Threatening biomarkers in lupus pregnancy: Biochemistry and genetic challenges

Objective

Using genetic markers and miRs work strongly beside other sensitive biomarkers in lupus management during sensitive period of pregnancy.

Methods

PubMed and Google Scholar databases were searched from 2000 to 2017 using the terms “lupus,” “lupus pregnancy,” “biomarkers,” “micro-RNA,” “polymorphisms,” “anti-phospholipid antibodies,” and “cluster differentiation markers.”

Discussion

Complement is a valuable biomarker in lupus pregnancy. However, the complement profile has ambiguous meaning because decreased levels of C3 and C4 reflect inflammation and because they are also prognostic biomarkers for abortion. Furthermore, increased C3 and C4 levels indicate hepatic protein synthesis in hepatocytes. Anti-phospholipid (APL) antibodies are present in 25% to 50% of lupus patients, and can lead to thrombotic and obstetric complications in some pregnancies and increase the risk of abortion, especially in a pregnant woman in the active phase of lupus. Several studies have associated APL with HELLP syndrome. However, other pregnancy complications have not been associated with APL. Autoantibodies against the major vault protein and anti-double strand DNA antibodies are valuable biomarkers in evaluating lupus activity. The expression pattern of micro-RNAs (miRs) differs in various diseases. Current studies have demonstrated the potential of miRs as diagnostic and prognostic biomarkers in various diseases; for example, the level of miR-126 is higher in lupus.

Conclusion

Mir-223-3p and miR-451 are informative biomarkers in estimating disease activity. TWEAK, BAFF, and APOL1 genes, and their polymorphisms are informative in estimating disease activity, especially renal effects, and in monitoring higher-risk pregnant women. Further studies of these genes and their relevant polymorphisms are needed.

     

Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.     

Efavirenz Dissolution Enhancement IV—Antisolvent Nanocrystallization by Sonication,Physical Stability,and Dissolution

Efavirenz is a fundamental drug in the HIV therapy; however, it has a low bioavailability due to low water solubility. Particle nanonization should enhance its dissolution and therefore its bioavailability. Nanocrystallization is a promising technique for preparing drug nanocrystals. A solution containing efavirenz (EFV) and methanol was added to an aqueous solution of particle stabilizers, under sonication. The adequate polymer stabilizer and its concentration and drug load were evaluated. Particle size and zeta potential of suspensions were measured. Nanosuspensions were freeze-dried and the resulting powder was characterized by some techniques, with special attention to dissolution. Particle size and zeta potential analysis showed that HMPC and PVP were the most suitable polymers. All samples prepared with these stabilizers had nanosized particles and proper zeta potential; however, sedimentation and particle growth were detected with Turbiscan?. Time-related destabilization occurred when the lowest polymer concentration of 20% was used. SEM analysis of the dried powder shows film formation for suspensions with 40% of polymer and particle aggregation in samples with less polymer. Dissolution profiles of samples were higher than EFV raw material, although the lower the polymer concentration, the higher the dissolution.     

Evaluation of protein Z plasma level in beta-thalassemia major patients in Ahvaz city in Iran

Objectives

Thrombotic episodes occurred frequently in beta-thalassemia major (BTM) patients, leading to hypercoagulability of plasma. Protein Z (PZ) is a vitamin-K-dependent anti-coagulation factor that plays a role in the human homeostatic process. The objective of the current study is to investigate the distribution pattern of PZ plasma concentrations between BTM patients and the normal population in Ahvaz city, the center of Khuzestan province, southwest of Iran.

Material and Methods

Forty confirmed BTM patients and 40 healthy volunteers were evaluated for complete blood count (CBC) indices and PZ plasma levels. CBC samples were measured using an automated cell counter, and PZ was assayed with an immunoassay method. Statistical analysis was conducted using SPSS software. The ROC curve and binary logistic regression estimated the sensitivity, specificity, and Odd’s ratio for PZ measurement.

