Arsenic redox disequilibrium in geogenic contaminated groundwater: Bioenergetic insights from organic molecular characterization and gene-informed modeling.

Biotransformation of arsenic (As) influences its speciation and mobility, obscuring mechanistic comprehension on spatiotemporal variation of As concentration in geogenic contaminated groundwater. In particular, unresolved processes underlying As redox disequilibrium in comparison to major redox couples discourage practical efforts to rehabilitate the As-contaminated groundwater. Here, quantitative metagenomic sequencing and ultrahigh-resolution mass spectrometry (FT-ICR-MS) were jointly applied to reveal the links between vertical distribution of As metabolic gene assemblages and that of free energy density of dissolved organic matter (DOM) in As-contaminated groundwater of Datong Basin. Observed small excess of Gibbs free energy available by DOM relative to that required for As(V)-to-As(III) reduction exerts thermodynamic constraint on metabolism-mediated redox transformation of As. Accordingly, the vertical distribution of dissolved As(V)/As(III) ratio correlated significantly with that of ars+acr3 and arr encoding As(V) reduction and aio encoding As(III) oxidation in the moderately/strongly reducing groundwater. Further gene-informed biogeochemical modeling suggests that a net effect of these kinetics-restricted bidirectional metabolic pathways leads to co-preservation of As(V) and As(III) even at relatively high rates of ars+acr3 encoded As(V) reduction. This study therefore provides new insights into bioenergetic constraints on As hydrobiogeochemical behavior, with implications for other redox-sensitive contaminants in the groundwater systems.

Mathematical modelling of Alzheimer's disease biomarkers: Targeting Amyloid beta, Tau protein, Apolipoprotein E and Apoptotic pathways.

Introduction: The kinetics of brain cell death in Alzheimer's disease (AD) is being studied using mathematical models. These mathematical models utilize techniques like differential equations, stochastic processes, and network theory to explore crucial signalling pathways and interactions between different cell types. One crucial area of research is the intentional cell death known as apoptosis, which is crucial for the nervous system. The main purpose behind the mathematical modelling of this is for identification of which biomarkers and pathways are most influential in the progression of AD. In addition, we can also predict the natural history of the disease, by which we can make early diagnosis. Mathematical modelling of AD: Current mathematical models include the Apolipoprotein E (APOE) Gene Model, the Tau Protein Kinetics Model, and the Amyloid Beta Peptide Kinetic Model. The Bcl-2 and Bax apoptosis theories postulate that the balance of pro- and anti-apoptotic proteins in cells determines whether a cell experiences apoptosis, where the Bcl-2 model, depicts the interaction of pro- and anti-apoptotic proteins, it is also being used in research on cell death in a range of cell types, including neurons and glial cells. How peptides are produced and eliminated in the brain is explained by the Amyloid beta Peptide (Aß) Kinetics Model. The tau protein kinetics model focuses on production, aggregation, and clearance of tau protein processes, which are hypothesized to be involved in AD. The APOE gene model investigates the connection between the risk of Alzheimer's disease and the APOE gene. These models have been used to predict how Alzheimer's disease would develop and to evaluate how different inhibitors will affect the illness's course. Conclusion: These mathematical models reflect physiological meaningful characteristics and demonstrates robust fits to training data. Incorporating biomarkers like Aß, Tau, APOE and markers of neuronal loss and cognitive impairment can generate sound predictions of biomarker trajectories over time in Alzheimer's disease.

Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues.

Seqrutinator is an objective, flexible pipeline that removes sequences with sequencing and/or gene model errors and sequences from pseudogenes from complex, eukaryotic protein superfamilies. Testing Seqrutinator on major superfamilies BAHD, CYP, and UGT removes only 1.94% of SwissProt entries, 14% of entries from the model plant Arabidopsis thaliana, but 80% of entries from Pinus taeda's recent complete proteome. Application of Seqrutinator on crude BAHDomes, CYPomes, and UGTomes obtained from 16 plant proteomes shows convergence of the numbers of paralogues. MSAs, phylogenies, and particularly functional clustering improve drastically upon Seqrutinator application, indicating good performance.

