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Nucleic acid - protein fingerprints. Novel protein classification based on nucleic acid - protein recognition
Full text linkProtein chemistry uses protein description and classification based on molecular mass and isoelectric point as general features. Enzymes are also compared by enzymatic reaction constants, namely Km and kcat values. Proteins are also studied by binding to different oligonucleotides. Here we suggest a simple experimental method for such a comparison of DNA binding proteins, which we call "nucleic acid-protein fingerprints". The experimental design of the method is based on an use of short oligonucleotides immobilized inside microarray of hydrogel cells - biochip. As a first stage, we solved a simple experimental task: what is the shortest single strand oligonucleotide to be recognized by protein? We tested binding of oligonucleotides from 2 to 12 bases, and we have obtained unexpected result that tetranucleotide one is long enough for specific protein binding. This 4-mer can contain two universal bases - 5-nitroindole nucleoside analogue (Ni) and only two meaningful bases, like A, G, T and C. The result obtained opens a way for constructing the simplest protein binding microarray. This microarray consists of 16 meaningful dinucleotides, like AA, AG, CT, GG etc. Physical sequences of all the nucleotides were NiNiAA, etc, where Ni is bound to gel through the amino linker. We prepared such an array and tested it for specific binding of several DNA/RNA binding proteins, labeled with fluorescent dyes like Texas Red of Bodipy. We tested RNase A and Binase for binding on the simplest microarray. It contains only 16 units, and there is a significant difference in the binding patterns. The microarray based on 3-mers must contains 64 units and must have much more specificity. The new principle of protein classification based on nucleic acid-protein recognition has been proposed and experimentally proved. Such an experimental approach must lead to a universal classification of specific DNA/RNA binding proteins
Docking studies to explore novel inhibitors against human beta-site APP cleaving enzyme (BACE-1) involved in Alzheimer’s disease
Full text linkAlzheimer’s disease (AD) is one of the most prominent neurodegenerative disorders, particularly in elder persons over 65 age. It is characterized by progressive cognitive deterioration together with declining activities. Amyloid precursor protein (APP) cleaves at A-beta (Aβ) peptide by rate limiting factor of Beta-site APP cleaving enzyme (BACE-1) in amyloidogenic pathway. Elevated level of BACE-1 leads to the accumulation of an insoluble form of Aβ peptides (Senile Plaques), an important hallmark in the pathogenesis of Alzheimer disease. Five published inhibitors of BACE-1, thiazolidinediones, rosiglitazone, pioglitazone, Sc7 and tartaric acid are available with poor pharmacological properties and intolerable side effects. Therefore, a computational approach was undertaken to design novel inhibitors against human BACE-1. The crystal structure of human BACE-1 was retrieved from the protein data bank and optimized by applying OPLS force field in Maestro v9.2. An ASINEX database (115,000 ligands) was downloaded and compounds were prepared using LigPrep. The optimized ligand dataset was docked into the BACE-1 through sequential application of Glide HTVS, SP and XP methods that penalizes more stringently for minor steric classes subsequently. Finally, seven leads were reported and ranked based on XPGscore with better binding affinity and good pharmacological properties compared with existing inhibitors. Six leads were proposed for human BACE-1. Among the six, lead 1, with XPGscore -8.051Kcal/mol, would be intriguing for rational drug design against Alzheimer’s disease and would be highly encouraging for future Alzheimer’s therapy if tested in animal models
Inferring decoding strategy from choice probabilities in the presence of noise correlations
Full text linkThe activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship often quantified by choice probabilities. While choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. Here, we derive the mathematical relationship between choice probabilities, read-out weights and noise correlations within the standard neural decision making model. Our solution allows us to prove and generalize earlier observations based on numerical simulations, and to derive novel predictions. Importantly, we show how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we present a test to decide whether the decoding weights of individual neurons are optimal, even without knowing the underlying noise correlations. We confirm the practical feasibility of our approach using simulated data from a realistic population model. Our work thus provides the theoretical foundation for a growing body of experimental results on choice probabilities and correlations
Mechanistic mechanisms of competition and biodiversity
Full text linkThe nature of competition and biodiversity are open basic questions since Darwin. To investigate mechanisms of interspecific competition and their contribution in biodiversity as closely as possible we offer a white-box modelling method based on physically interpreted ecological axioms. These models are implemented as deterministic individual-based cellular automata and able to give a direct physico-mechanistic insight into studied phenomena. Competition of two trophically identical but fitness different species, competing for one limiting resource in one stable uniform habitat (which is closed for immigration, emigration, predation, herbivory and parasitism) has been investigated in conditions, which are the most unfavourable for their coexistence. The species are per capita identical in fecundity, ontogeny, regeneration features of microhabitats, and in habitat requirements. We have modelled following 8 mechanistic mechanisms of interspecific competition: 
