Complete Mitogenomes of Three Carangidae (Perciformes) Fishes: Genome Description and Phylogenetic Considerations

Carangidae are ecologically and economically important marine fish. The complete mitogenomes of three Carangidae species (Alectis indicus, Decapterus tabl, and Alepes djedaba) were sequenced, characterized, and compared with 29 other species of the family Carangidae in this study. The length of the three mitogenomes ranged from 16,530 to 16,610 bp, and the structures included 2 rRNA genes (12S rRNA and 16S rRNA), 1 control region (a non-coding region), 13 protein-coding genes, and 22 tRNA genes. Among the 22 tRNA genes, only tRNA-Ser (GCT) was not folded into a typical cloverleaf secondary structure and had no recognizable DHU stem. The full-length sequences and protein-coding genes (PCGs) of the mitogenomes of the three species all had obvious AT biases. The majority of the AT-skew and GC-skew values of the PCGs among the three species were negative, demonstrating bases T and C were more plentiful than A and G. Analyses of Ka/Ks and overall p-genetic distance demonstrated that ATP8 showed the highest evolutionary rate and COXI/COXII were the most conserved genes in the three species. The phylogenetic tree based on PCGs sequences of mitogenomes using maximum likelihood and Bayesian inference analyses showed that three clades were divided corresponding to the subfamilies Caranginae, Naucratinae, and Trachinotinae. The monophyly of each superfamily was generally well supported. The divergence time analyses showed that Carangidae evolved during three geological periods, the Cretaceous, Paleogene, and Neogene. A. indicus began to differentiate from other species about 27.20 million years ago (Mya) in the early Miocene, while D. tabl (21.25 Mya) and A. djedaba (14.67 Mya) differentiated in the middle Oligocene.

Comparative Characterization of the Complete Mitochondrial Genomes of the Three Apple Snails (Gastropoda: Ampullariidae) and the Phylogenetic Analyses

The apple snails Pomacea canaliculata, Pomacea diffusa and Pomacea maculate (Gastropoda: Caenogastropoda: Ampullariidae) are invasive pests causing massive economic losses and ecological damage. We sequenced and characterized the complete mitochondrial genomes of these snails to conduct phylogenetic analyses based on comparisons with the mitochondrial protein coding sequences of 47 Caenogastropoda species. The gene arrangements, distribution and content were canonically identical and consistent with typical Mollusca except for the tRNA-Gln absent in P. diffusa. An identifiable control region (d-loop) was absent. Bayesian phylogenetic analysis indicated that all the Ampullariidae species clustered on the same branch. The genus Pomacea clustered together and then with the genus Marisa. The orders Architaenioglossa and Sorbeoconcha clustered together and then with the order Hypsogastropoda. Furthermore, the intergenic and interspecific taxonomic positions were defined. Unexpectedly, Ceraesignum maximum, Dendropoma gregarium, Eualetes tulipa and Thylacodes squamigerus, traditionally classified in order Hypsogastropoda, were isolated from the order Hypsogastropoda in the most external branch of the Bayesian inference tree. The divergence times of the Caenogastropoda indicated that their evolutionary process covered four geological epochs that included the Quaternary, Neogene, Paleogene and Cretaceous periods. This study will facilitate further investigation of species identification to aid in the implementation of effective management and control strategies of these invasive species.

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets

There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products.