Overview of Next-Generation Sequencing Technologies

A qRT-PCR Method Capable of Quantifying Specific Microorganisms Compared to NGS-Based Metagenome Profiling Data

Metagenome profiling research using next-generation sequencing (NGS), a technique widely used to analyze the diversity and composition of microorganisms living in the human body, especially the gastrointestinal tract, has been actively conducted, and there is a growing interest in the quantitative and diagnostic technology for specific microorganisms. According to recent trends, quantitative real-time PCR (qRT-PCR) is still a considerable technique in detecting and quantifying bacteria associated with the human oral and nasal cavities, due to the analytical cost and time burden of NGS technology. Here, based on NGS metagenome profiling data produced by utilizing 100 gut microbiota samples, we conducted a comparative analysis for the identification and quantification of five bacterial genera (Akkermansia, Bacteroides, Bifidobacterium, Phascolarctobacterium, and Roseburia) within same metagenomic DNA samples through qRT-PCR assay in parallel. Genus-specific primers, targeting the particular gene of each genus for qRT-PCR assay, allowed a statistically consistent quantification pattern with the metagenome profiling data. Furthermore, results of bacterial identification through Sanger validation demonstrated the high genus-specificity of each primer set. Therefore, our study suggests that an approach to quantifying specific microorganisms by applying the qRT-PCR method can compensate for the concerns (potential issues) of NGS while also providing efficient benefits to various microbial industries.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

What Has the Undiagnosed Diseases Network Taught Us About the Clinical Applications of Genomic Testing?

Genetic testing has undergone a revolution in the last decade, particularly with the advent of next-generation sequencing and its associated reductions in costs and increases in efficiencies. The Undiagnosed Diseases Network (UDN) has been a leader in the application of such genomic testing for rare disease diagnosis. This review discusses the current state of genomic testing performed within the UDN, with a focus on the strengths and limitations of whole-exome and whole-genome sequencing in clinical diagnostics and the importance of ongoing data reanalysis. The role of emerging technologies such as RNA and long-read sequencing to further improve diagnostic rates in the UDN is also described. This review concludes with a discussion of the challenges faced in insurance coverage of comprehensive genomic testing as well as the opportunities for a larger role of testing in clinical medicine.

Large-Area Interfaces for Single-Molecule Label-free Bioelectronic Detection

Bioelectronic transducing surfaces that are nanometric in size have been the main route to detect single molecules. Though enabling the study of rarer events, such methodologies are not suited to assay at concentrations below the nanomolar level. Bioelectronic field-effect-transistors with a wide (μm²-mm²) transducing interface are also assumed to be not suited, because the molecule to be detected is orders of magnitude smaller than the transducing surface. Indeed, it is like seeing changes on the surface of a one-kilometer-wide pond when a droplet of water falls on it. However, it is a fact that a number of large-area transistors have been shown to detect at a limit of detection lower than femtomolar; they are also fast and hence innately suitable for point-of-care applications. This review critically discusses key elements, such as sensing materials, FET-structures, and target molecules that can be selectively assayed. The amplification effects enabling extremely sensitive large-area bioelectronic sensing are also addressed.

Selective Single-Cell Sorting Using a Multisectorial Electroactive Nanowell Platform

Current approaches in targeted patient treatments often require the rapid isolation of specific rare target cells. Stream-based dielectrophoresis (DEP) based cell sorters have the limitation that the maximum number of sortable cell types is equivalent to the number of output channels, which makes upscaling to a higher number of different cell types technically challenging. Here, we present a microfluidic platform for selective single-cell sorting that bypasses this limitation. The platform consists of 10 000 nanoliter wells which are placed on top of interdigitated electrodes (IDEs) that facilitate dielectrophoresis-driven capture of cells. By use of a multisectorial design formed by 10 individually addressable IDE structures, our platform can capture a large number of different cell types. The sectorial approach allows for fast and straightforward modification to sort complex samples as different cell types are captured in different sectors and therefore removes the need for individual output channels per cell type. Experimental results obtained with a mixed sample of benign (MCF-10A) and malignant (MDA-MB-231) breast cells showed a target to nontarget sorting accuracy of over 95%. We envision that the high accuracy of our platform, in addition to its versatility and simplicity, will aid clinical environments where reliable sorting of varying complex samples is essential.

A simple guide to de novo transcriptome assembly and annotation

A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.

Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma

Background

Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM.

Materials and Methods

This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting.

Results

The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy.

Conclusions

As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

End-User Perspectives on Using Quantitative Real-Time PCR and Genomic Sequencing in the Field

Quantitative real-time PCR and genomic sequencing have become mainstays for performing molecular detection of biological threat agents in the field. There are notional assessments of the benefits, disadvantages, and challenges that each of these technologies offers according to findings in the literature. However, direct comparison between these two technologies in the context of field-forward operations is lacking. Most market surveys, whether published in print form or provided online, are directed to product manufacturers who can address their respective specifications and operations. One method for comparing these technologies is surveying end-users who are best suited for discussing operational capabilities, as they have hands-on experience with state-of-the-art molecular detection platforms and protocols. These end-users include operators in military defense and first response, as well as various research scientists in the public sector such as government and service laboratories, private sector, and civil society such as academia and nonprofit organizations performing method development and executing these protocols in the field. Our objective was to initiate a survey specific to end-users and their feedback. We developed a questionnaire that asked respondents to (1) determine what technologies they currently use, (2) identify the settings where the technologies are used, whether lab-based or field-forward, and (3) rate the technologies according to a set list of criteria. Of particular interest are assessments of sensitivity, specificity, reproducibility, scalability, portability, and discovery power. This article summarizes the findings from the end-user perspective, highlighting technical and operational challenges.

