No abstract

Saliva is a readily accessible biofluid that is important for the overall health, aiding in the chewing, swallowing, and tasting of food as well as the regulation mouth flora. As a first step to determining and understanding the human saliva metabolome, we have measured salivary metabolite concentrations under a variety of conditions in a healthy population with reasonably good oral hygiene. Using (1)H NMR spectroscopy, metabolite concentrations were measured in resting (basal) and stimulated saliva from the same subject and compared in a cohort of healthy male non-smoking subjects (n = 62). Almost all metabolites were higher in the unstimulated saliva when compared to the stimulated saliva. Comparison of the salivary metabolite profile of male smokers and non-smokers (n = 46) revealed citrate, lactate, pyruvate, and sucrose to be higher and formate to be lower in concentration in smokers compared with non-smokers (p < 0.05). Gender differences were also investigated (n = 40), and acetate, formate, glycine, lactate, methanol, propionate, propylene glycol, pyruvate, succinate, and taurine were significantly higher in concentration in male saliva compared to female saliva (p < 0.05). These results show that differences between male and female, stimulated and unstimulated, as well as smoking status may be observed in the salivary metabolome.

BackgroundMicroRNAs (miRs) are small non‐coding RNAs that regulate gene (mRNA) expression. Although the pathological role of miRs have been studied in muscle wasting conditions such as myotonic and muscular dystrophy, their roles in cancer cachexia (CC) are still emerging.ObjectivesThe objectives are (i) to profile human skeletal muscle expressed miRs; (ii) to identify differentially expressed (DE) miRs between cachectic and non‐cachectic cancer patients; (iii) to identify mRNA targets for the DE miRs to gain mechanistic insights; and (iv) to investigate if miRs show potential prognostic and predictive value.MethodsStudy subjects were classified based on the international consensus diagnostic criteria for CC. Forty‐two cancer patients were included, of which 22 were cachectic cases and 20 were non‐cachectic cancer controls. Total RNA isolated from muscle biopsies were subjected to next‐generation sequencing.ResultsA total of 777 miRs were profiled, and 82 miRs with read counts of ≥5 in 80% of samples were retained for analysis. We identified eight DE miRs (up‐regulated, fold change of ≥1.4 at P < 0.05). A total of 191 potential mRNA targets were identified for the DE miRs using previously described human skeletal muscle mRNA expression data (n = 90), and a majority of them were also confirmed in an independent mRNA transcriptome dataset. Ingenuity pathway analysis identified pathways related to myogenesis and inflammation. qRT‐PCR analysis of representative miRs showed similar direction of effect (P < 0.05), as observed in next‐generation sequencing. The identified miRs also showed prognostic and predictive value.ConclusionsIn all, we identified eight novel miRs associated with CC.

IntroductionPancreatic and periampullary adenocarcinomas are associated with abnormal body composition visible on CT scans, including low muscle mass (sarcopenia) and low muscle radiodensity due to fat infiltration in muscle (myosteatosis). The biological and clinical correlates to these features are poorly understood.MethodsClinical characteristics and outcomes were studied in 123 patients who underwent pancreaticoduodenectomy for pancreatic or non-pancreatic periampullary adenocarcinoma and who had available preoperative CT scans. In a subgroup of patients with pancreatic cancer (n = 29), rectus abdominus muscle mRNA expression was determined by cDNA microarray and in another subgroup (n = 29) 1H-NMR spectroscopy and gas chromatography-mass spectrometry were used to characterize the serum metabolome.ResultsMuscle mass and radiodensity were not significantly correlated. Distinct groups were identified: sarcopenia (40.7%), myosteatosis (25.2%), both (11.4%). Fat distribution differed in these groups; sarcopenia associated with lower subcutaneous adipose tissue (P<0.0001) and myosteatosis associated with greater visceral adipose tissue (P<0.0001). Sarcopenia, myosteatosis and their combined presence associated with shorter survival, Log Rank P = 0.005, P = 0.06, and P = 0.002, respectively. In muscle, transcriptomic analysis suggested increased inflammation and decreased growth in sarcopenia and disrupted oxidative phosphorylation and lipid accumulation in myosteatosis. In the circulating metabolome, metabolites consistent with muscle catabolism associated with sarcopenia. Metabolites consistent with disordered carbohydrate metabolism were identified in both sarcopenia and myosteatosis.DiscussionMuscle phenotypes differ clinically and biologically. Because these muscle phenotypes are linked to poor survival, it will be imperative to delineate their pathophysiologic mechanisms, including whether they are driven by variable tumor biology or host response.

