The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb

High-dimensional cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with the potential for biological relevance being overlooked. Here, we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments, such as identifying parent populations of interest, filtering of low-frequency populations and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with a local computer or server-based use. In testing of PhenoComb’s performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimensional single-cell data. The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb. Supplementary data are available at Bioinformatics Advances online.