Predictive Biomarkers for Checkpoint Inhibitor-Based Immunotherapy in Hepatocellular Carcinoma: Where Do We Stand?

Hepatocellular carcinoma (HCC) remains the sixth most commonly diagnosed malignancy worldwide, still representing an important cause of cancer-related death. Over the next few years, novel systemic treatment options have emerged. Among these, immune checkpoint inhibitors (ICIs) have been widely evaluated and are under assessment, as monotherapy or in combination with other anticancer agents in treatment-naïve and previously treated patients. In particular, the approval of the PD-L1 inhibitor atezolizumab plus the antiangiogenic agent bevacizumab as front-line treatment for advanced HCC has led to the adoption of this combination in this setting, and the IMbrave 150 phase III trial has established a novel standard of care. However, several questions remain unanswered, including the identification of reliable predictors of response to ICIs in HCC patients. In the current paper, we will provide an updated overview of potentially useful predictive biomarkers of response to immunotherapy in advanced HCC. A literature search was conducted in September 2021 of Pubmed/Medline, Cochrane library and Scopus databases.

Production of mannosylerythritol lipids: biosynthesis, multi-omics approaches, and commercial exploitation

Compilation of resources regarding MEL biosynthesis, key production parameters; available omics resources and current commercial applications, for smut fungi known to produce MELs.

Virus-Mediated Protein Overexpression (VOX) in Monocots to Identify and Functionally Characterize Fungal Effectors

One of the important armories that pathogens utilize to successfully colonize the plants is small secreted effector proteins, which could perform a variety of functions from suppression of plant innate immunity to manipulation of plant physiology in favor of the disease. Plants, on the other hand, evolved disease resistance genes that recognize some of the effectors or avirulence (Avr) proteins. Both, identification of the Avr proteins and understanding of the mechanisms of action of other effectors, are important areas of research in the molecular plant-pathogen interactions field as this knowledge is critical for the development of new effective pathogen control measures. To enable functional analysis of the effectors, it is desirable to be able to overexpress them readily in the host plants. Here we describe detailed experimental protocols for transient effector overexpression in wheat and other monocots using binary Barley stripe mosaic virus (BSMV)- and Foxtail mosaic virus (FoMV)-derived vectors. This functional genomics tool, better known as VOX (Virus-mediated protein OvereXpression), is rapid and relatively simple and inexpensive.

Reconstruction of full antibody sequences in NGS datasets and accurate VL:VH coupling by cluster coordinate matching of non-overlapping reads

Next-generation sequencing (NGS) is an indispensable tool in antibody discovery projects. However, the limits on NGS read length make it difficult to reconstruct full antibody sequences from the sequencing runs, especially if the six CDRs are randomized. To overcome that, we took advantage of Illumina’s cluster mapping capabilities to pair non-overlapping reads and reconstruct full Fab sequences with accurate V_L:V_H pairings. The method relies on in silico cluster coordinate information, and not on extensive in vitro manipulation, making the protocol easily deployable and less prone to PCR-derived errors. This work maintains the throughput necessary for antibody discovery campaigns, and a high degree of fidelity, which potentiates not only phage-display and synthetic library-based discovery methods, but also the NGS-driven analysis of naïve and immune libraries.

Genome-wide core sets of SNP markers and Fluidigm assays for rapid and effective genotypic identification of Korean cultivars of lettuce (Lactuca sativa L.)

Lettuce is one of the economically important leaf vegetables and is cultivated mainly in temperate climate areas. Cultivar identification based on the distinctness, uniformity, and stability (DUS) test is a prerequisite for new cultivar registration. However, DUS testing based on morphological features is time-consuming, labor-intensive, and costly, and can also be influenced by environmental factors. Thus, molecular markers have also been used for the identification of genetic diversity as an effective, accurate, and stable method. Currently, genome-wide single nucleotide polymorphisms (SNPs) using next-generation sequencing technology are commonly applied in genetic research on diverse plant species. This study aimed to establish an effective and high-throughput cultivar identification system for lettuce using core sets of SNP markers developed by genotyping by sequencing (GBS). GBS identified 17 877 high-quality SNPs for 90 commercial lettuce cultivars. Genetic differentiation analyses based on the selected SNPs classified the lettuce cultivars into three main groups. Core sets of 192, 96, 48, and 24 markers were further selected and validated using the Fluidigm platform. Phylogenetic analyses based on all core sets of SNPs successfully discriminated individual cultivars that have been currently recognized. These core sets of SNP markers will support the construction of a DNA database of lettuce that can be useful for cultivar identification and purity testing, as well as DUS testing in the plant variety protection system. Additionally, this work will facilitate genetic research to improve breeding in lettuce.

What sequencing technologies can teach us about innate immunity

For years, we have taken a reductionist approach to understanding gene regulation through the study of one gene in one cell at a time. While this approach has been fruitful it is laborious and fails to provide a global picture of what is occurring in complex situations involving tightly coordinated immune responses. The emergence of whole-genome techniques provides a system-level view of a response and can provide a plethora of information on events occurring in a cell from gene expression changes to splicing changes and chemical modifications. As with any technology, this often results in more questions than answers, but this wealth of knowledge is providing us with an unprecedented view of what occurs inside our cells during an immune response. In this review, we will discuss the current RNA-sequencing technologies and what they are helping us learn about the innate immune system.

Analysis of Gut Microbiome Using Fecal Samples

With the gut microbiome being implicated in many diseases and even offering the potential for future therapies, its study has been gaining interest over the years. However, to gain more in-depth insight and understanding of the impact of the microbiome on our health, it is critical to have accurate microbial data that is reproducible and comparable across studies. Variation in experimental techniques, particularly DNA extraction and sequencing methods, may result in variability and the inability to compare results. In this chapter, we describe in detail our microbiome analysis methodology focusing on stool samples.

