Overview of DNA microarrays: types, applications, and their future

Accelerated, high-quality photolithographic synthesis of RNA microarrays in situ

Nucleic acid photolithography is the only microarray fabrication process that has demonstrated chemical versatility accommodating any type of nucleic acid. The current approach to RNA microarray synthesis requires long coupling and photolysis times and suffers from unavoidable degradation postsynthesis. In this study, we developed a series of RNA phosphoramidites with improved chemical and photochemical protection of the 2'- and 5'-OH functions. In so doing, we reduced the coupling time by more than half and the photolysis time by a factor of 4. Sequence libraries that would otherwise take over 6 hours to synthesize can now be prepared in half the time. Degradation is substantially lowered, and concomitantly, hybridization signals can reach over seven times those of the previous state of the art. Under those conditions, high-density RNA microarrays and RNA libraries can now be synthesized at greatly accelerated rates. We also synthesized fluorogenic RNA Mango aptamers on microarrays and investigated the effect of sequence mutations on their fluorogenic properties.

Transcriptional reprogramming post-peripheral nerve injury: A systematic review

Neuropathic pain is characterised by periodic or continuous hyperalgesia, numbness, or allodynia, and results from insults to the somatosensory nervous system. Peripheral nerve injury induces transcriptional reprogramming in peripheral sensory neurons, contributing to increased spinal nociceptive input and the development of neuropathic pain. Effective treatment for neuropathic pain remains an unmet medical need as current therapeutics offer limited effectiveness and have undesirable effects. Understanding transcriptional changes in peripheral nerve injury-induced neuropathy might offer a path for novel analgesics. Our literature search identified 65 papers exploring transcriptomic changes post-peripheral nerve injury, many of which were conducted in animal models. We scrutinize their transcriptional changes data and conduct gene ontology enrichment analysis to reveal their common functional profile. Focusing on genes involved in 'sensory perception of pain' (GO:0019233), we identified transcriptional changes for different ion channels, receptors, and neurotransmitters, shedding light on its role in nociception. Examining peripheral sensory neurons subtype-specific transcriptional reprograming and regeneration-associated genes, we delved into downstream regulation of hypersensitivity. Identifying the temporal program of transcription regulatory mechanisms might help develop better therapeutics to target them effectively and selectively, thus preventing the development of neuropathic pain without affecting other physiological functions.

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body's reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review

Cytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations.

Integration of Image Pattern Recognition and Photon Sensor for Analyzing Cytokine Gene Expression Using πCode MicroDisc

Current quantitative gene expression detection in genomic and transcriptomic research heavily relies on quantitative real-time PCR (qPCR). While existing multiplex gene detection techniques offer simultaneous analysis of multiple targets, we present an alternative assay capable of detecting gene expression simultaneously within a single well. This highly sensitive method utilizes πCode MicroDiscs, featuring unique identification patterns and fluorescent detection. Our study compared this multiplex πCode platform with a qPCR platform for profiling cytokine gene expression. The πCode MicroDisc assay successfully demonstrated the expression of polymerization markers for M1- and M2-like macrophages generated from THP-1-derived macrophages in a qualitative assay. Additionally, our findings suggest a pattern agreement between the πCode assay and the qPCR assay, indicating the potential of the πCode technology for comparative gene expression analysis. Regarding the inherent sensitivity and linearity, the developed πCode assay primarily provides qualitative gene expression to discriminate the polarization of macrophages. This remarkable capability presents substantial advantages for researchers, rendering the technology highly suitable for high-throughput applications in clinical diagnosis and disease monitoring.

An evaluation of nucleic acid-based molecular methods for the detection of plant viruses: a systematic review

Precise and timely diagnosis of plant viruses is a prerequisite for the implementation of efficient management strategies, considering factors like globalization of trade and climate change facilitating the spread of viruses that lead to agriculture yield losses of billions yearly worldwide. Symptomatic diagnosis alone may not be reliable due to the diverse symptoms and confusion with plant abiotic stresses. It is crucial to detect plant viruses accurately and reliably and do so with little time. A complete understanding of the various detection methods is necessary to achieve this. Enzyme-linked immunosorbent assay (ELISA), has become more popular as a method for detecting viruses but faces limitations such as antibody availability, cost, sample volume, and time. Advanced techniques like polymerase chain reaction (PCR) have surpassed ELISA with its various sensitive variants. Over the last decade, nucleic acid-based molecular methods have gained popularity and have quickly replaced other techniques, such as serological techniques for detecting plant viruses due to their specificity and accuracy. Hence, this review enables the reader to understand the strengths and weaknesses of each molecular technique starting with PCR and its variations, along with various isothermal amplification followed by DNA microarrays, and next-generation sequencing (NGS). As a result of the development of new technologies, NGS is becoming more and more accessible and cheaper, and it looks possible that this approach will replace others as a favoured approach for carrying out regular diagnosis. NGS is also becoming the method of choice for identifying novel viruses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00863-0.