Evidence-based unification of potato gene models with the UniTato collaborative genome browser.

Potato (Solanum tuberosum) is the most popular tuber crop and a model organism. A variety of gene models for potato exist, and despite frequent updates, they are not unified. This hinders the comparison of gene models across versions, limits the ability to reuse experimental data without significant re-analysis, and leads to missing or wrongly annotated genes. Here, we unify the recent potato double monoploid v4 and v6 gene models by developing an automated merging protocol, resulting in a Unified poTato genome model (UniTato). We subsequently established an Apollo genome browser (unitato.nib.si) that enables public access to UniTato and further community-based curation. We demonstrate how the UniTato resource can help resolve problems with missing or misplaced genes and can be used to update or consolidate a wider set of gene models or genome information. The automated protocol, genome annotation files, and a comprehensive translation table are provided at github.com/NIB-SI/unitato.

Updated Gene Prediction of the Cucumber (9930) Genome through Manual Annotation.

Thorough and precise gene structure annotations are essential for maximizing the benefits of genomic data and unveiling valuable genetic insights. The cucumber genome was first released in 2009 and updated in 2019. To increase the accuracy of the predicted gene models, 64 published RNA-seq data and 9 new strand-specific RNA-seq data from multiple tissues were used for manual comparison with the gene models. The updated annotation file (V3.1) contains an increased number (24,145) of predicted genes compared to the previous version (24,317 genes), with a higher BUSCO value of 96.9%. A total of 6231 and 1490 transcripts were adjusted and newly added, respectively, accounting for 31.99% of the overall gene tally. These newly added and adjusted genes were renamed (CsaV3.1_XGXXXXX), while genes remaining unaltered preserved their original designations. A random selection of 21 modified/added genes were validated using RT-PCR analyses. Additionally, tissue-specific patterns of gene expression were examined using the newly obtained transcriptome data with the revised gene prediction model. This improved annotation of the cucumber genome will provide essential and accurate resources for studies in cucumber.

ToxCodAn-Genome: an automated pipeline for toxin-gene annotation in genome assembly of venomous lineages.

BACKGROUND: The rapid development of sequencing technologies resulted in a wide expansion of genomics studies using venomous lineages. This facilitated research focusing on understanding the evolution of adaptive traits and the search for novel compounds that can be applied in agriculture and medicine. However, the toxin annotation of genomes is a laborious and time-consuming task, and no consensus pipeline is currently available. No computational tool currently exists to address the challenges specific to toxin annotation and to ensure the reproducibility of the process. RESULTS: Here, we present ToxCodAn-Genome, the first software designed to perform automated toxin annotation in genomes of venomous lineages. This pipeline was designed to retrieve the full-length coding sequences of toxins and to allow the detection of novel truncated paralogs and pseudogenes. We tested ToxCodAn-Genome using 12 genomes of venomous lineages and achieved high performance on recovering their current toxin annotations. This tool can be easily customized to allow improvements in the final toxin annotation set and can be expanded to virtually any venomous lineage. ToxCodAn-Genome is fast, allowing it to run on any personal computer, but it can also be executed in multicore mode, taking advantage of large high-performance servers. In addition, we provide a guide to direct future research in the venomics field to ensure a confident toxin annotation in the genome being studied. As a case study, we sequenced and annotated the toxin repertoire of Bothrops alternatus, which may facilitate future evolutionary and biomedical studies using vipers as models. CONCLUSIONS: ToxCodAn-Genome is suitable to perform toxin annotation in the genome of venomous species and may help to improve the reproducibility of further studies. ToxCodAn-Genome and the guide are freely available at https://github.com/pedronachtigall/ToxCodAn-Genome.