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

Comprehensive analysis of chloroplast genome of Albizia julibrissin Durazz. (Leguminosae sp.)

The Albizia julibrissin chloroplasts have a classical chloroplast genome structure, containing 93 coding genes and 34 non-coding genes. Our research provides basic data for plant phylogenetic evolutionary studies. There is limited genomic information available for the important Chinese herb Albizia julibrissin Durazz. In this study, we constructed the chloroplast (Cp) genome of A. julibrissin. The length of the assembled Cp genome was 175,922 bp consisting of four conserved regions: a 5145 bp small single-copy (SSC) region, a 91,323 bp large single-copy (LSC) region, and two identical length-inverted repeat (IR) regions (39,725 bp). This Cp genome included 34 non-coding RNAs and 93 unique genes, the former contains 30 transfer and 4 ribosomal RNA genes. Gene annotation indicated some of the coding genes (82) in the A. julibrissin Cp genome classified in the Leguminosae family, with some to other related families (11). The results show that low GC content (36.9%) and codon bias towards A- or T-terminal codons may affect the frequency of gene codon usage. The sequence analysis identified 30 forward, 18 palindrome, and 1 reverse repeat > 30 bp length, and 149 simple sequence repeats (SSR). Fifty-five RNA editing sites in the Cp of A. julibrissin were predicted, most of which are C-to-U conversions. Analysis of the reverse repeat expansion or contraction and divergence area between several species, including A. julibrissin, was performed. The phylogenetic tree revealed that A. julibrissin was most closely related to Albizia odoratissima and Albizia bracteata, followed by Samanea saman, forming an evolutionary branch with Mimosa pudica and Leucaena trichandra. The research results are helpful for breeding and genetic improvement of A. julibrissin, and also provide valuable information for understanding the evolution of this plant.

Target-enriched sequencing enables accurate identification of bloodstream infections in whole blood

Bloodstream infections are within the top ten causes of death globally, with a mortality rate of up to 70%. Gold standard blood culture testing is time-consuming, resulting in delayed, but accurate, treatment. Molecular methods, such as RT-qPCR, have limited targets in one run. We present a new Ampliseq detection system (ADS) combining target amplification and next-generation sequencing for accurate identification of bacteria, fungi, and antimicrobial resistance determinants directly from blood samples. In this study, we included removal of human genomic DNA during nucleic acid extraction, optimized the target sequence set and drug resistance genes, performed antimicrobial resistance profiling of clinical isolates, and evaluated mock specimens and clinical samples by ADS. ADS successfully identified pathogens at the species-level in 36 h, from nucleic acid extraction to results. Besides pathogen identification, ADS can also present drug resistance profiles. ADS enabled detection of all bacteria and accurate identification of 47 pathogens. In 20 spiked samples and 8 clinical specimens, ADS detected at least 92.81% of reads mapped to pathogens. ADS also showed consistency with the three culture-negative samples, and correctly identified pathogens in four of five culture-positive clinical blood specimens. This Ampliseq-based technology promises broad coverage and accurate pathogen identification, helping clinicians to accurately diagnose and treat bloodstream infections.

Improving microalgae for biotechnology - From genetics to synthetic biology - Moving forward but not there yet

Microalgae are a diverse group of photosynthetic organisms that can be exploited for the production of different compounds, ranging from crude biomass and biofuels to high value-added biochemicals and synthetic proteins. Traditionally, algal biotechnology relies on bioprospecting to identify new highly productive strains and more recently, on forward genetics to further enhance productivity. However, it has become clear that further improvements in algal productivity for biotechnology is impossible without combining traditional tools with the arising molecular genetics toolkit. We review recent advantages in developing high throughput screening methods, preparing genome-wide mutant libraries, and establishing genome editing techniques. We discuss how algae can be improved in terms of photosynthetic efficiency, biofuel and high value-added compound production. Finally, we critically evaluate developments over recent years and explore future potential in the field.

Utilizing CRISPR-Cas in Tropical Crop Improvement: A Decision Process for Fitting Genome Engineering to Your Species

Adopting modern gene-editing technologies for trait improvement in agriculture requires important workflow developments, yet these developments are not often discussed. Using tropical crop systems as a case study, we describe a workflow broken down into discrete processes with specific steps and decision points that allow for the practical application of the CRISPR-Cas gene editing platform in a crop of interest. While we present the steps of developing genome-edited plants as sequential, in practice parts can be done in parallel, which are discussed in this perspective. The main processes include 1) understanding the genetic basis of the trait along with having the crop’s genome sequence, 2) testing and optimization of the editing reagents, development of efficient 3) tissue culture and 4) transformation methods, and 5) screening methods to identify edited events with commercial potential. Our goal in this perspective is to help any lab that wishes to implement this powerful, easy-to-use tool in their pipeline, thus aiming to democratize the technology.