Investigating the influence of solvent type and pH on protein adsorption onto silica surface by evanescent-wave cavity ring-down spectroscopy

Several studies have explored the adsorption of various proteins onto solid-liquid interfaces, revealing the crucial role of buffer solutions in biological processes. However, a comprehensive evaluation of the buffer's influence on protein absorption onto fused silica is still lacking. This study employs evanescent-wave cavity ring-down spectroscopy (EW-CRDS) to assess the influence of buffer solutions and pH on the adsorption kinetics of three globular proteins: hemoglobin (Hb), myoglobin (Mb), and cytochrome c (Cyt-C) onto fused silica. The EW-CRDS tool, with a ring-down time of 1.4 μ s and a minimum detectable absorbance of 1 × 10 - 6 , enabled precise optical measurements at solid-liquid interfaces. The three heme proteins' adsorption behavior was investigated at pH 7 in three different solvents: deionized (DI) water, tris(hydroxymethyl)-aminomethane hydrochloride (Tris-HCl), and phosphate buffered saline (PBS). For each protein, the surface coverage, the adsorption and desorption constants, and the surface equilibrium constant were optically measured by our EW-CRDS tool. Depending on the nature of each solvent, the proteins showed a completely different adsorption trend on the silica surface. The adsorption of Mb on the silica surface was depressed in the presence of both Tris-HCl and PBS buffers compared with unbuffered (DI water) solutions. In contrast, Cyt-C adsorption appears to be relatively unaffected by the choice of buffer, as it involves strong electrostatic interactions with the surface. Notably, Hb exhibits an opposite trend, with enhanced protein adsorption in the presence of Tris-HCl and PBS buffer. The pH investigations demonstrated that the electrostatic interactions between the proteins and the surface had a major influence on protein adsorption on the silica surface, with adsorption being greatest when the pH values were around the protein's isoelectric point. This study demonstrated the ability of the highly sensitive EW-CRDS tool to study the adsorption events of the evanescent-field-confined protein species in real-time at low surface coverages with fast resolution, making it a valuable tool for studying biomolecule kinetics at solid-liquid interfaces.

Employing emerging technologies such as motion capture to study the complex interplay between genotype and power-related performance traits

Exercise genomics has progressed alongside advancements in molecular genetic technologies that have enhanced our understanding of associations between genes and performance traits. This novel field of research incorporates techniques and tools from epidemiology, molecular genetics, exercise physiology and biostatistics to investigate the complex interplay between genotype and specific quantitative performance traits, such as muscle power output. Here I aimed to illustrate how interdisciplinary training can ensure the effective use of new emerging technologies, such as motion capture, to examine the influence of genetic and epigenetic factors on power-related quantitative performance traits. Furthermore, this study raises awareness about the present research trends in this field, and highlights current gaps and potential future developments. The acquired knowledge will likely have important future implications in the biotech industry, with a focus on gene therapy to combat age-related muscle power decline, personalized medicine and will drive advancements in exercise program design.

A Molecular Approach for Detecting Bacteria and Fungi in Healthcare Environment Aerosols: A Systematic Review

Molecular methods have become integral to microbiological research for microbial identification. This literature review focuses on the application of molecular methods in examining airborne bacteria and fungi in healthcare facilities. In January 2024, a comprehensive electronic search was carried out in esteemed databases including PubMed, Web of Science, and Scopus, employing carefully selected keywords such as ((bacteria) OR (virus) OR (fungi)) AND (aerosol) AND ((hospital) OR (healthcare) OR (dental office)) AND ((molecular) OR (PCR) OR (NGS) OR (RNA) OR (DNA) OR (metagenomic) OR (microarray)), following the PRISMA protocol. The review specifically targets healthcare environments with elevated concentrations of pathogenic bacteria. A total of 487 articles were initially identified, but only 13 met the inclusion criteria and were included in the review. The study disclosed that the prevalent molecular methodology for appraising aerosol quality encompassed the utilization of the PCR method, incorporating either 16S rRNA (bacteria) or 18S rRNA (fungi) amplification techniques. Notably, five diverse molecular techniques, specifically PFGE, DGGE, SBT, LAMP, and DNA hybridization methods, were implemented in five distinct studies. These molecular tests exhibited superior capabilities compared to traditional bacterial and fungal cultures, providing precise strain identification. Additionally, the molecular methods allowed the detection of gene sequences associated with antibiotic resistance. In conclusion, molecular testing offers significant advantages over classical microbiological culture, providing more comprehensive information.

