Mendelian randomization (MR) plays an increasingly important role in drug discovery, yet its full potential and optimized framework for accurately predicting drug targets have not been firmly established. This study aimed to evaluate the efficacy of multiple MR models in predicting effective drug targets and to propose the optimal selection of models and instrumental variables for MR analyses. We meticulously constructed datasets using approved drug indications and a range of IVs, encompassing cis-expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs). Our analytical approach incorporated diverse models, including Wald’s ratio, inverse-variance weighted (IVW), MR‒Egger, weighted median, and MRPRESSO, to evaluate MR's validity in drug target identification. The findings highlight MR efficacy, demonstrating approximately 70% accuracy in predicting effective drug targets. For the selection of instrumental variables, tissue-specific eQTLs in disease-related tissues emerged as superior IVs. We identified a r² threshold below 0.3 as optimal for excluding redundant SNPs. To optimize the MR model, we recommend IVW as the primary computational model, complemented by the weighted median and MRPRESSO for robust analyses. This finding is consistent with current findings in the literature. Notably, a P value of < 0.05, without false discovery rate correction, is the most effective for identifying significant drug targets. With the optimal strategies we summarized, we identified new potential therapeutic targets for IBD and its subtypes, including ERAP1, HLA-DQA1, IRF5 and other genes. This study provides a refined, optimized strategy for MR application in drug discovery. Our insights into the selection of instrumental variables, model preferences, and parameter thresholds significantly enhance MR's predictive capacity, offering a comprehensive guide for future drug development research.

The conservation and breeding of the western honey bee (Apis mellifera) is central dependent on accurate subspecies assignment, but the most commonly used methods are labor-intensive classical morphometrics and costly molecular assays. We developed an XGBoost-based classification framework using a compact set of routinely measurable characters. A curated dataset of labeled workers was measured under harmonized protocols; features were screened according to embedded importance, and model performance was assessed using five-fold cross-validation, outperforming standard machine learning baselines. The resulting model using only the top 10 characters—primarily forewing venation angles and abdominal plate metrics—achieved high performance (accuracy = 0.98; F1 = 0.99) and an area under the receiver operating characteristic curve (AUC) of 0.99 (95% CI = 0.995–0.999). SHAP analyses confirmed the discriminatory contributions of these features, while error inspection suggested that misclassifications were concentrated in morphologically overlapping lineages. The model’s performance supports its use as a rapid triage tool alongside genetic testing, providing a scalable and interpretable tool for researchers to create and deploy custom morphometric models, demonstrated here for A. mellifera but portable to other insect taxa.

Jujube flower disease (JFD) is a recurrent disorder affecting honeybees during the flowering period of Ziziphus jujuba, but its molecular basis remains unclear. To characterize JFD-associated molecular changes, we integrated widely targeted metabolomics and RNA sequencing (RNA-seq) in exposed asymptomatic jujube flower foragers (HC) and in bees showing characteristic JFD symptoms. Filtered metabolomic profiles showed clear separation between HC and JFD samples and revealed two major metabolite-level alterations. Tryptophan metabolism was the strongest enriched pathway among differentially abundant metabolites (Rich Factor = 14/16; FDR = $9.15\times {10}^{-7}$), whereas metabolites decreased in JFD were enriched mainly in galactose metabolism (Rich Factor = 7/16; FDR = $1.23\times {10}^{-4}$), amino sugar and nucleotide sugar metabolism (Rich Factor = 5/15; FDR = 0.00762), biosynthesis of nucleotide sugars (Rich Factor = 4/15; FDR = 0.0318), and starch and sucrose metabolism (Rich Factor = 3/8; FDR = 0.0318). RNA-seq detected 10,259 genes and identified 23 differentially expressed genes under the selected threshold, including 20 increased and 3 decreased genes in JFD bees. Although individual-gene differential expression was limited, preranked gene-set enrichment analysis identified moderate negative enrichment of the sugar supply/storage gene set (NES = −1.96; FDR = 0.024). The tryptophan/aromatic amino acid gene set was not enriched at the transcriptomic level, indicating that the tryptophan-related alteration was mainly observed at the metabolite level. Within these gene sets, HK showed the largest downward RNA-seq trend among the five displayed sugar-axis enzyme genes and a directionally concordant qRT-PCR trend; on this basis, HK was retained only as a candidate for future functional validation. Overall, these findings suggest that JFD is associated with altered tryptophan-related metabolism and reduced carbohydrate/nucleotide sugar-related metabolite abundance, with partial transcriptomic support for altered sugar supply and storage. This molecular profile provides candidate pathways and genes for future mechanistic investigation rather than established biomarkers, regulatory mechanisms, or evidence of disease causation.

Glycolysis plays a crucial role in neuronal homeostasis and is regulated by neuroendocrine signals, particularly dopamine, yet the underlying mechanisms remain unclear. Metabolomics showed that bromocriptine (BRC), a dopamine receptor agonist, increased glycolytic flux in forager bees, as indicated by higher glucose levels, accumulation of glycolytic intermediates, and reduced pentose-phosphate pathway metabolites. Single-cell and single-nucleus RNA-seq further indicated that acetylcholine (ACh) released by Kenyon cells may mediate dopamine-regulated glycolysis in surface glia (SG). We also observed coordinated insulin/insulin-like growth factor signaling (IIS) between cortex glia (CG) and SG. Mechanistically, co-administration of a nicotinic ACh receptor agonist with BRC attenuated the BRC-induced upregulation of SLC2A1, HK, and IGF transcripts, consistent with the proposed pathway. Together, these findings are consistent with a dual regulatory scheme in which a dopamine-ACh axis operates in concert with IIS to shape glial metabolic plasticity with cell-type specificity.

Background: Honeybees sustain vital ecological roles through foraging behavior, which provides pollination services and is likely regulated by dopamine signaling coupled to brain energy metabolism. However, the genetic and metabolic mechanisms underlying this regulation remain unclear. Methods: We treated honeybee workers with the dopamine receptor agonist bromocriptine and employed an integrative approach, combining liquid chromatography–mass spectrometry (LC–MS) metabolomics with single-nucleus RNA sequencing (snRNA-seq). Results: Metabolomics revealed increased levels of N6-carboxymethyllysine (CML) and a coordinated shift in central carbon metabolites, including higher glucose, pyruvate, and lactate within glycolysis, and ribose-5-phosphate in the pentose phosphate pathway (PPP). Integration with transcriptomics showed heterogeneous responses: glial cells exhibited higher glycolysis pathway scores and upregulated hexokinase expression compared to neurons, whereas major PPP enzymes were upregulated in both glial and neuronal subsets. Conclusions: These findings suggest that dopamine receptor activation is associated with altered whole-brain metabolic profiles and concurrent, cell-type-specific upregulation of glycolytic and PPP enzyme genes, particularly in glia. This study characterizes these neuro-metabolic associations, offering insights into the cellular and metabolic basis of foraging behavior in worker bees.