Omics Platforms Research Articles

Abstract 2588: Bioengineering of site-specific antibody labeling from basic research to spatial omics platforms

Abstract Over the last few years, spatial multi-omics platforms have emerged as a powerful tool for characterizing cancer biology in multiple dimensions. Notably, these systems typically rely on multiplex immunolabeling. Conventionally, multiplexing is accomplished through carefully selecting primary antibodies from different animal species, and in combination with their corresponding polyclonal secondary antibodies. However, given the practical complication related to the species availability of primary antibodies, the multiplexing labeling is still under constraint due to the necessity for matched secondary species labels and the availability. For decades, non-specific labeling of primary antibodies through amine- or thiol-reactive chemistries was widely performed. Evolving research and commercial products have sought to overcome this antibody-based multiplexing limitations by developing enzymatic reaction-directed chemical reaction or antibody targeting proteins that could contain affinity to the Fc or Fab regions of antibodies. However, these methods mentioned above have substantive limitations and side-effects on affinity, conjugation number, buffer incompatibilities, storage incompatibilities, insufficient conjugation, and uncontrolled stoichiometric labeling. Herein, we introduce an efficient, sustainable, reproducible site-specific antibody covalent labeling strategy through engineering anti-IgG secondary nanobodies. These infer the stability, high-affinity of species specific and easy-to-produce IgG binders with the permanence of controlled activation for proximity-driven intermolecular covalent conjugation, with a promise for efficient multiplexity. This labeling technology addresses antibody labeling challenges through currently available approaches, and would substantially enhance multiplexity in a wide variety of research and clinical settings. Citation Format: Kai Tao, Matthew J. Rames, MingChong Dai, Gulsu Sener, Samuel Olson, Srivathsan Ranganathan, Sila Ozdemir, Adam Oken, Michael Brasino, Randy Armstrong, Yu-Jui Chiu, Ece Eksi, Bruce Branchaud, Sadik Esener. Bioengineering of site-specific antibody labeling from basic research to spatial omics platforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2588.

Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution.

Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Microbial, proteomic, and metabolomic profiling of the estrous cycle in wild house mice.

Symbiotic microbial communities affect the host immune system and produce molecules contributing to the odor of an individual. In many mammalian species, saliva and vaginal fluids are important sources of chemical signals that originate from bacterial metabolism and may act as honest signals of health and reproductive status. In this study, we aimed to define oral and vaginal microbiomes and their dynamics throughout the estrous cycle in wild house mice. In addition, we analyzed a subset of vaginal proteomes and metabolomes to detect potential interactions with microbiomes. 16S rRNA sequencing revealed that both saliva and vagina are dominated by Firmicutes and Proteobacteria but differ at the genus level. The oral microbiome is more stable during the estrous cycle and most abundant bacteria belong to the genera Gemella and Streptococcus, while the vaginal microbiome shows higher bacterial diversity and dynamics during the reproductive cycle and is characterized by the dominance of Muribacter and Rodentibacter. These two genera cover around 50% of the bacterial community during estrus. Proteomic profiling of vaginal fluids revealed specific protein patterns associated with different estrous phases. Highly expressed proteins in estrus involve the keratinization process thus providing estrus markers (e.g., Hrnr) while some proteins are downregulated such as immune-related proteins that limit bacterial growth (Camp, Clu, Elane, Lyz2, and Ngp). The vaginal metabolome contains volatile compounds potentially involved in chemical communication, for example, ketones, aldehydes, and esters of carboxylic acids. Data integration of all three OMICs data sets revealed high correlations, thus providing evidence that microbiomes, host proteomes, and metabolomes may interact.IMPORTANCEOur data revealed dynamic changes in vaginal, but not salivary, microbiome composition during the reproductive cycle of wild mice. With multiple OMICs platforms, we provide evidence that changes in microbiota in the vaginal environment are accompanied by changes in the proteomic and metabolomics profiles of the host. This study describes the natural microbiota of wild mice and may contribute to a better understanding of microbiome-host immune system interactions during the hormonal and cellular changes in the female reproductive tract. Moreover, analysis of volatiles in the vaginal fluid shows particular substances that can be involved in chemical communication and reproductive behavior.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review.

