Identification of sequence changes in myosin II that adjust muscle contraction velocity.

The speed of muscle contraction is related to body size; muscles in larger species contract at slower rates. Since contraction speed is a property of the myosin isoform expressed in a muscle, we investigated how sequence changes in a range of muscle myosin II isoforms enable this slower rate of muscle contraction. We considered 798 sequences from 13 mammalian myosin II isoforms to identify any adaptation to increasing body mass. We identified a correlation between body mass and sequence divergence for the motor domain of the 4 major adult myosin II isoforms (β/Type I, IIa, IIb, and IIx), suggesting that these isoforms have adapted to increasing body mass. In contrast, the non-muscle and developmental isoforms show no correlation of sequence divergence with body mass. Analysis of the motor domain sequence of β-myosin (predominant myosin in Type I/slow and cardiac muscle) from 67 mammals from 2 distinct clades identifies 16 sites, out of 800, associated with body mass (padj 0.05). Both clades change the same small set of amino acids, in the same order from small to large mammals, suggesting a limited number of ways in which contraction velocity can be successfully manipulated. To test this relationship, the 9 sites that differ between human and rat were mutated in the human β-myosin to match the rat sequence. Biochemical analysis revealed that the rat-human β-myosin chimera functioned like the native rat myosin with a 2-fold increase in both motility and in the rate of ADP release from the actin-myosin crossbridge (the step that limits contraction velocity). Thus, these sequence changes indicate adaptation of β-myosin as species mass increased to enable a reduced contraction velocity and heart rate

Branched-Chain Amino Acids and Risk of Breast Cancer

Background Circulating branched-chain amino acid (BCAA) levels reflect metabolic health and dietary intake. However, associations with breast cancer are unclear. Methods We evaluated circulating BCAA levels and breast cancer risk within the Nurses’ Health Study (NHS) and NHSII (1997 cases and 1997 controls). A total of 592 NHS women donated 2 blood samples 10 years apart. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer risk in multivariable logistic regression models. We conducted an external validation in 1765 cases in the Women’s Health Study (WHS). All statistical tests were 2-sided. Results Among NHSII participants (predominantly premenopausal at blood collection), elevated circulating BCAA levels were associated with lower breast cancer risk (eg, isoleucine highest vs lowest quartile, multivariable OR = 0.86, 95% CI = 0.65 to 1.13, Ptrend = .20), with statistically significant linear trends among fasting samples (eg, isoleucine OR = 0.74, 95% CI = 0.53 to 1.05, Ptrend = .05). In contrast, among postmenopausal women, proximate measures (\u3c10 years from blood draw) were associated with increased breast cancer risk (eg, isoleucine OR = 1.63, 95% CI = 1.12 to 2.39, Ptrend = .01), with stronger associations among fasting samples (OR = 1.73, 95% CI = 1.15 to 2.61, Ptrend = .01). Distant measures (10-20 years since blood draw) were not associated with risk. In the WHS, a positive association was observed for distant measures of leucine among postmenopausal women (OR = 1.23, 95% CI = 0.96 to 1.58, Ptrend = .04). Conclusions No statistically significant associations between BCAA levels and breast cancer risk were consistent across NHS and WHS or NHSII and WHS. Elevated circulating BCAA levels were associated with lower breast cancer risk among predominantly premenopausal NHSII women and higher risk among postmenopausal women in NHS but not in the WHS. Additional studies are needed to understand this complex relationship

Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis

Objective: To identify prediagnostic plasma metabolomic biomarkers associated with amyotrophic lateral sclerosis (ALS). Methods: We conducted a global metabolomic study using a nested case-control study design within 5 prospective cohorts and identified 275 individuals who developed ALS during follow-up. We profiled plasma metabolites using liquid chromatography–mass spectrometry and identified 404 known metabolites. We used conditional logistic regression to evaluate the associations between metabolites and ALS risk. Further, we used machine learning analyses to determine whether the prediagnostic metabolomic profile could discriminate ALS cases from controls. Results: A total of 31 out of 404 identified metabolites were associated with ALS risk (p < 0.05). We observed inverse associations (n = 27) with plasma levels of diacylglycerides and triacylglycerides, urate, purine nucleosides, and some organic acids and derivatives, while we found positive associations for a cholesteryl ester, 2 phosphatidylcholines, and a sphingomyelin. The number of significant associations increased to 67 (63 inverse) in analyses restricted to cases with blood samples collected within 5 years of onset. None of these associations remained significant after multiple comparison adjustment. Further, we were not able to reliably distinguish individuals who became cases from controls based on their metabolomic profile using partial least squares discriminant analysis, elastic net regression, random forest, support vector machine, or weighted correlation network analyses. Conclusions: Although the metabolomic profile in blood samples collected years before ALS diagnosis did not reliably separate presymptomatic ALS cases from controls, our results suggest that ALS is preceded by a broad, but poorly defined, metabolic dysregulation years before the disease onset

Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass

Ammonia is a ubiquitous by-product of cellular metabolism; however, the biological consequences of ammonia production are not fully understood, especially in cancer. We found that ammonia is not merely a toxic waste product but is recycled into central amino acid metabolism to maximize nitrogen utilization. In our experiments, human breast cancer cells primarily assimilated ammonia through reductive amination catalyzed by glutamate dehydrogenase (GDH); secondary reactions enabled other amino acids, such as proline and aspartate, to directly acquire this nitrogen. Metabolic recycling of ammonia accelerated proliferation of breast cancer. In mice, ammonia accumulated in the tumor microenvironment and was used directly to generate amino acids through GDH activity. These data show that ammonia is not only a secreted waste product but also a fundamental nitrogen source that can support tumor biomass

Circulating Amino Acids and Amino Acid-Related Metabolites and Risk of Breast Cancer Among Predominantly Premenopausal Women

Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses\u27 Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value \u3c 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value \u3c 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value \u3c 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses&rsquo; Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups

Selective transcriptomic dysregulation of metabolic pathways in liver and retina by short- and long-term dietary hyperglycemia

Summary: A high glycemic index (HGI) diet induces hyperglycemia, a risk factor for diseases affecting multiple organ systems. Here, we evaluated tissue-specific adaptations in the liver and retina after feeding HGI diet to mice for 1 or 12 month. In the liver, genes associated with inflammation and fatty acid metabolism were altered within 1 month of HGI diet, whereas 12-month HGI diet-fed group showed dysregulated expression of cytochrome P450 genes and overexpression of lipogenic factors including Srebf1 and Elovl5. In contrast, retinal transcriptome exhibited HGI-related notable alterations in energy metabolism genes only after 12 months. Liver fatty acid profiles in HGI group revealed higher levels of monounsaturated and lower levels of saturated and polyunsaturated fatty acids. Additionally, HGI diet increased blood low-density lipoprotein, and diet-aging interactions affected expression of mitochondrial oxidative phosphorylation genes in the liver and disease-associated genes in retina. Thus, our findings provide new insights into retinal and hepatic adaptive mechanisms to dietary hyperglycemia

Prediagnostic plasma branched-chain amino acids and the risk of amyotrophic lateral sclerosis

Objective: To assess whether prediagnostic levels of plasma branched-chain amino acids (BCAAs) are associated with amyotrophic lateral sclerosis (ALS) risk. Methods: We included participants from 5 large cohort studies—The Nurses' Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition, the Multiethnic Cohort Study, and the Women's Health Initiative—and identified 275 individuals who developed ALS during follow-up. Two controls were randomly selected for each case, matched on cohort, age, sex, fasting status, and time of blood draw. We measured metabolites using liquid chromatography–mass spectrometry and used conditional logistic regression to estimate rate ratios (RRs) and 95% confidence intervals (CIs) for the association of individual BCAAs with ALS risk. Results: None of the 3 BCAAs was associated with a higher ALS risk. The risk estimates were similar for leucine (RR top vs bottom quartile: 0.87, 95% CI 0.57–1.33), isoleucine (RR top vs bottom quartile: 0.81, 95% CI 0.52–1.24), and valine (RR top vs bottom quartile: 0.80, 95% CI 0.52–1.23) in a multivariable analysis adjusted for body mass index, smoking, level of education, and physical activity. The estimates did not vary significantly by sex, fasting status, or time interval between blood draw and disease onset. Conclusion: The results from this study do not support the hypothesis that BCAAs are risk factors for ALS

Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes

Unbiased, “nontargeted” metabolite profiling techniques hold considerable promise for biomarker and pathway discovery, in spite of the lack of successful applications to human disease. By integrating nontargeted metabolomics, genetics, and detailed human phenotyping, we identified dimethylguanidino valeric acid (DMGV) as an independent biomarker of CT-defined nonalcoholic fatty liver disease (NAFLD) in the offspring cohort of the Framingham Heart Study (FHS) participants. We verified the relationship between DMGV and early hepatic pathology. Specifically, plasma DMGV levels were correlated with biopsy-proven nonalcoholic steatohepatitis (NASH) in a hospital cohort of individuals undergoing gastric bypass surgery, and DMGV levels fell in parallel with improvements in post-procedure cardiometabolic parameters. Further, baseline DMGV levels independently predicted future diabetes up to 12 years before disease onset in 3 distinct human cohorts. Finally, we provide all metabolite peak data consisting of known and unidentified peaks, genetics, and key metabolic parameters as a publicly available resource for investigations in cardiometabolic diseases