Gut microbiota in relationship to diabetes mellitus and its late complications with a focus on diabetic foot syndrome: A review

Diabetes mellitus is a chronic disease affecting glucose metabolism. The pathophysiological reactions underpinning the disease can lead to the development of late diabetes complications. The gut microbiota plays important roles in weight regulation and the maintenance of a healthy digestive system. Obesity, diabetes mellitus, diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are all associated with a microbial imbalance in the gut. Modern technical equipment and advanced diagnostic procedures, including xmolecular methods, are commonly used to detect both quantitative and qualitative changes in the gut microbiota. This review summarises collective knowledge on the role of the gut microbiota in both types of diabetes mellitus and their late complications, with a particular focus on diabetic foot syndrome.

Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm

Background

Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes.

Methods

In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs.

Results

The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively.

Conclusions

The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Standardized Pre-clinical Surgical Animal Model Protocol to Investigate the Cellular and Molecular Mechanisms of Ischemic Flap Healing

BACKGROUND

Some of the most complex surgical interventions to treat trauma and cancer include the use of locoregional pedicled and free autologous tissue transfer flaps. While the techniques used for these reconstructive surgery procedures have improved over time, flap complications and even failure remain a significant clinical challenge. Animal models are useful in studying the pathophysiology of ischemic flaps, but when repeatability is a primary focus of a study, conventional in-vivo designs, where one randomized subset of animals serves as a treatment group while a second subset serves as a control, are at a disadvantage instigated by greater subject-to-subject variability. Our goal was to provide a step-by-step methodological protocol for creating an alternative standardized, more economical, and transferable pre-clinical animal research model of excisional full-thickness wound healing following a simulated autologous tissue transfer which includes the primary ischemia, reperfusion, and secondary ischemia events with the latter mimicking flap salvage procedure.

RESULTS

Unlike in the most frequently used classical unilateral McFarlane's caudally based dorsal random pattern skin flap model, in the herein described bilateral epigastric fasciocutaneous advancement flap (BEFAF) model, one flap heals under normal and a contralateral flap-under perturbed conditions or both flaps heal under conditions that vary by one within-subjects factor. We discuss the advantages and limitations of the proposed experimental approach and, as a part of model validation, provide the examples of its use in laboratory rat (Rattus norvegicus) axial pattern flap healing studies.

CONCLUSIONS

This technically challenging but feasible reconstructive surgery model eliminates inter-subject variability, while concomitantly minimizing the number of animals needed to achieve adequate statistical power. BEFAFs may be used to investigate the spatiotemporal cellular and molecular responses to complex tissue injury, interventions simulating clinically relevant flap complications (e.g., vascular thrombosis) as well as prophylactic, therapeutic or surgical treatment (e.g., flap delay) strategies in the presence or absence of confounding risk factors (e.g., substance abuse, irradiation, diabetes) or favorable wound-healing promoting activities (e.g., exercise). Detailed visual instructions in BEFAF protocol may serve as an aid for teaching medical or academic researchers basic vascular microsurgery techniques that focus on precision, tremor management and magnification.

Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base-pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization of the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base-pairing to minimize the barrier height.

Correlated Hybrid DNA Structures Explored by the oxDNA Model

Thermodynamically, perfect DNA hybridization can be formed between probes and their corresponding targets due to the favorable energy. However, this is not the case dynamically. Here, we use molecular dynamics (MD) simulations based on the oxDNA model to investigate the process of DNA microarray hybridization. In general, correlated hybrid DNA structures are formed, including one probe associated with several targets as well as one target hybrid with multiple probes leading to the target-mediated hybridization. The formation of these two types of correlated structures largely depends on the surface coverage of the DNA microarray. Moreover, DNA sequence, DNA length, and spacer length have an impact on the structural formation. Our findings shed light on the dynamics of DNA hybridization, which is important for the application of DNA microarray.

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.

Diagnosis and screening of abnormal hemoglobins

Hemoglobin (Hb) abnormalities, such as thalassemia and structural Hb variants, are among the most prevalent inherited diseases and are associated with significant mortality and morbidity worldwide. However, there were not comprehensive reviews focusing on different clinical analytical techniques, research methods and artificial intelligence (AI) used in clinical screening and research on hemoglobinopathies. Hence the review offers a comprehensive summary of recent advancements and breakthroughs in the detection of aberrant Hbs, research methods and AI uses as well as the present restrictions anddifficulties in hemoglobinopathies. Recent advances in cation exchange high performance liquid chromatography (HPLC), capillary zone electrophoresis (CZE), isoelectric focusing (IEF), flow cytometry, mass spectrometry (MS) and polymerase chain reaction (PCR) etc have allowed for the definitive detection by using advanced AIand portable point of care tests (POCT) integrating with smartphone microscopic classification, machine learning (ML) model, complete blood counts (CBC), imaging-based method, speedy immunoassay, and electrochemical-, microfluidic- and sensing-related platforms. In addition, to confirm and validate unidentified and novel Hbs, highly specialized genetic based techniques like PCR, reverse transcribed (RT)-PCR, DNA microarray, sequencing of genomic DNA, and sequencing of RT-PCR amplified globin cDNA of the gene of interest have been used. Hence, adequate utilization and improvement of available diagnostic and screening technologies are important for the control and management of hemoglobinopathies.

