A population‐based temporal logic gate for timing and recording chemical events

Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.     

Post-Prandial Protein Handling: You Are What You Just Ate

Background

Protein turnover in skeletal muscle tissue is highly responsive to nutrient intake in healthy adults.

Objective

To provide a comprehensive overview of post-prandial protein handling, ranging from dietary protein digestion and amino acid absorption, the uptake of dietary protein derived amino acids over the leg, the post-prandial stimulation of muscle protein synthesis rates, to the incorporation of dietary protein derived amino acids in de novo muscle protein.

Design

12 healthy young males ingested 20 g intrinsically [1-¹³C]-phenylalanine labeled protein. In addition, primed continuous L-[ring-²H₅]-phenylalanine, L-[ring-²H₂]-tyrosine, and L-[1-¹³C]-leucine infusions were applied, with frequent collection of arterial and venous blood samples, and muscle biopsies throughout a 5 h post-prandial period. Dietary protein digestion, amino acid absorption, splanchnic amino acid extraction, amino acid uptake over the leg, and subsequent muscle protein synthesis were measured within a single in vivo human experiment.

Results

55.3±2.7% of the protein-derived phenylalanine was released in the circulation during the 5 h post-prandial period. The post-prandial rise in plasma essential amino acid availability improved leg muscle protein balance (from -291±72 to 103±66 μM·min^-1·100 mL leg volume^-1; P<0.001). Muscle protein synthesis rates increased significantly following protein ingestion (0.029±0.002 vs 0.044±0.004%·h^-1 based upon the muscle protein bound L-[ring-²H₅]-phenylalanine enrichments (P<0.01)), with substantial incorporation of dietary protein derived L-[1-¹³C]-phenylalanine into de novo muscle protein (from 0 to 0.0201±0.0025 MPE).

Conclusion

Ingestion of a single meal-like amount of protein allows ~55% of the protein derived amino acids to become available in the circulation, thereby improving whole-body and leg protein balance. About 20% of the dietary protein derived amino acids released in the circulation are taken up in skeletal muscle tissue following protein ingestion, thereby stimulating muscle protein synthesis rates and providing precursors for de novo muscle protein synthesis.

Trial Registration

trialregister.nl 3638     

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Background

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.

Objective

To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.

Design

Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.

Results

Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.

Conclusions

A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.

Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00390637","term_id":"NCT00390637"}}NCT00390637     

An innovative approach for testing bioinformatics programs using metamorphic testing

Background  

Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Gene-gene interaction filtering with ensemble of filters

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.     

云南省米虾属一新种(十足目：匙指虾科)

报道了米虾属一新种-昆明米虾Caridina kunmingensis Wang et Liang,sp.nov.。昆明米虾与石林米虾Caridina shilinica Liang et Cai2000有相似之处,但前者额角较短,前两对步足腕节和螯短而粗,雄性第1腹肢内肢、雄附肢形态和结构均与后者不同。