BackgroundCachexia affects the majority with advanced cancer. Based on current demographic and clinical factors, it is not possible to predict who will develop cachexia or not. Such variation may, in part, be due to genotype. It has recently been proposed to extend the diagnostic criteria for cachexia to include a direct measure of low skeletal muscle index (LSMI) in addition to weight loss (WL). We aimed to explore our panel of candidate single nucleotide polymorphism (SNPs) for association with WL +/− computerized tomography‐defined LSMI. We also explored whether the transcription in muscle of identified genes was altered according to such cachexia phenotypeMethodsA retrospective cohort study design was used. Analysis explored associations of candidate SNPs with WL (n = 1276) and WL + LSMI (n = 943). Human muscle transcriptome (n = 134) was analysed using an Agilent platform.ResultsSingle nucleotide polymorphisms in the following genes showed association with WL alone: GCKR, LEPR, SELP, ACVR2B, TLR4, FOXO3, IGF1, CPN1, APOE, FOXO1, and GHRL. SNPs in LEPR, ACVR2B, TNF, and ACE were associated with concurrent WL + LSMI. There was concordance between muscle‐specific expression for ACVR2B, FOXO1 and 3, LEPR, GCKR, and TLR4 genes and LSMI and/or WL (P < 0.05).ConclusionsThe rs1799964 in the TNF gene and rs4291 in the ACE gene are new associations when the definition of cachexia is based on a combination of WL and LSMI. These findings focus attention on pro‐inflammatory cytokines and the renin–angiotensin system as biomarkers/mediators of muscle wasting in cachexia.

Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5♂, 5♀) to n = 120 (60♂, 60♀)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134), we identified 717 differentially expressed transcripts (p<0.0001) and we were able predict sex with ∼90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5♂, 5♀), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ∼50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex.

Cancer-associated muscle wasting is associated with reduction in functional status, in response to treatment and in life expectancy. Methods currently used to assess muscle loss involve diagnostic imaging techniques such as computed tomography (CT), which are costly, inconvenient, invasive, time consuming and have limited ability to detect early or slowly evolving wasting. We present a novel approach using single time-point urinary metabolite profiles to determine whether a patient is experiencing muscle wasting. We analyzed 93 random urine samples from patients with cancer using 1 H-NMR. Using two successive CT images we assessed their lumbar skeletal muscle area (cm 2 ) to estimate the rate of muscle change (% loss or gain over time) for each patient. The average muscle change over time was -4.71%/100 days in the muscle-losing group and ?3.91%/100 days in the comparator group. Bivariate statistics identified metabolites related with muscle loss, including constituents and metabolites of muscle (creatine, creatinine, 3-OH-isovalerate), amino acids (Leu, Ile, Val, Ala, Thr, Tyr, Gln, Ser) and intermediary metabolites. We also applied machinelearning techniques to identify patterns of urinary metabolites that identify which patients are likely to lose muscle mass. We evaluated the predictive performance of 8 machine-learning approaches using fivefold cross validation and permutation testing, and found that SVM provided the best generalization accuracy (82.2%). These results suggest that 1 H-NMR analysis of a single random urine sample may be a fast, cheap, safe and inexpensive tool to screen and monitor muscle loss, and that useful classifiers for predicting related metabolic conditions are possible with the methodology presented.

Cancer cachexia is a life-threatening syndrome that affects most patients with advanced cancers and causes severe body weight loss, with rapid depletion of skeletal muscle. No treatment is available. We analyzed microarray data sets to identify a subset of genes whose expression is specifically altered in cachectic muscles of Yoshida hepatoma-bearing rodents but not in those with diabetes, disuse, uremia or fasting. Ingenuity Pathways Analysis indicated that three genes belonging to the C-X-C motif chemokine receptor 4 (CXCR4) pathway were downregulated only in muscles atrophying because of cancer: stromal cell-derived factor 1 (SDF1), adenylate cyclase 7 (ADCY7), and p21 protein-activated kinase 1 (PAK1). Notably, we found that, in the Rectus Abdominis muscle of cancer patients, the expression of SDF1 and CXCR4 was inversely correlated with that of two ubiquitin ligases induced in muscle wasting, atrogin-1 and MuRF1, suggesting a possible clinical relevance of this pathway. The expression of all main SDF1 isoforms (α, β, γ) also declined in Tibialis Anterior muscle from cachectic mice bearing murine colon adenocarcinoma or human renal cancer and drugs with anticachexia properties restored their expression. Overexpressing genes of this pathway (that is, SDF1 or CXCR4) in cachectic muscles increased the fiber area by 20%, protecting them from wasting. Similarly, atrophying myotubes treated with either SDF1α or SDF1β had greater total protein content, resulting from reduced degradation of overall long-lived proteins. However, inhibiting CXCR4 signaling with the antagonist AMD3100 did not affect protein homeostasis in atrophying myotubes, whereas normal myotubes treated with AMD3100 showed time- and dose-dependent reductions in diameter, until a plateau, and lower total protein content. This further confirms the involvement of a saturable pathway (that is, CXCR4). Overall, these findings support the idea that activating the CXCR4 pathway in muscle suppresses the deleterious wasting associated with cancer.