Summary of ChIP-Seq Methods and Description of an Optimized ChIP-Seq Protocol

Chromatin immunoprecipitation (ChIP) is an invaluable method to characterize interactions between proteins and genomic DNA, such as the genomic localization of transcription factors and post-translational modification of histones. DNA and proteins are reversibly and covalently crosslinked using formaldehyde. Then the cells are lysed to release the chromatin. The chromatin is fragmented into smaller sizes either by micrococcal nuclease (MN) or sonication and then purified from other cellular components. The protein-DNA complexes are enriched by immunoprecipitation (IP) with antibodies that target the epitope of interest. The DNA is released from the proteins by heat and protease treatment, followed by degradation of contaminating RNAs with RNase. The resulting DNA is analyzed using various methods, including polymerase chain reaction (PCR), quantitative PCR (qPCR), or sequencing. This protocol outlines each of these steps for both yeast and human cells. This chapter includes a contextual discussion of the combination of ChIP with DNA analysis methods such as ChIP-on-Chip, ChIP-qPCR, and ChIP-Seq, recent updates on ChIP-Seq data analysis pipelines, complementary methods for identification of binding sites of DNA binding proteins, and additional protocol information about ChIP-qPCR and ChIP-Seq.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

Multiplex Microarrays in 96-Well Plates Photoactivated with 4-Azidotetrafluorobenzaldehyde for the Identification and Quantification of β-Lactamase Genes and Their RNA Transcripts

Antibiotic-resistant bacteria represent a global issue that calls for novel approaches to diagnosis and treatment. Given the variety of genetic factors that determine resistance, multiplex methods hold promise in this area. We developed a novel method to covalently attach oligonucleotide probes to the wells of polystyrene plates using photoactivation with 4-azidotetrafluorobenzaldehyde. Then, it was used to develop the technique of microarrays in the wells. It consists of the following steps: activating polystyrene, hybridizing the probes with biotinylated target DNA, and developing the result using a streptavidin-peroxidase conjugate with colorimetric detection. The first microarray was designed to identify 11 different gene types and 16 single-nucleotide polymorphisms (SNPs) of clinically relevant ESBLs and carbapenemases, which confer Gram-negative bacteria resistance to β-lactam antibiotics. The detection of bla genes in 65 clinical isolates of Enterobacteriaceae demonstrated the high sensitivity and reproducibility of the technique. The highly reproducible spot staining of colorimetric microarrays allowed us to design a second microarray that was intended to quantify four different types of bla mRNAs in order to ascertain their expressions. The combination of reliable performance, high throughput in standard 96-well plates, and inexpensive colorimetric detection makes the microarrays suitable for routine clinical application and for the study of multi-drug resistant bacteria.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

A Canvas of Spatially Arranged DNA Strands that Can Produce 24-bit Color Depth

Nucleic acid microarray photolithography combines density, throughput, and positional control in DNA synthesis. These surface-bound sequence libraries are conventionally used in large-scale hybridization assays against fluorescently labeled, perfect-match DNA strands. Here, we introduce another layer of control for in situ microarray synthesis─hybridization affinity─to precisely modulate fluorescence intensity upon duplex formation. Using a combination of Cy3-, Cy5-, and fluorescein-labeled targets and an ensemble of truncated DNA probes, we organize 256 shades of red, green, and blue intensities that can be superimposed and merged. In so doing, hybridization alone is able to produce a large palette of 16 million colors or 24-bit color depth. Digital images can be reproduced with high fidelity at the micrometer scale by using a simple process that assigns sequence to any RGB value. Largely automated, this approach can be seen as miniaturized DNA-based painting.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Content-based filter queries on DNA data storage systems

Recent developments in DNA data storage systems have revealed the great potential to store large amounts of data at a very high density with extremely long persistence and low cost. However, despite recent contributions to robust data encoding, current DNA storage systems offer limited support for random access on DNA storage devices due to restrictive biochemical constraints. Moreover, state-of-the-art approaches do not support content-based filter queries on DNA storage. This paper introduces the first encoding for DNA that enables content-based searches on structured data like relational database tables. We provide the details of the methods for coding and decoding millions of directly accessible data objects on DNA. We evaluate the derived codes on real data sets and verify their robustness.

