Multi-omic analysis of longitudinal acute myeloid leukemia patient samples reveals potential prognostic markers linked to disease progression.

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.

Exploring the impact of intra-tumoural heterogeneity on liquid biopsy cell-free DNA methylation and copy number in head and neck squamous cell carcinoma.

Liquid biopsy profiling is gaining increasing promise towards biomarker-led identification and disease stratification of tumours, particularly for tumours displaying significant intra-tumoural heterogeneity (ITH). For head and neck squamous cell carcinoma (HNSCC), which display high levels of genetic ITH, identification of epigenetic modifications and methylation signatures has shown multiple uses in stratification of HNSCC for prognosis, treatment, and HPV status. In this study, we investigated the potential of liquid biopsy methylomics and genomic copy number to profile HNSCC. We conducted multi-region sampling of tumour core, tumour margin and normal adjacent mucosa, as well as plasma cell-free DNA (cfDNA) across 9 HNSCC patients. Collectively, our work highlights the prevalence of methylomic ITH in HNSCC, and demonstrates the potential of cfDNA methylation as a tool for ITH assessment and serial sampling.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases. IMPORTANCE: We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns.

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δß >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Multi-omic analyses identified SFRP4 as a novel biomarker in abnormal uterine bleeding with ovulatory dysfunction.

The goal of the study was to explore the mechanism underlying the progression from abnormal uterine bleeding with ovulatory dysfunction (AUB-O) to AUB with atypical hyperplasia/malignancy (AUB-M). AUB-O, AUB-M and control endometrial tissues were subjected to multi-omic analyses to identify biomarkers. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs), including SFRP4, between the AUB-O and AUB-M groups were identified. The expression of SFRP4 was upregulated in endometrial tissues from AUB-O groups compared to that from AUB-M groups. SFRP4 knockdown in human endometrial epithelial cells (hEECs) promoted cell migration, invasion, proliferation and colony formation but inhibited apoptosis. Furthermore, the levels of key Wnt pathway proteins were altered by SFRP4 knockdown: Wnt-5A was downregulated and Wnt-7A was upregulated. In conclusion, we identified SFRP4 as an AUB-O-related molecule. SFRP4 might play a key role in hEECs apoptosis, migration, invasion, proliferation and colony formation via the Wnt pathway. SFRP4 may serve as a repressive factor regarding the progression of AUB-O to AUB-M. However, further studies are warranted to elucidate the exact mechanism.

Efficient and low-energy degradation of chlorobenzene via catA-mediated cleavage and bedC1 docking in a novel Burkholderia stabilis TF-2.

In this study, a novel strain Burkholderia stabilis TF-2 capable of assimilatory and co-metabolic degradation of chlorobenzenes was obtained. The interaction between chlorobenzene (CB) and target enzymes, as well as the metabolic pathways in TF-2, were elucidated using multi-omics and molecular docking techniques. Results of degradation experiments indicated that TF-2 assimilated CB at a rate of 0.22-0.66 mg·gcell-1·h-1 in concentrations of 20-200 mg L-1. Additionally, TF-2 also used sodium succinate and sodium citrate as substrates to co-metabolize CB, with degradation rates of 0.26-2.00 and 0.31-1.72 mol·gcell-1·h-1, respectively. Whole-genome sequencing revealed over 18 novel genes associated with aromatic hydrocarbon degradation in TF-2. Transcriptomic analysis showed that CB induced the high expression of 119 genes involved in CB metabolism and late mineralization. The significant up-regulation of the bedC1 (encoding a ring-hydroxylated dioxygenase), CatA (chlorocatechol 1,2-dioxygenase), pcaJ (3-oxoadipate CoA-transferase alpha subunit) and fadA (acetyl-CoA acyltransferase) genes facilitated CB metabolism. Based on these findings, a metabolic pathway for CB was constructed, with the key step involving ortho cleavage of the aromatic ring under the action of the catA gene. Furthermore, molecular docking revealed that CB bound to bedC1 with -4.5 kcal mol-1 through hydrophobic bonds, π-stacking, and a halogen bond. These results provide strong support for development of efficient strains to enhance the removal of chlorinated organic compounds.

Gut microbiota metabolically mediate intestinal helminth infection in zebrafish.

