RNA diagnostics: real-time RT-PCR strategies and promising novel target RNAs

From degraded to deciphered: ATAC-seq's application potential in forensic diagnosis

In recent years, molecular biology-based diagnostic techniques have made remarkable strides and are now extensively utilized in clinical practice, providing invaluable insights for disease diagnosis and treatment. However, forensic medicine, especially forensic pathology, has witnessed relatively limited progress in the application of molecular biology technologies. A significant challenge in employing molecular techniques for forensic diagnoses lies in the quantitative and qualitative changes observed in diagnostic markers due to sample degradation-a recognized and formidable obstacle. Inspired by the success of DNA sequencing in forensic practices, which enables accurate individual identification even in cases involving degraded and deteriorated tissues and organs, we propose the application of the assay for transposase-accessible chromatin with sequencing (ATAC-seq) to identify targets at the transcriptional onset, exploring chromatin and DNA-level alterations for injury and disease inference in forensic samples. This study employs ATAC-seq to explore alterations in chromatin accessibility post-injury and their subsequent changes over a 2-h degradation period, employing traumatic brain injury (TBI) as a representative model. Our findings reveal high sensitivity of chromatin accessibility sites to injury, evidenced by shifts in thousands of peak positions post-TBI. Remarkably, these alterations remain largely unaffected by early degradation. Our results robustly endorse the notion that integrating and incorporating these specific loci for injury and disease diagnosis in forensic samples holds tremendous promise for practical application. We further validated the above results using human cortical tissue, which supported that early degradation did not significantly affect chromatin accessibility. This pioneering advancement in molecular diagnostic techniques may revolutionize the field of forensic science, especially forensic pathology.

When the answer is a maybe: a retrospective review of indeterminate SARS-CoV-2 results and their associations

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using reverse transcriptase polymerase chain reaction (RT-PCR) may generate indeterminate results (with a cycle threshold ≥30), requiring further investigation to determine the clinical significance. Patient variables which could predict a definitive result ('Detected'/'Not detected') post indeterminate result would aid in optimization of bed management and utilization of limited resources. A retrospective observational study of indeterminate SARS-CoV-2 results in an Irish tertiary hospital from March 2020 to March 2022 was performed to determine whether demographics, comorbidities and immunosuppression were associated with a definitive result upon subsequent investigation. Data was obtained from patient and laboratory records. Analysis of association was explored using Fisher's exact test, and predictability was tested using logistic regression. Of 411 patients with an initial indeterminate test, investigation showed that 299 (72.2%) patients had a subsequent definitive result; 29 were Detected and 270 were Not detected. In the Detected group, a prior diagnosis of COVID-19 was associated with a reduced risk of becoming Detected (crude odds ratio (COR) = 0.10, 95% CI 0.03-0.35). In the Not detected group, vaccinated patients were less likely to have a Not detected result on subsequent testing (adjusted odds ratio (AOR) = 0.57, 95% CI 0.34-0.94). Patients with previous COVID-19 infection were less likely to have a Detected result and vaccinated patients were less likely to have a Not detected result upon investigation of an indeterminate result. This study emphasizes the need for a good clinical and medical history in the management of SARS-CoV-2.

Microfluidic Device-Based Virus Detection and Quantification in Future Diagnostic Research: Lessons from the COVID-19 Pandemic

The global economic and healthcare crises experienced over the past three years, as a result of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted the commonplace habits of humans around the world. SARS-CoV-2, the virus responsible for the coronavirus 2019 (COVID-19) phenomenon, has contributed to the deaths of millions of people around the world. The potential diagnostic applications of microfluidic devices have previously been demonstrated to effectively detect and quasi-quantify several different well-known viruses such as human immunodeficiency virus (HIV), influenza, and SARS-CoV-2. As a result, microfluidics has been further explored as a potential alternative to our currently available rapid tests for highly virulent diseases to better combat and manage future potential outbreaks. The outbreak management during COVID-19 was initially hindered, in part, by the lack of available quantitative rapid tests capable of confirming a person's active infectiousness status. Therefore, this review will explore the use of microfluidic technology, and more specifically RNA-based virus detection methods, as an integral part of improved diagnostic capabilities and will present methods for carrying the lessons learned from COVID-19 forward, toward improved diagnostic outcomes for future pandemic-level threats. This review will first explore the context of the COVID-19 pandemic and how diagnostic technology was shown to have required even greater advancements to keep pace with the transmission of such a highly infectious virus. Secondly, the historical significance of integrating microfluidic technology in diagnostics and how the different types of genetic-based detection methods may vary in their potential practical applications. Lastly, the review will summarize the past, present, and future potential of RNA-based virus detection/diagnosis and how it might be used to better prepare for a future pandemic.