Advances in protein glycosylation and its role in tissue repair and regeneration

After tissue damage, a series of molecular and cellular events are initiated to promote tissue repair and regeneration to restore its original structure and function. These events include inter-cell communication, cell proliferation, cell migration, extracellular matrix differentiation, and other critical biological processes. Glycosylation is the crucial conservative and universal post-translational modification in all eukaryotic cells [1], with influential roles in intercellular recognition, regulation, signaling, immune response, cellular transformation, and disease development. Studies have shown that abnormally glycosylation of proteins is a well-recognized feature of cancer cells, and specific glycan structures are considered markers of tumor development. There are many studies on gene expression and regulation during tissue repair and regeneration. Still, there needs to be more knowledge of complex carbohydrates' effects on tissue repair and regeneration, such as glycosylation. Here, we present a review of studies investigating protein glycosylation in the tissue repair and regeneration process.

An ultra-high-throughput screen for the evaluation of peptide HLA-Binder interactions

Peptide human leukocyte antigen (pHLA) targeting therapeutics like T-cell receptor based adoptive cell therapy or bispecific T cell engaging receptor molecules hold great promise for the treatment of cancer. Comprehensive pre-clinical screening of therapeutic candidates is important to ensure patient safety but is challenging because of the size of the potential off-target space. By combining stabilized peptide-receptive HLA molecules with microarray printing and screening, we have developed an ultra-high-throughput screening platform named ValidaTe that enables large scale evaluation of pHLA-binder interactions. We demonstrate its potential by measuring and analyzing over 30.000 binding curves for a high-affinity T cell Engaging Receptor towards a large pHLA library. Compared to a dataset obtained by conventional bio-layer interferometry measurements, we illustrate that a massively increased throughput (over 650 fold) is obtained by our microarray screening, paving the way for use in pre-clinical safety screening of pHLA-targeting drugs.

Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays

Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018–2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

The interplay of long noncoding RNAs and hepatitis B virus

Hepatitis B Virus (HBV) infections remain a major global health burden with an estimated 296 million people living with a chronic infection and 884,000 HBV-related deaths annually. Notably, patients with a chronic hepatitis B (CHB) infection are at a 30-fold greater risk of developing hepatocellular carcinoma (HCC), the most common type of primary liver cancer, which is the 3rd deadliest cancer worldwide. Several groups have assessed HBV-related aberrant expression of host-cell long noncoding RNAs (lncRNAs) and how altered expression of specific lncRNAs affects HBV replication and progression to associated disease states. Given the challenges in establishing effective HBV models and analyzing transcriptomic data, this review focuses on lncRNA expression data primarily collected from clinical patient samples and primary human hepatocytes, with the subsequent mechanism of action analysis in cell lines or other model systems. Ultimately, understanding HBV-induced lncRNA-expression dysregulation could lead to new treatments and biomarkers for HBV infection and its associated diseases.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Current molecular approach for diagnosis of MRSA: a meta-narrative review

Introduction:

Detection and diagnosis of methicillin-resistant Staphylococcus aureus (MRSA) are important in ensuring a correct and effective treatment, further reducing its spread. A wide range of molecular approaches has been used for the diagnosis of antimicrobial resistance (AMR) in MRSA. This review aims to study and appraise widely used molecular diagnostic methods for detecting MRSA.

Methods:

This meta-narrative review was performed by searching PubMed using the following search terms: (molecular diagnosis) AND (antimicrobial resistance) AND (methicillin-resistant Staphylococcus aureus). Studies using molecular diagnostic techniques for the detection of MRSA were included, while non-English language, duplicates and non-article studies were excluded. After reviewing the libraries and a further manual search, 20 studies were included in this article. RAMESES publication standard for narrative reviews was used for this synthesis.

Results:

A total of 20 full papers were reviewed and appraised in this synthesis, consisting of PCR technique (n = 7), deoxyribonucleic acid (DNA) Microarray (n = 1), DNA sequencing (n = 2), Xpert MRSA/SA BC assay (n = 2), matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) (n = 2), MLST (n = 4), SCCmec typing (n = 1) and GENECUBE (n = 1).

Discussion:

Different diagnostic methods used to diagnose MRSA have been studied in this review. This study concludes that PCR has been extensively used due to its higher sensitivity and cost-effectiveness in the past five years

Efficient cross-validation traversals in feature subset selection

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.

Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain

Methods for acquiring spatially resolved omics data from complex tissues use barcoded DNA arrays of low- to sub-micrometer features to achieve single-cell resolution. However, fabricating such arrays (randomly assembled beads, DNA nanoballs, or clusters) requires sequencing barcodes in each array, limiting cost-effectiveness and throughput. Here, we describe a vastly scalable stamping method to fabricate polony gels, arrays of ∼1-micrometer clonal DNA clusters bearing unique barcodes. By enabling repeatable enzymatic replication of barcode-patterned gels, this method, compared with the sequencing-dependent array fabrication, reduced cost by at least 35-fold and time to approximately 7 h. The gel stamping was implemented with a simple robotic arm and off-the-shelf reagents. We leveraged the resolution and RNA capture efficiency of polony gels to develop Pixel-seq, a single-cell spatial transcriptomic assay, and applied it to map the mouse parabrachial nucleus and analyze changes in neuropathic pain-regulated transcriptomes and cell-cell communication after nerve ligation.