Intestinal helminth parasite (IHP) infection induces alterations in the composition of microbial communities across vertebrates, although how gut microbiota may facilitate or hinder parasite infection remains poorly defined. In this work, we utilized a zebrafish model to investigate the relationship between gut microbiota, gut metabolites, and IHP infection. We found that extreme disparity in zebrafish parasite infection burden is linked to the composition of the gut microbiome and that changes in the gut microbiome are associated with variation in a class of endogenously produced signaling compounds, N-acylethanolamines, that are known to be involved in parasite infection. Using a statistical mediation analysis, we uncovered a set of gut microbes whose relative abundance explains the association between gut metabolites and infection outcomes. Experimental investigation of one of the compounds in this analysis reveals salicylaldehyde, which is putatively produced by the gut microbe Pelomonas, as a potent anthelmintic with activity against Pseudocapillaria tomentosa egg hatching, both in vitro and in vivo. Collectively, our findings underscore the importance of the gut microbiome as a mediating agent in parasitic infection and highlight specific gut metabolites as tools for the advancement of novel therapeutic interventions against IHP infection. IMPORTANCE: Intestinal helminth parasites (IHPs) impact human health globally and interfere with animal health and agricultural productivity. While anthelmintics are critical to controlling parasite infections, their efficacy is increasingly compromised by drug resistance. Recent investigations suggest the gut microbiome might mediate helminth infection dynamics. So, identifying how gut microbes interact with parasites could yield new therapeutic targets for infection prevention and management. We conducted a study using a zebrafish model of parasitic infection to identify routes by which gut microbes might impact helminth infection outcomes. Our research linked the gut microbiome to both parasite infection and to metabolites in the gut to understand how microbes could alter parasite infection. We identified a metabolite in the gut, salicylaldehyde, that is putatively produced by a gut microbe and that inhibits parasitic egg growth. Our results also point to a class of compounds, N-acyl-ethanolamines, which are affected by changes in the gut microbiome and are linked to parasite infection. Collectively, our results indicate the gut microbiome may be a source of novel anthelmintics that can be harnessed to control IHPs.

Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination.

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Comprehensive multiomics analysis of the signatures of gastric mucosal bacteria and plasma metabolites across different stomach microhabitats in the development of gastric cancer.

PURPOSE: As an important component of the microenvironment, the gastric microbiota and its metabolites are associated with tumour occurrence, progression, and metastasis. However, the relationship between the gastric microbiota and the development of gastric cancer is unclear. The present study investigated the role of the gastric mucosa microbiome and metabolites as aetiological factors in gastric carcinogenesis. METHODS: Gastric biopsies from different stomach microhabitats (n = 70) were subjected to 16S rRNA gene sequencing, and blood samples (n = 95) were subjected to untargeted metabolome (gas chromatography‒mass spectrometry, GC‒MS) analyses. The datasets were analysed using various bioinformatics approaches. RESULTS: The microbiota diversity and community composition markedly changed during gastric carcinogenesis. High Helicobacter. pylori colonization modified the overall diversity and composition of the microbiota associated with gastritis and cancer in the stomach. Most importantly, analysis of the functional features of the microbiota revealed that nitrate reductase genes were significantly enriched in the tumoral microbiota, while urease-producing genes were significantly enriched in the microbiota of H. pylori-positive patients. A panel of 81 metabolites was constructed to discriminate gastric cancer patients from gastritis patients, and a panel of 15 metabolites was constructed to discriminate H. pylori-positive patients from H. pylori-negative patients. receiver operator characteristic (ROC) curve analysis identified a series of gastric microbes and plasma metabolites as potential biomarkers of gastric cancer. CONCLUSION: The present study identified a series of signatures that may play important roles in gastric carcinogenesis and have the potential to be used as biomarkers for diagnosis and for the surveillance of gastric cancer patients with minimal invasiveness.

A novel integrated extraction protocol for multi-omic studies in heavily degraded samples.

The combination of multi-omic techniques, such as genomics, transcriptomics, proteomics, metabolomics and epigenomics, has revolutionised studies in medical research. These techniques are employed to support biomarker discovery, better understand molecular pathways and identify novel drug targets. Despite concerted efforts in integrating omic datasets, there is an absence of protocols that integrate all four biomolecules in a single extraction process. Here, we demonstrate for the first time a minimally destructive integrated protocol for the simultaneous extraction of artificially degraded DNA, proteins, lipids and metabolites from pig brain samples. We used an MTBE-based approach to separate lipids and metabolites, followed by subsequent isolation of DNA and proteins. We have validated this protocol against standalone extraction protocols and show comparable or higher yields of all four biomolecules. This integrated protocol is key to facilitating the preservation of irreplaceable samples while promoting downstream analyses and successful data integration by removing bias from univariate dataset noise and varied distribution characteristics.

