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.

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.

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.

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".

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.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications.

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors.

Antioxidant peptides can protect against free radical-mediated diseases, especially food-derived antioxidant peptides are considered as potential competitors among synthetic antioxidants due to their safety, high activity and abundant sources. However, wet experimental methods can not meet the need for effectively screening and clearly elucidating the structure-activity relationship of antioxidant peptides. Therefore, it is particularly important to build a reliable prediction platform for antioxidant peptides. In this work, we developed a platform, AnOxPP, for prediction of antioxidant peptides using the bidirectional long short-term memory (BiLSTM) neural network. The sequence characteristics of peptides were converted into feature codes based on amino acid descriptors (AADs). Our results showed that the feature conversion ability of the combined-AADs optimized by the forward feature selection method was more accurate than that of the single-AADs. Especially, the model trained by the optimal descriptor SDPZ27 significantly outperformed the existing predictor on two independent test sets (Accuracy = 0.967 and 0.819, respectively). The SDPZ27-based AnOxPP learned four key structure-activity features of antioxidant peptides, with the following importance as steric properties > hydrophobic properties > electronic properties > hydrogen bond contributions. AnOxPP is a valuable tool for screening and design of peptide drugs, and the web-server is accessible at http://www.cqudfbp.net/AnOxPP/index.jsp.

Genome-wide characterization of PEBP gene family in Perilla frutescens and PfFT1 promotes flowering time in Arabidopsis thaliana.

Phosphatidylethanolamine-binding proteins (PEBP) family plays important roles in regulating plant flowering time and morphogenesis. However, geneme-wide identification and functional analysis of PEBP genes in the rigorous short-day plant Perilla frutescens (PfPEBP) have not been studied. In this study, 10 PfPEBP were identified and divided into three subfamilies based on their phylogenetic relationships: FT-like, TFL1-like and MFT-like. Gene structure analysis showed that all PfPEBP genes contain 4 exons and 3 introns. Motifs DPDxP and GIHR essential for anion-binding activity are highly conserved in PfPEBP. A large number of light-responsive elements were detected in promoter regions of PfPEBP. Gene expression of PfFT1 exhibited a diurnal rhythm. It was highly expressed in leaves under the short-day photoperiod, but higher in flowers and seeds under the long-day photoperiod. Overexpression of PfFT1 in Arabidopsis thaliana not only promoted early flowering of Col-0 or Ler, but also rescued the late flowering phenotype of ft-1 mutant. We concluded that PfFT1 promotes early flowering by regulating the expression of flowering-related genes AtAP1, AtLFY, AtFUL and AtSOC1. In conclusion, our results provided valuable information for elucidating the functions of PfPEBP in P. frutescens and shed light on the promoting effect of PfFT1 on flowering.

Genome-wide analysis of DGAT gene family in Perilla frutescens and functional characterization of PfDGAT2-2 and PfDGAT3-1 in Arabidopsis.

Diacylglycerol acyltransferase (DGAT) is the rate-limiting enzyme that catalyzes the final step in triacylglycerol biosynthesis, however, members of DGAT gene family of Perilla frutescens has not yet been identified and characterized. In this study, a total of 20 PfDGAT genes were identified from the genome of Perilla frutescens and were divided into four groups (PfDGAT1, PfDGAT2, PfDGAT3, PfWS/DGAT) according to their phylogenetic relationships. These were unevenly distributed across the 12 chromosomes. Sequence analysis revealed that PfDGAT members of the same subfamily have highly conserved gene structures, protein motifs and cis-acting elements in their promoters. Gene duplication analysis showed that random duplication and segmental duplication contributed to the expansion of the DGAT family in P. frutescens. RNA-seq and qRT-PCR analysis suggested that they may play a role in the growth and development of Perilla, especially in the accumulation of seed oil. Compared with the wild-type, seed length, width, and 1000-seed weight of transgenic PfDGAT2-2 and PfDGAT3-1 Arabidopsis were significantly increased, as well as the seed oil content increased by 7.36-15.83 %. Over-expression of PfDGAT2-2 could significantly increase the content of C18:3 and C20:1 in Arabidopsis, while over-expression of PfDGAT3-1 could significantly enhance the content of C18:2 and C18:3. In conclusion, in this study the characteristics and potential functions of the PfDGAT family members were elucidated. Our findings provided basic information for further functional studies and helped to increase the yield and quality of Perilla oil.

DFBP: a comprehensive database of food-derived bioactive peptides for peptidomics research.

MOTIVATION: Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence-structure-activity relationship of FBPs in a large dataset. RESULTS: Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence-structure-activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. AVAILABILITY AND IMPLEMENTATION: DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning.

Background: Melanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME. Methods: Three gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis. Results: The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively. Conclusion: Confirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.

Genome-wide identification and expression profiling of odorant receptor genes in the malaria vector Anopheles sinensis.

BACKGROUND: The olfactory system plays a crucial role in regulating insect behaviors. The detection of odorants is mainly mediated by various odorant receptors (ORs) that are expressed in the dendrites of olfactory neurons of chemosensilla. Anopheles sinensis is a major malaria vector in Eastern Asia and its genome has recently been successfully sequenced and annotated. In this study, we present genome-wide identification and expression profiling of OR genes in different chemosensory tissues of An. sinensis. METHODS: The OR genes were identified using the available genome sequences of An. sinensis. A series of bioinformatics analyses were conducted to investigate the structure, genome distribution, selective pressure and phylogenetic relationships of OR genes, the conserved domains and specific functional sites in the OR amino acid sequences. The expression levels of OR genes were analyzed from transcriptomic data from An. sinensis antennae, proboscis and maxillary palps of both sexes. RESULTS: A total of 59 putative OR genes have been identified and characterized in An. sinensis. This number is significantly less than that in An. gambiae. Whether this difference is caused by the contraction or expansion of OR genes after divergence of the two species remains unknown. The RNA-seq analysis showed that AsORs have obvious tissue- and sex-specific expression patterns. Most AsORs are highly expressed in the antennae and the expression pattern and number of AsORs expressed in antennae are similar in males and females. However, the relative levels of AsOR transcripts are much higher in female antennae than in male antennae, which indicates that the odor sensitivity is likely to be increased in female mosquitoes. Based on the expression patterns and previous studies, we have speculated on the functions of some OR genes but this needs to be validated by further behavioral, molecular and electrophysiological studies. Further studies are necessary to compare the olfactory-driven behaviors and identify receptors that respond strongly to components of human odors that may act in the process of human recognition. CONCLUSIONS: This is the first genome-wide analysis of the entire repertoire of OR genes in An. sinensis. Characterized features and profiled expression patterns of ORs suggest their involvement in the odorous reception of this species. Our findings provide a basis for further research on the functions of OR genes and additional genetic and behavioral targets for more sustainable management of An. sinensis in the future.

Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends.

Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.

Comparative Mitogenomic Analysis of Two Cuckoo Bees (Apoidea: Anthophila: Megachilidae) with Phylogenetic Implications.

Bees (Hymenoptera, Apoidea and Anthophila) are distributed worldwide and considered the primary pollinators of angiosperm. Megachilidae is one of the largest families of Anthophila. In this study, two complete mitogenomes of cuckoo bees in Megachilidae, namely Coelioxys fenestrata and Euaspis polynesia, were amplified and sequenced, with a length of 17,004 bp (C. fenestrata) and 17,682 bp (E. polynesia). The obtained results show that 37 mitogenomic genes and one putative control region were conserved within Hymenoptera. Truncated stop codon T was found in the cox3 gene of E. polynesia. The secondary structure of small (rrnS) and large (rrnL) rRNA subunits contained three domains (28 helices) and five domains (44 helices) conserved within Hymenoptera, respectively. Compared with ancestral gene order, gene rearrangement events included local inversion and gene shuffling. In order to reveal the phylogenetic position of cuckoo bees, we performed phylogenetic analysis. The results supported that all families of Anthophila were monophyletic, the tribe-level relationship of Megachilidae was Osmiini + (Anthidiini + Megachilini) and Coelioxys fenestrata was clustered to the Megachile genus, which was more closely related to Megachile sculpturalis and Megachile strupigera than Euaspis polynesia.

ExoBCD: a comprehensive database for exosomal biomarker discovery in breast cancer.

