A novel TMD-based peroxidase-mimicking nanozyme: From naked eye detection of leukocytosis-related diseases to sensing different bioanalytes.

Being emerged as alternatives to natural enzymes, nanozymes have recently drawn much attention in sensing. Herein, the first multicomponent transition metal dicalchogenide (TMD)-based nanozyme (MCFS/rGO) was synthesized by a facile hydrothermal method and characterized. This peroxidase-mimic nanozyme follows the typical Michaelis-Menten kinetics, showing a higher affinity for H2O2 substrate (Km = 9 µM) compared to that of natural peroxidase (Km = 3700 µM). The remarkable potential of the MCFS/rGO nanozyme to detect H2O2 provided us with a great opportunity to design some simple and fast colorimetric sensing systems. Coupling the efficient peroxidase-mimicking activity of the nanozyme with the H2O2 production capacity of white blood cells (WBCs) leads to the development of a novel, simple, rapid, and efficient colorimetric method to distinguish leukocytosis-related patients from healthy people by the naked eye. This pioneering diagnostic technique can also be utilized to quantitatively measure the WBC count. Moreover, we coupled the mentioned nanozyme-based system with the activity of glucose oxidase enzyme available in different types of honey samples, an innovative mechanism proved to be an effective quality indicator of the samples. Last but not least, the MCFS/rGO nanozyme is also able to determine the quantity of some biologically significant analytes, including glutathione (GSH), ascorbic acid (AA), and mercury ions (Hg2+), of which the limit of detection (LOD) was 9.3 nM, 22.5 nM, and 0.32 µM, respectively. Our results, however, demonstrated the superior performance of the MCFS/rGO nanozyme to determine the first two mentioned bioanalytes compared with other TMDs. Overall, this novel nanozyme-based sensor system can be considered a suitable candidate for developing multipurpose biosensors for medical and biochemical applications.

High efficient production of plant flavonoids by microbial cell factories: Challenges and opportunities.

Plant flavonoids are secondary metabolites containing a benzo-γ-pyrone structure, which are widely present in plants and have a variety of physiological and pharmacological activities. However, current flavonoid production from plant extraction or chemical synthesis does not meet the requirements of green and sustainable development. Fortunately, microbial synthesis of flavonoids has shown the potential for large-scale production with the advantages of being controllable and environmentally friendly, and a variety of microorganisms have been developed as microbial cell factories (MCFs) to synthesize plant flavonoids owing to the feasibility of genetic manipulations. However, most of MCFs have not yet been commercialized and industrialized because of the challenges posed by unbalanced metabolic flux among various pathways and conflict between cell growth and production. Here, strategies for coping with the challenges are summarized in terms of enzymes, pathways, metabolic networks, host cells. And combined with protein structure prediction, de novo protein design, artificial intelligence (AI), biocatalytic retrosynthesis, and intelligent stress resistance, it provides new insights for the high efficient production of plant flavonoids and other plant natural products in MCFs.

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.

Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes.

BACKGROUND: The underlying strategies used by influenza A viruses (IAVs) to adapt to new hosts while crossing the species barrier are complex and yet to be understood completely. Several studies have been published identifying singular genomic signatures that indicate such a host switch. The complexity of the problem suggested that in addition to the singular signatures, there might be a combinatorial use of such genomic features, in nature, defining adaptation to hosts. RESULTS: We used computational rule-based modeling to identify combinatorial sets of interacting amino acid (aa) residues in 12 proteins of IAVs of H1N1 and H3N2 subtypes. We built highly accurate rule-based models for each protein that could differentiate between viral aa sequences coming from avian and human hosts. We found 68 host-specific combinations of aa residues, potentially associated to host adaptation on HA, M1, M2, NP, NS1, NEP, PA, PA-X, PB1 and PB2 proteins of the H1N1 subtype and 24 on M1, M2, NEP, PB1 and PB2 proteins of the H3N2 subtypes. In addition to these combinations, we found 132 novel singular aa signatures distributed among all proteins, including the newly discovered PA-X protein, of both subtypes. We showed that HA, NA, NP, NS1, NEP, PA-X and PA proteins of the H1N1 subtype carry H1N1-specific and HA, NA, PA-X, PA, PB1-F2 and PB1 of the H3N2 subtype carry H3N2-specific signatures. M1, M2, PB1-F2, PB1 and PB2 of H1N1 subtype, in addition to H1N1 signatures, also carry H3N2 signatures. Similarly M1, M2, NP, NS1, NEP and PB2 of H3N2 subtype were shown to carry both H3N2 and H1N1 host-specific signatures (HSSs). CONCLUSIONS: To sum it up, we computationally constructed simple IF-THEN rule-based models that could distinguish between aa sequences of avian and human IAVs. From the rules we identified HSSs having a potential to affect the adaptation to specific hosts. The identification of combinatorial HSSs suggests that the process of adaptation of IAVs to a new host is more complex than previously suggested. The present study provides a basis for further detailed studies with the aim to elucidate the molecular mechanisms providing the foundation for the adaptation process.

Sesquiterpene action, and morphogenetic signaling through the ortholog of retinoid X receptor, in higher Diptera.

Morphogenetic signaling by small terpenoid hormones is a common feature of both vertebrate and invertebrate development. Most attention on insect developmental signaling by small terpenoids has focused on signaling by juvenile hormone through bHLH-PAS proteins (e.g., the MET protein), especially as that signaling axis intersects with ecdysteroid action through the receptor EcR. However, a series of endocrine and pharmacological studies on pupariation in cyclorrhaphous Diptera have remained persistently refractory to explanation with the above two-axis model. Recently, the terpenoid compound methyl farnesoate has been physicochemically demonstrated to exist in circulation at physiological concentrations, in several mecopterid orders, including Diptera. In addition, it has also been recently demonstrated that the receptor to which methyl farnesoate binds with nanomolar affinity (ultraspiracle, an ortholog of retinoid X receptor) requires a functioning ligand binding pocket to sustain the morphogenetic transition to puparium formation. This review evaluates endocrine and pharmacological evidence for developmental pathways reached by methyl farnesoate action, and assesses the participation of the retinoid X receptor ligand pocket in signal transduction to those developmental endpoints.

Recent developments and applications of immobilized laccase.

Laccase is a promising biocatalyst with many possible applications, including bioremediation, chemical synthesis, biobleaching of paper pulp, biosensing, textile finishing and wine stabilization. The immobilization of enzymes offers several improvements for enzyme applications because the storage and operational stabilities are frequently enhanced. Moreover, the reusability of immobilized enzymes represents a great advantage compared with free enzymes. In this work, we discuss the different methodologies of enzyme immobilization that have been reported for laccases, such as adsorption, entrapment, encapsulation, covalent binding and self-immobilization. The applications of laccase immobilized by the aforementioned methodologies are presented, paying special attention to recent approaches regarding environmental applications and electrobiochemistry.

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors.

MOTIVATION: Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. RESULTS: We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. AVAILABILITY: A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm.