Integrated Analysis Research Articles

Viologen-Derived Covalent Organic Frameworks: Advancing PFAS Removal Technology with High Adsorption Capacity.

The escalating presence of per- and polyfluoroalkyl substances (PFAS) in drinking water poses urgent public health concerns, necessitating effective removal. This study presents a groundbreaking approach, using viologen to synthesize covalent organic framework nanospheres: MELEM-COF and MEL-COF. Characterized by highly crystalline features, these nanospheres exhibit exceptional affinity for diverse anionic PFAS compounds, achieving simultaneous removal of multiple contaminants within 30 min. Investigating six anionic PFAS compounds, MEL- and MELEM-COFs achieved 90.0-99.0% removal efficiency. The integrated analysis unveils the synergistic contributions of COF morphology and functional properties to PFAS adsorption. Notably, MELEM-COF, with cationic surfaces, exploits electrostatic and dipole interactions, with a 2500mg g-1 adsorption capacity-surpassing all reported COFs to date. MELEM-COF exhibits rapid exchange kinetics, reaching equilibrium within 30min. These findings deepen the understanding of COF materials and promise avenues for refining COF-based adsorption strategies.

A multi-layered integrative analysis reveals a cholesterol metabolic program in outer radial glia with implications for human brain evolution.

The definition of molecular and cellular mechanisms contributing to brain ontogenetic trajectories is essential to investigate the evolution of our species. Yet their functional dissection at an appropriate level of granularity remains challenging. Capitalizing on recent efforts that have extensively profiled neural stem cells from the developing human cortex, we develop an integrative computational framework to perform (i) trajectory inference and gene regulatory network reconstruction, (ii) (pseudo)time-informed non-negative matrix factorization for learning the dynamics of gene expression programs, and (iii) paleogenomic analysis for a higher-resolution mapping of derived regulatory variants in our species in comparison to our closest relatives. We provide evidence for cell type-specific regulation of gene expression programs during indirect neurogenesis. In particular, our analysis uncovers a key role for a cholesterol program in outer radial glia, regulated by zinc-finger transcription factor KLF6. A cartography of the regulatory landscape impacted by Homo sapiens-derived variants reveals signals of selection clustering around regulatory regions associated with GLI3, a well-known regulator of radial glial cell cycle, and impacting KLF6 regulation. Our study contributes to the evidence of significant changes in metabolic pathways in recent human brain evolution.

Integrated analysis of disulfidoptosis-related genes identifies NRP1 as a novel biomarker promoting proliferation of gastric cancer via glutamine mediated energy metabolism

The incidence and mortality of gastric cancer rank fifth and fourth worldwide among all malignancies, respectively. Additionally, disulfidoptosis, a recently identified form of cellular demise, is closely linked to the initiation and advancement of malignancies. This study aims to create a novel signature of disulfidptosis-related genes (DRGs) and to further explore its association with the tumor immune microenvironment. Based on our comprehensive study, a prognostic signature consisting of 31 DRGs in stomach adenocarcinoma (STAD) was identified and characterized. Through the integrative analyses involving gene expression profiling, machine learning algorithms, and Cox regression models, the prognostic significance of these DRGs was demonstrated. Our findings highlight their strong predictive power in assessing overall survival across diverse patient datasets, and their better performance than traditional clinicopathological factors. Moreover, the DRGs signature showed association with the characteristics of the tumor microenvironment, which has implications for the immune modulation and therapeutic strategies in STAD. Specifically, NRP1 emerged as a key DRG with elevated expression in STAD, showing correlation with the advanced stages of diseases and poorer outcomes. Functional studies further revealed the role of NRP1 in promoting STAD cell proliferation through the modulation of glutamine metabolism. Overall, our study underscores the clinical relevance of DRGs as biomarker and potential therapeutic targets in STAD management, providing insights into disease biology and personalized treatments.

