Complex Signatures of Natural Selection at the Duffy Blood Group Locus

The Duffy blood group locus (FY) has long been considered a likely target of natural selection, because of the extreme pattern of geographic differentiation of its three major alleles (FY*B, FY*A, and FY*O). In the present study, we resequenced the FY region in samples of Hausa from Cameroon (fixed for FY*O), Han Chinese (fixed for FY*A), Italians, and Pakistanis. Our goals were to characterize the signature of directional selection on FY*O in sub-Saharan Africa and to understand the extent to which natural selection has also played a role in the extreme geographic differentiation of the other derived allele at this locus, FY*A. The data from the FY region are compared with the patterns of variation observed at 10 unlinked, putatively neutral loci from the same populations, as well as with theoretical expectations from the neutral-equilibrium model. The FY region in the Hausa shows evidence of directional selection in two independent properties of the data (i.e., level of sequence variation and frequency spectrum), observations that are consistent with the FY*O mutation being the target. The Italian and Chinese FY data show patterns of variation that are very unusual, particularly with regard to frequency spectrum and linkage disequilibrium, but do not fit the predictions of any simple model of selection. These patterns may represent a more complex and previously unrecognized signature of positive selection

Molecular dynamics simulations reveal canonical conformations in different pMHC/TCR interactions

The major defense system against microbial pathogens in vertebrates is the adaptive immune response and represents an effective mechanism in cancer surveillance. T cells represent an essential component of this complex system. They can recognize myriads of antigens as short peptides (p) originated from the intracellular degradation of foreign proteins presented by major histocompatibility complex (MHC) proteins. The clonotypic T-cell antigen receptor (TCR) is specialized in recognizing pMHC and triggering T cells immune response. It is still unclear how TCR engagement to pMHC is translated into the intracellular signal that initiates T-cell immune response. Some work has suggested the possibility that pMHC binding induces in the TCR conformational changes transmitted to its companion CD3 subunits that govern signaling. The conformational changes would promote phosphorylation of the CD3 complex ζ chain that initiates signal propagation intracellularly. Here, we used all-atom molecular dynamics simulations (MDs) of 500 ns to analyze the conformational behavior of three TCRs (1G4, ILA1 and ILA1α1β1) interacting with the same MHC class I (HLA-A*02:01) bound to different peptides, and modelled in the presence of a lipid bilayer. Our data suggest a correlation between the conformations explored by the β-chain constant regions and the T-cell response experimentally determined. In particular, independently by the TCR type involved in the interaction, the TCR activation seems to be linked to a specific zone of the conformational space explored by the β-chain constant region. Moreover, TCR ligation restricts the conformational space the MHC class I groove

Functional constraints on the constitutive androstane receptor inferred from human sequence variation and cross-species comparisons

<p>Abstract</p> <p>Members of the NR1I subfamily of nuclear receptors play a role in the transcriptional activation of genes involved in drug metabolism and transport. NR1I3, the constitutive androstane receptor (CAR), mediates the induction of several genes involved in drug response, including members of the <it>CYP3A</it>, <it>CYP2B </it>and <it>UGT1A </it>subfamilies. Large inter-individual variation in drug clearance has been reported for many drug metabolising enzyme genes. Sequence variation at the <it>CAR </it>locus could potentially contribute to variation in downstream targets, as well as to the substantial variation in expression level reported. We used a comparative genomics-based approach to select resequencing segments in 70 subjects from three populations. We identified 21 polymorphic sites, one of which results in an amino acid substitution. Our study reveals a common haplotype shared by all three populations which is remarkably similar to the ancestral sequence, confirming that CAR is under strong functional constraints. The level and pattern of sequence variation is approximately similar across populations, suggesting that interethnic differences in drug metabolism are not likely to be due to genetic variation at the <it>CAR </it>locus. We also identify several common non-coding variants that occur at highly conserved sites across four major branches of the mammalian phylogeny, suggesting that they may affect <it>CAR </it>expression and, ultimately, the activity of its downstream targets.</p

2D Zernike polynomial expansion: finding the protein-protein binding regions

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order

A novel strategy for molecular interfaces optimization: the case of ferritin-transferrin receptor interaction

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces

Applying the EFuNN Evolving Paradigm to the Recognition of Artefactual Beats in Continuous Seismocardiogram Recordings

