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The TBX1 gene plays a critical role in the development of 22q11.2 deletion syndrome (22q11.2DS), a complex genetic disorder associated with various phenotypic manifestations. In this study, we performed in-silico analysis to identify potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) within the TBX1 gene and evaluate their functional and structural impact on 22q11.2DS. A comprehensive analysis pipeline involving multiple computational tools was employed to predict the pathogenicity of nsSNPs. This study assessed protein stability and explored potential alterations in protein-protein interactions. The results revealed the rs751339103(C>A), rs780800634(G>A), rs1936727304(T>C), rs1223320618(G>A), rs1248532217(T>C), rs1294927055 (C>T), rs1331240435 (A>G, rs1601289406 (A>C), rs1936726164 (G>A), and rs911796187(G>A) with a high-risk potential for affecting protein function and stability. These nsSNPs were further analyzed for their impact on post-translational modifications and structural characteristics, indicating their potential disruption of molecular pathways associated with TBX1 and its interacting partners. These findings provide a foundation for further experimental studies and elucidation of potential therapeutic targets and personalized treatment approaches for individuals affected by 22q11.2DS. [ABSTRACT FROM AUTHOR]

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81–0.97). While the best models most frequently employed gene content, an average accuracy score of 0.90 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic. [ABSTRACT FROM AUTHOR]

Recombination has the potential to facilitate adaptation. In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations, empirical evidence to test these theories is still scarce. We examined the effect of recombination on adaptation on a large-scale empirical fitness landscape in HIV-1 based on in vitro fitness measurements. Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size, mutation rate and recombination rate. The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined. We also found that both fitness effects of individual mutations and epistatic fitness interactions cause recombination to accelerate adaptation. The estimated epistasis in the adapting populations is significantly negative. Our results highlight the importance of recombination in the evolution of HIV-I. [ABSTRACT FROM AUTHOR]

There is ample empirical evidence revealing that fitness landscapes are often complex: the fitness effect of a newly arisen mutation can depend strongly on the allelic state at other loci. However, little is known about the effects of recombination on adaptation on such fitness landscapes. Here, we investigate how recombination influences the rate of adaptation on a special type of complex fitness landscapes. On these landscapes, the mutational trajectories from the least to the most fit genotype are interrupted by genotypes with low relative fitness. We study the dynamics of adapting populations on landscapes with different compositions and numbers of low fitness genotypes, with and without recombination. Our results of the deterministic model (assuming an infinite population size) show that recombination generally decelerates adaptation on these landscapes. However, in finite populations, this deceleration is outweighed by the accelerating Fisher-Muller effect under certain conditions. We conclude that recombination has complex effects on adaptation that are highly dependent on the particular fitness landscape, population size and recombination rate. [ABSTRACT FROM AUTHOR]