Identification of differentially expressed tumour-related genes regulated by UHRF1-driven DNA methylation

Ubiquitin-like with PHD and RING finger domains 1 (UHRF1) is an epigenetic regulator that plays critical roles in tumours. However, the DNA methylation alteration patterns driven by UHRF1 and the related differentially expressed tumour-related genes remain unclear. In this study, a UHRF1-shRNA MCF-7 cell line was constructed, and whole-genome bisulfite sequencing and RNA sequencing were performed. The DNA methylation alteration landscape was elucidated, and DNA methylation-altered regions (DMRs) were found to be distributed in both gene bodies and adjacent regions. The DMRs were annotated and categorized into 488 hypermethylated/1696 hypomethylated promoters and 1149 hypermethylated/5501 hypomethylated gene bodies. Through an integrated analysis with the RNA sequencing data, 217 methylation-regulated upregulated genes and 288 downregulated genes were identified, and these genes were primarily enriched in nervous system development and cancer signalling pathways. Further analysis revealed 21 downregulated oncogenes and 15 upregulated TSGs. We also showed that UHRF1 silencing inhibited cell proliferation and migration and suppressed tumour growth in vivo. Our study suggested that UHRF1 and the oncogenes or TSGs it regulates might serve as biomarkers and targets for breast cancer treatment.

Integrative Proteomics-Metabolomics of In Vitro Degeneration of Cardiovascular Cell Lines.

Long-term cell culture is an important biological approach but is also characterized by degeneration in cellular morphology, proliferation rate, and function. To explore this phenomenon in a systematic way, we conducted an integrative proteomics-metabolomics measurement of two cardiovascular cell lines of AC16 and HUVECs. The 18th culturing passages, i.e., G18, showed as the turning points by cell metabolism profiles, in which the metabolomic changes demonstrated the dysfunction of energy, amino acid, and ribonucleotide metabolism metabolic pathways. Although active protein networks showed mitochondria abundance AC16 and oxidative/nitrative sensitive HUVECs indicated the different degeneration patterns, the G18 and G30 proteomics evidenced the senescence by processes of signal transduction, signaling by interleukins, programmed cell death, cellular responses to stimuli, cell cycle, mRNA splicing, and translation. Some crucial proteins (RPS8, HNRNPR, SOD2, LMNB1, PSMA1, DECR1, GOT2, OGDH, PNP, CBS, ATIC, and IMPDH2) and metabolites (L-glutamic acid, guanine, citric acid, guanosine, guanosine diphosphate, glucose 6-phosphate, and adenosine) that contributed to the dysregulation of cellular homeostasis are identified by using the integrative proteomic-metabolomic analysis, which highlighted the increased cellular instability. These findings illuminate some vital molecular processes when culturing serial passages, which contribute holistic viewpoints of in vitro biology with emphasis on the replicative senescence of cardiovascular cells.

Integrative transcriptome analysis reveals the molecular events underlying impaired T-cell responses in EGFR-mutant lung cancer

EGFR mutations are critical oncogenic drivers in lung adenocarcinoma (LUAD). However, the mechanisms by which they impact the tumor microenvironment (TME) and tumor immunity are unclear. Furthermore, the reasons underlying the poor response of EGFR-mutant (EGFR-MU) LUADs to immunotherapy with PD-1/PD-L1 inhibitors are unknown. Utilizing single-cell RNA (sc-RNA) and bulk RNA sequencing datasets, we conducted high-dimensional weighted gene coexpression network analysis to identify key genes and immune-related pathways contributing to the immunosuppressive TME. EGFR-MU cancer cells downregulated MHC class I genes to evade CD8+ cytotoxic T cells, expressed substantial levels of MHC class II molecules, and engaged with CD4+ regulatory T cells (Tregs). EGFR-MU tumors may recruit Tregs primarily through the CCL17/CCL22/CCR4 axis, leading to a Treg-enriched TME. High levels of MHC class II-positive cancer-associated fibroblasts and tumor endothelial cells were found within EGFR-MU tumors. Owing to the absence of costimulatory factors, they may inhibit rather than activate the tumor antigen-specific CD4+ T-cell response, contributing further to immune suppression. Multiplex immunohistochemistry analyses in a LUAD cohort confirmed increased expression of MHC class II molecules in cancer cells and fibroblasts in EGFR-MU tumors. Our research elucidates the highly immunosuppressive TME in EGFR-MU LUAD and suggests potential targets for effective immunotherapy.

Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI

Breast cancer diagnosis and treatment have been revolutionized by multiparametric Magnetic Resonance Imaging (mpMRI), encompassing T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and Dynamic Contrast-Enhanced MRI (DCE-MRI). We conducted a retrospective analysis of mpMRI data from 194 breast cancer patients (September 2019 to October 2023). Using ‘pyradiomics’ for radiomics feature extraction and MOVICS for unsupervised clustering. Interestingly, we identified two distinct patient clusters associated with significant differences in molecular subtypes, particularly in Luminal A subtype distribution (p = 0.03), estrogen receptor (ER) (p = 0.01), progesterone receptor (PR) (p = 0.04), mean tumor size (p < 0.01), lymph node metastasis (LNM) (p = 0.01), and edema (p < 0.01). Our study emphasizes mpMRI’s potential in breast cancer by using radiomics-based cluster analysis to categorize tumors, uncovering heterogeneity, and aiding in personalized treatment strategies.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

The genomic landscape of 2,023 colorectal cancers.

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

The underlying mechanisms by which boron mitigated copper toxicity in Citrus sinensis leaves revealed by integrated analysis of transcriptome, metabolome, and physiology.

Both copper (Cu) excess and boron (B) deficiency are often observed in some citrus orchard soils. The molecular mechanisms by which B alleviates excessive Cu in citrus are poorly understood. Seedlings of sweet orange (Citrus sinensis (L.) Osbeck cv. Xuegan) were treated with 0.5 (Cu0.5) or 350 (Cu350 or Cu excess) μM CuCl2 and 2.5 (B2.5) or 25 (B25) μM HBO3 for 24weeks. Thereafter, this study examined the effects of Cu and B treatments on gene expression levels revealed by RNA-Seq, metabolite profiles revealed by a widely targeted metabolome, and related physiological parameters in leaves. Cu350 upregulated 564 genes and 170 metabolites, and downregulated 598 genes and 58 metabolites in leaves of 2.5μM B-treated seedlings (LB2.5), but it only upregulated 281 genes and 100 metabolites, and downregulated 136 genes and 40 metabolites in leaves of 25μM B-treated seedlings (LB25). Cu350 decreased the concentrations of sucrose and total soluble sugars, and increased the concentrations of starch, glucose, fructose, and total nonstructural carbohydrates (TNC) in LB2.5, but it only increased the glucose concentration in LB25. Further analysis demonstrated that B addition reduced the oxidative damage and alterations in primary and secondary metabolisms caused by Cu350; and alleviated the impairment of Cu350 to photosynthesis and cell wall metabolism, thus improving leaf growth. LB2.5 exhibited some adaptive responses to Cu350 to meet the increasing need for the dissipation of excessive excitation energy (EEE) and the detoxification of reactive oxygen species (reactive aldehydes) and Cu. Cu350 increased photorespiration, xanthophyll cycle-dependent thermal dissipation, nonstructural carbohydrate accumulation, and secondary metabolite biosynthesis and abundances; and upregulated tryptophan metabolism and related metabolite abundances, and some antioxidant-related gene expression, and some antioxidant abundances. Additionally, this study identified some metabolic pathways, metabolites, and genes that might lead to Cu tolerance in leaves.

Integrated Histological and Molecular Analysis of Filarial Species and Associated Wolbachia Endosymbionts in Human Filariasis Cases Presenting Atypically in Thailand.

Atypical presentations of filariasis have posed diagnostic challenges due to the complexity of identifying the causative species and the difficulties in both diagnosis and treatment. In this study, we present the integrative histological and molecular analysis of seven atypical filariasis cases observed in regions of nonendemicity of Thailand. All filariasis cases were initially diagnosed based on histological findings. To confirm the causative species, molecular characterization based on both filarial mitochondrial (mt 12S rRNA and COI genes) and nuclear ITS1 markers was performed, together with the identification of associated Wolbachia bacterial endosymbionts. Among the cases studied, Brugia pahangi (N = 3), Brugia malayi (N = 1), Dirofilaria sp. "hongkongensis" (N = 2), and a suspected novel filarial species genetically related to Pelecitus copsychi (N = 1) were identified. By targeting the 16S rRNA gene, Wolbachia was also molecularly amplified in two cases of infection with Dirofilaria sp. "hongkongensis." Phylogenetic analysis further revealed that the detected Wolbachia could be classified into supergroups C and F, indicating the high genetic diversity of this endosymbiont in Dirofilaria sp. "hongkongensis." Furthermore, this study demonstrates the consistency between histological findings and species identification based on mitochondrial loci rather than on the nuclear ITS1. This suggests the utility of mitochondrial markers, particularly COI, as a highly sensitive and reliable diagnostic tool for the detection and differentiation of filarial species in clinical specimens. Precise identification of the causative species will facilitate accurate diagnosis and treatment and is also essential for the development of epidemiological and preventive strategies for filariasis.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States.