Results

The mean±SD of the PZ plasma level in normal individuals was 1.68±0.63 μg/mL, and in BTM patients, it was 1.10±0.52 μg/mL. This shows a significant reduction of PZ in BTM patients statistically (CI = 0.99; p<0.001). Further, the mean±SD of the PZ plasma levels in BTM patients who received washed red blood cells was not significantly different from that of patients undergoing packed red blood cell therapy (CI = 0.95; p = 0.320). The area under the curve (AUC) for PZ was 0.759 (p = 0.00). The cut-off value = 1.4 μg/mL of the PZ plasma level had at least 70% sensitivity and specificity in BTM patients.

Discussion

Several epidemiologic studies have shown thromboembolism episodes in BTM patients. In the current study, PZ was reduced significantly in BTMs.

Conclusion

We noticed that BTMs have lower plasma PZ concentration might be predisposed to BTM.

     

A Newton Cooperative Genetic Algorithm Method for In Silico Optimization of Metabolic Pathway Production

This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.     

The effect of coagulation factors polymorphisms on abortion

Objective

Recent studies showed coagulation factors play important role in controlling pregnancy duration in addition to controlling homeostasis. Recent studies showed several polymorphisms of coagulation factors genes increase the clot formation and lead to abortion. In this study, we evaluated the polymorphisms of coagulation factors and their effects on the development of the fetus.

Material and Methods

Relevant literature was identified by a PubMed search (1988-2017) of English language papers using the terms Abortion, pregnancy woman, coagulation factor and polymorphism.

Result

Several polymorphisms of coagulation factors disturb the exchange of food and other materials between the fetus and the mother, and impairs the formation of the placenta during embryonic stages.

Discussion

Evaluation of functional polymorphisms in coagulation factors gene during fetal development can be used as a prognostic factor in the prevention of the abortion.

     

An improved hybrid of SVM and SCAD for pathway analysis

Pathway analysis has lead to a new era in genomic research by providing further biological process information compared to traditional single gene analysis. Beside the advantage, pathway analysis provides some challenges to the researchers, one of which is the quality of pathway data itself. The pathway data usually defined from biological context free, when it comes to a specific biological context (e.g. lung cancer disease), typically only several genes within pathways are responsible for the corresponding cellular process. It also can be that some pathways may be included with uninformative genes or perhaps informative genes were excluded. Moreover, many algorithms in pathway analysis neglect these limitations by treating all the genes within pathways as significant. In previous study, a hybrid of support vector machines and smoothly clipped absolute deviation with groups-specific tuning parameters (gSVM-SCAD) was proposed in order to identify and select the informative genes before the pathway evaluation process. However, gSVM-SCAD had showed a limitation in terms of the performance of classification accuracy. In order to deal with this limitation, we made an enhancement to the tuning parameter method for gSVM-SCAD by applying the B-Type generalized approximate cross validation (BGACV). Experimental analyses using one simulated data and two gene expression data have shown that the proposed method obtains significant results in identifying biologically significant genes and pathways, and in classification accuracy.     

Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria

A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.     

Application of String Kernels in Protein Sequence Classification

INTRODUCTION: The production of biological information has become much greater than its consumption. The key issue now is how to organise and manage the huge amount of novel information to facilitate access to this useful and important biological information. One core problem in classifying biological information is the annotation of new protein sequences with structural and functional features. METHOD: This article introduces the application of string kernels in classifying protein sequences into homogeneous families. A string kernel approach used in conjunction with support vector machines has been shown to achieve good performance in text categorisation tasks. We evaluated and analysed the performance of this approach, and we present experimental results on three selected families from the SCOP (Structural Classification of Proteins) database. We then compared the overall performance of this method with the existing protein classification methods on benchmark SCOP datasets. RESULTS: According to the F1 performance measure and the rate of false positive (RFP) measure, the string kernel method performs well in classifying protein sequences. The method outperformed all the generative-based methods and is comparable with the SVM-Fisher method. DISCUSSION: Although the string kernel approach makes no use of prior biological knowledge, it still captures sufficient biological information to enable it to outperform some of the state-of-the-art methods.