ToxCodAn-Genome: an automated pipeline for toxin-gene annotation in genome assembly of venomous lineages

Background: The rapid development of sequencing technologies resulted in a wide expansion of genomics studies using venomous lineages. This facilitated research focusing on understanding the evolution of adaptive traits and the search for novel compounds that can be applied in agriculture and medicine. However, the toxin annotation of genomes is a laborious and time-consuming task, and no consensus pipeline is currently available. No computational tool currently exists to address the challenges specific to toxin annotation and to ensure the reproducibility of the process. Results: Here, we present ToxCodAn-Genome, the first software designed to perform automated toxin annotation in genomes of venomous lineages. This pipeline was designed to retrieve the full-length coding sequences of toxins and to allow the detection of novel truncated paralogs and pseudogenes. We tested ToxCodAn-Genome using 12 genomes of venomous lineages and achieved high performance on recovering their current toxin annotations. This tool can be easily customized to allow improvements in the final toxin annotation set and can be expanded to virtually any venomous lineage. ToxCodAn-Genome is fast, allowing it to run on any personal computer, but it can also be executed in multicore mode, taking advantage of large high-performance servers. In addition, we provide a guide to direct future research in the venomics field to ensure a confident toxin annotation in the genome being studied. As a case study, we sequenced and annotated the toxin repertoire of Bothrops alternatus, which may facilitate future evolutionary and biomedical studies using vipers as models. Conclusions: ToxCodAn-Genome is suitable to perform toxin annotation in the genome of venomous species and may help to improve the reproducibility of further studies. ToxCodAn-Genome and the guide are freely available at https://github.com/pedronachtigall/T oxCodAn-Genome.

Prediction of asthma using a four-locus gene model including IL13, IL4, FCER1B, and ADRB2 in children of Kazak nationality.

BACKGROUND: To study whether the four locus gene model consisting of ADRB2 rs1042713, IL4 rs2243250, FCER1B rs569108 and L13 rs20541 can predict asthma of the Kazak children in Xinjiang, China. METHODS: Four single nucleotide polymorphisms about the 4 genes were genotyped in asthma group and control group of Han children and Kazak children respectively. The frequencies of different genotypes and alleles were compared between the asthma group and the control group in the two nationalities. Different risk genotypes for asthma were evaluated in the two nationalities. RESULTS: The differences about frequencies of genotypes in ADRB2 rs1042713 and IL4 rs2243250 and IL13 rs20541 between asthma group and control group were statistically significant in Han children, as were the frequencies of alleles in the 3 single nucleotide polymorphisms, but there were no statistical differences in FCER1B rs569108(P > 0.05). For the Kazak children, no differences were existed among all the genotypes and alleles in asthma group and control group. For the Han children, more children were asthma high risk genotype in the asthma group than those in the control group and no difference was found in the Kazak children. CONCLUSIONS: The four locus gene model consisting of ADRB2 rs1042713, IL4 rs2243250, FCER1B rs569108 and L13 rs20541 can predict asthma of Han children but not for the Kazak children in Xinjiang, which illustrating that the difference of asthma prevalence between different races is closely related to the genetic background.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown. METHODS: Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets. RESULTS: Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD. CONCLUSION: We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

Major Susceptibility Gene Epistasis over Minor Gene Resistance to Spot Form Net Blotch in a Commercial Barley Cultivar.

Spot form net blotch, caused by Pyrenophora teres f. maculata, is a significant global disease of barley (Hordeum vulgare). Baudin, a barley cultivar that was until recently extensively grown in Western Australia, was reported as having minor seedling resistance. However, Baudin was highly susceptible to a local isolate, M3, suggesting that this isolate had gained virulence against a major susceptibility gene. M3 causes atypical lesions with pale centers early in the infection, with initial screens of a segregating population indicating that this was determined by a single locus in the Baudin genome. The susceptibility was semidominant in F1 progeny and the susceptibility gene, designated Spm1 (Susceptibility to P. teres f. maculata 1), mapped to a 190-kb section of the resistance gene-rich Mla region of chromosome 1H. Phenotyping with Ptm SP1, a non-M3 pathotype, identified a seedling resistance locus on 2H. Minor gene resistance is generally regarded as potentially durable, but our findings suggest the resistance to spot form net blotch in Baudin is nullified by strong susceptibility conferred by a separate locus on 1H. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Endoplasmic reticulum stress-related gene model predicts prognosis and guides therapies in lung adenocarcinoma.

