Identifying Protein Biomarkers in Blood for Alzheimer's Disease

Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis. Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we developed an iterative method based on disease similarity (IBDS). We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method. Results: We selected 39 diseases which are highly related to AD. They correspond 1,481 kinds of proteins. One hundred and eighty-four proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods. Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. Seven proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD. Availability and Implementation: https://github.com/zty2009/Identifying-Protein-Biomarkers-in-Blood-for-Alzheimer-s-Disease.

Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China: a multicenter observational study

BACKGROUND

Blood glucose control is closely related to type 2 diabetes mellitus (T2DM) prognosis. This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network (EN) with a machine-learning algorithm to predict glycemic control.

METHODS

Basic information, biochemical indices, and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017. An EN regression was used to address variable collinearity. Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status. Additionally, a stepwise logistic regression was performed to compare the machine learning models.

RESULTS

The well-controlled blood glucose rate was 45.82% in North China. The multivariable analysis found that hypertension history, atherosclerotic cardiovascular disease history, exercise, and total cholesterol were protective factors in glycosylated hemoglobin (HbA1c) control, while central adiposity, family history, T2DM duration, complications, insulin dose, blood pressure, and hypertension were risk factors for elevated HbA1c. Before the dimensional reduction in the EN, the areas under the curve of RF, SVM, and BP were 0.73, 0.61, and 0.70, respectively, while these figures increased to 0.75, 0.72, and 0.72, respectively, after dimensional reduction. Moreover, the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models (the sensitivity and accuracy of logistic were 0.52 and 0.56; RF: 0.79, 0.70; SVM: 0.84, 0.73; BP-ANN: 0.78, 0.73, respectively).

CONCLUSIONS

More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications. The EN and machine learning algorithms are alternative choices, in addition to the traditional logistic model, for building predictive models of blood glucose control in patients with T2DM.

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

Conformational variability of the stationary phase survival protein E from Xylella fastidiosa revealed by X-ray crystallography, small-angle X-ray scattering studies, and normal mode analysis

Xylella fastidiosa is a xylem-limited bacterium that infects a wide variety of plants. Stationary phase survival protein E is classified as a nucleotidase, which is expressed when bacterial cells are in the stationary growth phase and subjected to environmental stresses. Here, we report four refined X-ray structures of this protein from X. fastidiosa in four different crystal forms in the presence and/or absence of the substrate 3'-AMP. In all chains, the conserved loop verified in family members assumes a closed conformation in either condition. Therefore, the enzymatic mechanism for the target protein might be different of its homologs. Two crystal forms exhibit two monomers whereas the other two show four monomers in the asymmetric unit. While the biological unit has been characterized as a tetramer, differences of their sizes and symmetry are remarkable. Four conformers identified by Small-Angle X-ray Scattering (SAXS) in a ligand-free solution are related to the low frequency normal modes of the crystallographic structures associated with rigid body-like protomer arrangements responsible for the longitudinal and symmetric adjustments between tetramers. When the substrate is present in solution, only two conformers are selected. The most prominent conformer for each case is associated to a normal mode able to elongate the protein by moving apart two dimers. To our knowledge, this work was the first investigation based on the normal modes that analyzed the quaternary structure variability for an enzyme of the SurE family followed by crystallography and SAXS validation. The combined results raise new directions to study allosteric features of XfSurE protein.

Structures of mesophilic and extremophilic citrate synthases reveal rigidity and flexibility for function

Citrate synthase (CS) catalyses the entry of carbon into the citric acid cycle and is highly-conserved structurally across the tree of life. Crystal structures of dimeric CSs are known in both "open" and "closed" forms, which differ by a substantial domain motion that closes the substrate-binding clefts. We explore both the static rigidity and the dynamic flexibility of CS structures from mesophilic and extremophilic organisms from all three evolutionary domains. The computational expense of this wide-ranging exploration is kept to a minimum by the use of rigidity analysis and rapid all-atom simulations of flexible motion, combining geometric simulation and elastic network modeling. CS structures from thermophiles display increased structural rigidity compared with the mesophilic enzyme. A CS structure from a psychrophile, stabilized by strong ionic interactions, appears to display likewise increased rigidity in conventional rigidity analysis; however, a novel modified analysis, taking into account the weakening of the hydrophobic effect at low temperatures, shows a more appropriate decreased rigidity. These rigidity variations do not, however, affect the character of the flexible dynamics, which are well conserved across all the structures studied. Simulation trajectories not only duplicate the crystallographically observed symmetric open-to-closed transitions, but also identify motions describing a previously unidentified antisymmetric functional motion. This antisymmetric motion would not be directly observed in crystallography but is revealed as an intrinsic property of the CS structure by modeling of flexible motion. This suggests that the functional motion closing the binding clefts in CS may be independent rather than symmetric and cooperative.