New binding specificities evolve via point mutation in an invertebrate allorecognition gene.
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ABSTRACT:
SUBMITTER: Huene AL
PROVIDER: S-EPMC8282982 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sessile colonial invertebrates-animals such as sponges, corals, bryozoans, and ascidians-can distinguish between their own tissues and those of conspecifics upon contact [1]. This ability, called allorecognition, mediates spatial competition and can prevent stem cell parasitism by ensuring that colonies only fuse with self or close kin. In every taxon studied to date, allorecognition is controlled by one or more highly polymorphic genes [2-8]. However, in no case is it understood how the proteins encoded by these genes discriminate self from non-self. In the cnidarian Hydractinia symbiolongicarpus, allorecognition is controlled by at least two highly polymorphic allorecognition genes, Alr1 and Alr2 [3, 5, 9-12]. Sequence variation at each gene predicts allorecognition in laboratory strains such that colonies reject if they do not share a common allele at either locus, fuse temporarily if they share an allele at only one locus, or fuse permanently if they share an allele at both genes [5, 9]. Here, we show that the gene products of Alr1 and Alr2 (Alr1 and Alr2) are self-ligands with extraordinary specificity. Using an in vitro cell aggregation assay, we found that Alr1 and Alr2 bind to themselves homophilically across opposing cell membranes. For both proteins, each isoform bound only to itself or to an isoform of nearly identical sequence. These results provide a mechanistic explanation for the exquisite specificity of Hydractinia allorecognition. Our results also indicate that hydroids have evolved a molecular strategy of self-recognition that is unique among characterized allorecognition systems within and outside invertebrates.
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Project description:Chemoreceptor-based signaling pathways are among the major modes of bacterial signal transduction, and Pseudomonas aeruginosa PAO1 is an important model to study their function. Of the 26 chemoreceptors of this strain, PctA has a broad ligand range and responds to most of the proteinogenic amino acids, whereas PctB and PctC have a much narrower range and show strong ligand preference for l-glutamine and γ-aminobutyrate, respectively. Using several comparative genomics approaches, we show that these receptors are paralogs: pctA gene duplication in the common ancestor of the genus Pseudomonas led to pctC, whereas pctB originated through another, independent pctA duplication in the common ancestor of P. aeruginosa Thus, the broad-range amino acid chemoreceptor was evolutionarily older, and chemoreceptors that complemented "missing" amino acid sensing abilities arose later in specific Pseudomonas lineages. Using comparative sequence analysis, newly solved crystal structures of PctA, PctB, and PctC ligand-binding domains, and their molecular dynamics simulations, we identified a conserved amino acid recognition motif and changes in the ligand-binding pocket that led to novel ligand specificities. In addition, we determined major forces driving the evolution of this group of chemoreceptors.IMPORTANCE Many bacteria possess a large number of chemoreceptors that recognize a variety of different compounds. More than 60% of the genomes analyzed in this study contain paralogous chemoreceptors, suggesting that they emerge with high frequency. We provide first insight on how paralogous receptors have evolved and show that two chemoreceptors with a narrow ligand range have evolved from an ancestral protein with a broad chemoeffector spectrum. Protein structures show that multiple changes in the ligand-binding site account for the differences in the ligand spectrum. This work lays the ground for further studies aimed at establishing whether the principles of ligand-binding evolution reported here can be generalized for a wider spectrum of sensory proteins in bacteria.
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Project description:Duchenne/Becker muscular dystrophy (DMD/BMD) is the most common form of muscular dystrophy, accounting for over 50% of all cases. In this regard, in Russia we carry out a program of selective screening for DMD/BMD, which mainly involves male patients. The main inclusion criteria are an increase in the level of creatine phosphokinase (>2000 U/L) or an established clinical diagnosis. At the first stage of screening, patients are scanned for extended deletions and duplications in the DMD gene using multiplex ligase-dependent probe amplification (MLPA SALSA P034 and P035 DMD probemix, MRC-Holland). The second stage is the search for small mutations using a custom NGS panel, which includes 31 genes responsible for various forms of limb-girdle muscular dystrophy. In a screening of 1025 families with a referral Duchenne/Becker diagnosis, pathogenic and likely pathogenic variants in the DMD gene were found in 788 families (in 76.9% of cases). In the current study, we analyzed the mutation spectrum of the DMD gene in Russian patients and noted certain differences between the examined cohort and the multi-ethnic cohort. The analysis of the DMD gene mutation spectrum is essential for patients with DMD/BMD because the exact mutation type determines the application of a specific therapeutic method.