Synergy between oligosaccharides and probiotics: From metabolic properties to beneficial effects

Synbiotic is defined as the dietary mixture that comprises both probiotic microorganisms and prebiotic substrates. The concept has been steadily gaining attention owing to the rising recognition of probiotic, prebiotics, and gut health. Among prebiotic substances, oligosaccharides demonstrated considerable health beneficial effects in varieties of food products and their combination with probiotics have been subjected to full range of evaluations. This review delineated the landscape of studies using microbial cultures, cell lines, animal model, and human subjects to explore the functional properties and host impacts of these combinations. Overall, the results suggested that these combinations possess respective metabolic properties that could facilitate beneficial activities therefore could be employed as dietary interventions for human health improvement and therapeutic purposes. However, uncertainties, such as applicational practicalities, underutilized analytical tools, contradictory results in studies, unclear mechanisms, and legislation hurdles, still challenges the broad utilization of these combinations. Future studies to address these issues may not only advance current knowledge on probiotic-prebiotic-host interrelationship but also promote respective applications in food and nutrition.

Characterization of Genetic Risk of End-Stage Knee Osteoarthritis Treated with Total Knee Arthroplasty: A Genome-Wide Association Study

BACKGROUND

End-stage knee osteoarthritis (OA) is a highly debilitating disease for which total knee arthroplasty (TKA) serves as an effective treatment option. Although a genetic component to OA in general has been described, evaluation of the genetic contribution to end-stage OA of the knee is limited. To this end, we present a genome-wide association study involving patients undergoing TKA for primary knee OA to characterize the genetic features of severe disease on a population level.

METHODS

Individuals with the diagnosis of knee OA who underwent primary TKA were identified in the U.K. Biobank using administrative codes. The U.K. Biobank is a data repository containing prospectively collected clinical and genomic data for >500,000 patients. A genome-wide association analysis was performed using the REGENIE software package. Logistic regression was also used to compare the total genetic risk between subgroups stratified by age and body mass index (BMI).

RESULTS

A total of 16,032 patients with end-stage knee OA who underwent primary TKA were identified. Seven genetic loci were found to be significantly associated with end-stage knee OA. The odds ratio (OR) for developing end-stage knee OA attributable to genetics was 1.12 (95% confidence interval [CI], 1.10 to 1.14), which was lower than the OR associated with BMI (OR = 1.81; 95% CI, 1.78 to 1.83) and age (OR = 2.38; 95% CI, 2.32 to 2.45). The magnitude of the OR for developing end-stage knee OA attributable to genetics was greater in patients <60 years old than in patients ≥60 years old (p = 0.002).

CONCLUSIONS

This population-level genome-wide association study of end-stage knee OA treated with primary TKA was notable for identifying multiple significant genetic variants. These loci involve genes responsible for cartilage development, cartilage homeostasis, cell signaling, and metabolism. Age and BMI appear to have a greater impact on the risk of developing end-stage disease compared with genetic factors. The genetic contribution to the development of severe disease is greater in younger patients.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

A Novel Transcriptome Approach to the Investigation of the Molecular Pathology of Vitreous and Retinal Detachment

Retinal detachment (RD) is one of the most common, sight-threatening ocular conditions requiring emergency intervention. Posterior vitreous detachment (PVD) occurs in the majority of an aging population whereby the vitreous body separates from the retina. It is well established that PVD is the common precursor to the most common forms of RD; however, it remains unknown why in most individuals PVD will cause no/few complications (physiological PVD) but in a small percentage will cause retinal tears and detachment (pathological PVD). Despite over 100 years of scientific research, the anatomical definitions of PVD and its pathogenesis remain controversial. Recent research has identified a novel cell population (laminocyte), present at significantly higher numbers in pathological PVD when compared to physiological PVD. We review and summarise the seven distinct clinical sub-groups of retinal breaks and focus on the role of the laminocyte in those secondary to PVD and the transcriptomic profile of this unique cell. Provisional whole transcriptome analysis using bulk RNA-Seq shows marked differentially expressed genes when comparing physiological PVD with PVD associated with RD. The limitations of bulk RNA-Seq are considered and the potential to address these using spatial transcriptomics are discussed. Understanding the pathogenesis of PVD-related retinal tears will provide a baseline for the development of novel therapeutic targets and prophylactic treatments.

