Omics Studies Research Articles

Immunometabolic interplay in Edwardsiella tarda-infected crucian carp (Carassius auratus) and in vitro identification of the antimicrobial activity of apolipoprotein D (ApoD) by utilization of multiomics analyses

Edwardsiella tarda is an intracellular pathogenic bacteria that can imperil the health of farmed fish. However, the interactive networks of immune regulation and metabolic response in E. tarda-infected fish are still unclear. In this investigation, we aimed to explore immunometabolic interplay in crucian carp after E. tarda infection by utilizing multiomics analyses. Crucian carp (Carassius auratus) receiving E. tarda infection showed increased levels of tissue damage and oxidative injury in liver. Multiomics analyses suggested that carbon and amino acid metabolism may be considered as crucial metabolic pathways in liver of crucian carp following E. tarda infection, while spaglumic acid, isocitric acid and tetrahydrocortisone were the crucial liver biomarkers. After that, a potential antimicrobial peptide (AMP) sequence called apolipoprotein D (ApoD) was identified from omics study. Then, tissue-specific analysis indicated that liver CaApoD showed the highest expression among isolated tissues. After Aeromonas hydrophila stimulated, CaApoD expressions increased sharply in immune-related tissues. Moreover, CaApoD fusion protein could mediate the in vitro binding to A. hydrophila and E. tarda, attenuate bacterial growth as well as diminish bacterial biofilm forming activity. These findings may have a comprehensive implication for understanding immunometabolic response in crucian carp upon infection.

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging.

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.

ShinySperm: navigating the sperm proteome landscape.

Context Integrated omics studies hold a crucial role in improving our understanding of reproductive biology. However, the complex datasets so generated are often only accessible via supplementary data files, which lack the capacity for interactive features to allow users to readily interrogate and visualise data of interest. Aims The intent of this technical note was to develop an interactive web-based application that enables detailed interrogation of a representative sperm proteome, facilitating a deeper understanding of the proteins identified, their relative abundance, classifications, functions, and associated phenotypes. Methods We developed a Shiny web application, ShinySperm (https://reproproteomics.shinyapps.io/ShinySperm/ ), utilising R and several complementary libraries for data manipulation (dplyr), interactive tables (DT), and visualisation (ggplot2, plotly). ShinySperm features a responsive user interface, dynamic filtering options, interactive charts, and data export capabilities. Key results ShinySperm allows users to interactively search, filter, and visualise sperm proteomics data based on key features (e.g. protein classification, sperm cell domain, presence, or absence at different maturation stages). This application responds live to filtering options and enables the generation of interactive plots and tables, thus enhancing user engagement and understanding of the data. Conclusions ShinySperm provides a robust platform for the dynamic exploration of epididymal sperm proteome data. It significantly improves accessibility and interpretability of complex datasets, allowing for effective data-driven insights. Implications This technical note highlights the potential of interactive web applications in reproductive biology and provides a plug and play script for the field to produce applications for meaningful researcher interaction with complex omic datasets.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Appropriate Biological Standardisation to Bring Confidence to Omics Data: The Example of the Microbiome

Abstract Research into the microbiome can provide invaluable information which is being increasingly used to improve knowledge on its fundamental influence over human health, and crucially, its potential to aid diagnostic and therapeutic efforts. However, the methods used to study this ecosystem are flawed and introduce bias at every stage of the process, from sample collection to DNA extraction and sequencing analysis. Groups across the world have rushed to conduct sophisticated omics studies of the microbiome with limited knowledge of these biases, as they are not yet fully understood. This has led to contradictory findings, a lack of comparability between seemingly similar studies, and a deterioration of overall trust in the field. Tackling this major issue requires biological standardisation; physical reference materials that provide a ground truth to which microbiome data can be compared. Through this comparison, we can identify the nature of biases and the stages at which they are introduced, and crucially, how the biases can be overcome by optimising and eventually improving the methods used. With the use of biological standards, the field can thus become harmonised, and the accuracy and reliability of the data produced can be improved. This would facilitate innovation across the different disciplines involved, through the development of novel approaches to overcome the current hurdles encountered when translating the vast promise of the microbiome to fulfil public health needs. Information © The Authors 2024

