Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review

Introduction

Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction.

Methods

We performed a literature search for this review using the Boolean search in the PubMed database.

Results

This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues.

Discussion

Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Psoriasis: Striving for Potential Biomarkers

Psoriasis is a chronic disease that is caused by multiple factors and is identified by itchiness, unpleasant, red, or white scaly patches on the skin, particularly on regularly chafed body regions such as the lateral areas of the limbs. Reports suggest that globally around 2%-3% of the population suffers from psoriasis. In this review, we have discussed the clinical classification of psoriasis and also the ideal characteristics of the biomarkers. An overview regarding the discovery of the biomarker and method for validating the study has been discussed. A growing body of research suggests a link to certain other systemic symptoms such as cardiovascular disorder, metabolic syndrome, and few other comorbidities such as hypertension and nonalcoholic fatty liver disease. Natural killer (NK) cells are lymphocyte cells that concentrate on the destruction of virally infected and malignant cells; these tend to produce a wide range of inflammatory cytokines, some of which are associated with the etiology of psoriasis. Detailed information on the molecular pathogenesis of psoriasis in which interleukin (IL)-17, IL-23, tumor necrosis factor-α (TNF-α), and CCL20 play a very significant role in the development of psoriasis. In this review, we have discussed an overview of the recent state of the biomarkers available for the diagnosis and treatment of psoriasis by emphasizing on the available biomarkers such as epigenomic, transcriptomic, glycomic, and metabolomic. The most recent advancements in molecular-targeted therapy utilizing biologics and oral systemic therapy (methotrexate, apremilast) enable to adequately treat the most serious psoriatic symptoms and also the studies have validated the efficacy of biologic therapy such as TNF-α antagonist (infliximab, adalimumab), IL-23 antagonist (guselkumab, risankizumab), and IL-17 antagonist (secukinumab, ixekizumab). Finally, an overview about the technological opportunities as well as various challenges has been discussed.

A high-throughput and highly automated genotoxicity screening assay

The increasing number of compounds under development and chemicals in commerce that require safety assessments pose a serious challenge for regulatory agencies worldwide. In vitro screening using toxicogenomic biomarkers has been proposed as a first-tier screen in chemical assessments and has been endorsed internationally. We previously developed, evaluated, and validated an in vitro transcriptomic biomarker responsive to DNA-damage-inducing (DDI) agents, namely TGx-DDI, for genotoxicity testing in human cells, and demonstrated the feasibility of using TGx-DDI in a medium-throughput, cell-based genotoxicity testing system by implementing this biomarker with the Nanostring nCounter system. In this current study, we took the advantage of Nanostring nCounter Plexset technology to develop a highly automated, multiplexed, and high-throughput genotoxicity testing assay, designated as the TGx-DDI Plexset assay, which can increase the screening efficiency by eight-fold compared to standard nCounter technology while decreasing the hands-on time. We demonstrated the high-throughput capability of this assay by eliminating concentration determination and RNA extraction steps without compromising the specificity and sensitivity of TGx-DDI. Thus, we propose that this simple, highly automated, multiplexed high-throughput pipeline can be widely used in chemical screening and assessment.

Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

Diagnostic potential of three serum microRNAs as biomarkers for equine sarcoid disease in horses and donkeys

BACKGROUND

MicroRNAs (miRNAs) are potential biomarkers for equine sarcoids (ES).

OBJECTIVES

To assess eca-miR-331, eca-miR-100, and eca-miR-1 as serum biomarkers for ES disease.

ANIMALS

Sixty-eight ES cases (56 horses, 12 donkeys), 69 tumor-free controls (60 horses, 9 donkeys), and 20 horses with other skin tumors.

METHODS

For this case-control study, expression of serum eca-miR-331, eca-miR-100, and eca-miR-1 in ES-affected equids was compared to tumor-free age-, sex-, and breed-matched control horses and donkeys with other skin tumors using reverse transcription quantitative PCR (polymerase chain reaction) for relative miRNA quantification. Biological, preanalytical, and clinical variable influences on miRNA expression were examined. Receiver operator characteristic (ROC) curve analyses were used to determine differences in miRNA expression between groups.