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

An Integrative Multi‐Omics Approach Reveals Molecular Signatures Associated with Age and High‐Fat Diet in Mouse Models of Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease (AD) is a complex, multifactorial pathology with high heterogeneity in biological alterations. Our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. Therefore, it is required to integrate the information from multiple data modalities for thorough exploration of endophenotype networks, biological interactions related to disease and thus accelerate our understanding of heterogeneity in Alzheimer’s disease.MethodIn this study, we performed multi‐level omics in a cohort of mouse models expressing humanized Abeta and two genetic risk factors (APOE4 and Trem2*R47H) at multiple ages for both sexes. Data from multiple omics platforms (transcriptomics, proteomics, and metabolomics) were analyzed at single‐omic level as well as integrated in an unbiased fashion, considering interaction between modalities using multi‐omics factor analysis (MOFA). We also systematically aligned multimodal mouse data to relevant human studies cohort.ResultMulti‐omics integration identified major components of heterogeneity explaining the variance within the cohort and differentially associated with age, sex, and high fat diet. Enrichment analysis of genes and protein associated with these components were significantly enriched for multiple AD‐related processes. Specifically, components associated with age and diet related heterogeneity exhibited overrepresentation of immune response and metabolic processes as well as increased levels of long‐chain acylcarnitine’s and reduced levels of spermidine in aged and high‐fat diet fed AD mouse models, similar to human AD. In addition, we observed weak correspondence between change in protein and RNA expression in mouse models compared to controls, similar to recent results from the ROSMAP cohort, which reported weak correlation between protein and RNA expression. We also identified a negative correlation for the change in RNA expression between male and female mice.ConclusionWe identified axes of variation within a cohort of LOAD mouse models using integrative multi‐omics approach. Our analysis revealed multiple interaction between distinct multi‐omics molecular signatures associated with Alzheimer’s disease. We determined that mRNA profiling alone provides an incomplete picture of molecular mechanism of AD. In this study, we highlighted that assembling multi‐omics measurements reveal interrelated pathway alterations in AD and the ability to identify biomarkers combinations that may inform clinical practice.

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BackgroundGestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression. AimTo comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches. MethodsWe conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6–9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms. ResultsAt baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group. ConclusionsOverall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

Oral Infection, Oral Pathology and Salivary Diagnostics of Mpox Disease: Relevance in Dentistry and OMICs Perspectives.

In this narrative review, we aim to point out the close relationship between mpox virus (MPXV) infection and the role of saliva as a diagnostic tool for mpox, considering the current molecular approach and in the perspective of OMICs application. The MPXV uses the host cell's rough endoplasmic reticulum, ribosomes, and cytoplasmic proteins to replicate its genome and synthesize virions for cellular exit. The presence of oral mucosa lesions associated with mpox infection is one of the first signs of infection; however, current diagnostic tools find it difficult to detect the virus before the rashes begin. MPXV transmission occurs through direct contact with an infected lesion and infected body fluids, including saliva, presenting a potential use of this fluid for diagnostic purposes. Currently available diagnostic tests for MPXV detection are performed either by real-time quantitative PCR (RT-qPCR) or ELISA, which presents several limitations since they are invasive tests. Despite current clinical trials with restricted sample size, MPXV DNA was detected in saliva with a sensitivity of 85%-100%. In this context, the application of transcriptomics, metabolomics, lipidomics, or proteomics analyses coupled with saliva can identify novel disease biomarkers. Thus, it is important to note that the identification and quantification of salivary DNA, RNA, lipid, protein, and metabolite can provide novel non-invasive biomarkers through the use of OMICs platforms aiding in the early detection and diagnosis of MPXV infection. Untargeted mass spectrometry (MS)-based proteomics reveals that some proteins also expressed in saliva were detected with greater expression differences in blood plasma when comparing mpox patients and healthy subjects, suggesting a promising alternative to be applied in screening or diagnostic platforms for mpox salivary diagnostics coupled to OMICs.

Novel biomarkers for acute radiation injury and countermeasures using large and small animal models and multi-omics approach.

Threats of radiological or nuclear disasters are of serious concern and a top priority for government agencies involved in domestic security and public health preparedness. There is a need for sensitive bioassays for biodosimetric assessments of radiation exposures originating from unanticipated nuclear/radiological events. The Food and Drug Administration Animal Rule approval pathway requires an in-depth understanding of the mechanisms of radiation injury, drug efficacy and biomarkers for radiation medical countermeasure approval. Biomarkers can be helpful for extrapolating the efficacious countermeasure dose in animals to humans. We summarised here our studies to identify candidate biomarkers for the acute radiation injury using various omic platforms (metabolomics/lipidomics, proteomics, microbiome and transcriptomics/microRNA) using murine and non-human primate models conducted in our laboratory. Multi-omic platforms appear to be highly useful in assessing radiation exposure levels and for identifying biomarkers of radiation injury and countermeasure efficacy, which can expedite the regulatory approval of countermeasures.

Predictive biomarkers for the early detection and management of heart failure.