Gut microbiome and metabolism alterations in schizophrenia with metabolic syndrome severity

BackgroundSchizophrenia (SCZ) patients undergoing antipsychotic treatment demonstrated a high prevalence and harmful effects of metabolic syndrome (MetS), which acted as the major cause of cardiovascular disease. The major clinical challenge is the lack of biomarkers to identify MetS episodes and prevent further damage, while the mechanisms underlying these drug-induced MetS remain unknown.MethodsThis study divided 173 participants with SCZ into 3 groups (None, High risk, and MetS, consisting of 22, 88, and 63 participants, respectively). The potential biomarkers were searched based on 16S rRNA gene sequence together with metabolism analysis. Logistic regression was used to test the effects of the genus-metabolites panel on early MetS diagnoses.ResultsA genus-metabolites panel, consisting of Senegalimassilia, sphinganine, dihomo-gamma-linolenoylcholine, isodeoxycholic acid, and MG (0:0/22:5/0:0), which involved in sphigolipid metabolism, fatty acid metabolism, secondary bile acid biosynthesis and glycerolipid metabolism, has a great discrimination efficiency to MetS with an area under the curve (AUC) value of 0.911 compared to the None MetS group (P = 1.08E-8). Besides, Senegalimassilia, 3-Hydroxytetradecanoyl carnitine, isodeoxycholic acid, and DG(TXB2/0:0/2:0) distinguished between subgroups robustly and exhibited a potential correlation with the severity of MetS in patients with SCZ, and may act as the biomarkers for early MetS diagnosis.ConclusionsOur multi–omics study showed that one bacterial genus-five lipid metabolites panel is the potential risk factor for MetS in SCZ. Furthermore, Senegalimassilia, 3-Hydroxytetradecanoyl carnitine, isodeoxycholic acid, and DG(TXB2/0:0/2:0) could serve as novel diagnostic markers in the early stage. So, it is obvious that the combination of bacterial genus and metabolites yields excellent discriminatory power, and the lipid metabolism provide new understanding to the pathogenesis, prevention, and therapy for MetS in SCZ.

A Simple Strategy for Identifying Conserved Features across Non-independent Omics Studies.

False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimating conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods devised to increase statistical power to detect molecular associations in one or more studies. Still, they are not immediately suited for identifying conserved molecular associations across multiple studies. Here, we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies while accounting for inter-study dependency. This method, which we call "adjusted maximum p-value" (AdjMaxP), is easy to implement with inter-study dependency and conservation estimated directly from the p-values from each study's molecular feature-level association testing results. Through simulation-based assessment, we demonstrate AdjMaxP's improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies. AdjMaxP offers an easily implementable strategy for improving the precision of analyses for biomarker discovery from cross-platform omics study designs, thereby facilitating the adoption of such protocols for robust inference from emerging omics technologies.

European and African-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites.

Initially focused on the European population, multiple genome-wide association studies (GWAS) of complex diseases, such as type-2 diabetes (T2D), have now extended to other populations. However, to date, few ancestry-matched omics datasets have been generated or further integrated with the disease GWAS to nominate the key genes and/or molecular traits underlying the disease risk loci. In this study, we generated and integrated plasma proteomics and metabolomics with array-based genotype datasets of European (EUR) and African (AFR) ancestries to identify ancestry-specific muti-omics quantitative trait loci (QTLs). We further applied these QTLs to ancestry-stratified T2D risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. We nominated five proteins and four metabolites in the European group and one protein and one metabolite in the African group to be part of the molecular pathways of T2D risk in an ancestry-stratified manner. Our study demonstrates the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse populations. Specifically, in the AFR proteomic findings on T2D, we prioritized the protein QSOX2; while in the AFR metabolomic findings, we pinpointed the metabolite GlcNAc sulfate conjugate of C21H34O2 steroid. Neither of these findings overlapped with the corresponding EUR results.

