Identification Of Disease Biomarkers Research Articles

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.

Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements.

Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate these patterns to accurately detect markers and comprehensively understand the diseases. Integrative analysis has emerged as a promising approach to address this challenge. However, existing studies have been limited by ignoring the adjacency structure of genomic measurements, such as single nucleotide polymorphisms (SNPs) and DNA methylations. In this study, we propose a structured integrative analysis method that incorporates a spline type penalty to accommodate this adjacency structure. We utilize a fused lasso type penalty to identify both heterogeneity and commonality across the groups. Extensive simulations demonstrate its superiority compared to several direct competing methods. The analysis of The Cancer Genome Atlas melanoma data with DNA methylation measurements and GENEVA diabetes data with SNP measurements exhibit that the proposed analysis lead to meaningful findings with better prediction performance and higher selection stability.

Statistical inference on qualitative differences in the magnitude of an effect.

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub-population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network-based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas.

Mass spectrometry in clinical laboratory: applications and future prospects

Innovations in laboratory medicine face additional challenges due to the unmet need for extremely precise methods of disease diagnosis. Developments in mass spectrometry-based disease biomarker identification are constantly expanding the field of clinical diagnosis. More mass spectrometry-based in vitro diagnostics are anticipated to move from the bench to the bedside shortly, although several are currently incorporated into standard clinical procedures. The molecular detection by mass spectrometry technique is very effective in diagnosis of the disease and therapeutic monitoring because of its extremely high sensitivity, specificity, and short turnaround time. Mass spectrometry is a potent analytical instrument that may be used to analyse a variety of materials and matrices; its use in clinical laboratory medicine applications is rising. For clinical prognostics and diagnostic purposes, mass spectrometry imaging has been widely utilized to distinguish between healthy and diseased tissues. Modern single-cell studies will benefit from its cutting-edge applications, which will offer comprehensive cellular biochemical data for mechanistic comprehension and, eventually, the development of therapeutic interventions. Within the field of laboratory medicine, the application of mass spectrometry is crucial, especially in terms of diagnostics and therapeutic drug monitoring. This review on the clinical application of mass spectrometry and its future prospects promises an insightful exploration of the evolving landscape of mass spectrometry in clinical settings, delving into its current applications and shedding light on its potential future advancements, for which a comprehensive search in the PubMed, ScienceDirect, and Web of Science databases was conducted.

Mass Specthoscope: A Hand-held Extendable Probe for Localized Noninvasive Sampling of Skin Volatome for Online Analysis.

Human skin emits a unique set of volatile organic compounds (VOCs). These VOCs can be probed in order to obtain physiological information about the individuals. However, extracting the VOCs that emanate from human skin for analysis is troublesome and time-consuming. Therefore, we have developed "Mass Specthoscope"─a convenient tool for rapid sampling and detecting VOCs emitted by human skin. The hand-held probe with a pressurized tip and wireless button enables sampling VOCs from surfaces and their transfer to the atmospheric pressure chemical ionization source of quadrupole time-of-flight mass spectrometer. The system was characterized using chemical standards (acetone, benzaldehyde, sulcatone, α-pinene, and decanal). The limits of detection are in the range from 2.25 × 10-5 to 3.79 × 10-5 mol m-2. The system was initially tested by detecting VOCs emanating from porcine skin spiked with VOCs as well as unspiked fresh and spoiled ham. In the main test, the skin of nine healthy participants was probed with the Mass Specthoscope. The sampling regions included the armpit, forearm, and forehead. Numerous skin-related VOC signals were detected. In the final test, one participant ingested a fenugreek drink, and the participant's skin surface was probed using the Mass Specthoscope hourly during the 8 h period. The result revealed a gradual release of fenugreek-related VOCs from the skin. We believe that this analytical approach has the potential to be used in metabolomic studies and following further identification of disease biomarkers─also in noninvasive diagnostics.

Desorption Electrospray Ionization Mass Spectrometry: 20 Years.

