Comparative Analyses by Sequencing of Transcriptomes during Skeletal Muscle Development between Pig Breeds Differing in Muscle Growth Rate and Fatness

Understanding the dynamics of muscle transcriptome during development and between breeds differing in muscle growth is necessary to uncover the complex mechanism underlying muscle development. Herein, we present the first transcriptome-wide longissimus dorsi muscle development research concerning Lantang (LT, obese) and Landrace (LR, lean) pig breeds during 10 time-points from 35 days-post-coitus (dpc) to 180 days-post-natum (dpn) using Solexa/Illumina's Genome Analyzer. The data demonstrated that myogenesis was almost completed before 77 dpc, but the muscle phenotypes were still changed from 77 dpc to 28 dpn. Comparative analysis of the two breeds suggested that myogenesis started earlier but progressed more slowly in LT than in LR, the stages ranging from 49 dpc to 77 dpc are critical for formation of different muscle phenotypes. 595 differentially expressed myogenesis genes were identified, and their roles in myogenesis were discussed. Furthermore, GSK3B, IKBKB, ACVR1, ITGA and STMN1 might contribute to later myogenesis and more muscle fibers in LR than LT. Some myogenesis inhibitors (ID1, ID2, CABIN1, MSTN, SMAD4, CTNNA1, NOTCH2, GPC3 and HMOX1) were higher expressed in LT than in LR, which might contribute to more slow muscle differentiation in LT than in LR. We also identified several genes which might contribute to intramuscular adipose differentiation. Most important, we further proposed a novel model in which MyoD and MEF2A controls the balance between intramuscular adipogenesis and myogenesis by regulating CEBP family; Myf5 and MEF2C are essential during the whole myogenesis process while MEF2D affects muscle growth and maturation. The MRFs and MEF2 families are also critical for the phenotypic differences between the two pig breeds. Overall, this study contributes to elucidating the mechanism underlying muscle development, which could provide valuable information for pig meat quality improvement

Computing XVA for American basket derivatives by machine learning techniques

Total value adjustment (XVA) is the change in value to be added to the price of a derivative to account for the bilateral default risk and the funding costs. In this paper, we compute such a premium for American basket derivatives whose payoff depends on multiple underlyings. In particular, in our model, those underlyings are supposed to follow the multidimensional Black-Scholes stochastic model. In order to determine the XVA, we follow the approach introduced by (Burgard and Kjaer in SSRN Electronic J 7:1–19, 2010) and afterward applied by (Arregui et al. in Appl Math Comput 308:31–53, 2017), (Arregui et al. in Int J Comput Math 96:2157–2176, 2019) for the one-dimensional American derivatives. The evaluation of the XVA for basket derivatives is particularly challenging as the presence of several underlings leads to a high-dimensional control problem. We tackle such an obstacle by resorting to Gaussian Process Regression, a machine learning technique that allows one to address the curse of dimensionality effectively. Moreover, the use of numerical techniques, such as control variates, turns out to be a powerful tool to improve the accuracy of the proposed methods. The paper includes the results of several numerical experiments that confirm the goodness of the proposed methodologies

Enhancing valuation of variable annuities in Lévy models with stochastic interest rate

This paper extends the valuation and optimal surrender framework for variable annuities with guaranteed minimum benefits in a Lévy equity market environment by incorporating a stochastic interest rate described by the Hull-White model. This approach frames a more dynamic and realistic financial setting compared to previous literature. We exploit a robust valuation mechanism employing a hybrid numerical method that merges tree methods for interest rate modeling with finite difference techniques for the underlying asset price. This method is particularly effective for addressing the complexities of variable annuities, where periodic fees and mortality risks are significant factors. Our findings reveal the influence of stochastic interest rates on the strategic decision-making process concerning the surrender of these financial instruments. Through comprehensive numerical experiments, and by comparing our results with those obtained through the Longstaff-Schwartz Monte Carlo method, we illustrate how our refined model can guide insurers in designing contracts that equitably balance the interests of both parties. This is particularly relevant in discouraging premature surrenders while adapting to the realistic fluctuations of financial markets. Lastly, a comparative statics analysis with varying interest rate parameters underscores the impact of interest rates on the cost of the optimal surrender strategy, emphasizing the importance of accurately modeling stochastic interest rates

Mutations in the m-AAA proteases AFG3L2 and SPG7 are causing isolated dominant optic atrophy

Objective To improve the genetic diagnosis of dominant optic atrophy (DOA), the most frequently inherited optic nerve disease, and infer genotype-phenotype correlations.MethodsExonic sequences of 22 genes were screened by new-generation sequencing in patients with DOA who were investigated for ophthalmology, neurology, and brain MRI.ResultsWe identified 7 and 8 new heterozygous pathogenic variants in SPG7 and AFG3L2. Both genes encode for mitochondrial matricial AAA (m-AAA) proteases, initially involved in recessive hereditary spastic paraplegia type 7 (HSP7) and dominant spinocerebellar ataxia 28 (SCA28), respectively. Notably, variants in AFG3L2 that result in DOA are located in different domains to those reported in SCA28, which likely explains the lack of clinical overlap between these 2 phenotypic manifestations. In comparison, the SPG7 variants identified in DOA are interspersed among those responsible for HSP7 in which optic neuropathy has previously been reported.ConclusionsOur results position SPG7 and AFG3L2 as candidate genes to be screened in DOA and indicate that regulation of mitochondrial protein homeostasis and maturation by m-AAA proteases are crucial for the maintenance of optic nerve physiology

Ancestral gene acquisition as the key to virulence potential in environmental Vibrio populations

Diseases of marine animals caused by bacteria of the genus Vibrio are on the rise worldwide. Understanding the eco-evolutionary dynamics of these infectious agents is important for predicting and managing these diseases. Yet, compared to Vibrio infecting humans, knowledge of their role as animal pathogens is scarce. Here we ask how widespread is virulence among ecologically differentiated Vibrio populations, and what is the nature and frequency of virulence genes within these populations? We use a combination of population genomics and molecular genetics to assay hundreds of Vibrio strains for their virulence in the oyster Crassostrea gigas, a unique animal model that allows high-throughput infection assays. We show that within the diverse Splendidus clade, virulence represents an ancestral trait but has been lost from several populations. Two loci are necessary for virulence, the first being widely distributed across the Splendidus clade and consisting of an exported conserved protein (R5.7). The second is a MARTX toxin cluster, which only occurs within V. splendidus and is for the first time associated with virulence in marine invertebrates. Varying frequencies of both loci among populations indicate different selective pressures and alternative ecological roles, based on which we suggest strategies for epidemiological surveys