FACT-seq: profiling histone modifications in formalin-fixed paraffin-embedded samples with low cell numbers

The majority of biopsies in both basic research and translational cancer studies are preserved in the format of archived formalin-fixed paraffin-embedded (FFPE) samples. Profiling histone modifications in archived FFPE tissues is critically important to understand gene regulation in human disease. The required input for current genome-wide histone modification profiling studies from FFPE samples is either 10-20 tissue sections or whole tissue blocks, which prevents better resolved analyses. But it is desirable to consume a minimal amount of FFPE tissue sections in the analysis as clinical tissues of interest are limited. Here, we present FFPE tissue with antibody-guided chromatin tagmentation with sequencing (FACT-seq), the first highly sensitive method to efficiently profile histone modifications in FFPE tissues by combining a novel fusion protein of hyperactive Tn5 transposase and protein A (T7-pA-Tn5) transposition and T7 in vitro transcription. FACT-seq generates high-quality chromatin profiles from different histone modifications with low number of FFPE nuclei. We proved a very small piece of FFPE tissue section containing ∼4000 nuclei is sufficient to decode H3K27ac modifications with FACT-seq. H3K27ac FACT-seq revealed disease-specific super enhancers in the archived FFPE human colorectal and human glioblastoma cancer tissue. In summary, FACT-seq allows decoding the histone modifications in archival FFPE tissues with high sensitivity and help researchers to better understand epigenetic regulation in cancer and human disease.

The storm of NGS in NSCLC diagnostic-therapeutic pathway: How to sun the real clinical practice

The increasing number of approved drugs along with next generation sequencing (NGS) technologies look out as potential revolution of biomolecular characterization of non-small-cell lung cancer (NSCLC). Nevertheless, several aspects impact on success rate of NGS in clinical practice: a multidisciplinary approach and thorough knowledge of strengths and limits of each technologic diagnostic tool are required. Crucial preliminary step is the selection of the best available sample before testing, aware of clinical condition and setting of disease. Genomic data should be than integrated in the clinical context and matched with available therapeutic options; Molecular Tumor Boards (MTB) are worldwide emerging interdisciplinary groups implemented to transfer the impact of precision medicine in clinical practice. In order to guarantee equity in treatment, these considerations should find their application widely and rapidly. Aim of this review is offering an overview of emerging biomarkers, relative upcoming targeted drugs, and new diagnostic chances with an authors' perspective about a real-life diagnostic-therapeutic algorithm useful for daily clinical practice.

Batch effects in population genomic studies with low-coverage whole genome sequencing data: Causes, detection and mitigation

Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a "batch-effect-naive" bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four-channel vs. two-channel), sequencing run, read type (single-end vs. paired-end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.

The Genetics of Inherited Cholestatic Disorders in Neonates and Infants: Evolving Challenges

Many inherited conditions cause cholestasis in the neonate or infant. Next-generation sequencing methods can facilitate a prompt diagnosis in some of these cases; application of these methods in patients with liver diseases of unknown cause has also uncovered novel gene-disease associations and improved our understanding of physiological bile secretion and flow. By helping to define the molecular basis of certain cholestatic disorders, these methods have also identified new targets for therapy as well patient subgroups more likely to benefit from specific therapies. At the same time, sequencing methods have presented new diagnostic challenges, such as the interpretation of single heterozygous genetic variants. This article discusses those challenges in the context of neonatal and infantile cholestasis, focusing on difficulties in predicting variant pathogenicity, the possibility of other causal variants not identified by the genetic screen used, and phenotypic variability among patients with variants in the same genes. A prospective, observational study performed between 2010-2013, which sequenced six important genes (ATP8B1, ABCB11, ABCB4, NPC1, NPC2 and SLC25A13) in an international cohort of 222 patients with infantile liver disease, is given as an example of potential benefits and challenges that clinicians could face having received a complex genetic result. Further studies including large cohorts of patients with paediatric liver disease are needed to clarify the spectrum of phenotypes associated with, as well as appropriate clinical response to, single heterozygous variants in cholestasis-associated genes.

Next-Generation Sequencing Targeted Panel in Routine Care for Metastatic Colon Cancers

Simple Summary

The place of Next-Generation-Sequencing (NGS) targeted panel in routine practice in digestive oncology should be addressed. The aim of our retrospective study was to assess the results and impact of NGS panel for metastatic colorectal cancer (mCRC) patients. In total, 210 patients with mCRC were included. Based on our findings, a major advantage of the NGS panel over single gene techniques is that, beyond the classical hotspots, it allows for an exhaustive search for molecular abnormalities in routinely recommended genes. In addition, routine NGS is a way to detect amplifications associated with resistance to anti-EGFR therapies and low-prevalence mutations in actionable genes, providing patients with the opportunity to access innovative targeted therapies. In conclusion, NGS targeted panel in mCRC is feasible in routine practice. Nevertheless, panels need to be regularly updated and in-depth studies are needed to better analyse the prognostic factors.

Abstract

In digestive oncology, the clinical impact of targeted next-generation sequencing (NGS) in routine practice should be addressed. In this work, we studied the impact of a 22-gene NGS amplicon-based panel with Ion Torrent Proton Sequencing, prospectively performed in routine practice. We analyzed the results of extended molecular testing, beyond RAS and BRAF, in metastatic colorectal cancer (mCRC) patients in a single-center, retrospective, observational study of consecutive mCRC patients followed up at the Georges Pompidou European Hospital between January 2016 and December 2018. Overall, 210 patients with mCRC were included. Median follow-up was 25.4 months (IQR: 14.9–39.5). The three most frequently mutated genes were: TP53 (63%), KRAS (41%) and PIK3CA (19%). A positive association was found between overall survival and performance status (PS) ≥ 2 (HR: 4.91 (1.84–13.1); p = 0.001) and differentiation (HR: 4.70 (1.51–14.6); p = 0.007) in multivariate analysis. The NGS panel enabled five patients to access a targeted therapy not currently registered for CRC. In conclusion, targeted NGS panels in mCRC are feasible in routine practice, but need to be regularly updated and in-depth studies are needed to better analyze the prognostic factors.