BACKGROUND: The prognosis and survival of lung adenocarcinoma (LUAD) patients are still not promising despite recent breakthroughs in treatment. Endoplasmic reticulum stress (ERS) is a self-protective mechanism resulting from an imbalance in quality control of unfolded proteins when cells are stressed, which plays an active role in lung cancer development, but the relationship between ERS and the pathological characteristics and clinical prognosis of LUAD patients remains unclear. METHODS: LASSO and Cox regression were applied based on sequencing information to construct the model, which was validated to be robust. The risk scores of the patients were calculated using the formula provided by the model, and the patients were divided into high and low-risk groups according to the median cut-off of risk scores. Cox regression analysis identifies independent prognostic factors for these patients, and enrichment analysis of prognosis-related genes was also performed. The relationship between risk scores and tumor mutation burden (TMB), cancer stem cell index, and drug sensitivity was explored. RESULTS: We constructed a 13-gene prognostic model for LUAD patients. Patients in the high-risk group had worse overall survival, lower immune score and ESTIMATE score, higher TMB, higher cancer stem cell index, and higher sensitivity to conventional chemotherapeutic agents. In addition, we constructed a nomogram that predicts 5-year survival in LUAD patients, which helps clinicians to foresee the prognosis from a new perspective. CONCLUSIONS: Our results highlight the association of ERS with LUAD and the potential use of ERS in guiding treatment.

The Joint Evolution of Herbivory Defense and Mating System in Plants: A Simulation Approach.

Agricultural losses brought about by insect herbivores can be reduced by understanding the strategies that plants use against insect herbivores. The two main strategies that plants use against herbivory are resistance and tolerance. They are, however, predicted to be mutually exclusive, yet numerous populations have them both (hence a mixed defense strategy). This has been explained, among other alternatives, by the non-linear behavior of the costs and benefits of resistance and tolerance and their interaction with plants' mating system. Here, we studied how non-linearity and mating system affect the evolutionary stability of mixed defense strategies by means of agent-based model simulations. The simulations work on a novel model that was built upon previous ones. It incorporates resistance and tolerance costs and benefits, inbreeding depression, and a continuously scalable non-linearity. The factors that promoted the evolutionary stability of mixed defense strategies include a multiplicative allocation of costs and benefits of resistance and tolerance, a concave non-linearity, non-heritable selfing, and high tolerance costs. We also found new mechanisms, enabled by the mating system, that are worth considering for empirical studies. One was a double trade-off between resistance and tolerance, predicted as a consequence of costs duplication and the inducibility of tolerance, and the other was named the resistance-cost-of-selfing, a term coined by us, and was derived from the duplication of costs that homozygous individuals conveyed when a single resistance allele provided full protection.

A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database.

This study explored prognostic genes of ovarian cancer and built a prognostic model based on these genes to predict patient's survival, which is of great significance for improving treatment of ovarian cancer. GSE26712 dataset was downloaded from Gene Expression Omnibus database as training set, while OV-AU dataset was downloaded from ICGC website as validation set. All genes in GSE26712 were analyzed by univariate Cox regression, Lasso regression, and multivariate Cox regression analyses. Then prognosis-related feature genes were screened to construct a multivariate risk model. Meanwhile, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed on samples in the high/low-risk groups using Gene Set Enrichment Analysis (GSEA) software. Finally, survival curve and receiver operating characteristic curve were drawn to verify the validity of the model. Ten feature genes related to prognosis of ovarian cancer were obtained: CMTM6, COLGALT1, F2R, GPR39, IGFBP3, RNF121, MTMR9, ORAI2, SNAI2, ZBTB16. GSEA enrichment analysis showed that there were notable differences in biological pathways such as gap junctions and homologous recombination between the high/low-risk groups. Through further verification of training set and validation set, the 10-gene prognostic model was found to be effective for the prognosis of ovarian cancer patients. In this study, we constructed a 10-gene prognostic model which predicted the prognosis of ovarian cancer patients well by integrating clinical prognostic parameters. It may have certain reference value for subsequent clinical treatment research of ovarian cancer patients and help in clinical treatment decision-making.

CBXs-related prognostic gene signature correlates with immune microenvironment in gastric cancer.