Genomic and transcriptomic profiling of inflammatory breast cancer reveals distinct molecular characteristics to non-inflammatory breast cancers.

Inflammatory breast cancer (IBC), a rare and highly aggressive form of breast cancer, accounts for 10% of breast cancer-related deaths. Previous omics studies of IBC have focused solely on one of genomics or transcriptomics and did not discover common differences that could distinguish IBC from non-IBC. Seventeen IBC patients and five non-IBC patients as well as additional thirty-three Asian breast cancer samples from TCGA-BRCA were included for the study. We performed whole-exon sequencing (WES) to investigate different somatic genomic alterations, copy number variants, and large structural variants between IBC and non-IBC. Bulk RNA sequencing (RNA-seq) was performed to examine the differentially expressed genes, pathway enrichment, and gene fusions. WES and RNA-seq data were further investigated in combination to discover genes that were dysregulated in both genomics and transcriptomics. Copy number variation analysis identified 10 cytobands that showed higher frequency in IBC. Structural variation analysis showed more frequent deletions in IBC. Pathway enrichment and immune infiltration analysis indicated increased immune activation in IBC samples. Gene fusions including CTSC-RAB38 were found to be more common in IBC. We demonstrated more commonly dysregulated RAS pathway in IBC according to both WES and RNA-seq. Inhibitors targeting RAS signaling and its downstream pathways were predicted to possess promising effects in IBC treatment. We discovered differences unique in Asian women that could potentially explain IBC etiology and presented RAS signaling pathway as a potential therapeutic target in IBC treatment.

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I–IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

An eco-friendly approach harnessing Trichoderma lixii ORT2 for reducing chemical phosphatic fertilizer dependency and groundwater phosphorus management through integrated in silico, in vitro and omic studies

Groundwater pollution has become an escalating global issue, with excessive application of phosphorus (P) fertilizers emerging as a major contaminant. The overuse of chemical phosphatic fertilizers has intensified the problem of P infiltration into water table, compromising soil health and exacerbating groundwater pollution. This underscores the urgent need to explore sustainable alternatives to chemical fertilizers to safeguard groundwater and soil health. The enzyme alkaline phosphatase (ALP) plays important role in efficient P-solubilization achieved by Trichoderma sp. a soil fungus known for its gradual transportation of P from fixed sources to plants. This mechanism not only minimizes P leaching into groundwater but also reduces dependency on fertilizers. To investigate the active role of fungal ALP in P-solubilization, amino acid/gene sequences of enzyme from 20 Trichoderma strains were retrieved from NCBI. Molecular docking revealed strong catalytic interaction (−6.93 kcal/mol) between complex-bound P and the ALP, involving two hydrogen bonds and key residues Gln286 and Arg340. To confirm ALP-mediated P-solubilization, 12 newly isolated Trichoderma spp., were assessed in vitro using media amended with tri-calcium phosphate (TCP), zinc phosphate (Zn3(PO4)2), or ferric phosphate (FePO4) as sole phosphorus source. Trichoderma lixii ORT2 demonstrated highest P-solubilization, yielding 547.40 μg/mL phosphate from TCP, 538.6 μg/mL from Zn3(PO4)2, and 423.45 μg/mL from FePO4, in 72 h. Solubilization was accompanied by simultaneous expression of ALP and siderophore in the same aliquot. TCP induced maximum expression of ALP (9.47 U/mL), followed by Zn3(PO4)2 and FePO4 showing 8.65 and 5.64 U/mL, respectively, in culture filtrates at 72h, that was further confirmed through proteomic analysis. FESEM micrographs confirmed P binding and biosorption into fungal mycelia as P-globules. Furthermore, Cicer arietinum inoculated with Trichoderma lixii ORT2 bio-formulations showed significant enhancement in growth parameters in TCP amended soil as compared to di-ammonium phosphate (DAP). The integrated approach of microbial P-solubilization and further utilization by plants for their growth as major nutrients merges microbial and phyto-remediation to address sustainability issues related to the management of soil health and phosphorus contamination. This strategy aims to substitute chemical phosphatic fertilizers, which are significant contributors to rising dissolved total phosphorus (DTP) in groundwater. This approach directly supports SDG 6 by reducing P leaching and improving groundwater quality, ensuring sustainable water and sanitation management for all.

