Cancer cachexia is a syndrome of weight loss that results from the selective depletion of skeletal muscle mass and contributes significantly to cancer morbidity and mortality. The driver of skeletal muscle atrophy in cancer cachexia is systemic inflammation arising from both the cancer and cancer treatment. While the importance of tumor derived inflammation is well described, the mechanism by which cytotoxic chemotherapy contributes to cancer cachexia is relatively unexplored. We found that the administration of chemotherapy to mice produces a rapid inflammatory response. This drives activation of the hypothalamic-pituitary-adrenal axis, which increases the circulating level of corticosterone, the predominant endogenous glucocorticoid in rodents. Additionally, chemotherapy administration results in a significant loss of skeletal muscle mass 18 hours after administration with a concurrent induction of genes involved with the ubiquitin proteasome and autophagy lysosome systems. However, in mice lacking glucocorticoid receptor expression in skeletal muscle, chemotherapy-induced muscle atrophy is completely blocked. This demonstrates that cytotoxic chemotherapy elicits significant muscle atrophy driven by the production of endogenous glucocorticoids. Further, it argues that pharmacotherapy targeting the glucocorticoid receptor, given in concert with chemotherapy, is a viable therapeutic strategy in the treatment of cancer cachexia.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

To begin to understand the contributions of maternal obesity and over-nutrition to human development and the early origins of obesity, we utilized a non-human primate model to investigate the effects of maternal high-fat feeding and obesity on breast milk, maternal and fetal plasma fatty acid composition and fetal hepatic development. While the high-fat diet (HFD) contained equivalent levels of n-3 fatty acids (FA's) and higher levels of n-6 FA's than the control diet (CTR), we found significant decreases in docosahexaenoic acid (DHA) and total n-3 FA's in HFD maternal and fetal plasma. Furthermore, the HFD fetal plasma n-6:n-3 ratio was elevated and was significantly correlated to the maternal plasma n-6:n-3 ratio and maternal hyperinsulinemia. Hepatic apoptosis was also increased in the HFD fetal liver. Switching HFD females to a CTR diet during a subsequent pregnancy normalized fetal DHA, n-3 FA's and fetal hepatic apoptosis to CTR levels. Breast milk from HFD dams contained lower levels of eicosopentanoic acid (EPA) and DHA and lower levels of total protein than CTR breast milk. This study links chronic maternal consumption of a HFD with fetal hepatic apoptosis and suggests that a potentially pathological maternal fatty acid milieu is replicated in the developing fetal circulation in the nonhuman primate.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

The variable predisposition to cachexia may, in part, be due to the interaction of host genotype. We analyzed 129 single nucleotide polymorphisms (SNPs) in 80 genes for association with cachexia based on degree of weight loss (>5, >10, >15%) as well as weight loss in the presence of systemic inflammation (C-reactive protein, > 10 mg/l). 775 cancer patients were studied with a validation association study performed on an independently recruited cohort (n = 101) of cancer patients. The C allele (minor allele frequency 10.7%) of the rs6136 (SELP) SNP was found to be associated with weight loss >10% both in the discovery study (odds ratio (OR) 0.52; 95% confidence intervals (CI), 0.29-0.93; p = 0.026) and the validation study (OR 0.09, 95% CI 0.01-0.98, p = 0.035). In separate studies, induction of muscle atrophy gene expression was investigated using qPCR following either tumour-induced cachexia in rats or intra-peritoneal injection of lipopolysaccharide in mice. P-selectin was found to be significantly upregulated in muscle in both models. Identification of P-selectin as relevant in both animal models and in cachectic cancer patients supports this as a risk factor/potential mediator in cachexia.