Comparative analysis of human facial skin microbiome between topical sites compared to entire face

BACKGROUND

Skin is an essential outer barrier and supports the growth of commensal microorganisms that protects a host from the offense of foreign toxic organisms. With the rapid development of next-generation sequencing (NGS)-based applications, skin microbiome research for facial health care has reached industry growth, such as therapy and cosmetic product development. Despite the acceleration of skin microbiome research, experimental standardization protocol has not yet been established in the facial site and method of sampling.

OBJECTIVE

Thus, we aimed to investigate the differences in microbial composition at each facial site (cheek, mouth, forehead, and entire face) using comprehensive microbiome analysis.

METHODS

Twelve specimens from three men (four specimens per one person) were collected. The hypervariable regions (V3-V4) of the bacterial 16S rRNA gene were targeted for 16S amplicon library construction and classification of bacterial taxonomy. Skin microbial composition for all specimens was investigated, and the differences site-by-site in skin microbial composition were analyzed and evaluated by the various statistical tests.

RESULTS

We were able to validate the independent correlation between the skin microbiome composition and the facial sites. The cheek site showed the highest alpha-diversity in richness and evenness scores compared to the forehead and mouth. The cheek and mouth sites showed a positive correlation (R² value > 0.93) with the entire face, while the forehead sites were negatively correlated (R² value < 0.2). Given the relative abundance based on statistical correlation analysis, we estimated that the cheek site could be considered an optimal topical site to replace the entire face.

CONCLUSION

Our study suggests that skin microbiome profiling of four facial sites confirms that the cheek shows the most similar skin flora with the entire face. This study would be informative for preventing bias caused by sampling methods before researching and understanding skin cosmetics development or skin diseases.

Short- and Long-Term Implications of Human Milk Microbiota on Maternal and Child Health

Human milk (HM) is considered the most complete food for infants as its nutritional composition is specifically designed to meet infant nutritional requirements during early life. HM also provides numerous biologically active components, such as polyunsaturated fatty acids, milk fat globules, IgA, gangliosides or polyamines, among others; in addition, HM has a “bifidogenic effect”, a prebiotic effect, as a result of the low concentration of proteins and phosphates, as well as the presence of lactoferrin, lactose, nucleotides and oligosaccharides. Recently, has been a growing interest in HM as a potential source of probiotics and commensal bacteria to the infant gut, which might, in turn, influence both the gut colonization and maturation of infant immune system. Our review aims to address practical approaches to the detection of microbial communities in human breast milk samples, delving into their origin, composition and functions. Furthermore, we will summarize the current knowledge of how HM microbiota dysbiosis acts as a short- and long-term predictor of maternal and infant health. Finally, we also provide a critical view of the role of breast milk-related bacteria as a novel probiotic strategy in the prevention and treatment of maternal and offspring diseases.

Non-invasive Technology Advances in Cancer-A Review of the Advances in the Liquid Biopsy for Endometrial and Ovarian Cancers

Improving cancer survival rates globally requires improvements in disease detection and monitoring, with the aim of improving early diagnosis and prediction of disease relapse. Traditional means of detecting and monitoring cancers rely largely on imaging and, where possible, blood-based protein biomarkers, many of which are non-specific. Treatments are being improved by identification of inherited and acquired genomic aberrations in tumors, some of which can be targeted by newly developed therapeutic interventions. Treatment of gynecological malignancy is progressively moving toward personalized therapy, as exemplified by application of PARP-inhibition for patients with BRCA-deficient tubo-ovarian cancers, or checkpoint inhibition in patients with mismatch repair-deficient disease. However, the more recent discovery of a group of biomarkers described under the umbrella term of “liquid biopsy” promises significant improvement in our ability to detect and monitor cancers. The term “liquid biopsy” is used to describe an array of tumor-derived material found in blood plasma and other bodily fluids such as ascites, pleural fluid, saliva, and urine. It includes circulating tumors cells (CTCs), circulating nucleic acids including DNA, messenger RNA and micro RNAs, and extracellular vesicles (EVs). In this review, we discuss recent advancements in liquid biopsy for biomarker detection to help in diagnosis, prognosis, and planning of treatment of ovarian and endometrial cancer.

Comparative RNA-Seq Analysis Reveals Potentially Resistance-Related Genes in Response to Bacterial Canker of Tomato

Tomato is one of the most important crops for human consumption. Its production is affected by the actinomycete Clavibacter michiganensis subsp. michiganensis (Cmm), one of the most devastating bacterial pathogens of this crop. Several wild tomato species represent a source of natural resistance to Cmm. Here, we contrasted the transcriptomes of the resistant wild tomato species Solanum arcanum LA2157 and the susceptible species Solanum lycopersicum cv. Ailsa Craig, during the first 24 h of challenge with Cmm. We used three analyses approaches which demonstrated to be complementary: mapping to S. lycopersicum reference genome SL3.0; semi de novo transcriptome assembly; and de novo transcriptome assembly. In a global context, transcriptional changes seem to be similar between both species, although there are some specific genes only upregulated in S. arcanum during Cmm interaction, suggesting that the resistance regulatory mechanism probably diverged during the domestication process. Although S. lycopersicum showed enriched functional groups related to defense, S. arcanum displayed a higher number of induced genes related to bacterial, oomycete, and fungal defense at the first few hours of interaction. This study revealed genes that may contribute to the resistance phenotype in the wild tomato species, such as those that encode for a polyphenol oxidase E, diacyl glycerol kinase, TOM1-like protein 6, and an ankyrin repeat-containing protein, among others. This work will contribute to a better understanding of the defense mechanism against Cmm, and the development of new control methods.