Optimised human stool sample collection for multi-omic microbiota analysis.

To accurately define the role of the gut microbiota in health and disease pathogenesis, the preservation of stool sample integrity, in terms of microbial community composition and metabolic function, is critical. This presents a challenge for any studies which rely on participants self-collecting and returning stool samples as this introduces variability and uncertainty of sample storage/handling. Here, we tested the performance of three stool sample collection/preservation buffers when storing human stool samples at different temperatures (room temperature [20 °C], 4 °C and - 80 °C) for up to three days. We compared and quantified differences in 16S rRNA sequencing composition and short-chain fatty acid profiles compared against immediately snap-frozen stool. We found that the choice of preservation buffer had the largest effect on the resulting microbial community and metabolomic profiles. Collectively analysis confirmed that PSP and RNAlater buffered samples most closely recapitulated the microbial diversity profile of the original (immediately - 80 °C frozen) sample and should be prioritised for human stool microbiome studies.

Multi-Omic Analysis Reveals Genetic Determinants and Therapeutic Targets of Chronic Kidney Disease and Kidney Function.

Chronic kidney disease (CKD) presents a significant global health challenge, characterized by complex pathophysiology. This study utilized a multi-omic approach, integrating genomic data from the CKDGen consortium alongside transcriptomic, metabolomic, and proteomic data to elucidate the genetic underpinnings and identify therapeutic targets for CKD and kidney function. We employed a range of analytical methods including cross-tissue transcriptome-wide association studies (TWASs), Mendelian randomization (MR), summary-based MR (SMR), and molecular docking. These analyses collectively identified 146 cross-tissue genetic associations with CKD and kidney function. Key Golgi apparatus-related genes (GARGs) and 41 potential drug targets were highlighted, with MAP3K11 emerging as a significant gene from the TWAS and MR data, underscoring its potential as a therapeutic target. Capsaicin displayed promising drug-target interactions in molecular docking analyses. Additionally, metabolome- and proteome-wide MR (PWMR) analyses revealed 33 unique metabolites and critical inflammatory proteins such as FGF5 that are significantly linked to and colocalized with CKD and kidney function. These insights deepen our understanding of CKD pathogenesis and highlight novel targets for treatment and prevention.

Integrated multi-omic analyses uncover the effects of aging on cell-type regulation in glucose-responsive tissues.

Aging significantly influences cellular activity and metabolism in glucose-responsive tissues, yet a comprehensive evaluation of the impacts of aging and associated cell-type responses has been lacking. This study integrates transcriptomic, methylomic, single-cell RNA sequencing, and metabolomic data to investigate aging-related regulations in adipose and muscle tissues. Through coexpression network analysis of the adipose tissue, we identified aging-associated network modules specific to certain cell types, including adipocytes and immune cells. Aging upregulates the metabolic functions of lysosomes and downregulates the branched-chain amino acids (BCAAs) degradation pathway. Additionally, aging-associated changes in cell proportions, methylation profiles, and single-cell expressions were observed in the adipose. In the muscle tissue, aging was found to repress the metabolic processes of glycolysis and oxidative phosphorylation, along with reduced gene activity of fast-twitch type II muscle fibers. Metabolomic profiling linked aging-related alterations in plasma metabolites to gene expression in glucose-responsive tissues, particularly in tRNA modifications, BCAA metabolism, and sex hormone signaling. Together, our multi-omic analyses provide a comprehensive understanding of the impacts of aging on glucose-responsive tissues and identify potential plasma biomarkers for these effects.

nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Non-linear Iterative Partial Least Squares.

Motivation: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. Results: We introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. Availability: nipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

Early detection of pancreatic cancer by liquid biopsy "PANLIPSY": a french nation-wide study project.