Seismocardiogram (SCG) recording is a novel method for the prolonged monitoring of the cardiac mechanical performance during spontaneous behavior. The continuous monitoring results in a collection of thousands of beats recorded during a variety of physical activities so that the automatic analysis and processing of such data is a challenging task due to the presence of artefactual beats and morphological changes over time that currently request the human expertise. On this premise, we propose the use of the Evolving Fuzzy Neural Network (EFuNN) paradigm for the automatic artifact detection in the SCG signal. The fuzzy logic processing method can be applied to model the human expertise knowledge using the learning capabilities of an artificial neural network. The evolving capability of the EFuNN paradigm has been applied to solve the issue of the physiological variability of the SGC waveform. Preliminary tests have been carried out to validate this approach and the obtained results demonstrate the effectiveness of the method and its scalability

Variabilidad de los caracteres morfométricos y morfológicos de una población de Criconemetia curvata ( Raski, 1952 ) Luc & Raski, 1981 (Nematoda: Tylenchida)

Se llevó a cabo un estudio de la variabilidad de los calacteres morfométricos y morfológicos de una pobación (hembras y larvas) del nematodo fitófago Criconemella C'urvata (Raski, 1952) Luc &amp; Raski, 1981 proveniente de Córdoba, Argentina. Dicha variablidad fue conocida en base a: análisis estadístico de cada uno de los caracteres morfométricos y análisis de la frecuencia relativa de cada carácter morfológico dentro de la población. En el caso particular de las larvas, se demuestra que la utilización de la longitud de la porción cónica del est Tete permite discriminar con precisión los diferentes estadios. Se efectuó un estudio crítico de la importancia de cada grupo de caracteres en la definición de la especi

Dietary determinants of postprandial blood glucose control in adults with type 1 diabetes on a hybrid closed-loop system

Aims/hypothesis: The aim of this work was to assess the relationship between meal nutrients and postprandial blood glucose response (PGR) in individuals with type 1 diabetes on a hybrid closed-loop system (HCLS). Methods: The dietary composition of 1264 meals (398 breakfasts, 441 lunches and 425 dinners) was assessed by 7-day food records completed by 25 individuals with type 1 diabetes on HCLSs (12 men/13 women, mean ± SD age 40 ± 12 years, mean ± SD HbA1c 51 ± 10 mmol/mol [6.9 ± 0.2%]). For each meal, PGR (continuous glucose monitoring metrics, glucose incremental AUCs) and insulin doses (pre-meal boluses, post-meal microboluses automatically delivered by the pump and adjustment boluses) over 6 h were evaluated. Results: Breakfast, lunch and dinner significantly differed with respect to energy and nutrient intake and insulin doses. The blood glucose postprandial profile showed an earlier peak after breakfast and a slow increase until 4 h after lunch and dinner (p < 0.001). Mean ± SD postprandial time in range (TIR) was better at breakfast (79.3 ± 22.2%) than at lunch (71.3 ± 23.9%) or dinner (70.0 ± 25.9%) (p < 0.001). Significant negative predictors of TIR at breakfast were total energy intake, per cent intake of total protein and monounsaturated fatty acids, glycaemic load and absolute amounts of cholesterol, carbohydrates and simple sugars consumed (p < 0.05 for all). No significant predictors were detected for TIR at lunch. For TIR at dinner, a significant positive predictor was the per cent intake of plant proteins, while negative predictors were glycaemic load and intake amounts of simple sugars and carbohydrate (p < 0.05 for all). Conclusions/interpretation: This study shows that nutritional factors other than the amount of carbohydrate significantly influence postprandial blood glucose control. These nutritional determinants vary between breakfast, lunch and dinner, with differing effects on postprandial blood glucose profile and insulin requirements, thus remaining a challenge to HCLSs. Graphical abstract: [Figure not available: see fulltext.]

Advances in Li-Ion battery management for electric vehicles

This paper aims at presenting new solutions for advanced Li-Ion battery management to meet the performance, cost and safety requirements of automotive applications. Emphasis is given to monitoring and controlling the battery temperature, a parameter which dramatically affects the performance, lifetime, and safety of Li-Ion batteries. In addition to this, an innovative battery management architecture is introduced to facilitate the development and integration of advanced battery control algorithms. It exploits the concept of smart cells combined with an FPGA-based centralized unit. The effectiveness of the proposed solutions is shown through hardware-in-the-loop simulations and experimental results