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Project description:Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00639-y.
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Project description:Noise is a prevalent and sometimes even dominant aspect of many biological processes. While many natural systems have adapted to attenuate or even usefully integrate noise, the variability it introduces often still delimits the achievable precision across biological functions. This is particularly so for visual phototransduction, the process responsible for converting photons of light into usable electrical signals (quantum bumps). Here, randomness of both the photon inputs (regarded as extrinsic noise) and the conversion process (intrinsic noise) are seen as two distinct, independent and significant limitations on visual reliability. Past research has attempted to quantify the relative effects of these noise sources by using approximate methods that do not fully account for the discrete, point process and time ordered nature of the problem. As a result the conclusions drawn from these different approaches have led to inconsistent expositions of phototransduction noise performance. This paper provides a fresh and complete analysis of the relative impact of intrinsic and extrinsic noise in invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Bayesian point process (Snyder) filters. An integrate-fire based algorithm is developed to reliably estimate photon times from quantum bumps and Snyder filters are then used to causally estimate random light intensities both at the front and back end of the phototransduction cascade. Comparison of these estimates reveals that the dominant noise source transitions from extrinsic to intrinsic as light intensity increases. By extending the filtering techniques to account for delays, it is further found that among the intrinsic noise components, which include bump latency (mean delay and jitter) and shape (amplitude and width) variance, it is the mean delay that is critical to noise performance. As the timeliness of visual information is important for real-time action, this delay could potentially limit the speed at which invertebrates can respond to stimuli. Consequently, if one wants to increase visual fidelity, reducing the photoconversion lag is much more important than improving the regularity of the electrical signal.
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Project description:Mutagen treatment of mouse tumor cell line P815 produces tum- variants that are rejected by syngeneic mice because they express new transplantation antigens. These tum- antigens are recognized by cytotoxic T lymphocytes (CTL) but induce no detectable antibody response. By transfecting P815 cell line P1.HTR with DNA of tum- variant P198, we obtained transfectants expressing tum- antigen P198 that could be identified on the basis of their ability to stimulate anti-P198 CTL. This was repeated with DNA of a cosmid library derived from variant P198, and a cosmid carrying the sequence encoding antigen P198 was recovered from a transfectant. Gene P198 is 3 kb long and contains eight exons. It shows no homology with previously identified tum- gene P91A, nor with any gene presently recorded in the data banks. The long open reading frame codes for a 23.5-kD protein. The antigenic allele of gene P198 differs from the normal allele by a point mutation located in exon 7. This mutation causes an Ala to Thr change, and was shown by site-directed mutagenesis to be responsible for the expression of the antigen. An 11-amino acid synthetic peptide covering the sequence surrounding the tum- mutation rendered P815 cells sensitive to lysis by anti-P198 CTL. The homologous peptide corresponding to the normal sequence of the gene did not, but it was able to compete for binding to major histocompatibility complex molecule Kd. We conclude that tum- mutation P198 generates a new epitope recognized by syngeneic T cells. As observed with gene P91A, we found that a fragment of gene P198 that contained only exons 3-7, cloned in nonexpression vectors, transferred efficiently the expression of the antigen.
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Project description:The mitochondria-ER cortex anchor (MECA) is required for proper mitochondrial distribution and functions by tethering mitochondria to the plasma membrane. The core component of MECA is the multidomain protein Num1, which assembles into clusters at the cell cortex. We show Num1 adopts an extended, polarized conformation. Its N-terminal coiled-coil domain (Num1CC) is proximal to mitochondria, and the C-terminal pleckstrin homology domain is associated with the plasma membrane. We find that Num1CC interacts directly with phospholipid membranes and displays a strong preference for the mitochondria-specific phospholipid cardiolipin. This direct membrane interaction is critical for MECA function. Thus, mitochondrial anchoring is mediated by a protein that interacts directly with two different membranes through lipid-specific binding domains, suggesting a general mechanism for interorganelle tethering.
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