Cardiovascular disease (CVD) is a serious public health concern whose incidence has been on a rise and is projected by the World Health Organization to be the leading global cause of mortality by 2030. Heart failure (HF) is a complicated syndrome resulting from various CVDs of heterogeneous etiologies and exhibits varying pathophysiology, including activation of inflammatory signaling cascade, apoptosis, fibrotic pathway, and neuro-humoral system, thereby leading to compromised cardiac function. During this process, several biomolecules involved in the onset and progression of HF are released into circulation. These circulating biomolecules could serve as unique biomarkers for the detection of subclinical changes and can be utilized for monitoring disease severity. Hence, it is imperative to identify these biomarkers to devise an early predictive strategy to stop the deterioration of cardiac function caused by these complex cellular events. Furthermore, measurement of multiple biomarkers allows clinicians to divide HF patients into sub-groups for treatment and management based on early health outcomes. The present article provides a comprehensive overview of current omics platform available for discovering biomarkers for HF management. Some of the existing and novel biomarkers for the early detection of HF with special reference to endothelial biology are also discussed.

Single-nucleus multiomic mapping of m6A methylomes and transcriptomes in native populations of cells with sn-m6A-CT

N6-methyladenosine (m6A) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global m6A profiling in bulk sequencing, single-cell technologies for analyzing m6A heterogeneity are not extensively established. Here, we developed single-nucleus m6A-CUT&Tag (sn-m6A-CT) for simultaneous profiling of m6A methylomes and transcriptomes within a single nucleus using mouse embryonic stem cells (mESCs). m6A-CT is capable of enriching m6A-marked RNA molecules in situ, without isolating RNAs from cells. We adapted m6A-CT to the droplet-based single-cell omics platform and demonstrated high-throughput performance in analyzing nuclei isolated from thousands of cells from various cell types. We show that sn-m6A-CT profiling is sufficient to determine cell identity and allows the generation of cell-type-specific m6A methylome landscapes from heterogeneous populations. These indicate that sn-m6A-CT provides additional dimensions to multimodal datasets and insights into epitranscriptomic landscape in defining cell fate identity and states.

Boldine Alters Serum Lipidomic Signatures after Acute Spinal Cord Transection in Male Mice.

Traumatic spinal cord injury (SCI) results in wide-ranging cellular and systemic dysfunction in the acute and chronic time frames after the injury. Chronic SCI has well-described secondary medical consequences while acute SCI has unique metabolic challenges as a result of physical trauma, in-patient recovery and other post-operative outcomes. Here, we used high resolution mass spectrometry approaches to describe the circulating lipidomic and metabolomic signatures using blood serum from mice 7 d after a complete SCI. Additionally, we probed whether the aporphine alkaloid, boldine, was able to prevent SCI-induced changes observed using these 'omics platforms'. We found that SCI resulted in large-scale changes to the circulating lipidome but minimal changes in the metabolome, with boldine able to reverse or attenuate SCI-induced changes in the abundance of 50 lipids. Multiomic integration using xMWAS demonstrated unique network structures and community memberships across the groups.

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

Towards deciphering glioblastoma intra-tumoral heterogeneity: The importance of integrating multidimensional models.

Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.

Causal relationship between kidney stones and gut microbiota contributes to the gut-kidney axis: a two-sample Mendelian randomization study.

Gut microbiota, particularly Oxalobacter formigenes, has been previously reported to be associated with kidney stones. However, the conflicting results from both observational and intervention studies have created substantial uncertainty regarding the contribution of Oxalobacter formigenes to the formation of kidney stone. We employed a two-sample MR analysis to investigate the causal relationship between gut microbiota and kidney stones using GWASs summary statistics obtained from the MiBioGen and FinnGen consortia. Moreover, we conducted a reserve MR analysis to assess the direction of the causal associations between gut microbiota and kidney stones. The inverse variance weighted (IVW) approach represents the primary method of Mendelian Randomization (MR) analysis. Our analyses do not yield supportive evidence for a causal link between the genus Oxalobacter (OR = 0.99, 95% CI: 0.90-1.09, p = 0.811) and the formation of kidney stones. The order Actinomycetales (OR = 0.79, 95% CI: 0.65-0.96, p = 0.020), family Actinomycetaceae (OR = 0.79, 95% CI: 0.65-0.96, p = 0.019), family Clostridiaceae 1 (OR = 0.80, 95% CI: 0.67-0.96, p = 0.015), genus Clostridiumsensustricto 1 (OR = 0.81, 95% CI: 0.67-0.98, p = 0.030) and genus Hungatella (OR = 0.86, 95% CI: 0.74-0.99, p = 0.040) had protective effects on kidney stones, and the genus Haemophilus (OR = 1.16, 95% CI: 1.01-1.33, p = 0.032), genus Ruminococcaceae (UCG010) (OR = 1.38, 95% CI: 1.04-1.84, p = 0.028), genus Subdoligranulum (OR = 1.27, 95% CI: 1.06-1.52, p = 0.009) were risk factors for kidney stones. Differential abundance analysis provide no evidence of a association between Oxalobacter formigenes and kidney stones, and showed genus Subdoligranulum were risk factors for kidney stones. Reverse MR analysis did not indicate any causal association of kidney stones on gut microbiota. No considerable heterogeneity of instrumental variables or horizontal pleiotropy was observed. Our two-sample MR study did not find any causal relationship between genus Oxalobacter and kidney stones. The association between gut microbiota and kidney stones does not solely depend on the presence of genus Oxalobacter/Oxalobacter formigenes. A more integrated approach using multiple omics platforms is needed to better understand the pathogenesis of kidney stones in the context of complex gene-environment interactions over time.

Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.

Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach.

Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress. The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented. Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na+, Cl-, and K+, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.

Evaluation of Rouxiella badensis Subsp Acadiensis (Canan SV-53) as a Potential Probiotic Bacterium.

The advent of omic platforms revealed the significant benefits of probiotics in the prevention of many infectious diseases. This led to a growing interest in novel strains of probiotics endowed with health characteristics related to microbiome and immune modulation. Therefore, autochthonous bacteria in plant ecosystems might offer a good source for novel next-generation probiotics. The main objective of this study was to analyze the effect of Rouxiella badensis acadiensis Canan (R. acadiensis) a bacterium isolated from the blueberry biota, on the mammalian intestinal ecosystem and its potential as a probiotic microorganism. R. acadiensis, reinforced the intestinal epithelial barrier avoiding bacterial translocation from the gut to deep tissues, even after feeding BALB/c mice for a prolonged period of time. Moreover, diet supplementation with R. acadiensis led to increases in the number of Paneth cells, well as an increase in the antimicrobial peptide α defensin. The anti-bacterial effect of R. acadiensis against Staphylococcus aureus and Salmonella enterica serovar Typhimurium was also reported. Importantly, R. acadiensis-fed animals showed better survival in an in vivo Salmonella enterica serovar Typhimurium challenge compared with those that received a conventional diet. These results demonstrated that R. acadiensis possesses characteristics of a probiotic strain by contributing to the reinforcement and maintenance of intestinal homeostasis.

Protocol for a nested case-control study design for omics investigations in the Environmental Determinants of Islet Autoimmunity cohort

Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D. Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case’s onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood. Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation. KEY MESSAGES The ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored.

Abstract 4311: Using multiomics analysis to identify dysregulated transcription factors in non-small cell lung cancer (NSCLC) to drive the expression of a cancer-activated synthetic biomarker

Abstract Early detection of cancer is an important driver of increased survival, quality of life and reduced healthcare costs. Earli is developing a highly sensitive, orthogonal approach that uses a genetic construct that usurps dysregulated pathways and actively forces cancer cells to drive the expression of a detectable ‘synthetic’ biomarker. Identification of abnormally elevated transcription factors (TF) in cancer is crucial when developing a cancer-activated expression platform. Evaluating TF function in a genome wide fashion is a challenge, especially since TF activity is not solely reflected by just its RNA expression or even by protein abundance, but also its post-translational modification including phosphorylation. In this study, we use the publicly available multiomics dataset from Clinical Proteomic Tumor Analysis Consortium (CPTAC) which includes RNA-seq based transcriptional profiling, MS based protein abundance and phosphorylation from 211 paired tumor and normal adjacent samples derived from NSCLC patients. Performing Multiomics Factor Analysis (MOFA) analysis, we identified a short list of 34 dysregulated TFs in NSCLC that displayed high expression levels in at least two of the three omics platforms relative to normal tissues, six of which scored highly across all three. A subset of candidate TF binding sequences were subcloned as multimers into expression constructs with a basal promoter and empirically evaluated in multiple cancer cell lines, including patient-derived xenograft (PDX) cell lines and normal cell lines.Transfection experiments demonstrated that these novel chimeric synthetic promoters could produce robust levels of expression in PDX-derived cell lines that was 10-20x higher than expression mediated by known cancer-activated promoters such as the endogenous survivin (BIRC5) promoter. Furthermore, levels achieved were only 3-4 fold lower than a control EF-1a promoter, one of the strongest promoters, typically used to drive constitutive expression in mammalian cells. Currently, a small subset of these chimeric constructs is being tested in murine tumor models and in large animal cancer models such as oncopigs. These experiments will further drive the development of this approach for use in early cancer detection. Citation Format: Yue Wendy Zhang, Shireen Rudina, Dariusz Wodziak, Chloe Xia, Maggie Louie, Albert Park, David Suhy. Using multiomics analysis to identify dysregulated transcription factors in non-small cell lung cancer (NSCLC) to drive the expression of a cancer-activated synthetic biomarker. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4311.