Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches.

The liver is the largest solid organ in the body, yet it remains incompletely characterized. Here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single-cell CITE-seq, single-nuclei sequencing, spatial transcriptomics, and spatial proteomics. By integrating these multi-omic datasets, we provide validated strategies to reliably discriminate and localize all hepatic cells, including a population of lipid-associated macrophages (LAMs) at the bile ducts. We then align this atlas across seven species, revealing the conserved program of bona fide Kupffer cells and LAMs. We also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. We demonstrate that LAMs are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while Kupffer cell development crucially depends on their cross-talk with hepatic stellate cells via the evolutionarily conserved ALK1-BMP9/10 axis.

HiCAR is a robust and sensitive method to analyze open-chromatin-associated genome organization.

The long-range interactions of cis-regulatory elements (cREs) play a central role in gene regulation. cREs can be characterized as accessible chromatin sequences. However, it remains technically challenging to comprehensively identify their spatial interactions. Here, we report a new method HiCAR (Hi-C on accessible regulatory DNA), which utilizes Tn5 transposase and chromatin proximity ligation, for the analysis of open-chromatin-anchored interactions with low-input cells. By applying HiCAR in human embryonic stem cells and lymphoblastoid cells, we demonstrate that HiCAR identifies high-resolution chromatin contacts with an efficiency comparable with that of in situ Hi-C over all distance ranges. Interestingly, we found that the "poised" gene promoters exhibit silencer-like function to repress the expression of distal genes via promoter-promoter interactions. Lastly, we applied HiCAR to 30,000 primary human muscle stem cells and demonstrated that HiCAR is capable of analyzing chromatin accessibility and looping using low-input primary cells and clinical samples.

An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity.

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27- early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

Molecular bases of comorbidities: present and future perspectives.

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

PhyloAln: A Convenient Reference-Based Tool to Align Sequences and High-Throughput Reads for Phylogeny and Evolution in the Omic Era.

The current trend in phylogenetic and evolutionary analyses predominantly relies on omic data. However, prior to core analyses, traditional methods typically involve intricate and time-consuming procedures, including assembly from high-throughput reads, decontamination, gene prediction, homology search, orthology assignment, multiple sequence alignment, and matrix trimming. Such processes significantly impede the efficiency of research when dealing with extensive data sets. In this study, we develop PhyloAln, a convenient reference-based tool capable of directly aligning high-throughput reads or complete sequences with existing alignments as a reference for phylogenetic and evolutionary analyses. Through testing with simulated data sets of species spanning the tree of life, PhyloAln demonstrates consistently robust performance compared with other reference-based tools across different data types, sequencing technologies, coverages, and species, with percent completeness and identity at least 50 percentage points higher in the alignments. Additionally, we validate the efficacy of PhyloAln in removing a minimum of 90% foreign and 70% cross-contamination issues, which are prevalent in sequencing data but often overlooked by other tools. Moreover, we showcase the broad applicability of PhyloAln by generating alignments (completeness mostly larger than 80%, identity larger than 90%) and reconstructing robust phylogenies using real data sets of transcriptomes of ladybird beetles, plastid genes of peppers, or ultraconserved elements of turtles. With these advantages, PhyloAln is expected to facilitate phylogenetic and evolutionary analyses in the omic era. The tool is accessible at https://github.com/huangyh45/PhyloAln.

Intricacies of single-cell multi-omics data integration.

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

Multi-omic Mitoprotease Profiling Defines a Role for Oct1p in Coenzyme Q Production.

Mitoproteases are becoming recognized as key regulators of diverse mitochondrial functions, although their direct substrates are often difficult to discern. Through multi-omic profiling of diverse Saccharomyces cerevisiae mitoprotease deletion strains, we predicted numerous associations between mitoproteases and distinct mitochondrial processes. These include a strong association between the mitochondrial matrix octapeptidase Oct1p and coenzyme Q (CoQ) biosynthesis-a pathway essential for mitochondrial respiration. Through Edman sequencing and in vitro and in vivo biochemistry, we demonstrated that Oct1p directly processes the N terminus of the CoQ-related methyltransferase, Coq5p, which markedly improves its stability. A single mutation to the Oct1p recognition motif in Coq5p disrupted its processing in vivo, leading to CoQ deficiency and respiratory incompetence. This work defines the Oct1p processing of Coq5p as an essential post-translational event for proper CoQ production. Additionally, our data visualization tool enables efficient exploration of mitoprotease profiles that can serve as the basis for future mechanistic investigations.

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.

Gene-environment interaction elicits dystonia-like features and impaired translational regulation in a DYT-TOR1A mouse model.

DYT-TOR1A dystonia is the most common monogenic dystonia characterized by involuntary muscle contractions and lack of therapeutic options. Despite some insights into its etiology, the disease's pathophysiology remains unclear. The reduced penetrance of about 30% suggests that extragenetic factors are needed to develop a dystonic phenotype. In order to systematically investigate this hypothesis, we induced a sciatic nerve crush injury in a genetically predisposed DYT-TOR1A mouse model (DYT1KI) to evoke a dystonic phenotype. Subsequently, we employed a multi-omic approach to uncover novel pathophysiological pathways that might be responsible for this condition. Using an unbiased deep-learning-based characterization of the dystonic phenotype showed that nerve-injured DYT1KI animals exhibited significantly more dystonia-like movements (DLM) compared to naive DYT1KI animals. This finding was noticeable as early as two weeks following the surgical procedure. Furthermore, nerve-injured DYT1KI mice displayed significantly more DLM than nerve-injured wildtype (wt) animals starting at 6 weeks post injury. In the cerebellum of nerve-injured wt mice, multi-omic analysis pointed towards regulation in translation related processes. These observations were not made in the cerebellum of nerve-injured DYT1KI mice; instead, they were localized to the cortex and striatum. Our findings indicate a failed translational compensatory mechanisms in the cerebellum of phenotypic DYT1KI mice that exhibit DLM, while translation dysregulations in the cortex and striatum likely promotes the dystonic phenotype.