1.	A case of the competitive exclusion when competing species differ only in fitness. 
2.	Coexistence based on periodic dominance changeovers as a consequence of environmental changes. Competing species differ only in fitness. 
3.	A strong violation of the competitive exclusion principle due to the lowered fecundity of both competitors. Competing species differ only in fitness.
4.	Coexistence based on the competition–colonisation trade-off when greater fitness is compensated by r-strategy.
5.	A competition–colonisation trade-off based on differences in ontogeny. 
6.	Competitive exclusion when recessive species drives out the dominant one having four times greater fecundity than the dominant one in stable environment (the greater fitness cannot compensate r-strategy). 
7.	An inverted competitive exclusion when recessive species drives out the dominant one by strategy of anticipatory deprivation of resources for competitor’s offsprings propagation. Recessive species drives out the dominant one in stable environment and both competing species have identical fecundity (tripod neighbourhood). Paradoxically, but the greater fitness cannot save the dominant species when the all other parameters of the species are equal.
8.	Both competing species die because the regeneration of a limiting environmental resource takes too much time and they cannot propagate. 
The revealed mechanisms of competition can be useful not only in conservation biology, but also in economics and politics. Additionally, we speculate that the simplest way to maintain biodiversity is a controlled reduction of human fertility as the decrease in biodiversity occurs largely due to humankind overloading of biosphere resources. 

Complex Systems Analysis of Arrested Neural Cell Differentiation during Development and Analogous Cell Cycling Models in Carcinogenesis
Full text linkA new approach to the modular, complex systems analysis of nonlinear dynamics of arrested neural cell Differentiation--induced cell proliferation during organismic development and the analogous cell cycling network transformations involved in carcinogenesis is proposed. Neural tissue arrested differentiation that induces cell proliferation during perturbed development and Carcinogenesis are complex processes that involve dynamically inter-connected biomolecules in the intercellular, membrane, cytosolic, nuclear and nucleolar compartments. Such 'dynamically inter-connected' biomolecules form numerous inter-related pathways referred to as 'molecular networks'. One such family of signaling pathways contains the cell cyclins. Cyclins are proteins that link several critical pro-apoptotic and other cell cycling/division components, including the tumor suppressor gene TP53 and its product, the Thomsen-Friedenreich antigen (T antigen), Rb, mdm2, c-Myc, p21, p27, Bax, Bad and Bcl-2, which play major roles in various neoplastic transformations of many tissues. 