Clinical Application Value of Circulating Cell-free DNA in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and a leading cause of cancer-related deaths. Due to late diagnosis, early intrahepatic metastasis and nonresponse to systemic treatments, surgical resection and/or biopsy specimens remain the gold standard for disease staging, grading and clinical decision-making. Since only a small amount of tissue was obtained in a needle biopsy, the conventional tissue biopsy is unable to represent tumor heterogeneity in HCC. For this reason, it is imperative to find a new non-invasive and easily available diagnostic tool to detect HCC at an early stage and to monitor HCC recurrence. The past decade has witnessed considerable evolution in the development of liquid biopsy technologies with the emergence of next-generation sequencing. As a liquid biopsy approach, molecular analysis of cell-free DNA (cfDNA), characterized by noninvasiveness and real-time analysis, may accurately represent the tumor burden and comprehensively reflect genetic profile of HCC. Therefore, cfDNA may be used clinically as a predictive biomarker in early diagnosis, outcome assessment, and even molecular typing. In this review, we provide an update on the recent advances made in clinical applications of cfDNA in HCC.

Metagenomic and Metatranscriptomic Insight Into Oral Biofilms in Periodontitis and Related Systemic Diseases

The oral microbiome is one of the most complex microbial communities in the human body and is closely related to oral and systemic health. Dental plaque biofilms are the primary etiologic factor of periodontitis, which is a common chronic oral infectious disease. The interdependencies that exist among the resident microbiota constituents in dental biofilms and the interaction between pathogenic microorganisms and the host lead to the occurrence and progression of periodontitis. Therefore, accurately and comprehensively detecting periodontal organisms and dissecting their corresponding functional activity characteristics are crucial for revealing periodontitis pathogenesis. With the development of metagenomics and metatranscriptomics, the composition and structure of microbial communities as well as the overall functional characteristics of the flora can be fully profiled and revealed. In this review, we will critically examine the currently available metagenomic and metatranscriptomic evidence to bridge the gap between microbial dysbiosis and periodontitis and related systemic diseases.

Metabolic Fate of Dietary Glucosinolates and Their Metabolites: A Role for the Microbiome

Robust evidence shows that phytochemicals from cruciferous vegetables, like broccoli, are associated with numerous health benefits. The anti-cancer properties of these foods are attributed to bioactive isothiocyanates (ITCs) and indoles, phytochemicals generated from biological precursor compounds called glucosinolates. ITCs, and particularly sulforaphane (SFN), are of intense interest as they block the initiation, and suppress the progression of cancer, through genetic and epigenetic mechanisms. The efficacy of these compounds is well-demonstrated in cell culture and animal models, however, high levels of inter-individual variation in absorption and excretion of ITCs is a significant barrier to the use of dietary glucosinolates to prevent and treat disease. The source of inter-individual ITC variation has yet to be fully elucidated and the gut microbiome may play a key role. This review highlights evidence that the gut microbiome influences the metabolic fate and activity of ITCs. Human feeding trials have shown inter-individual variations in gut microbiome composition coincides with variations in ITC absorption and excretion, and some bacteria produce ITCs from glucosinolates. Additionally, consumption of cruciferous vegetables can alter the composition of the gut microbiome and shift the physiochemical environment of the gut lumen, influencing the production of phytochemicals. Microbiome and diet induced changes to ITC metabolism may lead to the decrease of cancer fighting phytochemicals such as SFN and increase the production of biologically inert ones like SFN-nitrile. We conclude by offering perspective on the use of novel “omics” technologies to elucidate the interplay of the gut microbiome and ITC formation.

Intestinal flora alterations in patients with ulcerative colitis and their association with inflammation

Ulcerative colitis (UC), which is a type of inflammatory bowel disease, is a chronic intestinal disorder of multifactorial etiology. Numerous studies have indicated an association between UC and intestinal bacteria. However, a limited number of studies regarding the expression of interleukin-17 (IL-17) and interleukin-23 (IL-23) in association with intestinal bacteria have been performed. The aim of the current study was to investigate the gut microbiota alterations in patients with UC, at a number of taxonomic levels, and their relationship with intestinal inflammation by analyzing the protein expression of IL-17 and IL-23. Specimens were collected from 10 healthy controls and 16 patients with UC. A histological examination was performed in colonic tissues, IL-17 and IL-23 protein expression was detected by immunohistochemistry, fecal samples were sequenced using 16S rDNA sequencing and bioinformatics analysis was performed. The UC group exhibited an increased histological score (P<0.01) and upregulated IL-17 and IL-23 expression (P<0.01). At the order level, the bacterial diversity of the UC group was decreased. β-diversity analyses, including principal component analysis, principal coordinate analysis and non-metric multidimensional scaling, demonstrated that the two groups of samples were separated into two taxonomic categories, as distinct variations were observed in the analysis of group differences (P=0.001). Regarding the differences in species composition between the groups, Enterococcus was indicated to be the species with the greatest difference in abundance compared with the healthy control group (P<0.01), followed by Lactobacillus (P<0.05), Escherichia-Shigella (P<0.05), Bifidobacterium and Bacteroides. In addition, the average optical density of IL-17 was positively correlated with the histological score (ρ=0.669; P=0.035), Enterococcus (r=0.843; P<0.001), Lactobacillus (r=0.737; P=0.001), Bifidobacterium (r=0.773; P<0.001) and Escherichia-Shigella (r=0.663; P=0.005), and the average optical density of IL-23 was positively correlated with the histological score (ρ=0.733; P=0.016), Enterococcus (r=0.771; P<0.001), Lactobacillus (r=0.566; P=0.022), Bifidobacterium (r=0.517; P=0.041) and Escherichia-Shigella (r=0.613; P=0.012). The results of the present study indicated that the intestinal microbiota of patients with UC differed from that of healthy controls at multiple taxonomic levels. The alterations of the intestinal microflora were closely associated with the degree of inflammation. The IL-23/IL-17 axis, as a key factor in the development of UC, maybe associated with the alterations of intestinal microflora. The interaction between intestinal microflora and the IL-23/IL-17 axis may serve an important role in the pathogenesis of UC.