ConspectusMass spectrometry (MS) is one of the most widely used technologies in the chemical sciences. With applications spanning the monitoring of reaction products, the identification of disease biomarkers, and the measurement of thermodynamic parameters and aspects of structural biology, MS is well established as a universal analytical tool applicable to small compounds as well as large molecular complexes. Regardless of the application, the generation of gas-phase ions from neutral compounds is a key step in any MS experiment. However, this ionization step was for many years limited to high-energy approaches that required gas-phase analytes and thus it was restricted to volatile samples. Over the last few decades, new methodologies have been developed to address this limitation and facilitate ionization of biological molecules. Electrospray ionization (ESI) is the most broadly used of these methods, as it facilitates the ionization of intact polar compounds from solution.Twenty years ago, our group reported a new ionization method that uses a charged solvent spray to impact a surface, generating ions from objects rather than just solutions and doing so directly in the ambient environment with no vacuum requirements and little to no sample preparation. This method was termed desorption electrospray ionization (DESI), and it initiated a new field that would come to be known as ambient mass spectrometry. The simplicity and wide applicability of the DESI technology─and the tens of ambient ionization methods developed subsequently─revolutionized the MS analysis of complex materials for their organic components, especially for in situ applications.This Account describes the history of DESI, starting with the development of the technique from early electrosonic spray ionization (ESSI) experimental observations as well as the studies leading to the understanding of its mechanism as a "droplet pick-up" phenomenon involving sequential events (i.e., thin film formation, solid-liquid extraction, secondary droplet generation, and ESI-like ionization from these droplets). We also overview the developments and applications of the technology that have been demonstrated by our group during the last two decades. In particular, we describe (i) the use of DESI for tissue imaging, one of its more significant applications to date, and its extension to intraoperative clinical diagnosis; (ii) the integration of the technology with portable instrumentation for in situ analysis, especially when coupled with tandem mass spectrometry (MS/MS); (iii) the use of DESI microdroplets as microvessels to accelerate organic reactions by orders of magnitude compared to those in bulk solution; and (iv) the combination of all these capabilities for automated high-throughput experiments aimed at accelerating drug discovery.

Intrinsic Disorder in the Human Tear Proteome.

We aimed to characterize the proteome of human tears and assess for the presence of intrinsically disordered proteins (IDPs). IDPs, despite lacking a rigid three-dimensional structure, maintain biological functionality and could shed light on the molecular interactions within tears. We analyzed a dataset of 1475 proteins identified in the tear film of three healthy subjects. We employed several computational tools, including the Compositional Profiler, Rapid Intrinsic Disorder Analysis Online, Search Tool for the Retrieval of Interacting Genes, and Database of Disordered Protein Predictors to evaluate the intrinsic disorder, protein interactions, and functional characterization of the disordered regions within this proteome. Our analysis showed a notable inclination toward intrinsic disorder. Two out of 10 order-promoting residues and five out of 10 disorder-promoting residues were found enriched. Using the Predictor of Natural Disordered Regions (PONDR) VSL2 output, 95% of these proteins were classified as highly or moderately disordered. We revealed an extensive protein-protein interaction network with significant interaction enrichment. The most disordered proteins exhibited higher disorder binding sites and diverse posttranslational modifications compared to the most ordered ones. To the best of our knowledge, our study is the first comprehensive analysis of intrinsic disorder in the human tear film proteome, and it revealed an abundance of IDPs and their role in protein function and interaction networks. These findings suggest that variations in the intrinsic disorder of a tear film could be impacted by systemic and ocular conditions, offering promising avenues for disease biomarker identification and drug target development. Further research is needed to understand the implications of these findings in human health and disease.

Serum Neurofilament Light-Chain Concentrations in Essential Tremor: a Case-Control Study.

Essential tremor (ET) is a common neurological disorder, with clinical and pathophysiological links to the cerebellum. Inquiries into the etiology, pathophysiology, and nosology of ET stand to benefit from the identification of disease biomarkers. Serum neurofilament light chain (NfL) has emerged as a novel signature of conditions in which neuronal injury reflects an outcome of the ongoing disease process. We sought to investigate the concentrations of NfL in ET patients and healthy controls. In this case-control study, our powered study population of 41 ET patients and 40 age-matched healthy controls underwent clinical assessments and measurement of serum NfL concentration using Simoa technology. Serum NfL was elevated in ET patients - mean log-transformed serum NfL concentration = 1.23 ± 0.19 (95% confidence interval [CI] = 1.17-1.29) vs. 1.08 ± 0.15 (95% CI = 1.03-1.13), p = 0.0002. This difference persisted after accounting for age, sex and Montreal Cognitive Assessment score in a multiple linear regression model (p = 0.002) and in an age-matched sample subset of 35 ET cases and 35 controls (p = 0.006). There was no association between tremor severity and serum NfL levels (p = 0.73). In this sample of ET patients and controls, serum NfL concentrations were significantly higher in ET. Studies in additional cohorts of ET cases would be of value in attempting to replicate these results and assessing diagnostic utility.