PD-L1-related IncRNAs are associated with malignant characteristics and immune microenvironment in glioma

BACKGROUND

The expression of long non-coding RNA (lncRNA) can function as diagnostic and therapeutic biomarker for tumors. This research explores the role of PD-L1-related lncRNAs in affecting malignant characteristics and the immune microenvironment of glioma.

METHODS

Downloading gene expression profiles and clinicopathological information of glioma from TCGA and CGGA databases, 6 PD-L1-related lncRNAs were identified through correlation analysis, Cox and LASSO regression analysis, establishing the risk score model based on them. Bioinformatics analysis and cell experiments in vitro were adopted to verify the effects of LINC01271 on glioma.

RESULTS

Risk scores based on 6 PD-L1-related lncRNAs (AL355974.3, LINC01271, AC011899.3, MIR4500HG, LINC02594, AL357055.3) can reflect malignant characteristics and immunotherapy response of glioma. Patients with high LINC01271 expression had a worse prognosis, a higher abundance of M1 subtype macrophages in the immune microenvironment, and a higher degree of tumor malignancy. Experiments in vitro confirmed its positive regulatory effect on the proliferation and migration of glioma cells.

CONCLUSIONS

The risk score model based on 6 PD-L1-related lncRNAs can reflect the malignant characteristics and prognosis of glioma. LINC01271 can independently be used as a new target for prognosis evaluation and therapy.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

rRT-PCR for SARS-CoV-2: Analytical considerations

The COVID-19 pandemic remains a significant problem involving health systems worldwide. Accurate and early detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is critical for minimizing spread and initiating treatment. Among test methods, real-time reverse-transcriptase polymerase-chain-reaction (rRT-PCR) is considered the gold standard. Although this test has high specificity and relatively high sensitivity, the occurrence of falsely negative results in symptomatic patients and/or having a positive CT scan remains a challenge. Sources of error can be pre-analytical (sampling, storage and processing), analytical (RNA extraction, cDNA synthesis and amplification) and post-analytical (interpretation and analysis and test reporting). These potential sources of error and efforts to mitigate are reviewed in this article with an emphasis on the analytical phase.

Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders

The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology.

Synthetic Elaboration of Native DNA by RASS (SENDR)

Controlled site-specific bioconjugation through chemical methods to native DNA remains an unanswered challenge. Herein, we report a simple solution to achieve this conjugation through the tactical combination of two recently developed technologies: one for the manipulation of DNA in organic media and another for the chemoselective labeling of alcohols. Reversible adsorption of solid support (RASS) is employed to immobilize DNA and facilitate its transfer into dry acetonitrile. Subsequent reaction with P(V)-based Ψ reagents takes place in high yield with exquisite selectivity for the exposed 3' or 5' alcohols on DNA. This two-stage process, dubbed SENDR for Synthetic Elaboration of Native DNA by RASS, can be applied to a multitude of DNA conformations and sequences with a variety of functionalized Ψ reagents to generate useful constructs.

Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N1-methyladenosine (m1A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m1G and m22G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.

Identification of endogenous microRNA references in porcine serum for quantitative real-time PCR normalization

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that regulate the expression of genes, and they affect important biological and physiological states. Circulating miRNAs in blood are useful markers of metabolism and economic traits. Expression levels of circulating miRNAs have been estimated using quantitative real-time PCR (qPCR). Proper normalization is critical for accurate miRNA expression analysis. However, there is no study which systematically presented endogenous reference genes for evaluating circulating miRNA expression in pigs. In this study, ten porcine miRNAs (let-7a, miR-16, miR-17, miR-23a, miR-26a, miR-93, miR-103, miR-107, miR-127 and miR-191), based on the literature, were chosen as candidate reference miRNAs in serum. We evaluated the expression stability value of these miRNAs in Berkshire, Duroc, Landrace and Yorkshire pigs using geNorm and NormFinder. We determined the optimal combination of reference miRNAs for qPCR experiments: miR-127 and miR-17 in Berkshire pigs; miR-127 and miR-93 in Duroc and Landrace pigs; miR-127 and miR-16 in Yorkshire pigs. miR-127 was the best reference gene in pigs, regardless of the breed. Our study is crucial for the discovery of novel biomarkers in pigs. The reference miRNAs presented in this study could be used as appropriate reference genes for the measurement of circulating miRNA levels in studies of physiological blood metabolites.

Diagnostic molecular techniques in haematology: recent advances

Hematopoietic disorders are often driven by genetic mutations and epigenetic alterations. New advanced technologies including next-generation sequencing, ultra-deep PCR and whole-genome and exome sequencing were proved very efficient in detecting several mutations implicated in the pathogenesis of hematological diseases. Emerging evidence indicates that genomic data can be useful in all aspects of clinical practice including diagnosis, prognosis and prediction of response to specific treatments, as well as in the development of novel targeted treatments for patients with hematological disorders.