BRAFV600E Mutant Allele Frequency (MAF) Influences Melanoma Clinicopathologic Characteristics

BACKGROUND

Cutaneous melanoma shows high variability regarding clinicopathological presentation, evolution and prognosis.

METHODS

Next generation sequencing was performed to analyze hotspot mutations in different areas of primary melanomas (MMp) and their paired metastases. Clinicopathological features were evaluated depending on the degree of variation of the BRAFV600E mutant allele frequency (MAF) in MMp.

RESULTS

In our cohort of 14 superficial spreading, 10 nodular melanomas and 52 metastases, 17/24 (71%) melanomas had a BRAFV600E mutation and 5/24 (21%) had a NRASQ61 mutation. We observed a high variation of BRAFV600E MAF (H-BRAFV600E) in 7/17 (41%) MMp. The H-BRAFV600E MMp were all located on the trunk, had lower Breslow and mitotic indexes and predominantly, a first nodal metastasis. Regions with spindled tumor cells (Spin) and high lymphocytic infiltrate (HInf) were more frequent in the H-BRAFV600E patients (4/7; 57%), whereas regions with epithelial tumor cells (Epit) and low lymphocytic infiltrate (LInf) were predominant (6/10; 60%) and exclusive in the low BRAFV600E MAF variation tumors (L-BRAFV600E). The H-BRAFV600E/Spin/HInf MMp patients had better prognostic features and nodal first metastasis.

CONCLUSIONS

The H-BRAFV600E MMp were located on the trunk, had better prognostic characteristics, such as lower Breslow and mitotic indexes as well as high lymphocytic infiltrate.

Label-Free Identification of Single Mononucleotides by Nanoscale Electrophoresis

Nanoscale electrophoresis allows for unique separations of single molecules, such as DNA/RNA nucleobases, and thus has the potential to be used as single molecular sensors for exonuclease sequencing. For this to be envisioned, label-free detection of the nucleotides to determine their electrophoretic mobility (i.e., time-of-flight, TOF) for highly accurate identification must be realized. Here, for the first time a novel nanosensor is shown that allows discriminating four 2-deoxyribonucleoside 5'-monophosphates, dNMPs, molecules in a label-free manner by nanoscale electrophoresis. This is made possible by positioning two sub-10 nm in-plane pores at both ends of a nanochannel column used for nanoscale electrophoresis and measuring the longitudinal transient current during translocation of the molecules. The dual nanopore TOF sensor with 0.5, 1, and 5 µm long nanochannel column lengths discriminates different dNMPs with a mean accuracy of 55, 66, and 94%, respectively. This nanosensor format can broadly be applicable to label-free detection and discrimination of other single molecules, vesicles, and particles by changing the dimensions of the nanochannel column and in-plane nanopores and integrating different pre- and postprocessing units to the nanosensor. This is simple to accomplish because the nanosensor is contained within a fluidic network made in plastic via replication.

Genetic Diagnosis in Hereditary Hemochromatosis: Discovering and Understanding the Biological Relevance of Variants

BACKGROUND

Hereditary hemochromatosis (HH) is a genetic disease, leading to iron accumulation and possible organ damage. Patients are usually homozygous for p. Cys282Tyr in the homeostatic iron regulator gene but may have mutations in other genes involved in the regulation of iron. Next-generation sequencing is increasingly being utilized for the diagnosis of patients, leading to the discovery of novel genetic variants. The clinical significance of these variants is often unknown.

CONTENT

Determining the pathogenicity of such variants of unknown significance is important for diagnostics and genetic counseling. Predictions can be made using in silico computational tools and population data, but additional evidence is required for a conclusive pathogenicity classification. Genetic disease models, such as in vitro models using cellular overexpression, induced pluripotent stem cells or organoids, and in vivo models using mice or zebrafish all have their own challenges and opportunities when used to model HH and other iron disorders. Recent developments in gene-editing technologies are transforming the field of genetic disease modeling.

SUMMARY

In summary, this review addresses methods and developments regarding the discovery and classification of genetic variants, from in silico tools to in vitro and in vivo models, and presents them in the context of HH. It also explores recent gene-editing developments and how they can be applied to the discussed models of genetic disease.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Next Generation Sequencing Technology in Lung Cancer Diagnosis

Simple Summary

Lung cancer is still one of the most commonly diagnosed and deadliest cancers in the world. Its diagnosis at an early stage is highly necessary and will improve the standard of care of this disease. The aim of this article is to review the importance and applications of next generation sequencing in lung cancer diagnosis. As observed in many studies, next generation sequencing has been proven as a very helpful tool in the early detection of different types of cancers, including lung cancer, and has been used in the clinic, mainly due to its many advantages, such as low cost, speed, efficacy, low quantity usage of biological samples, and diversity.

Abstract

Lung cancer is still one of the most commonly diagnosed cancers, and one of the deadliest. The high death rate is mainly due to the late stage of diagnosis and low response rate to therapy. Previous and ongoing research studies have tried to discover new reliable and useful cbiomarkers for the diagnosis and prognosis of lung cancer. Next generation sequencing has become an essential tool in cancer diagnosis, prognosis, and evaluation of the treatment response. This article aims to review the leading research and clinical applications in lung cancer diagnosis using next generation sequencing. In this scope, we identified the most relevant articles that present the successful use of next generation sequencing in identifying biomarkers for early diagnosis correlated to lung cancer diagnosis and treatment. This technique can be used to evaluate a high number of biomarkers in a short period of time and from small biological samples, which makes NGS the preferred technique to develop clinical tests for personalized medicine using liquid biopsy, the new trend in oncology.