Effective and safe implementation of precision oncology for breast cancer is a vital strategy to improve patient outcomes, which relies on the application of reliable biomarkers. As 'liquid biopsy' and novel resource for biomarkers, exosomes provide a promising avenue for the diagnosis and treatment of breast cancer. Although several exosome-related databases have been developed, there is still lacking of an integrated database for exosome-based biomarker discovery. To this end, a comprehensive database ExoBCD (https://exobcd.liumwei.org) was constructed with the combination of robust analysis of four high-throughput datasets, transcriptome validation of 1191 TCGA cases and manual mining of 950 studies. In ExoBCD, approximately 20 900 annotation entries were integrated from 25 external sources and 306 exosomal molecules (49 potential biomarkers and 257 biologically interesting molecules). The latter could be divided into 3 molecule types, including 121 mRNAs, 172 miRNAs and 13 lncRNAs. Thus, the well-linked information about molecular characters, experimental biology, gene expression patterns, overall survival, functional evidence, tumour stage and clinical use were fully integrated. As a data-driven and literature-based paradigm proposed of biomarker discovery, this study also demonstrated the corroborative analysis and identified 36 promising molecules, as well as the most promising prognostic biomarkers, IGF1R and FRS2. Taken together, ExoBCD is the first well-corroborated knowledge base for exosomal studies of breast cancer. It not only lays a foundation for subsequent studies but also strengthens the studies of probing molecular mechanisms, discovering biomarkers and developing meaningful clinical use.

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 â†’ trn M-I-Q, trn W-C-Y â†’ trn C-W-Y, and trn K-D â†’ 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.

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.

Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration.

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

Excess melanin precursors rescue defective cuticular traits in stony mutant silkworms probably by upregulating four genes encoding RR1-type larval cuticular proteins.

Melanin and cuticular proteins are vital cuticle components in insects. Cuticular defects caused by mutations in cuticular protein-encoding genes can obstruct melanin deposition. The effects of changes in melanin on the expression of cuticular protein-encoding genes, the cuticular and morphological traits, and the origins of these effects are unknown. We found that the cuticular physical characteristics and the expression patterns of larval cuticular protein-encoding genes markedly differed between the melanic and non-melanic integument regions. By using four p multiple-allele color pattern mutants with increasing degrees of melanism (+p, pM, pS, and pB), we found that the degree of melanism and the expression of four RR1-type larval cuticular protein-encoding genes (BmCPR2, BmLcp18, BmLcp22, and BmLcp30) were positively correlated. By modulating the content of melanin precursors and the expression of cuticular protein-encoding genes in cells in tissues and in vivo, we showed that this positive correlation was due to the induction of melanin precursors. More importantly, the melanism trait introduced into the BmCPR2 deletion strain Dazao-stony induced up-regulation of three other similar chitin-binding characteristic larval cuticular protein-encoding genes, thus rescuing the cuticular, morphological and adaptability defects of the Dazao-stony strain. This rescue ability increased with increasing melanism levels. This is the first study reporting the induction of cuticular protein-encoding genes by melanin and the biological importance of this induction in affecting the physiological characteristics of the cuticle.

Consistent Biomarkers and Related Pathogenesis Underlying Asthma Revealed by Systems Biology Approach.

Asthma is a common chronic airway disease worldwide. Due to its clinical and genetic heterogeneity, the cellular and molecular processes in asthma are highly complex and relatively unknown. To discover novel biomarkers and the molecular mechanisms underlying asthma, several studies have been conducted by focusing on gene expression patterns in epithelium through microarray analysis. However, few robust specific biomarkers were identified and some inconsistent results were observed. Therefore, it is imperative to conduct a robust analysis to solve these problems. Herein, an integrated gene expression analysis of ten independent, publicly available microarray data of bronchial epithelial cells from 348 asthmatic patients and 208 healthy controls was performed. As a result, 78 up- and 75 down-regulated genes were identified in bronchial epithelium of asthmatics. Comprehensive functional enrichment and pathway analysis revealed that response to chemical stimulus, extracellular region, pathways in cancer, and arachidonic acid metabolism were the four most significantly enriched terms. In the protein-protein interaction network, three main communities associated with cytoskeleton, response to lipid, and regulation of response to stimulus were established, and the most highly ranked 6 hub genes (up-regulated CD44, KRT6A, CEACAM5, SERPINB2, and down-regulated LTF and MUC5B) were identified and should be considered as new biomarkers. Pathway cross-talk analysis highlights that signaling pathways mediated by IL-4/13 and transcription factor HIF-1α and FOXA1 play crucial roles in the pathogenesis of asthma. Interestingly, three chemicals, polyphenol catechin, antibiotic lomefloxacin, and natural alkaloid boldine, were predicted and may be potential drugs for asthma treatment. Taken together, our findings shed new light on the common molecular pathogenesis mechanisms of asthma and provide theoretical support for further clinical therapeutic studies.