RESULTS

The expression of eca-miR-100 was affected by age (P = .003) and expression of eca-miR-100 and eca-miR-1 were affected by hemolysis (both P < .001). Eca-miR-331 was unaffected by biological variation, hemolysis, ES type, and disease severity. Eca-miR-331 concentrations were higher in ES-affected compared to tumor-free controls (P = .002). The ROC curve analysis indicated an area under the curve of 0.65 (P = .002) with a sensitivity of 60%, specificity of 71%, and positive and negative likelihood ratios of 2.1 and 0.56, respectively, to diagnose ES. Eca-miR-331 expression did not discriminate between horses with ES and other skin tumors. Expression of eca-miR-100 and eca-miR-1 was not different between groups.

CONCLUSIONS AND CLINICAL IMPORTANCE

Serum eca-miR-331 expression is neither sensitive nor specific enough as a single ES biomarker. If combined with other miRNAs, it may be helpful for ES diagnosis.

Assessment of the performance of the TGx-DDI biomarker to detect DNA damage-inducing agents using quantitative RT-PCR in TK6 cells

Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx-DDI transcriptomic biomarker can accurately distinguish DNA damage-inducing (DDI) from non-DDI exposures based on changes in the expression of 64 biomarker genes. The 64 genes were previously derived from whole transcriptome DNA microarray profiles of 28 reference agents (14 DDI and 14 non-DDI) after 4 h treatments of TK6 human lymphoblastoid cells. To broaden the applicability of TGx-DDI, we tested the biomarker using quantitative RT-PCR (qPCR), which is accessible to most molecular biology laboratories. First, we selectively profiled the expression of the 64 biomarker genes using TaqMan qPCR assays in 96-well arrays after exposing TK6 cells to the 28 reference agents for 4 h. To evaluate the classification capability of the qPCR profiles, we used the reference qPCR signature to classify 24 external validation chemicals using two different methods-a combination of three statistical analyses and an alternative, the Running Fisher test. The qPCR results for the reference set were comparable to the original microarray biomarker; 27 of the 28 reference agents (96%) were accurately classified. Moreover, the two classification approaches supported the conservation of TGx-DDI classification capability using qPCR; the combination of the two approaches accurately classified 21 of the 24 external validation chemicals, demonstrating 100% sensitivity, 81% specificity, and 91% balanced accuracy. This study demonstrates that qPCR can be used when applying the TGx-DDI biomarker and will improve the accessibility of TGx-DDI for genotoxicity screening. Environ. Mol. Mutagen. 60: 122-133, 2019. © 2018 Her Majesty the Queen in Right of Canada Environmental and Molecular Mutagenesis.

Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs

Standard in vitro assays to assess genotoxicity frequently generate positive results that are subsequently found to be irrelevant for in vivo carcinogenesis and human cancer risk assessment. Currently used follow-up methods, such as animal testing, are expensive and time-consuming, and the development of approaches enabling more accurate mechanism-based risk assessment is essential. We developed an in vitro transcriptomic biomarker-based approach that provides a robust biomarker reflecting stress-signaling responses. The biomarker correctly identifies the vast majority of irrelevant genotoxicity results from in vitro chromosome damage assays. TGx-DDI, a multigene biomarker for DNA damage-inducing agents, is the first biomarker that not only shows convincing interlaboratory and intralaboratory reproducibility, but also performs accurately in a system suitable for high-throughput screening.

Interpretation of positive genotoxicity findings using the current in vitro testing battery is a major challenge to industry and regulatory agencies. These tests, especially mammalian cell assays, have high sensitivity but suffer from low specificity, leading to high rates of irrelevant positive findings (i.e., positive results in vitro that are not relevant to human cancer hazard). We developed an in vitro transcriptomic biomarker-based approach that provides biological relevance to positive genotoxicity assay data, particularly for in vitro chromosome damage assays, and propose its application for assessing the relevance of the in vitro positive results to carcinogenic hazard. The transcriptomic biomarker TGx-DDI (previously known as TGx-28.65) readily distinguishes DNA damage-inducing (DDI) agents from non-DDI agents. In this study, we demonstrated the ability of the biomarker to classify 45 test agents across a broad set of chemical classes as DDI or non-DDI. Furthermore, we assessed the biomarker’s utility in derisking known irrelevant positive agents and evaluated its performance across analytical platforms. We correctly classified 90% (9 of 10) of chemicals with irrelevant positive findings in in vitro chromosome damage assays as negative. We developed a standardized experimental and analytical protocol for our transcriptomics biomarker, as well as an enhanced application of TGx-DDI for high-throughput cell-based genotoxicity testing using nCounter technology. This biomarker can be integrated in genetic hazard assessment as a follow-up to positive chromosome damage findings. In addition, we propose how it might be used in chemical screening and assessment. This approach offers an opportunity to significantly improve risk assessment and reduce cost.