The novel theoretical analysis presented here is based on recently published studies of arrested cell differentiation that normally leads to neural system formation during early developmental stages; the perturbed development may involve cyclin signaling and cell cycling responsible for rapidly induced cell proliferation without differentiation into neural cells in such experimental studies
Synaptic state matching: a dynamical architecture for predictive internal representation and feature perception
Full text linkHere we consider the possibility that a fundamental function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single parsimonious computational framework
Phylogenetic Codivergence Supports Coevolution of Mimetic Heliconius Butterflies
Full text linkThe unpalatable and warning-patterned butterflies _Heliconius erato_ and _Heliconius melpomene_ provide the best studied example of mutualistic Müllerian mimicry, thought – but rarely demonstrated – to promote coevolution. Some of the strongest available evidence for coevolution comes from phylogenetic codivergence, the parallel divergence of ecologically associated lineages. Early evolutionary reconstructions suggested codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and this was initially hailed as the most striking known case of coevolution. However, subsequent molecular phylogenetic analyses found discrepancies in phylogenetic branching patterns and timing (topological and temporal incongruence) that argued against codivergence. We present the first explicit cophylogenetic test of codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and re-examine the timing of these radiations. We find statistically significant topological congruence between multilocus coalescent population phylogenies of _H. erato_ and _H. melpomene_, supporting repeated codivergence of mimetic populations. Divergence time estimates, based on a Bayesian coalescent model, suggest that the evolutionary radiations of _H. erato_ and _H. melpomene_ occurred over the same time period, and are compatible with a series of temporally congruent codivergence events. This evidence supports a history of reciprocal coevolution between Müllerian co-mimics characterised by phylogenetic codivergence and parallel phenotypic change
Structural and functional validation of Microsystin synthetases
Full text linkThe structure and function prediction for the Microsystin synthetases from Microsystis aerogenosa (LNSAMB) were carried out for verifying the authenticity of the sequenced genes. The genes for Microsystin synthetases (mcyA, mcyB, mcyD and mcyE,), were predicted by the application of computational methods and Bioinformatics web tools. The probable function prediction for the proteins was done by using Bioinformatics web tools like CDD-BLAST, INTERPROSCAN, PFAM and COGs by searching protein databases for the presence of conserved domains. While tertiary structures were constructed using PS2 Server- Protein Structure Prediction server. This study revealed structural and functional validation of sequenced genes
Donor mesenchymal stem cells trigger chronic graft-versus-host disease following minor antigen-mismatched bone marrow transplantation
Full text linkChronic graft-versus-host disease (cGVHD) is a complication after minor antigen mismatched bone marrow transplantation (BMT) characterized by an autoimmune-type reaction in various organs. Aberration in T cell regulation is involved, with donor mesenchymal stem cells (MSCs) playing a possible role in immunomodulation. In a minor-antigen mismatched mouse BMT model, transplantation of mismatched, but not syngeneic MSCs triggered the onset of cGVHD, and was associated with fibrosis, increased IL-6 secretion, decreased Foxp3+ regulatory T cells and increased Th17 in the peripheral blood. Mismatched MSCs alone were sufficient to induce cGVHD, while removal of donor MSCs rescued mice from cGVHD. RAG2 knockout recipient mice did not suffer cGVHD, indicating that host T cells were involved. Residual host-derived T cells were significantly higher in cGVHD patients compared to non-cGVHD patients. In conclusion, donor MSCs react with residual host T cells to trigger the progression of cGVHD
Diet transition to a high-fat diet for 3 weeks reduces brain omega-3-fatty acid levels, alters BDNF signaling and induces anxiety & depression-like behavior in adult rats
Full text linkBackground: The consumption of diets high in calories and low in nutrient value is becoming increasingly common in modern society, which can lead to metabolic disorders like diabetes and obesity, and potentially to psychiatric disorders. We have performed studies to assess how the shift from a healthy diet rich in omega-3 fatty acids to a diet rich in saturated fatty acid affects the substrates for brain plasticity and function, and anxiety and depression-like behavior. Methods: Pregnant rats were fed with omega-3 supplemented diet from their 2nd day of gestation period as well as their male pups for 12 weeks. Afterwards, the animals were randomly assigned to either a group fed on the same diet or a group fed on a high-fat diet (HFD) rich in saturated fats for 3 weeks. Anxiety and depression-like behaviors were assessed by using open field (OF) and elevated plus maze (EPM). Molecular assessments were performed in the frontal cortex and hippocampus as dysfunctions in these brain regions are main contributors towards depression, anxiety-like behavior and stress. Results: We found that the HFD increased vulnerability for anxiety and depression-like behavior, and that these modifications harmonized with changes in the anxiety-related neuropeptide Y (NPY)-1 receptor. The HFD reduced levels of brain-derived neurotrophic factor (BDNF), and the BDNF signaling receptor pTrkB, as well as the cyclic AMP response element binding protein (CREB), in these brain regions. Brain DHA contents were significantly associated with the levels of anxiety and depression-like behavior in these rats. Conclusions: These results suggest that the change in dietary lifestyle leading to alteration of dietary n3/n-6 fatty acids levels imposes a risk factor for anxiety-like behaviors. Dietary DHA might help for building cognitive reserve that can resist psychiatric disorders