Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients' overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, IRF1, JAK2, CD8A, IRF8, STAT5B, and SELL may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.

Symbiosis between Dendrobium catenatum protocorms and Serendipita indica involves the plant hypoxia response pathway

Mycorrhizae are ubiquitous symbioses established between fungi and plant roots. Orchids, in particular, require compatible mycorrhizal fungi for seed germination and protocorm development. Unlike arbuscular mycorrhizal fungi, which have wide host ranges, orchid mycorrhizal fungi are often highly specific to their host orchids. However, the molecular mechanism of orchid mycorrhizal symbiosis is largely unknown compared to that of arbuscular mycorrhizal and rhizobial symbiosis. Here, we report that an endophytic Sebacinales fungus, Serendipita indica, promotes seed germination and the development of protocorms into plantlets in several epiphytic Epidendroideae orchid species (6 species in 2 genera), including Dendrobium catenatum, a critically endangered orchid with high medicinal value. Although plant-pathogen interaction and high meristematic activity can induce the hypoxic response in plants, it has been unclear whether interactions with beneficial fungi, especially mycorrhizal ones, also involve the hypoxic response. By studying the symbiotic relationship between D. catenatum and S. indica, we determined that hypoxia-responsive genes, such as those encoding alcohol dehydrogenase (ADH), are highly induced in symbiotic D. catenatum protocorms. In situ hybridization assay indicated that the ADH gene is predominantly expressed in the basal mycorrhizal region of symbiotic protocorms. Additionally, the ADH inhibitors puerarin and 4-methylpyrazole both decreased S. indica colonization in D. catenatum protocorms. Thus, our study reveals that S. indica is widely compatible with orchids and that ADH and its related hypoxia-responsive pathway are involved in establishing successful symbiotic relationships in germinating orchids.

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.

Complete mitogenome of Anopheles sinensis and mitochondrial insertion segments in the nuclear genomes of 19 mosquito species

Anopheles sinensis is a major malarial vector in China and Southeast Asia. The mitochondria is involved in many important biological functions. Nuclear mitochondrial DNA segments (NUMTs) are common in eukaryotic organisms, but their characteristics are poorly understood. We sequenced and analyzed the complete mitochondrial (mt) genome of An. sinensis. The mt genome is 15,418 bp long and contains 13 protein-coding genes (PCGs), two rRNAs, 22 tRNAs and a large non-coding region. Its gene arrangement is similar to previously published mosquito mt genomes. We identified and analyzed the NUMTs of 19 mosquito species with both nuclear genomes and mt genome sequences. The number, total length and density of NUMTs are significantly correlated with genome size. About half of NUMTs are short (< 200 bp), but larger genomes can house longer NUMTs. NUMTs may help explain genome size expansion in mosquitoes. The expansion due to mitochondrial insertion segments is variable in different insect groups. PCGs are transferred to nuclear genomes at a higher frequency in mosquitoes, but NUMT origination is more different than in mammals. Larger-sized nuclear genomes have longer mt genome sequences in both mosquitoes and mammals. The study provides a foundation for the functional research of mitochondrial genes in An. sinensis and helps us understand the characteristics and origin of NUMTs and the potential contribution to genome expansion.

Potential biomarkers and lncRNA-mRNA regulatory networks in invasive growth hormone-secreting pituitary adenomas

PURPOSE

Growth hormone-secreting pituitary adenomas (GH-PAs) are common subtypes of functional PAs. Invasive GH-PAs play a key role in restricting poor outcomes. The transcriptional changes in GH-PAs were evaluated.

METHODS

In this study, the transcriptome analysis of six different GH-PA samples was performed. The functional roles, co-regulatory network, and chromosome location of differentially expressed (DE) genes in invasive GH-PAs were explored.

RESULTS

Bioinformatic analysis revealed 101 DE mRNAs and 70 DE long non-coding RNAs (lncRNAs) between invasive and non-invasive GH-PAs. Functional enrichment analysis showed that epithelial cell differentiation and development pathways were suppressed in invasive GH-PAs, whereas the pathways of olfactory transduction, retinol metabolism, drug metabolism-cytochrome P450, and metabolism of xenobiotics by cytochrome P450 had an active trend. In the protein-protein interaction network, 11 main communities were characterized by cell- adhesion, -motility, and -cycle; transport process; phosphorus and hormone metabolic processes. The SGK1 gene was suggested to play a role in the invasiveness of GH-PAs. Furthermore, the up-regulated genes OR51B6, OR52E4, OR52E8, OR52E6, OR52N2, MAGEA6, MAGEC1, ST8SIA6-AS1, and the down-regulated genes GAD1-AS1 and SPINT1-AS1 were identified in the competing endogenous RNA network. The RT-qPCR results further supported the aberrant expression of those genes. Finally, the enrichment of DE genes in chromosome 11p15 and 12p13 regions were detected.