Choices on sampling, sequencing, and analyzing DNA influence the estimation of community composition of plant fungal symbionts

Plant root symbionts, namely mycorrhizal fungi, can be characterized using a variety of methods, but most of these rely on DNA. While Sanger sequencing still fulfills particular research objectives, next-generation sequencing currently dominates the field, thus understanding how the two methods differ is important for identifying both opportunities and limitations to characterizing fungal communities. In addition to testing sequencing methods, we also examined how roots and soils may yield different fungal communities and how disturbance may affect those differences. We sequenced DNA from ectomycorrhizal fungi colonizing roots of Pinus banksiana and found that operational taxonomic unit richness was higher, and compositional variance lower, for Illumina MiSeq-sequenced communities compared to Sanger-sequenced communities. We also found that fungal communities associated with roots were distinct in composition compared to those associated with soils and, moreover, that soil-associated fungi were more clustered in composition than those of roots. Finally, we found community dissimilarity between roots and soils was insensitive to disturbance; however, rarefying read counts had a sizeable influence on trends in fungal richness. Although interest in mycorrhizal communities is typically focused on the abiotic and biotic filters sorting fungal species, our study shows that the choice of methods to sample, sequence, and analyze DNA can also influence the estimation of community composition.

Combinatorial technology revitalized by DNA-encoding

Combinatorial chemistry invented nearly 40 years ago was welcomed with enthusiasm in the drug research community. The method offered access to a practically unlimited number of new compounds. The new compounds however are mixtures, and methods had to be developed for the identification of the bioactive components. This was one of the reasons why the method could not providethe expected cornucopia of new drugs. Among the different screening methods, two approaches seem to offer the best results. One of them is based on the intrinsic property of the combinatorial split and pool solid-phase synthesis: One compound forms on each bead of the solid support. Different methods have been developed to encode the beads and identify the structure of compounds formed on them. The most important method applies DNA oligomers for encoding. As a second approach in screening, DNA-encoded combinatorial libraries are synthesized omitting the solid support and the mixtures are screened in solution using affinity binding methods. Libraries containing billions and even trillions of components are synthesized and successfully tested, which led to the identification of a significant number of new leads.

Helicobacter pylori infection and antibiotic resistance - from biology to clinical implications

Helicobacter pylori is a major human pathogen for which increasing antibiotic resistance constitutes a serious threat to human health. Molecular mechanisms underlying this resistance have been intensively studied and are discussed in this Review. Three profiles of resistance - single drug resistance, multidrug resistance and heteroresistance - seem to occur, probably with overlapping fundamental mechanisms and clinical implications. The mechanisms that have been most studied are related to mutational changes encoded chromosomally and disrupt the cellular activity of antibiotics through target-mediated mechanisms. Other biological attributes driving drug resistance in H. pylori have been less explored and this could imply more complex physiological changes (such as impaired regulation of drug uptake and/or efflux, or biofilm and coccoid formation) that remain largely elusive. Resistance-related attributes deployed by the pathogen cause treatment failures, diagnostic difficulties and ambiguity in clinical interpretation of therapeutic outcomes. Subsequent to the increasing antibiotic resistance, a substantial drop in H. pylori treatment efficacy has been noted globally. In the absence of an efficient vaccine, enhanced efforts are needed for setting new treatment strategies and for a better understanding of the emergence and spread of drug-resistant bacteria, as well as for improving diagnostic tools that can help optimize current antimicrobial regimens.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.

PD-L1, TMB, and other potential predictors of response to immunotherapy for hepatocellular carcinoma: how can they assist drug clinical trials?

INTRODUCTION

Hepatocellular carcinoma (HCC) represents the sixth most commonly diagnosed malignancy worldwide, accounting for millions of deaths annually. Despite immune checkpoint inhibitors (ICIs) reported important results, only a minority of HCC patients benefit from these treatments, and the identification of predictive biomarkers of response still remains a highly unmet need.

AREAS COVERED

Herein, we provide a timely overview of available evidence on biochemical predictors of response to immunotherapy in advanced HCC patients; we speculate on how PD-L1, TMB, and other emerging biomarkers could assist drug clinical trials in the near future. A literature search was conducted in June 2021 using Pubmed/Medline, Cochrane library, and Scopus databases.

EXPERT OPINION

Reliable predictors of response to ICIs are of pivotal importance to allow a proper stratification and selection of HCC patients that could derive more benefit from immunotherapy. Well-designed, multicenter clinical trials specifically focused on predictive biomarkers are warranted in this setting, where most of evidence currently derives from retrospective, single-center studies with small sample size.

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

Identifying and adopting industrial applications for proteins and enzymes derived from fungi strains have been at the focal point of several studies in recent times. To facilitate such studies, it is necessary that advancements and innovation in mycological and molecular characterisation are concomitant. This review aims to provide a detailed overview of the necessary steps employed in both qualitative and quantitative research using the omics technologies that are pertinent to fungi characterisation. This stems from the understanding that data provided from the functional characterisation of fungi and their metabolites is important towards the techno-economic feasibility of large-scale production of biological products. The review further describes how the functional gaps left by genomics, internal transcribe spacer (ITS) regions are addressed by transcriptomics and the various techniques and platforms utilised, including quantitive reverse transcription polymerase chain reaction (RT-qPCR), hybridisation techniques, and RNA-seq, and the insights such data provide on the effect of environmental changes on fungal enzyme production from an expressional standpoint. The review also offers information on the many available bioinformatics tools of analysis necessary for the analysis of the overwhelming data synonymous with the omics approach to fungal characterisation.