Gastrointestinal Tract

Based on the fact that the interactive compounds often involve in similar biological activities [11] (link), it is feasible to predict the ATC-class of a query drug using the information of chemical-chemical interactions, as described below.
STITCH (Search tool for interactions of chemicals) [19] (link) is a large database containing known and predicted interactions between chemicals and between proteins derived from experiments, literature and other databases. We downloaded the information of chemical-chemical interactions from http://stitch.embl.de:8080/download/chemical_chemical.links.v2.0.tsv.gz. Each of these interactions was evaluated by a confidence score, ranging from 1 to 1000, to reflect the likelihood of its occurrence. For any two drugs d₁ and d₂, their interaction confidence score was denoted by . Particularly, if the interaction between d₁ and d₂ does not exist in STITCH, their interaction confidence score was set as zero, i.e., .
Suppose that a training dataset consists of n drugs , and that the 14 main ATC-classes are denoted by , where C₁ represents “Alimentary tract and metabolism”, C₂ “Blood and blood forming organs”, and so forth (see Table 1). The ATC-classes of any drug d_i can be formulated as where According to the chemical-chemical interaction approach, the likelihood for a query drug belonging to C_j, denoted as , can be calculated by where means that is an element of the training dataset . According Eq.6, the likelihood that belongs to C_j can be formulated as the maximum of the interaction confidence scores between and those drugs that belong to C_j in the training dataset . Obviously, the larger the score is, the more likely that belongs to . When , it means that the probability for the drug belonging to the class C_j is zero. Given a query drug compound , suppose the outcome derived from Eq.6 is which means that the highest probability for the drug belonging to the ATC-class is (“Antineoplastic and immunomodulating agents”), followed by (“Alimentary tract and metabolism”), and so forth (cf. Table 1). If there is a tie between two terms in Eq.7, then the probabilities for the drug belonging to the two corresponding classes are the same. But this kind of tie case rarely happened.
Note that the outcome of Eq.6 might turn out to be trivial, i.e., indicating that no chemical-chemical interaction exists for the query drug in the training dataset ; i.e., Under such a circumstance, no meaningful result would be obtained by the “interaction-based” method, and we should instead use the “similarity-based method as described in the next section.

To analyze the expression levels of HongrES1 in different tissues of R. dorsalis, the whole body, alimentary canal, reproductive organs and salivary gland were dissected from 30 RdFV-free males or virgin females at 5-days post eclosion. The relative expression of HongrES1 in different tissues was detected by RT-qPCR assays. To verify the expression patterns of HongrES1, the total proteins were extracted from various tissues of 30 RdFV-free males or females, and then analyzed by western blot assays. Antibodies against HongrES1 and histone H3 (0.5 μg/μl) served as the primary antibodies, and goat anti-rabbit IgG-peroxidase (0.5 μg/μl) served as the secondary antibody.
We also detected the effects of RdFV or RGDV infection on the expression levels of HongrES1 in the male reproductive system. The reproductive organs were dissected from 30 RdFV-free, RdFV-positive, or RGDV and RdFV co-positive males. The relative expression of HongrES1 was detected by RT-qPCR assays. In the corresponding western blot assay, antibodies against HongrES1, RdFV CP, RGDV P8, and histone H3 (0.5 μg/μl) served as the primary antibodies, and goat anti-rabbit IgG-peroxidase (0.5 μg/μl) served as the secondary antibody. At least three biological replicates were performed.