Signaling via the type 4-melanocortin receptor (MC4R) is an important determinant of body weight in mice and humans, where loss of function mutations lead to significant obesity. Humans with mutations in the MC4R experience an increase in lean mass. However, the simultaneous accrual of fat mass in such individuals may contribute to this effect via mechanical loading. We therefore examined the relationship of fat mass and lean mass in mice lacking the type-4 melanocortin receptor (MC4RKO). We demonstrate that MC4RKO mice display increased lean body mass. Further, this is not dependent on changes in adipose mass, as MC4RKO mice possess more lean body mass than diet-induced obese (DIO) wild type mice with equivalent fat mass. To examine potential sources of the increased lean mass in MC4RKO mice, bone mass and strength were examined in MC4RKO mice. Both parameters increase with age in MC4RKO mice, which likely contributes to increases in lean body mass. We functionally characterized the increased lean mass in MC4RKO mice by examining their capacity for treadmill running. MC4R deficiency results in a decrease in exercise performance. No changes in the ratio of oxidative to glycolytic fibers were seen, however MC4RKO mice demonstrate a significantly reduced heart rate, which may underlie their impaired exercise performance. The reduced exercise capacity we report in the MC4RKO mouse has potential clinical ramifications, as efforts to control body weight in humans with melanocortin deficiency may be ineffective due to poor tolerance for physical activity.

Acute Myeloid Leukemia (AML) develops due to the acquisition of mutations from multiple functional classes. Here, we demonstrate that activating mutations in the granulocyte colony stimulating factor receptor (CSF3R), cooperate with loss of function mutations in the transcription factor CEBPA to promote acute leukemia development. The interaction between these distinct classes of mutations occurs at the level of myeloid lineage enhancers where mutant CEBPA prevents activation of a subset of differentiation associated enhancers. To confirm this enhancer-dependent mechanism, we demonstrate that CEBPA mutations must occur as the initial event in AML initiation. This improved mechanistic understanding will facilitate therapeutic development targeting the intersection of oncogene cooperativity.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Up to 80% of pancreatic cancer patients suffer from cachexia, a devastating condition that exacerbates underlying disease, reduces quality of life, and increases treatment complications and mortality. Tumour-induced inflammation is linked to this multifactorial wasting syndrome, but mechanisms and effective treatments remain elusive. Myeloid differentiation factor (MyD88), a key component of the innate immune system, plays a pivotal role in directing the inflammatory response to various insults. In this study, we tested whether MyD88 signalling is essential in the development of pancreatic cancer cachexia using a robust mouse tumour model.

Animals respond to inflammation by suppressing normal high-energy activities, including feeding and locomotion, in favor of diverting resources to the immune response. The cytokine interleukin-1 beta (IL-1β) inhibits normal feeding and locomotor activity (LMA) via its actions in the central nervous system (CNS). Behavioral changes in response to IL-1β are mediated by myeloid differentiation factor 88 (MyD88) in non-hematopoietic cells. It is unknown whether IL-1β acts directly on neurons or requires transduction by non-neuronal cells.

Skeletal muscle catabolism is a co-morbidity of many chronic diseases and is the result of systemic inflammation. Although direct inflammatory cytokine action on muscle promotes atrophy, nonmuscle sites of action for inflammatory mediators are less well described. We demonstrate that central nervous system (CNS)-delimited interleukin 1β (IL-1β) signaling alone can evoke a catabolic program in muscle, rapidly inducing atrophy. This effect is dependent on hypothalamic-pituitary-adrenal (HPA) axis activation, as CNS IL-1β-induced atrophy is abrogated by adrenalectomy. Furthermore, we identified a glucocorticoid-responsive gene expression pattern conserved in models of acute and chronic inflammatory muscle atrophy. In contrast with studies suggesting that the direct action of inflammatory cytokines on muscle is sufficient to induce catabolism, adrenalectomy also blocks the atrophy program in response to systemic inflammation, demonstrating that glucocorticoids are requisite for this process. Additionally, circulating levels of glucocorticoids equivalent to those produced under inflammatory conditions are sufficient to cause profound muscle wasting. Together, these data suggest that a significant component of inflammation-induced muscle catabolism occurs indirectly via a relay in the CNS.

Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.