BACKGROUND: Chromobox (CBX) proteins are important Polycomb family proteins in the development of gastric cancer. Nonetheless, the relationship between CBXs and gastric cancer microenvironment remains unclear. METHODS: Multiple databases were used for the analysis of CBXs expression and clinical value in gastric cancer patients. A Cox regression analysis was used to evaluate the prognostic importance of CBXs. Thereafter, regression analysis of LASSO Cox was used to construct the prognostic model. Spearman's correlation between risk score and immune infiltration was analyzed using the McP-counter algorithm. A predicted nomogram was developed to predict the overall survival of gastric cancer patients after 1, 2, and 3 years. RESULTS: In contrast with normal tissues, mRNA and protein expression levels of CBX2/3 were significantly high in gastric cancer tissues, whereas those of CBX6/7 were low. CBXs significantly correlated with immune subtypes and molecular subtypes. A prognostic gene model based on five CBX genes (CBX1, CBX2, CBX3, CBX7, and CBX8) predicted the overall survival of gastric cancer patients. A significant correlation was noted between the risk score of the CBXs-related prognostic gene model and immune-cell infiltration. Low risk patients could achieve a better response to immune checkpoint inhibitors. A predictive nomogram constructed using the above five CBX genes revealed that overall survival rates over 1, 2, and 3 years could be reasonably predicted. Therefore, the roles of CBXs were associated with chromatin modifications and histone methylation, etc. Conclusion: In summary, we identified a prognostic CBXs model comprising five genes (CBX1, CBX2, CBX3, CBX7, and CBX8) for gastric cancer patients through bioinformatics analysis.

A Manually Curated Gene Model Set for an Ascidian, Ciona robusta (Ciona intestinalis Type A).

Gene/transcript model sets predicted from decoded genome sequences are an important resource for a wide range of biological studies. Accuracy of gene models is therefore critical for deducing accurate conclusions. Computationally predicted models are sometimes inconsistent with experimental data from cDNA clones and RNA-sequencing. In an ascidian, Ciona robusta (Ciona intestinalis type A), a manually curated gene/transcript model set, which was constructed using an assembly in which 68% of decoded sequences were associated with chromosomes, had been used during the last decade. Recently a new genome assembly was published, in which over 95% of decoded sequences are associated with chromosomes. In the present study, we provide a high-quality version of the gene/transcript model set for the latest assembly. Because the Ciona genome has been used in a variety of studies such as developmental biological studies, evolutionary studies, and physiological studies, the current gene/transcript model set provides a fundamental biological resource.

TGF-ß Signaling Pathway-Based Model to Predict the Subtype and Prognosis of Head and Neck Squamous Cell Carcinoma.

Background: Although immunotherapy with immune checkpoint therapy has been used to treat head and neck squamous cell carcinoma (HNSCC), response rates and treatment sensitivity remain limited. Recent studies have indicated that transforming growth factor-ß (TGF-ß) may be an important target for novel cancer immunotherapies. Materials and methods: We collected genomic profile data from The Cancer Genome Atlas and Gene Expression Omnibus. The least absolute shrinkage and selection operator method and Cox regression were used to establish a prognostic model. Gene set enrichment analysis was applied to explore biological functions. Tracking of indels by decomposition and subclass mapping algorithms were adopted to evaluate immunotherapy efficiency. Result: We established a seven TGF-ß pathway-associated gene signature with good prediction efficiency. The high-risk score subgroup mainly showed enrichment in tumor-associated signaling such as hypoxia and epithelial-mesenchymal transition (EMT) pathways; This subgroup was also associated with tumor progression. The low-risk score subgroup was more sensitive to immunotherapy and the high-risk score subgroup to cisplatin, erlotinib, paclitaxel, and crizotinib. Conclusion: The TGF-ß pathway signature gene model provides a novel perspective for evaluating effectiveness pre-immunotherapy and may guide further studies of precision immuno-oncology.

[Establishment and Validation of Immune Risk Score for Predicting Survival of Patients with Acute Myeloid Leukemia].