Microfluidics-Based Urine Biopsy for Cancer Diagnosis: Recent Advances and Future Trends

Urine biopsy, allowing for the detection, analysis and monitoring of numerous cancer-associated urinary biomarkers to provide insights into cancer occurrence, progression and metastasis, has emerged as an attractive liquid biopsy strategy with enormous advantages over traditional tissue biopsy, such as noninvasiveness, large sample volume, and simple sampling operation. Microfluidics enables precise manipulation of fluids in a tiny chip and exhibits outstanding performance in urine biopsy owing to its minimization, low cost, high integration, high throughput and low sample consumption. Herein, we review recent advances in microfluidic techniques employed in urine biopsy for cancer detection. After briefly summarizing the major urinary biomarkers used for cancer diagnosis, we provide an overview of the typical microfluidic techniques utilized to develop urine biopsy devices. Some prospects along with the major challenges to be addressed for the future of microfluidic-based urine biopsy are also discussed.

Single-Cell Sequencing Yields Insights in the Evolution of Foot-and-Mouth Disease Virus Persistent Infection

Foot-and-mouth disease virus (FMDV) could cause acute infection in host cells, or they could coexist with host cells to generate persistent infection. In persistent infection, the virus could survive for a long time in the host and could be transmitted between different host cells. In the case of FMDV-persistent infection cell line, there is a remarkable significant cellular heterogeneity in the FMDV-persistent infection cell line due to differences of viral load in the individual cells within the cell line. However, the mechanisms of FMDV-persistent infection are not well understood. It is now generally accepted that multiple factors contribute to the coevolution of viruses and cells during the course of persistent infection. The outcome would influence the development of persistent FMDV infection conjointly, reaching a state of equilibrium ultimately. Therefore, in order to elucidate the mechanism of cellular heterogeneity in FMDV-persistent infection cell line, single-cell sequencing was performed on BHK-Op, and pseudotime trajectory plot was draw through cell cluster. Based on the cell clusters, we predicted the development and progression of the FMDV-persistent infection. It could be well explained by the fact that, in BHK-Op cells, there are a fraction of infected cells and a fraction of virus-exposed but uninfected bystander cells. By further comparing the transcripts in cell clusters, we found that these genes were involved in changes in ribosome biogenesis, cell cycle, and intracellular signaling including the interferon signaling pathway and mitogen-activated protein kinase (MAPK) signaling pathway. Through comprehensive cross-tabulation analysis of differential expressed genes in various cluster of cells, we identified a high association of Fos, a downstream transcription factor of the MAPK/extracellular signal–regulated kinase (ERK) signaling pathway, with viral replication during the formation of FMDV-persistent infection. Through the further study of Fos, we found that downregulation of Fos facilitates viral clearance during FMDV-persistent infection. Upregulation of c-Raf, which is the upstream of the MAPK/ERK signaling pathway, could promote FMDV replication through downregulation of Fos. Our research is the first to provide insight into the mechanism of the formation FMDV-persistent infection through single-cell sequencing using persistent infection cell line. Pseudotime trajectory analysis was the first time to apply for FMDV-persistent infection cell line. Our work highlights the detailed overview of the evolution of FMDV-persistent infection. We also analyzed the differential expressed genes in the replication or elimination of FMDV within the host. We found that the MAPK/ERK signaling pathway and its downstream transcription factor Fos play an important role in FMDV-persistent infection.

Current Scenario of Pathogen Detection Techniques in Agro-Food Sector

Over the past-decade, agricultural products (such as vegetables and fruits) have been reported as the major vehicles for foodborne diseases, which are limiting food resources. The spread of infectious diseases due to foodborne pathogens poses a global threat to human health and the economy. The accurate and timely detection of infectious disease and of causative pathogens is crucial in the prevention and treatment of disease. Negligence in the detection of pathogenic substances can be catastrophic and lead to a pandemic. Despite the revolution in health diagnostics, much attention has been paid to the agro-food sector regarding the detection of food contaminants (such as pathogens). The conventional analytical techniques for pathogen detection are reliable and still in operation. However, laborious procedures and time-consuming detection via these approaches emphasize the need for simple, easy-to-use, and affordable detection techniques. The rapid detection of pathogens from food is essential to avoid the morbidity and mortality originating from the suboptimal nature of empiric pathogen treatment. This review critically discusses both the conventional and emerging bio-molecular approaches for pathogen detection in agro-food.