Disease biomarker identification based on sample network optimization

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

Serum Neurofilament Light-chain Concentrations in Essential Tremor: A Case-Control Study (P7-11.003)

<h3>Objective:</h3> We sought to investigate the concentrations of neurofilament light chain (NfL) in essential tremor (ET) patients and healthy controls. <h3>Background:</h3> Inquiries into the etiology, pathophysiology, and nosology of essential tremor (ET) stand to benefit from the identification of disease biomarkers. Serum neurofilament light chain (NfL) has emerged as a novel signature of conditions in which neuronal injury reflects an outcome of the ongoing disease process. <h3>Design/Methods:</h3> In this cross-sectional, case-control, observational study, 41 ET patients and 41 healthy controls underwent clinical assessments and measurement of serum NfL concentration using Simoa technology. <h3>Results:</h3> Serum NfL was elevated in ET patients (median = 16.6 pg/mL) versus controls (median = 11.3pg/mL) (p &lt;0.001). NfL concentrations remained higher in ET patients than controls after stratification by sex (p = 0.006 and 0.02, for female and male participants, respectively). This difference persisted after accounting for age, sex and Montreal Cognitive Assessment score in a multiple linear regression model (p = 0.00947, Benjamini-Hochberg correction). <h3>Conclusions:</h3> In this sample of ET patients and controls, serum NfL concentrations were significantly higher in ET. Studies in additional cohorts of ET cases would be of value in attempting to replicate these results and assessing diagnostic utility. <b>Disclosure:</b> The institution of Dr. Salinas has received research support from North Texas VA Health Care System New Investigator Award. The institution of an immediate family member of Dr. Salinas has received research support from Veterans Health Administration. Nora Hernandez has nothing to disclose. Dr. Louis has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Wolters Kluwer - Merritt’s Textbook of Neurology. The institution of Dr. Louis has received research support from National Institutes of Health.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis.

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Machine Learning Applications in Drug Discovery and Development

Machine learning has emerged as a powerful tool in the field of drug discovery and development, revolutionizing the way pharmaceutical research is conducted. This abstract provides a concise overview of the key applications and impacts of machine learning in this domain. Drug discovery and development is a complex, time-consuming, and costly process that traditionally relies on trial-and-error experimentation. Machine learning, with its ability to analyze vast datasets and extract meaningful insights, has significantly accelerated and optimized various aspects of this process. One crucial application of machine learning in drug discovery is the prediction of potential drug candidates. Machine learning models can also aid in the identification of biomarkers for diseases, enabling more targeted drug development. In the clinical trial phase, machine learning algorithms assist in patient selection, monitoring, and optimization of trial protocols. Predictive models can identify patient subpopulations most likely to respond to a particular treatment, leading to more efficient trials and better patient outcomes. Additionally, machine learning has streamlined drug repurposing efforts by identifying existing drugs with potential new applications. This approach has the potential to save significant time and resources by leveraging existing safety and efficacy data. Furthermore, machine learning enhances the drug development pipeline by optimizing drug formulation and dosage, predicting adverse reactions, and assisting in regulatory compliance. While machine learning offers tremendous promise in drug discovery and development, it also presents challenges related to data quality, model interpretability, and regulatory approval. Addressing these challenges will be crucial for maximizing the potential of machine learning in the pharmaceutical industry.

High Throughput Epigenomic Mapping of Hematopoietic Stem and Progenitor Cells Using Low Cell Input Acoustic Enhanced Immunoprecipitation

Acute myeloid leukemia (AML) is a complex disease with several etiologies characterized by incomplete cellular differentiation and clonal proliferation of hematopoietic stem and progenitor cells (HSPCs) in place of normal hematopoiesis. Non-mutational epigenetic reprogramming is an emerging hallmark and enabling characteristic of cancer pathogenesis. Epigenetic regulation of gene activity is an essential component of chromatin stability and maintenance of proper cellular function and differentiation. Modifications of gene and histone landscape methylation and acetylation, three-dimensional chromatin structure, and protein-controlled activating and silencing switches are all regulated by epigenetic components, and thus are integral to understanding gene regulatory mechanisms during cell fate changes, disease onset, progression, as well as for optimizing therapeutic regime. In addition, this information holds immense potential for accurate identification of disease biomarkers, cell of origin, and targets for epigenetic therapy. However, existing chromatin immunoprecipitation sequencing (ChIP-Seq) workflows require significant cellular material and are very time-consuming, requiring cumbersome optimization. Moreover, cellular heterogeneity can influence accuracy of results from the perspective of sample preparation efficiency and also population-biased effects. Both normal and malignant bone marrow are heterogeneous in cell type and stage of differentiation that leads to imprecise results with regard to epigenetic determinates. In our study, we implemented Covaris's Adaptive Focused Acoustics® (AFA®) technology to miniaturize sample volumes for both chromatin shearing and immunoprecipitation in a 96-well format. This approach enables a standardized, robust, automatable, half-day workflow for both ChIP and Hi-ChIP applications. AFA ChIPseq- was applied to flow sorted hematopoietic stem and progenitor cells (HSPC) from individual mice. We utilized a range of epitopes including major histone modifications (H3K9me1/2/3, H3K27ac) and factors (CTCF, cohesin). Interestingly, we found epitope-dependent differences in AFA-enhanced binding with CTCF saturating at 30 minutes, whereas H3K27ac required 4 hours of AFA pulse to facilitate chromatin binding. Additionally, we evaluated low input in vitro and in vivo samples, with limited dilution of lineage negative HSPCs ranging from 500,000 to 5,000 cells with comparable sheared chromatin yield and immunoprecipitated material. Overlap of peaks called by MACS2 was consistent among the various cell inputs as well as when compared with gold standard CTCF ChIPseq- data previously performed on pooled samples from our previously published work. The low cell input capability permitted deconvolution of dynamic epigenetic changes in protein binding and chromatin structure during normal lineage specification of hematopoietic stem cells. Epigenetic remodeling of these sites was consistent with established gene programs essential for myeloid differentiation. Data were found to be in alignment with standard ChIPseq- and Hi-C datasets generated from similar cell populations. The simplified and significantly shortened (Hi)ChIP-seq processes showcased in this study will be highly beneficial for both research and clinical diagnostic applications owing to their requirements for using 96-well plate format and their ability to become integrated into automated workflows. Taken together, this technology and workflow represents a high throughput epigenetic system for small cell numbers of cells with a multitude of applications enabling laboratories focused in cell biology and translational research.

Patient-Derived Multiple Myeloma 3D Models for Personalized Medicine-Are We There Yet?

Despite the wide variety of existing therapies, multiple myeloma (MM) remains a disease with dismal prognosis. Choosing the right treatment for each patient remains one of the major challenges. A new approach being explored is the use of ex vivo models for personalized medicine. Two-dimensional culture or animal models often fail to predict clinical outcomes. Three-dimensional ex vivo models using patients' bone marrow (BM) cells may better reproduce the complexity and heterogeneity of the BM microenvironment. Here, we review the strengths and limitations of currently existing patient-derived ex vivo three-dimensional MM models. We analyze their biochemical and biophysical properties, molecular and cellular characteristics, as well as their potential for drug testing and identification of disease biomarkers. Furthermore, we discuss the remaining challenges and give some insight on how to achieve a more biomimetic and accurate MM BM model. Overall, there is still a need for standardized culture methods and refined readout techniques. Including both myeloma and other cells of the BM microenvironment in a simple and reproducible three-dimensional scaffold is the key to faithfully mapping and examining the relationship between these players in MM. This will allow a patient-personalized profile, providing a powerful tool for clinical and research applications.

CBDCEM: An effective centrality based differential co-expression method for critical gene finding

Recent years have seen rise in applications of differential co-expression analysis (DCE) for disease biomarker identification. This paper presents a centrality-based hub-gene centric method called Centrality Based Differential Co-Expression Method (CBDCEM), for crucial gene finding for critical diseases. A prominent task of DCE is the identification of hub-gene(s) for each differentially co-expressed module. We propose a consensus-based algorithm that employs seven centrality measures to detect hub-genes. Our method has been validated on three Esophageal Squamous Cell Carcinoma (ESCC) datasets and is found to perform well for denser modules. Through comparison of our proposed hub-gene finding algorithm with 4 other hub-gene finding methods, we have observed that the former was able to identify unique potential biomarkers that were undetected by the rest. CBDCEM is able to identify 15 genes as potential biomarkers for ESCC. Eleven other genes identified by CBDCEM have some evidence of being potential biomarkers for other Squamous Cell Carcinoma and can be considered candidates for ESCC, but require further in-depth validation.

Ceramide Acyl Chain Length and Its Relevance to Intracellular Lipid Regulation.

