Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.     

Androgen-dependent fibrinolytic activity in a murine mammary carcinoma (Shionogi SC-115 cells) in vitro.

Physiological concentrations of dihydrotestosterone (DHT) can specifically induce the release of fibrinolysin from androgen-dependent mammary carcinoma (Shionogi SC-115) cells in vitro. No fibrinolytic activity was observed in cells cultured either in the absence of DHT or presence of pharmacological concentrations of estrogen. Furthermore, an autonomous tumor derived from the Shionogi SC-115 cells produced fibrinolytic activity independent of added DHT or estrogen. These observations suggest a close correlation between fibrinolytic activity of a tumor and its ability to grow in vivo.     

The New Rga Locus Encodes a Negative Regulator of Gibberellin Response in Arabidopsis Thaliana

We have identified a new locus involved in gibberellin (GA) signal transduction by screening for suppressors of the Arabidopsis thaliana GA biosynthetic mutant ga1-3. The locus is named RGA for repressor of ga1-3. Based on the recessive phenotype of the digenic rga/ga1-3 mutant, the wild-type gene product of RGA is probably a negative regulator of GA responses. Our screen for suppressors of ga1-3 identified 17 mutant alleles of RGA as well as 10 new mutant alleles at the previously identified SPY locus. The digenic (double homozygous) rga/ga1-3 mutants are able to partially repress several defects of ga1-3 including stem growth, leaf abaxial trichome initiation, flowering time, and apical dominance. The phenotype of the trigenic mutant (triple homozygous) rga/spy/ga1-3 shows that rga and spy have additive effects regulating flowering time, abaxial leaf trichome initiation and apical dominance. This trigenic mutant is similar to wild type with respect to each of these developmental events. Because rga/spy/ga1-3 is almost insensitive to GA for hypocotyl growth and its bolting stem is taller than the wild-type plant, the combined effects of the rga and spy mutations appear to allow GA-independent stem growth. Our studies indicate that RGA lies on a separate branch of the GA signal transduction pathway from SPY, which leads us to propose a modified model of the GA response pathway.     

Cutting edge: lymphoproliferative disease in the absence of CTLA-4 is not T cell autonomous.

Mice deficient for the expression of CTLA-4 develop a lethal lymphoproliferative syndrome and multiorgan inflammation leading to death at about 4 wk of age. Here we show that RAG2-deficient mice reconstituted with CTLA-4-deficient bone marrow do not develop a lymphoproliferative syndrome despite lymphocyte infiltration mainly into pericardium and liver. Moreover, RAG2-deficient mice reconstituted with a mixture of normal and CTLA-4-deficient bone marrow remain healthy and do not develop any disease. Thus, the lethal disease observed in CTLA-4-deficient mice is not T cell autonomous and can be prevented by factors produced by normal T cells.     

Feasibility of using EMG driven neuromusculoskeletal model for prediction of dynamic movement of the elbow.

Neuromusculoskeletal (NMS) modeling is a valuable tool in orthopaedic biomechanics and motor control research. To evaluate the feasibility of using electromyographic (EMG) signals with NMS modeling to estimate individual muscle force during dynamic movement, an EMG driven NMS model of the elbow was developed. The model incorporates dynamical equation of motion of the forearm, musculoskeletal geometry and musculotendon modeling of four prime elbow flexors and three prime elbow extensors. It was first calibrated to two normal subjects by determining the subject-specific musculotendon parameters using computational optimization to minimize the root mean square difference between the predicted and measured maximum isometric flexion and extension torque at nine elbow positions (0-120 degrees of flexion with an increment of 15 degrees ). Once calibrated, the model was used to predict the elbow joint trajectories for three flexion/extension tasks by processing the EMG signals picked up by both surface and fine electrodes using two different EMG-to-activation processing schemes reported in the literature without involving any trajectory fitting procedures. It appeared that both schemes interpreted the EMG somewhat consistently but their prediction accuracy varied among testing protocols. In general, the model succeeded in predicting the elbow flexion trajectory in the moderate loading condition but over-drove the flexion trajectory under unloaded condition. The predicted trajectories of the elbow extension were noted to be continuous but the general shape did not fit very well with the measured one. Estimation of muscle activation based on EMG was believed to be the major source of uncertainty within the EMG driven model. It was especially so apparently when fine wire EMG signal is involved primarily. In spite of such limitation, we demonstrated the potential of using EMG driven neuromusculoskeletal modeling for non-invasive prediction of individual muscle forces during dynamic movement under certain conditions.