Progress in mitochondrial and omics studies in Alzheimer's disease research: from molecular mechanisms to therapeutic interventions.

Alzheimer's disease (Alzheimer's disease, AD) is a progressive neurological disorder characterized by memory loss and cognitive impairment. It is characterized by the formation of tau protein neurofibrillary tangles and β-amyloid plaques. Recent studies have found that mitochondria in neuronal cells of AD patients exhibit various dysfunctions, including reduced numbers, ultrastructural changes, reduced enzyme activity, and abnormal kinetics. These abnormal mitochondria not only lead to the loss of normal neuronal cell function, but are also a major driver of AD progression. In this review, we will focus on the advances of mitochondria and their multi-omics in AD research, with particular emphasis on how mitochondrial dysfunction in AD drives disease progression. At the same time, we will focus on summarizing how mitochondrial genomics technologies have revealed specific details of these dysfunctions and how therapeutic strategies targeting mitochondria may provide new directions for future AD treatments. By delving into the key mechanisms of mitochondria in AD related to energy metabolism, altered kinetics, regulation of cell death, and dysregulation of calcium-ion homeostasis, and how mitochondrial multi-omics technologies can be utilized to provide us with a better understanding of these processes. In the future, mitochondria-centered therapeutic strategies will be a key idea in the treatment of AD.

Emerging importance of stool preservation methods in OMICS studies with special focus on cancer biology.

The intricate consortium of microorganisms in the human gut plays a crucial role in different physiological functions. The complex known-unknown elements of the gut microbiome are perplexing and the absence of standardized procedures for collecting and preserving samples has hindered continuous research in comprehending it. The technological bias produced because of lack of standard protocols has affected the reproducibility of results. The complex nature of diseases like colorectal cancer, gastric cancer, hepatocellular carcinoma and breast cancer require a thorough understanding of its etiology for an efficient and timely diagnosis. The designated protocols for collection and preservation of stool specimens have great variance, hence generate inconsistencies in OMICS studies. Due to the complications associated to the nature of sample, it is important to preserve the sample to be studied later in a laboratory or to be used in the future research purpose. Stool preservation is gaining importance due to the increased use of treatment options like fecal microbiota transplantation to cure conditions like recurrent Clostridium difficile infections and for OMICS studies including metagenomics, metabolomics and culturomics. This review provides an insight into the importance of omics studies for the identification and development of novel biomarkers for quick and noninvasive diagnosis of various diseases.

Penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers

BackgroundThe matched case–control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case–control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account.ResultsWe present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case–control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model.ConclusionsThe proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case–control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Open science in biomedicine: Overcoming barriers in omics studies

Open science in biomedicine: Overcoming barriers in omics studies

High-temperature adaptation mechanisms and biotechnological potentials of thermophilic cyanobacteria