Ceramides are a class of sphingolipids which are implicated in skin disorders, obesity, and other metabolic diseases. As a class with pleiotropic effects, recent efforts have centred on discerning specific ceramide species and their effects on atopic dermatitis, obesity, type 2 diabetes, and cardiovascular diseases. This delineation has allowed the identification of disease biomarkers, with long acyl chain ceramides such as C16- and C18-ceramides linked to metabolic dysfunction and cardiac function decline, while ultra-long acyl chain ceramides (>25 carbon acyl chain) were reported to be essential for maintaining a functional skin barrier. Given the intricate link between free fatty acids with ceramides, especially the de novo synthetic pathway, intracellular lipid droplet formation is increasingly viewed as an important mechanism for preventing accumulation of toxic ceramide species. Here, we review recent reports of various ceramide species involved in skin abnormalities and metabolic diseases, and we propose that promotion of lipid droplet biogenesis can be seen as a potential protective mechanism against deleterious ceramides.

Novel paradigms of macrophage biology and function: identification of disease biomarkers and therapeutic targets

Novel paradigms of macrophage biology and function: identification of disease biomarkers and therapeutic targets

MicroRNA expression biomarkers of chronic venous disease

Abstract Chronic venous disease (CVD) is a common disease caused by hemodynamic disorders of the venous circulation in the lower extremities. The clinical image of this disease is complex and includes such signs as telangiectases, varicose veins, leg edema and skin changes, usually accompanied with ache, pain, tightness, heaviness, swelling and muscle cramps of legs. Venous ulcers develop in the advanced stages of the disease and lead to significant impairment of patient abilities and reduction of the quality of life. CVD is diagnosed based on physical and image examinations, and main treatment options include compression therapy, invasive treatments like endovenous ablation and foam sclerotherapy, as well as pharmacotherapy. Currently, there is no biochemical and molecular biomarkers utilized in diagnosis or treatment of CVD. With regard to this situation, one of the most investigated fields for identification of disease biomarkers is microRNA (miRNA). These constitute a pool of small, non-coding RNAs that play crucial roles in maintaining cellular homeostasis through posttranscriptional regulation of genes expression. Dysregulations of miRNA expression profiles have been found in patients with various diseases, and this situation provides information about potential miRNA signatures involved in pathophysiology. In this review, the studies focused on investigations of miRNA expression patterns in patients with CVD were collected. The performed literature analysis provides contemporary knowledge in the field of miRNA-dependent mechanisms involved in the etiopathogenesis of CVD and shows gaps that need to be filled in further studies.

Abstract 3765: Epigenomics at the speed of sound: High throughput epigenomic mapping of low cell input samples using acoustic enhanced immunoprecipitation

Abstract Mapping the epigenetic landscape of Myelodysplastic Syndrome (MDS) and Acute Myeloid Leukemia (AML) is essential for understanding gene regulatory mechanisms during cell fate changes, disease onset, progression, as well as for optimizing therapeutic regime. In addition, these information hold immense potential for accurate identification of disease biomarkers, cell of origin, and targets for epigenetic therapy. However, existing chromatin immunoprecipitation sequencing (ChIP-Seq) workflows require significant cellular material and are very time-consuming, requiring cumbersome optimization. Moreover, cellular heterogeneity can influence the accuracy of results from the perspective of sample preparation efficiency and also population-biased effects. Both normal and malignant bone marrow are heterogeneous in cell type and stage of differentiation that leads to imprecise results with regard to epigenetic determinates.In our study, we implemented Covaris’ Adaptive Focused Acoustics® (AFA®) technology to miniaturize sample volumes for both chromatin shearing and immunoprecipitation in a 96-well format. This approach enables a standardized, robust, automatable, half-day workflow for both ChIP and Hi-ChIP applications. Additionally, we evaluated low input in vitro and in vivo samples, where we utilized a range of epitopes including major histone modifications (H3K27ac) and factors (CTCF). AFA was applied to flow-sorted stem and progenitor populations from individual mice. The low cell input capability permitted deconvolution of dynamic epigenetic changes in protein binding and chromatin structure during normal lineage specification of hematopoietic stem cells. Epigenetic remodeling of these sites were consistent with established gene programs essential for myeloid differentiation. Data were found to be in alignment with standard ChIP-seq and Hi-C datasets generated from similar cell populations.The simplified and significantly shortened (Hi)ChIP-seq processes showcased in this study will be highly beneficial for both research and clinical diagnostic applications owing to their requirements for using 96-well plate format and their ability to become integrated into automated workflows. Taken together, this technology and workflow represent a high throughput epigenetic system for small cell numbers of cells with a multitude of applications enabling laboratories focused on cell biology and translational research. Citation Format: Besmira Alija, Stephanie Braunstein, Jane Xu, Tom O'Hare, Angela Garding, Aaron Viny. Epigenomics at the speed of sound: High throughput epigenomic mapping of low cell input samples using acoustic enhanced immunoprecipitation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3765.