CONCLUSION

Our findings provide a new perspective for studies evaluating the underlying mechanism of invasive GH-PAs.

Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis

BACKGROUND

Pituitary adenoma (PA) is a prevalent intracranial tumor. Metabolites differ between pituitary tumor and healthy tissues or among different tumor subtypes. However, the transcriptional changes in metabolic enzymes, which are usually seemed as targets for metabolic therapy, remain unidentified.

METHODS

Using microarray data for 160 samples from the Gene Expression Omnibus database, across the four most common tumor subtypes, we present the integrated identification of differentially expressed genes (DEGs) between tumors and controls.

RESULTS

Subtype-specific DEGs revealed 1081 prolactin tumor-specific DEGs, 437 nonfunctioning tumor-specific DEGs, and 217 common DEGs among the four subtypes. Functional enrichment showed that a lot of biological functions related to metabolism had changed. Twenty-one prolactin and twenty-three nonfunctioning tumor-specific metabolic-related DEGs are mainly involved in fatty acid and nucleotide metabolism, redox reaction, and gluconeogenesis. Eighteen metabolic-related DEGs enriched in the metabolism of xenobiotics by the cytochrome P450 pathway, sulfur metabolism, retinoid metabolism, and glucose homeostasis were abnormal in all subtypes of PA.

CONCLUSION

Based on a comprehensive bioinformatics analysis of the available PA-related transcriptomics data, we identified specific DEGs related to metabolism, and some of them might be new attractive therapeutic targets. Especially, PDK4 and PCK1 might be new attractive biomarkers and therapeutic targets.

Stoichiogenomics reveal oxygen usage bias, key proteins and pathways associated with stomach cancer

Stomach cancer involves hypoxia-specific microenvironments. Stoichiogenomics explores environmental resource limitation on biological macromolecules in terms of element usages. However, the patterns of oxygen usage by proteins and the ways that proteins adapt to a cancer hypoxia microenvironment are still unknown. Here we compared the oxygen and carbon contents ([C]) between proteomes of stomach cancer (hypoxia) and two stomach glandular cells (normal). Key proteins, genome locations, pathways, and functional dissection associated with stomach cancer were also studied. An association of oxygen content ([O]) and protein expression level was revealed in stomach cancer and stomach glandular cells. For differentially expressed proteins (DEPs), oxygen contents in the up regulated proteins were3.2%higherthan that in the down regulated proteins in stomach cancer. A total of 1,062 DEPs were identified; interestingly none of these proteins were coded on Y chromosome. The up regulated proteins were significantly enriched in pathways including regulation of actin cytoskeleton, cardiac muscle contraction, pathway of progesterone-mediated oocyte maturation, etc. Functional dissection of the up regulated proteins with high oxygen contents showed that most of them were cytoskeleton, cytoskeleton associated proteins, cyclins and signaling proteins in cell cycle progression. Element signature of resource limitation could not be detected in stomach cancer for oxygen, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins was adapted to the rapid growth and fast division of the stomach cancer cells. In addition, oxygen usage bias, key proteins and pathways identified in this paper laid a foundation for application of stoichiogenomics in precision medicine.

A novel exosome-derived prognostic signature and risk stratification for breast cancer based on multi-omics and systematic biological heterogeneity

Tumor heterogeneity remains a major challenge for disease subtyping, risk stratification, and accurate clinical management. Exosome-based liquid biopsy can effectively overcome the limitations of tissue biopsy, achieving minimal invasion, multi-point dynamic monitoring, and good prognosis assessment, and has broad clinical prospects. However, there is still lacking comprehensive analysis of tumor-derived exosome (TDE)-based stratification of risk patients and prognostic assessment for breast cancer with systematic dissection of biological heterogeneity. In this study, the robust corroborative analysis for biomarker discovery (RCABD) strategy was used for the identification of exosome molecules, differential expression verification, risk prediction modeling, heterogenous dissection with multi-ome (6101 molecules), our ExoBCD database (306 molecules), and 53 independent studies (481 molecules). Our results showed that a 10-molecule exosome-derived signature (exoSIG) could successfully fulfill breast cancer risk stratification, making it a novel and accurate exosome prognostic indicator (Cox P = 9.9E-04, HR = 3.3, 95% CI 1.6-6.8). Interestingly, HLA-DQB2 and COL17A1, closely related to tumor metastasis, achieved high performance in prognosis prediction (86.35% contribution) and accuracy (Log-rank P = 0.028, AUC = 85.42%). With the combined information of patient age and tumor stage, they formed a bimolecular risk signature (Clinmin-exoSIG) and a convenient nomogram as operable tools for clinical applications. In conclusion, as an extension of ExoBCD, this study conducted systematic analyses to identify prognostic multi-molecular panel and risk signature, stratify patients and dissect biological heterogeneity based on breast cancer exosomes from a multi-omics perspective. Our results provide an important reference for in-depth exploration of the "biological heterogeneity - risk stratification - prognosis prediction".

MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning

In transcriptomics, differentially expressed genes (DEGs) provide fine-grained phenotypic resolution for comparisons between groups and insights into molecular mechanisms underlying the pathogenesis of complex diseases or phenotypes. The robust detection of DEGs from large datasets is well-established. However, owing to various limitations (e.g., the low availability of samples for some diseases or limited research funding), small sample size is frequently used in experiments. Therefore, methods to screen reliable and stable features are urgently needed for analyses with limited sample size. In this study, MSPJ, a new machine learning approach for identifying DEGs was proposed to mitigate the reduced power and improve the stability of DEG identification in small gene expression datasets. This ensemble learning-based method consists of three algorithms: an improved multiple random sampling with meta-analysis, SVM-RFE (support vector machines-recursive feature elimination), and permutation test. MSPJ was compared with ten classical methods by 94 simulated datasets and large-scale benchmarking with 165 real datasets. The results showed that, among these methods MSPJ had the best performance in most small gene expression datasets, especially those with sample size below 30. In summary, the MSPJ method enables effective feature selection for robust DEG identification in small transcriptome datasets and is expected to expand research on the molecular mechanisms underlying complex diseases or phenotypes.

Stoichioproteomics reveal oxygen usage bias, key proteins and pathways in glioma

BACKGROUND

The five-year survival rate and therapeutic effect of malignant glioma is low. Identification of key/associated proteins and pathways in glioma is necessary for developing effective diagnosis and targeted therapy of glioma. In addition, Glioma involves hypoxia-specific microenvironment, whether hypoxia restriction influences the stoichioproteomic characteristics of expressed proteins is unknown.

METHODS

In this study, we analyzed the most comprehensive immunohistochemical data from 12 human glioma samples and 4 normal cell types of cerebral cortex, identified differentially expressed proteins (DEPs), and researched the oxygen contents of DEPs, highly and lowly expressed proteins. Further we located key genes on human genome to determine their locations and enriched them for key functional pathways.

RESULTS

Our results showed that although no difference was detected on whole proteome, the average oxygen content of highly expressed proteins is 6.65% higher than that of lowly expressed proteins in glioma. A total of 1480 differentially expressed proteins were identified in glioma, including 226 up regulated proteins and 1254 down regulated proteins. The average oxygen content of up regulated proteins is 2.56% higher than that of down regulated proteins in glioma. The localization of differentially expressed genes on human genome showed that most genes were on chromosome 1 and least on Y. The up regulated proteins were significantly enriched in pathways including cell cycle, pathways in cancer, oocyte meiosis, DNA replication etc. Functional dissection of the up regulated proteins with high oxygen contents showed that 51.28% of the proteins were involved in cell cycle and cyclins.

CONCLUSIONS

Element signature of oxygen limitation could not be detected in glioma, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins and DEPs were adapted to the fast division of glioma cells. This study can help to reveal the molecular mechanism of glioma, and provide a new approach for studies of cancer-related biomacromolecules. In addition, this study lays a foundation for application of stoichioproteomics in precision medicine.

Preparation of phosphorus‐doped cow milk‐derived carbon quantum dots and detection of Au3+

A new fluorescent probe based on phosphorus‐doped cow milk‐derived carbon quantum dots (P‐CMCQDs) for the detection of Au3+ was studied in water samples. P‐CMCQDs were prepared by hydrothermal method with cow milk as carbon source and phosphoric acid as dopant. P‐CMCQDs show green fluorescence under ultraviolet light and can be partially quenched by Au3+. However, the excellent fluorescence characteristics of P‐CMCQDs‐ascorbic acid (AA) were obtained and can be used to detect trace Au3+ by simply mixing the P‐CMCQDs with AA. The sensing mechanism is based on the reduction of Au3+ to AuNPs by P‐CMCQDs and AA, and then AuNPs induce the fluorescence quenching of P‐CMCQDs through synergistic static quenching and internal filtering effect. The fluorescence intensity of P‐CMCQDs‐AA varies with the concentration of Au3+ (10–140 μmol/L), the detection limit is 4.2 nmol/L, indicating that the P‐CMCQDS‐AA probe has excellent sensitivity. In the detection of Au3+ in tap water and lake water, it is found that the recovery is between 99.6% and 100.5%, and the relative standard deviation is between 1.7% and 3.2%. This method is fast, efficient, and sensitive, and can be used to detect Au3+ in actual water samples.