OBJECTIVE: To establish an immune gene prognostic model of acute myeloid leukemia (AML) and explore its correlation with immune cells in bone marrow microenvironment. METHODS: Gene expression profile and clinical data of TCGA-AML were downloaded from TCGA database. Immune genes were screened by LASSO analysis to construct prognosis prediction model, and prediction accuracy of the model was quantified by receiver operating characteristic curve and area under the curve. Survival analysis was performed by Log-rank test. Enriched pathways in the different immune risk subtypes were evaluated from train cohort. The relationship between immune prediction model and bone marrow immune microenvironment was verified by flow cytometry in the real world. RESULTS: Patients with low-risk score of immune gene model had better prognosis than those with high-risk score. Multivariate analysis showed that the immune gene risk model was an independent prognostic factor. The risk ratio for AML patients in the training concentration was HR=24.594 (95%CI: 6.180-97.878), and the AUC for 1-year, 3-year, and 5-year overall survival rate was 0.811, 0.815, and 0.837, respectively. In addition, enrichment analysis of differential gene sets indicated activation of immune-related pathways such as cytokines and chemokines as well as autoimmune disease-related pathways. At the same time, real world data showed that patients with high immune risk had lower numbers of CD8+T cells and B lymphocytes compared with low immune risk patients. CONCLUSION: We constructed a stable prognostic model for AML, which can not only predict the prognosis of AML, but also reveal the dysregulation of immune microenvironment.

Establishment and Validation of Immune Risk Score for Predicting Survival of Patients with Acute Myeloid Leukemia / 中国实验血液学杂志

OBJECTIVE@#To establish an immune gene prognostic model of acute myeloid leukemia (AML) and explore its correlation with immune cells in bone marrow microenvironment.@*METHODS@#Gene expression profile and clinical data of TCGA-AML were downloaded from TCGA database. Immune genes were screened by LASSO analysis to construct prognosis prediction model, and prediction accuracy of the model was quantified by receiver operating characteristic curve and area under the curve. Survival analysis was performed by Log-rank test. Enriched pathways in the different immune risk subtypes were evaluated from train cohort. The relationship between immune prediction model and bone marrow immune microenvironment was verified by flow cytometry in the real world.@*RESULTS@#Patients with low-risk score of immune gene model had better prognosis than those with high-risk score. Multivariate analysis showed that the immune gene risk model was an independent prognostic factor. The risk ratio for AML patients in the training concentration was HR=24.594 (95%CI: 6.180-97.878), and the AUC for 1-year, 3-year, and 5-year overall survival rate was 0.811, 0.815, and 0.837, respectively. In addition, enrichment analysis of differential gene sets indicated activation of immune-related pathways such as cytokines and chemokines as well as autoimmune disease-related pathways. At the same time, real world data showed that patients with high immune risk had lower numbers of CD8+T cells and B lymphocytes compared with low immune risk patients.@*CONCLUSION@#We constructed a stable prognostic model for AML, which can not only predict the prognosis of AML, but also reveal the dysregulation of immune microenvironment.

A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma.

BACKGROUND: Cutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma. METHODS: Cutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms. RESULTS: A five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment. CONCLUSION: Herein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

Reference Transcriptome Data in Silkworm Bombyx mori.

Herein, we performed RNA-seq analysis of ten major tissues/subparts of silkworm larvae. The sequences were mapped onto the reference genome assembly and the reference transcriptome data were successfully constructed. The reference data provided a nearly complete sequence for sericin-1, a major silk gene with a complex structure. We also markedly improved the gene model for other genes. The transcriptomic expression was investigated in each tissue and a number of transcripts were identified that were exclusively expressed in tissues such as the testis. Transcripts strongly expressed in the midgut formed tight genomic clusters, suggesting that they originated from tandem gene duplication. Transcriptional factor genes expressed in specific tissues or the silk gland subparts were also identified. We successfully constructed reference transcriptome data in the silkworm and found that a number of transcripts showed unique expression profiles. These results will facilitate basic studies on the silkworm and accelerate its applications, which will contribute to further advances in lepidopteran and entomological research as well as the practical use of these insects.