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized. We utilized cellular indexing of transcriptomes and epitopes by sequencing (cellular indexing of transcriptomes and epitopes by sequencing) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes. This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi, monocyte-platelet aggregates, as well as nonclassical, sand several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia

Chronic Lymphocytic Leukemia (CLL) is the most common B-cell malignancy in the Western world, characterized by frequent relapses despite temporary remissions. Our study integrated publicly available proteomic, transcriptomic, and patient survival datasets to identify key differences between healthy and CLL samples. We exposed approximately 1000 proteins that differentiate healthy from cancerous cells, with 608 upregulated and 415 downregulated in CLL cases. Notable upregulated proteins include YEATS2 (an epigenetic regulator), PIGR (Polymeric immunoglobulin receptor), and SNRPA (a splicing factor), which may serve as prognostic biomarkers for this disease. Key pathways implicated in CLL progression involve RNA processing, stress resistance, and immune response deficits. Furthermore, we identified three existing drugs—Bosutinib, Vorinostat, and Panobinostat—for potential further investigation in drug repurposing in CLL. We also found limited correlation between transcriptomic and proteomic data, emphasizing the importance of proteomics in understanding gene expression regulation mechanisms. This generally known disparity highlights once again that mRNA levels do not accurately predict protein abundance due to many regulatory factors, such as protein degradation, post-transcriptional modifications, and differing rates of translation. These results demonstrate the value of integrating omics data to uncover deregulated proteins and pathways in cancer and suggest new therapeutic avenues for CLL.

Integrated analysis of remote sensing with meteorological and health data for allergic rhinitis forecasting in Tianjin.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

An evaluation of the use of plastic waste in the manufacture of plastic bricks

An integrated analysis is performed for the present study, including an evaluation of the mechanical and durability properties of plastic brick, a cost-effectiveness analysis, and an eco-benefit analysis of recycling plastic waste into bricks. To promote sustainable construction practices, pp waste is used in plastic brick to replace waste material often discarded in landfills and burned. This study involved the preparation of 36 (230 mm × 110 mm × 90 mm) bricks. Modified fly ash containing 40, and 45% of the cement weight was incorporated into the samples. There is an increase in waste production among the Indian population. Some of these wastes pose a threat to the environment. There is also a significant amount of plastic waste in the country. Even though it is not biodegradable, it poisons trees and aquatic organisms. Thus, waste plastics were repurposed for use in the construction industry. Cement, fly ash, M sand, and plastic waste fibers are used to make plastic bricks. Polypropylene (PP) has a high deformation capacity and is a cost-effective material. Plastic waste is used in this study in the form of polypropylene fibers. To produce the bricks, different mix ratios were used in which PP waste was used in proportions of 5%, 10%, and 15% (represented as Mix1, Mix2, and Mix3), based on the total mixing of the polycarbonate substance volume of bricks. The purpose of this study is to investigate the feasibility of utilizing plastic wastes to produce a cost-effective and lightweight plastic brick for use in construction. Compressive strength, water absorption, soundness, efflorescence, and hardness tests were carried out on the bricks. The compressive strength of mix 2 at a load of 426 kg is the highest of the three plastic bricks tested. A comparison of this proportion with other mix proportions and conventional brick effectiveness.

Enhanced Catalytic Synthesis of Flavonoid by UV-B Radiation in Artemisia argyi

Enzymatic synthesis of specific active substances is an important foundation for biological adaptations to various stresses. In this study, we investigated the metabolic response of the medicinal herb Artemisia argyi to UV-B radiation through transcriptome and metabolome analysis. In all tested samples, there were 544 shared differentially expressed genes, most of which were linked to the metabolism of flavonoids and fatty acids. A total of 283 differential metabolites were identified and classified into 10 categories, with flavonoids being the largest category. Through an integrated analysis of genes and metabolites involved in flavonoid biosynthesis, flavonoids were predicted to be critical for the adaptation of A. argyi to UV radiation. The increased plant hormones methyl jasmonate and salicylic acid were considered as key regulatory approaches for catalyzing the large-scale synthesis of flavonoids. We explored this by investigating the flavonoid production of A. argyi grown at different altitudes. It showed that total flavonoid content of A. argyi planted in high-altitude areas was 45% higher than that in low-altitude areas. These findings not only deepen our understanding of flavonoid anabolism and its regulation but also provide a reliable strategy for improving flavonoid content in the genus Artemisia.