BACKGROUND: Pancreatic cancer, predominantly characterized by ductal adenocarcinoma (PDAC) accounts for 90% of cases and is the fourth leading cause of cancer-related deaths globally. Its incidence is notably increasing. This poor prognosis is primarily due to late-stage diagnosis (approximately 70% to 80% of patients are diagnosed at an advanced stage), aggressive tumor biology, and low sensitivity to chemotherapy. Consequently, it is crucial to identify and develop a simple, feasible and reproducible blood-based signature (i.e., combination of biomarkers) for early detection of PDAC. METHODS: The PANLIPSY study is a multi-center, non-interventional prospective clinical trial designed to achieve early detection of PDAC with high specificity and sensitivity, using a combinatorial approach in blood samples. These samples are collected from patients with resectable, borderline or locally advanced, and metastatic stage PDAC within the framework of the French Biological and Clinical Database for PDAC cohort (BACAP 2). All partners of the BACAP consortium are eligible to participate. The study will include 215 PDAC patients, plus 25 patients with benign pancreatic conditions from the PAncreatic Disease Cohort of TOuLouse (PACTOL) cohort, and 115 healthy controls, totaling 355 individuals. Circulating biomarkers will be collected in a total volume of 50 mL of blood, divided into one CellSave tube (10 mL), two CELL-FREE DNA BCT® preservative tubes (18 mL), and five EDTA tubes (22 mL in total). Samples preparation will adhere to the guidelines of the European Liquid Biopsy Society (ELBS). A unique feature of the study is the AI-based comparison of these complementary liquid biopsy biomarkers. Main end-points: i) to define a liquid biopsy signature that includes the most relevant circulating biomarkers, ii) to validate the multi-marker panel in an independent cohort of healthy controls and patients, with resectable PDAC, and iii) to establish a unique liquid biopsy biobank for PDAC study. DISCUSSION: The PANLIPSY study is a unique prospective non-interventional clinical trial that brings together liquid biopsy experts. The aim is to develop a biological signature for the early detection of PDAC based on AI-assisted detection of circulating biomarkers in blood samples (CTCs, ctDNA, EVs, circulating immune system, circulating cell-free nucleosomes, proteins, and microbiota). TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06128343 / NCT05824403. Registration dates: June 8,2023 and April 21, 2023.

Multi-omic integration reveals alterations in nasal mucosal biology that mediate air pollutant effects on allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common inflammatory condition of the nasal mucosa that imposes a considerable health burden. Air pollution has been observed to increase the risk of developing allergic rhinitis. We addressed the hypotheses that early life exposure to air toxics is associated with developing allergic rhinitis, and that these effects are mediated by DNA methylation and gene expression in the nasal mucosa. METHODS: In a case-control cohort of 505 participants, we geocoded participants' early life exposure to air toxics using data from the US Environmental Protection Agency, assessed physician diagnosis of allergic rhinitis by questionnaire, and collected nasal brushings for whole-genome DNA methylation and transcriptome profiling. We then performed a series of analyses including differential expression, Mendelian randomization, and causal mediation analyses to characterize relationships between early life air toxics, nasal DNA methylation, nasal gene expression, and allergic rhinitis. RESULTS: Among the 505 participants, 275 had allergic rhinitis. The mean age of the participants was 16.4 years (standard deviation = 9.5 years). Early life exposure to air toxics such as acrylic acid, phosphine, antimony compounds, and benzyl chloride was associated with developing allergic rhinitis. These air toxics exerted their effects by altering the nasal DNA methylation and nasal gene expression levels of genes involved in respiratory ciliary function, mast cell activation, pro-inflammatory TGF-ß1 signaling, and the regulation of myeloid immune cell function. CONCLUSIONS: Our results expand the range of air pollutants implicated in allergic rhinitis and shed light on their underlying biological mechanisms in nasal mucosa.

An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients.

Background: Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches. Methods: The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis. Results: In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions. Conclusion: In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Navigating Amaryllidaceae Alkaloids: Bridging Gaps and Charting Biosynthetic Territories - A Comprehensive Review.

Amaryllidaceae alkaloid (AAs) biosynthesis has garnered significant attention in recent years, particularly with the commercialisation of galanthamine as a treatment for the symptoms of Alzheimer's disease. A significant amount of research work over the last 8 decades has focused on the understanding of AA biosynthesis, starting from early radiolabelling studies to recent multi-omics analysis with modern biotechnological advancements. Those studies enabled the identification of hundreds of metabolites, the characterisation of biochemical pathway, an understanding of the environmental stimuli, and of the molecular regulation of these pharmaceutically and agriculturally important metabolites. Despite the numerous works there remain significant gaps in understanding their biosynthesis in Amaryllidaceae plants. As such, further research is needed to fully elucidate the metabolic pathway and facilitate their production. This review aims to provide a comprehensive overall summary of the current state of knowledge on AAs biosynthesis, from elicitation of transcription factors expression in the cell nucleus to alkaloid transport in the apoplast, and to highlight the challenges that need to be overcome for further advancement.