Practical Applications

Although the element gold is chemically inert, soluble gold salts remaining in the environment in the form of ions are toxic and serious damage the peripheral nervous system, causing other health issues. Therefore, it seems necessary to design an efficient and sensitive method to detect Au3+ in environment. Among many detection methods, fluorescence sensing method is more suitable for on‐site, rapid detection. However, the complex system, high limit of detection and environmental pollution caused by metal luminescence limit its application. In this paper, the biomass P‐CMCQDs was synthesized from cow milk by hydrothermal method. By adding AA in the synthesized P‐CMCQDs, the limit of detection (LOD) was greatly improved, and the trace Au3+ detection in tap water and lake water was highly selective and sensitive.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

The complete mitogenome of Lasioglossum affine (Hymenoptera: Halictidae) and phylogenetic analysis

The complete mitogenome of Lasioglossum affine (Hymenoptera: Halictidae) was sequenced and analyzed. The whole mitogenome is 17,352 bp (AT%=84.1%) and encodes 37 typical eukaryotic mitochondrial genes, including 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and an AT-rich region. Further analysis found three gene rearrangements, where trn I-Q-M → trn M-I-Q, trn W-C-Y → trn C-W-Y, and trn K-D → trn D-K were shuffled. The phylogenetic relationships of 19 species of Hymenoptera were established using maximum-likelihood method based on 13 concatenated PCGs. The result showed that Lasioglossum affine is a sister of Lasioglossum sp. SJW-2017.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines.

RESULTS

We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation.

CONCLUSIONS

No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Lung Cancer Biomarker Database (LCBD): a comprehensive and curated repository of lung cancer biomarkers

BACKGROUND

Lung cancer remains a leading cause of cancer-related mortality, primarily because of the lack of effective diagnostic and therapeutic biomarkers. To address the issue of fragmented biomarker data across numerous publications, we have developed the Lung Cancer Biomarker Database (LCBD, http://lcbd.biomarkerdb.com ).

METHODS

We comprehensively reviewed biomarker-related studies up to June 30, 2023, and extracted relevant biomarker information. The identified biomarkers were systematically annotated, including genes, proteins, GO terms, KEGG pathways, biomarker types, molecular types, developmental stages, discovery methods, sources, populations, and sample sizes. The LCBD online platform was developed to integrate lung cancer biomarker data, and provide search, browsing, and data download functions for researchers. To validate the data in the LCBD, we conducted three case studies comparing models with and without LCBD data.

RESULTS

After deduplication and summarization, we collected 1,447 unique biomarkers that were systematically annotated. We then developed the LCBD specifically for use in lung cancer diagnosis. The validity of the biomarkers in the LCBD was confirmed using prognostic models, diagnostic models, and immune infiltration models.

CONCLUSION

The LCBD provides a centralized platform for lung cancer biomarkers, facilitating early screening and personalized treatment. This database is poised to become a valuable resource for lung cancer research and therapeutic strategies.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Common Methods for Phylogenetic Tree Construction and Their Implementation in R

A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Comparative Transcriptomic Analysis Revealing the Potential Mechanisms of Erythritol-Caused Mortality and Oviposition Inhibition in Drosophila melanogaster

Erythritol has shown excellent insecticidal performance against a wide range of insect species, but the molecular mechanism by which it causes insect mortality and sterility is not fully understood. The mortality and sterility of Drosophila melanogaster were assessed after feeding with 1M erythritol for 72 h and 96 h, and gene expression profiles were further compared through RNA sequencing. Enrichment analysis of GO and KEGG revealed that expressions of the adipokinetic hormone gene (Akh), amylase gene (Amyrel), α-glucosidase gene (Mal-B1/2, Mal-A1-4, Mal-A7/8), and triglyceride lipase gene (Bmm) were significantly up-regulated, while insulin-like peptide genes (Dilp2, Dilp3 and Dilp5) were dramatically down-regulated. Seventeen genes associated with eggshell assembly, including Dec-1 (down 315-fold), Vm26Ab (down 2014-fold) and Vm34Ca (down 6034-fold), were significantly down-regulated or even showed no expression. However, there were no significant differences in the expression of three diuretic hormone genes (DH44, DH31, CAPA) and eight aquaporin genes (Drip, Big brain, AQP, Eglp1, Eglp2, Eglp3, Eglp4 and Prip) involved in osmolality regulation (all p value > 0.05). We concluded that erythritol, a competitive inhibitor of α-glucosidase, severely reduced substrates and enzyme binding, inhibiting effective carbohydrate hydrolysis in the midgut and eventually causing death due to energy deprivation. It was clear that Drosophila melanogaster did not die from the osmolality of the hemolymph. Our findings elucidate the molecular mechanism underlying the mortality and sterility in Drosophila melanogaster induced by erythritol feeding. It also provides an important theoretical basis for the application of erythritol as an environmentally friendly pesticide.