Weighted Gene Correlation Network Analysis (WGCNA) of Arabidopsis Somatic Embryogenesis (SE) and Identification of Key Gene Modules to Uncover SE-Associated Hub Genes

Somatic embryogenesis (SE), which occurs naturally in many plant species, serves as a model to elucidate cellular and molecular mechanisms of embryo patterning in plants. Decoding the regulatory landscape of SE is essential for its further application. Hence, the present study was aimed at employing Weighted Gene Correlation Network Analysis (WGCNA) to construct a gene coexpression network (GCN) for Arabidopsis SE and then identifying highly correlated gene modules to uncover the hub genes associated with SE that may serve as potential molecular targets. A total of 17,059 genes were filtered from a microarray dataset comprising four stages of SE, i.e., stage I (zygotic embryos), stage II (proliferating tissues at 7 days of induction), stage III (proliferating tissues at 14 days of induction), and stage IV (mature somatic embryos). This included 1,711 transcription factors and 445 EMBRYO DEFECTIVE genes. GCN analysis identified a total of 26 gene modules with the module size ranging from 35 to 3,418 genes using a dynamic cut tree algorithm. The module-trait analysis revealed that four, four, seven, and four modules were associated with stages I, II, III, and IV, respectively. Further, we identified a total of 260 hub genes based on the degree of intramodular connectivity. Validation of the hub genes using publicly available expression datasets demonstrated that at least 78 hub genes are potentially associated with embryogenesis; of these, many genes remain functionally uncharacterized thus far. In silico promoter analysis of these genes revealed the presence of cis-acting regulatory elements, “soybean embryo factor 4 (SEF4) binding site,” and “E-box” of the napA storage-protein gene of Brassica napus; this suggests that these genes may play important roles in plant embryo development. The present study successfully applied WGCNA to construct a GCN for SE in Arabidopsis and identified hub genes involved in the development of somatic embryos. These hub genes could be used as molecular targets to further elucidate the molecular mechanisms underlying SE in plants.

Comparison of High-Throughput Technologies in the Classification of Adult-Onset Still's Disease Patients

A meta-analysis study was conducted to compare high-throughput technologies in the classification of Adult-Onset Still's Disease patients, using differentially expressed genes from independent profiling experiments. We exploited two publicly available datasets from the Gene Expression Omnibus and performed a separate differential expression analysis on each dataset to extract statistically important genes. We then mapped the genes of the two datasets and subsequently we employed well-established machine learning algorithms to evaluate the denoted genes as candidate biomarkers. Using next-generation sequencing data, we managed to achieve the maximum (100%) classification accuracy, sensitivity and specificity with the Gradient Boosting and the Random Forest classifiers, compared to the 83% of the DNA microarray data. Clinical Relevance- When biomarkers derived from one study are applied to the data of another, in many cases the results may diverge significantly. Here we establish that in cross-profiling meta-analysis approaches based on differential expression analysis, next-generation sequencing data provide more accurate results than microarray experiments in the classification of Adult-Onset Still's Disease patients.

Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge

The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians’ diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles.

Investigation and validation of labelling loop mediated isothermal amplification (LAMP) products with different nucleotide modifications for various downstream analysis

Loop mediated isothermal amplification (LAMP) is one of the best known and most popular isothermal amplification methods. It's simplicity and speed make the method particularly suitable for point-of-care diagnostics. Nevertheless, false positive results remain a major drawback. Many (downstream) applications are known for the detection of LAMP amplicons like colorimetric assays, in-situ LAMP or CRISPR-Cas systems. Often, modifications of the LAMP products are necessary for different detection applications such as lateral flow assays. This is usually achieved with pre-modified primer. The aim of this study is to evaluate amplicon labelling with different modified nucleotides such as Cy5-dUTP, biotin-dUTP and aminoallyl-dUTP as an alternative to pre-labelled primers. To realise this, the effects on amplification and labelling efficiency were studied as a function of molecule size and nucleotide amount as well as target concentration. This research shows that diverse labelling of LAMP amplicons can be achieved using different, modified nucleotides during LAMP and that these samples can be analysed by a wide range of downstream applications such as fluorescence spectroscopy, gel electrophoresis, microarrays and lateral flow systems. Furthermore, microarray-based detection and the ability to identify and distinguish false positives were demonstrated as proof of concept.

Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

BACKGROUND

In their everyday life, clinicians face an overabundance of biological indicators potentially helpful during a disease therapy. In this context, to be able to reliably identify a reduced number of those markers showing the ability of optimising the classification of treatment outcomes becomes a factor of vital importance to medical prognosis. In this work, we focus our interest in inflammatory bowel disease (IBD), a long-life threaten with a continuous increasing prevalence worldwide. In particular, IBD can be described as a set of autoimmune conditions affecting the gastrointestinal tract whose two main types are Crohn’s disease and ulcerative colitis.

AIM

To identify the minimal signature of microRNA (miRNA) associated with colorectal cancer (CRC) in patients with one chronic IBD.

METHODS

We provide a framework of well-established statistical and computational learning methods wisely adapted to reconstructing a CRC network leveraged to stratify these patients.

RESULTS

Our strategy resulted in an adjusted signature of 5 miRNAs out of approximately 2600 in Crohn’s Disease (resp. 8 in Ulcerative Colitis) with a percentage of success in patient classification of 82% (resp. 81%).

CONCLUSION

Importantly, these two signatures optimally balance the proportion between the number of significant miRNAs and their percentage of success in patients’ stratification.