Integrative Analyses Reveal the Correlation Between the Airway Microbiome and Host Metabolism in Severe Community-acquired Pneumonia.

Community-acquired pneumonia (CAP) is a significant global health concern, responsible for high mortality and morbidity. Recent research has revealed a potential link between disordered microbiome and metabolism in pneumonia, although the precise relationship between these factors and severe CAP remains unclear. To address this knowledge gap, we conducted a comprehensive analysis utilizing 16S sequencing and LC-MS/MS metabolomics data to characterize the microbial profile in sputum and metabolic profile in serum in patients with severe community-acquired pneumonia (sCAP). Our analysis identified 13 genera through LEfSe analysis and 15 metabolites meeting specific criteria (P < 0.05, VIP ≥ 2, and |Log2(FC)| ≥ 2). The findings of this study demonstrate the presence of altered coordination between the microbiome of the lower respiratory tract and host metabolism in patients with sCAP. The observed concentration trends of specific metabolites across different disease stages further support the potential involvement of the serum metabolism in the development of sCAP. These correlations between the airway microbiome and host metabolism in sCAP patients have important implications for optimizing early diagnosis and developing individualized therapeutic strategies.

Understanding and Therapeutic Application of Immune Response in Major Histocompatibility Complex (MHC) Diversity Using Multimodal Artificial Intelligence

Human Leukocyte Antigen (HLA) is like a device that monitors the internal environment of the body. T lymphocytes immediately recognize the HLA molecules that are expressed on the surface of the cells of the different individual, attacking it defeats microorganisms that is one of the causes of rejection in organ transplants performed between people with unmatched HLA types. Over 2850 and 3580 different polymorphisms have been reported for HLA-A and HLA-B respectively, around the world. HLA genes are associated with the risk of developing a variety of diseases, including autoimmune diseases, and play an important role in pathological conditions. By using a deep learning method called multi-task learning to simultaneously predict the gene sequences of multiple HLA genes, it is possible to improve accuracy and shorten execution time. Some new systems use a model called convolutional neural network (CNNs) in deep learning, which uses neural networks consisting of many layers and can learn complex correlations between SNP information and HLA gene sequences based on reference data for HLA imputation, which serves as training data. The learned model can output predicted values of HLA gene sequences with high accuracy using SNP information as input. To investigate which part of the input information surrounding the HLA gene is used to make learning predictions, predictions were made using not only a small number of nearby SNP information but also many SNP information distributed over a wider area by visualizing the learning information of the model. While conventional methods are strong at learning using nearly SNP information and not good at learning using SNP information located at distant locations, some new systems are thought that prediction accuracy may have improved because this problem was overcome. HLA genes are involved in the onset of a variety of diseases and are attracting attention. As an important area from the perspective of elucidating pathological conditions and realizing personalized medicine. The applied multi-task learning to two different HLA imputation reference panels—a Japanese panel (n = 1118) and type I diabetes genetics consortium panel (n = 5122). Through 10-fold cross-validation on these panels, the multi-task learning achieved higher imputation accuracy than conventional methods, especially for imputing low-frequency and rare HLA alleles. The increased prediction accuracy of HLA gene sequences is expected to increase the reliability of HLA analysis, including integrated analysis between different racial populations, and is expected to greatly contribute to the identification of HLA gene sequences associated with diseases and further elucidation of pathological conditions.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization.

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

The natural variance of Arabidopsis secondary metabolism on extended darkness

In plants due to their sessile nature, secondary metabolites are important components against different abiotic and biotic stress, such as extended darkness. For this reason, the variation of secondary metabolite content of the Arabidopsis thaliana HapMap natural population following 0-and 6-d darkness treatment were detected and the raw data of different accessions at two timepoints were deposited in the Zenodo database. Moreover, the annotated secondary metabolites of these samples are presented in this data descriptor, which we believe will be a usefully re-usable resource for future integrative analysis with dark-treated transcripts, proteins or other phenotypic data in order to comprehensively illustrate the multiomic landscape of Arabidopsis in response to the stresses exerted by extended darkness.