Thermophilic cyanobacteria are prokaryotic organisms that possess exceptional heat-resistant characteristics. This group serves as an excellent model for investigating the heat tolerance of higher photosynthetic organisms, including higher plants, some protists (such as algae and euglena), and bacteria. Analyzing the mechanisms of high-temperature adaptation in thermophilic cyanobacteria can enhance our understanding of how photosynthetic organisms and microorganisms tolerate high temperatures at the molecular level. Additionally, these thermotolerant cyanobacteria have the potential to contribute to breeding heat-tolerant plants and developing microbial cell factories. This review summarizes current research on thermophilic cyanobacteria, focusing on their ecology, morphology, omics studies, and mechanisms of high-temperature tolerance. It offers insight into the potential biotechnological applications of thermophilic cyanobacteria and highlights future research opportunities. Specifically, attention is given to the photosynthetic physiology and metabolism of cyanobacteria, and the molecular basis of heat-tolerance mechanisms in thermophilic cyanobacteria is explored.

Targeting histone deacetylase in cardiac diseases.

Histone deacetylases (HDAC) catalyze the removal of acetylation modifications on histones and non-histone proteins, which regulates gene expression and other cellular processes. HDAC inhibitors (HDACi), approved anti-cancer agents, emerge as a potential new therapy for heart diseases. Cardioprotective effects of HDACi are observed in many preclinical animal models of heart diseases. Genetic mouse models have been developed to understand the role of each HDAC in cardiac functions. Some of the findings are controversial. Here, we provide an overview of how HDACi and HDAC impact cardiac functions under physiological or pathological conditions. We focus on in vivo studies of zinc-dependent classical HDACs, emphasizing disease conditions involving cardiac hypertrophy, myocardial infarction (MI), ischemic reperfusion (I/R) injury, and heart failure. In particular, we review how non-biased omics studies can help our understanding of the mechanisms underlying the cardiac effects of HDACi and HDAC.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. https://github.com/jianzou75/MICA.

A Regularized Bayesian Dirichlet-multinomial Regression Model for Integrating Single-cell-level Omics and Patient-level Clinical Study Data.

The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.

Construction of a Full-Length Transcriptome of Western Honeybee Midgut Tissue and Improved Genome Annotation.

Honeybees are an indispensable pollinator in nature with pivotal ecological, economic, and scientific value. However, a full-length transcriptome for Apis mellifera, assembled with the advanced third-generation nanopore sequencing technology, has yet to be reported. Here, nanopore sequencing of the midgut tissues of uninoculated and Nosema ceranae-inoculated A. mellifera workers was conducted, and the full-length transcriptome was then constructed and annotated based on high-quality long reads. Next followed improvement of sequences and annotations of the current reference genome of A. mellifera. A total of 5,942,745 and 6,664,923 raw reads were produced from midguts of workers at 7 days post-inoculation (dpi) with N. ceranae and 10 dpi, while 7,100,161 and 6,506,665 raw reads were generated from the midguts of corresponding uninoculated workers. After strict quality control, 6,928,170, 6,353,066, 5,745,048, and 6,416,987 clean reads were obtained, with a length distribution ranging from 1 kb to 10 kb. Additionally, 16,824, 17,708, 15,744, and 18,246 full-length transcripts were respectively detected, including 28,019 nonredundant ones. Among these, 43,666, 30,945, 41,771, 26,442, and 24,532 full-length transcripts could be annotated to the Nr, KOG, eggNOG, GO, and KEGG databases, respectively. Additionally, 501 novel genes (20,326 novel transcripts) were identified for the first time, among which 401 (20,255), 193 (13,365), 414 (19,186), 228 (12,093), and 202 (11,703) were respectively annotated to each of the aforementioned five databases. The expression and sequences of three randomly selected novel transcripts were confirmed by RT-PCR and Sanger sequencing. The 5' UTR of 2082 genes, the 3' UTR of 2029 genes, and both the 5' and 3' UTRs of 730 genes were extended. Moreover, 17,345 SSRs, 14,789 complete ORFs, 1224 long non-coding RNAs (lncRNAs), and 650 transcription factors (TFs) from 37 families were detected. Findings from this work not only refine the annotation of the A. mellifera reference genome, but also provide a valuable resource and basis for relevant molecular and -omics studies.