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.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability.

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Inferring secretory and metabolic pathway activity from omic data with secCellFie.

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Severe Allergy as a Chronic Inflammatory Condition From a Systems Biology Perspective.

Persistent and unresolved inflammation is a common underlying factor observed in several and seemingly unrelated human diseases, including cardiovascular and neurodegenerative diseases. Particularly, in atopic conditions, acute inflammatory responses such as those triggered by insect venom, food or drug allergies possess also a life-threatening potential. However, respiratory allergies predominantly exhibit late immune responses associated with chronic inflammation, that can eventually progress into a severe phenotype displaying similar features as those observed in other chronic inflammatory diseases, as is the case of uncontrolled severe asthma. This review aims to explore the different facets and systems involved in chronic allergic inflammation, including processes such as tissue remodelling and immune cell dysregulation, as well as genetic, metabolic and microbiota alterations, which are common to other inflammatory conditions. Our goal here was to deepen on the understanding of an entangled disease as is chronic allergic inflammation and expose potential avenues for the development of better diagnostic and intervention strategies.

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.

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.

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.

Patterns of differential expression by association in omic data using a new measure based on ensemble learning.

The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.

Microbial oases in the ice: A state-of-the-art review on cryoconite holes as diversity hotspots and their scientific connotations.

Cryoconite holes, small meltwater pools on the surface of glaciers and ice sheets, represent extremely cold ecosystems teeming with diverse microbial life. Cryoconite holes exhibit greater susceptibility to the impacts of climate change, underlining the imperative nature of investigating microbial communities as an essential module of polar and alpine ecosystem monitoring efforts. Microbes in cryoconite holes play a critical role in nutrient cycling and can produce bioactive compounds, holding promise for industrial and pharmaceutical innovation. Understanding microbial diversity in these delicate ecosystems is essential for effective conservation strategies. Therefore, this review discusses the microbial diversity in these extreme environments, aiming to unveil the complexity of their microbial communities. The current study envisages that cryoconite holes as distinctive ecosystems encompass a multitude of taxonomically diverse and functionally adaptable microorganisms that exhibit a rich microbial diversity and possess intricate ecological functions. By investigating microbial diversity and ecological functions of cryoconite holes, this study aims to contribute valuable insights into the broader field of environmental microbiology and enhance further understanding of these ecosystems. This review seeks to provide a holistic overview regarding the formation, evolution, characterization, and molecular adaptations of cryoconite holes. Furthermore, future research directions and challenges underlining the need for long-term monitoring, and ethical considerations in preserving these pristine environments are also provided. Addressing these challenges and resolutely pursuing future research directions promises to enrich our comprehension of microbial diversity within cryoconite holes, revealing the broader ecological and biogeochemical implications. The inferences derived from the present study will provide researchers, ecologists, and policymakers with a profound understanding of the significance and utility of cryoconite holes in unveiling the microbial diversity and its potential applications.

New biomarkers underlying acetic acid tolerance in the probiotic yeast Saccharomyces cerevisiae var. boulardii.

Evolutionary engineering experiments, in combination with omics technologies, revealed genetic markers underpinning the molecular mechanisms behind acetic acid stress tolerance in the probiotic yeast Saccharomyces cerevisiae var. boulardii. Here, compared to the ancestral Ent strain, evolved yeast strains could quickly adapt to high acetic acid levels (7 g/L) and displayed a shorter lag phase of growth. Bioinformatic-aided whole-genome sequencing identified genetic changes associated with enhanced strain robustness to acetic acid: a duplicated sequence in the essential endocytotic PAN1 gene, mutations in a cell wall mannoprotein (dan4Thr192del), a lipid and fatty acid transcription factor (oaf1Ser57Pro) and a thiamine biosynthetic enzyme (thi13Thr332Ala). Induction of PAN1 and its associated endocytic complex SLA1 and END3 genes was observed following acetic acid treatment in the evolved-resistant strain when compared to the ancestral strain. Genome-wide transcriptomic analysis of the evolved Ent acid-resistant strain (Ent ev16) also revealed a dramatic rewiring of gene expression among genes associated with cellular transport, metabolism, oxidative stress response, biosynthesis/organization of the cell wall, and cell membrane. Some evolved strains also displayed better growth at high acetic acid concentrations and exhibited adaptive metabolic profiles with altered levels of secreted ethanol (4.0-6.4% decrease), glycerol (31.4-78.5% increase), and acetic acid (53.0-60.3% increase) when compared to the ancestral strain. Overall, duplication/mutations and transcriptional alterations are key mechanisms driving improved acetic acid tolerance in probiotic strains. We successfully used adaptive evolutionary engineering to rapidly and effectively elucidate the molecular mechanisms behind important industrial traits to obtain robust probiotic yeast strains for myriad biotechnological applications. KEY POINTS: •Acetic acid adaptation of evolutionary engineered robust probiotic yeast S. boulardii •Enterol ev16 with altered genetic and transcriptomic profiles survives in up to 7 g/L acetic acid •Improved acetic acid tolerance of S. boulardii ev16 with mutated PAN1, DAN4, OAF1, and THI13 genes.

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.