Metal nanoparticle assisted polymerase chain reaction for strain typing of Salmonella Typhi

Salmonella enterica serotype Typhi (S. Typhi) is the causative agent of typhoid fever and remains a major health threat in most of the developing countries. The prompt diagnosis of typhoid directly from the patient's blood requires high level of sensitivity and specificity. Some of us were the first to report PCR based diagnosis of typhoid. This approach has since then been reported by many scientists using different genomic targets. Since the number of bacteria circulating in the blood of a patient can be as low as 0.3 cfu ml(-1), there is always a room for improvement in diagnostic PCR. In the present study, the role of different types of nanoparticles was investigated to improve the existing PCR based methods for diagnosis and strain typing of S. Typhi (targeting Variable Number of Tandem Repeats [VNTR]) by using optimized PCR systems. Three different types of nanoparticles were used i.e., citrate stabilized gold nanoparticles, rhamnolipid stabilized gold and silver nanoparticles, and magnetic iron oxide nanoparticles. The non-specific amplification was significantly reduced in VNTR typing when gold and silver nanoparticles were used in an appropriate concentration. More importantly, the addition of nanoparticles decreased the non-specificity to a significant level in the case of multiplex PCR thus further validating the reliability of PCR for the diagnosis of typhoid.

Temperature effects on the fidelity of a thermostable HIV-1 reverse transcriptase

Transcriptomics and gene expression analysis are largely dependent of the availability of efficient thermostable reverse transcriptases (RTs). However, the intrinsic fidelity of DNA synthesis catalyzed by retroviral RTs is low. Reported error rates are in the range 1.2 × 10(-5)-6.7 × 10(-4), with oncoretroviral RTs being the most faithful enzymes. Wild-type HIV-1 group O (HIV-1O) RT is a thermostable polymerase that is able to synthesize cDNA at temperatures as high as 70 °C. At 37 °C, its error rate has been estimated at 5.8 × 10(-5) in M13mp2 lacZ-based forward mutation assays. However, at higher temperatures (e.g. 50 and 55 °C), the accuracy of HIV-1O RT is increased by approximately two- to five-fold. At 55 °C, the HIV-1O RT error rate (1.3 × 10(-5)) was similar to that shown by the AffinityScript (Agilent Technologies Inc., La Jolla, CA, USA) RT, a commercially available thermostable murine leukaemia virus RT. At higher temperatures, the increased accuracy of the HIV-1 enzyme results from a lower base substitution error rate, although it shows a higher tendency to introduce frameshifts. Kinetic studies carried out with model template-primers suggest minor differences in nucleotide discrimination, although, at higher temperatures, HIV-1O RT showed a reduced ability to extend mispaired template-primers.

Thermostable group II intron reverse transcriptase fusion proteins and their use in cDNA synthesis and next-generation RNA sequencing

Mobile group II introns encode reverse transcriptases (RTs) that function in intron mobility ("retrohoming") by a process that requires reverse transcription of a highly structured, 2-2.5-kb intron RNA with high processivity and fidelity. Although the latter properties are potentially useful for applications in cDNA synthesis and next-generation RNA sequencing (RNA-seq), group II intron RTs have been difficult to purify free of the intron RNA, and their utility as research tools has not been investigated systematically. Here, we developed general methods for the high-level expression and purification of group II intron-encoded RTs as fusion proteins with a rigidly linked, noncleavable solubility tag, and we applied them to group II intron RTs from bacterial thermophiles. We thus obtained thermostable group II intron RT fusion proteins that have higher processivity, fidelity, and thermostability than retroviral RTs, synthesize cDNAs at temperatures up to 81°C, and have significant advantages for qRT-PCR, capillary electrophoresis for RNA-structure mapping, and next-generation RNA sequencing. Further, we find that group II intron RTs differ from the retroviral enzymes in template switching with minimal base-pairing to the 3' ends of new RNA templates, making it possible to efficiently and seamlessly link adaptors containing PCR-primer binding sites to cDNA ends without an RNA ligase step. This novel template-switching activity enables facile and less biased cloning of nonpolyadenylated RNAs, such as miRNAs or protein-bound RNA fragments. Our findings demonstrate novel biochemical activities and inherent advantages of group II intron RTs for research, biotechnological, and diagnostic methods, with potentially wide applications.

Transcriptomic biomarkers of cardiovascular disease

Transcriptomics is the study of how our genes are regulated and expressed in different biological settings. Technical advances now enable quantitative assessment of all expressed genes (ie, the entire "transcriptome") in a given tissue at a given time. These approaches provide a powerful tool for understanding complex biological systems and for developing novel biomarkers. This chapter will introduce basic concepts in transcriptomics and available technologies for developing transcriptomic biomarkers. We will then review current and emerging applications in cardiovascular medicine.